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Metabolites
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Metabolomics Tools to Accelerate Natural Product Discovery
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Metabolomics Tools to Accelerate Natural Product Discovery

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 20634

Special Issue Editor

Dr. Samuel Bertrand

E-Mail Website
Guest Editor
Faculty of Pharmaceutical and Biological Sciences, University of Nantes, MMS-EA2160, 9, rue Bias BP 53508, CEDEX 1, 44035 Nantes, France
Interests: bioinformatics; data mining; dereplication/annotation; induction strategies; metabolomics; fungal natural products
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since recent years, innovations in metabolite profiling, bioassays and chemometrics have initiated a paradigm shift in natural product (NP) chemistry. This was encouraged by the constant rediscovery of widely reported chemical structures. Thus, research strategies have evolved toward the early selection of extract, fraction and/or compounds with high chemical novelty potential based on accurate biological and chemical profiling.

The development of novel NP Discovery strategies yielded the emergence of a large variety of tools. They provide, for example, the possibility to analyse in-depth profiling data, link biological and chemical information, in order to accurately target compound isolation.

Now, such tools need to gain in visibility for the NP community, however, many of them remain rather obscure in their use. Therefore, a huge effort in providing user-friendly interfaces in nowadays mandatory to spread further into this field.

This special issue is focussed on publishing:

  • novel metabolomic tools and strategies applied to novel NP discovery,
  • improvement of tools and strategies with a particular focus on user-friendly interface,
  • example of their application in NP research.

The Special Issue is now open for submission and accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website.

Do not hesitate to contact me for any questions and indicating me in advance for any submission

Dr. Samuel Bertrand
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Annotation
  • Bioinformatics
  • Data Mining
  • Dereplication
  • Genomics
  • Metabolomics
  • Natural Products

Published Papers (5 papers)

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Research

19 pages, 2806 KiB  
Article
Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics Using Probabilistic Modeling
by Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu and Soha Hassoun
Metabolites 2020, 10(5), 183; https://doi.org/10.3390/metabo10050183 - 03 May 2020
Cited by 8 | Viewed by 4018
Abstract
Motivation: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computational advances, interpreting collected measurements and determining their biological role remains a challenge. Results: To interpret measurements, we present an inference-based approach, termed [...] Read more.
Motivation: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computational advances, interpreting collected measurements and determining their biological role remains a challenge. Results: To interpret measurements, we present an inference-based approach, termed Probabilistic modeling for Untargeted Metabolomics Analysis (PUMA). Our approach captures metabolomics measurements and the biological network for the biological sample under study in a generative model and uses stochastic sampling to compute posterior probability distributions. PUMA predicts the likelihood of pathways being active, and then derives probabilistic annotations, which assign chemical identities to measurements. Unlike prior pathway analysis tools that analyze differentially active pathways, PUMA defines a pathway as active if the likelihood that the path generated the observed measurements is above a particular (user-defined) threshold. Due to the lack of “ground truth” metabolomics datasets, where all measurements are annotated and pathway activities are known, PUMA is validated on synthetic datasets that are designed to mimic cellular processes. PUMA, on average, outperforms pathway enrichment analysis by 8%. PUMA is applied to two case studies. PUMA suggests many biological meaningful pathways as active. Annotation results were in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures. Importantly, PUMA annotates many measurements, suggesting 23 chemical identities for metabolites that were previously only identified as isomers, and a significant number of additional putative annotations over spectral database lookups. For an experimentally validated 50-compound dataset, annotations using PUMA yielded 0.833 precision and 0.676 recall. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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20 pages, 2273 KiB  
Article
Profiling of Chlorogenic Acids from Bidens pilosa and Differentiation of Closely Related Positional Isomers with the Aid of UHPLC-QTOF-MS/MS-Based In-Source Collision-Induced Dissociation
by Anza-Tshilidzi Ramabulana, Paul Steenkamp, Ntakadzeni Madala and Ian A. Dubery
Metabolites 2020, 10(5), 178; https://doi.org/10.3390/metabo10050178 - 29 Apr 2020
Cited by 42 | Viewed by 3974
Abstract
Bidens pilosa is an edible herb from the Asteraceae family which is traditionally consumed as a leafy vegetable. B. pilosa has many bioactivities owing to its diverse phytochemicals, which include aliphatics, terpenoids, tannins, alkaloids, hydroxycinnamic acid (HCA) derivatives and other phenylpropanoids. The later [...] Read more.
Bidens pilosa is an edible herb from the Asteraceae family which is traditionally consumed as a leafy vegetable. B. pilosa has many bioactivities owing to its diverse phytochemicals, which include aliphatics, terpenoids, tannins, alkaloids, hydroxycinnamic acid (HCA) derivatives and other phenylpropanoids. The later include compounds such as chlorogenic acids (CGAs), which are produced as either regio- or geometrical isomers. To profile the CGA composition of B. pilosa, methanol extracts from tissues, callus and cell suspensions were utilized for liquid chromatography coupled to mass spectrometric detection (UHPLC-QTOF-MS/MS). An optimized in-source collision-induced dissociation (ISCID) method capable of discriminating between closely related HCA derivatives of quinic acids, based on MS-based fragmentation patterns, was applied. Careful control of collision energies resulted in fragment patterns similar to MS2 and MS3 fragmentation, obtainable by a typical ion trap MSn approach. For the first time, an ISCID approach was shown to efficiently discriminate between positional isomers of chlorogenic acids containing two different cinnamoyl moieties, such as a mixed di-ester of feruloyl-caffeoylquinic acid (m/z 529) and coumaroyl-caffeoylquinic acid (m/z 499). The results indicate that tissues and cell cultures of B. pilosa contained a combined total of 30 mono-, di-, and tri-substituted chlorogenic acids with positional isomers dominating the composition thereof. In addition, the tartaric acid esters, caftaric- and chicoric acids were also identified. Profiling revealed that these HCA derivatives were differentially distributed across tissues types and cell culture lines derived from leaf and stem explants. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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19 pages, 1291 KiB  
Article
Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics
by Neda Hassanpour, Nicholas Alden, Rani Menon, Arul Jayaraman, Kyongbum Lee and Soha Hassoun
Metabolites 2020, 10(4), 160; https://doi.org/10.3390/metabo10040160 - 21 Apr 2020
Cited by 11 | Viewed by 3353
Abstract
Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer [...] Read more.
Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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15 pages, 1898 KiB  
Article
Metabolic Fingerprinting with Fourier-Transform Infrared (FTIR) Spectroscopy: Towards a High-Throughput Screening Assay for Antibiotic Discovery and Mechanism-of-Action Elucidation
by Bernardo Ribeiro da Cunha, Luís P. Fonseca and Cecília R.C. Calado
Metabolites 2020, 10(4), 145; https://doi.org/10.3390/metabo10040145 - 09 Apr 2020
Cited by 17 | Viewed by 3866
Abstract
The discovery of antibiotics has been slowing to a halt. Phenotypic screening is once again at the forefront of antibiotic discovery, yet Mechanism-Of-Action (MOA) identification is still a major bottleneck. As such, methods capable of MOA elucidation coupled with the high-throughput screening of [...] Read more.
The discovery of antibiotics has been slowing to a halt. Phenotypic screening is once again at the forefront of antibiotic discovery, yet Mechanism-Of-Action (MOA) identification is still a major bottleneck. As such, methods capable of MOA elucidation coupled with the high-throughput screening of whole cells are required now more than ever, for which Fourier-Transform Infrared (FTIR) spectroscopy is a promising metabolic fingerprinting technique. A high-throughput whole-cell FTIR spectroscopy-based bioassay was developed to reveal the metabolic fingerprint induced by 15 antibiotics on the Escherichia coli metabolism. Cells were briefly exposed to four times the minimum inhibitory concentration and spectra were quickly acquired in the high-throughput mode. After preprocessing optimization, a partial least squares discriminant analysis and principal component analysis were conducted. The metabolic fingerprints obtained with FTIR spectroscopy were sufficiently specific to allow a clear distinction between different antibiotics, across three independent cultures, with either analysis algorithm. These fingerprints were coherent with the known MOA of all the antibiotics tested, which include examples that target the protein, DNA, RNA, and cell wall biosynthesis. Because FTIR spectroscopy acquires a holistic fingerprint of the effect of antibiotics on the cellular metabolism, it holds great potential to be used for high-throughput screening in antibiotic discovery and possibly towards a better understanding of the MOA of current antibiotics. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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9 pages, 1041 KiB  
Communication
Extensive Metabolic Profiles of Leaves and Stems from the Medicinal Plant Dendrobium officinale Kimura et Migo
by Hua Cao, Yulu Ji, Shenchong Li, Lin Lu, Min Tian, Wei Yang and Han Li
Metabolites 2019, 9(10), 215; https://doi.org/10.3390/metabo9100215 - 04 Oct 2019
Cited by 46 | Viewed by 4853
Abstract
Dendrobium officinale Kimura et Migo is a commercially and pharmacologically highly prized species widely used in Western Asian countries. In contrast to the extensive genomic and transcriptomic resources generated in this medicinal species, detailed metabolomic data are still missing. Herein, using the widely [...] Read more.
Dendrobium officinale Kimura et Migo is a commercially and pharmacologically highly prized species widely used in Western Asian countries. In contrast to the extensive genomic and transcriptomic resources generated in this medicinal species, detailed metabolomic data are still missing. Herein, using the widely targeted metabolomics approach, we detect 649 diverse metabolites in leaf and stem samples of D. officinale. The majority of these metabolites were organic acids, amino acids and their derivatives, nucleotides and their derivatives, and flavones. Though both organs contain similar metabolites, the metabolite profiles were quantitatively different. Stems, the organs preferentially exploited for herbal medicine, contained larger concentrations of many more metabolites than leaves. However, leaves contained higher levels of polyphenols and lipids. Overall, this study reports extensive metabolic data from leaves and stems of D. officinale, providing useful information that supports ongoing genomic research and discovery of bioactive compounds. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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