Responses to β-alanine and carnosine supplementation of mussel Mytilus coruscus as revealed by UPLC–MS/MS based untargeted metabolomics

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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Phytocannabinomics: Untargeted metabolomics as a tool for cannabis chemovar differentiation

Talanta ◽

10.1016/j.talanta.2021.122313 ◽

2021 ◽

Vol 230 ◽

pp. 122313

Author(s):

Andrea Cerrato ◽

Cinzia Citti ◽

Giuseppe Cannazza ◽

Anna Laura Capriotti ◽

Chiara Cavaliere ◽

...

Keyword(s):

Untargeted Metabolomics

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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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