Rough Periwinkles at Emersion Presence Or Absence of Response in Gene Expression of Aspartate Aminotransferase?

Rough periwinkles at emersion Presence or absence of response in gene expression of aspartate aminotransferase?

CH-14

Cecilia Helmerson

Degree project for Master of Science (Two Years) in Marine Sciences and Biology

Degree course in Marine ecology 45 hec Spring and Autumn 2014

Department of Biological and Environmental Sciences University of Gothenburg

Examiner: Kerstin Johannesson Department of Biological and Environmental Sciences University of Gothenburg

Supervisors: Marina Panova and Olga Ortega Martinez Department of Biological and Environmental Sciences University of Gothenburg

Illustration: Cecilia Helmerson 2014

Index

ABSTRACT ...... 4 1. INTRODUCTION ...... 5 2. MATERIALS AND METHODS ...... 7 2.1 SAMPLING AND ACCLIMATION ...... 7 2.2 EMERSION EXPERIMENT ...... 8 2.3 DISSECTION AND EXTRACTION ...... 10 2.4 QUALITY CHECK AND REVERSE TRANSCRIPTION ...... 10 2.5 QUANTITATIVE PCR ...... 10 2.6 DATA ANALYSIS ...... 13 3. RESULTS ...... 14 3.1 EFFECT OF EMERSION ...... 14 3.2 CORRELATIONS AND COMPARISONS BETWEEN TARGETS ...... 16 4. DISCUSSION ...... 18 4.1 MAIN RESULTS ...... 18 4.2 HUMIDITY ...... 18 4.3 REGULATION OF GENE EXPRESSION ...... 18 4.4 CORRELATION AND COMPARISONS BETWEEN TARGETS ...... 19 4.5 INDIVIDUAL VARIATION ...... 20 4.6 TECHNICAL ASPECTS ...... 20 4.7 ONE GENE OF MANY ...... 20 5. CONCLUSION ...... 21 6. ACKNOWLEDGEMENTS ...... 21 7. REFERENCES ...... 21 7. SUPPLEMENTARY MATERIAL ...... 24 S1- EXTRACTION PROTOCOL ...... 24 S2 – MOPS GEL AND BUFFERS ...... 24 S2.1 Agarose gel for RNA ...... 24 S2.2 Running buffert MOPS ...... 24 * S3 - CDNA SYNTHESIS ...... 24 S4 – PLATE DESIGN ...... 25 S5 – STANDARD CURVES ...... 26 S6 NORMALITY TESTS ...... 28 S. 6.1 Between treatments ...... 28 S. 6.2 Between targets ...... 29 S7 TEST ASSESSING HOMOGENEITY OF VARIANCES ...... 30 S. 7.1 Between treatments ...... 30 S8 KRUSKAL WALLIS ANOVA ...... 31 S9 GEOMETRICAL MEAN ...... 32

Rough periwinkles at emersion Presence or absence of response in gene expression of aspartate aminotransferase?

Cecilia Helmerson

Supervisors: Marina Panova1 and Olga Ortega-Martinez2

Degree Project for a Master of Science in Marine Sciences and Biology, 45 hec

Department of Biology and Environmental Sciences, Sven Lovén Centre of Marine Sciences - 1Tjärnö/2Kristineberg, University of Gothenburg

E-mail address: [email protected] or [email protected]

Phone: 0046-739804988

Abstract

Many selective forces shape evolution in rocky shore organisms. In the intertidal snail Littorina saxatilis desiccation at emersion is thought to be an important selective force. One approach to study responses to desiccation is through measuring gene expression of genes putatively involved in coping with the exposure. In the exposed ecotype of L. saxatilis a candidate gene for studying response to desiccation is aspartate aminotransferase (Aat). Aat is an enzyme initially active in anaerobic energy production that may be used at emersion. There are two allozyme alleles Aat120 and Aat100 and the allele frequencies vary with shore height. The activity of the enzyme varies depending on the genotype. This study investigated if there was an up-regulation of the enzyme at transcript level during emersion. Snails were sampled at an exposed atidal shore in the Aat hybrid zone and acclimated to laboratory conditions. An emersion event was simulated and gene expression quantified at different time points with quantitative PCR. The study found no significant response in gene expression for cytosolic Aat or mitochondrial Aat, but found a significant correlation between the two. The hypothesis of up-regulation in response to desiccation however cannot be rejected. Humidity throughout the experimental emersion event was low, but higher than dry air conditions. Snails might therefore not utilise Aat at the humidity and timeframe of the experimental setup. Based on results, the directions for further studies will be testing extreme stress, difference between genotypes and investigating expression difference at tissue level. The measuring of gene expression and enzyme activity together also allows testing an alternative hypothesis - that regulation is not at transcript level. Simulated emersion and other experimental approaches also should be compared to natural emersion events.

Keywords desiccation, anaerobic energy production, glutamate-oxaloacetate transaminase, gastropod

4 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

1. Introduction

Rocky shore marine organisms face several strong environmental forces at emersion (reviewed e.g. Raffaelli & Hawkins 1999) including changes in humidity, salinity and temperature. Changes in humidity and temperature lead to a loss of water, desiccation at emersion. At emersion mobile animals such as snails might therefore seek refugia in moist crevices and minimise locomotion and feeding (Hand & Menze 2007). Metabolic depression, utilisation of pathways for anaerobic metabolism and even severe metabolic arrest might follow as desiccation proceeds (Hand & Menze 2007). The prosobranch gastropod, Littorina saxatilis, the rough periwinkle is one species experiencing desiccation stress. Adaptions such as metabolic rate depression and improved water conservation have been documented for the species (Sokolova & Pörtner 2001, Sokolova et al. 2000). It is a common inhabitant of the littoral zone in the North Atlantic and occurs over a wide range of habitats and conditions. Factors such as wave exposure and crab predation are thought to drive formation of ecotypes (e.g. Reid 1996, Johannesson 2003). One of several described ecotypes (Reid 1996), is the Swedish E-ecotype or E-morph (Janson 1982) on wave exposed rocky shore (Figure 1A- C).

A B C

Figure 1 A) Exposed rocky shore B) Littorina saxatilis E –ecotype in natural environment and C) E-ecotype D) Reaction facilitated by aspartate aminotransferase. Photos and illustrations by C. Helmerson

5 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

The enzyme Aspartate aminotransferase (Aat-1) (EC 2.6.1.1) or L-aspartate-2- oxoglutarate aminotransferase is a candidate gene for studying selection in the E-ecotype and on exposed shore, with two different allozyme alleles Aat120 and Aat100 coupled to high or low shore, respectively (Johannesson & Johannesson 1989). The Aat cline has been shown to be a result of strong selection (Johannesson et al. 1995). Enzyme activity varies with genotype and the enzyme is thought to be involved in anaerobic energy production under desiccation (Panova & Johannesson 2004, Sokolova & Pörtner 2001). Aspartate is used to generate ATP in the aspartate-succinate pathway (DeZwaan & Putzer 1985 and references therein). It is one of many enzymes involved in eukaryotic anaerobic energy production (List in Müller et al. 2012). Aat catalyses the reversible transfer of an amino group from L-aspartate to a-ketoglutarate (also called 2-oxoglutarate) resulting in the formation of oxaloacetate and L-glutamate (Figure 1D). There are two isoenzymes, cytosolic aspartate aminotransferase and mitochondrial aspartate aminotransferase. The enzyme is a dimer of two units and in heterozygotes both copies of the gene are expressed, resulting in three distinct bands on allozyme analysis (personal communication Marina Panova). Homozygotes for Aat100 have higher activity than homozygous Aat120 and heterozygotes have intermediate activity (Panova & Johannesson 2004). Aspartate is thought to be used initially as an energy source during the first 5 to 10h of anaerobiosis at emersion by anaerobe-tolerant invertebrates, in a pathway resulting with the accumulation of succinate (DeZwaan & Putzer 1985 and references therein). One study suggests that aspartate might be used even after 10h. Sokolova & Pörtner (2001) measured concentration in the foot of L. saxatilis in a 48h long experiment at 30°C and found a decline throughout the experiment of L- aspartate. Furthermore, the study showed that the relative contribution of anaerobic metabolism only was 1-2% of ATP turnover (Sokolova & Pötner 2001). In the Pacific oyster Crassostera gigas a response in gene-expression has been found under hypoxia (Boutet et al. 2005). The coding sequence of Aat is identified and there are two amino-acid changes between Aat120 and Aat100 alleles (Mittermayer 2013). The goals of this project are to quantify and compare gene expression at different time points of a simulated emersion event. The tested hypothesis is that the gene is up-regulated as a response to desiccation, and the expression may be higher initially at emersion as predicted from DeZwaan & Putzer (1985) and Sokolova & Pörtner (2001) with a decline in relation to aspartate concentration, assuming transcriptional regulation. Real-time quantitative PCR (qPCR) is a common method to measure gene expression and was applied here to test this hypothesis. qPCR has previously been used for L.saxatilis by Martínez-Fernández et al. (2010) for assessing difference in COI expression between the two Spanish ecotypes SU and RB. With qPCR a quantitative estimate is made by monitoring the fluorescence of a dye as SYBR® Green. SYBR® Green binds to double stranded DNA and fluoresce more when bound. Thus while monitoring PCR reactions an approximate measure can be made of the starting amount of the gene transcripts in the sample. When the amount of double stranded DNA increases during PCR the signal get stronger. When the signal is significantly higher than the baseline (background signal) the threshold cycle Ct is reached. A lower Ct in one sample indicates higher number of transcript copies. Here the relative quantitation method was used, when expression of the target gene is compared to expression level in control housekeeping genes (not responding to treatment). This is done to control for variation of total amount of nucleic acids between samples due to imprecise RNA concentration measurements, variation in RNA integrity and pipetting errors.

6 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

2. Materials and methods

Figure 2 shows the workflow for the main experiment, but qPCR was also optimized for L. saxatilis, as described below.

Figure 2 Experiment setup starting with (1) sampling of rough periwinkles, followed by (2) acclimation. Emersion experiment was simulated (3) and snails sampled at defined time points. Snails were then fixated (4), dissected (5) and RNA extracted (6). Quality check was performed (7) before reverse transcription. Finally samples were analysed with qPCR (8).

2.1 Sampling and acclimation

L. saxatilis snails were sampled (n ∼ 400) on January 21, 2014 in the hybrid-zone of the Aat cline at an exposed rocky shore on Ursholmen, Bohuslän, Sweden (N 58° 49.879', E 10° 59.470') in the Kosterhavet National Park. Sampled snails were approximately in the size range 3.5-8 mm. Snails were taken at the same height to minimise variation and ensure sampling of both genotypes. Variation between snails at different shore height have been previously documented in the form of evaporative loss as emersion when exposed to elevated temperature and behavioural response (Sokolova et al. 2000). Snails were stored in refrigerator over night and acclimatised in an indoor aquarium (1551 L) until April 16, 2014 (84 days) in a flow through system (developed by K. Johannesson and M. Ogemark). Water was all the way up to the lid of the aquarium, ensuring immersion. Initial water temperature measurements were performed January 29-30. Temperature varied between the aquaria in the aquaria system, with lowest value being around 7.5 °C and highest around 14.7 °C (tested on 3 aquaria). In order to reduce habitat-confounding, temperature was measured several times every week after 31st of January and flow adjusted. When aquaria were over-

1 Based on measurements with tape measure (175 L when calculating with measurements made from outside of aquaria). 7 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

grown by algae, cleaning was performed and common periwinkles L. littorea was added. Removal of faeces was performed when necessary to avoid impairment of water quality. While cleaning, removed snails were kept moist and then returned to aquaria. Some mortality was encountered (> 84 snails or > 21%, juveniles excluded) throughout the acclimation period for the aquarium with the snails. Mortality was attributed to snails being unable to flip back when landing upside down. When possible, snails were flipped back with a stick through a hole in the lid of the aquaria. In long term studies in this type of aquaria there might be artificial selection on this trait only having generations of snails being able to turn back (personal communication K. Johannesson). Other plausible causes of death could be damage at sampling or cleaning of aquaria and filter. Dead snails were removed at cleaning. Snails were sometimes also lost at cleaning. Snails dropped on floor or found crawling on the outside of aquaria were removed from the experiment.

2.2 Emersion experiment

The emersion simulation event was performed at room temperature and laboratory humidity (Figure 3A and 4A). Humidity on the tray was 49-51 %RH and 49-52%RH at the desk before starting the experiment. Relative humidity was measured with a hygrometer (hobby quality) before or after every treatment sampling. Snails were picked from the aquarium and put into 1000 mL plastic jars with water from the aquarium.

A B

Figure 3 A) Humidity variation of the simulated emersion event and B) illustration of snails as when first on paper and timepoint defined as 0h. Illustration by C. Helmerson.

While awaiting start of experiment, jars were kept cold by putting them into a bucket with flowing water (same source as to the aquarium). Representative sized snails, roughly measured by the eye to cover the desired variation in size (n=30 for each control) were allocated to control jars (1000 mL) that were put back into aquaria. Jars were covered with lids with mesh filled holes in order to ensure water circulation and to avoid snails escaping or entering the jar. The rest of the snails (n=210) were put on a

8 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

tray evenly spread (opening facing paper), with double folded filter paper (Figure 5) in order to reduce the effect of huddling on temperature and humidity experienced of snails (e.g. Rojas et al. 2013). The paper also ensured that snails did not escape or aggregate (personal observation).

A B

Figure 4 A) Temperature variation of the simulated emersion event B) Illustration showing how size of snail was measured (width measure similar to Janson & Sundberg 1983).

Time until all or a majority of the snails had withdrawn into shells was measured by a stopwatch (Asaklitt/Cielo Stopwatch WT035)(Time ∼1h). Some shells were turned over to check for withdrawal. Snails were then picked at 0h (when fully withdrawn Figure 3B), 2h, 4h, 6h, 12h, 24h and 48h (n=30 at each time point) (Time points were chosen to match Sokolova & Pörtner’s (2001) experiment). In order to account for possible effect of sampling time, the starting and stop time for sampling from the tray at each treatment was taken. Sampling took between 40-59 minutes. For all treatments, samples were picked from all parts of the tray (middle, upper, lower part and including snails from the edge to include possible edge effects). Controls were sampled after 0h and 48h. The width of the shells (Figure 4B) was measured with a calliper and then snails were crushed by the Figure 5 Emersion experiment. Showing tray with snails on paper. back of a tweezer while enfolded in tissue and put into Photo: C. Helmerson RNAlater® medium (Ambion). Samples were kept at room temperature (< 12h) and then stored in the refrigerator before freezing at -20°C.

9 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

2.3 Dissection and extraction

Snails with a size around 5 mm (4-5.5 mm) were chosen for extraction. Samples were thawed on ice and dissected under stereo-microscope (50X) on paper tissue, removing most or all of shell and operculum. The breeding pouch was removed from female snails (or removal of embryos without removing pouch when possible) in order to only get RNA from one individual. Larger pieces such as the foot were cut to aid homogenisation. Homogenisation of samples and extraction of RNA was made with TRI-Reagent® (Sigma-Aldrich) following the protocol in S1.

2.4 Quality check and reverse transcription

RNA extractions quality was checked with a NanoDrop® ND-1000 Spectrophotometer. Quality was assessed via concentration of nucleic acid by measurement at 260/280 nm and 260/230 nm ratios. Extracted samples were stored at -80°C. A subset of samples, representing different extractions and treatments were checked for degradation. This was done using denaturing agarose gel electrophoresis. Approx. 1000ng of RNA were mixed with RNAse free water to 15 µl volume and 3 µl of Formaldehyde Load Dye (Ambion), incubated at 65°C for 5 min and cooled on ice for 1 min. Samples were loaded on MOPS-gel (See S2) and run at 50-100V for ∼ 20-30 min (or until good separation of bands) in MOPS (3-(N-morpholino) propanesulfonic acid) running buffer (S2) in a RunOne Electrophoresis cell (Embi Tec). Bands were inspected under UV. RNA was converted to single-stranded complementary DNA (cDNA) by reverse transcription reaction. iScriptTM Select cDNA synthesis kit (Bio-Rad Laboratories Inc) with Random primers was used following the manufacturers instruction (Details in S3). Incubation was performed in an Eppendorf Mastercycler gradient PCR machine or an ABI VeritiTM 96-Well Thermal Cycler for the incubation.

2.5 Quantitative PCR

2.5.1 Quantification

Quantification of gene expression was measured by relative qPCR in an StepOnePlusTM Real-Time PCR System (96 wells, Applied Biosystem Inc) in 20 µl reactions using 10 µl Fast SYBR® Green Master Mix (Applied Biosystems Inc), 4 µl cDNA (1:10 dilution), 2 µl RNAase/Nuclease free water (non template controls set using 6ul water and no cDNA), 2 µl forward primer and 2 µl reverse primer (Primer sequences provided in Table 1). Optimisation of qPCR conditions performed before the final experiment is described below (2.5.2). Cycling conditions were at 95°C for 20 sec, 40 cycles at 95°C for 3 sec + 60°C for 30 sec followed by a meltcurve stage. Cycling for meltcurve was 95° 15 sec, 60° 1 min, 95° 15 sec. (Default parameters) Gene expression for the cytosolic and mitochondrial targets was measured together with five control genes18S, Ubiq, RibP1, H3.3, EF2 (Table 1). All primers for target genes (two pairs for cyt-Aat: Aatc2, Aatc4 and one for mt-Aat, Aatmt) were designed in Primer Express® Software v. 2.0 (Applied Biosystems Inc) as well as for three of the control genes: Small ribosomal subunit (18S), Ubiqutin (Ubiq) and Ribosomal protein 1 (RibP1). Sequences for cytosolic Aat was from Mittermayer (2013), for other genes from the Littorina sequence database (Canbäck et al. 2012). In addition, the primers for the other two control genes (Histone H3.3 and Elongation Factor 2, EF2) were obtained from Martínez-Fernández et al. (2010). Five individuals were run on each plate with targets and controls run on same plate. (Design in S4). The order of individuals and treatments were random at first. Last plates were being 10 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

run with samples for treatments missing or in need for rerun. This was done in order to get 3-4 individuals for each treatment. In total the final experiment included 7 plates. For 2 individuals either one or two of the control genes were rerun on other plates due to possible technical error and for one individual targets and control genes were on different plates because of a mistake with the master mix. In the first two cases reruns made little difference so old runs were kept. In the other case output values were similar to other individuals in the same treatment.

2.5.2 Optimisation and validation

Due to SYBR®Green dye binding to non-specific PCR products and primer dimers (i.e. any double- stranded DNA), primer optimization is necessary before the measurement. The purpose of the optimization step is to determine minimum primer concentration that gives the lowest threshold cycle

Ct and minimizes non-specific amplification and primer dimer formation. Optimisation was made with the same cycling conditions (including meltcurve stage) as in 2.5.1. Initial primer check was first performed with a 10 µM primer concentration and cDNA (1:10 dilution). This test cDNA was synthesised from a pooled RNA sample (0.5 µg put into reaction) using 2-3 individuals from several treatments (0h control, 2h treatment and 48h treatment). An equal amount of RNA was taken to pool from each sample. Optimisation was further explored using cDNA (again 1:10 dilution, reaction made from pooled RNA) with primer concentrations of 0.5, 1, 2 and 4 µM. 0.5 µM was found to be optimal, with least amplification in no-template control (NTC). A standard curve was made for each gene, as a validation necessary for final calculation and comparison of PCR efficiencies. Validation qPCR was performed with serial dilutions of 1:2, 1:4, 1:8, 1:16 and 1:32 of cDNA (1:10 dilution, reaction RNA pool). A gene was considered optimised if its standard curve had a slope of between - 3.58 and -3.10, and efficiency of 100% ±10% and no outliers. Efficiency reflects the rate at which PCR amplicon is generated in the PCR reaction. Efficiency at 100% (corresponding slope is -3.32) tells that PCR product is doubled each cycle during the geometric phase of the reaction (see standard curves in S5). E values were calculated from slope according to Equation 1.

� = 10!!/!"#$% − 1 (Equation 1)

All targets were optimal according to these conditions but not all control genes. However, excluding all control genes with un-optimal PCR efficiency would make relative quantification rely solely on 18S. Since 18S is highly expressed this would not be preferable (Life technologies 2012).

11 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Target

Name Primer sequences (forward and reverse) Function Slope , E Efficiency % Aatc2 5´-TGGTCAAGACTGTGGAGAATCAA-3´ Trans- -3.162, Cytosolic aspartate 5´-CGGCAGATATTCGTGGTTCA-3´ aminase 2.071 aminotransferase (107%)

Aatc4 5´- CGCCCAGTCGTTCTCCAA-3´ Trans- -3.228, Cytosolic aspartate 5´- CGATACACAGGTTGCCAATTCTC-3´ aminase 2.041 aminotransferase (104%)

Aatmt 5´-AATGTGGGCTACTTGGCTAAGG-3´ Trans- -3.291, Mitochondrial aspartate 5´-TGACGGCAAGACGCTCACT-3´ aminase 2.013 aminotranferase (101%)

Control genes

Name Primer sequences (forward and reverse) Function Slope, E Efficiency % 18S 5´- CGTCCCTGCCCTTTGTACAC-3´ Part of -3.341, 18S rRNA 5´- CCCTCACTAAACCGTTCAATCG-3´ ribosome 1.992 (99%)

RibP1 Part of -2.957, Ribosomal protein P1 5´- CGTTTATGCCGCACTTATCTTG -3´ ribosome 2.179 5´- CAGCTTCTCGCCCGTGAT-3´ (118%)

H3-q* Mark active -2.946, Histone H3.3 5´- AGAGTGCTCCCTCAACTGGA-3´ chromatin 2.185 5´- GTCCTCAAAGAGACCCACCA-3´ (118%)

EF2-q* Trans- -2.988, Elongation factor 2 5´-ACGCATGTTCTCCTCACACA-3´ lational 2.161 5´-CGCTACCTGGTGGACAACTT-3´ elongation (116%)

Ubiq Mark added -2.864 Ubiquitin 5´- CAACATCCGCCCCAAGAA-3´ to protein 2.234 5´- AGCACAGCGAGAGGAAGCA-3´ for (123%) degradation

Table 1 Abbreviations, full names and primer sequences for target and control genes used in final experiment. *Two primer pairs for control genes obtained form study by Martínez-Fernández et al. (2010). Primers for cytosolic Aat designed from the sequences obtained by Felix Mittermayer (2013). Other primers designed from sequences obtained from the Littorina sequence database (Canbäck et al. 2012).

12 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

2.6 Data analysis

2.6.1 Quantification of gene expression

Ct values were calculated as the mean of the two technical replicates (Equation 2) for each individual and gene and corrected by the E values (efficiencies) obtained from validation (Equation 3). ΔCt were calculated in relation to Ct control (Equation 4 and Equation 5), the latter being the geometrical mean of the control genes (Vandelsompele et al. 2002)

�! !"#! + �! !"#! �! = 2 (Equation 2)

�! !"##$!%$& = �! ∗ ��!� (Equation 3)

! �! !"#$%"& !"#$ = �! !"#$%"& !"#" ! + �! !"#$%"& !"#" ! + ⋯ . �! !"#$%"& !"#" ! (Equation 4)

∆�! = �! !"#$%! − �! !"#$%"& (Equation 5)

2.6.2 Statistical analysis

Statistical tests were first made to check fulfilment of tests criteria, then to test for expression difference between target genes and between treatments. The latter statistically testing the hypothesis of ΔCt (gene expression level) correlated between targets and changed with treatment. All statistical tests were made in IBM® SPSS® Statistics v. 22.0.0, and having p=0.05 as the critical level of rejection. ΔCt data was assumed to be unrelated between individuals and between treatments, but related between targets, especially the two primer pairs for cytosolic Aat. Before testing difference between treatments test criteria was assessed by a test of Normality, followed by Levene´s test, testing for normality and homogeneity of variances. Because of non-normality a non-parametric Levene´s test were used to assess homogeneity of variances (Nordstokke & Zumbo 2010). Looking at difference between treatments, data were showing non- normality (S6) but homogeneity of variances (S7). Therefore a Kruskal Wallis ANOVA (nonparametric test) was used to test for significant differences between treatments. Before testing the difference or relationship between targets, test criteria were assessed by normality tests. In case of Aatc2 and Aatc4, a normality test looking at the difference between them was also made (non significant result criteria for paired t-test). Variances were assumed to be homogenous. Aatc2 and Aatc4 were normally distributed (and their difference as well) but Aatmt was not (S6). Therefore a parametric paired sample t-test was made to test the expression difference between primer combinations of cytosolic target, accompanied by a parametric correlation test. Pairwise correlations between Aatmt and the other were made with Spearman’s correlation test due to the non-normality detected in the Aatmt data. 13 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

3. Results 3.1 Effect of emersion

The primer combination for Aatc2 showed no significant difference between treatments i.e no effect of 2 emersion on gene expression level (Kruskal Wallis: χ 2 =4.873, n1=4, n2=4 n3=3, n4=3, n5=4, n6=4, n7=4, n8=4, n9=3, p=0.771)(S8), with much variation within each treatment (sampling point) rather than between treatments (Figure 6).

Figure 6 Gene expression given as ΔCt for primer combination for Aatc2 for immersed controls (C1 and C2 taken after 0h and 48h sampling) and emersion treatment for 0-48 h at room temperature and humidity. Scale of x-axis not proportional to time.

14 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Figure 7 Gene expression given as ΔCt for primer combination for Aatc4 for immersed controls (C1 and C2 taken after 0h and 48h sampling) and emersion treatment for 0-48 h at room temperature and humidity. Scale of x-axis not proportional to time.

Primer combination Aatc4 for cytosolic Aat did not show any significant difference with emersion either, variation being between individuals within sampling points rather than between (Kruskal 2 Wallis: χ 2 =11.317, n1=4, n2=4 n3=3, n4=3, n5=4, n6=4, n7=4, n8=4, n9=3, p=0.184)(Figure 7, S8), 2 neither did the mitochondrial target Aatmt (Kruskal Wallis: χ 2 =5.459, n1=4, n2=4 n3=3, n4=3, n5=4, n6=4, n7=4, n8=4, n9=3, p=0.708)(Figure 8, S8).

Figure 8 Gene expression given as ΔCt for primer combination for Aatmt for immersed controls (C1 and C2 taken after 0h and 48h sampling) and emersion treatment for 0-48 h at room temperature and humidity. Scale of x-axis not proportional to time. 15 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

3.2 Correlations and comparisons between targets

Visual inspection of scatters (Figure 6 and 7) revealed a difference in expression between the two primer combinations targeting the cytosolic aspartate aminotransferase, Aatc2 and Aatc4 (Figure 9A). The paired t-test for difference between the means of Aatc2 and Aatc4 was significant t=7.496 df=32 p=0.000 (Figure 9B), with Aatc2 primer pair showing generally higher level of expression. The pairwise correlation was non-significant (Pearson’s Correlation=0.142, p=0.429) (Scatter Figure 9C). A B

Figure 9 A) Gene-expression as ΔCt for Aatc2 and Aatc4 showing both data sets together. Scale of x-axis not proportional to time. B) Mean gene-expression with 95% confidence for Aatc2 and Aatc4. C) Scatter showing ΔCt Aatc2 and Aatc4, pairwise correlation being non-significant.

Pearson’s r = 0.142 p=0.429

16 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Correlation between Aatc2 and Aatmt was non-significant (Spearman’s rho= -0.070, p=0.699, Figure 10). Aatc4 and Aatmt showed a significant weak positive correlation (Spearman’s rho=0.369 p=0.035, Figure 11)

Spearman’s rho=-0.070 p=0.699

Figure 10 Scatter showing correlation between ΔCt of Aatc2 and Aatc4.

Spearman’s rho=0.369 p=0.035

Figure 11 Scatter showing correlation between ΔCt of Aatc4 and Aatmt. Correlation weak and positive but significant.

17 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

4. Discussion

4.1 Main results

Expression levels between treatments were similar across treatments, i.e. no up-regulation of cytosolic or mitochondrial Aat genes during any stage of emersion. Experimental results indicate that variation in gene expression was larger between individuals within the same treatment than between treatments. Furthermore, the experiment indicated correlation between cytosolic and mitochondrial expression of Aat (even if not perfectly linear and only for one primer pair). The two primer pairs targeting cytosolic Aat showed significantly different levels of expression that did not correlate with each other across the treatments.

4.2 Humidity

A possibility for the absence of response in gene expression is that relative humidity throughout experiment was too high to desiccate snails. However, snails did withdraw and except for two snails at 24h, no snails were observed to come out of the shells (snails not constantly watched). The activity of the two individuals suggests that a properly desiccated state was not reached. Air exposure time at the humidity level of the experiment may be a poor indicator for when snails enter anaerobic metabolism. Sokolova & Pötner (2001) measured a decline in substrate L- aspartate in the foot of L. saxatilis throughout their experiment using dry air at 30°C. This is considerably drier than the RH of 50-57% measured at sampling in this experiment. Santini et al. (2001) were not able to measure a decrease compared to control in substrate L-aspartate concentration in limpets at 55% RH either at 6h or 18h, but when in anoxic water for 6h and 18h they found L- aspartate to decrease. Brinkhoff et al. (1983) measured metabolites in molluscs both under experimental hypoxia (water bubbled by N2) and at natural emersion. They found that despite fining a decrease in L-aspartate under hypoxia in Patella vulgata and Scrobicularia plana (0h-24h), at normal emersion periods out in nature (0-6h) there was no such decline (no humidity data provided)(Brinkhoff et al. 1983). One way to test gene expression under real life humidity conditions would be to sample snails at natural periods of desiccation, with immediate fixation in RNAlater®. One approach would be to sample after prolonged desiccation. Out in nature snails in the high shore could experience much severe conditions, desiccating at 40°C for days and possibly even weeks (personal communication K. Johannesson). The problem of such an experiment would be to have proper controls knowing at which state of desiccation snails starts at. Sampling at immersion or humid conditions would be one way of solving it. Humidity and temperature measurements should be performed together with sampling. A simplified approach would be measuring humidity and temperature at emersion in nature and redesigning indoor experiment.

4.3 Regulation of gene expression

Another explanation for absence in response could be that the aspartate aminotransferase activity is not transcriptionally regulated - that is there is no correlation between the levels of mRNA and enzyme activity. A way to test this could be measuring mRNA expression and enzyme activity in the same samples (I. Sokolova, personal communication), but this cannot be done with RNAlater® fixed samples. One way to circumvent this would be to rerun the emersion experiment with new snails and

18 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

taking some snails for activity analysis and some for RNAlater® fixation. Then activity levels could be compared with gene expression even if not in same individuals. Boutet et al. (2005) reported the presence of motifs in UTRs of cytosolic Aat in C. gigas associated with transcript stability. They also noted that abiotic stress such as salinity and hypoxia did not seem to affect mRNA levels the same way as hydrocarbons. They emphasised the need for looking at mRNA expression and activity together and suggested that mRNA levels and activity do not have to reflect each other. In vertebrates lack of correlation between Aat enzyme activity have already been reported in rats by Abruzzese et al. (1995), who despite finding increase of mRNA with age did not observe change in enzyme activity. A point of consideration may also be that at each time-point both old and new transcripts are fixed, extracted and measured. The half-life of Aat transcripts is unknown (as far as the author is aware). However in yeast median half-life of most mRNAs is 11 min (Miller et al. 2011). With similar half-life the time frames of this experiment should not be prone to carry over. But this should not be taken for granted. L. saxatilis could for example have the motifs found in C. gigas reported by Boutet et al. (2005). Fixed genetic variation in Aat and that there are allozymes “adapted” to high and low shore does not exclude the possibility of regulation of this gene at the expression level. Changes in coding sequence can be combined with changes in regulatory sequence that may as well be adaptive, as seen for lactate dehydrogenase-B in the fish Fundus heteroclitus. In F. heteroclitus there are coding differences between northern and southern populations but also a measurable difference in the expression in the liver due to changes in the regulatory sequence that is thought to be the main reason for the observed difference in activity (reviewed by Schulte 2001). Arginine kinase in L. saxatilis is an example where an enzyme is expressed differently between Spanish ecotypes occupying different height on the shore and shelter – exposure (Diz et al. 2012). In a closely related species, Littorina fabalis there is "a near fixation" of different arginine kinase allozyme alleles between the sheltered and exposed Swedish ecotypes (Duvetorp et al., unpublished). Whether there are expressional differences between these ecotypes remains to be tested.

4.4 Correlation and comparisons between targets

The correlation between mitochondrial Aat and cytosolic Aat is logical but it is hard to say anything about the exact nature. Both are a part of amino acid metabolism and catalyse the same reaction. However the correlation is weak and not perfectly linear. The difference between Aatc2 and Aatc4 and absence of correlation between the two is unexpected since they target the same gene. One difference between these two primer pairs is their genomic position (Marina Panova, personal communication); the Aatc2 primers are both situated within the same exon (and the fragment could be amplified also using genomic DNA as template) while Aatc4 primers are in two different exons. It is possible that the higher level of Aatc2 fragment could be due to DNA contamination of RNA extractions. This is a possibility since samples were not DNAse treated before reverse transcription to cDNA. One way to asses for contamination would be run a PCR with extracted RNA using the qPCR primers. An alternative is comparing qPCR results for sample before and after DNAse treatment. In addition, the analysis of the latest assembly of L. saxatilis genome suggests that there are two copies of cytosolic Aat gene one after the other; the second however having a frameshift breaking translation and is unlikely to code for a functional protein (Marina Panova, pers. communication). While the second copy may be still transcribed, it is unlikely that the expression level of this copy was measured because the designed primers have some mismatches to it. Further, 19 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

both primers pairs showed the same pattern (i.e. perfect match to the first gene copy, mismatches to the second). Thus, the second copy cannot explain the differences in expression level observed between the primer pairs.

4.5 Individual variation

In the population there will also be three different genotypes and genotype specific expression might be present. Testing whether variation between individuals could be due to genotype specific expression would be interesting - since enzyme activity empirically varies between genotypes (Panova & Johannesson 2004). Genotype information could possibly identify “expression clusters” within the data. The individual variation also shows that sample size should be increased. Since there are few replicates the full variation may not be captured. Even if snails did become anaerobic, changes in gene expression of Aat cannot be ruled out in specific tissues since analysis here was confined to whole snails. In order to detect where gene expression is important in situ hybridization could be an option in future studies. Snails analysed from the experiment were also confined to a size around 5 mm so size or age-dependent changes are not possible to rule out. Unanalysed samples from the experiment could be analysed to assess this.

4.6 Technical aspects

At each stage of this study there will be added technical variation or error: during dissection, extraction, cDNA conversion, qPCR plate preparation. Variation in dissection could for example result in some tissues being unrepresented, but for whole snails this should not be a huge problem. Variation in extraction and cDNA conversion should affect both targets and controls in a similar matter, i.e. relation between target and control should be the same. Variation between the plates could potentially be an issue and one way to address this could be to exclude 3 treatments and only proceed with 5 treatments, (i.e. having the whole experiment in one plate including treatment controls). The sub-optimal efficiency of all control-genes except 18S could also affect the quality of the data. For the control genes another consideration could be metabolic rate depression and possible down-regulation of over all transcription. However in this experiment the geometrical mean of the controls was relatively stable and no statistical difference detected (S9).

4.7 One gene of many

Aat is one gene of many involved in anerobic metabolism. Other genes of interest could also be added in future studies. For assessing the anaerobic pathway where Aat is involved, genes such as alanine transaminase, malate dehydrogenase, fumarase involved in the same pathway as Aat could be suitable. Other interesting genes to study could be the genes that have been found to be up- regulated in L. littorea under anoxia: COII, ribosomal protein L26, ferritin heavy chain, granulin and kvn (Larade & Storey 2002a). The latter gene kvn, could be specially interesting. It is a previously unknown gene with mRNA found to be increasing in hepatopancreas of L. littorina under anoxia and it is thought to be both transcribed and translated during anoxic stress (Larade & Storey 2002b).

20 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

5. Conclusion

This study cannot cannot reject the null-hypothesis of no difference in gene expression of aspartate aminotransferase at emersion in the E-morph of L. saxatilis. The study cannot tell the nature of the correlation between cytosolic and mitochondrial Aat. The directions for further studies would be to test whether there is genotype specific expression, increase sample size and test more extreme and realistic stress. Measurement of expression and activity together is also desirable and comparison to real-life conditions.

6. Acknowledgements

This project would never have come into place without three persons: Marina Panova my supervisor, my second supervisor Olga Ortega-Martinez who introduced me to the world of qPCR and who designed some of the primers and Felix Mittermayer. I also would like to thank Kerstin, (and Marina), Zusannah, Bo, Mårten and Simon for joint sampling that cold winter morning in January at Ursholmen. For advise with aquaria Martin Ogemark. For help spreading out snails on tray Amandine. Also special thanks to Anna-Karin, Helene, Ricardo, Laura, Henrik, Irena and all others at Tjärnö. I would also like to thank my parents and Daniel for their support.

7. References

Abruzzese F., Greco M., Perlino E., Doonan S., Marra E. 1995. Lack of correlation between mRNA expression and enzymatic activity of the aspartate aminotransferase isoenzymes in various tissues of the rat FEBS Letters 366:170-172

Brinkhoff W., Stöckmann K., Grieshaber M. 1983. Natural occurrence of anaerobiosis in molluscs from intertidal habitats. Oecologica 57: 151-155

Boutet I., Meistertzheim A-L., Tanguy A.,Thébault M-T., Moraga D. 2005. Molecular characterization and expression of the gene encoding aspartate aminotransferase from the Pacific oyster Crassostrea gigas exposed to environmental stressors. Comparative Biochemistry and Physiology Part C 140: 69–78

Canbäck, B., André, C., Galindo, J., Johannesson, K., Johansson, T., Panova, M., Tunlid, A., and Butlin, R. 2012. The Littorina sequence database (LSD)–an online resource for genomic data. Molecular Ecology Resources 12:142–148.

De Zwaan A.D., Putzer V. 1985 Metabolic adaptions of intertidal invertebrates to environmental hypoxia (a compairson of environmental anoxia to exercise anoxia). In M.S Laverack (eds.): Physiological Adaptions of Marine Animals. Pp. 33-62 Symposia of the Society for Experimental Biology Symposium XXXIX, Cambridge: The Company of Biologists Limited

Diz A.P., Martínez-Fernández M., Rólan-Alvarez E. 2012. Proteomics in evolutionary ecology: linking the genotype with the phenotype. Molecular Ecology 21: 1060–1080

Hand S.C., Menze M.A. 2007. Desiccation stress. In M.W Denny & S.D Gaines (eds.): Encyclopedia of tidepools and rocky shore. Pp. 173-177. Berkley: University of California Press.

Janson K. 1982. Phenotypic differentiation in Littorina saxatilis Olivi (Mollusca, Prosobranchia) in a small area on the Swedish west coast Journal of Molluscan Studies 48:167-173

21 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Janson K., Sundberg P. 1983. Multivariate morphometric analysis of two varieties of Littorina saxatilis from the Swedish west coast Marine Biology 74: 49-53

Johannesson K. 2003. Evolution in Littorina: ecology matters. Journal of Sea Research 49:107-117

Johannesson K., Johannesson B. 1989. Differences in allele frequencies of Aat between high- and mid-rocky shore populations of Littorina saxatilis (Olivi) suggest selection in enzyme locus. Genetics Research 54:7-11

Johanesson K., Johanesson B., Lundgren U. 1995. Strong natural selection causes microscale allozyme variation in a marine snail. Proceedings of the National Academy of Science 92:2602-2606

Larade K., Storey K.B. 2002a. Chapter 3 A Profile of the Metabolic Responses to Anoxia in Marine Invertebrates. In K.B Storey & J.M Storey (eds.) Sensing, Signaling and Cell Adaption. Pp. 27-46. Netherlands: Elsevier Science

Larade K., Storey K.B. 2002b. Characterization of a novel gene up-regulated during anoxia exposure in the marine snail, Littorina littorea. Gene 283:145–154

Life Technologies Corporation. 2012. Real-time PCR handbook. CO32085 0812

Martínez-Fernández M., Bernatchez L., Rolán-Alvarez E., Quesada H. 2010. Insights into the role of differential gene expression on the ecological adaption of the snail Littorina saxatilis. BMC Evolutionary Biology 10:356

Miller C., Schwalb B., Maier K., Schulz D., Dümcke S., Zacher B., Mayer A., Sydow J., Marcinowski L., Dölken L., Martin D.E., Tresch A., Cramer P. 2011. Dynamic transcriptome analysis measure rates of mRNA synthesis and decay in yeast. Molecular Systems Biology 7:458

Mittermayer F. 2013. Sequencing a Gene under Strong Selection. Aspartate Aminotransferase in North Atlantic Littorina Degree project for Master of Science (Two Years) in Biology, Universiy of Gothenburg, Department of Biological and Environmental Sciences

Müller M., Mentel M., van Hellemond J.J., Henze K., Woehle C., Gould S.B., Yu R-Y., van der Giezen M., Tielens A.G.M., Martin W.F. 2012. Biochemistry and Evolution of Anaerobic Energy Metabolism in Eukaryotes. Microbiology and Molecular Biology Reviews 76(2):444. DOI:10.1128/MMBR.05024-11.

Nordstokke D.W., Zumbo B.D. 2010. A New Nonparametric Levene Test for Equal Variances Psicológica 31(2): 401-430

Panova M., Johannesson K. 2004. Microscale variation in Aat (aspartate aminotransferase) is supported by activity differences between upper and lower shore allozymes of Littorina saxatilis Marine Biology 144: 1157– 1164

Raffaelli D., Hawkins S. 1999: 1 The shore environment: major gradients. In Intertidal Ecology (second ed.). Pp. 1-35. Dordrecht, Netherlands: Kluwer Academic Publishers.

Reid D.G. 1996. Systematics and evolution of Littorina. London: Ray Society, Henry Ling Limited (The Dorset Press, Great Britain, Dorchester, Dorset)

Rojas J.M., Castillo S.B., Escobar J.B., Shinen J.L., Bozinovic F. 2013. Huddling up in a dry environment: the physiological benefits of aggregation in an intertidal gastropod Marine Biology 160:1119–1126

22 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Santini G., Bruschini C., Pazzagli L., Pieraccini G., Moneti G., Chelazzi G. 2001. Metabolic responses of the limpet Patella caerulea (L.) to anoxia and dehydration. Comparative Biochemistry and Physiology Part A 130:1-8

Schulte P.M. 2001. Environmental adaptations as windows on molecular evolution. Comparative Biochemistry and Physiology Part B 128:597-611

Sokolova I.M., Granovitch A.I., Berger V.Ja., Johannesson K. 2000 Intraspecific physiological variability of the gastropod Littorina saxatilis related to the vertical shore gradient in the White and North Seas. Marine Biology 137:297-308

Sokolova I.M., Pörtner H.O. 2001. Physiological adaptions to high intertidal life involve improved water conservation abilities and metabolic rate depression in Littorina saxatilis. Marine Ecology Progress Series 224:171-186

Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A., Speleman F. 2002. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3(7):research0034.1–0034.11

23 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

7. Supplementary material

S1- Extraction protocol

1) Sample + 1.0 mL TRI-Reagent (or TRIzol), homogenize 10 min with metal beads in a shaker (Retsch Mixer Mill Type MM301)(+ 10 min more if visible pieces of tissue). 2) Incubation 5 min. 3) Removal metal beads with tweezer (rinse tweezer with DEPC (Diethyl pyrocarbonate) treated water and dry with tissue paper between each sample). 4) Freezing samples at -80°C (if protocol run in two days) 5) Thawing samples (if protocol run in two days). 6) Add 100 µl 1-bromo-3-chloropropan and vortex 15s and incubate at room temperature for 15 min (Transfer to Heavy Gel Lock Coulums if colums (pre-spun) should be used). 7) Centrifuge 5 min at 12 000 x g 4°C 8) Pipette out top-phase to new tubes or pour if using columns. 9) Add 250 µl isoporopanol and 250 µl high salt solution (0.8 M sodium citrate and 1.2 M NaCl) 10) Vortex 10 s 11) Incubate 10 min at room temperature 12) Centrifuge 30 min at 12 000 x g 4°C 13) Remove supernatant by pipetting or pouring 14) Wash pellet twice with 1 mL 75% RNAse free ethanol by adding ethanol and centrifuge 5 min at 12 000 x g 4°C and then remove ethanol 15) add 15 µl RNAse free water (Ambion), pipette up and down a few times, briefly heat at 55-60°C in heating block 17) Put on ice

(Protocol provided by Marina Panova)

S2 – MOPS gel and buffers

S2.1 Agarose gel for RNA

RNA 1.0 g agarose (Ambion), 90 mL ddH2O or MilliQ H2O, 10mL MOPS, 3.2µl GelRed

S2.2 Running buffert MOPS

(300 ml) 30ml 10xMOPS + 270ml ddH2O

S3 - cDNA synthesis*

*Following Bio-Rad Inc (the iScriptTM Select cDNA Synthesis Kit’s manufacturers) instructions:

Reaction mix (20 µl): 4.0 µl iScript reaction mix, 1 µl iScript reverse transcriptase, 2 µl Random primer mix, x µl Nuclease-free water, x µl RNA template to get 0.5 ug of RNA

Incubation: 25°C 5 min, 42°C 30 min, 85°C 5 min, 4°C ∞

24 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S4 – plate design

25 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S5 – Standard curves

Standard curves as obtained from qPCR machine.

Aatc2 Aatc4

Aatmt 18S

26 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

RibP1 H3-q

EF2-q Ubiq

27 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S6 Normality tests S. 6.1 Between treatments

Tests of Normality

Treatment Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Aatc2 0h 0.258 4 . 0.917 4 0.52 2h 0.349 3 . 0.832 3 0.193 4h 0.342 3 . 0.845 3 0.227 6h 0.241 4 . 0.902 4 0.439 12h 0.267 4 . 0.945 4 0.684 24h 0.281 4 . 0.881 4 0.344 48h 0.263 4 . 0.907 4 0.466 C1 0.213 4 . 0.955 4 0.747 C2 0.378 3 . 0.768 3 0.039* Aatmt 0h 0.291 4 . 0.864 4 0.273 2h 0.336 3 . 0.857 3 0.259 4h 0.253 3 . 0.964 3 0.636 6h 0.408 4 . 0.687 4 0.008* 12h 0.207 4 . 0.985 4 0.932 24h 0.266 4 . 0.873 4 0.311 48h 0.213 4 . 0.941 4 0.662 C1 0.185 4 . 0.968 4 0.827 C2 0.381 3 . 0.759 3 0.02* Aatc4 0h 0.159 4 . 0.99 4 0.959 2h 0.271 3 . 0.947 3 0.557 4h 0.374 3 . 0.777 3 0.061 6h 0.267 4 . 0.893 4 0.396 12h 0.2 4 . 0.982 4 0.912 24h 0.269 4 . 0.949 4 0.713 48h 0.211 4 . 0.966 4 0.814 C1 0.394 4 . 0.737 4 0.029* C2 0.336 3 . 0.857 3 0.258 a Lilliefors Significance Correction

28 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S. 6.2 Between targets

Tests of Normality

Target Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Aatc2 0.148 33 0.064 0.951 33 0.147 Aatmt 0.191 33 0.004 0.930 33 0.035* Aatc4 0.092 33 0.200b 0.969 33 0.450

a Lilliefors Significance Correction b This is a lower bound of the true significance

Tests of Normality a Target Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Difference Aatc2 .145 33 .077 .953 33 .162 – Aatc4 a. Lilliefors Significance Correction

29 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S7 Test assessing homogeneity of variances S. 7.1 Between treatments

Nonparametric Levene’s Testa

Target Sum of Squares df Mean Square F Sig. Aatc2b Between Groups 284.441 8 35.555 2.303 .055 Within Groups 370.558 24 15.440 Total 654.999 32 Aatc4b Between Groups 277.238 8 34.655 2.207 .064 Within Groups 376.891 24 15.704 Total 654.129 32 Aatmtb Between Groups 209.940 8 26.242 1.573 .185 Within Groups 400.363 24 16.682 Total 610.303 32

a ANOVA made on data first being treated as in b. b Data being ranked, then calculating difference between mean rank for treatment group (C1, 0h…) – individual rank (use absolute values) (Nordstokke & Zumbo 2010).

30 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S8 Kruskal Wallis Anova

Ranks a,b Test Statistics Treatment N Mean Rank Aatc2 Aatc4 Aatmt Aatc2 C1 4 17.25 Chi-Square 4.873 11.317 5.459 0h 4 17.75 df 8 8 8 2h 3 10.33 Asymp. Sig. .771 .184 .708 4h 3 11.33 6h 4 16.25 a. Kruskal Wallis Test 12h 4 20.50 b. Grouping Variable: Treatment 24h 4 16.75 48h 4 23.50

C2 3 16.00 Total 33 Aatc4 C1 4 20.25

0h 4 10.00 2h 3 26.67 4h 3 13.00 6h 4 14.00 12h 4 18.00 24h 4 14.50 48h 4 25.75 C2 3 10.67

Total 33 Aatmt C1 4 23.00 0h 4 9.50 2h 3 16.33 4h 3 18.33 6h 4 18.25 12h 4 17.75 24h 4 16.25

48h 4 20.50 C2 3 12.00 Total 33

31 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

S9 Geometrical mean

ANOVA, test of normality and Levene´s test for homogenity of variances included below

ANOVA Geomed Sum of Squares df Mean Square F Sig. Between Groups 11.976 8 1.497 1.447 .228 Within Groups 24.832 24 1.035 Total 36.808 32

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk Treatment Statistic df Sig. Statistic df Sig. Geomed C1 .250 4 . .917 4 .523

Ct 0h .179 4 . .976 4 .880 2h .237 3 . .976 3 .706 4h .279 3 . .938 3 .521 6h .161 4 . .990 4 .956 12h .290 4 . .933 4 .612 24h .260 4 . .892 4 .391 48h .165 4 . .985 4 .933 C2 .237 3 . .976 3 .706 a. Lilliefors Significance Correction

32 Degree Project for a Master of Science in Marine Sciences and Biology Cecilia Helmerson University of Gothenburg

Test of Homogeneity of Variances Geomed Levene Statistic df1 df2 Sig. 1.754 8 24 .137