Differentiating signals to make biological sense – a guide through databases for MS-based non-targeted metabolomics

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their on-line ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as ID assignment, structural assignment and interpretation of results

Metabolic clustering analysis as a strategy for compound selection in the drug discovery pipeline for leishmaniasis

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis–mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs

Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample

The type and use of quality control (QC) samples is a ‘hot topic’ in metabolomics. QCs are not novel in analytical chemistry; however since the evolution of using QCs to control the quality of data in large scale metabolomics studies (first described in 2011), the need for detailed knowledge of how to use QCs and the effects they can have on data treatment is growing. A controlled experiment has been designed to illustrate the most advantageous uses of QCs in metabolomics experiments. For this, samples were formed from a pool of plasma whereby different metabolites were spiked into two groups in order to simulate biological biomarkers. Three different QCs were compared: QCs pooled from all samples, QCs pooled from each experimental group of samples separately and QCs provided by an external source (QC surrogate). On the experimentation of different data treatment strategies, it was revealed that QCs collected separately for groups offers the closest matrix to the samples and improves the statistical outcome, especially for biomarkers unique to one group. A novel quality assurance plus procedure has also been proposed that builds on previously published methods and has the ability to improve statistical results for QC pool. For this dataset, the best option to work with QC surrogate was to filter data based only on group presence. Finally, a novel use of recursive analysis is portrayed that allows the improvement of statistical analyses with respect to the ratio between true and false positives

Flow Cytometry Has a Significant Impact on the Cellular Metabolome

The characterization of specialized cell subpopulations in a heterogeneous tissue is essential for understanding organ function in health and disease. A popular method of cell isolation is fluorescence-activated cell sorting (FACS) based on probes that bind surface or intracellular markers. In this study, we analyze the impact of FACS on the cell metabolome of mouse peritoneal macrophages. Compared with directly pelleted macrophages, FACS-treated cells had an altered content of metabolites related to the plasma membrane, activating a mechanosensory signaling cascade causing inflammation-like stress. The procedure also triggered alterations related to energy consumption and cell damage. The observed changes mostly derive from the physical impact on cells during their passage through the instrument. These findings provide evidence of FACS-induced biochemical changes, which should be taken into account in the design of robust metabolic assays of cells separated by flow cytometry.FJ.R., J.G., and D.R. acknowledge funding from the Ministerio de Economia y Competitividad (CTQ2014-55279-R). This study was also supported by Ministerio de Economia y Competitividad grant BIO2015-67580-P through the Carlos III Institute of Health (ISCIII) and the Fundacion La Marato TV3 to J.V and to M.R (201605-30-31-32). J.V. laboratory is a member of Proteored, PRB3 and is supported by grant PT17/0019, of the PE I+D+i 2013-2016, funded by ISCIII and European Regional Development Fund (ERDF). M.R. received grants from the Ministerio de Economia y Competitividad (SAF2015-64287R, SAF2017-90604-REDT). J.V and M.R received funding from the People Programme (Marie Curie Actions) of the European Union Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no 608027 (CardioNext Initial Training Networks project). A.B. is a FP7-PEOPLE-2013-ITN-Cardionext fellow. The CNIC is supported by the Ministerio de Ciencia, Innovacion y Universidades (MCNU) and the Pro-CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

In-Vial Dual Extraction for Direct LC-MS Analysis of Plasma for Comprehensive and Highly Reproducible Metabolic Fingerprinting

Metabolic fingerprinting of biological tissues has become an important area of research, particularly in the biomarker discovery field. Methods have inherent analytical variation, and new approaches are necessary to ensure that the vast numbers of intact metabolites present in biofluids are detected. Here, we describe an in-vial dual extraction (IVDE) method and a direct injection method that shows the total number of features recovered to be over 4500 from a single 20 μL plasma aliquot. By applying a one-step extraction consisting of a lipophilic and hydrophilic layer within a single vial insert, we showed that analytical variation was decreased. This was achieved by reducing sample preparation stages including procedures of drying and transfers. The two phases in the vial, upper and lower, underwent HPLC-QTOF analysis on individually customized LC gradients in both positive and negative ionization modes. A 60 min lipid profiling HPLC-QTOF method for the lipophilic phase was specifically developed, enabling the separation and putative identification of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. The aqueous phase of the extract underwent direct injection onto a 45 min gradient, enabling the detection of both polarities. The IVDE method was compared to two traditional extraction methods. The first method was a two-step ether evaporation and IPA resuspension, and the second method was a methanol precipitation typically used in fingerprinting studies. The IVDE provided a 378% increase in reproducible features when compared to evaporation and a 269% increase when compared to the precipitate and inject method. As a proof of concept, the method was applied to an animal model of diabetes. A 2-fold increase in discriminant metabolites was found when comparing diabetic and control rats with IVDE. These discriminant metabolites accounted for around 600 entities, out of which 388 were identified in available databases

METABOLOMIC ASSESSMENT WITH LC-QTOF-MS OF THE NUTRACEUTICAL EFFECT OF ROSMARINUS OFFICINALIS EXTRACTS ON PLASMA SAMPLES OF DIABETIC RATS

Diabetes mellitus is one of the major worldwide health problems. The peculiarity of this disease is to have high levels of oxidative stress, which plays a crucial role in the pathogenesis of other complications, such as nephropathy, neuropathy and retinopathy. Rosmarinus officinalis L. extracts, processed by supercritical fluid extraction (SFE), have long been recognized as having antioxidant properties. To investigate the potential nutraceutical properties of these extracts on type 1 diabetes, a metabolomic fingerprinting study was carried out using LC-QTOF-MS. Metabolomic fingerprinting is a technique which study as many analytes as possible to get a complete view of a disease and of the different levels of metabolites involved, and finally to identify possible biomarkers.In this work, Sprague-Dawley male rats (12 \ub1 2 weeks of age), treated with streptozotocin, were used in controlled conditions in which they received, by intragastric administration, five doses of rosemary extract containing 10% folic acid (used to improve the endothelial progenitor cell function) dispersed in 1 mL vehicle or only 1 mL of vehicle. There were also healthy rats that received the same treatment, thus generating four groups of samples: treated or non-treated (only vehicle) controls, treated and non-treated diabetics.Plasma fingerprints of control and diabetic rats, focused on the most polar constituents, were obtained by LC-QTOF-MS. Retention of polar compounds from plasma in hydrophilic interaction liquid chromatography (HILIC) mode and use of solvents readily compatible with mass spectrometry is a new approach that was initially explored by testing different stationary phases. The HILIC columns used in this study included an Ascentis Si (15 cm 7 2.1 mm, 5 \ub5m, Supelco), a TSK gel Amide-80 (5 cm 7 2.0 mm, 3 \ub5m, Tosoh), a Luna HILIC (10 cm 7 2.0 mm, 5 \ub5m, Phenomenex) and a ZIC-HILIC (10 cm 7 2.1 mm, 3.5 \ub5m, Merck). Unfortunately, the results were not as good as it was expected and this was mainly due to the lack of precision. It was therefore decided to analyze the plasma samples under reversed-phase (RP) conditions, using a Discovery HS C18 column (15 cm 7 2.1 mm, 3 \ub5m, Supelco), with a mobile phase composed by water (with 0.1% of formic acid) and ACN (with 0.1% of formic acid), under gradient elution. The injection volume was 10 \ub5L; the flow rate was 0.6 mL/min and the column temperature was set at 30 \ub0C. Data were collected in the positive ion mode with a QTOF mass analyzer, operated in the full scan mode from 50 to 1000 m/z. Experimental data from RP-HPLC were further processed by multivariate analysis to find compounds with statistically significant changes. Then, MS/MS experiments were performed under the same experimental conditions and identifications were carried out by studying the fragmentation pattern of the target analytes or using reference standards. Finally, the biochemical interpretation of potential biomarkers associated with the disease was performed. The compounds that were found to be affected by the nutraceutical treatment include:\u2022Lysophosphatidylcholines (LPCs), represent the first group of identified compounds. LPCs are generated from ox-LDL or from inflammatory cells, and are very important intermediates of different metabolic pathways. These compounds were found to increase in diabetic rats after the treatment.\u2022Pipecolic acid, the major metabolite of lysine degradation. This compound was found to increase in diabetic rats after the treatment.\u2022Lipoic acid, an antioxidant synthesized in mitochondria. It is a necessary co-factor for mitochondrial \u3b1-ketoacid dehydrogenases, and thus plays a critical role in mitochondrial energy metabolism. This metabolite was found to increase in diabetic rats after the treatment.\u2022Carnitine and derivates, which is important to transport fatty acids into the mitochondria. Also these compounds were found to be affected by the treatment.In conclusion, the LC-QTOF-MS analysis allowed us to study a large number of compounds, very useful in metabolomic fingerprinting, and, with the help of chemometric analysis, to select masses with statistically significant changes. The levels of LPCs, pipecolic acid, lipoic acid, carnitine and derivates were found to be improved after the nutraceutical treatment, that can be considered useful to counteract some of the deleterious effects of type 1 diabetes

Plasma Metabolic Signature of Atherosclerosis Progression and Colchicine Treatment in Rabbits.

Balloon catheter endothelial denudation in New Zealand white rabbits fed high cholesterol diet is a validated atherosclerosis model. Well-characterized in terms of atherosclerosis induction and progression, the metabolic changes associated with the atherosclerosis progression remain indeterminate. Non-targeted metabolomics permits to develop such elucidation and allows to evaluate the metabolic consequences of colchicine treatment, an anti-inflammatory drug that could revert these changes. 16 rabbits underwent 18 weeks of atherosclerosis induction by diet and aortic denudation. Thereafter animals were randomly assigned to colchicine treatment or placebo for 18 weeks while on diet. Plasma samples were obtained before randomization and at 36 weeks. Multiplatform (GC/MS, CE/MS, RP-HPLC/MS) metabolomics was applied. Plasma fingerprints were pre-processed, and the resulting matrixes analyzed to unveil differentially expressed features. Different chemical annotation strategies were accomplished for those significant features. We found metabolites associated with either atherosclerosis progression, or colchicine treatment, or both. Atherosclerosis was profoundly associated with an increase in circulating bile acids. Most of the changes associated with sterol metabolism could not be reverted by colchicine treatment. However, the variations in lysine, tryptophan and cysteine metabolism among others, have shown new potential mechanisms of action of the drug, also related to atherosclerosis progression, but not previously described.M.A.I. received funding from Airbus Defense and Space through the CLX-2 program developed with Comando da Aeronáutica (COMAER) and the Brazilian Government. This work has been partially funded by the Ministerio de Ciencia, Innovación y Universidades of Spain (MICINN RTI2018-095166-B-I00), the Spanish Society of Cardiology (“Proyecto investigación traslacional” to BI), the Carlos III Institute of Health (ISCiii FIS-FEDER PI14-01427 to JM), the Ministerio de Economía, Industria y Competitividad (MINECO SAF2017-84494-C2-1R to JR-C), a project granted by the BBVA Foundation to JR-C. This work was performed under the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (MDM-2017-0720). The CNIC is supported by the ISCiii, and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S