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Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products.

ABSTRACT: Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypti. We analyse the Brazilian plant Annona crassiflora using different platforms to annotate the active compounds in different extracts/fractions of various plant parts. The MetaboAnalyst and GNPS platforms, which consider LC-MS and LC-MS/MS data, respectively, were chosen to identify compounds that differentiate active and inactive samples. Bio-guided isolation was subsequently performed to confirm compound activity. Results proved the capacity of metabolomics to predict metabolite differences between active and inactive samples using LC-MS and LC-MS/MS data. Moreover, we discuss the limitations, possibilities, and strategies to have a broad view of vast data.

SUBMITTER: Demarque DP

PROVIDER: S-EPMC6978511 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products.

Demarque Daniel P DP Dusi Renata G RG de Sousa Francisco D M FDM Grossi Sophia M SM Silvério Maira R S MRS Lopes Norberto P NP Espindola Laila S LS

Scientific reports 20200123 1

Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypt ...[more]

PMID: 31974423

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Project description:BackgroundAssessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.ResultsWe apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.ConclusionsThis research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

Dataset Information

Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products.

Publications

Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products.

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