Heat tolerance and gene expression responses to

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Journal of Thermal Biology journal homepage: www.elsevier.com/locate/jtherbio

Heat tolerance and gene expression responses to heat stress in threespine sticklebacks from ecologically divergent environments Karin Brydsø Dammark, Anne-Laure Ferchaud1, Michael M. Hansen, Jesper G. Sørensen

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Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark

A R T I C LE I N FO

A B S T R A C T

Keywords: Climate change Thermal adaptation QPCR CTmax

Ectothermic animals like fishes are extremely dependent on temperature, as they are not able to change body temperature physiologically. When populations are found in isolated water bodies such as small lakes they will have to respond to stressful high temperatures by behavioral avoidance, phenotypic plasticity or microevolutionary change. We analyzed threespine sticklebacks from two large and two small lakes, representing different isolated populations. We determined maximum critical thermal limits (CTmax) and the associated gene expression responses in three heat shock (hsp60, hsp70, hsp90) and two key metabolic (idh2, fbp2) genes at ecologically relevant moderate heat stress (26 °C) as well as at the critical thermal limit (CTmax). CTmax showed slight variation across populations with no strong indication of local adaptation. Likewise, there was no strong evidence for local adaptation at the level of gene expression. The expression of the metabolic genes indicated a shift from aerobic towards anaerobic energy production with extreme heat stress. We conclude that threespine sticklebacks do not show severe stress during the warmest temperatures they are likely to encounter during current temperature regimes in Denmark, and following this show no sign of local adaptation even in small, isolated water bodies.

1. Introduction Anthropogenic climate change is increasingly causing elevated global temperatures (Kerr, 2007; Marcott et al., 2013; Moss et al., 2010), and effects on plants and animals are already evident (Geerts et al., 2015; Parmesan, 2006; Sydeman et al., 2015). The effects of climate change on living organisms are complex and manifold. Temperature in itself, and particularly extreme summer temperatures may cause heat stress (Deutsch et al., 2008; Hoffmann and Sgro, 2011; Pörtner and Knust, 2007; Pörtner and Peck, 2010). Whether organisms will cope with rapid changes in environmental conditions via shortterm plastic responses (i.e. acclimatization), local adaptation, or a combination of these mechanisms is not well understood. In addition, it is not clear if local adaptation can occur rapidly enough to track changes in climate. Some studies have demonstrated genetic change at ecologically important traits and markers coinciding with climate change (Balanya et al., 2006; Bradshaw and Holzapfel, 2001; Geerts et al., 2015; Pulido and Berthold, 2010; Umina et al., 2005), but in many other cases it is unclear if observed responses reflect phenotypic plasticity or evolutionary genetic change (Crozier and Hutchings, 2014; Hansen et al., 2012; Merila and Hendry, 2014). ⁎

Fishes are [with a few exceptions (Dickson and Graham, 2004)] ectothermic and thereby unable to change body temperature physiologically. The physiology of fishes is therefore highly influenced by temperature that affects general metabolism and thereby activity, reproduction and growth (Ficke et al., 2007; Hill et al., 2008; Wootton, 1998). So far, most research on temperature adaptation and climate change adaptability in fishes has focused on relatively few model systems. These studies have found local or environment-dependent temperature-related adaptation in life-history traits (Cote et al., 2016; Haugen and Vøllestad, 2000; Jensen et al., 2008; Koskinen et al., 2002; Metzger et al., 2016a). Examples include associations between thermal regimes and loci under possible diversifying selection (Bradbury et al., 2010; Hecht et al., 2015; Narum et al., 2010), responses to selection for important temperature-related physiological traits (Munoz et al., 2015), differences in gene expression reaction norms between populations (Fangue et al., 2006; Meier et al., 2014; Niu et al., 2008; Papakostas et al., 2014), and associations between thermal regimes and oxygen limitation (Anttila et al., 2013; McBryan et al., 2016; Pörtner and Knust, 2007; Schulte, 2015). Altogether, these results demonstrate potentials for plastic and evolutionary responses to climate change, although only recently studies have specifically focused on how populations and

Corresponding author. E-mail address: [email protected] (J.G. Sørensen). Present address: Institut de Biologie Intégrative et des Systèmes (IBIS), Pavillon Charles-Eugène-Marchand, 1030, Avenue de la Médecine, Local 1145 Université Laval, Québec (Québec), Canada G1V 0A6. 1

https://doi.org/10.1016/j.jtherbio.2018.06.003 Received 19 December 2017; Received in revised form 25 April 2018; Accepted 3 June 2018 $YDLODEOHRQOLQH-XQH (OVHYLHU/WG$OOULJKWVUHVHUYHG


K.B. Dammark et al.

species respond when extreme summer temperatures occur (Brown et al., 2016; Fangue et al., 2011; Gallant et al., 2017; Tomalty et al., 2015). Here we investigate a temperate model fish species, the threespine stickleback, Gasterosteus aculeatus. This species has a wide geographical distribution, shows pronounced ecological, phenotypic and behavioral variation (Bell and Foster, 1994; Östlund-Nilsson et al., 2007) and its genome has been sequenced and assembled (Jones et al., 2012). Furthermore, its generation time is short, generally 1–3 years in temperate regions (Bell and Foster, 1994; DeFaveri and Merila, 2013) which makes it a candidate for adapting to local environmental conditions and environmental change on a contemporary time scale. In Denmark it is found in a wide range of habitats, including coastal marine regions, rivers, larger freshwater lakes and small lakes and ponds that are often man-made and isolated with no possibilities for migration. In particular, small isolated ponds may experience high temperatures during particularly warm periods of summer. We therefore investigated the heat stress response in threespine stickleback populations from four isolated environments: two small, shallow and two larger, deeper lakes. Experiments were conducted with wild-caught individuals, which were acclimated to a common environment prior to the experiments in order to reduce environmentally induced differences. From a physiological perspective, maximum critical thermal limit (CTmax) is assumed to be a key parameter when assessing the maximum temperature a population can survive in the short term during e.g. extreme climatic events. However, empirical support for adaptive variation in heat tolerance of sticklebacks is lacking. Earlier studies reported evidence for cold tolerance adaptation but not heat tolerance adaptation in the form of CTmax in threespine stickleback (Barrett et al., 2011; Gibbons et al., 2016). Metzger et al. (2016a) found differences in CTmax as induced by acclimation to different salinities, but no differences in CTmax between marine and freshwater sticklebacks. Hence, CTmax might be similar across different populations, but if differences should exist, we expect to find them among populations genetically adapted to different thermal environments. In the present study, we expected that fish from smaller lakes experienced higher temperatures during summer, and we hypothesized that sticklebacks from environments experiencing the warmest temperatures would also be the most resilient to heat stress, either through previous acclimatization and/or through genetically based adaptation. Second, we investigated the response to high temperatures at the level of gene expression. The investigated genes encompassed three Heat Shock Proteins (HSPs). HSPs are an important part of the cellular stress response and responsible for the maintenance of homeostasis within the cell and up-regulated proportionally to accumulation of stress (e.g. heat) induced damage (Sørensen et al., 2003). Further, we investigated the gene expression of two metabolic proteins (fructose bisphosphatase2 and isocitrate dehydrogenase) involved in key energy production pathways (glycolysis/gluconeogenesis and the citric acid cycle, respectively). Previous studies confirm that the expression levels of different heat shock paralogs and metabolic genes generally are sensitive to temperature in fish (e.g. Tomalty et al., 2015). To address the acute response we investigated gene expression at the temperature where fish reached their critical thermal limit (CTmax; defined as observed loss of equilibrium). Measuring responses to sub-lethal temperatures could be more useful than CTmax when studying implications of elevated temperature for the long-term survival of populations. Thus, we also investigated the responses in gene expression to more short-term chronic exposures (24 and 48 h) to a temperature within the upper range that sticklebacks would encounter in their native environments (26 °C). We hypothesized that all populations would up-regulate HSPs under maximal stressful conditions. For the metabolic genes, we expected expression to be up-regulated with increasing sub-lethal temperature (short-term chronic exposure). With severely stressful conditions (CTmax) we expected expression of metabolic genes to be negatively correlated with up-regulation of HSPs, thus indicating a metabolic cost

Denmark HAL

BOT

KNU THO

50 km Fig. 1. Map showing the location of the studied populations in Denmark: Lake Hald (HAL; geographical coordinates 56.367302, 9.333352), Lake Knud (KNU; 56.099821, 9.783578), Botanical Garden Lake (BOT; 56.160444, 10.190847) and Thors Mølle Lake (THO; 56.121454, 10.213345).

of the stress response. For the 24 and 48 h exposures to sub-lethal elevated temperature we expected population differences to reflect adaptive differences in acclimation ability. Normal levels of gene expression would indicate the ability to maintain metabolic activity, while up-regulation of HSPs and down-regulation of metabolic genes at a sub-lethal temperature could indicate sub-lethal stress. 2. Materials and methods 2.1. Study area and lake temperatures Four collection sites were selected, all situated in Jutland, Denmark (Fig. 1). These included two small, shallow lakes situated in parks in the municipality of Aarhus: The Botanical Garden Lake (BOT) and Thors Mølle Lake (THO) (both ca. 900 m2, max depth < 2 m). The lakes are fed by water from small inlets and in the case of THO possibly also groundwater. Migration into and from both lakes is restricted due to impassable dams. The remaining two collection sites were larger lakes and both parts of the Gudenaa River system: Lake Hald (HAL; 3.3 km2, max depth: 31 m) and Lake Knud (KNU; 2.0 km2, max depth: 29 m). HAL is isolated due to an impassable dam built ca. 500 years ago, and KNU is also isolated by dams ca. 5 km upstream (ca. 500 years old) and 10 km downstream (ca. 150 years old) in the Gudenaa River system. However, sticklebacks have the option to migrate to cooler areas during periods of high temperature, either by moving into deeper water or by migrating into cooler inlet rivers. Water temperatures at the four collection sites were measured using temperature loggers (HOBO U22, Onset Computer Corporation, Bourne, MA, USA), situated ca. 3 m from the shore on the lake bottom and set to log every 30 min. In HAL, temperatures were measured from 31/7/2014-23/8/2014, in KNU from 31/7/2014-15/8/2014, in THO from 25/6/2014-8/9/2014 and in BOT temperature loggers were deployed from 20/6/2014-8/8/2014. More temperature loggers were deployed but could not be recovered. Loggers were presumably discovered and removed by persons bathing in HAL and KNU and persons walking in the park areas surrounding THO and


K.B. Dammark et al.

Maya, 2006) we increased the temperature of the CTmax assay in two steps over two days; this would also be closer to a natural setting, where water temperature would not be likely to increase continuously at the same rate during the day (and night). On the first day the temperature was increased from 18° to 25°C at a rate of 1.5 °C/h. On the following day the temperature was increased gradually from 25 °C to LoE at a rate of 1.5 °C/h, with a slightly decreasing rate at temperatures higher than 29 °C (ca. 1.2 °C/h).

BOT. 2.2. Sampling and maintenance in the laboratory Threespine sticklebacks were sampled by minnow traps or dip nets in August-September 2014 at the localities where temperatures were logged. The number of fish per population ranged from ca. 80–100. Water temperature at the time of sampling was 17 °C in HAL, 14 °C in THO and 19 °C in BOT. Water temperature was not recorded at the time that KNU was sampled. After return to the laboratory, the collected fish were acclimated for a minimum of four weeks at constant 16 °C in separate 1000 L tanks (one tank per population). Whereas mortality during catch and transport encompassed < 10 individuals per population, a large number (26) of individuals from BOT died during the acclimation period in the tanks. Each tank provided the fish with artificial plants for shelter and a continuous supply of oxygenated freshwater, which was circulated among all tanks. Given this, and as the density of fish was not drastically different among tanks, we assume that tank effects were not an important concern. Fish were fed mosquito larvae or trout pellets each day ad libitum. The project was approved by the Animal Experiments Inspectorate of the Ministry of Food, Agriculture and Fisheries (permit no. 2013-15-2934-967).

2.5. Exposure to short-term chronic heat stress Based on data from the CTmax experiment, a temperature of 26 °C was chosen for the short-term sub-lethal temperature exposure. This temperature was estimated to be sufficiently high to stress the fish and likely to occur in lakes during summer. The heat stress experiment was conducted in four blocks, switching populations between compartments as described for the CTmax experiment. Each block of the experiment encompassed a total of six fish per population/compartment of which three were exposed to elevated temperature for 24 h and the remaining three for 48 h. On the first day sticklebacks from the holding containers were moved to the experimental container (18 °C). The thermostat was then set to 26 °C, and the water gradually heated to this temperature with a rate of increase of ca. 1.5 °C/h. Exactly 24 and 48 h, respectively, after initiating water heating, three sticklebacks from each population were sampled. As a control group a sample of three individuals from each population were retrieved from the holding tanks. They were treated as the other sticklebacks in the experiment with respect to handling, but were not moved to the experimental aquarium.

2.3. Experimental setup Critical thermal maximum (CTmax) and heat stress exposure for gene expression assays were conducted simultaneously for all four populations in a single aquarium of 130 L split up into four equal compartments using stainless steel mesh (size of 2 mm x 2 mm) mounted on steel frames. A combined water pump and thermostat (EHEIM professional 3, EHEIM GmbH & CO. KG, Deizisau, Germany) was used to achieve precise and homogenous regulation of the water temperature. An aquarium air pump provided oxygenation of the water to decrease the risk of oxygen limitation as a main stress contribution. To minimize potential bias, each experiment was run four times, with populations assigned to a different compartment in each run. During all experiments the temperature was monitored using two HOBO U22 temperature loggers submerged into the aquarium. Gene expression and CTmax could be influenced by sex (Knag and Taugbol, 2013) and size (reflecting age) of individuals (Wootton, 1998). Morphology could also play a role, where differences in numbers of lateral armor plates may be present among individuals (Colosimo et al., 2005) and potentially may affect growth rates in freshwater (Barrett et al., 2008). Therefore, all experimental animals were investigated for length, plate-morphology (number of lateral plates) and sex. After experimentation all experimental fish were immediately euthanized using benzocaine (2.5 mL benzocaine in 0.5 L water), and length and plate-morphology was determined immediately after, the latter using a stereo microscope. In fish designated for gene expression assays the liver was dissected out and transferred to 2 mL Eppendorf tubes and stored at − 80 °C in RNAlater (Life Technologies, Nærum, Denmark) for subsequent gene expression assay. Sex was subsequently determined using primers targeting sex-specific variation in the Isocitrate dehydrogenase (idh) gene (Peichel et al., 2004).

2.6. RNA extraction RNA was extracted from four different treatment groups (untreated controls, CTmax, 24 h and 48 h at 26 °C). RNA-extraction from the stored liver tissue was performed using the E.Z.N.A.® MicroElute Total RNA Kit (Omega Bio-Tek, Inc. Norcross, GA, USA). The standard procedure for extraction from animal tissue was used according to the manufacturer's recommendations. At the end of the extraction procedure, the sample was eluted twice with 21 µL of DEPC water. After RNA extraction genomic DNA contamination was removed using the Turbo DNA-free™ Kit (Life Technologies, Nærum, Denmark). The RNA concentration was measured using a Qubit fluorometer (Qubit® RNA BR Assay Kit, Invitrogen, Life technologies, Nærum, Denmark) according to the manufacturer's recommendations. Samples were diluted to a RNA concentration of ca. 50 ng/µL. 2.7. qPCR gene expression analysis Three heat shock protein coding genes (hsp60, hsp70, hsp90), two metabolic genes (isocitrate dehydrogenase 2 (idh2) and fructose biphosphatase 2 (fbp2)) and three potential reference genes for normalization (tubb2b, ef1α, rpl13a) were investigated. It has recently been shown that hsp70 encompasses two paralogs (Metzger et al., 2016b), but our assay did not allow for separating them. Reference genes were selected based on the suggestions from earlier studies (Hibbeler et al., 2008; Knag and Taugbol, 2013). Sequences of the genes (including introns and exons) were retrieved from Ensembl (see Table 1 for Ensembl Gene IDs), and except for hsp60 (which does not contain introns) primers were designed such as to span an intron, thus minimizing problems with potential genomic DNA contamination of RNA samples. Primer sequences along with Taqman probe sequences used for gene expression assays on the Fluidigm Biomark platform (Fluidigm Corporation, San Francisco, CA, USA) are listed in Table 1. The real-time quantitative PCR (qPCR) assay was outsourced to AROS Applied Biotechnology A/S, Aarhus, Denmark, using the Fluidigm BioMark System and a Fluidigm 192.24 Dynamic Array (Fluidigm, San Francisco, CA, USA) according to the manufacturer's

2.4. Critical thermal maximum assay Critical thermal maximum (CTmax) of the sticklebacks was defined as ‘Loss of Equilibrium’ (LoE), the temperature at which the fish were losing balance or exhibiting abnormal swimming behaviour. The fish were under constant observation during the experiments and were immediately removed and euthanized when LoE occurred (and treated as described above). The experiment was run four times, with five sticklebacks from each population for a total of twenty fish per population. To allow time for thermal acclimation while reducing stress effects due to longer time of exposure (Beitinger et al., 2000; Mora and


K.B. Dammark et al.

Table 1 Ensembl gene ID, primer (F: forward, R: reverse) and taqman probe sequences for the target and reference genes used for the gene expression assay. Target genes

Ensembl gene ID

Primer/probe sequences

hsp60 heat shock 60kD protein 1 (chaperonin) hsp90 heat shock protein 90, alpha (cytosolic) hsp70 heat shock 70 kDa protein 8 idh2 Isocitrate dehydrogenase 2 (mitochondrial) fbp2 Fructose-1,6-bisphosphatase 2 Reference genes tubb2b beta-tubulin ef1α Elongation factor 1 alpha rpl13a ribosomal protein L13a

ENSGACG00000008860

F: TCCGACGGCGTGGC/R: TCATTCACCTCCACGTCGC Probe: TCGTTCCTCCGATCTTGAG F: TCCTTAGAGAGCTGATCTCC/R: TGTCAAACTCTCATATCTGATTTTGT Probe: CCAAAGCATCTGAGGAATT F: GCCCTGAACCCCAACAACA/R: CAAACCGACGGCCAATGAG Probe: CCGTATTTGATGCAAAGCGT F: ACTCGCATCATATGGGAGTTCA/R: CTTCAGCTCAACGTCCACATG Probe: CAGGATGAGCTTCTCTTTG F: CGCCGTGCGCAAAGC/R: CCCGTCACGTTGGTGC Probe: TCGCTCACCTTTACGGCATC

ENSGACG00000012875 ENSGACG00000010517 ENSGACG00000016476 ENSGACG00000014527

ENSGACG00000003471

F: GCCAATGCGGAAACCAGATC/R: AATGCCGTGCTCATCACTGA Probe: CCTCCCAGAACTTTGC F: GCCCCTGCAGGACGTCTA/R: GGCCGACTGGGACTGTT Probe: CCAATACCTCCGATTTTG F: TGCGCCTGAAACCCACTC/R: CGACCTCGTGTGCCAGAC Probe: AAGGAGCGCAAACTTG

ENSGACG00000002182 ENSGACG00000009310

recommendations. All 188 individuals were analyzed (using three technical replicates) for the eight genes on the same array. Pre-amplification cycles (14 cycles) were conducted in order to increase yields of cDNA for qPCR. A stock sample made by mixing RNA from additional stickleback samples was used to make standards, involving a dilution series of 1, 0.2, 0.04, 0.008, 0.0016, and 0.00032. For each gene both no-enzyme controls (NEC; with no reverse transcriptase) and no-template controls (NTC; with no RNA template) were run. Data were quality controlled with the criteria that Ct values should be between 26 and 6 and quality score of the amplification curve > 0.65 (Fluidigm default). Data not fulfilling these requirements were excluded from further analysis. Ct values were converted into X0 values (Thomsen et al., 2010), which are positively related to expression levels. To achieve a robust target gene normalization we used the geometric mean expression of two reference genes tubb2b and rpl13a (Vandesompele et al., 2002), as ef1α was deemed unsuitable as a reference gene due to a low average Ct-value.

3.2. Population characteristics We assayed the length and morph of all fish. Sticklebacks from KNU were considerably shorter and sticklebacks from THO were considerably longer than the two other populations (Table 2). Based on the number of lateral plates, sticklebacks were divided into three groups; low plated (≤10 plates), partially plated (11–20 plates) and full plated (> 20 plates) (Le Rouzic et al., 2011). There were differences in the numbers of different morphs among freshwater populations (Table 2), as previously observed in Denmark (Ferchaud and Hansen, 2016; Pujolar et al., 2017). As lateral morphs and populations would be confounded, we did not consider this aspect further, but note that some of the population differences observed (see below) could potentially be associated with morph type. 3.3. Heat tolerance (CTmax) As populations showed a large difference in size (length), the effect of size on heat tolerance was confounded by population differences and could not be evaluated across populations. To evaluate the general relationship between size and CTmax (i.e. does CTmax of relatively larger fish within populations differ from the CTmax of smaller fish) we therefore standardized the length of each fish to the population mean length. The overall correlation between CTmax and length of the fish showed that length did not significantly affect CTmax (Pearson's product-moment correlation, t = −0.35(71), P = 0.73). Correlations between size (in mm) and CTmax for each population conformed to this, all four being non-significant. Initial ANOVA analysis of CTmax showed no effect of population (F = 1.2(3,68), P = 0.30), sex (F = 1.8(1,68), P = 0.19) or the interaction between the two (F = 0.9(3,65), P = 0.47). Sexes were pooled as they did not deviate significantly from each other. Visual inspection of the residuals identified a single outlier from each of three populations (Bot, Hal and Knu). Removal of these three data points had little impact of the model coefficient estimates, while the reduced variance led to the population effect now being significant (F = 3.96(3,65), P = 0.012). TukeyHSD post hoc comparisons only showed a significant difference between KNU and THO (Padj = 0.008). The outliers all had negative residuals due to very low CTmax estimates. While unknown, it is possible that these low CTmax estimates were caused by factors other than true heat tolerance (e.g. handling damage or illness) and thus represent true outliers. In this case the population differences could be considered significant. However, it is also possible that the observations represent the natural variation present in the population. Even though the population with the highest CTmax originated from the warmest lake (Fig. 2, plot based on all data including the potential outliers), the dataset does not allow to draw conclusions

2.8. Statistics We analyzed CTmax and gene expression by ANOVA based on linear models using population, treatment and sex as fixed factors. We assessed the fixed effects and their interactions by comparing the full model including all factors and interactions to a reduced model, where the interaction among factors and factors were sequentially removed using F-tests to find the minimum adequate model that explained our data (Crawley, 2013). Upon obtaining significant differences between models, the reduction was halted for the involved factors. Post hoc comparisons were performed by TukeyHSD tests. The assumptions for running ANOVAs were visually investigated based on Q-Q and residual plots. The effects of length were investigated by correlations with CTmax and gene expression (within treatments and populations). All the analyses were performed using the “lme4″ package in R (v.1.1-5; https://cran.r-project.org/web/packages/lme4/index.html).

3. Results 3.1. Lake temperatures The mean of the temperatures measured in the lakes was calculated for the period 31/7/14 − 8/8/14 for all locations (Table 2). The temperature was lower and more stable at THO compared to the other lakes, with KNU showing the highest mean temperature.


K.B. Dammark et al.

Table 2 Size (length in mm ± St. dev), number of different morph types (Low: ≤10 plates, Partially: 11–20 plates, Full: > 20 plates), CTmax (Mean ± St. dev) of the investigated populations. For all traits ‘n’ represents sample size. Additionally, the mean lake temperature (temperature range in square brackets) for the period 31/ 7/14 − 8/8/14 is given. The investigated populations were: BOT = Botanical Garden Lake, THO = Thors Mølle Lake, KNU = Lake Knud, HAL = Lake Hald. Note that the minimum temperature at death in the BOT, HAL and KNU populations was a result of a single individual dying much sooner than all other individuals. Note that sample size for sex is not equal to sample size for size and plate morphology, as not all individuals successfully were assigned to sex.

Mean length (mm) ± St. dev (n) Morph Low (n) Partially (n) Full (n) Sex (males/females) CTmax [Mean ± St. dev (n)] Mean lake temperature [range]

BOT

HAL

KNU

THO

49.3 ± 3.8 (58) 13 19 26 25/11 31.5 ± 0.5 (17) 20.1 [18.8–21.1]

46.8 ± 8.6 (57) 53 4 0 14/21 31.7 ± 0.6 (18) 21.8 [20.3–23.4]

37. ± 5.5 (57) 2 14 41 16/21 32.0 ± 0.5 (19) 23.3 [22.3–24.4]

56.2 ± 6.3 (57) 45 10 2 12/26 31.4 ± 0.7 (19) 14.5 [13.9–15.1]

and Treatment (Table 3). The coefficients of the minimal adequate model are available in Suppl. Table 2. Inspection of a diagnostic plot did identify four data points with high leverage (high Cook's Distance), i.e. had large impact on the results. Inspecting these data points further revealed no apparent pattern (they were all from different populations and treatment combinations). Statistical analyses with and without these data points yielded no differences in the final model or statistical significances in post hoc comparisons and, thus they had no effect on the conclusions. The reported statistics are with these points included. Post hoc comparisons among populations identified overall significantly lower expression in HAL and KNU compared to BOT, and in KNU compared to THO (Table 4, Suppl. Table 2). Post hoc comparisons among treatments showed significant differences among all comparisons, except for the two long term sub-lethal exposures (24 and 48 h). Thus, Control had the lowest expression level followed by short-term chronic heat exposure (24 and 48 h) and with the CTmax treatment showing the highest expression (Table 5, Suppl. Table 2). For hsp90 the final model showed significant effect of Treatment and the Population x Treatment interaction (Table 3). The coefficients of the minimal adequate model are available in Suppl. Table 2. Inspection of a diagnostic plot did identify four data points with high leverage, and additionally two data points with highly deviating residuals. Inspecting these data points revealed that all data points of the control treatments from populations HAL and THO were affected. Due to the significant interaction between Population and Treatment, the significant effect of Treatment was investigated for each population separately. Post hoc comparisons showed very similar patterns among populations, with hsp90 up-regulated by any heat treatment (but especially CTmax) and with no significant difference between 24 and 48 h treatments for any population. The only exception was a lack of

regarding a connection between lake temperature and heat tolerance.

3.4. Gene expression The effect of body length was investigated by correlating the gene expression separately for each gene and treatment against size (standardized to the population mean length). Only in a single gene and treatment combination (out of a total of twenty correlations) was a significant relationship identified (idh2, 24 h short-term chronic heat), testifying to a general lack of effect of size on gene expression. Thus, size was not considered further in the analyses of gene expression (Suppl. Table 1). The gene expression results are shown in Fig. 3. For hsp70 the minimal adequate model showed significant effect of Population, Treatment, Sex and the interaction between Treatment and Sex (Table 3). The coefficients of the minimal adequate model are shown in Suppl. Table 2. Inspection of a diagnostic plot did not cause the evaluation of the effect of any specific data points. As no interactions included Population, the Population effect could be investigated directly by Post hoc comparisons, which identified significant differences between THO and HAL, and between THO and KNU (Table 4). Due to the significant interaction between Treatment and Sex, the effects of Treatment were investigated for each sex separately. Here, post hoc comparisons for females showed that gene expression was significantly increased for CTmax compared to all other treatments, while the shortterm chronic heat treatments did not induce significantly increased expression of hsp70. For males the gene expression at CTmax as well as the 24 h and 48 h treatments were higher than Control (Table 5; Suppl. Table 2). For hsp60 the final model showed significant effect of Population

Fig. 2. CTmax ( ± sem) of sticklebacks from four different Danish lakes (BOT = Botanical Garden Lake, THO = Thors Mølle Lake, KNU = Lake Knud, HAL = Lake Hald) plotted against the average water temperature of each lake measured for the period 31st July to 8th August 2014.


K.B. Dammark et al.

Fig. 3. Gene expression results (fold change, normalized to BOT Control = 1). Symbols represent population means ± sem.

significant differences between Control and short-term chronic heat treatments (24 and 48 h) in the HAL and THO populations, driven be the slightly inflated control samples with high leverage. Thus, the significant Population x Treatment interaction was likely driven by these deviating control values for two of the four populations (Table 4; Suppl. Table 2). For idh2 the final model showed significant effect of Treatment, Population, Sex and the Treatment x Population interaction (Table 3). The coefficients of the minimal adequate model are available in Suppl. Table 2. Inspection of a diagnostic plot did not identify any data point with high leverage in need of further investigation. The main effect of sex was due to a generally higher expression in males as compared to females (Suppl. Table 2). Due to the significant interaction between Population and Treatment, the significant effect of treatment was investigated for each population separately. Post hoc comparisons among populations showed no differences at control conditions. After the 24 h

Table 4 TukeyHSD pairwise population comparisons. For hsp70, hsp60 and fbp2 no interaction between population and any other factors was found, allowing general comparisons. No significant effect of population was found for hsp90. For idh2 a significant population by treatment interaction was found, which explains the treatment specific population comparisons. BOT = Botanical Garden Lake, THO = Thors Mølle Lake, KNU = Lake Knud, HAL = Lake Hald. hsp70 (Padj)

hsp60 (Padj)

hsp90 (Padj)

Population HAL-BOT KNU-BOT THO-BOT KNU-HAL THO-HAL THO-KNU

0.90 0.53 0.12 0.91 0.02 0.002

0.01 8e-7 0.71 0.09 0.14 6e-5

– – – – – –

idh2 (Padj)

fbp2 (Padj)

Control

24 h

48 h

CTmax

0.97 0.99 0.95 0.90 0.76 0.99

0.004 0.02 0.006 0.97 0.99 0.99

0.22 0.88 0.55 0.045 0.92 0.17

0.01 0.77 0.75 0.10 0.001 0.21

0.03 0.12 0.85 0.93 0.18 0.46

Table 3 ANOVA results from the analysis of the expression levels of each gene, obtained by sequential model reduction. Factors in the minimal adequate model for each gene in bold, while ¤ represent the non-significant factors and interactions removed by the sequential model reduction. hsp70

Population Treatment Sex Treat x Sex Treat x Pop Pop x Sex Treat x Pop x Sex

hsp60

hsp90

idh2

fbp2

F(d.f.)

P

F(d.f.)

P

F(d.f.)

P

F(d.f.)

P

F(d.f.)

P

6.5(3,134) 465.9(3,134) 9.5(1,134) 5.1(3,134) 1.8(9,134)¤ 0.5(3,125)¤ 0.7(8,122)¤

0.0004 < 2.2e-16 0.0025 0.002 0.07¤ 0.68¤ 0.72¤

12.2(3139) 64.2(3139) 2.7(1, 139)¤ 1.2(3, 138)¤ 0.3(9, 135)¤ 0.2(3126)¤ 0.6(8123)¤

4.0e-7 < 2.2e-16 0.10¤ 0.31¤ 0.98¤ 0.94¤ 0.77¤

1.4(3,125) 231.7(3,125) 1.5(1,125)¤ 0.6(3,124)¤ 2.5(9,125) 0.7(3,121)¤ 0.7(7,118)¤

0.24 < 2.2e-16 0. 23¤ 0. 64¤ 0.012 0. 57¤ 0.69¤

8.9(3,129) 9.3(3,129) 5.9(1,129) 0.3(3,129)¤ 2.9(9,129) 1.6(3,126)¤ 0.7(8,123)¤

2.2e-5 1.4e− 5 0.02 0.79¤ 0.004 0.20¤ 0.66¤

3.4(3,139) 23.5(3,139) 0.9(1,139)¤ 0.6(3,135)¤ 0.1(9,132)¤ 1.3(3,138)¤ 0.3(8,123)¤

0.02 2.3e-12 0.34¤ 0.63¤ 0.99¤ 0.29¤ 0.95¤


K.B. Dammark et al.

Table 5 TukeyHSD pairwise treatment comparisons. For hsp60 and fbp2 no interaction between treatment and any other factors was found, allowing general comparisons. For hsp70 there was a significant treatment x sex interaction leading to sex specific treatment comparisons. For idh2 and hsp90 a significant population by treatment interaction was found, which explains the population specific treatment comparisons. Treatments were: Control (16 °C), CTmax (heating until Loss of equilibrium), 24 h and 48 h (short-term chronic heat at 26 °C for 24 or 48 h, respectively). BOT = Botanical Garden Lake, THO = Thorsmølle Lake, KNU = Lake Knud, HAL = Lake Hald. hsp70 (Padj)

hsp60 (Padj)

Treatment

Males

Females

48–24 h Control-24 h CTmax-24 h Control-48 h CTmax-48 h CTmax-Control

0.99 0.0008 < 1e-7 0.001 < 1e-7 < 1e-7

0.09 0.97 < 1e-7 0.62 < 1e-7 < 1e- 7

0.90 0.011 < 1e-7 0.034 < 1e-7 < 1e-7

hsp90 (Padj)

idh2 (Padj)

fbp2 (Padj)

BOT

HAL

KNU

THO

BOT

HAL

KNU

THO

0.99 0.014 < 1e-7 0.023 < 1e-7 < 1e-7

0.41 0.67 < 1e-7 0.31 < 1e- 7 1e-5

0.71 0.047 < 1e-7 0.009 < 1e-7 < 1e- 7

0.99 0.53 < 1e-7 0.57 < 1e-7 6e-5

0.97 0.12 0.79 0.07 0.56 0.37

0.61 0.83 0.37 0.38 0.045 0.99

0.00005 0.83 0.89 0.001 0.0006 0.56

0.37 0.99 0.06 0.56 0.75 0.23

0.99 0.01 < 1e-7 0.006 < 1e-7 0.40

match our initial expectation, we did observe variation in CTmax among populations. The effect size of the difference in CTmax was modest (< 1 °C), which is not surprising as former studies encompassing a wide range of species generally find limited evidence for adaptation with respect to upper thermal tolerance (Bradshaw and Holzapfel, 2008; Williams et al., 2008). As a notable example this is supported by studies of Drosophila species where lack of standing genetic variation in species already living at their upper thermal limits constrains further adaptation (Kellermann et al., 2012). We note that the study presented here represents a limited geographical region of this species, which is otherwise widespread in the Northern Hemisphere (Östlund-Nilsson et al., 2007). It would be interesting to investigate variation in CTmax across populations covering the full distribution range, e.g. from the Mediterranean to Arctic regions such as Greenland. Even though the population with the highest CTmax did originate from the lake with the highest measured temperature, the small differences in CTmax did not show clear relationships with temperatures of the lakes. Most likely, especially for larger lakes, fish would be able to behaviourally avoid extreme temperatures. This would lead to a decreased need for strong genetic adaptation. Even if differences in CTmax did exist, it is not clear if they would reflect local adaptation or phenotypic plasticity. The fish were all kept for four weeks at common garden conditions before experimentation, which suggests that the CTmax did at least not reflect easily reversible phenotypic plasticity. The population samples differed in body size (length) and in lateral plate morphology. Length was not related to CTmax (or gene expression), which has otherwise been reported for e.g. Cottus gobio (Wootton, 1998) and Zoarces viviparus (Pörtner and Knust, 2007). Differences in lateral plate morphology were confounded by populations of origin and its possible association with CTmax could not be tested. We do not rule out interactions between lateral plate morphology or the major gene affecting this (Ectodysplacin [Eda]; (Colosimo et al., 2005)) and CTmax, as previous studies have revealed association between Eda and several physiological and behavioral traits (Barrett et al., 2009; Spence et al., 2012). Thus, size and plate morphology may be an integrated part of the observed population differences in CTmax. Further studies are needed in determine whether these could represent innate, potentially adaptive genetic differences. The gene expression results showed a strong acute heat stress response in all three heat shock genes (hsp60, hsp90 and hsp70), although at a variable degree of fold change. In parallel, the metabolic genes showed a tendency for a slight down-regulation. In many other organisms the up-regulation of the heat stress chaperones occurs with a marked and simultaneous general down-regulation of the metabolic machinery (Feder and Hofmann, 1999; Sørensen et al., 2005). The lack of a strong depression of the metabolism was not caused by insufficient stress levels, as the sticklebacks were at their CTmax and high fold change induction of hsps was observed. Possibly, metabolism was generally increased as a consequence of the increasing temperature,

heat treatment, BOT showed significantly lower expression compared to the other populations. (Table 4, Suppl. Table 2). After 48 h this difference had disappeared, and only KNU and HAL was different, due to the very low expression in KNU. At CTmax conditions HAL showed elevated expression making it significantly higher than BOT and THO. The fold changes were, however, quite modest. Post hoc comparisons among treatments showed no significant differences among any treatment for BOT and THO, and only a single for HAL (expression at CTmax increased compared to 48 h). For the last population, KNU, the low expression of the 48 h treatment made it significantly different from all other treatments (Table 5, Suppl. Table 2). For fbp2 the final model showed significant effect of Population and Treatment (Table 3). The coefficients of the minimal adequate model are available in Suppl. Table 2. Inspection of a diagnostic plot did not identify any points of high leverage in need of further investigation. Post hoc comparisons among populations identified significantly elevated expression in HAL compared to BOT (Table 4, Suppl. Table 2). Post hoc comparisons among treatments showed that both short-term chronic heat treatment (24 and 48 h) induced higher expression compared to both control and CTmax. with no differences between neither control and CTmax nor between 24 and 48 h treatments (Table 5, Suppl. Table 2).

4. Discussion Contrary to expectations we found that the two largest lakes, HAL and KNU in fact showed the highest temperatures during a particularly warm period of the summer of 2014. It should be noted that temperature loggers were deployed at the same sites where fish were sampled, and sticklebacks were observed both at the time of deployment and retrieval of loggers. Hence, the temperature data is expected to be representative of the living conditions of sticklebacks in the lakes, though with important caveats for the larger lakes (see below). Denmark is composed of lowland and all the studied lakes are found within a radius of ca. 50 km. Hence, the differences in water temperature are unlikely to reflect differences in air temperature but rather innate properties of the lakes. Specifically, THO may be affected by upwelling groundwater, and significant cover from surrounding trees may provide sufficient shade to keep temperatures low. The large surface areas of HAL and KNU may in contrast absorb heat resulting in warm surface water. Nevertheless, despite the warm surface water in the two large lakes sticklebacks may still escape stressful temperatures by moving into deeper water or migrating into inlet rivers. The results are qualitatively similar to measurements of winter temperatures in a set of Danish rivers that showed pronounced differences reflecting feeding by groundwater or surface water (Jensen et al., 2008). Our general result of a CTmax between 31 and 32 °C seems to be a general characteristic of stickleback populations (Barrett et al., 2011). Nevertheless, whereas the thermal characteristics of the lakes did not


K.B. Dammark et al.

need for molecular chaperones at the molecular level. The exposure to 24 or 48 h at sub-lethal high temperature (26 °C) did lead to a quite modest stress response, while all individuals mounted a strong upregulation of heat shock proteins at their critical thermal limit (CTmax). The expression patterns of the metabolic genes investigated could be cautiously interpreted as an indication of a shift from aerobic towards anaerobic energy production with heat stress. We conclude that threespine sticklebacks do not show severe stress during the warmest temperatures they are likely to encounter during current temperature regimes, although future climate change could severely affect populations, especially in small, isolated water bodies.

which might have counter acted on the net expression levels of metabolic genes. In aquatic organisms heat tolerance has been suggested to be limited by the inability of organisms to meet the increasing demand for oxygen with increasing temperature (Pörtner, 2001; Pörtner and Knust, 2007) while this seems of less importance for terrestrial organisms (Verberk et al., 2016). It is possible that differences between aquatic and terrestrial ectotherms also exist in the types of metabolic regulation with stress. However, this warrants further investigation, and in general the oxygen- and capacity-limited thermal tolerance hypothesis is subject to controversy (Clark et al., 2013). The short-term chronic stress treatment at 26 °C was well within the temperature range that sticklebacks could encounter under natural conditions. This might be stressful, as even a small temperature increase of only + 2 °C was shown to affect rainbow trout, Oncorhynchus mykiss, negatively during late summer (Morgan et al., 2001). That this treatment was stressful was also confirmed by the detection of slightly increased expression of heat shock genes, especially hsp60 and hsp70 (although with an interesting sex difference for the latter gene). At this treatment an up-regulation of metabolic genes would be expected at higher temperatures during non-stressful conditions, as increased temperatures increase metabolism (Hill et al., 2008). This was observed for fbp2. The gene product is involved in gluconeogenesis which maintains blood glucose level in order for muscles and brain to meet their metabolic needs (Berg et al., 2006). In contrast, short-term chronic stress seemed to slightly decrease idh2 expression. Thus, rather than a general decrease in metabolism with stress, the expression levels of metabolic genes could be interpreted as an aerobic limitation, as idh2 is involved in the aerobic Citric Acid Cycle (Berg et al., 2006) and a shift towards anaerobic metabolism represented by the gluconeogenesis. This was found generally for all populations, and even if some post hoc comparisons indicated significant differences between populations, the variation did not seem related to variation in CTmax or stress sensitivity (hsp expression). Furthermore, there was only a low stress response at the level of gene expression of hsps in the short-term chronic stress treatments. Interestingly, the expression of hsp70 was up-regulated in males but not females. The sex of sticklebacks has been suggested to influence gene expression (Knag and Taugbol, 2013), and hormones can possibly affect the regulation of hsp levels in fish (Basu et al., 2002). In sticklebacks, males invest a lot of energy in reproduction (ÖstlundNilsson et al., 2007). Elevated temperatures have been found to increase mortality of males expressing parental-care, and fertilization success has been shown to be negatively correlated with temperature (Mehlis and Bakker, 2014; Östlund-Nilsson et al., 2007) Even if our experiment was conducted after the mating season, it is possible that the up-regulation of hsp70 in response to mild stress in males reflects higher stress sensitivity, rather than a stronger adaptive response (Sørensen, 2010). Among population differences indicate that the hsp expression levels should indeed be interpreted as a larger need for chaperone protection under severe thermal stress. At CTmax, the two highly inducible hsps (hsp70 and hsp90) showed the highest levels in THO (the coldest lake). This leads to the hypothesis that high stress sensitivity (strongly elevated hsp expression), low heat tolerance and low environmental temperature might be associated in isolated stickleback populations.

Acknowledgements We thank Johannes Overgaard for access to excellent facilities and for important suggestions and comments on the study and paper and Heidi Meldgaard Jensen for daily maintenance of fish. We acknowledge the Villum Foundation (grant no. VKR022523) and EU Interreg (Øresund-Kattegat-Skagerrak) funds (MARGEN) (MMH) and Aarhus University Research Foundation, Starting Grant (AUFF-E-2015-FLS-872) (JGS) for funding. Conflicts of interest The authors declare no conflicts of interest Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jtherbio.2018.06.003. References Anttila, K., Dhillon, R.S., Boulding, E.G., Farrell, A.P., Glebe, B.D., Elliott, J.A.K., Wolters, W.R., Schulte, P.M., 2013. Variation in temperature tolerance among families of Atlantic salmon (Salmo salar) is associated with hypoxia tolerance, ventricle size and myoglobin level. J. Exp. Biol. 216, 1183–1190. Balanya, J., Oller, J.M., Huey, R.B., Gilchrist, G.W., Serra, L., 2006. Global genetic change tracks global climate warming in Drosophila subobscura. Science 313, 1773–1775. Barrett, R.D.H., Paccard, A., Healy, T.M., Bergek, S., Schulte, P.M., Schluter, D., Rogers, S.M., 2011. Rapid evolution of cold tolerance in stickleback. Proc. R. Soc. B 278, 233–238. Barrett, R.D.H., Rogers, S.M., Schluter, D., 2008. Natural selection on a major armor gene in threespine stickleback. Science 322, 255–257. Barrett, R.D.H., Vines, T.H., Bystriansky, J.S., Schulte, P.M., 2009. Should I stay or should I go? The Ectodysplasin locus is associated with behavioural differences in threespine stickleback. Biol. Lett. 5, 788–791. Basu, N., Todgham, A.E., Ackerman, P.A., Bibeau, M.R., Nakano, K., Schulte, P.M., Iwama, G.K., 2002. Heat shock protein genes and their functional significance in fish. Gene 295, 173–183. Beitinger, T.L., Bennett, W.A., McCauley, R.W., 2000. Temperature tolerances of North American freshwater fishes exposed to dynamic changes in temperature. Environ. Biol. Fish. 58, 237–275. Bell, M.A., Foster, S.A., 1994. Introduction to the evolutionary biology of the threespine stickleback. In: Bell, M.A., Foster, S.A. (Eds.), The Evolutionary Biology of the Threespine Stickleback. Oxford University Press, New York, pp. 1–27. Berg, J.M., Tymoczko, J.L., Stryer, L., 2006. Biochemistry. Freeman, New York, NY, pp. 10010. Bradbury, I.R., Hubert, S., Higgins, B., Borza, T., Bowman, S., Paterson, I.G., Snelgrove, P.V.R., Morris, C.J., Gregory, R.S., 2010. Parallel adaptive evolution of Atlantic cod on both sides of the Atlantic Ocean in response to temperature. Proc. R. Soc. Lond., Ser. B: Biol. Sci. 277, 3725–3734. Bradshaw, W.E., Holzapfel, C.M., 2001. Genetic shift in photoperiodic response correlated with global warming. P Natl. Acad. Sci. USA 98, 14509–14511. Bradshaw, W.E., Holzapfel, C.M., 2008. Genetic response to rapid climate change: it's seasonal timing that matters. Mol. Ecol. 17, 157–166. Brown, L.R., Komoroske, L.M., Wagner, R.W., Morgan-King, T., May, J.T., Connon, R.E., Fangue, N.A., 2016. Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish. PLoS One 11. Clark, T.D., Sandblom, E., Jutfelt, F., 2013. Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J. Exp. Biol. 216, 2771–2782. Colosimo, P.F., Hosemann, K.E., Balabhadra, S., Villarreal, G., Dickson, M., Grimwood, J., Schmutz, J., Myers, R.M., Schluter, D., Kingsley, D.M., 2005. Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles. Science 307,

5. Conclusion We found that threespine sticklebacks from a set of Danish populations show slightly different CTmax, with no strong signal of local adaptation or adaptive phenotypic plasticity. At short-term chronic exposure to 26 °C, we observed slight signals of stress at the gene expression level in all populations. We found no strong evidence for local adaptation at the level of gene expression. We found that increased expression levels of the heat shock proteins, hsp70 and hsp90, were most highly induced in the fish from the coldest lake. This leads to proposing the hypothesis that stress sensitivity is reflected as a higher


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