Feeding Activity and Xenobiotics Modulate Oxidative Status in

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Feeding activity and xenobiotics modulate oxidative status in Daphnia magna: implications for ecotoxicological testing Sara Furuhagen, Birgitta Liewenborg, Magnus Breitholtz, and Elena Gorokhova Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es5044722 • Publication Date (Web): 23 Sep 2014 Downloaded from http://pubs.acs.org on September 28, 2014

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Environmental Science & Technology

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Feeding activity and xenobiotics modulate oxidative status in

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Daphnia magna: implications for ecotoxicological testing

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Sara Furuhagen*, Birgitta Liewenborg, Magnus Breitholtz and Elena Gorokhova

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Department of Applied Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden

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*Corresponding author:

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Sara Furuhagen

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Department of Applied Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden

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+46 8 674 72 79

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[email protected]

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ABSTRACT

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To apply biomarkers of oxidative stress in laboratory and field settings, an understanding of

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their responses to changes in physiological rates is important. The evidence is accumulating

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that caloric intake can increase production of reactive oxygen species and thus affect

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background variability of oxidative stress biomarkers in ecotoxicological testing. This study

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aimed to delineate effects of food intake and xenobiotics on oxidative biomarkers in Daphnia

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magna. Antioxidant capacity measured as ORAC (oxygen radical absorbance capacity) and

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lipid peroxidation assayed as TBARS (thiobarbituric acid reactive substances) were measured.

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Food intake was manipulated by varying food densities or by exposing the animals to

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chemicals inhibiting feeding rate (pharmaceutical haloperidol and pesticide lindane). Feeding

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rate proved to affect both protein, ORAC and TBARS in unexposed daphnids. However, there

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was no significant effect of feeding rate on the protein-specific ORAC values. Both

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substances affected individual protein and ORAC levels and changed their relationship to

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feeding rate. Our results show that inhibition of feeding rate influenced the interpretation of

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biomarker response and further emphasize the importance of understanding (1) baseline

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variability in potential biomarkers due to variations in metabolic state, and (2) the

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contribution of feeding rate on toxic response of biomarkers.

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INTRODUCTION

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To counteract pro-oxidative processes in aerobic organisms, homeostasis is maintained

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between the cellular production of reactive oxygen species (ROS) and the endogenous

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antioxidant defense. The balance between ROS production and the antioxidant defense can be

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affected by physiological factors, such as age and disease1 as well as by environmental

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factors, such as hypoxia2 and pollutants3. Caloric restriction (CR) has been identified as an

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important factor affecting the cellular production of ROS as low-calorie diets hamper ROS

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production4. Studies on isolated rat mitochondria showed that CR results in decreased

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substrate oxidation activity, leading to lowered mitochondrial membrane potential and

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increased proton leakage and thus a diminished generation of ROS5. This effect appears to be

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a general mechanism as the effects of CR have been shown for a variety of species6. As an

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excess of ROS can be harmful to DNA, lipids and proteins7, the levels of oxidative damages

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on these biomolecules have been found to be lower in animals given a CR diet6.

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Chemical substances can affect cellular ROS concentrations through different mechanisms.

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Pro-oxidative substances can increase generation of ROS and other radicals by entering redox

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cycles8, but ROS production can be also induced by increasing metabolic rate as a response to

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stress or through induction of enzymes involved in xenobiotic metabolism, such as CYP450

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and NAD(P)H9. Moreover, the oxidative homeostasis can be disrupted by depletion of

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antioxidative substances in xenobiotic metabolism7, 9. Consequently, biomarkers of oxidative

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stress are widely used in ecotoxicology and ecology as indicators of stressful conditions10,

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although specific causes of the observed biochemical alterations are not always identifiable

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because of the plethora of factors that may affect the oxidative status of organisms. Therefore,

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to facilitate biomarker application and interpretation of measured responses to stress factors,

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we need to understand the magnitude and causes of background variability for the biomarker

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of interest11. In particular, variability in feeding rate has a high potential to affect biomarkers 3 ACS Paragon Plus Environment


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of oxidative stress, because of the effect of caloric intake on the production of ROS. Feeding

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rate is a sensitive and ecologically relevant end point in ecotoxicological assays as exposure

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to a wide range of substances has been reported to cause feeding inhibition in various test

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species12-13. In stress assessment, feeding inhibition assay proved to be about 50-fold more

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sensitive than standardized acute ecotoxicological assays employing survival as an end

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point14. Moreover, although effects of ad libitum feeding on many endpoints have been

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identified as a serious methodological issue in tests with vertebrates15-17, no attempt has been

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made so far to study the effects of feeding rate and xenobiotic exposure on oxidative

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biomarkers in invertebrates. The objectives of this study were to (1) establish relationships

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between feeding rate and oxidative biomarkers in Daphnia magna and (2) understand the

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contribution of variation in feeding rate to toxic response.

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Two model substances, haloperidol and lindane, were used to address these objectives.

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Haloperidol is a dopamine receptor antagonist that has been found to cause feeding inhibition

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in rats by blocking hunger perception18; and it has indeed been found to cause feeding

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inhibition in D. magna (Furuhagen, unpubl. data). Lindane is a neurotoxic insecticide19 that

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has been shown to inhibit feeding in daphnids by reducing the movement of filtering limbs12,

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20-21

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varying food levels in the absence of a toxic compound or to varying substance concentrations

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at a constant food level. As proxies for antioxidant capacity and oxidative damage we assayed

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oxygen radical absorbance capacity (ORAC) and lipid peroxidation levels measured as

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thiobarbituric acid reactive substances (TBARS). Both biomarkers have been used before to

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assess oxidative stress in microcrustaceans, including daphnids22-23. Two sets of hypotheses

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were tested with regard to the effects of food intake and chemical exposure in this study. We

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hypothesized that individual protein content will be positively related to feeding rate in the

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unexposed daphnids (Hypothesis 1), due to increased protein synthetic activity at higher food

. In our study, we manipulated feeding activity in daphnids by exposing them to either

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intake. Moreover, the oxidative biomarkers will be positively related to the increase in protein

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content of animals feeding at higher rates and thus having higher ROS production (Hypothesis

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2). In addition to feeding inhibition and corresponding effects on the biomarker response, the

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exposure to toxicants was hypothesized to have effects on the relationships between feeding

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rate and protein allocation, and between protein content and oxidative biomarkers. In

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particular, a negative effect on individual protein content was expected in the haloperidol-

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exposed daphnids, whereas lindane was hypothesized to have a positive effect on the protein

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content. The rationale for this expectation was based on the reported negative effect of

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haloperidol on the expression of heat shock proteins (hsp)24 and the induction of hsp after

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lindane exposure25 (Hypothesis 3). An increase in oxidative damage, independent of feeding

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rates, was expected in exposed daphnids, whereas the antioxidant capacity could be both

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positively and negatively affected by xenobiotics depending on the stress levels (Hypothesis

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4).

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METHODS

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Test organism

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The daphnids originated from a single clone (environmental pollution test strain Klon 5, the

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State office for nature, environment, and customer protection North-Rhine Westfalia, Bonn,

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Germany; originally from the Federal Environment Agency, Berlin, Germany). They were

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cultured in 2 L M7 medium (OECD standard 202 and 211) at a stock density of ~20

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individuals in each beaker and fed three times a week with a mixture of Pseudokirchneriella

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subcapitata and Scenedesmus subspicatus.

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Functional response experiments

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Using varying densities of algal suspension we manipulated daphnid feeding rate. The

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individuals feeding at varying rates were also used to measure individual protein content,

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ORAC (Experiment I and II), and TBARS (Experiment II) (Table 1). The feeding rate of

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juvenile daphnids (96 h old) as a function of food density was measured in M7 medium with 5

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daphnids in each replicate. Fluorescence of the algae was measured using Turner designs 10-

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AU Fluorometer at the start of the test and after 24 h to assess the feeding rate of the

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daphnids. All experiments were conducted in darkness, at 20˚C to minimize algal growth

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during the incubation. An additional replicate without daphnids served as a control to assess

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algal growth. Incubation for ORAC and TBARS measurements (Experiment II) was done

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using starved animals and those incubated at the intermediate food concentration (1.5 µg C

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mL-1) and at the near-saturation concentration (7 µg C mL-1).

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Feeding inhibition tests

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Feeding inhibition tests (haloperidol: Experiment III and lindane: Experiment V; test

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conditions as in Experiment I) were performed according to Barata, et al.

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modifications. In these experiments, feeding rate was measured at a constant food

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concentration (1.5 µg C mL-1) and the animals were used for measurements of individual

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protein content and ORAC. Exposure for TBARS and ORAC (Experiments IV and VI for

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haloperidol and lindane, respectively) were conducted at a food concentration of 1.5 µg C mL-

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1

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lindane were dissolved in dimethyl sulfoxide (DMSO) for use in the bioassays. Solvent

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control with a DMSO concentration corresponding to a volume of 0.1‰ of the total

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incubation volume was used in all exposure tests.

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with minor

and using the same test conditions as in Experiment II (Table 1). Both haloperidol and


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Biochemical analyses

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At the termination of each experiment, live daphnids were pooled within replicates, rinsed in

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potassium phosphate buffer (PPB) and transferred to Eppendorf tubes. All samples were snap

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frozen and stored at -80˚C until analysis.

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Protein quantification

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Samples for protein and ORAC measurements were homogenized in 150 µL PPB buffer (3.1

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mM, pH 7.4). Protein content (µg ind-1) was determined by bicinchoninic acid method27 using

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PierceTM BCA Protein Assay kit (Thermo Scientific) according to microplate procedure with

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some modifications. Transparent microplate was used and total volume in the wells was 150

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µL, with 10 µL homogenate, 10 µL PPB and 130 µL working solution. Absorbance was

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measured at 540 nm using FluoStar Optima plate reader (BMG Lab Technologies, Germany).

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Oxygen radical absorbance capacity (ORAC)

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Total antioxidant capacity was assayed as ORAC according to Ou, et al.

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modifications. Fluorescein was used as a fluorescent probe (106 nM) and 2,2- azobis(2-

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amidinopropane) dihydrochloride (AAPH) (152.66 mM) as a source of peroxyl radicals.

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Trolox (218 µM, Sigma-Aldrich) was used as standard. In each assay, 20 µL sample was

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added to each well and mixed with 30 µL AAPH and 150 µL fluorescein. ORAC levels were

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expressed as ORAC ind-1. Additionally, ORAC values were normalized to protein content


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, with minor


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(ORACp) before related to feeding rate because of the high contribution of proteins to ORAC

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values29.

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Lipid peroxidation

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Two exposures were performed for unexposed and exposed daphnids for TBARS analysis

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(Experiment II, IV, VI; Table 1) with high (A) and low (B) population density, respectively.

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Daphnids were assayed for lipid peroxidation by a modified TBARS method for measuring

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the aldehydic lipid peroxidation decomposition derivatives, which form fluorescent products

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after reacting with thiobarbituric acid (TBA)30. Daphnids were homogenized in 200 µL PPB,

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sonicated 3×10s and aliquoted. Subsamples of 125 µL tissue homogenate were treated with

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125 µL 10% trichloroacetic acid. The sample was further mixed with 150 µL TBA (2 mM)

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and incubated at 100˚C for 1 h. After cooling to room temperature, 220 µL of

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butanol:pyridine (volume ratio 15:1) mixture were added, vortexed (2×10s) and centrifuged

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for 5 minutes at 4000 g. The organic phase was used for fluorometric determination

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(540nm/590nm) of malondialdehyde (MDA) concentration (µM MDA equivalents ind-1). The

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MDA concentrations were expressed as TBARS ind-1. Following the common procedure for

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biochemical end point, TBARS were normalized to protein content (TBARSp) before related

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to feeding rate.

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Data analysis and statistics

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Feeding rate was calculated according to Båmstedt, et al.

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unexposed daphnids was fitted to a sigmoid function32-33

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= /1 +

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. The functional response of

(1) 8 ACS Paragon Plus Environment

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where F is feeding rate (µg C h-1 ind-1), Fmax is the theoretical maximum feeding rate, e is

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Euler´s number, k is a constant, C is food concentration (µg C mL-1) and Cm is the half-

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saturation constant.

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To test hypotheses 1 and 2, the relationships between feeding rate and biomarkers were

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analyzed by linear regression analysis. The effect of chemical exposure on feeding rate was

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evaluated using Hill equation

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= + − × / +

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where F is feeding rate (µg C h-1 ind-1), Fmin is the minimum feeding rate (µg C h-1 ind-1) and

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Fmax the maximum feeding rate (µg C h-1 ind-1), C is the logarithm of the substance

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concentration (mg L-1) +1 and n is the Hill constant34. The substance concentration

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(logarithmic value) that causes 50% feeding inhibition is represented by EC50 (mg L-1).

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Hypotheses 3 and 4 were addressed using general linear models to test the impact of

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substance exposure on the relationships between feeding and protein allocation and between

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protein content and oxidative biomarkers; experimental run (A and B) was used as a co-

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variable to account for the differences in experimental design. All values were normalized to

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the mean value of the control group for each treatment. To facilitate statistical comparisons

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and interpretation, the independent variables were centered by their respective mean values

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using response interval covering both exposed and unexposed daphnids. Centering input

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variables removes the collinearity between the main effects and the interaction predictors,

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therefore allowing the interpretation of main effects35. The interactions between explanatory

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variables were removed from the model when non-significant. Assumptions of normality and

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homoscedasticity were fulfilled for all data as confirmed by residual plot analysis. Significant

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level was ˂0.05 for all tests and all analyses were carried out in R 2.13.2.


(2)


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Projection to latent structures by means of partial least squares (PLS) was applied to visualize

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directions in a multivariate space for maximum separation of observations (biomarkers and

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physiological variables across an individual) belonging to different groups (treatments). The

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amount of variation attributed to each explanatory variable was determined by regressing each

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explanatory variable against the response variable (Treatment as a categorical variable) in the

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absence of the other explanatory variables36 as implemented in STATISTICA 8 (StatSoft, Inc.

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2013).

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RESULTS

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Feeding and biomarker responses in unexposed animals

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Feeding rate increased with increasing food concentration, reaching saturation at ~6.7 µg C

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mL-1 (Figure 1). Moreover, individual protein content was significantly positively related to

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feeding rate (Figure 3). Also, there was a significant positive relationship between individual

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ORAC and protein values. However, no significant relationship between ORACp and feeding

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rate was found. Finally, individual protein content and TBARS as well as TBARSp and

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feeding rate were positively related to each other (Table 2; Figure 3).

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Feeding and biomarker responses in exposed daphnids

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Neither haloperidol nor lindane caused mortality within the tested concentration range.

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However, both substances had inhibitory effect on feeding rate, following a sigmoid dose-

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response and resulting in nearly complete feeding inhibition in the highest concentrations of

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both substances. Haloperidol EC50 was 1.62 mg L-1 (95% confidence interval: 1.37 to 1.90 mg


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L-1) and lindane EC50 was 1.27 mg L-1 (95% confidence interval: 1.03 to 1.55 mg L-1) (Figure

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2).

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Similar to the unexposed daphnids, individual protein content was significantly positively

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related to feeding rate in exposed daphnids. Moreover, haloperidol had a significant negative

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effect on the baseline protein content and a positive effect on the slope on the relationship

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between protein and feeding as indicated by the significant interaction term of feeding rate

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and treatment. By contrast, lindane had no significant effect on the individual protein content

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(Table 3A; Figure 3).

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Again, similar to the unexposed daphnids, ORAC was positively related to protein content in

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both haloperidol and lindane exposed daphnids. Moreover, haloperidol had a positive effect

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on the ORAC – protein relationship, as indicated by the significant interaction between

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treatment and protein content. Lindane significantly decreased the baseline ORAC values as

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indicated by the significantly different intercept (Table 3B).

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In contrast to the unexposed daphnids, a significant positive relationship between ORACp and

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feeding rate was observed in the animals exposed to haloperidol or lindane. Moreover, both

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substances had significant negative effects on the ORACp baseline as indicated by the

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significant treatment effect (Table 3C).

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Haloperidol had no significant effect on the relationship between TBARS and protein content.

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However, the significant interaction for lindane and protein indicates a significantly negative

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effect on the TBARS-protein relationship due to lindane exposure (Table 3D). Neither

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haloperidol nor lindane had any significant effect on the relationship between TBARSp and

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feeding rate (Table 3E).

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The PLS analysis showed a clear pattern related to the differences between the lindane-

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exposed and unexposed daphnids revealing relevant ORACp, feeding rate and TBARSp in 11 ACS Paragon Plus Environment


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this separation. The two PLS components accounted for 69% of the total variance. No clear

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separation between the haloperidol-exposed and unexposed animals or animals exposed to

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low lindane concentrations was observed, with variability in both haloperidol-exposed and

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unexposed groups being largely affected by variations in feeding and individual protein

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content (Figure 3).

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DISCUSSION

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In Daphnia magna, biomarkers of oxidative status changed in concert with feeding rate, that

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thus was established as a confounding factor for oxidative biomarkers in this standard test

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species. In addition to inducing feeding inhibition, exposure to our model substances,

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haloperidol and lindane, altered the relationship between feeding rate, protein and oxidative

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biomarkers, emphasizing the complexity of the biomarker responses.

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Hypothesis 1, linking protein content to feeding rate, was confirmed by the significant

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positive relationship between feeding rate and protein content, indicating that protein

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synthesis increases as more resources become available for protein production37. Since protein

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content is commonly used to normalize enzyme activities and other biochemical constituents

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used as biomarkers in ecology and ecotoxicology, this response is important for biomarker

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applications in general.

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Hypothesis 2, linking oxidative biomarkers to individual protein content, was also supported.

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In both exposed and unexposed daphnids, individual ORAC values were positively related to

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protein content, most probably due to increased production and intake of low-molecular

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compounds and proteins with antioxidative properties. The antioxidant defense consists of

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enzymes as well as other proteins and low-molecular-mass agents, such as ascorbic acid,

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reduced glutathione, methionine and uric acid7. The antioxidative activity of the water soluble 12 ACS Paragon Plus Environment

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fraction of these compounds is measured in the ORAC assay28. The lack of correlation

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between ORACp and feeding rate in the unexposed daphnids suggests that the allocation of

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proteins to the antioxidant defense did not increase in response to increased caloric intake,

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which is in agreement with previous findings38. A baseline relationship between the feeding

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rate and alterations in antioxidant defense has thus been established, which also facilitates

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application of ORACp as a biomarker in ecological and ecotoxicological studies11.

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Even though EC50 values for feeding inhibition were similar between haloperidol and lindane

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as evidenced by the overlapping confidence intervals, their effects on protein and ORAC

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differed, which may be related to differences in their mode of action18, 20. By including protein

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and substance exposure in the statistical models we could confirm Hypothesis 3 and show that

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haloperidol had a positive effect on the relationship between individual protein content and

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feeding rate. This was likely due to decreased protein synthesis with increasing haloperidol

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concentration39. By contrast to haloperidol, lindane did not have any effect on protein content.

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Hence, any difference in protein content in response to increasing lindane concentration was

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solely due to the decrease in filtering activity and thus feeding rate in the exposed animals.

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Without including feeding rate as an explanatory variable to the model, this effect would have

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been missed and observed alterations in protein content, and in the biomarker normalized to

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protein content, may erroneously have been attributed direct toxic mechanisms on protein

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metabolism.

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The Hypothesis 4 also found support as both haloperidol and lindane affected the relationship

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between protein and ORAC. Exposure to haloperidol resulted in lower allocation of resources

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to the antioxidant defense in relation to protein content, which is consistent with a general

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response to starvation, whereas lindane caused a significant decrease in the baseline ORAC

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values. Due to the effects of haloperidol and lindane on protein allocation and ORAC, there

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were significant positive effects on the relationship between ORACp and feeding rate in the 13 ACS Paragon Plus Environment


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exposed daphnids. The altered ORAC-protein relationship and the positive relation between

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ORACp and feeding rate indicate reduced resource allocation to antioxidant defense and/or a

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depletion of antioxidative compounds due to the chemical exposure. One can speculate that

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these effects may be related to detoxification as some agents contributing to the antioxidant

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defense are also involved in xenobiotic metabolism. One of these, glutathione (GSH), is a co-

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factor in the detoxification of ROS7 and depletion of GSH is used as a biomarker of oxidative

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stress due its involvement in antioxidative reactions40. Both haloperidol and lindane have been

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shown to cause GSH depletion due to its involvement in ROS detoxification and xenobiotic

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metabolism41-43. Hence, depletion of GSH may partly explain the decrease in ORAC in

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relation to protein content observed for exposed daphnids.

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Contrary to Hypothesis 4, predicting that lipid peroxidation levels would increase in response

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to xenobiotic exposure, haloperidol did not altered the relationship between protein content

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and TBARS, thus indicating that lipid peroxidation levels were only affected by feeding rate

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(Figure 3). These results contradict reported effects of this drug on lipid peroxidation in

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humans that had been administrated haloperidol (10 mg day-1) for two weeks41. However, the

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tested concentrations in our study may have been too low to induce oxidative damages during

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the 24-h exposure and the antioxidant defense could successfully counteracted ROS effects

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with non-detectable effects on lipid peroxidation levels. The high survival rate in this study

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could be indicative of such responses, albeit high survivorship does not necessarily mean that

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an oxidative stress response is absent23. Lindane significantly lowered the TBARS levels at

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increasing protein content compared to the unexposed daphnids. Due to the positive effect of

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lindane on hsp, and thus protein composition25, normalizing TBARS to the total protein

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concentrations in the sample could introduce additional uncertainty and even be misleading

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for understanding of chemical exposure effects. Assessment of normalization strategies for


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oxidative stress biomarkers is needed to facilitate interpretation of these responses in the field

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and laboratory settings.

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The observed effects of calorie intake on TBARS levels (Table 2, Figure 3) raise questions

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about optimal testing design for studies employing oxidative stress biomarkers to evaluate

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effects of various stressors. The ad libitum feeding regime used in many ecotoxicological tests

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may lead to high lipid peroxidation levels in actively feeding non-stressed individuals and,

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further, to erroneous interpretation of the results. Hence, bioassays with D. magna should be

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performed at moderate food levels to avoid effects on biochemical markers that are solely a

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consequence of ad libitum feeding. Moderate dietary restriction has also been shown to

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increase the sensitivity and response to toxic substances in both rodents15 and rotifers44, and

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our results indicate that this is also the case for oxidative biomarkers in D. magna.

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In conclusion, our results indicate that alterations in oxidative biomarker response due to

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xenobiotic exposure may not only be a consequence of direct interactions with oxidative

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processes, but can also be a result of indirect response via feeding inhibition. Since protein

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content, ORAC and TBARS were positively correlated with food intake, it is necessary to

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account for inhibitory effects of xenobiotics on feeding rate when interpreting responses of

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these biomarkers. Without considering confounding factors representing nutritional status and

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metabolism when evaluating biomarker response to toxic exposure, there is a risk of making

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erroneous conclusions about toxicity effects and mechanisms. To further increase the value of

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biomarkers in general and oxidative biomarkers in particular, the importance of confounding

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factors for interpretation of biomarker responses needs to be addressed routinely in biomarker

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validation.

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ACKNOWLEDGMENTS 15 ACS Paragon Plus Environment


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This study was supported by the Swedish Research Council for Environment, Agricultural

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Science and Spatial Planning (FORMAS), Stockholm University’s strategic marine

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environmental research program “Baltic Ecosystem Adaptive Management” and the Swedish

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Foundation for Strategic Environmental Research (MISTRA; MistraPharma).

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SUPPORTING INFORMATION

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

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13. Domene, X.; Natal-da-Luz, T.; Alcaniz, J. M.; Andres, P.; Sousa, J. P., Feeding inhibition in the soil collembolan Folsomia candida as an endpoint for the estimation of organic waste ecotoxicity. Environmental Toxicology and Chemistry 2007, 26 (7), 1538-1544. 14. Barata, C.; Alanon, P.; Gutierrez-Alonso, S.; Riva, M. C.; Fernandez, C.; Tarazona, J. V., A Daphnia magna feeding bioassay as a cost effective and ecological relevant sublethal toxicity test for Environmental Risk Assessment of toxic effluents. Science of the Total Environment 2008, 405 (1-3), 78-86. 15. Hart, R. W.; Keenan, K.; Turturro, A.; Abdo, K. M.; Leakey, J.; Lyncook, B., Caloric restriction and toxicity. Fundamental and Applied Toxicology 1995, 25 (2), 184-195. 16. Keenan, K. P.; Ballam, G. C.; Soper, K. A.; Laroque, P.; Coleman, J. B.; Dixit, R., Diet, caloric restriction, and the rodent bioassay. Toxicological Sciences 1999, 52 (2), 24-34. 17. Pugh, T. D.; Klopp, R. G.; Weindruch, R., Controlling caloric consumption: protocols for rodents and rhesus monkeys. Neurobiology of Aging 1999, 20 (2), 157-165. 18. Ninan, I.; Kulkarni, S. K., Dopamine receptor sensitive effect of dizocilpine on feeding behaviour. Brain Research 1998, 812 (1-2), 157-163. 19. Videla, L. A.; Barros, S. B. M.; Junqueira, V. B. C., Lindane-induced liver oxidative stress. Free Radical Biology and Medicine 1990, 9 (2), 169-179. 20. Gliwicz, M. Z.; Sieniawska, A., Filtering activity of Daphnia in low concentrations of a pesticide. Limnology and Oceanography 1986, 31 (5), 1132-1138. 21. Hartgers, E. M.; Heugens, E. H. W.; Deneer, J. W., Effect of lindane on the clearance rate of Daphnia magna. Archives of Environmental Contamination and Toxicology 1999, 36 (4), 399404. 22. Vehmaa, A.; Hogfors, H.; Gorokhova, E.; Brutemark, A.; Holmborn, T.; Engstrom-Ost, J., Projected marine climate change: effects on copepod oxidative status and reproduction. Ecology and Evolution 2013, 3 (13), 4548-4557. 23. Barata, C.; Varo, I.; Navarro, J. C.; Arun, S.; Porte, C., Antioxidant enzyme activities and lipid peroxidation in the freshwater cladoceran Daphnia magna exposed to redox cycling compounds. Comparative Biochemistry and Physiology C-Toxicology & Pharmacology 2005, 140 (2), 175-186. 24. Nakki, R.; Nickolenko, J.; Chang, J. M.; Sagar, S. M.; Sharp, F. R., Haloperidol prevents ketamine- and phencyclidine-induced HSP70 protein expression but not microglial activation. Exp. Neurol. 1996, 137 (2), 234-241. 25. Saradha, B.; Vaithinathan, S.; Mathur, P. P., Lindane alters the levels of HSP70 and clusterin in adult rat testis. Toxicology 2008, 243 (1-2), 116-123. 26. Barata, C.; Baird, D. J.; Minarro, A.; Soares, A., Do genotype responses always converge from lethal to nonlethal toxicant exposure levels? Hypothesis tested using clones of Daphnia magna straus. Environmental Toxicology and Chemistry 2000, 19 (9), 2314-2322. 27. Smith, P. K.; Krohn, R. I.; Hermanson, G. T.; Mallia, A. K.; Gartner, F. H.; Provenzano, M. D.; Fujimoto, E. K.; Goeke, N. M.; Olson, B. J.; Klenk, D. C., Measurement of protein using bicinchonic acid. Anal. Biochem. 1985, 150 (1), 76-85. 28. Ou, B. X.; Hampsch-Woodill, M.; Prior, R. L., Development and validation of an improved oxygen radical absorbance capacity assay using fluorescein as the fluorescent probe. Journal of Agricultural and Food Chemistry 2001, 49 (10), 4619-4626. 29. Cao, G. H.; Prior, R. L., Comparison of different analytical methods for assessing total antioxidant capacity of human serum. Clinical Chemistry 1998, 44 (6), 1309-1315. 30. Hodges, D. M.; DeLong, J. M.; Forney, C. F.; Prange, R. K., Improving the thiobarbituric acid-reactive-substances assay for estimating lipid peroxidation in plant tissues containing anthocyanin and other interfering compounds. Planta 1999, 207 (4), 604-611. 31. Båmstedt, U.; Gifford, D. J.; Irigoien, X.; Atkinson, A.; Roman, M., 8 - Feeding. In ICES Zooplankton Methodology Manual, Huntley, R. H. W. L. R. S., Ed. Academic Press: London, 2000; pp 297-399. 32. Jeschke, J. M.; Kopp, M.; Tollrian, R., Consumer-food systems: why type I functional responses are exclusive to filter feeders. Biological Reviews 2004, 79 (2), 337-349. 17 ACS Paragon Plus Environment


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33. Sarnelle, O.; Wilson, A. E., Type III functional response in Daphnia. Ecology 2008, 89 (6), 1723-1732. 34. Barton, H. A.; Andersen, M. E.; Allen, B. C., Dose-response characteristics of uterine responses in rats exposed to estrogen agonists. Regulatory Toxicology and Pharmacology 1998, 28 (2), 133-149. 35. Schielzeth, H., Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution 2010, 1 (2), 103-113. 36. Wold, S.; Sjostrom, M.; Eriksson, L., PLS-regression: a basic tool of chemometrics. Chemometrics Intell. Lab. Syst. 2001, 58 (2), 109-130. 37. Hoppe, S.; Bierhoff, H.; Cado, I.; Weber, A.; Tiebe, M.; Grummt, I.; Voit, R., AMPactivated protein kinase adapts rRNA synthesis to cellular energy supply. Proceedings of the National Academy of Sciences of the United States of America 2009, 106 (42), 17781-17786. 38. Sohal, R. S.; Ku, H. H.; Agarwal, S.; Forster, M. J.; Lal, H., Oxidative damage, mitochondrial oxidant generation and antioxidant defense during aging and in response to food restriction in the mouse. Mechanisms of Ageing and Development 1994, 74 (1-2), 121-133. 39. Barrientos, A.; Marin, C.; Miro, O.; Casademont, J.; Gomez, M.; Nunes, V.; Tolosa, E.; Urbano-Marquez, A.; Cardellach, F., Biochemical and molecular effects of chronic haloperidol administration on brain and muscle mitochondria of rats. Journal of Neuroscience Research 1998, 53 (4), 475-481. 40. Doyotte, A.; Cossu, C.; Jacquin, M. C.; Babut, M.; Vasseur, P., Antioxidant enzymes, glutathione and lipid peroxidation as relevant biomarkers of experimental or field exposure in the gills and the digestive gland of the freshwater bivalve Unio tumidus. Aquatic Toxicology 1997, 39 (2), 93-110. 41. Pai, B. N.; Janakiramaiah, N.; Gangadhar, B. N.; Ravindranath, V., Depletion of glutathione and enhanced lipid-peroxidation in the CSF of acute psychotic following haloperidol administration. Biological Psychiatry 1994, 36 (7), 489-491. 42. Tanaka, K.; Kurihara, N.; Nakajima, M., Metabolism of lindane in house-flies metabolic desaturation, dehydrogenation and dehydrochlorination, and conjugation with glutathione. Pesticide Biochemistry and Physiology 1976, 6 (4), 392-399. 43. Anguiano, O. L.; de Castro, A. C.; de D'Angelo, A. M. P., The role of glutathion conjugation in the regulation of early toad embryos' tolerance to pesticides. Comparative Biochemistry and Physiology C-Toxicology & Pharmacology 2001, 128 (1), 35-43. 44. Kailasam, M.; Kaneko, G.; Oo, A. K. S.; Ozaki, Y.; Thirunavukkarasu, A. R.; Watabe, S., Effects of calorie restriction on the expression of manganese superoxide dismutase and catalase under oxidative stress conditions in the rotifer Brachionus plicatilis. Fisheries Science 2011, 77 (3), 403-409. 45. Rousseeuw, P. J.; Ruts, I.; Tukey, J. W., The bagplot: A bivariate boxplot. Am. Stat. 1999, 53 (4), 382-387.

481 482 483


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484

485 486 487 488 489 490

Table 1. Summary of the experiments, specific conditions and measured biomarkers

Exp. I no test substance, varying food levels

Exp. II no test substance, varying food levels

Exp. III

Exp. IV

Exp. V

Exp. VI

Haloperidol

Haloperidol

Lindane

Lindane

Concentrations of the test substance (mg L-1)

0

0

0.2-3.1

0.2-3.1

0.2-1.6

0.2-1.6

Food concentration (µg C mL-1)

0-9.4

0-7

1.5

1.5

1.5

1.5

Feeding rate, Mortality

Mortality Feeding rate (B)





Protein, ORAC

Protein, ORAC, TBARS

Protein, ORAC


Protein, ORAC


Test volume (mL)

50

900 (A) 50 (B)

50

900 (A) 50 (B)

50

900 (A) 50 (B)

Individuals/replicate

5

33-35 (A) 5 (B)

5

33-35 (A) 5 (B)

5

33-35 (A) 5 (B)

5

3 (A) 18 pooled to 3 (B)

Exposure

Physiological end points Biomarkers measured

3 (A) 3 (A) 18 pooled to 5 18 pooled to 3 3 (B) (B) A – Exposure with high population density, B – Exposure with low population density Replicates/conc.

5



491

492 493 494 495

Figure 1. Functional response of juvenile D. magna.fed P. subcapitata. The horizontal broken line represents the maximum feeding rate and the vertical line represents saturation level 6.7 µg C/ml, calculated using the lower 95% confidence interval for the theoretical Fmax.

496 497 498 499 500 501 502 503


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504 505 506

Figure 2. Feeding inhibition in Daphnia magna exposed to haloperidol (A) and lindane (B). The vertical lines represent EC50 values and the grey, broken lines represent upper and lower confidence interval (95%).

507 508 Table 2. Linear regression for unexposed daphnids.

Response variable

Explanatory variable

a

b

df

R2

p

Protein

FR

3.81

5.45

23

0.70

***

ORAC

Protein

0.10

0.059

23

0.71

***

ORACp

FR

-0.014

0.12

23

0.10

˃ 0.05

TBARS

Protein

1.15

-2.99

7

0.47

*

TBARSp

FR

0.47

0.51

7

0.54

*

Linear regressions (y = ax + b) for the relationships between feeding rate and biomarker responses and between biomarkers and individual protein content. Asterisks indicate level of significance for the slope (a): p ≤ 0.05 (*); p ≤ 0.01 (**); p ≤ 0.001 (***). FR – Feeding rate, ORAC – Oxygen radical absorbance capacity, ORACp - proteinspecific ORAC, TBARS – Thiobarbituric acid reactive substances, TBARSp - protein-specific TBARS

509 510 511 512 Table 3. General linear models testing effects of the xenobiotics and feeding rate on the individual protein content, ORAC,



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ORACp, TBARS and TBARSp.

Haloperidol

Lindane

Estimate

SE

t

p value

Estimate

SE

t

p value

FR

0.54

0.11

4.77

***

0.15

0.017

8.77

***

Treatment

0.16

0.043

3.80

***

-0.06

0.042

-1.42

˃ 0.05

FR*Treatment

-0.27

0.13

-2.04

*

Protein

1.7

0.15

11.45

***

0.98

0.17

5.81

***

Treatment

0.023

0.043

0.54

˃ 0.05

0.27

0.056

4.89

***

Protein*Treatment

-0.60

0.22

-2.76

**

FR

0.93

0.12

7.67

***

0.38

0.15

2.55

*

Treatment

0.11

0.046

2.48

*

0.24

0.056

4.22

***

FR*Treatment

-0.98

0.14

-7.01

***

-0.44

0.17

-2.56

*

Protein

0.51

0.10

4.89

***

0.25

0.16

1.66

˃ 0.05

Treatment

0.022

0.044

0.49

˃ 0.05

0.025

0.042

0.58

˃ 0.05

0.39

0.19

2.07

*

A) Protein

B) ORAC

C) ORACp

D) TBARS

Protein*Treatment E) TBARSp FR

-0.20

0.12

-1.72

˃ 0.05

-0.20

0.12

-1.76

˃ 0.05

Treatment

-0.063

0.099

-0.64

˃ 0.05

-0.16

0.098

-1.61

˃ 0.05

Treatment group was set as a reference. Asterisks indicate level of significance: p ≤ 0.05 (*); p ≤ 0.01 (**); p ≤ 0.001 (***).FR – Feeding rate, ORAC – Oxygen radical absorbance capacity, ORACp – protein-specific ORAC, TBARS – Thiobarbituric acid reactive substances, TBARSp - protein-specific TBARS

513 514 515


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516 517 518 519 520 521 522 523 524 525 526 527

Figure 3. Standardized biplot visualizing PLS model by scatter plots termed scores or loadings plots and showing how predictors form the space of the latent variables and how they are combined with the observations. Each point on the score plot represents an individual Daphnia sample projected in the bivariate space and loading scores provide the correlation between the original variables and the new component variables. The model is for Treatment as a response variable and feeding rate (FR), individual protein content (Protein), protein-specific ORAC and TBARS values (ORACp and TBARSp, respectively) as explanatory variables. The structure of the multivariate point cloud is represented by the shaded areas in the bagplot, analogous to a box-and-whiskers plot but also visualizing the spread, correlation, skewness, and tails of the data45. The small square colored according to the treatment represents the depth median (the deepest location), the dark area corresponds to 50% of the dataset, and the light area is a fence augmented by a default factor of 1.5. Sample points outside the shaded areas are outliers.



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Feeding Activity and Xenobiotics Modulate Oxidative Status in

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