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Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation

  • Louis-Félix Nothias
    Louis-Félix Nothias
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France
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    Orcidhttp://orcid.org/0000-0001-6711-6719
  • , 
  • Mélissa Nothias-Esposito
    Mélissa Nothias-Esposito
    Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France
    Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France
    More by Mélissa Nothias-Esposito
  • , 
  • Ricardo da Silva
    Ricardo da Silva
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    More by Ricardo da Silva
  • , 
  • Mingxun Wang
    Mingxun Wang
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    More by Mingxun Wang
  • , 
  • Ivan Protsyuk
    Ivan Protsyuk
    European Molecular Biology Laboratory, EMBL, Heidelberg, Germany
    More by Ivan Protsyuk
  • , 
  • Zheng Zhang
    Zheng Zhang
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    More by Zheng Zhang
  • , 
  • Abi Sarvepalli
    Abi Sarvepalli
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    More by Abi Sarvepalli
  • , 
  • Pieter Leyssen
    Pieter Leyssen
    Laboratory for Virology and Experimental Chemotherapy, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium
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  • , 
  • David Touboul
    David Touboul
    Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France
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    Orcidhttp://orcid.org/0000-0003-2751-774X
  • , 
  • Jean Costa
    Jean Costa
    Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France
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  • , 
  • Julien Paolini
    Julien Paolini
    Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France
    More by Julien Paolini
    Orcidhttp://orcid.org/0000-0002-3109-1430
  • , 
  • Theodore Alexandrov
    Theodore Alexandrov
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    European Molecular Biology Laboratory, EMBL, Heidelberg, Germany
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  • , 
  • Marc Litaudon
    Marc Litaudon
    Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France
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    Orcidhttp://orcid.org/0000-0002-0877-8234
  • , and 
  • Pieter C. Dorrestein*
    Pieter C. Dorrestein
    Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States
    Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    *Tel: 858-534-6607. Fax: 858-822-0041. E-mail: [email protected]
    More by Pieter C. Dorrestein
    Orcidhttp://orcid.org/0000-0002-3003-1030
Cite this: J. Nat. Prod. 2018, 81, 4, 758–767
Publication Date (Web):March 2, 2018
https://doi.org/10.1021/acs.jnatprod.7b00737

Copyright © 2018 American Chemical Society and American Society of Pharmacognosy. This publication is licensed under these Terms of Use.

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Abstract

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It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.

Nature has inspired the discovery of numerous therapeutics from organisms such as microbes, plants, and insects. (1) The discovery of new bioactive natural products as leads for therapeutic development can be inspired by ethnopharmacological knowledge or achieved by screening a collection of extracts for bioactivity, (1−3) using in vitro, in cellulo, and even in vivo assays. (4) When a natural extract is found to be bioactive, a bioassay-guided fractionation is usually carried out and is generally comprised of the following steps: (i) extraction of metabolites from the biomass using solvents, (ii) fractionation of the resulting extract by chromatography, (iii) bioassay screening of each fraction, (iv) isolation of the molecule(s) from bioactive fractions, and (v) identification of the isolated molecules and evaluation of their bioactivity. (1,5,6) Although the bioassay-guided fractionation method seems to have been in use by chemists, pharmacologists, and toxicologists since the early 1900s, (7) it was formally conceptualized after the 1950s (7,8) and is still used today for the discovery of therapeutic leads by researchers in academia, government, and industrial laboratories worldwide.
Loss of activity or failure in isolating the bioactive compounds during the bioassay-guided fractionation workflow is a very common and costly pitfall in natural products isolation attempts. Generally, the unsuccessful outcome of these efforts are simply not published or are finally reported as investigations aiming at characterizing the chemical components. Some of the reasons for these pitfalls are (1) the bioactive compounds were degraded during the purification process, (2) the bioactive compounds were present in too low concentrations to be efficiently isolated, and (3) the bioactivity was due to synergistic effects between multiple compounds. It is therefore important to detect candidate bioactive molecules early in the purification scheme in order to rationalize the isolation procedure applied toward these substances.
In addition, the application of bioassay-guided fractionation procedures in the last decades has resulted in the repeated isolation of molecules already described. To avoid the isolation of those known molecules, researchers have long used a preliminary structural assessment step called “dereplication”. Dereplication aims at detecting known and bioactive compounds in natural product extracts and ideally prior to the start of in-depth investigation. In practice, dereplication is often performed when the molecule is at least partially, if not fully, purified. (9,10) Dereplication is generally achieved by first separating the molecular constituents using chromatography and then by looking at their spectral signatures obtained from mass spectrometry, (11−13) nuclear magnetic resonance, (14) or UV–vis spectroscopy, and then searching for compounds having identical properties (such as the molecular formula or core chromophores) in natural product databases (13) or in dedicated open source cheminformatics tools. (15) Some of the most commonly used natural product dereplication databases are “MarineLit”, (16) “Antibase”, (17) and the “Dictionary of Natural Products”. (18) The use of untargeted tandem mass spectrometry to dereplicate known natural products has gained popularity, thanks to the introduction of a public spectral library and new bioinformatic tools. (13,19−22) The recent introduction of the Global Natural Product Social molecular networking (GNPS) Web-platform (http://gnps.ucsd.edu) enables the automatic spectral mining of large experiments with up to thousands of samples in a few hours. (22) In addition to the annotation of the molecules with reference MS/MS spectra publicly available on GNPS, it is also possible to propagate an annotation to an unknown molecule using the “analogues search mode” and MS/MS molecular networking, which expand and accelerate dereplication capabilities to similar structures. (22−25) Annotation can then be propagated using differences in molecular formulas of features. For example, a difference of 14 Da between two precursor ions would suggest a putative CH2 increment, a difference of 16 Da on oxygenation, while a difference of 34 Da in the substitution of proton by a chlorine atom. Careful interpretation of the mass defects and the fragmentation patterns observed may even enable an investigator to decipher the part of the molecule that is modified. For this reason, GNPS and molecular networking have been used to prioritize biomass in in-depth investigations based on the observation of known and unknown analogues or of unique clusters of MS/MS spectra referred to as “molecular families”. (22,24−27) However, because molecular networking on GNPS was designed for MS/MS spectral comparisons, this approach alone does not consider the relative abundance of the molecules between samples, which is crucial for downstream statistical analysis of data.
Bioactivity screening and LC-MS (i.e., without MS/MS acquisition) have been employed in order to (i) prioritize biomass selection (28) and (ii) speed up the isolation process for the discovery of bioactive molecules. (29−31) These studies used statistical approaches to estimate a bioactivity score, defined as the probability of a molecule as being bioactive. For each molecule, a bioactivity score can be calculated using its relative abundance between samples and the quantitative result of a bioassay for those same samples. (28) Multivariate statistical models can be used to identify bioactive compounds from LC-MS data. (29) Especially, partial least squares (PLS) regression has been shown to be an effective method to detect the association between the spectral data of a molecule and the result of a bioassay. (29) However, although the above-mentioned approaches can provide the molecular formula of the predicted bioactive moieties, they are unable to provide the high-throughput annotation that GNPS and molecular networking offer. Recently, molecular networking was introduced for the prioritization of bioactive natural extracts in large collections (26,32,33) and for the annotation of bioactive fractions. (34−36)
In the present work, this gap is bridged by providing a workflow procedure, named “bioactive molecular networking”, which integrates MS/MS molecular networking and bioactivity scoring to assist in bioassay-guided fractionation. By highlighting molecular families and putative bioactive candidate molecules, prior to undertaking a time-consuming isolation step, the use of bioactivity scoring and molecular networking can accelerate natural-products-based drug discovery. (24) Herein, we demonstrate the utility of bioactive molecular networking by reinvestigating the data obtained in our previous study. (37) In this previous study, the chromatographic bioactive fractions from Euphorbia dendroides L. (Euphorbiaceae) latex, a Mediterranean tree spurge, were investigated to elucidate the components responsible for the potent and selective inhibition of the initial extract against chikungunya virus (CHIKV) replication. Despite a phytochemical effort that led to the isolation and the identification of nearly 20 molecules from the most bioactive fractions, no compound with selective and significant inhibition activity against CHIKV replication was found. By reinvestigating E. dendroides samples using bioactive molecular networking, it was possible to better capture the untapped nature of molecules associated with the bioactivity. The findings were validated by conducting a mass spectrometry targeted purification, and it was demonstrated that the new diterpene esters isolated in trace amounts (isolation yield from the extract ranging from 0.04% to 0.28%) were indeed active against the CHIKV replication in a cell-based bioassay. The workflow we have developed relies on a combination of open bioinformatic tools, including MZmine2 (38) and Optimus workflow (39) (using OpenMS (40)), and the GNPS Web-platform. The approach can be readily adopted by researchers in the field of natural products drug discovery and also adapted to metabolomics and chemical ecology applications with other quantitative variables instead of the bioactivity level.

Results and Discussion

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Development of the Workflow Procedure

In the context of a bioassay-guided fractionation study, the employment of the bioactive molecular networking workflow procedure (Figure 1) required first the performance of an untargeted LC-MS/MS analysis and the evaluation of the bioactivity level for each chromatographic fraction. The bioactivity molecular networking consisted of three steps (I–III). The first step (I) was LC-MS/MS spectral data processing with the Optimus workflow, (41) based on OpenMS algorithms, (40) or with the MZmine2 toolbox. (38) This step allowed the detection and relative quantification of LC-MS/MS spectral features (ions) across the chromatographic fractions. The second step (II) consisted in the calculation of a bioactivity score using the Pearson correlation between feature intensity across samples and the bioactivity level associated with each sample. The needed script was prepared as a Jupyter notebook using R language. (42) Jupyter notebook (http://jupyter.org/) (43) is an open-source Web application that facilitates reproducibility and does not require advanced computational skills to be reemployed. The third and final step (III) was achieved by analyzing MS/MS data on the GNPS Web-platform (22) and visualizing the corresponding molecular networks in Cytoscape. (44) GNPS is a Web-platform that serves both as a data repository and a data analysis infrastructure with molecular networking and spectral library search capabilities. In recent years, this crowd-sourced platform has become an essential toolbox for both the natural products and metabolomics scientific communities. Indeed, public spectral libraries facilitate the dereplication of known molecules, and molecular networks allow annotation propagation to unknown related molecules. The objective of the last step of this workflow procedure was to annotate detected molecules using both spectral library annotations and the network topology, in particular those molecules with significant predicted bioactivity scores. For this purpose, the data curation relied on two complementary approaches. First a targeted approach, where prior knowledge can be used, such as chemotaxonomic information and the set of previously described bioactive molecules. Second, an untargeted approach can be undertaken by looking for consistent patterns of bioactive candidates in the networks, such as clusters with a high frequency of bioactive candidates, which could translate to the presence of a bioactive pharmacophore.

Figure 1

Figure 1. Bioactive natural products discovery pipelines. Workflow procedure (A) for the classical bioassay-guided fractionation approach and (B) by integrating bioactive molecular networking.

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Evaluation of the Workflow Procedure

To evaluate the efficiency of bioactive molecular networking, an extract of Euphorbia dendroides latex was reinvestigated. This plant has documented ethnobotanical and ethnopharmacological uses to treat warts since the days of Ancient Rome. (37,45) Previous bioactivity screening of a Corsican specimen of E. dendroides showed that the latex extract displayed potent antiviral activity against CHIKV replication in a cell-based assay (Vero cells, green monkey kidney). (46,47) However, none of the compounds isolated during subsequent bioassay-guided fractionation of this E. dendroides extract showed selective antiviral activity against CHIKV. (37,48) Some of its fractions (F12, F13, and F17) exhibited a selective inhibition of CHIKV replication with an effective concentration (EC50) below 1.2 μg/mL (EC50 = 0.27, 0.17, and 1.13 μg/mL, respectively) and high selectivity indices of 41, 140, and 57, respectively. (EC50 is defined as the concentration that reduces the replication of the CHIKV by 50%, and the SI as the ratio EC50/CC50 where CC50 is the cytotoxic concentration of the compound to reduce natural growth of host cells by 50%.) Nineteen diterpenoid jatrophane esters (1–19), including four terracinolides (12–15), were isolated from these bioactive fractions, but none of these molecules showed significant selective inhibition of CHIKV replication. (37) Thus, the nature of the compounds responsible for antiviral activity has not yet been elucidated. The assumption was made that the bioactive molecule(s) either occurred in trace amounts or were degraded, or that multiple molecules were required for activity (synergistic activity). To find the molecules with anti-CHIKV replication inhibition property, the extract and fractions were analyzed by LC-MS/MS and the presently described workflow was applied.
A crucial factor to predict a significant bioactivity score is to have multiple samples of known concentration that could have different, but related, molecular profiles with corresponding associated bioactivity levels. These could be chromatographic fractions of a single extract or multiple extracts from different but related sources. In this study, 18 fractions were obtained from a chromatographic separation of the extract of E. dendroides latex (F1–F18), and these fractions were subjected to LC-MS/MS and anti-CHIKV bioassays. Their mass spectrometric data, along with the results of their selective inhibition of CHIKV replication, were then processed with our procedure using the Optimus workflow, and bioactive molecular networks were generated in order to prioritize candidate molecules responsible for such bioactivity.
The bioactive molecular networks for the bioassay-guided fractionation of E. dendroides fractions are represented in Figures 2 and 3. As depicted in the legend (Figure 2A), the result of bioactivity scoring is visualized directly in the molecular network with the node size proportionally representing the molecule predicted bioactivity score. The bioactivity score was defined herein as the Pearson correlation coefficient (r) between the molecule relative abundance derived from the LC-MS peak (area under the curve) and the selectivity index (SI) value obtained from the evaluation of fraction antiviral activity. The largest nodes represent molecules with statistically significant bioactivity score [strong Pearson correlation value (r > 0.85) and a significance (FDR corrected, p value <0.03) after Bonferroni correction]. In addition, the relative abundance of detected molecule in each fraction can be visualized in each node with a pie chart diagram [colors correspond to the fraction bioactivity level]. In total, 8.4% (24/285) of molecules were predicted as having a statistically significant bioactivity score. These bioactive candidates represented 10.7% (3/28) of the nodes in MN1 and 8.2% (21/257) of the nodes in MN2.

Figure 2

Figure 2. Bioactive molecular networking of bioassay-guided fractionation of Euphorbia dendroides latex extract. (A) Legend of bioactive molecular networks. (2) Previously isolated molecules (1–19). (C) Bioactive molecular network 1 (MN1) and 2 (MN2) of E. dendroides latex extract and cluster annotations (I–V). See Figure 3 for a detailed view for the cluster V of molecular network 2 (MN2).

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Figure 3

Figure 3. View of bioactive molecular networking results for the cluster V of molecular network 2 (MN2). The molecules 20–22 with significant bioactivity scores (large nodes, see legend) were annotated as deoxyphorbol ester derivatives and isolated in the present study. Evaluation of the biological activity indicated that 21 and 22 are selective inhibitors of chikungunya virus replication in the submicromolar range. The deoxyphorbol ester 23 was also isolated and found to have a lower selective antiviral activity in comparison to compounds 20–22.

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A detailed examination of the molecular networks allowed the annotation of two main molecular families (Figure 2C). The first molecular family (MN1) was composed of the known diterpene esters (1–15), including jatrophane esters (1–11) belonging to the 14-oxojatropha-6(17),11E-diene type and terracinolides (12–15). These molecules did not show strong anti-CHIKV activity in a previous study (49) and were readily annotated (square node in the molecular networks) because their spectral data were available in the GNPS public spectral library. We also observed that the aggregation of reference compounds in clusters I–IV was based on two main structural features: first, the number of esters such as hexa-esterified jatrophane derivatives (cluster I) versus penta- and tetra-esterified jatrophane derivatives (clusters III) or the presence of specific esters such as nicotinoyl esters (cluster IV) and the presence of a lactone for the terracinolides (cluster II).
In addition, it was possible to expand the annotation of MN1 to unknown related molecules using the “analogues search function” available on GNPS. Compounds that were annotated as analogues are represented with a circular colored node, with the color being representative of diterpene-type matching. Interestingly, it was observed that analogues were mostly found in the same clusters as the reference matching spectra. Reference compounds belonging to each cluster of MN1 (I–IV) were previously evaluated against CHIKV replication, (37,48) and none of them showed inhibition activity. For the above reasons, it was assumed that the analogues present in clusters I–V were not the most promising bioactive candidates, and subsequent efforts were focused on MN2, for which no candidate was previously isolated.
The molecular network 2 contained three spectral features with significant bioactivity score at m/z 591.326 (20), 589.311 (21), and 563.296 (23) and at the retention times 1626 s, 1520 s, and 1291 s, respectively. Still, no spectral annotation could be retrieved from public spectral libraries on GNPS, including using analogue searching. An available in silico annotation tool for molecular networking was used. (50) The results indicated heterogeneous candidate structures, including many unlikely candidates from a chemotaxonomic standpoint, which were not considered further. Examination of spectra showed that the ion at m/z 591.326 was a sodium adduct corresponding to a molecular formula of C34H48O7 (m/zcalc 591.3298, [M + Na]+, Δm/z = 3.0 ppm). This observation explained the result of the in silico annotation tool, which considers all ions to be protonated adducts. (50) A search for molecules with the same molecular formula was carried out in the Dictionary of Natural Products (18) and indicated a diterpene diester isolated from Euphorbia prolifera (51) named Euphorbia factor Pr4 [12-O-(2,4-decadienoyl)-13-O-(2-methylpropanoyl)-4-deoxyphorbol]. The examination of the MS/MS fragmentation pattern of this diterpenoid (Figure S3, Supporting Information) showed two neutral losses of 88 Da [M – 88 + Na]+ and 168 Da [M – 168 + Na]+, indicating that the detected molecules possessed a C4H7O acyl (potentially an isobutyryl or a butyryl) and a C10H17O acyl moiety (potentially a decadienyl). Moreover, the observations of characteristic diterpene backbone fragment ions at m/z 313, 295, and 285 were supportive of this molecule being indeed of the phorboid diterpene ester, of either deoxyphorbol or ingenol type. (47)
The annotation of these diagnostic bioactive Euphorbiaceae diterpenoids (52−54) was of significance, as some phorboid derivatives are well-known inhibitors of human immunodeficiency virus replication, (46,55) and recently also against CHIKV, (56,57) due to their protein kinase C (PKC) modulatory ability. Thus, based on (i) the significant bioactivity score in MN2 for the nodes at m/z 591.326, 589.311, and 563.296 (compounds 20–22, with respectively retention times at 1630, 1523, and 1289 s, an r value of 0.91, 0.87, and 0.90, and p values of 5 × 10–6, 4.5 × 10–5, and 7.3 × 10–6) and (ii) the molecular network-based annotation indicating that they were likely phorboid diterpene esters, it was decided to perform a targeted isolation of these putative bioactive molecules. Inspection of the pie-chart diagram showed that the concentration of compounds 20–22 was higher in F13, and this fraction was hence selected for targeted isolation. In addition, it was proposed to isolate compound 23 (m/z 589.31, with a retention time 1609 s), because it was likely a positional isomer of compound 21 with low predicted bioactivity score (r ≃ 0 and p value 0.99), and it was thus valuable to test the approach.
The residual sample from the previous bioassay-guided fractionation of F13 (sub-F13) was fractionated by preparative HPLC using the UV trace as a proxy to the LC-MS/MS analysis. The targeted compounds 20–23 were purified using preparative HPLC and were then identified by 1D- and 2D-NMR data analysis (Tables 1 and 2). As suggested by molecular networking analysis (Figure 3), these four new compounds were indeed 4β-deoxyphorbol esters (20–23) (4-dPEs). The absolute configuration of each stereocenter was assumed based on prior studies on diterpenes of Euphorbia species (X-ray diffraction and total synthesis). (54,58) Following their identification, the reference MS/MS spectra of compound 20–23 were uploaded to the GNPS library and an analogue search was performed on molecular network MN2. Figure 3 showed that MN2 was essentially composed of deoxyphorbol ester analogues (nodes colored in green). The four isolated 4-dPEs (20–23) were evaluated for antiviral activity against CHIKV replication (Table S26, Supporting Information), and compounds 21 and 22 were found to be the most potent and selective CHIKV replication inhibitors with EC50 values of 0.40 ± 0.02 μM and 0.60 ± 0.06 μM, and SI = 34 and 41, respectively. The low SI values for compounds 20 and 23 indicated that they are associated with high host-cell cytotoxicity (SI = 6 and 4, respectively). These four 4β-deoxyphorbol esters possess the same isobutyrate group at C-13, but differ in the nature of the C-12 acyl moiety. Compound 20 has a 2Z,4E-decadienoyl group at position C-12, while compound 22 has a 2Z,4E-octadienyl group. The two compounds 21 and 23 are indeed isobaric compounds with a 12-acyl substituent of deca-2Z,4E,6E-trienoyl and deca-2Z,4E,7Z-trienoyl, respectively. While being closely related to compounds 20–22, compound 23 had a lower selectivity index against chikungunya virus replication (EC50 = 12.6 ± 8.4 μM, SI = 4), in agreement with the prediction of the bioactivity scoring. This result was consistent with previously described structure–activity relationships for phorbol esters, (59) showing that the nature of the acyl chain residue modulates the antiviral activity. It was remarkable to observe that the bioactive molecules 20 and 22 had also isobaric counterparts at m/z 591.33 and 563.296 with low bioactivity scores predicted. These isobaric counterparts had almost identical MS/MS spectra but showed different retention times (1707, 1607, and 1406 s). They were annotated as positional isomers, or epimers, of compounds 20, 21, and 22, as commonly found in the Euphorbiaceae. (54,60,61) In the context of bioactivity score prediction, it was observed that despite the isobaric counterparts being abundant (9.2E7, 3.0E8, and 1.2E8 vs 6.5E7, 1.9E8, and 6.7E7 for compounds 20–22, respectively, in the latex extract), they did not have a significant bioactivity score.
Table 1. 1H NMR Spectroscopic Data for Compounds 20–22 (300 MHz) and 23 (500 MHz) in CDCl3 at 300 K (δH in ppm, J in Hz)
position 20 21 22 23
1 7.54 br t (1.7) 7.54 br t (1.7) 7.54 br t (1.7) 7.54 br t (1.7)
4 2.44 td (9.5, 4.5) 2.44 td (9.5, 4.5) 2.44 td (9.5, 4.5) 2.46 td (9.5, 4.5)
5α 2.82 dd (18.0, 10.0) 2.82 dd (17.7 9.6) 2.82 dd (18.0, 10.0) 2.82 dd (18.0, 10.0)
5β 2.12 dd (18.0, 10.0) 2.12 dd (17.7, 8.1) 2.12 dd (18.0, 10.0) 2.13 dd (18.0, 10.0)
7 5.52 d (5.4) 5.52 d (5.4) 5.52 d (5.4) 5.52 d (5.4)
8 2.35 t (5.4) 2.35 t (5.4) 2.35 t (5.4) 2.35 t (5.4)
10 3.23 m 3.23 m 3.23 m 3.24 m
11 1.56 m 1.56 m 1.56 m 1.58 m
12 5.43 d (9.8) 5.44 d (9.6) 5.43 d (9.8) 5.43 d (9.8)
14 1.01 d (5.4) 1.01 d (5.4) 1.01 d (5.4) 1.01 d (5.4)
16 1.18 s 1.18 s 1.18 s 1.21 s
17 1.18 s 1.18 s 1.18 s 1.20 s
18 0.91 d (6.4) 0.91 d (6.4) 0.91 d (6.4) 0.90 d (6.4)
19 1.70 dd (2.4, 1.2) 1.70 dd (2.4, 1.2) 1.70 dd (2.4, 1.2) 1.70 dd (2.4, 1.2)
20 4.01 b rs 4.01 dd (19.5, 13.3) 4.01 br s 4.00 br s
OH-9 5.81 br s 5.81 br s 5.81 br s 5.85 br s
OH-20 5.27 br s 3.46 br s 5.27 br s 5.28 br s
OR-14        
1′ 5.52 d (11.3) 5.56 d (11.1) 5.52 d (11.3) 5.53 d (11.3)
2′ 6.56 t (11.3) 6.59 t (11.1) 6.56 t (11.3) 6.57 t (11.3)
3′ 7.29 dd (15.6, 11.5) 7.35 dd (15.1, 11.1) 7.29 dd (15.6, 11.5) 7.31 dd (15.6, 11.5)
4′ 6.07 ddd (15.6, 7.1, 6.8) 6.46 dd (14.8, 10.8) 6.07 ddd (15.6, 7.1, 6.8) 6.04 ddd (15.6, 7.1, 6.8)
5′ 2.15 dd (13.6, 7.1) 6.20 dd (14.8, 10.8) 2.16 dd (14.9, 7.1) 2.92 m
6′ 1.41 m 5.92 ddd (14.8, 7.1, 6.8) 1.45 dd (14.9, 7.4) 5.33 dd (11.2, 5.7)
7′ 1.27 m 2.11 dd (14.6, 6.8) 0.90 t (7.4) 5.46 dd (11.2, 8.0)
8′ 1.28 m 1.42 dd (14.6, 7.5)   2.02 m
9′ 0.87 t (6.9) 0.90 t (7.5)   0.94 t (6.9)
OiBu-13        
2″ 2.57 q (7.0) 2.57 q (7.0) 2.57 q (7.0) 2.57 q (7.0)
Me-3″ 1.18 d (7.0) 1.18 d (7.0) 1.18 d (7.0) 1.18 d (7.0)
Me-4″ 1.15 d (7.0) 1.15 d (7.0) 1.15 d (7.0) 1.15 d (7.0)
Table 2. 13C NMR Spectroscopic Data for Compounds 20–22 (75 MHz) and 23 (125 MHz) in CDCl3 at 300 K (δC in ppm, J in Hz)
position 20 21 22 23
1 160.1 160.1 160.1 160.1
2 136.5 136.5 136.5 136.5
3 210.1 210.1 210.1 210.1
4 44.4 44.4 44.4 44.4
5 29.8 29.8 29.8 29.8
6 142.1 142.1 142.1 142.1
7 126.7 126.7 126.7 126.7
8 42.2 42.2 42.2 42.2
9 77.9 77.9 77.9 77.9
10 54.4 54.4 54.4 54.4
11 42.7 42.7 42.7 42.7
12 76.2 76.2 76.2 76.2
13 65.1 65.1 65.1 65.1
14 35.8 35.8 35.8 35.8
15 26.1 26.1 26.1 26.1
16 23.9 23.9 23.9 23.9
17 16.9 16.9 16.9 16.9
18 15.2 15.2 15.2 15.2
19 10.4 10.4 10.4 10.4
20 67.7 67.7 67.7 67.7
OR-12 166.1 166.1 166.1 166.2
1′ 115 113.4 115 115.0
2′ 146.3 143.5 146.3 146.3
3′ 127 128.1 127 127.0
4′ 146.6 140.4 146.6 144.0
5′ 33.2 124.2 35.4 31.1
6′ 28.6 138.6 22.1 124.7
7′ 31.6 32.8 13.8 134.1
8′ 22.7 19.7   20.6
9′ 14.2 11.3   14.2
OiBu-13        
1″ 179.7 179.7 179.7 179.7
2″ 34.4 34.4 34.4 34.4
Me-3″ 18.8 18.8 18.8 18.8
Me-4″ 18.7 18.7 18.7 18.6
Bioactive molecular networks have permitted the detection of bioactive molecules from an antiviral fraction of E. dendroides latex, for which bioactive constituents were unknown despite the carrying out of a previous bioassay-guided fractionation procedure. (49) The present work showed that bioactive molecular networking can accelerate dereplication and a rationalized isolation procedure in bioassay-based drug discovery. It has been shown that, prior to any time-consuming isolation procedure, bioactive molecular networking is able to (i) narrow down the number of bioactive candidates by 10;, (ii) speed up spectral annotation with MS/MS molecular networking, and (iii) discriminate even isomers as a result of LC-MS feature detection tools. The workflow procedure can be readily implemented and adapted for any bioassay-guided fractionation with LC-MS/MS data associated. The approach can be easily reproduced since it relies on open tools (Optimus or MZmine2), the GNPS Web-platform, and a Jupyter notebook. Yet, like any other statistical approach, the accuracy and utility of a bioactive molecular network depends on (i) the reproducibility of the bioassay procedure and (ii) the number of samples included in the data set. Furthermore, the annotation capability of MS/MS spectra depends on public MS/MS spectral libraries, which are expanding rapidly as a result of contributions from the scientific community.
Herein is presented the concept of bioactive molecular networking, and it can be adopted as a systematic strategy for the annotation of bioactive molecules in a complex plant extract. It was applied to the discovery of antiviral diterpenoids in a E. dendroides latex extract, an extract on which a classical bioassay-guided effort was previously unsuccessful. From bioactive molecular networking, two bioactive compounds (21 and 22) were isolated. Compounds 21 and 22 were found to be active against CHIKV replication at submicromolar concentrations. Chikungunya virus is an emerging virus that is causing massive outbreaks (1 million estimated cases in South America and in the Caribbean region in 2014) (62) and associated with high morbidity and for which no antiviral chemotherapy is available. The present results demonstrate that bioactive molecular networking can speed up the process of drug-lead discovery by revealing bioactive candidates and facilitating their dereplication and/or annotation at the very first step of any bioassay-guided purification procedure. For this reason, we expect that bioactive molecular networking can be a cornerstone concept/methodology for the discovery of bioactive molecules from natural product extracts.
To facilitate its acceptance by various communities, the bioactive molecular networking workflow is freely available (https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks) and can be readily applied or adapted without the need for advanced computational skills, as it relies on popular LC-MS feature detection (MZmine2 or Optimus), (38,39) accessible bioinformatics tools (Jupyter notebook), (63) and the crowd-sourced GNPS Web-platform (http://gnps.ucsd.edu). (22) Moreover, the workflow will directly benefit from the development of new modules and bioinformatics tools, in particular those for improved LC-MS feature detection (38,40) or spectral (64) and in silico annotation. (65−67)
Bioactive molecular networking has the retroactive potential to reinvestigate successful bioassay-guided fractionation or those where investigators failed to find the active components, as long as corresponding MS/MS data were acquired or can still be generated. Currently, while 352 studies mentioned “bioassay-guided” and “MS/MS” for the year 2016, (68) none have deposited MS/MS data in public repositories such as MetaboLights (http://www.ebi.ac.uk/metabolights/) (69) or GNPS-MassIVE (http://massive.ucsd.edu). Therefore, as the scientific community pushes toward transparency (70) and as mandated by many funding agencies, the availability of all MS/MS data in public repositories will increase (similar to genomic efforts decades ago) and will enable both the detection of a presently untapped reservoir of bioactive analogues and the optimization of bioactivity scoring.
Finally, since bioactive molecular networking is agnostic to the nature of the quantitative variable employed (here the value of the bioassay results), any other quantitative outcome parameters can be employed instead. Thus, the workflow described herein can be adapted for applications outside of natural products research. Among the potential use of the current workflow, it could be employed in microbial ecology to annotate compounds impacted by an environmental variable (such as the pH), or perhaps it could be used in exposomics to annotate biomarkers associated with a level of exposure to a chemical contaminant.

Experimental Section

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General Experimental Procedures

Optical rotations ([α]D) were measured with the sodium D line monochromatic light source in EtOH at 24 °C. The instrument used was an MCP 300 Anton Paar polarimeter. A PerkinElmer Spectrum BX FT-IR system was used to record IR spectra. UV spectra were measured in a 1 cm quartz tank using a Varian Cary 100 scan spectrophotometer. The 1D (1H and 13C) and 2D (COSY, HSQC, HMBC, and ROESY) NMR spectra were obtained in CDCl3 for compounds 20–22 on a Bruker Avance 300 MHz instrument, and for compound 23 on a 500 MHz instrument, using a 1.7 mm microprobe. HPLC separation was performed on analytical C18 columns (Kromasil, 250 × 4.6 mm i.d., 5 μm, Thermo Scientific) and on semipreparative C18 columns for fractionation. A Waters autopurification system equipped with a binary pump (Waters 2525), a UV–vis diode array detector (190–600 nm, Waters 2996), and a PL-ELS 1000 ELSD Polymer Laboratory detector was used. Preparative HPLC separation was performed on a semipreparative C18 column (Kromasil, 250 × 10 mm; i.d. 5 μm). A Dionex autopurification system equipped with a binary pump (P580), a UV–vis array detector (200–600 nm, Dionex UVD340U), and a PL-ELS 1000 ELSD Polymer Laboratory detector was used. A hybrid linear ion trap/Orbitrap mass spectrometer (LTQ-XL Orbitrap, ThermoFisher Scientific, Les Ulis, France) was used for LC-MS/MS analysis. An ESI source operating in positive ionization mode was used. Mass spectra were acquired between m/z 150 and m/z 1000. In the full-scan mode, full width at half-maximum mass resolution of the Orbitrap mass analyzer was fixed at 30 000 for mass spectra and at 15 000 for tandem mass spectra. The data-dependent MSn mode was used to monitor the one to three most intense ions with an exclusion duration of 40 s after eight repetitions. Instrumental parameters were set as follows: source voltage 5 kV, lens 1 voltage −15 V, capillary temperature 275 °C, gate lens voltage −35 V, capillary voltage 25 V, tube lens voltage 65 V. The CID parameters were set as follow: CE at 30% of the maximum and an activation time of 30 ms. HPLC was performed with an HPLC Ultimate 3000 system (Dionex, Voisins-le-Bretonneux, France) consisting of a degasser, a quaternary pump, an autosampler, a column oven, and a photodiode array detector. Separation was achieved using an octadecyl silica gel column (Sunfire, 150 × 2.1 mm × 3.5 μm; Waters, Guyancourt, France), equipped with a guard column. The column oven temperature was set at 25 °C. Elution was conducted with a mobile phase consisting of water + 0.1% formic acid (A) and CH3CN + 0.1% formic acid (B), following the gradient 5% to 95% B in 40 min, then maintaining 100% B for 10 min at a flow rate of 250 μL/min. To enable the semiquantification of the detected ions, the samples were prepared at a concentration of 2.5 mg/mL in MeOH, and the injection volume was fixed at 10 μL. The LC-MSn analysis of the CH3CN latex extract, the fractions of E. dendroides, and the reference MS/MS spectra were deposited on GNPS (http://gnps.ucsd.edu), under the reference number MSV000079385.

Plant Material

The latex of Euphorbia dendroides was collected by L.-F.N. in August 2012 on 20 specimens at Ficaghjola Beach (Piana) in the western region of Corsica. Botanical identification was performed by L.-F.N., and a voucher specimen (LF-026) was deposited at the Herbarium of the University of Corsica (Laboratoire de Chimie des Produits Naturels, Corte).

Extraction and Isolation

The latex extract of E. dendroides (4.5 g) was subjected to a bioassay-guided fractionation protocol as described in a previous publication, (37) suggesting that F13 (1600 mg) contains phorboid diterpenes with significant bioactivity score predictions. The previous bioassay-guided isolation procedure on fraction F13 (EC50 = 0.17 μg/mL, SI = 140) allowed the isolation of euphodendroidin E (804 mg, with an isolation yield of 17% from the extract) and of the subfraction sub-F13 (486 mg). This subfraction was analyzed by LC-MS/MS, and ions of interest were annotated and compounds present purified by preparative HPLC (Kromasil C18, isocratic H2O–CH3CN/MeOH (5/5) + 0.1% formic acid, 8:92 in 40 min) using the UV–vis detector as a reference between chromatographic gradients. This purification procedure afforded four new compounds, compound 20 (12.8 mg), 21 (5.0 mg), 22 (5.1 mg), and compound 23 (1.8 mg), with isolation yields from the extract of 0.28%, 0.11%, 0.11%, and 0.04%, respectively.

12β-O-[Deca-2Z,4E-dienoyl]-13α-isobutyryl-4β-deoxyphorbol (20):

amorphous powder; [α]25D −2 (c 1, EtOH); UV (EtOH) λmax (log ε) 262 (4.32), 207 (4.08) nm; IR νmax 2925, 1706, 1634, 1164, 1131, 1082, 780 cm–1; 1H (300 MHz) and 13C (75 MHz) NMR data, see Tables 1 and 2; HRESIMS m/z 591.3272 [M + Na]+ (calcd for C34H48O7Na, 591.3298) deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00000840337#%7B%7D.

12β-O-[Deca-2Z,4E,6E-trienoyl]-13α-isobutyryl-4β-deoxyphorbol (21):

amorphous powder; [α]25D +5 (c 1, EtOH); UV (EtOH) λmax (log ε) 301 (4.04), 262 (4.10), 208 (4.10) nm; for 1H (300 MHz) and 13C NMR data, see Tables 1 and 2; HRESIMS m/z 589.3127 [M + Na]+ (calcd for C34H46O7Na, 589.3141); deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00000840338#%7B%7D.

12β-O-[Octa-2Z,4E-dienoyl]-13α-isobutyryl-4β-deoxyphorbol (22):

amorphous powder; [α]25D −1 (c 1, EtOH); UV (EtOH) λmax (log ε) 262 (4.10), 207 (3.90) nm; for 1H (300 MHz) and 13C NMR data, see Tables 1 and 2; HRESIMS m/z 563.3010 [M + Na]+ (calcd for C32H44O7Na, 563.2985); deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00000840336#%7B%7D.

12β-O-[Deca-2Z,4E,7Z-trienoyl]-13α-isobutyryl-4β-deoxyphorbol (23):

amorphous powder; [α]25D +3 (c 1, EtOH); UV (EtOH) λmax (log ε) 261 (3.79), 206 (3.76) nm; for 1H and 13C NMR data, see Tables 1 and 2; HRESIMS m/z 589.3167 [M + Na]+ (calcd for C34H46O7Na, 589.3141); deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00000840339#%7B%7D.

Spectral Feature Detection

This step was performed with the Optimus workflow for feature detection, integration, filtering, and visualization (https://github.com/MolecularCartography), (39) based on using OpenMS algorithms. (40) The Optimus workflow was executed with the KNIME Analytics Platform version 3.2.1 on macOS 10.12. The workflow must be run with the option “Presence of MS/MS”. The .MGF file generated by the Optimus workflow (version 1.2.0) that contains MS/MS spectra (spectral_data_MS2.mgf) for each feature was uploaded on GNPS, and a Data Analysis was performed as described below. The CSV table generated by Optimus (feature_quantification_matrix.csv) was used for both the calculation of the bioactivity score and the visualization of the relative abundance into the molecular networks. More details on parameters used for Optimus and OpenMS are provided in the Supporting Information. Note that alternatively the popular MZmine2 toolbox (release 2.30) can be used for the LC-MS feature detection step. The Dorrestein laboratory implemented the needed features in MZmine2. (38,71) See the release of MZmine 2.28 in July 2017 and the corresponding online documentation on GitHub (https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks). MZmine2 can now output similar files to those outputted by the Optimus workflow thanks to the option named “filter MS/MS for GNPS” and “Reset the peak number ID” in the peak list rows filter module and the MGF export module “Export for GNPS”. The CSV table with relative quantitative information is exported using the CSV export module. Note that an offline multisoftware workflow procedure was recently developed for the integration of MZmine2 and molecular networking. (72) While the use of this workflow is theoretically compatible with the present bioactivity molecular networking workflow procedure, it has not been tested.

Molecular Networking

Tandem mass spectrometry molecular networks were created using the GNPS platform (http://gnps.ucsd.edu). (22) Data were first converted to the.mzML format with MS-Convert. (73) The spectral information (MGF file) was generated by Optimus and uploaded on GNPS. This file was used to generate an MS/MS molecular network using the GNPS Data Analysis workflow, with the MS-Cluster deactivated. The precursor ion mass tolerance was set to 0.01 Da and to a product ion tolerance of 0.0075 Da (allowing a maximum error of 25 ppm at m/z 300 and 12 ppm at m/z 600). The fragment ions below 10 counts were removed from the MS/MS spectra. MNs networks were generated using 12 minimum matched peaks and a cosine score of 0.7. Data were open and visualized using Cytoscape 3.4.0 software. (44) A force-directed layout modulated by cosine score factor was used for data visualizations. Data of LC-MS/MS analysis were deposited in the MassIVE Public GNPS data set (http://massive.ucsd.edu, MSV000079856). The molecular networking job on GNPS can be found at http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=169c80d3192d4c29a6399729cdc8c9a4 and with analogue search http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=ce2a564dbd704c0595494e04798b0233. The corresponding Cytoscape file is available as Supporting Information. The annotated MS/MS spectra were deposited in the GNPS spectral library under references CCMSLIB00000840337, CCMSLIB00000840338, CCMSLIB00000840336, and CCMSLIB00000840339 for compounds 20–23, respectively.

Prediction of Bioactivity Score Significance and Mapping onto the Molecular Networks

An R-based Jupyter notebook, (43) called Bioactive Molecular Networks, was created and is available on GitHub, https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks, along with a step-by-step tutorial and the representative input/output files needed. The table of spectral features’ intensity (CSV format) obtained with Optimus workflow was used in the analysis. Note that to enable LC-MS-based semiquantitation, and thus a reliable prediction of the bioactivity score, each sample has to be analyzed at a known concentration. If the concentration differs between samples, a normalization has to be applied prior to using the workflow. The bioactive molecular networks Jupyter notebook is composed of three steps: scaling samples by normalizing the intensity to the TIC, (74) calculating the Pearson correlation score (r) and its significance (r_pval) between each feature and the bioactivity value, as well as the multiple hypothesis testing correction (the Bonferroni correction), and outputting the node attribute table. This table is conveniently formatted for import into the Cytoscape software to map back the bioactivity score onto the corresponding MS/MS molecular network. For the visualization of bioactive molecular networks in Cytoscape, the Select function in Cytoscape was used to filter nodes based on their r and the FDR corrected p value. The style of these nodes is then bypassed with a larger node size. In the current study, only molecules having a bioactivity score above r > 0.85 and p value of <0.03 were represented as having a large node in the bioactive molecular networks.

Virus-Cell-Based Antialphavirus Assay

The protocol used was described in a previous publication. (49)

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jnatprod.7b00737.

  • Results of bioassays against CHIKV replication and 1D and 2D NMR spectra (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Pieter C. Dorrestein - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States;  Orcidhttp://orcid.org/0000-0002-3003-1030;  Email: [email protected]
  • Authors
    • Louis-Félix Nothias - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States;  Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France;  Orcidhttp://orcid.org/0000-0001-6711-6719
    • Mélissa Nothias-Esposito - Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France;  Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France
    • Ricardo da Silva - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    • Mingxun Wang - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    • Ivan Protsyuk - European Molecular Biology Laboratory, EMBL, Heidelberg, Germany
    • Zheng Zhang - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    • Abi Sarvepalli - Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California 92093, United States;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
    • Pieter Leyssen - Laboratory for Virology and Experimental Chemotherapy, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium
    • David Touboul - Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France;  Orcidhttp://orcid.org/0000-0003-2751-774X
    • Jean Costa - Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France
    • Julien Paolini - Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134, University of Corsica, 20250, Corte, France;  Orcidhttp://orcid.org/0000-0002-3109-1430
    • Theodore Alexandrov - Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States;  European Molecular Biology Laboratory, EMBL, Heidelberg, Germany
    • Marc Litaudon - Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301, Université Paris-Sud, 91198, Gif-sur-Yvette, France;  Orcidhttp://orcid.org/0000-0002-0877-8234
  • Notes
    The authors declare the following competing financial interest(s): P. C. Dorrenstein is an advisor for Sirenas, a company that employs molecular networking for the discovery of bioactive natural products from marine resources. The work in that company does not overlap with the work presented in this paper.

    Reference MS/MS spectra of isolated compounds were deposited in the GNPS public spectral library (CCMSLIB00000840316 to CCMSLIB00000840340). LC-MS data have been deposited in the MassIVE repository http://gnps.ucsd.edu under accession number MSV000079856. The MS/MS molecular networks can be consulted at http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=ce2a564dbd704c0595494e04798b0233. The corresponding Cytoscape file of the molecular networks and the code for bioactive molecular network Jupyter notebook are available at https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks.

Acknowledgments

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The authors would like to thank J.-F. Gallard (CNRS) for help with the NMR acquisition and I. Schmitz-Afonso (CNRS) for the LC-MS/MS analysis. We thank the NIH for supporting this work under NIH-UCSD Center for Computational Mass Spectrometry P41 GM103484 and the NIH grant on reuse of metabolomics data R03 CA211211. This work was also supported by an “Investissement d’Avenir” grant managed by Agence Nationale de la Recherche (CEBA, ANR-10-LABX-25-01). D.T. was supported by the Agence Nationale de la Recherche (Grant ANR-16-CE29-0002-01 CAP-SFC-MS). This project benefited from European Commission funding (Horizon 2020 Research and Innovation Program) from the Marie Skłodowska-Curie Action MSCA-IF-2016, 3D-Plant2Cells, No. 704786 (to L.F.N), and from the grant agreement No. 634402 (to T.A. and I.P).

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    Natural Product Reports (2013), 30 (4), 525-545CODEN: NPRRDF; ISSN:0265-0568. (Royal Society of Chemistry)
    A review. Covering: 2008 to 2012Since the last comprehensive review by Otto Sticher on natural product isolation in NPR, a plethora of new reports on isolation of secondary compds. from higher plants, marine organisms and microorganisms has been published. Although methods described earlier like the liq.-solid chromatog. techniques (VLC, FC, MPLC, HPLC) or partition chromatog. methods are still the major tools for isolating pure compds., some developments like hydrophilic interaction chromatog. (HILIC) have not been fully covered in previous reviews. Furthermore, examples of using different preparative solid-phase extn. (SPE) columns including mol. imprinting technol. have been included. Special attention is given to chiral stationary phases in isolation of natural products. Methods for proper identification of plant material, problems of post-harvest changes in plant material, extn. methods including application of ionic liqs., de-replication procedures during natural product isolation are further issues to be discussed by the review. Selected work published between 2008 and mid-2012 is covered.
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    A review. The success of modern methods in anal. chem. sometimes obscures the problem that the ever increasing amt. of anal. data does not necessarily give more insight of practical relevance. As alternative approaches, toxicity- and bioactivity-based assays can deliver valuable information about biol. effects of complex materials in humans, other species or even ecosystems. However, the obsd. effects often cannot be clearly assigned to specific chem. compds. In these cases, the establishment of an unambiguous cause-effect relationship is not possible. Effect-directed anal. tries to interconnect instrumental anal. techniques with a biol./biochem. entity, which identifies or isolates substances of biol. relevance. Successful application has been demonstrated in many fields, either as proof-of-principle studies or even for complex samples. This review discusses the different approaches, advantages and limitations and finally shows some practical examples. The broad emergence of effect-directed anal. concepts might lead to a true paradigm shift in anal. chem., away from ever growing lists of chem. compds. The connection of biol. effects with the identification and quantification of mol. entities leads to relevant answers to many real life questions.
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    A review. Metabolite profiling is crit. in many aspects of the life sciences, particularly natural product research. Obtaining precise information on the chem. compn. of complex natural exts. (metabolomes) that are primarily obtained from plants or microorganisms is a challenging task that requires sophisticated, advanced anal. methods. In this respect, significant advances in hyphenated chromatog. techniques (LC-MS, GC-MS and LC-NMR in particular), as well as data mining and processing methods, have occurred over the last decade. Together, these tools, in combination with bioassay profiling methods, serve an important role in metabolomics for the purposes of both peak annotation and dereplication in natural product research. In this review, a survey of the techniques that are used for generic and comprehensive profiling of secondary metabolites in natural exts. is provided. The various approaches (chromatog. methods: LC-MS, GC-MS, and LC-NMR and direct spectroscopic methods: NMR and DIMS) are discussed with respect to their resoln. and sensitivity for ext. profiling. In addn. the structural information that can be generated through these techniques or in combination, is compared in relation to the identification of metabolites in complex mixts. Anal. strategies with applications to natural exts. and novel methods that have strong potential, regardless of how often they are used, are discussed with respect to their potential applications and future trends.
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    A review. Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodol. advances, opening up pioneering research prospects in this field.
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    Covering: up to 2016In this highlight, we describe the current landscape for dereplication and discovery of natural products based on the measurement of the intact mass by LC-MS. Often it is assumed that because better mass accuracy (provided by higher resoln. mass spectrometers) is necessary for abs. chem. formula detn. (≤1 part-per-million), that it is also necessary for dereplication of natural products. However, the av. ability to dereplicate tapers off at ∼10 ppm, with modest improvement gained from better mass accuracy when querying focused databases of natural products. We also highlight some recent examples of how these platforms are applied to synthetic biol., and recent methods for dereplication and correlation of substructures using tandem MS data. We also offer this highlight to serve as a brief primer for those entering the field of mass spectrometry-based natural products discovery.
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    Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites
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    Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biol. exts., which contain inventories of all extractable small mols. produced by an organism or consortium. Historically, compd. isolation prioritization has been driven by obsd. biol. activity and/or relative metabolite abundance and followed by dereplication via accurate mass anal. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various anal. and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.
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    Complete structural elucidation of natural products is commonly performed by NMR spectroscopy (NMR), but annotating compds. to most likely structures using high-resoln. tandem mass spectrometry is a faster and feasible first step. The CASMI contest 2016 (Crit. Assessment of Small Mol. Identification) provided spectra of eighteen compds. for the best manual structure identification in the natural products category. High resoln. precursor and tandem mass spectra (MS/MS) were available to characterize the compds. We used the Seven Golden Rules, Sirius2 and MS-FINDER software for detn. of mol. formulas, and then we queried the formulas in different natural product databases including DNP, UNPD, ChemSpider and REAXYS to obtain mol. structures. We used different in-silico fragmentation tools including CFM-ID, CSI:FingerID and MS-FINDER to rank these compds. Addnl. neutral losses and product ion peaks were manually investigated. This manual and time consuming approach allowed for the correct dereplication of thirteen of the eighteen natural products.
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    MassBank: a public repository for sharing mass spectral data for life sciences
    Horai, Hisayuki; Arita, Masanori; Kanaya, Shigehiko; Nihei, Yoshito; Ikeda, Tasuku; Suwa, Kazuhiro; Ojima, Yuya; Tanaka, Kenichi; Tanaka, Satoshi; Aoshima, Ken; Oda, Yoshiya; Kakazu, Yuji; Kusano, Miyako; Tohge, Takayuki; Matsuda, Fumio; Sawada, Yuji; Hirai, Masami Yokota; Nakanishi, Hiroki; Ikeda, Kazutaka; Akimoto, Naoshige; Maoka, Takashi; Takahashi, Hiroki; Ara, Takeshi; Sakurai, Nozomu; Suzuki, Hideyuki; Shibata, Daisuke; Neumann, Steffen; Iida, Takashi; Tanaka, Ken; Funatsu, Kimito; Matsuura, Fumito; Soga, Tomoyoshi; Taguchi, Ryo; Saito, Kazuki; Nishioka, Takaaki
    Journal of Mass Spectrometry (2010), 45 (7), 703-714CODEN: JMSPFJ; ISSN:1076-5174. (John Wiley & Sons Ltd.)
    MassBank is the first public repository of mass spectra of small chem. compds. for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry(EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MSn data of 2337 authentic compds. of metabolites, 11 545 EI-MS and 834 other-MS data of 10 286 volatile natural and synthetic compds., and 3045 ESI-MS2 data of 679 synthetic drugs contributed by 16 research groups (Jan. 2010). ESI-MS2 data were analyzed under nonstandardized, independent exptl. conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more exptl. conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calcd. by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS2 data. MassBank also provides a merged spectrum for each compd. prepd. by merging the analyzed ESI-MS2 data on an identical compd. under different collision-induced dissocn. conditions. Data merging has significantly improved the precision of the identification of a chem. compd. by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chem. compds. and the publication of exptl. data.
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    Smith, C. A.; O’Maille, G.; Want, E. J.; Qin, C.; Trauger, S. A.; Brandon, T. R.; Custodio, D. E.; Abagyan, R.; Siuzdak, G. Ther. Drug Monit. 2005, 27, 747– 751,  DOI: 10.1097/01.ftd.0000179845.53213.39
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    Therapeutic Drug Monitoring (2005), 27 (6), 747-751CODEN: TDMODV; ISSN:0163-4356. (Lippincott Williams & Wilkins)
    Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass anal. METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalog of high-resoln. Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.
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    Gowda, H.; Ivanisevic, J.; Johnson, C. H.; Kurczy, M. E.; Benton, H. P.; Rinehart, D.; Nguyen, T.; Ray, J.; Kuehl, J.; Arevalo, B.; Westenskow, P. D.; Wang, J.; Arkin, A. P.; Deutschbauer, A. M.; Patti, G. J.; Siuzdak, G. Anal. Chem. 2014, 86, 6931– 6939,  DOI: 10.1021/ac500734c
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    Analytical Chemistry (Washington, DC, United States) (2014), 86 (14), 6931-6939CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
    XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" vs. "disease" exptl. design. Here, the authors introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-anal., and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-anal. facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, the authors have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data anal. in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extd.-ion chromatograms and box plots. The interactive principal component anal. includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic anal. of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.
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    Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
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    Nature Biotechnology (2016), 34 (8), 828-837CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
    The potential of the diverse chemistries present in natural products (NP) for biotechnol. and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Mol. Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide ref. MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanal. of deposited data.
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    Watrous, J.; Roach, P.; Alexandrov, T.; Heath, B. S.; Yang, J. Y.; Kersten, R. D.; van der Voort, M.; Pogliano, K.; Gross, H.; Raaijmakers, J. M.; Moore, B. S.; Laskin, J.; Bandeira, N.; Dorrestein, P. C. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, E1743– E1752,  DOI: 10.1073/pnas.1203689109
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    Mass spectral molecular networking of living microbial colonies
    Watrous, Jeramie; Roach, Patrick; Alexandrov, Theodore; Heath, Brandi S.; Yang, Jane Y.; Kersten, Roland D.; van der Voort, Menno; Pogliano, Kit; Gross, Harald; Raaijmakers, Jos M.; Moore, Bradley S.; Laskin, Julia; Bandeira, Nuno; Dorrestein, Pieter C.
    Proceedings of the National Academy of Sciences of the United States of America (2012), 109 (26), E1743-E1752, SE1743/1-SE1743/42CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Integrating the governing chem. with the genomics and phenotypes of microbial colonies has been a holy grail in microbiol. This work describes a highly sensitive, broadly applicable, and cost-effective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample prepn. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and mol. networking, enabled monitoring of metabolite prodn. from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. By using these tools to visualize small mol. changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this exptl. platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi. The antifungal effect of strain SH C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. The authors' technol., in combination with their recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in the ability to understand the spatiotemporal dynamics of metabolite prodn. in live microbial colonies and communities.
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  24. 24
    Quinn, R. A.; Nothias, L.-F.; Vining, O.; Meehan, M.; Esquenazi, E.; Dorrestein, P. C. Trends Pharmacol. Sci. 2017, 38, 143– 154,  DOI: 10.1016/j.tips.2016.10.011
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    Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy
    Quinn, Robert A.; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C.
    Trends in Pharmacological Sciences (2017), 38 (2), 143-154CODEN: TPHSDY; ISSN:0165-6147. (Elsevier Ltd.)
    Mol. networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chem. of mols. dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related mols. are likely to display similar fragment ion spectra. Mol. networking organizes the MS/MS data as a relational spectral network thereby mapping the chem. that was detected in an MS/MS-based metabolomics expt. Although the wider utility of mol. networking is just beginning to be recognized, in this review we highlight the principles behind mol. networking and its use for the discovery of therapeutic leads, monitoring drug metab., clin. diagnostics, and emerging applications in precision medicine.
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  25. 25
    Yang, J. Y.; Sanchez, L. M.; Rath, C. M.; Liu, X.; Boudreau, P. D.; Bruns, N.; Glukhov, E.; Wodtke, A.; de Felicio, R.; Fenner, A.; Wong, W. R.; Linington, R. G.; Zhang, L.; Debonsi, H. M.; Gerwick, W. H.; Dorrestein, P. C. J. Nat. Prod. 2013, 76, 1686– 1699,  DOI: 10.1021/np400413s
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    Molecular Networking as a Dereplication Strategy
    Yang, Jane Y.; Sanchez, Laura M.; Rath, Christopher M.; Liu, Xueting; Boudreau, Paul D.; Bruns, Nicole; Glukhov, Evgenia; Wodtke, Anne; de Felicio, Rafael; Fenner, Amanda; Wong, Weng Ruh; Linington, Roger G.; Zhang, Lixin; Debonsi, Hosana M.; Gerwick, William H.; Dorrestein, Pieter C.
    Journal of Natural Products (2013), 76 (9), 1686-1699CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biol. exts. We demonstrate here that mol. networking, an approach that organizes MS/MS data based on chem. similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with mol. networks requires MS/MS spectra of the natural product mixt. along with MS/MS spectra of known stds., synthetic compds., or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Mol. networking not only dereplicates known mols. from complex mixts., it also captures related analogs, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based mol. networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 mols. including analogs.
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    Winnikoff, J. R.; Glukhov, E.; Watrous, J.; Dorrestein, P. C.; Gerwick, W. H. J. Antibiot. 2014, 67, 105– 112,  DOI: 10.1038/ja.2013.120
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    Quantitative molecular networking to profile marine cyanobacterial metabolomes
    Winnikoff, Jacob R.; Glukhov, Evgenia; Watrous, Jeramie; Dorrestein, Pieter C.; Gerwick, William H.
    Journal of Antibiotics (2014), 67 (1), 105-112CODEN: JANTAJ; ISSN:0021-8820. (Nature Publishing Group)
    Untargeted LC-MS is used to rapidly profile crude natural product (NP) exts.; however, the quantity of data produced can become difficult to manage. Mol. networking based on MS/MS data visualizes these complex data sets to aid their initial interpretation. Here, we developed an addnl. visualization step for the mol. networking workflow to provide relative and abs. quantitation of a specific compd. in an ext. The new visualization also facilitates combination of several metabolomes into one network, and so was applied to an MS/MS data set from 20 crude exts. of cultured marine cyanobacteria. The resultant network illustrates the high chem. diversity present among marine cyanobacteria. It is also a powerful tool for locating producers of specific metabolites. In order to dereplicate and identify culture-based sources of known compds., we added MS/MS data from 60 pure NPs and NP analogs to the 20-strain network. This dereplicated six metabolites directly and offered structural information on up to 30 more. Most notably, our visualization technique allowed us to identify and quant. compare several producers of the bioactive and biosynthetically intriguing lipopeptide malyngamide C. The most prolific producer, a Panamanian strain of Okeania hirsuta (PAB10FEB10-01), was found to produce at least 0.024 mg of malyngamide C per mg biomass (2.4%, w/dw) and is now undergoing genome sequencing to access the corresponding biosynthetic machinery.
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    Nguyen, D. D.; Wu, C.-H.; Moree, W. J.; Lamsa, A.; Medema, M. H.; Zhao, X.; Gavilan, R. G.; Aparicio, M.; Atencio, L.; Jackson, C.; Ballesteros, J.; Sanchez, J.; Watrous, J. D.; Phelan, V. V.; van de Wiel, C.; Kersten, R. D.; Mehnaz, S.; De Mot, R.; Shank, E. A.; Charusanti, P.; Nagarajan, H.; Duggan, B. M.; Moore, B. S.; Bandeira, N.; Palsson, B. Ø.; Pogliano, K.; Gutiérrez, M.; Dorrestein, P. C. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, E2611– E2620,  DOI: 10.1073/pnas.1303471110
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    MS/MS networking guided analysis of molecule and gene cluster families
    Nguyen, Don Duy; Wu, Cheng-Hsuan; Moree, Wilna J.; Lamsa, Anne; Medema, Marnix H.; Zhao, Xiling; Gavilan, Ronnie G.; Aparicio, Marystella; Atencio, Librada; Jackson, Chanaye; Ballesteros, Javier; Sanchez, Joel; Watrous, Jeramie D.; Phelan, Vanessa V.; van de Wiel, Corine; Kersten, Roland D.; Mehnaz, Samina; De Mot, Rene; Shank, Elizabeth A.; Charusanti, Pep; Nagarajan, Harish; Duggan, Brendan M.; Moore, Bradley S.; Bandeira, Nuno; Palsson, Bernhard O.; Pogliano, Kit; Gutierrez, Marcelino; Dorrestein, Pieter C.
    Proceedings of the National Academy of Sciences of the United States of America (2013), 110 (28), E2611-E2620, SE2611/1-SE2611/23CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    The ability to correlate the prodn. of specialized metabolites to the genetic capacity of the organism that produces such mols. has become an invaluable tool in aiding the discovery of biotechnol. applicable mols. Here, the authors accomplish this task by matching mol. families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one mol. to one organism at a time, such as how it is traditionally done. They can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. The authors matched the mol. families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular mols. with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a mol. family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779T. The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at mol. family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.
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    Kurita, K. L.; Glassey, E.; Linington, R. G. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 11999– 12004,  DOI: 10.1073/pnas.1507743112
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    Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries
    Kurita, Kenji L.; Glassey, Emerson; Linington, Roger G.
    Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (39), 11999-12004CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compd. structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compds. with unique biol. and/or chem. properties. By integrating image-based phenotypic screening in HeLa cells with high-resoln. untargeted metabolomics anal., we have developed a new platform, termed Compd. Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product ext. library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compd. modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chem. features and biol. function of bioactive metabolites are known early in the screening workflow, and lead compds. can be rationally selected based on biol. and/or chem. novelty. We demonstrate the utility of the Compd. Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biol. measurements from a library of 234 natural products exts. and integrating these two datasets to identify 13 clusters of fractions contg. 11 known compd. families and four new compds. Using Compd. Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.
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    Kellogg, J. J.; Todd, D. A.; Egan, J. M.; Raja, H. A.; Oberlies, N. H.; Kvalheim, O. M.; Cech, N. B. J. Nat. Prod. 2016, 79, 376– 386,  DOI: 10.1021/acs.jnatprod.5b01014
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    Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive Compounds
    Kellogg, Joshua J.; Todd, Daniel A.; Egan, Joseph M.; Raja, Huzefa A.; Oberlies, Nicholas H.; Kvalheim, Olav M.; Cech, Nadja B.
    Journal of Natural Products (2016), 79 (2), 376-386CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    A central challenge of natural products research is assigning bioactive compds. from complex mixts. The gold std. approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixt. components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodol. was statistical modeling of the integrated biol. and chem. data sets (biochemometric anal.). Three data anal. approaches for biochemometric anal. were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Exts. from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric anal. incorporating the selectivity ratio performed best in identifying bioactive ions from these exts. early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., resp. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixt. components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single anal.
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    Bertrand, S.; Azzollini, A.; Nievergelt, A.; Boccard, J.; Rudaz, S.; Cuendet, M.; Wolfender, J.-L. Molecules 2016, 21, 259,  DOI: 10.3390/molecules21030259
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    Aligiannis, N.; Halabalaki, M.; Chaita, E.; Kouloura, E.; Argyropoulou, A.; Benaki, D.; Kalpoutzakis, E.; Angelis, A.; Stathopoulou, K.; Antoniou, S.; Sani, M.; Krauth, V.; Werz, O.; Schütz, B.; Schäfer, H.; Spraul, M.; Mikros, E.; Skaltsounis, L. A. ChemistrySelect 2016, 1, 2531– 2535,  DOI: 10.1002/slct.201600744
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    Olivon, F.; Allard, P.-M.; Koval, A.; Righi, D.; Genta-Jouve, G.; Neyts, J.; Apel, C.; Pannecouque, C.; Nothias, L.-F.; Cachet, X.; Marcourt, L.; Roussi, F.; Katanaev, V. L.; Touboul, D.; Wolfender, J.-L.; Litaudon, M. ACS Chem. Biol. 2017, 12, 2644– 2651,  DOI: 10.1021/acschembio.7b00413
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    Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks
    Olivon, Florent; Allard, Pierre-Marie; Koval, Alexey; Righi, Davide; Genta-Jouve, Gregory; Neyts, Johan; Apel, Cecile; Pannecouque, Christophe; Nothias, Louis-Felix; Cachet, Xavier; Marcourt, Laurence; Roussi, Fanny; Katanaev, Vladimir L.; Touboul, David; Wolfender, Jean-Luc; Litaudon, Marc
    ACS Chemical Biology (2017), 12 (10), 2644-2651CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
    Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these mols. with exceptional biol. properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chem. complexity of the biol. matrixes in which they are found. The purifn. of single constituents from such matrixes requires such a significant amt. of work that it should be ideally performed only on mols. of high potential value (i.e., chem. novelty and biol. activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compd. identification within complex mixts. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated anal. strategy for bioactive natural products prioritization. Our approach uses massive mol. networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chem. diversity of crude exts. collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biol. targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural ext. screening should speed up and rationalize the isolation of bioactive natural products.
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    Brito, Â.; Gaifem, J.; Ramos, V.; Glukhov, E.; Dorrestein, P. C.; Gerwick, W. H.; Vasconcelos, V. M.; Mendes, M. V.; Tamagnini, P. Algal Res. 2015, 9, 218– 226,  DOI: 10.1016/j.algal.2015.03.016
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    Naman, C. B.; Rattan, R.; Nikoulina, S. E.; Lee, J.; Miller, B. W.; Moss, N. A.; Armstrong, L.; Boudreau, P. D.; Debonsi, H. M.; Valeriote, F. A.; Dorrestein, P. C.; Gerwick, W. H. J. Nat. Prod. 2017, 80, 625– 633,  DOI: 10.1021/acs.jnatprod.6b00907
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    Integrating Molecular Networking and Biological Assays To Target the Isolation of a Cytotoxic Cyclic Octapeptide, Samoamide A, from an American Samoan Marine Cyanobacterium
    Naman, C. Benjamin; Rattan, Ramandeep; Nikoulina, Svetlana E.; Lee, John; Miller, Bailey W.; Moss, Nathan A.; Armstrong, Lorene; Boudreau, Paul D.; Debonsi, Hosana M.; Valeriote, Frederick A.; Dorrestein, Pieter C.; Gerwick, William H.
    Journal of Natural Products (2017), 80 (3), 625-633CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    Integrating LC-MS/MS mol. networking and bioassay guided fractionation enabled the targeted isolation of a new and bioactive cyclic octapeptide, samoamide A (1), from a sample of cf. Symploca sp. collected in American Samoa. The structure of 1 was established by detailed 1D and 2D NMR expts., HRESIMS data, and chem. degrdn./chromatog. (e.g., Marfey's anal.) studies. Pure compd. 1 was shown to have in vitro cytotoxic activity against several human cancer cell lines in both traditional cell culture and zone inhibition bioassays. Although there was no particular selectivity between the cell lines tested for samoamide A, the most potent activity was obsd. against H460 human nonsmall cell lung cancer cells (IC50 = 1.1 μM). Mol. modeling studies suggested that one possible mechanism of action for 1 is the inhibition of the enzyme dipeptidyl peptidase (CD26, DPP4) at a reported allosteric binding site, which could lead to many downstream pharmacol. effects. However, this interaction was moderate when tested in vitro at up to 10 μM, and only resulted in about 16% peptidase inhibition. Combining bioassay screening with the cheminformatics strategy of LC-MS/MS mol. networking as a discovery tool expedited the targeted isolation of a natural product possessing both a novel chem. structure and a desired biol. activity.
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    Esposito, M.; Nothias, L.-F.; Retailleau, P.; Costa, J.; Roussi, F.; Neyts, J.; Leyssen, P.; Touboul, D.; Litaudon, M.; Paolini, J. J. Nat. Prod. 2017, 80, 2051– 2059,  DOI: 10.1021/acs.jnatprod.7b00233
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    Isolation of Premyrsinane, Myrsinane, and Tigliane Diterpenoids from Euphorbia pithyusa Using a Chikungunya Virus Cell-Based Assay and Analogue Annotation by Molecular Networking
    Esposito, Melissa; Nothias, Louis-Felix; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Neyts, Johan; Leyssen, Pieter; Touboul, David; Litaudon, Marc; Paolini, Julien
    Journal of Natural Products (2017), 80 (7), 2051-2059CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    Six new premyrsinol esters, (1)(I),(2-6), and one new myrsinol ester (8) were isolated from an aerial parts ext. of Euphorbia pithyusa, together with a known premyrsinol (7) and two known dideoxyphorbol esters (9 and 10), following a bioactivity-guided purifn. procedure using a Chikungunya virus (CHIKV) cell-based assay. The structures of the new diterpene esters (1-6 and 8) were elucidated by MS and NMR spectroscopic data interpretation. Compds. I-10 were evaluated against CHIKV replication, and results showed that the β-dideoxyphorbol ester 10 was the most active compd., with an EC50 value of 4.0 ± 0.3 μM and a selectivity index of 10.6. To gain more insight into the structural diversity of diterpenoids produced by E. pithyusa, the initial ext. and chromatog. fractions were analyzed by LC-MS/MS. The generated data were annotated using a mol. networking procedure and revealed that dozens of unknown premyrsinane, myrsinane, and tigliane analogs were present.
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  36. 36
    Nothias, L.-F.; Boutet-Mercey, S.; Cachet, X.; De La Torre, E.; Laboureur, L.; Gallard, J.-F.; Retailleau, P.; Brunelle, A.; Dorrestein, P. C.; Costa, J.; Bedoya, L. M.; Roussi, F.; Leyssen, P.; Alcami, J.; Paolini, J.; Litaudon, M.; Touboul, D. J. Nat. Prod. 2017, 80, 2620– 2629,  DOI: 10.1021/acs.jnatprod.7b00113
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    Environmentally Friendly Procedure Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking for the Discovery of Potent Antiviral Compounds from Euphorbia semiperfoliata
    Nothias, Louis-Felix; Boutet-Mercey, Stephanie; Cachet, Xavier; De La Torre, Erick; Laboureur, Laurent; Gallard, Jean-Francois; Retailleau, Pascal; Brunelle, Alain; Dorrestein, Pieter C.; Costa, Jean; Bedoya, Luis M.; Roussi, Fanny; Leyssen, Pieter; Alcami, Jose; Paolini, Julien; Litaudon, Marc; Touboul, David
    Journal of Natural Products (2017), 80 (10), 2620-2629CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    A supercrit. fluid chromatog.-based targeted purifn. procedure using tandem mass spectrometry and mol. networking was developed to analyze, annotate, and isolate secondary metabolites from complex plant ext. mixt. This approach was applied for the targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant ext. The anal. of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorbol esters, were present in the samples. The purifn. procedure using semipreparative supercrit. fluid chromatog. led to the isolation and identification of two new jatrophane esters (13 and 14) and one known (15) and three new 4-deoxyphorbol esters (16-18). The structure and abs. configuration of compd. 16 were confirmed by X-ray crystallog. This compd. was found to display antiviral activity against Chikungunya virus (EC50 = 0.45 μM), while compd. 15 proved to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC50 = 13 nM). This study showed that a supercrit. fluid chromatog.-based protocol and mol. networking can facilitate and accelerate the discovery of bioactive small mols. by targeting mols. of interest, while minimizing the use of toxic solvents.
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  37. 37
    Esposito, M.; Nothias, L.-F.; Nedev, H.; Gallard, J.-F.; Leyssen, P.; Retailleau, P.; Costa, J.; Roussi, F.; Iorga, B. I.; Paolini, J.; Litaudon, M. J. Nat. Prod. 2016, 79, 2873– 2882,  DOI: 10.1021/acs.jnatprod.6b00644
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    Euphorbia dendroides latex as a source of jatrophane esters: Isolation, structural analysis, conformational study, and anti-CHIKV activity
    Esposito, Melissa; Nothias, Louis-Felix; Nedev, Hirsto; Gallard, Jean-Francois; Leyssen, Pieter; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Iorga, Bogdan I.; Paolini, Julien; Litaudon, Marc
    Journal of Natural Products (2016), 79 (11), 2873-2882CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    An efficient process was used to isolate six new jatrophane esters, euphodendroidins J (3), K (5), L (6), M, (8), N (10), and O (11), along with seven known diterpenoid esters, namely, euphodendroidins A (4), B (9), E (1), and F (2), jatrophane ester (7), and 3α-hydroxyterracinolides G and B (12 and 13), and terracinolides J and C (14 and 15) from the latex of Euphorbia dendroides. Their 2D structures and relative configurations were established by extensive NMR spectroscopic anal. The abs. configurations of compds. 1, 11, and 15 were detd. by X-ray diffraction anal. Euphodendroidin F (2) was obtained in 18% yield from the diterpenoid ester-enriched ext. after two consecutive flash chromatog. steps, making it an interesting starting material for chem. synthesis. Euphodendroidins K and L (5 and 6) showed an unprecedented NMR spectroscopic behavior, which was investigated by variable-temp. NMR expts. and mol. modeling. The structure-conformation relationships study of compds. 1, 5, and 6, using DFT-NMR calcns., indicated the prominent role of the acylation pattern in governing the conformational behavior of these jatrophane esters. The antiviral activity of compds. 1-15 was evaluated against Chikungunya virus (CHIKV) replication.
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    MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data
    Pluskal Tomas; Castillo Sandra; Villar-Briones Alejandro; Oresic Matej
    BMC bioinformatics (2010), 11 (), 395 ISSN:.
    BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
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    Protsyuk, I.; Melnik, A. M.; Nothias, L.-F.; Rappez, L.; Phapale, P.; Aksenov, A. A.; Bouslimani, A.; Ryazanov, S.; Dorrestein, P. C.; Alexandrov, T. Nat. Protoc. 2018, 13, 134– 154,  DOI: 10.1038/nprot.2017.122
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    Röst, H. L.; Sachsenberg, T.; Aiche, S.; Bielow, C.; Weisser, H.; Aicheler, F.; Andreotti, S.; Ehrlich, H.-C.; Gutenbrunner, P.; Kenar, E.; Liang, X.; Nahnsen, S.; Nilse, L.; Pfeuffer, J.; Rosenberger, G.; Rurik, M.; Schmitt, U.; Veit, J.; Walzer, M.; Wojnar, D.; Wolski, W. E.; Schilling, O.; Choudhary, J. S.; Malmström, L.; Aebersold, R.; Reinert, K.; Kohlbacher, O. Nat. Methods 2016, 13, 741– 748,  DOI: 10.1038/nmeth.3959
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    OpenMS: a flexible open-source software platform for mass spectrometry data analysis
    Rost, Hannes L.; Sachsenberg, Timo; Aiche, Stephan; Bielow, Chris; Weisser, Hendrik; Aicheler, Fabian; Andreotti, Sandro; Ehrlich, Hans-Christian; Gutenbrunner, Petra; Kenar, Erhan; Liang, Xiao; Nahnsen, Sven; Nilse, Lars; Pfeuffer, Julianus; Rosenberger, George; Rurik, Marc; Schmitt, Uwe; Veit, Johannes; Walzer, Mathias; Wojnar, David; Wolski, Witold E.; Schilling, Oliver; Choudhary, Jyoti S.; Malmstrom, Lars; Aebersold, Ruedi; Reinert, Knut; Kohlbacher, Oliver
    Nature Methods (2016), 13 (9), 741-748CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
    High-resoln. mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomol. structural information and characterizing cellular signaling networks. However, the rapid growth in the vol. and complexity of MS data makes transparent, accurate and reproducible anal. difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible anal. of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS addnl. provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quant. mass spectrometric analyses with ease.
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    Cytoscape: A software environment for integrated models of biomolecular interaction networks
    Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S.; Wang, Jonathan T.; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey
    Genome Research (2003), 13 (11), 2498-2504CODEN: GEREFS; ISSN:1088-9051. (Cold Spring Harbor Laboratory Press)
    Cytoscape is an open source software project for integrating biomol. interaction networks with high-throughput expression data and other mol. states into a unified conceptual framework. Although applicable to any system of mol. components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other mol. states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of addnl. computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined phys./functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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    Esposito, M.; Nim, S.; Nothias, L.-F.; Gallard, J.-F.; Rawal, M. K.; Costa, J.; Roussi, F.; Prasad, R.; Di Pietro, A.; Paolini, J.; Litaudon, M. J. Nat. Prod. 2017, 80, 479– 487,  DOI: 10.1021/acs.jnatprod.6b00990
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    Evaluation of Jatrophane Esters from Euphorbia spp. as Modulators of Candida albicans Multidrug Transporters
    Esposito, Melissa; Nim, Shweta; Nothias, Louis-Felix; Gallard, Jean-Francois; Rawal, Manpreet Kaur; Costa, Jean; Roussi, Fanny; Prasad, Rajendra; Di Pietro, Attilio; Paolini, Julien; Litaudon, Marc
    Journal of Natural Products (2017), 80 (2), 479-487CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    Twenty-nine jatrophane esters and 1 lathyrane diterpenoid ester isolated from Euphorbia species were evaluated for their capacity to inhibit drug-efflux activities of the primary ABC-transporter CaCdr1p and the secondary MFS-transporter CaMdr1p of Candida albicans, in yeast strains overexpressing the corresponding transporter. These diterpenoid esters were obtained from Euphorbia semiperfoliata, E. insularis, and E. dendroides, and included 5 new compds., euphodendroidins P-T. Jatrophane esters I and II inhibited the efflux of Nile Red (NR) mediated by the 2 multidrug transporters, at 85-64% for CaCdr1p, and 79-65% for CaMdr1p. In contrast, III was selective for CaCdr1p and induced a strong inhibition (92%), whereas IV was selective for CaMdr1p, with a 74% inhibition. The potency and selectivity are sensitive to the substitution pattern on the jatrophane skeleton. However, these compds. were not transported, and showed no synergism with fluconazole cytotoxicity.
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    Esposito, M.; Nothias, L.-F.; Nedev, H.; Gallard, J.-F.; Leyssen, P.; Retailleau, P.; Costa, J.; Roussi, F.; Iorga, B. I.; Paolini, J.; Litaudon, M. J. Nat. Prod. . 2016. 79 2873 DOI: 10.1021/acs.jnatprod.6b00644
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    Euphorbia dendroides latex as a source of jatrophane esters: Isolation, structural analysis, conformational study, and anti-CHIKV activity
    Esposito, Melissa; Nothias, Louis-Felix; Nedev, Hirsto; Gallard, Jean-Francois; Leyssen, Pieter; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Iorga, Bogdan I.; Paolini, Julien; Litaudon, Marc
    Journal of Natural Products (2016), 79 (11), 2873-2882CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    An efficient process was used to isolate six new jatrophane esters, euphodendroidins J (3), K (5), L (6), M, (8), N (10), and O (11), along with seven known diterpenoid esters, namely, euphodendroidins A (4), B (9), E (1), and F (2), jatrophane ester (7), and 3α-hydroxyterracinolides G and B (12 and 13), and terracinolides J and C (14 and 15) from the latex of Euphorbia dendroides. Their 2D structures and relative configurations were established by extensive NMR spectroscopic anal. The abs. configurations of compds. 1, 11, and 15 were detd. by X-ray diffraction anal. Euphodendroidin F (2) was obtained in 18% yield from the diterpenoid ester-enriched ext. after two consecutive flash chromatog. steps, making it an interesting starting material for chem. synthesis. Euphodendroidins K and L (5 and 6) showed an unprecedented NMR spectroscopic behavior, which was investigated by variable-temp. NMR expts. and mol. modeling. The structure-conformation relationships study of compds. 1, 5, and 6, using DFT-NMR calcns., indicated the prominent role of the acylation pattern in governing the conformational behavior of these jatrophane esters. The antiviral activity of compds. 1-15 was evaluated against Chikungunya virus (CHIKV) replication.
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    Allard, P.-M.; Péresse, T.; Bisson, J.; Gindro, K.; Marcourt, L.; Pham, V. C.; Roussi, F.; Litaudon, M.; Wolfender, J.-L. Anal. Chem. 2016, 88, 3317– 3323,  DOI: 10.1021/acs.analchem.5b04804
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    Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
    Allard, Pierre-Marie; Peresse, Tiphaine; Bisson, Jonathan; Gindro, Katia; Marcourt, Laurence; Pham, Van Cuong; Roussi, Fanny; Litaudon, Marc; Wolfender, Jean-Luc
    Analytical Chemistry (Washington, DC, United States) (2016), 88 (6), 3317-3323CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
    Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biol. matrixes. In this context, liq.-chromatog. coupled to high resoln. mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS expts., massive amts. of detailed information on the chem. compn. of crude exts. can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as mol. networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chem. of complex NPs exts., dereplicate metabolites, and annotate analogs of database entries.
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    Oligo- and macrocyclic diterpenes in Thymelaeaceae and Euphorbiaceae occurring and utilized in Yunnan (Southwest China). 6. Tigliane type diterpene esters from latex of Euphorbia prolifera.
    Wu, Dagang; Sorg, B.; Hecker, E.
    Phytotherapy Research (1994), 8 (2), 95-9CODEN: PHYREH; ISSN:0951-418X.
    Five tigliane-type diterpene esters were isolated from the latex of E. prolifera by Craig distribution, followed by TLC. Structural elucidation by spectroscopic methods (MS, NMR) revealed 4,20-dideoxyphorbol 12-benzoate 13-isobutyrate (I), 4,20-dideoxy-5ζ-hydroxyphorbol 12-benzoate 13-isobutyrate (II) and 12,13-diisobutyrate (III) and a mixt. of 4-deoxyphorbol 12-(2,4-decadienoate) 13-isobutyrate (IV) with 4-deoxyphorbol 12-(2,4,6-decatrienoate) 13-isobutyrate (V). All compds. were assayed on the mouse ear for irritant activity. I was weakly active, and the IV-V mixt. was highly active. II and III were inactive.
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    A comparative phytochemical study of the diterpenes of some species of the genera Euphorbia and Elaeophorbia (Euphorbiaceae)
    Evans, F. J.; Kinghorn, A. D.
    Botanical Journal of the Linnean Society (1977), 74 (1), 23-35CODEN: BJLSAF; ISSN:0024-4074.
    Nearly 60 species from Euphorbia and Elaeophorbia were investigated for diterpenes of the classes tiglianes, ingenanes, and ortho-esters. The diterpenes were isolated as their acetates by a micro-technique from latex or fresh herb material collected from several countries around the world. Authentication of the diterpenes was by chromatog. and spectroscopic methods. These compds. were absent from only 9% of the species examd. The most commonly occurring diterpene was ingenol, followed closely by 12-deoxy-phorbol. The ingenane deriv. 5-deoxyingenol was always detected as a minor companion to ingenol from species of the section Tithymalus. Species of the section Euphorbium contained mainly the diterpene ingenol, although both tigliane and ortho-ester diterpenes were also present in some species. Species from the sections Anisophyllum and Poinsettia did not contain diterpenes of the type in question, whereas species from the Elaeophorbia yielded ingenol. These results provide addnl. chem. evidence concerning recent suggestions on the subgeneric and generic status of Anisophyllum, Poinsettia, Tithymathus, and Elaeophorbia.
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    Ingenane Diterpenoids
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    Progress in the Chemistry of Organic Natural Products (2016), 102 (), 1-90CODEN: POPRDK; ISSN:2192-4309. (Springer International Publishing AG)
    A review. The phytochem., biogenesis, bioactivity, pharmacol., SAR, chem. and spectroscopic properties of the ingenane class of diterpenoids was reviewed along with their isolation, total synthesis and reactivity in synthesis.
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    Euphorbia Diterpenes: Isolation, Structure, Biological Activity, and Synthesis (2008-2012)
    Vasas, Andrea; Hohmann, Judit
    Chemical Reviews (Washington, DC, United States) (2014), 114 (17), 8579-8612CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
    A review. Euphorbiaceae is one of the largest families of higher plants, comprising about 50 tribes, 300 genera, and 7500 species, with probably the highest species richness in many habitat. Isolation, structure, classification, and biol. activity of diterpenes of Euphorbia plants and their synthesis are reviewed.
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    Nothias-Scaglia, L.-F.; Pannecouque, C.; Renucci, F.; Delang, L.; Neyts, J.; Roussi, F.; Costa, J.; Leyssen, P.; Litaudon, M.; Paolini, J. J. Nat. Prod. 2015, 78, 1277– 1283,  DOI: 10.1021/acs.jnatprod.5b00073
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    Antiviral Activity of Diterpene Esters on Chikungunya Virus and HIV Replication
    Nothias-Scaglia, Louis-Felix; Pannecouque, Christophe; Renucci, Franck; Delang, Leen; Neyts, Johan; Roussi, Fanny; Costa, Jean; Leyssen, Pieter; Litaudon, Marc; Paolini, Julien
    Journal of Natural Products (2015), 78 (6), 1277-1283CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
    Recently, new daphnane, tigliane, and jatrophane diterpenoids have been isolated from various Euphorbiaceae species, of which some have been shown to be potent inhibitors of chikungunya virus (CHIKV) replication. To further explore this type of compd., the antiviral activity of a series of 29 com. available natural diterpenoids was evaluated. Phorbol-12,13-didecanoate (11) proved to be the most potent inhibitor, with an EC50 value of 6.0 ± 0.9 nM and a selectivity index (SI) of 686, which is in line with the previously reported anti-CHIKV potency for the structurally related 12-O-tetradecanoylphorbol-13-acetate (13). Most of the other compds. exhibited low to moderate activity, including an ingenane-type diterpene ester, compd. 28, with an EC50 value of 1.2 ± 0.1 μM and SI = 6.4. Diterpene compds. are known also to inhibit HIV replication, so the antiviral activities of compds. 1-29 were evaluated also against HIV-1 and HIV-2. Tigliane- (4β-hydroxyphorbol analogs 10, 11, 13, 15, 16, and 18) and ingenane-type (27 and 28) diterpene esters were shown to inhibit HIV replication in vitro at the nanomolar level. A Pearson anal. performed with the anti-CHIKV and anti-HIV data sets demonstrated a linear relationship, which supported the hypothesis made that PKC may be an important target in CHIKV replication.
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    In this review article, the authors summarize the phytochem. progress and list all of the compds. isolated from the genus Euphorbia over the past few decades. Also included are the biol. activities of compds. isolated in recent years and structure-activity relationships.
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    Vasas, A.; Rédei, D.; Csupor, D.; Molnár, J.; Hohmann, J. Eur. J. Org. Chem. 2012, 2012, 5115– 5130,  DOI: 10.1002/ejoc.201200733
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    BACKGROUND: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. RESULTS: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. CONCLUSION: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.
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  • Abstract

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    Figure 1

    Figure 1. Bioactive natural products discovery pipelines. Workflow procedure (A) for the classical bioassay-guided fractionation approach and (B) by integrating bioactive molecular networking.

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    Figure 2

    Figure 2. Bioactive molecular networking of bioassay-guided fractionation of Euphorbia dendroides latex extract. (A) Legend of bioactive molecular networks. (2) Previously isolated molecules (1–19). (C) Bioactive molecular network 1 (MN1) and 2 (MN2) of E. dendroides latex extract and cluster annotations (I–V). See Figure 3 for a detailed view for the cluster V of molecular network 2 (MN2).

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    Figure 3

    Figure 3. View of bioactive molecular networking results for the cluster V of molecular network 2 (MN2). The molecules 20–22 with significant bioactivity scores (large nodes, see legend) were annotated as deoxyphorbol ester derivatives and isolated in the present study. Evaluation of the biological activity indicated that 21 and 22 are selective inhibitors of chikungunya virus replication in the submicromolar range. The deoxyphorbol ester 23 was also isolated and found to have a lower selective antiviral activity in comparison to compounds 20–22.

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      Smith, Colin A.; O'Maille, Grace; Want, Elizabeth J.; Qin, Chuan; Trauger, Sunia A.; Brandon, Theodore R.; Custodio, Darlene E.; Abagyan, Ruben; Siuzdak, Gary
      Therapeutic Drug Monitoring (2005), 27 (6), 747-751CODEN: TDMODV; ISSN:0163-4356. (Lippincott Williams & Wilkins)
      Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass anal. METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalog of high-resoln. Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhtlSrtr%252FO&md5=8f8e0ec35da7e1c9e212bfdb0c7525f2
    21. 21
      Gowda, H.; Ivanisevic, J.; Johnson, C. H.; Kurczy, M. E.; Benton, H. P.; Rinehart, D.; Nguyen, T.; Ray, J.; Kuehl, J.; Arevalo, B.; Westenskow, P. D.; Wang, J.; Arkin, A. P.; Deutschbauer, A. M.; Patti, G. J.; Siuzdak, G. Anal. Chem. 2014, 86, 6931– 6939,  DOI: 10.1021/ac500734c
      21
      Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses
      Gowda, Harsha; Ivanisevic, Julijana; Johnson, Caroline H.; Kurczy, Michael E.; Benton, H. Paul; Rinehart, Duane; Nguyen, Thomas; Ray, Jayashree; Kuehl, Jennifer; Arevalo, Bernardo; Westenskow, Peter D.; Wang, Junhua; Arkin, Adam P.; Deutschbauer, Adam M.; Patti, Gary J.; Siuzdak, Gary
      Analytical Chemistry (Washington, DC, United States) (2014), 86 (14), 6931-6939CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" vs. "disease" exptl. design. Here, the authors introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-anal., and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-anal. facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, the authors have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data anal. in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extd.-ion chromatograms and box plots. The interactive principal component anal. includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic anal. of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.
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    22. 22
      Wang, M.; Carver, J. J.; Phelan, V. V.; Sanchez, L. M.; Garg, N.; Peng, Y.; Nguyen, D. D.; Watrous, J.; Kapono, C. A.; Luzzatto-Knaan, T.; Porto, C.; Bouslimani, A.; Melnik, A. V.; Meehan, M. J.; Liu, W.-T.; Crüsemann, M.; Boudreau, P. D.; Esquenazi, E.; Sandoval-Calderón, M.; Kersten, R. D.; Pace, L. A.; Quinn, R. A.; Duncan, K. R.; Hsu, C.-C.; Floros, D. J.; Gavilan, R. G.; Kleigrewe, K.; Northen, T.; Dutton, R. J.; Parrot, D.; Carlson, E. E.; Aigle, B.; Michelsen, C. F.; Jelsbak, L.; Sohlenkamp, C.; Pevzner, P.; Edlund, A.; McLean, J.; Piel, J.; Murphy, B. T.; Gerwick, L.; Liaw, C.-C.; Yang, Y.-L.; Humpf, H.-U.; Maansson, M.; Keyzers, R. A.; Sims, A. C.; Johnson, A. R.; Sidebottom, A. M.; Sedio, B. E.; Klitgaard, A.; Larson, C. B.; Boya P, C. A.; Torres-Mendoza, D.; Gonzalez, D. J.; Silva, D. B.; Marques, L. M.; Demarque, D. P.; Pociute, E.; O’Neill, E. C.; Briand, E.; Helfrich, E. J. N.; Granatosky, E. A.; Glukhov, E.; Ryffel, F.; Houson, H.; Mohimani, H.; Kharbush, J. J.; Zeng, Y.; Vorholt, J. A.; Kurita, K. L.; Charusanti, P.; McPhail, K. L.; Nielsen, K. F.; Vuong, L.; Elfeki, M.; Traxler, M. F.; Engene, N.; Koyama, N.; Vining, O. B.; Baric, R.; Silva, R. R.; Mascuch, S. J.; Tomasi, S.; Jenkins, S.; Macherla, V.; Hoffman, T.; Agarwal, V.; Williams, P. G.; Dai, J.; Neupane, R.; Gurr, J.; Rodríguez, A. M. C.; Lamsa, A.; Zhang, C.; Dorrestein, K.; Duggan, B. M.; Almaliti, J.; Allard, P.-M.; Phapale, P.; Nothias, L.-F.; Alexandrov, T.; Litaudon, M.; Wolfender, J.-L.; Kyle, J. E.; Metz, T. O.; Peryea, T.; Nguyen, D.-T.; VanLeer, D.; Shinn, P.; Jadhav, A.; Müller, R.; Waters, K. M.; Shi, W.; Liu, X.; Zhang, L.; Knight, R.; Jensen, P. R.; Palsson, B. Ø.; Pogliano, K.; Linington, R. G.; Gutiérrez, M.; Lopes, N. P.; Gerwick, W. H.; Moore, B. S.; Dorrestein, P. C.; Bandeira, N. Nat. Biotechnol. 2016, 34, 828– 837,  DOI: 10.1038/nbt.3597
      22
      Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
      Wang, Mingxun; Carver, Jeremy J.; Phelan, Vanessa V.; Sanchez, Laura M.; Garg, Neha; Peng, Yao; Nguyen, Don Duy; Watrous, Jeramie; Kapono, Clifford A.; Luzzatto-Knaan, Tal; Porto, Carla; Bouslimani, Amina; Melnik, Alexey V.; Meehan, Michael J.; Liu, Wei-Ting; Crusemann, Max; Boudreau, Paul D.; Esquenazi, Eduardo; Sandoval-Calderon, Mario; Kersten, Roland D.; Pace, Laura A.; Quinn, Robert A.; Duncan, Katherine R.; Hsu, Cheng-Chih; Floros, Dimitrios J.; Gavilan, Ronnie G.; Kleigrewe, Karin; Northen, Trent; Dutton, Rachel J.; Parrot, Delphine; Carlson, Erin E.; Aigle, Bertrand; Michelsen, Charlotte F.; Jelsbak, Lars; Sohlenkamp, Christian; Pevzner, Pavel; Edlund, Anna; McLean, Jeffrey; Piel, Jorn; Murphy, Brian T.; Gerwick, Lena; Liaw, Chih-Chuang; Yang, Yu-Liang; Humpf, Hans-Ulrich; Maansson, Maria; Keyzers, Robert A.; Sims, Amy C.; Johnson, Andrew R.; Sidebottom, Ashley M.; Sedio, Brian E.; Klitgaard, Andreas; Larson, Charles B.; Boya P, Cristopher A.; Torres-Mendoza, Daniel; Gonzalez, David J.; Silva, Denise B.; Marques, Lucas M.; Demarque, Daniel P.; Pociute, Egle; O'Neill, Ellis C.; Briand, Enora; Helfrich, Eric J. N.; Granatosky, Eve A.; Glukhov, Evgenia; Ryffel, Florian; Houson, Hailey; Mohimani, Hosein; Kharbush, Jenan J.; Zeng, Yi; Vorholt, Julia A.; Kurita, Kenji L.; Charusanti, Pep; McPhail, Kerry L.; Nielsen, Kristian Fog; Vuong, Lisa; Elfeki, Maryam; Traxler, Matthew F.; Engene, Niclas; Koyama, Nobuhiro; Vining, Oliver B.; Baric, Ralph; Silva, Ricardo R.; Mascuch, Samantha J.; Tomasi, Sophie; Jenkins, Stefan; Macherla, Venkat; Hoffman, Thomas; Agarwal, Vinayak; Williams, Philip G.; Dai, Jingqui; Neupane, Ram; Gurr, Joshua; Rodriguez, Andres M. C.; Lamsa, Anne; Zhang, Chen; Dorrestein, Kathleen; Duggan, Brendan M.; Almaliti, Jehad; Allard, Pierre-Marie; Phapale, Prasad; Nothias, Louis-Felix; Alexandrov, Theodore; Litaudon, Marc; Wolfender, Jean-Luc; Kyle, Jennifer E.; Metz, Thomas O.; Peryea, Tyler; Nguyen, Dac-Trung; Van Leer, Danielle; Shinn, Paul; Jadhav, Ajit; Muller, Rolf; Waters, Katrina M.; Shi, Wenyuan; Liu, Xueting; Zhang, Lixin; Knight, Rob; Jensen, Paul R.; Palsson, Bernhard O.; Pogliano, Kit; Linington, Roger G.; Gutierrez, Marcelino; Lopes, Norberto P.; Gerwick, William H.; Moore, Bradley S.; Dorrestein, Pieter C.; Bandeira, Nuno
      Nature Biotechnology (2016), 34 (8), 828-837CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
      The potential of the diverse chemistries present in natural products (NP) for biotechnol. and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Mol. Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide ref. MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanal. of deposited data.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaitLnE&md5=e6ca23ede2d85dd1460a5d73da542444
    23. 23
      Watrous, J.; Roach, P.; Alexandrov, T.; Heath, B. S.; Yang, J. Y.; Kersten, R. D.; van der Voort, M.; Pogliano, K.; Gross, H.; Raaijmakers, J. M.; Moore, B. S.; Laskin, J.; Bandeira, N.; Dorrestein, P. C. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, E1743– E1752,  DOI: 10.1073/pnas.1203689109
      23
      Mass spectral molecular networking of living microbial colonies
      Watrous, Jeramie; Roach, Patrick; Alexandrov, Theodore; Heath, Brandi S.; Yang, Jane Y.; Kersten, Roland D.; van der Voort, Menno; Pogliano, Kit; Gross, Harald; Raaijmakers, Jos M.; Moore, Bradley S.; Laskin, Julia; Bandeira, Nuno; Dorrestein, Pieter C.
      Proceedings of the National Academy of Sciences of the United States of America (2012), 109 (26), E1743-E1752, SE1743/1-SE1743/42CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Integrating the governing chem. with the genomics and phenotypes of microbial colonies has been a holy grail in microbiol. This work describes a highly sensitive, broadly applicable, and cost-effective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample prepn. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and mol. networking, enabled monitoring of metabolite prodn. from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. By using these tools to visualize small mol. changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this exptl. platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi. The antifungal effect of strain SH C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. The authors' technol., in combination with their recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in the ability to understand the spatiotemporal dynamics of metabolite prodn. in live microbial colonies and communities.
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    24. 24
      Quinn, R. A.; Nothias, L.-F.; Vining, O.; Meehan, M.; Esquenazi, E.; Dorrestein, P. C. Trends Pharmacol. Sci. 2017, 38, 143– 154,  DOI: 10.1016/j.tips.2016.10.011
      24
      Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy
      Quinn, Robert A.; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C.
      Trends in Pharmacological Sciences (2017), 38 (2), 143-154CODEN: TPHSDY; ISSN:0165-6147. (Elsevier Ltd.)
      Mol. networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chem. of mols. dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related mols. are likely to display similar fragment ion spectra. Mol. networking organizes the MS/MS data as a relational spectral network thereby mapping the chem. that was detected in an MS/MS-based metabolomics expt. Although the wider utility of mol. networking is just beginning to be recognized, in this review we highlight the principles behind mol. networking and its use for the discovery of therapeutic leads, monitoring drug metab., clin. diagnostics, and emerging applications in precision medicine.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhslGru7vJ&md5=6459b0d087969f208dc5321732619916
    25. 25
      Yang, J. Y.; Sanchez, L. M.; Rath, C. M.; Liu, X.; Boudreau, P. D.; Bruns, N.; Glukhov, E.; Wodtke, A.; de Felicio, R.; Fenner, A.; Wong, W. R.; Linington, R. G.; Zhang, L.; Debonsi, H. M.; Gerwick, W. H.; Dorrestein, P. C. J. Nat. Prod. 2013, 76, 1686– 1699,  DOI: 10.1021/np400413s
      25
      Molecular Networking as a Dereplication Strategy
      Yang, Jane Y.; Sanchez, Laura M.; Rath, Christopher M.; Liu, Xueting; Boudreau, Paul D.; Bruns, Nicole; Glukhov, Evgenia; Wodtke, Anne; de Felicio, Rafael; Fenner, Amanda; Wong, Weng Ruh; Linington, Roger G.; Zhang, Lixin; Debonsi, Hosana M.; Gerwick, William H.; Dorrestein, Pieter C.
      Journal of Natural Products (2013), 76 (9), 1686-1699CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      A major goal in natural product discovery programs is to rapidly dereplicate known entities from complex biol. exts. We demonstrate here that mol. networking, an approach that organizes MS/MS data based on chem. similarity, is a powerful complement to traditional dereplication strategies. Successful dereplication with mol. networks requires MS/MS spectra of the natural product mixt. along with MS/MS spectra of known stds., synthetic compds., or well-characterized organisms, preferably organized into robust databases. This approach can accommodate different ionization platforms, enabling cross correlations of MS/MS data from ambient ionization, direct infusion, and LC-based methods. Mol. networking not only dereplicates known mols. from complex mixts., it also captures related analogs, a challenge for many other dereplication strategies. To illustrate its utility as a dereplication tool, we apply mass spectrometry-based mol. networking to a diverse array of marine and terrestrial microbial samples, illustrating the dereplication of 58 mols. including analogs.
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    26. 26
      Winnikoff, J. R.; Glukhov, E.; Watrous, J.; Dorrestein, P. C.; Gerwick, W. H. J. Antibiot. 2014, 67, 105– 112,  DOI: 10.1038/ja.2013.120
      26
      Quantitative molecular networking to profile marine cyanobacterial metabolomes
      Winnikoff, Jacob R.; Glukhov, Evgenia; Watrous, Jeramie; Dorrestein, Pieter C.; Gerwick, William H.
      Journal of Antibiotics (2014), 67 (1), 105-112CODEN: JANTAJ; ISSN:0021-8820. (Nature Publishing Group)
      Untargeted LC-MS is used to rapidly profile crude natural product (NP) exts.; however, the quantity of data produced can become difficult to manage. Mol. networking based on MS/MS data visualizes these complex data sets to aid their initial interpretation. Here, we developed an addnl. visualization step for the mol. networking workflow to provide relative and abs. quantitation of a specific compd. in an ext. The new visualization also facilitates combination of several metabolomes into one network, and so was applied to an MS/MS data set from 20 crude exts. of cultured marine cyanobacteria. The resultant network illustrates the high chem. diversity present among marine cyanobacteria. It is also a powerful tool for locating producers of specific metabolites. In order to dereplicate and identify culture-based sources of known compds., we added MS/MS data from 60 pure NPs and NP analogs to the 20-strain network. This dereplicated six metabolites directly and offered structural information on up to 30 more. Most notably, our visualization technique allowed us to identify and quant. compare several producers of the bioactive and biosynthetically intriguing lipopeptide malyngamide C. The most prolific producer, a Panamanian strain of Okeania hirsuta (PAB10FEB10-01), was found to produce at least 0.024 mg of malyngamide C per mg biomass (2.4%, w/dw) and is now undergoing genome sequencing to access the corresponding biosynthetic machinery.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXht12jsrs%253D&md5=87e93779f786842b774446ce52c738b2
    27. 27
      Nguyen, D. D.; Wu, C.-H.; Moree, W. J.; Lamsa, A.; Medema, M. H.; Zhao, X.; Gavilan, R. G.; Aparicio, M.; Atencio, L.; Jackson, C.; Ballesteros, J.; Sanchez, J.; Watrous, J. D.; Phelan, V. V.; van de Wiel, C.; Kersten, R. D.; Mehnaz, S.; De Mot, R.; Shank, E. A.; Charusanti, P.; Nagarajan, H.; Duggan, B. M.; Moore, B. S.; Bandeira, N.; Palsson, B. Ø.; Pogliano, K.; Gutiérrez, M.; Dorrestein, P. C. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, E2611– E2620,  DOI: 10.1073/pnas.1303471110
      27
      MS/MS networking guided analysis of molecule and gene cluster families
      Nguyen, Don Duy; Wu, Cheng-Hsuan; Moree, Wilna J.; Lamsa, Anne; Medema, Marnix H.; Zhao, Xiling; Gavilan, Ronnie G.; Aparicio, Marystella; Atencio, Librada; Jackson, Chanaye; Ballesteros, Javier; Sanchez, Joel; Watrous, Jeramie D.; Phelan, Vanessa V.; van de Wiel, Corine; Kersten, Roland D.; Mehnaz, Samina; De Mot, Rene; Shank, Elizabeth A.; Charusanti, Pep; Nagarajan, Harish; Duggan, Brendan M.; Moore, Bradley S.; Bandeira, Nuno; Palsson, Bernhard O.; Pogliano, Kit; Gutierrez, Marcelino; Dorrestein, Pieter C.
      Proceedings of the National Academy of Sciences of the United States of America (2013), 110 (28), E2611-E2620, SE2611/1-SE2611/23CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      The ability to correlate the prodn. of specialized metabolites to the genetic capacity of the organism that produces such mols. has become an invaluable tool in aiding the discovery of biotechnol. applicable mols. Here, the authors accomplish this task by matching mol. families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one mol. to one organism at a time, such as how it is traditionally done. They can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. The authors matched the mol. families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular mols. with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a mol. family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779T. The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at mol. family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.
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    28. 28
      Kurita, K. L.; Glassey, E.; Linington, R. G. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 11999– 12004,  DOI: 10.1073/pnas.1507743112
      28
      Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries
      Kurita, Kenji L.; Glassey, Emerson; Linington, Roger G.
      Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (39), 11999-12004CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compd. structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compds. with unique biol. and/or chem. properties. By integrating image-based phenotypic screening in HeLa cells with high-resoln. untargeted metabolomics anal., we have developed a new platform, termed Compd. Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product ext. library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compd. modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chem. features and biol. function of bioactive metabolites are known early in the screening workflow, and lead compds. can be rationally selected based on biol. and/or chem. novelty. We demonstrate the utility of the Compd. Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biol. measurements from a library of 234 natural products exts. and integrating these two datasets to identify 13 clusters of fractions contg. 11 known compd. families and four new compds. Using Compd. Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsV2ntLnO&md5=36b4c261ec51d891a3818ba99dd0af6e
    29. 29
      Kellogg, J. J.; Todd, D. A.; Egan, J. M.; Raja, H. A.; Oberlies, N. H.; Kvalheim, O. M.; Cech, N. B. J. Nat. Prod. 2016, 79, 376– 386,  DOI: 10.1021/acs.jnatprod.5b01014
      29
      Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive Compounds
      Kellogg, Joshua J.; Todd, Daniel A.; Egan, Joseph M.; Raja, Huzefa A.; Oberlies, Nicholas H.; Kvalheim, Olav M.; Cech, Nadja B.
      Journal of Natural Products (2016), 79 (2), 376-386CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      A central challenge of natural products research is assigning bioactive compds. from complex mixts. The gold std. approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixt. components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodol. was statistical modeling of the integrated biol. and chem. data sets (biochemometric anal.). Three data anal. approaches for biochemometric anal. were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Exts. from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric anal. incorporating the selectivity ratio performed best in identifying bioactive ions from these exts. early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., resp. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixt. components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single anal.
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    30. 30
      Bertrand, S.; Azzollini, A.; Nievergelt, A.; Boccard, J.; Rudaz, S.; Cuendet, M.; Wolfender, J.-L. Molecules 2016, 21, 259,  DOI: 10.3390/molecules21030259
      There is no corresponding record for this reference.
    31. 31
      Aligiannis, N.; Halabalaki, M.; Chaita, E.; Kouloura, E.; Argyropoulou, A.; Benaki, D.; Kalpoutzakis, E.; Angelis, A.; Stathopoulou, K.; Antoniou, S.; Sani, M.; Krauth, V.; Werz, O.; Schütz, B.; Schäfer, H.; Spraul, M.; Mikros, E.; Skaltsounis, L. A. ChemistrySelect 2016, 1, 2531– 2535,  DOI: 10.1002/slct.201600744
      There is no corresponding record for this reference.
    32. 32
      Olivon, F.; Allard, P.-M.; Koval, A.; Righi, D.; Genta-Jouve, G.; Neyts, J.; Apel, C.; Pannecouque, C.; Nothias, L.-F.; Cachet, X.; Marcourt, L.; Roussi, F.; Katanaev, V. L.; Touboul, D.; Wolfender, J.-L.; Litaudon, M. ACS Chem. Biol. 2017, 12, 2644– 2651,  DOI: 10.1021/acschembio.7b00413
      32
      Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks
      Olivon, Florent; Allard, Pierre-Marie; Koval, Alexey; Righi, Davide; Genta-Jouve, Gregory; Neyts, Johan; Apel, Cecile; Pannecouque, Christophe; Nothias, Louis-Felix; Cachet, Xavier; Marcourt, Laurence; Roussi, Fanny; Katanaev, Vladimir L.; Touboul, David; Wolfender, Jean-Luc; Litaudon, Marc
      ACS Chemical Biology (2017), 12 (10), 2644-2651CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
      Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these mols. with exceptional biol. properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chem. complexity of the biol. matrixes in which they are found. The purifn. of single constituents from such matrixes requires such a significant amt. of work that it should be ideally performed only on mols. of high potential value (i.e., chem. novelty and biol. activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compd. identification within complex mixts. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated anal. strategy for bioactive natural products prioritization. Our approach uses massive mol. networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chem. diversity of crude exts. collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biol. targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural ext. screening should speed up and rationalize the isolation of bioactive natural products.
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    33. 33
      Brito, Â.; Gaifem, J.; Ramos, V.; Glukhov, E.; Dorrestein, P. C.; Gerwick, W. H.; Vasconcelos, V. M.; Mendes, M. V.; Tamagnini, P. Algal Res. 2015, 9, 218– 226,  DOI: 10.1016/j.algal.2015.03.016
      There is no corresponding record for this reference.
    34. 34
      Naman, C. B.; Rattan, R.; Nikoulina, S. E.; Lee, J.; Miller, B. W.; Moss, N. A.; Armstrong, L.; Boudreau, P. D.; Debonsi, H. M.; Valeriote, F. A.; Dorrestein, P. C.; Gerwick, W. H. J. Nat. Prod. 2017, 80, 625– 633,  DOI: 10.1021/acs.jnatprod.6b00907
      34
      Integrating Molecular Networking and Biological Assays To Target the Isolation of a Cytotoxic Cyclic Octapeptide, Samoamide A, from an American Samoan Marine Cyanobacterium
      Naman, C. Benjamin; Rattan, Ramandeep; Nikoulina, Svetlana E.; Lee, John; Miller, Bailey W.; Moss, Nathan A.; Armstrong, Lorene; Boudreau, Paul D.; Debonsi, Hosana M.; Valeriote, Frederick A.; Dorrestein, Pieter C.; Gerwick, William H.
      Journal of Natural Products (2017), 80 (3), 625-633CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      Integrating LC-MS/MS mol. networking and bioassay guided fractionation enabled the targeted isolation of a new and bioactive cyclic octapeptide, samoamide A (1), from a sample of cf. Symploca sp. collected in American Samoa. The structure of 1 was established by detailed 1D and 2D NMR expts., HRESIMS data, and chem. degrdn./chromatog. (e.g., Marfey's anal.) studies. Pure compd. 1 was shown to have in vitro cytotoxic activity against several human cancer cell lines in both traditional cell culture and zone inhibition bioassays. Although there was no particular selectivity between the cell lines tested for samoamide A, the most potent activity was obsd. against H460 human nonsmall cell lung cancer cells (IC50 = 1.1 μM). Mol. modeling studies suggested that one possible mechanism of action for 1 is the inhibition of the enzyme dipeptidyl peptidase (CD26, DPP4) at a reported allosteric binding site, which could lead to many downstream pharmacol. effects. However, this interaction was moderate when tested in vitro at up to 10 μM, and only resulted in about 16% peptidase inhibition. Combining bioassay screening with the cheminformatics strategy of LC-MS/MS mol. networking as a discovery tool expedited the targeted isolation of a natural product possessing both a novel chem. structure and a desired biol. activity.
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    35. 35
      Esposito, M.; Nothias, L.-F.; Retailleau, P.; Costa, J.; Roussi, F.; Neyts, J.; Leyssen, P.; Touboul, D.; Litaudon, M.; Paolini, J. J. Nat. Prod. 2017, 80, 2051– 2059,  DOI: 10.1021/acs.jnatprod.7b00233
      35
      Isolation of Premyrsinane, Myrsinane, and Tigliane Diterpenoids from Euphorbia pithyusa Using a Chikungunya Virus Cell-Based Assay and Analogue Annotation by Molecular Networking
      Esposito, Melissa; Nothias, Louis-Felix; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Neyts, Johan; Leyssen, Pieter; Touboul, David; Litaudon, Marc; Paolini, Julien
      Journal of Natural Products (2017), 80 (7), 2051-2059CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      Six new premyrsinol esters, (1)(I),(2-6), and one new myrsinol ester (8) were isolated from an aerial parts ext. of Euphorbia pithyusa, together with a known premyrsinol (7) and two known dideoxyphorbol esters (9 and 10), following a bioactivity-guided purifn. procedure using a Chikungunya virus (CHIKV) cell-based assay. The structures of the new diterpene esters (1-6 and 8) were elucidated by MS and NMR spectroscopic data interpretation. Compds. I-10 were evaluated against CHIKV replication, and results showed that the β-dideoxyphorbol ester 10 was the most active compd., with an EC50 value of 4.0 ± 0.3 μM and a selectivity index of 10.6. To gain more insight into the structural diversity of diterpenoids produced by E. pithyusa, the initial ext. and chromatog. fractions were analyzed by LC-MS/MS. The generated data were annotated using a mol. networking procedure and revealed that dozens of unknown premyrsinane, myrsinane, and tigliane analogs were present.
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    36. 36
      Nothias, L.-F.; Boutet-Mercey, S.; Cachet, X.; De La Torre, E.; Laboureur, L.; Gallard, J.-F.; Retailleau, P.; Brunelle, A.; Dorrestein, P. C.; Costa, J.; Bedoya, L. M.; Roussi, F.; Leyssen, P.; Alcami, J.; Paolini, J.; Litaudon, M.; Touboul, D. J. Nat. Prod. 2017, 80, 2620– 2629,  DOI: 10.1021/acs.jnatprod.7b00113
      36
      Environmentally Friendly Procedure Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking for the Discovery of Potent Antiviral Compounds from Euphorbia semiperfoliata
      Nothias, Louis-Felix; Boutet-Mercey, Stephanie; Cachet, Xavier; De La Torre, Erick; Laboureur, Laurent; Gallard, Jean-Francois; Retailleau, Pascal; Brunelle, Alain; Dorrestein, Pieter C.; Costa, Jean; Bedoya, Luis M.; Roussi, Fanny; Leyssen, Pieter; Alcami, Jose; Paolini, Julien; Litaudon, Marc; Touboul, David
      Journal of Natural Products (2017), 80 (10), 2620-2629CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      A supercrit. fluid chromatog.-based targeted purifn. procedure using tandem mass spectrometry and mol. networking was developed to analyze, annotate, and isolate secondary metabolites from complex plant ext. mixt. This approach was applied for the targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant ext. The anal. of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorbol esters, were present in the samples. The purifn. procedure using semipreparative supercrit. fluid chromatog. led to the isolation and identification of two new jatrophane esters (13 and 14) and one known (15) and three new 4-deoxyphorbol esters (16-18). The structure and abs. configuration of compd. 16 were confirmed by X-ray crystallog. This compd. was found to display antiviral activity against Chikungunya virus (EC50 = 0.45 μM), while compd. 15 proved to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC50 = 13 nM). This study showed that a supercrit. fluid chromatog.-based protocol and mol. networking can facilitate and accelerate the discovery of bioactive small mols. by targeting mols. of interest, while minimizing the use of toxic solvents.
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    37. 37
      Esposito, M.; Nothias, L.-F.; Nedev, H.; Gallard, J.-F.; Leyssen, P.; Retailleau, P.; Costa, J.; Roussi, F.; Iorga, B. I.; Paolini, J.; Litaudon, M. J. Nat. Prod. 2016, 79, 2873– 2882,  DOI: 10.1021/acs.jnatprod.6b00644
      37
      Euphorbia dendroides latex as a source of jatrophane esters: Isolation, structural analysis, conformational study, and anti-CHIKV activity
      Esposito, Melissa; Nothias, Louis-Felix; Nedev, Hirsto; Gallard, Jean-Francois; Leyssen, Pieter; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Iorga, Bogdan I.; Paolini, Julien; Litaudon, Marc
      Journal of Natural Products (2016), 79 (11), 2873-2882CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      An efficient process was used to isolate six new jatrophane esters, euphodendroidins J (3), K (5), L (6), M, (8), N (10), and O (11), along with seven known diterpenoid esters, namely, euphodendroidins A (4), B (9), E (1), and F (2), jatrophane ester (7), and 3α-hydroxyterracinolides G and B (12 and 13), and terracinolides J and C (14 and 15) from the latex of Euphorbia dendroides. Their 2D structures and relative configurations were established by extensive NMR spectroscopic anal. The abs. configurations of compds. 1, 11, and 15 were detd. by X-ray diffraction anal. Euphodendroidin F (2) was obtained in 18% yield from the diterpenoid ester-enriched ext. after two consecutive flash chromatog. steps, making it an interesting starting material for chem. synthesis. Euphodendroidins K and L (5 and 6) showed an unprecedented NMR spectroscopic behavior, which was investigated by variable-temp. NMR expts. and mol. modeling. The structure-conformation relationships study of compds. 1, 5, and 6, using DFT-NMR calcns., indicated the prominent role of the acylation pattern in governing the conformational behavior of these jatrophane esters. The antiviral activity of compds. 1-15 was evaluated against Chikungunya virus (CHIKV) replication.
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    38. 38
      Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. BMC Bioinf. 2010, 11, 395,  DOI: 10.1186/1471-2105-11-395
      38
      MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data
      Pluskal Tomas; Castillo Sandra; Villar-Briones Alejandro; Oresic Matej
      BMC bioinformatics (2010), 11 (), 395 ISSN:.
      BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
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    39. 39
      Protsyuk, I.; Melnik, A. M.; Nothias, L.-F.; Rappez, L.; Phapale, P.; Aksenov, A. A.; Bouslimani, A.; Ryazanov, S.; Dorrestein, P. C.; Alexandrov, T. Nat. Protoc. 2018, 13, 134– 154,  DOI: 10.1038/nprot.2017.122
      There is no corresponding record for this reference.
    40. 40
      Röst, H. L.; Sachsenberg, T.; Aiche, S.; Bielow, C.; Weisser, H.; Aicheler, F.; Andreotti, S.; Ehrlich, H.-C.; Gutenbrunner, P.; Kenar, E.; Liang, X.; Nahnsen, S.; Nilse, L.; Pfeuffer, J.; Rosenberger, G.; Rurik, M.; Schmitt, U.; Veit, J.; Walzer, M.; Wojnar, D.; Wolski, W. E.; Schilling, O.; Choudhary, J. S.; Malmström, L.; Aebersold, R.; Reinert, K.; Kohlbacher, O. Nat. Methods 2016, 13, 741– 748,  DOI: 10.1038/nmeth.3959
      40
      OpenMS: a flexible open-source software platform for mass spectrometry data analysis
      Rost, Hannes L.; Sachsenberg, Timo; Aiche, Stephan; Bielow, Chris; Weisser, Hendrik; Aicheler, Fabian; Andreotti, Sandro; Ehrlich, Hans-Christian; Gutenbrunner, Petra; Kenar, Erhan; Liang, Xiao; Nahnsen, Sven; Nilse, Lars; Pfeuffer, Julianus; Rosenberger, George; Rurik, Marc; Schmitt, Uwe; Veit, Johannes; Walzer, Mathias; Wojnar, David; Wolski, Witold E.; Schilling, Oliver; Choudhary, Jyoti S.; Malmstrom, Lars; Aebersold, Ruedi; Reinert, Knut; Kohlbacher, Oliver
      Nature Methods (2016), 13 (9), 741-748CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
      High-resoln. mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomol. structural information and characterizing cellular signaling networks. However, the rapid growth in the vol. and complexity of MS data makes transparent, accurate and reproducible anal. difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible anal. of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS addnl. provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quant. mass spectrometric analyses with ease.
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    41. 41
      Optimus https://github.com/MolecularCartography/Optimus (accessed May 24, 2017).
      There is no corresponding record for this reference.
    42. 42
      Ihaka, R.; Gentleman, R. J. Comput. Graph. Stat. 1996, 5, 299– 314,  DOI: 10.1080/10618600.1996.10474713
      There is no corresponding record for this reference.
    43. 43
      Perez, F. Project Jupyter, 2015 http://jupyter.org/about.html.
      There is no corresponding record for this reference.
    44. 44
      Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N. S.; Wang, J. T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Genome Res. 2003, 13, 2498– 2504,  DOI: 10.1101/gr.1239303
      44
      Cytoscape: A software environment for integrated models of biomolecular interaction networks
      Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S.; Wang, Jonathan T.; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey
      Genome Research (2003), 13 (11), 2498-2504CODEN: GEREFS; ISSN:1088-9051. (Cold Spring Harbor Laboratory Press)
      Cytoscape is an open source software project for integrating biomol. interaction networks with high-throughput expression data and other mol. states into a unified conceptual framework. Although applicable to any system of mol. components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other mol. states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of addnl. computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined phys./functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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    45. 45
      Appendino, G.; Szallasi, A. Life Sci. 1997, 60, 681– 696,  DOI: 10.1016/S0024-3205(96)00567-X
      There is no corresponding record for this reference.
    46. 46
      Nothias-Scaglia, L.-F.; Dumontet, V.; Neyts, J.; Roussi, F.; Costa, J.; Leyssen, P.; Litaudon, M.; Paolini, J. Fitoterapia 2015, 105, 202– 209,  DOI: 10.1016/j.fitote.2015.06.021
      There is no corresponding record for this reference.
    47. 47
      Nothias-Scaglia, L.-F.; Schmitz-Afonso, I.; Renucci, F.; Roussi, F.; Touboul, D.; Costa, J.; Litaudon, M.; Paolini, J. J. Chromatogr. A 2015, 1422, 128– 139,  DOI: 10.1016/j.chroma.2015.09.092
      There is no corresponding record for this reference.
    48. 48
      Esposito, M.; Nim, S.; Nothias, L.-F.; Gallard, J.-F.; Rawal, M. K.; Costa, J.; Roussi, F.; Prasad, R.; Di Pietro, A.; Paolini, J.; Litaudon, M. J. Nat. Prod. 2017, 80, 479– 487,  DOI: 10.1021/acs.jnatprod.6b00990
      48
      Evaluation of Jatrophane Esters from Euphorbia spp. as Modulators of Candida albicans Multidrug Transporters
      Esposito, Melissa; Nim, Shweta; Nothias, Louis-Felix; Gallard, Jean-Francois; Rawal, Manpreet Kaur; Costa, Jean; Roussi, Fanny; Prasad, Rajendra; Di Pietro, Attilio; Paolini, Julien; Litaudon, Marc
      Journal of Natural Products (2017), 80 (2), 479-487CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      Twenty-nine jatrophane esters and 1 lathyrane diterpenoid ester isolated from Euphorbia species were evaluated for their capacity to inhibit drug-efflux activities of the primary ABC-transporter CaCdr1p and the secondary MFS-transporter CaMdr1p of Candida albicans, in yeast strains overexpressing the corresponding transporter. These diterpenoid esters were obtained from Euphorbia semiperfoliata, E. insularis, and E. dendroides, and included 5 new compds., euphodendroidins P-T. Jatrophane esters I and II inhibited the efflux of Nile Red (NR) mediated by the 2 multidrug transporters, at 85-64% for CaCdr1p, and 79-65% for CaMdr1p. In contrast, III was selective for CaCdr1p and induced a strong inhibition (92%), whereas IV was selective for CaMdr1p, with a 74% inhibition. The potency and selectivity are sensitive to the substitution pattern on the jatrophane skeleton. However, these compds. were not transported, and showed no synergism with fluconazole cytotoxicity.
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    49. 49
      Esposito, M.; Nothias, L.-F.; Nedev, H.; Gallard, J.-F.; Leyssen, P.; Retailleau, P.; Costa, J.; Roussi, F.; Iorga, B. I.; Paolini, J.; Litaudon, M. J. Nat. Prod. . 2016. 79 2873 DOI: 10.1021/acs.jnatprod.6b00644
      49
      Euphorbia dendroides latex as a source of jatrophane esters: Isolation, structural analysis, conformational study, and anti-CHIKV activity
      Esposito, Melissa; Nothias, Louis-Felix; Nedev, Hirsto; Gallard, Jean-Francois; Leyssen, Pieter; Retailleau, Pascal; Costa, Jean; Roussi, Fanny; Iorga, Bogdan I.; Paolini, Julien; Litaudon, Marc
      Journal of Natural Products (2016), 79 (11), 2873-2882CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      An efficient process was used to isolate six new jatrophane esters, euphodendroidins J (3), K (5), L (6), M, (8), N (10), and O (11), along with seven known diterpenoid esters, namely, euphodendroidins A (4), B (9), E (1), and F (2), jatrophane ester (7), and 3α-hydroxyterracinolides G and B (12 and 13), and terracinolides J and C (14 and 15) from the latex of Euphorbia dendroides. Their 2D structures and relative configurations were established by extensive NMR spectroscopic anal. The abs. configurations of compds. 1, 11, and 15 were detd. by X-ray diffraction anal. Euphodendroidin F (2) was obtained in 18% yield from the diterpenoid ester-enriched ext. after two consecutive flash chromatog. steps, making it an interesting starting material for chem. synthesis. Euphodendroidins K and L (5 and 6) showed an unprecedented NMR spectroscopic behavior, which was investigated by variable-temp. NMR expts. and mol. modeling. The structure-conformation relationships study of compds. 1, 5, and 6, using DFT-NMR calcns., indicated the prominent role of the acylation pattern in governing the conformational behavior of these jatrophane esters. The antiviral activity of compds. 1-15 was evaluated against Chikungunya virus (CHIKV) replication.
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    50. 50
      Allard, P.-M.; Péresse, T.; Bisson, J.; Gindro, K.; Marcourt, L.; Pham, V. C.; Roussi, F.; Litaudon, M.; Wolfender, J.-L. Anal. Chem. 2016, 88, 3317– 3323,  DOI: 10.1021/acs.analchem.5b04804
      50
      Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
      Allard, Pierre-Marie; Peresse, Tiphaine; Bisson, Jonathan; Gindro, Katia; Marcourt, Laurence; Pham, Van Cuong; Roussi, Fanny; Litaudon, Marc; Wolfender, Jean-Luc
      Analytical Chemistry (Washington, DC, United States) (2016), 88 (6), 3317-3323CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biol. matrixes. In this context, liq.-chromatog. coupled to high resoln. mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS expts., massive amts. of detailed information on the chem. compn. of crude exts. can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as mol. networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chem. of complex NPs exts., dereplicate metabolites, and annotate analogs of database entries.
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    51. 51
      Wu, D.; Sorg, B.; Hecker, E. Phytother. Res. 1994, 8, 95– 99,  DOI: 10.1002/ptr.2650080209
      51
      Oligo- and macrocyclic diterpenes in Thymelaeaceae and Euphorbiaceae occurring and utilized in Yunnan (Southwest China). 6. Tigliane type diterpene esters from latex of Euphorbia prolifera.
      Wu, Dagang; Sorg, B.; Hecker, E.
      Phytotherapy Research (1994), 8 (2), 95-9CODEN: PHYREH; ISSN:0951-418X.
      Five tigliane-type diterpene esters were isolated from the latex of E. prolifera by Craig distribution, followed by TLC. Structural elucidation by spectroscopic methods (MS, NMR) revealed 4,20-dideoxyphorbol 12-benzoate 13-isobutyrate (I), 4,20-dideoxy-5ζ-hydroxyphorbol 12-benzoate 13-isobutyrate (II) and 12,13-diisobutyrate (III) and a mixt. of 4-deoxyphorbol 12-(2,4-decadienoate) 13-isobutyrate (IV) with 4-deoxyphorbol 12-(2,4,6-decatrienoate) 13-isobutyrate (V). All compds. were assayed on the mouse ear for irritant activity. I was weakly active, and the IV-V mixt. was highly active. II and III were inactive.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXltVCjsb0%253D&md5=818fc8990e79af330e7894562c234e0c
    52. 52
      Evans, F. J.; Kinghorn, A. D. J. Chromatogr. A 1973, 87, 443– 448,  DOI: 10.1016/S0021-9673(01)91746-7
      There is no corresponding record for this reference.
    53. 53
      Evans, F. J.; Kinghorn, A. D. Bot. J. Linn. Soc. 1977, 74, 23– 35,  DOI: 10.1111/j.1095-8339.1977.tb01163.x
      53
      A comparative phytochemical study of the diterpenes of some species of the genera Euphorbia and Elaeophorbia (Euphorbiaceae)
      Evans, F. J.; Kinghorn, A. D.
      Botanical Journal of the Linnean Society (1977), 74 (1), 23-35CODEN: BJLSAF; ISSN:0024-4074.
      Nearly 60 species from Euphorbia and Elaeophorbia were investigated for diterpenes of the classes tiglianes, ingenanes, and ortho-esters. The diterpenes were isolated as their acetates by a micro-technique from latex or fresh herb material collected from several countries around the world. Authentication of the diterpenes was by chromatog. and spectroscopic methods. These compds. were absent from only 9% of the species examd. The most commonly occurring diterpene was ingenol, followed closely by 12-deoxy-phorbol. The ingenane deriv. 5-deoxyingenol was always detected as a minor companion to ingenol from species of the section Tithymalus. Species of the section Euphorbium contained mainly the diterpene ingenol, although both tigliane and ortho-ester diterpenes were also present in some species. Species from the sections Anisophyllum and Poinsettia did not contain diterpenes of the type in question, whereas species from the Elaeophorbia yielded ingenol. These results provide addnl. chem. evidence concerning recent suggestions on the subgeneric and generic status of Anisophyllum, Poinsettia, Tithymathus, and Elaeophorbia.
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    54. 54
      Appendino, G. Prog. Chem. Org. Nat. Prod. 2016, 102, 1– 90,  DOI: 10.1007/978-3-319-33172-0_1
      54
      Ingenane Diterpenoids
      Appendino, Giovanni
      Progress in the Chemistry of Organic Natural Products (2016), 102 (), 1-90CODEN: POPRDK; ISSN:2192-4309. (Springer International Publishing AG)
      A review. The phytochem., biogenesis, bioactivity, pharmacol., SAR, chem. and spectroscopic properties of the ingenane class of diterpenoids was reviewed along with their isolation, total synthesis and reactivity in synthesis.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtFCqtLbF&md5=9db3117f80eea9def32795ec963babba
    55. 55
      Gulakowski, R. J.; McMahon, J. B.; Buckheit, R. W., Jr.; Gustafson, K. R.; Boyd, M. R. Antiviral Res. 1997, 33, 87– 97,  DOI: 10.1016/S0166-3542(96)01004-2
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      Abdelnabi, R.; Staveness, D.; Near, K. E.; Wender, P. A.; Delang, L.; Neyts, J.; Leyssen, P. Biochem. Pharmacol. 2016, 120, 15– 21,  DOI: 10.1016/j.bcp.2016.09.020
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    57. 57
      Abdelnabi, R.; Amrun, S. N.; Ng, L. F. P.; Leyssen, P.; Neyts, J.; Delang, L. Antiviral Res. 2017, 139, 79– 87,  DOI: 10.1016/j.antiviral.2016.12.020
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      Vasas, A.; Hohmann, J. Chem. Rev. 2014, 114, 8579– 8612,  DOI: 10.1021/cr400541j
      58
      Euphorbia Diterpenes: Isolation, Structure, Biological Activity, and Synthesis (2008-2012)
      Vasas, Andrea; Hohmann, Judit
      Chemical Reviews (Washington, DC, United States) (2014), 114 (17), 8579-8612CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review. Euphorbiaceae is one of the largest families of higher plants, comprising about 50 tribes, 300 genera, and 7500 species, with probably the highest species richness in many habitat. Isolation, structure, classification, and biol. activity of diterpenes of Euphorbia plants and their synthesis are reviewed.
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    59. 59
      Nothias-Scaglia, L.-F.; Pannecouque, C.; Renucci, F.; Delang, L.; Neyts, J.; Roussi, F.; Costa, J.; Leyssen, P.; Litaudon, M.; Paolini, J. J. Nat. Prod. 2015, 78, 1277– 1283,  DOI: 10.1021/acs.jnatprod.5b00073
      59
      Antiviral Activity of Diterpene Esters on Chikungunya Virus and HIV Replication
      Nothias-Scaglia, Louis-Felix; Pannecouque, Christophe; Renucci, Franck; Delang, Leen; Neyts, Johan; Roussi, Fanny; Costa, Jean; Leyssen, Pieter; Litaudon, Marc; Paolini, Julien
      Journal of Natural Products (2015), 78 (6), 1277-1283CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)
      Recently, new daphnane, tigliane, and jatrophane diterpenoids have been isolated from various Euphorbiaceae species, of which some have been shown to be potent inhibitors of chikungunya virus (CHIKV) replication. To further explore this type of compd., the antiviral activity of a series of 29 com. available natural diterpenoids was evaluated. Phorbol-12,13-didecanoate (11) proved to be the most potent inhibitor, with an EC50 value of 6.0 ± 0.9 nM and a selectivity index (SI) of 686, which is in line with the previously reported anti-CHIKV potency for the structurally related 12-O-tetradecanoylphorbol-13-acetate (13). Most of the other compds. exhibited low to moderate activity, including an ingenane-type diterpene ester, compd. 28, with an EC50 value of 1.2 ± 0.1 μM and SI = 6.4. Diterpene compds. are known also to inhibit HIV replication, so the antiviral activities of compds. 1-29 were evaluated also against HIV-1 and HIV-2. Tigliane- (4β-hydroxyphorbol analogs 10, 11, 13, 15, 16, and 18) and ingenane-type (27 and 28) diterpene esters were shown to inhibit HIV replication in vitro at the nanomolar level. A Pearson anal. performed with the anti-CHIKV and anti-HIV data sets demonstrated a linear relationship, which supported the hypothesis made that PKC may be an important target in CHIKV replication.
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    60. 60
      Shi, Q.-W.; Su, X.-H.; Kiyota, H. Chem. Rev. 2008, 108, 4295– 4327,  DOI: 10.1021/cr078350s
      60
      Chemical and Pharmacological Research of the Plants in Genus Euphorbia
      Shi, Qing-Wen; Su, Xiao-Hui; Kiyota, Hiromasa
      Chemical Reviews (Washington, DC, United States) (2008), 108 (10), 4295-4327CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      In this review article, the authors summarize the phytochem. progress and list all of the compds. isolated from the genus Euphorbia over the past few decades. Also included are the biol. activities of compds. isolated in recent years and structure-activity relationships.
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    61. 61
      Vasas, A.; Rédei, D.; Csupor, D.; Molnár, J.; Hohmann, J. Eur. J. Org. Chem. 2012, 2012, 5115– 5130,  DOI: 10.1002/ejoc.201200733
      61
      Diterpenes from European Euphorbia Species Serving as Prototypes for Natural-Product-Based Drug Discovery
      Vasas, Andrea; Redei, Dora; Csupor, Dezso; Molnar, Joseph; Hohmann, Judit
      European Journal of Organic Chemistry (2012), 2012 (27), 5115-5130CODEN: EJOCFK; ISSN:1099-0690. (Wiley-VCH Verlag GmbH & Co. KGaA)
      A review. Diterpenes occurring in plants of the Euphorbiaceae family are of considerable interest in the context of natural product drug discovery programs because of their wide range of potentially valuable biol. activities and their broad structural diversity due to their different polycyclic and macrocyclic skeletons and the various aliph. and arom. ester groups. Euphorbia species have provided many lead compds. (resiniferatoxin, prostratin, jatrophane and pepluane esters) for drug development, some of which are currently involved in preclin. or clin. studies, but the importance of natural products of this type can be demonstrated primarily by the recent approval of ingenol 3-angelate (ingenol mebutate) by the FDA for the treatment of actinic keratosis. An appreciable time has passed since a new plant chemotype - a natural product without structural modification - has been introduced into clin. practice. Ingenol 3-angelate, a Euphorbia peplus metabolite, serves as a new prototype for natural product-based drug discovery. The aim of this paper is to provide an overview of the chem. and pharmacol. potential of European Euphorbia species. Besides the main aspects of the development of ingenol 3-angelate as a drug, screening methods, isolation strategies, the chem. characteristics of Euphorbia diterpenes and the results of pharmacol. investigations are surveyed.
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    62. 62
      Powers, A. Res. Rep. Trop. Med. 2015, 6, 11– 19
      There is no corresponding record for this reference.
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      Project Jupyter website, 2018, http://jupyter.org/install.
      There is no corresponding record for this reference.
    64. 64
      Scheubert, K.; Hufsky, F.; Petras, D.; Wang, M.; Nothias, L.-F.; Dührkop, K.; Bandeira, N.; Dorrestein, P. C.; Böcker, S. Nat. Commun. 2017, 8, 1494,  DOI: 10.1038/s41467-017-01318-5
      64
      Significance estimation for large scale metabolomics annotations by spectral matching
      Scheubert Kerstin; Hufsky Franziska; Duhrkop Kai; Bocker Sebastian; Hufsky Franziska; Petras Daniel; Wang Mingxun; Nothias Louis-Felix; Bandeira Nuno; Dorrestein Pieter C; Petras Daniel; Nothias Louis-Felix; Bandeira Nuno; Dorrestein Pieter C
      Nature communications (2017), 8 (1), 1494 ISSN:.
      The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
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    65. 65
      Gerlich, M.; Neumann, S. J. Mass Spectrom. 2013, 48, 291– 298,  DOI: 10.1002/jms.3123
      65
      MetFusion: integration of compound identification strategies
      Gerlich, Michael; Neumann, Steffen
      Journal of Mass Spectrometry (2013), 48 (3), 291-298CODEN: JMSPFJ; ISSN:1076-5174. (John Wiley & Sons Ltd.)
      Mass spectrometry (MS) is an important anal. technique for the detection and identification of small compds. The main bottleneck in the interpretation of metabolite profiling or screening expts. is the identification of unknown compds. from tandem mass spectra. Spectral libraries for tandem MS, such as MassBank or NIST, contain ref. spectra for many compds., but their limited chem. coverage reduces the chance for a correct and reliable identification of unknown spectra outside the database domain. On the other hand, compd. databases like PubChem or ChemSpider have a much larger coverage of the chem. space, but they cannot be queried with spectral information directly. Recently, computational mass spectrometry methods and in silico fragmentation prediction allow users to search such databases of chem. structures. We present a new strategy called MetFusion to combine identification results from several resources, in particular, from the in silico fragmenter MetFrag with the spectral library MassBank to improve compd. identification. We evaluate the performance on a set of 1062 spectra and achieve an improved ranking of the correct compd. from rank 28 using MetFrag alone, to rank 7 with MetFusion, even if the correct compd. and similar compds. are absent from the spectral library. On the basis of the evaluation, we extrapolate the performance of MetFusion to the KEGG compd. database.
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    66. 66
      Hufsky, F.; Scheubert, K.; Böcker, S. Nat. Prod. Rep. 2014, 31, 807,  DOI: 10.1039/c3np70101h
      66
      New kids on the block: novel informatics methods for natural product discovery
      Hufsky, Franziska; Scheubert, Kerstin; Boecker, Sebastian
      Natural Product Reports (2014), 31 (6), 807-817CODEN: NPRRDF; ISSN:0265-0568. (Royal Society of Chemistry)
      A review Covering: 2008 to 2014 Mass spectrometry is a key technol. for the identification and structural elucidation of natural products. Manual interpretation of the resulting data is tedious and time-consuming, so methods for automated anal. are highly sought after. In this review, we focus on four recently developed methods for the detection and investigation of small mols., namely MetFrag/MetFusion, ISIS, FingerID, and FT-BLAST. These methods have the potential to significantly advance the field of computational mass spectrometry for the research of natural products. For example, they may help with the dereplication of compds. at an early stage of the drug discovery process; i.e., the detection of mols. that are identical or highly similar to known drugs or drug leads. Furthermore, when a potential drug lead has been detd., these tools may help to identify it and elucidate its structure.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXotFekurk%253D&md5=5ace01eee7bcce05e1f06abf88995cc1
    67. 67
      Dührkop, K.; Shen, H.; Meusel, M.; Rousu, J.; Böcker, S. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 12580– 12585,  DOI: 10.1073/pnas.1509788112
      67
      Searching molecular structure databases with tandem mass spectra using CSI:FingerID
      Duehrkop, Kai; Shen, Huibin; Meusel, Marvin; Rousu, Juho; Boecker, Sebastian
      Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (41), 12580-12585CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics expts. usually rely on tandem MS to identify the thousands of compds. in a biol. sample. Today, the vast majority of metabolites remain unknown. The authors present a method for searching mol. structure databases using tandem MS data of small mols. The authors' method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown mol. The authors use the fragmentation tree to predict the mol. structure fingerprint of the unknown compd. using machine learning. This fingerprint is then used to search a mol. structure database such as PubChem. The authors' method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsFamsLzF&md5=8ccf25648836ca6271ebe8516b4c257c
    68. 68
      “bioassay-guided” MS/MS - Google Scholar https://scholar.google.com/scholar?q=%22bioassay-guided%22+MS%2FMS&hl=en&as_sdt=0%2C5&as_ylo=2016&as_yhi=2016 (accessed May 31, 2017).
      There is no corresponding record for this reference.
    69. 69
      Haug, K.; Salek, R. M.; Conesa, P.; Hastings, J.; de Matos, P.; Rijnbeek, M.; Mahendraker, T.; Williams, M.; Neumann, S.; Rocca-Serra, P.; Maguire, E.; González-Beltrán, A.; Sansone, S.-A.; Griffin, J. L.; Steinbeck, C. Nucleic Acids Res. 2013, 41, D781– D786,  DOI: 10.1093/nar/gks1004
      69
      MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data
      Haug, Kenneth; Salek, Reza M.; Conesa, Pablo; Hastings, Janna; de Matos, Paula; Rijnbeek, Mark; Mahendraker, Tejasvi; Williams, Mark; Neumann, Steffen; Rocca-Serra, Philippe; Maguire, Eamonn; Gonzalez-Beltran, Alejandra; Sansone, Susanna-Assunta; Griffin, Julian L.; Steinbeck, Christoph
      Nucleic Acids Research (2013), 41 (D1), D781-D786CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw exptl. data and assocd. metadata, maintained by one of the major open-access data providers in mol. biol. Metabolomic profiling is an important tool for research into biol. functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of exptl. methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their ref. spectra as well as their biol. roles, locations, concns. and raw data from metabolic expts. Studies automatically receive a stable unique accession no. that can be used as a publication ref. (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvV2ktrnO&md5=b4fd7aa651ff814b038cb87db58bb10f
    70. 70
      Spicer, R. A.; Steinbeck, C. Metabolomics 2018, 14, 16,  DOI: 10.1007/s11306-017-1309-5
      There is no corresponding record for this reference.
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      Katajamaa, M.; Miettinen, J.; Oresic, M. Bioinformatics 2006, 22, 634– 636,  DOI: 10.1093/bioinformatics/btk039
      71
      MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data
      Katajamaa, Mikko; Miettinen, Jarkko; Oresic, Matej
      Bioinformatics (2006), 22 (5), 634-636CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      New addnl. methods are presented for processing and visualizing mass spectrometry based mol. profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large no. of files, support for parallel processing, new methods for calcg. peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance anal. for data visualization and exploratory anal.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhvVeks7o%253D&md5=b0d0e995be707835237b2e1de389dcfa
    72. 72
      Olivon, F.; Grelier, G.; Roussi, F.; Litaudon, M.; Touboul, D. Anal. Chem. 2017, 89, 7836– 7840,  DOI: 10.1021/acs.analchem.7b01563
      72
      MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability
      Olivon, Florent; Grelier, Gwendal; Roussi, Fanny; Litaudon, Marc; Touboul, David
      Analytical Chemistry (Washington, DC, United States) (2017), 89 (15), 7836-7840CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      Mol. networking is becoming more and more popular into the metabolomic community to organize tandem mass spectrometry (MS2) data. Even though this approach allows the treatment and comparison of large data sets, several drawbacks related to the MS-Cluster tool routinely used on the Global Natural Product Social Mol. Networking platform (GNPS) limit its potential. MS-Cluster cannot distinguish between chromatog. well-resolved isomers as retention times are not taken into account. Annotation with predicted chem. formulas is also not implemented and semiquantification is only based on the no. of MS2 scans. The authors propose to introduce a data-preprocessing workflow including the preliminary data treatment by MZmine 2 followed by a homemade Python script freely available to the community that clears the major previously mentioned GNPS drawbacks. The efficiency of this workflow is exemplified with the anal. of six fractions of increasing polarities obtained from a sequential supercrit. CO2 extn. of Stillingia lineata leaves.
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    73. 73
      Chambers, M. C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D. L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; Hoff, K.; Kessner, D.; Tasman, N.; Shulman, N.; Frewen, B.; Baker, T. A.; Brusniak, M.-Y.; Paulse, C.; Creasy, D.; Flashner, L.; Kani, K.; Moulding, C.; Seymour, S. L.; Nuwaysir, L. M.; Lefebvre, B.; Kuhlmann, F.; Roark, J.; Rainer, P.; Detlev, S.; Hemenway, T.; Huhmer, A.; Langridge, J.; Connolly, B.; Chadick, T.; Holly, K.; Eckels, J.; Deutsch, E. W.; Moritz, R. L.; Katz, J. E.; Agus, D. B.; MacCoss, M.; Tabb, D. L.; Mallick, P. Nat. Biotechnol. 2012, 30, 918– 920,  DOI: 10.1038/nbt.2377
      73
      A cross-platform toolkit for mass spectrometry and proteomics
      Chambers, Matthew C.; MacLean, Brendan; Burke, Robert; Amodei, Dario; Ruderman, Daniel L.; Neumann, Steffen; Gatto, Laurent; Fischer, Bernd; Pratt, Brian; Egertson, Jarrett; Hoff, Katherine; Kessner, Darren; Tasman, Natalie; Shulman, Nicholas; Frewen, Barbara; Baker, Tahmina A.; Brusniak, Mi-Youn; Paulse, Christopher; Creasy, David; Flashner, Lisa; Kani, Kian; Moulding, Chris; Seymour, Sean L.; Nuwaysir, Lydia M.; Lefebvre, Brent; Kuhlmann, Frank; Roark, Joe; Rainer, Paape; Detlev, Suckau; Hemenway, Tina; Huhmer, Andreas; Langridge, James; Connolly, Brian; Chadick, Trey; Holly, Krisztina; Eckels, Josh; Deutsch, Eric W.; Moritz, Robert L.; Katz, Jonathan E.; Agus, David B.; MacCoss, Michael; Tabb, David L.; Mallick, Parag
      Nature Biotechnology (2012), 30 (10), 918-920CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
      Mass spectrometry-based proteomics has become an important component of biol. research. There have been several calls for improvements and standardization of proteomics data anal. frameworks, as well as for an application programming interface for proteomics data access. In response, ProteoWizard Toolkit was developed, a robust set of opensource, software libraries and applications designed to facilitate proteomics research. With version 3.0 of the ProteoWizard Toolkit8, the challenges in the field can be mitigated through open-source, permissively licensed, cross-platform software. The Toolkit has two components: first, a suite of libraries that facilitate the development and comparison of tools for proteomics data anal. and second, a set of tools, developed using these libraries, that performs a wide array of common proteomics analyses. ProteoWizard is built upon a modular framework of many independent libraries grouped in dependency levels.
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    74. 74
      van den Berg, R. A.; Hoefsloot, H. C. J.; Westerhuis, J. A.; Smilde, A. K.; van der Werf, M. J. BMC Genomics 2006, 7, 142,  DOI: 10.1186/1471-2164-7-142
      74
      Centering, scaling, and transformations: improving the biological information content of metabolomics data
      van den Berg Robert A; Hoefsloot Huub C J; Westerhuis Johan A; Smilde Age K; van der Werf Mariet J
      BMC genomics (2006), 7 (), 142 ISSN:.
      BACKGROUND: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. RESULTS: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. CONCLUSION: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.
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  • Supporting Information

    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jnatprod.7b00737.

    • Results of bioassays against CHIKV replication and 1D and 2D NMR spectra (PDF)


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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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