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19 pages, 2366 KiB

Open AccessArticle

Optimisation of the HS-SPME/GC-MS Approach by Design of Experiments Combined with Chemometrics for the Classification of Cretan Virgin Olive Oils

by Artemis Lioupi, Ioannis Sampsonidis, Christina Virgiliou, Vassiliki T. Papoti, Kyriaki G. Zinoviadou, Apostolos Spyros and Georgios Theodoridis

Metabolites 2022, 12(2), 114; https://doi.org/10.3390/metabo12020114 - 25 Jan 2022

Cited by 16 | Viewed by 4172

Abstract

A headspace-solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) method was developed herein for the analysis of virgin olive oil volatile metabolome. Optimisation of SPME conditions was performed by Design of Experiments (DoE) and Response Surface Methodology (RSM) approaches and factors, such as sample volume, [...] Read more.

A headspace-solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) method was developed herein for the analysis of virgin olive oil volatile metabolome. Optimisation of SPME conditions was performed by Design of Experiments (DoE) and Response Surface Methodology (RSM) approaches and factors, such as sample volume, sample stirring, extraction temperature and time, and desorption temperature and time, were examined to reach optimal microextraction conditions. The potential of the optimised method was then investigated for its use in the classification of Cretan virgin olive oil samples with the aid of multivariate statistical analysis. Certain markers were identified with significance in the geographical classification of Cretan extra-virgin olive oil (EVOO) samples. In total, 92 volatile organic compounds were tentatively identified and semi-quantified, and the data obtained confirm that the method is robust, reliable, and analytically powerful for olive oil classification. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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13 pages, 1877 KiB

Open AccessArticle

Stability of Wheat Floret Metabolites during Untargeted Metabolomics Studies

by Kristin Whitney, Gerardo Gracia-Gonzalez and Senay Simsek

Metabolites 2022, 12(1), 62; https://doi.org/10.3390/metabo12010062 - 11 Jan 2022

Cited by 2 | Viewed by 1992

Abstract

A typical metabolomic analysis consists of a multi-step procedure. Variation can be introduced in any analysis segment if proper care in quality assurance is not taken, thus compromising the final results. Sample stability is one of those factors. Although sophisticated studies addressing sample [...] Read more.

A typical metabolomic analysis consists of a multi-step procedure. Variation can be introduced in any analysis segment if proper care in quality assurance is not taken, thus compromising the final results. Sample stability is one of those factors. Although sophisticated studies addressing sample decay over time have been performed in the medical field, they are emerging in plant metabolomics. Here, we focus on the stability of wheat floret extracts on queue inside an auto-injector held at 25 °C. The objective was to locate an analytical time window from extraction to injection with no significant difference occurring in the sample. Total ion current chromatograms, principal component analysis, and volcano plots were used to measure changes in the samples. Results indicate a maximum work window time of 7:45 h for Steele-ND wheat methanolic extractions in an auto-sampler at 25 °C. Comparisons showed a significant gradual increase in the number and intensity of compounds observed that may be caused by the degradation of other molecules in the sample extract. The approach can be applied as preliminary work in a metabolite profiling study, helping to set the appropriate workload to produce confident results. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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16 pages, 1615 KiB

Open AccessArticle

Kiwifruit Metabolomics—An Investigation of within Orchard Metabolite Variability of Two Cultivars of Actinidia chinensis

by Daryl Rowan, Helen Boldingh, Sarah Cordiner, Janine Cooney, Duncan Hedderley, Katrin Hewitt, Dwayne Jensen, Trisha Pereira, Tania Trower and Tony McGhie

Metabolites 2021, 11(9), 603; https://doi.org/10.3390/metabo11090603 - 6 Sep 2021

Cited by 3 | Viewed by 2468

Abstract

Plant metabolomics within field-based food production systems is challenging owing to environmental variability and the complex architecture and metabolic growth cycles of plants. Kiwifruit cultivars of Actinidia chinensis are vigorous perennial vines grown as clones in highly structured orchard environments, intensively managed to [...] Read more.

Plant metabolomics within field-based food production systems is challenging owing to environmental variability and the complex architecture and metabolic growth cycles of plants. Kiwifruit cultivars of Actinidia chinensis are vigorous perennial vines grown as clones in highly structured orchard environments, intensively managed to maximize fruit yield and quality. To understand the metabolic responses of vines to orchard management practices, we needed to better understand the various sources of metabolic variability encountered in the orchard. Triplicate composite leaf, internode and fruit (mature and immature) samples were collected from each of six Actinidia chinensis var. deliciosa ‘Hayward’ and A. chinensis var. chinensis ‘Zesy002’ kiwifruit vines at three times during the growing season and measured by LC-MS. In general, there was more variation in metabolite concentrations within vines than between vines, with ‘Hayward’ showing a greater percentage of within-vine variability than ‘Zesy002’ (c. 90 vs. 70% respectively). In specific tissues, the sampler, infection by Pseudomonas syringae var. actinidiae and the rootstock also influenced metabolite variability. A similar pattern of metabolic variability was observed from quantitative analysis of specific carbohydrates and phytohormones. High within-vine metabolic variability indicates that it is more important to obtain sufficient replicate samples than to sample from multiple vines. These data provide an objective basis for optimizing metabolite sampling strategies within kiwifruit orchards. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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12 pages, 2972 KiB

Open AccessArticle

Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

by Brechtje Hoegen, Alan Zammit, Albert Gerritsen, Udo F. H. Engelke, Steven Castelein, Maartje van de Vorst, Leo A. J. Kluijtmans, Marleen C. D. G. Huigen, Ron A. Wevers, Alain J. van Gool, Christian Gilissen, Karlien L. M. Coene and Purva Kulkarni

Metabolites 2021, 11(9), 568; https://doi.org/10.3390/metabo11090568 - 26 Aug 2021

Cited by 13 | Viewed by 3717

Abstract

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted [...] Read more.

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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16 pages, 1534 KiB

Open AccessArticle

Urinary Metabolites Reveal Hyperinsulinemia and Insulin Resistance in Polycystic Ovarian Syndrome (PCOS)

by Anna Maria Fulghesu, Cristina Piras, Angelica Dessì, Claudia Succu, Luigi Atzori, Roberta Pintus, Cecilia Gentile, Stefano Angioni and Vassilios Fanos

Metabolites 2021, 11(7), 437; https://doi.org/10.3390/metabo11070437 - 2 Jul 2021

Cited by 9 | Viewed by 3322

Abstract

The identification of insulin resistance and hyperinsulinemia in polycystic ovary syndrome (PCOS) is not a minor issue. The homeostasis model assessment of insulin resistance index (HOMA) is the most used index of IR (Insulin Resistance), validated in overweight and obese patients but not [...] Read more.

The identification of insulin resistance and hyperinsulinemia in polycystic ovary syndrome (PCOS) is not a minor issue. The homeostasis model assessment of insulin resistance index (HOMA) is the most used index of IR (Insulin Resistance), validated in overweight and obese patients but not in normal-weight PCOS subjects, who can still present with increased insulin secretion by an oral glucose tolerance test (OGTT). The evaluation of insulin secretion and resistance represents a still unresolved problem. The aim of this study is to identify a possible yet noninvasive method to properly evaluate the insulin metabolism in young non-diabetic subjects. Girls aged 14–22 years, afferent to the center of Gynecological Diseases in Childhood and Adolescence of Cagliari (Italy), were screened for PCOS. A total of 42 subjects comprised the study group. Hormonal assays, OGTT, transabdominal (TA) or transvaginal (TV) US, and urine collection for ¹H-NMR analysis were assayed in the early follicular phase. A ¹H-NMR coupled multivariate statistical analysis was performed. The OPLS model indicated that the NMR profile of urine had a good fit and prediction ability for the AUC OGTT with R² = 0.813. Metabolomics can be a promising tool to the potential identification of biomarkers of an exaggerated insulin response to OGTT and can encourage substantial progress for a more accurate and early diagnosis in PCOS. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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15 pages, 1999 KiB

Open AccessArticle

Towards Standards for Human Fecal Sample Preparation in Targeted and Untargeted LC-HRMS Studies

by Farideh Hosseinkhani, Anne-Charlotte Dubbelman, Naama Karu, Amy C. Harms and Thomas Hankemeier

Metabolites 2021, 11(6), 364; https://doi.org/10.3390/metabo11060364 - 7 Jun 2021

Cited by 11 | Viewed by 5093

Abstract

Gut microbiota and their metabolic products are increasingly being recognized as important modulators of human health. The fecal metabolome provides a functional readout of the interactions between human metabolism and the gut microbiota in health and disease. Due to the high complexity of [...] Read more.

Gut microbiota and their metabolic products are increasingly being recognized as important modulators of human health. The fecal metabolome provides a functional readout of the interactions between human metabolism and the gut microbiota in health and disease. Due to the high complexity of the fecal matrix, sample preparation often introduces technical variation, which must be minimized to accurately detect and quantify gut bacterial metabolites. Here, we tested six different representative extraction methods (single-phase and liquid–liquid extractions) and compared differences due to fecal amount, extraction solvent type and solvent pH. Our results indicate that a minimum fecal (wet) amount of 0.50 g is needed to accurately represent the complex texture of feces. The MTBE method (MTBE/methanol/water, 3.6/2.8/3.5, v/v/v) outperformed the other extraction methods, reflected by the highest extraction efficiency for 11 different classes of compounds, the highest number of extracted features (97% of the total identified features in different extracts), repeatability (CV < 35%) and extraction recovery (≥70%). Importantly, optimization of the solvent volume of each step to the initial dried fecal material (µL/mg feces) offers a major step towards standardization, which enables confident assessment of the contributions of gut bacterial metabolites to human health. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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24 pages, 9271 KiB

Open AccessArticle

Automated Sequential Analysis of Hydrophilic and Lipophilic Fractions of Biological Samples: Increasing Single-Injection Chemical Coverage in Untargeted Metabolomics

by Kristian Pirttilä, Göran Laurell, Curt Pettersson and Mikael Hedeland

Metabolites 2021, 11(5), 295; https://doi.org/10.3390/metabo11050295 - 5 May 2021

Cited by 3 | Viewed by 3140

Abstract

In order to increase metabolite coverage in LC–MS-based untargeted metabolomics, HILIC- and RPLC-mode separations are often combined. Unfortunately, these two techniques pose opposite requirements on sample composition, necessitating either dual sample preparations, increasing needed sample volume, or manipulation of the samples after the [...] Read more.

In order to increase metabolite coverage in LC–MS-based untargeted metabolomics, HILIC- and RPLC-mode separations are often combined. Unfortunately, these two techniques pose opposite requirements on sample composition, necessitating either dual sample preparations, increasing needed sample volume, or manipulation of the samples after the first analysis, potentially leading to loss of analytes. When sample material is precious, the number of analyses that can be performed is limited. To that end, an automated single-injection LC–MS method for sequential analysis of both the hydrophilic and lipophilic fractions of biological samples is described. Early eluting compounds in a HILIC separation are collected on a trap column and subsequently analyzed in the RPLC mode. The instrument configuration, composed of commercially available components, allows easy modulation of the dilution ratio of the collected effluent, with sufficient dilution to obtain peak compression in the RPLC column. Furthermore, the method is validated and shown to be fit for purpose for application in untargeted metabolomics. Repeatability in both retention times and peak areas was excellent across over 140 injections of protein-precipitated blood plasma. Finally, the method has been applied to the analysis of real perilymph samples collected in a guinea pig model. The QC sample injections clustered tightly in the PCA scores plot and showed a high repeatability in both retention times and peak areas for selected compounds. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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9 pages, 4877 KiB

Open AccessArticle

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

by Kristina E. Haslauer, Philippe Schmitt-Kopplin and Silke S. Heinzmann

Metabolites 2021, 11(5), 285; https://doi.org/10.3390/metabo11050285 - 29 Apr 2021

Cited by 6 | Viewed by 2805

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight [...] Read more.

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine ¹H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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21 pages, 4450 KiB

Open AccessArticle

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

by Alison Woodward, Alina Pandele, Salah Abdelrazig, Catherine A. Ortori, Iqbal Khan, Marcos Castellanos Uribe, Sean May, David A. Barrett, Richard G. Grundy, Dong-Hyun Kim and Ruman Rahman

Metabolites 2021, 11(4), 240; https://doi.org/10.3390/metabo11040240 - 14 Apr 2021

Cited by 1 | Viewed by 4032

Abstract

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells [...] Read more.

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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18 pages, 3111 KiB

Open AccessArticle

HPTLC-Based Chemical Profiling: An Approach to Monitor Plant Metabolic Expansion Caused by Fungal Endophytes

by Luis F. Salomé-Abarca, Cees A. M. J. J. van den Hondel, Özlem Erol, Peter G. L. Klinkhamer, Hye Kyong Kim and Young Hae Choi

Metabolites 2021, 11(3), 174; https://doi.org/10.3390/metabo11030174 - 17 Mar 2021

Cited by 7 | Viewed by 3026

Abstract

Fungal endophytes isolated from two latex bearing species were chosen as models to show their potential to expand their host plant chemical diversity. Thirty-three strains were isolated from Alstonia scholaris (Apocynaceae) and Euphorbia myrsinites (Euphorbiaceae). High performance thin layer chromatography (HPTLC) was used [...] Read more.

Fungal endophytes isolated from two latex bearing species were chosen as models to show their potential to expand their host plant chemical diversity. Thirty-three strains were isolated from Alstonia scholaris (Apocynaceae) and Euphorbia myrsinites (Euphorbiaceae). High performance thin layer chromatography (HPTLC) was used to metabolically profile samples. The selected strains were well clustered in three major groups by hierarchical clustering analysis (HCA) of the HPTLC data, and the chemical profiles were strongly correlated with the strains’ colony size. This correlation was confirmed by orthogonal partial least squares (OPLS) modeling using colony size as “Y” variable. Based on the multivariate data analysis of the HPTLC data, the fastest growing strains of each cluster were selected and used for subsequent experiments: co-culturing to investigate interactions between endophytes-phytopathogens, and biotransformation of plant metabolites by endophytes. The strains exhibited a high capacity to fight against fungal pathogens. Moreover, there was an increase in the antifungal activity after being fed with host-plant metabolites. These results suggest that endophytes play a role in plant defense mechanisms either directly or by biotransformation/induction of metabolites. Regarding HPTLC-based metabolomics, it has proved to be a robust approach to monitor the interactions among fungal endophytes, the host plant and potential phytopathogens. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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15 pages, 1436 KiB

Open AccessArticle

A Study of Blood Fatty Acids Profile in Hyperlipidemic and Normolipidemic Subjects in Association with Common PNPLA3 and ABCB1 Polymorphisms

by Thomai Mouskeftara, Antonis Goulas, Despoina Ioannidou, Charikleia Ntenti, Dimitris Agapakis, Andreana Assimopoulou and Helen Gika

Metabolites 2021, 11(2), 90; https://doi.org/10.3390/metabo11020090 - 4 Feb 2021

Cited by 4 | Viewed by 2001

Abstract

Adiponutrin (patatin-like phospholipase domain-containing 3; PNPLA3), encoded in humans by the PNPLA3 gene, is a protein associated with lipid droplet and endoplasmic reticulum membranes, where it is apparently involved in fatty acid redistribution between triglycerides and phospholipids. A common polymorphism of PNPLA3 (I148M, [...] Read more.

Adiponutrin (patatin-like phospholipase domain-containing 3; PNPLA3), encoded in humans by the PNPLA3 gene, is a protein associated with lipid droplet and endoplasmic reticulum membranes, where it is apparently involved in fatty acid redistribution between triglycerides and phospholipids. A common polymorphism of PNPLA3 (I148M, rs738409), linked to increased PNPLA3 presence on lipid droplets, is a strong genetic determinant of non-alcoholic fatty liver disease (NAFLD) and of its progression. P-glycoprotein (Pgp, MDR1—multidrug resistance protein 1, ABCB1—ATP-binding cassette sub-family B member 1), encoded by the ABCB1 gene, is another membrane protein implicated in lipid homeostasis and steatosis. In the past, common ABCB1 polymorphisms have been associated with the distribution of serum lipids but not with fatty acids (FA) profiles. Similarly, data on the effect of PNPLA3 I148M polymorphism on blood FAs are scarce. In this study, a gas chromatography-flame ionization detection (GC-FID) method was optimized, allowing us to analyze twenty FAs (C14: 0, C15: 0, C15: 1, C16: 0, C16: 1, C17: 0, C17: 1, C18: 0, C18: 1cis, C18: 2cis, C20: 0, C20: 1n9, C20: 2, C20: 3n6, C20: 4n6, C20: 5, C23: 0, C24: 0, C24: 1 and C22: 6) in whole blood, based on the indirect determination of the fatty acids methyl esters (FAMES), in 62 hyperlipidemic patients and 42 normolipidemic controls. FA concentrations were then compared between the different genotypes of the rs738409 and rs2032582 (ABCB1 G2677T) polymorphisms, within and between the hyperlipidemic and normolipidemic groups. The rs738409 polymorphism appears to exert a significant effect on the distribution of blood fatty acids, in a lipidemic and fatty acid saturation state-depending manner. The effect of rs2032582 was less pronounced, but the polymorphism did appear to affect the relative distribution of blood fatty acids between hyperlipidemic patients and normolipidemic controls. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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16 pages, 3119 KiB

Open AccessArticle

Mass Spectrometry-Based Flavor Monitoring of Peruvian Chocolate Fabrication Process

by Stephanie Michel, Luka Franco Baraka, Alfredo J. Ibañez and Madina Mansurova

Metabolites 2021, 11(2), 71; https://doi.org/10.3390/metabo11020071 - 26 Jan 2021

Cited by 16 | Viewed by 4072

Abstract

Flavor is one of the most prominent characteristics of chocolate and is crucial in determining the price the consumer is willing to pay. At present, two types of cocoa beans have been characterized according to their flavor and aroma profile, i.e., (1) the [...] Read more.

Flavor is one of the most prominent characteristics of chocolate and is crucial in determining the price the consumer is willing to pay. At present, two types of cocoa beans have been characterized according to their flavor and aroma profile, i.e., (1) the bulk (or ordinary) and (2) the fine flavor cocoa (FFC). The FFC has been distinguished from bulk cocoa for having a great variety of flavors. Aiming to differentiate the FFC bean origin of Peruvian chocolate, an analytical methodology using gas chromatography coupled to mass spectrometry (GC-MS) was developed. This methodology allows us to characterize eleven volatile organic compounds correlated to the aromatic profile of FFC chocolate from this geographical region (based on buttery, fruity, floral, ethereal sweet, and roasted flavors). Monitoring these 11 flavor compounds during the chain of industrial processes in a retrospective way, starting from the final chocolate bar towards pre-roasted cocoa beans, allows us to better understand the cocoa flavor development involved during each stage. Hence, this methodology was useful to distinguish chocolates from different regions, north and south of Peru, and production lines. This research can benefit the chocolate industry as a quality control protocol, from the raw material to the final product. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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15 pages, 1829 KiB

Open AccessArticle

HPLC-HRMS Global Metabolomics Approach for the Diagnosis of “Olive Quick Decline Syndrome” Markers in Olive Trees Leaves

by Alberto Asteggiano, Pietro Franceschi, Michael Zorzi, Riccardo Aigotti, Federica Dal Bello, Francesca Baldassarre, Francesco Lops, Antonia Carlucci, Claudio Medana and Giuseppe Ciccarella

Metabolites 2021, 11(1), 40; https://doi.org/10.3390/metabo11010040 - 8 Jan 2021

Cited by 8 | Viewed by 3149

Abstract

Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the [...] Read more.

Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the most widely applied technologies in metabolomics, as it provides a blend of rapid, sensitive, and selective qualitative and quantitative analyses with the ability to identify metabolites. The purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves. Results obtained via multivariate analysis through an HPLC-ESI HRMS platform (LTQ-Orbitrap from Thermo Scientific) show a clear separation between HP and OP samples. Among the differentially expressed metabolites, 18 different organic compounds highly expressed in the OP group were annotated; results obtained by this metabolomic approach could be used as a fast and reliable method for the biochemical characterization of OQDS and to develop targeted MS approaches for OQDS detection by foliage analysis. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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15 pages, 3819 KiB

Open AccessArticle

Development of Tandem Mass Tag Labeling Method for Lipid Molecules Containing Carboxy and Phosphate Groups, and Their Stability in Human Serum

by Suzumi M. Tokuoka, Yoshihiro Kita, Masaya Sato, Takao Shimizu, Yutaka Yatomi and Yoshiya Oda

Metabolites 2021, 11(1), 19; https://doi.org/10.3390/metabo11010019 - 30 Dec 2020

Cited by 5 | Viewed by 2935

Abstract

In clinical lipidomics, it is a challenge to measure a large number of samples and to reproduce the quantitative results. We expanded the range of application of the tandem mass tag (TMT) method, which is widely used in proteomics, to lipidomic fields. There [...] Read more.

In clinical lipidomics, it is a challenge to measure a large number of samples and to reproduce the quantitative results. We expanded the range of application of the tandem mass tag (TMT) method, which is widely used in proteomics, to lipidomic fields. There are various types of lipid molecule, for example, eicosanoids have a carboxyl group and phosphatidic acid has a phosphate group. We modified these functional groups simultaneously with TMT. This approach allows for a single analysis by mixing six samples and using one of the six samples as a bridging sample; the quantitative data can be easily normalized even if the number of measurements increases. To accommodate a large number of samples, we utilize a pooled serum sample of 300 individuals as a bridging sample. The stability of these lipid molecules in serum was examined as an analytical validation for the simultaneous TMT labeling. It was found that the stability of these lipid molecules in serum differs greatly depending on the lipid species. These findings reaffirmed the importance of proper sample preparation and storage to obtain reliable data. The TMT labeling method is expected to be a useful method for lipidomics with high-throughput and reliable reproducibility. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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17 pages, 2037 KiB

Open AccessArticle

Metabolic Drug Response Phenotyping in Colorectal Cancer Organoids by LC-QTOF-MS

by Sylvia K. Neef, Nicole Janssen, Stefan Winter, Svenja K. Wallisch, Ute Hofmann, Marc H. Dahlke, Matthias Schwab, Thomas E. Mürdter and Mathias Haag

Metabolites 2020, 10(12), 494; https://doi.org/10.3390/metabo10120494 - 1 Dec 2020

Cited by 21 | Viewed by 4628

Abstract

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid [...] Read more.

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (<500 cells/injection) in the presence of an extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on the p-value and fold change cut-offs, which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2′-deoxyuridine, 2′-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2′-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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17 pages, 1966 KiB

Open AccessArticle

Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics

by Julian Pezzatti, Víctor González-Ruiz, Julien Boccard, Davy Guillarme and Serge Rudaz

Metabolites 2020, 10(11), 464; https://doi.org/10.3390/metabo10110464 - 15 Nov 2020

Cited by 12 | Viewed by 3795

Abstract

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion [...] Read more.

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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14 pages, 2138 KiB

Open AccessArticle

A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows

by Gabriel Riquelme, Nicolás Zabalegui, Pablo Marchi, Christina M. Jones and María Eugenia Monge

Metabolites 2020, 10(10), 416; https://doi.org/10.3390/metabo10100416 - 16 Oct 2020

Cited by 31 | Viewed by 8589

Abstract

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent [...] Read more.

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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Review

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31 pages, 2167 KiB

Open AccessReview

Defining Blood Plasma and Serum Metabolome by GC-MS

by Olga Kiseleva, Ilya Kurbatov, Ekaterina Ilgisonis and Ekaterina Poverennaya

Metabolites 2022, 12(1), 15; https://doi.org/10.3390/metabo12010015 - 24 Dec 2021

Cited by 32 | Viewed by 8371

Abstract

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles [...] Read more.

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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17 pages, 693 KiB

Open AccessReview

Metabolomics in Bone Research

by Jingzhi Fan, Vahid Jahed and Kristaps Klavins

Metabolites 2021, 11(7), 434; https://doi.org/10.3390/metabo11070434 - 1 Jul 2021

Cited by 23 | Viewed by 5819

Abstract

Identifying the changes in endogenous metabolites in response to intrinsic and extrinsic factors has excellent potential to obtain an understanding of cells, biofluids, tissues, or organisms’ functions and interactions with the environment. The advantages provided by the metabolomics strategy have promoted studies in [...] Read more.

Identifying the changes in endogenous metabolites in response to intrinsic and extrinsic factors has excellent potential to obtain an understanding of cells, biofluids, tissues, or organisms’ functions and interactions with the environment. The advantages provided by the metabolomics strategy have promoted studies in bone research fields, including an understanding of bone cell behaviors, diagnosis and prognosis of diseases, and the development of treatment methods such as implanted biomaterials. This review article summarizes the metabolism changes during osteogenesis, osteoclastogenesis, and immunoregulation in hard tissue. The second section of this review is dedicated to describing and discussing metabolite changes in the most relevant bone diseases: osteoporosis, bone injuries, rheumatoid arthritis, and osteosarcoma. We consolidated the most recent finding of the metabolites and metabolite pathways affected by various bone disorders. This collection can serve as a basis for future metabolomics-driven bone research studies to select the most relevant metabolites and metabolic pathways. Additionally, we summarize recent metabolic studies on metabolomics for the development of bone disease treatment including biomaterials for bone engineering. With this article, we aim to provide a comprehensive summary of metabolomics in bone research, which can be helpful for interdisciplinary researchers, including material engineers, biologists, and clinicians. Full article

(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)

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