Lifelong Exposure to PCBs in the Remote Norwegian Arctic Disrupts

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Lifelong exposure to PCBs in the remote Norwegian Arctic disrupts the plasma stress metabolome in Arctic charr Patrick T. Gauthier, Anita Evenset, Guttorm N. Christensen, Even H. Jørgensen, and Mathilakath M. Vijayan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05272 • Publication Date (Web): 13 Dec 2017 Downloaded from http://pubs.acs.org on December 21, 2017

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Environmental Science & Technology

Gauthier et al.

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Lifelong exposure to PCBs in the remote Norwegian Arctic disrupts the plasma stress metabolome in Arctic charr

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Patrick T. Gauthier1, Anita Evenset2, Guttorm N. Christensen2, Even H. Jorgensen3, and Mathilakath M. Vijayan1,*

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Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada T2N1N4 Akvaplan-niva AS, Fram Centre-High North Research Centre for Climate and the Environment, Hjalmar Johansens Gate 14, 9007 Tromsø, Norway 3 Department of Arctic and Marine Biology, UiT the Arctic University of Norway, NO-9037, Tormsø, Norway 2

*Corresponding author: [email protected]

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Manuscript summary Manuscript Format: Abstract word count: Manuscript word count: Small figures (300 words): Large figures (600 words): Small tables (300 words): Large tables (600 words): Word-equivalent: References: Supporting information:

Research Article – 7,000 word-equivalent limit. 192 4,100 Figure 1-3 – 300 x 3 = 900 Figure 4-5 – 600 x 2 = 1,200 Table 1-2 – 300 x 2 = 600 na – 600 x 0 = 0 192 + 4,100 + 900 + 1,200 + 600 = 6992 44 Metabolite library, Tables S1, S2, and S3

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Abstract Lake Ellasjøen on the remote Norwegian island of Bjørnøya is populated by Arctic charr

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(Salvelinus alpinus) having 20-fold higher body burdens of polychlorinated biphenyls (PCB)

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compared to charr from the neighbouring Lake Laksvatn. This provides a natural setting to test

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the hypothesis that lifelong exposure to PCBs compromises the energy metabolism in this

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northernmost living salmonid. To test this, blood was sampled from charr from both lakes

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immediately after capture and following a 1 h handling and confinement stressor to assess

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possible differences in their energy metabolism and energy substrate mobilization, respectively.

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The plasma metabolome of charr was assessed by metabolite detection/separation with LC-MS.

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Plasma metabolite profiles revealed differences in key pathways involved in amino acid

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metabolism between charr from each lake, underscoring an impact of PCBs on energy

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metabolism in Arctic charr residing in Lake Ellasjøen. Subjecting charr from either lake to an

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acute stressor altered the plasma metabolite profiles and revealed distinct stress metabolome in

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Lake Ellasjøen charr, suggesting a reduced metabolic capacity. Taken together, lifelong exposure

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to PCBs in Ellasjøen charr disrupts the plasma metabolome, and may impair the adaptive

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metabolic response to stressors, leading to a reduced fitness.

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KEYWORDS: Arctic, energy metabolism, metabolomics, PCBs, salmonid, stress performance,

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wildlife

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1.0. Introduction On the remote island of Bjørnøya (74° 30′N, 19° 00′E) in the Norwegian arctic, an

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interesting case of environmental contamination occurs. Lake Ellasjøen is frequented by

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migratory seabirds that breed in cliffs along the coast of the island and use the lake as a resting

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area during summer months,1 during which a large amount of seabird guano is deposited directly

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into the lake. This seabird guano is enriched with organohalogenated compounds, including

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polychlorinated biphenyls (PCBs), which contribute up to 80% of reported PCBs within the

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lake.1 Several other lakes on Bjørnøya, including Lake Laksvatn, are not visited by seabirds, and

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thus have no contributions of PCB-rich guano.1 Lake Ellasjøen and Lake Laksvatn are

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oligotrophic lakes with no point-source of pollution, located within ca. 15 km of each other, and

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contain only one species of fish, the Arctic charr (Salvelinus alpinus), which are land-locked.2

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Therefore, these island lakes of Bjørnøya provide an excellent opportunity to study the

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ecological effects of life-long exposure to PCBs on a high-latitude freshwater fish in a natural

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setting.

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Exposure to PCBs can have adverse toxicological effects in fish, including the

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modulation of stress performances via disruption in the functioning of the hypothalamus-

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pituitary-interrenal (HPI) axis.3 It has already been shown that PCBs bioaccumulate in Arctic

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charr that inhabit Lake Ellasjøen, and disrupt the molecular mechanisms involved in the

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activation of the HPI axis in this species.4,5 Activation of the HPI axis initiates a cascade of

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events eventually leading to the release of cortisol, the primary glucocorticoid in teleosts,6

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triggering the mobilization of energy reserves to cope with the stressor. A key role for cortisol

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during stress adaptation involves an increase in the intermediary metabolism, including enhanced

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activity of alanine aminotransferase, aspartate aminotransferase, glutamate dehydrogenase and

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glutamine synthetase, which facilitates the mobilization of amino acids substrates for oxidation

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and/or gluconeogenesis.6

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The magnitude of plasma cortisol levels in response to an acute stressor exposure is

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widely applied as a biomarker of stress performance in fish.3 Yet, despite changes in HPI

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transcript abundance observed in charr from Lake Ellasjøen when compared to charr from Lake

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Laksvatn, PCB exposure did not modify the plasma cortisol response to a stressor.5 However,

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whether the lifelong exposure to PCBs may have downstream metabolic effects, including

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disruption in mobilization of energy reserves to cope with stressor insults, are far from clear.7,8

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For instance, exposure to PCBs has been shown to modulate the activities of enzymes involved

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in amino acid metabolism, including alanine aminotransferase in rainbow trout (Oncorhynchus

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mykiss) and Arctic charr liver.9-11 Also, lifelong exposure to PCBs increases the liver transcript

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abundance of the glucocorticoid receptor, a key protein involved in cortisol signalling and

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mediating the metabolic response to stress,12 and this could potentially make the animal more

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sensitive to corticosteroid action.5 Consequently, the lower body mass observed in charr from

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Lake Ellasjøen compared to Lake Laksvatn may suggest an increased metabolic demand and

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reduced anabolic capacity in response to lifelong PCBs exposure, but this was not tested

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previously.5

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In order to better understand the metabolic consequences of lifelong PCB exposure on

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feral charr, we assessed the whole plasma metabolome, as well as plasma lactate and glucose

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levels, of charr caught from Lake Ellasjøen and Lake Laksvatn. Our hypothesis was that charr

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from the contaminated lake have a lower metabolic capacity and this will be reflected in the

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altered plasma metabolome in response to an acute secondary stressor. We sampled plasma from

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charr from Lake Ellasjøen and Lake Laksvatn before and after they had been subjected to an

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acute handling/confinement stressor in situ. Plasma metabolomes were quantified with LC-MS,

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and the data processed with a non-metric multidimensional scaling (NMDS)-permutational

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multivariate analysis of variance (PERMANOVA) to determine the pre-stress differences in

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metabolite profiles between charr from the two lakes, as well as their response to an acute

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stressor exposure. Metabolite set enrichment and pathway topology analyses were utilized to

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identify metabolic pathways impacted due to lifelong PCB exposure and modulated by stressor

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exposure.

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2.0. Methods

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2.1. Animal and plasma sampling

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Arctic charr sampling and the stress protocol have been described previously.5 Briefly,

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charr were caught from Lake Ellasjøen and Lake Laksvatn by hook and line in September 2014

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(Table 1). Only large (> 400 g) immature fish were used for sampling, as this size class has

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historically had the highest level of accumulated PCBs.12 Fish were anaesthetized with 60 mg L-1

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benzocaine and a maximum of 1 mL of blood was drawn from the caudal vein within 4 min of

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hooking by Li-heparinized Vacutainers. Sampled fish were then tagged with Floy FTF-69

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fingerling tags (MGF, Seattle, WA, USA) to identify individuals following a confinement

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stressor that involved fish being contained in a holding container filled with ca. 50 L of 5 °C lake

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water. After 1 h, fish were anaesthetised in 120 mg L-1 benzocaine and again sampled for blood

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for metabolome analysis. Blood samples were centrifuged at × 4000 g for 5 min and plasma was

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collected and stored at -80 °C for later metabolite and metabolome analyses. Permission for

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fieldwork was granted by the Governor of Svalbard and the experimental work was approved by

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the Norwegian animal research authority (Norwegian Food Safety Authority).

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2.2. Glucose and lactate levels Plasma glucose and lactate concentrations were measured in plasma samples prior to the

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acute stressor and 1 h after stressor exposure according to protocols described previously.14,15

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2.3. Plasma metabolome analysis

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We used a methanol extraction for detection of polar metabolites by hydrophilic

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interaction liquid chemistry (HILIC)16. Plasma samples were centrifuged at ×17,000 g for 1 min

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and 50 µL of supernatant was transferred to 450 µL of 50% MeOH in clean 1 mL centrifuge

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tubes and vortexed. The diluted samples were centrifuged at ×17,000 g for 1 min and then frozen

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at -80 °C for ca. 12 h prior to analysis. An additional centrifuge step was included if there was

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visible precipitate in the supernatant. The supernatant was used for mass spectrometry (MS)

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analysis at the Calgary Metabolomics Research Facility (CMRF), University of Calgary.

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Metabolites were detected by liquid chromatography mass spectrometry (LC-MS) with a

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Vanquish™ UHPLC system (Thermo-Fisher, Waltham, MA, USA) and Q Exactive™ HF Hybrid

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Quadrupole-Orbitrap™ mass spectrometer (Thermo-Fisher). Metabolites were separated with a

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Syncronis HILIC 1.7 µm 2.1 × 100 mm column (Thermo-Fisher).

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2.4. MS data processing

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Spectral intensity data were matched to an in-house metabolite library within

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MAVEN17,18 provided by the CMRF (see supporting information). A minimum peak intensity of

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100,000 ions excluded low intensity metabolite matches, which were further screened for quality

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of peak alignment according to Clasquin et al.18 Data were exported from MAVEN and imported

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in R19 for subsequent processing. Metabolite names were matched to the Kyoto Encyclopedia of

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Genes and Genomes (KEGG) compound database. In cases where KEGG compound accession

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identifiers were unavailable, the compound was removed from the dataset.

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2.5. Statistics The effects of lake, stress, sex and their interaction on glucose and lactate were tested

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using a linear mixed-model to account for repeated measurements of pre- and post-stress

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samplings with the ‘lme’ function of the ‘nlme’ package in R version 3.3.2.19,20 Results from the

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glucose and lactate analyses are presented as means ± standard errors.

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Non-metric multidimensional scaling was carried out using the ‘metaMDS’ function from

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the ‘vegan’ package21 to ordinate similarities among treatments and metabolites. Spectral

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intensity data were square root transformed and Wisconsin double standardization was

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performed prior to calculating the Euclidean distance matrix for NMDS. Ordination results were

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centre-scaled and axes were rotated to maximally represent variation in the first dimension.

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Ellipses were drawn around the four treatment groups, excluding sex (i.e., Ellasjøen pre- and

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post-stressor, and Laksvatn pre- and post-stressor) using the ‘ordiellipse’ function of the ‘vegan’

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package to illustrate standard deviations of NMDS ordinations scores based on replicates (i.e.,

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plasma samples) within each treatment group. Following NMDS, the effects of lake, stress, sex

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and their interactions on metabolite spectral intensity data were analyzed with a PERMANOVA

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using the Euclidean distance matrix.22 The PERMANOVA was performed with the ‘adonis’

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function of the ‘vegan’ package.

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When no interaction was detected from the PERMANOVA, subsequent reporting of main

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effects represent changes exclusive to that treatment (i.e., lake effect independent of stressor

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effect and stressor effect independent of lake effect).

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Metabolite set enrichment analysis was carried out to determine metabolite pathways that

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were active in fish from Lake Ellasjøen and Lake Laksvatn. An over-representation analysis

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(ORA) was applied according to Xia and Wishart.23 We obtained the KEGG metabolite pathway

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database for zebrafish (Danio rerio) in R using the ‘keggGet’ function from the ‘pathview’ 7



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package.24 We applied a hypergeometric test using the ‘phyper’ function from the ‘stats’

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package20 to determine the probability of randomly matching the metabolites present in the charr

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plasma samples to those of each pathway. The ORA was carried out independent of treatment

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effects as metabolites occurred ubiquitously across all plasma samples, despite any potential

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differences in their spectral intensities. A false discovery rate correction was applied using the

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‘p.adjust’ function of ‘stats’ package to reduce the risk of type 1 error associated with

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independent ORAs for each pathway.

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Metabolite pathway topology was analyzed to determine the relative impact of

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metabolites in each pathway. Pathway topology assists in objectively measuring the importance

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of over-represented pathways in terms of metabolites present in plasma samples. We applied

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relative betweenness centrality (RBC) as our centrality measure for topology analyses. Briefly,

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RBC first determines the shortest paths between all metabolite pairs in the pathway, and then

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quantifies the number of shortest paths that intersect with a given metabolite, and divides that

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number by the total number of shortest paths in the pathway.25 Metabolites that have a greater

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number of intersecting shortest paths will have a higher RBC. Pathway maps were downloaded

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as .xml files from the KEGG database and imported into R using the ‘parseKGML’ function of

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the ‘KEGGgraph’ package.26 Metabolite information was then translated into graph objects using

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the ‘KEGGpathway2reactionGraph’ function of the ‘KEGGgraph’ package. Graph objects

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contained the necessary metabolite (i.e., nodes) and linkage (i.e., edges) information to determine

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RBC using the ‘brandes.betweenness.centrality’ function of the ‘RBGL’ package.27 After

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determining the RBC for each metabolite, total pathway impact was calculated by dividing the

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summed RBCs of matched metabolites (i.e., metabolites within each pathway that were present

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in charr plasma samples) by the total RBC score from all metabolites in the pathway.

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Pathways were plotted according to their log p-values from ORA and pathway impact.

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We chose to focus on a subset of pathways based on a threshold of their combined ORA log p-

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values and pathway impact. Because total pathway impact always has a maximum of 1, a line

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having a negative slope of minimum log p-value connects the maximum values of each axis.

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Pathways that were on the origin-side of this line were excluded from further analysis. This

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conservative threshold allowed us to focus only on the most important pathways. Pathways that

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were deemed important were mapped using the ‘Rgraphviz’ package28 with log2 fold-changes in

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metabolite spectral peak intensities to illustrate patterns among treatment groups. Spectral

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intensity data were median-normalized prior to log2 fold-change calculations.

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We recognize the growing concern of biases and errors associated with metabolomics

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data.29 Biological variance from selection bias was reduced by sampling similarly aged fish with

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a near-equal sex ratio during the same sampling period.5 Analytical variance was reduced by

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having samples prepared for LC-MS by the same user during a 2 h window, with all samples

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being analyzed by LC-MS the following day. Once data were obtained, only strong peaks were

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retained within the dataset. The NMDS and PERMANOVA analyses, both non-parametric tests,

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avoided assumptions on the distribution of errors in the dataset. The biases associated with ORA

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were in part reduced by imposing strict and objective criteria to screen metabolic pathways

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having the greatest likelihood of being relevant to the charr metabolome. For example, 29

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pathways were identified as being over-represented, yet after combining the ORA with a

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pathway impact analysis to develop a selection threshold, only 7 over-represented pathways were

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retained for further analysis. Our prediction was that lifelong exposure to PCBs would alter the

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metabolome in terms of stress performance and energy substrate metabolism, and irrespective of

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our selection threshold, the identified pathways corroborated this expectation.

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3.0. Results

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3.1. Plasma glucose and lactate analysis

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Plasma glucose (t(16) = 3.54; p = 0.0027) and lactate (t(16) = 8.57; p < 0.0001) levels

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increased by 1.97 ± 0.55 and 5.23 ± 0.61 mM, respectively, following the handling/confinement

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stressor. There was no effect of sex, lake, or interactive effect of lake, stressor, and sex on

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plasma glucose and lactate concentrations in charr plasma (Figure 1).

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3.2 Metabolome analysis

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Analysis and screening of spectral intensity data in MAVEN identified 165 metabolites

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present in the KEGG database (Tables S1 and S2). The ordination by NMDS separated

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metabolites by lake and stressor treatments (Figure 2). The ORA identified 27 pathways in which

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metabolites were over-represented (Table 1). Within these over-represented pathways, total

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pathway impact varied from 0 to 1, indicating that some identified pathways had none or all of

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the metabolites with RBC scores greater than 0 present (Table 1). Pathways that surpassed the

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threshold ratio of log(p-values) from ORA and pathway impact were aminoacyl-tRNA

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biosynthesis (KEGGpid 00970), alanine, aspartate, and glutamate metabolism (KEGGpid

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00250), glycine, serine and threonine metabolism (KEGGpid 00260), arginine biosynthesis

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(KEGGpid 00220), phenylalanine metabolism (KEGGpid 00360), caffeine metabolism

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(KEGGpid 00232), and D-glutamine and D-glutamate metabolism (KEGGpid 00471; Figure 3).

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As no sex-related effects were observed on the charr metabolome and plasma glucose and lactate

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concentrations, we omitted sex from our final analyses of the plasma metabolome.

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3.2.1 Lake effect

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The PERMANOVA revealed metabolites varied between lakes (F(1,36) = 5.1; p = 0.011), with log2 fold-changes of metabolites ranging from -5.33 to 6.67 between the lakes. The majority

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of metabolites measured were lower in Lake Ellasjøen charr compared to Lake Laksvatn charr,

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with 32% and 68% of metabolites being up- and down-regulated, respectively (Table S1).

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Metabolites detected within the phenylalanine metabolism pathway were all up-regulated in

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Lake Ellasjøen charr, whereas metabolites detected within the caffeine, and D-glutamine and D-

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glutamate metabolism pathways were all down-regulated in Lake Ellasjøen compared to

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Laksvatn charr (Figure 4). For alanine, aspartate, and glutamate metabolism, glycine, serine, and

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threonine metabolism, arginine biosynthesis, and aminoacyl t-RNA biosynthesis, 63.6%, 45.5%,

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50%, and 52.9% of detected plasma metabolites were down-regulated respectively in Lake

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Ellasjøen charr compared to Lake Laksvatn charr (Figure 4).

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3.2.2 Stressor effect

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The PERMANOVA revealed that metabolites varied prior to and in response to stressor

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exposure (F(1,36) = 14.5; p = 0.0009), with log2 fold-changes of metabolites ranging from -1.47 to

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3.21 pre-and post-stressor. The majority of metabolites were down-regulated following the

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confinement stressor, with 39% and 61% of metabolites being up- and down-regulated

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respectively (Table S2). In comparison with metabolite log2 fold-changes from the effect of lake,

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61% of metabolites had opposite changes in response to the confinement stressor. Metabolites

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detected within the phenylalanine metabolism pathway were all down-regulated following the

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confinement stressor, whereas metabolites detected within the caffeine metabolism pathway

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were all up-regulated following the confinement stressor (Figure 5). For alanine, aspartate, and

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glutamate metabolism, glycine, serine, and threonine metabolism, arginine biosynthesis, D-

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glutamine and D-glutamate metabolism, and aminoacyl t-RNA biosynthesis, 36.4%, 54.5%,

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62.5%, 66.6% and 82.4% of detected metabolites were down-regulated post-stressor,

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respectively (Figure 5). The PERMANOVA did not detect an interactive effect of lake and

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stressor exposure (F(1,36) = 1.23; p = 0.26). However, there were differences in metabolites

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between post-stressed fish from each lake, with log2-fold changes ranged between -5.9 to 7.4

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(Table S3).

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4.0. Discussion Although the Arctic environment lacks a point-source for PCB contamination, studies

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have clearly shown that animals residing in this pristine environment are exposed to

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contaminants from different off-target sources.13,30 Our companion study recently demonstrated

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that charr from Lake Ellasjøen exhibited altered gene expressions suggestive of endocrine

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disruption of the stress axis.5 Using a metabolomics approach, our results suggest for the first

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time that lifelong exposure to PCBs may also affect energy metabolism in Arctic charr, leading

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to disruption in energy substrate mobilization that is critical for coping with additional stressors.

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4.1. Effect of PCBs on charr plasma metabolome

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The levels of PCB contamination in Lake Ellasjøen and bioaccumulation in charr within

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the lake have been monitored for over two decades.2 Sediment concentrations of PCBs in Lake

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Ellasjøen have ranged from 2 to 600 times higher than sediments from other arctic lakes around

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the world.13 When compared with charr we caught from Lake Laksvatn, muscle PCB

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concentrations in charr from Lake Ellasjøen were 29 ng g-1 ww, approximately 750% higher.5

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Also, previous surveys have reported muscle PCB concentrations as high as 5175 ng g-1 ww in

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charr from Lake Ellasjøen.13 Exposure to these levels of PCBs is sufficient to induce

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reproductive toxicity in rainbow trout.2 Thus, it is expected that charr from Lake Ellasjøen have

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been experiencing toxicological effects with life-long exposure to PCBs at these concentrations.

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The continual exposure to PCBs throughout their lifetime may subject charr to a higher metabolic cost as indicated by the strong (10-fold) up-regulation of cytochrome P450 1A, a key

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protein involved in PCB detoxification.31 This increased energy demand associated with critical

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protein synthesis for biotransformation may, at least partly, explain the lower body mass of charr

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from Lake Ellasjøen compared to the less contaminated charr of the same age from Lake

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Laksvatn.5,31 Along with this, the distinct plasma metabolome observed in charr from Lake

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Ellasjøen further supports an enhanced metabolic demand due to PCB-exposure compared to

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charr from Lake Laksvatn. The most significant differences in charr plasma metabolome from

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Lake Ellasjøen compared to Lake Laksvatn were related to amino acid metabolism, with the

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majority (i.e., 87.8%) of metabolites related to alanine, aspartate, and glutamate metabolism,

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caffeine metabolism, and D-glutamine and D-glutamate metabolism being lower in Lake

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Ellasjøen charr compared to Lake Laksvatn charr. A decrease in plasma amino acid

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concentrations (Tables S1 and S2), including alanine, and lysine, glutamine, and glutamate

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suggests a lowering of oxidative and gluconeogenic substrates in the plasma in response to PCB

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contamination. The lowering of plasma amino acid concentration is normally associated with

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extended fasting,32 and we propose that fish in the contaminated lake may have a lower feeding

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or food-conversion efficiency supporting a reduced anabolic capacity due to PCB contamination.

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However, a reduction in plasma amino acids may also be indicative of an increased utilization

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within various tissues due to increased metabolic demand.33 Specifically, pyruvate and 2-

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oxoglutarate, two metabolites critical to energy metabolism,34 were lower in plasma from charr

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inhabiting Lake Ellasjøen. This along with an up-regulation of cocarboxylase, a coenzyme

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fundamental to energy metabolism via the decarboxylation of pyruvate and 2-oxoglutarate,35

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suggests an increased tissue utilization of pyruvate and 2-oxoglutarate supporting an increase in

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metabolic demand. The combination of higher liver cytochrome P450 1a mRNA,5 a plasma

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metabolome profile indicative of disruption of energy metabolism, and the observed reduced

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growth in charr from Lake Ellasjøen,5 suggests that lifelong exposure to PCB may increase the

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metabolic demand and curtail the anabolic capacity in Lake Ellasjøen charr.

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4.2. Effect of PCBs on charr stress metabolome

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The response of fish to a stressor, including handling and confinement, involves the

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activation of the sympathetic system and the HPI axis initiating a cascade of events, including

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hormone release, ultimately leading to the mobilization of energy reserves to cope with the

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increased energy demand.4-6,8 While the sympathetic system activation is essential for the rapid

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response to stressors, the HPI axis activation and the associated release of cortisol, the main

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glucocorticoid in teleosts, plays a key role in the mobilization and reallocation of energy

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substrates to cope with the stressor, as well as re-establish homeostasis.4,8,36 Glucose is the main

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fuel to meet the increased energy demand during stress, and this is produced mainly in the liver

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in response to stress hormone stimulation.4,6,36,37 In addition to glucose, lactate and amino acids

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have also been used as substrates for oxidation and gluconeogenesis in fish hepatocytes in

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response to stress and cortisol stimulation.9 However, the plasma metabolite changes during an

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acute stressor exposure are far from clear in fishes. In the present study, plasma glucose and

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lactate levels were elevated in response to an acute stressor in charr from both lakes, supporting

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enhanced energy substrate mobilization.6

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The metabolomics approach allowed us to identify pathways that may be important in

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affecting stress performance and energy metabolism in Arctic charr. In the present study,

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stressor-mediated changes in plasma metabolome points to increases in mobilization of energy

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substrates, including pyruvate and 2-oxoglutarate. In general, the majority of pathways we

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identified through ORA and total pathway impact are involved in the production of amino acids

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that are either substrates for oxidation and/or gluconeogenesis.6 We propose the acute stress

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plasma metabolome is most likely indicative of a mobilization of energy substrates from

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muscles, for use by target tissues, including liver, for oxidation and also for gluconeogenesis.6,8,36

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The elevation in plasma cortisol levels in response to a stressor, as was seen in the present

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study,5 may be playing a key role in the energy substrate mobilization. For instance, activation of

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the HPI axis has been shown to increase the activity of glutamine synthetase, aspartate

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aminotransferase, tyrosine aminotransferase, and glutamate dehydrogenase in the muscle and

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liver,6 all of which are critical enzymes involved in amino acid metabolism and may be involved

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in the altered plasma amino acid patterns in the post-stressed charr plasma (i.e., arginine

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biosynthesis, alanine, aspartate, and glutamate metabolism, and phenylalanine metabolism).

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However, most of these enzyme changes were reported after several hours of cortisol treatment

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in fishes,6,8 leading to the proposal that the rapid changes in plasma amino acids after an acute

344

stress may be due to other metabolic hormones stimulation and/or a nongenomic action of

345

cortisol, but remains to be determined.8

346

In general, there was a lowering of plasma metabolites following the confinement

347

stressor, regardless of the lakes. However, because the baseline plasma metabolome (prior to

348

stress) was different between the two lakes (see section 4.1), the post-stressor plasma metabolites

349

profile of post-stressed fish from Lake Ellasjøen was also distinct from that of Lake Laksvatn

350

(Figure 2). In fact, metabolite log2 fold-changes were greatest when comparing post-stressed fish

351

between the two lakes (Tables S1-S3). These changes in plasma stress metabolite profiles

352

between the two lakes suggest that lifelong PCB-exposure may compromise the metabolic

353

adjustments essential for coping with stressors in Arctic charr. Moreover, 61% of metabolites

354

that showed log2 fold-changes in the pre-stress lake comparisons (Table S1) also differed in

355

response to stressor exposure, but in the opposite direction (Tables S2; Figures 4 and 5). This

15



Gauthier et al. 356

would further support our notion that the capacity for charr from Lake Ellasjøen to mobilize

357

energy substrates to subsequent stressors may be impaired to a greater extent due to their already

358

compromised pre-stress metabolic capacity.

359

Although predominant, PCBs are not the only contaminants present in Lake Ellasjøen38

360

and in the resident charr tissues.1 However, historic records from Lake Ellasjøen indicate low

361

levels of trace metals.39 Moreover, the effects of PCBs on the physiological and cellular stress

362

responses in charr have already been demonstrated in controlled laboratory studies40,41 and

363

support our findings of compromised amino acid metabolism.42,43,44 As both lakes are

364

oligotrophic,2 guano contributions to Lake Ellasjøen have likely increased nutrient loading in the

365

lake, which would influence primary productivity and growth rates in charr.13 However, charr

366

from Lake Ellasjøen were smaller in size compared to Lake Laksvatn, leading us to propose that

367

changes in energy repartitioning associated with biotransformation of contaminants may in part

368

play a role in reducing the anabolic capacity of charr in the contaminated lake. The combination

369

of altered metabolic capacity, including a lower body mass and an impaired metabolite response

370

to stress may reduce the fitness of charr in Lake Ellasjøen, but this needs to be ascertained.

371

Future studies involving early life-stages may also greatly aid in our understanding of the

372

molecular mechanisms leading to the development of PCB-related health dysfunction in the wild

373

populations of this northernmost teleost species.

374 375 376 377

Supporting information Supporting information includes a list of all metabolites within the CMRF library, and tables S1, S2, and S3, which describe log2 fold-changes in metabolites.

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Gauthier et al. 378 379 380

Acknowledgements The study was funded by The Research Council of Norway (project no. 221371/E40) and

381

the Norwegian Ministry of Environment through the Fram Centre flagship (Tromsø, Norway)

382

"Hazardous substances – effects on ecosystems and human health", and by the Natural Sciences

383

and Engineering Research Council (NSERC) of Canada Discovery Grant. We thank Ryan

384

Groves at the Calgary Metabolomics Research Facility for developing the LC-MS protocol and

385

running our samples, Nawamaka Merah for plasma glucose and lactate analysis, and Jenny

386

Bytingsvik and Marianne Frantzen for valuable assistance with fieldwork.

387 388

References

389 390 391

(1) Evenset, A., Carroll, J., Christensen, G.N., Kallenborn, R., Gregor, D., Gabrielsen, G.W. Seabird guano is an efficient conveyer of persistent organic pollutants (POPs) to arctic lake ecosystems. Environ. Sci. Technol. 2007, 41: 1173-1179.

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(2) Bytingsvik, J., Frantzen, M., Gotsch, A., Heimstad, E.S., Christensen, G., Evenset, A. Current status, between-year comparisons and maternal transfer of organohalogenated compounds (OHCs) in Arctic char (Salvelinus alpinus) from Bjørnøya, Svalbard (Norway). Sci. Total Environ. 2015, 521-522: 421-430.

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(3) Hontela, A., Vijayan, M.M. In Adrenal Toxicology; Harvey, P.W., Everett, D., Springall, C., Eds.; CRC Press; Boca Raton, 2008; pp 233-256.

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(5) Jørgensen, E.H., Maule, A.G., Evenset, A., Christensen, G., Bytningsvik, J., Frantzen, M., Nikiforov, V., Faught, E., Vijayan, M.M. Biomarker response and hypothalamus-pituitaryinterrenal axis functioning in Arctic charr from Bjørnøya (73°30’N), Norway, with high levels of organohalogenated compounds. Aquat. Toxicol. 2017, 187: 64-71.

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(7) Trenzado, C.E., Carrick, T.R., Pottinger, T.G. Divergence of endocrine and metabolic responses to stress in two rainbow trout lines selected for differing cortisol responsiveness to stress. Gen. Comp. Endocrinol. 2003, 133: 332-340.

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(10) Řehulka, J., Minařík, B. Effect of polychlorinated biphenyls (Declor 103) on haematological and enzyme parameters of the rainbow trout Oncorhynchus mykiss. Dis. Aquat. Organ. 2004, 62: 147-153.

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(12) Wiseman, S., Jørgensen, E.H., Maule, A.G., Vijayan, M.M. Contaminant loading in remote Arctic lakes impact cellular stress-related proteins expression in feral charr. Polar Biol. 2011, 34: 933-937.

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(13) Evenset, A., Christensen, G.N., Skotvold, T., Fjeld, E., Schlabach, M., Wartena, E., Gregor, D. A comparison of organic contaminants in two high Arctic lake ecosystems, Bjørnøya (Bear Island), Norway. Sci. Total Environ. 2004, 318:125-141.

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(29) Moseley, H.N.B. Error analysis and propagation in metabolomics data analysis. Comput. Struct. Biotechnol. J. 2013, 4: e201301006.

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(30) Letcher, R.J., Bustnes, J.O., Dietz, R., Jenssen, B.M., Jørgensen, E.H., Sonne, C., Verreault, J., Vijayan, M.M., Gabrielsen, G.W. Exposure and effects assessment of persistent organohalogen contaminants in arctic wildlife and fish. Sci. Total Environ. 2010, 408: 29953043.

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(31) Jørgensen, E.H., Vijayan, M.M., Killie, J.-E.A., Aluru, N., Aas-Hansen, Ø., Maule, A. Toxicokinetics and effects of PCBs in Arctic fish: a review of studies on Arctic charr. J. Toxicol. Environ. Health A 2016, 9:37-52.

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(32) Pottinger, T.G., Rand-Weaver, M., Sumpter, J.P. Overwinter fasting and re-feeding in rainbow trout: plasma growth hormone and cortisol levels in relation to energy mobilisation. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 2003, 136: 403-417.

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(33) Hori, T.S.F., Avilez, I.M., Inoue, L.K., Moreas, G. 2006. Metabolic changes induced by chronic phenol exposure in matrinxã Brycon cephalus (teleostei: characidae) juveniles. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2006, 143: 67-72.

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(34) Enes, P., Panserat, S., Kaushik, S., Oliva-Telesm A. Nutritional regulation of hepatic glucose metabolism in fish. Fish Physiol. Biochem. 2009, 35: 519-539.

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(35) Longsdale, D. A Review of the biochemistry, metabolism and clinical benefits of thiamin(e) and its derivatives. Evid. Based Complement. Alternat. Med. 2006, 3: 49-59.

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(36) Aluru, N., Vijayan, M.M. Stress transcriptomics in fish: a role for genomic cortisol signalling. Gen. Comp. Endocrinol. 2009, 164: 312-329.

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(37) Faught, E., Aluru, N., Vijayan, M.M. In Biology of Stress in Fish: Fish Physiology 35; Schreck, C.B., Tort, L., Farrell, A.P., Brauner, C.J., Eds.; Academic Press/Elsevier Inc.; New York, 2016: pp 113-166.

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(38) Evenset, A., Christensen, G.N., Carroll, J., Zaborska, A., Berger, U., Herzke, D., Gregor, D. Historical trends in persistent organic pollutants and metals recorded in sediment from Lake Ellasjøen, Bjørnøya, Norwegian Arctic. Environ. Pollut. 2007, 146: 196-205.

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(39) Nasjonal innsjøundersøkelse 2004 – 2006, Del I: Vannkjemi. Status for forsuring, næringssalter og metaller; Akvaplan NIVA as: Oslo, 2008; http://www.miljodirektoratet.no/old/klif/publikasjoner/2361/ta2361.pdf

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(40) Jørgensen, E.H., Vijayan, M.M. Aluru, N., Maule, A.G. Fasting modifies Aroclor 1254 impact on plasma cortisol: glucose and lactate responses to handling disturbance in Arctic charr. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2002b, 132: 235-245.

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(41) Aluru, N., Jørgensen, E.H., Maule, A., Vijayan, M.M. PCB disruption of the hypothalamuspituitary-interrenal axis involves brain glucocorticoid receptor downregulation in anadromous Arctic charr. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2004, 287: R787-R793.

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(42) Vijayan, M.M., Aluru, N., Maule, A.G., Jørgensen, E.H. Fasting augments PCB impact on liver metabolism in anadromous Arctic char. Toxicol. Sci. 2006, 91: 431-439.

496 497 498

(43) Bellehumeur, K., Lapointe, D., Cooke, S.J., Moon, T.W. Exposure to sublethal levels of PCB-126 impacts fuel metabolism and swimming performance in rainbow trout. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 2016, 199: 97-104.

499 500 501

(44) Nault, R., Al-Hameedi, S., Moon, T.W. Effects of polychlorinated biphenyls on whole animal energy mobilization and hepatic cellular respiration in rainbow trout, Oncorhyncus mykiss. Chemosphere 2012, 87: 1057-1062.

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Gauthier et al. 503

Figure captions

504

Figure 1. Plasma glucose and lactate levels in charr from Lake Ellasjøen or Lake Laksvatn

505

sampled prior to and after a 1 h of handling/confinement stressor. Values represent means ±

506

SEM (n = 18). Glucose (t(16) = 3.54; p = 0.0027) and lactate (t(16) = 8.57; p < 0.0001) increased

507

following the handling/confinement stressor, while there was no effect of lake or interactive

508

effect of lake and stressor. Different letters indicate significant differences in glucose and lactate

509

between pre- and post-stressed fish.

510 511

Figure 2. Nonmetric multidimensional scaling (NMDS) of Arctic charr plasma metabolome by

512

site and stressor. Axes represent arbitrary distances calculated through NMDS. Objects that are

513

closer together on the plot are more closely associated with one another. Black circles represent

514

metabolites. Red and blue circles represent fish plasma samples from Lake Laksvatn and Lake

515

Ellasjøen respectively. Red and blue shaded and striped ellipses represent standard deviations of

516

charr plasma sample scores for Lake Laksvatn and Lake Ellasjøen pre-and post-stressed

517

respectively. A permutational multivariate analysis of variance (PERMANOVA) detected that

518

the charr plasma metabolome changed following the handling/confinement stress (F(1,36) = 14.6;

519

p = 0.0009) and differed between lakes (F(1,36) = 5.1; p = 0.011).

520 521

Figure 3. Metabolomics pathway enrichment analysis. The y-axis represents results from over-

522

representation analysis (ORA; see section 2.5.) reported as log(p-values). The x-axis represents

523

results from the metabolite pathway topology analysis reported as total pathway impact. Closed

524

black circles represent pathways. The dashed blue line represents our threshold for further

525

pathway analysis. Pathways that exceeded the threshold are identified with text.

526

21



Gauthier et al. 527

Figure 4. Metabolomics pathway topology analysis, including log2 fold-change differences in

528

charr from Ellasjøen in reference to charr from Laksvatn. Circles represent metabolites. Arrows

529

represent reactions. Circles coloured gray indicate metabolites within the pathway that were

530

absent in the charr plasma samples. The size of each circle indicates its relative betweenness

531

centrality (RBC) within the pathway. Changes observed for metabolites having a higher RBC

532

(i.e., larger circle) are likely to reflect a greater impact on the pathway as a whole compared to

533

metabolites with lower RBC scores.

534

535

Figure 5. Metabolomics pathway topology analysis, including log2 fold-change differences post-

536

stressor (bottom half of circles). Circles represent metabolites. Arrows represent reactions.

537

Circles coloured gray indicate metabolites within the pathway that were absent in the charr

538

plasma samples. The size of each circle indicates its relative betweenness centrality (RBC)

539

within the pathway. Changes observed for metabolites having a higher RBC (i.e., larger circle)

540

are likely to reflect a greater impact on the pathway as a whole compared to metabolites with

541

lower RBC scores.

22


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Gauthier et al.

Table 1. Summary of characteristics of fish sampled from Lake Laksvatn and Lake Ellasjøen modified from Jørgensen et al.5. Sex ratio [Female (F):male (M)], age, fork length (FL), body mass (BM), condition factor (CF), and muscle fat (MF) contents are represented as means ± SEM. n F:M

Lake Laksvatn Ellasjøen

10 10

1:1 2:3

age (yr)

FL (cm)

BM (g)

CF

MF (%)

10.2 ± 0.4 11.3 ± 0.5

48.9 ± 1.1 40.6 ± 0.8

1046 ± 52.9 595 ± 31.1

0.89 ± 0.03 0.88 ± 0.01

0.9 ± 0.18 0.36 ± 0.04

23



Page 24 of 30

Gauthier et al. Table 2. Over-representation analysis (ORA) and pathway impact of Arctic charr metabolome from lakes Ellasjøen and Laksvatn, Norway. ORA and pathway impact calculations are carried out irrespective metabolite levels (i.e., treatment effects), and identify pathways within the plasma samples of all sampled fish. p-values indicate results from over-representation analysis. Pathway impact is calculated as the summed relative-betweenness centrality (RBC) scores for metabolites present in plasma samples proportional to the total RBC score from all metabolites in the pathway. pathway

KEGGpid

p

Impact

Aminoacyl-tRNA biosynthesis Alanine, aspartate and glutamate metabolism Glycine, serine and threonine metabolism Arginine biosynthesis Tyrosine metabolism Butanoate metabolism Phenylalanine metabolism Pantothenate and CoA biosynthesis Taurine and hypotaurine metabolism Propanoate metabolism Arginine and proline metabolism Pyrimidine metabolism Caffeine metabolism Cysteine and methionine metabolism beta-Alanine metabolism Nicotinate and nicotinamide metabolism Galactose metabolism Purine metabolism Ascorbate and aldarate metabolism Glutathione metabolism Citrate cycle (TCA cycle) Pyruvate metabolism Valine, leucine and isoleucine degradation D-Glutamine and D-glutamate metabolism Sulfur metabolism Lysine biosynthesis Synthesis and degradation of ketone bodies Histidine metabolism Glyoxylate and dicarboxylate metabolism

00970 00250 00260 00220 00350 00650 00360 00770 00430 00640 00330 00240 00232 00270 00410 00760 00052 00230 00053 00480 00020 00620 00280 00471 00920 00300 00072 00340 00630

5.22 × 10-10 7.79 × 10-8 1.75 × 10-7 1.48× 10-5 5.05 × 10-5 5.18 × 10-5 8.88 × 10-5 5.26 × 10-4 8.11 × 10-4 8.40 × 10-4 2.15 × 10-3 3.02 × 10-3 4.30 × 10-3 5.30 × 10-3 6.29 × 10-3 8.34 × 10-3 9.30 × 10-3 9.53 × 10-3 1.18 × 10-2 1.47 × 10-2 2.00 × 10-2 3.32 × 10-2 2.33 × 10-2 2.36 × 10-2 2.64 × 10-2 3.74 × 10-2 3.75 × 10-2 3.83 × 10-2 4.35 × 10-2

0.17 0.78 0.63 0.48 0.09 0.14 0.62 0.01 0.60 0.04 0.34 0.21 1.00 0.24 0.45 0.00 0.42 0.12 0.00 0.13 0.17 0.24 0.02 1.00 0.00 0.00 0.60 0.36 0.15

24


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Gauthier et al. FIGURE 1

25



26

FIGURE 2

26


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Page 27 of 30


27

FIGURE 3

27



Page 28 of 30

28

FIGURE 4

Downregulated Alanine, aspartate, and glutamate metabolism

Glycine, serine and threonine Glycine, serine and threonine metabolism metabolism

Alanine, aspartate and glutamate metabolism

2-Oxoglutarate L-Glutamate

L-Threonine Betaine L-Glutamate Glycine

Pyruvate

L-Aspartate

Arginine biosynthesis Arginine biosynthesis

Pyruvaldehyde

L-Alanine

N-Acetyl-Laspartate

Upregulated

2-Oxobutanoate L-Glutamine Sarcosine

L-Serine

Fumarate

Pyruvate

Fumarate

Succinate semialdehyde

Creatine 5-Aminolevulinate

2-Oxoglutarate

L-Asparagine

L-Glutamine

Aspartate Citruline

Succinate

L-Arginine L-Ornithine

D-Glutamine and D-glutamate metabolism

Caffeine metabolism Caffeine metabolism

Phenylalanine metabolism

Phenylalanine metabolism

D-Glutamine and D-glutamate metabolism

L-Glutamine

L-Phenylalanine Theobromine Paraxanthine Phenylpyruvate

L-Glutamate

2-hydroxyphenylacetate

2-Oxoglutarate

Phenylacetate

Aminoacyl t-RNA biosynthesis Aminoacyl-tRNA biosynthesis L-Serine

L-Methionine L-Glutamate

L-Tryptophan

L-Threonine

L-Leucine

L-Isoleucine

L-Lysine

L-Alanine

L-Valine

L-Aspartate

Glycine

L-Glutamine

L-Arginine

L-Phenylalanine

L-Histidine

L-Asparagine

28


Page 29 of 30


29

FIGURE 5 Down regulated Alanine, aspartate, and glutamate metabolism

Glycine, serine and threonine Glycine, serine and threonine metabolism metabolism

Alanine, aspartate and glutamate metabolism

2-Oxoglutarate L-Glutamate

L-Threonine Betaine L-Glutamate Glycine

Pyruvate

L-Aspartate

Arginine biosynthesis Arginine biosynthesis

Pyruvaldehyde

L-Alanine

N-Acetyl-Laspartate

Up regulated

2-Oxobutanoate L-Glutamine Sarcosine

L-Serine

Fumarate

Pyruvate

Fumarate

Succinate semialdehyde

Creatine 5-Aminolevulinate

2-Oxoglutarate

L-Asparagine

L-Glutamine

Aspartate Citruline

Succinate

L-Arginine L-Ornithine

D-Glutamine and D-glutamate metabolism D-Glutamine and D-glutamate metabolism

Caffeine metabolism Caffeine metabolism

Phenylalanine Phenylalanine metabolism metabolism

L-Glutamine

L-Phenylalanine Theobromine Paraxanthine Phenylpyruvate

L-Glutamate

2-hydroxyphenylacetate

2-Oxoglutarate

Phenylacetate

Aminoacyl t-RNA biosynthesis Aminoacyl-tRNA biosynthesis L-Serine

L-Methionine L-Glutamate

L-Tryptophan

L-Threonine

L-Leucine

L-Isoleucine

L-Lysine

L-Alanine

L-Valine

L-Aspartate

Glycine

L-Glutamine

L-Arginine

L-Phenylalanine

L-Histidine

L-Asparagine

29



30

ABSTRACT TOC

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Lifelong Exposure to PCBs in the Remote Norwegian Arctic Disrupts

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