Knowledge-based metabolite annotation tool: CEU Mass Mediator

Gil-de-la-Fuente, Alberto; Godzień, Joanna; Fernández-López, Mariano; Rupérez, Javier; Barbas, Coral; Otero, Abraham

doi:10.1016/j.jpba.2018.02.046

Cited by 80 publications

(63 citation statements)

References 30 publications

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“…Tentative annotation of the metabolites presenting statistically significant differences was achieved by matching the accurate m/z with different databases available online. The tool employed was the search engine CEU Mass Mediator (CMM) [35,36] that comprises 332,665 real compounds integrated from several metabolomics databases, including HMDB [37], METLIN [38], KEGG [39], and LIPID MAPS [40]. The annotation was based on (i) mass accuracy (maximum error mass was set on 20 ppm); (ii) retention time; (iii) possibility of cation and anion formation; and (iv) adduct formation [41].…”

Section: Data Processing and Multivariate Statistical Analysismentioning

confidence: 99%

Metabolic changes during respiratory syncytial virus infection of epithelial cells

et al. 2020

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Viral infections induce substantial metabolic changes in infected cells to optimize viral production while cells develop countermeasures to restrict that infection. Human respiratory syncytial virus (HRSV) is an infectious pathogen that causes severe lower respiratory tract infections (LRTI) in infants, the elderly, and immunocompromised adults for which no effective treatment or vaccine is currently available. In this study, variations in metabolite levels at different time points post-HRSV infection of epithelial cells were studied by untargeted metabolomics using liquid chromatography/mass spectrometry analysis of methanol cell extracts. Numerous metabolites were significantly upregulated after 18 hours post-infection, including nucleotides, amino acids, amino and nucleotide sugars, and metabolites of the central carbon pathway. In contrast, most lipid classes were downregulated. Additionally, increased levels of oxidized glutathione and polyamines were associated with oxidative stress in infected cells. These results show how HRSV infection influences cell metabolism to produce the energy and building blocks necessary for virus reproduction, suggesting potential therapeutic interventions against this virus.

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Section: Data Processing and Multivariate Statistical Analysismentioning

confidence: 99%

Metabolic changes during respiratory syncytial virus infection of epithelial cells

et al. 2020

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“…PERMANOVA analysis was done with the vegan package (version 2.5.2). A level-5 putative identification of metabolite identities was conducted by searching the exact mass and composite spectrum of molecular features against public chemical databases including METLIN (Smith et al, 2005), HMDB (Wishart et al, 2018), and Kegg (Kanehisa et al, 2006) with the use of CEU Mass Mediator (Schymanski et al, 2014;Gil de la Fuente et al, 2018). Search results matching to synthetic metabolites, drugs or animal-derived metabolites were filtered.…”

Section: Metabolomics Data Preprocessing and Statistical Analysismentioning

confidence: 99%

The Influence of Contrasting Microbial Lifestyles on the Pre-symbiotic Metabolite Responses of Eucalyptus grandis Roots

et al. 2019

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Plant roots co-inhabit the soil with a diverse consortium of microbes of which a number attempt to enter symbiosis with the plant. These microbes may be pathogenic, mutualistic, or commensal. Hence, the health and survival of plants is heavily reliant on their ability to perceive different microbial lifestyles and respond appropriately. Emerging research suggests that there is a pivotal role for plant root secondary metabolites in responding to microbial colonization. However, it is largely unknown if plants are able to differentiate between microbes of different lifestyles and respond differently during the earliest stages of pre-symbiosis (i.e., prior to physical contact). In studying plant responses to a range of microbial isolates, we questioned: (1) if individual microbes of different lifestyles and species caused alterations to the plant root metabolome during pre-symbiosis, and (2) if these early metabolite responses correlate with the outcome of the symbiotic interaction in later phases of colonization. We compared the changes of the root tip metabolite profile of the model tree Eucalyptus grandis during pre-symbiosis with two isolates of a pathogenic fungus (Armillaria luteobubalina), one isolate of a pathogenic oomycete (Phytophthora cinnamomi), two isolates of an incompatible mutualistic fungus (Suillus granulatus), and six isolates of a compatible mutualistic fungus (Pisolithus microcarpus). Untargeted metabolite profiling revealed predominantly positive root metabolite responses at the pre-symbiosis stage, prior to any observable phenotypical changes of the root tips. Metabolite responses in the host tissue that were specific to each microbial species were identified. A deeper analysis of the root metabolomic profiles during pre-symbiotic contact with six strains of P. microcarpus showed a connection between these early metabolite responses in the root with later colonization success. Further investigation using isotopic tracing revealed a portion of metabolites found in root tips originated from the fungus. RNA-sequencing also showed that the plant roots undergo complementary transcriptomic reprogramming in response to the fungal stimuli. Taken together, our results demonstrate that the early metabolite responses of plant roots are partially selective toward the lifestyle of the interacting microbe, and that these responses can be crucial in determining the outcome of the interaction.

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“…Only features with variable importance in the projection (VIP) values of the first component of the PLS-DA models higher or equal than 1.0 were considered as significant and were subjected to the identification process. Metabolite identification was carried out by comparing the obtained accurate mass values in the CEU Mass Mediator (in which the search of metabolites is performed in different databases such as KEGG, METLIN, LipidMass, and HMDB) [34] and in the FoodDB database (http://foodb.ca/) (an error of 30 ppm was considered).…”

Section: Metabolite Identificationmentioning

confidence: 99%

Untargeted HILIC-MS-Based Metabolomics Approach to Evaluate Coffee Roasting Process: Contributing to an Integrated Metabolomics Multiplatform

et al. 2020

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An untargeted metabolomics strategy using hydrophilic interaction chromatography-mass spectrometry (HILIC-MS) was developed in this work enabling the study of the coffee roasting process. Green coffee beans and coffee beans submitted to three different roasting degrees (light, medium, and strong) were analyzed. Chromatographic separation was carried out using water containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase A) and acetonitrile containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase B). A total of 93 molecular features were considered from which 31 were chosen as the most statistically significant using variable in the projection values. 13 metabolites were tentatively identified as potential biomarkers of the coffee roasting process using this metabolomic platform. Results obtained in this work were complementary to those achieved using orthogonal techniques such as reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) since only one metabolite was found to be common between HILIC-MS and RPLC-MS platforms (caffeoylshikimic acid isomer) and other between HILIC-MS and CE-MS platforms (choline). On the basis of these results, an untargeted metabolomics multiplatform is proposed in this work based on the integration of the three orthogonal techniques as a powerful tool to expand the coverage of the roasted coffee metabolome.

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Knowledge-based metabolite annotation tool: CEU Mass Mediator

Cited by 80 publications

References 30 publications

Metabolic changes during respiratory syncytial virus infection of epithelial cells

Metabolic changes during respiratory syncytial virus infection of epithelial cells

The Influence of Contrasting Microbial Lifestyles on the Pre-symbiotic Metabolite Responses of Eucalyptus grandis Roots

Untargeted HILIC-MS-Based Metabolomics Approach to Evaluate Coffee Roasting Process: Contributing to an Integrated Metabolomics Multiplatform

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