Development and Validation of an On-Line HPLC-DAD-Antioxidant Assay (ORAC)/ESI-HRMS System to Identify Antioxidant Compounds in Complex Mixtures.

An online high-performance liquid-chromatography-diode-array detector coupled with detection of antioxidant compounds using oxygen radical absorbance capacity (ORAC) assay and electrospray ionization-high-resolution mass spectrometer (HPLC-DAD-antioxidant assay (ORAC)/ESI-HRMS) was developed for the identification of antioxidant compounds in complex mixtures. The method was validated using quercetin and a mixture of antioxidant compounds with different antioxidant activities (resveratrol, dihydroxymethoxy-dihydrochalcone, ferulic acid, baicalein and luteolin). Accuracy of the system was established by comparing the results from the developed system with those from ORAC microplate assay determination and reveals the ability of the system to determine the respective contribution of antioxidant compounds to the whole activity of complex mixtures. Application of the system to the identification of antioxidants in a commercial Yerba Mate extract (Ilex paraguariensis St. Hil.) reveals the occurrence of seven actives, which were characterized as chlorogenic acids isomers (3-O-caffeoylquinic acid, 4-O-caffeoylquinic acid and 5-O-caffeoylquinic acid), dicaffeoylquinic acid isomers (3,4-di-O-caffeoylquinic acid, 3,5-di-O-caffeoylquinic acid and 4,5-di-O-caffeoylquinic acid) and rutin based on UV/Vis spectra, HRMS and MS/MS data. This on-line system is able to generate HPLC-DAD fingerprints, UV/Vis spectra, ORAC activity profile and high-resolution mass spectrometric data.

Comprehensive characterization of naturally occurring antioxidants from the twigs of mulberry (Morus alba) using on-line high-performance liquid chromatography coupled with chemical detection and high-resolution mass spectrometry.

INTRODUCTION: The mulberry tree (Morus alba L.) is a prolific source of biologically active compounds. There is considerable growing interest in probing M. alba twigs as a source of disruptive antioxidant lead candidates for cosmetic skin care product development. OBJECTIVE: An integrated approach using high-performance liquid chromatography (HPLC) coupled with either chemical detection (CD) or high-resolution mass spectrometry (HRMS) was applied to the hydroalcoholic extract of M. alba to detect and identify lead antioxidant compounds, respectively. MATERIAL AND METHODS: The twigs were weighed, powdered and homogenized using a mill and the extract was prepared using 70% aqueous ethanol. The antioxidant metabolites were detected with HPLC coupled with CD (based on the ORAC assay) and their structural identification was carried out using a Q-Exactive Orbitrap MS instrument. RESULTS: Using this approach, 13 peaks were detected as overall contributors to the antioxidant activity of M. alba, i.e. mulberrosides (A & E), oxyresveratrol & its derivatives, moracin & its derivatives and a dihydroxy-octadecadienoic acid, which together accounted for >90% of the antioxidant activity, highlighting the effectiveness of the integrated approach based on HPLC-CD and HPLC-HRMS. Additionally, a (3,4-dimethoxyphenyl-1-O-ß-D-apiofuranosyl-(1″ → 6')-O-ß-D-glucopyranoside was also discovered for the first time from the twig extract and is presented here. CONCLUSION: To our knowledge, this is the first report from M. alba twigs using HPLC-CD and HPLC-HRMS that identifies key compounds responsible for the antioxidant property of this native Chinese medicinal plant.

Vitamin C prevents epidermal damage induced by PM-associated pollutants and UVA1 combined exposure.

Particulate matter is suspected to be substantially involved in pollution-induced health concerns. In fact, ultrafine particles (UFPs) contain polycyclic aromatic hydrocarbons (PAHs) known as mutagenic, cytotoxic and sometimes phototoxic. Since UFPs reach blood circulation from lung alveoli, deep skin is very likely contaminated by PAHs coming from either skin surface or blood. As photoreactive, benzo(a)pyrene (BaP) or indenopyrene (IcdP) is involved in the interplay between pollution and sunlight. In order to better characterize this process, experiments were carried out on reconstructed human epidermis (RHE) in a protocol mimicking realistic exposure. Concentrations of PAHs comparable to those generally reported in blood were used together with chronic irradiation to low dose UVA1. On a histological level, damaged cells mainly accumulated in a suprabasal situation, thus reducing living epidermis thickness. Stress markers such as IL1-α or MMP3 secretion increased, and surprisingly, the histological position of Transglutaminase-1 within epidermis was disturbed, whereas position of other differentiation markers (keratin-10, filaggrin, loricrin) remained unchanged. When vitamin C was added in culture medium, a very significant protection involving all markers was noticed. In conclusion, we provide here a model of interest to understand the epidermal deleterious consequences of pollution and to select efficient protective compounds.

biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data.

High-throughput technologies such as transcriptomics, proteomics, and metabolomics show great promise for the discovery of biomarkers for diagnosis and prognosis. Selection of the most promising candidates between the initial untargeted step and the subsequent validation phases is critical within the pipeline leading to clinical tests. Several statistical and data mining methods have been described for feature selection: in particular, wrapper approaches iteratively assess the performance of the classifier on distinct subsets of variables. Current wrappers, however, do not estimate the significance of the selected features. We therefore developed a new methodology to find the smallest feature subset which significantly contributes to the model performance, by using a combination of resampling, ranking of variable importance, significance assessment by permutation of the feature values in the test subsets, and half-interval search. We wrapped our biosigner algorithm around three reference binary classifiers (Partial Least Squares-Discriminant Analysis, Random Forest, and Support Vector Machines) which have been shown to achieve specific performances depending on the structure of the dataset. By using three real biological and clinical metabolomics and transcriptomics datasets (containing up to 7000 features), complementary signatures were obtained in a few minutes, generally providing higher prediction accuracies than the initial full model. Comparison with alternative feature selection approaches further indicated that our method provides signatures of restricted size and high stability. Finally, by using our methodology to seek metabolites discriminating type 1 from type 2 diabetic patients, several features were selected, including a fragment from the taurochenodeoxycholic bile acid. Our methodology, implemented in the biosigner R/Bioconductor package and Galaxy/Workflow4metabolomics module, should be of interest for both experimenters and statisticians to identify robust molecular signatures from large omics datasets in the process of developing new diagnostics.

Annotation of the human cerebrospinal fluid lipidome using high resolution mass spectrometry and a dedicated data processing workflow.

INTRODUCTION: Due to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches. OBJECTIVES: This study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach. METHODS: Cerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to high-resolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species. RESULTS: Using this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.e., free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF. CONCLUSION: This work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.

Annotation of the Staphylococcus aureus Metabolome Using Liquid Chromatography Coupled to High-Resolution Mass Spectrometry and Application to the Study of Methicillin Resistance.

Staphylococcus aureus can cause a variety of severe disease patterns and can readily acquire antibiotic resistance; however, the mechanisms by which this commensal becomes a pathogen or develops antibiotic resistance are still poorly understood. Here we asked whether metabolomics can be used to distinguish bacterial strains with different antibiotic susceptibilities. Thus, an efficient and robust method was first thoroughly implemented to measure the intracellular metabolites of S. aureus in an unbiased and reproducible manner. We also placed special emphasis on metabolome coverage and annotation and used both hydrophilic interaction liquid chromatography and pentafluorophenyl-propyl columns coupled to high-resolution mass spectrometry in conjunction with our spectral database developed in-house to identify with high confidence as many meaningful S. aureus metabolites as possible. Overall, we were able to characterize up to 210 metabolites in S. aureus, which represents a substantial ∼50% improvement over previously published data. We then preliminarily compared the metabolic profiles of 10 clinically relevant methicillin-resistant and susceptible strains harvested at different time points during the exponential growth phase (without any antibiotic exposure). Interestingly, the resulting data revealed a distinct behavior of "slow-growing" resistant strains, which show modified levels of several precursors of peptidoglycan and capsular polysaccharide biosynthesis.

Lipidomic analysis of cerebrospinal fluid by mass spectrometry-based methods.

Lipids are natural substances found in all living organisms. Essential to the integrity of cell membranes, they also have many biological functions linked to energy storage and cell signaling, and are involved in a large number of heterogeneous diseases such as cancer, diabetes, neurological disorders, and inherited metabolic diseases. Lipids are challenging to analyze because of their huge structural diversity and numerous species. Up to now, lipid analysis has been achieved by targeted approaches focusing on selected families and relying on extraction protocols and chromatographic methods coupled to various detectors including mass spectrometry. Thanks to the technological improvements achieved in the fields of chromatography, high-resolution mass spectrometry and bioinformatics, it is possible to perform global lipidomic analyses enabling the concomitant detection, identification and relative quantification of many lipid species belonging to different families. The aim of this review is to focus on mass spectrometry-based methods to perform lipid and lipidomic analyses and on their application to the analysis of cerebrospinal fluid.

Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry.

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum.