Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Beniddir, Mehdi A.; Kang, Kyo Bin; Genta‐Jouve, Grégory; Huber, Florian; Rogers, Simon; Hooft, Justin J. J. van der

doi:10.1039/d1np00023c

Cited by 113 publications

(103 citation statements)

References 154 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Substructure discovery may provide insight into the backbone of metabolites, such as the functional groups, building blocks and/core structures. This is feasible as complex metabolite structures may be synthesised from common biosynthetic pathways, thus sharing the same building blocks [21,47]. Thus, the MolNetEnhancer approach integrates result outputs from molecular mining tools (molecular networking and MS2LDA), in silico annotation tools (NAP and DEREPLI-CATOR) and class annotation through ClassyFire terms to provide a comprehensive visualisation of the chemical space within a metabolome.…”

Section: Detailed Exploration Of the Chemical Space Of Momordica Speciesmentioning

confidence: 99%

“…In combination with developing computational tools, a broader view of metabolomes has been achieved as in maize plant treated with biostimulants [19] and comprehensive annotation of flavonoids in Chrysobalanaceae plants was achieved [20]. The computational tools and strategies make it possible to decompose complex metabolite mixtures into substructures and chemical class information, thereby aiding in the annotation of known and unknown metabolites [21,22]. As such, metabolomics is an indispensable approach to decode and comprehensively characterize the metabolite profiles/phytochemistry of Momordica species.…”

Section: Introductionmentioning

confidence: 99%

“…Molecular networking (MN) approaches, in the Global Natural Products Social Molecular Networking (GNPS) ecosystem [23] were applied to process and analyze the generated spectral data. The workflows used herein build on the fundamentals that metabolites within complex mixtures are diversified forms originating from the same building blocks (substructures) [21]. Molecules sharing similar MS 2 spectra were thus grouped into molecular families using MS-Cluster algorithm while also matching them to MS 2 spectral libraries (e.g., NIST, Respect and MassBank).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metabolomics and Molecular Networking to Characterize the Chemical Space of Four Momordica Plant Species

et al. 2021

View full text Add to dashboard Cite

Momordica plant species (Cucurbitaceae), have been used for centuries in traditional medicine and for nutritional purposes. Plants from this family are thus claimed to be phytochemically rich, representing an inexhaustible source of natural products. However, the chemical space of these Momordica species has not yet been fully decoded, and due to the inherent complexity of plant metabolomes, the characterization of the Momordica phytochemistry remains challenging. Thus, in this study we propose the use of molecular networking to unravel the molecular families within the metabolomes of four Momordica species (M. cardiospermoides, M. balsamina, M. charantia and M. foetida) and highlight the relevance of molecular networking in exploring the chemotaxonomy of these plants. In silico annotation tools (Network Annotation Propagation and DEREPLICATOR) and an unsupervised substructure identification tool (MS2LDA) were also explored to complement the classical molecular networking output and integration using MolNetEnhancer within GNPS. This allowed for the visualisation of chemical classes and the variety of substructures within the molecular families. The use of computational tools in this study highlighted various classes of metabolites, such as a wide range of flavonoids, terpenoids and lipids. Herein, these species are revealed to be phytochemically rich plants consisting of many biologically active metabolites differentially distributed within the different species, with the metabolome of M. cardiospermoides dereplicated in this paper for the first time.

show abstract

Section: Detailed Exploration Of the Chemical Space Of Momordica Speciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metabolomics and Molecular Networking to Characterize the Chemical Space of Four Momordica Plant Species

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Whilst there is a lot of promise in these kinds of approaches, exactly how they benefit and improve plant metabolomics workflows is yet to be witnessed. We refer the interested reader to a recent review that further explains the workings of substructure-based, chemical compound class-based, and network-based strategies for metabolite annotation [245].…”

Section: Chemical Compound Class-based Annotationmentioning

confidence: 99%

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

et al. 2021

Self Cite

View full text Add to dashboard Cite

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

show abstract

“…These features make of 13 C NMR a useful tool for NP dereplication. A general review article about NP mixture analysis has been recently published [21]; it includes references to the methods that benefit from taxonomy focused NMR databases such as CARAMEL [10], DerepCrude [12], or MixONat [13], but in which the step of 13 C NMR chemical shift prediction still constitutes a bottleneck.…”

Section: Introductionmentioning

confidence: 99%

Taxonomy-Focused Natural Product Databases for Carbon-13 NMR-Based Dereplication

Nuzillard

2021

Analytica

View full text Add to dashboard Cite

The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order, etc.). A set of freely available python scripts, CNMR_Predict, is proposed for the quick supplementation of taxon oriented search results from the naturaL prOducTs occUrrences database (LOTUS, lotus.naturalproducts.net) with predicted carbon-13 nuclear magnetic resonance data from the ACD/Labs CNMR predictor and DB software (acdlabs.com) to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as a taxon example.

show abstract

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Abstract: This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

Cited by 113 publications

References 154 publications

Metabolomics and Molecular Networking to Characterize the Chemical Space of Four Momordica Plant Species

Metabolomics and Molecular Networking to Characterize the Chemical Space of Four Momordica Plant Species

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Taxonomy-Focused Natural Product Databases for Carbon-13 NMR-Based Dereplication

Contact Info

Product

Resources

About