Da Dalton

Zhicheng Liu

Mohamed N Triba

Roland Amathieu

Xiangping Lin

Nadia Bouchemal

Edith Hantz

Laurence Le Moyec

Philippe Savarin
email: philippe.savarin@univ-paris13.fr

Alanine aminotransferase ANOVA Analysis of Variance APCI Atmospheric Pressure Chemical Ionization AST Aspartate aminotransferase

Keywords: Assisted Laser Desorption Ionisation MRE Magnetic resonance elastography 1 H nuclear magnetic resonance spectroscopy, Metabolomics, Septic shock, Outcome prediction

Quantitative Not sensitive (LOD = 5 μM) Non-destructive High start-up cost (>US$1 million) Fast (2-3 min per sample) Large instrument footprint Requires no derivatization Cannot detect or identify salts and inorganic ions Requires no separation Cannot detect non-protonated compounds Detects most organic classes Requires larger sample volumes (0.1-0.5 mL) Allows identification of novel chemicals Most spectral features are identifiable Robust, mature technology Can be used for metabolite imaging (fMRI or MRS) Can be fully automated Compatible with liquids and solids Long instrument lifetime (over 20 years) Robust, mature technology Destructive (sample not recoverable) Modest start-up cost (~$150,000) Requires sample derivatization Quantitative (with calibration) Requires separation Modest sample volume (0.1-0.2 mL) Slow (20-40 min per sample) Good sensitivity (LOD = 0.5 μM) Cannot be used in imaging Large body of software and databases for metabolite identification Not compatible with solids Detects most organic and some inorganic molecules Novel compound identification is difficult Excellent separation reproducibility Many spectral features are identifiable Can be mostly automated Compatible with gases and liquids Superb sensitivity (LOD = 0.5 nM) Destructive (sample not recoverable) Very flexible technology Not very quantitative Detects most organic and some inorganic molecules Higher start-up cost (>$300,000) Small sample volumes (10-100 μL) Slow (15-40 min per sample) Can be used in metabolite imaging (MALDI or DESI) Usually requires separation Can be done without separation (direct injection) Poor separation resolution and lower reproducibility versus GC-MS Has the potential to detect the largest portion of metabolome Less-robust instrumentation than NMR or GC-MS Can be mostly automated Most spectral features are not yet identifiable Compatible with solids and liquids Novel compound identification is difficult Short instrument lifetime (<9 years) NMR spectroscopy GC-MS LC-MS 3.5.2 Nuclear magnetic resonance-based serum metabolomic analysis reveals different disease evolution profiles between septic shock survivors and nonsurvivors

ACKNOWLEDGMENTS

First, I would like to thank my PhD. supervisor, Pr. Philippe SAVARIN, the director of CSPBAT (Chimie, Structures, Propriétés de Biomatériaux et d'Agents Thérapeutiques) laboratory, for offering me the possibility of the present PhD. thesis opportunity, and for his guidance and advice both scientifically and personally throughout my thesis work.

Next, I would like to thank Pr. Guowang XU, the director of Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, who offer me a chance to perform part of my thesis work in his laboratory, where I can access the cutting edge MS laboratory equipment and very enthusiastic research environment, thanks also for his invaluable scientific advice.

Then, I would like also to express my deepest appreciation to Dr. Claudine MANACH, who offer me the opportunity to begin in metabolomics. I was really lucky to work with great people in our small metabolomics group: Dr. Mohamed N. TRIBA, Pr. Edith HANTZ, Dr. Nadia BOUCHEMAL, Dr. Zhicheng LIU, Dr. Tony PALAMA, Dr. Roland AMATHIEU and Msc. Agnes VICTOR BALA, and all staffs in Pr. XU's laboratory, for all of their help, accompaniment and advice during my thesis work. They suggestions have always enlightened me and helped me to think in different perspectives. I would like to thank separately Dr. Xinyu LIU, for her constructive suggestions to my thesis work and for all her help during my stay in Dalian.

Thanks also to all my thesis committee members, Dr Christophe Junot, Pr François Mesnard, Dr Carina Prip-Buus, for their thoughtful input and they time spend in the reviewing of my Thesis. Here, I also would like to thank the Doctoral school 146 « Sciences, Technologies, Santé -Galilée » and the French government for offering me opportunity to perform a PhD thesis in France, and for they financial support.

Lastly, I would like to thank my parents, my family especially my elder sister for all they support and patience over the years. They bring sunshine to my life. Without them, I may never have gotten to where I am today.

LIST OF FIGURES

. An overview from genomics to metabolomics. Figure 2. A schematic representation of metabolomic applications. Figure 3. The liver and nearby organs. Figure 4. Changes in core metabolic pathways in prostate cancer. Figure 5. the main steps of the metabolomic analysis. Figure 6. A simplified explanation of the principle behind NMR spectroscopy. Figure 7. Simplified illustration of an NMR spectrometer. Figure 8. Simplified illustration of a Mass spectrometer. Figure 9. Simplified explanation of Electrospray Ionization (ESI). Figure 10. Simplified schematic of a Quadrupoles mass analyzer. Figure 11. Simplified schematic of a TOF mass analyzer. Figure 12. Simplified schematic of an Orbitrap Figure 13. A misaligned set of human urine NMR spectra before and after icoshift. Figure 14. Schematic representation in 3 dimensions of a LC-MS chromatogram. Figure 15. A schematic of quantile normalization Figure 16. PLS (left) separates the variability in the X matrix in two parts, the systematic variability (R2X) and the residual variability (E). Figure 17. Score plot of an OPLS-DA model, within group and between group variations are separated on both components. Figure 18. S-plots of an OPLS-DA model, p(corr) indicates the reliability of a variable as a marker while the loading, p, indicates the influence of the variables in the model. Figure 19. A, Score plot of OPLS-DA. Figure 19. B, 200 times permutation test for OPLS-DA. Figure 19. C, Covariance plot of OPLS-DA. Figure 20. Heatmap shows metabolites changes between NL, NAFL and NASH patients. Figure 21. PCA Score Plot for all analyzed plasma samples. Figure 22. Score Plot of OPLS-DA model for classification between NL (bleu) and NASH (red) patients. Figure 23. S-plot for OPLS-DA models highlight important metabolites (red) in the classification between NL and NASH patients. Figure 24. Heatmap shows lipids changes between NL, NAFL and NASH patients. Figure 25. PCA Score Plot for all analyzed plasma samples. Figure 26. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients. Figure 27. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients. Figure 28. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients. Figure 29. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients. Figure 30. Box plots of plasma levels of selected metabolites or lipids in NL (0), NAFL (1) and NASH (2) patients. Figure 31. Score Plot of PCA for all analyzed plasma samples. Figure 32. Score Plot of OPLS-DA models for Estimation cohort. Figure 33. 999 times permutation test of OPLS-DA model for R2 (red) and Q2 (bleu). Figure 34. ROC curve for validation cohort.

LIST OF TABLES

Table 1. Summary of recent metabolomics studies in NAFLD Table 2. Summary of recent metabolomics studies in NASH Table 3. Nonalcoholic fatty liver disease subtype classification, Table 4. A comparison of different metabolomic analytical technologies. Table 5. Schematic representation of a typical metabolomic data matrix. Table 6. Overview of common centering, scaling and transformation methods. Table 7. List and concentration of used internal standard (IS). Table 8. list and concentration of internal standard (IS). Table 9. Characteristics of NL, NAFL and NASH Patients. Table 10. Summary of OPLS-DA models for metabolomics analysis. Table 11. Confusion matrix of the sample by OPLS-DA models. Table 12. List of metabolites highlighted by S-plot for OPLS-DA models. Table 13. Summary of OPLS-DA models for lipidomics analysis. Table 14. List of lipids highlighted by S-plot for OPLS-DA models. Table 15. Confusion matrix of the sample by OPLS-DA models. Table 16. Confusion matrix of the sample by OPLS-DA in Estimation cohort. Table 17. List of metabolites selected with VIPpred of OPLS-DA. Table 18. List of variables in the logistic regression equation. Table 19. Confusion matrix of the sample by OPLS-DA in validation cohort.

Part One GENERAL INTRODUCTION

Metabolites, metabolome and metabolomics

Metabolites are low molecular weight biochemicals (chemically defined as small molecules, typically MW < 1500 Da) [1], such as sugars, fatty acids, amino acids (Tryptophan, Phenylalanine …), but also some peptides (Glutathione …), organic acids, vitamins, steroids, xenobiotics and other exogenous molecules, which are intermediates and products of metabolic reactions (metabolism) catalyzed by various enzymes that naturally occur within cells [2].

They cover a wide range of chemical formulas, for example, the Human Metabolome Database (HMDB) (version 4.0) contains 114,100 metabolite entries including both watersoluble and lipid soluble metabolites as well as metabolites that would be regarded as either abundant (> 1 µM) or relatively rare (< 1 nM). In Plant Kingdom, it has been estimated that there is at least 200,000 different metabolites, and between 7000 and 15,000 within an individual species [3], [4].

Depending on their origin, metabolites can be distinguished as endogenous metabolites that are naturally produced by an organism (such as amino acids, organic acids, nucleic acids, fatty acids, amines, sugars, vitamins, co-factors, pigments, antibiotics, etc.) as well as exogenous metabolites (such as drugs, environmental contaminants, food additives, toxins and other xenobiotics) that are not naturally produced by an organism.

Endogenous metabolites can be further classified as primary and secondary metabolites.

A primary metabolite is directly involved in the normal growth, development, and reproduction (such as sugars, organic acids, amino acids, phosphorylated sugars). A secondary metabolite is not directly involved in those processes, but usually has other functions (such as terpenes, flavonoids, alkaloids, drugs, toxins, xenobiotics, etc).

Primary metabolites are ubiquitous (bacterial, plant and animal kingdoms), the term of "secondary metabolites" is particularly used for plant or microbial metabolites.

Secondary metabolites may include pigments, antibiotics or waste products derived from partially metabolized xenobiotics [5].

The term of metabolome was initially proposed in the literature in 1998 by Oliver et al [1],

and Tweeddale et al [6]. In parallel to the terms of genome, transcriptome and proteome, the term of metabolome represents all metabolites contained in a biofluids (such as urine, blood plasma…), tissue or cell of a living organism (Figure 1).

Figure 1. An overview from genomics to metabolomics (inspired by Dettmer et al, [7]).

Compared to genome, transcriptome and proteome studies, the metabolome study has complementary advantages. One of great advantage is that metabolites are endpoint products of interactions between biological systems, genome and environment, thus, compare with genome, transcriptome and proteome, metabolome may better reflect molecular phenotypic behavior of a living organism [7].

Metabolomics is the science designed to comprehensively study the metabolome, which gives the broadest insight into theses chemical fingerprints (metabolites) by identifying and quantifying them, eg. establishing a profile of the metabolites of a studied sample.

By identifying and quantifying metabolites, metabolomics gives a comprehensive snapshot of the physiological state of the studied extract or cell [8], [9].

Technically speaking, metabolomics can be also defined as systematic analysis of metabolites in biofluids [10], [11], tissues [12], [13] or cells [14], [15] and investigate metabolites changes (or perturbations) during diseases (eg., cancer) [16]- [18], physiological processes (eg., aging) [19] or external stimulus (eg., drug treatment) [20], [21]. Thus, measuring metabolites by using metabolomics is a very important complementary to genome, transcriptome and proteome studies, which may improve

Gene

Genome Genomics mRNA (Retro-)Transcriptome (Retro-)Transcriptomics

Proteins

Proteome Proteomics

Metabolites Metabolome Metabolomics

our understanding of how genetics, environment, the microbiome, disease, drug exposure, diet, and lifestyle all influence the phenotype [2], [22], [23].

Metabolomic study

The two main approaches frequently used in metabolomics are untargeted and targeted metabolomics, just like all method, each has their own advantages and drawbacks. Other approaches which have been proposed in the literature such as Pseudotargeted metabolomics and Imaging metabolomics are also discussed in the present section.

However, in order to facilitate the understanding of the methods used in the following part, I will briefly describe these types of metabolomic studies here, a more exhaustive description of each metabolomics study can be found in these books [24]- [27].

Untargeted metabolomics

Untargeted or global metabolomic analysis usually involves comparing the metabolome of control and test groups (such as disease or treated group) to identify differences between their metabolite profiles [10], [28]- [30].

An untargeted experimental workflow is usually composed of three steps: 1. Profiling, in order to seek the metabolites with statistically significant variations in abundance within a set of experimental and control samples; 2. Identification of metabolite, and elicitation of the chemical structure; 3. Interpretations, which makes connections between the identified metabolites and the biological processes.

Untargeted metabolomics try to cover as many metabolites as possible present in a biological sample, which is a very useful tool during the primary phase in the biomarker discovery as this approach is non-hypothesis driven, with a wide range of metabolite classes. That's why this method was also called discovery metabolomics. A lot of new biomarkers has identified by using this method. Now, between 200 and 500 metabolites can be detected by untargeted metabolomics, However, it is not yet possible to detect all metabolite classes as uncompleted NMR and MS database, convolution and ionization problem exist. Furthermore, a lot of compounds detected in this method remain unknown in metabolite databases [31].

Targeted metabolomics

In contrast to untargeted or global metabolomic analysis, targeted metabolomics aim at quantitation of a preselected set of metabolites (targeted metabolites) [32]- [34].

Targeted metabolomics is often used in the confirmation and validation stage in the biomarker discovery, with highly advantage in specificity and in quantitation. To estimate a metabolite concentration, a standard curve for a concentration range of the metabolite of interest by using they chemical standard is established. Thus, to perform targeted metabolomic analysis, the chemical standard for the metabolite of interest should be available or should be easy to synthetized [32], [35].

Pseudotargeted metabolomics

Pseudotargeted metabolomics was initially proposed in the literature by Prof. Xu's group where I performed part of my thesis work. This method is a new approach combining the advantages of both untargeted and targeted methods. Briefly, a pooled (2X or 3X times) concentrated Quality Control (QC) sample was analyzed at first by using untargeted method in order to cover the maximum metabolites features possible, then a targeted quantitative analysis with the detected metabolites in previous steps was performed with real samples in order to estimate they quantity [START_REF] Li | A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring[END_REF], [START_REF] Chen | Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry[END_REF].

Imaging metabolomics

Untargeted and targeted metabolomics which represented above involve the extraction of metabolites from a sample, and the homogenization of the samples before measurement. Consequently, all metabolite spatial distribution information is lost. In imaging metabolomics, in contrast, a thin section of sample (basically a small portion of tissue/organ) is measured by using mostly a mass spectrometer while leaving location information intact on the sample, thereby permitting measurement of metabolite distribution information [31], [START_REF] Gessel | MALDI imaging mass spectrometry: spatial molecular analysis to enable a new age of discovery[END_REF]- [START_REF] Patti | Innovation: Metabolomics: the apogee of the omics trilogy[END_REF].

Imaging mass spectrometry techniques, such as matrix-assisted laser desorption ionization (MALDI) [START_REF] Tanaka | Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry[END_REF], nanostructure-imaging mass spectrometry (NIMS) [START_REF] Northen | Clathrate nanostructures for mass spectrometry[END_REF],

desorption electrospray ionization mass spectrometry (DESI) [START_REF] Wiseman | Tissue imaging at atmospheric pressure using desorption electrospray ionization (DESI) mass spectrometry[END_REF] and secondary ion mass spectrometry (SIMS) [START_REF] Kraft | Phase separation of lipid membranes analyzed with high-resolution secondary ion mass spectrometry[END_REF] are frequently used techniques in Imaging metabolomics, among them, NIMS and DESI are particularly suited to the analysis of small molecules [31]. MS-imaging data can also combine with detailed optical microscope images in order to get more relevant biological information [START_REF] Buck | High-resolution MALDI-FT-ICR MS imaging for the analysis of metabolites from formalin-fixed, paraffin-embedded clinical tissue samples[END_REF], [START_REF] Amstalden Van Hove | A concise review of mass spectrometry imaging[END_REF].

Applications of Metabolomics

Unlike well-established approach such as transcriptomics, so far, metabolomics is still in its infancy, however, related studies revealed already its considerable potential applications in various research fields such as Agriculture (e.g. development of new pesticides, improve genetically modified plants) [START_REF] Dixon | Applications of metabolomics in agriculture[END_REF], [START_REF] Tian | Metabolomics, a Powerful Tool for Agricultural Research[END_REF], Precision medicine (e.g. newborn screening for inborn errors of metabolism, customize drug treatments) [START_REF] Clish | Metabolomics: an emerging but powerful tool for precision medicine[END_REF], [START_REF] Sandlers | The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism[END_REF],

Drug discovery (e.g. identify new pathways / novel drug targets, toxicology test) [START_REF] Lindon | Metabonomics in pharmaceutical R&D[END_REF]- [START_REF] Wishart | Emerging applications of metabolomics in drug discovery and precision medicine[END_REF] and Biomarker discovery (e.g. cancer biomarker, diabetes biomarker, Alzheimer's disease biomarkers …) [21], [31], [START_REF] Mamas | The role of metabolites and metabolomics in clinically applicable biomarkers of disease[END_REF], it is also important to know that its application will not be only limited in these fields. The Figure below list possible metabolomic applications.

Figure 2. A schematic representation of metabolomic applications (www.metabolon.com).

In health-related field, especially in clinic research, one important application of metabolomics is the discovery for early disease Biomarker [START_REF] Rhee | Metabolomics and cardiovascular biomarker discovery[END_REF]. According to Biomarkers Definitions Working Group, a biological marker (biomarker) is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [START_REF]Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework[END_REF].

My PhD thesis focus on the Biomarker discovery part of metabolomics, especially on ( Finally, the last part is the general conclusion and outlook of the thesis.

Metabolomics studies in NAFLD: State-of-the-art

The liver is one of the largest organs in our body. It plays many important functions in metabolism, such as carbohydrates, protein and lipids metabolism. In normal condition, it converts the macronutrients (such as carbohydrates, protein and lipids) in our diets into substances that the body can use (glucose, amino acids and fatty acids), stores these substances in form of glycogen or fatty acids, and supplies cells with glycogen or fatty acids when needed [START_REF] Information | Usa, How does the liver work? Institute for Quality and Efficiency in Health Care[END_REF]. However, in abnormal condition such as perturbation of lipids metabolism, liver may be subject to damage, even develop disease. One of the common chronic liver conditions is Non-alcoholic fatty liver disease (NAFLD) [START_REF] Younossi | Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[END_REF], [START_REF] Kim | OPTN/SRTR 2017 Annual Data Report: Liver[END_REF]. NAFLD characterized by abnormal accumulation of lipids mainly triacylglycerols (TGs) in the liver, based on clinicalhistologic characters, NAFLD spectrum range from simple fatty liver (NAFL) or steatosis to the advanced form termed NASH, without therapeutic intervention, a subset of patient with NASH will subsequently progress towards cirrhosis and, ultimately, hepatocellular carcinoma [START_REF] Castera | Noninvasive Assessment of Liver Disease in Patients With Nonalcoholic Fatty Liver Disease[END_REF].

So far, liver biopsy is the gold standard for diagnosis, staging and monitoring progression of NAFLD during treatments. However, biopsy has well-known limitations, such as invasiveness, poor acceptability by patients, sampling variability, and financial cost… which limit its application in large-scale population. Moreover, recently developed Noninvasive imaging biomarker assessment method, even the most accurate noninvasive liver elastography based methods, such as vibration-controlled transient elastography (VCTE), magnetic resonance elastography (MRE), shear-wave elastography and acoustic radiation force impulse have other limits including couldn't access inflammation, with very limited guidance (or even unavailable) for how clinicians should anticipate and manage the pitfalls of these tests [START_REF] Tapper | Noninvasive imaging biomarker assessment of liver fibrosis by elastography in NAFLD[END_REF]- [START_REF] Pappachan | Non-alcoholic Fatty Liver Disease: A Clinical Update[END_REF].

Thus, the development of an alternative noninvasive and familiar for clinicians' strategy such as using non-invasive biomarkers is an urgent need for prognostication, early detection, staging, selection of patients for treatment and monitoring of disease [START_REF] Younossi | Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[END_REF].

Metabolomics is the science designed to comprehensively study the metabolome, the repertoire of small molecule metabolites, which has been used to investigate in prognosis, risk estimation, early diagnosis, and identification of novel biomarkers of NAFLD. Recent metabolomic studies in NAFLD and NASH were summarized in tables below [START_REF] Safaei | Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases[END_REF] (Table 1, Table 2). In the further research, investigation subtypes of this heterogeneous and complex disease could be a novel perspective direction. Moreover, combine with other omics research such as transcriptomics, proteomics, and also clinical characteristics may improve novel subtyping approach of NAFLD patients, allowing further more precisely classification and staging of patients, in order to correctly interpret the biochemical processes behind the disease, which could contribute to the development of appropriate therapy and precision medicine-based management of patients.

Metabolomics studies in PCa: State-of-the-art

Prostate cancer (PCa) is the second most commonly diagnosed cancer and the second leading cause of cancer death (7.1% for incidence) among males [START_REF] Bray | Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[END_REF]. Currently, there is no single definitive test to identify prostate cancer in men [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF]. Prostate-Specific Antigen (PSA) test and digital rectal examination are screening methods used for PCa, for the definitive diagnosis, prostate biopsy and supplementary imaging are required [START_REF] Zadra | Metabolic Vulnerabilities of Prostate Cancer: Diagnostic and Therapeutic Opportunities[END_REF]. The PSA test is a relatively easy to perform test and applicable for population in large scale, however, it has well known limits such as sensitivity, specificity, and can lead to falsepositive and false-negative results [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF].

Although extensive efforts in biomarker discovery during the last decades, including the genome and transcriptome approach, which has contributed to the identification of predictive biomarkers, more sensitive and specific biomarkers are still very demanding in early detection, prognosis, monitoring, and clinical management of PCa patients [START_REF] Penney | mRNA expression signature of Gleason grade predicts lethal prostate cancer[END_REF]- [START_REF] Sharma | Biomarkers for prostate cancer: present challenges and future opportunities[END_REF]. Metabolomics, defined as systematic analysis of metabolites in biofluids [10], [11],

tissues [12], [13] or cells [14], [15] and investigate metabolites changes (or perturbations)

during diseases (eg., cancer) [16]- [18], physiological processes (eg., aging) [19] or external stimulus (eg., drug treatment) [20], [21], has shown to be a promising and powerful tool to identify novel PCa biomarkers [START_REF] Lucarelli | Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer[END_REF]- [START_REF] Kelly | Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence[END_REF]. The figure below displayed major metabolic pathways changes in the tissues (Figure 4). For studies with biofluid, one of remarkable example is sarcosine, an N-methyl derivative of glycine, Sreekumar et al. found that sarcosine is a differential metabolite that was highly increased during prostate cancer progression to metastasis, also, sarcosine can be detected non-invasively in urine [START_REF] Sreekumar | Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression[END_REF], which could be a very good biomarker candidate.

However, whether sarcosine could use as a reliable biomarker for prostate cancer is still in discussion in the scientific committee [START_REF] Lima | Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies[END_REF], some study confirm this finding [START_REF] Khan | The role of sarcosine metabolism in prostate cancer progression[END_REF], but

other not [START_REF] Struys | Serum sarcosine is not a marker for prostate cancer[END_REF]. Potentially however, serum PSA concentrations in relation to serum sarcosine concentrations might have additional diagnostic value [START_REF] Struys | Serum sarcosine is not a marker for prostate cancer[END_REF].

Apart from metabolism, there is other direction as well, which could promote application of metabolomics to prostate cancer, such as data processing. C. Pérez-Rambla et al. [START_REF] Pérez-Rambla | Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia[END_REF] show that variable selection such as the regression coefficient (b-coefficient) based method [START_REF] Diaz | Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes[END_REF], [START_REF] Quintás | Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool[END_REF] improved the classification predictiveness of model.

Accumulated evidence suggests that metabolic alterations specific to prostate carcinogenesis and progression may represent potential metabolic biomarkers. In the further research, validation of promising biomarkers should be a priori, and a number of approach such as transcriptomics, proteomics should be used as complement to promote and validate metabolomic findings in the study of prostate cancer [START_REF] Kelly | Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence[END_REF].

Part Two METHODOLOGY

General workflow of a Metabolomic study

Several very informative and well detailed protocols and reviews are available in the literature which described the different operations in a typical Metabolomic study [START_REF] Want | Global metabolic profiling procedures for urine using UPLC-MS[END_REF]- [START_REF] Alonso | Analytical methods in untargeted metabolomics: state of the art in 2015[END_REF]. Essentially, a metabolomic study consists of several steps, the main steps (partly represented in the Figure 5, which is an analysis workflow of an untargeted metabolomic) include experimental design (didn't represented in the Figure 5), sampling and storage, samples preparation, data acquisition, data processing and analyses, metabolites feature identification, and biological interpretation.

Almost any metabolomic approach start by one or more biological or clinical questions to which we wish to answer. Whether searching for early disease biomarkers, monitoring the effects of treatment or study effect of targeted gene in the regulation of metabolism … the experimental design must be carefully thought out to reduce as much as possible bias and avoid the introduction of irrelevant variables [START_REF] Dunn | The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans[END_REF].

Figure 5. Main steps of the metabolomic analysis [START_REF] Alonso | Analytical methods in untargeted metabolomics: state of the art in 2015[END_REF].

Also, when designing the study, it may be necessary to establish more specific recommendations or specific inclusion criteria in order to have the most homogeneous population possible.

The steps for samples preparation, data acquisition, data processing and analyses, metabolites feature identification, and biological interpretation will be described in the following section of the present part.

1 H NMR Spectroscopy based metabolomics

In order to study small molecules (metabolites) in biological samples (such as urine, blood plasma, blood serum, saliva, …), a number of different high throughput, sophisticated analytical instruments can be used, such as Nuclear Magnetic Resonance (NMR) spectrometer and Mass Spectrometer (MS).

Ongoing advances in analytical techniques including NMR Spectroscopy and Mass Spectrometry (MS) will certainly lead to a continuous improvement of the breadth and throughput of metabolomic analysis.

The NMR spectroscopy is one of the two most used techniques in metabolomic analysis.

Here, in order to facilitate the understanding of the results presented in the following part, I will shortly describe the basic principles of NMR. A more exhaustive explanation of principles and physics related to NMR can be found in books, for example, see [START_REF] Levitt | Spin Dynamics: Basics of Nuclear Magnetic Resonance[END_REF].

The basic principle of NMR

NMR is based on the magnetic properties of certain atomic nuclei (e.g. 1 H, 13 C, 31 P, 19 F).

In quantum mechanics and particle physics, spin is an intrinsic physical property of the atomic nucleus. It is characterized in particular by an intrinsic magnetic moment, if the moment is different from zero, the spin will give magnetic properties to the nuclei which will be exploitable in the NMR spectroscopy [START_REF] Levitt | Spin Dynamics: Basics of Nuclear Magnetic Resonance[END_REF].

Theoretically, without an external magnetic field, spins of a given atomic nucleus (for example the nuclei of hydrogen) have the same energy level, when an external magnetic field (B0) is applied, in the case of the spin 1/2 nucleus, nuclei spins will split into two energy levels, higher energy (-1/2 or b) and base energy (+1/2 or a) level (called

The Zeeman effect). The Boltzmann distribution describes the population of nuclei in each spin state, equation: (Eq. 0.1).

𝑁b/𝑁a = 𝑒 !"

DE !" # (Eq. 0.1)
where Na and Nb represent the number of spins expect to measure in the a and b states, ΔE is the difference in energy between the two states, k is the Boltzmann constant (1.381

x 10 -23 joules/°K), and T is the absolute temperature in Kelvin degrees.

And the difference in energy between the two spin states can be described as (Eq. 0.2):

ΔEα ® β = Eβ -Eα = (h/2π)γB0 (Eq. 0.2)
where ΔE is the difference in energy between the two states, h is the Planck constant, γ is the gyromagnetic ratio.

Between the b and the a energy states, an energy transfer is possible when an external magnetic field (B1) is applied. The energy transfer happens by the way of a wavelength that corresponds to the energy of radio frequencies received and when the spin returns to its base level, energy is emitted at the same frequency. The signal, called Free Induction Decay (FID) that correspond to this transfer is measured in time domain and processed into frequency domain (Fourier Transform) in order to yield an NMR spectrum for the nucleus studied [START_REF] Levitt | Spin Dynamics: Basics of Nuclear Magnetic Resonance[END_REF]. The schema below (Figure X) simplify resumes the principle of the NMR spectroscopy. Figure 6. A simplified explanation of the principle behind NMR spectroscopy [START_REF] Levitt | Spin Dynamics: Basics of Nuclear Magnetic Resonance[END_REF].

1. Without external magnetic field (B0), nucleus spins have the same energy state. When an external magnetic field (B0) is applied, nuclei spins will be split into two energy states, higher energy (b) and base energy (a) states.

2. Between the b and the a energy states, energy transfer is possible by application of B1 in form of a radio frequencies pulse, the energy corresponds to the energy of radio frequencies pulse received. When the spin returns to its base level, a magnetization can be recorded at the same frequency.

3. The signal, called free induction decay (FID) that correspond to this transfer is measured in time domain and processed into frequency domain (called "Fourier

Transform") in order to generate an NMR spectrum for the nucleus studied.

Chemical shift

For a given atomic nucleus, the effective magnetic field strength (Beff) at the nucleus is affected by the magnetic field generated by the movement of electron surround-by (electron shielding), which is dependent on the chemical environment of the nucleus. The relationship between the external field strength (B0) and the effective field strength (Beff)

for the nuclei can be described as:

Beff = B0 (1 -s) (Eq. 0.3)
where s is the screening constant.

The chemical shift (δ) is measured with respect to a reference signal depending on the frequency of the spectrometer by the following relation (Eq. 0.4). This value defines the position of the signal on the frequency axis.

δ = (νcompound -νref)/νref (Eq. 0.4)

where "νcompound" is the absolute resonance frequency of a test compound and "νref" is the absolute resonance frequency of a standard reference compound, measured in the same applied magnetic field B0. Usually, the numerator "νcompound -νref" in this equation is expressed in "hertz", and the denominator "νref" in "megahertz", thus, the chemical shift "δ", using in this equation, is not dependent on the magnetic field and it is expressed in "parts per million" (ppm) by frequency.

It is customary to adopt tetramethylsilane (TMS) as the proton reference frequency, because the precise resonance frequency shift of each nucleus depends on the magnetic field used. For TMS, which is set to "νref" in this equation, its chemical shift (δ) is "0".

NMR Instrumentation

An NMR spectrometer consists essentially of the superconducting magnet (composed of niobium titanium), a transmitter and a high radio frequency receiver (Figure 7). The sample to be analyzed is introduced into the measurement cell called probe, itself placed in the magnetic field B0. The probe excites the nucleus in sample with high radio frequency radiation and also receives the signal from the relaxed nucleus. The spectrum is then recorded after amplification and processing.

High Resolution Magic Angle Spinning Probes (HR-MAS Probes)

One of the advantages for NMR is that analysis of intact biological tissue samples is possible, HR-MAS is an established technique for analyzing intact biological tissue samples. By spinning at the magic angle (θ = 54.7°), line broadening effects due to dipolar interactions and susceptibility differences within the sample are removed resulting in high resolution quality spectra, a more detailed description about HR-MAS for metabolomic analysis can be found in the review of Beckonert et al 2010 [13], [START_REF] Gowda | NMR-Based Metabolomics: Methods and Protocols[END_REF].

Several important parameters for NMR data acquisition

In order to obtain comparable spectra, during NMR data acquisition, the experimental conditions must be comparable and therefore optimized for the analysis of all the samples. The main parameters to consider when acquiring a spectrum are listed below [START_REF][END_REF], [96], setting these parameters will determine the nature and quality of the NMR spectrum.

-Spectral Window (SW). It defines the range of frequencies observed.

RF Transmitter

Receiver & amplifier Magnet controller -Acquisition time of the FID (AQ). It must be long enough to maximize the amount of signal that is contained in the FID and ensure good digital resolution.

-Number of points acquired on the FID (TD, time domain data size) in other words, the NMR data size, in general, a larger the number of points defining the FID will correspond to a higher spectral resolution.

The relationship between acquisition time (AQ), the spectral window (SW) and the number of points (TD) can be represented by the following equation:

AQ = TD/2SW
(Eq. 0.5) -Relaxation time (d1) correspond to the return to equilibrium of the magnetization vector before each new sequence of pulses. The FID is decreasing exponentially with the relaxation times, it is usually recommended to wait 5 times the value of the highest T1 in order to obtain the complete return of the magnetization vector after a 90 ° pulse.

-Number of scans accumulations (NS). In order to improve the sensitivity, a series of n pulses can be applied immediately after the recording of the first signal allowing the recording of n signals which will accumulate before obtaining the spectrum. The signalto-noise ratio (S/N) being proportional to √n, a greater the number of accumulations will correspond to a better S/N.

-Receiver Gain (RG). The receive gain controls the amplitude of the FID which itself depends on the concentration of the sample. The optimum gain determined corresponds to the maximum value obtained for the intensity of the strongest signal. In order to be able to compare the spectra, it is recommended to have the same value of gain and to fix this value a little below the value that has been optimally determined to avoid saturating in one of the spectra, especially if the samples to be analyzed differ between them.

-The duration of the radiofrequency pulse sent is of the order of a few μs. P1 defines the time required to fully switch the magnetization vector of the Z axis in the XY plane for a 90 ° pulse. This duration is dependent on the intensity of the irradiation.

-Power (PW). PL sets the attenuation parameter on this irradiation intensity.

-Irradiation frequency, SFO1, corresponds to the frequency sent to excite the desired nucleus. It is composed of two terms: BF1, which corresponds to the base frequency recorded for the chosen kernel (500 MHz for 1H corresponding to a B0 of 11.7 tesla) and O1 ("offset value"), which makes it possible to adjust the exact value of the reference frequency. Setting of O1 makes it possible to focus on the spectral region of acquisition.

Mass Spectrometry based metabolomics

The other most used Analytical technologies in metabolomics is Mass Spectrometry (MS).

The principle of MS based metabolomics is measuring the mass-to-charge ratio (m/z) of ions to identify and quantify molecules. As in the NMR section, here, I will briefly represent the basic principles of MS in order to facilitate the understanding of the results presented in the following part. A more exhaustive explanation of principles and physics related to MS can be found in "Mass Spectrometry: A Textbook" [START_REF] Gross | Mass Spectrometry: A Textbook[END_REF].

The basic principle of Mass Spectrometry

The principle of the mass spectrometry is fully described in reviews, briefly, "the basic principle of MS is to generate ions from the sample molecules by thermally, by electric fields or by impacting energetic electrons, ions or photons, to separate these ions by their m/z and to detect them qualitatively and quantitatively by their respective m/z and abundance. The ions can be single ionized atoms, clusters, molecules or their fragments or associates. Ion separation is effected by static or dynamic electric or magnetic fields." [START_REF] Gross | Mass Spectrometry: A Textbook[END_REF], [START_REF] Kienitz | Massenspektrometrie[END_REF].

MS Instrumentation

Fundamentally, a mass spectrometer contains an ion source, a mass analyzer and an ion detector (Figure 8). The analyzer, detector, and often the ionization source too, are maintained under high vacuum to ensure the ions travelling through the instrument without any impact from air molecules (such as N2, O2). Samples are introduced into the mass spectrometer in liquid or gas form and then vaporized and ionized by the ion source [START_REF] Gross | Mass Spectrometry: A Textbook[END_REF].

Figure 8. Simplified illustration of a Mass spectrometer [START_REF] Gross | Mass Spectrometry: A Textbook[END_REF].

Ionization

Ionization is a process to generate charged ions from the sample molecules. Several ionization methods are proposed in the literature, such as Atmospheric Pressure Chemical Ionization (APCI), Chemical Ionisation (CI), Electron Impact (EI) and Matrix Assisted Laser Desorption Ionisation (MALDI). The detailed description for each ionization methods are available in these publications [START_REF] Frycák | Atmospheric pressure ionization mass spectrometry of purine and pyrimidine markers of inherited metabolic disorders[END_REF]- [START_REF] Awad | Mass Spectrometry, Review of the Basics: Ionization[END_REF]. The choice of the method should depend on the nature of molecules in samples to be studied.

During my thesis work, Electrospray Ionization (ESI) is the main method we used, ESI is one of the Atmospheric Pressure Ionization (API) techniques and is well-suited to the analysis of polar molecules with molecular mass ranging from less than 100 Da to more than 1,000,000 Da [START_REF] Fenn | Electrospray ionization for mass spectrometry of large biomolecules[END_REF], [START_REF] Fenn | Electrospray ionization-principles and practice[END_REF]. In ESI, the ionization mode can be positive or negative.

The principle of ESI is described in the figure 9.

Vacuum Pumps

The sample to be analyzed such as blood plasma (after preparation) is introduced into the ion source with a polar, volatile solvent (such as methanol or acetonitrile) and pumped (from a syringe pump or as the eluent flow from liquid chromatography) through a narrow, stainless steel capillary (75 -150 micrometers i.d.) with a flow rate between 1 µL/min and 1 mL/min.

A high voltage (from 2.5 to 5 kV) is applied to the tip of the capillary (right part in the figure), as a consequence, the sample emerging from the tip is dispersed into an aerosol of highly charged droplets (right part in the figure), a process that is aided by a co-axially introduced nebulizing gas flowing around the outside of the capillary (left part in the figure). This gas, usually nitrogen, helps to direct the spray emerging from the capillary tip towards the mass spectrometer.

The charged droplets diminish in size by solvent evaporation (right part in the figure), assisted by a warm flow of nitrogen known as the drying gas which passes across the front of the ionization source.

Finally, charged sample ions, free from solvent, are released from the droplets, some of which pass through a sampling cone or orifice into an intermediate vacuum region, and from there through a small aperture into the analyzer of the mass spectrometer, which is held under high vacuum. The lens voltages are optimized individually for each sample.

Mass Analyzer

The main function of the mass analyzer is to separate, or resolve, the ions formed in the ion source of the mass spectrometer according to their mass-to-charge (m/z) ratios.

There are a number of mass analyzers currently available, such as quadrupoles (Q) [START_REF] Dawson | Quadrupole mass analyzers: Performance, design and some recent applications[END_REF],

time-of-flight (TOF) [START_REF] Balcerzak | An overview of analytical applications of time of flight-mass spectrometric (TOF-MS) analyzers and an inductively coupled plasma-TOF-MS technique[END_REF] and ion trap mass analyzers [START_REF] March | Ion Trap Mass Spectrometers[END_REF]. These mass analyzers have different features, including the m/z range that can be covered, the mass accuracy, and the achievable resolution [START_REF] Gross | Mass Spectrometry: A Textbook[END_REF], [START_REF] Haag | Mass Analyzers and Mass Spectrometers[END_REF], [START_REF] El-Aneed | Mass Spectrometry, Review of the Basics: Electrospray, MALDI, and Commonly Used Mass Analyzers[END_REF]. During my thesis work, the Quadrupoles, TOF, and Orbitrap are the mass analyzers we used.

Quadrupoles mass analyzer

As presented in Fig. The applied potentials on the opposed pairs of rods varies sinusoidally as cos(ωt) cycles with time 't'. This results in ions being able to traverse the field free region along the central axis of the rods but with oscillations amongst the poles themselves. These oscillations result in complex ion trajectories dependent on the m/z of the ions.

Specific combinations of the potentials 'U' and 'V' and frequency 'ω' will result in specific ions being in resonance creating a stable trajectory through the quadrupole to the detector. All other m/z values will be non-resonant and will hit the quadrupoles and not be detected (Figure 10). The mass range and resolution of the instrument is determined by the length and diameter of the rods. Quadrupoles mass analyzers are very commonly used in combination with either gaschromatography (GC/MS) or liquid-chromatography (LC/MS) as a simple high throughput screening system. Quadrupoles can also be placed in tandem to enable them to perform fragmentation studies -the most common set-up is the triple quadrupole (Q1qQ3) [START_REF] Yost | Selected ion fragmentation with a tandem quadrupole mass spectrometer[END_REF] mass spectrometer, where Q1 and Q3 are mass filters, q, is the collision cell, which enables basic ion fragmentation studies (tandem mass spectrometry MS/MS) to be performed.

TOF mass analyzer

The principle of TOF mass analyzer is shown in the Figure 11, which is a linear TOF in this figure. The ions are introduced either directly from the ion source of the instrument or from a previous analyzer (eg. Q-TOF) as a pulse. This results in all the ions receiving the same initial kinetic energy. As they pass along the field free drift zone, they are separated by their masses, lighter ions travel faster. This enables the instrument to record all ions as they arrive at the detector and so accounts for the technique's high sensitivity.

Orbitrap mass analyzer

The Orbitrap is an ion trap mass analyzer that consists of two outer electrodes and a central electrode, which enable it to act as both an analyzer and detector.

Figure 12. Simplified schematic of an Orbitrap (https://www.chromacademy.com).

Ion introduction into Orbitrap can be performed after modification of the electric field at the injection port. This can be achieved by using a field compressor which is a small portion of the outer electrode (Figure 12). Ions entering the Orbitrap are captured through "electrodynamic squeezing" after which they oscillate around the central electrode and between the two outer electrodes. Different ions oscillate at different frequencies, resulting in their separation [START_REF] Hu | The Orbitrap: a new mass spectrometer[END_REF].

By measuring the oscillation frequencies induced by ions on the outer electrodes, the mass spectra of the ions are acquired using image current detection [START_REF] Badman | Differential non-destructive image current detection in a fourier transform quadrupole ion trap[END_REF].

The combination between these mass analyzers are also available, which is usually the case for modern mass spectrometer, such as Q-TOF, triple quadrupole-TOF, Q-Orbitrap…

Mass detector

The function of the detector is to respond to ions passing through the mass analyzer. It consists mainly of two parts: a high-energy dynode and an electron multiplier. Among them, the role of the high-energy dynode is to convert the charged ions into electrons, and the electron multiplier will amplify the generated electrons into electrical signals that the software can recognize. Then we have the m/z values of the ions are plotted against their intensities to show the number of components in the sample, the molecular mass of each component, and the relative abundance of the various components in the sample.

The most common types of ion detector used in modern instruments are the photomultiplier, the electron multiplier and the Faraday Cup detector [START_REF] Medhe | Mass Spectrometry: Detectors Review[END_REF].

Separation techniques coupled with mass spectrometry

Mass spectrometry is not only used to analyze pure compounds, but also used to analyze mixture compounds, the latter is even more common in metabolomics as biological samples are usually complex mixture. Although it is possible (and sometime desirable) to do total analysis of mixtures by direct injection, it is often preferable to combine on-line separation and/or chromatography with the mass spectrometry.

There are a number of combining techniques available and Gas Chromatography Mass Spectrometry (GC/MS) and Liquid Chromatography Mass Spectrometry (LC-MS) are the two methods we used during my thesis works.

Gas Chromatography Mass Spectrometry (GC/MS)

Gas chromatography mass spectrometry (GC/MS) comprising a gas chromatograph (GC) coupled to a mass spectrometer, by which complex mixtures of chemicals may be separated before MS analysis. The sample solution is injected into the GC inlet where it is vaporized and swept onto a chromatographic column by the carrier gas (usually helium).

The sample flows through the column and the compounds comprising the mixture of interest are separated by their relative interaction with the coating of the column (stationary phase) and the carrier gas (mobile phase). The latter part of the column passes through a heated transfer line and ends at the entrance to ion source where compounds eluting from the column are converted to ions.

In order for a compound to be analyzed by GC/MS, it must be sufficiently volatile and thermally stable. In addition, functionalized compounds may require chemical modification (derivatization), prior to analysis, to eliminate undesirable adsorption effects that would otherwise affect the quality of the data obtained.

Liquid chromatography coupled to mass spectrometry (LC-MS)

Liquid chromatography (LC) is a widely used method of sample separation prior to analysis and is frequently coupled with mass spectrometry. With LC-MS, solubilized compounds (the mobile phase) are passed through a column packed with a stationary (solid) phase. This effectively separates the compounds based on their weight and affinity for the mobile and stationary phases of the column. This also leads to fragmentation of the sample and its anionization through loss of H + ions.

During my thesis work, Ultra-Performance Liquid Chromatography [UPLC ® Technology] was used, UPLC is a combination of a 1.7μm reverse-phase packing material and a chromatographic system that can operate at pressures in the 6000-15000psi range, these configuration allow dramatic increases in resolution, speed and sensitivity compared to a conventional liquid chromatography [START_REF] Trenerry | 9.16 -Natural Products Research and Metabolomics[END_REF].

Comparison between NMR and MS based metabolomic analysis

In comparison with NMR and MS, each technique provides broad coverage of many classes of organic compounds, including lipids, amino acids, sugars, biogenic amines and organic acids. A detailed comparison of different analytical technologies used in metabolomics is presented in the Table 4.

Table 4. A comparison of different metabolomic analytical technologies.

Adapted from [START_REF] Wishart | Emerging applications of metabolomics in drug discovery and precision medicine[END_REF], [START_REF] Wishart | Advances in metabolite identification[END_REF]- [START_REF] Balog | Intraoperative tissue identification using rapid evaporative ionization mass spectrometry[END_REF].

Sample preparation

Sample types

A large range of biofluids, including blood serum, blood plasma, urine, saliva and cerebrospinal fluid are commonly used samples in metabolomic analysis. Apart biofluids, cell, tissue or even organ could be samples used in metabolomic analysis. In our laboratory, blood serum, blood plasma, urine, saliva, cell, and also liver biopsy are commonly used samples. During my thesis work, blood plasma is the main sample we used.

Quality Control (QC) sample preparation

Several good publications are available in the literature which detailed the QC sample preparation [START_REF] Want | Global metabolic profiling procedures for urine using UPLC-MS[END_REF], [START_REF] Dunn | The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans[END_REF], [START_REF] Vorkas | Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease[END_REF]. Using the QC samples is a way to assess the quality of the data when performing a Principal Component Analysis (PCA, this will be described in the section 2.7. The ideal QC sample is a pooled QC sample of each biological sample in the study.

However, sometimes, due to limited sample amounts or if the study involves large number of samples (such as thousands), then an alternative QC sample should be used.

In case of a large sample size (such as more than 500 samples) the QC may be prepared from the first batch of samples collected. In this situation, the recruitment of subjects should be randomized and the samples should be representative of the entire study group. Alternatively, a commercially available QC sample could be used, for example human serum purchased from commercial suppliers. If neither a pooled QC nor a commercial alternative is available for example in samples with low volumes such as tears or bile then a synthetic substitute may be used [START_REF] Dunn | The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans[END_REF].

Also, preparation of the QCs should follow the same sample procedure used in the preparation of the study biological samples, and the number of freeze-thaw cycles should be concordant between the QC and study biological samples.

The preparation of QC samples can be done by pool samples before or after extraction, depending on the objective to verify. If the objective is trying to investigate all the variation during preparation procedure, then it may be better to pool before extraction, in this case, the reproducibility of the sample preparation technique is the main variation, and this variation can be corrected by adding internal standards in the extraction solvent.

If the purpose is to use the pooled QC to correct for the behavior of metabolites in analytical system, that are not able to be corrected by the internal standard (e.g. metabolites that behave in an opposite pattern to the internal standard), then it is better to pool after extraction and then split into individual aliquots that are run throughout batch sequence at regular interval (such as 1 QC for every 10 samples). In this case, a homogenous identical mixture has created that theoretically should give identical chromatograms, but in fact will reflect any variations in the analytical system (this paragraph is inspired by exchange with Dr. David P. De Souza, Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne).

Sample preparation for NMR based metabolomic analysis

During my thesis work, for NMR based metabolomic analysis, the blood plasma samples were prepared as follow: plasma samples were stocked in the freezer at -80°C, before analysis, samples were at first thawed on the ice. Then, 250 μL of each plasma sample was added in a new clean 1.5 Eppendorf tube, and mixed with 350 μL of D2O which contain 10mM Phosphate-Buffered Saline (PBS), the pH of D2O was adjust to 7.48 at 21.1°C. After that, the mixture was centrifuged at 12 000 RCF, for 10 min at 4°C. Finally, 550 μL of supernatant was transferred into a clean 5mm NMR tube for analysis.

Sample preparation for LC/GC-MS based analysis

Basically, the sample preparation for LC-MS based analysis consists the following steps:

remove proteins, metabolites extraction, lyophilize and reconstitution.

Methanol and acetonitrile are commonly used in LC-MS based analysis to remove proteins and for metabolites extraction. During my thesis work, methanol / plasma 4:1 (volume / volume) was used. The mixture (methanol / plasma) was then centrifugated, the supernatant is drawn and lyophilized in order to concentrate extracted metabolites.

Before analysis with LC-MS, the lyophilized supernatant was reconstituted with usually 1:4 (volume / volume) methanol (or acetonitrile) / water mixed solution. For GC-MS studies, a derivatization step is needed by using derivatization reagent to protect active function group.

The detailed sample preparation method for LC-MS based analysis that we used during my thesis work will be described in the Part Three PRESENTATION OF THE THESIS WORK, section 3.1 "Protocol for blood plasma sample extraction for metabolomics" and section 3.2 "Protocol for blood plasma sample extraction for lipidomics".

Data processing: From raw data to data matrix

In this section, the properties of raw NMR and raw MS data, general preprocessing steps from raw NMR and raw LC-MS data to metabolomic data matrix will be described.

NMR data preprocessing

Raw NMR data, which are a series of intensity values collected as a function of time, thus, it is time domain data, usually with 16,000 or 32,000 entries. The data values are composed of two types, real and imaginary, which reflect the two channels of the NMR receiver. For each time point in the FID, there is a pair of data values and in the order real, imaginary, real, imaginary …, start with FID, the following steps are needed to be performed, during my thesis work, NMRPipe software [START_REF] Delaglio | NMRPipe: a multidimensional spectral processing system based on UNIX pipes[END_REF] was used to perform these steps:

Apodization

The goal of Apodization is to emphasize the early data (mostly signal) in the FID and deemphasize the later data (mostly noise). Which is usually achieved by multiply the FID by an exponential decay function, such as: , where LB is line-broadening, which is the additional line-width in Hertz, and LB is usually set to 0.3 (value used during my thesis work) for proton spectra and 1.0 for carbon spectra [START_REF] Rule | Fundamentals of Protein NMR Spectroscopy[END_REF].

Zeros filling

Zeros filling consists add zeros to the end of the FID, this operation has no effect on the peak positions, intensities, or linewidths of spectrum, but increase the digital resolution in the spectrum.

Fourier transform

The Fourier transform is a mathematical function which converts the time domain data (FID) into a frequency domain spectrum. The Fourier transform extracts from the FID about the different frequencies of the signal, their intensities, the rate at which they decay, which determines the linewidth of each peak in the spectrum. The signals which decay quickly are transformed into board peaks, while signals which decay during a long time will be transformed into sharp peaks.

Phase correction

After Fourier transform FID into frequency domain spectrum, due to imperfections in the RF electronics and variability of samples, it is impossible to start the FID at 0° for all acquisition, so phase correction (or Phasing) is necessary to correct phase errors in order to get absorptive peak shape. Two steps of phase correction may be necessary. The first one is order 0 phase correction. It applies the same phase correction to the entire spectrum and aims to account for any phase shift that may occur independently of the signal frequency. The second one is order 1 phase correction. This time, it applies a phase correction depending on the frequency of the signal. The order 0 phase correction could be sufficient for metabolomic study, it depends on the sequence that is used.

Setting the Reference

This step consists of selecting a reference peak (eg. Tetramethylsilane, TMS) and giving a chemical shift value to this reference, without this reference, the chemical shift scale of the spectrum will be approximative.

However, references such as Trimethylsilylpropanoic acid (TSP) or 4,4-dimethyl-4silapentane-1-sulfonic acid (DSS) are known to have a certain affinity with proteins such as human serum albumin and therefore cause a variation in the chemical shift of the signals [START_REF] Shimizu | Appropriateness of DSS and TSP as internal references for (1)H NMR studies of molten globule proteins in aqueous media[END_REF].

In absence of added reference, the signal of a metabolite whose chemical shift is known and not sensitive to experimental conditions can be used as reference (eg. the anomeric proton doublet of α-glucose at 5.23 ppm) [127].

Another problem, appearing during the statistical processing of the NMR data, is the absolute and the relative position of an NMR signal can be affected by several chemical and physical factors, for example, changes in the magnetic field, changes in pH, in temperature, a different saline concentration, or different relative concentrations of specific ions, and it is not always possible or desirable to eliminate these effects.

To remove misalignment of NMR signals, several algorithms have been proposed in the literature, such as interval-correlation-shifting (or icoshift) program [START_REF] Savorani | icoshift: A versatile tool for the rapid alignment of 1D NMR spectra[END_REF]. The icoshift algorithm derives its name from the basic coshift algorithm [START_REF] Van Den Berg | Warping: Investigation of NMR Preprocessing and Correction[END_REF], [130]. The basic idea is:

independently aligns each NMR signal to a target (which can optionally be an actual signal or a synthetic one like the average, or the median) by maximizing the crosscorrelation between user-defined intervals. The figure below (Figure 13) illustrates an overview of icoshift results when applied to a misaligned set of human urine NMR spectra zoomed into a strongly misaligned region [START_REF] Savorani | icoshift: A versatile tool for the rapid alignment of 1D NMR spectra[END_REF].

Baseline Correction

The baseline is the average of the noise part of the spectrum, ideally, this would be a straight, horizontal line representing zero intensity. In real experiment, it can drift, roll, and wiggle. These errors result for example from erroneous data which are collected at the very beginning of the FID, when the electronics is still recovering from the shock of the exciting RF pulse. This distortion of the baseline can be corrected by subtracting a polynomial function (here an order 1 polynomial function, which is a straight line) [START_REF] Gan | Baseline correction by improved iterative polynomial fitting with automatic threshold[END_REF].

Binning or Bucketing

Binning or Bucketing is an operation to reduce the NMR data dimension (named variables afterwards). In binning, the spectra are divided into bins (called buckets) and the total area within each bin is calculated to represent the original spectrum. The approach consists usually to divide all the spectra with uniform areas width (such as 10 -3 ppm used frequently for 1 H spectrum).

Due to the arbitrary division of peaks, one bin may contain pieces from two or more peaks which may affect the data analysis. Intelligent Binning method [START_REF] Meyer | NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm[END_REF] was then proposed, these methods attempt to split the spectra so that each area common to all spectra contains the same resonance, eg. belonging to the same metabolite. In such methods, the width of each area is then determined by the maximum difference of chemical shift among all spectra.

Exclusion of spectral regions

Spectrum regions that do not contain information on metabolites and are likely to introduce artifacts for statistical analysis are preferably excluded. Thus, spectral regions outside of 0 to 10 ppm are generally removed in metabolomics studies. Another part of the spectrum corresponding to the resonances of solvent signals such as those of water between 4.6 ppm and 5 ppm can also be excluded, because water signal is very strong in NMR spectrum, can lead to a very important variability, and have impact in further statistical analysis.

LC-MS data preprocessing

In LC-MS, the raw data consists of a set of chromatograms (1 for each sample). Each chromatogram representing the intensity of the total ion current as a function of the retention time, there is in fact a third dimension which corresponds to the resolution in mass to charge ration (m/z) of ions detected for each spectrum (Figure 14). Compared to the NMR, the data processing step is even more complex by the presence of a separation technique that brings an extra dimension, a large amount of background noise, artifacts and redundancy information.

Ó Daniel Norena-Caro, Wikipedia Fundamentally, the main steps are: detect masses from mass spectra, construct extracted-ion chromatogram (EICs), detect chromatographic peaks, their alignment and integration of peaks. Specific software and free workflow are available to complete these operations, which may cover the process from raw LC-MS data into data matrix, such as Thermo Scientific™ Compound Discoverer™ software (also preform ion feature identification), Workflow4Metabolomics [START_REF] Giacomoni | Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics[END_REF].

Common steps for NMR data and LC-MS data preprocessing

After preprocessing, raw data (NMR data or LC-MS data) were converted into the data matrix, which is represented in the Table below (Table 5).

Table 5. Schematic representation of a typical metabolomic data matrix.

"Variables" in column correspond to detected mass-TR for LC-MS data or spectral bin for NMR data, observation in row correspond to each analyzed samples and responses, the value in the matrix represents detected LC-MS peak area or NMR spectral bin intensities, here, missing value in response were replaced by "NaN" (Not a Number).

Group information was coded by 0 (control group) or 1 (disease group).

Basically, before further statistical analysis, the following operations (such as Missing value imputation, Normalization, Transformation and Scaling) on the metabolomic data matrix are needed to be performed.

Missing value imputation

The presence of missing values in metabolomics data occur widely and can originate from a number of sources, including for both technical and biological reasons: (1) metabolite is detected in one sample but is not present at any concentration in another sample;

(2) metabolite is present in a sample but at a concentration less than the

… … … … … … … … … … … … …
analytical method's limit of detection, and (3) metabolite is present in a sample at a concentration greater than the analytical method's limit of detection but the data processing software has not detected it and has not reported the metabolite [START_REF] Hrydziuszko | Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline[END_REF], [START_REF] Guida | Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling[END_REF].

Fundamentally, a feature (variable) with more than 20% of missing value will be excluded for further analysis, also, a lot of software such as SIMCA ® (Umetrics ® ) tolerate portion of missing value, thus, missing value is not a major concern in metabolomic data preprocessing.

During my thesis works, for LC-MS data matrix, K-nearest neighbour imputation (KNN)

was used as the missing value imputation method. Briefly, the missing values are replaced by the average of the corresponding (feature specific) non-missing values in the k (here k = 10) closest features in terms of Euclidean distance of the responses across all the samples. Therefore, a unique value is imputed for every missing value in a feature instead of using the same value multiple times [START_REF] Hrydziuszko | Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline[END_REF], [START_REF] Xia | Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst[END_REF]. A detailed Missing value imputation method was described in the publication Di Guida et al. 2016 [START_REF] Guida | Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling[END_REF].

Normalization: Integral, Quotient, Quantile

The objective of normalization is to conserve the maximum biologically variation and minimums errors during sample preparation and data acquisition. Normalization is not absolutely necessary, but in some case, it is crucial, especially for biological fluids.

Normalization is in particular important for urine samples, as metabolites concentration in urine are basically negative correlated with water intake quantity, animal drinking different quantity of water will result different concentration in metabolite among different individual, normalization try to reduce the variation of metabolites concentration which is not biological interesting (this is named the dilution effect).

Several metabolomic data normalization methods have been proposed in the literature.

In our laboratory, Integral, Quotient and Quantile Normalization are the most frequently methods used.

Integral Normalization

Each response (peak intensity or peak area) in a sample is divided by the total sum of the sample and multiplied by a constant (the appropriate constant defined by the response)

to restore the original response form (called "smooth") [START_REF] Craig | Scaling and normalization effects in NMR spectroscopic metabonomic data sets[END_REF].

Probabilistic Quotient Normalization

The probabilistic quotient normalization, which is introduced by Dieterle et al. [START_REF] Dieterle | Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics[END_REF]. This method is based on the calculation of a most probable dilution factor (such as median of quotients) by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum. The reference spectrum can be a QC sample or the median spectrum.

Quantile Normalization

It was initially developed for gene expression microarrays [START_REF] Bolstad | A comparison of normalization methods for high density oligonucleotide array data based on variance and bias[END_REF], [START_REF] Amaratunga | Analysis of Data From Viral DNA Microchips[END_REF] but today it is applied in a wide-range of data types. Quantile normalization is a nonlinear transformation that replaces each feature response (row) with the mean of the features across all the samples with the same rank or quantile. A schematic of quantile normalization is shown in Figure 15. (1) order the feature values within each sample (2) for each feature, average across the rows (3) substitute the raw feature value with the average (4) re-order the transformed values by placing in the original order.

Transformation

Transformations are generally applied to correct for heteroscedasticity, which is the situation that the variability of a variable is unequal across the range of values of the variable that predicts it [START_REF] Kvalheim | Preprocessing of analytical profiles in the presence of homoscedastic or heteroscedastic noise[END_REF], to convert multiplicative relations into additive relations, and to make skewed distributions more symmetric [START_REF] Van Den Berg | Centering, scaling, and transformations: improving the biological information content of metabolomics data[END_REF]. The log transformation is the commonly used method; however, it is unable to deal with the zero values. Thus, generalized logarithm (glog) transformation is proposed, which is a simple variation of ordinary log in order to deal with zero or negative values in the data set [START_REF] Durbin | A variance-stabilizing transformation for gene-expression microarray data[END_REF]. Its formula is:

glog ! (𝑥) = log ! "# √" ! #& ! ! (Eq. 0.6)
where a is a constant with a default value of 1.

Scaling: Mean-centering, UV scaling, Pareto scaling

The objective of "scaling" or "weighting" was to give all variables more reasonable weight (importance) in the modelling. This is especially important in cases where the variables being compared have different response units, such as measure of body weight and body height [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF].

For NMR and mass spectrometric data, variables correspond to peak intensities are areas and hence have the same units, so scaling is not absolutely essential, but is still usually useful. If the original data is not scaled, the variables with the largest variance will tend to dominate the Principal Component Analysis (PCA). For example, a very large variable which is approximately constant for all samples may dominate the first component of PCA (PC1).

Several data scaling methods have been proposed in the literature, the Unit Variance (UV)

scaling and the Pareto scaling (Par) are commonly used methods in our laboratory.

Table 6. Overview of common centering, scaling and transformation methods. In the Unit column, O represents the original Unit, and (-) presents dimensionless data.

Adapted from van den Berg et al. 2006 [START_REF] Van Den Berg | Centering, scaling, and transformations: improving the biological information content of metabolomics data[END_REF].

Comparing the UV scaling with the Pareto scaling, UV scaling gives all variables (signal or noise) equal weight (or importance) in the modeling, however, Pareto scaling gives important variables (mostly signal) more weight than noise, as a result, QC samples will be more closely in PCA scores plot after Pareto scaling than UV scaling, that is why the Pareto scaling is the preferred method in most of cases.

Statistical analysis and Interpretation

Univariate analysis

For two groups study (e.g. health control and disease), to compare each variable in the data, the student's t-test which relies on the comparison of the two-sample means is commonly used. It requires:

1, each sample population have a normal distribution;

2, there should be an equal variance of the two populations;

3, the data is independently sampled.

In case where two sample populations have unequal variances, Welch's t-test, or unequal variances t-test can be applied. For data contains more than two groups, one-way analysis of variance (ANOVA) can be utilized to test the difference between group means of each variable.

When the assumption of normal distribution is not met for the sample population, nonparametric analysis such as Mann-Whitney U test (also called Wilcoxon rank-sum test) can be used to test the difference between two independent samples by comparing their medians. Kruskal-Wallis test is the nonparametric equivalent of ANOVA for comparing data with multiple groups.

Multivariate analysis

To account for the impact of multiple variables (combination of two or more variables) on the outcome of measurement (e.g. health control or disease), or study several variables at one time, multivariate analysis is required. Several multivariate analysis methods have been proposed in the literature, Principal Component Analysis (PCA) and Partial Least Squares Projection to Latent Structures (PLS) are commonly used multivariate methods in metabolomics, as these models have better interpretability than other multivariate methods.

Principal Component Analysis (PCA)

Principal Component Analysis, or PCA, is a dimensionality-reduction method, the idea of PCA is to reduce the number of variables of a data set, while preserving as much information as possible. This is performed by transforming a large set of variables (highly dimension) into a new smaller set of uncorrelated variables (lower dimension), also called Principal Components (PC), that still contains most of the information in the large set.

Principal components are new variables that are constructed as linear combinations or mixtures of the initial variables.

Mathematically, PCA model shows the correlation structure of the data matrix X, approximating it by a matrix product of lower dimension (TP'), called the Principal Components plus a matrix of residuals (E). X = Xbar + TP' + E (Eq. 0.7)

Where

Xbar contains X average.

T is a matrix of scores that summarizes the X-variables.

P is a matrix of loadings showing the influence of the variables.

E is a matrix of residuals, the deviations between the original values and the projections.

This geometrically corresponds to fitting a line, plane or hyper plane to the data in the multidimensional space with the variables as axes [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF]. The scaling of the variables specifies the length and also the direction of the axes of this space.

Scores plot is generated to assess the clustering of different samples, with the corresponding loadings plot demonstrating the variables accounting for the most variation in the specified principal component.

As an unsupervised analysis method, that means without the prior knowledge of the sample classification in the model building, PCA is particularly useful in the first step in metabolomic data analysis to identify how one sample is different from another, which variables contribute most to this difference and whether those variables contribute in the same way (e.g. are correlated) or independently (e.g. uncorrelated) from each other.

A more detailed description of PCA can be found in the book of Jackson, J.E. (1991) [START_REF]A User's Guide to Principal Components[END_REF].

Partial Least Squares Projection to Latent Structures (PLS)

Partial Least Squares Projection to Latent Structures, or PLS, is a supervised analysis method, in contrast with unsupervised method, information of sample class labels (e.g.

health control or disease) are also used in the statistic models building. PLS finds the linear relationship between a matrix Y (dependent variables) and a matrix X (predictor variables), expressed as:

Y = f(X) + E (Eq. 0.8)
Where "E" is a matrix of residuals, the deviations between the original values and the projections [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF].

The PLS used in metabolomics is usually its discriminant version, called Partial Least Square-Discriminant Analysis (PLS-DA), which is a variant of the PLS regression that allows to build a model that maximizes the separation between the classes to which the samples belong. PLS-DA has the advantages of PLS: it can manage a large number of X variables, manage multicollinearities and missing data [START_REF] Wold | Partial Least Squares Projections to Latent Structures (PLS) in Chemistry[END_REF]. The difference between the

Orthogonal PLS modeling (OPLS)

The OPLS is a modification of PLS model, the difference between PLS and OPLS is in their handling of the variance of the X matrix. PLS separates the variability in X into two parts (Figure 16), the systematic and residual parts. The systematic part is the sum of the variability in X that is linearly related to Y (predictive part) and the variability in X that is uncorrelated to Y (orthogonal part). Only the variation related to Y is used to model Y [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF]. The OPLS can, like PLS-DA, be used for discrimination purposes (OPLS-DA).

Figure 16. PLS (left) separates the variability in the X matrix in two parts, the systematic variability (R2X) and the residual variability (E). OPLS (right) further splits the systematic variability, R2X, in two parts, the part that is linearly related to Y (predictive, R2Xpred)

and the part that is uncorrelated to Y (orthogonal, R2Xorth) [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF].

PLS divides the sum of squares of X in two parts, OPLS divides it in three parts. Also, within group and between group variations are separated on both components in OPLS-DA while they were not in PLS-DA (Figure 16), thus, those greatly facilitates the interpretation of the OPLS-DA model. However, OPLS-DA provides no predictive advantage over PLS-DA [START_REF] Tapp | Notes on the practical utility of OPLS[END_REF]- [START_REF] Worley | Multivariate Analysis in Metabolomics[END_REF]. The loading and S-plots [START_REF] Wiklund | Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models[END_REF] are usually used to identify what is different between classes. The S-plot (Figure 18) is one of methods to highlight putative biomarkers from a two group OPLS-DA model with NMR, MS based metabolomics data. Compare with loading plots, S-plot adds another dimension to the loading plot by also providing the p(corr) value. This value indicates the reliability of a variable as a marker while the loading, p, indicates the influence of the variables in the model [START_REF] Eriksson | Multi-and Megavariate Data Analysis Basic Principles and Applications[END_REF].

It should be noted that in the single-Y case (e.g. 1 health control and 1 disease groups), by theory, the OPLS model can only have one predictive component [START_REF] Trygg | Orthogonal projections to latent structures (O-PLS)[END_REF]. However, with multiple Y-variables there can be more than one predictive OPLS component (the case of O2PLS). The AU-ROC is equal to the probability that the classifier will score a randomly drawn positive sample higher than a randomly drawn negative sample. In fact, AU-ROC and Mann-Whitney U test are closely related. From the perspective of Mann-Whitney U statistic, AU-ROC can be explicated in this way, given 2 classes (0 and 1), randomly select one sample from class 1, randomly select the other sample from class 0, and then predict these two random samples with the classification model. The probability of predicting 1 as class 1 is p1, the probability of predicting 0 as class 1 is p0, and the AU-ROC is equal to the probability of p1 > p0.

Model diagnosis and validation

So, the AUC reflects the sorting ability of the classification model for the sample.

According to this explanation, if we classify the samples completely randomly, then the AUC should be close to 0.5. It is also worth noting that AUC is not sensitive to the consistency of sample categories, which is the reason why AU-ROC is usually use to evaluate classifier performance for unbalanced samples [163]- [START_REF] Zweig | Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine[END_REF]. Also, for a two groups classification problem, the Area Under the Receiver Operating characteristic Curve (AU-ROC) will be a better estimator than Q2 to access OPLS-DA model performance.

To Also, it allows to calculate several insightful performance metrics, such as Accuracy: how often is the classifier correct, defined as: "correct predictions / total predictions", calculated by "(TP + TN) / (TP + TN + FN + FP)", with:

True Positive (TP): Observation is positive, and is predicted as positive (eg. they do have the disease, and predicted as disease).

False Negative (FN): Observation is positive, but is predicted as negative.

True Negative (TN): Observation is negative, and is predicted as negative.

False Positive (FP): Observation is negative, but is predicted as positive.

A more detailed description of confusion matrix can be found in the book "Fundamentals of Clinical Data Science, Chapter 8 Prediction Modeling Methodology" [START_REF] Dankers | Prediction Modeling Methodology[END_REF].

Biological interpretation

Biological interpretation increases the information generated by metabolomic, and exploit the relational properties present in metabolomic data by analyzing metabolite patterns from an integrative point of view [START_REF] Alonso | Analytical methods in untargeted metabolomics: state of the art in 2015[END_REF].

In general, after identified important variable or metabolite of interest, the next step is trying to integrate the metabolite of interest into biological network especially metabolic pathway, which reveals metabolites changes (or perturbations) in biological network during diseases, physiological processes or external stimulus. And the possible enzymes controlling the metabolite levels in the cell could be then investigated, by testing its impact further on the metabolite level may promote understanding of biological mechanisms associated with the specific disease. Thus, it may improve our understanding of biological etiology of specific diseases, and providing insight further in the development of targeted treatment methods [9], [START_REF] Kirschner | The meaning of systems biology[END_REF], [START_REF] Suhre | Human metabolic individuality in biomedical and pharmaceutical research[END_REF].

Metabolic pathway can be assessed using biological databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [START_REF] Kanehisa | KEGG for integration and interpretation of large-scale molecular data sets[END_REF], Small Molecule Pathway DataBase (SMPDB) [START_REF] Jewison | SMPDB 2.0: big improvements to the Small Molecule Pathway Database[END_REF], EHMN [START_REF] Ma | The Edinburgh human metabolic network reconstruction and its functional analysis[END_REF], WikiPathways [START_REF] Kelder | WikiPathways: building research communities on biological pathways[END_REF], and MetaCyc [START_REF] Caspi | The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases[END_REF], these databases provide exhaustive information of a large number of metabolic pathways.

Part Three PRESENTATION OF THE THESIS WORK

In order to prepare blood plasma samples for LC-MS analysis, two protocols, with one for blood plasma sample extraction for metabolomics and another for blood plasma sample extraction for lipidomics, were adapted in consideration of the equipment, reagents and chemicals availability in the CSPBAT laboratory.

Protocol for blood plasma sample extraction for metabolomics

The

Protocol for blood plasma sample extraction for lipidomics

The

NMR and UPLC-HRMS based plasma metabolomic and lipidomic profiling in NonAlcoholic Fatty Liver Disease

Nota bene: this document is in preparation for submitting as a research article, the name listed below is a primary list for people who mainly involved in this work.

Xiangping LIN 1,2,3 , Xinyu LIU 3 , Mohamed N. TRIBA 1,2 , Nadia BOUCHEMAL

INTRODUCTION

Non-Alcoholic Fatty Liver Disease (NAFLD) is defined as the presence of steatosis in at least 5% of hepatocytes on liver biopsy assessment or on imaging in patients who have a history of little alcohol consumption (limited daily alcohol intake < 20 g for women and < 30 g for men) or no alcohol consumption at all and have no other cause of hepatic steatosis [START_REF] Hashimoto | Diagnosis and classification of nonalcoholic fatty liver disease and non-alcoholic steatohepatitis: Current concepts and remaining challenges[END_REF]- [START_REF] Estes | Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease[END_REF].

With increasing metabolic diseases (obesity, diabetes …) rates, NAFLD has emerged as a leading global cause of chronic liver disease with the prevalence of more than 25% in the global adult population in the past few decades [START_REF] Younossi | Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[END_REF], [START_REF] Kim | OPTN/SRTR 2017 Annual Data Report: Liver[END_REF].

Despite growing prevalence, the factors involved in NAFLD development and subsequent progression to non-alcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis and hepatocellular carcinoma are poorly understood, however, it is well considered that the pathogenesis of NAFLD is multifactorial, and the main risk factors are such as genetic predisposition (e.g., polymorphisms of patatine-like phospholipase domain-containing protein 3 (PNPLA3) gene) [START_REF] Polyzos | An update on the validity of irisin assays and the link between irisin and hepatic metabolism[END_REF], dietary factors (e.g., fructose), Insulin Resistance (IR) [START_REF] Polyzos | Nonalcoholic fatty liver disease: the pathogenetic roles of insulin resistance and adipocytokines[END_REF], obesity, type II diabetes mellitus, hyperlipidemia, endocrine disruptors [START_REF] Polyzos | The emerging role of endocrine disruptors in pathogenesis of insulin resistance: a concept implicating nonalcoholic fatty liver disease[END_REF] and the gut microbiota dysbiosis [START_REF] Doulberis | Nonalcoholic fatty liver disease: An update with special focus on the role of gut microbiota[END_REF], [START_REF] Leung | The role of the gut microbiota in NAFLD[END_REF].

NAFLD is characterized by an abnormal accumulation of lipids mainly triacylglycerols (TGs) in the liver, based on clinical-histologic characters, NAFLD spectrum range from simple fatty liver (NAFL) or steatosis to the advanced form termed NASH, without therapeutic intervention, a subset of patient with NASH will subsequently progress towards cirrhosis and, ultimately, hepatocellular carcinoma [START_REF] Castera | Noninvasive Assessment of Liver Disease in Patients With Nonalcoholic Fatty Liver Disease[END_REF].

So far, liver biopsy is the gold standard for diagnosis, staging and monitoring progression of NAFLD during treatments. However, biopsy has well-known limitations, such as invasiveness, poor acceptability by patients, sampling variability, and financial cost… which limit its application in a large population. Moreover, recently developed Noninvasive imaging biomarker assessment method, even the most accurate noninvasive liver elastography based methods, such as vibration-controlled transient elastography (VCTE), magnetic resonance elastography (MRE), shear-wave elastography and acoustic radiation force impulse have other limits including couldn't access inflammation, with very limited guidance (or even unavailable) for how clinicians should anticipate and manage the pitfalls of these tests [START_REF] Tapper | Noninvasive imaging biomarker assessment of liver fibrosis by elastography in NAFLD[END_REF].

Thus, the development of an alternative noninvasive and familiar for clinicians' strategy such as using non-invasive biomarkers is an urgent need [START_REF] Younossi | Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[END_REF] for prognostication, staging, selection of patients for treatment and monitoring of the disease.

Previous studies found that plasma metabolome was a better predictor for steatosis (80%) than noninvasive basal clinical data (predictive power of 58%) [START_REF] Hoyles | Molecular phenomics and metagenomics of hepatic steatosis in nondiabetic obese women[END_REF], [START_REF] Delzenne | Microbiome metabolomics reveals new drivers of human liver steatosis[END_REF].

Moreover, in our primary NMR study, lipids may be major patterns which could discriminate NASH patients from NL patients.

The objectives of our study were ( 1 and NASH from NL obese patients, by using appropriate statistical models.

MATERIAL AND METHODS

Reagents and chemicals

For metabolomics, HPLC grade solvent (acetonitrile, methanol) and formic acid were ISs were prepared in methanol as stock solution with the follow concentrations and stored in -20°C before use: PC (19:0/19:0) 0.67 μg/mL, LPC (19:0) 0.33 μg/mL, SM (d18:1/12:0) 0.17 μg/mL, Cer(d18:1/17:0) 0.17 μg/mL, TAG (15:0/15:0/15:0) 0.53 μg/mL, d3-FFA (C16:0) 0.67 μg/mL, and d3-FFA(C18:0) 0.67 μg/mL.

Biological samples

Between

Samples Preparation

For metabolomics, plasma samples were randomized, thaw on ice, then aliquots of each plasma sample were pooled as QC samples. For deproteinization and metabolites extraction, 150 μL of plasma was mixed with 600 μL methanol containing ISs, after vortexed for 2 min, centrifuged for 10 min at 16 000 g at 4 °C. The supernatant was lyophilized and then stock in -80°C before analysis, for quality control during sample preparation, a QC sample was prepared with every 5 plasma samples.

For lipidomics, plasma samples were randomized, then thaw on ice, 20 μL aliquot of each samples were pooled as QC samples. For deproteinization, 40 μL plasma was mixed with 300 μL ice-cold methanol containing ISs, after vortexed 30s, 1 mL MTBE was added in the mixture and vibrated at room temperature for 1 h for lipids extraction. Then, 300 μL water was added followed by vortexing 30 s and stay at 4°C for 10 min. After centrifugation at 14000 g for 15 min at 4 °C, 2 times 400 μL supernatants were transferred into two new Eppendorf tubes, supernatants were lyophilized, then stock in -80°C before analysis, for quality control during sample preparation, a QC sample was prepared for every 5 plasma samples.

The detailed sample preparation was descripted in Part Two METHODOLOGY, section For NMR analysis, 250 µL thawed plasma was prepared with 350 µL deuterated PBS (contain NaN3), which permits the deuterium frequency-field lock, after vortexed mix for 1 min, then centrifuged for 10 min at 12 000 g at 4 °C, 550 µL supernatant was transferred into a clean 5 mm NMR tube.

UPLC-HRMS based Metabolomic analysis

The supernatant was re-dissolved in methanol/water (1:4, v/v) solvent before analysis. 5

μL re-dissolved supernatant was used for Metabolomic analysis, which was performed on a Q Exactive HF (Thermo Fisher Scientific, Rockford, IL) system coupled with an ACQUITY Ultra Performance Liquid Chromatography (UPLC, Waters Corporation, Manchester, U.K.). Column temperature and automatic sampler temperature were set at 60°C and 6°C, respectively. In order to cover as many types of compounds as possible, different columns were used in the positive and negative ionization mode.

Analysis Sequences

For each plasma sample, NMR spectra were acquired from two complementary experiments: One-dimensional 1 H Nuclear Overhauser Effect Spectroscopy NOESY1D presat (NOESY1dgppr sequence) [START_REF] Giraudeau | Optimizing water suppression for quantitative NMR-based metabolomics: a tutorial review[END_REF] and Carr-Purcell-Meiboom-Gill (CPMG presat) [START_REF] Carr | Effects of Diffusion on Free Precession in Nuclear Magnetic Resonance Experiments[END_REF]. The spectral width was 10 kHz.

For MS based metabolomic analysis, the resolutions of full scan MS and ddMS2 were set at 120 000 and 60 000, respectively. The automatic gain control (AGC) target and maximum injection time in full scan MS settings were 1 × 10 6 and 200 ms, while their values were 1×10 5 and 50 ms in ddMS2 settings. The TopN (N, the number of top most abundant ions for fragmentation) was set to 10, and collision energy was set to 15 eV, 30

eV and 45 eV. A heated ESI source was used at positive and negative ion mode. The spray voltage was set as 3.5 kV. The capillary temperature and aux gas heater temperature were set as 300 and 350 °C, respectively. Sheath gas and aux gas flow rate were set at 45 and 10 (in arbitrary units), respectively. The S-lens rf level was 50.

For [START_REF] Howe | RNA-Seq analysis in MeV[END_REF], metabolites were selected with p-value < 0.05, the regrouping of metabolites was performed with pearson correlation based hierarchical clustering.

Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed using in house MATLAB OPLS script based on Trygg and Wold method [START_REF] Trygg | Orthogonal projections to latent structures (O-PLS)[END_REF], analyses were performed with MATLAB ® (R2016b) for macOS (Mathworks, Natick, Massachusetts, USA). Quality parameters of the models, the explained variance (R2Y) and the predictability of the model (Q2Y) were calculated. Q2Y

was calculated by a 7-fold cross validation and confirmed by exploring the impact of permutations in the dataset rows [START_REF] Triba | PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters[END_REF]. To evaluate the discriminatory power of the model, the area under the receiver operating curve during the cross validation (CV-AUROC) and

Confusion Matrix were calculated. The Confusion matrix was generated by a logistic regression, components in OPLS-DA model were used as variables, after a 7-fold cross validation, all samples have a predicted probability, and probability >= 0.5 consider as class 1, probability < 0.5 consider as class 0. Models were validated by permutation tests (n=200). S-plot [START_REF] Wiklund | Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models[END_REF] for OPLS-DA model were used to identify potentials biomarkers.

Identification

For NMR study, identification was performed with the help of 2D experience, Chenomex For MS based lipidomic analysis, identification was performed with MS/MS experience, in-house MS database, and LIPID MAPS [START_REF] Fahy | A comprehensive classification system for lipids[END_REF]- [START_REF] Kyle | LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data[END_REF].

RESULTS

Characteristics of NL, NAFL and NASH Patients

There were 66 females (80%) and 16 males (20%) involved in the present study, the diagnoses of NL, NAFL and NASH were established histologically in liver biopsy specimens. Patient's characteristics and clinical laboratory data are represented in the Table 9. Compared with NL obese patients, there were no significant differences in terms of BMI, Cholesterol and Phospholipid (in the liver) in patients with NAFL or NASH.

Patients with NASH had significant high level of ALT, ASAT, GGT, TG and Fasting blood glucose compared with both Normal Liver obese controls and NAFL patients.

As anticipated, it can be seen that patients with NAFLD (NAFL and NASH) are Insulin

Resistance (HOMA IR) [START_REF] Matthews | Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man[END_REF] and have significantly higher levels of triglycerides in liver.

Because the diagnoses of NL, NAFL and NASH patients was mainly based on these parameters. Comparison Test [START_REF] Dunn | Multiple Comparisons Using Rank Sums[END_REF], which is a post-hoc test for Kruskal-Wallis. This will compare the pairs of groups, but is statistically more sensible than doing pairwise Mann-Whitney tests, In summary, this first part of metabolomics analysis with NMR demonstrated that lipids such as VLDL, LDL and HDL were particularly important to discriminate between NL and NASH patients. To go father, we want to determine lipids or the exact lipid classes which contribute the most in the discrimination. As we know that these metabolites were important in the disease, it is logical to suggest that some lipids may help in discriminating of NL, NAFL and NASH patients. Thus, we performed further UPLC-HRMS based metabolomics and lipidomics analysis to cover as much as possible lipids and metabolites.

UPLC-HRMS based Metabolomics analysis

Global metabolites changes between NL, NAFL and NASH patients

There were 19 NL, 37 NAFL (lost 2 samples during reconstitution before MS analysis) and 24 NASH plasma samples analyzed with an UPLC-HRMS (Orbitrap) based metabolomics according to the procedure described in Material and Method, 198 distinct metabolites (positive and negative mode) were identified in the plasma samples. The changes in metabolites between NL, NAFL and NASH patients were compared by using Kruskal-Wallis test, there were 36 metabolites with p < 0.05, none of them were significative after false discovery rate (FDR) correction (FDR limit 0.05), however, when FDR limit set at 0.25 they were significative. These 36 metabolites were used to generate the Heatmap below (Figure 20). The complete list for detected mass with retention time used for metabolites identification is displayed in Annexe 1. In view of these results, it can be seen that certain metabolites may have a relative different concentration in different stage of NAFLD. Thus, we performed further multivariate statistical analysis to see if combination of several metabolites could be useful to class different stage of disease.

OPLS-DA models for comparison between NL, NAFL and NASH patients

After processing as procedure described in Material and Method, UPLC-HRMS data were used for multivariate statistical analysis, the PCA Score Plot of all analyzed plasma samples was represented in figure below (Figure 21). (Table 10). (bleu) and NASH (red) patients was represented below (Figure 22). Rows represent the real groups; columns list the predicted groupings by metabolomic analysis. The Confusion matrix was generated by using a logistic regression, the predictive component in OPLS-DA model was used as variable, after a 7-fold cross validation (7-fold CV), all samples have a predicted probability, and probability >= 0.5 consider as class 1, probability < 0.5 consider as class 0. Accuracy: Overall, how often is the classifier correct, calculated by (TP+TN)/total, with True Positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease.

True Negatives (TN): We predicted no, and they don't have the disease.

To highlight important metabolites which mainly responsible for the classification of NL, NAFL and NASH patients in OPLS-DA models, the S-plot for OPLS-DA model was employed. S-plot is one of methods used to highlight putative biomarkers from a two group OPLS-DA model. S-plot combines the model covariance (Variable Contribution, in X-axis) and model correlation (Variable Confidence, in Y-axis) from the OPLS-DA model, and project on a scatter plot, which allow to highlight discriminants variables in the OPLS-patients with highlighted important metabolites (red) was represented below (Figure 23), and the list of highlighted important metabolites were given in the table below (Table 12). Method. 419 distinct lipids were identified in patient's plasma (positive and negative mode). The changes in lipids between NL, NAFL and NASH patients were compared by using Kruskal-Wallis test, there were 97 lipids with p < 0.05, and all of them were significative after FDR correction (FDR limit: 0.05). The first 45 lipids with lowest p-value were used to generate the Heatmap below (Figure 24). The complete list for detected mass with retention time used for lipid ions identification is displayed in Annexe 2. Compared with NL obese patients, Sphingomyelin (SM), Phosphatidylcholine (PC) and Ceramides (Cer) were tend to have a lower relative concentration in NAFL and NASH obese patients. In contrast, Triglycerides (TG), Diglycerides (DG) were tend to have a higher relative concentration in NAFL and NASH obese patients than NL obese patients (Figure 24).

OPLS-DA models for comparison between NL, NAFL and NASH patients

After processing as procedure described in Material and Method, UPLC-HRMS data were used to multivariate statistical analysis, the PCA Score Plot of all analyzed plasma samples was represented in figure below (Figure 25). Several OPLS-DA models for classification of NL, NAFL and NASH patients have been investigated. A table which summarize OPLS-DA model's outcome is presented below (Table 13).

Table 13. Summary of OPLS-DA models for lipidomics analysis. The OPLS-DA models (Score Plot, S-plot) for classification of NL, NASH patients and NL, NAFL patients were represented below (Figure 26). The S-plot for OPLS-DA model (Figure 29) was used to highlight important lipids (red) which mainly responsible for the classification of NL and NASH patients. The list of highlighted important lipids was given in the table below (Table 14).

Groups

CONCLUSION AND DISCUSSION

NAFLD is becoming the most common chronic liver condition in the world, however the diagnosis of NAFLD remains challenge currently, and there is an urgent need of noninvasive biomarkers for prognostication, selection of patients for treatment and monitoring of the disease [START_REF] Younossi | Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[END_REF]. The objectives of our study were (1) To the best of our knowledge, this study was the first using NMR, UPLC-HRMS based metabolomics and lipidomics analysis with biopsy confirmed patients in NAFLD. UPLC-HRMS based plasma metabolomics and lipidomics analysis were performed to obtain at first a global view of metabolites and lipids changes in NAFLD patients, then to identify disease-related patterns and to identify further biochemical perturbations.

Our results revealed significant relative changes in certain metabolites and lipids, especially for lipids metabolism, Sphingomyelin (SM), Phosphatidylcholine (PC) and Ceramides (Cer), Phosphatidylethanolamine (PE) were tend to have a lower relative concentration in NAFL and NASH obese patients. In contrast, Triglycerides (TG), Diglycerides (DG), Lysophosphatidylcholines (LPC), lysophosphatidylethanolamine (LPE), Sphingosine, lactamide were tend to have a higher relative concentration in NAFL and NASH obese patients than NL obese patients (Figure 20, Figure 24, Figure 30). Compared with NL obese patients, the changes in the plasma metabolome and lipidome were more distinct in NASH patients than in NAFL patients.

Triglycerides (TGs) and Diglycerides (DG) are mainly lipids involved in NAFLD [START_REF] Cano | Deciphering non-alcoholic fatty liver disease through metabolomics[END_REF], our results confirm the utility of UPLC-HRMS based metabolomics and lipidomics analysis in NAFLD, especially lipidomics, the results from lipidomics (Figure 24) such as changes in TG were concordant with clinic biochemistry analysis (Table 9). For metabolomics analysis, the method used in this study doesn't cover TGs, this may explain why OPLS-DA models for lipidomics are slightly better than metabolomics in our study.

The classification of NAFL patients from NL patients is difficult in our study, this may due to the subjects are already in advanced stage of obesity with a very high BMI. The discrimination of NASH patients from NAFL patients remains a challenge. In classification of NL, NAFL and NASH patients, UPLC-HRMS based lipidomics and metabolomics analysis results in our study are concordant. Nevertheless, the classification model's outcome such as Q2, AU-ROC and confusion matrix are slightly better in lipidomics analysis than in metabolomics analysis.

The strengths of this study were the combination of robustness NMR, UPLC-HRMS based metabolomics and lipidomics analysis with biopsy confirmed samples. However, our study has limits. First, the subjects are already obese and with a very high BMI, this may have impact in metabolism such as lipids metabolism, thus, complicate interpretation of results. Second, it is a relatively small population, therefore, the number of patients in each group may not enough to achieve more strong significant statistic outcome. Third, the precision of LC-MS peak annotation was limited, especially for peaks which have a retention time less than 0.5 minute, as reverse phase UPLC column was used, these peaks correspond to high polarity metabolites, which not or less retained by stationary phase, thus, they retention time will be approximative, also, our in-house database used for metabolomics analysis may not cover other metabolites class which may have strong association with different stages of NAFLD. Moreover, validation in an independent cohort will be necessary.

In the following work, at first, we will interpret more profoundly metabolites and lipids changes, with integration in metabolism pathway. Then, we will try data filtering [START_REF] Hackstadt | Filtering for increased power for microarray data analysis[END_REF] and variable selection approach such as sparse PLS-DA (sPLS-DA) [START_REF] Diaz | Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes[END_REF], [START_REF] Lê Cao | Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems[END_REF], [START_REF] Chun | Sparse partial least squares regression for simultaneous dimension reduction and variable selection[END_REF], to investigate if we could find better model. Besides, validation in an independent cohort was also planned and in preparation.

In conclusion, this study suggested that UPLC-HRMS based metabolomics, especially lipidomics analysis could be promising approach to identify biomarkers in NAFLD.

Nevertheless, it should be underscored that NAFLD is a heterogeneous and complex multi-organ disease [START_REF] Kalhan | Plasma metabolomic profile in nonalcoholic fatty liver disease[END_REF], further investigation should be particularly focus on lipidomics, as well as investigation subtypes of patients, appropriate data processing and statistic model. Moreover, combine with other omics research such as transcriptomics, proteomics, and also clinical characteristics may improve novel subtyping approach of NAFLD patients, allowing further more precisely classification and staging of patients, in order to correctly interpret the biochemical processes behind the disease, which could contribute to the development of appropriate therapy and precision medicine-based management of patients.

UPLC-HRMS based untargeted plasma metabolomics in discovery of early biomarkers associated with risk of prostate cancer

Nota bene: this document is in preparation for submitting as a research article, the name listed below is a primary uncompleted list for people who mainly involved in this work.

Xiangping LIN

INTRODUCTION

Prostate cancer (PCa) is the second most commonly diagnosed cancer and the second leading cause of cancer death (7.1% for incidence) among males [START_REF] Bray | Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[END_REF]. Currently, there is no single definitive test to identify prostate cancer in men [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF]. Prostate-Specific Antigen (PSA) test and digital rectal examination are screening methods used for PCa, for the definitive diagnosis, prostate biopsy and supplementary imaging are required [START_REF] Zadra | Metabolic Vulnerabilities of Prostate Cancer: Diagnostic and Therapeutic Opportunities[END_REF]. The PSA test is a relatively easy to perform test and applicable for population in large scale, however, it has well known limits such as sensitivity, specificity, and can lead to falsepositive and false-negative results [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF].

Although extensive efforts in biomarker discovery during the last decades, including the genome and transcriptome approach, which has contributed to the identification of predictive biomarkers, more sensitive and specific biomarkers are still very demanding in early detection, prognosis, monitoring, and clinical management of PCa patients [START_REF] Penney | mRNA expression signature of Gleason grade predicts lethal prostate cancer[END_REF]- [START_REF] Sharma | Biomarkers for prostate cancer: present challenges and future opportunities[END_REF].

Metabolomics, defined as systematic analysis of metabolites in biofluids [10], [11],

tissues [12], [13] or cells [14], [15] and investigate metabolites changes (or perturbations)

during diseases (eg., cancer) [16]- [18], physiological processes (eg., aging) [19] or external stimulus (eg., drug treatment) [20], [21], has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids [START_REF] Lucarelli | Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer[END_REF]- [START_REF] Kelly | Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence[END_REF].

In this context, combination of UPLC-HRMS (Obitrap) based metabolomics and epidemiological approaches may open new perspectives in PCa research, especially for identifying novel biomarkers, evaluation of risk and investigation the etiology of PCa [START_REF] Mondul | Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study[END_REF]- [START_REF] Ankerst | A case control study of sarcosine as an early prostate cancer detection biomarker[END_REF]. In the present study, a prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort [START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF],

[210], with selected baseline plasma samples from 146 prostate cancer cases and 272 matched controls diagnosed during a 13-year follow-up.

The SU.VI.MAX study (clinicaltrials.gov; NCT00272428) was initially designed as a double-blind placebo-controlled trial with the purpose of assessing the influence of a daily supplementation with nutritional doses of antioxidants on the incidence of cardiovascular diseases and cancers [START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF]. The study design and methods have been previously detailed. Briefly, a total of 13 017 participants were enrolled in 1994-95 for an 8-year intervention trial and were followed up for health events until September 2007 [START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF], [START_REF] Hercberg | A primary prevention trial using nutritional doses of antioxidant vitamins and minerals in cardiovascular diseases and cancers in a general population: the SU.VI.MAX study--design, methods, and participant characteristics. SUpplementation en VItamines et Minéraux AntioXydants[END_REF].

The aim of our present study was to investigate whether UPLC-HRMS (Obitrap) based plasma untargeted metabolomic profiles, established from a simple baseline blood draw from healthy men, and appropriate statistical models, could identify biomarkers, if any, associated with the risk of developing prostate cancer within the following decade. And which may use further to improve our understanding of the aetiology of this complex disease. In order to guide therapy decisions, improve outcomes and reduce overtreatment.

MATERIAL AND METHODS

Reagents and chemicals

HPLC grade solvent (acetonitrile, methanol) and formic acid were purchased from Merck KGaA (Darmstadt, Germany). Ultrapure water (H2O) was collected from a Milli-Q system (Millipore, Billerica, MA). Internal standards (ISs) Carnitine C8:0-d3, Carnitine C16:0-d3, FFA C18:0-d3, CA-d4, CDCA-d5, Phe-d5 and Trp-d5 were purchased from Cambridge Isotope (Tewksbury, MA), FFA C16:0-d3 was purchased from C/D/N Isotopes Inc.

(Pointe-Claire, Québec) and LPC 19:0 was supplied by Avanti Polar Lipids (Alabaster, AL).

Biological samples

A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort [START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF], [START_REF] Hercberg | A primary prevention trial using nutritional doses of antioxidant vitamins and minerals in cardiovascular diseases and cancers in a general population: the SU.VI.MAX study--design, methods, and participant characteristics. SUpplementation en VItamines et Minéraux AntioXydants[END_REF], with selected plasma samples from 146 prostate cancer cases and 272 matched controls diagnosed during a 13-year follow-up. The SU.VI.MAX study (clinicaltrials.gov; NCT00272428) was initially designed as a double-blind placebo-controlled trial with the purpose of assessing the influence of a daily supplementation with nutritional doses of antioxidants on the incidence of cardiovascular diseases and cancers.

The study design and methods have been previously detailed. Briefly, a total of 13 017 participants were enrolled in 1994-95 for an 8-year intervention trial and were followed Information on socio-demographics, smoking habits, physical activity, medication use and health status were collected through self-administered questionnaires. Age at menopause was collected from the participants by follow-up questionnaires.

A 35 mL venous blood sample was collected in sodium heparin Vacutainer tubes (Becton Dickinson, Rungis, France) from all fasting participants. After centrifugation at 4°C, plasma aliquots were immediately prepared and stored frozen at -20°C during less than 2 days and then stored in liquid nitrogen.

Health events were self-reported by the participants in regular follow-up questionnaires.

Then, all relevant medical information and pathological reports were gathered through participants, physicians and/or hospitals and subsequently reviewed by an independent physician expert committee. Validated cases were classified according to the International Chronic Diseases Classification, 10th Revision (ICD-10) [START_REF] Organization | International classification of diseases and related health problems, 10^<th> revision[END_REF].

All participants with a first incident invasive prostate cancer, diagnosed between 1 year after their inclusion in the SU. and non-smokers) and season of blood draw (a priori-defined periods: October-November/December-January-February/March-April-May). The method for control selection was density sampling, i.e. every time a case was diagnosed, two controls were selected from other members of the cohort who, at that time, did not have diagnosed prostate cancer.

Samples Preparation

Plasma samples were randomized, balanced case and control, thaw on ice, then aliquots of each plasma sample were pooled as QC samples. For deproteinization and metabolites extraction, 150 μL of plasma was mixed with 600 μL methanol containing ISs, after vortexed for 2 min, centrifuged for 10 min at 16 000 g at 4 °C. The supernatant was lyophilized and then stock in -80°C before analysis, for quality control during sample preparation, a QC sample was prepared with every 10 plasma samples.

Data Acquisition

Analysis Equipment

An ACQUITY Ultra Performance Liquid Chromatography (UPLC, Waters Corporation, Manchester, U.K.) was coupled with a Q Exactive HF (Thermo Fisher Scientific, Rockford, IL) MS system.

Analyzes by LC/MS

The supernatant was re-dissolved in methanol/water (1:4, v/v) solvent before analysis. 5

μL re-dissolved supernatant was used for Metabolomic analysis, which was performed on a Q Exactive HF (Thermo Fisher Scientific, Rockford, IL) MS system coupled with an ACQUITY Ultra Performance Liquid Chromatography (UPLC, Waters Corporation, Manchester, U.K.). Column temperature and automatic sampler temperature were set at 60°C and 6°C, respectively. In order to cover as many types of compounds as possible, different columns were used in the positive and negative ionization mode.

For electrospray positive ion (ESI+) mode, BEH C8 (2.1 × 50 mm, Waters) column was used to ensure the separation of weakly polar compounds such as carnitine and lipids, the mobile phases were water (A) and acetonitrile (B) with 0.1% formic acid. The flow rate was 0.40 mL/min and the total run time was 12 min. The elution program started with 5%

B and was held for 0.5 min, then linearly increased to 40% B at 2 min and increased to 100% B in 8 min, maintained 2 min, then went back to 5% B in 0.1 min and kept for 1.9 min for post equilibrium.

For electrospray negative ion (ESI-) mode, HSS T3 (2.1 × 50 mm, Waters) column was used to ensure the retention and separation of polar compounds in reverse phase, the mobile phases were water (A) and methanol/water (95:5, v/v) (B) containing 6.5mM

Ammonium bicarbonate. The flow rate was 0.40 mL/min and the total run time was 12 min. The elution program started with 2% B and was held for 0.5 min, then linearly increased to 40% B at 2 min and increased to 100% B in 8 min, maintained 2 min, then went back to 2% B in 0.1 min and kept for 1.9 min for post equilibrium.

Analysis Sequences

The resolutions of full scan MS and ddMS2 were set at 120 000 and 60 000, respectively.

The automatic gain control (AGC) target and maximum injection time in full scan MS settings were 1 × 10 6 and 200 ms, while their values were 1×10 5 and 50 ms in ddMS2 settings. The TopN (N, the number of top most abundant ions for fragmentation) was set to 10, and collision energy was set to 15 eV, 30 eV and 45 eV. A heated ESI source was used at positive and negative ion mode. The spray voltage was set as 3.5 kV. The capillary temperature and aux gas heater temperature were set as 300 and 350 °C, respectively. Sheath gas and aux gas flow rate were set at 45 and 10 (in arbitrary units), respectively. The S-lens rf level was 50.

Data Processing

Statistical Analysis

Nonparametric analysis, Mann-Whitney U test, with Benjamini-Hochberg [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF] base false discovery rate (FDR) correction were performed with Multiple Experiment Viewer (V_4_8_1_r2727_mac) for macOS [START_REF] Howe | RNA-Seq analysis in MeV[END_REF]. Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were performed using in-house MATLAB OPLS script based on Trygg and Wold method [START_REF] Trygg | Orthogonal projections to latent structures (O-PLS)[END_REF], analyses were performed with MATLAB ® (R2016b) for macOS (Mathworks, Natick, Massachusetts, USA). Quality parameters of the models, the explained variance (R2Y) and the predictability of the model (Q2Y) were calculated. Q2Y was calculated by a 7-fold cross validation and confirmed by exploring the impact of permutations in the dataset rows [START_REF] Triba | PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters[END_REF]. To evaluate

RESULTS

UPLC-HRMS based Metabolomics analysis

There were 146 prostate cancer cases and 272 matched controls plasma samples analyzed with an UPLC-HRMS (Orbitrap) based metabolomics according to the procedure described in Material and Method.

For MS data matrix from TraceFinder software, 259 distinct metabolites (positive mode)

were identified in the plasma samples. The changes in metabolites between prostate cancer cases and matched controls samples were calculated by the ratio of their group means (also called "Fold change"). The statistical significance of the changes was analyzed by Mann-Whitney U test, with Benjamini-Hochberg base FDR correction, p < 0.05 considered to be significant. After FDR correction, there were 18 metabolites significative different between cancer and control group. The complete list for detected mass with retention time used for metabolites identification is displayed in Annexe 1.

For MS data matrix from Compound Discoverer software, 323 distinct metabolites (positive mode) were identified in the plasma samples. After primary multivariate analysis with 2 MS data matrixes from TraceFinder software and Compound Discoverer software.

There is an exploitable model with MS data matrixes from TraceFinder software, however, we haven't found exploitable multivariate model yet with MS data matrixes from Compound Discoverer software, with the limitation of time during the PhD, for the follow analysis, we focused only on MS data matrixes from TraceFinder software, and we will exploit MS data matrixes from Compound Discoverer software in a later stage.

OPLS-DA model for metabolomics analysis

Plasma samples of 418 male participants with prostate cancer cases (n=146), matched control (n=272) from SU.VI.MAX cohort were partitioned randomly into 2 cohorts: estimation (70%, Cases: n=102 / Control: n=190) and validation (30%, Cases: n= 44 / Control: n= 82) cohorts, with an equal proportion of case/control.

After processing as procedure described in Material and Method, UPLC-HRMS (Orbitrap) data were used for multivariate statistical analysis. The PCA Score Plot of all analyzed plasma samples was represented in figure below (Figure 31). As represented in the Table 16, confusion matrix confirm that our OPLS-DA model classification performance is good, with high Accuracy (0.83) and low Class error (0.11

for matched control, 0.28 for cancer case). To highlight important metabolites in the OPLS-DA model, the Variable Importance in Projection (VIP) values from the predictive component (VIPpred) was used, VIP value describes a quantitative estimation of the discriminatory power of each individual feature.

Here, 13 metabolites (VIPpred > 2) were selected as discriminant metabolites (Table 17). Logistic regression is a common and powerful regression method for binary classification problem, especially in epidemiology, which allow not only analyze multiple explanatory variables simultaneously, but also reducing the effect of confounding factors [START_REF] Sperandei | Understanding logistic regression analysis[END_REF]. With these selected 13 metabolites, a binary logistic regression analysis was performed in our Estimation cohort (70%, Cancer cases: n=102 / Matched Control: n=190). 7 of these selected 13 metabolites were further selected as biomarker candidate (Table 18). As represented in the figure above (Figure 34) , the area under the ROC curve is 0.9 (95% CI: 0.833 to 0.950), which means with the selected 7 metabolites, randomly select a cancer sample from all cancer samples, randomly select a control sample from all control samples, and then predict two random samples with our model. The probability of predicting cancer sample as cancer is p1, the probability of predicting control samples as cancer is p0, the probability of p1> p0 is 90%. This confirm the prediction power of our model is fairly good.

Moreover, with the OPLS-DA model from established with estimation cohort, we project samples of our validation cohort in the model. As represented in the (Figure 35), there is a clear discrimination of Cancer and Control groups in the projection, which confirm that our OPLS-DA model can well predict new data. Furthermore, confusion matrix calculated with our OPLS-DA model for prediction of sample in validation cohort was generated (Table 19). To sum-up, a scheme simplified different steps in identification and validation of putative Prostate cancer biomarkers in our study (Figure 36). Binary logistic regression analysis with Estimation cohort (70%) Selected 7 metabolites (VIP pred > 2)

Apply Binary logistic regression model in Validation cohort (30%) Calculated AUC candidate. Finally, to test these selected 7 metabolites in a new data set, the binary logistic regression model was applied in the validation cohort, the probability for every sample was calculated, and represented in form of area under the ROC curve.

For, these selected 7 metabolites, they plasma relative change in the cancer and control group were represented below (Figures 37).

CONCLUSION AND DISCUSSION

Prostate cancer (PCa) is the second most commonly diagnosed cancer and the second leading cause of cancer death among males [START_REF] Bray | Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[END_REF]. There is no single definitive test to identify prostate cancer in men currently [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF]. The exist tests like PSA test has limits such as sensitivity, specificity, and can lead to false-positive and false-negative results [START_REF] Nicholson | The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation[END_REF].

More sensitive and specific biomarkers are still very demanding in early detection, prognosis, monitoring, and clinical management of PCa patients [START_REF] Penney | mRNA expression signature of Gleason grade predicts lethal prostate cancer[END_REF]- [START_REF] Sharma | Biomarkers for prostate cancer: present challenges and future opportunities[END_REF]. The objective of our present study was to investigate whether UPLC-HRMS (Obitrap) based plasma untargeted metabolomic profiles, established from a simple baseline blood draw from healthy men, could identify biomarkers, if any, associated with the risk of developing prostate cancer within the following decade. UPLC-HRMS (Orbitrap) based plasma metabolomics analysis were performed with plasma samples of 418 male participants from a randomized, placebo-controlled trial SU.VI.MAX cohort [START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF].

To the best of our knowledge, this study was the first using a robustness 12 minutes UPLC-HRMS (Orbitrap) based metabolomics analysis to investigate the relationship between baseline plasma metabolites profiles and long-term prostate cancer risk in a large prospective male cohort. Our study revealed a panel of 7 metabolites (Table 18)

which may useful for prediction the risk of prostate cancer decade before, with AUC: 0.900 (95% CI: 0.833 to 0.950), Sensitivity: 81.82%; Specificity: 91.46% in our validation cohort (Cases: n= 44 / Control: n= 82). Men characterized by higher fasting plasma levels of Cis-11,14-Eicosadienoic acid, Ethyl oleate and phosphoric acid had a higher risk of developing prostate cancer during the 13-year follow-up (Table 18, Figure 37).

Our result show promising advantage compared with currently used PSA testing, with a cutoff of 4.0 ng/mL has a sensitivity of 67.5-80%, the specificity of PSA at levels higher than 4.0 ng/mL is 60-70% [216].

The strengths of the present study were the combination of a robustness 12 minutes UPLC-HRMS (Orbitrap) based metabolomics analysis with a prospective cohort design and long follow-up. Nevertheless, our study has limits. First, males included in this study were age between 45-60 years, which may not fully represent all male population. Second, metabolomic analysis was performed with a single blood draw, the intraindividual variability of metabolomic profile over time was not controlled in our study.

Third, the precision of LC-MS peak annotation was limited, especially for peaks which have a retention time less than 0.5 minute, as reverse phase UPLC column was used, these peaks correspond to high polarity metabolites, which not or less retained by stationary phase, thus, they retention time will be approximative, also, our in-house database used for metabolomics analysis may not cover other metabolites class which may have strong association with cancer risk. We will investigate latterly MS data matrix from Compound Discoverer software, which integrated a more complete database.

Moreover, validation of the panel of 7 metabolites will be necessary by using a targeted quantitative analysis in an independent prospective cohort.

In the following work, at first, we will interpret more profoundly these 7 metabolites in metabolism pathway, then by using data filtering [START_REF] Hackstadt | Filtering for increased power for microarray data analysis[END_REF] and variable selection approach such as sparse PLS-DA (sPLS-DA) [START_REF] Diaz | Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes[END_REF], [START_REF] Lê Cao | Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems[END_REF], [START_REF] Chun | Sparse partial least squares regression for simultaneous dimension reduction and variable selection[END_REF], to investigate MS data matrix from Compound Discoverer software.

In conclusion, this prospective study suggested that UPLC-HRMS (Obitrap) based plasma untargeted metabolomic profiles, established from a simple baseline blood draw from healthy men, may identify biomarkers, that associated with the risk of developing prostate cancer within the following decade. Still, our preliminary promising findings should be validated in other independent prospective studies, to allow the identification of more robustness biomarkers, that associated with prostate cancer risk. After validated, our study may contribute to (1) develop early screening strategies to predict prostate cancer risk well before symptoms appear, to (2) improve our understanding of the aetiology of this complex disease. In order to guide therapy decisions, improve outcomes and reduce overtreatment.

Metabolomic studies of sepsis and septic shock

Introduction of sepsis and septic shock

Sepsis is a serious medical condition characterized by an exaggerated, uncontrollable immune response to an infection [START_REF] Bone | Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee[END_REF]- [START_REF] Kaukonen | Systemic inflammatory response syndrome criteria in defining severe sepsis[END_REF]. Which occurs in up to 30% of patients in intensive care units (ICUs) [START_REF] Angus | Epidemiology of sepsis: an update[END_REF], [START_REF] Martin | Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes[END_REF]. The term "sepsis" is usually used to describe a progression of infection, with spectrum range from systemic inflammatory response syndrome (SIRS) to septic shock [START_REF] Bone | Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee[END_REF], which can result in multiple organ dysfunction syndrome (MODS) and death [START_REF] Lever | Sepsis: definition, epidemiology, and diagnosis[END_REF]. Currently, standard of care recommends aggressive appropriate antimicrobial therapy, which can led to drug-resistant [START_REF] Langley | Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?[END_REF], moreover, for patient management, there are no reliable biomarkers which can predict outcomes, aid clinical decision and direct more precise therapeutic intervention, thus, new approaches to more accurately phenotype sepsis is an urgent need.

Metabolomics is the science designed to comprehensively study the metabolome, the repertoire of small molecule metabolites, which has been used to investigate in prognosis, risk estimation, early diagnosis, and identification of novel biomarkers of sepsis.

Metabolomics provides novel level of detail, highlighting specific biochemical pathways, by investigating metabolites changes in the pathophysiology of sepsis [START_REF] Langley | Integrative 'omic' analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes[END_REF]- [START_REF] Eckerle | Metabolomics as a Driver in Advancing Precision Medicine in Sepsis[END_REF].

Accumulated results on metabolomics suggest that it is an important approach in prognosis, diagnosis, pathophysiology, and treatment of sepsis. Also, in complement with other systems biology approaches, such as transcriptomics, proteomics, to aid in defining specific sepsis phenotypes and to find novel predictive and prognostic biomarkers that can lead to more personalized management and therapeutics [START_REF] Langley | Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?[END_REF], [START_REF] Langley | Integrative 'omic' analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes[END_REF], [START_REF] Eckerle | Metabolomics as a Driver in Advancing Precision Medicine in Sepsis[END_REF]. Still, independent prospective validation studies are needed to translate metabolomics findings into the clinical applications in sepsis.

Background

Septic shock is the most severe phase of sepsis [1,2]. It is defined as sepsis complicated either by hypotension that is refractory to fluid resuscitation or by hyperlactacidemia and is often accompanied by acute organ failure. Mortality rates associated with septic shock are 20 to 30% in many series, principally due to multiple organ dysfunction syndrome (MODS) [3]. Common strategies for the treatment of septic shock include prompt initiation of therapy to treat the underlying infection with antibiotics, vasopressor therapy, and support for failing organs. In recent years, early goal-directed therapy (EGDT), which improves curative effect, has been extensively applied to improve rescue outcomes [4,5]. However, early personalized prognosis and diagnosis remain challenging due to the complicated etiology and pathogenesis of septic shock. Determination of an acute prognosis in the early stage of sepsis is of great importance to improve therapeutic efficacy and will aid in the development of adapted strategies for different cases. In fact, evaluation of existing biomarkers (e.g., TNF-α, IL-6, and PCT) and clinical scores such as the sequential organ failure assessment (SOFA) [6] have been applied prognostically but their performance (sensitivity, specificity) has not proven adequate for all cases [7]. Thus, new methods for reliable early prognosis are still urgently needed.

Metabolomics has been proven to be a promising tool that aid in the prognosis of sepsis. This is because metabolomics allows to provide comprehensive information of personalized metabolome and therefore to enable the prediction of personalized outcome for septic patients. Previous studies have shown that there are considerable differences in the metabolome fingerprints between septic shock survivors and non-survivors. However, notably, most of the previous studies in human septic patients were designed to be performed by analysis of one unique sampling, and no studies have derived dynamic alterations of patient metabolomes during clinical therapy. However, good outcomes for septic shock are associated with a less severe disease course and a positive therapeutic response to treatment. In a previous study, we reported comprehensive differences in the metabolic profiles between septic shock survivors and non-survivors at the admission to the intensive care unit (ICU), based on a liquid chromatography-mass spectrometry (LC-MS) approach [8]. In this current study, samples from the septic shock patients which were obtained 24 h after ICU admission were also included. The aim of the present study was to analyze the discriminatory ability of metabolic profiles between septic shock survivors and non-survivors at the beginning and 24 h after ICU admission and also to describe the evolution of metabolic profiles for septic shock patients during this period by using 1 H NMR spectroscopy-based metabolomics.

Materials and methods

Patient inclusion

Between January 2009 and December 2011, all consecutive adults admitted to our intensive care unit were enrolled in this study if they had an indisputable or probable septic shock in the first 24 h after ICU admission [9]. Septic shock was defined as the presence of a clinically or microbiologically documented infection and on-going treatment with vasopressor therapy (norepinephrine or epinephrine at a dose ≥ 0.25 μg per kilogram of body weight per minute or at least equal to 1 mg per hour) for at least 6 h to maintain a systolic blood pressure of at least 90 mmHg or a mean blood pressure of at least 65 mmHg. Non-inclusion criteria were (i) patient younger than 18 years, (ii) patient with solid cancer or blood cancer, and (iii) patient with liver cirrhosis or chronic kidney disease. Patients were treated according to the international guidelines for the management of sepsis and shock septic [5].

Biological parameters, hemodynamic parameters, and the use of catecholamine and mechanical ventilation were recorded at inclusion. Cause of septic shock was recorded. To evaluate the severity of the disease, the Sequential Organ Failure Assessment (SOFA) score was calculated during the first day of admission [10]. ICU and hospital length of stay and mortality were recorded. The survival status of each patient was noted 7 days after the first sample.

Sample collection

All the first samplings (H0) were obtained withdrawn just before or immediately after clinical vasopressor therapy initiation on the patients. The second samples were withdrawn 24 h after the beginning of the vasopressor introduction. Blood samples were collected in serum separator tubes (SST). SST were stored for at least 30 min and not more than 1 h and 30 min. After centrifugation (1000×g, 25 °C, 10 min), the serum was stored at -80 °C. All human serum samples were collected and stored, provided by the "center of biologic resources for liver disease", in Jean Verdier Hospital, Bondy, France (BB-0033-00027). Written informed consent was obtained from all subjects or their surrogate decision-maker. The local ethics committee approved the protocol.

Regrouping and matching of samples

As shown in Fig. 1, 122 samples from 70 patients were obtained. Seventy samples were drawn at ICU admission and are noted as H0 samples. 52 samples were obtained 24 h after the first sampling and are noted as H24 samples. During analysis, one H0 sample from a non-survivor who did not have a matching H24 sample was excluded as a spectral outlier. One H24 sample from a survivor was excluded while the H0 sample that belonged to the same patient was retained. The exclusion of the sample was due to the drastically affected NMR gain parameter. The spectrum of this sample was therefore found to be clearly different from the others. Among the non-survivors, 11 patients died prior to the H24 sampling and their H24 samples were therefore not available. For the other samples, each H24 sample was matched with the H0 sample which was collected from the same patient. In this case, 32 pairs for survivors and 19 pairs for non-survivors were obtained. For both septic shock survivor (SSS) and non-survivor (SSN) H0-H24 pairs, two thirds were randomly taken into the training set while the remaining were put into the test set.

Sample preparation and NMR data acquisition

Samples were defrosted at room temperature. A volume of 450 μL of each sample was diluted with 50 μL of D 2 O in an NMR tube of 5 mm diameter. All the samples were then analyzed with a 500-MHz NMR spectrometer (Advance III, Bruker, Germany) at 297 K. The free induction decay (FID) signals were collected onto 64k data points, with a spectral width of 6000 Hz. The 1D 1 H NMR spectra were recorded by the Carr-Purcell-Meiboom-Gill (CPMG) sequence [11] with 128 transients for each spectrum. For several samples, 2D NMR experiments (TOCSY and JRES sequences) were achieved to confirm spectral assignments. The mixing time of the TOCSY spectra was 80 ms with 32 transients.

Data processing

After the FIDS were acquired for all the samples, they were processed using the NMRPipe software [12]. All FIDs were multiplied by a 0.3-Hz exponential line broadening factor prior to Fourier transformation. Phasing of each spectrum was manually adjusted, and baselines were corrected using a linear method. All the spectra were divided into 0.001 ppm buckets between -1 and 10 ppm. The residual water signal (4.6 to 5.5 ppm) was excluded, and the spectral region from 3.16 to 4 ppm was also removed since signals observed in this section represented the infusion of hydroxyethyl starch (HES), which was applied in the ICU to heighten blood tension for the patients who suffered from hypotension. The spectra were then normalized using the probabilistic quotient method [13]. All the buckets were centered by the method of auto-scaling. The peaks were adequately assigned using the Human Metabolome Database (HMDB, www.hmdb.ca) NMR library, the Chenomx

Statistical analyses

All the multivariate analyses were achieved using an in-house code which is based on the code of Trygg and Wold [14], developed using Matlab software (version 2012b, MA, USA). Prior to the establishment of discriminatory analyses, a principal component analysis (PCA) with H0 samples from all the included non-survivors shows that the main variability among these samples does not correspond to the time of the death, as shown in Additional file 1: Figure S2. Another PCA for all the acquired spectra was performed to ensure that there were no outliers (Additional file 1: Figure S3). Orthogonal projections to latent structures-discriminant analyses (OPLS-DA) were performed for differentiating survivors from non-survivors with H0 and H24 samples, respectively. Samples obtained at H0 and H24 from the same patients were paired and analyzed in multilevel models to study the interindividual variability [15]. The paired samples were divided into survivor and non-survivor groups. Two OPLS-DA multilevel models were applied with the survivors and non-survivors, respectively, separating H0 from H24 samples. The models were all validated by cross-validation with 500 permutations of variable X and Y, where X represents the data matrix and Y represents the discriminatory variable for each model [15,16]. For the univariate analyses analyzing the significant differences between two groups, the P values were calculated with Student's T test. The false discovery rate (FDR) was calculated by the Multiple Experiment Viewer Toll (version 4.9.0, OriginLab, Northampton, USA); the correction of P value is performed with "adjusted Bonferroni correction" [17]. The threshold of FDR was set at 0.1 for the screening of the discriminatory metabolites, that is, the variables with FDRs superior to 0.1 were not considered as important discriminants. A significant difference between compared groups was defined with an adjusted P value inferior to 0.05.

Results

Baseline characteristics of patients

The baseline biological characteristics of all the included patients are shown in Table 1. Partial pressure of arterial oxygen (PaO 2 ) and the ratio of PaO 2 to the percentage of inspired oxygen (FiO 2 ) were significantly different between the survivors and non-survivors at H0. Lactate level in non-survivors was also found to be significantly increased than those in survivors. For the clinical scores, SAPSII and SOFA, SAPSII was able to discriminate between survivors and non-survivors. However, due to the sample size, it was not satisfying to predict mortality with SOFA in this study, according to the results both at H0 and H24.

Discriminatory analyses separating septic survivors from non-survivors with samples drawn before treatment (H0)

For the H0 samples, a total of 69 samples were analyzed using an OPLS-DA model (PCA models separating the survivors from the non-survivors prior to the exclusion of the outlier have been illustrated in Additional file 1: Figure S3). Among these, 40 samples were obtained from survivors and 29 were from non-survivors. As is shown in Fig. 2a for H0, a clear separation between the two groups of patients is demonstrated by the score plot. The Q 2 Y, which indicates the predictability of the model, was equal to 0.60 with three components, and the R 2 Y, which indicates the fraction of explained variance of the Y variable, was 0.75, where Y corresponded to the survival condition in the model for the SSS vs. SSN comparison. Cross-validation showed that the model was not over-fitted (Additional file 1: Figure S4). In the loading plot (Fig. 2c), the peaks are colored according to the correlation coefficients, which relate to their contribution to the discriminatory model. Corresponding discriminatory metabolites have been listed in Table 2 with their chemical shifts, multiplicity, correlations, variance importance projections (VIPs), and P values. The concentrations of various amino acids such as alanine, glutamate, glutamine, methionine, and aromatic amino acids were increased in the non-survivors as compared to the survivors. Significant variations between the two groups were also found in energy-associated metabolites including two tricarboxylic acid (TCA) cycle intermediates, citrate and fumarate, and lactate and pyruvate. Ketone bodies, 3-hydroxybutyrate, and acetate were also elevated in the non-surviving patients. The only decreased signal was observed for the N-acetyl moieties of glycoproteins. Together, the results showed considerable differences in the metabolic profiles between the survivors and the non-survivors at H0.

Discriminatory analyses separating septic survivors from non-survivors with samples drawn 24 h after ICU admission (H24)

A second OPLS-DA model differentiating metabolic profiles of SSS from those of SSN were performed with 51 H24 samples. Of these samples, 19 non-survivors were compared with 32 survivors. As shown in Fig. 2b, a separation between SSS and SSN was observed. The R 2 Y and Q 2 Y values in the model were equal to 0.86 and 0.46, respectively, and were calculated with three components. The validation by permutations is shown in Additional file 1: Figure S5. Significant discriminant metabolites were identified referring to the loading plot (Fig. 2d) and are listed in Table 3. Interestingly, increasing levels of most amino acids and energy-related metabolites, as well as the decreases of N-acetyl moieties of glycoproteins, were still detected in SSN, compared with SSS, in line with the findings of the H0 model. Besides, an increase in ketone bodies and diminishing lipid-related signals were only present at H24, but not at H0, in the non-survivors when compared to survivors. Both H0 and H24 unpaired models revealed extensive variations in the metabolic profiles between SSS and SSN at the admission and 24 h after ICU admission.

Discriminatory analysis of the evolution of septic shock from H0 to H24 for septic shock survivors and nonsurvivors On the basis of the separation found between SSS and SSN within the above two discriminant models, we hypothesized that the therapeutic response between SSS and SSN could be different. To verify this hypothesis, SSS and SSN groups were compartmentalized and studied by two multi-level OPLS-DA models which focused on the intraindividual variability of the metabolome between H0 and H24. The pairwise distance of metabolome variations between H0 and H24 samples were analyzed for the patients for whom both H0 and H24 samples were collected.

For the SSS group, 21 pairs were randomly included to establish a model separating the H0 sample from the H24 sample, as shown in Fig. 3a. The Q 2 Y was 0.78 with 2 components, and R 2 Y was 0.94. This model was subsequently applied to 11 other pairs. The predictions for these pairs are shown in Fig. 3b. R 2 Y for the prediction was 0.76, showing a prominent prediction of the classification among H0 and H24 samples. For the SSN model, 4 exhibited opposite H0-H24 metabolome evolutions between SSN and SSS. Accordingly, increases of amino acids, energy-related metabolites, and creatinine and a decline of glycoprotein could be observed during the evolution from H0 to H24 for the non-survivors. However, this was not the case for survivors.

Discrimination between SSS and SSN based on the relative quantification of key discriminators

Spectral signals corresponding to the metabolites in Table 4 were integrated for the spectra of paired samples. As shown in Table 4,t h em o l e c u l e sv a r i e d oppositely during the H0-H24 evolution between the survivors and non-survivors. The time-trend change of area, ΔSignal area H24-H0 ,w a sc a l c u l a t e df o re a c h metabolite. Average values for ΔSignal area H24-H0 of involved metabolites resulting from the SSN model were compared to those from the SSS model, as shown in Fig. 4a. Interestingly, the metabolites were also shown to be discriminant variables in the comparison between SSS and SSN in previously mentioned H0 and H24 unpaired models. We further calculated the area under the ROC curve for the metabolites in order to test their performance in the classification of surviving patients. ROCs for the discriminant metabolites in the H0 and H24 models, as well as for Δ H24-H0 , were performed and are shown in Table 5. Accordingly, based on our data, most ROCs for the metabolites showed slightly better performance in the classification of survival than SAPSII and SOFA, not only within the H0 and H24 models, but also with the value of Δ H24-H0 .

Fig. 2 OPLS-DA between septic shock survivors and non-survivors at H0 and H24. a, b Score plots for the H0 and H24 models, respectively. Blue dots represent the survivors and yellow dots represent the non-survivors. T pred : The components that predict the differences between the groups; Torth: components that do not predict the differences between the groups; c, d Loading plot for the H0 and H24 models, respectively. The color of the peaks indicates the correlation between the marked peak and the classification of the sample. Colors that are close to red correspond to a higher correlation. Positive peaks in the loading plot correspond to metabolites which increased in non-survivors; negative peaks correspond to metabolites that decreased in non-survivors

Discussion

Effective prognosis can help to improve outcomes for septic shock patients. However, septic shock prognosis can be complicated by patient-specific factors that affect responsiveness to therapy. With the use of metabolomic techniques, we have determined the serum metabolome fingerprint of septic shock patients with both H0 and H24 samples. We also investigate the metabolic footprint along with the evolution from H0 to H24. To our knowledge, our study is the first to reveal time-trend metabolic differences using NMR-based metabolomics between septic shock survivors and non-survivors within 24 h after ICU admission.

Metabolic variations for H0 and H24 unpaired models separating SSS from SSN

The H0 and H24 unpaired models reveal the differences of metabolome fingerprint between SSS and SSN at the admission to ICU and at 24 h after the admission. Regarding the common discriminatory metabolites found in both models, consistent increases in energy-related metabolites, creatinine, 1-MH, and several amino acids, as well as decreases in glycoproteins are observed as important signals in the non-survivors. Such variations found in SSN at both H0 and H24 are likely to reflect more severe sepsis-induced inflammatory responses and organ dysfunctions that contribute to poor outcomes. The deregulation of TCA cycle intermediates, such as more concentrated citrate found in the SSN, is one of the consequences of severe stress induced by sepsis [18]. Stress also results in an unregulated catabolism [19]. Enhanced degradation of glycoproteins indicates an aggravated stress in the non-survivors. Also, increases in various amino acids and ketone bodies at H24 in the non-survivors are known as effects of protein breakdown and enhanced lipid oxidation [20]. Notably, ketone bodies have recently been reported to be immune suppressors [21], and elevations in these metabolites may contribute to a negative response in critical illness [22].

Our results also provide evidence for the metabolic variations that are associated with severe organ dysfunction in non-survivors. As shown in Table 1, significantly lower oxygen pressures with higher blood lactate levels indicate the presence of more severe hypoxia in the non-survivors than in survivors. This may be due to mitochondrial disorder, defective TCA cycle [23], which results in dampened aerobic respiration and abnormal energy supply. Severe disorders in energy supply should be an import factor inducing organ failure [24][25][26]. Other variations involving organ dysfunction are found in creatinine and 1-MH. Their elevations in the comparison in SSN are also supported by some other previous studies [27,28]. For each listed metabolite, the sign of C1 is opposite to that of C2; Adj P1, adjusted P value (with Bonferroni correction) of the metabolite in the comparison between H0 and H24 samples for the survivors; Adj P2, adjusted P value of the metabolite in the comparison between H0 and H24 samples for the non-survivors; FDR1, false discovery rate for the P value calculated with the survivors; FDR2, false discovery rate for the P value calculated with the non-survivors; V1, variation in concentration for the metabolites from H0 to H24 for the survivors; V2, variation in concentration for the metabolites from H0 to H24 for the non-survivors; ↑, increased concentration of the metabolite at H24 compared with H0; ↓, decreased concentration of the metabolite at H24 compared with H0. NS, non-significant (Adj P > 0.05) variation

Different variations for some key discriminators between SSS and SSN H0-H24 multilevel models

As shown in Table 4, different pairwise alterations of relevant metabolites in the comparison between SSS and SSN groups indicate distinct trends in development along with clinical therapy. Interestingly, most of these metabolites related to septic shock evolution are in accordance with the discriminatory molecules found with the H0 and H24 unpaired models. Besides the deregulation of energy-related molecules, increases in four amino acids may be associated with severe protein breakdown and muscle wasting for the non-survivors. Notably, serum concentrations in some amino acids, such as alanine, glutamate, and phenylalanine, are otherwise documented to be involved with hemolysis associated with sepsis [29]. Moreover, as shown in Fig. 4b, glutamate is known to be a core amino acid for conversion into TCA cycle intermediates [30]. Its elevation, as well as the elevation of related amino acids such as glutamine and alanine, is associated with increases in citrate.

Phenylalanine can be converted into fumarate. Increases in phenylalanine in patients with poor outcomes have been also reported in other studies [31][32][33]. The conversion from amino acids to TCA cycle intermediates is likely to provide supplementary energy during severe anoxic conditions, however, is detrimental for the outcomes [34]. Creatinine is known to be an important indicator for monitoring renal injury. Decreases in N-acetyl glycoproteins may correspond to a breakdown of proteins. Interestingly, it has been demonstrated by DeCoux et al. [35] that inflammation-induced enriched extracellular glycoproteins are associated with an optimal response during septic shock. The present study not only provides support for the findings in our previous work, which investigated metabolic differences between SSS and SSN at H0 [8], but also reveals that different evolutions in the first 24 h after admission to ICU between septic shock survivors and non-survivors are linked to variations in metabolites identified by this study. The ROC results shown in Table 5 also show that the key metabolite discriminators are good classifiers for separating SSN from SSS during the first 24 h after ICU admission. We have reason to believe that sustained enrichment of energy-related metabolites and amino acids can provide early warning of a bad outcome.

Conclusion

In the present study, we have investigated metabolic differences between the survivors and non-survivors of septic shock with the samples obtained at ICU admission and with those obtained 24 h later. We have provided evidence that the sustained enrichment of energy-supply metabolites and amino acids is predictive of a bad outcome. We suggest that monitoring the relevant metabolites in the first 24 h may help to evaluate early therapeutic response.

Additional files

Additional file 1 Table S1. Assignment of spectra recorded with one exemplar serum sample from a septic shock patient. Figure S1. Assignment of spectra recorded with an example of a representative 1 H-NMR spectrum. The assigned peaks corresponding with the key metabolite discriminants have been marked in the figure. Figure S2. A PCA calculated with H0 samples from 11 nonsurvivors who died during the first 24 h (red dots) and those from the other non-survivors who died from the second day to the seventh day after the first sampling (blue dots). Figure S3. PCA model separating survivors from non-survivors with H0 samples before the exclusion of outlier. One sample of a non-survivor was observed as an outlier for the PCA. This outlier has been removed before statistical analyses. Blue dots: survivors, yellow dots: non-survivors. Figure S4 (respectively S5). Cross-validation by 200 times permutation between X and Y for the OPLS-DA model with H0 samples (respectively H24). The green dots stand for the obtained R 2 value and the blue dots stand for the obtained Q 2 value within the 200 permutations. The Y-axis represents R 2 and Q 2 calculated for every model while the X-axis represents the correlation coefficient between original and permuted response data. Figure S6. Loading plots for paired OPLS-DA models showing important discriminatory metabolites that contribute to the separation between H0 and H24 samples. The paired models for the survivors and non-survivors are shown separately. The peaks are assigned to corresponding discriminatory metabolites. The correlations between the assigned metabolites and the model have been shown with the colors. a: loading plot for the separation between H0 and H24 for the survivors; b: loading plot for the separation between H0 and H24 for the non-survivors. (DOCX 829 kb)

3. 3 . 4 Annexe 1 .Annexe 2 .

 3412 CONCLUSION AND DISCUSSION ......................................................................................................... 3.4 UPLC-HRMS based untargeted plasma metabolomics in discovery of early biomarkers associated with risk of prostate cancer .....................................................................................3.4.1 INTRODUCTION ............................................................................................................................... 3.4.2 MATERIAL AND METHODS ............................................................................................................... 3.4.2.1 Reagents and chemicals ........................................................................................................... 3.4.2.2 Biological samples .................................................................................................................... 3.4.2.3 Samples Preparation ................................................................................................................ 3.4.2.4 Data Acquisition ....................................................................................................................... 3.4.2.4.1 Analysis Equipment .......................................................................................................... 3.4.2.4.2 Analyzes by LC/MS ........................................................................................................... 3.4.2.4.3 Analysis Sequences .......................................................................................................... 3.4.2.5 Data Processing ........................................................................................................................ 3.4.2.6 Statistical Analysis .................................................................................................................... 3.4.2.7 Identification ............................................................................................................................ 3.4.3 RESULTS ........................................................................................................................................... 3.4.3.1 UPLC-HRMS based Metabolomics analysis .............................................................................. 3.4.3.2 OPLS-DA model for metabolomics analysis ............................................................................. 3.4.4 CONCLUSION AND DISCUSSION ....................................................................................................... 3.5 Metabolomic studies of sepsis and septic shock ................................................................ 3.5.1 Introduction of sepsis and septic shock ........................................................................................... 3.5.2 Nuclear magnetic resonance-based serum metabolomic analysis reveals different disease evolution profiles between septic shock survivors and non-survivors ..................................................................... Part Four GENERAL CONCLUSIONS AND OUTLOOK ........................................................ ANNEXES ....................................................................................................................... List of detected mass and retention time used for metabolites identification. ........ List of detected mass and retention time used for lipid ions identification. ............ BIBLIOGRAPHY .............................................................................................................. ABSTRACT .....................................................................................................................

Figure 35 .

 35 Figure1. An overview from genomics to metabolomics. Figure2. A schematic representation of metabolomic applications. Figure3. The liver and nearby organs. Figure4. Changes in core metabolic pathways in prostate cancer. Figure5. the main steps of the metabolomic analysis. Figure6. A simplified explanation of the principle behind NMR spectroscopy. Figure7. Simplified illustration of an NMR spectrometer. Figure8. Simplified illustration of a Mass spectrometer. Figure9. Simplified explanation of Electrospray Ionization (ESI). Figure10. Simplified schematic of a Quadrupoles mass analyzer. Figure11. Simplified schematic of a TOF mass analyzer. Figure12. Simplified schematic of an Orbitrap Figure13. A misaligned set of human urine NMR spectra before and after icoshift. Figure14. Schematic representation in 3 dimensions of a LC-MS chromatogram. Figure15. A schematic of quantile normalization Figure16. PLS (left) separates the variability in the X matrix in two parts, the systematic variability (R2X) and the residual variability (E). Figure17. Score plot of an OPLS-DA model, within group and between group variations are separated on both components. Figure18. S-plots of an OPLS-DA model, p(corr) indicates the reliability of a variable as a marker while the loading, p, indicates the influence of the variables in the model. Figure19. A, Score plot of OPLS-DA. Figure19. B, 200 times permutation test for OPLS-DA. Figure19. C, Covariance plot of OPLS-DA. Figure20. Heatmap shows metabolites changes between NL, NAFL and NASH patients. Figure21. PCA Score Plot for all analyzed plasma samples. Figure22. Score Plot of OPLS-DA model for classification between NL (bleu) and NASH (red) patients. Figure23. S-plot for OPLS-DA models highlight important metabolites (red) in the classification between NL and NASH patients. Figure24. Heatmap shows lipids changes between NL, NAFL and NASH patients. Figure25. PCA Score Plot for all analyzed plasma samples. Figure26. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients. Figure27. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients. Figure28. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients. Figure29. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients. Figure30. Box plots of plasma levels of selected metabolites or lipids in NL (0), NAFL (1) and NASH (2) patients. Figure31. Score Plot of PCA for all analyzed plasma samples. Figure32. Score Plot of OPLS-DA models for Estimation cohort. Figure33. 999 times permutation test of OPLS-DA model for R2 (red) and Q2 (bleu). Figure34. ROC curve for validation cohort. Figure35. Projection validation cohort samples in estimation cohort OPLS-DA model.

Figure 36 .

 36 Figure 36. Scheme for the identification and validation of putative Prostate cancer biomarkers.

Figure 3 .

 3 Figure 3. The liver and nearby organs. (Image credit: Don Bliss, National Cancer Institute)

NAFLD:

  Nonalcoholic fatty liver disease; SAMe: S-adenosylmethionine; PC-PUFA: Phosphatidylcholines containing polyunsaturated fatty acids; VLDLTG: Very low-density lipoprotein-triglycerides; DNL: de novo lipogenesis; KO: Knockout; MCD: Methionine and choline deficient.

Figure 4 .

 4 Figure 4. Changes in core metabolic pathways in prostate cancer[START_REF] Mcdunn | Metabolomics and its Application to the Development of Clinical Laboratory Tests for Prostate Cancer[END_REF]. Major metabolic pathways changes in the malignant tissues compared with normal prostate tissues are displayed. Metabolites in red boxes have been observed to be increased in prostate tumors relative to cancer-free prostate tissue, while green boxes indicate a decrease[START_REF] Trock | Application of metabolomics to prostate cancer[END_REF].Elevated metabolites are seen in pathways related to membrane phospholipid synthesis, methylation and oxidative stress. Increases in branched chain amino acid (BCAA) metabolism are suggested by an increase in BCAA related carnitines and an increase in the three BCAAs. TCA cycle intermediates were elevated along with glutamine and glutamate which can feed the TCA cycle through 2-ketoglutarate. Citrate, which acts as an intermediate in the TCA cycle and is also utilized in fatty acid synthesis, was observed to be lower in prostate cancers. Unlike many tumor types, prostate cancer tissue did not display a large increase in glycolysis intermediates typical of a shift in energy metabolism away from mitochondrial oxidative phosphorylation and toward aerobic glycolysisalthough lactate and alanine which can be markers of increased aerobic glycolysis were elevated. The inter-conversion of glycine and sarcosine is highlighted in the dashed box (Legend of figure adapted from E. McDunn et al.[START_REF] Mcdunn | Metabolomics and its Application to the Development of Clinical Laboratory Tests for Prostate Cancer[END_REF]).

Figure 7 .

 7 Figure 7. Simplified illustration of an NMR spectrometer [93].

Figure 9 .

 9 Figure 9. Simplified explanation of Electrospray Ionization (ESI). (by Paul J. Gates 2014)

Figure 10 .

 10 Figure 10. Simplified schematic of a Quadrupoles mass analyzer. (by Paul J. Gates 2014)

Figure 11 .

 11 Figure 11. Simplified schematic of a TOF mass analyzer. (by Paul J. Gates 2014)

Figure 13 .

 13 Figure 13. A misaligned set of human urine NMR spectra before (a) and after(b) icoshift [128].

Figure 14 .

 14 Figure 14. Schematic representation in 3 dimensions of a LC-MS chromatogram.

Figure 15 .

 15 Figure 15. A schematic of quantile normalization (Stephanie C. et al. 2014) [147].

  PLS and the PLS-DA is based on the nature of the Y variables, for a classical PLS the Y are quantitative variables, for the PLS-DA the Y variables may be qualitative or categorical.PLS, as well as PLS-DA allows the construction of an explanatory model. This model thus makes it possible to highlight metabolites (variables) whose intensity is characteristic of a given biological state and which contribute to the separation of the different groups. PLS uses variable importance to projection (VIP) scores to demonstrate the contribution of each variable in the PLS model, a metabolite with VIP score > 1 considered an important variable in classification as the average VIP scores is 1. A more detailed description of PLS can be found in Wold et al, 2001[START_REF] Wold | PLS-regression: a basic tool of chemometrics[END_REF].

Figure 17 .

 17 Figure 17. Score plot of an OPLS-DA model, within group and between group variations are separated on both components [151].

Figure 18 .

 18 Figure 18. S-plots of an OPLS-DA model, p(corr) indicates the reliability of a variable as a marker while the loading, p, indicates the influence of the variables in the model [151].

  Several diagnostic statistics approaches are currently employed in the optimization and the assessment of performance of PLS-DA models in metabolomics data analyses, such as cross-validated explained variation Q2 and the Area Under the Curve of a Receiver Operating Characteristic (AU-ROC) analysis. The Q2 estimates the predictability of the model, which means the ability of model to correctly class a new set of data, the more the value of Q2 close to 1 the better the predictability of the model [163].

  The basic of Cross Validation (CV) involves splitting the data into a training set and a test set. The training set is used to build the classification models (e.g. PLS-DA model), and the model is then applied to predict the outcome of the test set, the process will repeat several times until all subjects will predicted. Leave-one-out CV and k-fold CV are commonly used method for Cross Validation (7-fold CV is the default method used in SIMCA ® Umetrics ® ). A permutation test can assess whether the classification based on true sample class is significantly better than classification based on randomly assigned sample class. The principle of permutation test is to compare model outcomes between the classification based on true sample class (really Y) and the classification based on randomly assigned sample class (randomly assigned Y).

  3 a) Preparation of QC and plasma for NAFLD samples: 3 a1)-Day 1, randomize sample order and balance case and control then thaw all samples at room temperature, mix on ice 50 μL of each (81x50μL= 4050 then add excluded samples to yield 5000 μL) into a 15mL Centrifuge tube, vortex mix 60s to ensure mixing 3 a2) -Aliquot of 100 μL mix into 30 new Labeled 1.5 mL Ep tubes (aliquot of 40 μL mix into 30 new Labeled 2mL Ep tubes for lipidomics) 3 a3)-(take 40 samples and 15 QC, then stock the rest into -80°C) For day 2, take 41 samples and 15 QC, thaw at room temperature, transfer 100 μL of plasma or QC sample into new labeled 1.5 mL Ep tubes and add 400 μL methanol solvent. Vortex mix 60s to ensure mixing 3 a4)-Centrifuge @ 16 000xg at 4°C for 10 min, then transfer 2 times 200 μL of the supernatant into 2 new Labeled 1.5 mL Ep tubes (one for +, one for -) 3 b) Lyophilize QC and plasma samples -Lyophilize QC and plasma samples then stock into -80°CREFERENCES:[START_REF] Dunn | The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans[END_REF],[START_REF] Wu | Altered Lipid Metabolism in Recovered SARS Patients Twelve Years after Infection[END_REF]-[START_REF] Gika | Sample preparation prior to the LC-MS-based metabolomics/metabonomics of blood-derived samples[END_REF]

  3 a) Preparation of QC and plasma, NAFLD samples: 3 a1)-Day 1, randomize sample order and balance case and control then thaw all samples at room temperature, mix on ice 50 μL of each (81x50μL= 4050 then add excluded samples to yield 5000 μL) into a 15mL Centrifuge tube, vortex mix 60s to ensure mixing 3 a2) -Aliquot of 100 μL mix into 30 new Labeled 1.5 mL Ep tubes (aliquot of 40 μL mix into 30 new Labeled 2mL Ep tubes for lipidomics) 3 a3)-(take 40 samples and 15 QC, then stock the rest into -80°C) For day 2, take 41 samples and 15 QC, thaw at room temperature, transfer 40 μL of plasma or QC sample into new labeled 2mL Ep tubes and add 300 μL methanol solvent. Vortex mix 30s to ensure mixing Add under Fume hood 1mL of MTBE into each tube, and shaken at room temperature with an oscillator for 1 hour, then add 250 μL milli-Q water 3 a4)-Centrifuge @ 10 621xg at 4°C for 10 min, then transfer 2 times 400 μL of the supernatant into 2 new Labeled 2mL Ep tubes (one for +, one for -) 3 b) Lyophilize QC and plasma samples -Lyophilize QC and plasma samples then stock into -80°CREFERENCES:[START_REF] Dunn | The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans[END_REF],[START_REF] Wu | Altered Lipid Metabolism in Recovered SARS Patients Twelve Years after Infection[END_REF]-[START_REF] Gika | Sample preparation prior to the LC-MS-based metabolomics/metabonomics of blood-derived samples[END_REF]

  ) describe the relative plasma metabolome and lipidome changes in Nonalcoholic fatty liver (NAFL) and in Non-Alcoholic SteatoHepatitis (NASH) compared with Normal Liver (NL) obese patients, (2) investigated whether Ultra-Performance Liquid Chromatography (UPLC) coupled with High-resolution mass spectrometry (HRMS) based plasma metabolomics and lipidomics analysis could help to identify potentials biomarkers, if any, associated with different stages of NAFLD (NAFL, NASH), and (3) identify metabolomic or lipidomic patterns which could discriminate NAFL

  purchased from Merck KGaA (Darmstadt, Germany). Ultrapure water (H2O) was collected from a Milli-Q system (Millipore, Billerica, MA). Internal standards (ISs) Carnitine C8:0-d3, Carnitine C16:0-d3, FFA C18:0-d3, CA-d4, CDCA-d5, Phe-d5 and Trp-d5 were purchased from Cambridge Isotope (Tewksbury, MA), FFA C16:0-d3 was purchased from C/D/N Isotopes Inc. (Pointe-Claire, Québec) and LPC 19:0 was supplied by Avanti Polar Lipids (Alabaster, AL). For lipidomics, HPLC grade solvent (acetonitrile, methanol and isopropanol), ammonium acetate and tert-butyl methyl ether (MTBE) were purchased from Merck KGaA (Darmstadt, Germany). Ultrapure water (H2O) was collected from a Milli-Q system (Millipore, Billerica, MA). Internal standards (ISs) phosphatidylcholine PC(19:0/19:0), lysophosphatidylcholine LPC(19:0), sphingomyelin SM(d18:1/12:0), ceramide Cer(d18:1/17:0) were purchased from Avanti Polar Lipids (Alabaster, AL). Free fatty acid d3-FFA (C16:0), free fatty acid d3-FFA (C18:0) and triacylglycerol TAG(15:0/15:0/15:0) were purchased from C/D/N Isotopes Inc. (Pointe-Claire, Québec), Cambridge Isotope (Tewksbury, MA), and Merck KGaA (Darmstadt, Germany), respectively.

  June 2011 and May 2015, 82 obese patients were recruited into the digestive and metabolic surgery service in the Jean Verdier University Hospital, Bondy, France. These patients were candidates for a bariatric surgery (Laparoscopic adjustable gastric banding, longitudinal sleeve gastrectomy or Roux-en-Y Gastric Bypass). A total of 82 blood plasma were collected from 82 patients. Biopsies were performed intraoperatively by laparotomy during bariatric surgery. All patients included in the cohort signed a consent covering intraoperative liver biopsy and subsequent use of frozen specimens and blood plasma. Inclusion criteria were: (1) age 18 and over with morbid obesity: BMI ≥ 40 or BMI ≥ 35 with at least one associated comorbidity (hypertension arterial disease, type 2 diabetes, dyslipidemia, obstructive sleep apnea syndrome, osteoarthritis and / or NAFLD); (2) absence of other hepatic infection (exclusion criteria): autoimmune or infectious hepatic disease (chronic viral hepatitis), hemochromatosis, history of chemotherapy or hepatotoxic drugs, alcohol consumption over 20 g / day for women and over 30g / day for men ; (3) Physical and psychological eligibility for a bariatric surgical procedure (decided at the multidisciplinary consultation meeting in the surgery service).Liver biopsy was performed on the outer edge of the left lobe of the liver with a depth of 2 cm minimum in the parenchyma. This biopsy was immediately divided into three portions: 1) a part for metabolic flow analyzes; 2) a part for histological analysis; 3) a part for direct freezing at -80° C. Histological analysis based on an algorithm described by Bedossa et al.[START_REF] Bedossa | Histopathological algorithm and scoring system for evaluation of liver lesions in morbidly obese patients[END_REF], which allowed the classification of liver biopsies patients in three categories: Normal Liver (NL; steatosis, inflammation and ballooning = 0); steatosis (NAFL; steatosis 1-3 + either inflammation 1-3 or ballooning 1-2) and NASH (steatosis 1-3 with 1-3 inflammation and 1-2 ballooning + 1-4 fibrosis).

For

  electrospray positive ion (ESI+) mode, BEH C8 (2.1 × 50 mm, Waters) column was used to ensure the separation of weakly polar compounds such as carnitine and lipids, the mobile phases were water (A) and acetonitrile (B) with 0.1% formic acid. The flow rate was 0.40 mL/min and the total run time was 12 min. The elution program started with 5% B and was held for 0.5 min, then linearly increased to 40% B at 2 min and increased to 100% B in 8 min, maintained 2 min, then went back to 5% B in 0.1 min and kept for 1.9 min for post equilibrium.For electrospray negative ion (ESI-) mode, HSS T3 (2.1 × 50 mm, Waters) column was used to ensure the retention and separation of polar compounds in reverse phase, the mobile phases were water (A) and methanol/water (95:5, v/v) (B) containing 6.5 mM Ammonium bicarbonate. The flow rate was 0.40 mL/min and the total run time was 12 min. The elution program started with 2% B and was held for 0.5 min, then linearly increased to 40% B at 2 min and increased to 100% B in 8 min, maintained 2 min, then went back to 2% B in 0.1 min and kept for 1.9 min for post equilibrium.

  software and Human Metabolome Database (HMDB) (version 4.0). For MS based metabolomic analysis, identification was performed with MS/MS experience, in-house MS database, TraceFinder software (version 4.1, Thermo Fisher Scientific, Rockford, IL) and Human Metabolome Database (HMDB) (version 4.0).

  significant differences between groups (p < 0.05) are represented by *: NL -NAFL, †: NL -NASH, and ‡ : NAFL -NASH. BMI: Body Mass Index, TG: Triglycerides, AST: Aspartate transaminase, ALT: Alanine transaminase, GGT: Gamma-Glutamyl Transferase, HOMA: Homeostatic model assessment [199], is a method for assessing β-cell function and Insulin Resistance (IR), NS: Non Significant, NL: Normal Liver, NAFL: Nonalcoholic fatty liver or steatosis, NASH: Non-Alcoholic SteatoHepatitis, SD: Standard deviation.

  OPLS-DA models for classification of NL, NAFL and NASH patients were investigated, the model for classification of NL and NASH patients was tested firstly as these 2 groups patients represent normal and the advanced stage of NAFLD, respectively, which means they have the maximum difference in the stage of NAFLD. An OPLS-DA model for classification of NL and NASH patients was obtained with 2 components (1 predictive and 1 orthogonal) as represented in Figure 1. The score plot of the OPLS-DA model for classification of NL and NASH patients was represented in Figure 1. A. To estimate the model performance, OPLS-DA models were evaluated by 200 times permutation tests (as represented in Figure 19. B), and repeated (n = 200) 7-fold CV-AUC, with R2Xcum: 0.64, R2Ycum: 0.48, R2 is the indicator of how model fit the data, the more closely to 1 the better the fit, Q2cum: 0.39, Q2 is the capacity of model to correctly class a new dataset, the more closely to 1 the better the model predictability. CV-AUC: 0.896, which means the probability that the OPLS-DA model will score a randomly chosen NASH classed as NASH patient higher than a randomly chosen NASH classed as NL patient is 89.6%. All of which indicate that our OPLS-DA model for classification of NL and NASH patients is fairly stable. The variables in the model that contributed importantly in the classification of NL and NASH patients are represented in figure 19.C. The covariance plot of OPLS-DA was generated, the covariance plot restores the form of NMR spectra, and colored it with correlation score with NASH, the red spectra regions were having a correlation > 0.5 with NASH.

Figure 19 .Figure 19 .

 1919 Figure 19. A, Score plot of OPLS-DA (NL vs NASH, 1D NOESY Data)

Figure 20 .

 20 Figure 20. Heatmap shows metabolites changes between NL, NAFL and NASH patients. Metabolites were selected with p-value < 0.05, the regrouping of metabolites was performed with hierarchical clustering (pearson correlation).

Figure 21 .

 21 Figure 21. PCA Score Plot for all analyzed plasma samples. NL (bleu, n=19), NAFL (green, n=37) and NASH (red, n=24).

Figure 22 .

 22 Figure 22. Score Plot of OPLS-DA model for classification between NL (bleu) and NASH (red) patients (Metabolomics analysis).

Figure 23 .

 23 Figure 23. S-plot for OPLS-DA models highlight important metabolites (red) in the classification between NL and NASH patients (Metabolomics analysis).

Figure 24 .

 24 Figure 24. Heatmap shows lipids changes between NL, NAFL and NASH patients. Lipids were selected with p-value < 0.05, the regrouping of metabolites was performed with hierarchical clustering (pearson correlation).

Figure 25 .

 25 Figure 25. PCA Score Plot for all analyzed plasma samples. NL (bleu, n=19), NAFL (green, n=39) and NASH (red, n=24).

Figure 26 .

 26 Figure 26. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients (Lipidomics analysis).

Figure 27 .

 27 Figure 27. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients (Lipidomics analysis).

Figure 28 .

 28 Figure 28. Score Plot of OPLS-DA models for classification between NL (bleu) and NASH (red) patients (Lipidomics analysis).

Figure 29 .

 29 Figure 29. S-plot for OPLS-DA models highlight important lipids (red) in the classification between NL and NASH patients (Lipidomics analysis).

Figure 30 .

 30 Figure 30. Box plots of plasma levels of selected metabolites or lipids in NL (0), NAFL(1) and NASH (2) patients. Probabilistic Quotient normalized areas are presented on the yaxis. The circle represents a sample, the red triangle represents a "far out" value.

  up for health events until September 2007. The written informed consent for each participant was obtained. Participants were advised against taking any self-prescribed supplements during the trial. The study was conducted according to the Declaration of Helsinki guidelines and was approved by the Ethics Committee for Studies with Human Subjects of Paris-Cochin Hospital (CCPPRB 706/2364) and the 'Commission Nationale de l'Informatique et des Libertés' (CNIL 334641/907094)[START_REF] Hercberg | The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals[END_REF],[START_REF] Hercberg | A primary prevention trial using nutritional doses of antioxidant vitamins and minerals in cardiovascular diseases and cancers in a general population: the SU.VI.MAX study--design, methods, and participant characteristics. SUpplementation en VItamines et Minéraux AntioXydants[END_REF]. At enrolment, all participants underwent a clinical examination by the study nurses and physicians, with anthropometric measurements and a blood draw, occurring after a 12-hour fasting period.

  VI.MAX cohort in 1994-95 and September 2007, were included in this nested case-control study (n = 146). Incident prostate cancers diagnosed during the first year of follow-up were excluded to avoid reverse causality bias and guarantee the prospective design. For each case, two controls were randomly selected and matched for baseline age (45-49 years/50-54 years/55-59 years/>60 years), body mass index (BMI) (underweight, normal weight and overweight/obese), intervention group of the initial SU.VI.MAX trial (placebo/supplemented), smoking status (current smokers

Raw

  MS data were collected, to optimize MS data extraction, TraceFinder software (version 4.1, Thermo Fisher Scientific, Rockford, IL) and Compound Discoverer software (version 3.0, Thermo Fisher Scientific, Rockford, IL) were used for peak extraction and integration. 2 MS data matrixes were generated, with one from TraceFinder software, the other from Compound Discoverer software.

  the discriminatory power of the model, the area under the receiver operating curve during the cross validation (CV-AUROC) and Confusion Matrix were calculated. The Confusion matrix was generated by a logistic regression, components in OPLS-DA model were used as variables, after a 7-fold cross validation, all samples have a predicted probability, and probability >= 0.5 consider as class 1, probability < 0.5 consider as class 0. Models were validated by permutation tests (n=200). VIPpred of OPLS-DA model were used to identify potentials biomarkers. Binary logistic regression analysis for biomarker selection was performed with IBM ® SPSS ® Statistics V25.0 for macOS (Armonk, NY: IBM Corp.). For variable selection, forward conditional selection method was used. MedCalc Statistical Software version 19.1 (MedCalc Software bv, Ostend, Belgium; https://www.medcalc.org; 2019) was used for ROC curve and the box-and-whisker plot (Tukey, 1977), ROC curve use the method of DeLong et al. (1988)[START_REF] Delong | Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach[END_REF], the Youden index J, is defined as: J = max (sensitivityc + specificityc -1) where c ranges over all possible criterion values[START_REF] Youden | Index for rating diagnostic tests[END_REF]. Equal weight is given to sensitivity and specificity. For box-and-whisker plot, outside and far out values are according to the original definitions of Tukey (1977)[START_REF] Chatfield | Exploratory data analysis[END_REF].

3. 4 . 2 . 7 Identification

 427 Identification was performed with Compound Discoverer software (version 3.0, Thermo Fisher Scientific, Rockford, IL), MS/MS experience in QC samples, in-house MS database, TraceFinder software (version 4.1, Thermo Fisher Scientific, Rockford, IL) and Human Metabolome Database (HMDB) (version 4.0).

Figure 31 .

 31 Figure 31. Score Plot of PCA for all analyzed plasma samples. Prostate cancer cases (red, n=146), matched control (bleu, n=272).

Figure 32 .

 32 Figure 32. Score Plot of OPLS-DA models for Estimation cohort. Prostate cancer cases (red), matched controls (bleu). Components: 1+3; Q2cum: 0.387; Cases: n=102 / Control: n=190.

Figure 33 .

 33 Figure 33. 999 times permutation test of OPLS-DA model for R2 (green) and Q2 (bleu).

Figure 34 .

 34 Figure 34. ROC curve for validation cohort. Cases: n= 44 / Control: n= 82, AUC: 0.900 (95% CI: 0.833 to 0.950), Youden index J: 0.733. Associated criterion: > 0.481 (Sensitivity: 81.82%; Specificity: 91.46%).

Figure 35 .

 35 Figure 35. Projection validation cohort samples in estimation cohort OPLS-DA model. Validation cohort, prostate cancer cases: n= 44 (red), matched controls: n= 82 (bleu).

Figure 36 .

 36 Figure 36. Scheme for the identification and validation of putative Prostate cancer biomarkers. Firstly, plasma samples of 418 male participants from SU.VI.MAX cohort with prostate cancer cases (n=146), matched control (n=272) were partitioned randomly into 2 cohorts: estimation (70%) and validation (30%) cohorts, with an equal proportion of cancer/control. Then, an OPLS-DA model for classification of prostate cancer cases and matched controls was established with Estimation cohort, and 13 metabolites were selected with VIPpred > 2. After, a binary logistic regression analysis was performed in our Estimation cohort, 7 of these selected 13 metabolites were further selected as biomarker

Figure 37 .

 37 Figure 37. Box plots of plasma levels of selected 7 metabolites in prostate cancer and matched controls subjects. Median normalized areas are presented on the y-axis. The circle represents a sample, the red triangle represents a "far out" value.

Fig. 1

 1 Fig.1Regrouping and matching of samples. One H24 sample from a survivor was excluded due to the problem of NMR gain parameter; one H0 sample from a non-survivor was excluded as it was found to be an outlier in PCA. For the paired H0-H24 samples obtained from the same patients, the pairs have been divided into training set and test set. The pairs of survivors and non-survivors were analyzed separately. Samples in the training set were analyzed for establishing discriminatory models between H0 and H24 samples. The pairs in the test set were reanalyzed in the established models

  separation between H0 and H24 samples was also observed, as shown in Fig.3c. Thirteen pairs were used to set up a training model, and 6 pairs were included in the test set. Consequently, R 2 Y and Q 2 Y were 0.57 and 0.91, respectively, and R 2 Y for the reanalysis was 0.33 (as shown Fig.3d). The loading plots for the two paired models are shown in Additional file 1: FigureS6. Metabolites which are listed in Table

Fig. 3

 3 Fig. 3 Score plots of OPLS-DA separating H0 from H24. For the patients whose H0 and H24 are both available, their H0 and H24 samples are matched in the discriminatory models. The pairs from the survivors and non-survivors are analyzed in two separated paired models. Blue dots represent the H24 samples and yellow dots represent the H0 samples. a Score plot for the training set separating H0 from H24 for the survivors. b Reanalysis of test set samples of survivors in the established H0-H24 multi-level model. c Score plot for the training set separating H0 from H24 for the non-survivors. d Reanalysis of test set samples of non-survivors in the established H0-H24 multi-level model

Fig. 4

 4 Fig.4Levels of key discriminatory metabolites and their relevant metabolic pathway in the comparison between SSS and SSN patients during the H0-H24 evolution. a Levels of key discriminatory metabolites in the SSS and in SSN. The levels of the metabolites are calculated with the average of time-trend change (ΔH24-H0). Averages of ΔH24-H0 for the survivors and non-survivors have been respectively shown and calibrated by the standard deviation. *: p < 0.05. **: p < 0.01. a.u.: arbitrary unit; b relevant metabolic pathway for energy-related metabolites and amino acids that vary differentially between SSS and SSN in the H0-H24 evolution. Metabolites marked by red color are those that increase in the SSN compared to SSS in all the models. Solid flashes express a direct conversion between two metabolites and dotted lines represent undirect conversions between two metabolites, according to KEGG metabolic pathway database

  The first part is a general introduction about metabolomics, the definition of different term used in metabolomics, the different types of metabolomic study, some applications of metabolomics were also discussed with further focus on the applications of metabolomics in NAFLD, in Prostate Cancer (PCa).

	The second part is methodology, in this section, sample preparation, Data acquisition,
	Analytical technologies, Data preprocessing, Data analysis and Interpretation used in
	metabolomics study were detailed.
	The third part is the presentation of my PhD thesis work, application NMR and UPLC-

1) using NMR and UPLC-HRMS based metabolomic and lipidomic profiling, to identify novel plasma biomarkers which characterize the different stages: normal liver (NL), Nonalcoholic fatty liver (NAFL) or simple steatosis and nonalcoholic steatohepatitis (NASH) of Non-alcoholic fatty liver disease (NAFLD), by (2) combining UPLC-HRMS based untargeted metabolomics with epidemiology approach, to identify plasma biomarkers which are associated with the risk of developing prostate cancer (PCa) within the following decade, and (3) application of NMR based metabolomics in Sepsis and Septic shock. My thesis is composed of 4 parts: HRMS based Metabolomics in Non-alcoholic fatty liver disease (NAFLD), in Prostate Cancer Biomarker Discovery and in Sepsis and Septic shock.

Table

Table 2 .

 2 Summary of recent metabolomics studies in NASH.

Adapted from Safaei A, et al.

[START_REF] Safaei | Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases[END_REF]

.

NAFLD is a heterogeneous and complex disease

[START_REF] Mato | Biomarkers and subtypes of deranged lipid metabolism in non-alcoholic fatty liver disease[END_REF]

, Alonso C. et al., identified 2 major subtypes of NAFLD, M-subtype and non-M-subtype

[START_REF] Alonso | Metabolomic Identification of Subtypes of Nonalcoholic Steatohepatitis[END_REF]

, characteristics of each subtype were detailed in the table below.

Table 3 .

 3

Nonalcoholic fatty liver disease subtype classification, Mato et al.

[START_REF] Mato | Biomarkers and subtypes of deranged lipid metabolism in non-alcoholic fatty liver disease[END_REF]

.

  validate outcomes of the multivariate models such as PLS-DA model, Confusion Matrix, Cross Validation (CV) and permutation test are commonly used Internal Validation techniques. The confusion matrix is a table that lists the correct and false predictions versus the actual observations, which is usually used to describe the performance of a classification model (classifier) on a new set of test data for which the true values (such as groups

information: health, disease…) are known previous. It allows easily identify confusion or error (like one class is misclassed as the other) of classification model when it makes predictions.

Table 7 .

 7 List and concentration of used internal standard (IS). Preparation of methanol solvent containing internal standard:-prepare stock solution of all internal standard as in the Table1with pure LC-MS grade Methanol to 1mg/mL or other concentration and stock in 4°C.

	Carnitine C8-d3	0,1		0,047
	LPC 19:0	0,75		0,349
	Carnitine C16:0-d3	0,15		0,070
	FFA C16:0-d3	2,5		1,180
	FFA C18:0-d3	2,5	44,4 of 465 mL	1,180
	CA-d4	1,854		0,862
	CDCA-d4	1,485		0,691
	Phe-d5	3,6125		1,680
	Trp-d5	4,25		1,976

present Standard Operating Procedure (SOP) applies to blood plasma sample extraction and protein precipitation for LC-MS based global metabolomics analysis. This SOP was edited by Xiangping LIN, reviewed by Zhicheng LIU, Philippe SAVARIN and Xinyu LIU (Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences) in 10 Oct. 2017. I) Protein Precipitation & Metabolites extraction MATERIELS: -Reagents: Blood Plasma, LC-MS grade Methanol, Internal Standard as listed -Equipment: Pipettes and Pipette Tips (100, 200, 1000 µL), Eppendorf (tube1.5, 2mL), Centrifuge tube (15mL, 50mL), 500mL glass bottle, gloves, protective goggles, timer, Fume hood, centrifuge and lyophilizer. Stable isotope labeled IS final.Con(ug/mL) v methanol (mL) Total (mg)

  present SOP applies to blood plasma sample extraction and protein precipitation for

	LC-MS based lipidomic analysis.
	I) Protein Precipitation & Metabolites extraction
	MATERIELS:
	-Reagents: Blood Plasma, LC-MS grade Methanol, milli-Q water, MTBE, Internal
	Standard (IS)
	PC(19:0/19:0) chlor	36	0,67 24,12 Avanti Lipids Polar
	LPC19:0 (lipidom) chlor	36	0,33 11,88 Avanti Lipids Polar
	SM12:0 (15,9ug) etha	36	0,17	6,12 Avanti Lipids Polar
	Cer17:0	36	0,17	6,12 Avanti Lipids Polar
	FFA C16:0-d3 (for lipidom)	36	0,67 24,12 cdnisotopes
	FFA C18:0-d3 (lipidom)	36	0,67 24,12 Cambridge Isotope
	TG45:0	36	0,53 19,08 Sigma-Aldrich
	PROCEDURE:
	1) Preparation of methanol solvent containing internal standard:
	-prepare stock solution of all internal standard as in the table1 with pure LC-MS grade
	Methanol to 1mg/mL or other concentration and stock in 4°C.
	-add adequate quantity of IS stock solution into pure LC-MS grade Methanol to have
	desired concentration and volume as in the table1.
	2) Preparation of plasma before freeze-drying for NAFLD samples:
	Divided operation into 2 days, with 55 samples/day QC included, calculate the total

-Equipment: Pipettes and Pipette Tips

(100, 200,

1000 µL), Eppendorf (tube 2mL), Centrifuge tube (15mL, 50mL), 500mL glass bottle, gloves, protective goggles, timer, Fume hood, centrifuge and lyophilizer. Table 8. list and concentration of internal standard (IS). number of QC samples n (n >= N/10 + 20, N: the total number of all plasma samples, about 81, 1QC per 10 samples, about 30 QC) internal standard (ID) MeOH(ml) 300ul/sample final conc. ug/ml in MeOH Total ug Source

  1,2 , Zhicheng LIU 4 , Tony PALAMA 1,2 , Laurence LE MOYEC 5 , Marianne ZIOL 8 , Nada HELMY 7 , Corinne VONS 7 , Guowang XU 3, *, Carina PRIP-BUUS 6, *, Philippe SAVARIN 1,2, *

  2.5.3 Sample preparation for NMR based metabolomic analysis, 2.5.4 Sample preparation for LC/GC-MS based analysis, section 3.1 Protocol for blood plasma sample extraction for metabolomics and section 3.2 Protocol for blood plasma sample extraction

	for lipidomics.
	3.3.2.4 Data Acquisition
	3.3.2.4.1 Analysis Equipment
	TripleTOF™ 5600 plus mass spectrometer system (AB SCIEX™, Framingham, MA) for
	UPLC-HRMS based Metabolomic analysis and UPLC-HRMS based Lipidomic analysis,
	respectively.
	3.3.2.4.2 Analysis by NMR and LC/MS
	3.3.2.4.2.1 NMR Analysis

For NMR study, samples were analyzed by using a 500 MHz Bruker AVANCE III 1 H NMR spectrometer (Advance III, Bruker, Germany) with automatic sample changer.

For UPLC-HRMS based analysis, an ACQUITY Ultra Performance Liquid Chromatography (UPLC, Waters Corporation, Manchester, U.K.) was coupled with a Q Exactive HF (Thermo Fisher Scientific, Rockford, IL) system and an AB SCIEX

  Before analysis, the supernatant was re-dissolved in the mix of 20 μL solution A (chloroform : methanol, 2 : 1 (v/v)) and 80 μL solution B (water : isopropanol : acetonitrile, 5 : 30 : 65 (v/v/v) containing 5mmol/L ammonium acetate), after vortexed for 1 min and centrifuged at 14 000 g for 5 min at 4°C, lipidomic analysis was performed on the AB SCIEX TripleTOF™ 5600 plus mass spectrometer system (AB SCIEX™, Framingham, MA) coupled with an Ultra-high-performance liquid chromatography (Waters, Milford, MA), equipped a reversed-phase UPLC ACQUITY C8 BEH column (2.1 mm × 100 mm × 1.7 μm, Waters, Milford, USA), the column temperature was 55°C in electrospray positive and negative ionization (ESI+ and ESI-) modes. Acetonitrile : water, 6 : 4 (v/v) containing 10 mM ammonium acetate was used as mobile phase A. Isopropanol : acetonitrile, 9 : 1 (v/v) containing 10 mM ammonium acetate was used as mobile phase B. The flow rate was 0.26 mL/min, with the elution gradient as follows: 32% B was firstly maintained for 1.5 min, then linearly increased to 85% B in 14 min, linearly increased to 97% B from 15.5 min to 15.6 min, finally maintained for 2.4 min and followed by equilibration with 32% B in next 2 min.

3.3.2.4.2.3 UPLC-HRMS based Lipidomic analysis

  MS based lipidomic analysis, data acquisition was performed both in full scan (with mass range from 200 to 1000m/z for ESI+, 90 to 1000m/z for ESI-) and IDA mode (with mass range from 100 to 1000m/z for ESI+, 50 to 1000m/z for ESI-). Mass spectrometry parameters were as follows: ion spray voltage, 5500V for ESI+ and -4500V for ESI-;The missing values are replaced by the average of the corresponding (feature specific) non-missing values in the k (here k = 10) closest features in terms of Euclidean distance of the responses across all the samples. After processing, MS features with relative standard deviation (RSD) < 30 were used for further statistical analysis, before the flow statistical analysis, MS data were Probabilistic Quotient normalized.

	curtain gas was 35 psi; declustering potential, full scan mode: 100V for ESI+ and -100V
	for ESI-, IDA mode: 80V for ESI+ and -100V for ESI-; collision energy, full scan mode:
	10V for ESI+ and -10V for ESI-, IDA mode: 35V for ESI+ and -35V for ESI-, collision energy 3.3.2.6 Statistical Analysis
	spread was 15 in ESI+ and ESI-mode; interface heater temperature, 500°C for ESI+ and
	550°C for ESI-. Dunn's Multiple Comparison Test [177] was performed with IBM ® SPSS ® Statistics V25.0
	for macOS (Armonk, NY: IBM Corp.), Heatmap and Kruskal-Wallis test with false
	3.3.2.5 Data processing discovery rate (FDR) correction were performed with Multiple Experiment Viewer
	(V_4_8_1_r2727_mac) for macOS
	3.3.2.5.1 NMR Data processing
	For MS based metabolomic analysis, raw MS data were collected and processed on
	TraceFinder software (version 4.1, Thermo Fisher Scientific, Rockford, IL), for peak
	extraction and integration.
	For MS based lipidomic analysis, raw MS data were collected and processed on
	PeakView® Software (version 2.2, AB SCIEX™, Framingham, MA) and MultiQuant™
	Software (version 3.0.3, AB SCIEX™, Framingham, MA), for peak extraction and
	integration, respectively.

The detailed processing steps were descripted in the section 2.6.1 NMR data preprocessing. Briefly, a Fourier transformation was applied on NMR data with linebroadening (LB) at 0.3 Hz. Spectra were phased and baseline corrected. All spectra were aligned on glucose doublet at 5.23 ppm. After processing and calibration, each 1D NMR spectrum was sliced into buckets of 0.001 ppm, containing NMR signals. These steps were performed by using NMRPipe software

[START_REF] Delaglio | NMRPipe: a multidimensional spectral processing system based on UNIX pipes[END_REF]

. Spectra bin were further normalized to the median of intensities before statistical analysis.

3.3.2.5.2 UPLC-HRMS Data processing

Table 9 .

 9 Characteristics of NL, NAFL and NASH Patients.

	Characteristics	NL (n=19) NAFL (n=39) NASH(n=24)	p-value
	Age (year)	32.2 (8.7) 36.2 (10.2)	41.7 (10) †	< 0.01
	Gender (♂/♀)	(1/18)	(5/34)	(10/14)	-
	BMI (kg/m2)	42.9 (4.6) 45.3 (5.1)	44.5 (5.3)	NS
	ALT (IU/L)	18.7 (7.3) 28.5 (20.3) ‡	50.0 (28.3) †	< 0.001
	AST (IU/L)	20.1 (5.1) 25.3 (11.7) ‡	34.7 (15.5) †	< 0.002
	GGT (IU/L)	19.9 (5.5) 35.2 (38.5) ‡	47.2 (31.9) †	< 0.001
	Phosphate alcaline	68.9 (16.3) 74.1 (18.9)	69.0 (24.4)	NS
	TG (mM)	1.0 (0.5)	1.3 (0.6) ‡	2.3 (1.5) †	< 0.001
	Cholesterol (mM)	4.8 (0.9)	4.7 (1.0)	5.0 (1.1)	NS
	Fasting blood Glucose (mM) 5.0 (0.6)	5.3 (1.2) ‡	6.7 (2.6) †	< 0.002
	Fasting Insulin (pM)	71.5 (39.3) 142.2 (109.0)* 171.8 (150.6) † < 0.02
	HOMA IR	2.4 (1.3)	5.2 (4.5)*	7.2 (5.6) †	< 0.004
	Triglycerides (mg/g of liver)	6.1 (2.9)	16.0 (12.4)* ‡ 25.5 (14.6) †	< 0.001
	Phospholipids (mg/g of liver) 0.8 (0.7)	0.8 (0.6)	0.8 (0.4)	NS
	Diacylglycerols (mg/g of liver) 0.6 (0.4)	0.7 (0.3)	0.9 (0.5) †	< 0.04

Data are expressed as "mean (SD)". p-value were calculated with Dunn's Multiple

  3.3.3.2 Metabolomic and lipidomic profiles of NL, NAFL and NASH patients3.3.3.2.1 NMR AnalysisThe first part of metabolomics analysis was performed with an NMR spectrometer, as NMR analysis requires minimal sample preparation and there is NMR spectrometer available in our laboratory. After processing as procedure described in Material and Method, 1D NOESY NMR data were used to build multivariate statistical models for classification of NL, NAFL and NASH patients. In our study, blood samples were collected with tube containing Sodium Citrate which was used as anticoagulant during the preparation of blood plasma from blood, consequently, data of NMR spectra region of Citrate (between 2.5 -2.7ppm) were excluded for multivariate statistical analysis. Also, 1 NL and 8 NASH patients were excluded for multivariate statistical analysis due to presence of strong sugar signal. The presence of this abnormal sugar signal lead to a very large variability on the spectra and have impact in statistical analysis.

Table 10 .

 10 Summary of OPLS-DA models for metabolomics analysis.

	Groups	Model	A	Q2	CV-AUC (median, n=200)
	NL vs NASH (16 vs 19)	OPLS-DA 1+1	0.378	0.84 (0.83-0.86)
	NL vs NAFL (16 vs 33)	OPLS-DA 1+0	0.108	0.71 (0.69-0.72)
	NAFL vs NASH (34 vs 20)	OPLS-DA (1)*+0 -0.033	0.61 (0.58-0.65)

A, number of significant components given by cross-validation, *, non-significant component. CV-AUC, median (1 st -3 rd quartile) with 200 times cross-validation. Outliers identified by Principal Component Analysis (PCA) were excluded in building OPLS-DA models.

As represented in the table above (Table

X

), the classification of NL and NASH patients gives better result in term of model quality (Q2: 0.378; AU-ROC: 0.85) than the classification of NL, NAFL (Q2: 0.108; AU-ROC: 0.69) and NAFL, NASH (Q2: -0.033; AU-ROC: 0.62) patients. The Score Plot of OPLS-DA model for classification between NL

Table 11 .

 11 Confusion matrix of the sample by OPLS-DA models.

		NL	NASH Accuracy Class error
	NL	13	3	0.19
			0.83
	NASH 3	16	0.16
		NL	NAFL Accuracy Class error
	NL	4	12	0.75
			0.67
	NAFL	5	28	0.15
		NAFL NASH Accuracy Class error
	NAFL	30	4	0.12
			0.65
	NASH 15	5	0.75

Table 12

 12

		Metabolites high in NL	Metabolites high in NASH
		Cysteine-glutathione disulfide,	Phenyl sulfate,
		Malic acid,	Eicosapentaenoic acid,
	NL vs NASH	Melezitose	Glycodeoxycholic acid, LPC 14:0,
			LPC 16:1,
			PC 30:0

. List of metabolites highlighted by S-plot for OPLS-DA models.

Table 14 .

 14 List of lipids highlighted by S-plot for OPLS-DA models.

		Lipids high in NL Lipids high in NASH
	NL vs NASH	SM(d16:0/26:4), SM(d16:0/24:3), SM(d22:2/19:1), SM(d22:1/19:1)	TG(18:1/12:0/14:0), TG(16:0/14:0/16:0), TG(16:0/14:0/16:1), TG(16:0/14:0/14:0), TG(16:1/14:0/14:0)
	OPLS-DA models classification performance were further evaluated by confusion matrix.
	As represented in Table 15, OPLS-DA model has difficulty to distinguish NL patients from

NAFL patients (the model looks like straggled in the classification of NL patients), and to distinguish NAFL patients from NASH patients (the model is more likely confused in the classification of NASH patients). The results are concordant (Table

11

and Table

15

) with our UPLC-HRMS based metabolomics analysis results.

Table 15 .

 15 Confusion matrix of the sample by OPLS-DA models.

		NL	NASH Accuracy Class error
	NL	15	3	0.17
			0.86
	NASH 3	21	0.13
		NL	NAFL Accuracy Class error
	NL	8	10	0.56
			0.70
	NAFL	7	32	0.18
		NAFL NASH Accuracy Class error
	NAFL	34	5	0.13
			0.68
	NASH 15	9	0.63

Rows represent the real groups; columns list the predicted groupings, 7-fold CV.

  1,2,4 , Lucie LÉCUYER 3 , Xinyu LIU 4 , Mohamed N. TRIBA 1,2 , Tony PALAMA 1,2 , Agnès VICTOR BALA 1,2 , Zhicheng LIU 5 , Nadia BOUCHEMAL 1,2 , Philippe SAVARIN 1,2, *, Guowang XU 4, *, Mathilde TOUVIER 3, *

Table 16 .

 16 Confusion matrix of the sample by OPLS-DA in Estimation cohort.

		Matched control Cancer cases Accuracy Class error
	Matched control 170	20	0.83	0.11
	Cancer cases	29	73		0.28
	Rows represent the real groups; columns list the predicted groupings. Estimation cohort,
	Cancer cases: n=102 / Matched Control: n=190. 7-fold CV.

Table 17 .

 17 List of metabolites selected with VIPpred of OPLS-DA.

	Metabolites	VIPpred
	Sphingosine	3.99
	Gly-Tyr	3.89
	Sphinganine	3.28
	Cis-11,14-Eicosadienoic acid	3.11
	2-Hydroxy-6-aminopurine	2.68
	Guanine	2.66
	4-Acetamidophenol	2.65
	Ethyl oleate	2.43
	FFA C18:1	2.42
	FFAD C18:1	2.36
	Glutamic acid	2.23
	Cis-8,11,14-Eicosatrienoic acid	2.07
	Orthophosphoric acid	2.03

Table 19 .

 19 Confusion matrix of the sample by OPLS-DA in validation cohort.

		Matched control Cancer cases Accuracy Class error
	Matched control 74	8	0.80	0.10
	Cancer cases	17	27		0.39
	Rows represent the real groups; columns list the predicted groupings. validation cohort
	(30%, Cases: n= 44 / Control: n= 82).

Table 1

 1 Baseline characteristics of the patients recorded at admission to the ICU FiO2 percentage of inspired oxygen, SOFA Sepsis-related Organ Failure Assessment, SOFAH0 SOFA measured at H0, SOFAH24 SOFA measured at H24, SAPSII new simplified acute physiology score, LOS length of stay, FDR false discovery rate, Adj P P value adjusted with Bonferroni correction

		Total/average	Survivors	Non-survivors	Adj P	FDR
	Number of patients	70	40	30
	Male (%)	40 (57%)	27 (67%)	13 (32%)	0.07	0.09
	Age	70.1 ± 0.16	68.5 ± 0.29	72.1 ± 0.36	0.12	0.23
	Temperature (°C)	37.3 ± 0.02	37.1 ± 0.03	36.9 ± 0.05	0.32	0.35
	Mean arterial pressure (mmHg) at admission	72.8 ± 2,44	71,0 ± 3,25	75.2 ± 3.72	0.41	0.35
	pH at admission	7.3 ± 0.00	7.31 ± 0.00	7.29 ± 0.00	0.43	0.53
	PaO2 (mmHg)H0	144.7 ± 1.54	167.5 ± 2.83	113.6 ± 3.12	0.05	0.13
	PaCO2 (mmHg)	37.6 ± 0.18	37.6 ± 0.31	37.5 ± 0.45	0.66	0.63
	PaO2/FiO2 ratioH0	212.5 ± 4.10	168.6 ± 3.19	242.2 ± 5.59	0.04	0.07
	Lactate (mmol/L)H0	5.0 ± 0.07	3.7 ± 0.11	6.6 ± 0.17	0.01	0.03
	Creatininemia (μmol/L)H0	212.2 ± 3.88	200.1 ± 4.34	241.9 ± 3.55	0.07	0.15
	Glycemia (mmol/L)	9.8 ± 0.09	9.9 ± 0.20	9.6 ± 0.23	0.91	0.95
	Hemoglobin (mmol/L)	10.5 ± 0.03	10.1 ± 0.06	10.9 ± 0.07	0.09	0.06
	Albumin (g/L)	24.6 ± 0.13	22.2 ± 0.15	26.8 ± 0.38	0.11	0.21
	Platelet (g/L)	156.7 ± 1.53	151.8 ± 2.61	162.2 ± 3.70	0.33	0.13
	Total bilirubin (μmol/L)	38.4 ± 0.90	37.8 ± 1.90	39.1 ± 1.53	0.54	0.77
	CRP (mg/dL)	162.3 ± 2.15	174.0 ± 3.98	147.4 ± 4.71	0.49	0.67
	PCT (mg/dL)	25.2 ± 0.52	28.0 ± 1.15	21.6 ± 1.39	0.74	0.88
	SAPSII	59.0 ± 0.24	55.2 ± 0.39	64.3 ± 0.58	0.02	0.07
	SOFAH0	11.7 ± 0.06	10.9 ± 0.11	12.4 ± 0.12	0.10	0.15
	SOFAH24	9.4 ± 0.06	8.7 ± 0.11	10.3 ± 0.10	0.07	0.11
	ICU LOS (day)	9.16 ± 1.21	15.1 ± 1.36	3.62 ± 0.09	0.05	0.09
	Mechanical ventilation (%)	84%	75%	93%
	Hospital-acquired infection (%)	45%	35%	60%
	Sepsis causes (%)
	Pulmonary	54%	55%	53%
	Abdominal	30%	22%	40%
	Urinary tract	7%	7%	6%
	Others	8%	15%	0%
	All the data is represented as mean ± standard error of mean (SEM)
	PaO2 partial pressure of arterial oxygen,

Table 2

 2 Metabolites found to discriminate between SSS and SSN at H0Chemical shifts for the assigned metabolites are shown in the peak column. The superscripts for the peaks represent the multiplicity of the peaks. s, singlet; d, doublet; t, triplet; q, quadruplet; m, multiplet. A positive correlation indicates an increased level of the metabolite in the non-survivor while negative correlation indicates a decreased level of the metabolite. The threshold of FDR was set at 0.1. Similar expressions are also applied for Tables3 and 4Adj P P values that are calculated by Student's T test are adjusted with Bonferroni correction, FDR false discovery rate

	Peaks	Assignment	VIP Correlation Adj P FDR
	1.06 d	3-Hydroxyisobutyrate 3.06 0.52	0.0001 0.0001
	5.79 s	Urea	2.64 0.45	0.002 0.003
	7.31 m 7.36 m Phenylalanine	2.64 0.44	0.01	0.01
	2.12 m , 2.32 m Glutamate	2.6 0.44	0.02	0.02
	2.43 m	Glutamine	2.55 0.43	0.03	0.01
	3.03 s	Creatinine	2.43 0.41	0.03	0.04
	1.32 d 4.11 q Lactate	2.38 0.4	0.02	0.04
	2.14 s	Methionine	2.17 0.37	0.06	0.05
	1.46 d	Alanine	2.12 0.26	0.07	0.08
	6.88 d 7.18 d Tyrosine	2.02 0.34	0.03	0.04
	2.36 s	Pyruvate	2.01 0.34	0.03	0.01
	2.52 d 2.62 d Citrate	1.94 0.33	0.03	0.04
	1.7 m	Lysine	1.91 0.27	0.09	0.08
	6.52 s	Fumarate	1.9 0.32	0.04	0.05
	7.67 s	1-Methylhistidine	1.66 0.28	0.07	0.06
	2.03 s	Glycoprotein (N-acetyl) 1.64 -0.28	0.08	0.03
	1.91 s	Acetate	1.56 0.25	0.09	0.10
	1.16 d	Isopropanol	1.53 0.26	0.09	0.03

Table 3

 3 Metabolites found to discriminate between SSS and SSN at 24 h after admission to ICU

	Peaks	Assignment	Correlation	VIP	Adj P	FDR
	2.37 s	Pyruvate	0.52	3.55	0.0001	0.0001
	2.52 d 2.62 d	Citrate	0.52	3.5	0.0002	0.0003
	7.31 m 7.36 m	Phenylalanine	0.48	3.25	0.001	0.001
	6.88 d , 7.18 d	Tyrosine	0.45	3.03	0.004	0.004
	2.72 m	Lipids (fatty acid residues)	-0.44	2.99	0.01	0.01
	2.43 m	Glutamine	0.44	2.96	0.01	0.01
	1.32 d , 4.41 q	Lactate	0.44	2.9	0.01	0.02
	1.06 d	2-Hydroxyisovalerate	0.41	2.77	0.02	0.03
	3.03 s	Creatinine	0.37	2.51	0.03	0.05
	2.03 s	Glycoprotein (N-acetyl)	-0.37	2.48	0.05	0.05
	7.03 s 7.67 s	1-Methylhistidine	0.35	2.37	0.06	0.07
	2.12 m, 2.33 m	Glutamate	0.33	2.22	0.07	0.09
	1.7 m	Lysine	-0.29	1.95	0.09	0.07
	1.46 d	Alanine	0.28	1.9	0.13	0.10

Table 4

 4 Discriminatory metabolites with different variations along the H0-H24 evolution between the non-survivor group and the survivor group C1, correlation of the metabolite to the discriminatory model for the survivors; C2, correlation of the metabolite to the discriminatory model for the non-survivors.

	Peaks	Assignment	C1	Adj P1	FDR1	V1	C2	Adj P2	FDR2	V2
	2.12 m 2.32 m	Glutamate	-0.62	0.0001	0.001	↓	0.49	0.03	0.02	↑
	2.52 d 2.66 d	Citrate	-0.59	0.0001	0.001	↓	0.59	0.002	0.004	↑
	7.32 d 7.36 d	Phenylalanine	-0.53	0.0004	0.003	↓	0.4	0.08	0.07	↑
	2.07 m 2.43 m	Glutamine	-0.49	0.001	0.01	↓	0.44	0.03	0.03	↑
	1.47 d	Alanine	-0.42	0.004	0.03	↓	0.42	0.05	0.06	↑
	1.32 d 4.11 q	Lactate	-0.62	0.0001	0.002	↓	0.2	0.26	0.21	NS
	2.37 s	Pyruvate	-0.38	0.04	0.05	↓	0.06	0.42	0.33	NS
	2.03 s	Glycoprotein (N-acetyl)	0.26	0.08	0.09	NS	-0.48	0.01	0.01	↓
	3.02 s	Creatinine	-0.21	0.15	0.13	NS	0.47	0.02	0.03	↑

Table 5

 5 Area under ROC for key metabolites that separate septic shock survivors from non-survivors lipidomics analysis could be promising approach in NAFLD. Further investigation should be particularly focus on lipidomics, as well as investigation subtypes, appropriate data processing and statistic model.

		AUROC H0	AUROC H24	AUROC H24-H0
		(n = 69)	(n = 51)	(n = 51)
	Lactate	0.74	0.75	0.73
	Alanine	0.78	0.78	0.67
	Glycoprotein (N-acetyl)	0.71	0.60	0.65
	Glutamate	0.61	0.81	0.71
	Glutamine	0.80	0.70	0.74
	Pyruvate	0.81	0.83	0.79
	Citrate	0.82	0.72	0.72
	Creatinine	0.79	0.69	0.70
	Phenylalanine	0.84	0.73	0.79
	SOFA	0.60	0.64	0.61
	SAPSII	0.62
	n number of patients

lysophosphatidylethanolamine, Sphingosine and lactamide. Compared with NL obese patients, the changes in the plasma metabolome and lipidome were more distinct in NASH patients than in NAFL patients. The study suggested that UPLC-HRMS based metabolomics, especially characterized by higher fasting plasma levels of Cis-11,14-Eicosadienoic acid, Ethyl oleate and phosphoric acid had a higher risk of developing prostate cancer during the 13-year follow-up. Which suggested that UPLC-HRMS (Obitrap) based plasma untargeted metabolomic profiles, established from a simple baseline blood draw from healthy men, ANNEXES Annexe 1.

List of detected mass and retention time used for metabolites identification.

	Annexe 2.	Tryptophan Ethyl oleate Carnitine C18:2-OH LPE 22:4 -2 SM 36:3 15-Oxoete FFA C16:1(Hexadecenoic acid) L-Asparagine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	188.0699 311.2928 440.3362 530.32466 4.51 0.97 6.97 3.52 727.5733 6.7 317.2126 5.74 253.2172 6.7 131.034 0.55
		N8-Acetylspermidine Fatty amide C20:0 Carnitine C18:1-OH LPC 19:0 SM 36:2 1-Stearoyl-Sn-Glycerol-3-Phosphocholine FFA C16:2 Leucine/Isoleucine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	188.1749 312.3263 442.3532 538.386 729.5894 522.3562 251.2014 130.0863	0.31 6.31 3.8 5.02 6.97 8.07 6.4 0.76
		3-Indolepropionic acid Phe-Phe Carnitine C18-OH Cer(d18:1/16:0)-2 SM 36:2 SM(d18:1/18:1) 2,5-Dichlorobenzoic Acid FFA C17:0(Heptadecanoic acid) L-Glutamic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	190.0862 313.1542 444.3694 538.5187 729.5894 188.9376 269.2492 146.045	2.2 1.78 4.03 7.68 7.01 0.38 7.44 0.33
		Cotinine N-oxide Carnitine C10:1 Glycodeoxycholic acid Cer(d18:0/16:0)-1 PC 32:2 2-Aminoethylphosphonic acid FFA C17:1(Heptadecenoic acid) L-Glutamine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	193.10186 0.94 314.2319 2.5 450.3189 3.05 540.5317 7.76 730.5371 7 124.0077 0.34 267.2324 6.98 145.061 0.36
		L-Altrose Carnitine C10:0 LPE 16:1 Cer(d18:0/16:0)-2 SM 36:1 2-Phosphoenol pyruvate FFA C18:0(Stearic acid) L-Homoserine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	203.0527 316.2477 452.278 540.5328 731.6049 166.9739 283.2633 118.0499	0.36 2.66 3.79 7.05 7.23 0.29 7.67 0.38
		Symmetric dimethylarginine Phytosphingosine LPE 16:0 sn-1 LPC 20:5 sn-1 PC 32:1 3-(1-Pyrazolyl)-L-alanine FFA C18:1(Vaccenic acid-2) Lithocholic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	203.1494 318.3004 454.2922 542.3233 732.5503 154.0609 281.2489 375.2911	0.36 3.36 4.15 3.8 7.8 0.37 7.31 6.33
		Carnitine C2:0 Methyl arachidonate LPE 16:0 sn-2 LPC 20:5 sn-2 PC 32:0 3-Indolepropionic acid FFA C18:2(β-Linoleic acid) LPC16:0	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	204.1228 319.2627 454.2922 542.3233 734.5677 188.071 279.2333 494.3245	0.39 6.3 4.04 3.8 7.54 2.02 6.99 7.53
		Metabolites L-Tryptophan Cis-8,11,14-Eicosatrienoic acid Methyl ester LPC 14:0 LPC 20:4 sn-2 PE 36:4 3-Methylhistidine FFA C18:3(Linolenic acid) L-Tryptophan	Mode m/z ESI+ 205.0962 ESI+ 321.2719 ESI+ 468.3061 ESI+ 544.3373 ESI+ 740.5248 ESI-168.0771 ESI-277.2158 ESI-203.0819	RT 0.95 2.61 3.61 3.99 7.42 0.41 6.67 1.66
		Pyrrolidine Kynurenine N-Oleoylethanolamine LPE 18:2 sn-1 LPC 20:4 sn-1 PE 36:3 5(S),6(R)-Lipoxin a4 FFA C19:0(Nonadecanoic acid) Mannose	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	72.0821 209.0921 326.3051 478.2921 544.339 742.5362 351.2172 297.279 215.0325	0.39 0.62 5.11 4.01 4.39 7.47 4.82 7.81 0.37
		TMAO IS Tryptophan-d5 Docosahexaenoic acid LPE 18:2 sn-2 LPC 20:3 sn-1 PC 33:2 5-Oxoete FFA C19:1(Cis-10-Nonadecenoic acid) Myoinositol	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	76.0762 210.128 329.2477 478.2921 546.3544 744.5525 317.2098 295.2626 179.0547	0.36 0.91 5.45 3.91 4.27 7.17 6.21 7.56 0.36
		Lactamide Carnitine C3:0 all-cis-4,7,10,13,16-Docosapentaenoic acid LPE 18:1 LPC 20:3 PC O-34:2 6-Phosphogluconic acid FFA C20:0(Arachidic acid) Na-Acetylarginine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	90.056 218.1384 331.2631 480.3114 546.3576 744.5903 275.0173 311.2946 215.0317	0.37 0.53 5.77 4.36 4.28 7.52 0.29 8.06 0.34
		Orthophosphoric acid Gluconate Glycerol 1-hexadecanoate LPC O-16:1 LPC 20:2 sn-1 PC 33:1 8(R)-Hydroxy-(5Z,9E,11Z,14Z)-eicosatetraenoic acid FFA C20:1(Eicosenoic acid) N-Acetylaspartic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	98.9843 219.0462 331.2836 480.3439 548.3716 746.5676 319.2269 309.2798 174.0398	0.41 0.37 5.33 4.36 4.56 7.42 6 7.77 0.31
		Choline Zeatin Fatty amide C22:2 LPE 18:0 sn-1 LPC 20:2 sn-2 PE 33:1 9-Oxoode FFA C20:2(Cis-11,14-Eicosadienoic acid) N-Acetylglutamic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	104.1072 220.1181 336.326 482.3223 548.3716 746.5696 293.211 307.2636 188.0541	0.36 0.77 6.05 4.74 4.45 7.95 5.7 7.51 0.31
		Proline L-Carnosine FFA C22:1 LPE 18:0 sn-2 LPC 20:1 PC O-34:1 Acetanilide FFA C20:3(Cis-8,11,14-Eicosatrienoic acid) N-Acetylneuraminic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	116.0707 227.1135 338.3402 482.3223 550.3861 746.6047 134.0601 305.2469 308.099	0.4 0.31 6.42 4.62 4.9 7.7 1.57 7.24 0.39
		Valine Leu-pro Erucamide LPC 15:0 sn-1 LPC 20:0 sn-1 PE O-38:7 Adenosine5-phosphosulfate FFA C20:4(Arachidonic acid) N-Oleoylethanolamine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	118.0857 229.1543 338.341 482.3238 552.4014 748.5257 426.0188 303.2334 324.29	0.38 0.38 6.38 3.89 5.32 7.4 0.32 7.03 7.92
		Indoline Carnitine C4:0 Fatty amide C22:0 LPC 15:0 sn-2 LPC 20:0 sn-2 PE O-38:6 a-ketoglutaric acid FFA C20:5(Eicosapentaenoic acid) Oleamide	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	120.0802 232.1541 340.3558 482.3238 552.4014 750.541 145.0134 301.2164 280.2637	0.68 0.91 6.82 3.79 5.21 7.59 0.36 6.73 7.97
		Salicylic acid Gly-Tyr Carnitine C12:1 LPC O-16:0 LPC 22:6 sn-1 PC 34:4 Ala-Gly FFA C21:0(Heneicosanoic acid) Oleoyl-L-α-lysophosphatidic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	121.0279 239.1067 342.2633 482.3586 568.3391 754.537 145.0611 325.3091 435.2465	4.46 0.39 2.88 4.32 4.09 7 0.34 8.21 7.19
		Nicotinamide Carnitine C5:1 Carnitine C12:0 LPE 18:0-2 sn-2 LPC 22:6 sn-2 PC 34:3 Carnosine FFA C22:0(Behenic acid) Orthophosphoric acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	123.0548 244.1539 344.2788 482.3596 568.3391 756.5524 225.0991 339.3272 96.9704	0.47 1.31 3.12 4.72 4.09 7.11 0.38 8.4 0.31
		D-Pipecolinic acid Carnitine C5:0 Adenosine 5'-monophosphate LPC 16:1 sn-2 LPC 22:6 PC 34:2(16:0/18:2) Chenodeoxycholic acid FFA C22:1(Erucic acid) Oxypurinol	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	130.0874 246.1696 348.0688 494.3233 568.3395 758.5676 391.2848 337.3119 151.0261	0.32 1.45 0.48 3.81 4 7.4 5.78 8.13 0.38
		Creatine N-Acetyl-D-tryptophan Anandamide LPC 16:1 Cer(d18:0/18:0)-1 PC 34:2 Cholic acid FFA C22:2 Palmitoylethanolamide	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	132.0777 247.1076 348.2868 494.3236 568.5627 758.5683 407.2797 335.294 298.2744	0.37 1.82 4.79 3.82 8.04 7.35 5.23 7.81 7.66
		Leucine Octopine 15-Ketoprostaglandin F2α LPC 16:0 sn-1 LPC 22:5 PC 34:1 Citraconic acid FFA C22:5(All-cis-4,7,10,13,16-docosapentaenoic acid) ESI-ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-P-cresol sulfate ESI-	132.10176 0.53 247.14367 1.26 353.2315 2.29 496.3388 4.18 570.3537 4.24 760.5836 7.58 129.0188 0.31 329.247 7.09 187.0064 2.07
		Ornithine Carnitine C4-OH Phenol red LPC 16:0 sn-2 LPC 22:5 sn-1 PC 34:0 Citric acid FFA C22:6(Docosahexaenoic acid) P-cresyl glucuronide	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	133.0971 248.147 355.0641 496.3389 570.3542 762.6016 191.0201 327.2317 283.0821	0.3 1.26 1.91 4.07 4.35 7.85 0.29 7.03 1.79
		Hypoxanthine Palmitoleic acid MAG 18:1 LPE 20:4 sn-1 LPC 22:5 sn-2 PE 38:6 Creatinine FFA C22:7 Phenol red	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	137.04567 0.46 255.2311 5.56 357.3007 7.26 502.2919 4.07 570.3542 4.23 764.5208 7.29 112.0505 0.49 325.2145 6.88 353.0476 2.8
		1-Amlnocydohexanecarboxylic acid Hexadecanamide Hydrocortisone LPE 20:4 LPC 22:4 PC O-36:5 Cystathionine FFA C24:0(Tetracosanoic acid) Phenyl sulfate	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	144.1018 256.2651 363.2161 502.2931 572.3698 766.5767 221.0583 367.3565 172.9909	0.4 5.11 2.27 4.07 4.52 7.5 0.32 8.73 1.51
		Acetylchloline Choline glycerophosphate Alpha-Lactose LPE 20:3 LPC 22:2 PC O-36:4 Cysteine-glutathione gisulfide FFA C24:1(Nervonic acid) phenylacetylglutamine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	146.1162 258.1117 365.1069 504.3062 576.4091 768.5504 425.0767 365.3418 263.1035	0.38 0.39 0.37 4.25 4.35 7.21 0.31 8.46 1.78
		L-Glutamine Carnitine C6:0 Carnitine C14:2 LPE 20:2 Biliverdin PE 38:4 Cytidine-3(2)-Monophisphoric acid FFA C24:2 Phenylalanine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	147.0757 260.1853 368.2788 506.3262 583.2543 768.5518 322.042 363.3253 164.0709	0.39 1.8 3.12 4.53 2.86 7.67 0.32 8.19 1.43
		L-Lysine γ-Glu-Leu Carnitine C14:1 LPE 20:1 L-Glutathione PC 35:3 D-3-Phosphoglyceric acid FFA C24:5 proline	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	147.1127 261.1439 370.2945 508.3381 613.1594 770.5673 184.9849 357.2785 114.0549	0.31 1.28 3.38 4.88 0.47 7.29 0.29 7.64 0.44
		L-Glutamic acid Phe-pro Carnitine C14:0 LPC O-18:1 SM 30:1 PC 36:5 D-Citramalic acid FFA C24:6 Pyroglutamic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	148.0609 263.1386 372.3101 508.3754 647.5105 780.5526 147.029 355.2623 128.0358	0.38 1.55 3.61 4.5 5.97 7.12 0.31 7.44 0.36
		α-Keto-γ-(methylthio)butyric acid Phenylacetylglutamine 3-Hydroxydecanoic acid LPE 20:0 SM 32:2 PC 36:4 Dehydroepiandrosterone Sulfate FFA C8:0 S-(5-Adenosy)-L-homocysteine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	149.0226 265.1179 377.2982 510.354 673.5269 782.5679 367.158 143.1076 383.1109	4.45 1.45 2.99 5.3 6.1 7.36 4.05 3.21 1.47
		D-Methionine 1-Hexadecanol Sphingosine-1-phosphate LPC 17:0 SM 32:1 SM(d18:1/14:0) SM 40:3 D-Glucose 6-phosphate FFA C9:0 Succinic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	150.0582 265.2524 380.2552 510.3548 675.5414 783.6359 259.0209 157.1225 117.0187	0.46 5.88 3.57 4.4 6.46 7.39 0.31 3.99 0.31
		Guanine FFAD C18:1 Carnitine C14-OH LPC O-18:0 SM 32:1 PC 36:3 Dimethyluric acid Fructose 1,6-bisphosphate Taurochenodesoxycholic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	152.0567 282.2786 388.306 510.3909 675.5426 784.5845 195.05 338.9879 498.2877	0.48 5.33 3.05 4.9 6.43 7.47 0.34 0.27 5.63
		2-Hydroxy-6-aminopurine FFA C18:1 Diisooctyl phthalate LPC 18:3 sn-2 PC(28:0) PC 36:2 DL-Aspartic acid Glutathione Taurocholic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	152.0567 283.2627 391.2839 518.3205 678.5077 786.5996 132.0298 611.1467 514.2824	0.39 5.85 6.71 3.75 6.82 7.71 0.32 0.31 5.12
		4-Acetamidophenol 8-hydroxy-2'-deoxyguanosine Carnitine C16:2 LPC 18:3 sn-1 SM 33:1 PC 36:1 DL-malic acid Glycerylphosphorylethanolamine trans-3-Hydroxy-L-proline	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	152.0693 284.0981 396.312 518.3207 689.5584 788.6178 133.0133 214.0481 130.0488	0.8 0.48 3 4.18 6.65 7.92 0.31 0.34 0.31
		Xanthine stearamide Carnitine C16:1 LPC 18:3 SM 33:1 SM(d18:1/15:0) PC o-38:5 D-Mannitol Glycocholic acid trans-9-Octadecenoic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	153.0404 284.295 398.3258 518.3224 689.5588 794.6044 181.0706 464.3011 563.5007	0.47 5.73 3.81 3.75 6.67 7.54 0.37 5.16 7.19
		Histidine Carnitine C8:1 Carnitine C16:0 LPC 18:2 sn-1 PE O-34:3 PC 38:7 FFA 11:0 Glycodeoxycholic acid Tyrosine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	156.0755 286.2013 400.3416 520.3388 700.5278 804.5516 185.1547 448.3052 180.0657	0.37 2.06 4.13 4.04 7.55 7.07 5.1 5.66 0.47
		L-Carnitine Carnitine C8:0 7-Ketocholestero LPC 18:2 sn-2 SM 34:2 PC 38:6 FFA 18:2 Glycoursodeoxycholic acid Uric acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	162.1122 288.2165 401.3407 520.3391 701.5579 806.5686 279.2331 448.3046 167.0202	0.38 2.28 5.96 4.04 6.55 7.3 6.92 4.7 0.38
		trans-P-Coumaric acid IS Carnitine C8:0-d3 20α-Hydroxy Cholesterol LPC 18:2 SM 34:2 SM(d18:1/16:1) PC 38:5 FFA 18:4 Hexadecatrienoic acid/FFA 16:3 Uridine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	165.0539 291.23526 2.27 0.48 403.3562 6.28 520.3391 4.03 701.5597 6.57 808.5833 7.44 275.2021 6.22 249.1846 6.13 243.0616 0.46
		L-Phenylalanine glycated valine c16-d3 LPC 18:1 sn-1 SM 34:1 PC 38:4 FFA 23:0 Hippuric acid Ursolic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	166.08589 0.68 296.067 0.36 403.3611 4.06 522.3543 4.36 703.5739 6.85 810.5989 7.54 353.3425 5.4 178.0511 1.56 455.349 7.27
		IS L-Phenylalanine-d5 Palmitoylethanolamide GCDCA LPC 18:1 sn-2 SM 34:1 SM(d18:1/16:0) CA-d4 FFA 25:0 Hypoxanthine Valine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-ESI-	171.1173 300.29 414.3001 522.3547 703.5748 411.3022 381.3732 135.032 116.0734	0.66 4.88 2.63 4.25 6.88 5.18 8.72 0.47 0.4
	L-arginine Sphingosine carnitine C16:0-OH LPC 18:0 sn-1 SM 34:0 FFA 16:0-d3 FFA 26:0 Hypoxanthine-9-beta-D-arabinofuranosine N-acetylaspartate Sphinganine carnintine C18:3 LPC 18:0 sn-2 PC 30:0 FFA 18:0-d3 FFA C10:0 Indolelactic acid m/z: detected mass, RT: retention time (minutes).	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	175.1188 300.2902 416.3368 524.3701 705.5802 258.2527 395.3889 267.0729 176.0697 302.3046 422.3268 524.3703 706.5371 286.2827 171.1383 204.0664	3.51 3.54 4.76 6.97 7.14 8.87 1.21 0.33 2 3.64 3.72 4.62 7.16 7.67 4.65 1.67
		Serotonin Eicosapentaenoic acid carnitine C18:2 LPE 22:6 sn-1 SM 35:2 (19S)-Hydroxyicosatetraenoic acid FFA C12:0 Indoxyl sulfate potassium salt	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	177.10223 0.48 303.2305 5.17 424.3413 4.01 526.292 4.07 715.5737 6.79 319.2241 6.69 199.1697 5.71 212.0029 1.6
		1,7-Dimethylxanthine Arachidonic acid Carnitine C18:1 LPE 22:6 sn-2 PE 34:2 1,3-dimethyluric acid FFA C13:0 L(+)-Ornithine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	181.0729 305.2471 426.357 526.292 716.5234 217.0293 213.1855 131.0829	0.97 5.5 4.29 4.07 7.41 0.36 6.14 0.43
		L-Tyrosine cis-8,11,14-Eicosatrienoic acid L-Cysteine-Glutathione LPE 22:6 SM 35:1 1,5-Anhydro-D-Glucitol FFA C14:0(Myristic acid) Lactamide	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	182.0806 307.264 427.0954 526.2928 717.5889 163.0603 227.2023 88.0415	0.48 5.73 0.39 3.99 7.09 0.43 6.51 0.38
		D-Mannitol Cis-11,14-Eicosadienoic acid Carnitine C18:0 D(+)-Melezitose PE O-36:5 11,12-Epoxy-(5z,8z,14z)-Eicosatrienoic Acid FFA C15:0(Pentadecanoic acid) Lactic acid	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	183.0843 309.2776 428.3729 527.1579 724.5263 319.2284 241.2171 89.0233	0.46 6.04 4.6 0.38 7.49 7.13 6.8 0.36
		Epinephrine fatty amide C20:1 carnitine C18:0 LPE 22:4 -1 PC(32:4) 13-cis-acitretin FFA C16:0(Palmitic acid) L-Arginine	ESI+ ESI+ ESI+ ESI+ ESI+ ESI-ESI-ESI-	184.0943 310.3103 428.3749 528.30977 4.21 0.34 5.89 4.58 726.5023 7.01 325.1848 6.78 255.2338 7.02 173.1049 0.42

List of detected mass and retention time used for lipid ions identification.

	PC(16:1/19:1)+H TG(14:0/18:2/20:5)+Na TG(16:0/16:0/22:5)+NH4 TG(16:1/15:1/18:2)+NH4 TG(18:0/20:4/22:5)+NH4 TG(18:3/18:2/18:3)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	772.584494 7.27 871.679147 11.22 898.784725 12.15 830.722771 11.65 974.816944 12.33 892.739109 11.14
	PC(16:1/20:4)+H TG(14:0/18:2/22:6)+NH4 TG(16:0/16:1/16:2)+NH4 TG(16:1/16:1/18:2)+Na TG(18:0p/16:0/18:1)+NH4 TG(18:3/18:2/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	780.552103 6.19 892.738763 11.53 818.723037 12.00 849.693143 11.81 862.824588 13.32 918.755071 11.29
	PC(16:1/20:5)+H TG(14:0/18:3/18:3)+NH4 TG(16:0/16:1/18:1)+NH4 TG(16:1/18:1/18:1)+NH4 TG(18:1/12:0/14:0)+NH4 TG(18:3/18:2/20:5)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	778.536595 5.72 840.709078 10.90 848.769364 12.52 874.786909 13.91 766.690701 11.52 916.740474 10.93
	PC(16:1/24:2)+H TG(14:0/18:3/22:6)+NH4 TG(16:0/16:1/18:2)+NH4 TG(16:1/18:1/18:2)+NH4 TG(18:1/14:0/18:2)+NH4 TG(18:3/18:2/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	840.649593 8.71 890.724422 11.05 846.753462 12.17 872.772893 12.51 846.75431 12.72 942.754816 11.11
	Lipid Ion PC(16:1/24:7)+H TG(14:0/20:4/20:4)+NH4 TG(16:0/16:1/18:3)+NH4 TG(16:1/18:2/18:2)+NH4 TG(18:1/14:0/18:3)+NH4 TG(18:3/18:3/18:3)+NH4	Mode Obs m/z ESI+ 830.567963 6.13 Rt ESI+ 892.738778 11.52 ESI+ 844.737901 11.81 ESI+ 870.753615 11.73 ESI+ 844.738495 12.05 ESI+ 890.725365 10.66
	Cer(d16:1/24:0)+H PC(16:1p/18:2)+H TG(14:0/20:4/22:6)+NH4 TG(16:0/16:1/22:6)+NH4 TG(16:1/18:2/18:3)+NH4 TG(18:1/18:1/18:1)+NH4 TG(18:3/20:5/20:5)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	622.614159 10.05 740.558014 6.69 916.739714 11.28 894.754128 11.87 868.73841 11.55 902.817133 12.93 921.695838 10.93
	Cer(d16:1/24:1)+H PC(16:1p/19:1)+H TG(15:0/14:0/18:1)+NH4 TG(16:0/17:0/18:1)+NH4 TG(16:1/18:2/20:5)+NH4 TG(18:1/18:1/18:2)+NH4 TG(18:3/20:5/22:6)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	620.598963 9.26 756.587625 7.77 808.737944 12.21 864.801465 12.96 892.738733 11.33 900.801379 12.58 947.710337 11.10
	Cer(d18:1/16:0)+H PC(16:1p/20:4)+H TG(15:0/14:0/18:2)+NH4 TG(16:0/17:0/20:4)+NH4 TG(16:1/18:2/22:5)+NH4 TG(18:1/18:1/18:3)+NH4 TG(18:4/12:0/18:2)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	538.518718 7.52 764.554142 6.50 806.722321 11.83 886.785248 12.59 920.769472 11.65 898.785709 12.34 795.647429 11.16
	Cer(d18:1/17:0)+H PC(16:1p/24:2)+H TG(15:0/15:0/15:0)+NH4 TG(16:0/17:0/22:6)+NH4 TG(16:1/18:2/22:6)+NH4 TG(18:1/18:1/19:0)+NH4 TG(18:4/14:0/16:0)+Na	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	552.534637 7.95 824.654945 8.72 782.722166 12.09 910.784836 12.39 918.75365 11.47 918.851189 13.33 821.662306 11.41
	Cer(d18:1/20:0)+H PC(16:1p/24:7)+H TG(15:0/18:1/18:2)+NH4 TG(16:0/17:1/18:1)+NH4 TG(16:1/18:3/18:3)+NH4 TG(18:1/18:1/19:1)+NH4 TG(18:4/14:0/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	594.581365 9.17 814.571096 7.06 860.769408 12.25 862.786037 12.66 866.724487 11.00 916.834595 13.12 840.707989 11.08
	Cer(d18:1/22:0)+H PC(16:2/18:3)+H TG(15:0/18:1/19:1)+NH4 TG(16:0/17:1/18:2)+NH4 TG(16:2/18:2/18:3)+NH4 TG(18:1/18:1/20:2)+NH4 TG(18:4/15:0/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	622.613621 9.85 752.521534 5.22 876.800993 12.82 860.770359 12.47 866.724347 11.19 928.831644 12.57 854.723895 11.28
	Cer(d18:1/24:0)+H PC(16:2/20:5)+H TG(15:0/18:1/20:4)+NH4 TG(16:0/17:1/19:1)+NH4 TG(16:2/18:2/22:6)+NH4 TG(18:1/18:1/20:3)+NH4 TG(18:4/16:1/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	650.645906 10.49 776.527463 5.56 884.769577 12.23 876.793652 12.45 916.738938 11.10 926.816528 12.60 866.723989 11.18
	Cer(d18:1/24:1)+H PC(17:0/20:4)+H TG(15:0/18:1/22:5)+NH4 TG(16:0/17:1/20:4)+NH4 TG(17:0/18:1/18:1)+NH4 TG(18:1/18:1/20:4)+NH4 TG(19:1/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	648.630884 9.81 796.584017 7.31 910.783597 12.16 884.768908 12.25 890.818063 13.00 924.801122 12.73 912.801668 12.37
	Cer(d18:1/24:2)+H PC(17:0/22:6)+H TG(15:0/18:1/22:6)+NH4 TG(16:0/18:1/18:1)+NH4 TG(17:0/18:1/18:2)+NH4 TG(18:1/18:1/20:5)+NH4 TG(20:0/18:1/18:1)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	646.614616 9.27 820.584203 7.06 908.769758 11.99 876.801605 12.80 888.801097 12.64 922.785868 11.48 932.866539 13.55
	Cer(d18:2/24:1)+H PC(17:1/18:2)+H TG(15:0/18:2/18:2)+NH4 TG(16:0/18:1/18:2)+NH4 TG(17:0/18:1/20:4)+NH4 TG(18:1/18:1/22:1)+NH4 TG(20:0/18:1/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	646.613141 9.31 770.568269 6.74 858.753386 11.89 874.785208 12.53 912.797497 12.56 958.881396 13.49 930.85051 13.30
	CerG1(d18:1/16:0)+H PC(18:0/16:0)+H TG(15:0/18:2/18:3)+NH4 TG(16:0/18:1/18:3)+NH4 TG(17:0/18:1/22:5)+NH4 TG(18:1/18:1/22:3)+NH4 TG(20:1/17:0/18:1)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	700.57225 762.600482 8.23 6.85 856.738268 11.54 872.769637 12.26 938.817671 12.57 954.848776 12.96 918.851074 13.32
	CerG1(d18:1/22:0)+H PC(18:0/18:1)+H TG(15:0/18:2/20:4)+NH4 TG(16:0/18:1/20:4)+NH4 TG(17:0/18:1/22:6)+NH4 TG(18:1/18:1/22:4)+NH4 TG(20:1/18:1/18:1)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	784.665875 9.33 788.615852 8.37 882.752762 11.86 898.785796 12.40 936.800841 12.38 952.832897 12.77 930.850096 13.26
	CerG1(d18:1/24:0)+H PC(18:0/18:2)+H TG(15:0/18:2/20:5)+NH4 TG(16:0/18:1/22:0)+NH4 TG(17:0/18:2/18:2)+NH4 TG(18:1/18:1/22:5)+NH4 TG(20:1/18:1/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	812.697856 10.02 786.600033 7.71 880.7377 11.44 934.880655 13.79 886.7853 12.28 950.816725 12.56 928.832447 12.89
	CerG1(d18:1/24:1)+H PC(18:0/19:1)+H TG(15:0/18:2/22:5)+NH4 TG(16:0/18:1/22:1)+NH4 TG(17:0/18:2/22:5)+NH4 TG(18:1/18:1/22:6)+NH4 TG(20:3/18:2/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	810.682137 9.33 802.634148 8.63 908.769656 11.80 932.866034 13.45 936.800092 12.24 948.800365 12.22 946.784585 11.83
	CerG1(d18:2/22:0)+H PC(18:0/24:1)+H TG(15:0/18:2/22:6)+NH4 TG(16:0/18:1/22:4)+NH4 TG(17:0/18:2/22:6)+NH4 TG(18:1/18:1/24:0)+NH4 TG(20:5/18:2/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	782.651878 8.76 872.712048 10.50 906.753924 11.60 926.817353 12.72 934.785358 12.06 988.926441 14.17 942.755328 11.29
	CerG1(d18:2/24:0)+H PC(18:0/24:2)+H TG(15:0/18:3/20:5)+H TG(16:0/18:1/22:5)+NH4 TG(17:1/18:1/18:1)+NH4 TG(18:1/18:1/24:1)+NH4 TG(20:5/18:2/20:5)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	810.682968 9.52 870.696379 9.98 861.694052 11.54 924.801463 12.36 888.799835 12.84 986.911693 13.84 940.739357 10.85
	CerG1(d18:2/24:1)+H PC(18:0e/16:0)+H TG(15:1/18:1/18:1)+NH4 TG(16:0/18:1/22:6)+NH4 TG(17:1/18:1/18:2)+NH4 TG(18:1/18:1/24:6)+NH4 TG(20:5/18:2/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	808.667568 8.74 748.62389 8.72 860.769198 12.42 922.785102 12.18 886.784664 12.45 976.831679 12.54 966.754958 11.01
	CerG2(d16:1/16:0)+H PC(18:0e/18:2)+H TG(15:1/18:1/18:2)+NH4 TG(16:0/18:1/24:0)+NH4 TG(17:1/18:1/19:1)+NH4 TG(18:1/18:2/18:2)+NH4 TG(20:5/20:5/22:6)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	834.593748 5.63 772.620937 8.25 858.753482 12.07 962.911328 14.13 902.816654 12.87 898.785293 12.22 971.710998 11.01
	CerG2(d18:1/16:0)+H PC(18:0e/20:4)+H TG(15:1/18:1/19:1)+NH4 TG(16:0/18:1/24:1)+NH4 TG(17:1/18:1/22:5)+NH4 TG(18:1/18:2/18:3)+NH4 TG(21:4/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	862.623385 6.53 796.621175 8.25 874.78536 12.47 960.896763 13.79 936.800268 12.24 896.769553 11.87 934.785891 11.88
	CerG2(d18:1/24:1)+H PC(18:0e/22:5)+H TG(15:1/18:2/18:2)+NH4 TG(16:0/18:2/18:2)+NH4 TG(17:1/18:2/18:2)+NH4 TG(18:1/18:2/19:1)+NH4 TG(22:0/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	972.734889 9.06 822.636978 8.19 856.737919 11.71 872.769429 12.09 884.769556 12.05 914.817149 12.74 956.866036 13.34
	CerG2(d18:2/16:0)+H PC(18:0e/22:6)+H TG(16:0/12:0/14:0)+NH4 TG(16:0/18:2/18:3)+NH4 TG(17:1/18:2/18:3)+NH4 TG(18:1/18:2/20:2)+NH4 TG(22:4/18:2/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	860.607722 5.75 820.620533 7.44 740.675383 11.46 870.753845 11.91 882.753504 11.67 926.816868 12.61 972.801531 11.93
	CerG3(d18:1/16:0)+H PC(18:0e/24:2)+H TG(16:0/12:0/16:1)+NH4 TG(16:0/18:2/20:4)+NH4 TG(17:1/18:2/20:4)+NH4 TG(18:1/18:2/20:4)+NH4 TG(22:5/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	1024.68042 6.36 856.716519 10.22 766.69057 11.51 896.76955 12.05 908.769439 11.95 922.785398 12.05 946.784837 11.83
	CE 15:0 PC(18:0e/24:6)+H TG(16:0/12:0/18:3)+NH4 TG(16:0/18:2/20:5)+NH4 TG(17:1/18:2/22:5)+NH4 TG(18:1/18:2/22:1)+NH4 TG(24:1/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	628.603 848.653386 8.22 9.85 790.691148 11.33 894.753766 11.69 934.785242 11.88 956.865656 13.28 982.880675 13.29
	CE 16:0 PC(18:0e/24:7)+H TG(16:0/14:0/14:0)+NH4 TG(16:0/18:2/21:4)+NH4 TG(18:0/16:0/16:0)+NH4 TG(18:1/18:2/22:4)+NH4 TG(24:4/18:1/18:3)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	642.618 846.636886 7.97 12.75 768.706629 11.91 910.784286 12.17 852.802964 13.09 950.8164 12.37 959.804288 12.98
	CE 16:1 PC(18:0p/18:2)+H TG(16:0/14:0/15:1)+NH4 TG(16:0/18:2/22:5)+NH4 TG(18:0/16:0/18:1)+NH4 TG(18:1/18:2/22:5)+NH4 TG(4:0/16:0/16:0)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	640.603 770.604848 7.69 12.30 780.705666 11.81 922.785139 12.01 878.817277 13.16 948.800365 12.19 656.583559 9.94
	CE 17:0 PC(18:0p/20:4)+H TG(16:0/14:0/16:0)+NH4 TG(16:0/18:2/22:6)+NH4 TG(18:0/16:0/20:4)+Na TG(18:1/18:2/24:1)+NH4 TG(4:0/16:0/18:1)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	656.634 794.606089 8.13 10.50 796.738138 12.35 920.769591 11.83 905.757226 12.76 984.897112 13.56 682.598987 10.04
	CE 17:1 PC(18:0p/22:6)+H TG(16:0/14:0/16:1)+NH4 TG(16:0/18:3/20:4)+NH4 TG(18:0/17:0/18:1)+NH4 TG(18:1/19:1/19:1)+NH4 TG(6:0/16:0/16:0)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	654.618 818.601338 7.80 9.85 794.72225 11.95 894.754048 11.89 892.834668 13.26 930.846567 12.94 684.614507 10.56
	CE 18:1 PC(18:0p/24:2)+H TG(16:0/14:0/18:1)+NH4 TG(16:0/18:3/22:6)+NH4 TG(18:0/17:0/20:4)+NH4 TG(18:1/20:3/20:5)+NH4 TG(6:0/16:0/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	668.634 854.700519 9.69 12.77 822.752489 12.50 918.754113 11.56 914.817612 12.92 946.784195 11.67 708.61543 10.11
	CE 18:2 PC(18:0p/24:7)+H TG(16:0/14:0/18:2)+NH4 TG(16:0/22:5/22:6)+NH4 TG(18:0/18:0/18:1)+NH4 TG(18:1/20:4/20:4)+NH4 TG(8:0/16:0/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	666.618 844.619306 8.27 12.36 820.738448 11.97 970.785172 11.74 906.850521 13.49 946.785165 12.01 736.646195 10.62
	CE 18:3 PC(18:1/18:2)+H TG(16:0/14:0/18:3)+NH4 TG(16:0/22:6/22:6)+NH4 TG(18:0/18:0/20:4)+NH4 TG(18:1/20:4/20:5)+NH4 TG(8:0/16:0/20:4)+H	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	664.603 784.584342 7.14 11.92 818.722195 11.77 968.76919 11.55 928.833895 13.12 944.769337 11.65 743.616492 11.11
	CE 20:2 PC(18:1/20:4)+H TG(16:0/14:0/20:4)+NH4 TG(16:0e/16:0/18:1)+NH4 TG(18:0/18:1/18:1)+NH4 TG(18:1/20:4/22:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	694.650 808.583846 6.76 12.75 844.738186 11.99 836.807857 13.28 904.834802 13.30 974.816223 12.31
	CE 20:3 PC(18:1/22:6)+H TG(16:0/14:0/20:5)+Na TG(16:0e/18:1/18:1)+Na TG(18:0/18:1/18:2)+NH4 TG(18:1/20:4/22:5)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	692.634 832.584082 6.67 12.40 847.678407 11.63 867.779787 13.34 902.818251 13.88 972.800675 11.93
	CE 20:4 PC(18:1/24:2)+H TG(16:0/14:0/22:6)+NH4 TG(16:0e/18:1/18:2)+NH4 TG(18:0/18:1/20:3)+NH4 TG(18:1/20:4/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	690.618 868.680408 9.25 12.04 868.738501 11.73 860.807264 13.02 928.834394 13.08 970.784787 11.74
	CE 20:5 PC(18:1/24:6)+H TG(16:0/15:0/16:0)+NH4 TG(16:1/12:0/18:1)+NH4 TG(18:0/18:1/20:4)+NH4 TG(18:1/22:6/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	688.603 860.614603 7.56 11.63 810.754688 12.59 792.70609 11.76 926.816956 12.90 994.784759 11.58
	CE 22:5 PC(18:1/24:7)+H TG(16:0/15:0/18:1)+NH4 TG(16:1/12:0/18:2)+NH4 TG(18:0/18:1/22:5)+NH4 TG(18:2/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	716.634 858.599801 6.79 12.08 836.769541 12.60 790.691584 11.15 952.832696 12.74 896.768796 11.78
	CE 22:6 PC(18:1p/22:5)+H TG(16:0/15:0/18:2)+NH4 TG(16:1/14:0/14:0)+Na TG(18:0/18:1/22:6)+NH4 TG(18:2/18:2/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	714.618 818.603069 7.81 11.78 834.753697 12.25 771.646217 11.52 950.817377 12.67 920.769847 11.68
	DG(16:0/18:1)+NH4 PC(18:1p/24:2)+H TG(16:0/15:1/18:2)+NH4 TG(16:1/14:0/18:1)+NH4 TG(18:0/18:2/20:4)+NH4 TG(18:2/18:2/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	612.557247 8.86 852.683912 9.15 832.737573 12.03 820.737606 12.15 924.800974 12.76 944.768995 11.48
	DG(16:0/18:2)+NH4 PC(18:1p/24:7)+H TG(16:0/16:0/16:0)+NH4 TG(16:1/14:0/18:2)+NH4 TG(18:0/20:0/20:4)+H TG(18:2/20:4/20:4)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	610.540219 8.41 842.602324 7.47 824.769619 12.68 818.722148 11.76 939.8367 13.79 944.769875 11.67
	DG(16:0/18:3)+NH4 PC(18:2p/16:0)+H TG(16:0/16:0/17:0)+NH4 TG(16:1/14:0/18:3)+NH4 TG(18:0/20:1/20:4)+NH4 TG(18:2/20:4/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	608.524485 7.90 742.573364 6.92 838.785397 12.92 816.707071 11.39 954.849309 13.10 968.769887 11.37
	DG(16:0/20:4)+NH4 PC(18:2p/17:1)+H TG(16:0/16:0/18:1)+NH4 TG(16:1/14:0/22:6)+NH4 TG(18:0/20:3/22:5)+NH4 TG(18:2/22:6/22:6)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	634.540345 8.17 754.573327 6.98 850.785184 12.87 866.722786 11.37 976.831036 12.30 992.770169 11.19
	DG(16:0/22:5)+NH4 PC(18:2p/18:0)+H TG(16:0/16:0/18:2)+NH4 TG(16:1/15:0/18:2)+NH4 TG(18:0/20:4/20:4)+NH4 TG(18:3/18:2/18:2)+NH4	ESI+ ESI+ ESI+ ESI+ ESI+ ESI+	660.555883 8.17 770.604069 7.57 848.771969 12.88 832.73805 11.84 948.801105 12.37 894.754021 11.51

Acknowledgements

We thank Dr. Pierre Nahon for his help with sample collection and for his constructive suggestions for the study design. We also would like to express our appreciation to Agnes Victor Bala for her help in the assignment of NMR spectra. We thank Sandy Field, PhD, for medical editing assistance. We acknowledge the NMR-PF facility (University Paris 13 France).

Funding

This study is supported by the funding of Anhui Medical University (XJ201729) and by the funding from the University of Paris 13.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

To my parents

Changmei LIU, Jialiang LIN and my elder sister, Xiaojun LIN

Ethics approval and consent to participate

Written informed consent was obtained from all subjects or their surrogate decision-maker. The local ethics committee approved the protocol.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

ABSTRACT

Metabolomics is the science designed to comprehensively study the metabolome, the repertoire of small molecule metabolites, which gives a comprehensive snapshot of the physiological state of the biofluid, extracts or cells studied. Measuring metabolites by using metabolomics is a key complementary to genome, transcriptome and proteome studies, which may improve our understanding of how genetics, environment, the microbiome, disease, drug exposure, diet, and lifestyle influence the phenotype. One of important application of metabolomics in clinical research is the discovery of novel biomarkers. The present PhD thesis focus on biomarkers discovery by applying metabolomics, the objectives were: (1) by using NMR and UPLC-HRMS based metabolomic and lipidomic profiling, to identify novel plasma biomarkers, if any, which characterize the different stage of Non-alcoholic fatty liver disease (NAFLD), and ( 2) by combining UPLC-HRMS based untargeted metabolomics with epidemiology approach, to identify plasma biomarkers which associated with the risk of developing prostate cancer (PCa) within the following decade.

Keywords: Metabolomics, NMR, LC-MS, multivariate analysis, prostate cancer, NAFLD, biomarkers

RESUME

La métabolomique consiste en l'étude approfondie du métabolome, qui correspond à l'ensemble des métabolites présent dans un organisme. Le métabolome donne un aperçu de l'état physiologique de l'organisme, de l'extrait ou des cellules étudiées. La mesure des métabolites par l'approche métabolomique est un complément important aux études sur le génome, le transcriptome et le protéome, qui peut améliorer notre compréhension sur comment la génétique, l'environnement, le microbiome, les maladies, l'exposition aux médicaments, l'alimentation et le mode de vie influencent le phénotype. L'une des applications importantes de la métabolomique en recherche clinique est la découverte de nouveaux biomarqueurs. La présente thèse porte sur la partie découverte de biomarqueurs par métabolomique. Deux études sont réalisées :

(1) la première utilise l'approche métabolomique et lipidomique basée sur la RMN et l'UPLC-HRMS, pour identifier de nouveaux biomarqueurs plasmatiques qui caractérisent les différents stades de la stéatose hépatique non alcoolique (NAFLD) (2) la seconde combine la métabolomique non ciblée basée sur UPLC-HRMS avec une approche épidémiologique pour identifier les biomarqueurs plasmatiques associés au risque de développer un cancer de la prostate (PCa) au cours de la décennie suivante.

Mots-clés : Métabolomique, RMN, LC-MS, analyses multivariées, cancer prostate, NAFLD, biomarqueurs