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Metabolomic Approach Assisted High Resolution LCESI-MS Based Identification of a Xenobiotic Derivative of Fenhexamid Produced by Lactobacillus casei Jozsef Lenart, Erika Bujna, Bela Robert Kovacs, Eszter Bekefi, Leonora Szaraz, and Mihaly Dernovics J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf4022493 • Publication Date (Web): 24 Aug 2013 Downloaded from http://pubs.acs.org on August 25, 2013

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Journal of Agricultural and Food Chemistry

Metabolomic Approach Assisted High Resolution LC-ESI-MS Based Identification of a Xenobiotic Derivative of Fenhexamid Produced by Lactobacillus casei

József Lénárta,b, Erika Bujnac, Béla Kovácsb, Eszter Békefia, Leonóra Száraza, Mihály Dernovicsa*

a

Department of Applied Chemistry, Faculty of Food Science, Corvinus University of Budapest,

Villányi út 29-43, H-1118, Budapest, Hungary c

Faculty of Agricultural and Food Sciences and Environmental Management, Centre for

Agricultural and Applied Economic Sciences, University of Debrecen, Böszörményi út 138, H4032, Debrecen, Hungary c

Department of Brewing and Distilling, Faculty of Food Science, Corvinus University of Budapest,

Ménesi út 45, H-1118, Budapest, Hungary

Corresponding Author * E-mail: [email protected] Tel: +36 1 482 6161. Fax: +36 1 466 4272

1 Environment ACS Paragon Plus


1

ABSTRACT

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Fenhexamid is a widely used fungicide with one of the highest maximum tolerance limits approved

3

for fruits and vegetables. The goal of this study was to examine if fenhexamid is metabolized by a

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non-target organism, a Lactobacillus species (Lactobacillus casei Shirota), a probiotic strain of the

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human gastro-intestinal tract. The assignment of bacterial derivatives of the xenobiotic fenhexamid

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was substantially facilitated by a metabolomic software based approach optimized for the extraction

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of molecular features of chlorine-containing compounds from liquid chromatography – electrospray

8

ionization – quadrupole time-of-flight mass spectrometry data with an untargeted compound search

9

algorithm. After validating the software with a set of seventeen chlorinated pesticides and manually

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verifying the result lists, eleven molecular features out of 4363 turned out to be bacterial derivatives

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of fenhexamid, revealing the O-glycosyl derivative as the most abundant one that arose from the

12

fermentation medium of Lactobacillus casei Shirota in the presence of 100 µg/ml fenhexamid.

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KEYWORDS

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xenobiotics, LC-ESI-QTOF-MS, metabolomics, fenhexamid-O-glucoside

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INTRODUCTION

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The gastro intestinal tract (GIT) is highly exposed to xenobiotics since compounds from numerous

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sources can occur within. The intestinal tract is therefore particularly important in toxicology both

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as a target organ and as a site for xenobiotics absorption to the organism. The most important

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xenobiotic source is the oral ingestion of drugs, food components and environmental pollutants.1

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Additionally, reactive metabolites can also be formed through the activity of drug-metabolizing

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enzymes expressed by intestinal cells and finally the interaction of xenobiotics with the gut

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microbiota can also lead to the production of a significant amount of derivatives.2, 3 The microbial

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activity significantly affects the metabolism and bioavailability of various compounds from external

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origin, e.g., from food and drug compounds.4 This microbiome related source should be highlighted

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since the absorption in the distal gut results in around 10% of the metabolites in the host systemic

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blood flow from bacterial origin.5

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Regarding the human GIT, lactic acid bacteria are among the abundant species.6 The most common

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genera are Lactobacillus, Streptococcus and Enterococcus. Besides their well established role in

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food industry, Lactobacilli have gained attention for their probiotic properties7 and the production

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of a wide variety of enzymes: intracellular β-galactosidase production of L. acidophilus,8 β-

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glucoronidase-like activity in more Lactobacillus species9-11 and oleuropeinase and β-glucosidase

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activities in L. plantarum12, 13 were already observed in previous studies.

35

Fruit and vegetable consumption is an important part of a healthy diet and as a consequence

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fungicides and their residues on the surface of these products may represent a significant source of

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xenobiotics and precursors of xenobiotics in the gut. There are huge public concerns about the

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extensive utilization of pesticides including fungicides since potential harmful effects on the human

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health are attributed to these bio-active chemicals.14,

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applied microbiota in dairy and meat production were presented in previous studies where it was

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concluded that in general the reproduction of lactic acid bacteria was hindered and their metabolism

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was also changed (however to different extent).16, 17 It is also noticeable lactic acid bacteria used in

15

The effects of relevant pesticides on the



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meat production could degrade the pesticides in their environment, which positively influenced the

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quality of the product.18

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Concerning the generally applied pesticides, special emphasis should be attributed to fenhexamid

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(N-(2,3-dichloro-4-hydroxyphenyl)-1-methylcyclohexane carboxamide; C14H17Cl2NO2; CAS No.

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126833-17-8). Fenhexamid is a low water soluble hydroxy-anilide fungicide and is applied

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worldwide in case of numerous horticultural crops, fruits and vegetables.19 According to the actual

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regulation it is a fungicide with one of the highest maximum tolerance level (MTL) / maximum

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residue limit (MRL) value allowed even up to 30 mg/kg (for chervil, chrysanthemum, coriander,

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corn salad, cress, sorrel, endive, lettuce, orach, parsely, purslane, red chicory, spinach) and 40

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mg/kg (for lettuce) in the US and in the EU, respectively. Moreover, there has been a recent opinion

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published by the European Food Safety Authority (EFSA) allowing to raise the MRL value of

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fenhexamid in the case of some fruits.19

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The information about the effects of fenhexamid on non-target organisms is extremely scarce

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however fenhexamid was introduced in 1998.20 Facing the high impact of xenobiotics on human

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microbiota21 and the occurrence of fenhexamid residues on fruits and vegetables,22, 23 we decided to

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investigate in vitro the possible detoxification or metabolized products of fenhexamid produced by

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L. casei. However, the assignment of supposedly low abundant arising compounds in complex

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sample matrices is extremely difficult to carry out without a metabolomic software assisted

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approach requiring high resolution chromatographic and mass spectrometric techniques (LC-ESI-

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QTOF-MS or LC-ESI-Orbitrap-MS). This approach has been successfully applied to reveal the

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metabolomic aspect of the symbiosis of leguminous plants and rhizobial bacteria24 and to

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characterize the effect of beta-lactam antibiotics on the microbiome of exposed rat models.25

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Therefore, in order to cover the highest possible number of fenhexamid derived xenobiotics of

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bacterial origin, our study was based on metabolomic data mining.

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MATERIALS AND METHODS

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Reagents

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Fenhexamid was obtained by the chromatographic clean-up of Teldor® (Bayern Cropscience,

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Monheim am Rhein, Germany) commercial pesticide product.26 Fifteen other, also chlorine

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containing

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fenbuconazole, haloxyfop, imazalil, linuron, metconazole, prochloraz, pyraclostrobin, tebuconazole,

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tetraconazole and thiacloprid) and formic acid were obtained from the Sigma–Aldrich group (St.

76

Louis, MO, USA). Penconazole was ordered from Dr. Ehrenstorfer Gmbh (Augsburg, Germany),

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HPLC gradient grade acetonitrile (ACN) and ethanol were ordered from Fisher Scientific (VWR,

78

Radnor, PA, USA). Ultra pure water (>18 MΩ cm−1) was obtained from a Millipore Milli-Q system

79

(Bedford, USA).

pesticides

(acetamiprid,

boscalid,

dichlorvos,

difenoconazol,

epoxiconazole,

80 81

Chemicals

82

Fenhexamid-O-glucoside (purity >98%; CAS No. 1392231-43-4) was synthesized according to the

83

protocol described by Polgár et al.26 Fenhexamid stock solutions (~4000 mg L−1) were prepared in

84

pure ethanol and were stored at -18 °C until analysis.

85 86

Maintenance of the cell cultures

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L. casei Shirota species originated from the National Collection of Agricultural and Industrial

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Microorganisms (NCAIM, Budapest). The cells were kept on traditional MRS slants and were sub-

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cultured before conducting the experiments to reach the appropriate vitality of the cultures. One

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hundred µl volumes of cell cultures were subsequently inoculated into 20 ml MRS broths

91

containing 20.0 g D-glucose L-1 (pH=6.5, Himedia M369; Himedialabs, Mumbai, India) at 37 °C.

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Fenhexamid standard stock solutions sterilized by filtration through 0.22 µm disposable PTFE

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filters of 4000 µg/ml concentration (25, 200 and 500 µl) were added to the three separate broths (20

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ml) to set the 5, 40 and 100 µg/ml final fenhexamid concentrations, respectively. Ethanol



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concentration was set in all samples to 2.5 % v/v. The broths were kept for 72 hours at 37 °C. Cell

96

density was determined by OD600 measurements using Thermo Scientific Ecolution 300 EB

97

(Thermo Scientific, Bellefonte, PA, USA) spectrophotometer.

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An additional cell-free control sample was also set, containing 100 µg/ml fenhexamid in MRS

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broth, 2.5 % v/v ethanol.

100 101

Sample preparation

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Samples were collected in sterile 2.0 ml Eppendorf tubes after 24, 48 and 72 hours then the cells

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were separated by centrifugation at 10,000×g at 4 °C in a Hettich Mikro 22R centrifuge (Tuttlingen,

104

Germany). Ten times dilutions were performed on the collected supernatants adjusting a final ACN

105

concentration of 5 % v/v. Before injection the samples were filtered through a disposable 0.45 µm

106

PTFE syringe filter.

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The remaining ~14 ml samples were stored at -18 °C and used to perform solid phase extraction

108

(SPE) enrichment procedure. The enrichment was performed on Waters Sep-PaK® Vac C18 3cc

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disposable cartridges (Waters, Milford, MA, USA) that were applied on a Thermo Scientific

110

Hypersep 16-port SPE vacuum manifold (Thermo Scientific) equipped with an oil free vacuum

111

pump. For the enrichment 5 ml supernatants were collected by centrifugation from the remaining

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~14 ml samples. In order to keep the octadecyl groups of the SPE cartridges unfolded (active), 1 ml

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ACN was added to the 5 ml sample leading to 16.7% v/v ACN concentration. Steps of the

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enrichment process are shown in the Supporting Information (SI - Table 1).

115

The concentrated samples were then evaporated under argon flow to total dryness and resuspended

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in 400 µl 20% v/v ACN:H2O. Before injection the samples were filtered through a 0.45 µm PTFE

117

syringe filter.

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HPLC-ESI-MS analyses

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The HPLC–ESI-MS couplings were carried out with an Agilent 1100 HPLC system (Agilent

123

Technologies, Palo Alto, CA, USA). The samples were dissolved in 20% v/v ACN:H2O and 10 µl

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was injected on a Zorbax Eclipse XDB-C18 reversed phase column (3.5 µm, 2.1 mm x 50 mm;

125

Agilent). The HPLC flow rate was set to 400 µl/min. For the chromatographic gradient, solution A

126

was water with 0.1% v/v formic acid, and solution B was ACN. Gradient elution was set as follows:

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0–3 min 5% B; 3–7 min up to 9% B; 7–10 min 9% B, 10-20 min up to 20% B; 20–22 min 20% B,

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22-40 min up to 100% B; 40–42 min 100% B, 42-43 min down to 5% B; 43–47 min 5% B.

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For the full scan screening of the samples, a 6530 Accurate Mass QTOF LC/MS system (Agilent)

130

was applied. The QTOF-MS instrument was operated with an Agilent 6220 instrument derived dual

131

ESI ion source in positive and negative ionization modes. Mass calibration was performed by

132

introducing a mass calibration solution by direct infusion according to the manufacturer’s

133

instructions. The instrument performed automatic continuous mass correction by introducing a

134

reference solution. This solution contains internal reference masses purine (C5H5N4/[M+H]+/at m/z

135

121.050873, /[M-H]-/at m/z 119.036320) and HP-0921 ([hexakis-(1H,1H,3H-tetrafluoropentoxy)-

136

phosphazene] (C18H19O6N3P3F24)/[M+H]+/at m/z 922.009798, /[M+HCOO]-/at m/z 966.000725.

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Due to the continuous mass correction mass accuracy was ~3 ppm and mass resolution was above

138

10,000 (FWHM) at m/z 400. The instrumental parameters and settings are summarized in the

139

Supporting Information (SI - Table 2).

140 141

Data mining strategy: seeking for chlorine containing metabolites

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The full-scan data recorded was processed with Mass Hunter Qualitative Analysis software

143

(Version B.06.00; Agilent) and untargeted compound search (general unknown screening) was

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performed by the Molecular Feature Extraction algorithm (MFE). During MFE the software

145

annotates the detected ions to create molecular features. Ions are grouped together to create a given

146

molecular feature whose extracted ion chromatograms (EIC) share similar elution profiles and



147

additionally their mass differences can be explained via isotope ratios, adduct or dimer formations

148

or neutral losses and in-source fragmentation events. The software takes into consideration user

149

defined parameters regarding mass accuracy, mass resolution, minimal signal and compound

150

intensities, minimum number of ions per compound etc.

151

According to the known degradation and detoxification pathways of fenhexamid,27-30 the

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intermediates and by-products still possess the chlorine containing residue, therefore searching for

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chlorinated molecules may give a hint to find fenhexamid related compounds. As chlorine has two

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abundant isotopes, Cl35 and Cl37, the chlorine containing compounds share a characteristic isotopic

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pattern with a distinctive, intensive M+2 isotope. Additionally, there is a mass defect (defined as the

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difference in exact mass between the masses of the isotopes of an element minus the integer mass

157

difference, where the reference isotope is the monoisotopic mass)31 caused by Cl37 isotope giving an

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optional remark to the identification of chlorine containing compounds.

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By the settings of molecular formula generation at least one chlorine atom was set up as an

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obligatory element which enabled to focus only on the chlorine containing compounds and filter out

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the others. Considering the mentioned marks of chlorine content of the detected compounds

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molecular formulae were generated to determine their elemental composition.

163

In order to find the optimal settings of MFE for assigning chlorine containing metabolites, a 10

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ng/ml pesticide standard mix of the 17 different, chlorine containing pesticides was prepared.

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During optimization, the isotope spacing tolerance parameter was reduced from the default value (5

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mDa + 7 ppm) to 2 mDa + 3 ppm. As a result, MFE could assign all of the 17 pesticides correctly

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by detecting at least four isotopologues per pesticide.

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MFE may result in false positive and negative hits even after optimization, consequently each

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differential chlorine containing compound was verified manually considering the isotopic pattern,

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mass defect and the shape of correlating EICs.

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Differential expression analysis

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Differential expression analysis of the samples after processing by Mass Hunter Qualitative analysis

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was performed by Mass Profiler Professional software (Version 2.1.5; Agilent). Among the

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assigned molecular features the non-differentiating ones between the experimental and control

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samples were filtered out by the following statistical methods: filter by frequency, t-test (p1000

2.5

Polgár et al.26

footnote: n.a. = not applicable



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FIGURE GRAPHICS x 107

(a)

1 0.5 x 106 2

(b)

1 x 107 4

(c)

2 x 106 2

(d)

1 5

10

15

20 25 30 Counts vs. Acquisition Time (min)

Figure 1


35

40

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x 106 1.5 464.1252

x 106 302.0727 2

(a)

304.0698

x 106 2

1

(b)

466.1226

x 106

x 106 2 302.0723

1.5

464.1253

304.0694

x 105

1

0.5

466.1225 0.5

5 302

1

305

308

464

467 470

302

2.5 150

150 350 550 750 950 1150 1350 1550 1750 Counts vs. Mass-toMass-to-Charge (m/z) x 106 (c) 2

x 105 4

EIC of m/z 302.0727 EIC of m/z 464.1252

350

305

308

464

467 470

550 750 950 1150 Counts vs. Mass-toMass-to-Charge (m/z)

1350

EIC of m/z 302.0723 EIC of m/z 464.1253

(d)

2

1

26

26.5 27 27.5 Counts vs. Acquisition Time (min)

x 104 (e)

28 x 10 4 8

302.0709

4 97.1017

2

26

4

55.054 55.0549 50

100

26.5 27 27.5 Counts vs. Acquisition Time (min)

(f)

28

302.0699

97.1011 55.0552

150 200 250 300 350 400 Counts vs. Mass-toMass-to-Charge (m/z)

450

50

100

150

200 250 300 350 400 Counts vs. Mass-toMass-to-Charge (m/z)

Figure 2


450


TABLE OF CONTENTS GRAPHICS


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