Survival and gene expression under different temperature and humidity regimes in ants

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Survival and gene expression under different temperature and humidity regimes in ants Stucki, Dimitri; Freitak, Dalial; Sundström, Liselotte Stucki, D., Freitak, D., & Sundström, L. (2017). Survival and gene expression under different temperature and humidity regimes in ants. PLoS ONE, 12(7), Article e0181137. https://doi.org/10.1371/journal.pone.0181137 2017 RESEARCH ARTICLE Survival and gene expression under different temperature and humidity regimes in ants Dimitri Stucki 1,2 *, Dalial Freitak 1,2,3 , Liselotte Sundstro ¨m 1,2 1Centre of Excellence in Biological Interactions / Department of Biosciences, University of Helsinki, Helsinki, Finland, 2Tva¨rminne Zoological Station, University of Helsinki, Hanko, Finland, 3Centre of Excellence in Biological Interactions, University of Jyva ¨skyla ¨, Jyva ¨skyla ¨, Finland *[email protected] Abstract Short term variation in environmental conditions requires individuals to adapt via changes in behavior and/or physiology. In particular variation in temperature and humidity are common, and the physiological adaptation to changes in temperature and humidity often involves alterations in gene expression, in particular that of heat-shock proteins. However, not only traits involved in the resistance to environmental stresses, but also other traits, such as immune defenses, may be influenced indirectly by changes in temperature and humidity. Here we investigated the response of the ant F.exsecta to two temperature regimes (20˚C & 25˚C), and two humidity regimes (50% & 75%), for two populations. We measured the survival and the expression of six metabolismand immunity-related genes, and furthermore compared the expression levels in each condition with the pre-experiment expression levels. Both populations survived equally well at the two humidities, but one population showed higher mortality at 25˚C than 20˚, at 50% humidity. Similarly, the two populations showed striking differences in their gene expression before the experiment, and in their responses to the environmental conditions. Surprisingly, instead of converging to similar expression levels in the same environmental conditions, gene expression diverged further apart. This indicates different reaction norms to both temperature and humidity for the two populations. Furthermore, our results suggest that also immune defenses are indirectly affected by environmental conditions. Introduction Individuals respond to short term changes in environmental conditions, either via altering behavior or via adjusting physiological processes mediated by modifications in gene expression patterns [1]. These responses are often not identical among individuals within a species, although the conditions may be similar, but are determined by reaction norms specific to e.g. populations or individuals [2]. These reaction norms depend on the genetic makeup of an individual, but are also determined during development [3], or via transgenerational effects [4]. Furthermore, short-term acclimatization to different temperatures can result in differential expression of heat-shock genes [5]. The resistance of an individual to environmental stress, PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Stucki D, Freitak D, Sundstro¨m L (2017) Survival and gene expression under different temperature and humidity regimes in ants. PLoS ONE 12(7): e0181137. https://doi.org/10.1371/ journal.pone.0181137 Editor: Suzannah Rutherford, Fred Hutchinson Cancer Research Center, UNITED STATES Received: March 4, 2017 Accepted: June 27, 2017 Published: July 31, 2017 Copyright: ©2017 Stucki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data underlying the analyses are provided as supplementary material. Funding: This work was supported by Academy of Finland (grants #251337, #252411, #284666) (http://www.aka.fi/en): Financial funding; University of Helsinki (https://www.helsinki.fi/en): Financial funding; Societas pro Flora et Fauna Fennica (http://www.societasfff.fi/?lang=en): Financial funding (Dimitri Stucki); and Doctoral Programme in Wildlife Biology Research (http://www. societasfff.fi/?lang=en): Financial funding (Dimitri such as heatand cold-tolerance, or resistance to desiccation, is often sculpted by the conditions met during development [6,7]. In particular, temperature and moisture can shape reaction norms, as they often vary in space and time. Such variation in temperature, or moisture, can result in a differential expression of genes directly related to coping with changes in these conditions, such as the expression of heat-shock proteins [8]. Also other genes not directly linked to temperature or moisture, such as immune defenses, can show changes in expression patterns [9–11]. For example, Drosophila flies from different populations showed reduced immune defenses at temperatures that differed from their naturally encountered temperature [11]. This indicates that the expression of immune defense traits may depend on the reaction norm to the experienced conditions. The most frequent physiological response to variation in temperatures and humidity is by altering the expression of heatand cold-shock proteins [12–15]. Also other genetic pathways have been linked to environment-dependent processes, such as the insulin-like protein pathway in overwintering insects [16], and metabolism-related genes under drought [17]. For example, immune genes from the Toll and PPO pathways are differentially expressed under different temperature regimes in the alfalfa leafcutting bee Megachile rotundata [18], and and metabolism-related genes were generally down regulated during desiccation in the Antarctic mite, Belgica Antarctica [17]. Similarly, a recent study on two ant species showed a high amount of genes to be temperature-sensitive, including potential immune genes [19]. Given that temperature and humidity often affect the development of fungal pathogens [20–22], a reaction norm that raises immune defenses at higher temperatures and/or humidities may thus increase the fitness of an individual, but may also incur a fitness cost in the absence of an infection. Therefore, although environmental conditions can have a direct influence on the expression of immune traits, the precise nature of this influence depends on the reaction norms of an individual. Given such complex interactions, it may be difficult to predict how individuals regulate immune defenses under different environmental conditions. Nevertheless, this information is important for understanding host-parasite interactions in the light of eco-evolutionary processes under variable environmental conditions. Here we investigated how workers of two natural populations, one island population and on mainland population, of the ant F.exsecta respond to different environmental conditions in a controlled laboratory experiment. We transferred field-collected ants from the two populations to the laboratory, maintained them at four temperature-humidity combinations, and measured the change in the expression of six immunityand metabolism-related genes. We selected a heat-shock protein (HSP75), an insulin receptor (IR3), a hemolymph storage protein, which however also shows immune functions (Arylphorin), and three immune genes (β1,3-glucan-binding protein, Pro-Phenoloxidase, Toll-receptor). In addition, we recorded the survival of the ants under each environmental condition. The colonies of the island population are mostly located on dry soil and exposed to the sun, whereas the colonies of the mainland population are located in a forest with closed canopy, and a rich vegetation. Thus, we expected the ants of the two colonies to respond differently to the conditions, in particular, we expected a stronger response to the conditions the colonies are less accustomed to. Furthermore, we expected gene expression to be elevated in low temperatures, as well as in the dry conditions. Material & methods Sampling & bioassay We collected Formica exsecta workers from inside the nest, together with ~200ml nest material, between the 20 th and 23 rd of June 2014. In total we collected 1932 worker ants from 21 colonies (92 ants per colony), twelve of which originated from an island population (Furuska¨r: Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 2 / 15 Stucki). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. 59°50'00"N23°16'05"E), and nine from a mainland population (Pra¨stkulla: 59°58'45"N23° 20'51"E). We henceforth refer to these populations as island population (Furuska¨r) and mainland population (Pra¨stkulla). From each colony, we immediately sampled four biological replicates to measure gene expression before starting the experiment. Each biological replicate consisted of three ants, which we fixed (directly cut into small pieces) in 300μl Isol-RNA Lysis Reagent (5 Prime), and stored them at -80˚C until further processing. With the remaining ants from the respective colonies, we established the laboratory treatments. From each colony we placed 20 workers into each of four Fluon1coated pots (Ø: 7cm, h: 5cm) with plaster lining (i.e. four pots per colony). Each pot was then randomly assigned to one of two temperaturetreatments (20˚C and 25˚C) and to one of two humidity-treatments (50% and 75%) in a full factorial design. The pots were placed in incubators set to the respective temperature and 50% relative air humidity. We also added an open 1.5ml tube filled with water and a piece of cotton to each pot of the 75% humidity-treatment, and confirmed that the humidity indeed increased and was maintained at ca. 75%. The pots in the 50% humidity treatment remained without a water tube. For simplicity, we refer to the conditions as cold/hot (20˚C / 25˚C) and dry/moist (50% / 75%). We then fed the ants daily with ~200μl Bhatkar-Whitcomb diet [23], and removed and recorded dead individuals from the pots daily. On day ten after the onset of the laboratory treatments, we sampled three biological replicates from each pot for gene expression analysis to determine the effect of the laboratory conditions. For each biological sample, we fixed three ants (directly cut into small pieces) into 300μl Isol-RNA Lysis Reagent, and stored them at -80˚C until further processing. Gene expression analysis We first homogenized the thawed samples in a total volume of 600μl RNA Isol-RNA Lysis Reagent, with two stainless steel beads using a TissueLyser (Qiagen). Subsequently we added 400μl Isol-RNA Lysis Reagent and 150μl 1-Bromo-5-Chloropentane (Sigma). After mixing we centrifuged the samples for 10min at 13,000rpm at 4˚C. We then transferred the upper, transparent phase, containing the RNA, to a new 1.5ml tube and supplemented with 500μl isopropanol (Sigma). After mixing, we let the suspended RNA precipitate over night at -20˚C, and then centrifuged the samples for 30min at 13,000rpm at 4˚C to sediment the RNA. After removal of the supernatant, we washed the pellet on ice with 500μl 80% EtOH (Altia Oyj) and centrifuged for 10min at 13,000rpm at 4˚C. After drying the pellet we dissolved the RNA in RNA storage solution (AMBION). We measured concentration and quality of the RNA photospectrometrically with a NanoDrop (PEQ-Lab) and eliminated possible DNA contamination by DNAse digest (TURBO DNase; AMBION) before cDNA synthesis (iScript cDNA Synthesis Kit; Bio-Rad). For cDNA synthesis we used 1μg RNA for each sample and afterwards diluted the resulting 20μl cDNA in 80μl autoclaved ddH 2 O. As target genes we chose six genes, five of which were previously shown to be altered upon infection in workers of this species (unpublished data). We chose three immune genes coding for the antifungal peptide β-1,3-glucan-binding protein (B1,3g), and two cascade molecules, Pro-Phenoloxidase (PPO) and Toll-receptor (Toll). We also included three metabolism-related genes coding for the storage protein Arylphorin (Aryl), the insulin receptor 3 (IR3), and the heat shock protein 75 (HSP75). However, both Arylphorin and the insulin pathway have also been shown to be linked to immune responses [24,25]. A list of the primer sequences is provided in the supplementary table A in S1 File. We designed qRT-PCR primers using the online Primer3 internet-based interface (http:// www.ncbi.nlm.nih.gov/tools/primer-blast/) [26]. Primers were designed by the rules of highest maximum efficiency and sensitivity to avoid formation of selfand hetero-dimers, hairpins Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 3 / 15 and self-complementarity. Gene-specific primers were designed on the basis of the sequences obtained from the F.exsecta transcriptome [27]. Q-RT-PCR was performed on 384-well plates on a CFX384 Touch™Real Time PCR Detection System (Biorad) using iQ™SYBR1Green Supermix (Bio-Rad), with a 3-min initiation phase at 95˚C, followed by 40 cycles of 15sec at 95˚C for denaturation, and 45sec at 58˚C for annealing/extension, and a final step of 7min at 95˚C. All Q-RT-PCR assays were run using two technical replicates, and assessed for consistency and possible outliers, and subsequently averaged before normalization. Non-detects (no amplification signal within 40 qPCR cycles) were set to the maximal cycle number (i.e. Ct = 40), or removed if the second technical replicate showed amplification. Statistical analysis We used a mixed effects Cox proportional-hazard model to compare the survival across the environmental conditions, as well as between the populations. We specified temperature, humidity, and population as fixed factors, and included all possible interactions. Colony of origin was added as random factor. Subsequently we ran post-hoc contrasts on all pairwise comparisons, which were adjusted for false discovery rates [28]. We then tested for differences in gene expression with Principal Component Analyses on the inverted normalized Ct-values. We inverted the Ct-values because they are negatively correlated with specific transcript level (i.e. higher Ct-values indicate lower gene expression levels). To normalize gene expression we used the NORMA-Gene algorithm, which does not require reference genes for normalization [29]. As suggested by the authors, we nevertheless included the reference gene RPS9 to stabilize the normalization, but not as a gene of interest. Hence we do not report the results for this gene. We used two PCAs, one to test for differences in pre-experiment gene expression between the populations, and a second to test for differences in gene expression across the experimental conditions and between the populations. We used unrotated PCAs for component selection and retained all components with an Eigenvalue >1.0 (Table B in S1 File). We also consulted the scree plot of the Eigenvalues for consistency with the suggested number of components to retain. For the PCA on pre-experiment gene expression the scree plot indicated a high relative weight of a component despite an Eigenvalue <1.0, and in this case we also included this component. We then rotated the scores of the retained components using oblique (oblimin) rotation. The rotated scores were then used as dependent variables in two linear mixed effects models for each retained cluster (PC), with the PC score as dependent variable. In the preexperiment analysis we entered Population as a fixed factor, and Colony as a random effect, whereas in the second analysis across the experimental conditions and between the populations we entered the Population, Temperature and Humidity as a fixed factors, and Colony as a random effect. We then performed planned post-hoc comparisons, where we compared for each PC the pairwise differences between the two populations (separately for each experimental condition), and each population the pairwise differences between temperatures (at each humidity) and humidities (at each temperature). We then repeated the same analyses separately for each gene, in order to assess the congruence between the genes highlighted by the PCA, and the specific genes showing differential expression between the population, and treatments. We here arbitrarily define the association of each gene with a principal component as strong (loading 0.67–1.0), moderate (loading 0.33–0.66), or weak (0.0–0.32). Finally, we compared the pre-experimental status of gene expression with that after ten days of exposure to the laboratory conditions, in linear mixed effects models for each gene separately. We specified the factors as above, and included all possible interactions. To test for pairwise differences, we ran planned pairwise contrasts on the pre-experimental status against Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 4 / 15 the state of gene expression in each environmental condition, and adjusted for false discovery rates as above. Four samples did not yield sufficient RNA for analysis and were thus removed. For the statistical analyses we used R 3.2.3 [30], and the packages survival [31], coxme [32], lme4 [33], lmerTest [34], and multcomp [35]. For all statistical tests we used a significance threshold of α = 0.05, and correction for false discovery rates where applicable. Variation is indicated as standard errors, unless specified otherwise. Results Overall, survival did not differ significantly between the two populations, but temperature had a significant effect on the survival of the ants in one of the two populations, whereas humidity had no effect on survival (Table 1). In particular, ants from the island population died at a significantly faster rate at hot/dry compared to cold/dry conditions, whereas survival did not differ significantly between hot/moist and cold/moist conditions, despite a similar trend (Table B in S1 File,Fig 1). The survival of ants from the mainland population did not differ between the two temperatures, at either of the two humidity levels (Table B in S1 File,Fig 1). In the PCA we ran on pre-experiment gene expression, two components had an eigenvalue above 1.0 and explained 68% of the variation (Table C in S1 File). However, the scree plot (Fig 1A) suggested to include also PC3 for the analysis, which raised the variation explained to 84%. PC1 explained 47% of the variation, but was not significantly associated with population differences (β±SE = 0.25±0.32, t = 0.77, p = 0.45), whereas both PC2 (21% of variation explained), and PC3 (16% of variation explained) indicated significant differences in gene expression between the populations, with higher PC scores (i.e. gene expression) in ants from the mainland population than the island population (PC2: β±SE = 1.13±0.20, t = 5.75, p<0.0001; PC3: β±SE = 0.91±0.25, t = 3.65, p = 0.0025). The loadings of each PC partly reflected the gene functions. PC1 showed a strong positive association with the two immune genes B1,3g and Toll, and a strong negative association with the gene coding for the storage/immune protein Aryl, PC2 showed a strong positive association with the gene coding for the heat-shock protein HSP75 and the insulin receptor gene IR3, and PC3 showed a strong, respectively moderate, positive association with the two immune genes PPO and B1,3g (Table D in S1 File). This suggests that four of the six genes (HSP75,IR3,PPO,B1,3g) are more expressed in ants from the mainland population than the island population, which the gene-by-gene also fully supports (B1,3g:β ±SE = -0.88±0.31, t = -2.84, p = 0.0158; HSP75:β±SE = -1.51±0.26, t = -5.71, p = 0.0001; IR3:β ±SE = -1.13±0.31, t = -3.70, p = 0.0043; PPO:β±SE = -1.76±0.50, t = -3.53, p = 0.0043; Fig 2D– 2I). The remaining two genes (Aryl &Toll) did not show significant differences in expression Table 1. Survival regression models. Factor Chi 2 p-value Temp 89.5609 <0.0001 Hum 0.3139 0.58 Pop 0.8167 0.37 Temp:Hum 1.5391 0.22 Temp:Pop 11.5649 0.0007 Hum:Pop 1.4893 0.22 Temp:Hum:Pop 0.7441 0.39 Results of the Cox proportional-hazard regression on survival. Temp indicates the effect of temperature (cold vs hot), Hum the effect of humidity (dry vs moist) and Pop the populations (Island vs Mainland). https://doi.org/10.1371/journal.pone.0181137.t001 Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 5 / 15 between the populations, despite the strong negative association of Aryl with PC1 (Aryl:β±SE = -0.80±0.73, t = -1.10, p = 0.34; Toll:β±SE = -0.21±0.46, t = -0.46, p = 0.65; Fig 2D–2I). The PCA for the experimental conditions produced two components with an eigenvalue above 1.0, which explained 69% of the variation (Table C in S1 File). The scree plot also suggested two components to be sufficient (Fig A in S1B File). Both PCs separated between populations, with higher scores for the mainland population than the island population (Fig 3A & 3B, Tables 2& E in S1 File), again indicating higher overall expression levels in the mainland population. The loadings on these two PCs captured all the genes under investigation, with PC1 capturing the heat-shock protein HSP75, the insulin receptor IR3, the storage/immune gene Aryl, and the two immune genes B1,3g and PPO (strong: Aryl,HSP75 &IR3; moderate: B1,3g &PPO), and PC2 capturing the three immune genes (strong: B1,3g &Toll, moderate: PPO; Table D in S1 File). The high and consistently unidirectional (positive) factor loadings suggest that the expression levels, and apparent collinearity among the genes, were strongly influenced by the differences between the populations. In the gene-by-gene analysis the same (six) genes Aryl,B1,3g,HSP75,IR3,PPO,Toll were significantly differently expressed between the two populations (Fig 3C–3H, Tables 2& E in S1 File). Apart from capturing overall population differences PC1 predominantly separated between temperatures, whereas PC2 mainly captured effects of humidity on gene expression (Table 2). Both PCs, however, also brought out differences between the populations in how they responded to the combined effects of humidity and temperature, as suggested by the significant interaction terms in the analysis. The PC1 scores for the island population were lower at hot/dry than cold/dry conditions, but not different between hot/moist and cold/moist conditions, whereas the scores for the mainland population were lower at both hot/dry and hot/ moist conditions than at cold/dry and cold/moist conditions, respectively (Fig 3A, Table E in S1 File). This suggests that the expression of the genes associated with PC1 (i.e. Aryl,B1,3g, HSP75,IR3, and PPO) was in general higher at cold than hot temperatures. Also within each temperature regime, humidity variation produced different responses: the PC1 scores for the island population did not differ between the cold/dry and cold/moist conditions, but at the Fig 1. Condition-dependent survival. Cumulative survival during the laboratory experiment for each laboratory condition, separated by population. Solid lines indicate the survival in dry conditions, and dashed lines indicate the survival in moist conditions. Survival at cold temperatures is shown in black, and survival at hot temperatures is shown in red. Mind that the y-axis ranges only between 0.8 and 1.0. https://doi.org/10.1371/journal.pone.0181137.g001 Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 6 / 15 scores were higher at hot/moist than hot/dry conditions. The pattern was reversed in the mainland population, for which the scores were higher at cold/dry than cold/moist conditions, as well as at hot/dry than hot/moist conditions (Fig 3A, Table E in S1 File). Thus, overall, gene expression (PC1) appeared to be higher at dry conditions in the island population, and higher at moist conditions in the mainland population. The PC2 scores did not differ between cold/dry and cold/moist conditions, or between hot/ dry and hot/moist conditions for the island population. For the mainland population the PC2 scores were significantly lower at hot/moist than cold/moist conditions, but did not differ between hot/dry and cold/dry conditions. Within each temperature regime, neither of the populations showed a difference between cold/dry and cold/moist conditions, whereas both populations responded, but in opposite directions in the hot temperature treatment, so that Fig 2. Pre-experiment population differences in gene expression. Population-differences in PC scores (panels a-c) representing pre-experiment gene expression of the six genes (panels d-i). Error bars indicate 95% confidence intervals. https://doi.org/10.1371/journal.pone.0181137.g002 Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 7 / 15 the PC2 scores were higher in hot/moist than hot/dry conditions for the island population and lower in hot/moist than hot/dry conditions for the mainland population (Fig 3B; Table E in S1 File). The pattern shown by the gene-by-gene analysis mirrored the PCA, except that some of the genes with high loadings on the PCs did not show a significant effect for the treatment in the island population, although the direction of change was similar. Thus, in the island population within each humidity regime the gene coding for the heat-shock protein HSP75, the insulin receptor gene IR3 and the immune gene PPO showed a significantly higher expression at cold/ dry than hot/dry conditions, whereas no significant differences were found for the other genes, or between cold/moist and hot/moist conditions (Fig 3C–3H, Tables 3& F in S1 File). Fig 3. Condition-dependent gene expression levels. PC scores (panels a & b) representing gene expression of six genes (panels c-h) after ten days acclimatization to one of four laboratory conditions. Squares indicate gene expression at dry conditions, and circles indicate the gene expression at humid conditions. Filled symbols represent gene expression at cold temperatures, and open symbols represent gene expression at hot temperatures. Error bars indicate 95% confidence intervals. https://doi.org/10.1371/journal.pone.0181137.g003 Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 8 / 15 46. Dekoninck W, Maebe K, Breyne P, Hendrickx F. Strong genetic structuring in Formica rufa polygyny and strong genetic structuring within an isolated population of the wood ant Formica rufa. J Hymenopt Res. 2014; 41: 95–111. https://doi.org/10.3897/ Responses to temperature and humidity regimes in ants PLOS ONE | https://doi.org/10.1371/journal.pone.0181137 July 31, 2017 15 / 15