Comparative Population Analysis of Metallothionein Promoter Alleles

Apr 19, 2008 - Animal Ecology, de Boelelaan 1085, 1081 HV Amsterdam,. The Netherlands ... contained a signature of balancing selection in a population...
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Environ. Sci. Technol. 2008, 42, 3873–3878

Comparative Population Analysis of Metallothionein Promoter Alleles Suggests Stress-induced Microevolution in the Field THIERRY K. S. JANSSENS,† RICARDO DEL RIO LOPÉZ,‡ JANINE MARIËN,† MARTIJN J. T. N. TIMMERMANS,† K. MONTAGNE-WAJER,† NICO M. VAN S T R A A L E N , † A N D D I C K R O E L O F S * ,† Vrije Universiteit Amsterdam, Faculty of Earth and Life Sciences, Institute of Ecological Science, Department of Animal Ecology, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands and Universitat de les Illes Balears, Departament de Biologia, Carretera Valldemossa, km 7.5, C.P. 07122 - Palma de Mallorca, Spain

Received October 19, 2007. Revised manuscript received January 19, 2008. Accepted February 6, 2008.

We investigate a model system for microevolution of transcriptional regulation: metallothionein expression in springtails. A previous survey of the metallothionein promoter in Orchesella cincta (Collembola) revealed nine alleles with differential basal activities and responses to cadmium and oxidative stress. In this study, 23 woodlands, with a divergent degree of pollution, were sampled, and heavy metals were measured. When grouped to their contamination degree, they were discriminated best on the pmtD2 metallothionein promoter allele frequency, which was higher in populations from heavily polluted sites. Taken together with previous work showing high inducibility of the pmtD2 promoter allele by Cd in a reporter assay, this suggests a fitness advantage of the pmtD2 allele in polluted sites. Redundancy analysis revealed associations between allele frequencies and specific metals in the environment, resulting in a subdivision between pollution associated alleles and others. A positive relationship between the pmtD2 allele frequency and the Cd content of the soil as well as between pmtE and Ni in the litter emerged. An increase of genetic diversity was observed with increasing Pb in the soil, reached through substitution of the pmtA1 allele, suggesting balancing selection. Our results illustrate that environmental factors can exert selection on promoter polymorphisms and cause adaptation through altered transcriptional regulation.

Introduction Adaptive novelties may result from changes in gene regulation, which occur by reshuffling of modular transcription factor binding sites (1). This form of regulatory evolution has been demonstrated for ecologically relevant traits, such as Ldh-B expression as a compensatory mechanism in fish * Corresponding author phone: +31 - (0)20 5987078; fax: +31 (0)20 5987123; e-mail: [email protected]. † Vrije Universiteit Amsterdam. ‡ Universitat de les Illes Balears. 10.1021/es702618s CCC: $40.75

Published on Web 04/19/2008

 2008 American Chemical Society

populations inhabiting different temperature regimes (2) and tissue-specific overexpression of Cyp6g1 in insecticide resistance in D. melanogaster (3). These studies show that natural selection can act upon genetic variation of transcriptional regulation, in addition to structural variation of proteins (4). In this paper we study the microevolution of transcriptional regulation in field populations. Anthropogenic modifications of the environment often exhibit selection processes on genomes (5, 6). Frequently, this type of natural selection results in a specific transcriptional response (7, 8). It is suggested that polymorphisms of promoter sequences are often responsible for variation in expression (1, 3, 9), but surveys in which promoter allele frequencies were analyzed in association with environmental pollution are scarce. Orchesella cincta is a common Holarctic soil arthropod (10, 11) living in association with accumulated pollutants in the soil and litter. The heavy metal-tolerant phenotype of this species, observed in populations from heavily polluted sites, shows an increased excretion efficiency of Cd and Pb (12). The reduction of growth upon Cd exposure, which is inversely related to the Cd excretion efficiency, is less pronounced in the tolerant phenotype (12). Heavy metal tolerance also involves overexpression of a Cd-binding metallothionein (13–15), of which the Cd-induced expression is an additive genetic trait (16). Nine alleles were discovered in this gene’s promoter (pmt); some did show different basal activities and responsiveness to Cd and oxidative stress in a luciferase reporter assay (17). Their architecture differed in the number and spacing of putative transcription factor binding sites relevant to the transcriptional regulation of metallothioneins. Additionally, the sequences of this locus contained a signature of balancing selection in a population from a historically heavy metal contaminated site. The evolution of heavy metal tolerance in the springtail Orchesella cincta by increased metallothionein expression is used as a model to study the role of transcriptional regulation in the evolution of ecologically relevant traits. In this study we assess the association between metallothionein promoter (pmt) allele frequencies and the heavy metal content of the environment.

Experimental Sampling. Twenty-three populations were sampled in a wide variety of soils contaminated with heavy metals (Figure 1). Because of the subdivision of population genetic structure of Orchesella cincta between different European regions (18), we decided to restrict the survey to a part of northwestern Europe. Heavy metal pollution was due to mining of Pb-Zn ores (19), the vicinity of heavy metal smelters, dredging sludge landfills (20), or different levels of natural background concentrations. See Table S1 (Supporting Information) for geographic position, background information, and references of the sampled populations (14, 19–33). We aimed at genotyping 96 Orchesella cincta individuals per site. However, fewer individuals were sampled at sites where O. cincta abundance was low (24–93 ind.). All animals were taken alive to the laboratory, and separately frozen in 96 well plates until analysis. Additionally, three soil samples were taken at each site up to 10 cm depth as well as three litter samples. The pH-KCl in distilled water was measured in fresh soil and litter samples. Heavy Metal Analysis of Soil and Litter. Both 0.01 M CaCl2 exchangeable, which is an estimate of the biologically available fraction, and total concentrations of Cd, Pb, Zn, Cu, Ni, and Fe were determined in soil and litter samples, VOL. 42, NO. 10, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. (A) Map of Belgium and parts of the surrounding countries showing the sampled sites. (B) Classification of the sampled sites according to their degree of soil heavy metal pollution, summarized as the W index. using the same methods as Timmermans et al. (14). In the present study, the 0.01 M CaCl2 extracts were filtered over a 0.45 µm filter instead of centrifuged. To rank the sites with respect to the degree of pollution, an index (34) was calculated. This index was calculated by taking all measured metals into account, except Fe, in relation to their background concentrations in standard Dutch soils (35). Locations were considered as unpolluted, polluted, and heavily polluted when their pollution indices were e0, e2, and >2, respectively. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). Genomic DNA from separate animals (∼1 mg fresh weight) was isolated with the Wizard SV Genomic DNA purification kit (Promega, Inc.). The pmt locus was amplified according to Roelofs et al. (16). Five microliter aliquots from this PCR product were digested

overnight with 0.5–1 U of Cla I (Roche), Ssp I, Tau I, Taq I, and Mnl I (Fermentas), according to the manufacturers instructions. The digests were separated by 2% agarose gel electrophoresis in 0.5× TBE. Allele-specific restriction patterns were detected by scoring specific bands (Figure S1, Supporting Information). Data Analysis. Deviations from Hardy–Weinberg equilibrium using the χ2 test and the Shannon information index were calculated with Genalex 6 (36). Additionally, the exact tests for Hardy–Weinberg equilibrium, implemented in Arlequin (37) and Genepop (38), were executed on the data set. With Genepop alternative hypotheses, such as heterozygote excess and deficiency, were tested by a Markov chain method (39). Nei’s genetic diversity (40), an expected heterozygosity measure and the FIS (41) were calculated using Arlequin (37).

TABLE 1. Number of sampled individuals, allele frequencies, Nei’s genetic diversity, Shannon information index, p-values for χ2-test, and exact test for deviations from Hardy–Weinberg equilibrium and the FIS are given

AK1 AK2 LHG LD1 LD2 MAK LMM SID HOB MER ABS BAL BUD MA1 MA2 VSL STO KEL PLO ROG NOY FDM OVP a

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No. of pmt No. alleles A1

pmt A2

pmt B

pmt C

pmt D1

93 77 89 24 50 86 88 91 92 85 85 85 53 81 91 25 87 87 79 67 49 84 76

10.2 7.8 1.7 6.3 4.0 7.0 6.8 1.6 3.8 17.1 14.7 5.9 15.1 4.9 14.8 8.0 16.1 5.2 7.6 15.7 16.3 9.5 14.5

0.5 0.0 2.2 0.0 3.0 0.6 1.7 0.5 0.5 3.5 2.9 4.7 2.8 3.1 3.3 22.0 7.5 18.4 24.7 10.4 0.0 1.8 1.3

15.6 7.1 12.4 8.3 7.0 18.0 13.6 12.6 20.1 10.0 4.7 2.9 11.3 14.2 14.8 4.0 13.2 11.5 11.4 2.2 6.1 10.7 15.8

12.4 2.7 0.0 1.1 0.0 17.5 10.4 1.9 1.9 0.0 11.2 0.6 0.0 0.0 0.0 22.9 0.0 2.1 4.2 0.0 17.0 3.0 3.0 1.0 0.0 10.5 5.2 0.6 0.0 0.0 18.2 1.7 1.7 2.3 0.0 28.0 2.2 0.0 2.7 0.0 2.7 7.6 14.1 17.4 0.5 14.1 0.6 3.5 2.9 0.0 17.6 0.0 16.5 1.8 2.9 12.9 0.0 1.8 2.4 7.1 14.2 1.9 12.3 0.0 1.9 18.5 1.9 6.2 2.5 6.8 13.7 2.2 6.6 0.5 1.6 20.0 0.0 2.0 6.0 0.0 19.0 7.5 0.6 1.7 1.1 8.0 2.3 11.5 0.0 0.0 10.1 8.9 0.6 1.9 1.9 13.4 1.5 6.0 3.0 0.7 17.3 15.3 2.0 4.1 0.0 20.8 6.5 0.6 0.0 0.0 12.5 2.0 3.9 5.9 1.3

7 7 6 6 8 7 8 7 9 8 8 8 8 9 9 7 9 7 9 9 7 7 9

57.5 53.2 71.9 56.3 62.0 58.1 54.0 52.2 33.2 48.2 38.2 62.4 40.6 42.0 42.3 38.0 33.3 43.1 32.9 47.0 38.8 50.0 42.8

pmt D2

pmt E

pmt pmt p-value F BAL χ2-testa

p-value Nei’s Shannon exact genetic information test diversity index

NS

NS

b

c

NS

NS NS

NS NS NS NS NS NS NS NS NS

b

b

NS NS NS NS NS NS NS NS NS NS NS

NS NS NS NS NS NS NS NS

b

NS NS NS NS b

b

NS NS

The p-values of the χ2-test are adjusted for the number of tests executed.

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b

0.62 0.67 0.46 0.63 0.58 0.61 0.65 0.64 0.80 0.71 0.77 0.59 0.77 0.76 0.76 0.77 0.80 0.75 0.80 0.73 0.77 0.69 0.75

Heterozygote FIS -0.056 -0.366 -0.083 0.144 0.144 -0.003 -0.024 0.066 -0.093 -0.029 -0.078 0.137 -0.004 0.076 0.012 0.067 0.012 -0.013 0.001 -0.008 0.102 0.080 0.094

1.27 1.42 0.92 1.25 1.27 1.26 1.39 1.24 1.74 1.53 1.68 1.32 1.68 1.73 1.67 1.60 1.78 1.61 1.78 1.61 1.64 1.42 1.68

p < 0.05. c p < 0.001.

deficit excess

d

p < 0.01.

NS NS NS NS NS NS NS NS NS NS NS d

NS NS NS NS NS NS NS NS NS NS NS

NS c

NS NS NS NS NS NS b

NS NS NS NS NS NS NS NS NS NS NS NS NS NS

FIGURE 2. Mean allele frequency of the pmtD2 allele.

TABLE 2.

Linear Discriminant Function Analysisa

step 1

reference reference polluted heavily polluted

F Sig. F Sig. F Sig.

polluted 0.046 0.832

0.046 0.832 9.834 0.005

heavily polluted 9.834 0.005 6.810 0.017

6.810 0.017

a The stepwise selection procedure (Wilks’ λ ) 0.625, F ) 6, p ) 0.009) revealed a single variable, the pmtD2 allele frequency, which best described the membership of the populations to the classes of soil metal pollution level at the respective sites. On the basis of the F-statistic, the test for equality of the pmtD2 allele frequency of each class pair is given.

The allele frequency data (ratios) were arcsine(square root) transformed to achieve normality. The environmental variables were log(x + 1) transformed, except the pH and the W index. To assess associations of allele frequencies with environmental pollution, linear discriminant function analysis was conducted in SPSS v. 15.0.1. The stepwise selection procedure was chosen to minimize the Wilk’s λ statistic. A Box’s M test was performed to test the assumption of equality of covariance matrices. The sites were classified in three pollution classes, according to the W index of the soil (see Figure 1B). Ordinations were performed with CANOCO v 4.5 (42). A detrended correspondence analysis (DCA) was executed in order to detect the length of the ordination gradient. After detrending by segments, the length of the gradient for the allele and genotype frequency data were 0.067 and 1.067, respectively. Because they were smaller than three times the standard deviation, a linear response is assumed (43), and redundancy analysis (RDA), a direct ordination method, was chosen. The scaling was set to interspecies correlations to reflect the correlations of species scores with the environmental variables scores. Also, the species scores were standardized through division by their standard deviation. The variation introduced by the geographic location of the populations is corrected by using the coordinates as covariables, because the pmtA1 and pmtB frequencies were significantly affected by the geographic location (Mantel test,

FIGURE 3. RDA ordination biplot based on the pmt allele frequencies and the significantly contributing environmental variables from the forward selection procedure. Pearson correlation, 999 permutations, Brodgar V2.5.2, Highland Statistics). Correlations and regressions were calculated in SPSS v 15.0.1. The significance levels of the correlations were adjusted for the number of executed test by using the Benjamini-Yekutieli false-discovery rate correction (44). The number of executed tests for the diversity measures amounted to 58 (all environmental variables and the W index) and for the allele frequencies 63 (restricted to the significant environmental variables from the RDA analysis).

Results and Discussion Concentrations of metals in soil and litter as well as the pHs are summarized in Table S2 (Supporting Information). The total Cd and Pb concentrations span 3 orders of magnitude. To condense the information, pollution indices were calculated (Figure 1). They summarize the total heavy metal content in relation to Dutch background concentrations. The three cleanest sites were MAK, MER, and LHG, and the three most polluted were PLO, NOY, and STO. The number of genotyped individuals, allele frequencies, tests for deviations from Hardy–Weinberg equilibrium, FIS, and heterozygote deficit and excess are given in Table 1. The observed alleles are in decreasing abundance pmtA1, pmtD1, pmtC, pmtA2, pmtB, pmtE, pmtD2, pmtF, and pmtBAL. The most abundant allele, pmtA1, reached its highest and lowest frequency in LHG and PLO, respectively. A G-test showed significant differences in allele frequencies between the populations (p < 0.001). Five populations exhibited a deviation from the Hardy–Weinberg (H-W) equilibrium, namely, AK2, LD1, HOB, BAL, and NOY, although LD1 and HOB showed a deviation only in the χ2-test. The deviation in AK2 and HOB could be attributed to an excess of heterozygotes (Markov chain method implemented in Genepop). In BAL, a heterozygote deficit was observed, and NOY did not show any significant deficit or excess of heterozygotes. The NOY population did not deviate significantly from H-W equilibrium when the p-value of the χ2test was corrected for the number of executed test, although the exact test revealed a deviation. To detect if the subdivision of the sampled populations in three groups with a different grade of heavy metal pollution was reflected in the allele frequency, a linear discriminant VOL. 42, NO. 10, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Summarizing scatter plots of the most important Pearson correlations (A) The Shannon’s information index and the Nei’s genetic diversity were both positively correlated with the transformed CaCl2 exchangeable Pb fraction in the soil (R ) 0.693 and p ) 0.039; and R ) 0.658 and p ) 0.040, respectively). (B) The Shannon’s information index and the Nei’s genetic diversity were both negatively and strongly correlated with the transformed allele frequency of pmtA1 (R ) -0.948 and p ) 0.036; and R ) -0.984 and p ) 0.036, respectively). (C) The transformed allele frequency of pmtA1 is negatively correlated with the transformed CaCl2 exchangeable Pb fraction in the soil (R ) -0.720 and p ) 0.040). (D) The transformed allele frequency of pmtD2 is positively correlated with the transformed total Cd content of the soil (R ) 0.627 and p ) 0.041). (E) The transformed allele frequency of pmtE is positively correlated with the transformed CaCl2 exchangeable Ni fraction in the litter (R ) 0.585 and p ) 0.043). All p-values were corrected for the number of executed tests by using the Bejamini-Yekutiele false discovery rate correction. function analysis was performed by applying the stepwise selection procedure. The only variable explaining the discrimination of the populations in the three pollution classes is pmtD2, implying a single discriminant function (Figure 2). The mean allele frequency of the pmtD2 allele was significantly different between reference and heavily polluted, and polluted and heavily polluted populations, respectively (Table 2). Moreover, Pearson correlation revealed a significant relationship between the transformed pmtD2 allele frequency and the W index (R ) 0.565, two-tailed p ) 0.005). The forward selection procedure in the RDA analysis selected the environmental variables, which best explained the variability in pmt allele frequency composition (Table S3 part A, Supporting Information) for a new RDA restricted to the environmental variables that showed a significant contribution (Monte Carlo permutation test, 499 random permutations). Geographic location, as a covariable, explained 28.4% of the variation in pmt allele frequency data (Table S3 part B, Supporting Information). The first two axes captured 36.4% of the remaining variance. Environmental variables explained 56.1% of the corrected variation, from this relationship 64.8% is explained by the first two axes. The ordination biplot is represented in Figure 3. Apparently, three alleles were not at all associated with any metal fraction in the environment, that is, pmtA1, pmtB, and pmtD1. The pmtC, pmtA2, and 3876

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pmtBAL allele frequencies are represented as relatively short arrows and, hence, contribute little to the explanation of the variation. Most heavy metal fractions are positively correlated with RDA axis 1, are negatively correlated with RDA axis 2, and are positively correlated with the pmtD2 and pmtF allele frequencies. The CaCl2-exchangeable Ni fraction of the litter was positively correlated to RDA axis 2 and, hence, was more correlated with pmtE and pmtF allele frequencies. The most important correlations from the present study are summarized in Figure 4. Their p-values were corrected for the number of tests. Both the Shannon information index and Nei’s genetic diversity were positively correlated with the CaCl2 exchangeable soil concentration of Pb. This increase in genetic diversity at the pmt locus could be completely attributed to a decrease in the frequency of the pmtA1 allele. The correlation coefficients of the latter relationships were very high (R < -0.9). The pmtA1 allele frequency was negatively correlated with the CaCl2 exchangeable soil concentration of Pb, although it was also negatively correlated with other metal fractions (data not shown). The frequency of the pmtD2 allele was positively correlated with the total Cd content of the soil. In addition, the allele frequency of pmtE was positively correlated by the CaCl2 exchangeable litter concentration of Ni. Our study shows that the pmt locus exhibits a moderate but clear increase of allelic diversity with heavy metal

concentrations of the environment, primarily with the exchangeable soil concentration of Pb. Moreover, this increase in diversity operates by replacement of the (most abundant) pmtA1 allele and may be described as balancing selection. An increase of genetic diversity may be caused by overdominance (higher fitness of the heterozygote) (45) or by environmental heterogeneity (46–48). Balancing selection at a certain locus can also be associated with epistasis (48, 49), epigenetic processes (50), and physiological costs of homozygotes (51). Other studies, based on mutational spectra, documenting balancing selection acting upon promoters include the human interleukin 10 promoter (52), MHC genes promoters (53, 54), and CC cytokine receptor 5 (CCR5) (55), whose gene products are involved in immunological responses in which low and high expressing variants are balanced by costs and benefits. This type of selection on regulatory regions allows organisms to fine-tune expression profiles to environmental conditions by decoupling the regulatory variation from protein function (56). Some studies attribute an increase of genetic diversity with environmental pollution to asymmetric gene flow and increased mutation rates (57). The two latter explanations are less likely in our study, because microsatellite and AFLP markers did not reveal a relation between heavy metal pollution and genetic diversity in Orchesella cincta (58). Furthermore, conserved RFLP patterns observed at the pmt locus throughout all sampled populations make it less likely that the higher diversity emerged due to increased mutation rates in heavy metal contaminated sites. Despite the general pattern of balancing selection, a clear and significant correlation of the total Cd content of the soil and the CaCl2-exchangeable Ni in the litter with the allele frequencies of pmtD2 and pmtE, respectively, was observed (Figure 4). This suggests that they are under metal-specific directional selection. It is also plausible that the observed increase in genetic diversity is the result of gene flow from surrounding populations, weakening the direct selection described here above (59). This may explain why the two alleles under directional selection are never fixed. Our results are confirmed by a luciferase reporter assay, in which the pmtD2 is highly inducible by Cd, in absolute inducibility as well as the slope of the dose response curve and the basal promoter activity (17). Because increased expression of metallothionein contributes to metal-tolerance, we consider it likely that the pmtD2 plays a role in the fitness advantage of the cadmium-tolerant phenotype. Other studies have shown selection on alleles of the coding region of metallothioneins (25, 60) and on expression levels (14, 61, 62), but a shift in the occurrence of regulatory polymorphisms has not been observed before in field populations. In this survey we have sampled 23 populations from multiple reference and polluted populations in a broad range of different soil types and heavy metal pollution levels, albeit a small biogeographical area. The range of Cd contamination (as a measure for heavy metal pollution) in our samples approaches the EC50 for survival of a reference O. cincta population during chronic exposure to Cd (180 mg/kg (63)), when total concentration in litter is considered as concentration in the food. Our data provide sound evidence for balancing selection on metallothionein expression but also for specific directional selection by Cd and Ni on the allele frequencies of pmtD2 and pmtE, respectively.

Acknowledgments The authors would like to thank The Netherlands Organization for Scientific Research (NWO) (grant No. NWO-ALW 813.04.006) for funding this research and Bart Vandecasteele (Flemish Institute for Forestry and Game Management, Geraardsbergen, Belgium) for advice concerning the sampling.

Supporting Information Available Table S1, detailed overview of the sampled sites; Table S2, overview of the measured environmental variables; Table S3, output of the redundancy analysis (RDA); Figure S1, physical map of the pmt alleles on which the PCR-RFLP method is based. This information is available free of charge via the Internet at http://pubs.acs.org.

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