Automated Detection of Natural Halogenated Compounds from LC-MS

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Automated detection of natural halogenated compounds from LC-MS profiles – Application to the isolation of bioactive chlorinated compounds from marine-derived fungi Catherine Roullier, Yann GUITTON, Marine Valery, Séverine Amand, Soizic Prado, Thibaut Robiou du Pont, Olivier Grovel, and Yves François Pouchus Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02128 • Publication Date (Web): 18 Aug 2016 Downloaded from http://pubs.acs.org on August 20, 2016

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Analytical Chemistry

Automated detection of natural halogenated compounds from LCMS profiles – Application to the isolation of bioactive chlorinated compounds from marine-derived fungi Catherine Roullier,*,†,‡ Yann Guitton,†,‡,§ Marine Valery,† Séverine Amand,║ Soizic Prado,║ Thibaut Robiou du Pont,† Olivier Grovel,†,‡ Yves François Pouchus†,‡ †

University of Nantes, Faculty of Pharmacy, MMS-EA2160, 44035 Nantes, France ThalassOMICS, Plateforme Corsaire, Biogenouest, 44035 Nantes, France ║ Molécules de Communication et Adaptation des Micro-organismes, UMR 7245 MNHN/CNRS, Muséum National d’Histoire Naturelle, 75231 Paris Cedex 05, France ‡

ABSTRACT: A collection of culture extracts obtained from several marine-derived fungal strains collected on the French Atlantic coast was investigated by High Performance Liquid Chromatography-High Resolution Mass Spectrometry (HPLC-HRMS) in order to prospect for halogenated compounds and to identify potentially new ones. To achieve a fast, automated and efficient data analysis, a bioinformatics tool named MeHaloCoA (Marine Halogenated Compound Analysis) was developed and included into R. After extraction of all the peaks from the metabolic fingerprints and their associated mass spectra, a mathematical filter based on mass isotopic profiles allowed the selective detection of halogenated (Cl and Br) molecules. Integrating MeHaloCoA into a dereplication approach allowed the identification of known and new halogenated compounds in a competitive amount of time. Subsequent targeted purification led to the isolation of several chlorinated metabolites including two new natural products with bioactive potential, griseophenone I and chlorogriseofulvin from a marine-derived Penicillium canescens strain.

Seventy percent of the earth's surface is occupied by the sea, which salinity and abundance in reactive halogens turn into a very special environment. Consequently, it is not surprising that numerous marine organisms have been reported to sequester and incorporate these halogens in their metabolism, resulting in 15-20% of all newly discovered marine natural products to be organohalogens.1-4 The presence of organohal-

ogens in marine organisms has led attention for different reasons. Firstly, they are frequently encountered in environmental surveys, which most prominently include polyhalogenated pollutants (PHP) from human industrial development. This is a potential threat to the biodiversity of marine ecosystems.5,6 Secondly, organohalogens are also of interest to natural product chemists looking for new bioactive compounds. Effectively, halogenated compounds constitute a wide class of natural products, many of them exhibiting remarkable bioactivities.7 When studying the drugs that have been released to the market over the past 30 years, it appears that more than 15% contain one or more halogen atoms in their structure, not taking into account the halogenated salts.8 Effectively, halogenated compounds usually provide good leads for drug discovery. Many of them have been described from natural sources and are considered biologically important metabolites. As an example, the chlorinated drug vancomycin originally isolated from Streptomyces orientalis has found a commercial use as an antibiotic for more than fifty years.9 It is active against penicillin-resistant bacterial infections, and is frequently used in the control of methicillin-resistant Staphylococcus aureus (MRSA). It was clearly demonstrated that the presence of both

chlorine atoms in the molecule is required for its activity.10 The Penicillium-produced griseofulvin may also be mentioned as this chlorinated drug has been on the market for a long time for its antifungal properties. Other halogenated natural products are in clinical development as new anticancer agents.11 As an example, PM00104 is a novel synthetic fluorinated tetrahydroisoquinoline alkaloid related to both the marine natural compound jorumycin (extracted from the skin and mucus of the nudibranch Joruna funebris) and the family of renieramycins, produced by sponges and tunicates. It turned out to be the most potent agent tested against multiple myeloma, with IC50 values from picomolar to low nanomolar ranges. Salinosporamide A (Marizomib®), a chlorinated compound isolated from a new microbial source, the marine bacterium Salinospora tropica, also entered clinical trials in 2006 as a cytotoxic proteasome inhibitor. The evidence of its anti-proliferative activity was seen in a number of tumor types such as melanoma and myeloma.12 Initially, marine natural products drug discovery focused on macroorganisms such as algae and sponges, but over the past few years, researchers have increasingly turned to smaller creatures. First ignored, marine microorganisms such as marine-derived fungi have proven to be rich and renewable sources of structurally novel and biologically active secondary metabolites, which have become significant resources for drug discovery.13–15 Many compounds previously isolated from macroorganisms actually turned out to be metabolic products from their associated microbes.16 Turning to marine-derived fungi and considering the widely studied fungal genus Penicillium, it is quite surprising to find out that no more than 16

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chlorinated compounds have been reported in the literature.17 However, all of these compounds presented interesting biological activities such as the chlorinated sesquiterpene ligerin, which exhibited a selective antiproliferative activity against osteosarcoma cell lines and in vivo antitumor activity.18, 19 Considering the potential of marine-derived fungi as a promising resource for new bioactive metabolites and their under-investigated halogenated metabolome, this work focused on the detection of new halogenated compounds among a collection of marine-derived fungal strains. As chlorine and bromine present typical isotopic patterns, which can be visualized by high resolution mass spectrometry (HRMS), a novel approach combining metabolomics tools and targeted purification techniques was developed in order to provide a fast and focused access to new halogenated fungal metabolites with expected bioactive potential. The term “halogenated” in this work is then restricted to chlorinated and brominated as other halogen atoms (I and F) are monoisotopic elements.

EXPERIMENTAL SECTION General experimental procedures. HRESIMS analyses were carried out on a Shimadzu IT-TOF mass spectrometer. NMR experiments were recorded on Bruker Ultra Shield 500 MHz and on Bruker Avance III HD 400 MHz and 600 MHz spectrometers (Wissembourg, France) equipped with a BBFO Plus Smartprobe and a triple resonance TCI cryoprobe, respectively. Chemical shifts are expressed in δ (ppm), and are referenced to the residual non-deuterated solvent signals. For optical rotation measurements, a 341 model polarimeter (Perkin-Elmer) was used. Flash chromatography separation was conducted on Interchim PuriFlash®430 using a PuriFlash cartridge HP-Sil column (25 g, 50 µm). Semi-preparative HPLC was carried out on an Agilent semi-preparative HPLC system using a diode array detector and an Interchim Uptisphere ODB C18 column (250 mm × 4.6 mm, 5 µm). Chemicals and Materials. The twelve halogenated standards, diazoxide (1), lamotrigine (2), α-hydroxy-alprazolam (4), furosemide (5), griseofulvin (6), aceclofenac (7), haloperidol (8), ketoconazole (10), mometasone furoate (11) and amlodipine besylate (12) were purchased from Sigma-Aldrich (St Louis, USA). 7-chloro-fumagillol (3) and ligerin (9) were obtained as previously described by Blanchet et al.19 Acetonitrile and methanol used for sample preparation and HPLC-MS analyses were of UPLC/MS grade quality and purchased from Biosolve BV (Dieuze, France). Solvents used for the purification of compounds were of analytical grade and distilled before use. Preparation of the standard-mix. Seven standard-mix samples were prepared by spiking a halogenated compoundsfree fungal extract (#113, see Table S-1) with the twelve standard compounds. For this purpose, a stock solution was prepared at a concentration of 50 µg/mL of each compound in MeOH, together with dilutions 25, 10, 5, 1, 0.5 and 0.1 µg/mL. 200 µL of the solutions were then added to 0.1 mg of the fungal extract. Fungal strains, culture conditions and preparation of extracts for the screening. For this study, 32 different strains of the marine fungal strain collection of the laboratory were selected. They were 20 strains of the genus Penicillium, 2 Aspergillus, 2 Fusarium, 2 Phoma, 2 Trichoderma, 1 Chrysosporium, 1 Cladosporium, 1 Pycnidiophora and 1 Scopulariopsis. They are listed in Table S-1. Cultures followed standard

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protocols on agar-based media at 27 °C during 11 days. In order to enhance their metabolic production, strains were grown on different media (Tables S-1 and S-2) following the OSMAC approach (One Strain MAny Compounds).20 Extractions were performed either on mycelium, agar phase or the totality (totum) of cultures using different solvent systems as listed in Table S-1 leading to 136 crude extracts. Culture conditions and preparation of extracts for halogenated metabolites isolation. In order to purify the halogenated compounds detected in extracts of Penicillium canescens MMS460 (GenBank accession numbers for ITS and β-tubulin regions KU720405 and KU720399), it was grown on solid YES medium for 11 days at 27 °C. Cultures were performed in a series of 46 Erlenmeyers flasks containing 50 mL of solid medium each. After incubation, culture medium and mycelia were ground together and extracted with 100 mL of CH2Cl2/EtOAc (1:1, v/v). The ground mixture was then ultrasonicated for 30 min. After filtration, the solid residue was again macerated with 100 mL of CH2Cl2/EtOAc (1:1, v/v), and shaken overnight. Organic phases were pooled and dehydrated on Na2SO4. After filtration under vacuum through a regenerated cellulose membrane (0.45 µm, Sartorius), the organic phase was evaporated to dryness, leading to the crude extract (5.87 g). HPLC-HRMS metabolic profiling of extracts. Analyses of the 7 halogenated enriched standard-mixes and of the 136 samples from the MMS extract library were performed on a Shimadzu UFLC-HRMS-IT-TOF instrument, which was composed of two LC-20ADxr pumps, a SIL-20ACxr autosampler, a CTO-20AC column oven, an SPD-M20A PDA detector, a CBM-20A system controller, an ESI ion source, and an ITTOF mass spectrometer (Shimadzu, Kyoto, Japan). High performance liquid chromatography analyses were performed on a Kinetex ™ C18 column (100 x 2.1 mm, 2.6 µm), heated in a oven equilibrated at 40 °C. A mobile phase consisting of CH3CN-H2O (acidified with 0.1% formic acid) was used, starting with 15:85 during 2 min, then increasing linearly to 100% CH3CN within 23 min, holding at 100% CH3CN for another 5 min then returning to the starting conditions within 1 min, and holding for 4 min, for a total run time of 35 min and a flow rate of 0.3 mL/min. Samples were prepared at 500 µg/mL in MeOH (UPLC/MS grade) and kept at 4 °C before injection of 3 µL for each. UV-vis spectra were collected from 190 to 600 nm. MS data were recorded in the ESI positive and negative modes in the mass range m/z 100-10000, using the following parameters: heat block and curved desolvation line temperatures 200 °C; nebulizing nitrogen gas flow 1.5 L/min; interface voltage: (+) 4.5 kV, (−) −3.5 kV; detector voltage of the TOF analyzer 1.6 kV. MS data screening for automatic detection of halogenated compounds. The method developed used the open access R environment21 to create an algorithm able to detect from a LC-MS chromatogram in the appropriate format (netCDF, mzXML, mzDATA, mzML), any chlorinated or brominated pattern-suggesting compound. The packages XCMS (version 1.40)22 and CAMERA (version 1.18)23 were the two main dependencies used behind the new R script named MeHaloCoA (Marine Halogenated Compound Analysis) which is available on GitHub (http://yguitton.github.io/MeHaloCoA) (version 1.2). The last step of the process corresponded to the generation of .csv files with the list of positive hits, giving for each m/z-Rt couple the values of the different calculations


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performed. The files generated were: 1) a table for each sample, which gives for each ion considered as a peak: pcgroup (group of ions associated), Rt, m/z for M, m/z for [M+1], m/z for [M+2], [M+1]-M value,[M+2]-M value, [M+1]/M relative intensity percentage, [M+2]/M relative intensity percentage, mass defect filters test result, indication of type of halogen atom, i.e. Cl or Br based on [M+2]/M relative intensity percentage (> 30% Cl OK; > 90% Br OK); 2) a condensed table for multisample analyses giving: sample reference by file name, Rt, same indications of type of halogen atom as described above, list of m/z. 3) a plot of the isotopic filtered chromatogram, overlaid with the base peak chromatogram of the sample, with arrows highlighting best hits (all filters passed). For parameter optimization as discussed in this article, the an.rda file generated by the peak picking step using XCMS was automatically saved in a separate folder with date and time of processing, together with the defined parameters (settingslist). The an.rda file contained all the data obtained after the peak picking step such as for each m/z, Rt, “into” value, “maxo” value, “sn” value. For the analysis of the 272 chromatograms (136 in positive mode, 136 in negative mode), the following parameters for peak picking and halogenated detection steps were used: fwhm=8, snthresh=3, mzdiff=0.05, step=0.1, steps=2, profmethod=binlinbase, perfwhm=1, Rt range=2-30 min, m1=1.003, m2=1.997, Thresh=20, mdiff=0.005, ppmerr=30, m1need=FALSE, val=maxo. Manual checking was conducted by opening all chromatograms under Shimadzu software (LabSolutions) and tracing extracted ion chromatograms of detected chlorinated ions for M and M+2. Co-elution issues were then evaluated based on overlaid chromatograms. When co-elution issues were discarded, the chlorinated nature of automatically detected ions was assessed visually by the M+2/M intensity ratio and by measuring the mass defects between M+2 and M m/z values. Detected ions were then ranked as “ok” for confirmed chlorinated species, “no” for confirmed non-chlorinated species or “unclear” when the trace remains doubtful, usually corresponding to very low signals. Dereplication. For dereplication of the halogenated compounds highlighted, the databases used were the Dictionary of Natural Products (DNP 22.2 Copyright © 2014) and Antibase (release 2011).24 Isolation of identified chlorinated compounds. The crude extract (5 g) was subjected to vacuum liquid chromatography on silica, using a non linear step gradient of MeOH in CH2Cl2 from 0% to 50% to afford 8 fractions. 500 mg of fraction 4 (1.9 g) obtained through elution with CH2Cl2/MeOH (94:6, v/v) were then resuspended in CH2Cl2 and subjected to flash chromatography, using a non linear step gradient of MeOH in CH2Cl2 from 0% to 10% to afford 13 fractions. Fractions 4 (22 mg) and 5 (13 mg) obtained through elution with CH2Cl2/MeOH (97:3, v/v) and (96:4, v/v) respectively were subjected to semi-preparative reverse phase HPLC. The elution was performed with H2O (+ 0.1% HCOOH)/MeOH (+ 0.1% HCOOH) (35:65, v/v) from 0 to 45 min, followed by a gradient from 65% to 100% of MeOH (+ 0.1% HCOOH) from 45 to 55 min, for an overall run of 65 min at a 2 mL/min flow rate, to afford griseophenone G (1.2 mg) eluting at 30.5 min, 1 (0.4 mg) eluting at 51.6 min and 2 (0.1 mg) eluting at 55.5 min. Griseophenone G: UV (MeOH, H+) λmax 203, 286, 354 nm; 1 H NMR (MeOH-d4,500 MHz) δ 6.25 (1H, s, H-12), 6.22 (1H,

s, H-10), 3.84 (3H, s, OCH3-3’), 3.66 (3H, s, OCH3-9’), 2.07 (3H, s, CH3-14); 13C NMR (MeOH-d4, 125 MHz) δ 160.2 (C, C-3), 158.0 (C, C-9), 136.0 (C, C-13), 126.9 (C, C-8), 109.8 (CH, C-12), 97.5 (CH, C-10), 60.5 (OCH3, C-3’), 55.8 (OCH3, C-9’), 19.3 (CH3, C-14); HRESIMS m/z [M-H]- 371.0068 (calcd for C16H13O6Cl2-, 371.0094); MS2 fragments: HRESI(-) m/z 338.9837, 323.9585, HRESI(+) m/z 165.0554. Chlorogriseofulvin (1): [α]20D +222 (c 0.03, (CH3)2CO); UV (MeOH, H+) λmax 229, 273, 345 nm; 1H NMR (DMSO-d6,500 MHz) δ 5.67 (1H, s, H-3’), 4.06 (3H, s, OCH3-6), 4.00 (3H, s, OCH3-4), 3.65 (3H, s, OCH3-2’), 2.89 (1H, qdd, J = 6.7, 13.1, 5.2 Hz, H-6’), 2.64 (1H, dd, J = 16.7, 13.1 Hz, H-5’a), 2.42 (1H, dd, J = 16.7, 5.2 Hz, H-5’b), 0.85 (3H, d, J = 6.7 Hz, CH3-6’); 13C NMR (DMSO-d6, 125 MHz) δ 195.4 (C, C-4’), 192.6 (C, C-3), 169.3 (C, C-2’), 167.7 (C, C-7a), 160.9 (C, C4), 152.9 (C, C-6), 115.1 (C), 110.0 (C), 106.2 (C), 105.2 (CH, C-3’), 90.7 (C, C-2), 63.0 (6-OCH3), 61.6 (4-OCH3), 57.4 (2’OCH3), 40.1 (CH2, C-5’), 35.6 (CH, C-6’), 13.9 (6’-CH3); HRESIMS m/z [M-H]- 385.0242 (calcd for C17H15O6Cl2-, 385.0251); MS2 fragments: HRESI(+) m/z 319.0129, 248.9697,165.0553. Griseophenone I (2): UV (MeOH, H+) λmax 203, 281, 352 nm; 1H NMR (MeOH-d4,600 MHz) δ 6.31 (1H, s, H-12), 6.29 (1H, s, H-10), 3.94 (3H, s, OCH3-3’), 3.60 (3H, s, OCH3-9’), 3.33 (3H, s, OCH3-5’), 2.20 (3H, s, CH3-14); 13C NMR (MeOH-d4, 150 MHz) δ 201.1 (C, C-7), 161.3 (C, C-11), 160.6 (C, C-9), 159.1 (C, C-3), 158.3 (C, C-5), 139.4 (C, C13), 124.0 (C, C-8), 110.5 (CH, C-12), 97.4 (CH, C-10), 62.2 (OCH3, C-5’), 61.4 (OCH3, C-3’), 56.0 (OCH3, C-9’), 19.9 (CH3, C-14); 1H NMR (CH3CN-d3,600 MHz) δ 13.24 (1H, s, OH-1), 6.33 (2H, s, H-10, H-12), 3.95 (3H, s, OCH3-3’), 3.60 (3H, s, OCH3-9’), 3.29 (3H, s, OCH3-5’), 2.14 (3H, s, CH314); 13C NMR (CH3CN-d3, 150 MHz) δ 201.7 (C, C-7), 160.2 (C, C-11), 159.8 (C, C-1), 159.7 (C, C-9), 159.8 (C, C-3), 158.9 (C, C-5), 138.5 (C, C-13), 124.4 (C, C-8), 115.5 (C, C-6 or C-2), 109.6 (CH, C-12), 97.1 (CH, C-10), 62.3 (OCH3, C5’), 61.8 (OCH3, C-3’), 56.0 (OCH3, C-9’), 19.4 (CH3, C-14); HRESIMS m/z [M-H]- 385.0242 (calcd for C17H15O6Cl2-, 385.0251); MS2 fragments: HRESI(-) m/z 352.9981, 337.9762, HRESI(+) m/z 165.0544. Cytotoxicity assays. KB cells (human oral epidermoid carcinoma ATCC CCL 17, Rockville, MD) were cultivated in BME supplemented with 20% (v/v) foetal calf serum, 1% (v/v) glutamine 200 mM and 1% (v/v) streptomycin/penicillin 10 mg/mL (all Biochrom KG, Berlin, Germany). Cells were cultivated in flasks (Falcon, Becton Dickinson Labware) at 37 °C in a 5% CO2 enriched atmosphere. After an incubation period of 48 h, trypsinized cells were suspended as a 200,000 cells/mL suspension and 50 µL were put in each well of 96well microplates (Nuclon, Nunc). Compounds to be tested were prepared in methanol at a 0.4 mM stock solution and 10fold diluted in the culture medium. Solvent negative control was a 10% methanolic solution in supplemented BME. External wells were used as control: 50 µL of culture medium (3 wells) and 50 µL of solvent negative control (3 wells) were added to the initial 50-µL cell suspension. Other wells received 50 µL of compound solutions in 10% methanolic culture medium. The samples were then tested as a 5% (v/v) methanolic solution in pure culture medium. Six concentrations were tested ranging from 0.06 to 20 µM. All the in vitro assays were performed in triplicate. After 72 h of incubation,



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the cell viability was evaluated by the colorimetric MTT bioassay according to previously described methods.25,26

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pointing out the ions for which confidence in halogenated annotation is high (Blue arrows in Figure 1).

RESULTS AND DISCUSSION Development of a new transposable computer-aided method for the automatized detection of halogenated compounds. Considering the importance of halogenated metabolites and their typical isotopic pattern (Figure S-3), automatized detection by applying isotopic filtering could be very valuable. This study aimed at developing a freely available tool based on the free R software environment,21 dedicated to the search of halogenated compounds, which is consistent with natural product chemists’ concerns. It was named MeHaloCoA (Marine Halogenated Compound Analysis). For all steps a dedicated graphical user interface was designed and allows users not familiar with R to easily launch calculations. The first step of MeHaloCoA (Figure 1) consists in peak picking using the XCMS package.22 Generally, the number of generated spectra in an LC-MS run is in the order of 5,000 to 10,000 (corresponding to the number of scans) depending on the instrument, the method and the defined parameters. This procedure reduces this number to usually less than one thousand depending on the complexity of the sample. Averaging mass spectra from all the scans corresponding to the same peak avoids artefacts detection, decreases data treatment time and reduces the rate of false positives. This step reduces the number of mass spectra to be analyzed by calculating a mean mass spectrum for each chromatographic peak. The second step analyses all mean mass spectra corresponding to each detected peak and annotates all its isotopes, especially M, M+1 and M+2 using the CAMERA package.23 This step also combines all the ions aligning in the same peak in a group named “pcgroup”. This is particularly interesting because if multiple ions within the same pcgroup (molecular ion, adducts and fragments) are found to be halogenated, there is an increased likelihood that the pertaining compound indeed is halogenated. The third step is the halogen presence detection using their characteristic isotope patterns (as shown in Figure S-3). It applies a filter which selects all ions for which the annotated [M+2] ion intensity is over a defined relative percentage of the M ion intensity. This value named “Thresh” in the algorithm, was first set at 30%, which is consistent with chlorinated and brominated compounds (Figure S-3). A report list of all peaks with annotated masses corresponding to either chlorinated or brominated compounds is generated. This method was implemented by a second filter based on the relative isotopic mass defect, and following the “rule of three” as described by Thurman and Ferrer.27 Effectively, halogenated molecules are characterized by a uniquely negative mass defect (difference between nominal and exact mass). This adds a further and more reliable criterion as to whether or not the molecule is halogenated. For a positive detection, mass differences between [M] and [M+1] and between [M] and [M+2] must be close to 1.003 and 1.997 accounting for differences between 12 C-13C and 35Cl-37Cl (or 79Br-81Br) respectively (∆1 and ∆2 in Figure 1). This calculation introduces an additional criterion, which in combination with the isotope pattern increases the reliability of detecting chlorinated or brominated compounds. As an example, it prevents from detecting co-eluting molecules different from one double bond (2H), because their accurate masses difference is higher (around 2.015). It allows

Figure 1. Workflow for an automated halogenated compound detection. After data acquisition and conversion into a readable format (netCDF, mzXML, mzDATA, mzML), the script developed under R first carries out a peak picking step using the XCMS package. Each mean mass spectrum for each peak is then annotated for isotopes using the CAMERA package. The critical step for halogen detection corresponds to the calculation of different values including ∆1 and ∆2 and the [M+2]/M ratio. If these values are in accordance with user-defined thresholds as explained in the text, then the peak is considered as a hit (likely to contain chlorine or bromine). A final report is then generated with the hit list for each chromatogram in a .csv file together with a graphical overlay of the total ion chromatogram with extracted ion chromatograms for each hit (highlighted in red), with blue arrows indicating high confidence peaks.

Validation and optimization of the method. In order to validate the method, an Aspergillus sp. extract, previously determined as containing no halogenated compounds, was enriched with 12 pure chlorinated standards at different concentrations ranging from 0.1 to 50 µg/mL. After LC-MS analysis, data obtained were analyzed using the developed method (results are reported in Table S-4). First, parameters used for peak picking were evaluated. Full width at half maximum (“fwhm”) together with the mass error allowed for m/z values (“mzdiff”) had to be defined according to the chromatographic system and the mass spectrometer used. For the parameters determining the width (in m/z units) of the chromatographic slices first obtained (“step”) and the number of chromatographic slices to be combined (“steps”) in the XCMS process, default values were maintained. Indeed, reducing the slice width increased data treatment time but did not lead to any improvement of the method. As these settings tend to differ from instrument to instrument and also with regard to the matrix, their optimization typically is a difficult and subjective process.28 The signal to noise ratio threshold (“snthresh”) had to be set to 3 to allow detection of compounds at 0.1 µg/mL, especially for low intensity isotopes ([M+1]) and avoid noise interferences. Whereas some compounds such as haloperidol still displayed a satisfactory signal to noise ratio at 0.1 µg/mL (s/n at 176, 35 and 67 for M, M+1 and M+2 respectively as


listed in Table S-4), other compounds including amlodipine, mometasone, aceclofenac, furosemide, 7-chlorofumagillol or diazoxide could not be detected at this concentration. The algorithm chosen for the profiling method (“profmethod”) in XCMS occurred to be critical here. Effectively, among the four options, “binlinbase” allowed to increase signal to noise ratio for all of the standards added (Figure S-5). The accuracy of the peak picking using “binlinbase” was found to be the most effective as it was in accordance with manually extracted ion chromatograms: each time, XCMS failed picking a peak for a specific molecule, it couldn’t be retrieved manually either. The “binlinbase” profiling method uses linear interpolation between successive data points (separated by 0.15 m/z units) and otherwise inserts a basal intensity value set to half of the minimum observed intensity, contrary to the simplest “bin” algorithm, which leaves matrix cells at zero when no signal is present.29 In fact, as data acquisition was performed with polarity switching after each scan, the alternation of positive and negative modes may generate many zero values in the “bin” matrix. The grouping of ions using the CAMERA package was ruled by the tolerated shift in the retention time, which is set by the “perfwhm” parameter. In the current study, a value of 0.6 to 1 allowed all isotopes of the same ion to be grouped in the same peak (“pcgroup”) for the 12 compounds at each concentration, together with their adducts and fragments when present. Overall, the peak picking parameters were assessed by plotting the measured values for each compound at each concentration. For each peak, two outputs were used: the integrated area under the curve (AUC) (named “into” in the algorithm) and the maximum intensity (“maxo”). It appeared that both values increased linearly with concentration (data not shown), allowing validation of the peak picking parameters used in this method. Concerning the critical detection step, different parameters were optimized in order to assign a peak as being chlorinated or brominated. First, we investigated the relative percentage for the threshold which the [M+2]/M ratio has to exceed (“Thresh”). It appeared that this value in some cases should be set lower than expected (below 30%), especially for monochlorinated compounds when highly diluted (Figure 2, Table S-4). The value used for calculation in the algorithm, namely “into” (Figure 2A) or “maxo”(Figure 2B), also had an influence on the reliability of the detection of bromine or chlorine. Based on the results obtained for the twelve chlorinated standards, we concluded that a value of 20% for “Thresh” in combination with the “maxo” setting was more robust than setting “Thresh” to 30% (as figured by red lines in Figure 2) and using the “into” setting. Moreover, our analysis revealed that compounds with [M+1] isotope below the signal to noise threshold, could be missed. This led us to introduce another parameter into the algorithm, “m1need”, to account for these cases. As an example, [M+1] isotopes of the standards lamotrigine and ketoconazole could not be detected at 0.1 µg/mL while their M and [M+2] were detected (Table S-4). When setting “m1need” to false, the presence of the [M+1] isotope is not needed anymore to detect these compounds and assign them as being chlorinated. This parameter led to add some false positives to the hit list (potential brominated or chlorinated compounds) but, with close examination of the results, these false positives could also be retrieved in the blank samples and then be eliminated from the hit list.

A 100 90

Percentage of [M+2] vs M (AUC)

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80 70 60 50 40 30 20 10 0 0.1

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1 Concentration (µg/mL)

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Percentage of [M+2] vs M (intensities)

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90 80 70 60 50 40 30 20 10 0

Figure 2. Evaluation of the critical parameters for detection of brominated or chlorinated compounds. The percentage of the [M+2] isotope relative to the M ion, when using AUC (“into” values) (A) or intensities (“maxo” values) (B) as generated by the peak detection step, within the whole range of concentrations tested was investigated (red and green lines representing threshold 30% and 20 % respectively). An Aspergillus extract was spiked with a mixture of the following authentic chlorinated standards at 0.1, 0.5, 1, 5, 10, 25 and 50 µg/mL each: diazoxide (), lamotrigine (), 7-chloro-fumagillol (), α-hydroxy-alprazolam (×), furosemide (), griseofulvin (), aceclofenac (+), haloperidol (-), ligerin (-), ketoconazole (), mometasone (), amlodipine (). The parameters used for peak picking by XCMS were: snthresh = 3, fwhm = 15, mzdiff = 0.05, max = 50, step = 0.1, steps = 2, profmethod = “binlinbase”.

Relevance of MeHaloCoA method. Most modern MS instruments come with specialized commercial software packages, some of which provide isotopic filtering functionalities, which in principle can be used for the detection of brominated or chlorinated compounds. However, these programs are not dedicated to this type of work, and moreover, they often are restricted to the instrument manufacturer's proprietary data format, and it is difficult to transpose data generated by other devices. They usually perform file per file data treatment and cannot process a whole batch in a row (as in MetID software provided with the Shimadzu IT-TOF MS instrument used in this study). Other algorithms have also been reported, such as the previously described AMSA-IFC algorithm developed on R by Zhu et al.,30 which would have been interesting to compare because of similar functions. Despite our best efforts, it was not available to us, and we therefore decided to re-encode it in accordance to the referring article, in order to be compared to the MeHaloCoA method. AMSA-IFC method processes data in a “scan by scan” approach, without any filtering of the data based on the chromatogram. It appeared that the peak picking step available in MeHaloCoA drastically reduces the pro-



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cessing time by decreasing the load of data to be analyzed. For 272 chromatograms processed on a Windows 7 PC with 4 GB RAM and 2.40 GHz CPU Intel Xeon CPU, 85 min were needed with MeHaloCoA for the peak picking step and 30 min for the halogen detection, which corresponds to a total data treatment time of 25 s per chromatogram. Even when scans were averaged 5 by 5 before halogen detection as described in the AMSA-IFC method (with the in-house developed equivalent R script), the length of data treatment is too long to be feasible for multi-sample analyses (> 30 min per chromatogram). Additionally, in this case, as several positive hits per halogenated peak are generated, it results in a massive number of hits to be addressed and summarized. However, this option was made available in the MeHaloCoA method. A user-friendly interface was also developed in order to be easily accessible to nonexpert users without any knowledge of R (Figure S-6). The detection method was evaluated by measuring its sensitivity, specificity and other statistical values (Table S-7). As expected, the rate of true positive hits occurred to be better with the “m1need” parameter set to “false” for low concentrations (0.67 vs 0.33 for “true” at 0.1 µg/mL). This value increases with concentration, reaching 1.00 at 5 µg/mL. This is in accordance with the [M+2] ion being intense enough to be detected over the signal to noise ratio of 3. The specificity of the method, corresponding to the rate of true negative nonobservations is quite high ranging between 0.92 and 1.00, leading to a negative predictive value of 0.96-1.00. If a peak is detected as not being chlorinated or brominated, the confidence in this assertion is then very high, which prevents from omitting any of these interesting compounds. The sensitivity of the method depends on the intensity of the M, [M+1] and [M+2] ions. As stated above, whereas the “m1need” parameter set to “false” increases the sensitivity for low concentrations, it also tends to increase the false discovery rate at high concentrations. Ultimately, even if the method generates some false positives, it seems easier to exclude them afterwards rather than omitting any chlorinated or brominated compound. Nevertheless the possibility of switching the “m1need” parameter from “false” to “true” relatively to the [M1] height has to be considered in a future version of the procedure. MeHaloCoA in its current state is restricted to monocharged and low molecular weight molecules (up to 800), which usually correspond to a majority of interesting natural products in the field of drug discovery. It has proven powerful in the detection of mono- and di-chlorinated compounds so far, as well as mono-brominated analogs (Figure S-8). Its application to poly-brominated products will be envisaged in the future, as this could be very valuable for the analysis of sponge extracts. Screening marine-derived fungal extracts for halogenated compounds. The method developed was employed for the investigation of 32 different fungal strains isolated from the marine environment with regard to their ability to produce halogenated compounds. A total of 136 extracts obtained from these fungi cultivated on different sea-water based culture media were analyzed by HPLC-HRMS either in positive and negative modes generating 272 chromatograms. Data obtained were processed using MeHaloCoA with optimized parameters, leading to the detection of 172 hits (from 108 extracts out of the 136). After manual checking, 106 peaks were confirmed as exhibiting a typical chlorinated profile. The 66 peaks that were

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ruled out correspond to: 1) clearly false positives such as when co-elution occurred which could not be resolved by the peak picking step, or 2) to very low intensity signals which were very difficult to ascertain. Among the 106 halogenated peaks, more than a half did not match the searched databases. Attention was given to the 67 peaks for which at least 2 different ions (adducts or fragments) were observed in the same analysis, or which were detected in at least two different extracts. In most cases, this allowed for deducing the charge state and the type of adducts, leading to establishing the molecular masses and the calculation of candidate molecular formulae. After a database search in DNP and Antibase, 20 of these 67 selected hits did not match any reported compounds. They were thus considered potentially unknown chlorinated compounds. This number is substantial, given that the number of chlorinated compounds currently reported for Penicillium species is around 75. After compiling all analyses results, 20 out of the 32 explored fungal strains were deemed to produce putatively new chlorinated compounds (corresponding to a total of 56 unique features). Among them some Penicillium species appeared as prolific producers of such molecules, in particular P. ligerum or P. canescens (Tables 1 and S-9). Table 1. Compilation of the results obtained after treatment by the MeHaloCoA method of the 136 extracts profiles obtained from the 32 marine-derived fungal strains. strain code MMS n° 351 646 417 460 194 556 29 330 906 5 747 15 270 404 719 940 14 50 266 393 927 946 1513 231 42 770 797 850 854 1053 1058 1061

Number of with no hit in halogenated peaks databases 29 18 Penicillium ligerum 16 3 Penicillium sp. 12 5 Penicillium restrictum 10 6 Penicillium canescens 8 3 Penicillium canescens 7 5 Penicillium atramentosum 7 6 Penicillium citreonigrum 6 3 Penicillium ubiquetum 6 2 Penicillium radicum 6 5 Penicillium chrysogenum 5 1 Penicillium ligerum 4 3 Penicillium antarcticum 3 2 Penicillium bialowiezense 3 2 Penicillium brevicompactum 3 2 Phoma exigua 3 3 Pycnidiophora dispersa 2 2 Penicillium antarcticum 2 0 Penicillium venetum 2 1 Penicillium marinum 2 2 Penicillium sp. 2 0 Trichoderma atroviride 2 0 Chrysosporium queenslandicum 2 0 Trichoderma atroviride 1 1 Penicillium polonicum Penicillium expansum 0 Aspergillus 0 sp. Phoma exigua 0 Scopulariopsis 0 sp. Aspergillus 0 sp. Fusarium 0 sp. Cladosporium 0 sp. Fusarium 0 sp. Genus

species

Application: targeted purification and isolation of potentially unknown chlorinated compounds detected by MeHaloCoA. Among the different marine-derived fungal strains


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identified as potential producers of halogenated compounds, strain P. canescens MMS 460 was selected for a mass-directed purification of its potentially unknown chlorinated metabolites. Following large scale culture on semi-solid medium, the whole culture was repeatedly extracted with CH2Cl2/EtOAc (1:1, v/v) and then fractioned by silica gel vacuum column chromatography to produce eight subfractions (1-8). Most of the chlorinated compounds were grouped in fractions 4 and 5. MS-guided fractionation of these fractions involved normal phase flash chromatography and reverse phase HPLC, and led to the isolation of 3 compounds identified as the new natural products (+)-5-chlorogriseofulvin (1) and griseophenone I (2) and the known griseophenone G [0.4 mg (0.007% yield), 0.1 mg (0.002% yield) and 1.2 mg (0.02% yield), respectively ] (Figure 3). Spectroscopic data for griseophenone G were in accordance with the literature.31 Complete structural elucidation of 1 and 2 was accomplished via spectroscopic analysis as described below.

Figure 3. Structure of the isolated halogenated natural products

HRESIMS of 1 showed a [M+H]+ peak at m/z 387.0375 exhibiting an isotopic pattern comprised of m/z 387/388/389/390/391 (100:20:70:13:12 ratio), clearly indicating the presence of two chlorine atoms. The molecular formula was thus established as C17H16O6Cl2 (calcd for C17H17O6Cl2+, 387.0396), corresponding to 9 degrees of unsaturation. Analysis of the 1H NMR spectrum of 1 showed resonances characteristic of one methyl group (δ 0.84), three methoxy groups (δ 3.65, δ 4.00 and δ 4.06), two mutually coupled methine and methylene groups (δ 2.42, δ 2.64 and δ 2.89), and one ethylenic methine proton (δ 5.67). The majority of these resonances were similar to signals observed for griseofulvin (Figure S10),32 except the aromatic proton on position 5 in griseofulvin (δ 6.50) which was absent in compound 1. This suggested the replacement of the latter by a chlorine atom. Comprehensive analysis of 1 by 2D NMR, including HSQC, HMBC and COSY confirmed this hypothesis. As chloro-substituted griseofulvin analogs have already been described as synthetic products,33-38 and on the basis of its optical activity, UV, MS and NMR data compound 1 was identified as (+)-5chlorogriseofulvin, which is reported here as a natural product for the first time. The second new natural product 2 was analyzed for C17H16O6Cl2 by HRESIMS of the [M-H]- peak (m/z 385.0242; calcd for C17H15O6Cl2-, 385.0251). Both 1 and 2 showed very similar isotopic patterns, consistent with the presence of two chlorine atoms. However, ionization of 2 in the negative mode was clearly enhanced, suggesting the presence of an acidic proton. Analysis of 1H and 13C NMR revealed structural similarities between 2 and the isolated griseophenone G. Indeed, 2 showed one methyl group (δ 2.20), two aromatic protons (δ 6.29 and δ 6.31) and two methoxy groups (δ 3.60 and δ 3.94). Compared to griseophenone G, an additional methoxy group was observed at δ 3.33, suggesting that the hydroxy group of griseophenone G in position 5 was methylated in 2. Furthermore, griseophenone G and 2 showed similarities in their UV spectra and MS2 fragmentations. Effectively, both molecules

presented neutral losses of 32 and 47 amu in the negative mode and a common fragment at m/z 165.0544 in the positive mode which is derived from the shared aromatic ring. Due to the low amount of the compound available, it was not possible to observe signals for all carbons in 13C NMR spectrum. Additionally, despite many efforts in various solvents and using different delays corresponding to various coupling constants in HMBC experiments (8, 6, 4 Hz), carbons and heteronuclear correlations of the aromatic ring bearing the chlorines could not be detected. However, the signal identified as OH-1 based on its downfield chemical shift presented cross peaks with δC 159.8 and 115.5 which could be assigned to carbons bearing methoxy and chlorine substituents, respectively. On the other hand, clear HMBC correlations allowed to assigning the second aromatic ring and its connection to the ketone. Indeed it included cross peaks between aromatic methines and the carbonyl at δC 201.7 as well as between carbon at δC 109.5 and CH3 at δH 2.10 (Figure S-15). On this basis, 2 was identified as a new natural product for which the name griseophenone I is proposed. Interestingly, this series of compounds have mostly been isolated during griseofulvin biosynthesis investigations (Table S-16).31,39,40 Antiproliferative activity of the isolated chlorinated compounds. The three purified compounds were evaluated for their cytotoxic effects on KB cells. At the minimal concentration tested (0.6 µM), chlorogriseofulvin (1), griseophenone I (2) and griseophenone G showed 49%, 58% and 47% growth inhibition, respectively. This type of activity confirms the fact that halogenated compounds may provide interesting bioactive molecules. Moreover, this is in accordance with recent studies focusing on griseofulvin analogs for their antiproliferative activity.41

CONCLUSION In this study, a new software tool (freely available and accessible) was developed under R to identify halogenated compounds in HPLC-MS profiles. This is particularly relevant for toxicological surveys and for marine natural products chemists, as halogenated compounds can either be considered a threat for marine environments or as new pharmacological agents for human health. The developed method was optimized and validated on a series of halogenated standards. It was applied to marine fungal extracts and allowed the identification and subsequent isolation of three compounds exhibiting antiproliferative activities. Two of them, chlorogriseofulvine and griseophenone I, isolated from a marine-derived Penicillium canescens strain, were here identified as new natural compounds. As highlighted in this work, the MehaloCoA method should help in the automated detection of halogenated natural products, and in the rapid and effective selection of putative new halogenated metabolites which constitute a promising source in terms of biological activity.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Additional tables and figures (S-1 to S-16) are provided as a pdf file.

AUTHOR INFORMATION


Analytical Chemistry (17)

Corresponding Author

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*E-mail: [email protected]. Tel: +33 (0)2 51125686 / +33 (0)2 5348-4193.

(18)

Present Addresses §

LUNAM Université, Oniris, Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), 44307 Nantes, France.

Author Contributions All authors have given approval to the final version of the manuscript.

(19) (20) (21)

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This work was supported by a financial grant from the ChiMiMar program (Région Pays de la Loire). HPLC-HRMS experiments, bioinformatics tool development and data treatment were performed on the ThalassOMICS technical plateau (Plateforme Corsaire, Biogenouest). The authors also thank Arnaud Bondon and Sandrine Pottier for part of the NMR experiments (PRISM platform, UMR CNRS 6226) and Pierre Huneau for its contribution to the standard mix analyses.

REFERENCES (1) (2) (3) (4) (5) (6)

(7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

Gribble, G. W. Naturally occurring organohalogen compounds - A comprehensive update; Springer: Wien, 2010. Gribble, G. W. J. Chem. Educ. 2004, 81, 1441. Pauletti, P. M.; Cintra, L. S.; Braguine, C. G.; da Silva Filho, A. A.; e Silva, M. L. A.; Cunha, W. R.; Januário, A. H. Mar. Drugs 2010, 8, 1526–1549. Paul, C.; Pohnert, G. Nat. Prod. Rep. 2011, 28, 186–195. Vetter, W. Organohalogen Compd. 2002, 59, 271–274. Vetter, W. In Reviews of Environmental Contamination and Toxicology; Ware, G. W., Whitacre, D. M., Albert, L. A., de Voogt, P., Gerba, C. P., Hutzinger, O., Knaak, J. B., Mayer, F. L., Morgan, D. P., Park, D. L., Tjeerdema, R. S., Yang, R. S. H., Gunther, F. A., Eds.; Springer: New York, 2006; pp 1–57. Gribble, G. W. Mar. Drugs 2015, 13, 4044–4136. Newman, D. J.; Cragg, G. M. J. Nat. Prod. 2012, 75, 311– 335. Levine, D. P. Clin. Infect. Dis. 2006, 42 (Supplement 1), S5– S12. Harris, C. M.; Kannan, R.; Kopecka, H.; Harris, T. M. J. Am. Chem. Soc. 1985, 107, 6652–6658. Petit, K.; Biard, J.-F. Anti-Cancer Agents Med. Chem. Former. Curr. Med. Chem.-Anti-Cancer Agents 2013, 13, 603– 631. Newman, D.J. ; Cragg, G.M., Mar. Drugs 2014, 12, 255– 278. Gerwick, W. H.; Moore, B. S. Chem. Biol. 2012, 19, 85–98. Bugni, T. S.; Ireland, C. M. Nat. Prod. Rep. 2004, 21, 143– 163. Rateb, M. E.; Ebel, R. Nat. Prod. Rep. 2011, 28, 290–344. Piel, J. Nat Prod Rep 2009, 26, 338–362.

(22) (23) (24) (25) (26) (27) (28) (29)

(30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40) (41)

Page 8 of 9

Taylor & Francis Group. The Dictionary of Natural Products online 24.2, 2016, CRC Press http://dnp.chemnetbase.com (accessed Feb 24, 2016). Vansteelandt, M.; Blanchet, E.; Egorov, M.; Petit, F.; Toupet, L.; Bondon, A.; Monteau, F.; Le Bizec, B.; Thomas, O. P.; Pouchus, Y. F.; Le Bot, R.; Grovel, O. J. Nat. Prod. 2013, 76, 297–301. Blanchet, E.; Vansteelandt, M.; Le Bot, R.; Egorov, M.; Guitton, Y.; Pouchus, Y. F.; Grovel, O. Eur. J. Med. Chem. 2014, 79, 244–250. Bode, H. B.; Bethe, B.; Höfs, R.; Zeeck, A. ChemBioChem 2002, 3, 619–627. Gentleman, R. C.; Carey, V. J.; Bates, D. M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.; Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; Li, C.; Maechler, M.; Rossini, A. J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney, L.; Yang, J. Y.; Zhang, J. Genome Biol. [Online] 2004, 5, R80. Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779–787. Kuhl, C.; Tautenhahn, R.; Böttcher, C.; Larson, T. R.; Neumann, S. Anal. Chem. 2012, 84, 283–289. Laatsch, H. AntiBase 2011, The Natural Compound Identifier; Wiley-VCH: Weinheim, Germany, 2011. Denizot, F.; Lang, R. J. Immunol. Methods 1986, 89, 271– 277. Mosmann, T. J. Immunol. Methods 1983, 65, 55–63. Thurman, E. M.; Ferrer, I. Anal. Bioanal. Chem. 2010, 397, 2807–2816. Wehrens, R.; Weingart, G.; Mattivi, F. J. Chromatogr. B 2014, 966, 109–116. Smith, C. A. LC/MS Preprocessing and analysis with xcms 2016. https://bioconductor.org/packages/devel/bioc/vignettes/xcms /inst/doc/xcmsPreprocess.pdf (accessed Jul 12, 2016). Zhu, P.; Tong, W.; Alton, K.; Chowdhury, S. Anal. Chem. 2009, 81, 5910–5917. Cacho, R. A.; Chooi, Y.-H.; Zhou, H.; Tang, Y. ACS Chem. Biol. 2013, 8, 2322–2330. Levine, S. G.; Hicks, R. E.; Gottlieb, H. E.; Wenkert, E. J. Org. Chem. 1975, 40, 2540–2542. Petersen, A. B.; Rønnest, M. H.; Larsen, T. O.; Clausen, M. H. Chem. Rev. 2014, 114, 12088–12107. Takeuchi, Y.; Watanabe, I.; Misumi, K.; Irie, M.; Hirose, Y.; Hirata, K.; Yamato, M.; Harayama, T. Chem. Pharm. Bull. (Tokyo) 1997, 45, 2011–2015. Taub, D.; Kuo, C. H.; Wendler, N. L. J. Org. Chem. 1963, 28, 3344–3348. Green, G. F. H.; Page, J. E.; Staniforth, S. E. J. Chem. Soc. Resumed 1964, No. 0, 144–148. Arison, B. H.; Wendler, N. L.; Taub, D.; Hoffsommer, R. D.; Kuo, C. H.; Slates, H. L.; Trenner, N. R. J. Am. Chem. Soc. 1963, 85, 627–631. Ballantine, J. A.; Fenwick, R. G. Org. Mass Spectrom. 1969, 2, 1145–1169. Rhodes, A.; Boothroyd, B.; McGonagle, M. P.; Somerfield, G. A. Biochem. J. 1961, 81, 28–37. Qin, C.; Lin, X.; Lu, X.; Wan, J.; Zhou, X.; Liao, S.; Tu, Z.; Xu, S.; Liu, Y. J. Antibiot. (Tokyo) 2015, 68, 121–125. Liéby-Muller, F.; Heudré Le Baliner, Q.; Grisoni, S.; Fournier, E.; Guilbaud, N.; Marion, F. Bioorg. Med. Chem. Lett. 2015, 25, 2078–2081.


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Automated Detection of Natural Halogenated Compounds from LC-MS

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