Response of bacterial communities to Pb smelter

Science of the Total Environment 605–606 (2017) 436–444

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Response of bacterial communities to Pb smelter pollution in contrasting soils Arnaud R. Schneider a,1, Maxime Gommeaux a,⁎, Jérôme Duclercq b, Nicolas Fanin c, Alexandra Conreux a, Abdelrahman Alahmad b, Jérôme Lacoux b, David Roger b, Fabien Spicher b, Marie Ponthieu a, Benjamin Cancès a, Xavier Morvan a, Béatrice Marin a a b c

GEGENAA EA3795, SFR Condorcet FR CNRS3417, URCA, Université de Champagne, 2 Esplanade Roland Garros, 51100 Reims, France CNRS FRE 3498 EDYSAN (Écologie et Dynamique des Systèmes Anthropisés), UPJV, 33 rue St-Leu, 80039 Amiens, France INRA, UMR 1391 ISPA, 71 avenue Edouard Bourlaux, CS 20032, F33882 Villenave-d'Ornon cedex, France

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Bacterial community and Pb content were investigated in industry-polluted soils. • Pb bioavailability varied with soil type. • Bacterial community was primarily shaped by soil type and secondarily by Pb content. • Specific bacterial groups responded consistently to Pb in all studied soils. • The Verrucomicrobia/Chlamydiae ratio is an indicator of trace-element contamination.

a r t i c l e

i n f o

Article history: Received 5 April 2017 Received in revised form 19 June 2017 Accepted 20 June 2017 Available online xxxx Editor: E. Capri Keywords: Pb contamination Pb bioavailability Bacterial diversity Bioindicator Chlamydiae Verrucomicrobia

a b s t r a c t Anthropogenic inputs of trace elements (TE) into soils constitute a major public and environmental health problem. Bioavailability of TE is strongly related to the soil physicochemical parameters and thus to the ecosystem type. In order to test whether soil parameters influence the response of the bacterial community to TE pollution, we collected soil samples across contrasting ecosystems (hardwood, coniferous and hydromorphic soils), which have been contaminated in TE and especially lead (Pb) over several decades due to nearby industrial smelting activities. Bacterial community composition was analysed using high throughput amplicon sequencing and compared to the soil physicochemical parameters. Multivariate analyses of the pedological and biological data revealed that the bacterial community composition was affected by ecosystem type in the first place. An influence of the contamination level was also evidenced within each ecosystem. Despite the important variability in bacterial community structure, we found that specific bacterial groups such as γ-Proteobacteria, Verrucomicrobia and Chlamydiae showed a consistent response to Pb content across contrasting ecosystems. Verrucomicrobia were less abundant at high contamination level whereas Chlamydiae and γ-Proteobacteria were more abundant. We conclude that such groups and ratio's thereof can be considered as relevant bioindicators of Pb contamination. © 2016 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail address: [email protected] (M. Gommeaux). 1 Presently at CEREGE UMR7330, Europôle Méditerranéen de l'Arbois, Avenue Louis Philibert, BP 80, 13545 Aix en Provence cedex 04, France.

http://dx.doi.org/10.1016/j.scitotenv.2017.06.159 0048-9697/© 2016 Elsevier B.V. All rights reserved.

A.R. Schneider et al. / Science of the Total Environment 605–606 (2017) 436–444

1. Introduction

2. Materials and methods

The development of anthropogenic activities such as industry and agriculture has led to an accumulation of several hazardous compounds in soils. So far, N6000 sites in France have been reported to be contaminated (BASOL, 2016). A major part of these sites correspond to metallurgical industry areas and agricultural regions surrounding them, which are often heavily polluted with various xenobiotics (Sofilić et al., 2008) and trace elements (TE), such as cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn) (Ettler et al., 2004; Rodella and Chiou, 2009). Contrary to organic pollutants, TE cannot be degraded and constitute a persistent environmental hazard that can influence negatively human health, soil fertility and ecosystem processes (Adriano, 2013; Ahmad et al., 2005; Wuana and Okieimen, 2011). The environmental risks associated with TE contamination (transfer to groundwater or availability to living organisms) are highly dependent on their speciation and lability in soils. For this reason, the relevance of using total or pseudo-total TE contents is currently questioned. Instead, or in complement, estimations of bioavailable TE concentrations (with which living organisms are supposedly in direct contact), using different physical or chemical extraction methods, are increasingly recognized as an essential measurement (Belén Hinojosa et al., 2010; Degryse et al., 2009). TE can be scavenged by a variety of soil compounds with adsorptive surfaces, and their behaviour modified accordingly: soil organic matter (SOM), clays and metal oxides and hydroxides (Al, Fe and Mn) (Alloway, 1995). In the case of Pb, SOM is the key parameter controlling its mobility in most soil contexts (An et al., 2015; Benedetti et al., 1996; Fleming et al., 2013; Weng et al., 2001). It is well established that the nature of SOM is complex and strongly related to the above-ground plant cover and litter quality (Gleixner, 2013), as well as below-ground processes such as root exudation and microbial transformation of SOM (Paul, 2016, 2014), reinforcing the need to study TE behaviour in various ecosystem types. Although some soil microorganisms have established various mechanisms of resistance (Naik and Dubey, 2013; Rathnayake et al., 2010), recent studies have demonstrated adverse effects on microbial activities, abundance and community structure (Epelde et al., 2015; Gołębiewski et al., 2014; Tsezos, 2009). An expanding literature aims at filling the remaining gaps with respect to the understanding of the soil microbial communities' response to metals exposure, its consistency in different ecosystems (Giller et al., 2009; Hagmann et al., 2015) and the microbial indicators that can be used to evaluate degradation status and remediation processes (Gómez-Sagasti et al., 2012). In complement to soil physico-chemical analyses, microbial bioindicators have several advantages: rapid response, sensitivity, ecological relevance, and capacity to provide information that integrates many environmental factors (Burges et al., 2015). In this context, estimations of microbial biomass, metabolic activity (by respiration, enzyme assays…) or diversity are increasingly performed as part of monitoring/remediation strategy (Burges et al., 2017). To understand the importance of the soil parameters versus Pb contamination in diverse ecosystems, on microbial community structure, we investigated the bacterial communities in soils surrounding a secondary Pb smelter. We tested the hypotheses that: (i) ecosystem type controls Pb bioavailability (via the soil physicochemical parameters), and in turn, (ii) the content in bioavailable Pb shapes the structure of soil bacterial communities. To this purpose, we collected and analysed soil samples with contrasting properties from three different ecosystem types (hardwood, coniferous, hydromorphic), with two levels of contamination. We compared soil properties and levels of CaCl2extractable Pb to the bacterial diversity analysed by high-throughput Illumina sequencing.

2.1. Site selection and soil sampling

437

We collected soil samples in the vicinity of a secondary lead smelter, which is still operating to date (49°53′20″ N–4°32′00″ E, Fig. 1). The main activity of the smelter is the recycling of lead-acid batteries, which caused a high contamination in Pb throughout the area. More information on the study area is provided by Schneider et al. (2016). Three different ecosystem types were studied. Two of them are luvic cambisols (IUSS Working Group WRB, 2007) developed on Quaternary loam. They are located under hardwood (S1) and coniferous (S2) forests, respectively. The third one (S3) is a hydromorphic gleysol due to predominant clays developed on altered black shales of the Upper Cambrian under mixed forest. For each soil ecosystem, a pair of heavily (H) and moderately (M) contaminated spots were selected according to Schneider et al. (2016) and by in situ TE measurements with a field portable X-ray fluorescence (FPXRF) using a Thermo Scientific Niton XL3t 980 GOLDD+ (Geometrically Optimised Large area Drift Detector) EDXRF (Energy Dispersive XRF). After characterization of a reduced number of samples performed to guide sampling (Appendix A and Table B.1), the sample set was collected as follows. In each ecosystem, four samples were collected within few centimetres horizontal distance and at two different depths (“top” 0–5 cm and “bottom” 5–10 cm), resulting in 48 soil samples.

2.2. Soil physicochemical analyses Large plant fragments and stones were removed on-site from the 48 soil samples, which were transferred into polyethylene sealing bags. Samples were then homogenized, ground and then divided into aliquots for physicochemical and biological analyses. Samples for biological analyses were kept at −20 °C until processed as described below. The samples for physicochemical analyses were air-dried. Soil pH was determined in a mixture of 1:2.5 soil:water (w/v). The total organic content (TOC) content was determined following the standard procedure ISO 14235 (AFNOR, 1998). Total Pb content of the soil samples was determined by FPXRF in the laboratory. Sample cups of 31 mm diameter were filled with approximately 6 g of soil and covered with 6 μm X-ray Mylar film. The soil was gently compacted with an agate pestle to press the sample against the window film.

Fig. 1. Distribution of surface soil lead content in the study area. This map was produced by ordinary kriging interpolation of 247 FPXRF measurements. Samplings points were selected in order to cover the three main ecosystem types and the two pollution levels. Their surface content was controlled with in-situ measurements before soil sampling.

438


Finally, extraction in an aqueous solution of CaCl2 0.01 M was used to get an estimation of the bioavailable Pb concentrations in the soil samples. The extraction was performed with a soil:solution ratio of 1:10 (g:ml) and a contact time of 2 h (Belén Hinojosa et al., 2010), and extracted Pb concentration was determined by inductively coupled plasma atomic emission spectrometer (ICP-AES, iCAP 6000, Thermo Scientific, United Kingdom). 2.3. Biological analyses We quantified soil DNA as indicator of the microbial biomass, from 1 g of oven-dried soil (70 °C for 48 h) according to a procedure standardized by the GenoSol platform (Dequiedt et al., 2011; Terrat et al., 2012). Soil DNA concentrations were determined in triplicate by electrophoresis in 1% agarose gel using calf thymus DNA as a reference. DNA was stained with GelGreen Nucleic Acid Gel Stain (Biotium, USA) and visualized in a Gel Doc 2000 system (Bio-Rad, USA). To perform the pyrosequencing of the bacterial 16S rRNA gene sequences, we performed a separate DNA extraction from 0.3 g of defrosted (but not dried) soil using the Fast DNA Spin Kit for Soil (MP Biomedicals, USA) and purified extracted DNA with the NucleoSpin gDNA Clean-up XS kit (Macherey-Nagel, USA) according to the manufacturer's instructions (Verzeaux et al., 2016). The concentration in each DNA sample was fluorometrically quantified with the AccuBlue High Sensitivity dsDNA Quantitation Kit (Biotium, USA) using a monochromator-based multimode microplate reader (Infinite M1000 PRO, Tecan Systems, USA). From the purified soil DNA, the bacterial 16S rRNA V3–V4 region was PCR amplified with the forward Bakt_341F (5′-CCTA-CGG-GNG-GCWGCA-G-3′) and reverse Bakt_805R (5′-GAC-TAC-HVG-GGT-ATC-TAATCC-3′) primers (Herlemann et al., 2011). The forward and reverse primers were designed containing overhang sequences compatible with Illumina Nextera XT index (forward primer overhang: TCG-TCGGCA-GCG-TCA-GAT-GTG-TAT-AAG-AGA-CAG, reverse primer overhang: GTC-TCG-TGG-GCT-CGG-AGA-TGT-GTA-TAA-GAG-ACA-G). For each sample, we used 5 ng of template DNA for PCR conducted under the following conditions: 95 °C for 3 min, 25 cycles of 30 s at 95 °C, 30 s at 55 °C and 30 s at 72 °C, followed by 5 min final elongation at 72 °C. We checked the success of the PCR (and absence of amplification in the negative controls) by agarose gel electrophoresis stained with GelGreen (Biotium, USA). We purified each amplicon product (and negative control) with the AMPure XP beads kit (Beckman Coulter, USA) and we quantified DNA with the AccuBlue High Sensitivity dsDNA Quantitation Kit. Illumina Nextera XT Index sequencing adapters were integrated into the amplicons by PCR. The final libraries were purified once again with the AMPure XP beads before quantification with the AccuBlue High Sensitivity dsDNA Quantitation Kit. To validate the library, 1 μl of a 1:50 dilution of the final library was used on a Bioanalyzer DNA 1000 chip using a Bioanalyzer 2100 (Agilent Technologies, USA) to verify the library size and to check the presence of primer dimer contamination. Purified libraries were pooled at equal molarity, denatured, diluted to 4 pM, spiked with a premade PhiX control library at 5% (PhiX control v2, Illumina, USA) and loaded into a MiSeq v2 Reagent Kit (500 Cycles PE, Illumina, USA) to be sequenced in a MiSeq system (Illumina, USA). 2.4. Bioinformatic processing of sequencing data We obtained the sequencing data as separate sets of forward and reverse paired-end reads. After a visual quality-check of the single reads using the FastQC software (included in the distribution Bio-Linux 8 installed on standard desktop PC), we performed the analysis using mothur (version 1.34.4; Schloss et al., 2009) on the ROMEO cluster of the Université de Reims Champagne-Ardenne, following the MiSeq SOP protocol (Kozich et al., 2013). All steps were performed in single-

node mode. We used the mothur compiled SILVA rRNA database (version nr_v119) as reference database (Quast et al., 2013). Briefly, the forward and reverse sequence reads were paired and trimmed of adapter sequences and multiplexing barcodes specific to each sample. Then, quality screening was performed to remove sequences that differed by one nucleotide (nt) from primer sequences or had at least one ambiguous base or excessive homopolymer (N 10 nt). We removed two samples with excessively low number of reads from the dataset. Sequences were then aligned against the SILVA rRNA database and pre-clustered. The dataset was screened for chimeras using the UCHIME program (included in mothur; Edgar et al., 2011). We excluded sequences corresponding to organisms other than Bacteria (i.e. Chloroplast, Mitochondria, Archaea, Eukaryota) or unknown after classification against the SILVA database, from the dataset. Then, we clustered the remaining sequences into operational taxonomic units (OTU) according to the taxonomic affiliation of their closest neighbours and we computed the proportions of OTU. We decided not to apply rarefaction analysis to the results, because of the lack of consensus regarding this statistical tool (McMurdie and Holmes, 2014). The detailed list of commands and parameters used is available as Appendix C. 2.5. Accession numbers All DNA sequences were deposited in the Sequence Read Archive (SRA) of NCBI under BioProject submission number SUB2795840. 2.6. Multivariate statistical analysis To test the significance of ecosystem type, soil horizon and contamination level in structuring bacterial communities, we ran permutational multivariate analyses of variance (PERMANOVA) using the UniFrac matrix (Lozupone et al., 2011) generated by mothur on the basis of the tree computed by FastTree (Price et al., 2009). We performed nonmetric multidimensional scaling (NMDS) on the UniFrac matrix to investigate the community-scale differences between soil samples' bacterial community structure. Finally, a canonical correspondence analysis (CCA) on the relative proportions of the main bacterial groups was used in each ecosystem type to explore relationships between the various bacterial phyla, used as dependent variables, and the selected non-collinear soil physicochemical properties used as constraining variables. Contrarily to NMDS, which allows maximizing rank-order correlation between distance measures and distance in ordination space, CCA axes are constrained to be linear combinations of the environmental variables (Ramette, 2007). All of the statistical tests were performed using R software (version 3.1.1, The R Foundation for Statistical Computing, Vienna, Austria) using vegan and ecodist packages. 3. Results 3.1. Soil physicochemical properties The physicochemical analyses (Tables 1 and B.2) revealed that all soil samples were acidic, with a slightly higher pH for S2 samples (soil under coniferous, pH = 4.7 ± 0.1) than for S1 (hardwood cover) and S3 (hydromorphic samples) (pH = 4.3 ± 0.1 in both S1 and S3). The TOC content ranged between 47 and 275 g·kg−1 across all soil samples (Fig. 2, a–c). S1 and S2 showed similar TOC levels, far lower than in S3: average contents were 81 ± 42, 56 ± 12 and 230 ± 49 g·kg−1, respectively. The total Pb (Pbt) content ranged between 186 and 11,666 mg·kg−1 across all samples (Table B.2), while bioavailable Pb (Pbba) content ranged between 1.7 and 129.4 mg·kg−1, as estimated by CaCl2 extraction. It resulted in an almost nine fold variation in the Pbba:Pbt ratio ranging from 0.3% to 2.6%. The average bioavailability percentages


439

Table 1 Soil physicochemical properties (mean ± SD) for each soil ecosystem type, depth and contamination level (n = 4, total of 48 samples): pH, total organic carbon (TOC); total Pb (Pbt) and bioavailable Pb (Pbba). S1 = hardwood, S2 = coniferous, S3 = hydromorphic; T = top, B = bottom; H = high, M = moderate. The full dataset is given in Table SOM-2. Sample

S1 H T S1 H B S1 M T S1 M B S2 H T S2 H B S2 M T S2 M B S3 H T S3 H B S3 M T S3 M B

pH

TOC

Pbba

Pbt −1

H2O

g·kg

4.3 ± 0.1 4.3 ± 0.1 4.3 ± 0.1 4.3 ± 0.1 4.7 ± 0.1 4.8 ± 0.2 4.6 ± 0.1 4.6 ± 0.0 4.3 ± 0.0 4.4 ± 0.0 4.2 ± 0.2 4.2 ± 0.1

131.2 ± 48.4 55.0 ± 11.4 86.1 ± 23.4 53.8 ± 2.1 70.4 ± 11.5 47.8 ± 6.5 59.0 ± 6.5 47.2 ± 0.8 274.8 ± 17.1 240.5 ± 32.1 232.5 ± 40.2 172.5 ± 35.3

mg·kg

−1

7864 ± 2830 1263 ± 925 552 ± 123 386 ± 85 4366 ± 1455 1709 ± 913 555 ± 12 441 ± 40 10,449 ± 481 6305 ± 2319 1105 ± 413 526 ± 292

mg·kg−1 92 ± 25 31 ± 23 7±1 9±3 35 ± 6 21 ± 13 4±0 3±0 68 ± 4 43 ± 12 5±1 6±2

were 1.81 ± 0.62%, 0.87 ± 0.29% and 0.78 ± 0.36% for S1, S2 and S3, respectively. Pb was by far the TE with the highest content in the studied soils (Pbt). Furthermore, very strong correlations existed between the content in Pbt and in the other TE measured by FPXRF (Cu: r2 = 0.90 with p b 0.001; Zn: r2 = 0.88 with p b 0.001; data not shown). 3.2. Microbial biomass and bacterial diversity The soil DNA concentrations (microbial biomass indicator) ranged between 17 and 134 μg DNA·g−1 across the different ecosystem types (Table B.2). We did not observe any relation between the estimated microbial biomass and TOC. For samples with the highest Pb contents (S1 and S3), lower DNA concentrations were found suggesting a lower biomass values were observed than for samples with low Pb content (i.e. Pbba b 10–20 mg·kg−1), even though a large dispersion of biomass values existed. The high-throughput sequencing of the V3-V4 region of the 16S rRNA gene generated 3,723,669 reads. After all quality-control steps, the sequences were clustered into OTUs with 97% identity threshold and 98.4 to 92.2% of the OTU arising were affiliated to the phylum taxonomic level. Over 90% of the sequences belonged to eight major phyla (with relative abundance N1% of the total OTU), which are, in order of their relative abundance: Acidobacteria, Proteobacteria, Actinobacteria, Verrucomicrobia, Chloroflexi, Planctomycetes, Bacteroidetes, and Gemmatimonadetes (Tables 2 and B.3). We found a lower proportion of Acidobacteria and Proteobacteria in the coniferous (S1: 52% of sequences) than in the hardwood (S2: 61%) and the hydromorphic (S3: 66%) soil samples. At the opposite, coniferous soil samples were characterized by a higher proportion of Actinobacteria (18%) than in the hardwood (12%) and in the hydromorphic (8%) soil samples.

Fig. 2. a–c: Bioavailable lead (Pbba, bars) and organic C (circles) content in soil samples for the three ecosystem types (S1-S2-S3), for topsoil (0–5 cm, T) and bottom (5–10 cm, B); green colour and letter M for the moderately contaminated samples, red colour and letter H for the highly contaminated samples. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3. Multivariate statistical analyses The PERMANOVA results (Table 3 and Table B.4) indicated that bacterial diversity was principally influenced by the ecosystem type (sum of squares [S.S.] for F2 = 64.6) and to a lower extent by the pollution level (S.S. for F3 = 5.9), but not by the soil depth (S.S. for F1 = 0.8). In addition to the effects of the main factors, the interaction between ecosystem type and pollution level also significantly affected the bacterial community structure (S.S. for F2 × F3 = 3.2). The NMDS plot displayed a clear separation among the different ecosystem types (Fig. 3). Highly and moderately contaminated samples were clearly separated within each ecosystem type (Fig. 3). Because of the strong effect of ecosystem type on bacterial communities, we investigated the relationships between soil properties and

bacterial groups in each ecosystem separately (Fig. 4, a–c). Each CCA plot displayed a separation between highly and moderately contaminated samples (Pbba associated with the former). The three most abundant bacterial phyla in our samples, Acidobacteria, Proteobacteria and Actinobacteria, showed diverse patterns on the CCA plots, corresponding to diverse relationship with soil parameters. The Acidobacteria were not influenced by the physicochemical parameters (pH, TOC or Pbba). The Proteobacteria showed a consistent positive association with Pbba and TOC for the three soil ecosystem types (Fig. 4, a–c). The Actinobacteria did not display the same association with soil parameters on the three CCA plots (positively associated with pH for S1, negatively associated with pH and Pbba for S2 and not influenced by soil parameters for S3).

440 Table 2 Abundance in % of bacterial phyla (mean ± SD) for each soil ecosystem type, depth and contamination level (n = 4, total of 46 samples). “C.D.” stands for “candidate division”. See Table 1 for the complete legend. The full dataset is given in Table SOM-3. Proteobacteria

Actinobacteria

Verrucomicrobia

Chloroflexi

Planctomycetes

Bacteroidetes

Gemmatimonadetes

Chlamydiae

C.D. OD1

C.D. TM7

Elusimicrobia

29.60 ± 0.99 36.55 ± 0.89 36.92 ± 0.31 25.49 ± 0.27 22.56 ± 0.54 32.89 ± 7.38 22.57 ± 6.07 31.22 ± 5.75 31.81 ± 3.10 28.24 ± 3.46 31.61 ± 7.28 45.84 ± 4.26

39.22 ± 0.98 20.72 ± 4.33 17.73 ± 0.46 37.18 ± 0.43 31.51 ± 2.52 24.33 ± 4.07 22.49 ± 3.14 20.36 ± 1.22 37.33 ± 4.83 35.64 ± 5.52 35.61 ± 7.22 18.88 ± 4.79

9.40 ± 0.34 21.55 ± 2.18 7.81 ± 0.50 8.03 ± 0.29 14.35 ± 3.29 11.07 ± 2.95 28.74 ± 13.41 17.69 ± 3.49 7.26 ± 0.71 7.76 ± 1.59 6.82 ± 0.72 9.38 ± 1.20

2.71 ± 0.36 1.70 ± 0.06 15.96 ± 1.12 7.06 ± 0.16 6.67 ± 2.06 7.10 ± 1.05 6.60 ± 1.81 7.60 ± 0.53 3.45 ± 1.07 4.40 ± 1.04 5.39 ± 0.63 3.12 ± 0.65

1.98 ± 0.67 8.16 ± 3.11 10.00 ± 0.29 3.96 ± 0.13 3.93 ± 1.69 5.75 ± 1.75 4.32 ± 1.68 7.28 ± 0.73 3.16 ± 0.38 4.25 ± 0.42 1.30 ± 0.12 8.92 ± 5.14

2.73 ± 0.12 1.67 ± 0.40 4.11 ± 0.18 4.47 ± 0.19 3.49 ± 1.06 3.92 ± 0.63 3.85 ± 1.45 4.26 ± 0.92 2.87 ± 0.32 3.40 ± 0.49 3.03 ± 0.40 1.75 ± 0.48

2.69 ± 0.21 19.95 ± 0.60 0.42 ± 0.06 2.37 ± 0.13 4.04 ± 1.65 1.93 ± 0.72 2.36 ± 1.31 1.16 ± 0.21 2.11 ± 0.59 1.87 ± 0.36 3.86 ± 0.79 1.78 ± 0.86

1.07 ± 0.20 0.34 ± 0.10 1.07 ± 0.11 2.30 ± 0.11 2.09 ± 0.56 2.28 ± 0.74 1.33 ± 0.20 1.67 ± 0.31 0.87 ± 0.22 1.09 ± 0.15 1.38 ± 0.31 0.66 ± 0.24

2.61 ± 0.25 1.11 ± 1.03 0.34 ± 0.04 0.48 ± 0.03 0.80 ± 0.24 0.85 ± 0.26 0.38 ± 0.13 0.53 ± 0.07 1.36 ± 0.14 1.44 ± 0.64 0.72 ± 0.09 0.50 ± 0.21

0.81 ± 0.12 0.18 ± 0.08 0.19 ± 0.03 0.85 ± 0.13 0.83 ± 0.52 0.45 ± 0.21 0.24 ± 0.05 0.16 ± 0.05 0.93 ± 0.43 1.51 ± 0.22 1.38 ± 0.22 0.73 ± 0.37

0.41 ± 0.07 0.42 ± 0.11 0.23 ± 0.02 1.47 ± 0.06 1.38 ± 0.68 0.53 ± 0.22 0.31 ± 0.10 0.19 ± 0.05 0.38 ± 0.10 0.25 ± 0.08 0.85 ± 0.22 0.35 ± 0.14

0.34 ± 0.09 0.14 ± 0.05 0.35 ± 0.04 0.55 ± 0.04 0.56 ± 0.17 0.56 ± 0.19 0.32 ± 0.07 0.36 ± 0.13 0.44 ± 0.10 0.57 ± 0.12 0.44 ± 0.04 0.38 ± 0.07

Table 2 2 (continued) Table Abundance in % of bacterial phyla (mean ± SD) for each soil ecosystem type, depth and contamination level (n = 4, total of 46 samples). “C.D.” stands for “candidate division”. See Table 1 for the complete legend. The full dataset is given in Table SOM-3.


Nitrospirae

TM6

Firmicutes

WD272

C.D. WS3

WCHB1-60

SM2F11

Armatimonadetes

Cyanobacteria

Spirochaetae

Chlorobi

Other bacterial taxa

0.24 ± 0.09 0.13 ± 0.06 0.51 ± 0.03 0.47 ± 0.05 0.61 ± 0.35 0.66 ± 0.16 0.32 ± 0.08 0.45 ± 0.11 0.31 ± 0.05 0.43 ± 0.17 0.26 ± 0.05 0.42 ± 0.12

0.45 ± 0.10 0.20 ± 0.04 0.71 ± 0.14 0.73 ± 0.07 0.26 ± 0.01 0.34 ± 0.09 0.17 ± 0.03 0.25 ± 0.06 0.28 ± 0.05 0.24 ± 0.06 0.15 ± 0.04 0.09 ± 0.02

0.11 ± 0.05 0.58 ± 0.12 0.19 ± 0.03 0.09 ± 0.04 0.20 ± 0.10 0.41 ± 0.33 0.34 ± 0.07 0.48 ± 0.17 0.13 ± 0.06 0.18 ± 0.04 0.34 ± 0.26 0.49 ± 0.09

0.53 ± 0.10 0.25 ± 0.07 0.46 ± 0.05 0.46 ± 0.02 0.20 ± 0.06 0.27 ± 0.07 0.21 ± 0.02 0.16 ± 0.07 0.16 ± 0.03 0.13 ± 0.03 0.38 ± 0.09 0.16 ± 0.04

0.01 ± 0.00 0.01 ± 0.01 0.53 ± 0.10 0.22 ± 0.02 0.52 ± 0.09 0.87 ± 0.34 0.21 ± 0.09 0.43 ± 0.06 0.07 ± 0.03 0.16 ± 0.04 0.01 ± 0.01 0.01 ± 0.00

0.06 ± 0.01 0.12 ± 0.01 0.31 ± 0.03 0.52 ± 0.06 0.41 ± 0.16 0.24 ± 0.05 0.23 ± 0.04 0.14 ± 0.04 0.06 ± 0.02 0.03 ± 0.01 0.20 ± 0.04 0.13 ± 0.07

0.16 ± 0.08 0.06 ± 0.03 0.06 ± 0.01 0.37 ± 0.03 0.31 ± 0.18 0.15 ± 0.08 0.09 ± 0.06 0.09 ± 0.02 0.17 ± 0.05 0.22 ± 0.02 0.29 ± 0.12 0.21 ± 0.13

0.12 ± 0.04 0.15 ± 0.04 0.14 ± 0.01 0.36 ± 0.09 0.14 ± 0.04 0.09 ± 0.03 0.11 ± 0.03 0.13 ± 0.02 0.16 ± 0.02 0.16 ± 0.05 0.17 ± 0.06 0.14 ± 0.01

0.20 ± 0.05 0.09 ± 0.02 0.11 ± 0.03 0.16 ± 0.04 0.09 ± 0.03 0.09 ± 0.01 0.09 ± 0.05 0.08 ± 0.02 0.09 ± 0.02 0.08 ± 0.04 0.21 ± 0.02 0.08 ± 0.04

0.04 ± 0.01 0.03 ± 0.01 0.02 ± 0.01 0.02 ± 0.01 0.04 ± 0.02 0.04 ± 0.02 0.01 ± 0.01 0.04 ± 0.02 0.08 ± 0.06 0.28 ± 0.22 0.18 ± 0.05 0.15 ± 0.03

0.01 ± 0.00 0.01 ± 0.01 0.01 ± 0.00 0.02 ± 0.01 0.02 ± 0.01 0.02 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 0.08 ± 0.02 0.41 ± 0.15 0.01 ± 0.01 0.21 ± 0.09

4.50 ± 0.19 3.88 ± 0.27 1.83 ± 0.14 2.39 ± 0.37 5.30 ± 0.70 5.17 ± 0.32 4.72 ± 1.08 5.25 ± 0.26 6.44 ± 0.65 7.25 ± 0.67 5.40 ± 0.49 5.61 ± 0.41



Acidobacteria


441

Table 3 Permutational multivariate ANOVA evaluating the effects of depth (F1), soil ecosystem type (F2) and contamination level (F3) on the soil microbial community structure based on the UniFrac distance matrix The column F.model corresponds to the Fisher coefficient, Df to the degrees of freedom, S.S. to the sum of squares, MS to the mean of squares. pValues are based on 999 permutations (significance is *** for p-value b 0.001, ** for p b 0.01, * for p b 0.05 and . for p b 0.1) and r2-values indicate the proportion of variation explained by each variable.

F1: Depth F2: Ecosystem type F3: Pollution level F1 × F2 F1 × F3 F2 × F3 Residual

Df

S.S.

MS

F.model

p

Signif.

r2

1 2 1 2 1 2 36

0.8 64.6 5.9 0.7 0.2 3.2 2.8

0.01 0.32 0.06 0.00 0.00 0.02 0.00

10.4 409.7 74.4 4.3 3.0 20.1

0.002 0.001 0.001 0.015 0.073 0.001

** *** *** * . ***

0.01 0.83 0.07 0.01 0.00 0.04 0.04

Three phyla with a lower abundance displayed a consistent association with the soil parameters on the CCA plots. The phylum Chlamydiae was positively associated with Pbba on the CCA plots and the abundances in the highly contaminated samples were higher than in the moderately contaminated ones (1.9 ± 1.1% and 0.4 ± 0.1% for S1, 0.8 ± 0.3% and 0.5 ± 0.1% for S2 and 1.4 ± 0.5% and 0.6 ± 0.2% for S3, respectively). Conversely, the phyla Chloroflexi and Verrucomicrobia appeared to be negatively associated with Pbba: the abundances in the highly contaminated samples were lower than in the moderately contaminated ones (Chloroflexi: 5.1 ± 3.8% and 6.6 ± 3.0% for S1, 4.8 ± 1.9% and 5.8 ± 2.0% for S2 and 3.7 ± 0.7% and 4.6 ± 5.1% for S3, for highly and moderately contaminated samples respectively; Verrucomicrobia: 2.2 ± 0.6% and 10.9 ± 4.5% for S1, 6.9 ± 1.7% and 7.1 ± 1.4% for S2 and 3.9 ± 1.2% and 4.4 ± 1.3% for S3, respectively). 4. Discussion Trace element contamination in soil causes diverse hazards to humans and ecosystems: direct contamination due to ingestion or contact with contaminated soil, accumulation along the food chain, reduction in land usability for agricultural production possibly causing food insecurity (Wuana and Okieimen, 2011). To get insight into this issue, we assessed the influence of the soil physicochemical properties on Pb bioavailability, and in turn, on the impact of pollution level on the structure of bacterial communities in the vicinity of a secondary Pb smelter. To do so, we collected soil samples in three contrasting ecosystem types (hardwood and coniferous cover and hydromorphic soils) at two soil depths. The pedological and

Fig. 3. Non-metric multidimensional scaling (NMDS) ordination based on the UniFrac distance matrix (stress = 0.112). With increasing distance between two points the communities were more dissimilar. The ellipses represent the average projection area of the samples from the centroid of each site (orange = hardwood, brown = coniferous, blue = hydromorphic). The colour of the circle outlines corresponds to the contamination level (red for high and green for moderate contamination) and the circle filling corresponds to the site (same colours as for the ellipses). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. a–c: Canonical correspondence analysis (CCA) ordination plots for the first two dimensions of constraining variables (TOC, pH, Pbba) and bacterial phyla as dependent variables according to the ecosystem type: (a) hardwood, (b) coniferous, (c) hydromorphic.

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geochemical analyses showed that the selected soils surrounding the Pb smelter had contrasting properties and different levels of TE contamination. In particular, the proportion of bioavailable Pb to total Pb varied between 0.3 and 2.6% in the three ecosystems but was in the same range as reported in other studies (Chen et al., 2014). The comparison of Pb bioavailability between hardwood (with low TOC) and hydromorphic soil samples (with high TOC, but pH values similar to those of the hardwood soil samples) confirmed the idea that SOM plays an important role in Pb speciation: higher TOC leads to lower Pb bioavailability (Sauvé et al., 2000). The comparison of the samples under hardwood and coniferous plant cover (both having low TOC, pH being approximately 0.4 unit higher for the coniferous than for the hardwood soil samples) indicated that pH also played a significant role in Pb speciation: higher pH leads to lower Pb bioavailability, as has been evidenced by numerous studies (e.g., Grobelak and Napora, 2015). The similar bioavailability values obtained for the coniferous and hydromorphic soil samples are likely the result of the counteracting effects of pH and TOC content. In line with our first hypothesis, these results taken together confirmed that ecosystem types, varying in their soil physicochemical properties, influence Pb bioavailability to an important degree in polluted environment. The large dispersion of microbial biomass values in soil samples with low Pbba content indicated that Pb does not impede microbial development at low contamination level (Chodak et al., 2013; Deng et al., 2015; Niklińska et al., 2005). However, we found an important decrease of the microbial biomass above thresholds of about 10–20 mg Pbba·kg−1, suggesting that the microbial growth was inhibited at high TE content. In agreement with our second hypothesis, we also found significant changes in the bacterial community structure at high levels of TE contamination. Some groups, such as Actinobacteria and Acidobacteria were not influenced by Pb contamination. Contrarily, Proteobacteria were more abundant in the highly- than in the moderately-contaminated samples in all ecosystem types. The Proteobacteria were positively associated with Pbba and TOC, in agreement with the findings of Sandaa et al. (1999) and Gołębiewski et al. (2014). Looking further into the phylum Proteobacteria, the proportions of the class γ-Proteobacteria revealed variations between ecosystem types: they were much higher for the hydromorphic than for the hardwood and coniferous soil samples (Table B.5). Across all ecosystem types, this class was more abundant in the highly than in the moderately polluted samples, suggesting that their abundance was also strongly influenced by Pb contamination level. Within the γ-Proteobacteria class, the order Xanthomonadales was dominant in the hardwood and coniferous soils while Pseudomonadales (and especially the species Pseudomonas mandelii) were dominant in the hydromorphic soil samples and displayed a much higher proportion in the highly- than in the moderatelycontaminated samples. In agreement with our data, Zhang et al. (2012) also observed a good correlation between DTPA-extractable Pb and γ-Proteobacteria clone sequences in contaminated rhizospheric soils and their cultivation experiments obtained a high number of Pseudomonas isolates. Furthermore, P. mandelii is frequently described in water-saturated and/or anoxic soils (Parmentier et al., 2014), which is consistent with its presence in the hydromorphic samples of our study. Recently, Staley et al. (2015) found that Pseudomonadales were the only bacterial order that was correlated to the frequency of metal resistance genes in river waters (Hg and Zn). In addition to the γ-Proteobacteria group, we also found that the phylum Chlamydiae was consistently positively associated with Pbba. Within that phylum, the family Parachlamydiaceae was the most abundant (Table B.5). The Chlamydiae, initially described as pathogens to humans and other animals (Jones et al., 1945), are also occasionally referred to as a minor group in studies of soil or underground bacterial diversity (Itävaara et al., 2011; Serkebaeva et al., 2013). The Chlamydiae are obligate intracellular bacteria found in free-living amoebae and other eukaryotic hosts (Horn, 2008). These hosts could serve as protective armor through their cyst forms (Kebbi-Beghdadi and Greub, 2014)

and could allow the Chlamydiae to resist high Pb levels. Together with the Chlamydiae, the phyla Planctomycetes and Verrucomicrobia make up the “PVC clade” (or “superphylum”) (Devos and Ward, 2014; Gupta et al., 2012; Lagkouvardos et al., 2014). These two phyla did not display the same consistent negative correlation with Pbba as the Chlamydiae. In our dataset, Verrucomicrobia displayed a negative association with Pbba, whereas Planctomycetes were not affected. Similar to the observed decrease in the proportion of Verrucomicrobia at high Pb levels, McGee et al. (2017) report a significant decrease of their relative abundance in soils amended with silver nano-particles. Verrucomicrobia have been reported to have an oligotrophic K-type development strategy (Bergmann et al., 2011; Tada and Inoue, 2000), but Männistö et al. (2016) observed a decrease of their abundance in N-amended soil microcosms. Due to their opposite behaviour towards TE contamination, the ratio Verrucomicrobia/Chlamydiae was consistently lower in highly- (1.2–8.3) than in moderately Pb-contaminated soils (7.0– 25.8). Thus, we propose that the Verrucomicrobia/Chlamydiae ratio could be used as an indicator of soil pollution level. 5. Conclusions Our combined physicochemical-microbiological analyses provide new insight into the relative effects of ecosystem types and Pb bioavailability on soil bacterial communities. Pb bioavailability was controlled by ecosystem type because of the differences in soil parameters, especially the pH and the TOC content. Although ecosystem type was the main driver of the bacterial community structure, the relationships between Pb contamination and specific groups of bacteria demonstrated that the level of pollution was also an important factor structuring soil bacterial communities. Our results indicated that the two bacteria phyla Verrucomicrobia and Chlamydiae were consistently, but in opposite directions, influenced by Pb contamination. Their ratio might constitute a bioindicator of alteration of the soil microbial community due to TE pollution and more generally, of soil health. Acknowledgements This work was funded by the Région Champagne-Ardenne (A210101 Essaimage) (PhD grant to A.S.) and by the Structure Fédérative de Recherche Condorcet FR CNRS3417. This work was completed using resources of the ROMEO high-performance computing center. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.06.159. References Adriano, D.C., 2013. Trace Elements in the Terrestrial Environment. Springer Science & Business Media. AFNOR, 1998. ISO 14235; Soil Quality - Determination of Organic Carbon by Sulfochromic Oxidation. Ahmad, I., Hayat, S., Ahmad, A., Inam, A., Samiullah, 2005. Effect of heavy metal on survival of certain groups of indigenous soil microbial population. J. Appl. Sci. Environ. Manag. 9. http://dx.doi.org/10.4314/jasem.v9i1.17267. Alloway, B.J., 1995. Heavy Metals in Soils. Blackie Academic and Professional. An, J., Jho, E.H., Nam, K., 2015. Effect of dissolved humic acid on the Pb bioavailability in soil solution and its consequence on ecological risk. J. Hazard. Mater. 286:236–241. http://dx.doi.org/10.1016/j.jhazmat.2014.12.016. Belén Hinojosa, M., Carreira, J.A., García-Ruíz, R., Rodríguez-Maroto, J.M., Daniell, T.J., Griffiths, B.S., 2010. Plant treatment, pollutant load, and soil type effects in rhizosphere ecology of trace element polluted soils. Ecotoxicol. Environ. Saf. 73:970–981. http://dx.doi.org/10.1016/j.ecoenv.2010.01.013. Benedetti, M.F., Van Riemsdijk, W.H., Koopal, L.K., 1996. Humic substances considered as a heterogeneous Donnan gel phase. Environ. Sci. Technol. 30:1805–1813. http://dx.doi. org/10.1021/es950012y. Bergmann, G.T., Bates, S.T., Eilers, K.G., Lauber, C.L., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol. Biochem. 43:1450–1455. http://dx.doi.org/10.1016/j.soilbio. 2011.03.012.

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