Lipids as key markers in maize response to fumonisin accumulation

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Food Safety and Toxicology

Lipids as key markers in maize response to fumonisin accumulation. Laura Righetti, Luigi Lucini, Paola Giorni, Sabrina Locatelli, Chiara Dall’Asta, and Paola Battilani J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b06316 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 20, 2019

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Journal of Agricultural and Food Chemistry

Lipids as key markers in maize response to fumonisin accumulation.

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Laura Righetti†, Luigi Lucini‡, Paola Giorni⸸, Sabrina Locatelli⸸, Chiara Dall’Asta*,†, Paola

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Battilani*,⸸.

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†

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Parma, Italy

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‡

Department of Food and Drug University of Parma, Parco Area delle Scienze 95/A, 43124

Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia

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Parmense 84, Piacenza, Italy

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⸸ Department

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Parmense 84, Piacenza, Italy

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⸸ Research

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Via Stezzano 24, Bergamo, Italy

of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia

Centre for Cereal and Industrial Crops, Council for Agricultural Research and Economics,

16 17 18

Corresponding authors details:

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Prof. Chiara Dall’Asta – [email protected]

20

Prof. Paola Battilani – [email protected]

21 22 23 24 25 26 27 1 ACS Paragon Plus Environment


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ABSTRACT

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The present field study offers new insights into the role played by plant lipid pathways in the

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modulation of fumonisin accumulation in maize Untargeted metabolomics was applied to better

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understand the multifactorial plant-pathogen interaction mechanisms, including host resistance. Our

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results showed a significant influence of the hybrid genotype, along with the environmental growing

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conditions, on fumonisin accumulation. A total of 25 significant metabolites have been identified,

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with glycerophospholipid and linoleic acid metabolism as the main pathways affected by the plant-

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pathogen interaction. This evidence highlighted the crucial role played by lipid signaling as an

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integrated part of the complex regulatory network in plant.

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Keywords:

hybrid

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glycerophospholipids

resistance,

fumonisins,

metabolomics,

lipid

signaling

pathways,

2 ACS Paragon Plus Environment

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INTRODUCTION

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Climate change significantly affects the mycotoxin contamination of crops worldwide. As a

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consequence, fungal infections and related diseases are of rising concerns, causing significant yield

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losses, quality reduction and mycotoxins accumulation. Attention has focused on fumonisins (FBs),

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mycotoxins mainly found in Fusarium verticillioides-infected maize grain, because of their

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widespread occurrence, acute toxicity to livestock, and their potential carcinogenicity. 1 Results from

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a global survey conducted from January to June 2017 indicated that FBs are between the most

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common mycotoxins found in 71% of all the analyzed maize samples (N° = 8472) at average level

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of 1,840 ppb. 2

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The production and accumulation of fumonisins during maize growth is a multifactorial process,

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driven by agronomic factors (i.e. soil pH and C:N ratio), the environment (i.e water activity and

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temperature) and the host plant. 3-5

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Among the strategies developed for reducing risk of FB contamination in maize available for food or

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feed consumption, selection and deployment of Fusarium ear mold-resistant maize germplasm is a

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high priority. Although the genetic base of commercial hybrids is quite limited, due to intensive

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breeding for improving technological properties of maize, the role of hybrids in FBs contamination

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is stated as important by many authors. 6 However, it must be underlined that environmental factors

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have a strong effect on the pathogen spreading and may therefore act as confounding factors when

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the genotype-related resistance is under investigation.

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On this account, several efforts have been focused on the events occurring in field along the growing

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season, and on the cross-talk between the plant and the fungus, to support the identification of resistant

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hybrids. 7-9

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Among the different maize macroconstituents, lipid composition of kernels has been suggested as

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relevant for FBs accumulation. 10,11 In particular, recent studies related the amount of total fatty acids

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to FBs accumulation in maize. According to these studies, maize hybrids with higher linoleic acid



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content showed a high FBs contamination. 7,8 In addition, oxylipins and phytoceramides were found

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significantly changed within the hybrids.

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Lipid partitioning in maize and in particular the different composition and distribution of endosperm

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lipids has been investigated in relation to their possible involvement in stress responses. 12 This is of

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great relevance considering that fungal colonization progresses from the outer layer of the kernel to

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the inner layer.

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higher FB amounts in the kernel. 14 The direct involvement of cuticular lipids in the plant–pathogen

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interaction has been pointed out representing a passive defense against most pathogens which

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colonize the plant surface. 15

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Although the correlation between FBs accumulation and plant lipid profile has been clearly

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demonstrated, next efforts should focus on the events occurring in field to clarify the host resistance

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mechanism and support the identification of resistant hybrids.

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hybrids are available on the market so far.

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The information derived from the use of novel untargeted “omics” tools such as genomics, proteomics

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and metabolomics might improve our limited understanding of the plant resistance mechanisms at

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the molecular level, deciphering the multifactorial nature of the resistance. Transcriptomics has been

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used to model the intracellular signaling cascade in maize cells against F. verticillioides infection and

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genome editing technologies have been recently considered to increase plant disease resistance. 6

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Novel research in mycotoxin analysis has been moving from the targeted analysis of individual

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compounds to untargeted analysis to detect all the metabolites that are involved in plant–fungi

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interactions. In parallel, the untargeted metabolomic approach might represent an effective strategy

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for understanding the biological pathways involved in mycotoxin accumulation, without any a priori

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hypothesis.

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Most of the studies performed so far to investigate cereal resistance mechanisms have been restricted

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to F. graminearum and F. culmorum in wheat and barley and deoxynivalenol accumulation in wheat.

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16-19

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Moreover, a lower surface wax content on the pericarp has been associated with

1

Indeed, no genetically resistant

Experiments have been performed under greenhouse with computer-controlled settings for light, 4 ACS Paragon Plus Environment

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temperature and relative air humidity and more recently, in environmental controlled growth

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chambers. 19, 20-23

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Very few reports have addressed till now the application of lipidomic analysis to maize responses to

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stress, especially in field conditions. 24,25 Even though in field studies are more challenging, because

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of several factors involved, the conclusions drawn are stronger, since confirmed independently on the

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natural variability.

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The work reported herein is aimed at underpinning differences in metabolic profiles between maize

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commercial hybrids in relation to fumonisin accumulation, under open field conditions.

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MATERIAL AND METHODS

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Chemicals and Reagents.

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FB1, FB2 and FB3 standard solutions (50 μg/ml in acetonitrile/water, 1:1 v/v) were purchased

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from Romer Labs (Tulln, Austria). Polytetrafluoroethylene (PTFE) 15 mL cuvettes were obtained

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from Greiner Bio-One (Kremsmünster, Austria). All the solvents used for samples extraction and

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chromatographic separation (methanol, ethanol, dicloromethane, 2-propanol and n-hexan) were

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purchased from Merck (Darmstadt, Germany). Ammonium formate and formic acid were supplied

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by Sigma–Aldrich (St. Luis, MO, USA). Water was purified by Milli-Q purification system

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(Millipore, Bedford, MA, USA).

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Plant material.

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In 2015, three different maize hybrids (N = 90) were sampled during the growing season in

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varietal fields grown in six Northern Italy regions: Lombardia (N=20), Friuli-Venezia Giulia (N=22),

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Emilia-Romagna (N=10), Toscana (N=10), Piemonte (N=18), Veneto (N=10). The maize hybrid

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name was not reported both because of the short market life of hybrids and the limited number of

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hybrids considered in the study; only codes were used (H20, H21, H22), indicating medium season

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hybrids (FAO classes 500-600). Ten ears were collected by hand from 15 m2 of each plot at harvest

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maturity in order to obtain a representative grain sample for the analyses. The ears were dried at 60

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°C at ca. 14% humidity using an Incas dryer (Cavallo, Milano, Italy) and shelled using an HP1 electric

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sheller (Vercella, Torino, Italy). All kernels from each plot (about 2 kg) were mixed thoroughly and

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ground using a ZM 200 Ultra Centrifugal Mill (Retsch GmbH, Haan, Germany), fitted with a 1 mm

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sieve to obtain a whole meal and stored at +4 °C until the analysis.

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Fumonisin determination.

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Maize samples were analyzed by LC-MS/MS to accurately quantify FBs content. Extraction

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and detection were performed according to an in-house validated method

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Supplementary Material).

7

(see Table S1,


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Untargeted Metabolomics.

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Sample preparation and UHPLC-QTOF detection method.

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1 g of grounded maize was extracted as previously described by Rubert et al., (2017) 26 using

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dicloromethane/methanol (50/50, v/v) as extraction mixture. Maize extracts were injected into a 1290

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UHPLC system (Agilent Technologies, Santa Clara, CA, USA) coupled with 6550 iFunnel QTOF

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mass spectrometer detector (Agilent Technologies, Santa Clara, CA, USA). Gradient elution and

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mass spectrometric detection was performed following the methods previously optimized. 26,27

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To address overall process variability, a set of eighteen samples (20% of the entire sample set)

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technical replicates and pooled quality control were injected. Samples injection order was randomized

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to avoid any possible time-dependent changes during UHPLC-QTOF analysis, which could result in

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false clustering.

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Data Processing and Chemometrics analysis.

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FBs target quantification data were statistically analysed using Two-Ways ANOVA followed

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by Tukey’s post hoc test (α = 0.05), using as factors both the hybrid and the harvesting area. The

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analysis was carried out using Statistica 13 (TIBCO Software Inc., Paolo Alto, CA, USA), plots were

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obtained using Sigma XL version 8 (SigmaXL Inc., Kitchener, Ontario, Canada).

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Deconvolution from raw data were carried out using the software Profinder B.07 (from Agilent

146

Technologies). 27 After data pre-processing (mass and retention time alignment, compounds filtering)

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compounds were filtered by abundance and by frequency

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(Umetrics, Malmo, Sweden) for multivariate modelling and significant markers selection. 26

27

and then exported into SIMCA 13

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Metabolites with VIP values and log2 fold change >1 were selected as significant markers and

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then submitted to the identification step using the software Profinder B.07 (from Agilent

151

Technologies), based on the ‘find-by-formula’ algorithm. Putative identification was achieved based

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on

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(http://www.genome.jp/kegg/genome.html), LIPID MAPS (http://www.lipidmaps.com) and using

the

database

exported

from

METLIN

(http://metlin.scripps.edu),

KEGG



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the entire isotopic profile (monoisotopic mass, isotope spacing and ratio). Some compounds

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putatively identified by LC–MS were confirmed by LC–HRMS/MS. Based on the strategy applied,

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identification was carried out according to ‘level III’ (putatively characterized) and when possible

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‘level II’, (putatively identified compounds), corresponding to compounds identified by UHPLC–

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HRMS/MS and matched to spectra from databases and literature, as set out by the Metabolomic

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Standard Initiative. 28

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Enrichment analysis of discriminant metabolites was performed with Pathway Tool in PlantCyc

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database (Plant Metabolic Network, https://www.plantcyc.org/) with biosource ‘Zea mais’ and Fisher

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Exact algorithm was used for over representation analysis.


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RESULTS

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Fumonisins accumulation.

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All the considered samples were found to be contaminated by fumonisins and, expressed as the sum

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of FB1, FB2 and FB3, ranged from 40 to 24770 µg Kg-1. The collected data showed a significant

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difference in FBs accumulation over geographical area (p

Lipids as key markers in maize response to fumonisin accumulation

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