Antioxidant Responses in Relation to Persistent Organic Pollutants

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Antioxidant responses in relation to persistent organic pollutants and metals in a low and a high-exposure population of seabirds Anette Antonsen Fenstad, A. John Moody, Markus Öst, Kim Jaatinen, Jan Ove Bustnes, Børge Moe, Sveinn Are Hanssen, Kristin Gabrielsen, Dorte Herzke, Syverin Lierhagen, Bjorn M. Jenssen, and Ase Krokje Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00478 • Publication Date (Web): 06 Apr 2016 Downloaded from http://pubs.acs.org on April 10, 2016

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Environmental Science & Technology

Antioxidant responses in relation to persistent organic pollutants

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and metals in a low and a high-exposure population of seabirds

2 3 4

Anette A. Fenstad†*, A. John Moody‡, Markus Öst§,ǁ, Kim Jaatinenǁ, Jan O. Bustnes#, Børge

5

Moe††, Sveinn A. Hanssen#, Kristin M. Gabrielsen†, Dorte Herzke‡‡, Syverin Lierhagen§§,

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Bjørn M. Jenssen† and Åse Krøkje*†

7 8

†

Biology, Realfagbygget, NTNU, 7491 Trondheim, Norway

9 10

‡

§

ǁ

Novia University of Applied Sciences (NOVIA), Coastal Zone Research Team, Raseborgsvägen 9, FI-10600 Ekenäs, Finland

15 16

Environmental and Marine Biology, Faculty of Science and Engineering, Åbo Akademy University, Artellerigatan 6, FI-20520 Turku, Finland

13 14

Plymouth University, School of Biological Sciences, Drake Circus, Plymouth, Devon PL4 8AA, UK

11 12

Norwegian University of Science and Technology (NTNU), Department of

#

Norwegian Institute for Nature Research (NINA), Framsenteret, Hjalmar Johansens gate 14, 9296 Tromsø, Norway

17 18

††

NINA, Høgskoleringen 9, 7034 Trondheim, Norway

19

‡‡

Norwegian Institute for Air Research (NILU), Framsenteret, Hjalmar Johansens gate 14, 9296 Tromsø, Norway

20 21 22

§§

Norwegian University of Science and Technology (NTNU), Department of Chemistry, Realfagbygget, NTNU, 7491 Trondheim, Norway

23 24

1 ACS Paragon Plus Environment


25

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Abstract

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Oxidative stress occurs when there is an imbalance between the production of

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reactive oxygen species (ROS) and antioxidant defence. Exposure to pollutants may

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increase ROS and affect antioxidant levels, and the resulting oxidative stress may

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negatively affect both reproduction and survival. We measured concentrations of 18

30

persistent organic pollutants (POPs) and 9 toxic elements in blood, as well as total

31

antioxidant capacity (TAC), total glutathione (tGSH), and carotenoids in plasma of

32

Baltic- and Arctic female common eiders (Somateria mollissima) (N = 54) at the end

33

of their incubation-related fasting. The more polluted Baltic population had higher

34

TAC and tGSH concentrations compared to the Arctic population. Carotenoid levels

35

did not differ between populations. The effect of mixtures of pollutants on the

36

antioxidants was assessed, and the summed molar blood concentrations of 14 POPs

37

were positively related to TAC. There was no significant relationship between the

38

analysed pollutants and tGSH concentrations. The adaptive improvement of the

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antioxidant defence system in the Baltic population may be a consequence of

40

increased oxidative stress. However, both increased oxidative stress and energy

41

allocation toward antioxidant defence may have adverse consequences for Baltic

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eiders at the incubation stage, when energy resources reach an annual minimum

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due to incubation-related fasting.

44 45 46 47

Key words: Arctic, Baltic, common eider, glutathione, carotenoids, antioxidant

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capacity

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Introduction

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Low levels of reactive oxygen species (ROS) are essential in the physiological

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control of cell functions in organisms,1 e.g. in immune defence and in cell signalling.2

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However, increased ROS production or impaired antioxidant defence may cause

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oxidative stress, which occurs when there is an imbalance between ROS production

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and elimination of ROS by antioxidants and antioxidant enzymes.1-3 Oxidative stress

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causes injury to cells and tissues through oxidative damage to membranes or

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biomolecules, such as DNA, proteins and lipids. Furthermore, oxidative stress may

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cause inflammation, degenerative diseases, accelerate the ageing process,2 and

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have negative effects on both the reproduction and survival of individuals.4, 5 Thus, it

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is important for all organisms to maintain the cellular ROS homeostasis in balance in

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order to avoid negative health effects.1, 3

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Exposure to chemical pollutants may increase ROS production.6 For instance,

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there is evidence that exposure to some persistent organic pollutants (POPs), such

64

as polychlorinated biphenyls (PCBs), may induce oxidative stress.7 It has also been

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suggested that the toxic effects of both reduction-oxidation (redox)-active metals,

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and redox-inactive metals and metalloids (with properties of both metal and non-

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metal) are partially due to metal- and metalloid-induced oxidative stress.8 Since

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wildlife and humans are exposed to complex mixtures of environmental chemicals,

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these mixtures have the potential for causing combined effects.9, 10 Individual

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chemicals in a mixture may cause additive effects by similar mechanisms or by

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dissimilar mechanisms with similar responses (dose addition and response addition,

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respectively). Furthermore, interactive effects (antagonism, synergism, potentiation)

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may occur between chemicals in specific combinations.10 Simultaneous exposure to



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mixtures of multiple toxic metals and POPs may therefore elicit additive or interactive

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oxidative effects.11

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Some of the most important protection mechanisms against toxic compounds

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involve the antioxidant defence system. When the levels of ROS increase above the

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natural range, cells can respond by increasing antioxidant production through ROS-

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based signalling.3, 12 Thus, antioxidants and their oxidation products are important

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indicators of oxidative stress in organisms.1 One of the most important antioxidants

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is glutathione (GSH).13, 14 It represents the major cellular redox buffer and is a

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representative indicator of the redox environment of the cell.12 Glutathione can react

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directly with ROS and may bind toxic metals.14, 15 Increased total glutathione (tGSH)

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concentrations are thought to indicate a long-term up-regulation of the GSH pool as

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a response to stress.8, 16, 17

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Other important contributors to antioxidant defence are the circulating

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antioxidants,18, 19 which can indirectly be measured as total antioxidant capacity

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(TAC) of plasma, i.e. the ability of a plasma sample to neutralize free radicals.20, 21

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Components such as uric acid, vitamin E, vitamin C, and nutritional carotenoids

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contribute to the TAC. In birds, uric acid is the most abundant circulating

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antioxidant.21 When antioxidant defence is activated it uses food-derived

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antioxidants, such as carotenoids,22 together with enzymatic antioxidants to cope

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synergistically with pro-oxidants.23, 24 Thus, there is a possibility that endogenous

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antioxidants, such as GSH, may be affected by the levels of nutritional carotenoids.25

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Several studies have documented either increased antioxidant levels26-28 or a

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depletion of antioxidants8, 26 as a response to chemical exposure. In the present

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study, we compared plasma levels of ROS neutralizing antioxidants (tGSH, TAC and

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carotenoids) in two populations of common eiders (Somateria mollissima, hereafter


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eider) with different body burdens of pollutants29: one high-polluted population in the

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Baltic Sea, and one low-polluted population in Svalbard, Norwegian Arctic. We

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measured blood concentrations of two different redox-active metals, seven redox-

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inactive metals and metalloids, and 18 different POPs at the end of the 26-day

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incubation fast in the eiders. The Baltic Sea has been considered one of the most

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polluted seas in the world,30 while Svalbard is considered a relatively clean area. As

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a consequence Baltic eiders have up to 26 times higher blood concentrations of

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certain POPs.29 The eider population in the Baltic Sea is declining and considered

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vulnerable,31 while in Svalbard the population has remained stable over the last three

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decades.32 During their incubation fast of approximately 26 days, female eiders may

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lose as much as 40% of their initial body mass.33 Incubating eiders are therefore

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exposed to a high degree of natural nutritional stress. At the end of incubation they

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are in poor body condition34-36 and concurrently their blood concentrations of POPs

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are elevated due to redistribution from fat stores during fasting.37, 38

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Our objective was first to investigate whether antioxidant status differed between

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the two eider populations, one breeding in a polluted area and one breeding in a

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relatively clean area. We hypothesised that the antioxidant protection in the more

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polluted Baltic population would be altered compared to the Svalbard population, as

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a response to the increased chemical exposure. Secondly, we examined the

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relationship between the concentration of 27 analysed pollutants and TAC and

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tGSH, to identify the mixture of compounds, congeners or elements that potentially

120

affect the antioxidant response of the eiders. Concentrations of POPs were analysed

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and published in a previous study,29 and element concentrations were analysed

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herein to study potential combined effects.

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Materials and Methods



124 125

Field sampling Blood samples were obtained from incubating female eiders in Tvärminne (N =

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25), Finland (~59º84’N, 23º21’E) and at Storholmen, Kongsfjorden (N = 29),

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Svalbard (78°56’N, 12°13’E) in 2011 as described by Fenstad et al.29 The females

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were sampled if the clutch had hatched or was near hatching based on egg

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floatation39 or direct signs of hatching (ducklings or cracks in the eggs). Additionally,

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plasma and red blood cells were separated in the field using a micro centrifuge

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(Labnet, Spectrafuge mini, 230 V, Woodbridge, UK), MC batteries (GEL, 12V/4Ah)

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and a transformer (Mascot 50W, Sine Wave Inverter, Type 2284, Output 230 V AC

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50 Hz and Input: 12 V DC). The plasma samples were immediately stored in a

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thermos flask containing a mix of salt and ice (~40 g NaCl/L ice, ~-10 °C). The

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samples were transported to the field station within six hours. The plasma was

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frozen (-80 °C) for later analysis of antioxidants. Body mass (to the nearest 10 g)

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was recorded using a spring balance (Pesola Medio-Line 42500, Ecotone-Poland,

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2500 g). The study complied with the Norwegian and Finnish regulation on animal

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experimentation and permissions of field work were granted by the Governor of

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Svalbard and the local authorities in Finland (Animal Experiment Board/State

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Provincial Office of Southern Finland, permit number ESLH-2009-02969/Ym-23).

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POP analysis

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The analysis of POPs was performed at Norwegian Institute for Air Research

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(NILU), Tromsø as described by Bustnes et al.40 and Fenstad et al.29, 38 The

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compounds that were over the LOD in more than 60% of the individuals included β-

146

HCH, HCB, trans-chlordane (t-chlordane), oxy-chlordane, trans-nonachlor (t-

147

nonachlor), cis-nonachlor (c-nonachlor), p,p’-DDE, Mirex and the PCBs 28, 99, 105,

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118, 138, 153, 180, 183, 187 and 194. For statistical calculations concentrations


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below the LOD were set to 50% of the detection limit. Blood POP concentrations

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reported in Fenstad et al.29 were calculated to molar concentrations (nmol/g wet

151

weight (ww)) in the present study.

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Metal analysis

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Blood samples of female eiders from Svalbard and Finland were analysed using

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High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS,

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Thermo Electronic Corporation, Waltham, MA, USA) at the Norwegian University of

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Science and Technology (NTNU), Department of Chemistry.

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For the detection of all elements, approximately 500 mg whole blood was

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transferred to acid-washed Teflon tubes, designed for UltraClave, and 0.5 mL 50%

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Scanpure nitric acid (HNO3, ultra-pure grade, 14.4 M) was added for digestion. The

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samples were digested using a high pressure microwave system, UltraClave

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(Milestone, Shelton, CT, USA) over two hours with temperature up to 240 °C and

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pressure of 160 bar. The blood samples were diluted to 12 ml with ion exchanged

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Milli-Q-water before metal analysis.

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To assure the quality of the analysis, two replicates of four of the blood samples

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were analysed in different runs. Three blank samples accompanied every run of the

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analysis. Four reference material samples (Seronorm, Trace Elements Whole Blood

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L-1, LOT MR4206, REF 201505, Sero, Billingstad, Norway) were analysed with the

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blood samples. The analysed reference material was within the approved range

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values for all analysed elements. The results were corrected from blank samples.

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The elements mercury (Hg), selenium (Se), lead (Pb), cadmium (Cd), arsenic (As),

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chromium (Cr), zinc (Zn), molybdenum (Mo) and copper (Cu) were selected to test

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effects on antioxidants based on previous documentations of oxidative effects and

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on potential environmental exposure at the two locations.8, 30, 41 The LOD ranged



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between 0.02 - 3.6 µg/kg for the analysed elements. Two individuals had negative

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blood Cr concentrations after correction from blank samples, and two individuals had

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blood Cr concentrations under the LOD (0.1 µg/kg). Negative Cr concentrations were

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set to zero and positive Cr concentrations under the LOD were set to 50% of the

178

detection limit.

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As for the POP compounds, the molar elemental concentrations (nmol/g ww) were

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used in statistical analyses.

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Determination of total antioxidant capacity

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The antioxidant measurements were performed at the School of Biological

183

Sciences, Plymouth University, UK. Total antioxidant capacity was determined using

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the ferric reducing ability of plasma (FRAP) assay.20 A detailed description is

185

included in supporting information (SI). Plasma samples (10 µl) were transferred to a

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96 well plate before mixing with the FRAP-reagent (240 µl). The absorbance at 595

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nm was measured in a plate reader (Optimax, Tunable microplate reader, B02118,

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Molecular Devices, US) for 45 minutes at 20 °C. Samples were run in triplicate and a

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blank and a standard of ferrous sulfate (FeSO4, 1 mM, BDH, 321-753) were included

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in each run. Blank results were subtracted from plasma results and the maximum

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absorbance point of each individual was used to calculate the FeSO4 equivalence

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(mM) from a standard curve.

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Determination of total glutathione in plasma

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Total glutathione (i.e. reduced glutathione (GSH), and oxidized glutathione

195

(GSSG)) content of plasma was determined using the glutathione reductase cycling

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assay,42 as described by Al-Subiai et al.43 with modifications specified in SI. Total

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glutathione in the plasma samples were measured in triplicate in a plate reader

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(Optimax, Tunable microplate reader, B02118, Molecular Devices, US) using 96 well


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plates at 412 nm at 22 °C for 30 minutes. A blank sample was included in each run

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and subtracted from the samples. Total glutathione (µM) was calculated from a

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standard included in each run (20 µM L-glutathione). All reagents were obtained from

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Sigma-Aldrich unless otherwise indicated.

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Extraction of carotenoids from plasma

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Plasma total carotenoids were extracted according to Donaldson44 with

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modification specified in SI. The absorbance spectra were measured between 350

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and 650 nm in a Jenway 7315 spectrophotometer (Bibby Scientific Limited, Stone,

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UK). There were no major differences in the absorbance spectra of extracted

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carotenoids between any of the samples, with λmax ranging from 446-449 nm with a

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median value of 447 nm. In order to partially correct for any turbidity in the extracts,

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A447 minus A520 was used as a measure of the concentration of carotenoids in each

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sample.

212

Spectrophotometric determination of haemoglobin in plasma

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Contamination of plasma with glutathione from lysis of red blood cells during

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sampling and treatment of samples prior to centrifugation is a potential problem.45

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Thus, haemoglobin (Hb) was quantified by three point wavelength quantification (see

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SI) using spectroscopy on each sample. A linear regression model with tGSH as

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dependent variable and population and Hb as independent variables, as well as the

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interaction between population and Hb variables, was used to check for correlations

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between tGSH and Hb content. The intercept differed (t50 = -3.7, p = 0.001), but

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plasma concentration of Hb was positively related to plasma tGSH concentrations

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(t50 = 3.3, p = 0.002) in both populations (non-significant interaction, t49 = 0.8, p =

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0.5). Thus, to control for the effect of potentially released GSH from lysis of red blood

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cells, Hb was included as an independent variable in the models where tGSH was



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the dependent variable. Haemoglobin in the plasma sample was not related to

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plasma TAC (t50 = 0.3, p = 0.8) or carotenoids (t47 = -0.8, p = 0.4).

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Statistical analysis

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The data were analysed with linear regression models using R 3.1.146 and

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principal component analyses (PCA) and partial least squares (PLS) modelling using

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Simca-P+ version 14 (Umetrics AB, Umeå, Sweden). Two linear models (lm

230

function), with population as the independent variable, were used to test the

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differences in the dependent variables TAC (N = 54), tGSH (N = 54) and carotenoids

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(N = 49) between the two populations of eiders. In the selection of individuals where

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both tGSH and carotenoids were measured, the independent variables carotenoids,

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population and the interaction between population and carotenoid levels were

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included in the model to investigate whether tGSH concentrations were associated

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with carotenoid levels (i.e. a confounding factor if birds with lower nutritional

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carotenoid levels increased endogenous production of GSH to compensate).

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Principal component analysis was conducted to visualize the differences in blood

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concentrations of pollutants and plasma TAC, tGSH and carotenoid levels between

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the two populations. In the PCA, ∑PCBs and ∑chlordanes were used to ease the

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readability of Figure 1. Partial least squares analysis was used to model antioxidant

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levels (TAC and tGSH) as a function of body mass and pollutant concentrations (10

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PCB congeners, four chlordanes, p,p’-DDE, HCH, HCB, mirex, Hg, Se, Cu, Cd, Zn,

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Mo, Cr, As and Pb). Partial least squares is an extension of multiple regression

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analysis, where associations are established with components extracted from

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independent variables that maximize the explained variance in the dependent

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variable.47, 48 Partial least squares analysis is particularly useful when the sample

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size is small and in case of severe multicollinearity,47 such as was the case in our


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analysis (Pearson’s correlation test: between PCB congeners: r > 0.6, p < 0.0001;

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chlordanes: r > 0.6, p < 0.0001; Hg:Se: r = -0.3, p = 0.02; p,p’-DDE-HCH-HCB: r >

251

0.3, p < 0.02). Separate PLS models were used for the two un-related dependent

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variables TAC and tGSH (Pearson’s correlation test: r = 0.08, p = 0.5) to ease

253

interpretation.48 Data were scaled to unit variance (UV) and centred to equalize the

254

variance within each variable before analysis.

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Coefficient plots were used to identify the independent variables that significantly

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affected the dependent variables according to the PLS model. The coefficient values

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represent the change in response when the variable varies from 0 to 1, in coded

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units (= 1 standard deviation (SD)). The coefficient is significant when the 95%

259

confidence interval does not contain zero. The final PLS model was selected by

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excluding unimportant variables (by using the variable influence on projection (VIP))

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to optimize the goodness of prediction (Q2) and the goodness of fit (R2Y) 48. An R2Y

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>0.7 and a Q2 > 0.4 is acceptable for biological data.49

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Linear regression models were used to verify the relationships between the

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pollutants identified in the PLS models and antioxidant variables. In these analyses,

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a single independent variable was created by summing the molar concentrations

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(nmol/g) of the pollutants positively affecting TAC, and a single independent variable

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was created by summing the concentration of the pollutants negatively affecting

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TAC. These two independent variables were used in separate models with TAC as

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the dependent variable. In the same way the summed concentration of the pollutants

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positively affecting tGSH and the summed concentration of the pollutants negatively

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affecting tGSH were used as independent variables in separate models with tGSH

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as dependent variable. Hence, in total four linear regression models were used to

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test the potential pollutant effect on plasma TAC and tGSH concentration. Population



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and the interaction between population and the summed concentration of pollutants

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were included as independent variables in all four models. The interaction was

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included to assess potential differences in the antioxidant response to pollutant

277

exposure between the two populations. In the models with tGSH as the dependent

278

variable, Hb was included as an independent variable to control for the effect of

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haemolysis on tGSH (see above). Independent variables and interactions with p >

280

0.1 were excluded from the starting model.

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Diagnostic plots were used to assess whether the data sufficiently met the

282

assumption of the linear model. All variables were loge-transformed to improve the

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linearity of the data in the PLS models (visually inspected curvature between X and

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Y, using the t1/u1 plot48). In the models testing the effect of the summed

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concentration of 14 and 12 pollutants on TAC and tGSH, the summed pollutant

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concentrations, Hb and tGSH were loge-transformed to improve the normal Q-Q plot,

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Scale-Location plot and Residuals vs Leverage plot of the linear models. Mean

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values are presented as mean ± SD. All tests were two-tailed, and the level of

289

significance was set at p < 0.05.

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Results

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Differences in antioxidant levels and pollutant patterns in Baltic and Svalbard

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eiders

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Baltic eiders had significantly higher TAC and plasma concentrations of tGSH

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compared to Svalbard eiders (Table 1), whereas carotenoid levels did not differ

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between the two populations (Table 1). Plasma tGSH concentrations were positively

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associated with carotenoid levels (t45 = 2.1, p = 0.04) in the Baltic eiders, but not the

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Svalbard eiders, as the slope (interaction term, t45 = -2.6, p = 0.01) and the intercept


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(t45 = -3.3, p = 0.002) for this relationship differed between the two populations (SI,

299

Figure S1).

300

In the PCA loading plot that included all analysed pollutants and all antioxidant

301

variables, the first two components explained 45% of the variation in the data (Figure

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1). The two eider populations were clearly separated, illustrating that Baltic eiders

303

(PC1= -0.7 - -0.1) had high blood concentrations of most POPs and Hg, higher tGSH

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concentrations and a higher TAC, while Svalbard eiders (PC1= 0 – 0.7) had high

305

blood concentrations of chlordanes, As, Se and Cd (Figure 1).

306

Relationships between the mixture of pollutants and antioxidant levels

307

The best-fitting PLS model with TAC as dependent variable (R2Y= 0.41, Q2=0.37)

308

included 17 of the 27 analysed pollutants, i.e. all 10 analysed PCBs, p,p’-DDE,

309

mirex, HCH, HCB, Cr, Se and Cd as independent variables (Figure 2A). Copper, Pb,

310

Zn, Mo, Hg, As and chlordanes were not included in the model. Plasma TAC was

311

best explained by the blood concentrations of all 10 PCBs, p,p’-DDE, mirex, HCH,

312

HCB, Se and Cd (Figure 2A). The blood concentration of Cr did not significantly

313

explain plasma TAC (Figure 2A). In summary, the model showed that TAC was

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positively associated with the blood concentrations of 14 POPs, including all 10

315

PCBs, p,p’-DDE, HCH, mirex and HCB (∑14pollutants), and negatively associated

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with Se and Cd (∑2pollutants, Figure 2A). The R2Y and Q2 of the PLS model was

317

slightly lower than preferred (>0.7 and > 0.4, respectively), but the results were

318

supported by the linear regression model, as TAC was strongly positively associated

319

with ∑14pollutants (t49 = 5.9, p < 0.0001, Figure 3A). The relationship between

320

∑14pollutants and TAC was the same in both eider populations, because the slope

321

(interaction term, t47 = -1.0, p = 0.3) and intercept (t48 = 1.5, p = 0.14) of this

322

relationship did not differ between the populations (Figure 3A). Hence, ∑14pollutants



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was important for explaining TAC, while population was not. The linear regression

324

models did not, however, support the negative relationship between TAC and

325

∑2pollutants (t51 = -0.3, p = 0.8), indicated by the PLS model within any of the two

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populations (interaction term, t50 = -0.1, p = 0.9) (Figure 3B). Hence, ∑2pollutants

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was not important for explaining plasma TAC in the eiders, but population was (t51 =

328

-2.9, p = 0.01), with higher plasma TAC for Baltic eiders.

329

The best-fitting PLS model explaining tGSH concentrations had a relatively low

330

predictive power (R2Y = 0.27, Q2 = 0.22). The model included all PCBs, with the

331

exception of PCB 194, and p,p’-DDE, HCH, Hg, Zn, oxy-chlordane, As, Cd, Se and

332

plasma Hb as independent variables (Figure 3C). Thus, in addition to PCB 194,

333

HCB, three chlordanes, mirex, Pb, Mo, Cr and Cu were excluded from the model.

334

Plasma Hb levels and blood concentration of Zn, oxy-chlordane and As did not

335

significantly explain tGSH concentrations (Figure 2B). Plasma tGSH concentration

336

was positively associated with PCB 153, 138, 99, 118, 180, 28, 105, 183 and 187,

337

HCH, p,p’-DDE and Hg (∑12pollutants), and negatively related to the blood

338

concentrations of Cd and Se (∑2*pollutants, Figure 2B). The linear regression model

339

did not, however, confirm the positive relationship between ∑12pollutants and plasma

340

tGSH concentrations (t46 = -1.1, p = 0.3, Figure 3C) in the eiders. The intercept (t46 =

341

-3.1, p = 0.003) for this relationship differed between the two populations, whereas

342

the slope (interaction term, t45 = 0.4, p = 0.68) did not. Furthermore, the negative

343

relationship between ∑2*pollutants and tGSH, indicated by the PLS model, was not

344

confirmed by the linear regression model (t49 = -0.6, p = 0.5, Figure 3D). The

345

intercept for this relationship (t49 = -2.4, p = 0.02), but not the slope (interaction term,

346

t48 = 0.7, p = 0.5), differed between the two populations.

347

Discussion


Page 15 of 31

348


Antioxidant levels in Baltic and Svalbard eiders

349

We found significantly higher plasma TAC and tGSH concentrations in the Baltic

350

eiders compared to the Svalbard eiders. This indicates an up-regulated antioxidant

351

defence system in the more polluted Baltic eiders29 (Figure 1) compared to the lower

352

contaminated Svalbard eiders. This is in accordance with the meta-analysis of

353

Isaksson,50 showing that wild birds tend to have up-regulated antioxidant defence in

354

polluted environments. Furthermore, other experimental and field studies have also

355

reported increased levels of circulating antioxidants51 and tGSH concentrations in

356

contaminated groups compared to controls27, 28. It is a general assumption that an

357

increase in the amount of antioxidants and higher antioxidant enzyme activities

358

indicate higher oxidative stress.52, 53 Nevertheless, the comparison of antioxidant

359

capacity between different populations may not by itself be sufficient to reach a

360

conclusion regarding possible differences in levels of oxidative stress.8, 54 This is

361

because the up-regulated antioxidant defence in Baltic eiders may also reflect an

362

over-expression of the antioxidant response and, thus, a sign of a better potential to

363

tolerate oxidative stress.8, 54 For example, chronic exposure to radiation (i.e.

364

increased ROS production) can lead to adaptive up-regulation of GSH

365

concentrations in wild birds.55

366

Total antioxidant capacity in response to mixtures of pollutants

367

Both the PLS and the linear model indicated that blood concentrations of all

368

PCBs, p,p’-DDE, HCH, mirex and HCB were positively related to plasma TAC in

369

eiders. Hence, environmental exposure to these POPs seems to cause an up-

370

regulation of plasma TAC in Baltic and Svalbard eiders. Cohen et al.21 suggested

371

that 90% of the variation in avian antioxidant capacity of plasma can be explained by

372

uric acid, based on a comparative study of 92 bird species. The results from the



Page 16 of 31

373

FRAP assay in the present study showed a fast and a slow phase of the FRAP

374

response, which indicated that there were two major contributors to the TAC (i.e.

375

ascorbate and uric acid, SI Figure S2). Thus, it is likely that the increased TAC in

376

relation to POP exposure in the present study was caused, at least in part, by an

377

increase in uric acid production. Uric acid is produced in response to lipid

378

peroxidation56 which results from ROS attack on lipids.3 The generation of ROS from

379

POPs is largely a result of cytochrome P450 (CYP450) monooxygenase-mediated

380

metabolism.57 In general, co-planar PCBs (non-, or one-ortho chlorine substituent,

381

PCB 105 and 118 in the present study) are substrates for the CYP1A isozymes and

382

may interact with the aryl hydrocarbon receptor to induce synthesis and activity of

383

CYP1A enzymes. Non-coplanar PCBs, p,p’-DDE, HCH, HCB and mirex, may induce

384

synthesis and activity of CYP 2B,58-61 2C and 3A enzymes.59, 60 Hence, it is plausible

385

that the positive relationship between ∑14pollutants and TAC in the present study

386

was caused by a dose addition effect, most likely linked to ROS production due to

387

CYP450 metabolism into reactive metabolites.62 Furthermore, ROS production from

388

other substrates for CYP enzymes63 may add to the ROS levels following a potential

389

induction of different CYP enzymes from pollutant exposure.64, 65

390

The PLS model indicated a negative relationship between Cd, Se and TAC.

391

However, when population was controlled for in the linear model, the negative

392

relationship between ∑2pollutants and TAC was not significant. Hence, this

393

seemingly negative relationship may be an artefact of pooling the samples from the

394

two populations.

395

Glutathione concentrations in response to mixtures of pollutants

396 397

Although the PLS model indicated that there was a positive relationship between tGSH and the blood concentration of Hg and 11 POPs, the Q2 (0.22) of the model


Page 17 of 31


398

was low and this relationship was not verified by the linear regression model. Thus,

399

the higher plasma tGSH concentrations in the more polluted population could not be

400

explained by the blood concentrations of chemicals analysed in the present study.

401

A restriction in food derived antioxidants, such as carotenoids, could affect GSH

402

homeostasis25 (i.e. a compensatory increase in GSH concentrations in individuals

403

with low carotenoid levels). There was, however, no difference in carotenoid levels in

404

the Baltic eiders compared to the Svalbard eiders, neither were there any indications

405

that nutritional carotenoid levels had a regulatory effect on tGSH concentrations.

406

This supports previous suggestions that carotenoids are minor antioxidants for adult

407

birds.22

408

Exposure to pollutants not analysed in the present study may have affected

409

plasma tGSH concentrations. For instance, polycyclic aromatic hydrocarbons (PAHs)

410

are known to cause oxidative effects.66 The sediment concentrations of PAHs are

411

much higher in the Baltic Sea30, 67 compared to Svalbard68, most likely due to several

412

sources of petroleum wastes there.30 Furthermore, the higher tGSH concentrations

413

in Baltic eiders may reflect a long-term up-regulation of the GSH pool as a response

414

to stress.8, 16, 17 Glucocorticoid stress hormones, such as corticosterone, regulate

415

oxidative stress genes69, 70 and can thereby elicit oxidative stress.69, 71 Females from

416

the Baltic population show higher baseline corticosterone levels72 than those nesting

417

in Svalbard.73 Furthermore, the more heavily polluted Baltic population has recently

418

experienced a marked increase in predation risk.74 Both stress from predation and

419

pollution may promote stress hormone secretion70, 75, 76 and, in turn, affect oxidative

420

balance. Hence, both pollutants and other stress factors may have contributed to the

421

significantly higher plasma tGSH concentration in Baltic compared to Svalbard

422

eiders. The extent to which these different putative stress factors affect oxidative



423

balance, potentially inducing higher tGSH concentrations in Baltic eiders, remains

424

unknown but warrants further study.

425

Page 18 of 31

As the seemingly negative relationship between Cd and Se and tGSH

426

concentrations, indicated by the PLS model, was not confirmed by the linear model,

427

it may be an artefact of pooling the samples from the two populations. Thus, neither

428

the redox-active elements (Cr and Cu) nor the redox inactive elements (Cd, Pb, Zn,

429

Mo, Hg and Se) affected plasma TAC or tGSH concentrations. Hence, exposure

430

levels to these elements in eiders may have been below the threshold for detecting

431

any effects.

432

In summary, we have demonstrated an up-regulation of antioxidant defences in

433

Baltic compared to Svalbard eiders. The up-regulation of TAC may potentially be a

434

response to environmental POP exposure, likely caused by increased ROS

435

generation in the more polluted Baltic population.

436

In addition to direct bimolecular effects of increased ROS production, oxidative

437

stress can negatively affect growth, survival and reproduction.4, 5 Alternatively,

438

increased TAC and higher concentrations of tGSH may suggest increased tolerance

439

for ROS and reduced oxidative stress. Nevertheless, ROS plays important roles in

440

multiple cell signalling pathways77, 78 and increased antioxidant production to

441

neutralize ROS may interfere with ROS-dependent functions.78 Either way, up-

442

regulation of oxidative defences may have particularly negative consequences for

443

incubating female eiders, for two reasons. First, energy resources reach an annual

444

low at the end of the incubation fast. As a result, important energy-demanding

445

physiological functions, such as immune defence, may be supressed with potentially

446

adverse consequences on reproduction in eiders.34, 35 Hence, females may suffer

447

negative health consequences irrespective of whether they are maintaining their


Page 19 of 31


448

oxidative balance by adaptive up-regulation of antioxidant defences against the

449

increased ROS production, or actually suffering from pollution-induced oxidative

450

stress. Second, female eiders are subject to other environmental stressors which

451

may potentially amplify the effects of oxidative stress. Chronic overproduction of

452

glucocorticoids such as corticosterone can enhance the mobilization of stored

453

energetic reserves.70 A combination of stress-induced increased energy expenditure

454

and pollution-induced oxidative stress may divert resources away from reproductive

455

effort in the Baltic population. This study was confined only to nesting females, which

456

incubated eggs until hatching. However, for other seabird species, both

457

environmental stress and pollution have been shown to reduce the breeding

458

probability or successfully hatched eggs.79-81 Thus, assessing the effects of up-

459

regulated antioxidant defence on the probability to initiate nesting and/or successful

460

hatching in both the Baltic and Svalbard eider population is one possible approach

461

for testing effects of pollutants on the reproductive potential of eider females. Clearly,

462

further investigations are warranted to assess the potential consequences of

463

oxidative responses in wild eiders exposed to a complex mixture of interacting

464

pollutants and stressors.

465 466

Acknowledgment―This work was supported by a Ph.D. fellowship provided by the

467

Faculty of Natural Sciences and Technology (70201200), Norwegian University of

468

Science and Technology (NTNU), Fram Flagship Hazardous substances and the

469

Research Council of Norway (Project 256934: AVITOX). Arctic Field Grants from

470

Svalbard Science Forum (2011) financed the field work in Svalbard. We thank the

471

staff at Sverdrup Station, Ny-Ålesund, and Tvärminne Zoological Station, Finland, for

472

logistic support. The work in Finland was funded by the Academy of Finland (grant



Page 20 of 31

473

no. 266208 to KJ and 128039 to MÖ) and the Swedish Cultural Foundation in

474

Finland (to MÖ). Finally we thank the Norwegian Ornithological Association (Norsk

475

ornitologisk forening), Kong Haakon den 7des utdannelsesfond for norsk ungdom,

476

University of Tromsø and Liv og Dag Vogts dyrevernfond, Oslo, Norway for financial

477

support.

478 479

*

Corresponding author: Telephone: +47 73596126, E-mail: [email protected],

480

Address: NTNU, Department of Biology, Realfagbygget, NTNU, 7491 Trondheim,

481

Norway.

482 483

Supporting Information Available: Method details for POP analysis, the FRAP assay,

484

glutathione measurements, and for carotenoid and haemoglobin determination.

485

Relationship between loge plasma total glutathione and loge plasma total carotenoids

486

in Baltic and Arctic common eiders (Figure S1), and the FRAP response measured

487

as absorbance (595) over time (sec) (Figure S2). This material is available free of

488

charge via the Internet at http://pubs.acs.org.

489 490 491 492 493 494 495 496 497


Page 21 of 31


498

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68. Jiao, L.; Zheng, G. J.; Minh, T. B.; Richardson, B.; Chen, L.; Zhang, Y.; Yeung, L. W.; Lam, J. C. W.; Yang, X.; Lam, P. K. S.; Wong, M. H. Persistent toxic substances in remote lake and coastal sediments from Svalbard, Norwegian Arctic: Levels, sources and fluxes. Environ. Pollut. 2009, 157 (4), 1342-1351. 69. You, J.-M.; Yun, S.-J.; Nam, K. N.; Kang, C.; Won, R.; Lee, E. H. Mechanism of glucocorticoid-induced oxidative stress in rat hippocampal slice cultures. Can. J. Physiol. Pharmacol. 2009, 87 (6), 440-447. 70. Sapolsky, R. M.; Romero, L. M.; Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 2000, 21 (1), 55-89. 71. Sato, H.; Takahashi, T.; Sumitani, K.; Takatsu, H.; Urano, S. Glucocorticoid Generates ROS to Induce Oxidative Injury in the Hippocampus, Leading to Impairment of Cognitive Function of Rats. J. Clin. Biochem. Nutr. 2010, 47 (3), 224232. 72. Jaatinen, K.; Seltmann, M. W.; Hollmen, T.; Atkinson, S.; Mashburn, K.; Ost, M. Context dependency of baseline glucocorticoids as indicators of individual quality in a capital breeder. Gen. Comp. Endocrinol. 2013, 191, 231-8. 73. Tartu, S.; Angelier, F.; Bustnes, J. O.; Moe, B.; Hanssen, S. A.; Herzke, D.; Gabrielsen, G. W.; Verboven, N.; Verreault, J.; Labadie, P.; Budzinski, H.; Wingfield, J. C.; Chastel, O. Polychlorinated biphenyl exposure and corticosterone levels in seven polar seabird species. Environ. Pollut. 2015, 197, 173-180. 74. Jaatinen, K.; Ost, M.; Lehikoinen, A., Adult predation risk drives shifts in parental care strategies: a long-term study. J. Anim. Ecol. 2011, 80 (1), 49-56. 75. Tartu, S.; Angelier, F.; Herzke, D.; Moe, B.; Bech, C.; Gabrielsen, G. W.; Bustnes, J. O.; Chastel, O., The stress of being contaminated? Adrenocortical function and reproduction in relation to persistent organic pollutants in female black legged kittiwakes. Sci. Total Environ. 2014, 476–477 (0), 553-560. 76. Nordstad, T.; Moe, B.; Bustnes, J. O.; Bech, C.; Chastel, O.; Goutte, A.; Sagerup, K.; Trouvé, C.; Herzke, D.; Gabrielsen, G. W. Relationships between POPs and baseline corticosterone levels in black-legged kittiwakes (Rissa tridactyla) across their breeding cycle. Environ. Pollut. 2012, 164 (0), 219-226. 77. D'Autreaux, B.; Toledano, M. B. ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis. Nat. Rev. Mol. Cell. Biol. 2007, 8 (10), 813-24. 78. Williams, T. D. Trade-offs and carry-over effects. In Physiological Adaptations for Breeding in Birds. Princeton University Press: Princeton, New Jersey, U.S 2012; pp 277-281. 79. Goutte, A.; Angelier, F.; Chastel, C. C.; Trouvé, C.; Moe, B.; Bech, C.; Gabrielsen, G. W.; Chastel, O. Stress and the timing of breeding: Glucocorticoidluteinizing hormones relationships in an arctic seabird. Gen. Comp. Endocrinol. 2010, 169 (1), 108-116. 80. Bustnes, J. O.; Erikstad, K. E.; Hanssen, S. A.; Tveraa, T.; Folstad, I.; Skaare, J. U. Anti-parasite treatment removes negative effects of environmental pollutants on reproduction in an Arctic seabird. Proceedings. Biological sciences / The Royal Society 2006, 273 (1605), 3117-22. 81. Goutte, A.; Barbraud, C.; Meillere, A.; Carravieri, A.; Bustamante, P.; Labadie, P.; Budzinski, H.; Delord, K.; Cherel, Y.; Weimerskirch, H.; Chastel, O. Demographic consequences of heavy metals and persistent organic pollutants in a vulnerable long-lived bird, the wandering albatross. Proc. R. Soc. Biol. Sci. Ser. B. 2014, 281 (1787), 20133313. 25 ACS Paragon Plus Environment


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743

Table 1: Mean plasma total antioxidant capacity, total glutathione and carotenoid

744

levels in Baltic and Svalbard eiders, and test statistics from the linear models

745

comparing the means between the two populations.

Baltic mean 1

746 747 748 749

Svalbard SD

test statistics

N

mean

SD

N

t

p

TAC (mM)

0.73

0.3

25

0.48

0.2

29

-3.99

0.0002

tGSH2 (µM)

1.8

1.5

25

0.9

0.8

29

-3.72

0.0005

Carotenoids (A447-520)

0.08

0.04

20

0.13

0.09

29

1.63

0.11

1) 2)

Total antioxidant capacity Total glutathione (reduced GSH and oxidized GSSG)

750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 26 ACS Paragon Plus Environment

Page 27 of 31


767

768 769

Figure 1: PCA loadings bi-plot including total antioxidant capacity (TAC, mM), total

770

glutathione (tGSH, µM) and carotenoids (absorbance at 447 nm) analysed in

771

plasma, as well as blood concentrations of the POPs p,p’-DDE, ∑10PCBs, HCB,

772

HCH, mirex, ∑4chlordanes, and the elements Se, Cd, As, Pb, Cu, Zn, Cr, Mo and

773

Hg, in Baltic (black circles) and Svalbard (blank circles) eiders (N = 54).

774 775 776 777



Page 28 of 31

778 779

Figure 2: A) The coefficient plot from the PLS model with total antioxidant capacity

780

(TAC, mM) of plasma as dependent variable and the blood concentration of the POP

781

compounds PCBs (PB) 28, 99, 105, 118, 138, 153, 180, 183, 187 and 194, p,p’-

782

DDE, HCH, mirex, HCB, and the elements chromium (Cr), selenium (Se) and

783

cadmium (Cd) (nmol/g ww), as independent variables (R2 = 0.41, Q2 = 0.37). B) The

784

coefficient plot from the PLS model with plasma total glutathione (GSH, µM) as

785

dependent variable and the blood concentrations of the POP compounds PB 28, 99,

786

105, 118, 138, 153, 180, 183 and 187, HCH, p,p’-DDE, oxy chlordane (oxy) and the

787

elements Hg, Zn, As, Cd and Se, as well as plasma haemoglobin levels (Hb) as

788

independent variables (R2 = 0.27, Q2 = 0.22). Boxes above zero are positively related

789

to the y-variable and boxes below zero are negatively related to the y-variable,


Page 29 of 31


790

respectively. The coefficients are significant when their 95% confidence intervals do

791

not cross zero. White bars vs. grey bars.

792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810



Page 30 of 31

811 812 813

Figure 3: The relationship between the dependent variables plasma total antioxidant

814

capacity (TAC, mM, A+B) and loge total glutathione (GSH, µM, C+D), and the

815

independent variables (identified from the PLS model, see text) summed molar blood

816

concentration (nmol/g ww) of A) 10 PCBs, p,p’-DDE, mirex, HCH, HCB (loge

817

sum14pollutants), B) selenium (Se) and cadmium (Cd) (sum2pollutants), C) 9 PCBs,

818

HCH, p,p’-DDE and mercury (Hg) (loge sum12pollutants), and D) Se and Cd

819

(sum2pollutants). The black and blank circles represent eiders from the Baltic and

820

Svalbard, respectively.

821 822 823 824 825 826 827 828 829


Page 31 of 31


830 831 832 833 834 835 836 837

TOC/Abstract art

838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853


Antioxidant Responses in Relation to Persistent Organic Pollutants

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