Phytocannabinomics: Untargeted metabolomics as a tool for cannabis chemovar differentiation

Apicomplexan parasites are responsible for devastating diseases, including malaria, toxoplasmosis, and cryptosporidiosis. Current treatments are limited by emerging resistance to, as well as the high cost and toxicity of existing drugs. As obligate intracellular parasites, apicomplexans rely on the uptake of many essential metabolites from their host. Toxoplasma gondii, the causative agent of toxoplasmosis, is auxotrophic for several metabolites, including sugars (e.g., myo-inositol), amino acids (e.g., tyrosine), lipidic compounds and lipid precursors (cholesterol, choline), vitamins, cofactors (thiamine) and others. To date, only few apicomplexan metabolite transporters have been characterized and assigned a substrate. Here, we set out to investigate whether untargeted metabolomics can be used to identify the substrate of an uncharacterized transporter. Based on existing genome- and proteome-wide datasets, we have identified an essential plasma membrane transporter of the major facilitator superfamily in T. gondii—previously termed TgApiAT6-1. Using an inducible system based on RNA degradation, TgApiAT6-1 was depleted, and the mutant parasite’s metabolome was compared to that of non-depleted parasites. The most significantly reduced metabolite in parasites depleted in TgApiAT6-1 was identified as the amino acid lysine, for which T. gondii is predicted to be auxotrophic. Using stable isotope-labeled amino acids, we confirmed that TgApiAT6-1 is required for efficient lysine uptake. Our findings highlight untargeted metabolomics as a powerful tool to identify the substrate of orphan transporters.

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Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽

10.3390/metabo11010008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 8

Author(s):

Michiel Bongaerts ◽

Ramon Bonte ◽

Serwet Demirdas ◽

Edwin H. Jacobs ◽

Esmee Oussoren ◽

...

Keyword(s):

Regression Model ◽

Inborn Errors Of Metabolism ◽

Untargeted Metabolomics ◽

Detection Accuracy ◽

Inborn Errors ◽

Mixed Effect ◽

Metabolomics Data ◽

Normalization Methods ◽

Metabolite Concentrations ◽

Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

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Untargeted Metabolomics Approach for the Discovery of Environment-Related Pyran-2-ones Chemodiversity in a Marine-Sourced Penicillium restrictum

Marine Drugs ◽

10.3390/md19070378 ◽

2021 ◽

Vol 19 (7) ◽

pp. 378

Author(s):

Van-Tuyen Le ◽

Samuel Bertrand ◽

Thibaut Robiou du Pont ◽

Fabrice Fleury ◽

Nathalie Caroff ◽

...

Keyword(s):

Culture Medium ◽

Blue Mussel ◽

Culture Media ◽

Principal Component ◽

Chemical Diversity ◽

Untargeted Metabolomics ◽

High Chemical ◽

Medium Mass ◽

Metabolomics Study ◽

Penicillium Restrictum

Very little is known about chemical interactions between fungi and their mollusc host within marine environments. Here, we investigated the metabolome of a Penicillium restrictum MMS417 strain isolated from the blue mussel Mytilus edulis collected on the Loire estuary, France. Following the OSMAC approach with the use of 14 culture media, the effect of salinity and of a mussel-derived medium on the metabolic expression were analysed using HPLC-UV/DAD-HRMS/MS. An untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA) and molecular networking (MN). It highlighted some compounds belonging to sterols, macrolides and pyran-2-ones, which were specifically induced in marine conditions. In particular, a high chemical diversity of pyran-2-ones was found to be related to the presence of mussel extract in the culture medium. Mass spectrometry (MS)- and UV-guided purification resulted in the isolation of five new natural fungal pyran-2-one derivatives—5,6-dihydro-6S-hydroxymethyl-4-methoxy-2H-pyran-2-one (1), (6S, 1’R, 2’S)-LL-P880β (3), 5,6-dihydro-4-methoxy-6S-(1’S, 2’S-dihydroxy pent-3’(E)-enyl)-2H-pyran-2-one (4), 4-methoxy-6-(1’R, 2’S-dihydroxy pent-3’(E)-enyl)-2H-pyran-2-one (6) and 4-methoxy-2H-pyran-2-one (7)—together with the known (6S, 1’S, 2’S)-LL-P880β (2), (1’R, 2’S)-LL-P880γ (5), 5,6-dihydro-4-methoxy-2H-pyran-2-one (8), (6S, 1’S, 2’R)-LL-P880β (9), (6S, 1’S)-pestalotin (10), 1’R-dehydropestalotin (11) and 6-pentyl-4-methoxy-2H-pyran-2-one (12) from the mussel-derived culture medium extract. The structures of 1-12 were determined by 1D- and 2D-MMR experiments as well as high-resolution tandem MS, ECD and DP4 calculations. Some of these compounds were evaluated for their cytotoxic, antibacterial, antileishmanial and in-silico PTP1B inhibitory activities. These results illustrate the utility in using host-derived media for the discovery of new natural products.

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Exploring metabolic alterations associated with death from asphyxia and the differentiation of asphyxia from sudden cardiac death by GC-HRMS-based untargeted metabolomics

Journal of Chromatography B ◽

10.1016/j.jchromb.2021.122638 ◽

2021 ◽

Vol 1171 ◽

pp. 122638

Author(s):

Kai Zhang ◽

Hui Yan ◽

Ruina Liu ◽

Ping Xiang ◽

Kaifei Deng ◽

...

Keyword(s):

Sudden Cardiac Death ◽

Cardiac Death ◽

Untargeted Metabolomics ◽

Metabolic Alterations

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Untargeted metabolomics reveals the mechanism of quercetin enhancing the bioavailability of ticagrelor

Biomedical Chromatography ◽

10.1002/bmc.5206 ◽

2021 ◽

Author(s):

Weijie Zhang ◽

Yaxin Sun ◽

Shuangyan Wei ◽

Bo Wei ◽

Xia Xu ◽

...

Keyword(s):

Untargeted Metabolomics

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Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics

Metabolomics ◽

10.1007/s11306-021-01782-7 ◽

2021 ◽

Vol 17 (3) ◽

Author(s):

Kelli Goodman ◽

Matthew Mitchell ◽

Anne M. Evans ◽

Luke A. D. Miller ◽

Lisa Ford ◽

...

Keyword(s):

Untargeted Metabolomics ◽

Plasma Samples

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Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers

TrAC Trends in Analytical Chemistry ◽

10.1016/j.trac.2020.116161 ◽

2021 ◽

Vol 135 ◽

pp. 116161

Author(s):

Leticia Lacalle-Bergeron ◽

David Izquierdo-Sandoval ◽

Juan V. Sancho ◽

Francisco J. López ◽

Félix Hernández ◽

...

Keyword(s):

Mass Spectrometry ◽

High Resolution ◽

High Resolution Mass Spectrometry ◽

Untargeted Metabolomics ◽

High Resolution Mass ◽

Resolution Mass

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An Untargeted Metabolomics Approach for Correlating Pulse Crop Seed Coat Polyphenol Profiles with Antioxidant Capacity and Iron Chelation Ability

Molecules ◽

10.3390/molecules26133833 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3833

Author(s):

Fatma M. Elessawy ◽

Albert Vandenberg ◽

Anas El-Aneed ◽

Randy W. Purves

Keyword(s):

Antioxidant Capacity ◽

Seed Coat ◽

Iron Chelation ◽

Iron Bioavailability ◽

Untargeted Metabolomics ◽

Black Bean ◽

Pulse Crops ◽

Pulse Crop ◽

Seed Coats ◽

Crop Seed

Pulse crop seed coats are a sustainable source of antioxidant polyphenols, but are typically treated as low-value products, partly because some polyphenols reduce iron bioavailability in humans. This study correlates antioxidant/iron chelation capabilities of diverse seed coat types from five major pulse crops (common bean, lentil, pea, chickpea and faba bean) with polyphenol composition using mass spectrometry. Untargeted metabolomics was used to identify key differences and a hierarchical analysis revealed that common beans had the most diverse polyphenol profiles among these pulse crops. The highest antioxidant capacities were found in seed coats of black bean and all tannin lentils, followed by maple pea, however, tannin lentils showed much lower iron chelation among these seed coats. Thus, tannin lentils are more desirable sources as natural antioxidants in food applications, whereas black bean and maple pea are more suitable sources for industrial applications. Regardless of pulse crop, proanthocyanidins were primary contributors to antioxidant capacity, and to a lesser extent, anthocyanins and flavan-3-ols, whereas glycosylated flavonols contributed minimally. Higher iron chelation was primarily attributed to proanthocyanidin composition, and also myricetin 3-O-glucoside in black bean. Seed coats having proanthocyanidins that are primarily prodelphinidins show higher iron chelation compared with those containing procyanidins and/or propelargonidins.

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