Monitoring metabolite production of aflatoxin biosynthesis by Orbitrap

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Monitoring metabolite production of aflatoxin biosynthesis by Orbitrap Fusion mass spectrometry and D-optimal mixture design method Huali Xie, xiupin wang, Liangxiao Zhang, Tong Wang, Wen Zhang, Jun Jiang, Perng-Kuang Chang, Zhi-yuan Chen, Deepak Bhatnagar, Qi Zhang, and Peiwu Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03703 • Publication Date (Web): 16 Nov 2018 Downloaded from http://pubs.acs.org on November 18, 2018

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Analytical Chemistry

1

Monitoring metabolite production of aflatoxin biosynthesis by Orbitrap

2

Fusion mass spectrometry and D-optimal mixture design method

3

Huali Xie§1,2,3, Xiupin Wang§1,3,4,5, Liangxiao Zhang1,3,4,5, Tong Wang1,2,3, Wen

4

Zhang1,3,4,5, Jun Jiang1,3,4,5, Perng-Kuang Chang6, Zhi-Yuan Chen7, Deepak

5

Bhatnagar6, Qi Zhang1,2,3*, Peiwu Li1,2,3,4,5*

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1. Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan,

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430061, People’s Republic of China.

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2. Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of

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Agriculture, Wuhan, 430061, People’s Republic of China.

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3. Key laboratory of Detection for Aflatoxins, Ministry of Agriculture, Wuhan, China.

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4. Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of

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Agriculture, Wuhan, 430061, People’s Republic of China.

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5. Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture,

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Wuhan, 430061, People’s Republic of China.

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6. Southern Regional Research Center, Agricultural Research Service, US Department

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of Agriculture, New Orleans, LA, 70124, USA.

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7. Department of Plant Pathology and Crop Physiology, Louisiana State University

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Agricultural Center, Baton Rouge, LA, 70803, USA.

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§These authors contributed equally to this work.

20 21

AUTHOR INFORMATION

22

Corresponding Author

23

*(Q.Z.) E-mail: [email protected]

24

*(PW.L.) E-mail: [email protected].

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Abstract: Aflatoxins, highly toxic and carcinogenic to humans, are synthesized via

27

multiple intermediates by a complex pathway in several Aspergilli including

28

Aspergillus flavus. Few analytical methods are available for monitoring the changes in

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metabolite profiles of the aflatoxin biosynthesis pathway under different growth and

30

environmental conditions. In the present study, we developed by D-optimal mixture

31

design a solvent system, methanol/dichloromethane/ethyl acetate/formic acid

32

(0.36/0.31/0.32/0.01), that was suitable for extracting the pathway metabolites. Matrix

33

effect from dilution of cell extracts was negligible. To facilitate the identification of

34

these metabolites, we constructed a fragmentation ion library. We further employed

35

liquid

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(UHPLC-HRMS) for simultaneous quantification of the metabolites. The limit of

37

detection (LOD) and limit of quantitation (LOQ) were 0.002-0.016 μg/kg and

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0.008-0.05 μg/kg, respectively. The spiked recovery rates ranged from 81.3% to 100.3%

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with intra-day and inter-day precision to be less than 7.6%. Using the method

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developed to investigate time-course aflatoxin biosynthesis, we found that precursors,

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including several possible toxins (with a carcinogenic group similar to aflatoxin B1)

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occurred together with aflatoxin, production increased rapidly at the early growth

43

stage, peaked on day four and then decreased substantially. The maximum production

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of aflatoxin B1 and aflatoxin B2 occurred one day later. Moreover, the dominant

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branch pathway was the one for aflatoxin B1 formation. We revealed that the

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anti-aflatoxigenicity mechanism of Leclercia adecarboxylata WT16 was associated

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with a factor upstream of the aflatoxin biosynthesis pathway. The design strategies

chromatography

coupled

with

high-resolution


mass

spectroscopy

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can be applied to characterize or detect other secondary metabolites to provide a

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snapshot of the dynamic changes during their biosynthesis.

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Key work: Aflatoxin, Biosynthesis pathway, Orbitrap mass spectrometer, D-optimal

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mixture design, Aspergillus flavus.

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INTRODUCTION

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Aspergillus flavus produces notorious carcinogens known as aflatoxins in

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susceptible crops. Consumption of food products contaminated with aflatoxins can

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lead to liver cancer, stunted growth in children and acute poisoning1,2. The occurrence

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of aflatoxins is a huge human health concern and has caused significant economic

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losses around the world3. Research on aflatoxins in terms of detection4 and risk

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assessment5 has been performed since the outbreak of Turkey X disease in the 1960s.

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Studies on aflatoxin biosynthesis including its regulation6,7 and structure of the

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aflatoxin gene cluster have attracted attention of many scientists since 1980s. The

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complete aflatoxin biosynthesis pathway involves over 20 genes in a 75 kb gene

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cluster and dozens of intermediates have been identified3,6,7. This pathway has been

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characterized in detail and it has become a roadmap to research secondary metabolism

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in eukaryotes and to explore biosynthesis of other mycotoxins3.

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Several pre-harvest control strategies have been proposed to prevent and reduce

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aflatoxin contamination of crops. Targeting the genes encoding enzymes involved in

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aflatoxin biosynthesis pathway has received considerable interest as a potential

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strategy to reduce aflatoxin contamination in susceptible crops8,9. Other strategies

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include suppression of growth of toxigenic fungal strains by utilizing atoxigenic A.

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flavus strains, biocontrol microbes10, and enhancing host resistance11. In addition,

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post-harvest control of fungal growth and aflatoxin production by controlling storage

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conditions or applying natural inhibitory agents (for example, plant volatile

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compounds)12 has been proposed. These applications have achieved certain levels of

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success. Although researchers are able to use real-time PCR technique10 and

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transcriptomic analysis13 to infer at the molecular level the possible anti-toxigenic and

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antigrowth mechanisms. However, the targets of these effective mechanism still are

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not well characterized. Obviously, both mechanisms can also affect cellular processes

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and change production of the metabolites in the aflatoxin biosynthesis pathway. Up to

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now, there is no method that can simultaneously quantify aflatoxins and the pathway

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intermediates efficiently and reliably. Development of such a high-resolution method


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will contribute to in-depth studies of cellular compartmentalization14 and aflatoxin

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transport mechanism15.

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Owing to the extremely low levels of aflatoxin pathway metabolites accumulated

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inside the fungal cells, any proposed detection method must greatly improve

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analytical sensitivity. In the so-called classical optimization method, only one variable

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is changed while other variables are fixed. Such design ignores interaction effects

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between process variables. This drawback can be remedied by use of optimal

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experimental design. Estimation of all solvent proportions on extraction efficiency of

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aflatoxin pathway metabolites has become experimentally feasible. Statistical

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experimental design, such as response surface design, has been applied in studying

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pharmaceuticals or food16,17. Mixture design, especially D-optimal mixture design,

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gives greater insights than the classical optimization design in regard to the optimal

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condition of each factor and its interaction with other variables. Several studies on

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detection of aflatoxins have been reported4,18. However, literature on investigating

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solvent interaction effects in the extraction of polyketide metabolites for improving

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the compound response intensity is scarce. In this context, a multi-response

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optimization approach to achieve the high sensitive detection of various intermediates

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in the aflatoxin biosynthesis pathway was developed to address this gap of

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knowledge.

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Here, we developed a method that combined liquid chromatography with

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high-resolution mass spectroscopy (UHPLC−HRMS). Because of rapid19 turnover of

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metabolites, we used a fast quenching technique20 to minimize perturbation of the

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metabolites before disruption of hyphal wall structure for solvent extraction. A

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reverse-phase chromatography column was employed to separate metabolites.

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Electrospray ionization (ESI) and a high-energy collision-induced dissociation (HCD)

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were performed with an Orbitrap Fusion mass spectrometer to analyze the metabolites.

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We showed that the positive/negative ion switching method of Orbitrap mass

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spectrometry and the fragmentation tree technology were ideal for selectively

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monitoring metabolite production in the aflatoxin biosynthesis pathway via analyses

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of complex cell extracts. Lastly, we demonstrated the applicability of our approach by



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examining time-course metabolite changes during aflatoxins biosynthesis and by

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revealing the anti-aflatoxigenic mechanism of the biocontrol bacterium Leclercia

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adecarboxylata WT 16 on aflatoxin production by A. flavus.

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MATERIALS AND METHODS

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Chemicals and materials

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Methanol (MeOH) and acetonitrile (ACN), both LC-MS grade, were obtained

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from Fisher Chemicals Co. (New Jersey, USA), whereas HPLC-grade MeOH and

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ACN, Ethyl acetate (EtOAc), Dichloromethane (DCM) were purchased from J&K

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Chemicals (Beijing, China). Sigma-Aldrich (Bornem, Belgium) supplied ammonium

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formate (HCOONH4) and Formic acid (HCOOH). Ultrapure H2O was produced by a

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Milli-Q Gradient System (Millipore, Brussels, Belgium). Detailed information of

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standards in this study was provided in Supporting Information.

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Strains and cultivation

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Aspergillus flavus 73, a toxigenic strain, was isolated from raw peanuts in

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Shangdong, China. Leclercia adecarboxylata WT16 was isolated from soil from

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peanut ground in Hubei, China. Detailed descriptions of the cultivation conditions are

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available in Supporting Information.

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Release intracellular metabolites

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To disrupt hyphal wall structures for liberating the maximum number and

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amounts of metabolites in their original state, we used organic solvents along with

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mechanical disruption methods of ultrasonics and grinding. Each mycelial sample

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(100 mg, 5 replicates) was dissolved in 2.0 mL of the solvent mixtures listed in Table

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S-1. The sample was vigorously vortexed for 30s, homogenized in a ball mill for 4

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min at 45Hz, incubated in ice water and ultrasound treated for 10 min, and filtered to

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separate the solution from the mycelial pellet. The crude extracts filtered through a

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0.22 µm membrane filter were then centrifuged at 13,000 g/min. The supernatant was

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transferred to vials of an autosampler. Lastly, the extract was diluted 4 times and 2 µl

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sample was injected into the UHPLC-HRMS system.

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Optimizing the extraction solvents by D-optimal mixture design


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Optimizing extraction efficiency greatly improves the range and quantitative

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capacity of recent studies21. Four commonly used organic solvents, methanol (MeOH),

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dichloromethane (DCM), acetonitrile (ACN), and ethyl acetate(EA), were collocated

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by the design of experiments(DOE) for the extraction of metabolites of the aflatoxin

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pathway. Selection of these solvents was based on pertinent literature studying

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mycotoxin detection4,22,23 and a review24on extraction of polar metabolites, where

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solvents with different XlogP values have different selectivity characteristics. A

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D-optimal mixture experimental design was employed to examine the effect of

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different extraction solvents. A total of 20 mixture reagents were prepared as shown

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in Table S-1(Because of the run order of 1 and 3; the run order of 7 and 13; the run

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order of 14 and 15; the run order of 10 and 17; and the run order of 8 and 19 are the

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same, there are actually only 15 mixed solvents). Experimental design calculations

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were performed with Expert design 8.0.6 (Stat-ease, USA) software.

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UHPLC-HRMS data acquisition

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The chromatographic separation was carried out on an Ultimate 3000 system, a

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column oven equipped with a switch valve, and an autosampler (Dionex, Sunnyvale,

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CA, USA). A C18 column (Hypersil Gold, 100 mm×2.1 mm (i.d.,3µm), Thermo Fisher

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Scientific, USA) was used. LC conditions and MS parameters were described in detail

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in the Supporting Information.

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Fragmentation ions trees as qualitative strategy

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Metabolite identification is a fundamental step in converting raw data into

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biological context. We have obtained various aflatoxin biosynthetic pathway

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intermediates to be used as standards. According to the metabolomics standards

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initiative (MSI)25, the first level of identified compounds includes obtained standard

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metabolites to avoid incorrect estimation of the number of unknown peaks detected in

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the sample. The third level of putatively characterized compound classes as a kind of

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strategy to identify the cannot obtain standard compounds or incapable searched by

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literature and database via mass spectral fragmentation ions analysis. Based on this

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recommendation, we constructed a small mass spectrometry database and studied the



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fragmentation patterns of these compounds as a qualitative strategy for this study. The

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mass spectral library includes compound name, chemical formula, the accurate mass

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of precursor ions, chromatography retention time, and characteristic fragment ion

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masses of the metabolites. We use a 1000ng/mL mixture standard solution and sample

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of hyphal to collect these chromatographic and mass data on the UHPLC-HRMS

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system under optimized instrument conditions. After that, a mass spectral library

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containing both precursor ions and all characteristic fragment ions was constructed. It

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could be used as a principal method of metabolite identification using a comparison of

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high-resolution mass spectrum. The mass fragmentation trees were computationally

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generated by the software of SIRIUS 426,27. The compound lacking in authentic

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standards was compared with analogue compound in this biosynthesis using the

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similarity of mass fragmentation trees to putatively annotate.

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Methods validation

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To ensure that the results were reliable before its application, the current method

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was validated by the limit of detection (LOD), limit of quantification (LOQ),

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precision, linearity, accuracy, and matrix effect. The LOD and LOQ were estimated

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for a signal-to-noise (S/N) ratio of more than 3 and 10, respectively. The precision of

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the method was calculated as RSD% of measurements in quintuplicate on the same

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day and three nonconsecutive days. Linearity was evaluated using standard solutions

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calibrations via analyzing in triplicate six concentrations levels. The accuracy was

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evaluated by recovery experiment in cell samples. The recovery experiments were

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carried out by spiking the sample in five replicates at two concentration levels:

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50ng/ml and 100ng/ml level (Table S-4). Owing to the presence of endogenous

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compounds in mycelium, the content of endogenous compounds in cells should be

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reduced to a minimum as much as possible by a 45 °C experiment condition, in which

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production of lipid continued unhindered but the formation of aflatoxin was

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suppressed. Matrix effect (ME%) was evaluated by establishing calibration curves in

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the pure solvent and in a blank extract. If one defines the peak areas obtained in neat

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solution standards as A(std), the corresponding peak areas for standards spiked after


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extraction into mycelium extracts as A(matrix + std), the matrix only as A(matrix),

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The matrix effects were expressed and calculated as follows:

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 A（matrix + std）− A(matrix)    100 % . A( std )  

ME% = 

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RESULTS AND DISCUSSION

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D-optimal mixture design results

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In the present study, the extraction efficiency in different proportions of organic

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solvents was evaluated. We used Expert design 8.0.6 (Stat-ease, USA) to construct

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mathematical models, performed statistical analyses (ANOVA) and optimized the

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results. The special cubic mixture model was developed based on four independent

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variables. Final equation as shown in supporting information of equation (1).

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Table S-2 shows that the special cubic mixture model is significant. The lack of

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fit is not significant as the p-value was 0.34. Hence, this model can be used for

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prediction. The variables of AB, AD, and ABD are highly significant (p-value was

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0.01) and AC and BC are significant (p-value < 0.05). The normal plot of residuals

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confirmed the normality assumptions and independence of the residuals (Fig 1 d). It

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also indicated that there is nearly no serious violation of the assumptions underlying

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the analyses17.

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Fig 1(a,b,c), show the effects of solvent interactions on the extraction of metabolites.

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A positive interaction was found between MeOH and DCM but a slightly negative

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interaction was found between DCM and ACN (Fig 1a). Similarly, a positive

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interaction was found between MeOH and EA and a negative interaction between

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MeOH and ACN (Fig 1b). MeOH，DCM and EA apparently have a synergistic effect

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on extraction of the metabolites(Fig 1c). Further optimization of solvent combination

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on metabolite extraction via model prediction (Fig 1d) concluded that the optimal

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extraction solution was MeOH: DCM: EA: HCOOH (36: 31: 32: 1, v/v/v/v). The

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developed solvent system was found to be superior to 4 other commonly used solvent

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systems (Fig S-1). Hence, it was employed as the extraction solvent system in

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subsequent experiments.



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The pathway fragmentation ions trees.

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The library of high-resolution fragmentation ions of aflatoxin pathway metabolites

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was established by standards and samples of mycelium. The aflatoxin biosynthesis

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pathway

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(http://www.genome.jp/kegg/pathway.html). However, not all these metabolites are

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readily available. In this study, the pathway metabolite high-resolution fragmentation

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ion library was established using commercial standard compounds and metabolites

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purified from mycelia. In total, 19 metabolites were analyzed (see Fig S-2). Standards

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of NOR, AVN, HAVN, AVF, VERB, VERA, ST, OMST, AFB1, AFB2, AFG1, AFG2,

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were used to acquired chromatography and mass spectrum information. However, no

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information is available from literature or database search for OAVN, DHOMST,

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DHST, HVN, VHA, VOAc. Figure S-2 shows that there was no interfering peak in the

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chromatograms for 15 compounds except for OAVN, HVN, VHA and VOAc. These

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four metabolites were putatively identified by analyses of retention time,

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high-resolution MS1 exact mass and fragmentation patterns (these information was

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presented in Table S-3, Figure 2 and Figure S-3,4). Table S-3 shows mass information

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of the 19 metabolites. Figure 2 and Figure S-4 shows the MS/MS spectrum and

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fragmentation trees of each endogenous metabolite as well as their fragmentation

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pattern comparison. Figure S-3 demonstrate the possible characteristic fragment

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structure of these fragments. In general, we used a standard compound as a reference

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to infer the identification of compounds without the standard by mass spectrometry

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fragmentation behavior, and recorded its fragment characteristic ion map for

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subsequent qualitative experiments.

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Method validation results

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The method was validated via analyzing 12 pathway metabolites (Table 1). The

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validation parameters included the linear range, limit of detection (LOD), limit of

253

quantification (LOQ), precision, accuracy, and matrix effect. The results showed that

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LOD and LOQ ranged from 0.002-0.1 μg/kg, 0.008-0.35 μg/kg, respectively.

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Correlation coefficients (R2) were higher than 0.9984 for pathway metabolites,

contains

24

compounds

in

KEGG


pathway

database

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256

indicating good linearity. The intra- and inter-day precision were 2.0–4.4% and

257

4.5–7.6%, respectively. It shows that the method is robust and suitable for detection of

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the metabolites in mycelia. As an example, the matrix effect was estimated using

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AFB1, AFB2, AFG1, AFG2, OMST, and ST (Table S-4). In order to reduce the matrix

260

effect, the extracts were diluted to 4 fold28. The matrix effect ranged from 88.3% to

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100.9%, which means that dilution of the mycelial sample extracts has negligible

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matrix effect on extraction of these metabolites. The recovery ranged from 81% to

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100.1% at the level of 50 μg/kg and the recovery ranged from 83% to 100.3% at the

264

level of 100 μg/kg.

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METHODS APPLICATION

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The time course changes of aflatoxin biosynthesis

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To demonstrate that the method is suitable for detecting dynamic changes of

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metabolite production during aflatoxin biosynthesis, we analyzed time-series A.flavus

269

mycelial samples (Fig S-5). The results showed that small amounts of precursors and

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the final products of AFB1 and AFB2 were synthesized on day one. The production of

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precursors increased rapidly, peaked on day four with the exception of versicolorin A

272

(Fig S-5a) and decreased greatly thereafter. In contrast, the maximum production of

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AFB1 and AFB2 was on day five (Fig S-5d). Taken together, it indicates that A. flavus

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converts most of the precursors to aflatoxins during active growth and stationary

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phases. Likely, most of the aflatoxins produced are transported out of cells29 because

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of their toxicity. A portion of aflatoxins may be converted to other compounds10,30 in

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cellular metabolism or degraded by some intracellular enzymes31, for instance, laccase.

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When intracellular aflatoxins were reduced to low levels, cells may start accumulating

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aflatoxins as seen in the period of day 8 to day 10 (Fig S-5d). The results also

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confirmed that the branch pathway that leads to aflatoxin B1 formation is dominant

281

and more efficient than the aflatoxin B2 branch pathway. This notion is supported by

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the extremely low amounts of detected VERA (Fig S-5a) and ST (Fig S-5c) in

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mycelia; both are precursors of aflatoxin B1(Fig S-5). The developed method may

284

also be applied to other secondary metabolites to provide a snapshot of the dynamic



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Page 12 of 27

changes during their production.

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As a conclusion, using the developed method, we found the amounts of

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precursors including several possible toxins (such as sterigmatocystin (ST) and

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O-methylsterigmatocystin (OMST) with a carcinogenic group similar to aflatoxin B132)

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occurred together with aflatoxin. However, these precursor toxins are often

290

overlooked in food safety monitoring.

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Target analysis of anti-toxigenic mechanisms

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L. adecarboxylata WT16, a biocontrol strain, produced yet-to-be characterized

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metabolites that inhibited aflatoxin production. We used the developed method to

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reveal the involved anti-aflatoxigenic mechanism. A. flavus grown in different

295

portions (0%, 50%, and 100%) of WT16 culture broth produced different levels of the

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17 examined metabolites (Fig 3) (This strain does not produce aflatoxin G1 and G2).

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The inhibition appeared to be dose dependent. The heatmap and boxplot show that the

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aflatoxin biosynthesis pathway was completely blocked when 100% WT16 culture

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broth was added. These results suggest that WT16 suppresses a key target (factor) in

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the upstream of the aflatoxin biosynthesis pathway. Aflatoxin pathway-specific

301

regulators such as AflR and AflJ33and other global regulators, for example,

302

components of the Velvet complex34 and developmental regulators35,36 may be

303

potential targets.

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CONCLUSIONS

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Our

method

simultaneously quantified

aflatoxin

biosynthesis

pathway

306

metabolites in a single assay in unprecedented detail. The reported high-resolution

307

fragmentation ion data will contribute greatly to future research that involves

308

characterization of these metabolites. The D-optimal mixture design optimized the

309

extraction solvent system, which allows capture of an intact metabolite profile that

310

reveals the dynamic changes of the biosynthesis pathway. The merits of the method

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included (1) high sensitivity, that is, limit of detection (LOD) and limit of quantitation

312

(LOQ) were 0.002-0.016 μg/kg and 0.008-0.05 μg/kg, respectively; (2) matrix effect

313

was not significant by dilution of the cell extracts; and (3) recoveries and intra-day


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and inter-day precision was satisfactory. Using this method, we found that precursors

315

were produced at the early growth stage and were efficiently converted to aflatoxins.

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We also confirmed that the branch pathway to aflatoxin B1 formation is the dominant

317

pathway. In the biocontrol experiments with the culture broth of L. adecarboxylata

318

WT16, the method was used successfully to identify possible mechanisms associated

319

with anti-aflatoxigenicity. These applications attest the utility of the developed

320

method, which will advance research on aflatoxin contamination, detection and

321

control.

322

Supporting Information Available:

323

Table of solvent mixture compositions used for extraction of aflatoxin biosynthesis

324

metabolites.

325

Table of ANOVA results for the special cubic mixture model.

326

Table of the high-resolution mass spectrometry information of aflatoxins biosynthesis

327

metabolites.

328

Table of recovery and matrix effect data.

329

Figure of comparison of four common solvents with proposed solvents.

330

Figure of chromatograms of aflatoxins biosynthesis compounds from the real

331

samples.

332

Figure of possible characteristic fragment structure of aflatoxin biosynthesis pathway

333

compounds.

334

Figure of the MS/MS mass spectra as well as fragmentation trees of aflatoxin

335

biosynthesis pathway metabolites.

336

Time course changes of aflatoxins biosynthesis metabolites.

337 338 339 340 341 342



343

Funding:

344

This work was supported by the national key research and development program

345

of China (2017YFC1601205), National Natural Science Foundation of China

346

(31640062), and the Major Project of Hubei Provincial Technical Innovation

347

(2018ABA081).

348

Conflict of Interest:

349

The authors declare no competing financial interest.

350 351 352 353 354 355 356 357 358 359 360 361


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Biochemistry 2015, 50, 2307-2313. (18) Arroyo-Manzanares, N.; Diana, D. M. J.; Uka, V.; Malysheva, S. V.; Cary, J. W.; Ehrlich, K. C.; Vanhaecke, L.; Bhatnagar, D.; De, S. S. Use of UHPLC high-resolution Orbitrap mass spectrometry to investigate the genes involved in the production of secondary metabolites in Aspergillus flavus. Food Additives & Contaminants 2015, 32, 1656-1673. (19) Xu, Y. J.; Wang, C.; Ho, W. E.; Ong, C. N. Recent developments and applications of metabolomics in microbiological investigations. Trends in Analytical Chemistry 2014, 56, 37-48. (20) Smart, K. F.; Aggio, R. B.; Van Houtte, J. R.; Villas-BãÁs, S. G. Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nature Protocols 2010, 5, 1709-1729. (21) Zheng, S.; Liu, X.; Tang, N. N.; Locasale, J. W. Extraction parameters for metabolomics from cultured cells. Analytical Biochemistry 2015, 475, 22-28. (22) Chen, D.; Cao, X.; Tao, Y.; Wu, Q.; Pan, Y.; Huang, L.; Wang, X.; Wang, Y.; Peng, D.; Liu, Z. Development of a sensitive and robust liquid chromatography coupled with tandem mass spectrometry and a pressurized liquid extraction for the determination of aflatoxins and ochratoxin A in animal derived foods. Journal of Chromatography A 2012, 1253, 110-119. (23) Skrbic; Biljana; Koprivica; Sanja; Godula; Michal. Validation of a method for determination of mycotoxins subjected to the EU regulations in spices: The UHPLC-HESI-MS/MS analysis of the crude extracts. Food Control 2013, 31, 461-466. (24) Cajka, T.; Fiehn, O. Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Analytical Chemistry 2016, 88, 524-545. (25) Goodacre, R.; Broadhurst, D.; Smilde, A. K.; Kristal, B. S.; Baker, J. D.; Beger, R.; Bessant, C.; Connor, S.; Capuani, G.; Craig, A. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 2007, 3, 231-241. (26) Böcker, S.; Letzel, M. C.; Lipták, Z.; Pervukhin, A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 2009, 25, 218-224. (27) Böcker, S.; Kai, D. Fragmentation trees reloaded. J Cheminformatics 2016, 8, 1-26. (28) Stahnke, H.; Kittlaus, S.; Kempe, G.; Alder, L. Reduction of Matrix Effects in Liquid Chromatography-Electrospray Ionization-Mass Spectrometry by Dilution of the Sample Extracts: How Much Dilution is Needed?. Analytical Chemistry 2012, 84, 1474-1482. (29) Chanda, A.; Roze, L. V.; Linz, J. E. A Possible Role for Exocytosis in Aflatoxin Export in Aspergillus parasiticus. Eukaryotic Cell 2010, 9, 1724. (30) Alberts, J. F.; Gelderblom, W. C.; Botha, A.; van Zyl, W. H. Degradation of aflatoxin B-1 by fungal laccase enzymes. International Journal of Food Microbiology 2009, 135, 47-52. (31) Adebo, O. A.; Njobeh, P. B.; Gbashi, S.; Nwinyi, O. C.; Mavumengwana, V. Review on microbial degradation of aflatoxins. Critical Reviews in Food Science & Nutrition 2017, 57, 3208-3217. (32) Richard, J.; Payne, G.; Desjardins, A.; Maragos, C.; Norred, W.; Pestka, J. Mycotoxins: Risks in plant, animal and human systems. AAHE-ERIC/Higher Education Research Report 2003, 9, 48–50. (33) Ehrlich, K. C.; Mack, B. M.; Wei, Q.; Li, P.; Roze, L. V.; Dazzo, F.; Cary, J. W.; Bhatnagar, D.; Linz, J. E. Association with AflR in Endosomes Reveals New Functions for AflJ in Aflatoxin Biosynthesis. Toxins 2012, 4, 1582-1600. (34) Chang, P. K.; Scharfenstein, L. L.; Li, P.; Ehrlich, K. C. Aspergillus flavus VelB acts distinctly from VeA in conidiation and may coordinate with FluG to modulate sclerotial production. Fungal Genetics & Biology 2013, 59, 71-79.


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(35) Cary, J. W.; Harris-Coward, P.; Scharfenstein, L.; Mack, B. M.; Chang, P. K.; Wei, Q.; Lebar, M.; Carter-Wientjes, C.; Majumdar, R.; Mitra, C. The Aspergillus flavus Homeobox Gene, hbx1, Is Required for Development and Aflatoxin Production. Toxins 2017, 9, 315. (36) Yao, G.; Zhang, F.; Nie, X.; Wang, X.; Yuan, J.; Zhuang, Z.; Wang, S. Essential APSES Transcription Factors for Mycotoxin Synthesis, Fungal Development, and Pathogenicity in Aspergillus flavus. Frontiers in Microbiology 2017, 8, 2277.

456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493



494 Analytes

Page 18 of 27

Table 1. The method validation parameters Regression equation

R2

Linear range

LOD

LOQ

RSD%

RSD%

(μg/kg)

(μg/kg)

(μg/kg)

intraday

interday

Norsolorinic acid

Y=(-5.4×106)+1.36×106X

0.9992

0.008-200

0.008

0.03

4.4

7.2

Averantin

Y=(-3.35×106)+3.20×106X

0.9990

0.07-500

0.07

0.25

4.2

6.6

Hydroxyaverantin

Y=(-1.35×106)+1.90×106X

0.9993

0.02-200

0.02

0.06

3.9

6.7

Averufin

Y=(-5.94×106)+1.27×106X

0.9996

0.01-500

0.01

0.04

4.2

7.4

Versicolorin B

Y=(4.90×106)+1.36×106X

0.9997

0.05-200

0.05

0.17

3.1

6.3

Versicolorin A

Y=(2.48×106)+9.99×105X

0.9984

0.1-500

0.1

0.35

2.8

5.5

Aflatoxin B1

Y=(-2.73×106)+1.49×106X

0.9996

0.01-500

0.008

0.03

3.4

6.8

Aflatoxin B2

Y=(-2.56×106)+ 1.22×106X

0.9993

0.01-500

0.005

0.02

3.1

5.9

Aflatoxin G1

Y=(-4.05×106)+1.97×106X

0.9998

0.02-200

0.002

0.008

4.1

7.6

Aflatoxin G2

Y=(-3.08×106)+1.46×106X

0.9997

0.02-200

0.003

0.01

2.3

4.5

O-methyl-sterigmatocystin

Y=(-3.43×106)+ 1.84×106X

0.9997

0.01-500

0.004

0.015

2.0

4.9

Sterigmatocystin

Y= (-3.15×105)+8.79×105X

0.9990

0.01-200

0.016

0.05

4.2

7.2

495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516


Page 19 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


517

FIGURE CAPTIONS

518

Figure 1. Interaction of solvents on the extract of metabolites. (a) Contour plots of interaction of

519

MeOH, ACN and DCM on extraction of metabolites; (b) Contour plots of interaction of MeOH,

520

ACN and EA; (c) Contour plots of interaction of MeOH, ACN and DCM; (d) Plots of normality

521

assumptions and independence of the residuals; and (e) Plots of optimization and confirmation of

522

different proportions of extraction solution.

523

Figure 2. The MS/MS mass spectra as well as fragmentation trees of aflatoxin biosynthesis

524

pathway metabolites.

525

Figure 3. Hierarchical cluster analysis (a) of the aflatoxin biosynthesis and boxplot visualized the

526

variation tendency for pathway metabolites (b) via comparing anti-toxigenic assay.

527 528



529 530

Figure 1. Interaction of solvents on the extract of metabolites. (a) Contour plots of interaction of

531

MeOH, ACN and DCM on extraction of metabolites; (b) Contour plots of interaction of MeOH,

532

ACN and EA on extraction of metabolites; (c) Contour plots of interaction of MeOH, ACN and

533

DCM on extraction of metabolites; (d) Plots of normality assumptions and independence of the

534

residuals; and (e) Plots of optimization and confirmation of different proportions of extraction

535

solution.

536 537 538 539


Page 20 of 27

Page 21 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


540 541

Figure 2. The MS/MS mass spectra as well as fragmentation trees of aflatoxin biosynthesis

542

pathway metabolites. (Using compound DHST, DHOMST, ST, OMST, AFB1 and AFB2 as an

543

example, other compound fragmentation ion trees are provided in the Supporting Information.)

544 545



546 547 548 549

Figure 3. Hierarchical cluster analysis (a) of the aflatoxin biosynthesis and boxplot visualized the variation tendency for pathway metabolites (b) via comparing anti-toxigenic assay.

550 551 552 553 554 555 556 557


Page 22 of 27

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558


For Table of Contents Only

559


Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Page 24 of 27


(d)

(e)

Page 25 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Analytical Chemistry -CH2 -CH2 -2H -CH2

-CH2 -CH2

-CH2

-2H -2H -2H



Page 26 of 27

Norsoloric acid (Nor)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

AveranƟn (AVN)

HydroxyaveranƟn(HAVN)

OxoaveranƟn(OAVN)

AveruĮn(AVF)

Hydroxyversicolorone(HVN)

(a) Hierarchical cluster analysis

Versiconal hemiacetal acetate(VHA)

Versiconol acetate(VOAc)

Versiconol(VOH)

Versicolorin B(VER B)

Versicolorin A(VER A)

SterigmatocysƟn (ST)

DihydrosterigmatocysƟn(DHST)

Dihydro-O-methylsterigmatocysƟn(DHOMST)

O-MethylsterigmatocysƟn(OMST) AŇatoxin G2

ACS Paragon Plus Environment AŇatoxin B1

AŇatoxin G1

AŇatoxin B2

(b) Aflatoxins biosynthesis

OH

O

OH

O

HO

OH O

Norsoloric acid (Nor)

Page 27 of 27 OH

O

HO

OH


OH

OH

O

Averan n (AVN)

OH

O

HO

OH

OH

Samples

OH

OH

O

Hydroxyaveran n(HAVN) OH

O

HO

OH

OH

O

OH

O

Oxoaveran n(OAV N) OH

O

OH

O HO

Solvent optimization

O

Averufin(AVF) O

OH

O

O

OH OH

1 2 3 4 5 6

O

HO O

Hydroxyversicolorone(HVN) O

OH

O

O

O

OH

O

LC-HRMS

OH

OH

HO

OH

O

Versiconol acetate(VOAc) OH

OH

O

OH OH OH

HO

Versiconol(VOH) O

OH

O

OH

H O H

O

HO

Versicolorin B(VER B) O

ACS Paragon Plus Environment O

OH

O

OH

H

H

H

O

O O

HO

H

O

O

O

Versicolorin A(VER A)

O

OH

Dihydrosterigmatocys n(DHST) O

O H

H

H

O

H

O

O

O

O

O

O

O

O

OH

Dihydro-O-methylsterigmatocys n(DHOMST)

Sterigmatocys n (ST) O

H

H

O

O

O

O

O

O

O

O

Aflatoxin B1

O

H

O

O O

H

O

O

O O

H

O

O

Aflatoxin G2

H

O

Aflatoxin B2

O

O

O

O

H

O

O

H

O

O

O

OH

O

HO

O

Versiconal hemiacetal acetate(VHA)

O-Methylsterigmatocys n(OMST)

O

ynthesis Aflatoxin Bios O

OH

O

Aflatoxin G1

High-resolution qualitative and quantitative

Monitoring metabolite production of aflatoxin biosynthesis by Orbitrap

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