Outdoor versus indoor cultivation: Effects on the metabolite profile of Agaricus subrufescens strains analyzed by untargeted metabolomics

2022 ◽  
Vol 374 ◽  
pp. 131740
Author(s):  
Caio de Oliveira Gorgulho Silva ◽  
Joice Raisa Barbosa Cunha ◽  
Aparecido Almeida Conceição ◽  
Euziclei Gonzaga Almeida ◽  
Diego Cunha Zied ◽  
...  
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Agnieszka Klupczynska ◽  
Szymon Plewa ◽  
Paweł Dereziński ◽  
Timothy J. Garrett ◽  
Vanessa Y. Rubio ◽  
...  

AbstractHoneybee (Apis mellifera) venom (HBV) has been a subject of extensive proteomics research; however, scarce information on its metabolite composition can be found in the literature. The aim of the study was to identify and quantify the metabolites present in HBV. To gain the highest metabolite coverage, three different mass spectrometry (MS)-based methodologies were applied. In the first step, untargeted metabolomics was used, which employed high-resolution, accurate-mass Orbitrap MS. It allowed obtaining a broad overview of HBV metabolic components. Then, two targeted metabolomics approaches, which employed triple quadrupole MS, were applied to quantify metabolites in HBV samples. The untargeted metabolomics not only confirmed the presence of amines, amino acids, carbohydrates, and organic acids in HBV, but also provided information on venom components from other metabolite classes (e.g., nucleosides, alcohols, purine and pyrimidine derivatives). The combination of three MS-based metabolomics platforms facilitated the identification of 214 metabolites in HBV samples, among which 138 were quantified. The obtaining of the wide free amino acid profiles of HBV is one of the project’s achievements. Our study contributed significantly to broadening the knowledge about HBV composition and should be continued to obtain the most comprehensive metabolite profile of HBV.


2015 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Soundarrajan Ilavenil ◽  
Srisesharam Srigopalram ◽  
Hyung Soo Park ◽  
Ki Choon Choi

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3377 ◽  
Author(s):  
Mohamed A. Farag ◽  
Asmaa M. Otify ◽  
Aly M. El-Sayed ◽  
Camilia G. Michel ◽  
Shaimaa A. ElShebiney ◽  
...  

Interest in developing coffee substitutes is on the rise, to minimizing its health side effects. In the Middle East, date palm (Phoenix dactylifera L.) pits are often used as a coffee substitute post roasting. In this study, commercially-roasted date pit products, along with unroasted and home-prepared roasted date pits, were subjected to analyses for their metabolite composition, and neuropharmacological evaluation in mice. Headspace SPME-GCMS and GCMS post silylation were employed for characterizing its volatile and non-volatile metabolite profile. For comparison to roasted coffee, coffee product was also included. There is evidence that some commercial date pit products appear to contain undeclared additives. SPME headspace analysis revealed the abundance of furans, pyrans, terpenoids and sulfur compounds in roasted date pits, whereas pyrroles and caffeine were absent. GCMS-post silylation employed for primary metabolite profiling revealed fatty acids’ enrichment in roasted pits versus sugars’ abundance in coffee. Biological investigations affirmed that date pit showed safer margin than coffee from its LD50, albeit it exhibits no CNS stimulant properties. This study provides the first insight into the roasting impact on the date pit through its metabolome and its neuropharmacological aspects to rationalize its use as a coffee substitute.


Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 476
Author(s):  
Joachim Kloehn ◽  
Matteo Lunghi ◽  
Emmanuel Varesio ◽  
David Dubois ◽  
Dominique Soldati-Favre

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.


Talanta ◽  
2021 ◽  
Vol 230 ◽  
pp. 122313
Author(s):  
Andrea Cerrato ◽  
Cinzia Citti ◽  
Giuseppe Cannazza ◽  
Anna Laura Capriotti ◽  
Chiara Cavaliere ◽  
...  

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  

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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