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Gene transcription in Daphnia magna: Effects of acute exposure to a carbamate insecticide and an acetanilide herbicide
Aquatic Toxicology 97 (2010) 268–276
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Gene transcription in Daphnia magna: Effects of acute exposure to a carbamate insecticide and an acetanilide herbicide Joana Luísa Pereira a,∗ , Christopher J. Hill b , Richard M. Sibly b , Viacheslav N. Bolshakov c , Fernando Gonc¸alves a , Lars-Henrik Heckmann b,d , Amanda Callaghan b a
Centre of Environmental and Marine Studies (CESAM) & Department of Biology, University of Aveiro, 3810-193 Aveiro, Portugal Environmental Biology Section, School of Biological Sciences, The University of Reading, Whiteknights, PO Box 68, Reading RG6 6BX, United Kingdom BioCentre Facility, The University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, United Kingdom d National Environmental Research Institute, Aarhus University, Department of Terrestrial Ecology, PO Box 314, DK-8600 Silkeborg, Denmark b c
a r t i c l e
i n f o
Article history: Received 2 October 2009 Received in revised form 23 December 2009 Accepted 29 December 2009 Keywords: Daphnia magna Methomyl Propanil Acute exposure Microarray Gene transcription
a b s t r a c t Daphnia magna is a key invertebrate in the freshwater environment and is used widely as a model in ecotoxicological measurements and risk assessment. Understanding the genomic responses of D. magna to chemical challenges will be of value to regulatory authorities worldwide. Here we exposed D. magna to the insecticide methomyl and the herbicide propanil to compare phenotypic effects with changes in mRNA expression levels. Both pesticides are found in drainage ditches and surface water bodies standing adjacent to crops. Methomyl, a carbamate insecticide widely used in agriculture, inhibits acetylcholinesterase, a key enzyme in nerve transmission. Propanil, an acetanilide herbicide, is used to control grass and broad-leaf weeds. The phenotypic effects of single doses of each chemical were evaluated using a standard immobilisation assay. Immobilisation was linked to global mRNA expression levels using the previously estimated 48 h-EC1 s, followed by hybridization to a cDNA microarray with more than 13,000 redundant cDNA clones representing >5000 unique genes. Following exposure to methomyl and propanil, differential expression was found for 624 and 551 cDNAs, respectively (one-way ANOVA with Bonferroni correction, P ≤ 0.05, more than 2-fold change) and up-regulation was prevalent for both test chemicals. Both pesticides promoted transcriptional changes in energy metabolism (e.g., mitochondrial proteins, ATP synthesis-related proteins), moulting (e.g., chitin-binding proteins, cuticular proteins) and protein biosynthesis (e.g., ribosomal proteins, transcription factors). Methomyl induced the transcription of genes involved in specific processes such as ion homeostasis and xenobiotic metabolism. Propanil highly promoted haemoglobin synthesis and up-regulated genes specifically related to defence mechanisms (e.g., innate immunity response systems) and neuronal pathways. Pesticide-specific toxic responses were found but there is little evidence for transcriptional responses purely restricted to genes associated with the pesticide target site or mechanism of toxicity. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The freshwater habitat is often contaminated with agrochemicals applied to control insect pests, weeds or pathogens. Pesticide contamination can result from spray drift during application, surface runoff and/or leaching (Brown et al., 1995; Carter, 2000; Reichenberger et al., 2007). Contemporary pesticides were developed in the mid-1970s as a less hazardous alternative to e.g., persistent organochlorines (Barr and Needham, 2002). Despite their relatively rapid degradation in the field these pesticides have been detected in water at concentra-
∗ Corresponding author. Tel.: +351 234 370 788 fax: +351 234 372 587. E-mail address: [email protected] (J.L. Pereira). 0166-445X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2009.12.023
tions frequently exceeding reference safety levels (e.g., Barr and Needham, 2002; Cerejeira et al., 2003; García de Llasera and Bernal-González, 2001; Guest et al., 2006; Wilson and Foos, 2006). The insecticide tested here, methomyl [S-methyl N(methylcarbamoyloxy)thioacetimidate] and the herbicide propanil (3,4-dichloropropioanilide) are examples of these agrochemicals. Methomyl is a monomethyl carbamate widely used to control a large range of insects and spider mites through direct contact and ingestion (Tomlin, 2001). Carbamates reversibly inhibit cholinesterase enzymes, such as acetylcholinesterase (AChE), which hydrolyses the cationic neurotransmitter acetylcholine at very high rates; these pesticides inactivate the enzyme through carbamylation of its active serine, hence compromising the normal neurotransmission function (Quinn, 1987). The potential
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of AChE inhibition as a biomarker of exposure to carbamates in Daphnia has been studied (Barata et al., 2004; Printes and Callaghan, 2004). However, these chemicals are able to significantly inhibit other esterases (Barata et al., 2004) and the relationship between the biomarker and the observed response at the individual level has already been shown to be dependent on the acting chemical (Printes and Callaghan, 2004). Such experimental evidence provides clues to the actual mechanism of carbamate toxicity to non-target organisms. Genomic investigation may provide further insight into the mechanism of carbamate toxicity. Propanil is an anilide herbicide that is commonly applied in the post-emergence of rice and acts through direct surface contact to control grass and broad-leaf weeds (Tomlin, 2001). Its specific mechanism of toxicity in target species involves an enzyme-mediated process of disruption of the electron flow in the Photosystem II, therefore inhibiting the light reaction of photosynthesis (e.g., Mitsou et al., 2006). Propanil is known to elicit deleterious effects in Daphnia related to survival, life-history and feeding (e.g., Pereira et al., 2007; Villarroel et al., 2003). Information on cellular and sub-cellular toxicological pathways of propanil in non-target systems is limited, but a few focussed studies are available in the vertebrate literature (Blyler et al., 1994; Cuff et al., 1996; Guilhermino et al., 1998; Li et al., 2003; Malerba et al., 2002). Daphnia have been widely used to study the effects of pesticides in freshwater ecosystems (e.g., Hanazato, 2001; Poynton and Vulpe, 2009; Sarma and Nandini, 2006) because they occupy a central position in the food web (e.g., Lampert, 2006) and are readily tested in the laboratory. Recent progresses in sequencing and annotating the Daphnia pulex genome and, to a lesser extent, Daphnia magna (Shaw et al., 2008; http://daphnia.cgb.indiana.edu; http://www.jgi.doe.gov) now allow to study their genomic responses. Effects of environmental stressors, such as pesticides, on non-target organisms have generally been assayed using wholeorganism or population responses. Despite providing valuable insight and useful information for regulatory purposes, such assessments rarely explain the mechanisms of toxicity underlying the observed response. The integration of genomic-based tools and ecotoxicology is a promising approach that may provide a broad view of how living systems respond to a given stressor (Neumann and Galvez, 2002; Robbens et al., 2007; Snape et al., 2004). Transcription profiling using microarrays (first described by Schena et al., 1995) is one of the most prominent genome-wide technologies within ecotoxicogenomics since it provides an overview of changes in gene expression linked to chemical exposure. With such an approach, we can try to establish a relationship between exposure and response effects. Very recently, cDNA microarray-related techniques have been successfully used to address transcriptional responses of D. magna to different environmental toxicants, including pharmaceuticals, heavy-metals, pesticides and PAHs (Connon et al., 2008; Heckmann et al., 2008; Soetaert et al., 2006, 2007a; Watanabe et al., 2007). Here we investigate phenotypic and molecular responses of D. magna to the pesticides methomyl and propanil and highlight the complex nature of molecular-level stress response resulting in immobility in this non-target organism. Our approach was to compare the response to equitoxic concentrations of each pesticide, using a previously estimated effect concentration (EC) EC1 . This allowed the use of strictly comparable exposure concentrations and hence responses. The EC1 concentration was chosen in order to detect sub-lethal transcriptional responses that could be linked to phenotypic responses.
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2. Materials and methods 2.1. Test organisms D. magna were obtained from the Water Research Centre (WRc), Medmenham, UK and cultured as a single clonal lineage at the University of Reading, UK for at least 2 years before testing. For full details of culturing conditions see Hooper et al. (2006). Third to fifth brood juveniles <24 h old and differing in age by <3 h were used for testing. 2.2. Chemicals and range-finding assays Methomyl (Pestanal® , 99.5% purity) and propanil (Pestanal® , 99.7% purity) were supplied by Sigma Aldrich (Seelze, Germany). Stock solutions were freshly prepared prior to experiments by directly dissolving methomyl or propanil in culture medium. The acute toxicity of each pesticide to D. magna was assessed following OECD guideline 202 (OECD, 2004). In brief, 48 h exposures were carried out under a static design using twenty juveniles (<24 h old) per treatment. Incubation conditions were as described for culturing (see Section 2.1). The tests were conducted in glass beakers, each containing 50 mL test solution. Dissolved oxygen and pH were monitored at the beginning and the end of the tests for validation purposes. Immobilised individuals were counted at the end of the test. Effect concentrations were estimated via Probit analysis (Finney, 1971). 2.3. Experimental treatments, RNA extraction and target labelling Neonate D. magna (<24 h old, 3 h age-range), were obtained from 40 bulk cultures (see Section 2.1) and were exposed to each treatment for 48 h (1-L test solution). A randomised block design with three treatments was followed: negative control, methomyl EC1 (10.5 g L−1 with a 95% confidence interval of 8.82–11.7 g L−1 ) and propanil EC1 (363 g L−1 with a 95% confidence interval of 302–401 g L−1 ). Five replicates were used per block and thirty juveniles were randomly assigned to each replicate. After the 48 h static exposure, the organisms were collected into sterile 1.5 mL micro-centrifuge tubes with 150 L RNAlater® (Ambion, UK), using a previously described approach (Heckmann et al., 2007) and stored at −80◦ C. Total RNA was extracted using the RNeasy Mini kit with on-column DNase treatment (Qiagen, UK), following the manufacturer’s instructions. RNA concentrations were determined on a GeneQuant Pro spectrophotometer (Biochrom, UK) and RNA integrity was verified using the BioAnalyser 2100 and RNA 6000 Nano Kit (Agilent Technologies, UK). For each sample, total RNA was amplified and labelled with Aminoallyl Message Amp aRNA Amplification Kit (Ambion, UK) from 400 ng of starting material. Reference material was created by pooling 10 g of aRNA from each sample followed by labelling with Alexa Fluor dye 555. Individual samples were labelled with Alexa Fluor 647. 2.4. Microarray experiments The D. magna microarray used in this study was produced at the Syngenta Central Toxicology Laboratory, Alderley Park, Macclesfield, UK. Good agreement between QPCR data and microarray data using this chip has already been confirmed in previous studies (e.g., Heckmann et al., 2008). This indicates good chip quality and validates its use in further ecotoxicological assessments. The chip cDNA content and manufacturing protocols, pre-hybridization and hybridization buffers and protocols are described in Connon et al. (2008). In brief, a mix of 5 g labelled sample and 5 g labelled reference material, together with blocking reagents, were hybridized in 50% formamide, 5× SSC and 0.1%SDS to individual microarray
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slides under a 2560 LifterSlipTM (Implen, UK) at 42◦ C for 16 h in a Techne HB-1 Hybridizer (Techne Ltd., UK). After hybridization, coverslips were removed in 2× SSC and slides were washed subsequently in 0.1%SDS/0.1× SSC; 0.1× SSC; 0.05× SSC and 100% isopropanol at room temperature. 2.5. Data analysis and annotation Slides were scanned on a GenePix Professional 4200A scanner and analysed using GenePixPro v.6 software (Molecular Devices, UK). During the scans, Auto-PMT function was used (saturation tolerance 0.005%, as recommended by the manufacturer) to avoid excess of saturated pixels. Spots with poor morphology, signalto-noise ratio less than three or with more than 50% of saturated pixels were removed from further analysis as unreliable. For the remaining spots, local background adjusted intensity data were imported into the TM4 software (Saeed et al., 2003) for normalization (block lowess), log2 transformation and identification of differentially expressed genes (one-way ANOVA with Bonferroni correction and t-test; P ≤ 0.05, more than 2-fold change between control and pesticide-treated conditions). D. magna sequences can be found at the Daphnia Base (http://daphnia.nibb.ac.jp) and at the website of the Daphnia research group of the University of Reading (http://www.reading.ac.uk/biologicalsciences/research/ environmentalbiology/biosci-daphnia.aspx). Microarray images and data are accessible through the public repository Array Express at the European Bioinformatics Institute (accession number: E-TABM-793). Sequences were annotated by BLASTX homology search against GenBank non-redundant protein sequences database (http://www.ncbi.nlm.nih.gov/). Sequences were only annotated if hits met an expect value (E-value) of <10−5 and a score of >50 against the database entry. GeneBank/UniProt accession number and species’ match were recorded with each annotation (Supplementary Table S1). cDNAs (i.e. microarray spots containing EST clones) with the same NCBI annotation were considered to represent the same gene. We used the mean expression and corresponding standard deviation to characterize the transcription level of gene groups represented by several cDNAs. Some gene groups recorded high variability of signal in representative cDNAs, which could be explained by their distance from the gene 3 -end, hybridization efficiency, or by cross-hybridization from members of large gene families (such as haemoglobin and cytochrome c oxidase) where numerous subunits or homologues carried identical annotation and may be differentially expressed in a tissue-specific manner. 3. Results 3.1. Global mRNA expression responses of genes 11,505 cDNAs were of sufficient quality to be analysed. When D. magna were exposed to the estimated EC1 concentrations of methomyl and propanil 2781 cDNAs differed significantly from controls in at least one treatment (one-way ANOVA with Bonferroni correction, P < 0.05). Of these, 768 cDNAs indicated 2-fold up or down-regulation of the mRNA of the associated gene. Propanil exposure significantly changed transcription levels in 551 cDNAs whereas methomyl significantly changed transcription levels in 624 cDNAs. Responses to both pesticides were similar in proportions of cDNAs up- and down-regulated. When the 768 cDNAs are plotted to compare responses between chemicals, it can be seen that many responses are similar, which may indicate general mechanisms of cellular response to chemical stress (Fig. 1). However, there are also clusters of
Fig. 1. Relative response of cDNAs following D. magna exposure to methomyl and propanil. Each dot refers to a cDNA, where >2-fold change in intensity was observed in at least one treatment compared to the control. M/C and P/C stand for the log2 transformed fold regulation (FR) of treated samples (M and P) to untreated control (C). Grey squares indicate cDNAs, which were up-regulated exclusively by methomyl (a) or propanil (b) and black squares depict stressor-specific down-regulation by methomyl (c) or propanil (d).
pesticide-specific responses (Fig. 1 – marked areas). Of the 768 differentially expressed cDNAs, only 354 were successfully annotated (see supplementary material for further information) and assigned functional groups (Figs. 2 and 3). Both pesticides elicited considerable differential transcription within protein biosynthesis, moulting and energy metabolism (Table 1). Few genes were toxicant-specific (Table 2) and, of these, more were up-regulated than were down-regulated (4 and 3 genes down-regulated by methomyl or propanil, respectively). 3.2. Responses to methomyl exposure After removal of redundant sequences, a final list representing 161 genes (98 up-regulated and 63 down-regulated) was established. Moulting, protein biosynthesis and energy metabolism were clearly the biological processes responding most to the exposure (Fig. 2). Genes associated with moulting represented 20.5% of the response, with many cuticular proteins and chitin deacetylases responding, some up-regulated by 8-fold (mean expression rate of 4.52 and 6.28, respectively) compared to the control (Table 1). Genes involved in protein biosynthesis (mostly 16S and 18S ribosomal RNA, different constitutive ribosomal proteins and proteins involved in folding, transfer across endoplasmic reticulum and the Golgi apparatus; see supplementary material for a detailed list) were up- and down-regulated by methomyl in identical proportions (around 8%) whereas there was more up- (14%) than down- (8.2%) regulated genes involved with energy metabolism, with mitochondrial genes and cytochrome oxidase up-regulated by 4–5-fold (Table 1). Within neuronal pathways, carboxylesterase and a predicted doughnut-like protein kinase were found downregulated by ca. 3- and 4-fold, respectively. Table 2 shows chemical-specific differential gene transcription. There was a chemical-specific induction of genes involved in ion homeostasis, namely the chloride–bicarbonate anion exchanger and Na, K ATPase (up-regulated by ca. 3- and 8-fold, respectively). Methomyl-specific up-regulation includes also genes involved in signalling pathways and proteins metabolism, as well as genes coding for structural proteins and for the protein sulfotransferase (ca. 6-fold-change), which should be directly related to the exposure to the xenobiotic. Chemical-specific down-regulation includes two
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Fig. 2. Proportional view of up- and down-regulated mRNA expression patterns of Daphnia magna genes following exposure to methomyl. The text box below the figure shows which proteins were assigned to each functional mechanism/pathway.
genes related to proteins metabolism (protease and phosphatase inhibitors), a protein of unknown function (PHB depolymerase) and a glycoprotein that may be related with sexual maturation (epididymal secretory protein). 3.3. Responses to propanil exposure After removal of redundant sequences, a final list of 126 genes (75 induced and 51 repressed) was produced. The main functional groups altered by exposure to the herbicide were moulting, protein biosynthesis, energy metabolism and oxygen transport (Fig. 3).
Energy metabolism accounted for the highest proportion of genes induced and repressed (14.5% and 10.6%, respectively; Fig. 3). Genes belonging to the mitochondrial genome contributing the most (17 up- and 21 down-regulated cDNAs), but additional differential transcription included ATP synthase, cytochrome C oxidase, ␣-amylase and enolase. Other important groups included genes associated with moulting (genes coding for cuticular proteins were strongly down-regulated by 8-fold and genes coding for chitin deacetylases were mostly up-regulated by 4-fold) and ribosomerelated proteins (Table 1). Propanil induced a strong (often more than 5-fold) up-regulation of oxygen transport genes (Table 1),
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Fig. 3. Proportional view of up- and down-regulated mRNA expression patterns of Daphnia magna genes following exposure to propanil. The text box below the figure shows which proteins were assigned to each functional mechanism/pathway.
namely genes coding for the four D. magna haemoglobin subunits. If one excludes lipid metabolism genes, a general trend for gene induction in the remaining functional processes was found; e.g., down-regulation of genes related to protein metabolism, cell cycle, neuronal and signalling pathways, structural proteins and stress response occurred at much lower rates than up-regulation of corresponding genes after exposure to propanil (Fig. 3). Propanil induced chemical-specific transcription of genes coding for proteins within generalised biological processes such as neuronal and signalling pathways, cell cycle, protein biosynthesis and lipid metabolism
(Table 2). mRNA for a gene coding for a cystatin precursor, which is involved in cell defence mechanisms, was specifically up-regulated (3.4-fold-change) by propanil whereas the transcription of the gene for stress-related protein Peroxinectin (4.8 average fold-change; 1.99 standard deviation) was down-regulated. 4. Discussion Exposure of D. magna neonates to low concentrations (EC1 ) of the pesticides methomyl and propanil resulted in highly signifi-
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Table 1 Responding cDNAs of similar annotation, indicating differential mRNA expression of represented genes following exposure to methomyl and propanil. The total number of responding cDNAs (No.) is subdivided, showing the abundance of the most representative cDNAs with the same annotation in each group. mRNA expression response of genes is represented by the average expression ratio (f–c) within each group, indicating the mean fold-change compared to the control. SD stands for standard deviation; ↑ and ↓ indicate up- and down-regulation compared to the control respectively. Methomyl
Propanil
↑
↓
No.
↑
↓
f–c (SD)
No.
f–c (SD)
No.
f–c (SD)
No.
f–c (SD)
Protein biosynthesis Ribosomal RNA Ribosomal proteins
24 7 17
4.7 (4.0) 4.3 (2.3)
25 6 10
3.3 (0.9) 3.1 (0.9)
18 6 10
7.8 (8.6) 3.7 (1.8)
18 5 8
5.3 (3.4) 4.6 (1.8)
Moulting Cuticular constituents Chitin-binding proteins Chitin deacetylase
60 49 4 7
4.5 (2.5) 2.5 (0.3) 6.3 (8.3)
20 16 4 –
3.4 (0.6) 3.9 (1.5) –
19 5 7 7
3.5 (0.8) 3.7 (0.4) 4.4 (0.8)
19 16 3 –
7.7 (2.6) 4.1 (2.3) –
Energy metabolism Mitochondrial genomea NADH dehydrogenase Cytochrome C oxidase ATP synthase Enolase
41 23 3 7 1 2
5.5 (2.9) 3.8 (0.8) 4.0 (2.3) 5.9 3.9 (2.2)
24 20 – 1 1 –
3.3 (0.9) – 4.5 5.9 –
33 16 2 8 1 2
8.7 (8.3) 5.4 (1.1) 4.2 (2.1) 2.5 5.1 (3.8)
24 20 – 1 1 –
5.3 (2.6) – 3.8 3.7 –
Oxygen transportb Haemoglobin domains
6 6
5.2 (2.1)
9 9
2.5 (0.7)
21 20
3.8 (2.0)
1 1
6.0
Lipids metabolism Vitellogenin Lipoprotein receptors Lipid transporters
3 1 2 –
3.6 7.2 (0.5) –
7 – 1 6
– 2.7 3.2 (0.9)
4 1 2 –
4.7 5.0 (0.4) –
7 – – 7
– – 3.3 (1.0)
10 6 3 –
3.8 (2.0) 2.8 (0.6) –
7 5 – 2
3.3 (0.9) – 2.2 (0.2)
7 4 3 –
2.9 (0.2) 3.4 (1.6) –
4 4 – –
5.0 (3.7) – –
Proteins metabolism Serine proteases Carboxypeptidases Protease inhibitors a b
Unspecified proteins. Haemoglobin.
cant changes in gene transcription. Although acute effects at EC1 are negligible, sub-lethal effects under chronic exposures during 21 days have already been demonstrated for these chemicals (Pereira et al., 2007; Pereira and Gonc¸alves, 2007). One such effect was on growth and this can be linked to changes in genes associated with moulting. Arthropods grow through a process of periodic shedding of the exoskeleton synchronised with the regeneration of the cuticle. Gene sequences related to the moulting process, such as
those generally involved in new exoskeleton synthesis (e.g., various cuticle proteins, chitin-binding proteins, structural constituents of cuticle) or in old endocuticle degradation (e.g., chitin deacetylase and eventually carboxypeptidases and serine proteases) have to be synchronised for successful moulting to occur. Moulting in crustaceans is regulated by a multi-hormonal system, where the immediate controllers are ecdysteroids (Chang et al., 1993); ecdysteroids regulate moulting-related gene activities at the transcriptional level in epidermal cells interfering with
Table 2 mRNA expression of genes that responded exclusively to methomyl (left-hand panel) or propanil (right-hand panel). In each panel, the gene name is given in the left-hand column, basic gene function (derived from bioinformatic analysis) is given in the centre column, and fold-change compared to the control (f–c) in the right-hand column. Every gene was represented by a single cDNA in the dataset except for peroxinectin, which was represented by two repeats (both expression ratios are shown). Methomyl
f–c
Up-regulated genes Actin Angiomotin cAMP-regulated protein Chemosensory protein Chloride/bicarbonate AE Ghitm-prov protein NA,K-ATPase Pros45 proteosome subunit Putative muscular protein20 Serine collagenase 1a Sulfotransferase TC tumor protein
[Structural proteins] [Other functions] [Signalling pathways] [Signalling pathways] [Ion homeostasis] [Other functions] [Ion homeostasis] [Protein metabolism] [Structural proteins] [Protein metabolism] [Xenobiotic metabolism] [Other functions]
7.09 2.61 5.16 3.13 2.31 2.30 7.71 2.48 2.06 3.38 5.64 6.70
Down-regulated genes Epididymal secretory proteinp Protein phosphatase 1K PHB depolymerase Serine-type protease inhibitor
[Other functions] [Protein metabolism] [Other functions] [Protein metabolism]
2.56 2.08 2.87 2.31
AE – anion exchanger; TC – translationally-controlled. a Percursor.
Propanil
f–c
Up-regulated genes Acyl-CoA desaturase Cystatin precursor Dopa decarboxylase Glutamine synthetase Innexin 2 Katanin 60 Myosin light chain NFkB protein Ornithine decarboxylase Syntaxin 6 Vang-like protein 2
[Lipid metabolism] [Defence mechanisms] [Neuronal pathways] [Other functions] [Structural proteins] [Cell cycle] [Structural proteins] [Protein biosynthesis] [Cell cycle] [Neuronal pathways] [Signalling pathways]
2.67 3.38 2.53 2.67 2.06 2.02 2.35 2.06 2.51 5.03 3.16
Down-regulated genes Headcase protein Peroxinectin Trehalose transporter
[Other functions] [Stress response (oxidative)] [Other functions]
2.01 6.18; 3.36 2.27
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either ecdysteroidogenesis or intracellular ecdysteroid signalling (Meng and Zou, 2009; Zou, 2005). Some endocrine disrupting chemicals (EDCs) are known to affect moulting in D. magna (e.g., Haeba et al., 2008; Palma et al., 2009; Soetaert et al., 2006, 2007b; Zou and Fingerman, 1997). Although neither methomyl nor propanil have previously been shown to affect endocrine systems, here they affected moulting-related gene transcription. Methomyl strongly up-regulated moulting-related genes, including those coding for various structural constituents of cuticle, cuticular proteins and chitin deacetylases; this suggests that the moulting cycle was accelerated in response to the chemical exposure. Propanil induced and repressed moulting-related genes; assuming that down-regulation of these genes means a chemicalinduced delay in the moulting cycle, daphnids may be able to compensate by enhancing the synthesis of different cuticle constituents. Ribosomes support growth since they are key actors in protein biosynthesis. RNA makes up 50–60% of the ribosome, which has a steady-state level comprising 80–90% of the total cellular RNA and daphnids are fast-growing crustaceans with high relative RNA content (ca. 10% RNA per unit of weight) (Elser et al., 2000). Thus, the changes in protein biosynthesis genes observed in our transcription dataset were expected; they represented more than 15% of all differentially expressed genes for both methomyl and propanil exposure. The induction of these genes may represent an attempt to overcome the environmental challenge and continue growth. Both pesticides induced transcription of genes coding for structural proteins associated with cell and tissue growth. However, previous studies have shown that D. magna somatic growth rates are significantly reduced by both methomyl and propanil in chronic exposures at lower concentrations (Pereira et al., 2007; Pereira and Gonc¸alves, 2007). This suggests that D. magna will not ultimately keep normal growth under an extended exposure to these pesticides, despite the initial investment (our exposure lasted for 48 h) in such a compensatory strategy. Survival and growth depend on energy availability and pesticides are known to reduce cellular energy budgets in daphnids (De Coen and Janssen, 2003). Both methomyl and propanil promoted differential transcription of energy-related genes. Induction of mRNAs of genes coding for ATP synthase and enzymes involved in the glycolysis and in the respiratory chain suggests that the organism needs energy to cope with the environmental challenge. The mRNAs of genes digestive enzyme ␣-amylase and diverse lipoproteins were up-regulated after exposure of D. magna to both pesticides; while the induction of ␣-amylase may follow the need for carbohydrate breakdown and further energy production, induction of lipid-related gene transcription is likely to indicate mobilisation of lipid reserves to maintain homeostasis during the toxicant exposure (De Coen and Janssen, 2003). mRNAs of genes for vitellogenin, a four-subunit lipoprotein which is the precursor of the major yolk protein vitellin (Kato et al., 2004; Tokishita et al., 2006), as well as vitellogenin fused with Cu/Zn superoxide dismutase, which may have a role in intermediate detoxification of superoxides resulting from vitellogenin metabolism (Kato et al., 2004), were also consistently induced after exposure. Previous studies using mature D. magna females, eggs, embryos or juveniles, have demonstrated differential expression of mRNAs of genes coding for both proteins in response to environmental toxicants (Connon et al., 2008; Soetaert et al., 2006, 2007a; Tokishita et al., 2006). Gene transcription changes regarding vitellogenin may be linked to chemical-induced impairment of reproduction, which would be particularly important in egg and embryo development and hence this gene has been suggested as a potential early marker to predict reproduction impairment in D. magna (Soetaert et al., 2006, 2007b; Tokishita et al., 2006). However, results from these studies con-
tradict ours, because they report vitellogenin to be essentially down-regulated. One may hypothesise that the vitellogenin upregulation found here should not translate into reproduction impairment, but may rather reflect enhanced energy demands to cope with toxicant stress and consequent mobilisation of lipid reserves where the lipoprotein may function as a general lipid transporter. Oxygen fuels cellular metabolic needs and oxygen transport is hence crucial for the organism’s survival and growth. In D. magna, oxygen is transported by extracellular, multi-subunit assembled haemoglobin (Hb), which is encoded by four well-characterized Hb genes (Kimura et al., 1999; Nunes et al., 2005). Daphnids are tolerant to hypoxia since they are able to increase Hb synthesis and its oxygen affinity in response to low environmental oxygen levels (Kobayashi et al., 1990; Seidl et al., 2005). Both methomyl and propanil induced the transcription of the four Hb D. magna genes. Hb synthesis appeared particularly after exposure to propanil (9% of the differentially expressed transcripts were up-regulated Hb sequences). Rider and LeBlanc (2006) found that the triazine herbicide atrazine induced expression of Hb genes and confirmed that this generally translated into an actual increase of Hb concentration in D. magna. Their results link the response of D. magna to that of vertebrates: e.g., Hb adducts were found in rats exposed to anilides (Beyerbach and Sabionni, 1999) and in agricultural workers exposed to propanil (Pastorelli et al., 1998). Changes in Hb expression in D. magna have also been found following exposure to cadmium (Connon et al., 2008) and ibuprofen (Heckman et al., 2008) which suggests that expression of Hb genes may indeed be useful as a biomarker of chemical exposure in D. magna. mRNAs of genes related to defence mechanisms and “general” stress responses (galactose-binding C-type lectins, cystatins and ferritins) were mostly up-regulated by both pesticides with the strongest response following exposure to propanil. Invertebrates lack adaptive immune systems and rather have innate immunity defence mechanisms against unspecific antigens (Janeway and Medzitov, 2002; Little et al., 2003; Muta and Iwanaga, 1996); the general immune response involves haemolymph coagulation driven by specialised haemocytes where lectins (involved in cell agglutination and adhesion) and cystatins (protease inhibitors) cooperate (Muta and Iwanaga, 1996). Ferritins are involved in the storage and scavenging of iron and have been found previously to be up-regulated by exposure to metals in D. magna (Connon et al., 2008; Poynton et al., 2007). Induction of ferritin has also been reported in human cell lines exposed to cadmium (Koizuma and Yamada, 2003) and has been directly linked with families of antioxidant response genes in the repair and prevention of oxidative damage (Hintze and Theil, 2005). Thus the induction of ferritin by methomyl and propanil may indicate oxidative stress, as suggested previously (El-Khawaga, 2005; Milatovic et al., 2006; Moraes et al., 2007). 4.1. Methomyl-specific changes Ion regulation in daphnids and other freshwater organisms is an essential process to counteract significant ion loss into the hypo-osmotic external medium and chloride and sodium are two major ions representative of this physiological process. Few studies have investigated ion regulation in D. magna but it is known that internal chloride and sodium concentrations are actively regulated. The ATP-dependent sodium–potassium pump (Na, K ATPase) plays a relevant role in sodium exchange processes across the basolateral membrane of the salt-transporting epithelia in daphnids (Bianchini and Wood, 2008). Chloride is regulated through the chloride-bicarbonate anion exchanger (Cl, HCO3 AE) and in D. magna this ion exchange process is inhibited by high concentrations of bicarbonate in the external medium (Hoke et al., 1992). Bianchini
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and Wood (2008) suggested that these two ion regulatory processes are linked, with Cl, HCO3 AE being coupled with Na,K ATPase to diffuse HCO3 produced during Na+ uptake by daphnids. mRNAs of both of these ion regulatory genes were up-regulated by methomyl and as far as we know, this is the first report of a pesticide affecting these systems in daphnids. Methomyl exposure also up-regulated mRNA of the gene coding for sulfotransferase. Sulfotransferases catalyse the conjugation of sulphate with several endogenous and xenobiotic compounds, including alcohols, thiols and amines (Josephy, 1997). Xenobiotics such as pyrene are conjugated to sulphate, via sulfotransferase in D. magna (Ikenaka et al., 2006) and therefore an up-regulation of gene coding for sulfotransferase may represent a detoxification process. 4.2. Propanil-specific changes Methomyl was expected to affect neuronal transmission since its known mode of action is to disrupt nerve transmission, whereas propanil was not expected to disrupt this function. However propanil elicited specific up-regulation of gene transcription within neuronal pathways, including dopa decarboxylase and syntaxin 6, whereas no differential transcription of these genes was found following exposure to methomyl. Dopa decarboxylase catalyses the conversion of dihydroxyphenylalanine (Dopa) to dopamine and 5-hydrotryptophan to serotonin in response to several endogenous or exogenous signals; and has already been shown to be involved in insect cuticle maturation, neuronal regulation, pigmentation patterning and innate immunity (Hodgetts and O’Keefe, 2006). Syntaxin 6 is a protein belonging to the synaptic vesicle release machinery and appears to regulate the presynaptic calcium channels activity (Wendler and Tooze, 2001; Zamponi, 2003). Both pesticides down-regulated the expression of mRNA of a gene for a carboxylesterase belonging to the AChE family (EF580101 – see supplementary material). 4.3. Conclusions Our comparison of transcriptional responses to two commonly used pesticides provides important insights into the molecular/cellular mechanisms underlying the effects of stress. There are now several D. magna microarray datasets where animals have been exposed to an array of chemicals, including heavymetals, pharmaceuticals and various classes of pesticides (Connon et al., 2008; Heckmann et al., 2008; Poynton et al., 2007). What is emerging from these and datasets from other invertebrate microarray studies is that where growth is impaired or animals immobilised, genes associated with energy production and (in the case of ecdysozoans) moulting, are nearly always affected. These genes are therefore likely to represent general stress responses and be directly linked to phenotypic responses. There is little evidence for expression responses purely restricted to genes associated with the pesticide target site. The toxic response is therefore more subtle and more complicated that first thought. Authors’ contributions All authors participated in designing the study. JLP and CJH contributed equally in conducting the experimental work, following protocols previously developed by LHH. Bioinformatics was handled by CJH, VNB and RMS (microarray imaging and statistics) and by JLP (gene annotation and functional analysis). The manuscript was drafted by JLP and CJH under the supervision of FG, RMS and AC. All authors contributed intellectually to the manuscript and approved the final version.
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Acknowledgements This study was part-funded by the Portuguese Foundation of Science and Technology (FCT, Portugal), as additional financial support to the PhD grant of Joana L. Pereira (SFRH/BD/13682/2003). Chris Hill was supported by a Natural Environment Research Council studentship NER/S/A/2006/14200. Microarray image acquisition and data analysis were funded and performed by the University of Reading BioCentre Facility. The microarray was produced from a NERC-funded grant (NER/D/S/2002/00413) to RMS and AC and from funds provided by Syngenta and AstraZeneca. The following people (in addition to AC, RMS, LHH and CJH) were responsible for the production and annotation of the microarray: R. Connon, H.L. Hooper, S.J. Maund, T.H. Hutchinson, F.L. Lim, D.J. Moore, H. Watanabe, A. Soetaert, K. Cook, J. Moggs, W. De Coen and T. Iguchi. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.aquatox.2009.12.023. References Barata, C., Solayan, A., Porte, C., 2004. Role of B-esterases in assessing toxicity of organophosphorus (chlorpyrifos, malathion) and carbamate (carbofuran) pesticides to Daphnia magna. Aquat. Toxicol. 66, 125–139. Barr, D.B., Needham, L.L., 2002. Analytical methods for biological monitoring of exposure to pesticides: a review. J. Chromatogr. B 778, 5–29. Beyerbach, A., Sabionni, G., 1999. Biomonitoring of arylamines: haemoglobin adducts of aniline derivatives. Biomarkers 4, 229–236. Bianchini, A., Wood, C.M., 2008. Sodium uptake in different life stages of crustaceans: the water flea Daphnia magna Straus. J. Exp. Biol. 211, 539–547. Blyler, G., Landreth, K.S., Lillis, T., Schafer, R., Theus, S.A., Gandy, J., Barnett, J.B., 1994. Selective myelotoxicity of propanil. Fundam. Appl. Toxicol. 22, 505– 510. Brown, C.D., Carter, A.D., Holis, J.M., 1995. Soils and pesticide mobility. In: Roberts, T.R., Kearney, P.C. (Eds.), Environmental Behaviour of Agrochemicals. John Wiley & Sons, West Sussex, pp. 131–179. Carter, A., 2000. How pesticides get into water – and proposed reduction measures. Pestic. Outlook, 149–156. Cerejeira, M.J., Viana, P., Batista, S., Pereira, T., Silva, E., Valério, M.J., Silva, A., Ferreira, M., Silva-Fernandes, A.M., 2003. Pesticides in Portuguese surface and ground waters. Water Res. 37, 1055–1063. Chang, E.S., Bruce, M.J., Tamone, S.L., 1993. Regulation of crustacean molting: a multihormonal system. Am. Zool. 33, 324–329. Connon, R., Hooper, H., Sibly, R.M., Lim, F.-L., Heckmann, L.-H., Moore, D.J., Watanabe, H., Soetaert, A., Cook, K., Maund, S.J., Hutchinson, T.H., Moggs, J., De Coen, W., Iguchi, T., Callaghan, A., 2008. Linking molecular and population stress responses in Daphnia magna exposed to cadmium. Environ. Sci. Technol. 42, 2181–2188. Cuff, C.F., Zhao, W., Nukui, T., Schafer, R., Barnett, J.B., 1996. 3,4Dichloropropionanilide-induced atrophy of the thymus: mechanisms of toxicity and recovery. Fundam. Appl. Toxicol. 33, 83–90. De Coen, W., Janssen, C.R., 2003. A multivariate biomarker-based model predicting population-level responses of Daphnia magna. Environ. Toxicol. Chem. 22, 2195–2201. El-Khawaga, O.A.Y., 2005. Role of selenium on antioxidant capacity in methomyltreated mice. J. Physiol. Biochem. 61, 501–506. Elser, J.J., Sterner, R.W., Gorokhova, E., Fagan, W.F., Markow, T.A., Cotner, J.B., Harrison, J.F., Hobbie, S.E., Odell, G.M., Weider, L.J., 2000. Biological stoichiometry from genes to ecosystem. Ecol. Lett. 3, 540–550. Finney, D.J., 1971. Probit Analysis. Cambridge University Press, Cambridge. García de Llasera, M.P., Bernal-González, M., 2001. Presence of carbamate pesticides in environmental waters from the Northwest of Mexico: determination by liquid chromatography. Water Res. 35, 1933–1940. Guest, R.K., Ikehata, K., El-Din, M.G., Smith, D.W., 2006. Pesticides and herbicides. Water Environ. Res. 78, 1755–1801. Guilhermino, L., Soares, A.M.V.M., Carvalho, A.P., Lopes, M.C., 1998. Acute effects of 3,4-dichloroaniline on blood of male Wistar rats. Chemosphere 37, 619–632. Haeba, M.H., Hilscherová, K., Mazurová, E., Blahá, L., 2008. Selected endocrine disrupting compounds (vinclozolin, flutamide, ketoconazole and dicofol): effects on survival, occurrence of males, growth, molting and reproduction of Daphnia magna. Environ. Sci. Pollut. Res. 15, 222–227. Hanazato, T., 2001. Pesticide effects on freshwater zooplankton: an ecological perspective. Environ. Pollut. 112, 1–10. Heckmann, L.-H., Bouetard, A., Hill, C.J., Sibly, R.M., Callaghan, A., 2007. A simple and rapid method for preserving RNA of aquatic invertebrates for ecotoxicogenomics. Ecotoxicology 16, 445–447. Heckmann, L.-H., Sibly, R.M., Connon, R., Hooper, H.L., Hutchinson, T.H., Maund, S.J., Hill, C.J., Bouetard, A., Callaghan, A., 2008. Systems biology meets stress ecology:
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Gene transcription in Daphnia magna: Effects of acute exposure to a carbamate insecticide and an acetanilide herbicide Joana Luísa Pereira a,∗ , Christopher J. Hill b , Richard M. Sibly b , Viacheslav N. Bolshakov c , Fernando Gonc¸alves a , Lars-Henrik Heckmann b,d , Amanda Callaghan b a
Centre of Environmental and Marine Studies (CESAM) & Department of Biology, University of Aveiro, 3810-193 Aveiro, Portugal Environmental Biology Section, School of Biological Sciences, The University of Reading, Whiteknights, PO Box 68, Reading RG6 6BX, United Kingdom BioCentre Facility, The University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, United Kingdom d National Environmental Research Institute, Aarhus University, Department of Terrestrial Ecology, PO Box 314, DK-8600 Silkeborg, Denmark b c
a r t i c l e
i n f o
Article history: Received 2 October 2009 Received in revised form 23 December 2009 Accepted 29 December 2009 Keywords: Daphnia magna Methomyl Propanil Acute exposure Microarray Gene transcription
a b s t r a c t Daphnia magna is a key invertebrate in the freshwater environment and is used widely as a model in ecotoxicological measurements and risk assessment. Understanding the genomic responses of D. magna to chemical challenges will be of value to regulatory authorities worldwide. Here we exposed D. magna to the insecticide methomyl and the herbicide propanil to compare phenotypic effects with changes in mRNA expression levels. Both pesticides are found in drainage ditches and surface water bodies standing adjacent to crops. Methomyl, a carbamate insecticide widely used in agriculture, inhibits acetylcholinesterase, a key enzyme in nerve transmission. Propanil, an acetanilide herbicide, is used to control grass and broad-leaf weeds. The phenotypic effects of single doses of each chemical were evaluated using a standard immobilisation assay. Immobilisation was linked to global mRNA expression levels using the previously estimated 48 h-EC1 s, followed by hybridization to a cDNA microarray with more than 13,000 redundant cDNA clones representing >5000 unique genes. Following exposure to methomyl and propanil, differential expression was found for 624 and 551 cDNAs, respectively (one-way ANOVA with Bonferroni correction, P ≤ 0.05, more than 2-fold change) and up-regulation was prevalent for both test chemicals. Both pesticides promoted transcriptional changes in energy metabolism (e.g., mitochondrial proteins, ATP synthesis-related proteins), moulting (e.g., chitin-binding proteins, cuticular proteins) and protein biosynthesis (e.g., ribosomal proteins, transcription factors). Methomyl induced the transcription of genes involved in specific processes such as ion homeostasis and xenobiotic metabolism. Propanil highly promoted haemoglobin synthesis and up-regulated genes specifically related to defence mechanisms (e.g., innate immunity response systems) and neuronal pathways. Pesticide-specific toxic responses were found but there is little evidence for transcriptional responses purely restricted to genes associated with the pesticide target site or mechanism of toxicity. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The freshwater habitat is often contaminated with agrochemicals applied to control insect pests, weeds or pathogens. Pesticide contamination can result from spray drift during application, surface runoff and/or leaching (Brown et al., 1995; Carter, 2000; Reichenberger et al., 2007). Contemporary pesticides were developed in the mid-1970s as a less hazardous alternative to e.g., persistent organochlorines (Barr and Needham, 2002). Despite their relatively rapid degradation in the field these pesticides have been detected in water at concentra-
∗ Corresponding author. Tel.: +351 234 370 788 fax: +351 234 372 587. E-mail address: [email protected] (J.L. Pereira). 0166-445X/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2009.12.023
tions frequently exceeding reference safety levels (e.g., Barr and Needham, 2002; Cerejeira et al., 2003; García de Llasera and Bernal-González, 2001; Guest et al., 2006; Wilson and Foos, 2006). The insecticide tested here, methomyl [S-methyl N(methylcarbamoyloxy)thioacetimidate] and the herbicide propanil (3,4-dichloropropioanilide) are examples of these agrochemicals. Methomyl is a monomethyl carbamate widely used to control a large range of insects and spider mites through direct contact and ingestion (Tomlin, 2001). Carbamates reversibly inhibit cholinesterase enzymes, such as acetylcholinesterase (AChE), which hydrolyses the cationic neurotransmitter acetylcholine at very high rates; these pesticides inactivate the enzyme through carbamylation of its active serine, hence compromising the normal neurotransmission function (Quinn, 1987). The potential
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of AChE inhibition as a biomarker of exposure to carbamates in Daphnia has been studied (Barata et al., 2004; Printes and Callaghan, 2004). However, these chemicals are able to significantly inhibit other esterases (Barata et al., 2004) and the relationship between the biomarker and the observed response at the individual level has already been shown to be dependent on the acting chemical (Printes and Callaghan, 2004). Such experimental evidence provides clues to the actual mechanism of carbamate toxicity to non-target organisms. Genomic investigation may provide further insight into the mechanism of carbamate toxicity. Propanil is an anilide herbicide that is commonly applied in the post-emergence of rice and acts through direct surface contact to control grass and broad-leaf weeds (Tomlin, 2001). Its specific mechanism of toxicity in target species involves an enzyme-mediated process of disruption of the electron flow in the Photosystem II, therefore inhibiting the light reaction of photosynthesis (e.g., Mitsou et al., 2006). Propanil is known to elicit deleterious effects in Daphnia related to survival, life-history and feeding (e.g., Pereira et al., 2007; Villarroel et al., 2003). Information on cellular and sub-cellular toxicological pathways of propanil in non-target systems is limited, but a few focussed studies are available in the vertebrate literature (Blyler et al., 1994; Cuff et al., 1996; Guilhermino et al., 1998; Li et al., 2003; Malerba et al., 2002). Daphnia have been widely used to study the effects of pesticides in freshwater ecosystems (e.g., Hanazato, 2001; Poynton and Vulpe, 2009; Sarma and Nandini, 2006) because they occupy a central position in the food web (e.g., Lampert, 2006) and are readily tested in the laboratory. Recent progresses in sequencing and annotating the Daphnia pulex genome and, to a lesser extent, Daphnia magna (Shaw et al., 2008; http://daphnia.cgb.indiana.edu; http://www.jgi.doe.gov) now allow to study their genomic responses. Effects of environmental stressors, such as pesticides, on non-target organisms have generally been assayed using wholeorganism or population responses. Despite providing valuable insight and useful information for regulatory purposes, such assessments rarely explain the mechanisms of toxicity underlying the observed response. The integration of genomic-based tools and ecotoxicology is a promising approach that may provide a broad view of how living systems respond to a given stressor (Neumann and Galvez, 2002; Robbens et al., 2007; Snape et al., 2004). Transcription profiling using microarrays (first described by Schena et al., 1995) is one of the most prominent genome-wide technologies within ecotoxicogenomics since it provides an overview of changes in gene expression linked to chemical exposure. With such an approach, we can try to establish a relationship between exposure and response effects. Very recently, cDNA microarray-related techniques have been successfully used to address transcriptional responses of D. magna to different environmental toxicants, including pharmaceuticals, heavy-metals, pesticides and PAHs (Connon et al., 2008; Heckmann et al., 2008; Soetaert et al., 2006, 2007a; Watanabe et al., 2007). Here we investigate phenotypic and molecular responses of D. magna to the pesticides methomyl and propanil and highlight the complex nature of molecular-level stress response resulting in immobility in this non-target organism. Our approach was to compare the response to equitoxic concentrations of each pesticide, using a previously estimated effect concentration (EC) EC1 . This allowed the use of strictly comparable exposure concentrations and hence responses. The EC1 concentration was chosen in order to detect sub-lethal transcriptional responses that could be linked to phenotypic responses.
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2. Materials and methods 2.1. Test organisms D. magna were obtained from the Water Research Centre (WRc), Medmenham, UK and cultured as a single clonal lineage at the University of Reading, UK for at least 2 years before testing. For full details of culturing conditions see Hooper et al. (2006). Third to fifth brood juveniles <24 h old and differing in age by <3 h were used for testing. 2.2. Chemicals and range-finding assays Methomyl (Pestanal® , 99.5% purity) and propanil (Pestanal® , 99.7% purity) were supplied by Sigma Aldrich (Seelze, Germany). Stock solutions were freshly prepared prior to experiments by directly dissolving methomyl or propanil in culture medium. The acute toxicity of each pesticide to D. magna was assessed following OECD guideline 202 (OECD, 2004). In brief, 48 h exposures were carried out under a static design using twenty juveniles (<24 h old) per treatment. Incubation conditions were as described for culturing (see Section 2.1). The tests were conducted in glass beakers, each containing 50 mL test solution. Dissolved oxygen and pH were monitored at the beginning and the end of the tests for validation purposes. Immobilised individuals were counted at the end of the test. Effect concentrations were estimated via Probit analysis (Finney, 1971). 2.3. Experimental treatments, RNA extraction and target labelling Neonate D. magna (<24 h old, 3 h age-range), were obtained from 40 bulk cultures (see Section 2.1) and were exposed to each treatment for 48 h (1-L test solution). A randomised block design with three treatments was followed: negative control, methomyl EC1 (10.5 g L−1 with a 95% confidence interval of 8.82–11.7 g L−1 ) and propanil EC1 (363 g L−1 with a 95% confidence interval of 302–401 g L−1 ). Five replicates were used per block and thirty juveniles were randomly assigned to each replicate. After the 48 h static exposure, the organisms were collected into sterile 1.5 mL micro-centrifuge tubes with 150 L RNAlater® (Ambion, UK), using a previously described approach (Heckmann et al., 2007) and stored at −80◦ C. Total RNA was extracted using the RNeasy Mini kit with on-column DNase treatment (Qiagen, UK), following the manufacturer’s instructions. RNA concentrations were determined on a GeneQuant Pro spectrophotometer (Biochrom, UK) and RNA integrity was verified using the BioAnalyser 2100 and RNA 6000 Nano Kit (Agilent Technologies, UK). For each sample, total RNA was amplified and labelled with Aminoallyl Message Amp aRNA Amplification Kit (Ambion, UK) from 400 ng of starting material. Reference material was created by pooling 10 g of aRNA from each sample followed by labelling with Alexa Fluor dye 555. Individual samples were labelled with Alexa Fluor 647. 2.4. Microarray experiments The D. magna microarray used in this study was produced at the Syngenta Central Toxicology Laboratory, Alderley Park, Macclesfield, UK. Good agreement between QPCR data and microarray data using this chip has already been confirmed in previous studies (e.g., Heckmann et al., 2008). This indicates good chip quality and validates its use in further ecotoxicological assessments. The chip cDNA content and manufacturing protocols, pre-hybridization and hybridization buffers and protocols are described in Connon et al. (2008). In brief, a mix of 5 g labelled sample and 5 g labelled reference material, together with blocking reagents, were hybridized in 50% formamide, 5× SSC and 0.1%SDS to individual microarray
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slides under a 2560 LifterSlipTM (Implen, UK) at 42◦ C for 16 h in a Techne HB-1 Hybridizer (Techne Ltd., UK). After hybridization, coverslips were removed in 2× SSC and slides were washed subsequently in 0.1%SDS/0.1× SSC; 0.1× SSC; 0.05× SSC and 100% isopropanol at room temperature. 2.5. Data analysis and annotation Slides were scanned on a GenePix Professional 4200A scanner and analysed using GenePixPro v.6 software (Molecular Devices, UK). During the scans, Auto-PMT function was used (saturation tolerance 0.005%, as recommended by the manufacturer) to avoid excess of saturated pixels. Spots with poor morphology, signalto-noise ratio less than three or with more than 50% of saturated pixels were removed from further analysis as unreliable. For the remaining spots, local background adjusted intensity data were imported into the TM4 software (Saeed et al., 2003) for normalization (block lowess), log2 transformation and identification of differentially expressed genes (one-way ANOVA with Bonferroni correction and t-test; P ≤ 0.05, more than 2-fold change between control and pesticide-treated conditions). D. magna sequences can be found at the Daphnia Base (http://daphnia.nibb.ac.jp) and at the website of the Daphnia research group of the University of Reading (http://www.reading.ac.uk/biologicalsciences/research/ environmentalbiology/biosci-daphnia.aspx). Microarray images and data are accessible through the public repository Array Express at the European Bioinformatics Institute (accession number: E-TABM-793). Sequences were annotated by BLASTX homology search against GenBank non-redundant protein sequences database (http://www.ncbi.nlm.nih.gov/). Sequences were only annotated if hits met an expect value (E-value) of <10−5 and a score of >50 against the database entry. GeneBank/UniProt accession number and species’ match were recorded with each annotation (Supplementary Table S1). cDNAs (i.e. microarray spots containing EST clones) with the same NCBI annotation were considered to represent the same gene. We used the mean expression and corresponding standard deviation to characterize the transcription level of gene groups represented by several cDNAs. Some gene groups recorded high variability of signal in representative cDNAs, which could be explained by their distance from the gene 3 -end, hybridization efficiency, or by cross-hybridization from members of large gene families (such as haemoglobin and cytochrome c oxidase) where numerous subunits or homologues carried identical annotation and may be differentially expressed in a tissue-specific manner. 3. Results 3.1. Global mRNA expression responses of genes 11,505 cDNAs were of sufficient quality to be analysed. When D. magna were exposed to the estimated EC1 concentrations of methomyl and propanil 2781 cDNAs differed significantly from controls in at least one treatment (one-way ANOVA with Bonferroni correction, P < 0.05). Of these, 768 cDNAs indicated 2-fold up or down-regulation of the mRNA of the associated gene. Propanil exposure significantly changed transcription levels in 551 cDNAs whereas methomyl significantly changed transcription levels in 624 cDNAs. Responses to both pesticides were similar in proportions of cDNAs up- and down-regulated. When the 768 cDNAs are plotted to compare responses between chemicals, it can be seen that many responses are similar, which may indicate general mechanisms of cellular response to chemical stress (Fig. 1). However, there are also clusters of
Fig. 1. Relative response of cDNAs following D. magna exposure to methomyl and propanil. Each dot refers to a cDNA, where >2-fold change in intensity was observed in at least one treatment compared to the control. M/C and P/C stand for the log2 transformed fold regulation (FR) of treated samples (M and P) to untreated control (C). Grey squares indicate cDNAs, which were up-regulated exclusively by methomyl (a) or propanil (b) and black squares depict stressor-specific down-regulation by methomyl (c) or propanil (d).
pesticide-specific responses (Fig. 1 – marked areas). Of the 768 differentially expressed cDNAs, only 354 were successfully annotated (see supplementary material for further information) and assigned functional groups (Figs. 2 and 3). Both pesticides elicited considerable differential transcription within protein biosynthesis, moulting and energy metabolism (Table 1). Few genes were toxicant-specific (Table 2) and, of these, more were up-regulated than were down-regulated (4 and 3 genes down-regulated by methomyl or propanil, respectively). 3.2. Responses to methomyl exposure After removal of redundant sequences, a final list representing 161 genes (98 up-regulated and 63 down-regulated) was established. Moulting, protein biosynthesis and energy metabolism were clearly the biological processes responding most to the exposure (Fig. 2). Genes associated with moulting represented 20.5% of the response, with many cuticular proteins and chitin deacetylases responding, some up-regulated by 8-fold (mean expression rate of 4.52 and 6.28, respectively) compared to the control (Table 1). Genes involved in protein biosynthesis (mostly 16S and 18S ribosomal RNA, different constitutive ribosomal proteins and proteins involved in folding, transfer across endoplasmic reticulum and the Golgi apparatus; see supplementary material for a detailed list) were up- and down-regulated by methomyl in identical proportions (around 8%) whereas there was more up- (14%) than down- (8.2%) regulated genes involved with energy metabolism, with mitochondrial genes and cytochrome oxidase up-regulated by 4–5-fold (Table 1). Within neuronal pathways, carboxylesterase and a predicted doughnut-like protein kinase were found downregulated by ca. 3- and 4-fold, respectively. Table 2 shows chemical-specific differential gene transcription. There was a chemical-specific induction of genes involved in ion homeostasis, namely the chloride–bicarbonate anion exchanger and Na, K ATPase (up-regulated by ca. 3- and 8-fold, respectively). Methomyl-specific up-regulation includes also genes involved in signalling pathways and proteins metabolism, as well as genes coding for structural proteins and for the protein sulfotransferase (ca. 6-fold-change), which should be directly related to the exposure to the xenobiotic. Chemical-specific down-regulation includes two
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Fig. 2. Proportional view of up- and down-regulated mRNA expression patterns of Daphnia magna genes following exposure to methomyl. The text box below the figure shows which proteins were assigned to each functional mechanism/pathway.
genes related to proteins metabolism (protease and phosphatase inhibitors), a protein of unknown function (PHB depolymerase) and a glycoprotein that may be related with sexual maturation (epididymal secretory protein). 3.3. Responses to propanil exposure After removal of redundant sequences, a final list of 126 genes (75 induced and 51 repressed) was produced. The main functional groups altered by exposure to the herbicide were moulting, protein biosynthesis, energy metabolism and oxygen transport (Fig. 3).
Energy metabolism accounted for the highest proportion of genes induced and repressed (14.5% and 10.6%, respectively; Fig. 3). Genes belonging to the mitochondrial genome contributing the most (17 up- and 21 down-regulated cDNAs), but additional differential transcription included ATP synthase, cytochrome C oxidase, ␣-amylase and enolase. Other important groups included genes associated with moulting (genes coding for cuticular proteins were strongly down-regulated by 8-fold and genes coding for chitin deacetylases were mostly up-regulated by 4-fold) and ribosomerelated proteins (Table 1). Propanil induced a strong (often more than 5-fold) up-regulation of oxygen transport genes (Table 1),
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Fig. 3. Proportional view of up- and down-regulated mRNA expression patterns of Daphnia magna genes following exposure to propanil. The text box below the figure shows which proteins were assigned to each functional mechanism/pathway.
namely genes coding for the four D. magna haemoglobin subunits. If one excludes lipid metabolism genes, a general trend for gene induction in the remaining functional processes was found; e.g., down-regulation of genes related to protein metabolism, cell cycle, neuronal and signalling pathways, structural proteins and stress response occurred at much lower rates than up-regulation of corresponding genes after exposure to propanil (Fig. 3). Propanil induced chemical-specific transcription of genes coding for proteins within generalised biological processes such as neuronal and signalling pathways, cell cycle, protein biosynthesis and lipid metabolism
(Table 2). mRNA for a gene coding for a cystatin precursor, which is involved in cell defence mechanisms, was specifically up-regulated (3.4-fold-change) by propanil whereas the transcription of the gene for stress-related protein Peroxinectin (4.8 average fold-change; 1.99 standard deviation) was down-regulated. 4. Discussion Exposure of D. magna neonates to low concentrations (EC1 ) of the pesticides methomyl and propanil resulted in highly signifi-
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Table 1 Responding cDNAs of similar annotation, indicating differential mRNA expression of represented genes following exposure to methomyl and propanil. The total number of responding cDNAs (No.) is subdivided, showing the abundance of the most representative cDNAs with the same annotation in each group. mRNA expression response of genes is represented by the average expression ratio (f–c) within each group, indicating the mean fold-change compared to the control. SD stands for standard deviation; ↑ and ↓ indicate up- and down-regulation compared to the control respectively. Methomyl
Propanil
↑
↓
No.
↑
↓
f–c (SD)
No.
f–c (SD)
No.
f–c (SD)
No.
f–c (SD)
Protein biosynthesis Ribosomal RNA Ribosomal proteins
24 7 17
4.7 (4.0) 4.3 (2.3)
25 6 10
3.3 (0.9) 3.1 (0.9)
18 6 10
7.8 (8.6) 3.7 (1.8)
18 5 8
5.3 (3.4) 4.6 (1.8)
Moulting Cuticular constituents Chitin-binding proteins Chitin deacetylase
60 49 4 7
4.5 (2.5) 2.5 (0.3) 6.3 (8.3)
20 16 4 –
3.4 (0.6) 3.9 (1.5) –
19 5 7 7
3.5 (0.8) 3.7 (0.4) 4.4 (0.8)
19 16 3 –
7.7 (2.6) 4.1 (2.3) –
Energy metabolism Mitochondrial genomea NADH dehydrogenase Cytochrome C oxidase ATP synthase Enolase
41 23 3 7 1 2
5.5 (2.9) 3.8 (0.8) 4.0 (2.3) 5.9 3.9 (2.2)
24 20 – 1 1 –
3.3 (0.9) – 4.5 5.9 –
33 16 2 8 1 2
8.7 (8.3) 5.4 (1.1) 4.2 (2.1) 2.5 5.1 (3.8)
24 20 – 1 1 –
5.3 (2.6) – 3.8 3.7 –
Oxygen transportb Haemoglobin domains
6 6
5.2 (2.1)
9 9
2.5 (0.7)
21 20
3.8 (2.0)
1 1
6.0
Lipids metabolism Vitellogenin Lipoprotein receptors Lipid transporters
3 1 2 –
3.6 7.2 (0.5) –
7 – 1 6
– 2.7 3.2 (0.9)
4 1 2 –
4.7 5.0 (0.4) –
7 – – 7
– – 3.3 (1.0)
10 6 3 –
3.8 (2.0) 2.8 (0.6) –
7 5 – 2
3.3 (0.9) – 2.2 (0.2)
7 4 3 –
2.9 (0.2) 3.4 (1.6) –
4 4 – –
5.0 (3.7) – –
Proteins metabolism Serine proteases Carboxypeptidases Protease inhibitors a b
Unspecified proteins. Haemoglobin.
cant changes in gene transcription. Although acute effects at EC1 are negligible, sub-lethal effects under chronic exposures during 21 days have already been demonstrated for these chemicals (Pereira et al., 2007; Pereira and Gonc¸alves, 2007). One such effect was on growth and this can be linked to changes in genes associated with moulting. Arthropods grow through a process of periodic shedding of the exoskeleton synchronised with the regeneration of the cuticle. Gene sequences related to the moulting process, such as
those generally involved in new exoskeleton synthesis (e.g., various cuticle proteins, chitin-binding proteins, structural constituents of cuticle) or in old endocuticle degradation (e.g., chitin deacetylase and eventually carboxypeptidases and serine proteases) have to be synchronised for successful moulting to occur. Moulting in crustaceans is regulated by a multi-hormonal system, where the immediate controllers are ecdysteroids (Chang et al., 1993); ecdysteroids regulate moulting-related gene activities at the transcriptional level in epidermal cells interfering with
Table 2 mRNA expression of genes that responded exclusively to methomyl (left-hand panel) or propanil (right-hand panel). In each panel, the gene name is given in the left-hand column, basic gene function (derived from bioinformatic analysis) is given in the centre column, and fold-change compared to the control (f–c) in the right-hand column. Every gene was represented by a single cDNA in the dataset except for peroxinectin, which was represented by two repeats (both expression ratios are shown). Methomyl
f–c
Up-regulated genes Actin Angiomotin cAMP-regulated protein Chemosensory protein Chloride/bicarbonate AE Ghitm-prov protein NA,K-ATPase Pros45 proteosome subunit Putative muscular protein20 Serine collagenase 1a Sulfotransferase TC tumor protein
[Structural proteins] [Other functions] [Signalling pathways] [Signalling pathways] [Ion homeostasis] [Other functions] [Ion homeostasis] [Protein metabolism] [Structural proteins] [Protein metabolism] [Xenobiotic metabolism] [Other functions]
7.09 2.61 5.16 3.13 2.31 2.30 7.71 2.48 2.06 3.38 5.64 6.70
Down-regulated genes Epididymal secretory proteinp Protein phosphatase 1K PHB depolymerase Serine-type protease inhibitor
[Other functions] [Protein metabolism] [Other functions] [Protein metabolism]
2.56 2.08 2.87 2.31
AE – anion exchanger; TC – translationally-controlled. a Percursor.
Propanil
f–c
Up-regulated genes Acyl-CoA desaturase Cystatin precursor Dopa decarboxylase Glutamine synthetase Innexin 2 Katanin 60 Myosin light chain NFkB protein Ornithine decarboxylase Syntaxin 6 Vang-like protein 2
[Lipid metabolism] [Defence mechanisms] [Neuronal pathways] [Other functions] [Structural proteins] [Cell cycle] [Structural proteins] [Protein biosynthesis] [Cell cycle] [Neuronal pathways] [Signalling pathways]
2.67 3.38 2.53 2.67 2.06 2.02 2.35 2.06 2.51 5.03 3.16
Down-regulated genes Headcase protein Peroxinectin Trehalose transporter
[Other functions] [Stress response (oxidative)] [Other functions]
2.01 6.18; 3.36 2.27
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either ecdysteroidogenesis or intracellular ecdysteroid signalling (Meng and Zou, 2009; Zou, 2005). Some endocrine disrupting chemicals (EDCs) are known to affect moulting in D. magna (e.g., Haeba et al., 2008; Palma et al., 2009; Soetaert et al., 2006, 2007b; Zou and Fingerman, 1997). Although neither methomyl nor propanil have previously been shown to affect endocrine systems, here they affected moulting-related gene transcription. Methomyl strongly up-regulated moulting-related genes, including those coding for various structural constituents of cuticle, cuticular proteins and chitin deacetylases; this suggests that the moulting cycle was accelerated in response to the chemical exposure. Propanil induced and repressed moulting-related genes; assuming that down-regulation of these genes means a chemicalinduced delay in the moulting cycle, daphnids may be able to compensate by enhancing the synthesis of different cuticle constituents. Ribosomes support growth since they are key actors in protein biosynthesis. RNA makes up 50–60% of the ribosome, which has a steady-state level comprising 80–90% of the total cellular RNA and daphnids are fast-growing crustaceans with high relative RNA content (ca. 10% RNA per unit of weight) (Elser et al., 2000). Thus, the changes in protein biosynthesis genes observed in our transcription dataset were expected; they represented more than 15% of all differentially expressed genes for both methomyl and propanil exposure. The induction of these genes may represent an attempt to overcome the environmental challenge and continue growth. Both pesticides induced transcription of genes coding for structural proteins associated with cell and tissue growth. However, previous studies have shown that D. magna somatic growth rates are significantly reduced by both methomyl and propanil in chronic exposures at lower concentrations (Pereira et al., 2007; Pereira and Gonc¸alves, 2007). This suggests that D. magna will not ultimately keep normal growth under an extended exposure to these pesticides, despite the initial investment (our exposure lasted for 48 h) in such a compensatory strategy. Survival and growth depend on energy availability and pesticides are known to reduce cellular energy budgets in daphnids (De Coen and Janssen, 2003). Both methomyl and propanil promoted differential transcription of energy-related genes. Induction of mRNAs of genes coding for ATP synthase and enzymes involved in the glycolysis and in the respiratory chain suggests that the organism needs energy to cope with the environmental challenge. The mRNAs of genes digestive enzyme ␣-amylase and diverse lipoproteins were up-regulated after exposure of D. magna to both pesticides; while the induction of ␣-amylase may follow the need for carbohydrate breakdown and further energy production, induction of lipid-related gene transcription is likely to indicate mobilisation of lipid reserves to maintain homeostasis during the toxicant exposure (De Coen and Janssen, 2003). mRNAs of genes for vitellogenin, a four-subunit lipoprotein which is the precursor of the major yolk protein vitellin (Kato et al., 2004; Tokishita et al., 2006), as well as vitellogenin fused with Cu/Zn superoxide dismutase, which may have a role in intermediate detoxification of superoxides resulting from vitellogenin metabolism (Kato et al., 2004), were also consistently induced after exposure. Previous studies using mature D. magna females, eggs, embryos or juveniles, have demonstrated differential expression of mRNAs of genes coding for both proteins in response to environmental toxicants (Connon et al., 2008; Soetaert et al., 2006, 2007a; Tokishita et al., 2006). Gene transcription changes regarding vitellogenin may be linked to chemical-induced impairment of reproduction, which would be particularly important in egg and embryo development and hence this gene has been suggested as a potential early marker to predict reproduction impairment in D. magna (Soetaert et al., 2006, 2007b; Tokishita et al., 2006). However, results from these studies con-
tradict ours, because they report vitellogenin to be essentially down-regulated. One may hypothesise that the vitellogenin upregulation found here should not translate into reproduction impairment, but may rather reflect enhanced energy demands to cope with toxicant stress and consequent mobilisation of lipid reserves where the lipoprotein may function as a general lipid transporter. Oxygen fuels cellular metabolic needs and oxygen transport is hence crucial for the organism’s survival and growth. In D. magna, oxygen is transported by extracellular, multi-subunit assembled haemoglobin (Hb), which is encoded by four well-characterized Hb genes (Kimura et al., 1999; Nunes et al., 2005). Daphnids are tolerant to hypoxia since they are able to increase Hb synthesis and its oxygen affinity in response to low environmental oxygen levels (Kobayashi et al., 1990; Seidl et al., 2005). Both methomyl and propanil induced the transcription of the four Hb D. magna genes. Hb synthesis appeared particularly after exposure to propanil (9% of the differentially expressed transcripts were up-regulated Hb sequences). Rider and LeBlanc (2006) found that the triazine herbicide atrazine induced expression of Hb genes and confirmed that this generally translated into an actual increase of Hb concentration in D. magna. Their results link the response of D. magna to that of vertebrates: e.g., Hb adducts were found in rats exposed to anilides (Beyerbach and Sabionni, 1999) and in agricultural workers exposed to propanil (Pastorelli et al., 1998). Changes in Hb expression in D. magna have also been found following exposure to cadmium (Connon et al., 2008) and ibuprofen (Heckman et al., 2008) which suggests that expression of Hb genes may indeed be useful as a biomarker of chemical exposure in D. magna. mRNAs of genes related to defence mechanisms and “general” stress responses (galactose-binding C-type lectins, cystatins and ferritins) were mostly up-regulated by both pesticides with the strongest response following exposure to propanil. Invertebrates lack adaptive immune systems and rather have innate immunity defence mechanisms against unspecific antigens (Janeway and Medzitov, 2002; Little et al., 2003; Muta and Iwanaga, 1996); the general immune response involves haemolymph coagulation driven by specialised haemocytes where lectins (involved in cell agglutination and adhesion) and cystatins (protease inhibitors) cooperate (Muta and Iwanaga, 1996). Ferritins are involved in the storage and scavenging of iron and have been found previously to be up-regulated by exposure to metals in D. magna (Connon et al., 2008; Poynton et al., 2007). Induction of ferritin has also been reported in human cell lines exposed to cadmium (Koizuma and Yamada, 2003) and has been directly linked with families of antioxidant response genes in the repair and prevention of oxidative damage (Hintze and Theil, 2005). Thus the induction of ferritin by methomyl and propanil may indicate oxidative stress, as suggested previously (El-Khawaga, 2005; Milatovic et al., 2006; Moraes et al., 2007). 4.1. Methomyl-specific changes Ion regulation in daphnids and other freshwater organisms is an essential process to counteract significant ion loss into the hypo-osmotic external medium and chloride and sodium are two major ions representative of this physiological process. Few studies have investigated ion regulation in D. magna but it is known that internal chloride and sodium concentrations are actively regulated. The ATP-dependent sodium–potassium pump (Na, K ATPase) plays a relevant role in sodium exchange processes across the basolateral membrane of the salt-transporting epithelia in daphnids (Bianchini and Wood, 2008). Chloride is regulated through the chloride-bicarbonate anion exchanger (Cl, HCO3 AE) and in D. magna this ion exchange process is inhibited by high concentrations of bicarbonate in the external medium (Hoke et al., 1992). Bianchini
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and Wood (2008) suggested that these two ion regulatory processes are linked, with Cl, HCO3 AE being coupled with Na,K ATPase to diffuse HCO3 produced during Na+ uptake by daphnids. mRNAs of both of these ion regulatory genes were up-regulated by methomyl and as far as we know, this is the first report of a pesticide affecting these systems in daphnids. Methomyl exposure also up-regulated mRNA of the gene coding for sulfotransferase. Sulfotransferases catalyse the conjugation of sulphate with several endogenous and xenobiotic compounds, including alcohols, thiols and amines (Josephy, 1997). Xenobiotics such as pyrene are conjugated to sulphate, via sulfotransferase in D. magna (Ikenaka et al., 2006) and therefore an up-regulation of gene coding for sulfotransferase may represent a detoxification process. 4.2. Propanil-specific changes Methomyl was expected to affect neuronal transmission since its known mode of action is to disrupt nerve transmission, whereas propanil was not expected to disrupt this function. However propanil elicited specific up-regulation of gene transcription within neuronal pathways, including dopa decarboxylase and syntaxin 6, whereas no differential transcription of these genes was found following exposure to methomyl. Dopa decarboxylase catalyses the conversion of dihydroxyphenylalanine (Dopa) to dopamine and 5-hydrotryptophan to serotonin in response to several endogenous or exogenous signals; and has already been shown to be involved in insect cuticle maturation, neuronal regulation, pigmentation patterning and innate immunity (Hodgetts and O’Keefe, 2006). Syntaxin 6 is a protein belonging to the synaptic vesicle release machinery and appears to regulate the presynaptic calcium channels activity (Wendler and Tooze, 2001; Zamponi, 2003). Both pesticides down-regulated the expression of mRNA of a gene for a carboxylesterase belonging to the AChE family (EF580101 – see supplementary material). 4.3. Conclusions Our comparison of transcriptional responses to two commonly used pesticides provides important insights into the molecular/cellular mechanisms underlying the effects of stress. There are now several D. magna microarray datasets where animals have been exposed to an array of chemicals, including heavymetals, pharmaceuticals and various classes of pesticides (Connon et al., 2008; Heckmann et al., 2008; Poynton et al., 2007). What is emerging from these and datasets from other invertebrate microarray studies is that where growth is impaired or animals immobilised, genes associated with energy production and (in the case of ecdysozoans) moulting, are nearly always affected. These genes are therefore likely to represent general stress responses and be directly linked to phenotypic responses. There is little evidence for expression responses purely restricted to genes associated with the pesticide target site. The toxic response is therefore more subtle and more complicated that first thought. Authors’ contributions All authors participated in designing the study. JLP and CJH contributed equally in conducting the experimental work, following protocols previously developed by LHH. Bioinformatics was handled by CJH, VNB and RMS (microarray imaging and statistics) and by JLP (gene annotation and functional analysis). The manuscript was drafted by JLP and CJH under the supervision of FG, RMS and AC. All authors contributed intellectually to the manuscript and approved the final version.
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Acknowledgements This study was part-funded by the Portuguese Foundation of Science and Technology (FCT, Portugal), as additional financial support to the PhD grant of Joana L. Pereira (SFRH/BD/13682/2003). Chris Hill was supported by a Natural Environment Research Council studentship NER/S/A/2006/14200. Microarray image acquisition and data analysis were funded and performed by the University of Reading BioCentre Facility. The microarray was produced from a NERC-funded grant (NER/D/S/2002/00413) to RMS and AC and from funds provided by Syngenta and AstraZeneca. The following people (in addition to AC, RMS, LHH and CJH) were responsible for the production and annotation of the microarray: R. Connon, H.L. Hooper, S.J. Maund, T.H. Hutchinson, F.L. Lim, D.J. Moore, H. Watanabe, A. Soetaert, K. Cook, J. Moggs, W. De Coen and T. Iguchi. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.aquatox.2009.12.023. References Barata, C., Solayan, A., Porte, C., 2004. Role of B-esterases in assessing toxicity of organophosphorus (chlorpyrifos, malathion) and carbamate (carbofuran) pesticides to Daphnia magna. Aquat. Toxicol. 66, 125–139. Barr, D.B., Needham, L.L., 2002. Analytical methods for biological monitoring of exposure to pesticides: a review. J. Chromatogr. B 778, 5–29. Beyerbach, A., Sabionni, G., 1999. Biomonitoring of arylamines: haemoglobin adducts of aniline derivatives. Biomarkers 4, 229–236. Bianchini, A., Wood, C.M., 2008. Sodium uptake in different life stages of crustaceans: the water flea Daphnia magna Straus. J. Exp. Biol. 211, 539–547. Blyler, G., Landreth, K.S., Lillis, T., Schafer, R., Theus, S.A., Gandy, J., Barnett, J.B., 1994. Selective myelotoxicity of propanil. Fundam. Appl. Toxicol. 22, 505– 510. Brown, C.D., Carter, A.D., Holis, J.M., 1995. Soils and pesticide mobility. In: Roberts, T.R., Kearney, P.C. (Eds.), Environmental Behaviour of Agrochemicals. John Wiley & Sons, West Sussex, pp. 131–179. Carter, A., 2000. How pesticides get into water – and proposed reduction measures. Pestic. Outlook, 149–156. Cerejeira, M.J., Viana, P., Batista, S., Pereira, T., Silva, E., Valério, M.J., Silva, A., Ferreira, M., Silva-Fernandes, A.M., 2003. Pesticides in Portuguese surface and ground waters. Water Res. 37, 1055–1063. Chang, E.S., Bruce, M.J., Tamone, S.L., 1993. Regulation of crustacean molting: a multihormonal system. Am. Zool. 33, 324–329. Connon, R., Hooper, H., Sibly, R.M., Lim, F.-L., Heckmann, L.-H., Moore, D.J., Watanabe, H., Soetaert, A., Cook, K., Maund, S.J., Hutchinson, T.H., Moggs, J., De Coen, W., Iguchi, T., Callaghan, A., 2008. Linking molecular and population stress responses in Daphnia magna exposed to cadmium. Environ. Sci. Technol. 42, 2181–2188. Cuff, C.F., Zhao, W., Nukui, T., Schafer, R., Barnett, J.B., 1996. 3,4Dichloropropionanilide-induced atrophy of the thymus: mechanisms of toxicity and recovery. Fundam. Appl. Toxicol. 33, 83–90. De Coen, W., Janssen, C.R., 2003. A multivariate biomarker-based model predicting population-level responses of Daphnia magna. Environ. Toxicol. Chem. 22, 2195–2201. El-Khawaga, O.A.Y., 2005. Role of selenium on antioxidant capacity in methomyltreated mice. J. Physiol. Biochem. 61, 501–506. Elser, J.J., Sterner, R.W., Gorokhova, E., Fagan, W.F., Markow, T.A., Cotner, J.B., Harrison, J.F., Hobbie, S.E., Odell, G.M., Weider, L.J., 2000. Biological stoichiometry from genes to ecosystem. Ecol. Lett. 3, 540–550. Finney, D.J., 1971. Probit Analysis. Cambridge University Press, Cambridge. García de Llasera, M.P., Bernal-González, M., 2001. Presence of carbamate pesticides in environmental waters from the Northwest of Mexico: determination by liquid chromatography. Water Res. 35, 1933–1940. Guest, R.K., Ikehata, K., El-Din, M.G., Smith, D.W., 2006. Pesticides and herbicides. Water Environ. Res. 78, 1755–1801. Guilhermino, L., Soares, A.M.V.M., Carvalho, A.P., Lopes, M.C., 1998. Acute effects of 3,4-dichloroaniline on blood of male Wistar rats. Chemosphere 37, 619–632. Haeba, M.H., Hilscherová, K., Mazurová, E., Blahá, L., 2008. Selected endocrine disrupting compounds (vinclozolin, flutamide, ketoconazole and dicofol): effects on survival, occurrence of males, growth, molting and reproduction of Daphnia magna. Environ. Sci. Pollut. Res. 15, 222–227. Hanazato, T., 2001. Pesticide effects on freshwater zooplankton: an ecological perspective. Environ. Pollut. 112, 1–10. Heckmann, L.-H., Bouetard, A., Hill, C.J., Sibly, R.M., Callaghan, A., 2007. A simple and rapid method for preserving RNA of aquatic invertebrates for ecotoxicogenomics. Ecotoxicology 16, 445–447. Heckmann, L.-H., Sibly, R.M., Connon, R., Hooper, H.L., Hutchinson, T.H., Maund, S.J., Hill, C.J., Bouetard, A., Callaghan, A., 2008. Systems biology meets stress ecology:
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