Pesticide Mixture Toxicity in Surface Water Extracts in Snails

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Pesticide mixture toxicity in surface water extracts in snails (Lymnaea stagnalis) by an in vitro acetylcholinesterase inhibition assay and metabolomics Sara Tufi, Pim N.H. Wassenaar, Victoria Osorio, Jacob De Boer, Pim E.G. Leonards, and Marja H. Lamoree Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b04577 • Publication Date (Web): 22 Feb 2016 Downloaded from http://pubs.acs.org on February 28, 2016

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Environmental Science & Technology

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Pesticide mixture toxicity in surface water extracts in

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snails (Lymnaea stagnalis) by an in vitro

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acetylcholinesterase inhibition assay and metabolomics

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Sara Tufi1, Pim N.H. Wassenaar1, Victoria Osorio2, Jacob de Boer1, Pim E.G.

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Leonards1, Marja H. Lamoree1*

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1081 HV Amsterdam, The Netherlands

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Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1087,

KWR Watercycle Research Institute, Nieuwegein, The Netherlands.

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* Corresponding author: Dr. Marja Lamoree Institute for Environmental Studies (IVM) VU University Amsterdam De Boelelaan 1085 1081 HV Amsterdam The Netherlands e-mail: [email protected] phone: + 31 (0)20 89573 Fax:

+ 31 (0)20 59 89553

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Abstract

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Many chemicals in use end up in the aquatic environment. The toxicity of water samples can be tested

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with bioassays, but a metabolomic approach has the advantage that multiple endpoints can be

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measured simultaneously and the affected metabolic pathways can be revealed. A current challenge in

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metabolomics is the study of mixture effects. This study aims at investigating the toxicity of an

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environmental extract and its most abundant chemicals identified by target chemical analysis of >100

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organic micro-pollutants and Effect-Directed Analysis (EDA) using the acetylcholinesterase (AChE)

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bioassay and metabolomics. Surface water from an agricultural area was sampled with a large volume

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solid phase extraction (LVSPE) device using three cartridges containing a neutral, anionic and cationic

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sorbents able to trap several pollutants classes like pharmaceuticals, pesticides, PAHs, PCBs and

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perfluorinated surfactants. Targeted chemical analysis and AChE bioassay were performed on the

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cartridge extracts. The extract of the neutral sorbent cartridge contained most of the targeted

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chemicals, mainly imidacloprid, thiacloprid and pirimicarb, and was the most potent AChE inhibitor.

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Using an EDA approach other AChE inhibiting candidates were identified in the neutral extract, such

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as carbendazim and esprocarb. Additionally, a metabolomics experiment on the central nervous

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system (CNS) of the freshwater snail Lymnaea stagnalis was conducted. The snails were exposed to

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the extract, the three most abundant chemicals individually and a mixture of these. The extract

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disturbed more metabolic pathways than the three most abundant chemicals individually, indicating

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the contribution of other chemicals. Most pathways perturbed by the extract exposure overlapped with

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those related to exposure to neonicotinoids, like the polyamine metabolism involved in CNS injuries.

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Metabolomics for the straightforward comparison between complex mixture and single compound

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toxicity is still challenging but, compared to traditional biotesting, is a promising tool due to its

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increased sensitivity.

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Introduction

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The number of chemicals produced and used in our modern world is constantly growing. Many of

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those chemicals, like pesticides, household chemicals and mixtures of various chemicals in industrial

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waste end up in the environment and can cause a negative impact on the environment1. Chemicals in

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the aquatic ecosystem can influence the water quality, which may potentially have adverse effects on

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drinking water quality and biodiversity2. Effect-Directed Analysis (EDA) is one of the strategies to

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assess water quality. It is proposed to be integrated in the Water Framework Directive (WFD)3. EDA

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can potentially identify chemicals in complex mixtures that show a toxic response in a bioassay4,5. The

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bioassays that are often used focus on one specific toxicological endpoint, like estrogenic activity or 2 ACS Paragon Plus Environment


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photosynthetic inhibition6–8. In the EDA approach environmental extracts that show toxicity are

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fractionated to reduce the complexity of the mixture of chemicals. Those fractions are tested with the

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bioassay and the active fractions are subjected to chemical analyses in order to identify the chemicals

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that cause the toxicity. Usually, chromatographic techniques such as gas chromatography (GC) or

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liquid chromatography (LC) coupled to mass spectrometry (MS) are applied to identify these

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chemicals9,10. A commonly used bioassay to determine a toxic effect induced by pesticide exposure is

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the Ellman’s acetylcholinesterase (AChE) bioassay, which is linked to organophosphate (OP) and/or

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carbamate pesticides. These classes of pesticides inhibit the enzyme AChE11 which led to the

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accumulation of acetylcholine in the synaptic cleft, inducing an over-stimulation of the neuronal cells.

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As a consequence, the cholinergic function in the central nervous system (CNS), the peripheral

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nervous system and the motor components are impaired. However, pesticides like the neonicotinoids

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cannot be detected with the AChE bioassay since they act as acetylcholine receptor agonists12.

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Currently, there is a gap between the chemicals mode of action and the bioassay designed to test

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chemicals toxicity. As an example, for testing neonicotinoids toxicity a bioassay has not been designed

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yet. Therefore, even if a battery of bioassays is applied to test the quality of water samples, the toxicity

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of some classes of compounds will never be detected. An alternative strategy to test water quality and

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overcome the major pitfall of current biotesting strategies is metabolomics11,12. With this approach

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multiple endpoints (metabolites) can be measured simultaneously after exposing the whole organism

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to an environmental sample, thus enabling the study of the metabolism in a more realistic

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environmental exposure scenario13. Many studies in environmental metabolomics have investigated

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the effect of single compounds or a simple mixture of compounds14–18. One of the current challenges in

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environmental metabolomics is the study of complex chemical mixture effects, like an environmental

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sample. In this study the toxicological effects of an environmental water extract and its most abundant

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chemicals were investigated by applying an EDA using the AChE bioassay, and metabolomics on the

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central nervous system (CNS) of the freshwater snail Lymnaea stagnalis.

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Surface water was sampled in an agricultural area of the Netherlands using a large solid phase

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extraction (LVSPE) device was equipped with three cartridges placed in series containing neutral,

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anionic and cationic sorbents. Target chemical analysis on 119 chemicals was performed on the

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extracts of the three cartridges. Since the sampling location was known to be affected by pesticide

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pollution, the AChE inhibition potency was tested which is commonly used to detect toxicity of

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organophosphate and carbamate pesticides. To identify other chemical in the environmental extract, an

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EDA study was carried out using an on-line high pressure liquid chromatography - time of flight

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(HPLC-ToF) micro-fractionation system. Since the AChE bioassay can target only certain classes of

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chemicals, targeted and non-targeted metabolomics was used as alternative test of contaminant 3 ACS Paragon Plus Environment

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mixtures to evaluate the toxic effects of the environmental extract. The effects on the snail L. stagnalis

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CNS were studied since most pesticides are affecting the nervous system. The snails were exposed to

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the extract of the neutral sorbent cartridge, to the three most abundant chemicals individually, and to a

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mixture of these chemicals. The biological interpretation was facilitated by the use of biochemical

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mapping to identify the correlation between the surface water toxicity and its most abundant

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chemicals.

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Materials and Methods

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Water sampling and extraction

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Forty-five L of surface water was sampled at a location in the province Zuid-Holland in the

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Netherlands (geographical coordinates of 52°00'08.5"N and 4°17'35.2"E). The sampling was carried

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out in a ditch close to an agricultural area. Chemicals in this surface water were extracted by a LVSPE

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device, in which three different filter cartridges were placed in series. The first cartridge contained a

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hydrophobic polystyrene-divinylbenzene copolymer (HR-X) (neutral sorbent); the second contained a

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weak anion exchanger (HR-XAW) and the last cartridge contained a cationic exchanger (HR-XCW).

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The choice of these sorbents has been made in order to increase the retention capability of different

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classes of chemicals from neutral compounds to strong acids with a pKa < 1 and strong bases with pKa

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> 10. In the manuscript, the neutral extract refers to the extract from the neutral sorbent cartridge

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whereas cationic and anionic extract correspond to the extracts from the cationic and anionic sorbent

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cartridges, respectively. The chemicals in the neutral cartridge were eluted with 200 mL methanol

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(MeOH):ethylacetate 50:50 v/v, the chemicals in the anionic cartridge with 100 mL MeOH 2%

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ammonia 7N, and the chemicals in the cationic cartridge with 100 mL 1.7% formic acid (Fluka,

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Steinheim, Germany). As blank, 45 L of bottled water (Gerolsteiner, Gerolstein, Germany), underwent

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the same processes.

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Each cartridge extract was divided into aliquots for the different experiments. For the AChE bioassay

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2 mL of each extract were concentrated by evaporation to 0.2 mL, corresponding to a 150 times higher

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concentration than the original water sample (environmental concentration factor, ECF). The same

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process was used for the EDA study. For the metabolomics experiment, 84 mL of the extract were

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dissolved in 1.2 L of copper free water suitable for the snail exposure. The final concentration in the

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snail exposure media corresponded to 16 ECF.

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Target chemical analysis

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A list of 119 emerging organic micro-pollutants (see Table S1) was selected for chemical screening of

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the surface water extracts. The selection criteria of the candidate substances were based on: (i)

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occurrence in surface water, (ii) inclusion of a broad range of physicochemical properties and (iii)

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biological activity. This long list was classified according to their different uses, namely:

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pharmaceuticals and metabolites (55), herbicides and metabolites (32), insecticides (7), fungicides (2),

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flame retardants (5), plasticizers (1), industrial chemicals (14), and artificial sweeteners (3). This target

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chemical analysis was conducted by using an Orbitrap interfaced with a HPLC-system (Thermo

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Electron GmbH, Bremen, Germany) (see Supporting information). 5 ACS Paragon Plus Environment

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Acetylcholinesterase bioassay

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The extracts of the neutral, anionic and cationic cartridges were tested with regard to AChE inhibition

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with the AChE bioassay. As a first step glycerol (purity ≥ 99%, Sigma-Aldrich) was added to all

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extracts, at a final concentration of 50 mg/mL. To the cationic extracts an additional 10% of

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ammonium hydroxide was added to buffer the pH. The extracts were dried with a gentle nitrogen flow

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and reconstituted with potassium-phosphate buffer (0.1 M, 7.5 pH) in such an amount that the extracts

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were concentrated with a factor of 10. In addition, three procedural blanks consisting of the same

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solvents as the cartridge extracts were prepared to check the effect of the solvent on AChE inhibition.

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The bioassay procedure is described in the Supporting information.

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Effect-directed analysis (EDA)

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The most active cartridge extract and its corresponding blank were fractionated in an EDA approach to

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identify the causative compounds of the AChE inhibition. Before fractionation, the neutral and the

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neutral blank extracts were dried completely with a gentle nitrogen flow and subsequently

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reconstituted with water (H2O):acetonitrile (ACN) 90:10 v/v. These two extracts were fractionated

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using an on-line HPLC-micro-fractionation system with a ToF-MS in parallel to identify the

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compounds of interest. The experimental setup is described in the Supporting information. Analytical

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standards were used to calibrate the fractionation system (Table S2).

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L. stagnalis exposure

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L. stagnalis snails were taken from a synchronized population and cultured at the VU University

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Amsterdam, The Netherlands as described by Tufi et al. (2015)19. The neutral and neutral blank

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extracts were used to expose the snails. 84 mL of the extract the blank were completely dried using a

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Buchi syncore coupled to a BUCHI vacuum pump V-700 with vacuum controller V885 and a Buchi

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recirculating chiller F-108 (Buchi, Flawil, Switzerland) for 3 h at 240 mbar with 300 rpm, a rack

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temperature of 50 °C and lid temperature of 60 °C, followed by 1 h at 120 mbar. Afterwards, the

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extracts were diluted with copper free water to a concentration factor of 16 ECF.

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Besides the neutral extract and the neutral blank, the snails were also exposed to i) the three most

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abundant chemicals individually at concentrations corresponding to the neutral extract (imidacloprid,

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thiacloprid, and pirimicarb which were exceeding the normative levels in the environmental extract),

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and ii) to a mixture of them at the same concentration levels. In addition, a control group was assessed

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without an exposure. Imidacloprid, thiacloprid, and pirimicarb were dissolved in copper free water.

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Each exposure group contained seven snails. Each snail was separately placed in a beaker glass with

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150 mL copper free water and fed with 250 µL Tetraphyll (133 g/L in copper free water; Tetra, Melle, 6 ACS Paragon Plus Environment


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Germany). After 2 days of conditioning, the snails were placed in a new beaker glass with 150 mL of

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the exposure medium. After 48 hours the snails were put in an aluminium box (Sanbio, Uden, The

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Netherlands), snap frozen in liquid nitrogen and the CNSs dissected. The CNS samples were stored in

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0.5 mL Precellys vials (Bertin Technologies, France) containing ± 10 ceramic beads (1.4 mm,

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zirconium oxide; MO Bio Laboratories, CA, USA) and snap frozen in liquid nitrogen. Subsequently,

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the samples were stored at -80 °C.

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Actual exposure concentrations

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Aliquots of the exposure media were collected at the start of each experiment and analysed with LC-

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MS to determine actual exposure concentrations of pirimicarb, imidacloprid and thiacloprid (see

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Supporting information and Tables S3-5).

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Targeted and non-targeted metabolomics

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The CNS samples were processed individually and analyzed with the non-targeted and targeted

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metabolomics approaches described by Tufi et al. (2015)19,20. The extracted metabolites were analyzed

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by using hydrophilic interaction chromatography (HILIC) coupled to a ToFMS (Bruker Daltonics).

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For the non-targeted platform >500 standards from the MS Metabolite Library of Standards (MSMLS;

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IROA Technologies, MI, USA) were analyzed to determine the retention time and mass spectrum. The

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chromatograms were processed with Bruker Daltonics Compass PathwayScreener (Bruker Daltonics)

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to perform batch targeted analyses by screening for metabolites of the MSMLS library. Multiple

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hypotheses testing was performed using the areas of the detected metabolites and Bruker Daltonics

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Compass ProfileAnalysis (Bruker Daltonics). A t-test analysis was conducted on areas of the detected

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metabolites comparing two different exposure groups and FDR correction (p-value < 0.05, t-test).

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Biochemical networks

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From the detected metabolites biochemical networks were built with Metamapp setting the threshold

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at 0.7 for chemical similarity21. The formed network, based on chemical similarities and biochemical

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interactions, was uploaded in Cytoscape (v3.1.0) in which metabolic changes between the control

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group and different exposure groups were visualized22. Fold changes were calculated by dividing the

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average levels of metabolites in exposed groups by the average levels of metabolites in the control

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group. Visualization was based on fold changes and p-values. Exposure to the neutral extract was

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compared to the neutral blank extract, and exposures to the individual chemicals and the mixture of

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chemicals were compared to the control group. Biological interpretation of the results was performed

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using the small molecular databases Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human

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Metabolome Database, the Small Molecule Pathway Database and the Biocyc database collection.

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Results and Discussions

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Selection of the most active environmental extract

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Forty chemicals were quantified in the extracts (Table S6) and the concentrations of those compounds

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detected above the level of 10 ng/L are shown in Table 1. The highest concentrations were found in

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the neutral extract suggesting a higher retention of this sorbent for organic compounds. High

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concentrations of some chemicals were also found in the anionic extract indicating a potential

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cartridge breakthrough due to the overloaded capacity of the first neutral sorbent.

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The most abundant chemicals in the neutral extract were the carbamate pesticide pirimicarb (0.27

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µg/l), and the two neonicotinoid pesticides imidacloprid (0.21 µg/l) and thiacloprid (0.16 µg/l) (Table

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1). Their concentrations exceed the normative levels. Pirimicarb exceeded the maximum permissible

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risk (MPR) concentration 4-fold, while the concentration of imidacloprid was four times higher than

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the annual average environmental quality standard (AA-EQS), and thiacloprid exceeded the AA-EQS

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up to 20-fold, when the concentrations quantified in the neutral, cationic and anionic extracts were

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summed.

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The high concentrations of pesticides are consistent with the sampling location which is an agricultural

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area with greenhouses. From other studies it is well known that pesticides can be transferred through

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runoff waters into surface waters23. Our results showed comparable levels as found in monitoring

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programs at this location that are reported in the Pesticides Atlas of The Netherlands24. Thiacloprid

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was not detected in the latest available data (2013) from these monitoring programs, suggesting that

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thiacloprid has been widely used in that area later.



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Table 1. Results of the target chemical analysis, showing the most abundant chemicals identified. The columns 4-9 show the concentrations of the compounds in the extracts in µg/L (ND = not detected). Ins: Insectides, Ph: Pharmaceuticals, Herb: Herbicides, Herb M: Herbicide metabolites, Fung: Fungicides, FR: Flame Retardants, OC: Organic compounds, IC: Industrial chemicals. AA-EQS: annual average environmental quality standard, MAC-EQS: maximum allowable concentration-EQS, MPR: (.

Compound

Class

Pirimicarb

Ins

Imidacloprid

Ins

Thiacloprid

Ins

Caffeine

Ph

MCPA

Herb

Carbendazim

Fung

2.4-Dinitrophenol

Herb

4.6-dinitro-o-cresol

Herb

2.6-Dichloro benzamide Triethyl phosphate

Herb. M. FR

Mecoprop

Herb

Metalaxyl-M HMMM 4-Methyl-1H-benzotriazole Paracetamol

Fung OC IC Ph

Norm levels (µg/L)1 0.090 (MPR) 0.067 (AA) 0.2 (MAC) 0.01 (AA) 0.11 (MAC) ND 1.4 (AA) 15 (MAC) 0.6 ( AA) 0.6 (MAC) 0.001 (MPR) 9.2 (AA) 9.2 (MAC) 1000 (MPR) 440 (MPR) 18 (AA) 160 (MAC) 9.7 (MPR) 810 (MPR) ND ND

Neutral Extract (µg/L)

Neutral Blank (µg/L)

Anionic Extract (µg/L)

Anionic Blank (µg/L)

Cationic Extract (µg/L)

Cationic blank (µg/L)

0.27

ND

0.099

ND

0.001

ND

0.21

ND

0.063

ND

0.001

ND

0.16

ND

0.044

ND

ND

ND

0.052

0.001

0.012

ND

0.004

0.003

0.034

ND

0.032

ND

0.001

ND

0.031

ND

0.017

ND

ND

ND

0.017

ND

0.023

ND

ND

ND

0.017

ND

0.013

ND

ND

ND

0.015 0.015

ND 0.7

0.006 0.005

ND 0.6

0.002 0.009

ND 0.009

0.014

ND

0.011

ND

0.1

ND

0.010 0.009 0.008 0.005

ND ND ND ND

0.003 0.005 0.005 0.005

ND 0.1 ND ND

ND ND ND 0.003

ND ND ND ND


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The neutral extract showed the inhibitoriest potential in the AChE assay (Figure 1).

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These results are in accordance with the target chemical analysis which showed that the neutral extract

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contained most chemicals. A surprising result was obtained for the anionic extract, which expressed

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AChE stimulation at 15 and 60 ECF. This trend was also observed for the cationic extract, however, not

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statistically different from the blank. In the literature AChE inhibiting effects have been described

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extensively. However, AChE stimulating effects are seldom reported. A study conducted by López et al.

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(2015) described an AChE activating effect of monoterpenoids, plant oils, which showed an AChE

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stimulation effect at low concentrations (0.04 mM) but an AChE inhibiting effect at high concentrations

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(5 mM)25. There is a possibility that the anionic and cationic extracts contain chemicals which exert such

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effects. Another possible explanation for the observed AChE stimulation might be the presence of humic

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substances, like humic and fulvic acids, which are major components of natural organic matter in soil and

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water. These acids have a light yellow to dark brown colour26 and the yellow colour was indeed observed

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in the anionic extract and might therefore indicate the presence of humic substances. Nevertheless, it has

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been shown that humic substances, co-extracted by solid-phase extraction, can have a suppressing effect

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on the AChE inhibition assay27. To verify the potential interference of the matrix we performed the AChE

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bioassay with the extract and the reagents but without the enzyme. Since we did not observe any change

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in AChE activity, we could exclude a matrix effect in the AChE bioassay.

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Effect-Directed Analysis

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The target chemical analysis showed that pirimicarb and the fungicide carbendazim could be responsible

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for the observed AChE inhibition. These chemicals are mainly interacting with mitotic division, but they

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also have AChE inhibiting properties28. In order to identify other potential candidates that contributed to

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the AChE inhibition in the neutral extract, the neutral extract and the blank underwent an EDA

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fractionation study coupled to the AChE bioassay. A screening approach was used to identify chemical in

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the fractions. The most active fractions (well position 12, 13, 15, 19, 23, 36 and 37) of the neutral extract

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are shown in Figure S1 and the identified chemicals in the active fractions are shown in Table S8. Most of

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the identified compounds in the active fractions belong to the class of carbamate pesticides. The pesticide

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aldicarb was found in fraction 13, pirimicarb in fraction 19, and esprocarb in fractions 36 and 37. These

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identified pesticides were confirmed by mass accuracy, chemical formula and isotope distribution

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matches. To provide an additional level of confidence in these compounds identification, analytical

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standards were bought and the retention time has been compared. Even though these chemicals could

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potentially explain the AChE inhibition of the neutral extract, the identification of the chemicals in the

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active fractions 15 and 23 was not successful.

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Metabolomics of the neutral surface water extract, its most abundant chemicals, and mixture 10 ACS Paragon Plus Environment


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Snails were exposed to: (i) the neutral extract, (ii) the three most abundant chemicals (imidacloprid,

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pirimicarb and thiacloprid) individually and (iii) a mixture of these chemicals at the same levels as the

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individual concentrations. The measured exposure concentrations of the metabolomics experiments are

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shown in Table S6. The results show that the imidacloprid concentrations were similar among the

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exposure groups (Table S6). Thiacloprid concentration in the mixture was about 20% higher than in the

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neutral extract and the individual exposures, whereas pirimicarb was 30% lower than in the neutral extract

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and 20% lower than in the mixture. The neutral extract induced many alterations at the molecular level in

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the CNS: the levels of eighteen metabolites were found to be altered. Even though some altered pathways

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could be correlated to the three most abundant chemicals, the overall metabolic change could not be

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explained by the simplified mixture. The environmental extract perturbed more metabolic pathways,

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indicating therefore the contribution of other chemicals and possibly synergistic effects to the observed

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toxicity.

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To further investigate the results of the metabolomics experiments, the data were visualized in

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biochemical networks to identify affected metabolite groups and pathways. The comparison between the

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neutral extract and the neutral blank extract showed 18 statistically significant metabolites of which most

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were down regulated (± 80%) (Figure 2). Exposure to the single pesticides, on the other hand, showed

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fewer statistically significant differences and in the mixture exposure there were only four significantly

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changed metabolites (Figure S2-S5).

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Exposure to pirimicarb showed to disturb only few metabolites including methionine, 4-hydroxy-L-

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proline, inosine and hypoxanthine (ca. 3 fold increase). Increased hypoxanthine levels were related to

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increased oxygen radicals29. A study conducted by Wang et al. (2014) showed that pirimicarb exposure

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caused increased oxidative stress, which might be associated to the observed increased level of

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hypoxanthine. Furthermore, pirimicarb was related to a disturbed energy metabolism30. The interpretation

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of the metabolite biochemical role has been based on the mammalian metabolic pathways reported in

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KEGG. This database is increasing the number of annotated species but the metabolism of the snail

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Lymnaea stagnalis has not been reported yet. Therefore some discrepancies might raise from the direct

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comparison of diverse species metabolisms, even though, metabolites are highly conserved among

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different species13.

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The metabolite patterns in the CNS of L. stagnalis exposed to imidacloprid or thiacloprid are expected to

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be comparable, since these chemicals have the same mode of action and therefore their effect is supposed

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to be additive (following the concentration addition model). Therefore, it was expected that they would

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affect the same metabolites. Thiacloprid and imidacloprid indeed showed many similar metabolic

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alterations. They decreased the levels of the metabolites ornithine and lysine. Ornithine is involved in 11 ACS Paragon Plus Environment

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multiple metabolic pathways, including the spermidine and spermine biosynthesis, and in arginine and

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proline metabolism. Imidacloprid showed a significant decrease in spermidine, proline and arginine

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levels, which indicates involvement of these pathways. A study conducted by Moser et al. (2015), who

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exposed rats to imidacloprid, showed the same altered pattern for ornithine, proline and spermidine.

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Furthermore, they showed that citrulline, also involved in the arginine and proline metabolism, was

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decreased as well31. This supports the hypothesis that neonicotinoids play a role in these pathways.

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Although imidacloprid and thiacloprid have overlapping metabolic patterns, some differences were also

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observed. For instance, glucosamine, involved in amino sugar metabolism, was up-regulated after

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imidacloprid exposure and downregulated after thiacloprid exposure. Glucosamine is related to chitin

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metabolism and is for example used as building block for the exoskeletons of arthropods, like insects and

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crustaceans, the shells of mollusks and the cell walls of fungi32. Dondero et al. (2010) showed that

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thiacloprid exposure, in comparison to imidacloprid, resulted in severely decreased chitinase mRNA in

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the marine mussel Mytilus galloprovincialis, which is also involved in chitin metabolism32.

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The exposures to the three single pesticides induced changes in a higher number of metabolites compared

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with the alteration shown after the exposure to their mixture. Indeed, only four metabolites were

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significantly different after the mixture exposure. For example, the decrease of lysine, observed for all

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three single compounds, was levelled out in the mixture. This effect has been already observed in other

302

studies as well30,32 and might be caused by interaction effects33. These interactions might be due to the

303

internal dose or bioavailable concentration of the chemical by its toxicokinetics (absorption, distribution,

304

metabolism and excretion) and/or to the chemical toxicodynamics through the binding of one of more

305

chemicals to a receptor through which toxicity may be mediated.

306

Moreover, it has been shown that chemicals with the same mode of action did not show additive effects

307

when mixed, suggesting that these chemicals have non-similar effects, probably due to differences in

308

compound-specific toxicodynamics14,32,34.

309

The neutral extract exposure showed many significantly changed metabolites and most of them were not

310

found after the single pesticide exposures indicating that other chemicals in this extract also play a big

311

role in the changes. Only few metabolites were also found to change significantly after the individual

312

pesticide exposures. For instance, the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA; ca. 3

313

fold decrease) was decreased with the same fold change as after the thiacloprid exposure. The metabolites

314

guanosine, adenosine, adenine, inosine and hypoxanthine were affected by the individual compounds as

315

well. These metabolites are involved in purine nucleotide degradation, which has been linked to

316

lymphocyte deficiencies35, and lymphocyte deficiencies have been related to thiacloprid exposure32.

317

Furthermore, imidacloprid has shown to affect purine nucleotide degradation by increasing the activity of 12 ACS Paragon Plus Environment


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xanthine oxidase, converting hypoxanthine to xanthine. This reaction is responsible for the release of

319

oxygen radicals, which can cause additional damage and disturbances in tissues and organs36. Pirimicarb

320

showed to disturb the purine nucleotide metabolism by increasing hypoxanthine.

321

The neutral extract also affected spermidine and spermine biosynthesis, which was also affected by

322

imidacloprid and thiacloprid but other metabolites changed in this pathway (ornithine and spermidine

323

instead of 5’-methylthioadenosine, N(1)-acetylspermine, N-acetylputrescine and putrescine) (Figure 3).

324

It seemed that chemicals in the neutral extract were blocking the putrescine break down, resulting in an

325

increased putrescine level. Putrescine can react with S-adenosylmethioninamine to form spermidine and

326

5’-methylthioadenosine, or can be degraded to N-acetylputrescine. Spermidine, 5’-methylthioadenosine

327

and N-acetylputrescine showed decreased fold changes, supporting the hypothesis that putrescine break

328

down is inhibited. Furthermore, N(1)-acetylspermine, a break down product of spermine or a building

329

block of spermidine, was decreased as well, which might indicate a shift in the formation of spermidine

330

through a different route. Increases in putrescine levels have been related to pathological changes that

331

cause cell injury in the CNS, like severe metabolic stress, exposure to neurotoxins and seizure37. These

332

increased putrescine levels are mainly caused by an increased activity of the enzyme ornithine

333

decarboxylase (ODC), which degrades ornithine into putrescine37–39. Furthermore, S-adenosylmethionine

334

(SAM) and the enzyme S-adenosylmethionine decarboxylase which transforms SAM into S-

335

adenosylmethioninamine (dcSAM) might be depleted or inhibited, respectively, after CNS injury40.

336

Additionally, most CNS injury cases39,40 induced unaltered or only slightly changed in the levels of

337

spermine and spermidine.

338

The observed effects of the neutral extract on the spermidine and spermine biosynthesis might partially be

339

explained by the neonicotinoids imidacloprid and thiacloprid. The observed effects of the neutral extract

340

on the metabolites histidine, kynurenic acid, trigonelline and cytosine cannot be assigned to one of the

341

three most abundant chemicals and, therefore, these effects were probably caused by other chemicals in

342

the extract. For instance, decreased kynurenic acid levels have been related to pyrethroid exposure, which

343

might be present in the extract41. Furthermore, paracetamol, which was detected in the neutral extract, has

344

been related to decreased trigonelline levels as well as decreased SAM42.

345

In conclusion, the observed toxicity of the neutral extract in the metabolomics experiment could not be

346

explained entirely by the most abundant chemicals in the extract, although some metabolic patterns were

347

overlapping with the neutral extract exposure. Nevertheless, the use of metabolomics in environmental

348

extract exposure studies is very promising since it showed to be more sensitive than traditional toxicity

349

test, capable to focus on many endpoints simultaneously and able to provide information on the pathways

350

affected. The use of an aquatic model organism requiring a smaller volume of water, like Daphnia 13 ACS Paragon Plus Environment

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351

magna, can be advantageous for further research. With such organisms dose response effects of water

352

extracts can be studied, even with small amounts of water extract.

353 354

Acknowledgments

355

This study was carried out within the Marie Curie Research Training Network EDA-EMERGE

356

(www.eda-emerge.eu) supported by the EU (MRTN-CT-2012-290100).

357 358

Supporting Information

359

This information is available free of charge via the Internet at http://pubs.acs.org.

360 361 362

References

363 364

(1)

Daughton, C. G. “Emerging” chemicals as pollutants in the environment: A 21st century perspective. Renew. Resour. J. 2005, 23, 6–23.

365 366

(2)

European Environmental Agency. 10 messages for 2010 Freshwater ecosystems; Copenhagen, 2010.

367 368 369 370

(3)

Brack, W.; Govender, S.; Schulze, T.; Krauss, M.; Hu, M.; Muz, M.; Hollender, J.; Schirmer, K.; Schollee, J.; Hidasi, A.; et al. EDA-EMERGE: an FP7 initial training network to equip the next generation of young scientists with the skills to address the complexity of environmental contamination with emerging pollutants. Environ. Sci. Eur. 2013, 25 (1), 18.

371 372

(4)

Brack, W. Effect-directed analysis: a promising tool for the identification of organic toxicants in complex mixtures? Anal. Bioanal. Chem. 2003, 377 (3), 397–407.

373 374 375

(5)

Weiss, J. M.; Hamers, T.; Thomas, K. V.; van der Linden, S.; Leonards, P. E. G.; Lamoree, M. H. Masking effect of anti-androgens on androgenic activity in European river sediment unveiled by effect-directed analysis. Anal. Bioanal. Chem. 2009, 394 (5), 1385–1397.

376 377 378 379

(6)

Simon, E.; van Velzen, M.; Brandsma, S. H.; Lie, E.; Løken, K.; de Boer, J.; Bytingsvik, J.; Jenssen, B. M.; Aars, J.; Hamers, T.; et al. Effect-Directed Analysis To Explore the Polar Bear Exposome: Identification of Thyroid Hormone Disrupting Compounds in Plasma. Environ. Sci. Technol. 2013, 47 (15), 8902–8912.

380 381 382 383

(7)

Booij, P.; Vethaak, A. D.; Leonards, P. E. G.; Sjollema, S. B.; Kool, J.; de Voogt, P.; Lamoree, M. H. Identification of photosynthesis inhibitors of pelagic marine algae using 96-well plate microfractionation for enhanced throughput in effect-directed analysis. Environ. Sci. Technol. 2014, 48 (14), 8003–8011. 14 ACS Paragon Plus Environment


384 385 386

(8)

Fang, Y.-X.; Ying, G.-G.; Zhao, J.-L.; Chen, F.; Liu, S.; Zhang, L.-J.; Yang, B. Assessment of hormonal activities and genotoxicity of industrial effluents using in vitro bioassays combined with chemical analysis. Environ. Toxicol. Chem. 2012, 31 (6), 1273–1282.

387 388

(9)

Brack, W. Effect-Directed Analysis in Ecotoxicology. In Encyclopedia of Aquatic Ecotoxicology; Férard, J.-F., Blaise, C., Eds.; Springer Netherlands: Dordrecht, 2013; pp 387–394.

389 390 391 392

(10)

Weiss, J. M.; Simon, E.; Stroomberg, G. J.; de Boer, R.; de Boer, J.; van der Linden, S. C.; Leonards, P. E. G.; Lamoree, M. H. Identification strategy for unknown pollutants using highresolution mass spectrometry: Androgen-disrupting compounds identified through effect-directed analysis. Anal. Bioanal. Chem. 2011, 400 (9), 3141–3149.

393 394

(11)

Ellman, G. L.; Courtney, K. D.; Andres jr., V.; Featherstone, R. M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 1961, 7 (2), 88–95.

395 396

(12)

Buckingham, S.; Lapied, B.; Corronc, H.; Sattelle, F. Imidacloprid actions on insect neuronal acetylcholine receptors. J. Exp. Biol. 1997, 200 (21), 2685–2692.

397 398

(13)

Bundy, J. G.; Davey, M. P.; Viant, M. R. Environmental metabolomics: a critical review and future perspectives. Metabolomics 2009, 5 (1), 3–21.

399 400 401

(14)

Baylay, A. J.; Spurgeon, D. J.; Svendsen, C.; Griffin, J. L.; Swain, S. C.; Sturzenbaum, S. R.; Jones, O. a. H. A metabolomics based test of independent action and concentration addition using the earthworm Lumbricus rubellus. Ecotoxicology 2012, 21 (5), 1436–1447.

402 403 404

(15)

Kluender, C.; Sans-Piché, F.; Riedl, J.; Altenburger, R.; Härtig, C.; Laue, G.; Schmitt-Jansen, M. A metabolomics approach to assessing phytotoxic effects on the green alga Scenedesmus vacuolatus. Metabolomics 2008, 5 (1), 59–71.

405 406 407

(16)

Ye, Y.; Wang, X.; Zhang, L.; Lu, Z.; Yan, X. Unraveling the concentration-dependent metabolic response of Pseudomonas sp. HF-1 to nicotine stress by 1H NMR-based metabolomics. Ecotoxicology 2012, 21 (5), 1314–1324.

408 409 410

(17)

Jones, O. A. H.; Swain, S., C.; Svendsen, C.; Griffin, J. L.; Sturzenbaum, S. R.; Spurgeon, D. J. Potential New Method of Mixture Effects Testing Using Metabolomics and Caenorhabditis elegans. J. Proteome Res. 2012, 11 (2), 1446–1453.

411 412 413 414

(18)

Collette, T. W.; Teng, Q.; Jensen, K. M.; Kahl, M. D.; Makynen, E. A.; Durhan, E. J.; Villeneuve, D. L.; Martinović-Weigelt, D.; Ankley, G. T.; Ekman, D. R. Impacts of an Anti-Androgen and an Androgen/Anti-Androgen Mixture on the Metabolite Profile of Male Fathead Minnow Urine. Environ. Sci. Technol. 2010, 44 (17), 6881–6886.

415 416 417

(19)

Tufi, S.; Lamoree, M. H.; De Boer, J.; Leonards, P. E. G. Cross-platform metabolic profiling: application to the aquatic model organism Lymnaea stagnalis. Anal. Bioanal. Chem. 2015, 407 (7), 1901–1912.

418 419 420

(20)

Tufi, S.; Lamoree, M.; de Boer, J.; Leonards, P. Simultaneous analysis of multiple neurotransmitters by hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry. J. Chromatogr. A 2015.


Page 16 of 21

Page 17 of 21


421 422 423 424

(21)

Barupal, D. K.; Haldiya, P. K.; Wohlgemuth, G.; Kind, T.; Kothari, S. L.; Pinkerton, K. E.; Fiehn, O. MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinformatics 2012, 13 (1), 99.

425 426

(22)

Lopes, C. T.; Franz, M.; Kazi, F.; Donaldson, S. L.; Morris, Q.; Bader, G. D. Cytoscape Web: an interactive web-based network browser. Bioinformatics 2010, 26 (18), 2347–2348.

427 428 429

(23)

Cerejeira, M. J.; Viana, P.; Batista, S.; Pereira, T.; Silva, E.; Valério, M. J.; Silva, A.; Ferreira, M.; Silva-Fernandes, A. M. Pesticides in Portuguese surface and ground waters. Water Res. 2003, 37 (5), 1055–1063.

430 431 432

(24)

de Snoo, G. R.; Tamis, W. L. M.; Vijver, M. G.; Musters, C.; van ’t Zelfde, M. Risk mapping of pesticides: the Dutch atlas of pesticide concentrations in surface waters: www.pesticidesatlas.nl. Commun. Agric. Appl. Biol. Sci. 2006, 71 (2 Pt A), 49–58.

433 434 435 436

(25)

López, M. D.; Campoy, F. J.; Pascual-Villalobos, M. J.; Muñoz-Delgado, E.; Vidal, C. J. Acetylcholinesterase activity of electric eel is increased or decreased by selected monoterpenoids and phenylpropanoids in a concentration-dependent manner. Chem. Biol. Interact. 2015, 229, 36– 43.

437 438

(26)

Humus Chemistry: Genesis, Composition, Reactions, Second Edition (Stevenson, F. J.). J. Chem. Educ. 1995, 72 (4), A93.

439 440

(27)

Neale, P. A.; Escher, B. I. Coextracted dissolved organic carbon has a suppressive effect on the acetylcholinesterase inhibition assay. Environ. Toxicol. Chem. SETAC 2013, 32 (7), 1526–1534.

441 442 443

(28)

Palanikumar, L.; Kumaraguru, A. K.; Ramakritinan, C. M.; Anand, M. Toxicity, biochemical and clastogenic response of chlorpyrifos and carbendazim in milkfish Chanos chanos. Int. J. Environ. Sci. Technol. 2013, 11 (3), 765–774.

444 445

(29)

Rieger, D. Relationships between energy metabolism and development of early mammalian embryos. Theriogenology 1992, 37 (1), 75–93.

446 447 448

(30)

Wang, P.; Wang, H.-P.; Xu, M.-Y.; Liang, Y.-J.; Sun, Y.-J.; Yang, L.; Li, L.; Li, W.; Wu, Y.-J. Combined subchronic toxicity of dichlorvos with malathion or pirimicarb in mice liver and serum: A metabonomic study. Food Chem. Toxicol. 2014, 70, 222–230.

449 450 451 452

(31)

Moser, V. C.; Stewart, N.; Freeborn, D. L.; Crooks, J.; MacMillan, D. K.; Hedge, J. M.; Wood, C. E.; McMahen, R. L.; Strynar, M. J.; Herr, D. W. Assessment of serum biomarkers in rats after exposure to pesticides of different chemical classes. Toxicol. Appl. Pharmacol. 2015, 282 (2), 161–174.

453 454 455

(32)

Dondero, F.; Negri, A.; Boatti, L.; Marsano, F.; Mignone, F.; Viarengo, A. Transcriptomic and proteomic effects of a neonicotinoid insecticide mixture in the marine mussel (Mytilus galloprovincialis, Lam.). Sci. Total Environ. 2010, 408 (18), 3775–3786.

456 457 458

(33)

Spurgeon, D. J.; Jones, O. A. H.; Dorne, J.-L. C. M.; Svendsen, C.; Swain, S.; Stürzenbaum, S. R. Systems toxicology approaches for understanding the joint effects of environmental chemical mixtures. Sci. Total Environ. 2010, 408 (18), 3725–3734.



459 460 461

(34)

Gomez-Eyles, J. L.; Svendsen, C.; Lister, L.; Martin, H.; Hodson, M. E.; Spurgeon, D. J. Measuring and modelling mixture toxicity of imidacloprid and thiacloprid on Caenorhabditis elegans and Eisenia fetida. Ecotoxicol. Environ. Saf. 2009, 72 (1), 71–79.

462 463

(35)

Fox, I. H. Metabolic basis for disorders of purine nucleotide degradation. Metabolism. 1981, 30 (6), 616–634.

464 465

(36)

Duzguner, V.; Erdogan, S. Chronic exposure to imidacloprid induces inflammation and oxidative stress in the liver & central nervous system of rats. Pestic. Biochem. Physiol. 2012, 104 (1), 58–64.

466 467 468

(37)

Paschen, W. Polyamine metabolism in different pathological states of the brain. Mol. Chem. Neuropathol. Spons. Int. Soc. Neurochem. World Fed. Neurol. Res. Groups Neurochem. Cerebrospinal Fluid 1992, 16 (3), 241–271.

469

(38)

Seiler, N. Oxidation of Polyamines and Brain Injury. Neurochem. Res. 2000, 25 (4), 471–490.

470 471

(39)

Shohami, E.; Nates, J. L.; Glantz, L.; Trembovler, V.; Shapira, Y.; Bachrach, U. Changes in brain polyamine levels following head injury. Exp. Neurol. 1992, 117 (2), 189–195.

472 473 474

(40)

Adibhatla, R. M.; Hatcher, J. F.; Sailor, K.; Dempsey, R. J. Polyamines and central nervous system injury: spermine and spermidine decrease following transient focal cerebral ischemia in spontaneously hypertensive rats. Brain Res. 2002, 938 (1–2), 81–86.

475 476 477

(41)

Zielińska, E.; Kocki, T.; Saran, T.; Borbely, S.; Kuc, D.; Vilagi, I.; Urbańska, E. M.; Turski, W. A. Effect of pesticides on kynurenic acid production in rat brain slices. Ann. Agric. Environ. Med. AAEM 2005, 12 (2), 177–179.

478 479 480 481

(42)

Sun, J.; Schnackenberg, L. K.; Holland, R. D.; Schmitt, T. C.; Cantor, G. H.; Dragan, Y. P.; Beger, R. D. Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS. J. Chromatogr. B 2008, 871 (2), 328–340.


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Figure 1. Results of the AChE bioassay. A) The AChE activity of the neutral extract with an IC50 of 33.39 ECF (n=3, p-values < 0.05, Tukey’s test). B) The AChE activity of the anionic and cationic extracts, not showing any AChE inhibition (n=3, p-values < 0.05, Tukey’s test). 105x39mm (300 x 300 DPI)



Figure 2. Biochemical network based on fold changes of metabolites, comparing the CNS of L. stagnalis exposed to the neutral extract (16 ECF) and the neutral blank extract. Red and green dots indicate, respectively, a reduced or increased fold change, whereas grey dots indicate not detected metabolites. Big dots symbolize significant metabolites (n=7, p-values < 0.05, t-test). Arrows describe metabolic interactions and dashed lines chemical similarities. 203x196mm (300 x 300 DPI)


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Figure 3. Polyamine metabolism pathway, highlighting the catabolic and synthetic reaction and metabolites and enzymes involved (A). Metabolites of the polyamine pathway after exposure to the neutral cartridge extract (NEUTRAL) and imidacloprid (IMI) and thiacloprid (THIA) (B). The metabolites permutation is shown with a green arrow indicating an up-regulation and with a red arrow a down-regulation. The amplitude of these changes is given by the corresponding fold changes. 185x94mm (300 x 300 DPI)


Pesticide Mixture Toxicity in Surface Water Extracts in Snails

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