Dataset Information

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

ABSTRACT:

Premise of the study

We describe a field collection, sample processing, and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) instrumental and bioinformatics method developed for untargeted metabolomics of plant tissue and suitable for molecular networking applications.

Methods and results

A total of 613 leaf samples from 204 tree species was collected in the field and analyzed using UHPLC-MS/MS. Matching of molecular fragmentation spectra generated over 125,000 consensus spectra representing unique molecular structures, 26,410 of which were linked to at least one structurally similar compound.

Conclusions

Our workflow is able to generate molecular networks of hundreds of thousands of compounds representing broad classes of plant secondary chemistry and a wide range of molecular masses, from 100 to 2500 daltons, making possible large-scale comparative metabolomics, as well as studies of chemical community ecology and macroevolution in plants.

SUBMITTER: Sedio BE

PROVIDER: S-EPMC5895185 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Sedio Brian E BE Boya P Cristopher A CA Rojas Echeverri Juan Camilo JC

Applications in plant sciences 20180301 3

<h4>Premise of the study</h4>We describe a field collection, sample processing, and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) instrumental and bioinformatics method developed for untargeted metabolomics of plant tissue and suitable for molecular networking applications.<h4>Methods and results</h4>A total of 613 leaf samples from 204 tree species was collected in the field and analyzed using UHPLC-MS/MS. Matching of molecular fragmentation spectra generat ...[more]

PMID: 29732263

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Project description:BackgroundThe COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum.MethodsSaliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques.ResultsPositive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified.ConclusionsIn this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

Dataset Information

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Premise of the study

Methods and results

Conclusions

Publications

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

Similar Datasets

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