Advancements in capturing and mining mass spectrometry data are transforming natural products research

Jarmusch, Scott A; Hooft, Justin J. J. van der; Dorrestein, Pieter C.; Jarmusch, Alan K.

doi:10.1039/d1np00040c

Cited by 52 publications

(37 citation statements)

References 132 publications

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“…The use of metabolomics is spreading widely and includes an increasing field of applications, spanning from the life sciences [ 1 , 2 ] to plant metabolites content [ 3 , 4 ], food science [ 5 , 6 ], natural products [ 7 ], up to advanced materials. Novel technologies in instrumental analytics are increasingly [ 8 , 9 ] improving to lower detection limits and the range of detectable substance classes is expanding, broadening the scope of accessible and potentially bioactive natural molecules. The potentiality of metabolomics in the field of human health is well known [ 10 ], with the study of biofluids.…”

Section: Introductionmentioning

confidence: 99%

NMR Characterization of Lignans

Consonni¹,

Ottolina²

2022

Molecules

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Lignans are particularly interesting secondary metabolites belonging to the phenyl-propanoid biosynthetic pathway. From the structural point of view, these molecules could belong to the aryltetralin, arylnaphtalene, or dibenzylbutyrolactone molecular skeleton. Lignans are present in different tissues of plants but are mainly accumulated in seeds. Extracts from plant tissues could be characterized by using the NMR-based approach, which provides a profile of aromatic molecules and detailed structural information for their elucidation. In order to improve the production of these secondary metabolites, elicitors could effectively stimulate lignan production. Several plant species are considered in this review with a particular focus on Linum species, well recognized as the main producer of lignans.

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Section: Introductionmentioning

confidence: 99%

NMR Characterization of Lignans

Consonni¹,

Ottolina²

2022

Molecules

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“…Significant efforts were made in the past decades to expand the coverage of spectral libraries. For annotation of unknown metabolites, due to lacking the knowledge of chemical structures, additional experiments or in-silico tools were generally required 5,11 . For example, Tsugawa and colleagues employed the stable-isotope labeling to determine formulas of unknown metabolites through identifying the labeled and non-labeled pair of metabolic peaks 14 .…”

Section: Introductionmentioning

confidence: 99%

“…These metabolic features come from known and unknown metabolites, as well as abiotic MS signals generated during ionization such as adducts, isotopes, neutral losses and other ions generated from in-source fragmentation 9,10 . Metabolite identification remains the central bottleneck in LC-MS based untargeted metabolomics 4,11 . For annotation of known metabolites, the most common approach is to search the exact mass of precursor ion (MS1 m/z ) and tandem mass spectrum (MS2 spectrum) against the standard spectral libraries 12,13 .…”

Section: Introductionmentioning

confidence: 99%

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network

Zhou

Luo

Zhang

et al. 2022

Preprint

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Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, we developed an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrated three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we applied KGMN in an in-vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites were validated with in-silico MS/MS tools. Finally, we successfully validated 5 unknown metabolites through the repository-mining and the syntheses of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites towards deciphering dark matters in untargeted metabolomics.

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“…Recent advancements in this area are represented by molecular networking tools 5 and by machine learning that, combined with publicly accessible databases, have greatly expedited metabolite annotation and prioritization for further investigations. 6,7 Particularly promising is the combination of metabolomic tools to poorly explored bacterial taxa. In recent work on bacterial strains belonging to the actinobacterial genus Planomonospora , we uncovered novel chemistry, which led to a family of unexpected biosynthetic gene clusters (BGCs), 8,9 which in turn helped uncover further novel chemistry.…”

Section: Introductionmentioning

confidence: 99%

Allopeptimicins: unique antibacterial metabolites generated by hybrid PKS-NRPS, with original self-defense mechanism in Actinoallomurus

Iorio¹,

Gentile²,

Brunati³

et al. 2022

Preprint

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In the search for structurally novel metabolites with antibacterial activity, innovative approaches must be implemented to increase the probability of discovering novel chemistry from microbial sources. Here we report on the application of metabolomic tools to the genus Actinoallomurus, a poorly explored member of the Actinobacteria. From examining extracts derived from 88 isolates belonging to this genus, we identified a family of cyclodepsipeptides acylated with a C20 polyketide chain, which we named allopeptimicins. These molecules possess unusual structural features, including several double bonds in the amino-polyketide chain and four non-proteinogenic amino acids in the octapeptide. Remarkably, allopeptimicins are produced as a complex of active and inactive congeners, the latter carrying a sulfate group on the polyketide amine. This modification is also a mechanism of self-protection in the producer strain. The structural uniqueness of allopeptimicins is reflected in a biosynthetic gene cluster showing a mosaic structure, with dedicated gene cassettes devoted to formation of specialized precursors and modular assembly lines related to those from different pathways.

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Advancements in capturing and mining mass spectrometry data are transforming natural products research

Abstract: This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Cited by 52 publications

References 132 publications

NMR Characterization of Lignans

NMR Characterization of Lignans

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network

Allopeptimicins: unique antibacterial metabolites generated by hybrid PKS-NRPS, with original self-defense mechanism in Actinoallomurus

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