Cerebral gene expression and neurobehavioural responses in mice pups exposed to methylmercury and docosahexaenoic acid through the maternal diet

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journal homepage: www.elsevier.com/locate/etap

Cerebral gene expression and neurobehavioural responses in mice pups exposed to methylmercury and docosahexaenoic acid through the maternal diet S. Jayashankar a,b,∗ , C.N. Glover c , K.I. Folven a , T. Brattelid a,b , C. Hogstrand a,d , A.-K. Lundebye a,b a

National Institute of Nutrition and Seafood Research (NIFES), Post box 2029 Nordnes 5817 Bergen, Norway Department of Biology, University of Bergen, P.O. box 7803, N-5020, Bergen, Norway c School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand d Metabolism Group, Nutritional Sciences Division, School of Medicine, King’s College London, SE1 9NH, United Kingdom b

a r t i c l e

i n f o

a b s t r a c t

Article history:

Methylmercury (MeHg) is an environmental neurotoxicant with adverse effects particularly

Received 5 February 2011

noted in the developing brain. The main source of MeHg exposure is seafood. However, fish

Received in revised form

is also an important source of n-3 fatty acids such as docosahexaenoic acid (DHA) which

24 September 2011

has neuroprotective effects, and which plays an important role during the prenatal devel-

Accepted 6 October 2011

opment of the central nervous system. The aim of the present study was to examine the

Available online 13 October 2011

effects of DHA and MeHg individually, and in combination, on development using accumulation, behavioural and transcriptomic endpoints in a mammalian model. Analyses were

Keywords:

performed on 15 day old mice which had been exposed to varying levels of DHA (8 or

Methylmercury

24 mg/kg) and/or MeHg (4 mg/kg) throughout development via the maternal diet. Supple-

Docosahexaenoic acid

mentation of the maternal diet with DHA reduced MeHg accumulation in the brain. An

Mice

accelerated development of grasping reflex was seen in mice offspring in the ‘MeHg + high

Grasping reflex

DHA’ group when compared to ‘MeHg’ and ‘control’. Exposure to MeHg and DHA had an

Microarray

impact on cerebral gene expression as assessed by microarray and qPCR analysis. The

Polyunsaturated fatty acids

results from the present study show the potential of DHA for alleviating toxicity caused by MeHg. This information may contribute towards refining risk/benefit assessment of seafood consumption and may enhance understanding of discrepancies between epidemiological studies of MeHg neurodevelopmental toxicity. © 2011 Elsevier B.V. All rights reserved.

1.

Introduction

The effects of seafood consumption on human health, particularly during pregnancy, are currently subject to much scientific debate (e.g. Genuis, 2008). One putative risk factor of seafood consumption is methylmercury (MeHg) exposure (Aschner

and Syversen, 2005; Clarkson, 1997). MeHg is well recognised as a neurotoxicant, and is especially damaging to the developing brain (Aschner and Syversen, 2005; Atchison, 2005). The major mechanisms proposed to mediate MeHg neurotoxicity are increased oxidative stress (Sarafian, 1999; Shanker and Aschner, 2003); impaired microtubule formation (Choi et al., 1980); excitotoxicity and altered glutamate homeostasis

∗ Corresponding author at: National Institute of Nutrition and Seafood Research, Post box 2029 Nordnes 5817 Bergen, Norway. Tel.: +47 41458038; fax: +47 55905299. E-mail address: [email protected] (S. Jayashankar). 1382-6689/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.etap.2011.10.001

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(Aschner et al., 2000); and disturbances in protein synthesis (Verity et al., 1975). These mechanisms of MeHg toxicity have been primarily characterised at high exposure levels, over acute time frames, and often using in vitro techniques. However, the effects of chronic, low dose MeHg exposure in vivo are comparatively poorly understood, despite the relevance of these exposures to human health. Some studies engaging chronic, low dose exposure protocols have demonstrated neurological deficits and behavioural disturbances in rodents perinatally exposed to MeHg (e.g. Goulet et al., 2003; Folven et al., 2009), but there appear to be fundamental differences in toxic mechanisms (Glover et al., 2009), relative to acute in vitro studies Exposure duration and concentration are key factors in determining MeHg impacts, but route of exposure may also be critical. Differences in responses related to oral versus subcutaneous dosing have been described (Bellum et al., 2007). Furthermore the nature of toxic responses following MeHg exposure via the diet, the most likely form of human exposure, appears to be significantly influenced by nutritional composition (Clarkson and Strain, 2003). An epidemiological study carried out on the human population of Faroe Islands showed that low level exposure to MeHg was the likely cause of mental deficiencies and learning disabilities in young children (Grandjean et al., 1997). However, a similar study conducted in the Seychelle Islands did not seem to produce any notable effects from low level exposure to MeHg (Myers et al., 2003). The discrepancies observed between the studies could be due to confounding factors such as differential nutritional composition between the main dietary sources of MeHg in these two studies (whale meat in the Faroe Islands, marine fish in the Seychelles; Clarkson and Strain, 2003). For example, some studies have suggested that dietary lipids such as docosahexaenoic acid (DHA), present in oily fish, may modulate MeHg-induced neurotoxicity (Chapman and Chan, 2000; Mahaffey et al., 2008). DHA is the most abundant n-3 poly unsaturated fatty acid (PUFA) present in the mammalian brain (Kim, 2007), and there is a large body of evidence that DHA is essential for the growth and development of nervous tissue (Kim, 2007; Uauy et al., 2001). The functions of DHA range from playing a critical role in neurogenesis (Ikemoto et al., 1997) to being antiapoptotic (Akbar and Kim, 2002) and protective against oxidative stress (Sarafian, 1999). DHA deficiency has been reported to be associated with impaired cognitive and behavioural performance (Innis, 2007). Many of these positive roles on neurological health directly compensate for some of the putative negative impacts of MeHg, providing a basis for the hypothesis that DHA is a candidate for ameliorating MeHg neurotoxicity. Although both DHA and MeHg have been researched individually, the mechanistic basis of DHA amelioration of MeHg-induced neurotoxicity has not been well explored. Levels of both MeHg and DHA can vary significantly depending on the type and amount of fish consumed (Mahaffey, 2004; Domingo et al., 2007). This complicates studies attempting to delineate interactions between these dietary components in real-world settings. However it is known that while reduced consumption of seafood limits MeHg exposure, it can also negatively impact both DHA levels and neurological function in offspring (Hibbeln et al., 2007). Most previous studies examining MeHg toxicity have used drinking water or

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injection as the route of administration of MeHg, which differ from the natural route of exposure, the diet. The aforementioned routes of exposure do not consider the complicating effects of nutrients on MeHg, which could affect its bioavailability or bioreactivity. Similarly, the biological form of MeHg present in fish is methylmercury cysteinate (MeHgCys; Harris et al., 2003). However, most studies of MeHg toxicity have used methylmercury chloride (MeHgCl), which differs from MeHgCys in its toxicological profile and impacts (Glover et al., 2009; Berntssen et al., 2004). A toxicogenomic approach was implemented to study the combined exposure of a neurotoxicant (MeHg) and neuroprotectant (DHA) in gestationally exposed mice. The route of intoxication occurred via the diet (natural route) using MeHgCys (natural form), which is the predominating MeHg species in fish (Harris et al., 2003). Studying gene expression perturbations elicited by MeHg in mice offspring provides insight into the mechanisms of action by highlighting which molecular pathways are targeted during cerebral development. Mice were used as a model owing to the extensive knowledge available on the sequence of the genome and a pharmacodynamic profile that resembles that of human mercury exposure (Lewandowski et al., 2003). By monitoring MeHg and DHA exposure impacts on gene expression, neurobehaviour and tissue accumulation in gestationally exposed mice, we aimed to understand how DHA, a seafood nutrient, may modulate neurological perturbations induced by MeHg at the most susceptible stages of development. The knowledge gained from this study may prove valuable in the refinement of future risk/benefit assessments and possibly address some of the neurodevelopmental discrepancies found in epidemiological studies of MeHg effects.

2.

Materials and methods

2.1.

Experimental diets

Five different dietary treatments were designed. The exposure groups included: Control; MeHg (in the naturally occurring cysteinate form (MeHgCys); ∼4 mg/kg); MeHg + high DHA (24 mg/kg); low DHA (8 mg/kg) and MeHg + low DHA. This level of MeHg was chosen based on previous studies (Stern et al., 2001; Markowski et al., 1998). In these studies, mice offspring gestationally exposed to MeHg exhibited toxic effects at the highest levels of exposure (3 ppm and 5 ppm). Studies such as Folven et al. (2009) and Glover et al. (2009), under similar experimental conditions, showed Hg accumulation in the brain of mice equivalent to environmentally and occupationally exposed humans (0.19–2.96 mg/kg; Falnoga et al., 2000), which resulted in significant, albeit minor, changes in behaviour and gene expression. Concentrations of DHA were chosen in order to maximise DHA supplementation without impacting the overall composition of the dietary lipid source. This ensured that observed effects were a consequence of DHA supplementation rather than a consequence of altered fatty acid profile relative to the control. Feed preparation was inhouse, using powdered casein as the protein source. The lipid content of the diets was 12% and sunflower oil was used as the lipid source. A standard commercial vitamin and mineral

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supplement (AIN-93G; Dyets Inc., U.S.) was used. MeHgCys was prepared according to the method described by Taylor et al. (1975). A stock solution of MeHgCl (Sigma Aldrich, Norway) dissolved in 96% ethanol was added in a 1:1 molar mixture to a stock solution of l-cysteine (Sigma–Aldrich, Norway) dissolved in water. Aliquots of the resulting MeHgCys solution were then added to the powdered diets as appropriate. Appropriate amounts of DHA were added in the form of oil (Napro-Pharm, Norway) to the experimental diets. The differences in the lipid content of the experimental diets were balanced with the addition of sunflower oil, making the diets iso-caloric. The diets were stored at −20 ◦ C throughout the course of the trial to minimise oxidation of DHA.

2.2.

Dietary exposure regime

A total of 76 balb/c mice (Taconic Inc., Denmark) were used in the experiment, allowing comparison to previous studies of gestational MeHg exposure that have utilised this strain (e.g. Folven et al., 2009; Glover et al., 2009; Jayashankar et al., 2010). The male mice (n = 22) were solely used for mating purposes. Female mice (n = 54) were housed three per cage, with cages randomly distributed within racks to minimise effects of any variations in housing conditions. Twelve dams were assigned to all groups with the exception of ‘Low DHA’ and ‘MeHg + Low DHA’ experimental groups which had nine dams. The mice were acclimated for a period of two weeks on control diet. Following acclimation, the mice received experimental diets ad libitum for three weeks prior to breeding, during breeding, throughout gestation and for two weeks post-partum (a total of nine weeks of exposure for dams by experiment termination). The amount of feed consumed was approximately 3.5 g per mouse prior to pregnancy, 4.5–6 g per mouse during gestation and 7–10 g per mouse during lactation. Dose (Table 1) was calculated for periods prior to and during gestation, based on the feed consumed per day per body weight of the mouse. The amount of feed provided to dams was based on consumption rates from a previous study conducted in our facility (Glover et al., 2009). Tap water was supplied ad libitum. The female mice were separated into individual cages on gestation day 16. A twelve hour light and dark cycle was maintained during the course of the trial. Temperature (25 ± 2 ◦ C) and humidity (50 ± 5%) of the room in which the mice were housed was controlled. The experimental protocol was approved by the Norwegian Animal Research Authority, and all procedures described were performed in accordance with their recommendations. For the behavioural analyses, pups were chosen randomly. The data from male pups and female pups were not treated separately. Similarly, tissues from pups for the chemical and transcriptomic analyses were not distinguished on the basis of gender. This approach follows a protocol that was used previously, in studies that failed to delineate any gender-specific impacts of MeHg on brain gene expression or neurobehaviour (Folven et al., 2009; Glover et al., 2009).

2.3.

Tissue sampling

Mice pups were sacrificed on postnatal day (PND) 15. The corresponding dams were also sacrificed at this time. Animals

were anaesthetised in an Isofluran Gas-chamber (Univentor 400 Anaesthesia unit, Isobavet), followed by cervical dislocation. Brain and liver were dissected out, weighed and snap frozen in liquid nitrogen. The samples were stored at −80 ◦ C for gene expression profiling and chemical analysis.

2.4.

Mercury analysis

Tissue and feed samples were subjected to microwave digestion in concentrated nitric acid and hydrogen peroxide, before being analysed for total Hg by inductively coupled plasma mass spectrometry (Agilent quadrupole 7500 C, Yokogawa Analytical System Inc., Tokyo, Japan). Rhodium was used as the internal standard, with a certified reference material (Dogfish muscle, DORM-2, Ottawa, Canada) to assess accuracy (Julshamn et al., 2007). The limit of quantification was 0.03 mg/kg dry weight.

2.5.

Fatty acid analysis

Fatty acid composition of total lipids in feed, pup brains and pup livers was determined. Lipids from the samples were extracted by adding chloroform–methanol (2:1, v/v) and 19:0 methyl esters were added as the internal standard. The samples were filtered, saponified, methylated and analysed using Trace Gas Chromatography (Fison CE instruments, Milan, Italy) as described by Lie and Lambertsen (1991) modified by Torstensen et al. (2008).

2.6.

Behavioural analysis

A battery of neurobehavioural analyses were performed on mice pups at PND 5, 7, 9, 12 and 15. The day the pups were born was considered PND 0. The testing was carried out in a separate room to minimise calls disturbing the mothers. Two pups per litter were subjected to analyses which were carried out around the same time of the day for pups of the same developmental stage. The testing was performed by an analyst blinded to the exposure groups.

2.6.1. Development of physical markers and early pup behaviour Body weights, development of physical markers (freeing of pinnae; eye opening; fur development; eruption of incisors) and reflex development in pups were monitored on PND 5, 7, 9, 12 and 15. A suite of behavioural tests for analysing reflex development in mice offspring was performed based on the methods implemented by Folven et al. (2009). The tests carried out were as follows: righting reflex; rooting reflex; grasping reflex and auditory startle. A litter score was calculated by combining the data from pups tested within a litter and this was used as a statistical unit (Folven et al., 2009). Odour cues were removed from the surface between each litter tested.

2.6.2.

Late stage pup and dam behaviour

Grip strength, hindlimb splay and elevated maze were tested in late stage pups (15 day old pups) and dams, according to the protocols of Folven et al. (2009) and Glover et al. (2009). The following parameters were monitored on the elevated maze: distance travelled; velocity of movement; number of entries

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into various zones; resting time and permanence time in each zone.

2.6.3.

Statistics on behavioural data

Data for each pup was averaged to provide a mean value per litter (i.e. n = number of litters). The data was tested for normality of distribution using Kolmogorov-Smirnov test and homogeneity using Levene’s test. One-way ANOVA was used to analyse the data if the normality and homogeneity tests passed and Fisher LSD post hoc test was used to establish the groups which were significantly different. If the tests failed, the data was subjected to non-parametric analysis using Kruskal–Wallis ANOVA. The level of significance was set at p < 0.05.

2.7.

RNA isolation

Frozen whole brain samples were homogenised using a bead grinding protocol (6000 rpm, 3 × 10 s; Precellys 24, Bertin Technologies). Total RNA was isolated using QIAzol reagent (Qiagen) on the BioRobot EZ1 work station. RNA concentration was measured spectrophotometrically (Nanodrop® ND-1000; NanoDrop Technologies, Wilmington, DE, USA). The integrity of the RNA samples was monitored using microcapillary electrophoresis (Agilent 2100 Bioanalyser; Agilent Technologies, Palo Alto, CA, USA).

2.8.

Microarray

RNA samples from pup brains were subjected to microarray analysis (Control, n = 3; MeHg, n = 3; Low DHA, n = 3; MeHg + low DHA, n = 4; MeHg + high DHA, n = 4). N values were assigned based on pups from individual mothers i.e. one pup was taken from each of the different dams exposed to the respective treatments. The reference pool was made using aliquots of all the RNA samples that were subjected to microarray analysis via a protocol identical to that described by Glover et al. (2009). The mouse oligonucleotides arrays were spotted on UltraGAPSTM coated slides (Corning Life Sciences, Promega), using a Qarray2 robot (Genetix Ltd.) at the King’s College London Genomics Centre, UK, utilising arrays with probes representing approximately 25,000 genes and 38,000 gene transcripts (mouse OpArray, 4.0, Operon, Huntsville, AL). In addition, 331 customised oligonucleotides and 23 Cl ScorecardTM were added to the array set and used for quality control. The method we used to quantify intensity data is standard for two-color microarrays and was previously published (Zheng et al., 2010; Glover et al., 2009; Jayashankar et al., 2010).

2.8.1.

Analysis

Images of hybridised slides were captured (ScanArray Express; Perkin Elmer, Waltham, MA), analysed and were subjected to a normalisation procedure (LOWESS) (Bluefuse; BlueGnome, Cambridge, UK). The output data was subsequently imported into GeneSpring GX 7.3 for further normalisation and statistical analysis. The data was first normalised “per spot and per gene”. A per-spot normalisation relates the signal strength of the target gene in the sample to the signal strength of the control (Cy3 dye alone). Per gene normalisation relates the

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signal strength of the target gene to the median value of every measurement of signal strength of that gene across the entire experiment. Each gene was normalised to control samples to generate fold-change values. The resulting data was then filtered on flags. ‘Flags’ were derived from the threshold of spot confidence. Quality of spots was first assessed manually within the BlueFuse software where bad spots were flagged for exclusion. In addition, BlueFuse returns a confidence value of 0–1, indicating the quality of each spot. These confidence estimates were assigned flags where Absent: 0–0.23, Marginal: 0.23–0.70, and Present: 0.70–1.0. The genes with “absent” flags were excluded (absent or unreliable spots) in more than 10 out of the 18 samples and above the signal intensity of 495. A fold-change filter on the resulting list was applied to include genes with relative expression levels 1.5 fold or more relative to the control in at least one out of the four comparisons, consistent with previous approaches examining cerebral gene expression changes on MeHg exposure in mice (Glover et al., 2009; Jayashankar et al., 2010). Genes with a fold-change relative to the control of <1.5-fold were excluded from statistical analysis because changes smaller than this were deemed of less biological importance. These restrictions left 5693 genes to be statistically tested for differences between treatments. The fold-change values of genes were log2 transformed and subjected to a parametric ANOVA without assuming equal variances (p < 0.05). The 5693 genes were also analysed for statistical differences using a Welch t-test on each of the four comparisons (relative to control; p < 0.05) to distinguish genes regulated by each treatment. A functional annotation enrichment analysis was performed on regulated genes in each group. We adopted a two-tiered approach where, in addition to the statistical criteria described above, a less stringent significance (Welch t-test p < 0.2) was accepted in order to increase the numbers of genes going into the annotation enrichment test. The threshold for statistical significance was raised because the annotation enrichment test used works better with larger gene-sets (Dennis et al., 2003). Even though this approach probably introduced more false positives (Type 1 error), it was considered appropriate for the purpose as a random gene list would not be expected to contain overrepresentation of any particular gene category and, thus, statistically significant enrichment in functional annotation would likely be caused by treatment. The gene lists resulting from ‘high DHA + MeHg’ and ‘low DHA + MeHg’ groups were combined for the functional annotation enrichment analysis to further increase the power of analysis. The functional annotation enrichment analysis was carried out using Internet-based Database for Annotation, Visualisation, and Integrated Discovery (DAVID; Dennis et al., 2003)), which analysed for enrichment of Gene Ontology (GO) terms and alternative functional and structural descriptors, using the mouse genome as background gene population. The DAVID functional annotation tool uses a one-tailed Fisher Exact test to calculate significant enrichment of genes with a particular annotation within a gene list. The gene lists used with DAVID (p < 0.2) were also used to perform pathway analysis with Ingenuity Pathway Analysis (IPA, Ingenuity Systems® ), to select genes for qPCR analysis. The rationale behind using non-stringent gene lists was again to increase the number of significantly enriched gene-sets.

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Table 1 – Mean total mercury and DHA levels in feed samples. Experimental group

Control Low DHA MeHg MeHg + Low DHA MeHg + High DHA a

Feed levelsa (mg/kg)

Estimated maternal consumption (␮g/kg body weight/day)

DHA

Hg

Prior to gestation

Gestation

0.00 7.70 ± 0.08 0.00 7.70 ± 0.02 23.64 ± 0.19

<0.03 <0.03 3.75 ± 0.14 4.00 ± 0.17 3.79 ± 0.10

– – 500 500 488

– – 640–780 650–760 630–750

Feed levels (means ± SD) were measured (2 and 4 parallels for mercury and DHA analysis) from randomly selected samples of feed. Limit of quantification = 0.03 mg/kg for mercury.

2.9.

Quantitative real time PCR

RNA isolated from brain tissue was subjected to reverse transcription polymerase chain reaction (RT PCR; Control, n = 6; MeHg, n = 6; Low DHA, n = 6; MeHg + low DHA, n = 6; MeHg + high DHA, n = 6). Reverse transcription and real-time PCR was performed using a LightCycler® 480 Real-Time PCR System (Roche Applied Sciences, Basel, Switzerland), according to a previously published protocol (Glover et al., 2009). Primers for the various selected genes were off-shelf products (QuantiTect primer assays, Qiagen).

2.9.1.

Analysis

The standard curve was plotted using two-fold dilutions of the reference pool (pool of target samples). Amplification efficiency for each quantitative RT-PCR reaction was calculated from slope of the corresponding standard curve with the formula: Efficiency = (10(−1/slope) ) − 1. The amplification efficiencies were between 90 and 100%. The standard curve method was used to calculate individual expression level for each mRNA analysed. Elongation factor 4 was selected to normalise the data from target genes. Statistical analysis on the qPCR data was performed using GraphPad Prism 5.0 software (GraphPad software, Inc.). A nonparametric Mann–Whitney test was used to determine the statistical differences between the respective treatment and control. A statistical difference of p < 0.05 was deemed significant. Six replicates were used for each of the respective treatments. Samples showing an SEM above 10% between duplicates were excluded from the analysis (a minimum of 3 replicates were maintained per exposure group).

3.

Results

3.1.

Dietary mercury and DHA levels and doses

The total Hg and DHA contents in the diets are provided in Table 1. Control and low DHA had Hg levels less than 0.03 mg/kg. The Low DHA supplemented groups and high DHA supplemented groups contained ∼8 and ∼24 mg/kg of DHA respectively. Mice in all groups consumed approximately 3.5 g feed/day prior gestation, 5 g feed/day during initial days of gestation and 7 g/day during latter stages of gestation. This resulted in an estimated maternal Hg consumption of–∼500, ∼650, and ∼780 ␮g/kg body weight/day, for animals in

MeHg-supplemented groups during pre-gestation, early and late gestation, respectively (Table 1).

3.2.

Tissue burdens of Hg and DHA

Tissue burdens of MeHg and DHA in brain and liver of mice offspring are represented in Figs. 1 and 2, respectively. Hg concentrations in the brain and liver of mice offspring prenatally exposed to low DHA and control were below quantification limits. Pups from the MeHg-supplemented groups had a significantly higher accumulation of Hg in brain and liver than the control group. Mice offspring perinatally exposed to ‘MeHg + low DHA’ and ‘MeHg + high DHA’ exhibited significantly lower Hg burdens in brain when compared to the ‘MeHg’ group. However, there were no significant differences in offspring brain Hg concentrations between the ‘MeHg + low DHA’ and ‘MeHg + high DHA’ groups. There was a small but significant increase in the DHA level in brain of mice offspring in the ‘MeHg’ group when compared to the ‘control’ group. No such difference in DHA level was found in liver of mice offspring between ‘control’ and ‘MeHg’ pups. DHA levels in the brain of mice offspring in DHA supplemented treatments were significantly higher than those in the ‘Control’ and ‘MeHg’ exposures. The DHA levels in pup livers in all groups were significantly higher than the control group. No significant differences in the DHA levels in pup brains and livers were observed between pups of dams exposed to DHA and those exposed to DHA and MeHg at each dietary DHA level. A significantly higher DHA level in pup brains was observed in MeHg + high DHA (5.8 mg/kg) when compared to MeHg + low DHA (5.1 mg/kg) treatments. However, this difference was not found in pup livers.

3.3. Reproductive success in dams and physical development in offspring No significant differences in the body weights of pups were observed among groups at any of the test days, nor were there significant differences in the mean litter sizes between the exposure groups (Table 2). Physical body indices such as hepatosomatic index and brain size did not differ between exposure groups. Pups in all groups developed fur between PND 5 and 7 had free pinnae on PND 5; opened eyes between PND 12 and PND 15; and displayed erupted incisors between PND 6 and 8. No significant differences in these markers of physical development were observed among exposure groups.

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Fig. 1 – Mean Hg concentrations in (a) pup brains and (b) pup livers following maternal exposure to respective treatment groups. The plotted points represent means ± SEM of four replicated sample measurements. The limit of quantification was 0.03 mg/kg. Sample size given in brackets. Mean values sharing letters are not significantly different (p < 0.05 one-way ANOVA).

3.4.

Behavioural endpoints

A battery of reflex tests was performed on mice offspring to assess motor and sensory development. Pups in the MeHg + High DHA supplemented group had a significantly lower day of development score than pups in the control group (Fig. 3). Pups in the MeHg + high DHA group showed a significantly lower day score when compared to pups in the MeHg

Fig. 2 – Mean DHA concentrations in (a) pup brains and (b) pup livers (PND 15) following maternal exposure to respective treatment groups. The plotted points represent means ± SEM of four replicated sample measurements. Sample size given in brackets. Mean values sharing letters are not significantly different (p < 0.05 one-way ANOVA).

Table 2 – Number of litters born and their mean litter sizes in respective experimental treatments. Experimental group

Litters/experimental group

MeHg + low DHA Low DHA Control MeHg MeHg + high DHA

8 7 8 7 8

Mean Litter size ± SD 6.13 6.14 5.63 5.57 5.75

± ± ± ± ±

2.30 3.28 2.39 1.32 1.67

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3.5.

Fig. 3 – Hind limb grasp in mice offspring (PND 15) in respective maternal dietary treatment groups. Data is presented as day score (see Section 2). The results are presented as group means ± SEM. Sample size given in brackets. Mean values sharing letters are not significantly different (p < 0.05).

group. A similar trend was observed on hind limb grasp at PND 5 (data not shown). All other behavioural analyses, including those conducted on the elevated plus maze, failed to show a significant impact of MeHg and/or DHA exposure.

Transcriptomic analysis

A total of 19980 transcripts scored as “present” on the arrays based on a spot confidence score of >23% and a signal intensity of 495. Twelve of these 5693 transcripts passed normalisation and filtering procedures, and showed a fold change regulation of ≥1.5. Three hundred and eighteen genes were determined to be significantly regulated in one or more comparisons (one way ANOVA on 4 comparisons). Fig. 4 represents the regulation (relative to control) of genes belonging to the respective exposure groups (p < 0.05). The genes were subjected to hierarchical clustering which grouped genes based on relative expression. The MeHg-supplemented treatments did not cluster together suggesting a dominant effect of DHA on neural gene expression. To obtain separate gene lists for each treatment, gene expression from each exposure group was tested pairwise against the control, using a Welch t-test at an alpha level of 0.05 (relative to control). This analysis resulted in 126 genes with MeHg; 209 genes with Low DHA; 108 genes with MeHg + high DHA; and 115 genes with MeHg + low DHA that were significantly differentially expressed. A number of functional classes were identified as being significantly enriched following gestational and lactational exposure to the experimental treatments (Table 3). Fig. 5

Table 3 – Over-represented functional groups among genes regulated by MeHg or DHA or MeHg + DHA relative to control.

Note: Functional clusters were derived from functional annotation enrichment analysis using DAVID (Dennis et al., 2003). Shading of the entry indicates the significance value of the annotation group under the respective exposure group. (dark grey, p < 0.001; grey with hatched bars, p < 0.01; light grey < p < 0.05). Data for MeHg + low DHA and MeHg + high DHA was pooled.

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Fig. 4 – Heat map showing gene expression of 50 genes of the 318 genes significantly regulated in brains of pups (PND 15) in one or more comparisons between groups resulting from 1-way ANOVA (FDR = 0.05). Values less than 1 (green) represent downregulated genes, while values greater than 1 (red) represent upregulated genes. The heat map was generated using MultiExperimentViewer (Saeed et al., 2003), with hierarchical clustering performed via Pearson Correlation. See supplemental Table 1 for further details on individual genes. The data is based on the following n-values: Control, n = 3; MeHg, n = 3; Low DHA, n = 3; MeHg + low DHA, n = 4; n = 4; MeHg + high DHA, n = 4. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

represents the regulation (relative to control) of genes belonging to the respective exposure groups in some of the most significant clusters. Distinct clusters of genes related to development, neurological processes, cell communication, metabolic process and protein modification processes were significantly enriched under DHA exposure, while those related to growth factor activity, cell morphology and function were significantly enriched under MeHg exposure (Table 3). The combined exposure to MeHg and DHA altered expression of genes related to cytoskeleton development, neurotransmission, cell equilibrium, apoptotic programming, intracellular organelle part and protein localisation and transport. The functional class ‘transmission of nerve impulse’ was common to DHA and MeHg + DHA exposure groups. Similarly, ‘intracellular organelle’ was an enriched functional cluster

among those genes differentially expressed in both DHA and MeHg exposure groups. While, ‘intracellular part’ overlapped between MeHg and MeHg + DHA treatments, there were no functional clusters common to MeHg, MeHg + DHA and DHA exposure groups.

3.6.

Quantitative PCR analysis

Quantitative PCR analysis of selected genes was used to verify the effects of dietary exposures on murine brain gene expression. The genes selected were based on differentially regulated ‘Top molecules’ that participated in some of the ‘Top networks’ identified by Ingenuity Pathway Analysis under each treatment (see Supplementary data). Genes selected were as follows: Excision repair cross-complementing rodent

34

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Fig. 5 – Heat maps showing expression of genes in brains of pups (PND 15) from some of the most significant functional annotation categories identified as being enriched in pup brains following MeHg exposure. Maps represent genes clustered in (a) GOTERM BP ALL “growth factor activity”, (b) GOTERM BP ALL “receptor binding”, (c) GOTERM BP ALL “transmission of nerve impulse”, (d) GOTERM BP ALL “microtubule cytoskeleton”, (e) GOTERM BP ALL “tubulin binding”. The heat map was generated using MultiExperimentViewer (Saeed et al., 2003), with hierarchical clustering performed via Pearson Correlation. Control, n = 3; MeHg, n = 3; Low DHA, n = 3; MeHg + low DHA, n = 4; MeHg + high DHA, n = 4.

repair deficiency, complementation group 5 (Ercc5); Fructose1,6-bisphosphatase 2 (Fbp2); ring finger protein 103 (Rnf103); synuclein, alpha (non A4 component of amyloid precursor) (Snca); mitogen-activated protein kinase kinase 6 (Map2k6); opsin 5 (Opn5); claudin 11 (Cldn11); DnaJ (Hsp40) homolog, subfamily A, member 2 (Dnaja2); desmoglein 1 (Dsg1); galanin prepropeptide (Gal); guanylate cyclase 2F, retinal (Gucy2f); LSM5 homolog, U6 small nuclear RNA associated (S. cerevisiae) (Lsm5); nuclear receptor subfamily 2, group F, member 1 (Nrf2); peroxisomal D3, D2-enoyl-CoA isomerise (Peci); superoxide dismutase 1, soluble (Sod1); U2 small nuclear RNA auxiliary factor 1 (U2af1); and Tetratricopeptide repeat domain 7B (Ttc7b). Of the 16 genes selected, 11 genes showed directional correspondence with the array data (Table 4) and genes Rnf103, Lsm5, Nrf2, Dnaja2 and Sod1 did not show differential regulation.

4.

Discussion

The present study investigated the effects of DHA on mice gestationally exposed to an environmentally relevant species of MeHg, through a natural route of exposure. The critical findings of the present study were: (a) DHA exposure led to a

decrease in MeHg accumulation in brains of mice offspring; (b) DHA influenced hindlimb grasping reflex in mice offspring; (c) Perinatal MeHg and DHA exposure induced transcriptional perturbations in murine brain and affected key functional classes of genes of biological relevance; (d) Identification of significantly differentially regulated gene clusters in response to the concomitant exposure of MeHg and DHA highlighted potential mechanisms of DHA amelioration of MeHg-induced toxicity.

4.1.

Effects of DHA on MeHg accumulation

DHA significantly reduced the accumulation of MeHg in the brain tissue of pups gestationally exposed through the maternal diet. This finding is novel, and may explain some published observations. For example, Berntssen and co-workers (2004) found a reduction in MeHg accumulation in the brains of rats fed with naturally contaminated fish when compared to diets spiked with MeHg. This effect may have been mediated by the presence of neuroprotectants such as DHA in the salmonbased feed. Furthermore, reduced cell-associated MeHg was observed in neuronal cell cultures supplemented with DHA (Kaur et al., 2007). The mechanism underlying this effect is unknown. A higher Hg accumulation in the pup brains was also noted when compared to pup livers. This observation

35

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Table 4 – Comparison of quantitative polymerase chain reaction (qPCR) and microarray gene expression values in the respective experimental groups relative to control. Exposure group

Gene Description

Gene (n)

Fold change Microarray

Low DHA MeHg MeHg MeHg MeHg MeHg + High DHA MeHg + High DHA MeHg + High DHA MeHg + High DHA MeHg + High DHA MeHg + Low DHA MeHg + Low DHA MeHg + Low DHA MeHg + Low DHA MeHg + Low DHA MeHg + Low DHA

Opsin 5 Excision repair cross-complementing rodent repair deficiency, complementation group 5 Synuclein, alpha (non A4 component of amyloid precursor) Fructose-1,6-bisphosphatase 2 Ring finger protein 103 Guanylate cyclase 2F, retinal U2 small nuclear RNA auxiliary factor 1 Galanin prepropeptide LSM5 homolog, U6 small nuclear RNA associated (S. cerevisiae) Nuclear receptor subfamily 2, group F, member 1 Peroxisomal D3, D2-enoyl-CoA isomerise Claudin 11 Tetratricopeptide repeat domain 7B Mitogen-activated protein kinase kinase 6 DnaJ (Hsp40) homolog, subfamily A, member 2 Superoxide dismutase 1, soluble

qPCR

qPCR SD

Opn5 (6) Ercc5 (6)

−1.9 −2.3

−1.23 −1.21

0.31 0.13

Snca (4) Fbp2 (3) Rnf103 (5) Gucy2f (3) U2af1(3) Gal (4) LSM5 (3)

2.9 −2.2 2.8 3.2 2.2 −3.6 2.3

1.14 −1.12 −1.03 1.31 1.12 −1.19 1.03

0.23 0.05 0.1 0.09 0.05 0.3 0.11

Nr2f1 (6) Peci (4) Cldn11 (5) Ttc7b (4) map2k6 (5) Dnaja2 (4) Sod 1 (6)

1.7 −6.9 −2.9 −2.6 −1.4 1.4 −2

1.02 −1.13 −1.28 −1.29 −1.11 −1.02 −1.03

0.12 0.03 0.15 0.05 0.094 0.07 0.04

Note: Values given in the brackets in column 3 represent the number of replicates used in qPCR. SD corresponds to standard deviation. Fold change values from microarray and qPCR were subjected to correlation analysis, which yielded Spearman R value of 0.85 and p < 0.0001.

could partially explain the higher susceptibility of brain to MeHg toxicity compared to liver (Aschner and Syversen, 2005).

4.2. Effects of DHA on neurobehaviour of mice offspring A more rapid development of the hindlimb grasp reflex was noted in pups exposed to MeHg + high DHA relative to the control and MeHg alone groups. However, no differences in hindlimb grasp development were observed between MeHg and control groups. Hindlimb grasp reflex is an assessment that reflects motor skill development in young rodents. The change observed in the present study was concomitant with an enhanced level of DHA accumulation in the brain of the mice in the DHA + MeHg exposed mice, and suggests an effect mediated by enhanced tissue-level DHA bioavailability. There have been previous studies supporting the influence of n-3 fatty acids on motor related functions. For example, in a study performed on rats, perinatal omega-3 supplementation exerted a beneficial effect on rotarod performance in juveniles, which indicated an improvement in balance and motor coordination (Coluccia et al., 2009). Similarly, fish oil supplementation in children (1–11 years old) affected by phenylketonuria improved motor skills (Beblo et al., 2006). The conclusions from these studies support the finding from the present study. Overall the data suggests that DHA improved neuromuscular development in mice offspring mediated through enhanced cerebral DHA accumulation but MeHg does not adversely impact this response.

4.3.

Effects on neural gene expression

The results from the microarray analysis not only provided novel insight into the molecular mechanisms underlying MeHg–DHA interactions, but also confirmed previously described effects. Although the hierarchical clustering on

experimental groups revealed discrimination of treatments based on gene expression pattern, microarrays were unable to completely discriminate between individual samples assigned to different treatment groups. Consistent with other studies (Glover et al., 2009; Jayashankar et al., 2010), only a small effect of MeHg on brain gene expression was noted, suggesting that gene expression in brain is, compared with many other tissues, remarkably stable. The combination of a relatively low dose and generally low fold-changes results in global gene expression profiles that are meaningful when averaged between biological replicates, but alone do not allow individual sample discrimination. The use of higher doses of MeHg would likely have generated greater effects and also more complete hierarchical clustering of samples according to treatments. However, the effects observed at higher (and unrealistic) doses of MeHg may very well be different from those that occur at lower doses.

4.3.1.

Effects of MeHg

Functional clusters of genes related to cell morphology and function were significantly altered under MeHg exposure in the present study, indicating generalised effects of MeHg on cellular processes. These results were in accordance with the findings from previous rodent brain gene expression studies, which revealed similar functional classes of genes affected by MeHg exposure (Glover et al., 2009; Padhi et al., 2008).

4.3.2.

Interactions between MeHg and DHA

In the current study, functional annotation enrichment analysis identified clusters of genes related to the cytoskeleton were regulated under the concomitant exposure of MeHg and DHA. Microtubules, which are integral components of the cytoskeleton, are one of the primary targets of MeHg neurotoxicity (Aschner and Syversen, 2005). MeHg inhibits neuronal growth by hindering the re-polymerisation of microtubules

36

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(Sager et al., 1982), resulting in the inhibition of neuronal migration (Choi et al., 1980). Since neural development relies on neuronal migration, an impact on this function would explain the widespread effects of MeHg on neurological functions (Igata, 1993). DHA is known to play an important role in various neurological functions including maintenance of cellular architecture. Modulation of the cytoskeleton by PUFA could influence a variety of neural functions ranging from cell shape, neuronal plasticity and polarity, stabilisation of axons and dendrites, vesicle formation, and transport (Kitajka et al., 2002). The appearance of functional groups of genes related to cytoskeleton under the concomitant exposure of MeHg and DHA suggests an influence of DHA on the regulation of genes involved in maintaining the cellular architecture of the brain, possibly to compensate for any neurotoxic insults related to MeHg exposure. These findings provide evidence for a putative mechanism of DHA amelioration of MeHg toxicity. However, extrapolating impacts from microarray expression data, especially from murine developing brain, is complex. Further studies are warranted to confirm the above findings since some of the gene expression changes observed might also be normal responses rather than gene expression perturbations. MeHg-induced oxidative stress is considered a key mechanism of MeHg neurotoxicity. DHA is known to influence MeHg-induced reactive oxygen species (ROS). Kaur et al. (2007) found DHA impacts on MeHg-induced ROS production in neuronal cells. Similarly, an in vivo study performed on rats reported modulation of MeHg-induced oxidative stress by dietary lipids (Jin et al., 2007). Contrary to expectation, however, no functional clusters of genes related to oxidative stress under the concurrent exposure of MeHg and DHA were enriched in the present study. One gene involved in antioxidant defence (superoxide dismutase 1) was regulated according to array results, but showed no change in expression when analysed using qPCR. There could have been several reasons for the disagreement between results from the two methods (qPCR and microarray). Most genes have several transcripts and it is possible that PCR amplicons did not always correspond to the region of probe binding (3 bias). Microarray is a semi quantitative technique, while qPCR is a more sensitive methodology, hence it is difficult to attain similar fold changes. The reduced sensitivity of array technology may stem in part from the lasers used to scan the arrays, which had a threshold limiting the accurate measurement of gene regulation. There are no such limits with regard to qPCR, where the intensity of the fluorescence is detected by the real-time PCR machine. The use of different data normalisation procedures in both the techniques could also contribute towards the differences observed in gene expression values. Although potentially contrasting with the established dogma of oxidative stress being a principal mechanism of neurotoxicity from MeHg, this result does find support in the literature. Some previous studies which followed a similar exposure paradigm to that used herein also failed to identify antioxidant defence as an important cluster impacted by MeHg using microarray studies in mice (Glover et al., 2009; Jayashankar et al., 2010). A number of factors may explain these differences. Single-time-point sampling employed in these studies may have precluded detection of oxidative stress

and/or ameliorating pathways. Furthermore, the exposure regime (sub-chronic exposure in utero) followed in the present study may have allowed the gradual development of antioxidant defence mechanisms which may not have been reflected by changes in gene expression at levels that met the definition of significant in the present study (i.e. changes less than 1.5 fold). Furthermore, in vivo studies have shown that MeHgCl and MeHgCys differ in their toxicological impacts (Glover et al., 2009; Berntssen et al., 2004), which could also account for differences observed between the present study and others with respect to the impacts of DHA on MeHg-induced oxidative stress. However, the lack of antioxidant gene expression might reflect an insufficiency of the brain to respond to oxidative stress, which would explain the susceptibility of the brain to free radical toxicity.

4.3.3.

Quantitative PCR confirmation

Qualitative validation by qPCR of gene expression data from 11 of 16 genes selected from the respective treatments confirmed the accuracy of the array results. Genes Ercc5 involved in excision repair (Shiomi et al., 1994); Snca participating in cell signalling (Takahashi et al., 2003; Alves da Costa, 2003); and Fbp2 involved in energy production (Tillmann and Eschrich, 1998), were regulated under MeHg exposure. The involvement of these genes in such universal cellular functions supports the pluripotency of MeHg impacts. Some of the genes regulated under the concurrent exposure of MeHg and DHA were confirmed by qPCR in the present study. These genes participated in an array of important functions. Gene Gal participates in a variety of physiological functions which include memory, spinal reflexes, and energy metabolism (Mechenthaler, 2008); Cldn11 is involved in cellular proliferation and migration during development (Bronstein et al., 2000); U2af1 plays a critical role in constitutive and enhancer-dependent RNA splicing (Zuo and Maniatis, 1996; McKee et al., 2005); Peci participates in beta-oxidation of unsaturated fatty acids (Geisbrecht et al., 1999); and Map2k6 takes part in a variety of cellular processes ranging from stress induced cell cycle arrest, to transcription and apoptosis (Raingeaud et al., 1996; Han et al., 1996). The regulation of these genes corroborates the impacts of concurrent exposure of MeHg and DHA observed on various functional classes in the array data.

4.4.

Summary

The current investigation utilised an integrated approach and showed that DHA and MeHg work both independently and interactively to alter chemical (accumulation), behavioural and gene expression endpoints within the brain of the developing mammal. Furthermore, the present study suggests that extrapolation of results to humans based on data from neurotoxicant exposure may only be appropriate if they also account for the concurrent exposure to putative neuroprotectants such as those found in seafood.

Conflict of interest statement This work was supported by the Research Council of Norway [NFR 173389] and the EU FRP6 integrated project “Aquamax” [016249–2].

e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 3 ( 2 0 1 2 ) 26–38

Acknowledgements The authors would like to thank Edel Erdal for technical assistance; Berit Solli for mercury analysis; Dr. Matthew Arno, King’s College London Genomics Centre for facilitating the microarray work and Dr. Parvinder Kaur and Prof. Tore Syversen for useful discussions. This study and the experimental facilities at NIFES were approved by the Norwegian Animal Research Authority.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.etap.2011.10.001.

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