Metabolomics reveals differential mechanisms of toxicity of hyperbranched poly(ethyleneimine)-derived nanoparticles to the soil-borne fungus Verticillium dahliae Kleb

Journal Pre-proof Metabolomics reveals differential mechanisms of toxicity of hyperbranched poly(ethyleneimine)-derived nanoparticles to the soil-borne fungus Verticillium dahliae Kleb

Maira Lykogianni, Evgenia-Anna Papadopoulou, Andreas Sapalidis, Dimitris Tsiourvas, Zili Sideratou, Konstantinos A. Aliferis PII:

S0048-3575(20)30024-9

DOI:

https://doi.org/10.1016/j.pestbp.2020.02.001

Reference:

YPEST 4535

To appear in:

Pesticide Biochemistry and Physiology

Received date:

9 October 2019

Revised date:

28 January 2020

Accepted date:

1 February 2020

Please cite this article as: M. Lykogianni, E.-A. Papadopoulou, A. Sapalidis, et al., Metabolomics reveals differential mechanisms of toxicity of hyperbranched poly(ethyleneimine)-derived nanoparticles to the soil-borne fungus Verticillium dahliae Kleb, Pesticide Biochemistry and Physiology (2020), https://doi.org/10.1016/ j.pestbp.2020.02.001

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© 2020 Published by Elsevier.

Journal Pre-proof

Essential title page information

Metabolomics reveals differential mechanisms of toxicity of hyperbranched poly(ethyleneimine)-derived

nanoparticles

to

the

soil-borne

fungus

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Verticillium dahliae Kleb

Maira Lykogianni1,2, Evgenia-Anna Papadopoulou 1, Andreas Sapalidis3,

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e-

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Dimitris Tsiourvas3, Zili Sideratou3, Konstantinos A. Aliferis 1,4*

1

Laboratory of Pesticide Science, Agricultural University of Athens, Iera Odos 75,

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118 55, Athens, Greece 2

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Laboratory of Biological Control of Pesticides, Benaki Phytopathological Institute,

3

Institute

of

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8 St. Delta str., 145 61, Kifissia, Attica, Greece Nanoscience

and

Nanotechnology,

NCSR

Demokritos,

Part.

Gregoriou & Neapoleos 27, Agia Paraskevi 153 44, Athens, Greece 4

Department of Plant Science, McGill University, Macdonald Campus, Ste-Anne-

de-Bellevue, QC H9X 3V9, Canada

*Corresponding author: [email protected] Laboratory of Pesticide Science, Agricultural University of Athens, Iera Odos 75, 118 55, Athens, Greece

Journal Pre-proof Abstract

There is a consensus on the urge for the discovery and assessment of alternative, improved sources of bioactivity that could be developed as plant protection products (PPPs), in order to combat issues that the agrochemical sector is facing. Based on the recent advances in nanotechnology, nanoparticles seem to have a great potential towards the development of the next generation nano-PPPs used

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as active ingredients (a.i.) per se or as nanocarriers in their formulation. Nonetheless, information on their mode(s)-of-action (MoA) and mechanisms of

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toxicity is yet largely unknown, representing a bottleneck in their further

fungitoxicity

of

hyperbranched

poly(ethyleneimine)

(HPEI),

quaternized

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the

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assessment and development. Therefore, we have undertaken the task to assess

hyperbranched poly(ethyleneimine) (QPEI), and guanidinylated hyperbranched

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poly(ethyleneimine) (GPEI) nanoparticles to the soil-born plant pathogenic fungus

metabolomics.

Results

revealed

that

functionalization

of HPEI

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GC/EI/MS

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Verticillium dahliae Kleb, and dissect their effects on its metabolism applying

nanoparticles with guanidinium end groups (GPEI) increases their toxicity to V. dahliae, while functionalization with quaternary ammonium end groups (QPEI) decreases it. The treatments with the nanoparticles affected the chemical homeostasis of the fungus, altering substantially its amino acid pool, energy production, and fatty acid content, causing additionally oxidative and osmotic stresses. To the best of our knowledge, this is the first report on the comparative toxicity of HPEI, QPEI, and GPEI to filamentous fungi applying metabolomics. The findings could be exploited in the study of the quantitative structure-activity

Journal Pre-proof relationship (QSAR) of HPEI-derived nanoparticles and their further development as nano-PPPs.

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Keywords: Nanotechnology, fungal metabolomics, nanopesticides

Journal Pre-proof Abbreviations

AA; amino acids AgNPs; silver nanoparticles a.i.; active ingredient(s) GPEI; guanidinylated hyperbranched poly(ethyleneimine) HCA; hierarchical cluster analysis

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HPEI; hyperbranched poly(ethyleneimine) KEGG; Kyoto Encyclopedia of Genes and Genomes

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MoA; mode(s)-of-action

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M.W.; molecular weight

PPPs; plant protection products

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OPLS-DA; Orthogonal partial least squares-discriminant analysis

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PUFAs; polyunsaturated fatty acids

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QPEI; quaternized hyperbranched poly(ethyleneimine)

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TIC; Total ion chromatogram

Journal Pre-proof Funding This research was co-financed by the Greek State and the European Union (European Social Fund- ESF) through the Operational Program «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the Greek State Scholarships

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Foundation (ΙΚΥ).

Journal Pre-proof 1. Introduction

There is a consensus on the necessity to discover new sources of bioactivity that could be potentially developed as novel, improved plant protection products (PPPs), assisting the agrochemical sector to combat some of the major challenges that it is facing, in order to safeguard food production and sustainability. The recently introduced requirements of the new legislative framework (Storck et al.,

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2017), the development of resistant to PPPs populations of pests, plant pathogens, and weeds (Barzman et al., 2015), the presence of PPPs’ residues in

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the food (Lechenet et al., 2017) and the environment (Barzman et al., 2015), and

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toxicity issues to non-target organisms (Mesnage and Séralini, 2018), are among

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the most significant of those challenges. Additionally, there are devastating soilborne plant diseases for which, currently, there is lack in registered PPPs (e.g.

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Verticillium and Fusarium wilts) (Jiménez-Díaz et al., 2015, Depotter et al., 2016).

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As a result, severe yield losses often occur, which in certain cases could lead to

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the eradication of the cultivation of certain crop species (Weber, 2002, Keller et al., 2005, Takagi, 2008).

Although it has been a great progress in the development of biological-based PPPs in the recent years (Pereira et al., 2003, Tisch and Schmoll, 2010), currently, the chemical PPPs are the backbone of plant protection globally. Nonetheless, there is an ineffectiveness to discover and introduce new active ingredients (a.i.) that exhibit new mode(s)-of-action (MoA) (Duke, 2012), which represents a bottleneck in the discovery and development of PPPs. Focusing on fungicides, to date, 54 different MoA have been reported (Aliferis and Jabaji, 2011, FRAC,

Journal Pre-proof 2019), with the latest group of fungicides exhibiting a new MoA being the strobilourins in the late ‘90s (Duke, 2012). In order to address the abovementioned challenges, the discovery and assessment of novel sources of bioactivity, and their further development as PPPs via the discovery of new types of formulations are necessary. Based on the recent advances in the field of nanotechnology, nanoparticles seem to have a great potential towards the development of the next generation PPPs (Figure 1), the so-

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called nano-PPPs (Kah, 2015, Balaure et al., 2017, Stadler, 2018). Although numerous applications of nanotechnology in medicine have been reported

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(Boisseau and Loubaton, 2011, Morigi et al., 2012), its application in the research

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and development (R&D) of PPPs is yet in its infancy (Kah and Hofmann, 2014,

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Walker et al., 2018).

Nanoparticles represent a novel source of bioactivity that could be used either

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per se as PPPs or as carriers in the formulation of nano-PPPs (Kah et al., 2018).

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The nanoformulation of a.i. using nanocarriers has already attracted the interest of

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many researchers (Balaure et al., 2017, Schallenhammer et al., 2017, Kah et al., 2018). It could lead to the development of nano-PPPs, exhibiting superior features such as, controlled release of the a.i. and improved toxicological profiles and selectivity (Balaure et al., 2017, Saini et al., 2020). Additionally, it could substantially improve their efficacy compared to the conventional formulations (Kah et al., 2018), and their leaf adhesion and penetration, which in turn is likely to reduce the required application frequencies and quantities, costs, and the associated risks of resistance development (Balaure et al., 2017). Furthermore, the nanoformulation could be important from a commercial perspective, especially for a.i. that have lost patent protection, and additionally, it could promote the

Journal Pre-proof sustainable agriculture. Based on the above, it is highly anticipated that nanoPPPs will become an important component of plant protection and the most important influx of xenobiotics into the environment in the near future (Kookana et al., 2014). Nanocarriers are nanoparticles that can attach and/or encapsulate a.i. in order to protect and transport them to the target-site, and ideally, release them at a specific time (controlled release) (Kurniasih, 2015, Ray et al., 2019). Several

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organic or inorganic materials that exhibit features such as, minute size and high surface-to-volume ratio could serve as nanocarriers (Kesharwani et al., 2014).

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Among nanocarriers, the dendritic nanocarriers exhibit properties favorable in

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nanoformulation (Caminade et al., 2015). The hyperbranched polymers are

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included in this category, which have a three-dimensional structure with a variety of functional groups at their end-points (Kurniasih, 2015). They are synthesized via

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one-step polymerization (Caminade et al., 2015), which reduces the related

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production costs (Kurniasih, 2015). A group of hyperbranched polymers exhibiting

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favourable features for the development of nano-PPPs is that of hyperbranched poly(ethyleneimine)

(HPEI),

whose

primary

amino

end

groups

can

be

functionalized by guanidinium and quaternary ammonium groups (Sideratou et al., 2000, Paleos et al., 2008).

Despite, however, the great potential of nanoparticles in plant protection, there are major challenges that have to be addressed prior to their development as nano-PPPs. Currently, there is a growing concern from consumers and regulatory agencies related to the legislative framework on their approval, marketing, distribution, application, and monitoring (Kah et al., 2013, Kookana et al., 2014, Kah, 2015). Their unique physicochemical properties due to their minute size,

Journal Pre-proof makes the establishment of protocols for the study of their toxicity to target and non-target organisms, fate in the environment and agricultural products, and MoA, challenging. The latter, is a very important feature of an a.i., which largely determines its potential to be further developed as a PPPs (Aliferis and Jabaji, 2011). To date, knowledge on the MoA of nanoparticles, if non-existent, is yet largely fragmented, which represents an obstacle towards the further development and

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application of nanoparticles in the agricultural practice (Anthony Hardy, 2018, Kah et al., 2018). There are studies on the MoA of metal nanoparticles on microbes

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(Prabhu and Poulose, 2012, Ingle et al., 2014). The toxicity of silver nanoparticles

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has been attributed to their interaction with the bacterial cell wall, resulting in its

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lysis (Danilczuk et al., 2006, Kim et al., 2007), the formation of silver ions, which interacts with enzymes causing their inactivation (Feng et al., 2000, Matsumura et

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al., 2003), or the DNA destruction via the reaction of silver with its sulphur and

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phosphorus atoms (Hatchett and White, 1996, Morones et al., 2005). In the case

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of copper nanoparticles, their bioactivity has been attributed to the inactivation of bacterial enzymes and the generation of hydrogen peroxide (Das et al., 2010), protein denaturation (Schrand et al., 2010), interference with the DNA (Ren et al., 2009), or the disruption of various biochemical processes (Stohs and Bagchi, 1995) and that of the cell membranes (Gopalakrishnan, 2012). In order to acquire the missing information on the MoA and mechanism of toxicity of nanoparticles, the implementation of advanced large-scale omics is required (Shin et al., 2018). Based on its superior capacities in monitoring the metabolism of fungi in response to treatments with bioactive compounds (Aliferis and Jabaji, 2011, Kalampokis et al., 2018, Sevastos et al., 2018), metabolomics

Journal Pre-proof could provide insights into the MoA and bioactivity of selected poly(ethyleneimine) (HPEI)-based nanoparticles to target plant pathogenic fungi. To date, it has been successfully

applied

in the

in

vitro

and

in

vivo

study of nanotoxicity

(Schnackenberg et al., 2012, Shin et al., 2018). Focusing on agricultural and environmental sciences, metabolomics has been applied in the R&D of PPPs (Aliferis and Chrysayi-Tokousbalides, 2010, Aliferis and Jabaji, 2011), ecotoxicology (Bundy et al., 2008, Kostopoulou et al., 2020),

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food science (Diez-Simon et al., 2019, Hatzakis, 2019), microbiology (yeast) (Reyes-Garcés and Gionfriddo, 2019), and plant sciences (Bowne, 2018, Saia et

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al., 2019, Schwachtje et al., 2019). Related to nanotechnology, it has been applied

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in the study of the effects of various nanoparticles on model systems such as,

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plants (Soria et al., 2017, Zhao et al., 2017), rats (Hadrup et al., 2012), cell lines (Carrola et al., 2016, Carrola et al., 2018) and other organisms (Lankadurai et al.,

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2015, Ratnasekhar, 2015). Such studies confirmed its potential and applicability in

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the assessment of the bioactivity of nanoparticles and the discovery of the

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corresponding biomarkers of effect. Based on the above, we have undertaken the task to evaluate the bioactivity of selected HPEI-based nanoparticles to the plant-pathogenic fungus Verticillium dahliae Kleb and dissect their effects on the fungal metabolism applying gas chromatography-electron

impact-mass

spectrometry

(GC/EI/MS)-based

metabolomics, in an effort to add critical information regarding their MoA. Furthermore, we have examined whether the HPEI-based nanoparticles being studied have the potential to become the new generation PPPs against Verticillium wilts, contributing towards the sustainability of agriculture. The latter is gaining momentum (Pascoli et al, 2019), largely based on the capabilities of nano-PPPs

Journal Pre-proof such as the controlled released of a.i. (Sangeetha et al., 2019; Singh et al., 2019; Saini et al., 2020), introduction of novel a.i. (Haq and Ijaz, 2019; RodríguezGonzálezet al., 2019), and their improved toxicological profiles (Oliveira et al., 2019). However, there are increasing concerns on their toxicity to non-target organisms (Besha et al., 2020), which represents a major challenge towards their further development and implementation in the agricultural practice. The fungus, is a devastating soil-borne plant pathogen that causes heavy yield losses, having a

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wide range of hosts (Daayf, 2015), including amongst others, vegetables (Guerrero et al., 2019) and olive trees (Colella et al., 2008). It has a unique biology

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(e.g. long-term survival in the soil) and additionally, there is lack of registered

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chemical PPPs, since soil fumigants, which used to be the main plant protection

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measure, have been recently banned, making crop protection against the fungus

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problematic (Milgroom et al., 2014, Carroll et al., 2017).

2. Materials and Methods

2.1 Chemicals and reagents

HPEI with an average molecular weight (M.W.) of 25.000 Da (99%, Lupasol® WF, water-free), was kindly provided by BASF (Ludwigshafen, Germany). For the synthesis of QPEI and GPEI, glycidyltrimethylammonium chloride, 1H-pyrazole-1carboxamidine

hydrochloride,

N,N-diisopropylethylamine,

triethylamine,

dimethylformamide (DMF), and dialysis tubes (M.W. cut-off: 1200) (Sigma-Aldrich

Journal Pre-proof Ltd, Steinheim, Germany), were used. The organic solvents ethyl acetate and methanol (GC/MS grade, 99.9% purity, Carlo Erba Reagents, val de Reuil, France) were used in the extraction of the V. dahliae endo-metabolome. The derivatization of samples for GC/EI/MS analysis, was performed using the reagents methoxylamine hydrochloride (98%, w/w), pyridine (99.8%, v/v) (SigmaAldrich), and N-Trimethylsilyl-N-methyl trifluoroacetamide (MSTFA, Macherey and

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Nagel, Düren, Germany).

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2.2 Biological material

The isolate V. dahliae PMG27 of the collection of the Pesticide Metabolomics

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Group (PMG) (Laboratory of Pesticide Science, Agricultural University of Athens,

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AUA), was used in the experiments. Starter cultures were initiated from frozen

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mycelial plugs that were stored in 50% glycerol in plastic 2 mL cryotubes (Thermo Fisher Scientific, Waltham, MA, USA), at -80oC. The plugs were transferred into Petri plates (5 cm-in diameter) containing potato dextrose agar (PDA, Difco, BD, Sàrl, Switzerland), and incubated at 22 oC, in the dark. Sub-culturing was performed bi-weekly using 4mm in-diameter plugs as the inocula.

Journal Pre-proof 2.3

Synthesis

of

functionalized

hyperbranched

poly(ethyleneimine)-based

nanoparticles

Quaternized

hyperbranched

poly(ethyleneimine)

(QPEI)

and

guanidinylated

hyperbranched poly(ethyleneimine) (GPEI) (Figure 2), were prepared in-house. The synthetic route of QPEI with a 50% degree substitution of primary amino groups was described in a previous publication (Sideratou et al., 2000, Sapalidis

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et al., 2018). The synthesis of GPEI with a 50% degree substitution of primary amino groups was also performed based on an analogous previously described

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methodology (Tziveleka et al., 2007). Briefly, HPEI with M.W. of 25,000 Da (0.01

e-

mmol) dissolved in 5 mL of dry DMF was added to a DMF solution (5 mL)

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containing 1H-pyrazole-1-carboxamidine hydrochloride (0.2 mmol) and N,Ndiisopropylethylamine (0.04 mmol). The mixture was allowed to react at room

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temperature for 24 h, under argon atmosphere. The crude product was

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precipitated using diethyl ether and dried under vacuum. The obtained solid was

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dissolved in water, purified by dialysis against deionized water and lyophilized. The chemical structure of GPEI was established by spectroscopy. 1H and

1

H and

13

C NMR

13

C NMR spectra were recorded in D 2O using a Bruker

Avance DRX spectrometer operating at 500 and 125.1 MHz, respectively. The successful attachment of guanidinium groups to the HPEI scaffold and the degree of guanidinylation were assessed using NMR spectroscopy. Moreover, HPEI, QPEI and GPEI nanoparticles was imaged by atomic force microscopy (AFM, Veeco model APCS-0001) in tapping mode at a scan rate of 0.5 Hz in 256x256 format. 4 μL of methanol solutions (2.5 μg mL-1) of each sample were deposited on freshly cleaved mica (Pelco Muscovite 9.9 mm diameter) and

Journal Pre-proof the solvent was removed under a gentle stream of dry N2. For each sample, three different depositions were made.

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2.4 Assessment of the toxicity of nanoparticles to Verticillium dahliae

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In order to be able to draw conclusions on the mechanisms by which bioactive

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compounds exert their toxicity to a biological system applying metabolomics, it is

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important to slightly disturb its metabolism, rather than completely disrupt it (Aliferis and Jabaji, 2011). Therefore, in a first step, we have assessed the toxicity

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of the nanoparticles being studied to V. dahliae, based on their effects on the radial growth of its cultures (Figure 3) using the software image J (Schneider,

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2012). Stock solutions of the nanoparticles were prepared in sterilized water in

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glass vials, which were stored at -20oC until further use. The highest concentration

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that was tested it was that of the 750 μg mL-1. Measurements were taken in 24 h intervals, and up to 20 days following the initial inoculation of the media in Petri plates with mycelium plugs (4 mm-in diameter). Inocula were taken from the edges of seven-day old V. dahliae cultures. Three replications were performed per treatment, and cultures, in which sterilized water in equivalent volume to that of the nanoparticles solutions was added, served as the controls. Because of the slow development of the cultures, the EC 50 values were calculated 14 days following treatments, using the software JMP v.13 (SAS Institute Inc., Cary, NC, USA).

Journal Pre-proof

2.5

Gas

chromatography/electron

impact/mass

(GC/EI/MS)

spectrometry

metabolomics for the monitoring of the effects of nanoparticles on the metabolism of Verticillium dahliae

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2.5.1 Growing conditions and experimental design

The mycelial plugs (4 mm in-diameter) that were used as the inocula for the

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initiation of new cultures, were taken aseptically from the edges of 14-day-old V.

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dahliae cultures. The new cultures were grown in Petri dishes (9 cm in-diameter)

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containing 15 mL of PDA, supplemented with HPEI, QPEI, or GPEI in concentrations equal to their corresponding EC 50 values or sterilized water. They

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were incubated for 14 days, at 22 oC, in the dark. In order to facilitate the analyses

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of the endo-metabolome (e.g. separation of the hyphae from the medium), the cultures were grown on sterilized cellophane membranes (UCB, North Augusta,

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USA). In total, 15 biological replications were performed per treatment, every three of which were finally pooled to give one pooled sample.

2.5.2 Sampling and metabolite extraction

Hyphae, excluding the area of the plug, were scraped off using a metallic spatula and immediately were pulverized to a fine powder using a pestle in a mortar under

Journal Pre-proof liquid nitrogen, which was used for metabolism quenching. The pulverized hyphae were immediately collected in plastic 1.5 mL Eppendorf tubes and stored at -80°C until further processing. For the extraction of the endo-metabolome, a previously developed protocol was applied (Kalampokis, Kapetanakis et al., 2018), with minor modifications. Briefly, 1 mL of methanol: ethyl acetate (50:50, v/v) was added in the pulverized hyphae (40 mg) in glass autosampler vials (2 mL) (MachereyNagel), followed by sonication in an ultrasonic bath (Branson 1210, Connecticut,

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USA) for 20 minutes, and then, stirring for two hours at 24°C in an orbital shaker (GFL 3006, Burgwedel, Germany). A solution of ribitol (Sigma-Aldrich Ltd,

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Steinheim, Germany) (20 μL of 0.2 mg mL-1 in methanol) was then added as the

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internal standard. The crude extracts were finally filtered through PTFE filters (0.2

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μm, Macherey-Nagel, Duren, Germany) for the removal of debris and evaporated

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to dryness in a vacuum concentrator (Labconco, Kansas City, MO, USA).

2.5.3 Methoxymation and silylation of extracts and GC/EI/MS analyses

The derivatization of the dried extracts was performed following previously developed protocols (Kalampokis et al., 2018, Kostopoulou et al., 2020). Briefly, initially, 80 μL of a methoxylamine hydrochloride solution in pyridine (20 mg mL-1) was added in the dried extracts, followed by incubation in a water bath at 30°C for 2 h, for methoxymation. Then, 80 μL of MSTFA was added, and the resulting solutions were incubated at 37°C for 90 minutes, for silylation. The derivatized

Journal Pre-proof samples were finally added in microinserters (180 μL) in glass autosampler vials (2 mL). In the analyses, an Agilent 6890N gas chromatography platform (Agilent Technologies Inc., Santa Clara, CA, USA), equipped with the 7683 automatic inert mass selective detector (MSD) and an HP-5MS ultra inert (UI) capillary column (30 m, i.d. 0.25 mm, and film thickness 0.25 μm, Agilent Technologies Inc.), was employed, applying positive electron ionization (70 eV). Full scanning spectra

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were acquired over the 50-800 Da mass range (scan rate of 4 scans per second) applying an initial delay of 10 minutes. The temperature of the MS source was set

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at 230°C and that of the quadruple at 150°C. The injector temperature was set to

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230°C and samples (1 μL) were injected at a 5:1 split ratio. The carrier gas was

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helium at a flow rate of 1 mL min-1. The initial temperature of the oven was 70°C for 5 min, increased at a rate of 5 °C min-1 to 310°C, kept for 1 min. Experimental

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blank samples were additionally analyzed for the detection of metabolic features

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not related to the analyzed biological sample (e.g. contamination of equipment,

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possible contamination of the source, column bleeding).

2.5.4 GC/EI/MS chromatogram pre-processing, construction of an open access metabolite database of Verticillium dahliae, and bioinformatics analyses for the discovery of trends and metabolites-biomarkers of the toxicity of the nanoparticles

Raw GC/EI/MS chromatograms were processed following a previously described pipeline (Kalampokis et al., 2018, Kostopoulou et al., 2020). Briefly, the AMDIS

Journal Pre-proof software v.2.66 (National Institute of Standards and Technology Library, NIST, Gaithersburg, MD, USA) was used for the initial deconvolution of the acquired chromatograms. The putative identification of metabolites was performed by matching their mass spectra to those of the NIST library (NIST ’08), while the absolute identification of selected metabolites was performed by matching their mass spectra and retention times to those of analytical standards that had been analyzed on the same platform under identical analytical conditions (Sumner et al.,

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2007). For the identified metabolites, information such as their Kyoto Encyclopedia of Genes and Genomes-GenomeNet (KEGG ID), Pubchem CID, CAS registry

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number, biosynthetic pathways, monoisotopic mass (Da), average molecular mass

(https://pubchem.ncbi.nlm.nih.gov/),

and

Golm

Metabolome

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PubChem

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(Da), and molecular formula was acquired from the KEGG (https://www.kegg.jp/),

(gmd.mpimp-golm.mpg.de/) repositories. The open-access online metabolite

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library containing the above information was constructed using the software

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Microsoft Expression Web v.4.0.

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For the discovery of trends and the corresponding metabolite-biomarkers of the toxicity of nanoparticles to V. dahliae, raw GC/EI/MS chromatograms (Agilent “ .D”) were transformed to the “*.cdf” format using the software ACD Labs Spectrus (ACD Labs, Toronto, Canada). The data were further processed (e.g. alignment) using the software MS-DIAL v.3.70 (Tsugawa et al., 2015), adapting the recommended for GC/EI/MS data sets settings. The obtained aligned matrix was then exported to MS Excel® for further curation, addition of the information related to the identified metabolites, and normalization to the total integrals. The processed matrix was finally exported to the bioinformatics software SIMCA-P + v.13.0.3 (Umetrics, Sartorius Stedim Biotech, Umeå, Sweden) for the

Journal Pre-proof discovery of trends and biomarkers. For the monitoring of the effects of the applied nanoparticles on the fungal metabolism, orthogonal partial least squaresdiscriminant analysis (OPLS-DA) and OPLS-hierarchical cluster analysis (OPLSHCA) was performed (Kalampokis, Kapetanakis et al., 2018). The discovery of the corresponding metabolites-biomarkers of toxicity was based on regression coefficients

based

on partial least square-discriminant analysis (PLS-DA)

regression coefficients (P<0.05) and standard errors were calculated based on

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Jack-knifing (Efron and Gong, 1983). Additionally, variable influence on projection (VIP) plots were used to project the importance of the X-variables (metabolites),

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with confidence intervals derived from jack-knifing (Galindo‐Prieto et al., 2014).

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Finally, for the robust visualization of the data heat maps were constructed using

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the software Matlab R2019a (Natick, Massachusetts, U.S.A.) (Kostopoulou et al.,

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2020).

3. Results and Discussion

3.1. The applied synthesis protocol facilitated the acquisition of the hyperbranched poly(ethyleneimine)

(HPEI)-derived

poly(ethyleneimine)

(QPEI),

nanoparticles, and

quaternized

guanidinylated

hyperbranched hyperbranched

poly(ethyleneimine) (GPEI)

The HPEI that was used in the synthesis of the nanoparticles QPEI and GPEI (see §2.3), was characterized by inverse-gated

13

C NMR analysis as previously

Journal Pre-proof described (Sideratou et al., 2018). Results of analysis, revealed that the ratio of primary : secondary : tertiary amino groups (ΝΗ2:ΝΗ:Ν) was 1.06:1.26:1.00, while the degree of branching was 0.68. The applied synthetic protocol resulted in the synthesis of the nanoparticles QPEI and GPEI exhibiting a 50% degree of substitution of primary amino groups. The synthetic route of QPEI as well as their structural characterization have been recently described (Sapalidis et al., 2018). On the other hand, the successful guanidinylation of HPEI was assessed by 1H 13

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C NMR spectroscopy; new peaks at 7.85 and 7.20 ppm that were attributed

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and

to the protons of the guanidinium groups, and another one at 3.15 ppm, which was

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assigned to the protons of the α-methylene groups relative to the guanidinium

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moieties, appeared in the 1H NMR spectrum of GPEI (Supplementary Figure 1).

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The recorded multiplet centered at 2.60 ppm was attributed to the protons of the HPEI scaffold. The guanidinylation degree of HPEI was calculated by the

al

comparison of the peak integrals at 3.15 and 2.60 ppm, and it was found that 50%

rn

of the primary amino groups of HPEI were substituted by guanidinium groups. The 13

C NMR spectroscopy; the successful

Jo u

structure of GPEI was further elucidated by

attachment of guanidinium groups to HPEI scaffold was confirmed by the presence in the

13

C NMR spectrum of GPEI (Supplementary Figure 2), of new

signals at 158.5 ppm, attributed to the Carbon of the guanidinium group and at 36 and 39 ppm attributed to the C 1-3 and C1-2 of the α-methylene groups relative to the guanidinium group, respectively. Furthermore, AFM microscopy was employed to image the HPEI, GPEI and QPEI nanoparticles placed on mica from methanol solutions. As shown in (Supplementary Figure 3), the nanoparticles upon deposition on the surface assumed a disk-like shape of approximately 30 nm diameter and 0.3 nm height for

Journal Pre-proof all derivatives, as expected since both GPEI and QPEI are derived from the functionalization of the same hyperbranched scaffold, i.e. HPEI.

3.2

The

functionalization

of

hyperbranched

poly(ethyleneimine)

(HPEI)

nanoparticles with guanidinium groups increases their toxicity to Verticillium

oo

f

dahliae, while functionalization with quaternary ammonium groups decreases it

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The assessment of the toxicity of the nanoparticles HPEI, QPEI, and GPEI to V.

e-

dahliae revealed that the latter is the most bioactive nanoparticle, causing a substantial reduction in the radial growth of its cultures compared to the untreated

Pr

ones (Figure 3), with an EC 50 value of 207 µg mL-1. HPEI was slightly less toxic than GPEI, with an EC 50 of 212 µg mL-1. On the other hand, QPEI was moderately

al

toxic at the maximum concentration of 750 μg mL-1 that was tested, however,

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substantially less toxic than the other two nanoparticles, causing a 45% decrease

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in the radial growth of the fungal cultures at the maximum applied concentration. Such results provide evidence that the substitution of primary amino groups of HPEI-derived nanoparticles with guanidinium groups increases the fungitoxicity, whereas on the opposite, substitution with quaternary ammonium groups (Figure 2), decreases it. To the best of our knowledge, this is the first report on the comparative toxicity of HPEI, QPEI, and GPEI to filamentous fungi. Nonetheless, information on their exact MoA and mechanisms of toxicity, if non-existent, is largely fragmented. The guanidinium groups are the cation forms of guanidine (Tan and Coles, 2014) and is also present on the side chain of arginine, with a well-studied bioactivity. They

Journal Pre-proof are known to strongly interact with cell membranes enabling cell penetration (Hamley, 2017), and the presence of the guanidine-like structure is highly correlated to their antibacterial activity against Escherichia coli (Qian et al., 2008), ESKAPE organisms (Fleeman et al., 2015, Wang et al., 2016), Enterobacter cloacae, and Acitenobacter baumannii (Zamperini et al., 2017). Our findings are in agreement with such observations, further supporting the role of guanidinium groups in the antimicrobial properties of nanoparticles.

oo

f

On the other hand, quaternary ammonium salts have been studied as antimicrobial polymer agents (Cheng et al., 2017, Schallenhammer et al., 2017).

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Similarly, the antimicrobial activity of quaternary ammonium functionalized hyper-

e-

branched and dendritic polymers, to E. coli (Feng et al., 2000, Bo et al., 2014),

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Staphylococcus aureus (Bo et al., 2014, Worley et al., 2014), Pseudomonas aeruginosa (Worley et al., 2014), and Vibrio fisheri (Feng et al., 2000), has been

al

demonstrated. Furthermore, it has been shown that quaternary ammonium groups

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increase the antifungal efficacy of chitosan against important phytopathogenic

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fungal species (Brunel et al., 2013, Richter et al., 2015, Hamley, 2017). Nonetheless, results of our study showed a reduced antimicrobial activity of the QPEI nanoparticles compared to the corresponding HPEI-derived GPEI, indicating that functionalization of HPEI with quaternary ammonium groups decreases the fungitoxicity of nanoparticles. Additionally, the external load of nanoparticles has a large impact on their bioactivity; the charge‐conversional modification of immunoglobulin G enables its delivery into the cytoplasm of hepatoma cells (Lee et al., 2010). Also, the coating of silver nanoparticles (AgNPs) with cationic polyelectrolyte layers, improves their environmental footprint and their adhesion to the bacterial cell membranes,

Journal Pre-proof making them toxic to species such as, E. coli, P. aeruginosa, and quaternaryamine-resistant Ralstonia sp. (Richter et al., 2015). Furthermore, changes in the functional groups of quaternary ammonium monomers alter their mechanical and antibacterial properties (Liang et al., 2018). Our findings, further confirm such observations, suggesting that the guanidinium group interact much stronger with the cell membrane compared to the quaternary ammonium group, which has a lower basicity than the guanidinium group; the pKa values of guanidinium and

oo

f

quaternary ammonium group are ~ 12.5 (Nishihara et al., 2005) and ~ 10 (He and Chu, 2013), respectively.

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The observed fungitoxicity of HPEI and HPEI-derived nanoparticles is lower

e-

than that of nanoparticles such as, AgNPs to phytopathogenic fungi (EC 50 < 50

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ppm) (Kim et al., 2012), Candida albicans (ΜIC=2 μg mL-1) (Kim et al., 2009), nano-mancozeb to Alternaria solani (ED 50 ~ 1.31-2.79 mg L-1),Sclerotium rolfsii

al

(ED 50 ~ 1.60-3.14 mg L-1) (Majumder et al., 2016), and nanosized validamicin to

rn

Rhizoctonia solani (EC 50 < 25 mg L-1) (Qian et al., 2010). On the contrary, they

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exhibit higher toxicity than chitosan-based nanoparticles against Alternaria alternata and Macrophomina phaseolina (EC 50 > 0.06%, w/v), and R. solani (EC 50 > 0.1%, w/v) (Saharan et al., 2013). Such results indicate that the HPEI-based nanoparticles, exhibit interesting fungitoxicity, comparable to that of other nanoparticles that have been studied, which further supports the notion of their potential use as PPPs per se or as carriers in the formulation of nano-PPPs. Additionally, they provide novel insights into the quantitative structure-activity relationship (QSAR) of HPEI-derived nanoparticles that could be further exploited in the R&D of nano-PPPs.

Journal Pre-proof

3.3 Construction of a species-specific GC/EI/MS metabolite library for Verticillium dahliae

For

high-throughput

metabolomics

analyses,

the

use

of

species-specific

f

metabolite libraries is highly preferable (Brown et al., 2009), since it provides

oo

confidence in metabolite identification and enables the robust deconvolution of the

pr

acquired large-scale metabolomics data sets. Therefore, here, in a first step, the

e-

open-access PMG for Verticillium dahliae GC/EI/MS metabolite library v.1.0 was constructed. The library is hosted at the repository of the Pesticide Metabolomics

Pr

Group (PMG, Laboratory of Pesticide Science, Agricultural University of Athens,

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rn

al

AUA, http://www.aua.gr/pesticide-metabolomicsgroup/Resources/default.html).

3.4 Overview of the Verticillium dahliae GC/EI/MS metabolomics analysis

The applied bioanalytical protocol enabled the recording and deconvolution of a portion of the V. dahliae endo-metabolome. To the best of our knowledge, this is the first metabolite profiling study on this soil-borne fungus. The acquired GC/EI/MS total ion chromatograms (TIC) were of high quality, as it is confirmed by the large number of the peaks, their shapes and baselines, and the achieved chromatographic separation (Supplementary Figures 4-5).

Journal Pre-proof In total, 155 metabolic features were reproducibly recorded among treatments, which were identified at various levels (Supplementary data set 1). Information on the metabolites (e.g. identifiers, biosynthetic pathways, chemical grouping) are also included in the data set. The absolutely or tentatively identified metabolites participate in 115 biosynthetic pathways, with their majority, belonging to carbohydrates

(27%),

carboxylic

acids

(15%),

and

amino

acids

(14%)

(Supplementary Figure 6). The original data set “Verticillium dahliae (PMG-05-

Group

(https://www.aua.gr/pesticide-

metabolomicsgroup/Resources/default.html).

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Metabolomics

oo

f

19)” in “*.cdf” format, can be freely accessed from the repository of the Pesticide

e-

The robustness of the experimental and bioanalytical protocols is confirmed by

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the strong clustering between the recorded metabolite profiles of the biological replications of the same treatments at the OPLS-DA score plot (Figure 4A) and

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the corresponding hierarchical cluster analysis (HCA) dendrogram (Figure 4B),

rn

and the obtained heat map (Figure 5). Furthermore, no outliers were observed

Jo u

(Figure 4). The global overview of fluctuations of the metabolome of the fungus in response to the treatments was achieved using a heat map (Figure 5).

3.5. Treatments of Verticillium dahliae with hyperbranched poly(ethyleneimine) (HPEI), quaternized hyperbranched poly(ethyleneimine) (QPEI), or guanidinylated hyperbranched poly(ethyleneimine) (GPEI) nanoparticles cause distinct changes in its endo-metabolome

Journal Pre-proof Results of metabolomics analyses revealed distinct changes in the endometabolome of V. dahliae in response to the toxicities caused by the applied HPEI-derived nanoparticles (Figures 4-8), which plausibly indicates the operation of distinct MoA and/or mechanisms of toxicity. It is evident that the treatment of fungal cultures with the nanoparticles alters their metabolite composition, with the metabolite profiles of GPEI-treated cultures positioned closer to those of the untreated, compared to the HPEI and QPEI-treated ones (Figures 4-5).

oo

f

Interestingly, such grouping of the metabolite profiles is not correlated to the bioactivity of the nanoparticles, for example, the metabolite profiles of cultures that

pr

were treated with the least toxic QPEI are grouped closer to those of HPEI-treated

e-

ones.

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The discovery of the corresponding biomarkers of toxicity of each nanomaterial was based on pairwise comparisons applying OPLS-DA, comparing each time the

al

metabolite profile of the untreated to those of the nanoparticle-treated cultures

rn

(Supplementary Figure 7). Such comparisons confirmed the distinct changes in

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the metabolism of V. dahliae in response to the treatments. Analyses revealed the general disturbance of the fungal metabolism, since metabolites of various chemical groups, which participate in multiple biosynthetic pathways, were discovered as biomarkers (Figures 5-8). Based on the results and information retrieved from the KEGG database (https://www.kegg.jp/), the differences in the recorded metabolic profiles regarding the biochemical pathways were further grouped into biochemical functions (Figure 6). Analyses revealed a similar pattern in the fluctuation of metabolites following treatments with HPEI and QPEI, with metabolites in the GPEI-treated cultures exhibiting distinct trends (Figure 6A). The levels of the amino acids (AA), marked

Journal Pre-proof the most interesting fluctuation, with HPEI and QPEI treatments resulting in substantial lower levels of the majority of AA, and GPEI treatment, and on the contrary, to higher levels compared to the untreated. The observed trends were consisted with those based on the participation of metabolites in functions (Figure 6B). Likewise, treatments of V. dahliae with HPEI and QPEI caused similar fluctuations in its endo-metabolome, whereas GPEI had a distinct impact. Nonetheless, all nanoparticles caused a general disturbance of the fungal

oo

f

carbohydrate, AA, secondary metabolite, and energy metabolisms. Additionally, in the VIP plot, carbohydrates (e.g. mannitol, a-a-trehalose, glucose), AA (e.g.

pr

serine, GABA, leucine), and fatty acids (e.g. palmitate, linoleate) were amongst the

e-

most influential biomarkers of the toxicity of nanoparticles to V. dahliae (Figure 7).

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In order to gain insights into the impact of the nanoparticles on the metabolism of the fungus and biologically interpret the results, the metabolic network of

al

Verticillium was de novo constructed based on the identified metabolites (Figure

rn

8). Significant changes in the metabolite content between treatments were

Jo u

discovered using the OPLS regression coefficients.

3.6

Treatment

of

Verticillium

dahliae

cultures

with

hyperbranched

poly(ethyleneimine) (HPEI)-based nanoparticles, quaternized hyperbranched poly(ethyleneimine)

(QPEI),

and

guanidinylated

hyperbranched

poly(ethyleneimine) (GPEI) alter their chemical homeostasis and energy production, causing oxidative and osmotic stresses

Journal Pre-proof In contrast to yeast metabolomics (Canelas et al., 2009, Ibáñez et al., 2013), that of plant pathogenic fungi is still in its infancy, and although a great effort has been given towards the study of the secondary fungal metabolism (Keller et al., 2005, Brakhage, 2013, Hautbergue et al., 2018), the primary metabolism and its significance in fungal responses to bioactive compounds is largely unexplored (Kalampokis et al., 2018, Sevastos et al., 2018). Furthermore, most of the metabolomics studies on the use of nanoparticles in agriculture have been

oo

f

performed on plants (Soria et al., 2017, Zhao et al., 2017). Primary metabolites do not only serve as the building blocks in the biosynthesis of primary and secondary

pr

metabolites and proteins, but additionally, many of them are involved in the

e-

metabolism regulation acting, among others, as signaling compounds (Chevrot et

Pr

al., 2006, Reyes-García et al., 2012, Mead et al., 2013, de Castro Fonseca et al., 2016, Ryan et al., 2019).

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Since the interpretation of the global metabolite profiling analysis was beyond

rn

the scope of the present research, here, fluctuations in the levels of key

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metabolites that were discovered as biomarkers of the toxicities of the applied HPEI-derived nanoparticles are being discussed, in an effort to conclude on the mechanisms by which they exert their toxicity to V. dahliae. Among those are AA such as, L-proline, GABA, L-tryptophane, L-tyrosine, the disaccharide α-αtrehalose, Krebs cycle intermediates (KCI), and fatty acids.

Journal Pre-proof 3.6.1 Treatment of Verticillium dahliae with HPEI-derived nanoparticles alters its amino acid (AA) pool

Treatments of V. dahliae with the HPEI-derived nanoparticles resulted in a substantial change in the content of its amino acid pool (Figures 6-8). With a few exceptions, HPEI and QPEI caused a decrease in the levels of AA content of the fungus, whereas treatment with GPEI, an increase. AA have a profound role in

oo

f

fungal physiology serving as the precursors in the biosynthesis of metabolites and proteins, and additionally many of them, serve as signaling compounds and are

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essential in fungal physiology (e.g. osmoprotection) (Feehily and Karatzas, 2013,

e-

Galindo‐Prieto et al., 2014, Kostopoulou et al., 2020). Therefore, the observed

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disturbance of their pool following treatments with the nanoparticles, is indicative of a general disturbance of the fungal metabolism and its biosynthetic operation.

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Nonetheless, the current data does not allow to draw solid conclusions on the

rn

cause of such disturbance; increased levels of AA could indicate increased

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proteolysis or reduced utilization in the biosyntheses. Similarly, their decreased levels could be indicative of increased biosynthesis of proteins or decreased AA biosynthesis.

The increased levels of the aromatic AA L-tryptophan, and L-tyrosine, following treatment of the fungus with GPEI, combined with the reduced energy production via the Krebs cycle and the corresponding AA pool fluctuation, plausibly is indicative of the reduced biosynthesis of secondary metabolites through the indole alkaloid, isoquinone alkaloid, phenylpropanoid, and tropane, piperidine, and pyridine alkaloid biosynthetic pathways (Figure 8). Given the importance of those metabolites in fungal physiology (Keller et al., 2005, Brakhage, 2013), it is

Journal Pre-proof expected that the GPEI nanoparticles will have a high impact on its physiological function (e.g. pathogenesis, stress responses). On the contrary, application of HPEI and QPEI does not seem to have a substantial impact on the biosynthesis of

f

the secondary fungal metabolites.

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3.6.2 The toxicity of HPEI-derived nanoparticles to Verticillium dahliae involves

e-

pr

oxidative and osmotic stresses

The fluctuations in the levels of metabolites with well-established roles in

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responses of organisms to, among others, oxidative and osmotic stresses, such as L-proline and α,α-trehalose, are indicative of the effect of the applied nanoparticles

al

on V. dahliae. L-proline is an AA that is being synthesized from glutamate (Figure

rn

8). It plays multiple roles in fungal physiology, impacting various processes, such

Jo u

as growth, differentiation, apoptosis, and stress responses (Liang et al., 2013). It is essential in protein and membrane stabilization via its reactive oxygen species (ROS)

scavenging

capacity

(Takagi, 2008). The

upregulation of proline

biosynthesis leads to the suppression of ROS and the ROS-mediated apoptosis of mammalian cells (Chen and Dickman, 2005, Krishnan et al., 2008). Its protective role has been also reported in plants and microbes against osmotic stress (Khedr et al., 2003, Xu et al., 2010). The discovery of the metabolite in substantially higher relative concentration in GPEI-treated V. dahliae cultures compared to the untreated, is an indication that the nanoparticle exerts its toxicity via a mechanism that plausibly results in oxidative and/or osmotic stresses. On

Journal Pre-proof the contrary, such mechanism seems not to operate in response to treatments with HPEI or QPEI. Additionally, results dictate a higher affinity of the guanidinium groups than the quaternary ammonium groups, with the negatively charged cell membranes (the cell membranes of bacteria are rich in acidic phospholipid) (Matsuzaki, 2009). It is well established that the guanidinium groups interact both electrostatically and through hydrogen bonding with the phosphate, carboxylate, or sulfate groups located on the cell surface because of the electronic

oo

f

and geometrical complementarity of these moieties, further facilitating the transport of dendritic polymers through liposomal and cell membranes (Pantos et

pr

al., 2008, Theodossiou et al., 2008). This process triggers a membrane

e-

stabilization mechanism, part of which is L-proline.

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The levels of the non-reducing disaccharide α,α-trehalose exhibited an opposite trend compared to those of L-proline, with HPEI and QPEI treatments leading to

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higher levels compared to the untreated cultures (Figure 8). The disaccharide is

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among the most well-known fungal metabolites associated with their responses to

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various stresses, sporulation, and pathogenesis (Iturriaga et al., 2009, Lowe et al., 2009, Kalampokis et al., 2018). Higher trehalose concentrations are associated with the static phase of fungal cells (Jorge et al., 1997). In the soil-borne fungal pathogen R. solani, its accumulation during sclerotia maturation safeguards the structure and functionality of their membranes under extreme conditions (Aliferis and Jabaji, 2010). Also, a positive correlation between the content of hyphae in α,α-trehalose and the susceptibility of Fusarium graminearum to the a.i. carbendazim has been reported (Sevastos et al., 2018). Based on the above, it seems that treatments of V. dahliae with HPEI and QPEI exert a similar stress level and trigger a similar metabolic response by the fungus in its effort to

Journal Pre-proof counteract their toxicity, however, GPEI, seems to trigger differential stress responses. Furthermore, the above findings are in agreement with previous reports on the oxidative stress being one of the major mechanisms by which

f

various nanoparticles exert their toxicity (Schnackenberg et al., 2012).

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3.6.3 Effects of the HPEI-derived nanoparticles on the operation of Krebs cycle

e-

Treatment with GPEI caused a distinct pattern in the fluctuation of the majority of the identified Krebs cycle intermediates (KCI) (e.g. malate, fumarate, 2-

Pr

oxoglutarate) (Figure 8). The Krebs or tricarboxylic acid cycle (TCA), operates in the mitochondrial matrix leading to the oxidation of carbohydrates and the

al

production of the reducing equivalents FADH2 and NADH in filamentous fungi

rn

(Ryan et al., 2019). Energy production represents an essential function that

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represents the target for many commercially successful fungicides such as the succinate dehydrogenase inhibitors (SDHI), Quinone outside inhibitors (QoIfungicides), and Quinone inside inhibitors (QiI - fungicides) (Aliferis and Jabaji, 2011, FRAC, 2019). It additionally plays an essential role in cells’ metabolism and energy homeostasis, and provides precursor molecules (e.g. α-ketoglutarate) for the biosyntheses of metabolites and proteins. Furthermore, recent evidence suggest the involvement of several KCI in non-metabolic signalling processes (de Castro Fonseca et al., 2016, Ryan et al., 2019). All treatments of V. dahliae with the nanoparticles caused a disturbance in the function of TCA as it is indicated by the substantial fluctuation of the levels of

Journal Pre-proof many of its components (Figure 8). However, treatment with GPEI seems to cause a downregulation of its function (possibly decreased respiration), since KCI such as, malate, fumarate, and 2-oxoglutarate were detected in substantially lower levels compared to the untreated. Such disturbance of the KCI activity is expected to destabilize the energy production of the fungus and affect its metabolism regulation. The distinct impact of the three nanoparticles on the function of Krebs cycle, represents an additional evidence on the operation of differential

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f

mechanism of toxicities.

Interestingly, substantially elevated levels of the non-protein amino acid γ-

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aminobutyrate (GABA), were recorded as a common response of the fungus to the

e-

treatments with the nanoparticles (Figure 8). GABA is a product of L-glutamate

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decarboxylation (Figure 8), which is further catabolized to succinate. The latter exhibited a similar trend to that of GABA following all treatments. Under certain

al

conditions, GABA can serve as a source of C and N by filamentous fungi

rn

(Solomon and Oliver, 2001). Additionally, in plants (Kinnersley and Turano, 2000,

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Gilliham and Tyerman, 2016) and microbes (Chevrot et al., 2006, Reyes-García et al., 2012, Mead et al., 2013), it plays an essential role as a signaling compound, regulating various physiological processes. Furthermore, GABA through the GABA shunt, seems to play another important role providing succinate, as an alternative pathway to various steps of the Krebs cycle (Kumar and Punekar, 1997, Feehily and Karatzas, 2013). Taken together, GABA and the activation of GABA shunt seem to be among the major responses of V. dahliae to the nanoparticle-induced toxicity. Nonetheless, further experimentation is required for the elucidation of the underlying operating mechanism. Also, results on the involvement of GABA are in agreement with observations on the ascomycete Stagonospora nodorum,

Journal Pre-proof suggesting an essential role of the amino acid in its responses to stresses and its asexual development (Kinnersley and Turano, 2000).

3.6.4 The HPEI-derived nanoparticles negatively affect the biosynthesis of fatty

oo

f

acids in Verticillium dahliae

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With the exception of the levels of oleate following treatments with GPEI and QPEI, elevated levels of the polyunsaturated fatty acids (PUFAs) were recorded

e-

following treatments of V. dahliae with the HPEI-derived nanoparticles (Figure 8).

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PUFAs regulate various functions related to conidial production, morphogenesis, and mycelial growth (Calvo et al., 2001, Tisch and Schmoll, 2010). Their

al

biosynthesis begins with the carboxylation of acetyl-CoA to malonyl-CoA by acetyl-

rn

CoA carboxylases (ACCs), which provides the necessary substrate to be utilized

Jo u

by fatty acid synthase (Hunkeler et al., 2016). Nonetheless, such process is controlled by a complex regulatory mechanism of the carbohydrate, lipid and amino acid biosynthetic pathways (Vorapreeda et al., 2012). The groups of aryloxyphenoxypropionates

(FOPs),

cyclohexanediones

(DIMs),

and

phenylpyrazolins (DENs) include herbicidal a.i. which exhibit their toxicity by inhibiting the biosynthesis of fatty acids by inhibiting the ACC (Aliferis and Jabaji, 2011, HRAC, 2019). Nonetheless, the current data and the complexity of PUFAs biosynthesis do not allow to conclude on the cause of their decreased levels in the fungal hyphae; the diversion of fungal resources to cover other biosynthetic

Journal Pre-proof priorities responding to the treatments with the nanoparticles or the inhibition of ACC are two plausible scenarios.

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4. Conclusion

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The recent advances in nanotechnology represent a major opportunity and at the

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same time a challenge, for applications in plant protection. To date, little is known

e-

on the QSAR and the mechanism(s) by which nanomaterials exert their bioactivity. Therefore, the acquisition of the relevant knowledge by implementing advanced

Pr

tools is demanding. Our study demonstrated that the bioactivity of HPEI-derived

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nanoparticles is highly correlated to the type of their functional end groups, with guanidinium groups increasing, while quaternary ammonium groups decreasing

rn

the bioactivity of the obtained nanoparticles. At the subcellular level, the

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nanoparticles being studied seem to exhibit a different MoA and mechanisms of toxicity, as it is revealed by the results of GC/EI/MS metabolomics analysis. Although no solid conclusions can be drawn on the exact MoA of the nanoparticles HPEI, QPEI, and GPEI, a general disturbance of the chemical homeostasis of V. dahliae, and oxidative and osmotic stresses were attributed to their toxicity. The results could be further exploited in the synthesis of HPEI-derived nanoparticles towards the optimization of their fungitoxicity and the development of new nanoPPPs, in which such material could be used per se as the a.i. or as nanocarriers. To the best of our knowledge, our study is the first report on the effects of nanoparticles on a phytopathogenic fungus applying metabolomics.

Journal Pre-proof Figure Legends

Figure 1. Diagrams displaying the total number of publications that were retrieved performing searches using the tools of the ISI Web of Knowledge SM, and acquiring for the terms “Nanomaterial(s)” and “agriculture” or “pesticide(s)”.

poly(ethyleneimine)

(HPEI),

guanidinylated

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Figure 2. Schematic representation of the chemical structures of hyperbranched hyperbranched

poly(ethyleneimine)

e-

pr

(GPEI), and quaternized hyperbranched poly(ethyleneimine) (QPEI) nanoparticles.

with

various

concentrations

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Figure 3. Phenotypes of untreated Verticillium dahliae cultures (Control) or treated of

hyperbranched

poly(ethyleneimine)

hyperbranched

poly(ethyleneimine)

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guanidinylated

hyperbranched

poly(ethyleneimine) (GPEI),

and

(HPEI),

quaternized

rn

(QPEI) nanoparticles, 14 days following

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treatments (A), and corresponding effects on their radial growth displayed using box plots (B). Three replications were performed per treatment and the different letters above boxes designate statically significant differences performing the Student’s t-test (P < 95%).

Figure 4. Orthogonal partial least squares-discriminant analysis (OPLS-DA) PC1/PC2 score plot (A) and hierarchical cluster analysis (HCA) (B) for the GC/EI/MS metabolite profiles of Verticillium dahliae 14 days following treatments with hyperbranched poly(ethyleneimine) (HPEI), guanidinylated hyperbranched poly(ethyleneimine) (GPEI), and quaternized hyperbranched poly(ethyleneimine)

Journal Pre-proof (QPEI) nanoparticles or untreated (C). HCA was performed applying the Ward’s linkage method. In total, 15 biological replications were performed per treatment, every three of which were pooled to give one pooled sample. Five pooled samples and one quality control sample were analyzed per treatment. The ellipse represents the Hotelling’s T2 with 95% confidence interval.

dahliae,

poly(ethyleneimine)

14

days

(HPEI),

following

guanidinylated

treatments

hyperbranched

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Verticillium

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f

Figure 5. Cluster heat map (A) of the recorded GC/EI/MS metabolite profiles of with

hyperbranched

poly(ethyleneimine)

e-

(GPEI), and quaternized hyperbranched poly(ethyleneimine) (QPEI) nanoparticles

Pr

(EC 50 concentrations) or untreated (C). Expansions of the selected regions (B) and (C) are also displayed. Two-dimensional (2D) hierarchical cluster analysis

al

(HCA) was performed applying the Ward’s linkage method. The rows represent

rn

metabolites or metabolic features and the columns the various treatments being

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performed. Each cell is color-coded based on changes of the relative concentration of the corresponding metabolite or metabolic feature using a colorscale ranging from -3 (light green), indicating low values, to 3 (light red) indicating high values. In total, 15 biological replications were performed per treatment, every three of which were pooled to give one pooled sample. Five pooled samples and one quality control sample were analyzed per treatment.

Figure 6. Effect of hyperbranched poly(ethyleneimine) (HPEI), guanidinylated hyperbranched

poly(ethyleneimine)

(GPEI),

and

quaternized

hyperbranched

Journal Pre-proof poly(ethyleneimine) (QPEI) nanoparticles (EC 50 concentrations) on the metabolite composition of Verticillium dahliae; grouping was based on the chemical group of metabolites (A) and the their participation in fungal metabolic pathways/functions, measured as instances, since each metabolite can be involved in multiple pathways (B). For each treatment bars with solid colour corresponds to metabolites whose concentration increased in response to the treatment, the wide

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relative concentration was not substantially altered.

f

upward diagonal coloured to those decreased, and dotted to metabolites, whose

e-

Figure 7. Variable influence on projection (VIP) plot displaying the VIP values of

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the identified Verticillium dahliae metabolites, sorted in descending order. Confidence intervals have been derived from jack-knifing (P < 95%). High values

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correspond to metabolites with the highest fluctuation in response to the

rn

treatments of V. dahliae cultures with hyperbranched poly(ethyleneimine)-based

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nanoparticles.

Figure 8. De novo constructed metabolic network of Verticillium dahliae displaying the fluctuations in the relative composition of metabolites 14 days following treatments

with

hyperbranched

hyperbranched poly(ethyleneimine)

poly(ethyleneimine)

(QPEI)

poly(ethyleneimine) (GPEI),

nanoparticles

and (EC 50

(HPEI),

guanidinylated

quaternized

hyperbranched

concentrations).

Metabolite

fluctuations are color-coded based on the means of scaled and centered PLS regression coefficients (CoeffCS). Dashed lines symbolize multi-step or not fully

Journal Pre-proof elucidated fungal biosynthetic pathway steps and solid lines consecutive steps in a biosynthetic pathway. The symbols underneath metabolites correspond, in order, to the fluctuation in the relative composition of HPEI, GPEI and QPEI-treated strains, compared to the corresponding untreated ones. For the construction of the network, data from the database of the Kyoto Encyclopedia of Genes and

e-

pr

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f

Genomes (KEGG) were used.

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Supplementary Material

Supplementary Figures

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Supplementary Figure 1. Proton nuclear magnetic resonance (1H NMR)

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rn

spectrum of guanidinylated hyperbranched poly(ethyleneimine) (GPEI).

Supplementary Figure 2. Carbon-13 nuclear magnetic resonance (13C NMR) spectrum of guanidinylated hyperbranched poly(ethyleneimine) (GPEI).

Supplementary Figure 3. AFM images of (A) hyperbranched poly(ethyleneimine) (HPEI),

(B)

guanidinylated

hyperbranched

poly(ethyleneimine)

(GPEI),

(C)

quaternized hyperbranched poly(ethyleneimine) (QPEI) nanoparticles deposited on mica from methanol solutions (2.5 μg mL -1).

Journal Pre-proof

Supplementary Figure 4. Representative total ion chromatograms (TIC) of Verticillium dahliae 14 days following treatments; control (A) and hyperbranched poly(ethyleneimine) (HPEI)-treated cultures (B). Annotations for representative

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identified metabolites are displayed. QC; quality control sample.

Supplementary Figure 5. Representative total ion chromatograms (TIC) of

(GPEI)

(A),

and

e-

poly(ethyleneimine)

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Verticillium dahliae 14 days following treatments; guanidinylated hyperbranched quaternized

hyperbranched

Pr

poly(ethyleneimine) (QPEI) nanoparticles (B). Annotations for representative

rn

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identified metabolites are displayed. QC; quality control sample.

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Supplementary Figure 6. The relative composition of the Verticillium dahliae endo-metabolome as recorded by GC/EI/MS. Major chemical groups of the identified metabolites are displayed.

Supplementary Figure 7. Orthogonal partial least squares-discriminant analysis (OPLS-DA) PC1/PC2 score plot for the pairwise comparisons between the GC/EI/MS metabolite profiles of untreated Verticillium dahliae cultures and those of hyperbranched poly(ethyleneimine) (HPEI) (A), guanidinylated hyperbranched poly(ethyleneimine) (GPEI) (B), or quaternized hyperbranched poly(ethyleneimine)

Journal Pre-proof (QPEI)-treated ones (C), 14 days post-treatment. The ellipse represents the Hotelling’s T2 with 95% confidence interval. Five pooled samples and one quality control sample were analyzed per strain. In total, three biological replications were

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pooled to provide each pooled sample.

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