ARTICLE pubs.acs.org/est
The Influence of Long-Term Copper Contaminated Agricultural Soil at Different pH Levels on Microbial Communities and Springtail Transcriptional Regulation Tjalf E. de Boer,†,§,# Neslihan Tas-,‡,§,||,# Martin Braster,‡,§ Erwin J. M. Temminghoff,^ Wilfred F. M. R€oling,‡,§ and Dick Roelofs†,§,* †
Department of Ecological Sciences, VU University, de Boelelaan 1085, 1081HV, Amsterdam, The Netherlands Department of Molecular Cell Physiology, VU University, de Boelelaan 1085, 1081HV, Amsterdam, The Netherlands § NGI Ecogenomics Consortium, Amsterdam, The Netherlands Department of Ecology, Lawrence Berkeley National Laboratory, One Cyclotron Road, MS 70A-3317, Berkeley, California 94720, United States ^ Environmental Sciences, Wageningen University, Soil Quality, Droevendaalse steeg 4, 6708PB, Wageningen, The Netherlands
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bS Supporting Information ABSTRACT: Copper has long been applied for agricultural practises. Like other metals, copper is highly persistent in the environment and biologically active long after its use has ceased. Here we present a unique study on the long-term effects (27 years) of copper and pH on soil microbial communities and on the springtail Folsomia candida an important representative of the soil macrofauna, in an experiment with a full factorial, random block design. Bacterial communities were mostly affected by pH. These effects were prominent in Acidobacteria, while Actinobacteria and Gammaroteobacteria communities were affected by original and bioavailable copper. Reproduction and survival of the collembolan F. candida was not affected by the studied copper concentrations. However, the transcriptomic responses to copper reflected a mechanism of copper transport and detoxification, while pH exerted effects on nucleotide and protein metabolism and (acute) inflammatory response. We conclude that microbial community structure reflected the history of copper contamination, while gene expression analysis of F. candida is associated with the current level of bioavailable copper. The study is a first step in the development of a molecular strategy aiming at a more comprehensive assessment of various aspects of soil quality and ecotoxicology.
’ INTRODUCTION Contamination of agricultural soils by copper has largely occurred through the long-term use of pig-manure and fungicides. Copper (Cu) accumulation can lead to irreversible effects on the biological functioning and quality of the soil.1 In order to determine the environmental risks associated with specific sites, the effects of Cu need to be established. However, the effect of metals on soil biota heavily depends on physical and chemical soil properties. Factors such as pH and soil organic matter are determinant in the (de)sorption characteristics, cation exchange capacity or Cu speciation and therefore strongly manage its bioavailability.24 Both Cu and pH 57 affect the composition and activities of soil flora and fauna.5,8,9 Unequivocal interpretation of results is often hampered by the presence of confounding factors relating to soil chemistry and physics, and by the short duration of experiments. Microorganisms are generally described as more sensitive to Cu and other heavy metal stresses than other organisms in soil biocenosis.9 The long-term contact with the metal can lead to r 2011 American Chemical Society
adaptation toward microbial communities whose members survive well in metal-polluted soil10 or have acquired resistance.11 Soil pH is a major factor affecting microbial community composition and diversity of soil microorganisms in different soil types across a variety of spatial scales.7,12 Copper is an essential trace element required for the catalytic activity of a number of enzymes that catalyze oxidation reduction reactions in soil fauna.13 As an essential trace metal, Cu homeostasis and binding is tightly maintained at the cellular level since the free Cu ion is extremely toxic even in low concentrations.14 Criel et al. spiked multiple European field soils, all with different soil characteristics, with Cu and determined the effect Special Issue: Ecogenomics: Environmental Received: April 20, 2011 Accepted: September 1, 2011 Revised: August 31, 2011 Published: September 01, 2011 60
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on two soil invertebrates: Eisenia fetida and Folsomia candida.15 Effect concentrations with 50% impact on reproduction in both species (EC50) varied widely in the different soils. Their conclusion was that variability in Cu toxicity on soil fauna could best be explained by the pH-dependent cation exchange capacity of the soil. A major challenge in soil ecotoxicology is the integrated analyses of both historical and current consequences of soil pollution using fast, cost-effective molecular tools. The Wildekamp location near Bennekom, The Netherlands, was spiked with four Cu concentrations at four pH values in a full factorial, random plot design in 1982.16 This experimental site gives us the unique possibility to study and compare the effects of nearly 27 years of Cu pollution on the microbial community structure and on gene expression in higher organisms such as collembolans, without major interference by confounding factors. Denaturing gradient gel electrophoresis (DGGE) facilitates the analysis of complex soil bacterial communities in a fast and cost-effective way (e.g., ref 17). We hypothesize that the in situ microbial community structure will reflect more the historical conditions and the time-integrated impact of toxicity. In contrast, microarray based transcriptional profiling on single species, such as the springtail Folsomia candida,18,19 incubated under laboratory controlled conditions, is expected to provide detailed, accurate molecular mechanistic insights on the actual pollution status in soil samples . The aim of this study is to combine both technologies to evaluate copper and pH effects, as a first step toward a molecular strategy for comprehensive assessment of various aspects of soil quality and ecotoxicology.
As a measure for the bioavailable Cu (CuCaCl2) concentration the soil samples were analyzed after extraction with 0.01 M CaCl2 for 2 h at 20 C.20 Previous studies showed that CaCl2 extractable Cu is bioavailable for both Collembolans21 and Bacteria.22 After pH measurements in the settling suspension the solution was centrifuged at 2056xg and 0.45 μm filtered. Copper was measured by HR-ICP-MS (Element 2, Thermo Finnigan) and dissolved organic carbon (DOC) was measured by a TOC analyzer (SK12 Skalar, Breda, The Netherlands). Folsomia candida Culture and Exposure. Folsomia candida (VU Berlin strain) was maintained in PVC containers with a plaster of Paris base containing 10% charcoal according to previously described methods.18 Test animals were synchronized beforehand according to ISO-11267.23 Synchronized collembolans were used for the experiments at an age of 23 days. Exposures were performed in 100 mL glass jars which contained 25 g of moistened soil (at 50% of its WHC) and 30 collembolans. For each Cu/pH condition four replicate field samples were used. Collembolans were removed from the soil after 2 days of exposure by floatation and snap-frozen in liquid nitrogen for RNA extraction. The 28-day reproduction and growth test was performed as described previously.18 RNA Extraction and Microarray Hybridization. Total RNA was extracted with the SV Total RNA kit from Promega according to manufacturer’s instructions. The RNA isolation included a DNase step to remove genomic DNA. RNA integrity and concentrations were measured on a Bioanalyzer (Agilent Technologies) and Nanodrop spectrophotometer (Fisher Scientific). Labeling of the RNA samples and microarray hybridization were described previously.18 Custom microarrays24 were manufactured by Agilent, contain 5069 unique F. candida gene fragments in triplicate, and are based on the F. candida EST sequence database Collembase.25 Microarray Experimental Design and Analysis. Microarray hybridization was done according to an interwoven loop design.26 Each of the 16 Cu/pH combinations was replicated four times and RNA samples from the four replicates were dye-swapped during labeling (twice labeled with Cy3 and twice with Cy5). The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO series accession number GSE29644. (http://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?token=nnethoiwqimmono&acc=GSE29644) Microarray intensities were extracted with Feature Extraction software (version 9.5.1, Agilent Technologies). Raw Intensities were background subtracted (Edwards method, offset of 30), normalized within arrays (LOESS) and between arrays (aquantile) using the Limma package in the R environment.27 The data set was divided into three groups according to CuCaCl2 (low 328 μg/ kg (89, medium 1474 μg/kg (163 and high 3761 μg/kg (904) and the lowest concentration was set as a reference. The groups (for Cu and pH) that were created were significantly different from each other according to ANOVA (data not shown) and contained at least 12 samples. The division according to pH was low 4.1 ( 0.06, medium 4.6 ( 0.04 and high 5.0 ( 0.12 with the highest pH set as reference. This categorization resulted in 41 soil samples to be included in the analyses. A linear model was used in the Limma package to determine significant differentially expressed genes between the medium and reference and the high and reference Cu concentration groups. Calculated P values were adjusted for multiple testing according to the method developed by Benjamini and Hochberg.28 A similar method was used for the pH calculation where the highest pH group was set as a reference.
’ MATERIALS AND METHODS Soil Sampling. Soil samples were collected from the experimental arable field Wildekamp, which is located in Bennekom, The Netherlands (51 590 34.8900 N, 5 400 15.8500 E). The original Cu concentration was 3.9 ( 0.4 mg/kg, at an initial pH of 4.7 ( 0.4 (Korthals et al. 1996). The field was then divided into eight blocks, each block consisted of 16 plots (6 11 m) to which different combinations of Cu and pH treatments were applied, in a fully factorial random block design.16 Each plot has eight replicates. Copper was applied once in 1982 as a powdered CuSO4 3 5H2O at the load of 0, 250, 500, and 750 kg ha1. Soil pH (KCl) was adjusted to 6.1, 5.4, 4.7, and 4.0 by addition of lime or sulfur powder. This field has been used as a normal agricultural field since Cu contamination, with a four year crop rotation consisting of barley, potato, oats and maize production. Every 5 to 6 years the pH was checked and readjusted if necessary. Four out of the eight field replicates closest to the average Cu concentration were selected for sampling based on previously measurements from 2001. Sixty-four plots; corresponding to four replicates for each Cu/pH combination, were sampled on 1 April 2009. Samples per plot consisted of three pooled subsamples taken in the center of the plot which were mixed on-site. A small subsample of the total mixed sample was stored at 20 C for molecular analysis and used for Cu measurements. The bulk of the sample was stored at 5 C. Very wet soil samples were dried at room temperature for a week and all samples were sieved over a 4 mm grid to remove stones and plant material. Field Chemistry Measurements. Total reactive Cu in the samples was determined with inductive coupled plasma-atomic emission spectroscopy (ICP-AES; IRIS, Thermo Scientific, MA) after HNO3 extraction (0.43 M, solid/solution ratio, SSR, 0.1 kg/L). 61
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Gene ontology enrichment analysis was performed using TopGO package in R as described previously.18,29 Denaturing Gradient Gel Electrophoresis (DGGE) Profiling and Analysis. DNA was extracted from 0.25 g soil using the PowerSoil DNA kit (MoBio Laboratories, Solana Beach, CA) following instructions of the manufacturer. ITS-region from Fungi and 16S rRNA genes from commonly occurring and important taxonomic groups of soil microorganisms; namely Acidobacteria, Actinobacteria, Bacteriodetes, Beta- and Gammaproteobacteria, were amplified with GC-clamped30 primers (see Supporting Information (SI) Table S1 for primers and amplification details). DGGE was performed as described by R€oling et al.17 DGGE profiles were analyzed in the GelCompar II software package (Applied Maths, Kortrijk, Belgium) using a band assignment-independent method (Pearson product-moment correlation coefficient and unweighted pair-group clustering method using arithmetic averages (UPGMA)). Quantification of Phylogenetic Groups of Soil Microorganisms. Real-time quantitative PCR (qPCR) assays were conducted in polypropylene 96 well plates on an ABI Prism 7000 sequence detection system (Applied Biosystems). Each 25 μL reaction contained the following: 12.5 μL of ABsolute qPCR Master Mix (ABgene), 0.5 μL of each primer (10 μM; Biolegio, The Netherlands), 0.5 μL bovine serum albumin (10 mg mL1; Promega), 6.0 μL H2O, and 5 μL template DNA (0.5 ng μL1). PCR conditions were 15 min at 95 C, followed by 40 cycles of 95 C for 1 min, 30 s at the annealing temperature, and 72 C for 1 min. ITS-region from Fungi and 16S rRNA gene from the major phylogenetic groups of soil microorganisms; namely Acidobacteria, Actinobacteria, Bacteriodetes, Firmicutes, Alpha-, Beta-, and Gammaproteobacteria, were targeted with primers listed in SI Table S1. Each plate included triplicate reactions per sample and standard curves were generated from triplicate dilution series of standards (please refer to SI for more information). Melting curve analysis of the PCR products was conducted following each assay to confirm that the fluorescence signal originated from specific PCR products and not from primer-dimers or other artifacts. For all of the qPCR assays, there was a linear relationship between the log of the plasmid DNA copy number and the calculated threshold cycle value across the specified concentration range (R2 > 0.991 and amplification efficiency 1.72.1; data not shown). As the number of 16S rRNA operons or ITS-regions per cell is variable, the 16S rRNA gene copy data were as relative 16S rRNA gene abundances: group-specific 16S rRNA gene copies/Bacteria 16S rRNA gene copies)*100%. Multivariate Analysis. The presence scores of DGGE bands were used in canonical correspondence analysis (CCA) to define the effect of environmental parameters on the composition of specific microbial groups. CCA analysis of F. candida gene expression was conducted on normalized signal intensities per sample. All ANOVA and multivariate analyses were carried out in R (2.10.0) software (R Development Core Team, Vienna, Austria) with vegan, MASS, ade4 and car packages. Results were considered significant at a p < 0.05 baseline and assumed to be high (strong) when r > 0.700. All of the environmental data except for pH data and normalized gene expressions were transformed as log10 (1 + x). A Monte Carlo permutation test based on 999 random permutations was used to test the null hypothesis of “microbial community structure or changes in the F. candida gene expression is not related to environmental variables”. Microbial community structure and F. candida gene
Figure 1. Soil (A) pH, (B) total copper, and (C) bioavailable CuCaCl2 concentrations in 2009 for experimental Wildekamp plots subjected to various pH (4.06.1) and Cu treatments (0750 kg/ha) in 1982.
expression were visualized via ordination plots with scaling focused on intersample differences.
’ RESULTS Soil Chemistry. Chemical analysis of the soils showed that pH adjustment and Cu addition still had significant effects on soil chemistry (the resulting pH, total Cu and CuCaCl2-) after 27 years (Figure 1, SI Table S2A-B). Plots contained less total Cu compared to the start of the experiment. The respective Cu concentrations at pH 4.0 and pH 6.1 were ∼55% and ∼45%, when an initial concentration of 200 mg/kg is considered. The mean CuCaCl2 was 2.15 ( 1.46% of the total Cu concentration where the pH ranged between 3.89 and 5.41. Folsomia candida Reproduction and Gene Expression. No significant effect of exposure to the Wildekamp soil samples on F. candida reproduction or growth was observed in a four week test according to ISO guidelines23 (data not shown). Thus, we can conclude that Cu levels and pH ranges did not exert sub lethal effects on F. candida. ANOVA on the microarray data with regard to the original (historical) pH and Cu classifications did not yield any significant differential gene expression for these terms. Also, no interaction effect was observed between pH and Cu with the historical categorization. Since F. candida is only exposed to the soil for two days, we propose that they experience the current soil situation rather than the historical setting. This is best represented by the bioavailable Cu (CuCaCl2) concentration. No significant genes were found (P-adjusted 0.05), where 5.2% corresponded to the changes in DOC content (p < 0.05). Grouping according to pH arrangement or CuCaCl2 addition (data not shown) did not result in clear separation of the samples. Additionally, a data matrix containing the combined presence scores for all DGGE bands in 64
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the profiles of the six taxonomic groups was subjected to CCA (Figure 3B). The analysis revealed that environmental chemistry could significantly explain 58.3% (p < 0.05) of the variation observed in the combined soil microbial communities (Table 1). Variation partitioning revealed pH adjustment, which explained 4.1% (p < 0.01) of the observed differences, as the major driver of microbial community composition. A clear separation between samples from pH 4.0 and 6.1 plots was observed (Figure 3B). Additionally, DOC significantly explained a smaller portion of the variation (3.5%, p < 0.05), whereas other soil characteristics were not significant.
high Cu (750 kg/ha), at a single pH level of 4.7. In particular high copper at low pHs led to significant reduction in crop yields between 1982 and 1992 at the Wildekamp location,16 and would have to be expected to influence soil DOC quantity and quality, e.g. via root exudates, and therewith microbial community composition. Overall Cu concentrations severely decreased between 199216 and 2009, especially at lower pH settings. Thus, possible crop yields recovered, and therewith differences in DOC quantity and quality became less between treatments as well as the differences in microbial community structure in 2009, compared to 1997. Unfortunately, no data on crop yields are available for 2009, but difference in DOC were minor and only significantly affected by pH. A straightforward comparison with the results of Smit et al.41 is also hampered due to the use of different methods to profile amplified 16S rRNA genes. High Cu bioavailability was previously also found to significantly reduce the number of bacteria-feeding nematodes, while hyphal-feeding nematodes increased.16 However, no significant effect of soil chemistry on fungi to bacterial ratio or fungal community composition was observed in 2009. Other researchers reported also more pronounced long-term Cu3,3337 and especially pH5,12,38 effects on relative abundances and community composition of bacteria and fungi. Significant correlations between soil pH and the community composition of Acidobacteria, Actinobacteria, Alpha-, Beta-, and Gammaproteobacteria and Bacteroidetes, and the relative abundances of Acidobacteria, Actinobacteria, and Bacteroidetes have been reported,12 but here only differences in Acidobacteria community composition were observed. It should be noted, however, that the initial range in pH was relatively small (4.06.1) compared to these studies.5,12,38 Also pH tends to revert to natural values over time (Figure 1A), possibly explaining why a relatively minor impact of pH on microbial abundances and communities was observed (SI Figure S1). Due to our chosen experimental strategy with rapid, cheap but relatively low resolution DGGE (in comparison to high resolution, but (currently) more expensive phylochips42 or bar-coded pyrosequencing 43) we cannot exclude that effects on some bacterial groups or species (i.e., Pseudomonas, Sphingomonas)3,11,36,37,39,44,45 might be overlooked. Copper-mediated changes in community structure relate especially to Cu bioavailability,39 which in turn relates to Cu concentration and pH, but also other factors such as organic matter and mineralogy. Our experimental approach largely controlled for confounding factors and we observed only minor effects of Cu bioavailability after 27 years, despite specifically targeting taxonomic groups that were previously found to change in relative abundance and/or composition upon Cu pollution.3,11,36,37,39,44,45 Our results, based on more elaborate soil sampling, indicate that microbial community composition has stabilized after the shift upon Cu contamination, when compared to a previous study at this site.41 Furthermore, Ranjard et al.44 noticed in microcosm experiments that changes in microbial community structure were transient.44 Community adaptation and resilience has been suggested to be controlled by the diversity of Cu resistance genes.46 These genes can be located on plasmids47 and might be exchanged between species leading to a community that has acquired Cu resistance but is otherwise largely similar in composition to communities not exposed to Cu. Horizontal gene transfer of Cu resistance genes might be revealed by comparing the 16S rRNA based phylogenies of copperresistant isolates with phylogenies based on Cu resistance gene sequences. Any incongruence between 16S rRNA and Cu
’ DISCUSSION Copper is an essential element for organisms at low levels and can be toxic at high levels. In addition, Cu bioavailability in the soil is strongly affected by pH.10 The Wildekamp random block design experiment was initiated in 1982 and consisted of four different Cu treatments on agricultural soil, at four pH levels.16 After 27 years, Cu decreased significantly in all plots. However, the classifications (low-high Cu) of the plots are still reflecting the original setting. A similar resemblance between measured soil chemistry and originally imposed conditions was observed in 1992.16 Hence, the Wildekamp location provided us with a unique opportunity to establish the historical effects of Cu and pH on in situ microbial soil communities, and to simultaneously study physiological responses of F. candida to current soil chemistry and, potentially, to different microbial soil communities. Previous experiments utilized far less sampling points, generally addressed either pH or Cu and a single target group of organisms, or were subjected to factors confounding data interpretation, such as differences in soil types and conditions.34,39,40 An effect of the long-term differences in pH and Cu on microbial community structure was observed, although these historical factors could only for a minor extent explain current community structures (
