Monitoring sublethal changes in fish physiology following exposure to a light, unweathered crude oil

Accepted Manuscript Title: Monitoring sublethal changes in fish physiology following exposure to a light, unweathered crude oil Authors: Sharon E. Hook, Julie Mondon, Andrew T. Revill, Paul A. Greenfield, Sarah A. Stephenson, Joanna Strzlecki, Patricia Corbett, Emily Armstrong, Jing Song, Hai Doan, Skye Barrett PII: DOI: Reference:

S0166-445X(18)30452-1 https://doi.org/10.1016/j.aquatox.2018.08.013 AQTOX 5007

To appear in:

Aquatic Toxicology

Received date: Revised date: Accepted date:

11-12-2017 14-8-2018 16-8-2018

Please cite this article as: Hook SE, Mondon J, Revill AT, Greenfield PA, Stephenson SA, Strzlecki J, Corbett P, Armstrong E, Song J, Doan H, Barrett S, Monitoring sublethal changes in fish physiology following exposure to a light, unweathered crude oil, Aquatic Toxicology (2018), https://doi.org/10.1016/j.aquatox.2018.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Monitoring sublethal changes in fish physiology following exposure to a light, unweathered crude oil Sharon E. Hook1,*, Julie Mondon2, Andrew T Revill3, Paul A Greenfield4, Sarah A Stephenson1, Joanna Strzlecki5, Patricia Corbett2, Emily Armstrong1,2, Jing Song2,6,# , Hai Doan7, Skye Barrett8†

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1. CSIRO Oceans and Atmosphere, Lucas Heights, NSW 2234 Australia 2. School of Life and Environmental Sciences, Deakin University, Warrnambool, VIC 3280, Australia 3. CSIRO Oceans and Atmosphere, Hobart, TAS, 7000 Australia 4. CSIRO Oceans and Atmosphere, North Ryde, NSW, 2113, Australia 5. CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Centre, Crawley WA, 6009 Australia 6. Graduate School of Fisheries Science and Environmental Studies, Nagasaki University, Nagasaki, Japan 7. CSIRO Land and Water, Glen Osmond, SA, 5064, Australia 8. South Australian Research and Development Institute Aquatic Sciences, West Beach, SA, 5024 Australia

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* Corresponding Author, [email protected]; +61 2 9710 6839 (p)

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# current address: College of Oceanography, Hohai University, 1, Xikang Road, Gulou District, Najing City, Jiangsu Province, China, Phone: +86-185-0254-9493, E-mail: [email protected]

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† current address: Fisheries Research and Development Corporation (FRDC) Adelaide SA 5000, Australia

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Article Highlights: 1. 2. 3. 4.

Potential sublethal toxic effects need to be quickly identified after an oil spill. Potential markers of toxic impact were measured in an oil-exposed benthic finfish. PAH metabolites, histology and transcriptome, but not EROD, indicated oil exposure. The transcriptome and histological profiles indicate potential cardiac toxicity.

Abstract

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Biomarkers are frequently used to determine the exposure of fish to petroleum hydrocarbons following an oil spill. These biomarkers must be chosen carefully if they are to be used to determine sublethal toxic impacts as well as oil exposure. Many commonly used biomarkers relate to the metabolism of high molecular weight, typically pyrogenic, polycyclic aromatic hydrocarbons (PAHs), which are not abundant in unweathered crude oil. The goal of this study was to compare the efficacy of different biomarkers, including histological examination and transcriptomic profiling, in showing exposure to oil and the potential for sublethal toxic impacts. To achieve these goals, subadults/adults of the spotted dragonet (Repomucenus calcaratus) was exposed to a representative light, unweathered Australian oil for 96 h, so that the physiological changes that occur with exposure could be documented. Fish were then transferred to clean sediment for 90 h to quantify recovery. Biomarker changes, including PAH metabolites, 7-ethoxyresorufin O-deethylase (EROD), and histopathology, are presented in this work. In addition, a de novo transcriptome for the spotted dragonet was assembled, and differential transcript abundance was determined for the gill and liver of petroleum-exposed fish relative to a control. Increased levels of some biliary phenanthrene metabolites were seen throughout the exposure period. EROD levels showed modest, but not significant, increases. Transcriptomic differences were noted in the abundances of transcripts with a role in inflammation, primary metabolism and cardiac function. The patterns of transcript abundance in the gill and the liver changed in a manner that reflected exposure and recovery. The histology showed elevated prevalence of lesions, most notably vacuolization in liver and heart tissue, multi-organ necrosis, and lamellar epithelial lifting and telangiectasia in the gill. These findings suggest that short-term exposures to low molecular weight PAHs could elicit changes in the health of fish that are well predicted by the transcriptome. Furthermore, when light oil is released into the environment, exposure and subsequent risk would be better estimated using phenanthrene metabolite levels rather than EROD. This study also adds to the weight of evidence that exposure to low molecular weight PAHs may cause cardiac problems in fish. Further study is needed to determine the impact of these changes on reproductive capacity, long-term survival, and other population specific parameters.

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Keywords: oil spill response, RNA Seq, gene expression, biomarkers, histopathology, benthic finfish, cardiac toxicity, condensate

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1. Introduction

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Following an oil spill, there is a need to rapidly assess any impacts on commercially and economically important fisheries species (Law et al., 2011; Hook et al., 2016). Oil exposure is frequently measured via induction of xenobiotic metabolising enzymes, or directly via biliary metabolites (reviewed in Collier et al., 2013; Whyte et al., 2000), to put spatial and temporal boundaries on exposure to oil, from both spills and produced formation water. However, measurement of the induction of oil exposure biomarkers is not synonymous with ecologically relevant impacts of exposure to oil, such as changes in survival, growth or reproduction. While induction of cytochrome p450 1A and other enzymes that potentiate PAH toxicity are critical steps in the adverse outcome pathway for carcinogenesis and oxidative stress-mediated responses (Schlenk et al., 2008), patterns of induction of CYP 1A or its experimental orthologues have not been directly associated with increased incidence of disease (Lee and Anderson, 2005; Oris and Roberts, 2007). There may be compensatory mechanisms, such as DNA repair, that allow the organism to maintain homeostasis following oil exposure (Wirgin and Waldman, 1998). Furthermore, many of the deleterious consequences of oil exposure have a mode of action that does not require induction of CYP1A (such as altered cardiac capacity), or have an unknown mode(s) of action (such as reduced reproductive output or immune suppression) (Bravo et al., 2011; Incardona et al., 2005; Johnson et al., 2008). As a consequence, induction of CYP1A may not predict these endpoints well.

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In addition, CYP1A, and its surrogate EROD (Whyte et al., 2000), are most strongly induced following exposure to high molecular weight, typically pyrogenic PAH, such as benzo[a]pyrene and benzanthracene (Lee and Anderson, 2005), which have low water solubility and are typically found in heavy oils and combustion products (Volkman et al., 1994). Lower molecular weight compounds, typically thought of as petrogenic PAH, such as naphthalene and phenanthrene, are more water soluble and found in many types of oil, including low viscosity Australian oils (Volkman et al., 1994). The degree to which traditional biomarkers respond to light oils, especially if these oils are not weathered, is not known. Also, the implications for organism physiology following short exposures to these light oils has not been well characterised.

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Examining the transcriptome could provide greater insight into these long-term consequences for animal health following short exposures than the use of traditional biomarkers alone. The transcriptome may identify targets for monitoring that may be both more ecologically relevant and better suited to showing exposure to unweathered oils. Global gene expression, measured initially via microarrays (reviewed by Hook, 2010), and in recent years via RNASeq (Mehinto et al., 2012), identifies all of the transcripts in a given tissue that have altered abundance relative to unexposed individuals. Using the adverse outcome pathway concept (e.g., Ankley et al., 2010; Groh et al., 2015; Villeneuve et al., 2014), the changes in signalling indicated by the altered transcriptome following the molecular initiating event of petroleum exposure may predict subsequent altered physiology resulting in individual and population level effects (e.g., Hook et al., 2017; Webster et al., 2013). Recent studies have used global gene expression–based approaches including RNASeq, to evaluate the impacts of petroleum exposure in fish collected following the Deepwater Horizon wellhead blowout (Dubansky et al., 2013; Garcia et al., 2012; Whitehead et al., 2012). However, it is not certain to what extent transcriptomic changes could be used to accurately predict adverse outcomes. Our goal was to identify monitoring targets that could be used in the event of an oil spill to make predictions about the potential for sublethal toxic impacts of the petroleum hydrocarbons on resident fish. Current oil spill response monitoring guidelines (e.g. Law et al., 2011., Hook et al., 2016) advocate using oil exposure biomarkers, such as EROD, biliary metabolites, and CYP1A 3

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transcription levels, to put temporal and spatial boundaries on the spill impact area. To determine the best molecular predictor of sublethal toxicity following brief exposures to oil, the responses of a variety of endpoints to petroleum exposure were compared. The goals of this study were to determine how well traditional oil exposure biomarkers, biliary metabolites and EROD, indicate exposure to sediments containing unweathered light crude oil, as compared to transcriptomic changes and tissue damage, measured via histological examination. To that end, the spotted dragonet, Repomucenus calcaratus, a local benthic fish, was exposed to environmentally relevant concentrations of a light Australian crude oil via contaminated sediment. The spotted dragonet is endemic to St. Vincent Gulf (South Australia), but belongs to a genus that is widely distributed in southern Australia (A. Williams, CSIRO, pers. comm). This fish was selected because it burrows into sediments and fills an ecological niche similar to better studied flat fish such as sole, dab or flounder (e.g. Amara et al., 2007, Arellono et al., 2009, Le-Du Lacoste et al., 2013; Brown-Peterson et al., 2015, 2017). The sections below describe: i) how traditional biomarkers of exposure were induced, ii) how a reference transcriptome was developed; iii) how differential transcript abundance in tissues collected from the control or high dose at different times in uptake or recovery was determined, as well as iv) how the traditional biomarkers and the transcriptome related to the histopathological changes.

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2. Materials and Methods 2.1 Animal Collections

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All work with animals was conducted with the approval of the CSIRO (SA) Animal Ethics Committee (AEC), permit number 808.

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Spotted dragonet (Repomucenus calcaratus) were collected from St. Vincent Gulf, near Adelaide, South Australia via trawl from the RV Ngerin. Spotted dragonet were held at the aquaculture facility of the South Australian Research and Development Institute (SARDI) in West Beach, South Australia, in flow-through, 945 L tanks with sand substrate, and fed diced pipis (or Goolwa cockle, Plebidonax deltoides) ad libitum. The animals used in our study were either sub-adults or adults, with an average length of 11.3 + 1.0 cm, and an average mass of 9.2 + 2 g. The variability in size and our uncertainty as to their sexual maturity reflects the fact that these are wild-caught fish. The water quality was measured every 48 h, temperature was 20-22 ° C, salinity 37 ppt, dissolved oxygen was greater than 92%. These benthic fish were kept in the dark to reduce stress to the animals. The flow rates of the aquaria were 34.6 L per aquaria per day, or 0.025 L/min. Animals were monitored at least twice daily to ensure they were in good condition. During the experiment, one fish from the highest exposure level died for unexplained reasons, and two fish (one from each exposure level) were startled and jumped from their tanks, resulting in mortality, but otherwise no mortality occurred. 2.2 Animal exposures

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Beach sand was collected at North Haven and Henley Beach (near Adelaide, Australia) and large material was removed. Previous, unpublished work has shown that this area has low abundance of anthropogenic contaminants. The two oil-sediment concentration mixtures were prepared as follows: One kg of sand was placed into an amber jar, to which 19.2 mL of oil (Northwest Shelf 2, with a density of 0.81 g/L) was added to make up a 2 mg/kg oil treatment, and 4.8 mL of the same oil was used for the 0.5 mg/kg treatment. Jars were then filled with seawater to eliminate head space, sealed tightly and put on sediment rollers for a week. After seven days, the overlying water was poured off, which will reduce concentrations of most the water-soluble PAHs, and the sediment 4

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was mixed by hand with 12 kg of clean beach sand. Nominal concentrations of PAHs were at the low end of what has been reported in Louisiana coastal marshes following the Deepwater Horizon wellhead blow out (Turner et al., 2014), so are thought to represent environmentally realistic, concentrations. As the "spiked" sediment was darker in colour than clean sediment, it was apparent when the sediments were adequately homogenised. After homogenisation, 3 kg of sediment was placed in the bottom of each 25 L treatment tank. The tanks were then filled with clean seawater. Replicates for each treatment were designated by letter (A-E). Replicates for each treatment were distributed evenly such that the replicate letters were grouped together instead of treatments (e.g. control A, 0.5A, 2A, followed by control B, 0.5B, 2B, etc.) to avoid biasing the results with placement effects.

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The overall experimental design is as follows: Fish were exposed to spiked oil-contaminated and control sediments up to 96 hours, during which time tissues (to measure exposure uptake response) were harvested at 24 and 90 hours. All remaining fish were then gently captured and transferred at 96 hours to clean sediment. Tissues were then harvested from fish at 20 hours and 90 hours postsettlement in clean sediment, to measure recovery response.

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A schematic of the exposure design is shown in Figure 1. Four animals were assigned at random to each of five replicate tanks per concentration exposure treatments (3 treatments, 15 tanks total), and monitored to ensure that they settled into the sediment. Animals were fed periodically throughout the experiment, monitored twice daily for mortality, and monitored daily for water temperature and dissolved oxygen content. Animals were exposed to contaminated sediment up to a 96 h duration, which was chosen because of the limited persistence of light oil in beach sand, followed by transfer to clean sediment tanks, with a recovery duration of up to 90 hours (representing a combined total experimental duration of 180 hours). Sediments were collected at the beginning (t=0 h) and end (t=96 h) of the oil exposure period.

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At each exposure measurement time point across the entire experiment (uptake at 0 and 90 hours, and recovery at 120 and 180 hours), one animal from each tank was captured (five individuals per treatment), and euthanised with Aqui-S (synthetic clove oil) at 250 mg/L. Length and weight were measured (provided in Table S1), and tissues were harvested for subsequent analyses. A section of gill and liver was excised for RNA analysis, submerged in RNA later© (Ambion), stored at 4° C overnight, then transferred to -20°C. The gall bladder was harvested for analysis of PAH metabolite levels and frozen at -20°C in amber vials. A section of gill, liver, spleen, kidney, heart, muscle and skin were harvested for histological examination and preserved in 10% buffered formalin at room temperature. A final section of the liver was placed in a cryotube and submerged in liquid nitrogen, for EROD analysis.

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The highest dose and control exposures only were used for transcriptome profiling. The liver was chosen for transcriptomic analysis as it is the primary site of metabolism of PAH, and integrates uptake across the gills, skin and intestine (Whitehead et al., 2012). Gill was also utilised for transcriptome analysis as dragonet burrow in the sand and irrigate their gills with pore water, hence gills are expected to be in direct contact with oil contaminated sediment, and likely to have the greatest exposure to oil.

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Figure 1. A schematic of the exposure design, showing the measurement time points when each fish was euthanised and tissues harvested. The number of replicate boxes at each time period represent the remaining number of fish per tank at each measurement point of the experiment.

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2.3 Analysis of exposure sediment

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Sediment samples were spiked with a mix of deuterated PAH standards at known concentration and extracted three times with a one-phase dichloromethane–methanol–water mixture (1:2:0.8 v/v/v) according to a modified version of the method of Bligh and Dyer (1959). Samples were ultrasonicated for 10 min during each extraction, centrifuged and the supernatant extracts combined. After phase separation, the lipids were recovered in the lower organic layer and the solvent removed in vacuo. As we expected minimal background organic content in the beach sand and wanted to avoid losses due to evaporation, samples were analysed as total extracts. PAH concentration was determined by gas chromatography-mass spectrometry (GC/MS) using a ThermoScientific 1310 GC coupled with a TSQ triple quadrupole. Samples were injected using a Tripleplus RSH auto sampler with a non-polar HP-5 Ultra 2 bonded-phase column (50 m x 0.32 mm i.d. x 0.17 µm film thickness) used. The initial oven temperature of 45°C was held for 1 min, followed by temperature programming at 6°C per min to 180°C then at 3°C per min to 315°C where it was held for 15 min. Helium was used as the carrier gas. Mass spectrometer operating conditions were: electron impact energy 70 eV; emission current 250 µA, transfer line 310°C; source temperature 240°C; The instrument was operated in selected ion monitoring (SIM) mode to detect each compound group. Data were acquired and processed with Thermo Scientific XcaliburTM software (Waltham, MA, USA) and analytes quantified by comparison of peak areas with the relevant deuterated standard.

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2.4 Analysis of tissues

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2.4.1 Biliary metabolites

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Due to the small size of the gall bladders, it was not possible to remove discrete bile samples, so 100 L of methanol was added to the bladders which were then mashed to “extract” metabolites. These were centrifuged, the supernatant removed for analysis and the remainder dried to obtain dry weights.

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The quantitative method for analysing fish bile was adapted from Le Dû-Lacoste et al., 2013. Briefly, whole bile was thawed and homogenized in pre-cooled buffered water (acetate buffer at pH 5.0; approximately 100 L of bile in 2 mL of buffer) and then 20 μL of mercaptoethanol and 20 μL of βglucuronidase and arylsulfatase were added to the samples along with the surrogate, the standard (1-hydroxypyrene-d9).

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Samples were then hydrolysed at 37°C in an oven for 18 h followed by ultrasonication and then centrifuged (10 min, 5,000 rpm, 20°C). Supernatants were transferred to Strata X cartridges and washed with 2 mL buffered water and then 2 mL water/methanol (70:30 v/v). Traces of water were removed by applying vacuum to the cartridges for 45 min. Metabolites were then eluted with 5 mL of methanol. Extracts were reduced to dryness under a stream of high purity nitrogen and redissolved in 1 mL methanol/methylene chloride (20:80 v/v) before purification using solid-phase HF PSA Varian cartridges. Extracts were again reduced to dryness under a stream of high purity nitrogen and redissolved in 50 μL dichloromethane along with 30 μL of derivatising agent (BSTFA). The derivatisation was then completed by incubation at 65°C for 30 min. After derivatisation and before GC/ MS analysis, a solution of deuterated internal standard (pyrene-d10, 20 μL) was added to the sample for recovery determination. Sample extracts were analysed by GC/MS operating in selected ion monitoring (SIM) mode (Thermo scientific TSQ8000 with Trace 1310 GC). From a total volume of 200 µL, 0.5 µL was injected onto a 7

DB-5 column using a PTV injector at 45 °C which was balistically heated post injection to 305°C at 3°C/s. The GC oven was initially held at 70°C for 2 minutes and then ramped to 180 °C at 5°C /min, held for 1 minute and then to 290°C at 10°C /min with a final hold time of 1 min. The MS was operated in SIM mode with 1.53 scan/s and a dwell time of 50 ms for each ion. Target compounds were quantified using the ions reported by Mazeas and Budzinski (2005): 1-OHN (m/z 201), 2-OHN (m/z 216), 2-OHBi (m/z 211), 9-OHFe (m/z 165), 9-OHPhe isomers (m/z 266), 1-OHP (m/z 290), 1OHC (m/z 316), 3-OHBaP (m/z 340), 1-OHP-d9 (m/z 299) and pyr-d10 (m/z 212). 2.4.2 EROD activity

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2.4.2.1 Preparing microsomal protein fractions

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Microsomal protein was extracted from spotted dragonet liver samples according to the procedure of Lavado et al. (2004). Briefly, liver samples were weighed and then washed with cold 1.15% KCl before homogenisation. Samples were homogenised in 1:5 w/v cold 100 mM KH2PO4/K2HPO4 buffer (pH 7.4), comprising of 1 mM EDTA, 150 KCl and protein inhibitors including 1 mM dithiothreitol (DTT), 0.1 mM phenanthroline and 0.1 mg/mL trypsine inhibitor. Homogenates were centrifuged for 15 min at 1,500 ×g, the supernatant was separated from the pellet containing cell membranes and large organelles. The supernatant was centrifuged for 20 min at 12,000 ×g, and the resultant supernatant was removed, then centrifuged for 1 hr at 100,000 ×g. The resultant supernatant removed, and the microsomal pellet resuspended in a 1:10 w/v of microsomal buffer containing 100 mM KH2PO4/K2HPO4, 1 mM EDTA, 150 KCl, 20 % glycerol and protein inhibitors including 1 mM dithiothreitol (DTT), 0.1 mM phenanthroline and 0.01 mg/mL trypsine inhibitor. Protein concentration was established by the Bradford (1976) method, using bovine serum albumin (BSA) as the standard.

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2.4.2.2 EROD activity

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2.4.5 Histology

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EROD activity was determined as previously reported by Lavado et al. (2006). Essentially, liver microsomal protein was incubated at 28˚C for 10 min in a final volume of 500 µL containing 100 mM KH2PO4/K2HPO4 buffer (pH 7.4), 5 mM NADPH and 50 µM 7-ethoxyresorufin. The reaction was stopped by adding ice cold methanol and the samples then centrifuged for 10 min at 900 ×g. Fluorescence was measured using SpectraMax M3 spectofluorimeter at 537/583 nm excitation/emission wavelengths.

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Histological preparation and evaluation was based on the procedure previously reported by (Mondon et al., 2001). Gill tissue comprising the second branchial arch (gill arch) was fixed in 10% buffered formalin, followed by liver, kidney, spleen, skin and attached muscle, and heart tissue. Sections of delicate tissue or very small tissue samples, e.g., gill and kidney, were placed between biopsy pads inside histology cassettes for fixation. Fixed specimens were dehydrated in ascending grades of ethanol (30-100%), and cleared in Histolene® using a Leica ASP 300S vaccum infiltration processor and embedded in paraffin. Embedded tissue was sectioned to 4 µm using a HM 325 Micron microtome, mounted on glass slides and stained using standard haematoxylin and eosin (H & E) protocol. Single sections from each tissue block were examined to ensure independence of data. Sections were viewed blind using a Zeiss AxioPlan microscope at 100 - 400 x magnification and images captured using AxioCam ERc 5s image capture software. Changes in gill lamellae were recorded from 2 complete gill filament sections per fish. Pathologies examined comprised circulatory disturbance (including haemorrhage and thrombus), regressive changes (including decrease in cell number and necrosis), progressive changes (including increase in cell number such as hyperplasia 8

and proliferation), tumour formation and metaplasia, and parasites. All pathologies were enumerated as present /absent per organ per fish and expressed as percentage of fish affected (prevalence) per treatment. 2.5 Statistical analysis

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All statistical analyses were carried out in SigmaPlot version 12.5. For the metabolite analysis, where data were normally distributed, significant differences were determined by ANOVA followed by Holm Sidak comparison. EROD data were log transformed when the assumption of normality could not be met, as determined via a Shapiro-Wilks test for normality. As the assumption of normality still could not be met, non-parametric tests (Kruskal-Wallis One Way Analysis of Variance on Ranks, followed by Dunn’s methods for pairwise comparisons) were used. 2.7 RNA Extractions

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RNA was extracted from gill and liver tissue, preserved in RNAlater at the time of harvest, using previously described methods (Hook et al., 2017). Briefly, approximately 10 mg of preserved tissue was submerged in TRIzol (Invitrogen), homogenised using the MP Biomedicals bead beater and lysing matrix D. Liver tissue was homogenised at maximum speed (6.5) for one minute, gill was homogenised at minimum speed (4.0) for twenty seconds. RNA was then extracted following the TRIzol protocol through the removal of the aqueous phase. RNA in the aqueous phase was further purified using the Qiagen RNeasy kit. RNA was checked for quantity and purity using Thermofisher’s nanodrop (using a minimum 260/280 ratio of 2.0) and for integrity using Agilent’s bioanalyser (minimum RIN of 8).

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2.8 Sequencing

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Sequencing was performed at the Ramaciotti Centre for gene function analysis. Libraries were prepared using 1 μg RNA following Illumina’s protocols. Two flow cells of HiSeq2500 100bp SR Rapid run v2 were run following the manufacturer’s protocols. The sequencing data (reads) were analysed using the workflow shown in Figure 2. Sequencing data was deposited into the NCBI SRA database and have been assigned accession numbers SAMN06806247 - SAMN06806304 under bioproject PRJNA383750.

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2.9 Transcriptome assembly

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The reads were quality trimmed with the Trimmomatic program (Bolger et al., 2014) v0.36 using the parameters HEADCROP:3, TRAILING:3, SLIDINGWINDOW:4:15 and MINLEN:120. A transcriptome composed of both liver and gill was synthesized. Reads were then assembled using the Trinity software package (version r2014-04-13) as described in (Grabherr et al., 2011; Haas et al., 2013). The assembly was run three times using a minimum Kmer coverage of 1, 2, and 5 in different iterations to optimise the number of unique contigs (overlapping reads, putative transcripts) while reducing redundancy. Each transcriptome was clustered using CD-Hit (Fu et al., 2012) at 98% similarity at the nucleotide level to further reduce the redundancy. Whether the transcriptome was complete was checked by BLAST against the medaka CEGMA orthologues (Parra et al., 2007) and by BUSCO analysis (version 2.0 beta 3, with comparison to lineage dataset is: actinopterygii_odb9 (Creation date: 2016-11-18, number of species: 20, number of BUSCOs: 4584) (Simão et al., 2015). 2.10 Read mapping Read mapping was performed using Trinity’s RSEM program (Grabherr et al., 2011; Haas et al., 2013). The percentage of reads mapped for each library is between 50-70%, and is presented in Table S2. All subsequent analysis was performed in CLC Genomics workbench (version 9.0.1). 9

Fragments per kilobyte mapped (FPKM) were used to normalise for differences in read number between libraries and in transcript length. To determine differential transcript abundance, read mappings from exposed fish for either liver or gill were compared to the time matched control for the same tissue. Data were filtered to remove instances where the median read count in all replicates was not at least 0, and the average FPKM was above 5. Differential abundance was determined using the EDGE-R algorithm (Zhou et al., 2014), with a P<0.05 and a fold change greater than 2. An FDR corrected p value was determined for each transcript and is reported, but it is not used in differential abundance determinations as that left too few targets to analyse functional pathways.

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2.11 Functional analysis

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Functional analysis was performed using the BLAST2GO program (Conesa et al., 2005) as provided by the CSIRO bioinformatics core. Once the contigs had GO ontology terms assigned (Huang et al., 2009A), these were loaded as an annotation into CLC Genomics workbench (version 9.0.1). Differentially abundant GO terms were calculated by Hypergeometric test against annotations, where the contigs with differential abundance (p<0.05) for each treatment relative to the time matched control, were compared to all contigs “present” (as explained above) in a given tissue at that time period. Contigs were further annotated BLASTx against the Uni Prot - SwissProt database, via BLAST2GO. Clustering of the functional annotation for the differentially abundant contig lists were also performed in DAVID to identify the most relevant altered functions (Huang et al., 2009).

Figure 2. An overview of the work flow used in analysing the transcriptomic data 3. Results 3.1 Chemical exposure The oil used, NW-2, is typical of Australian Northwest shelf oils in that it is very light (Qi et al., 2011). At 20° C, the density of the oil was 0.81 g/L, and the viscosity is 3.45 C/cP (Qi et al., 2011). A list of 10

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the PAH detected in this oil is provided in Supplemental Table S3. The oil has a relatively high proportion of alkylated homologues and PAH heavier than the phenanthrenes/dibenzothiophenes were not present above the limits of reporting (Table S3). The concentration of PAHs in the sediment, before and after exposure, as well as in the source oil, is shown in Figure 3. Our nominal controls, 0.5 mg/kg, and 2 mg/kg equated to measured PAH concentrations at the start of exposure of 10, 165, and 519 μg/kg, respectively. The exact PAH concentrations measured are also provided in Supplemental Table S3. There were trace amounts of naphthalene and phenanthrene in the control sediment both before and after exposure. This may be laboratory-based contamination, or may result from the sediment having been collected in an urban environment. There were a lower proportion of naphthalenes, the most water soluble of the PAHs, relative to the proportion in NWS-2 oil, in the exposure sediments at both the beginning and end of exposure (Figure 3). These compounds would have been discarded when the overlying water was poured off, as discussed in Section 2.2, and would have dissolved in the water column and been lost to flow through in the tanks. These water-soluble PAHs may rapidly dissipate during the weathering process in the environment as well (reviewed in Hook et al., 2016). As shown in Figure 3, oil was lost rapidly from the tanks during the four day exposures, suggesting that longer duration experiments would not have resulted in greater exposure to PAHs. The concentrations of PAHs decreased during the exposure period for both treatment sediments, likely resulting from desorption of PAHs from the sand as it was bioturbated by the fish, by uptake and metabolism of the material by the fish, sorption of PAH to the glass walls of the tank and by microbial degradation. A greater proportion of PAHs was lost from the higher concentration for unknown reasons.

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Figure 3. PAH composition measured in sediments or in the source oil. Nominal concentrations are presented in the figures, the initial measured concentrations in sediment were 10, 165, and 519 μg/kg respectively. N = naphthalene, F = fluorene, P = phenanthrene, DBT = dibenzofluorene. C1C4 are the alkyl congeners for each PAH. The exact composition is provided in Supplemental Table S3. Compounds heavier than DBT were below detection in both the source oil and the sediment. 3.2 Biliary metabolites

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The naphthalene, fluorene and phenanthrene metabolites detected using GC/MS is shown in Figure 4. Although there is a great deal of variability, both between different individuals and at different experimental time periods, PAH metabolites were often elevated in exposed fish relative to control during the exposure time periods. At 24 hours, fish from both crude oil treatments have elevated 2hydroxynapthalene metabolites, and the 165 μg/kg initial dose has elevated 2-hydroxyphenanthrene and 3-hydroxyphenanthrene metabolites. The 9 hydroxyfluorene metabolites in the exposed fish were measured at higher concentrations than in 4 of the 5 control fish, but these changes were not significant at 24 h (p=0.07). At 90 hours, after most of the naphthalenes had been lost from the sediment, the 2-hydroxyphenanthrene and 3-hydroxyphenanthrene metabolites are elevated in 519 μg/kg initial exposed fish and in 165 and 519 μg/kg exposed fish, respectively. 2hydroxynaphthalene and 9-hydoxyfluorene are not elevated in the fish bile, and the naphthalenes and fluorenes have been lost from the sediment (Figures 4 and 3, respectively).

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IP T SC R U N A M ED PT CC E A Figure 4. Metabolite level as determined via GC/MS. The dashed line indicates when exposures ended and animals were transferred to clean sediment for depuration and recovery. The legend shows the total PAH concentration initially measured in the sediment, as μg/kg. The data are plotted as individual points, with all replicates presented, to show the variability within 14

treatments. The asterix denotes a significant difference from controls (ANOVA, P < 0.05, followed by Holm-Sidak pairwise comparisons).

3.4 EROD

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The activity levels of hepatic EROD are shown in Figure 5. There was a high level of variability in control EROD levels, such that the trends in elevation in exposed treatments were obscured and not statistically significant.

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Figure 5. Activity of hepatic EROD in fish from the different exposure treatments with time. The legend shows the total PAH concentration measured in the sediment at time =0, as μg/kg. The dashed line indicates where exposure ended and fish were transferred to clean sediment. Because of the high degree of inter-individual variability, each data point is shown with data offset to show each treatment clearly. There were no significant differences from control.

3.5 Assembly and QA of the spotted dragonet transcriptome The sequencing efforts produced 677 billion reads, >98% of which were suitable to use in the assembly. Three iterations of the Trinity algorithm were performed to obtain an assembly that was complete without containing too many redundant contigs: one with the assembly parameter “kmermin =1”; one with the assembly parameter “kmermin =2”, one with the assembly parameter 15

“kmermin = 5”. All assemblies were clustered to 98% to further reduce redundancy. The assemblies were QAed as described in (Hook et al., 2014b). Supplemental Table S4 shows descriptive parameters for the three different assemblies. Since the “kmermin = 2” assembly had the highest proportion of orthologues in the Uniprot-Swissprot database, and had good coverage as determined in BUSCO (Simão et al., 2015), it was used in all subsequent analyses. Two libraries were generated for each treatment and tissue, with each library containing roughly 500,000 reads. The trinity RSEM algorithm was used to map reads from the each replicate, treatment, and time point. Approximately 55-60% of reads could be mapped to the assembly. 3.6 Differential transcript abundance

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The change is transcript abundance with exposure to crude oil in the gill, compared to time-matched controls, is shown in Supplemental Figure S1A and in the liver, also compared to time matched controls in Supplemental Figure S1B. As shown, the abundance of most transcripts did not change significantly, however, at each time point, there were a few transcripts with altered abundance. The most statistically significant changes in transcript abundance occurred in the liver during the two uptake time points. In the gill, the recovery time points had fewer transcripts with altered abundances, although the magnitude and significance of these changes was equivalent. The transcript annotations provide insight into the physiological response of the fish to light crude oil.

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Abundances of representative transcripts with known function are shown in Table 1 for illustrative purposes, and for clarity, gene abbreviations discussed in this section are provided in the table caption. In the liver, in the first 24 h following exposure, transcripts related to metabolism of PAHs had altered abundance (molecular function of hydrolase activity and glutathione transferase activity) (Table 1). The CYP1-encoding transcripts (active in phase 1 metabolism of PAHs) were modestly increased in abundance, while the GST-encoding transcripts (active in Phase 2 metabolism of PAHs) had much greater and statistically significant relative increases (Table 1). Transcripts associated with cardiac function, metabolism, fatty acid metabolism, immune response, and laminin signalling (cell surface signalling) also had altered abundances. Some of the transcripts that exemplify these categories include transcripts for the cardiac isoforms of TM, CKM, TnT, and ACTA; transcripts for the sugar catabolising enzymes PGM, ALDOA, and GYS2; as well as transcripts encoding the lipid metabolising enzymes MOGAT1, APOEB, ALDH3A2 (Table 1). At the end of the exposure (90 h), transcripts with altered abundances in the liver relative to their time-matched controls either had roles in translation, immune response, lipid metabolism, and primary metabolism; or functions aligned to ion channels and cardiac cell apoptosis. For example, transcripts encoding metabolic enzymes such as GYS2, AMY1A, PEP and MIOX, and lipid-metabolising transcripts MOGAT1 and ALDH3A2 had greater abundances in the liver at 90 h into exposure, whereas the immune-response transcripts C3 and B2M had decreased abundance, as do the transcripts encoding ribosomal constituents RPL27 an RPS11. Transcripts encoding traditional biomarkers were no longer enriched at 90-h exposure in the liver (Table 1). At the beginning of the recovery period, transcripts in the liver are involved in ossification, ion and ammonium transport, collagen catabolism, glycolysis, and well as heme binding, insulin cycling and oxidoreductase activity. Transcripts for GYS2 and AMY1A were substantially upregulated, as were GPX-encoding transcripts (Table 1). GST-encoding transcripts, however, had decreased abundance. Connective tissue transcripts encoding CK and ACTA had decreased abundance, as did those encoding the oxygen transport/ blood proteins HBA2 and CA-IV. Ninety h into the recovery period, transcript levels in the livers of exposed fish were still altered relative to their time-matched controls. Transcripts with roles in glycolysis, protein polymerisation, collagen catabolism, cell growth regulation and cytoskeleton had altered abundance. These trends are exemplified by the transcripts encoding CK, ACTA, CLDN and FN, for 16

example, all of which were sharply upregulated, as well as increases in transcripts encoding ALDOA and AMY1A. As shown in Table 1, protein degradation and immune response transcripts had decreased abundance.

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In the gill, transcripts that encode CYP1A, EPHX1, and UDPGT had greater relative abundances in the gills of exposed fish than in the liver (Table 1). The transcripts encoding GST isoforms, however, were not increased in abundance. Twenty-four h into the exposure, transcripts associated with antigen response, altered lipid and lipoprotein processing, translation, carbohydrate metabolism, oxygen transport and oxidoreductase activity had altered abundances. These trends are demonstrated in Table 1 by the increased abundances of immune-related transcripts (those encoding Ig, and MHC-I), decreased abundances of the transcripts encoding ribosomal components RPL3 and RPL27 (the transcripts are associated lipid metabolising genes encoding PAP2B and CH24H). At a 90 h exposure, the transcripts with different abundances relative to time matched controls included those involeved in cell surface signalling, oxidoreductase activity, and translation. For example, transcripts encoding SOD and HSP90 isoforms were some of the oxidative stress response related transcripts more abundant in the gill at 90 h, and many of the immune-responsive transcripts were more abundant as well (Table 1).

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Gene Ontology (GO) categories (Ashburner et al., 2000) were also used to assess differentially abundant transcript function. Transcripts encoding the traditional oil biomarker categories of xenobiotic metabolism and oxidative stress response were also assessed. The gene ontology terms to which the differentially abundant transcripts could be aligned provide some indication as to the physiological processes altered in each tissue during exposure to light crude oil, as well as recovery from this exposure. The results of the tests for GO category enrichment are presented in full in Supplemental Table S5, but are summarised in Table 2. In the liver, the functional categories associated with differentially abundant transcripts show a progression from xenobiotic metabolism and response to cellular damage, to repair of cellular damage (Table 2). The transcripts encoding ribosomal proteins are altered in both directions, however. At 90-h recovery, the transcripts with altered abundances were involved in regulation of transcription, ion transport, translation, stress response and protein metabolism. These included decreased abundance of transcripts encoding the ion transporters RHCG and VDAC2, for example, and decreases in some of the ribosomal proteins, as shown in Table 2.

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Pathway analysis was also conducted to determine the physiological changes as the oil exposure progresses but sediment concentrations decrease (from U24 to U90 hours), as the fish are transferred from oiled to clean sediment and recovery begins (U90 to R20 hours), and as recovery progresses (from R20 to R90 hours). As shown in Table 3, many of the same pathways and roles that were highlighted in the time-matched control comparisons were also identified in the comparison of different sampling times. For example, xenobiotic metabolism pathways were active in the initial exposures in the liver, whereas oxidoreductase activity becomes more important in the liver during recovery, but is active throughout the experiment in the gill (Table 3). Inflammation/ immune response related transcripts are dynamic in both tissues throughout the experiment. Transcripts for energetic responses are still more common in the liver than the gill (Table 3).

17

Table 2. Selected gene ontology terms, where comparisons are between oil exposed fish and their time matched controls. Numbers provided are the numbers in the gene list above what would be expected due to chance, all values are significantly enriched. Full results are provided in Table S5.

Ion transport

Inflammation/ immune response

ED

oxygen transport

PT

Haemoglobin activity Membrane activity Apoptosis/ altered cell cycle Collagen catabolism

A

Energetic Compensation

CC E

Repair of damage

Compensation for damage

Stress Response

Translation

Transcription glycolysis

Carbohydrate metabolism/ insulin regulation Protein regulation Lipid metabolism ATP synthesis

GO:0006412 GO:0030529 GO:0010628 GO:0008184 GO:0006096 GO:0005975 GO:0004365 GO:0050113 GO:0005520 GO:0016310 GO:0005319 GO:0006839

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11 2 2 3 4 3 3

2

2

2

4 6

2

5 2

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Xenobiotic metabolism Altered cardiac function

Gill Exposure Recovery 24 90H 90 H H 7 9 2

4 3

3

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Response to Damage

oxidoreductase activity

GO:0055114 GO:0016491 GO:0016624 GO:0006979 GO:0004602 GO:0016787 GO:0004364 GO:0002026 GO:0005861 GO:0006813 GO:0006814 GO:0006816 GO:0002474 GO:0042605 GO:0006955 GO:0019882 GO:0006950 GO:0009408 GO:0015671 GO:0019825 GO:0020037 GO:0005833 GO:0043236 GO:0000086 GO:0010659 GO:0030574

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Function

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Role

Liver Exposure Recovery 24 90 20 90 H H H H

2 7 4

3 3 2

3 2

2 5 6 2 2

2 4 4

2 2 6 9 5

2 3 4 2 2

2

5 4

9 6

3 2 3

3

7 9

5 4

5 2 5

9 4

2 2 3 3 2

4 22 4 3

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Table 3. Selected gene ontology terms, where comparisons are between exposed fish at different exposure time periods. Numbers provided are the numbers in the gene list above what would be expected due to chance, all values are significantly enriched. Full results are provided in Table S6.

Membrane activity

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oxygen transport Haemoglobin

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5 32

5 6 2 2 3 4 5 8

4 7 4 6

5 5 8 3

GO:0010628 GO:0006096 GO:0050113 GO:0052834 GO:0006002 GO:0008233 Protein regulation GO:0006508 GO:0005319 Lipid metabolism GO:0006869 ATP synthesis GO:0005524

5 15 13 9 10 13 10 6 5

U90-R90 7

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5 6 5 5

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Apoptosis/ altered cell cycle Collagen catabolism Transcription glycolysis Carbohydrate metabolism/ insulin regulation

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Repair of damage

Compensation for damage

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Gill U24-U90 7 8 3

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oxidoreductase activity

GO Term GO:0016491 GO:0055114 GO:0016712 GO:0016787 GO:0004364 GO:0006937 GO:0060048 GO:0005509 GO:0034220 GO:0005506 GO:0008519 GO:0006814 GO:0006813 GO:0042605 GO:0002474 GO:0019882 GO:0006955 GO:0019825 GO:0015671 GO:0020037 GO:0005215 GO:0055085 GO:0005391 GO:0006919 GO:0008630 GO:0008544

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2 2 18

9 13

9 11 36

3.6 Alignment to DAVID functional pathways To better understand the biological pathways that are being altered, differentially abundant transcripts with an orthologue in the Uniprot-SwissProt database were further functionally 19

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annotated and clustered in DAVID (Huang et al., 2009). The full results are provided in Supplemental Table S7, but a summary of these results are presented in Table 4 below. Many of the trends observed are similar to what was observed for the hypergeometric enrichment of GO terms. For instance, the progression of molecular responses in the liver following crude oil exposure proceeds from protein degradation during exposure to rebuilding cellular material in recovery, while the gill showed inflammatory responses and increased protein stabilisation during exposure and decreased cellular adhesion during recovery. To briefly summarise the trends, in the liver after 24-h uptake, the most enriched functional clusters related to the cytoskeleton (e.g., actin binding, and cardiac troponin) or immune responses for transcripts with increased abundance, whereas the functional annotation clusters most enriched for transcripts with decreased abundance in the liver after a 24-h exposure included those involved in heme synthesis and proteolytic activity. After 90-h uptake, there were fewer enriched categories. The transcripts with increased abundance included those involved in primary metabolism and reproduction, whereas sterol biosynthesis and keratin production were some of the significant functional cluster amongst the transcripts with decreased abundance. During 20-h recovery, transcripts that clustered into the functional categories of heme synthesis and glutamate metabolism pathways had increased abundance, whereas those associated with the cytoskeleton, ammonia transport and heparin binding had decreased abundances. At the end of recovery, the transcripts with increased abundance clustered into categories including transcription, cytoskeleton, and ATP synthesis, whereas those with decreased abundances clustered into the functional categories reproduction and heme binding. As with the GO comparisons, DAVID analyses were also performed to determine the differences in the transcriptomes of exposed fish as the oil exposure progresses but sediment concentrations decrease (from U24 to U90 hours), as the fish are transferred from oiled to clean sediment and recovery begins (U90 to R20 hours), and as recovery progresses (from R20 to R90 hours). As shown in Table 5, although there were fewer annotation clusters identified, many of the same functions as had been identified in the time matched controls were altered. For instance, immunity, protein degradation, and membrane stability were still among the enriched clusters.

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Different functional annotations were abundant in the gill. After a 24-h exposure, the functional annotation most enriched in the transcripts with increased abundance included troponin, whereas transcripts associated with calcium binding were less abundant. After a 90-h exposure, the transcripts with increased abundance were most often associated with the ribosome and electron transport, whereas those with decreased in abundance included transcripts associated with transcription. Ninety h after the fish were transferred to clean sediment, the functional clusters with increased transcript abundance included those involved in ATP synthesis and MHC I transcripts, whereas spectrin-related transcripts had decreased abundance. The analysis of changes in the transcriptome with time, as described above, identified far fewer enriched annotation clusters. However, this analysis showed many of the same changes, as shown in Table 5. For example, transcripts involved in protein degradation were abundant in the liver, and immune related transcripts were abundant in the gill.

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Table 4. Summary of the DAVID functional annotation clusters. Only those clusters with an enrichment score greater than 2 are presented. The enrichment scores are presented, and the arrows indicate whether the transcripts were increased or decreased in abundance. Function

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Translation

Cluster Ribosomal protein Elongation factor ATP-binding Electron transport guanido kinase ~NAD+ ADP-ribosyltransferase activity MHC I Scavenger receptor activity Immunoglobulin Thyroglobulin Membrane attack complex component/perforin (MACPF) domain Complement pathway Histidine catabolic process Threonine protease Protease inhibitor Trypsin Inhibitor Peptidase S1 Phenylalanine catabolism Serine protease inhibitor Tubulin Myosin II complex Tetraspanin Actin, conserved site Troponin Intermediate filament Spectrin

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Cytoskeleton

U24

U90

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R90

U24

Gills U90 ↑6.65

R90

↑5.06

↑2.48

↑2.81 ↑6.26 ↑4.88 ↑4.36 ↑4.11 ↑2.67 ↑2.84 ↑2.42

↑2.66

↑5.47 ↓2.28 ↓2.52 ↓2.89 ↑3.79 ↓4.29 ↑2.28

↑2.98 ↑2.29 ↓2.72 ↓3.88

↑2.34 ↑2.16

↓2.53

↓4.56

↑3.47 ↑3.20 ↑2.60 ↑3.37 ↑2.13

↓2.97 ↓3.65

↑2.11 ↑2.86

↓2.34 21

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Primary metabolism

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Folate metabolism Cell connectivity/ cell surface

↑2.29 ↓2.12 ↑2.81 ↓2.12 ↑2.70 ↑2.54 ↓2.29 ↓4.02 ↓6.85 ↓2.89

↓3.43 ↓2.29 ↓2.28 ↑2.45 ↑2.73

↓7.56 ↓6.00 ↓2.61

↓3.31

↓2.70 ↓4.02 ↓2.69

↑2.61

↑2.32

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↑3.32

↑2.24

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Heme synthesis

↓3.88 ↑2.60

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Calcium ion

Plectin repeat Glutamate metabolic process Transketolase UDP-glucose metabolic Calcium-dependent membrane targeting Calcium-binding GTP binding Zinc finger mRNA modification regulation of gene expression Zona pellucida domain Vitellogen Sterol biosynthesis Blood coagulation Fibronectin Pyridoxal phosphate Heme –binding Monooxygenase Serine hydroxymethyltransferase Keratin Ammonium transmembrane transport Chaperone proteins ATPase

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Table 5. Summary of the DAVID functional annotation clusters in transcripts that changed abundance between different exposure times. Only those clusters with an enrichment score greater than 2 are presented, with the exception of U90-R20 in the liver, where no clusters had an enrichment score greater than 2. Full results are available in Supplemental Table S8.

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Reproduction Membrane stability

U24-U90

Liver U90-R20 1.66

Gill R20-R90

U24-U90

U90-R90

2.18 2.51 2.85 6.43 3.38 2.33 2.28 2.59 2.09 8.01 2.59 3.72 3.16

2.03

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Heme synthesis Unknown/various

Cluster Immunity Immunoglobulin Peptidase S1 Serine protease Actin Troponin Spectrin Zona pellucida domain Ion-membrane transport Glycoprotein Cell-cell adhesion Heme –binding Oxidoreductase activity Muscle protein

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3.7 Histology

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Light crude oil exposure was associated with an increased prevalence of histological alterations in all tissues examined. Gill, liver, spleen and kidney exhibited the highest number of alterations overall (Supplemental Table S9), with the highest prevalence of histological alteration in individuals in the 165 and 519 μg/kg crude oil dose exposures (Figure 6; Supplemental Figure S2). Tissue alteration was evident beyond the cessation of exposure. Following transfer to clean sediments in the recovery treatments (R20 and R90), fish continued to exhibit a trend towards higher prevalence of tissue alterations, particularly in the gill, liver, heart, kidney and skin (Figure 6; Supplemental Figure S2).

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The histological alterations observed in this study are summarized in Supplemental Table S9 and Supplemental Figures S2, S3. Representative examples of identified histopathology are displayed in Figures 7 and 8. Representative images of reference healthy tissue are presented in Supplemental Figure S5. Most frequently identified changes included degenerative tissue loss in the form of necrosis and lipid accumulation, circulatory disruption in the form of bleeding and oedema, proliferative change in the form of increase in relative cell number and size, inflammation in the form of lymphocyte infiltration and melanomacrophage aggregates or centres (MMCs), and parasitism.

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Lesions associated with degenerative change, included multi-organ focal necrosis (indicated by discrete patches of necrotic tissue containing dead cells or dead cell debris) (Figure 7 A, C, D, F, H). Evidence of minor inflammation was present, mostly associated with necrosis (Figures 7 D, F; Figure 8B), and to a lesser extent with helminth parasitism and associated granuloma (Supplemental Figure S2). Multi-focal melanomacrophage aggregates (indicated by multiple clusters of pigmented phagocytic cells associated with removal of infected or damaged cells) were evident across multiple organs (Figure 7 A, B, E, F, G; Figure 8 B). Degenerative change also included focal partial ecdysis of the epidermal skin layer (Figure 8 E). Circulatory disruption was evident in isolated focal haemorrhage (localised bleeding), and secondary lamellar aneurysm in the form of telangiectasia (indicated by excessive dilation or bulging of the blood vessel in the gill lamellae), resulting in a highly enlarged encapsulated ball of blood cells at the lamellae apex) (Figure 8C). Secondary lamellae oedema was also evident, indicated by an abnormal build up, or trapping of fluids between the external epithelial cell layer of the gill lamellae and the sub-epithelial tissue layers beneath (Figure 8D). Proliferative alterations were also present in the form of hypertrophy (increase in size of cells) and hyperplasia (increase in number of cells) of the gill epithelium (Figure 8C), and the mucous cells in the epidermal layer of the skin (Figure 8F). Evidence of minor inflammation was present, mostly associated with necrosis (Figures 7 F, 8B), and to a lesser extent with helminth parasitism and associated granuloma (Supplemental Figure S5).

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Figure 6. Mean prevalence of histological alterations in spotted dragonet with treatment (light oil exposure; measured initial PAH concentrations as μg/kg) and time (h) per organ: U24 and U90 represent uptake exposure duration (hours); R20 and R90 represent recovery duration (hours).

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Figure 7. Representative histological lesions recorded for kidney, liver, spleen and heart in light oil exposed spotted dragonet: A. Kidney - showing moderate tubule vacuolization (arrow) and necrotic degenerative loss (dashed arrow), and a melanomacrophage centre (arrow head); B. Kidney showing haematopoietic tissue containing multiple small melanomacrophage centres (arrow), and vacuolation of tubule cells (dashed arrow); C. Liver - distal lobe section exhibiting low to moderate steatosis (arrow), hepatic parenchyma degeneration (dashed arrow) and degeneration of acinar cells in exocrine pancreatic tissue (arrow head); D. Liver- distal lobe section exhibiting inflammation 26

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(arrow), vacuolization (dashed arrow) and liquefactive necrosis (arrow head) indicating disintegration of cellular tissue to a liquid; E. Spleen - parenchyma showing multiple melanomacrophage centres, low level degenerative necrosis (arrow) and vacuolization (dashed arrow); F. Spleen - parenchyma showing individual melanomacrophage cells (arrow), inflammatory response and necrotic tissue (dashed arrow); G. Heart - cardiac ventricle showing myocardial tissue with melanomacrophage cells (arrow) and degenerative tissue associated with moderate level vacuolization (dashed arrow); H. heart - myocardial ventricular tissue with localised degenerative necrotic tissue (arrow).

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Figure 8. Representative histological lesions recorded for muscle, gill and skin in light oil exposed spotted dragonet: A. Skeletal muscle - transverse section showing melanomacrophage cells within muscle fibre bundles (arrow); B. Skeletal muscle - longitudinal section showing necrotic degenerative loss and inflammatory response in muscle fibre bundles (arrow); C. Gill - sagittal section across two exposed filaments showing telangiectasia (arrow), partial epithelial lifting (dashed arrow) and partial epithelial cell proliferation (arrow head); D. Gill - sagittal section across a filament, indicating dispersed oedema of the lacura (capillary lumen) intermittently along secondary lamellae (arrow); E. Skin - transverse section showing the epidermal layer comprising predominantly mucous and large goblet mucous cells (arrow), and expelled mucous (dashed arrow). A section of partial ecdysis is 27

visible (star) where epidermal tissue loss is evident; F. Skin - transverse skin section showing proliferation of enlarged elongated goblet mucous cells, with one section of epidermis almost at the point of full section thickness loss to the basal layer due to mucous cell disintegration (arrow).

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The strongest histological trends indicating an oil exposure response in this test species relate to fatty steatosis and vacuolization, predominantly in liver and heart tissue (Figures 7 C, G,H; Supplemental Table 9; Supplemental Figure S3 A, B), in addition to lamellar epithelial lifting and telangiectasia in gill lamellae (Figure 8 C, D; Supplemental Figure S3 C, D). Some alterations, including lipid or glycogen storage in the liver, were noted in every treatment and time period but exhibited elevated prevalence in oil exposed animals. Others, such as liquefaction necrosis present in heart, kidney, liver and spleen, were only observed in oil exposed animals (Supplemental Table S9).

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Fatty necrosis in this evaluation describes the occurrence of circular vacuoles of fat which have displaced and compressed the nucleus of the cell, and displaced or compressed surrounding cells, representative of a degenerative lesion (e.g. Figure 7 C). The classification of lipid or glycogen accumulation is used to differentiate between the characteristic circular fatty-laden cell, with elevated storage of lipid or glycogen producing what appears as clear vacuolated cells that continue to retain the overall cell shape; forming a ‘lacy’ rather than a ‘Swiss cheese’ architecture. Fatty metamorphosis represents reversible but abnormal accumulation of intracellular fat leading to disruption in cell function or potential cell loss. The majority of fish exhibited lipid or glycogen accumulation in the liver, irrespective of treatment, indicating they had been well fed prior to the crude oil dose exposure. Specific fatty necrosis occurred in fish after oil exposure and the highest prevalence occurred in fish exposed to the highest dosage treatments (Supplemental Table S9). Fatty necrosis was predominantly present in liver, and to a lesser extent in heart, kidney and spleen. Elevated prevalence in liver specifically indicates an increasing trend from the higher dose U90 treatment through to the higher dose R20 and R90 individuals relative to all controls and lower dose U24 treatment individuals (Supplemental Figures S3 A, B).

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Necrosis was detected in isolated foci; sometimes, but not always, associated with an inflammatory response (Supplemental Table S9; Supplemental Figure S3 A, F). Cell death associated with liquefaction and caseous type necrosis, was present at higher prevalence in fish exposed to oil treatments, with a trend towards higher occurrence in higher dose concentration and longer depuration treatments (Supplemental Table S9).

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Epithelial lifting caused by oedema in gill lamellae occurred in all exposure treatments, plus one control, with the exception of R20 treatments (Supplemental Table S9). Proliferative response in gill was limited to epithelial cell hyperplasia and mucous cell hypertrophy in R90 treatment individuals. Lamellae haemorrhage in the form of telangiectasia capillary aneurism was present in fish across treatments and controls but increased in prevalence at the highest dosage R90 treatment (Supplemental Figure S3 C). Proliferative response in skin involving epidermal and mucous cell hyperplasia and hypertrophy was restricted to R20 and R90 treatments (Supplemental Table S9; Supplemental Figure S3 G). Minimal partial ecdysis, ie. loss of epidermal skin cells, was also present in the same treatments. Melanomacrophage centres (MMCs) were present across multiple organs. The high overall contribution of MMC activity in both control and exposed treatments in spleen represents a nonspecific response in this test population. The higher prevalence of MMC in liver, heart and kidney

28

indicates a potential response to oil exposure treatment (Table 6; Supplemental Table S9; Supplemental Figure S3 A, B, E).

4. Discussion

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The objective of this study was to determine whether commonly used oil exposure biomarkers indicate short-term, low-level exposure to light crude oils, and whether they can predict changes in organism health, measured via histology, following short exposures. Although these monitoring targets would not be predictive of changes in organism resilience, as laboratory studies of swimming speed, prey capture ability, and tolerance to hypoxia or temperature changes, (e.g. Mauduit et al., 2016), they could be implemented in an oil spill response monitoring program and, if used in an adverse outcome pathway could be used to predict the potential for sublethal toxic effects. A suite of potential endpoints commonly used in the oil response literature was surveyed, including PAH metabolites, EROD, histology, and transcriptome profiling. PAH metabolites showed significant elevation in the bile of exposed fish, and while not each metabolite was not significant in all exposure treatments, this reflected the residence times of different PAH in the sediment. The commonly used EROD was not sensitive to this oil, as induction was modest and highly variable in all treatments and exposure duration. However, the RNASeq portion of study showed alterations in transcript abundance, including in the abundance of transcripts that code for xenobiotic metabolising enzymes such as cytochrome p450 1A and glutathione s transferase. Increased abundance of histopathlogical lesions was observed in every tissue, particularly the liver, gill and heart, although the small sample sizes used limit comprehensive interpretation of the data. In summary, the data presented suggest that short-term, low-level exposure to light oil could have greater consequences for the health and fitness of exposed fish than would be predicted using EROD, at least under laboratory conditions where avoidance of contaminated sediment is not possible. If an adverse outcome pathway framework (e.g. Ankley et al., 2010; Villeneuve et al., 2015; Groh et al., 2015), is used, EROD induction is not an appropriate cellular level response to measure to link to the organ level response (the changes in tissue structure measured via histological examination). The nominal concentrations used were similar to what has been reported in coastal systems after recent oil spills (e.g. Turner et al., 2014), and are in the same range as used in other recent studies that have exposed flatfish to sediment-bound crude oil (e.g. Brown-Peterson et al., 2015; 2017). These findings suggest that the risk posed to the environment by release of light oils may be underestimated if the exposure is estimated using conventional biomarkers such as EROD in isolation

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Given the composition of the fresh light crude oil, with a lack of detectable higher molecular weight PAHs, strong EROD induction was not expected. EROD activity has been shown to respond best to high molecular weight, pyrogenic PAHs (Lee and Anderson, 2005), to heavier oils, for example the oil from the Prestige oil spill (Martinez-Gomez et al., 2009), and to post-combustion sources of petroleum (reviewed in Stegeman and Lech, 1991). EROD induction frequently has not shown elevation following exposure to produced water that also frequently has abundant 2 and 3 ringed PAH but a low proportion of high molecular weight PAHs. For instance, EROD activity was induced in cod but not haddock collected from field sites where the fish would be exposed to produced water (Balk et al., 2011), and other studies monitoring North Sea oil installations have not measured EROD induction in cod, even though PAH metabolites were measured (Hylland et al., 2008). This lack of EROD induction does not necessarily correlate with a lack of CYP1A induction. In the transcriptome, elevated CYP1A was noted in both gill and liver tissue, although it was only 29

statistically significant in the gill. Other studies have also reported an increase in CYP1A transcripts without an increase in EROD activity. For instance, polar cod exposed to crude oil had increased CYP1A transcripts at periods in the exposure before EROD was induced (Nahrgang et al., 2009). Atlantic cod exposed to crude oil and diesel showed increased abundance of CYP1a transcripts, but EROD activity increased in fish exposed to oil only (Holth et al., 2014). Although all of these studies have different test species, exposure routes, exposure durations and doses, it may be the composition of the petroleum hydrocarbons that determine whether EROD is induced (e.g. Lee and Anderson, 2005). It is also possible that we did not measure EROD induction because the crude oil contained a CYP inhibitor (reviewed in Whyte et al., 2000).

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Also, there may have been more robust induction of EROD if it were measured in the gill rather than the liver. In this experiment, the route of exposure was via the gill as the fish buried beneath the sandy substrate and drew oil-contaminated water to irrigate their gills. Greater elevation of CYP 1A transcripts were seen in the gill than in the liver. Previous studies have also shown higher relative induction in the gill than the liver (Hook et al., 2010; Levine and Oris, 1999). This “first pass” metabolism of PAHs as it is taken up may decrease the overall levels of induction observed in the liver (Levine and Oris, 1999), however, gill EROD was not induced in caged fish exposed to produced water along a spatial gradient from a North Sea oil platform (Abrahamson et al., 2008), again suggesting that other metrics that measure CYP1A directly, either via transcripts or immunohistochemistry, may be more sensitive indicators of exposure to low molecular weight PAHs.

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The analysis of PAH metabolites in the bile showed a high degree of variability between individuals from the same exposure and between time points, but yielded more significant comparisons (Figure 4). Some of this variability may arise from differences in the rates at which compounds or oxygenated and conjugated, and differences in how frequently the gall bladder was emptied (the fish in the experiment were fed). The method used for quantifying PAH metabolites may affect the significance of the results. Overlapping ranges of biliary PAH metabolites measured via a fluorometric method, in exposed and reference organisms, were also observed in demersal species collected following the Deepwater Horizon wellhead blowout and the Exxon Valdez oil spill (Huggett et al., 2003; Murawski et al., 2014; Snyder et al., 2015). Other studies have observed lower variability in biliary metabolites in dab collected from the Seine Estuary using mass spectrometric method similar to those used in this study (Devier et al., 2013), and in turbot exposed to petroleumcontaminated sediments (Le Du-Lacoste et al., 2013), whereas, in Atlantic cod exposed to crude oil and diesel, the coefficients of variation could be greater than 50% (Holth et al., 2014).

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A high quality transcriptome for this species was created, and changes in transcript abundance were measured during the exposure and recovery period in the liver and gill of the spotted dragonet. The responses differed with tissue and time point. These differences in the transcriptome not only reflect the response of fish to the oil exposure, and the tissue alteration, but also to the decreasing concentrations of oil in sediment during exposure. In the liver, a progression from xenobiotic metabolism and damage responses to structural alteration was apparent, with energetic compensation always measurable. In the gill, response and compensation to molecular and tissue damage was apparent initially, followed by repair of damage comprising the dominant altered functional group at the end of the recovery period. Energetic responses in the gill were only apparent in the initial sampling period. Some of the changes measured in the transcriptome were apparently manifested at higher levels the organisms’ physiology, suggesting that changes in the transcriptome act as cellular level events in the adverse outcome pathway following oil exposure, as has been proposed previously (e.g. Ankley et al., 2010, Villeneuve et al., 2014, Groh et al., 2015). For example, many of the changes observed 30

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in the transcriptome are consistent with the modest biomarker level, where low but not significant changes were measured in transcripts encoding hepatic CYP1A, and similar changes in EROD. Previous studies that have analysed the transcriptomic responses in fishes exposed to crude oil have reported similar changes in transcript abundance. For example, similar patterns of changes in CYP1A transcripts to those reported here (modest, not statistically significant, increases) were noted in Gulf killifish collected from a heavily oiled site (Grand Terre, LA, USA) following the Deepwater Horizon wellhead blowout (Garcia et al., 2012). Previous studies examining zebrafish exposed to synthetic produced water also did not find significant increases in CYP1A transcript levels (Holth et al., 2008). Some studies reported greater elevations in CYP1A and other AhR related transcripts in the liver (e.g., de Cerio et al., 2017; Hook et al., 2010a, b; Pilcher et al., 2014), but many of these have used weathered oil (e.g. Pilcher et al., 2014), or a heavier oil (Hook et al., 2010b; de Cerio et al., 2017). Greater and significant induction of CYP1A transcripts and of CYP1B were found in the gill during the initial exposure in the current study. These transcripts also had increased abundance in the gill of Gulf killifish (Fundulus grandis) collected from Grand Terre, LA, USA, at peak oiling following the Deepwater Horizon wellhead blowout (Dubansky et al., 2013), and CYP1A protein levels (measured via immunohistochemistry) were elevated in the gills of the same organisms (Whitehead et al., 2012).

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Increased abundance of transcripts for GST isoforms was measured in the liver during exposure, and declines in abundance of these transcripts were measured during the recovery period. These enzymes are important in the phase II metabolism of PAHs (Livingstone, 1998). Numerous previous studies exposing fish to crude oil have measured increases in the activity level of this enzyme (Holth et al., 2008 , Hook et al., 2010a; Kerambrun et al., 2012; Martinez-Gomez et al., 2009; Nahrgang et al., 2009). Transcripts for oxidative stress enzymes are elevated in both organs we studied, which is consistent with other studies. The process of metabolising PAHs has been shown to generate free radicals (Mitchelmore et al., 1998). Previous studies have also measured increases in the abundances of transcripts for SOD, and heat-shock proteins, for example (Hook et al., 2010b) and other oxidative stress responses have been observed in fish exposed to crude oil (Martinez-Gomez et al., 2009: Nahrgang et al., 2009).

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Transcripts for cardiac-related proteins were elevated at the 24-h time point in the current study and changes were measured in the tissue morphology of the heart throughout exposure. Cardiac toxicity following oil exposure is of increasing concern (Mager et al., 2014; Nelson et al., 2016; Tissier et al., 2015). These changes in cardiac performance are thought to occur as a result of interruptions in calcium cycling and potassium channels (Brette et al., 2014). The transcriptomic changes measured here support this, with increased calcium binding and ion transport apparent in the liver during the recovery period, which also may have significance in the heart. The liver and other nonexcitable tissues have been shown to have similar calcium channels as the cardiac and nervous system (reviewed in Abramowitz and Birnbaumer, 2009), suggesting that the transcriptional changes we measured in the liver may be indicative of changes in the heart. Transcripts for cardiac related proteins also showed altered regulation in whole mahi–mahi embryos exposed to source oil from the Deepwater Horizon wellhead (Xu et al., 2016), but notably, they measured decreased abundance of troponin. Transcripts for enzymes involved in glucose metabolism and insulin response were elevated throughout exposure, and return to baseline levels at the end of the recovery period. Primary metabolism was also a significantly enriched GO term in the hepatic transcriptomes of rainbow trout exposed to 2 mg/L of ANS crude (Hook et al., 2010b). Transcripts involved in protein and lipid metabolism were also elevated in the liver during exposure. Zebrafish exposed to synthetic 31

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produced water also had elevated abundance of transcripts encoding lipid metabolising genes, lipid transport proteins, and microsomal cholesterol metabolising enzymes (Holth et al., 2008). Protein catabolism was one of the functional categories enriched in the hepatic transcriptomes of Gulf killifish collected from Grand Terre, LA, USA at the time of peak oiling following the Deepwater Horizon wellhead blowout (Whitehead et al., 2012). Transcripts for sterol lipids (important in the cell membrane) also showed changes in abundance in whole mahi–mahi embryos exposed to source oil from the Deepwater Horizon wellhead (Xu et al., 2016). These transcriptional changes may also relate to the lipid retention or steatosis observed in the liver tissue in our study. For instance, at 90 h we observed an increase in the abundance of transcripts encoding Glycogen [starch] synthase, indicating glycogen accumulation, and Fatty aldehyde dehydrogenase, and 2-acylglycerol Oacyltransferase 2A, indicating an increase in lipid metabolism and lipid signalling. The latter two transcript abundances suggest an enhanced metabolic response to utilise excess stored fat, and return to normal homeostasis in the liver.

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Immune-responsive transcripts, particularly those involved in inflammatory response, had altered abundance levels in both tissues. Different immunoglobulins (antibodies), which were elevated in both tissues following exposure to oiled sediments, are indicative of acute phase immune response (reviewed in Segner et al., 2012). The gene ontology terms acute phase response, inflammatory response and response to wounding were among the categories enriched in the gill transcriptomes of Gulf killifish (Fundulus grandis) collected from oiled sites following the Deepwater Horizon wellhead blowout (Dubansky et al., 2013), and in the immunoglobulin heavy chain and tumour necrosis factor in the liver transcriptomes of the same fish (Garcia et al., 2012). Zebrafish exposed to synthetic produced water also exhibit alterations in the abundance of transcripts related to immune function (Holth et al., 2008). Atlantic cod exposed to produced formation water in laboratory studies exhibit increased levels of beta-2-microglobulin transcripts, but decreased abundances in other immune related transcripts (Perez-Casanova et al., 2012). Additionally, turbot exposed to a heavy fuel oil also exhibited decreased abundances in immune related transcripts (de Cerio et al., 2017). These transcriptomic changes may relate to the histological changes, as inflammation was measured in multiple tissues in these fish.

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Other changes in the transcriptome reflect altered circulatory patterns observed in the spotted dragonet. Blood clotting factors had altered abundances in the liver. Heparin and fibronectin binding were also amongst the significantly enriched gene ontology categories in the transcriptomes of mahi-mahi embryos exposed to slick oil from Deepwater Horizon (Xu et al., 2016). Anthrombin and a blood coagulation factor were also down-regulated in turbot exposed to heavy fuel oil (de Cerio et al., 2017). Ion transporters had decreased abundances in the gill. This change could indicate decreased circulation and a reduced number of gill epithelial cells. Gill hyperplasia has been measured in fish exposed to crude oil (Whitehead et al., 2012), and was present in the spotted dragonet.

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Both increases and decreases in transcript abundances for ribosomal components were measured at multiple timepoints in both gill and liver tissues from this study. These changes have been measured in previous studies as well (de Cerio et al., 2017; Hook et al., 2010b; Xu et al., 2016), and may reflect an increase in protein synthesis as damaged molecules and cells are replaced. The reduction we observed in fibronectin (a cell adhesion molecule), and increase in collagen, keratin, and cytoskeletal components involved in cell formation could also reflect replacement of the necrotic cells observed in the histopathological analyses. The histological examination indicated cellular alteration impacts from this brief, low-level exposure to oil. Oil exposure elicits histopathological alteration in fish (Myers & Henricks 1985; Agamy 2013 32

a;b). Studies following Deepwater Horizon have added to the literature describing oil cytotoxicity in fish (Giari et al 2012, Morawski et al 2014, Brown-Peterson et al. 2015, Centeno-Chale 2015). Lesions observed in the spotted dragonet are similar to those identified in previous studies examining changes in fish following oil exposure (Argamy, 2012; Kennedy, 2015). Necrosis represents localised cell and partial tissue loss, which, while degenerative in function, lost cell volume can be replaced depending on the overall health status of the fish, and the duration and severity of impact. Necrosis was particularly evident in the current study, especially in the higher concentration exposures and later experimental time points.

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The occurrence of fatty necrosis in the liver, especially at higher exposure concentrations, follows similar oil-induced fatty degeneration response in fish liver and kidney (Gabriel et al. 2007, Codi et al. 2011). More recent evidence indicating fatty liver in Gulf of Mexico trigger fish, links alteration in lipid metabolism and accumulation to corresponding hepatic accumulation of PAH-derived compounds (Smeltz et al. 2017). Alteration in lipid metabolism was also evident in lipid vacuole accumulation in the heart in the current study, which increased in prevalence during both the uptake and recovery phase of the light oil exposure trial.

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Oedema causing epithelial lifting in secondary lamellae is representative of a potential immune system defence mechanism to increase the distance between blood and exogenous toxicants (Evans 1987, Arellano et al. 1999). Abnormal retention of fluid in the sub-epithelial tissue space causes swelling, potentially due to either the osmotic pressure exceeding that of the surrounding tissues, or a lower permeability of the pillar cells (Hoar and Randall, 1984), or alternatively, a degradation of the endothelial glycocalyx affecting vascular permeability (Weinbaum et al. 2007), resulting in an exudate fluid filtering from the gill capillaries into the adjacent epithelial space, forcing the epithelial layer to lift. Accumulation and swelling from the white blood cell serum exudate could further impair diffusion exchange across the gill, by markedly increasing the thickness of the blood-water barrier (Hughes, 1966), and corresponding reduced oxygen uptake (Mauduit et al., 2016). The oedema measured in this study is consistent with exposure to crude oils affecting gill structure and function in other fish species (Agame, 2013 a,b; Giari et al., 2012).

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Lamellae hemorrhage in the form of telangiectasia capillary aneurism at the higher doses and late environmental time points observed in our study follows similar oil – induced telangiectasia and epithelial proliferation found in flounder after chronic exposure to spiked oil sediment (BrownPeterson et al 2015; 2017), and in oil spill-impacted bream (Giari et al 2012). Other studies have also noted an increase in gill telangiectasia occurring over very short duration exposures (Kerambrun et al., 2012). Excessive dilation of a blood vessel by 50% greater diameter than normal is generally considered equivalent to an aneurysm; in the case of telangiectasia the aneurysm is effectively a haemorrhage resulting from rupture of pillar cells (Eiras et al., 2008), which normally act to prevent the lamellae from dilating due to blood pressure. Interestingly, transcription changes associated with activation of genes associated with ‘response to damage’ are elevated in gill tissue, but ‘repair of damage’ genes related to collagen catabolism are not. Moderate epithelial cell hyperplasia and associated lamellar fusion from light oil exposure indicates a further potential reduction in exchange efficiency of gill lamellae. Excessive proliferation of epithelial cells resulting in hyperplasia lamella fusion has been identified in flounder exposed to Deepwater Horizon oil contaminated sediment (Brown-Peterson et al. 2015; 2017). Epithelial cell hyperplasia is a common inflammatory response to gill injury (Powell, 2007), however the mechanism eliciting both onset and reduction in proliferation has not been documented. High prevalence of MMC activity in spleen, and to a lesser extent kidney and liver for both control and exposed treatments is not unexpected in wild caught fish. Interpretation of the relative 33

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importance of MMC presence is problematic to some extent, the number and size of aggregates generally increase with age, infection and toxicant exposure (Agius and Roberts, 2003). Elevated MMC proliferation potentially indicates stress either after infection and/or a cytotoxic response; the phagocytic activity of the macrophages removing foreign material, bacteria, dying or dead cells. Interestingly, benthic flounder exposed to crude oil contaminated sediment exhibited a decrease as opposed to increase in liver MMCs (Payne and Fancey, 1989), whereas sea trout and killifish from the Gulf of Mexico demonstrated an increase in splenic MMCs indicating likely environmental exposure to Macondo crude oil (Ali et al. 2014). In the context of this investigation, the prevalence of MMCs in spleen are not representative of a trend either way; MMCs were present in all treatment and control groups. However, the presence of cardiac tissue MMCs in oil exposed animals provides a potential a line of evidence for a degenerative immune response to the light oil.

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Low replicate numbers per treatment inhibits viable interpretation of pathologies noted in skin tissue. Presence of helminth parasites identified in spleen and liver also represents a pre-existing condition not correlated to oil dose-exposure response. Nonetheless, a recent investigation highlights that abundance of helminth parasites in wild flounder exposed to environmental oil-spill contaminants decreased with the host species exposure to hydrocarbons (Centeno-Chalé et al., 2015).

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Proliferative epidermal response including mucous cell hyperplasia and hypertrophy in epidermal skin tissue was restricted to the higher concentration, recovery phase treatments. Hypoplasia of epidermal cells resulting in partial ecdysis was also present in the same treatments. Very low replicate numbers per treatment restricts confidence in interpretation, however increase in prevalence of skin lesions in benthic fish exposed to environmental oil spill-derived PAHs has been recently documented (Morawski et al. 2014).

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We observed a progression of cumulative damage in fish tissues, with damage continuing to manifest after exposure had stopped. Although most previous studies have either examined histological alterations following exposure to petroleum hydrocarbons in the field (e.g Codi-King et al., 2001; Hylland et al., 2008; Amara et al, 2007; Whitehead et al., 2012; Smeltz et al., 2017), or following longer exposures (e.g. 10 days, Salamanca et al., 2008; 28 days, Brown-Peterson et al., 2017; 32 days, Brown-Peterson et al., 2015), our findings do not set precedent. The few studies that have examined accumulation of histological lesions over comparable time periods (Agamy, 2013, a,b) have identified a few significant differences at the first time period when tissue damage was examined, and an increase in frequency of lesions and types of lesions as time progresses, as was evident in this study (Table 6).

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The analysis of pathologies identified in light oil-exposed fish was undertaken with the caveat that test individuals were wild-caught fish with unknown history of prior exposures and pre-existing pathologies, and the replicate number of individuals per treatment was low. The histology data are presented as prevalence, indicating the relative percentage of individuals exhibiting presence of an alteration, but not the degree of severity. Nonetheless, an increased preponderance of lesions in petroleum exposed individuals was observed. Some lesions persisted throughout the exposure and depuration period, suggesting a temporal delay of onset of alteration in some cases, and/or a temporal delay in recovery or reversal after transfer of the individual to clean sediment. Elevated temporal persistence of histological damage, relative to the more rapid onset and reduction response at the molecular biomarker level, has been observed in other studies (Salamanca et al., 2008). The tissue alteration evident after a relatively short-term exposure is representative of a rapid onset of integrated molecular level alteration in cellular functioning (Hinton and Lauren, 1990), and indicative of potential early stage toxicant stress (Connell et al. 1999). The significance of such 34

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changes identified in the light oil-exposed stink fish, in relation to longer-term individual fitness, however, would depend markedly on the degree or severity of alteration, and whether the alteration, or combined suite of alterations, are severe enough, or sustained to the point of significant impairment to essential maintenance, growth and or reproductive capability at the individual level, or ultimately over the longer term, at the population level and above (Walker et al. 2012). It is also important to specify that the causative agent for these histological changes in light oil has not been identified. It is frequently assumed that PAHs cause the toxic impacts resulting from crude oil exposure (e.g. Di Toro et al., 2007), but crude oil is a complex mixture of thousands of compounds, the majority of which are not quantified in this or other studies. Previous studies have also identified toxic impacts that are not attributable solely to the concentrations of PAH (e.g. Rowland et al.. 2001; Pettigrove & Hoffmann, 2005; Saco-Alvarez et al. 2008; Incardona et al., 2012).

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5. Conclusions

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Following a major oil spill, estimating the impacts of exposure to oil on ecologically and economically important species such as fish is of paramount importance. Current guidance suggests using biomarkers to constrain the location and duration over which impacts are anticipated (e.g. Law et al., 2011; Hook et al., 2016). The results of this study suggest that these biomarkers should be chosen carefully. We recorded different physiological changes in fish exposed to environmentally realistic concentrations of a light crude oil. Biliary metabolites showed high variability between individuals and sampling periods, and but were consistently significant and reflected the PAHs measured in the sediment. EROD induction was inconclusive, suggesting that these metrics are more suitable for heavy oils and post-combustion sources of petroleum. A high-quality transcriptome database was constructed for the liver and gill. Differences in transcript levels at different time points in each organ were predictive of other changes in the organism (i.e., the transcriptomic changes in creatine kinase, tropomyosin and troponin that potentially correlate with cell death identified in the heart, and the numerous transcripts involved in immune system’s processes correlate with inflammatory response and cell death evidenced by necrosis present in both the gill and liver.

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The transcriptomic changes were statistically significant, demonstrating the power of this approach. The ability to preserve samples in RNAlater or similar also makes transcriptomic profiling a comparatively attractive option for the collection of field based samples. This work suggests that RNA seq-derived transcriptomic profiles might be useful to predict the potential for sublethal toxicity or other physiological consequences of petroleum exposure following an unplanned release of oil, as they correlate with other measures. Indeed, such studies have already added value to the damage assessments efforts following Deepwater Horizon (Garcia et al., 2012; Pilcher et al., 2014; Whitehead et al., 2012). However, the transcriptome changed with time (e.g. Tables 3 and 5), reflecting a progression from uptake, detoxification, and recovery. Because of the dynamic nature of the transcriptome during exposure and recovery, the technique may not be suitable to be used in isolation. This study also shows the value in profiling multiple tissues, not only the liver, as CYP1A transcripts were higher in the gill. There was evidence of potential ecologically relevant changes occurring in tissue structure, including potential for tissue degeneration and reduced circulatory capacity, which suggest consequences for organism health that exceed the duration of exposure. Although this biomarker based study can not measure every potential change in organism health, such as decreased resilience and physiological tolerance, it demonstrates the merit in using multiple monitoring metrics. Taken together, this study highlights the need to identify markers for rapid 35

assessment of impacts of lighter petroleum products that occur via adverse outcome pathways such as tissue degeneration in the gill and especially cardiac toxicity. Acknowledgements:

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This project was undertaken as part of the Great Australian Bight Deepwater Marine Program. The Great Australian Bight Deepwater Marine Program is a public-good research program led by CSIRO, and sponsored by Chevron Australia, with data generated to be made publically available. Xiaoxu Li, Grant Mann, Mark Gluis, Michael Drew, Matthew Heard and Troy Rogers and other staff at SARDI are thanked for technical support and access to facilities during the exposures. Mina Brock (CSIRO O&A) assisted with the metabolite analyses. Alan Williams, Andrew Ross, and Graeme Batley are thanked for valuable comments on an earlier version of this manuscript. The comments of two anonymous reviewers also improved the manuscript.

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Table1. Selected transcripts with differential abundance, representative of their functional categoriesa.

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Uptake Recovery name U24L U90L D20L D90L Xenobiotic metabolism CYP1A1 2.1 1.54 1.1 -3.69 CYP1A2 1.45 1.3 1.14 1.3 EPHX1 -2.38 1 2.13 1.64 UDPGT 1.25 1.36 1.17 -1.89 AhR 1.2 2.37 -1.12 -4.6 CYBB 93.75 4.12 -56.12 -117 GST α 18.07 -1.28 -6.65 36.1 GST Ω 13.63 -3.15 -1.88 5.53 Oxidative stress response CAT -1.06 -3.04 1.39 2.54 GPX 3.2 1.38 77.15 nd SOD -1.15 3.6 1.36 -5.42 HSP 71 -1.3 8.82 -1.53 1.07 Cardiac function proteins TM 196.39 1.09 -39.53 1.75 CKM 763.18 1.04 -65.04 158.55 TnT 503.6 nd -35.74 29.8 ACTA 4564.9 -1.07 -29.94 125.35 Primary metabolism PGM 287.18 -1.02 1.34 -1.62 ALDOA 185.74 -1.25 -5.51 12.4 GYS2 51.42 117.28 704.15 1.56 AMY1A -1.22 11.61 15.57 20.7 PEP nd 21.68 1.68 -2.24 MIOX 1.27 12.33 1.51 -7.19 Protein degradation CPB1 -24.2 -1.82 8.05 -58.72 CTSE -98 1.47 5.5 -3.22 CTSl -144.7 2.13 1.81 -2.41 UBE3A nd 8.77 nd 1.76 CTSF 13.5 -4.5 -2.29 -5.82 Lipid metabolism MOGAT1 3.28 38.6 4.1 -3.44 APOEB 17.22 -3.49 -8.08 19.48 ALDH3A2 15.97 -1.66 -1.69 -1.99 ALDH3A2 -2.49 106.55 -1.69 -1.99 APOC2 -25.19 -1.26 nd -2.4 CYP 4v2 -9.26 -1.69 -1.49 -2.4

IP T

Liver

46

Immune Function 6.74 2.91 3.66 -2.95 nd -35.28 -3.24 -37.49 1.41 -47.62 -4.78 1.95 35.08 1 2.21 -2.53 Connective Tissue CK 95.1 -6.5 -236 704.32 ACTA 4565 nd -26.53 125.34 CLDN 50.14 2.05 1.03 301.94 FN 5.79 -20.19 1.54 33.19 Ion Transporters NCX2 2.53 193.49 2.81 -4.61 CaM 9.4 -2.02 -12.83 24.36 Blood Clotting Proteins FGA 224.6 1.19 8.98 -172.1 F9 7.06 2.09 1.97 -2.11 FGB -1.29 -44.1 1.01 -1.51 F7 1.25 772.39 1.41 -1.52 AT -134.7 -2.2 -1.29 2.13 exostosin 1.15 -1.09 -1.12 -1.06 Translation RPL27 -6.71 -23.05 1.08 -11.65 RPS11 1.19 -18.03 -1.29 -1.17 oxygen transport/blood proteins HBA2 2.61 -1.66 -166.8 6.31 CA-IV 1.25 -1.12 -402.4 2185.4

A

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CFI B2M C3 HPX

47

IP T SC R U N A M

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Gill Uptake Recovery name U24G U90G D90G Xenobiotic metabolism CYP1A1 1.73 4.9 nd CYP1A2 3.63 5.07 nd CYP1B1 4.59 4.39 1.25 EPHX1 3.72 -1.09 -2.21 UDPGT 7.26 -3.38 -1.49 AhR -1.88 -1.23 -2.81 GST α 1 -1.28 -1.5 GST Ω 1.06 -1.03 1.12 Oxidative stress response CAT -1.18 1.92 8.17 GPX 1.09 -1.16 1.15 SOD -1.35 19.76 1.45 HSP 90-α 1 -1.68 22.42 -7.42 HSP 90-β -1.01 12.65 -5.88 HSP 71 1.91 1.51 -1.01 Cardiac function proteins TM 1.34 1 -1.62 CKM 1.41 1.62 -2.36 TnT 1.26 -1.22 -1.17 ACTA 10.98 -1.22 -1.02 ANGPT -16.83 20.34 1.69 Primary metabolism PGM 1.24 -1.04 -1.09 ALDOA 1.33 1.7 -1.56 PEP -1.02 -1.1 -1.22 Protein degradation CTSl nd -1.23 -1.2 UBE3A nd 66.62 1.15 CTSF 4.6 8 2 Lipid metabolism MOGAT1 1.43 15.39 1 PAP2B -19.44 1.24 1.12 CH24H -5.28 1.6 1.19 APOEB 1.36 1.17 2.78

48

Liver D90L

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-3.44 19.48 -1.99 -1.99 -2.4 -2.4

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A

704.32 125.34 301.94 33.19

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-2.95 -37.49 1.95 -2.53

-4.61 24.36

-172.1 -2.11 -1.51 -1.52 2.13 -1.06 -11.65 -1.17

6.31 2185.4

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M

Uptake Recovery name U24L U90L D20L Lipid metabolism MOGAT1 3.28 38.6 4.1 APOEB 17.22 -3.49 -8.08 ALDH3A2 15.97 -1.66 -1.69 ALDH3A2 -2.49 106.55 -1.69 APOC2 -25.19 -1.26 nd CYP 4v2 -9.26 -1.69 -1.49 Immune function CFI 6.74 2.91 3.66 B2M nd -35.28 -3.24 C3 1.41 -47.62 -4.78 HPX 35.08 1 2.21 Connective tissue CK 95.1 -6.5 -235.98 ACTA 4565 nd -26.53 CLDN 50.14 2.05 1.03 FN 5.79 -20.19 1.54 Ion transporters NCX2 2.53 193.49 2.81 CaM 9.4 -2.02 -12.83 Blood clotting proteins FGA 224.6 1.19 8.98 F9 7.06 2.09 1.97 FGB -1.29 -44.1 1.01 F7 1.25 772.39 1.41 AT -134.71 -2.2 -1.29 exostosin 1.15 -1.09 -1.12 Translation RPL27 -6.71 -23.05 1.08 RPS11 1.19 -18.03 -1.29 Oxygen transport/blood proteins HBA2 2.61 -1.66 -166.75 CA-IV 1.25 -1.12 -402.41

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SC R U N A

A

CC E

PT

ED

M

Uptake Recovery name U24G U90G D90G Immune Function MHC-I 1.93 366.14 nd MHC-i 5.56 1.47 -1.67 Ig 51.35 34.09 -2.41 FCGBP 1.22 193.62 -1.09 TCP1 -164 nd nd B2M -2256 1.84 nd Connective tissue CK 1.02 1.08 -1.18 CLDN 1.12 1.15 1.12 NEB 1.531168 29.51 nd Ion transporters RHCG -20.1 -245 -33.1 VDAC2 -1.72 -19.43 -29.43 CLCN5 1.13 -3.01 1.11 CaM 1.07 1.07 1.02 Blood clotting proteins F7 -1.36 2.18 -1.2 exostosin 1.44 -34.53 -1.17 Translation RPL3 -111.11 -1.05 -1.26 RPL27 -9.66 -26.69 -8.47 RBS2 -1.14 73.96 -1.86 Oxygen transport/blood proteins HBA2 -2.57 1.16 -2.72 CA-IV -1.21 -1.01 -1.01

IP T

Gill

50

N

U

SC R

IP T

Connective Tissue CK 1.02 1.08 -1.18 CLDN 1.12 1.15 1.12 NEB 1.5312 29.51 nd Ion Transporters RHCG -20.1 -245 -33.1 VDAC2 -1.72 19.43 29.43 CLCN5 1.13 -3.01 1.11 CaM 1.07 1.07 1.02 Blood Clotting Proteins F7 -1.36 2.18 -1.2 exostosin 1.44 34.53 -1.17 Translation RPL3 -111.1 -1.05 -1.26 RPL27 -9.66 26.69 -8.47 RBS2 -1.14 73.96 -1.86 aNumbers

A

CC E

PT

ED

M

A

are the mean fold change for the treatment, relative to a time-matched control. Red indicates transcripts with increased abundance, blue with decreased abundance. Italicised numbers show significance at p<0.05 with no multiple test correction. Bold italicised numbers show significance at p<0.05 with a FDR multiple test correction. Key to abbreviations: CYP1A1 = cytochrome p450 1A1; CYP1A2 = cytochrome p450 1A2; CYP1B = cytochrome p450 1B; EPHX-1 = epoxide hydrolase 1 microsomal; UDPGT = UDP-glucuronosyltransferase; AhR = aryl-hydrocarbon receptor; CYBB = cytochrome b-245 heavy chain; GST α = glutathione S transferase alpha; GST Ω = glutathione S transferase omega; CAT = catalase; GPX = glutathionee peroxidase; SOD = superoxide dismutase; HSP = Heat Shock Protein; TM= tropomysoin; CKM = creatine kinase muscle-type; TnT = troponin T; ANGPT = angiopoietin; ACTA = α actin; MIOX = inositol oxygenase; PGM = phosphoglycerate mutase; ALDOA = fructose-bisphosphate aldolase a; AMY1A = pancreatic alpha-amylase; PEP = phosphoenolpyruvate; GYS2 = glycogen [starch] synthase; UBE3A = Ubiquitin-protein ligase E3; CPB1 = Carboxypeptidase B; CTS = Cathepsin; MOGAT1= 2-acylglycerol O-acyltransferase; ALDH3A2 = Fatty aldehyde dehydrogenase; APO= apolipoprotein; MHC-I = class I major histocompatibility complex; FCGBP = immunoglobulin, fragment, crystalisable, binding protein; CFI = Complement factor I; HPX = hemopexin; TCP1 = T complex protein 1; B2M = beta-2-microglobulin; CK = cytokeratin; FN = fibronectin; CLDN = claudin; NEB = nebulin; RHCG = ammonium transporter Rh CG; VDAC2 = voltagedependent anion-selective channel protein 2; NCX =sodium potassium calcium exchanger; CLCN = h(+) cl(-) exchange transporter 5 isoform x3; FGA = Fibrogenin alpha; F9 = Coagulation factor IX; AT = antithrombin; EXT1 = exostosin -1; Complement C3= C3; 60S ribosomal protein L27 = RPL27; Eukaryotic translation initiation factor 3 subunit F =eIF3f; haemoglobin α2 = HBA2; carbonic anhydrase 4 = CA-IV; phospholipid phosphatase 3 = PAP2B; cholesterol 24-hydroxylase = CH24H; 40S ribosomal protein L27 = RPL3; immunoglobulin –Ig.

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