Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics - Archive ouverte HAL Access content directly
Journal Articles Analytical Chemistry Year : 2019

Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics

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Abstract

The rapid innovations in metabolite profiling, bioassays and chemometrics have led to a paradigm shift in natural product (NP) research. Indeed, having partial or full structure information about possibly “all” specialized metabolites and an estimation of their levels in plants or microorganisms provides a way to perform pharmacognostic or chemical ecology investigations from a new and holistic perspective. The increasing amount of accurate metabolome data that can be acquired on massive sample sets, notably through data-dependent LC-HRMS/MS and NMR profiling, allows the mapping of natural extracts at an unprecedented level of precision. Most progress made recently in accelerating metabolite identification has been pushed by the need for metabolomics to have tools that provide a confident annotation of the biomarkers highlighted as the results of data mining through multivariate analysis, often on important datasets of complex samples. Historically, NP chemists have been involved in the unambiguous full de novo identification of unknown compoundsfrom complex natural biological matrices. This process is classically performed by the tedious isolation of pure bioactive NPs through comprehensive bioactivity-guided isolation workflows involving orthogonal chromatographic steps at the preparative level. Increasingly advanced metabolomics metabolite profiling methods are of strategic importance in dereplication workflows in NP research as well as for the full metabolome composition assignment of relevant organisms from both drug discovery and chemical ecology perspectives. In this review, we describe the latest developments in metabolite profiling by both LC-MS and NMRbased methods and related databases from a natural product chemist perspective. We assess the current possibilities and limits of such methods and the workflows for manual and automated NP annotations by equally treating the MS and NMR approaches that are both key for the “as confident as possible” NP annotation in crude natural extracts. We also propose future lines of development in the field that are important for NP research but are also generally needed for metabolite annotation in metabolomics because NPs represent perfect candidate compounds for identification due to their intrinsic structural complexity and chemodiversity across organisms. This review does not aim to provide a comprehensive survey of all metabolite profiling applications made in NP research to date. Typical case studies are discussed, and an update of a selection of the latest advanced original studies and numerous specialized reviews is made with links to tools and DBs regarded as useful for their current or future usage in NP research. Evaluations of what can be readily implemented and what is still required for confident NP structural elucidation are made, especially concerning access to generic structural and spectral DBs as well as the use of orthogonal detection methods for improved confidence in metabolite annotation.
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hal-01992885 , version 1 (21-09-2021)

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Jean-Luc Wolfender, Jean-Marc Nuzillard, Justin van Der Hooft, Jean-Hugues Renault, Samuel Bertrand. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Analytical Chemistry, 2019, 91 (1), pp.704-742. ⟨10.1021/acs.analchem.8b05112⟩. ⟨hal-01992885⟩
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