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Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing
Ecotoxicology and Environmental Safety 144 (2017) 300–306
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing
MARK
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Honghong Guoa,b, Mubasher Nasira,b, Jialong Lva,b, , Yunchao Daia,b, Jiakai Gaoa,b a b
College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China Key Laboratory of Plant Nutrition and Agri-environment in Northwest China, Ministry of Agriculture, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Heavy metals Soil microbial community Tolerant bacterial groups Illumina sequencing
To improve the understanding of bacterial community in heavy metals contaminated soils, we studied the effects of environmental factors on the bacterial community structure in contaminated fields located in Shaanxi Province of China. Our results showed that microbial community structure varied among sites, and it was significantly affected by soil environmental factors such as pH, soil organic matter (SOM), Cd, Pb and Zn. In addition, Spearman's rank-order correlation indicated heavy metal sensitive (Ralstonia, Gemmatimona, Rhodanobacter and Mizugakiibacter) and tolerant (unidentified-Nitrospiraceae, Blastocatella and unidentifiedAcidobacteria) microbial groups. Our findings are crucial to understanding microbial diversity in heavy metal polluted soils of China and can be used to evaluate microbial communities for scientific applications such as bioremediation projects.
1. Introduction Steady economic development in China threatens soil ecosystems due to excessive mining activities (Shu et al., 2003). China produces more than 265.4 million tons of mining waste including Cd, As and other toxic substances (Li, 2006). Once these toxins invade agricultural soils, they affect food production and safety, posing a significant threat to human health via incorporation into the food chain (Williams et al., 2009; Li et al., 2014a; Zhuang et al., 2013; Liu et al., 2013). Zhang et al. (2010) found that grain yield was diminished by more than l.0 × 107 t due to heavy metal pollution, resulting in a direct loss up to 20 billion RMB. Approximately 20% of arable land in China has been contaminated by heavy metals, and the amount is expected to increase over the next few decades (Li et al., 2014b). The total concentration of heavy metals in soil is a weak index for indicating the actual concentrations that microorganisms are exposed to in soil solutions (Giller et al., 2009). According to environmental quality standards, the toxicity of heavy metals to soil microorganisms is directly related to heavy metal bioavailability (Wang et al., 2007a; Kot and Namiesńik, 2000). However, data regarding the relationship between soil microbial community structure and heavy metal bioavailability are limited (Wang et al., 2007a). Microbial community structure can play a role in the community's ability to achieve different functions, but also can resist environmental interference (Torsvik and Øvreås, 2002). Generally, heavy metal pollution reduces microbial diversity in soil because microorganisms that
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are sensitive to heavy metal toxicity will abruptly decline in abundance or even become extinct, whereas species that can tolerate high concentrations of heavy metals slowly become predominant (Klug and Reddy, 1984; Giller et al., 1998). Changes in microbial community structure may seriously affect the ability of soil microbes to degrade organic matter, leading to decreased soil fertility (Giller et al., 1998). Studies of tailings reclamation have reported that the diversity and stability of vegetation on reclaimed land are closely related to the diversity and abundance of soil microorganisms (Sherriff, 2005; Mummey et al., 2002; Mendez et al., 2008). In addition, the study of microbial community structure in contaminated soils may be helpful in isolating strains of bacteria that are tolerant to heavy metals and aid in the development of heavy metal resistant genes (Gremion et al., 2003; Pawlowska et al., 2000). The Earth Microbiological Project has demonstrated that the V4 region can be widely supported as a standard 16S rRNA region for general community assessment across a range of very different environments (Gilbert et al., 2014). This helps us to explore the interaction between heavy metals and soil microbial communities to elucidate the mechanism by which heavy metal pollution changes microbial community structures. For this purpose, an experiment was conducted in contaminated fields located in Zhen'an County of Shangluo City, Shaanxi Province, China. Soils at these sites were mostly polluted from lead–zinc and gold mining wastes, which caused concentrations of soil heavy metals such as Cd, As, Hg, Zn and Pb to exceed the national standard. Thus, the selected experimental site was an ideal environment for studying
Corresponding author at: College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China.
http://dx.doi.org/10.1016/j.ecoenv.2017.06.048 Received 6 December 2016; Received in revised form 14 June 2017; Accepted 16 June 2017 0147-6513/ © 2017 Published by Elsevier Inc.
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Fig. 1. Locations of all sampling sites contaminated with heavy metals.
2. Materials and methods
(Cd, Pb, Zn, Hg and As) and extractable heavy metals (Cd, Pb, and Zn) were measured using standard soil testing procedures (Bao, 2000). Extractable Hg extracted with 0.03% TGA-1/15 mol/L Na2HPO4 (Wang et al., 1983) and extractable As extracted with 0.05 mol/L NaH2PO4 (Wang, 2012a), were measured with an atomic fluorescence spectrophotometer.
2.1. Field description and soil collection
2.3. High-throughput sequencing
Twenty-four soil samples were collected from four farmlands contaminated with heavy metals, located in Zhen'an County of Shangluo City, Shaanxi Province, China (Fig. 1). The fields (G1, G2) and (L1, L2) were contaminated from gold mining and lead–zinc mining, respectively. Soils samples were collected from top 0 to 15 cm soil layer in December 2015 (L1-W, L2-W, G1-W, G2-W) and June 2016 (L1-S, L2-S, G1-S, G2-S). Five soil cores were collected for each sample and thoroughly mixed into a single composite sample. All composite samples were packed into plastic bags and taken to the lab for further processing. Soil samples were immediately stored in a freezer at −80 °C prior to DNA extraction and at 4 °C prior to soil chemical analysis. The total metal concentration of the sampling sites is shown in Table 1.
Community DNA from soil samples was extracted in accordance with the manufacturer's protocol using the MoBio Power soil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA). The concentration and purity of DNA were tested with 1% agarose gel, and DNA was diluted with sterile water to 1 ng/μl. The V4 region of the bacterial 16S rRNA genes was amplified using the specific primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′) which yields accurate taxonomic information and has few biases among various bacterial taxa (Bates et al., 2010). A previously described protocol for DNA amplification was followed (Caporaso et al., 2011). Purified PCR amplicons with a bright main band of 300 bp were sequenced with the Illumina MiSeq platform at Novogene (Beijing, China). After sequencing, initial DNA fragments were assembled by fast length adjustment of short reads (Flash) (Magoč and Salzberg, 2011). Chimeras were removed and 97% similarity OTUs was picked up with UPARSE software package (Edgar, 2010). Representative sequence analysis was performed using the QIIME pipeline (Caporaso et al., 2010) and distributed ribosomal database project (RDP) (Wang et al., 2007b) with the latest Greengenes database (Mcdonald et al., 2012). For each representative sequence, the Greengenes database was used based on RDP classifier algorithm to assign putative taxonomic position. The raw reads generated in the study has been submitted to the NCBI's SRA. Accession: PRJNA376845 ID: 376845.
microbial communities under in-situ conditions. The main objective of our study was to characterize the behavior of microbial communities in heavy metal contaminated soil and determine the effect of environmental factors on bacteria.
2.2. Soil chemical analysis Soil pH was measured with a calibrated pH meter (soil: water ratio of 1:2.5). Soil moisture content was determined by the oven drying method at 105 °C for 8 h. Cation exchange capacity (CEC) was determined using NaOAc flame photometry. Soil organic matter (SOM) was determined by the K2Cr2O7 colorimetric method. Total nitrogen (TN) was analyzed with a CN analyzer. Available phosphorus (AP) was extracted with 0.5 mol/L NaHCO3 and measured with a spectrophotometer. Available potassium (AK) was extracted with 1 mol/L NH4OAc and measured using flame photometry. Total heavy metals Table 1 Concentration of total heavy metals in all fields. Location
Total Cd (mg kg−1)
Total Pb (mg kg−1)
Total Zn (mg kg−1)
Total Hg (mg kg−1)
Total As (mg kg−1)
L1 L2 G1 G2
1.97 ± 0.17 1.12 ± 0.35 0.37 ± 0.02 0.36 ± 0.04
59.86 ± 13.35 63.59 ± 12.44 30.91 ± 1.71 34.41 ± 1.58
50.68 ± 2.26 45.98 ± 2.15 28.63 ± 1.54 32.42 ± 1.68
2.09 ± 1.12 0.87 ± 1.42 0.12 ± 0.73 0.24 ± 1.52
26.00 ± 0.24 34.59 ± 0.35 19.83 ± 0.02 79.81 ± 0.08
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(43.31%, 11.54%, 8.19%, 8.82%, 7.28%, 3.31%, 5.33%) respectively. Proteobacteria, Gemmatimonadetes, Actinobacteria and Firmicutes were more abundant in G1 and G2, but Acidobacteria, Bacteroidetes and Planctomycetes were more abundant in L1 and L2. In addition, Chloroflexi, Nitrospirae and Thaumarchaeota were detected in contaminated soils at low relative abundances. At the genera level, the more abundant groups in G1 and G2 samples as compared to L1 and L2 samples along with the variation in their relative abundance were Sphingomonas (5.57% in G-site soils, 4.00% in L-site soils), Acinetobacter (1.31% G, 0.04% L), Pseudomonas (0.91% G, 0.42% L), Gemmatimonas (1.54% G, 0.43% L), Ralstonia (0.42% G, 0.02% L), Mizugakiibacter (0.38% G, 0.02% L), Rhodanobacter (0.69% G, 0.02% L) and Arthrobacter (0.73% G, 0.24% L), whereas Acidibacter (0.82% L, 0.77% G), Blastocatella (0.87% L, 0.59% G), Flavobacterium (1.61% L, 0.52% G) and Pedobacter (0.95% L, 0.40% G) were more abundant in L1 and L2 soils as shown in Fig. 3. Although most of these predominant bacterial genera showed a significant reduction in L-site as compared to G-site, which may be due to the higher concentration of heavy metals in L-site. However, Acidibacter, Blastocatella, Flavobacterium and Pedobacter had increased in varying degrees in L-site.
2.4. Statistical analysis Based on the results of all sample species annotations, the top 10 phyla and the top 35 genera were analyzed (the relative abundance over 1%). Principal component analysis (PCA) was applied to reduce dimensionality of the original environmental data matrix, which was performed using R software (Version 2.15.3). Unweighted Pair-group Method with Arithmetic Means (UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the metric distance matrix using average linkage and cluster the genus of the bacteria by QIIME software (version 1.7.0). Canonical correspondence analysis (CCA) was conducted to investigate which environmental factors significantly affected microbial community structure and Spearman's correlation analysis was used to investigate correlations among environmental factors and species richness (alpha diversity), which were performed using R software (Version 2.15.3). Relevant data tables were obtained using Microsoft EXCEL (2016). 3. Results 3.1. Environmental parameters
3.3. Relationship between microbial community structure and environmental parameters
Soil pH, soil moisture content, TN, AP, AK, SOM and CEC are shown in Table 2 and concentrations of extractable heavy metals are summarized in Table 3. Extractable heavy metal concentrations in samples collected in December 2015 were significantly higher than those in samples collected in June 2016, except for As and Hg, and heavy metal pollution in soil from the lead–zinc mining site was substantially more serious than that in samples from the gold mining site. Extractable Cd, Pb, Zn, Hg and As contents respectively ranged from 0.33 to 0.7 g kg−1, 1.52–42.95 g kg−1, 3.04–18.27 g kg−1, 0.26–0.93 g kg−1 and 1.51–2.51 g kg−1 in 2015 and from 0.15 to 0.37 g kg−1, 0.78–24.01 g kg−1, 0.68–9.09 g kg−1, 0.26–0.93 g kg−1 and 1.53–2.80 g kg−1 in 2016.
Results of PCA revealed that bacterial communities from different soil samples were clustered according to their type (Fig. 4), which may be due to the influence of environmental factors. Results of CCA showed a clear association between microbial community structure and environmental factors (Fig. 5). The strength of the effect of environmental factors on microbial community structure is reflected by the length of the arrow representing each parameter. The effect of SOM, pH and Zn on the microbial community structure were highly significant (P < 0.01), and the effect of Cd, Pb and H2O were significant (P < 0.05), indicating that these environmental factors were the key factors affecting microbial community. However, As and Hg had no obvious effect on microbial community structure.
3.2. Microbial community structure 3.4. Relationship between microbial abundance and environmental parameters
A total of 1,469,245 high quality reads were obtained from 24 samples after filtering low quality reads and chimaeras, and trimming the adapters, primers, and barcodes. The maximum and minimum number of sequences per sample was (75342, 29218) with an average of 61219. All high-quality reads were classified taxonomically (phylum to genus) using the default settings of QIIME. Taxonomic information at the phylum level is shown in Fig. 2. The site-specific trend influenced microbial population. The predominant phyla in L1 and L2 soil samples were Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Planctomycetes and Firmicutes with an average relative abundance (37.56%, 15.86%, 8.63%, 6.26%, 6.35%, 5.20%, 4.54%) respectively, whereas bacterial assemblages in G1 and G2 soil samples were predominated by Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Planctomycetes and Firmicutes with an average relative abundance
Spearman's rank-order correlation was used to analyze correlations among environmental parameters and microbial abundance (Fig. 6). The abundance of Ralstonia and Gemmatimonas were negatively correlated with exchangeable Cd, Pb, Zn, Hg and pH (P < 0.01). Abundance of Mizugakiibacter and Rhodanobacter were negatively correlated with exchangeable Cd, Pb, Zn and SOM (P < 0.01) except Hg (P < 0.05), but Mizugakiibacter was positively correlated with As (P < 0.05). Unidentified-Nitrospiraceae, Blastocatella and unidentified-Acidobacteria were significantly and positively correlated with Pb, Cd, Zn, Hg and SOM (P < 0.05). In addition, Acidibacter was significantly and positively correlated with Zn and SOM (P < 0.01) and positively correlated with Pb and As (P < 0.05). The abundance of Flavobacterium and Pedobacter were positively correlated with SOM (P < 0.01 for Flavobacterium;
Table 2 Physicochemical properties of the selected soils. Location
pH
H2O (%)
TN/ (g kg−1)
AP/ (g kg−1)
AK/ (g kg−1)
OM/ (g kg−1)
CEC/ (cmol kg−1)
L1-W L2-W G1-W G2-W L1-S L2-S G1-S G2-S
8.07 ± 0.05 8.08 ± 0.03 7.90 ± 0.07 7.67 ± 0.31 8.26 ± 0.02 7.84 ± 0.05 7.80 ± 0.10 7.60 ± 0.20
18.91 ± 5.8 21.77 ± 0.9 16.50 ± 3.8 14.75 ± 1.1 15.33 ± 0.9 12.51 ± 0.4 14.19 ± 1.2 10.23 ± 1.0
1.95 ± 0.02 1.92 ± 0.05 1.49 ± 0.26 1.70 ± 0.11 1.75 ± 0.14 1.75 ± 0.07 1.37 ± 0.17 1.62 ± 0.11
15.29 ± 7.1 25.33 ± 4.5 4.28 ± 1.0 13.67 ± 2.9 23.70 ± 1.7 38.64 ± 7.6 8.23 ± 1.8 30.87 ± 7.6
77.32 ± 7.0 63.57 ± 2.9 118.66 ± 11.1 135.09 ± 18.3 63.04 ± 5.1 82.86 ± 16.2 108.00 ± 17.5 117.14 ± 17.7
34.82 ± 1.6 28.58 ± 0.5 22.54 ± 1.2 19.29 ± 4.1 20.27 ± 0.7 17.84 ± 1.4 13.85 ± 0.3 10.33 ± 0.1
27.12 ± 6.6 36.25 ± 4.1 30.87 ± 5.3 29.51 ± 4.3 25.63 ± 3.6 21.93 ± 3.3 36.12 ± 2.6 29.14 ± 4.2
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Table 3 Concentration of extractable heavy metals in selected soils. Location
L1 L2 G1 G2
Extractable Cd (mg kg−1)
Extractable Pb (mg kg−1)
Extractable Zn (mg kg−1)
Extractable Hg (mg kg−1)
Extractable As (mg kg−1)
2015
2016
2015
2016
2015
2016
2015
2016
2015
2016
0.33 ± 0.04 0.70 ± 0.04 ND ND
0.15 ± 0.03 0.37 ± 0.07 ND ND
16.85 ± 3.1 42.95 ± 2.31 1.88 ± 0.12 1.52 ± 0.02
6.86 ± 1.56 24.01 ± 3.86 0.87 ± 0.11 0.78 ± 0.07
18.27 ± 0.36 17.59 ± 0.47 3.04 ± 1.04 4.10 ± 2.00
9.07 ± 0.17 9.09 ± 0.19 0.68 ± 0.06 0.83 ± 0.09
0.44 ± 0.02 0.93 ± 0.12 0.26 ± 0.02 0.27 ± 0.05
0.47 ± 0.04 0.93 ± 0.15 0.29 ± 0.01 0.26 ± 0.03
2.36 ± 0.04 1.65 ± 0.07 1.51 ± 0.06 2.51 ± 0.10
2.51 ± 0.11 1.53 ± 0.07 1.56 ± 0.06 2.80 ± 0.53
ND: Extractable Cd was not detected in G1 and G2.
4. Discussion 4.1. Correlations among microbial community structure and environmental parameters SOM appears to be important for shaping microbial community structure. Kirkham (2006) believed that SOM is one of the most important factor influencing the bioavailability of heavy metals in soil. And the correlation between heavy metals and microbes is related to the nature of SOM (Lynch et al., 2013; Palmborg et al., 1998). Our results indicated that there was a correlation between SOM and a large number of microorganisms, and these microorganisms had similar correlations with the heavy metals Cd, Pb, Zn and Hg. Therefore, it might that SOM changed the concentration of heavy metals in the soil, leading to change the microbial community structure. Soil pH has been reported to be a dominant factor influencing microbial activity. It has been generally accepted that pH plays a significant role in terrestrial and aquatic environments due to its impact on the composition and overall diversity of microbial communities (Wang et al., 2012b; Kuang et al., 2012; Nicol et al., 2008). Similar to previous studies, our results also found that pH significantly influenced the microbial community structure. Any variation in pH exerts pressure on single-celled organisms, as the intracellular pH of most microorganisms is usually within 1 pH unit of neutral (Fierer and Jackson, 2006).
Fig. 2. Taxonomic classification of bacterial reads retrieved from different samples at phylum level using RDP classifier. Others represent the relative abundance of all other phyla outside the 10 phyla.
P < 0.05 for Pedobacter) explained the higher of these species in L-site which might be due to the higher SOM content. The relationship between microbial abundance and SOM, pH was similar to that of most soil microorganisms with Cd, Pb, Zn, and Hg.
Fig. 3. Heatmap analysis of the dominant genera distribution in the soil samples. The shift of the bacterial community compositions are depicted by the color intensity ranged from 4 to −4.
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Fig. 4. Principal component analysis (PCA) plot based on the 16S rRNA sequencing genes from soil samples. The scatter plot is of principal coordinate 1 (PC1) vs principal coordinate 2(PC2).
Environmental parameters such as nutrient availability and cation solubility are often related to soil pH (Brady and Weil, 1996) and these factors may cause changes in pH that affect the microbial community composition.
(2006) reported predominance of Proteobacteria (39%) in healthy soil samples, followed by Acidobacteria and Actinobacteria (20% and 13%); the remaining 28% of the community was composed of Verrucomicrobia, Bacteroidetes, Chloroflexi, Planctomycetes, and Gemmatimonadetes, whose individual proportions ranged from 2% to 7%. The microbial community of healthy soil reported by Janssen was different from the community found in our soil samples. Proteobacteria in our samples accounted for the majority of the population (37.56% L, 43.31% G), followed by Acidobacteria (16.86% L, 11.54% G), Bacteroidetes (8.63% L, 8.19% G) and Actinobacteria (6.26% L, 8.83% G). The microbial community structure of heavy metal contaminated soil was
4.2. Microbial community structure Microbial communities exposed to heavy metals pollution change, those microorganisms that are susceptible to toxins abruptly decrease while resistant organisms adapt to the altered conditions, thereby altering microbial community structure (Ranjard et al., 2006). Janssen
Fig. 5. Canonical correspondence analysis (CCA) of 16S rRNA gene data and environmental parameters. Arrows indicate the direction and magnitude of environmental parameters associated with bacterial community structure.
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Fig. 6. Spearman rank correlation to study the environmental factors and microbial species richness. The corresponding value of heat map is Spearman correlation coefficient where “r” value is between −1 and 1, r < 0 is negative correlation, r > 0 is positive correlation, marked * indicates significance test p < 0.05, marked ** indicates significance test p < 0.01.
environmental pollution control and biotechnology (Kahng et al., 2000; Bucheli-Witschel et al., 2009; Fava et al., 1995; Bruins et al., 2000). In our study, we found that the abundance of Ralstonia was negatively correlated with Cd, Zn, Pb and Hg in contaminated soils. Seriously heavy metals contaminated soil used in our experiment, which might surpass the maximum resistant level of Ralstonia, and certain members of the Ralstonia genus are not as resistant as the famous Ralstonia. In brief, there is no proper pathway which can explain the accumulation of heavy metals in bacteria. The structural complexity of the cell and the complexity of the interaction between the heavy metals and microorganism make it difficult to better understand the basic mechanism. Thus, it needs more efforts to deeply explore this mechanism for future research work.
significantly different from that of healthy soil, especially Acidobacteria and Actinobacteria. Compared with healthy soils, soil samples in this study have undergone significant changes in the composition of microbial communities under long-term heavy metal pollution stress conditions. These changes may be explained by the selection of tolerant groups, while sensitive ones were clearly reduced (Singh et al., 2014). Adaptation to heavy metal-rich environments of these groups was attributed to different activity of biosorption, bioprecipitation, extracellular precipitation and chelation (Haferburg and Kothe, 2008). 4.3. Bacterial groups identified in long-term heavy metal polluted soil Several studies have found that most bacteria living in extremely polluted soil belong to Proteobacteria (Diels and Mergeay, 1990; Nies et al., 2000). In our study, Sphingomonas was more abundant than other genera of Proteobacteria. Tangaromsuk et al. (2002) concluded that Sphingomonas sp. has the capacity to adsorb Cd, but more research reported that Sphingomonas can degrade a variety of aromatic chemical pollutant (Baraniecki et al., 2002). In general, there were few reports on the heavy metal resistance of Sphingomonas, and the mechanism of resistance to heavy metals was hardly reported. It can be concluded from the previous literature and our current study that Sphingomonas has a greater potential in environmental protection and thus it should be a good choice for future study. At present, domestic and foreign researchers have screened a large number of heavy metal-resistant microorganisms, where most of the bacteria studied was Ralstonia sp. (Nies, 2000). Previous studies have shown that Ralstonia sp. have a wide range of applications in the field of
5. Conclusion In summary, we described the behavior of microbial communities in heavy metal contaminated soils based on high throughput sequencing, physicochemical parameters and statistical analysis. We came to the conclusion that the microbial community of contaminated soil has changed compared with healthy soil, and environmental factors (SOM, pH, Zn, Cd, Pb and H2O) had a significant effect on microbial community structure. Ralstonia, Gemmatimonas, Rhodanobacter and Mizugakiibacter were highly sensitive groups to Cd, Pb, Zn and Hg, whereas unidentified-Nitrospiraceae, Blastocatella and unidentifiedAcidobacteria were highly tolerant groups to these heavy metals. The relationship between microorganisms and SOM, pH was similar to that of most soil microorganisms with Cd, Pb, Zn and Hg. These tolerant and 305
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sensitive microorganisms should be explored for potential use in bioremediation of heavy metal contaminated areas. Acknowledgments This research was supported by the National Key Technology R & D Program for the Twelfth Five year Plan (No: 2015BAD22B02). We thank Jialong Lv and Xiaomin Wei for their suggestions and assistance with the manuscript. References Bao, S.D., 2000. Soil and Agricultural Chemistry Analysis. Agriculture Publication, Beijing, pp. 355–356. Baraniecki, C.A., et al., 2002. Characterization of Sphingomonas sp. Ant 17, an aromatic hydrocarbon-degrading bacterium isolated from antarctic soil. Microb. Ecol. 43 (1), 44–54. Bates, S.T., et al., 2010. Examining the global distribution of dominant archaeal populations in soil. Isme J. 5 (5), 908–917. Brady, N.C., Weil, R.R., 1996. The Nature and Properties of Soils. Prentice-Hall Inc. Bruins, M.R., et al., 2000. Pseudomonas pickettii: a common soil and groundwater aerobics bacteria with pathogenic and biodegradation properties. Ecotoxicol. Environ. Saf. 47 (2), 105–111. Bucheli-Witschel, M., et al., 2009. Benzene degradation by Ralstonia pickettii PKO1 in the presence of the alternative substrate succinate. Biodegradation 20 (3), 419–431. Caporaso, J.G., et al., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7 (5), 335–336. Caporaso, J.G., et al., 2011. Global patterns of 16s rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108 (Suppl 1), 4516–4522. Diels, L., Mergeay, M., 1990. DNA probe-mediated detection of resistant bacteria from soils highly polluted by heavy metals. Appl. Environ. Microbiol. 56 (5), 1485–1491. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. Fava, F., et al., 1995. Aerobic degradation and dechlorination of 2-chlorophenol, 3chlorophenol and 4-chlorophenol by a Pseudomonas pickettii strain. Lett. Appl. Microbiol. 21 (5), 307–312. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103 (3), 626–631. Giller, K.E., et al., 1998. Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: a review. Soil Biol. Biochem. 30 (10–11), 1389–1414. Giller, K.E., et al., 2009. Heavy metals and soil microbes. Soil Biol. Biochem. 41 (10), 2031–2037. Gremion, F., et al., 2003. Comparative 16s rDNA and 16s rRNA sequence analysis indicates that Actinobacteria, might be a dominant part of the metabolically active bacteria in heavy metal-contaminated bulk and rhizosphere soil. Environ. Microbiol. 5 (10), 896–907. Gilbert, J.A., et al., 2014. The Earth Microbiome project: successes and aspirations. BMC Biol. 12, 69. Janssen, P.H., 2006. Identifying the dominant soil bacterial taxa in libraries of 16s rRNA and 16s rRNA genes. Appl. Environ. Microbiol. 72 (3), 1719–1728. Haferburg, G., Kothe, E., 2008. Microbes and metals: interactions in the environment. J. Basic Microbiol. 47 (6), 453–467. Kahng, H.Y., et al., 2000. Characterization and role of tbuX in utilization of toluene by Ralstonia pickettii PKO1. J. Bacteriol. 182 (5), 1232–1242. Kirkham, M.B., 2006. Cadmium in plants on polluted soils: effects of soil factors, hyperaccumulation, and amendments. Geoderma 137 (1–2), 19–32. Klug, M.J., Reddy, C.A., 1984. Current Perspectives in Microbial Ecology. Current Perspectives in Microbial Ecology. American Society for Microbiology. Kot, A., Namiesńik, J., 2000. The role of speciation in analytical chemistry. Trac Trends Anal. Chem. 19 (2–3), 69–79.
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Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing
MARK
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Honghong Guoa,b, Mubasher Nasira,b, Jialong Lva,b, , Yunchao Daia,b, Jiakai Gaoa,b a b
College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China Key Laboratory of Plant Nutrition and Agri-environment in Northwest China, Ministry of Agriculture, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Heavy metals Soil microbial community Tolerant bacterial groups Illumina sequencing
To improve the understanding of bacterial community in heavy metals contaminated soils, we studied the effects of environmental factors on the bacterial community structure in contaminated fields located in Shaanxi Province of China. Our results showed that microbial community structure varied among sites, and it was significantly affected by soil environmental factors such as pH, soil organic matter (SOM), Cd, Pb and Zn. In addition, Spearman's rank-order correlation indicated heavy metal sensitive (Ralstonia, Gemmatimona, Rhodanobacter and Mizugakiibacter) and tolerant (unidentified-Nitrospiraceae, Blastocatella and unidentifiedAcidobacteria) microbial groups. Our findings are crucial to understanding microbial diversity in heavy metal polluted soils of China and can be used to evaluate microbial communities for scientific applications such as bioremediation projects.
1. Introduction Steady economic development in China threatens soil ecosystems due to excessive mining activities (Shu et al., 2003). China produces more than 265.4 million tons of mining waste including Cd, As and other toxic substances (Li, 2006). Once these toxins invade agricultural soils, they affect food production and safety, posing a significant threat to human health via incorporation into the food chain (Williams et al., 2009; Li et al., 2014a; Zhuang et al., 2013; Liu et al., 2013). Zhang et al. (2010) found that grain yield was diminished by more than l.0 × 107 t due to heavy metal pollution, resulting in a direct loss up to 20 billion RMB. Approximately 20% of arable land in China has been contaminated by heavy metals, and the amount is expected to increase over the next few decades (Li et al., 2014b). The total concentration of heavy metals in soil is a weak index for indicating the actual concentrations that microorganisms are exposed to in soil solutions (Giller et al., 2009). According to environmental quality standards, the toxicity of heavy metals to soil microorganisms is directly related to heavy metal bioavailability (Wang et al., 2007a; Kot and Namiesńik, 2000). However, data regarding the relationship between soil microbial community structure and heavy metal bioavailability are limited (Wang et al., 2007a). Microbial community structure can play a role in the community's ability to achieve different functions, but also can resist environmental interference (Torsvik and Øvreås, 2002). Generally, heavy metal pollution reduces microbial diversity in soil because microorganisms that
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are sensitive to heavy metal toxicity will abruptly decline in abundance or even become extinct, whereas species that can tolerate high concentrations of heavy metals slowly become predominant (Klug and Reddy, 1984; Giller et al., 1998). Changes in microbial community structure may seriously affect the ability of soil microbes to degrade organic matter, leading to decreased soil fertility (Giller et al., 1998). Studies of tailings reclamation have reported that the diversity and stability of vegetation on reclaimed land are closely related to the diversity and abundance of soil microorganisms (Sherriff, 2005; Mummey et al., 2002; Mendez et al., 2008). In addition, the study of microbial community structure in contaminated soils may be helpful in isolating strains of bacteria that are tolerant to heavy metals and aid in the development of heavy metal resistant genes (Gremion et al., 2003; Pawlowska et al., 2000). The Earth Microbiological Project has demonstrated that the V4 region can be widely supported as a standard 16S rRNA region for general community assessment across a range of very different environments (Gilbert et al., 2014). This helps us to explore the interaction between heavy metals and soil microbial communities to elucidate the mechanism by which heavy metal pollution changes microbial community structures. For this purpose, an experiment was conducted in contaminated fields located in Zhen'an County of Shangluo City, Shaanxi Province, China. Soils at these sites were mostly polluted from lead–zinc and gold mining wastes, which caused concentrations of soil heavy metals such as Cd, As, Hg, Zn and Pb to exceed the national standard. Thus, the selected experimental site was an ideal environment for studying
Corresponding author at: College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China.
http://dx.doi.org/10.1016/j.ecoenv.2017.06.048 Received 6 December 2016; Received in revised form 14 June 2017; Accepted 16 June 2017 0147-6513/ © 2017 Published by Elsevier Inc.
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Fig. 1. Locations of all sampling sites contaminated with heavy metals.
2. Materials and methods
(Cd, Pb, Zn, Hg and As) and extractable heavy metals (Cd, Pb, and Zn) were measured using standard soil testing procedures (Bao, 2000). Extractable Hg extracted with 0.03% TGA-1/15 mol/L Na2HPO4 (Wang et al., 1983) and extractable As extracted with 0.05 mol/L NaH2PO4 (Wang, 2012a), were measured with an atomic fluorescence spectrophotometer.
2.1. Field description and soil collection
2.3. High-throughput sequencing
Twenty-four soil samples were collected from four farmlands contaminated with heavy metals, located in Zhen'an County of Shangluo City, Shaanxi Province, China (Fig. 1). The fields (G1, G2) and (L1, L2) were contaminated from gold mining and lead–zinc mining, respectively. Soils samples were collected from top 0 to 15 cm soil layer in December 2015 (L1-W, L2-W, G1-W, G2-W) and June 2016 (L1-S, L2-S, G1-S, G2-S). Five soil cores were collected for each sample and thoroughly mixed into a single composite sample. All composite samples were packed into plastic bags and taken to the lab for further processing. Soil samples were immediately stored in a freezer at −80 °C prior to DNA extraction and at 4 °C prior to soil chemical analysis. The total metal concentration of the sampling sites is shown in Table 1.
Community DNA from soil samples was extracted in accordance with the manufacturer's protocol using the MoBio Power soil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA). The concentration and purity of DNA were tested with 1% agarose gel, and DNA was diluted with sterile water to 1 ng/μl. The V4 region of the bacterial 16S rRNA genes was amplified using the specific primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′) which yields accurate taxonomic information and has few biases among various bacterial taxa (Bates et al., 2010). A previously described protocol for DNA amplification was followed (Caporaso et al., 2011). Purified PCR amplicons with a bright main band of 300 bp were sequenced with the Illumina MiSeq platform at Novogene (Beijing, China). After sequencing, initial DNA fragments were assembled by fast length adjustment of short reads (Flash) (Magoč and Salzberg, 2011). Chimeras were removed and 97% similarity OTUs was picked up with UPARSE software package (Edgar, 2010). Representative sequence analysis was performed using the QIIME pipeline (Caporaso et al., 2010) and distributed ribosomal database project (RDP) (Wang et al., 2007b) with the latest Greengenes database (Mcdonald et al., 2012). For each representative sequence, the Greengenes database was used based on RDP classifier algorithm to assign putative taxonomic position. The raw reads generated in the study has been submitted to the NCBI's SRA. Accession: PRJNA376845 ID: 376845.
microbial communities under in-situ conditions. The main objective of our study was to characterize the behavior of microbial communities in heavy metal contaminated soil and determine the effect of environmental factors on bacteria.
2.2. Soil chemical analysis Soil pH was measured with a calibrated pH meter (soil: water ratio of 1:2.5). Soil moisture content was determined by the oven drying method at 105 °C for 8 h. Cation exchange capacity (CEC) was determined using NaOAc flame photometry. Soil organic matter (SOM) was determined by the K2Cr2O7 colorimetric method. Total nitrogen (TN) was analyzed with a CN analyzer. Available phosphorus (AP) was extracted with 0.5 mol/L NaHCO3 and measured with a spectrophotometer. Available potassium (AK) was extracted with 1 mol/L NH4OAc and measured using flame photometry. Total heavy metals Table 1 Concentration of total heavy metals in all fields. Location
Total Cd (mg kg−1)
Total Pb (mg kg−1)
Total Zn (mg kg−1)
Total Hg (mg kg−1)
Total As (mg kg−1)
L1 L2 G1 G2
1.97 ± 0.17 1.12 ± 0.35 0.37 ± 0.02 0.36 ± 0.04
59.86 ± 13.35 63.59 ± 12.44 30.91 ± 1.71 34.41 ± 1.58
50.68 ± 2.26 45.98 ± 2.15 28.63 ± 1.54 32.42 ± 1.68
2.09 ± 1.12 0.87 ± 1.42 0.12 ± 0.73 0.24 ± 1.52
26.00 ± 0.24 34.59 ± 0.35 19.83 ± 0.02 79.81 ± 0.08
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(43.31%, 11.54%, 8.19%, 8.82%, 7.28%, 3.31%, 5.33%) respectively. Proteobacteria, Gemmatimonadetes, Actinobacteria and Firmicutes were more abundant in G1 and G2, but Acidobacteria, Bacteroidetes and Planctomycetes were more abundant in L1 and L2. In addition, Chloroflexi, Nitrospirae and Thaumarchaeota were detected in contaminated soils at low relative abundances. At the genera level, the more abundant groups in G1 and G2 samples as compared to L1 and L2 samples along with the variation in their relative abundance were Sphingomonas (5.57% in G-site soils, 4.00% in L-site soils), Acinetobacter (1.31% G, 0.04% L), Pseudomonas (0.91% G, 0.42% L), Gemmatimonas (1.54% G, 0.43% L), Ralstonia (0.42% G, 0.02% L), Mizugakiibacter (0.38% G, 0.02% L), Rhodanobacter (0.69% G, 0.02% L) and Arthrobacter (0.73% G, 0.24% L), whereas Acidibacter (0.82% L, 0.77% G), Blastocatella (0.87% L, 0.59% G), Flavobacterium (1.61% L, 0.52% G) and Pedobacter (0.95% L, 0.40% G) were more abundant in L1 and L2 soils as shown in Fig. 3. Although most of these predominant bacterial genera showed a significant reduction in L-site as compared to G-site, which may be due to the higher concentration of heavy metals in L-site. However, Acidibacter, Blastocatella, Flavobacterium and Pedobacter had increased in varying degrees in L-site.
2.4. Statistical analysis Based on the results of all sample species annotations, the top 10 phyla and the top 35 genera were analyzed (the relative abundance over 1%). Principal component analysis (PCA) was applied to reduce dimensionality of the original environmental data matrix, which was performed using R software (Version 2.15.3). Unweighted Pair-group Method with Arithmetic Means (UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the metric distance matrix using average linkage and cluster the genus of the bacteria by QIIME software (version 1.7.0). Canonical correspondence analysis (CCA) was conducted to investigate which environmental factors significantly affected microbial community structure and Spearman's correlation analysis was used to investigate correlations among environmental factors and species richness (alpha diversity), which were performed using R software (Version 2.15.3). Relevant data tables were obtained using Microsoft EXCEL (2016). 3. Results 3.1. Environmental parameters
3.3. Relationship between microbial community structure and environmental parameters
Soil pH, soil moisture content, TN, AP, AK, SOM and CEC are shown in Table 2 and concentrations of extractable heavy metals are summarized in Table 3. Extractable heavy metal concentrations in samples collected in December 2015 were significantly higher than those in samples collected in June 2016, except for As and Hg, and heavy metal pollution in soil from the lead–zinc mining site was substantially more serious than that in samples from the gold mining site. Extractable Cd, Pb, Zn, Hg and As contents respectively ranged from 0.33 to 0.7 g kg−1, 1.52–42.95 g kg−1, 3.04–18.27 g kg−1, 0.26–0.93 g kg−1 and 1.51–2.51 g kg−1 in 2015 and from 0.15 to 0.37 g kg−1, 0.78–24.01 g kg−1, 0.68–9.09 g kg−1, 0.26–0.93 g kg−1 and 1.53–2.80 g kg−1 in 2016.
Results of PCA revealed that bacterial communities from different soil samples were clustered according to their type (Fig. 4), which may be due to the influence of environmental factors. Results of CCA showed a clear association between microbial community structure and environmental factors (Fig. 5). The strength of the effect of environmental factors on microbial community structure is reflected by the length of the arrow representing each parameter. The effect of SOM, pH and Zn on the microbial community structure were highly significant (P < 0.01), and the effect of Cd, Pb and H2O were significant (P < 0.05), indicating that these environmental factors were the key factors affecting microbial community. However, As and Hg had no obvious effect on microbial community structure.
3.2. Microbial community structure 3.4. Relationship between microbial abundance and environmental parameters
A total of 1,469,245 high quality reads were obtained from 24 samples after filtering low quality reads and chimaeras, and trimming the adapters, primers, and barcodes. The maximum and minimum number of sequences per sample was (75342, 29218) with an average of 61219. All high-quality reads were classified taxonomically (phylum to genus) using the default settings of QIIME. Taxonomic information at the phylum level is shown in Fig. 2. The site-specific trend influenced microbial population. The predominant phyla in L1 and L2 soil samples were Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Planctomycetes and Firmicutes with an average relative abundance (37.56%, 15.86%, 8.63%, 6.26%, 6.35%, 5.20%, 4.54%) respectively, whereas bacterial assemblages in G1 and G2 soil samples were predominated by Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Planctomycetes and Firmicutes with an average relative abundance
Spearman's rank-order correlation was used to analyze correlations among environmental parameters and microbial abundance (Fig. 6). The abundance of Ralstonia and Gemmatimonas were negatively correlated with exchangeable Cd, Pb, Zn, Hg and pH (P < 0.01). Abundance of Mizugakiibacter and Rhodanobacter were negatively correlated with exchangeable Cd, Pb, Zn and SOM (P < 0.01) except Hg (P < 0.05), but Mizugakiibacter was positively correlated with As (P < 0.05). Unidentified-Nitrospiraceae, Blastocatella and unidentified-Acidobacteria were significantly and positively correlated with Pb, Cd, Zn, Hg and SOM (P < 0.05). In addition, Acidibacter was significantly and positively correlated with Zn and SOM (P < 0.01) and positively correlated with Pb and As (P < 0.05). The abundance of Flavobacterium and Pedobacter were positively correlated with SOM (P < 0.01 for Flavobacterium;
Table 2 Physicochemical properties of the selected soils. Location
pH
H2O (%)
TN/ (g kg−1)
AP/ (g kg−1)
AK/ (g kg−1)
OM/ (g kg−1)
CEC/ (cmol kg−1)
L1-W L2-W G1-W G2-W L1-S L2-S G1-S G2-S
8.07 ± 0.05 8.08 ± 0.03 7.90 ± 0.07 7.67 ± 0.31 8.26 ± 0.02 7.84 ± 0.05 7.80 ± 0.10 7.60 ± 0.20
18.91 ± 5.8 21.77 ± 0.9 16.50 ± 3.8 14.75 ± 1.1 15.33 ± 0.9 12.51 ± 0.4 14.19 ± 1.2 10.23 ± 1.0
1.95 ± 0.02 1.92 ± 0.05 1.49 ± 0.26 1.70 ± 0.11 1.75 ± 0.14 1.75 ± 0.07 1.37 ± 0.17 1.62 ± 0.11
15.29 ± 7.1 25.33 ± 4.5 4.28 ± 1.0 13.67 ± 2.9 23.70 ± 1.7 38.64 ± 7.6 8.23 ± 1.8 30.87 ± 7.6
77.32 ± 7.0 63.57 ± 2.9 118.66 ± 11.1 135.09 ± 18.3 63.04 ± 5.1 82.86 ± 16.2 108.00 ± 17.5 117.14 ± 17.7
34.82 ± 1.6 28.58 ± 0.5 22.54 ± 1.2 19.29 ± 4.1 20.27 ± 0.7 17.84 ± 1.4 13.85 ± 0.3 10.33 ± 0.1
27.12 ± 6.6 36.25 ± 4.1 30.87 ± 5.3 29.51 ± 4.3 25.63 ± 3.6 21.93 ± 3.3 36.12 ± 2.6 29.14 ± 4.2
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Table 3 Concentration of extractable heavy metals in selected soils. Location
L1 L2 G1 G2
Extractable Cd (mg kg−1)
Extractable Pb (mg kg−1)
Extractable Zn (mg kg−1)
Extractable Hg (mg kg−1)
Extractable As (mg kg−1)
2015
2016
2015
2016
2015
2016
2015
2016
2015
2016
0.33 ± 0.04 0.70 ± 0.04 ND ND
0.15 ± 0.03 0.37 ± 0.07 ND ND
16.85 ± 3.1 42.95 ± 2.31 1.88 ± 0.12 1.52 ± 0.02
6.86 ± 1.56 24.01 ± 3.86 0.87 ± 0.11 0.78 ± 0.07
18.27 ± 0.36 17.59 ± 0.47 3.04 ± 1.04 4.10 ± 2.00
9.07 ± 0.17 9.09 ± 0.19 0.68 ± 0.06 0.83 ± 0.09
0.44 ± 0.02 0.93 ± 0.12 0.26 ± 0.02 0.27 ± 0.05
0.47 ± 0.04 0.93 ± 0.15 0.29 ± 0.01 0.26 ± 0.03
2.36 ± 0.04 1.65 ± 0.07 1.51 ± 0.06 2.51 ± 0.10
2.51 ± 0.11 1.53 ± 0.07 1.56 ± 0.06 2.80 ± 0.53
ND: Extractable Cd was not detected in G1 and G2.
4. Discussion 4.1. Correlations among microbial community structure and environmental parameters SOM appears to be important for shaping microbial community structure. Kirkham (2006) believed that SOM is one of the most important factor influencing the bioavailability of heavy metals in soil. And the correlation between heavy metals and microbes is related to the nature of SOM (Lynch et al., 2013; Palmborg et al., 1998). Our results indicated that there was a correlation between SOM and a large number of microorganisms, and these microorganisms had similar correlations with the heavy metals Cd, Pb, Zn and Hg. Therefore, it might that SOM changed the concentration of heavy metals in the soil, leading to change the microbial community structure. Soil pH has been reported to be a dominant factor influencing microbial activity. It has been generally accepted that pH plays a significant role in terrestrial and aquatic environments due to its impact on the composition and overall diversity of microbial communities (Wang et al., 2012b; Kuang et al., 2012; Nicol et al., 2008). Similar to previous studies, our results also found that pH significantly influenced the microbial community structure. Any variation in pH exerts pressure on single-celled organisms, as the intracellular pH of most microorganisms is usually within 1 pH unit of neutral (Fierer and Jackson, 2006).
Fig. 2. Taxonomic classification of bacterial reads retrieved from different samples at phylum level using RDP classifier. Others represent the relative abundance of all other phyla outside the 10 phyla.
P < 0.05 for Pedobacter) explained the higher of these species in L-site which might be due to the higher SOM content. The relationship between microbial abundance and SOM, pH was similar to that of most soil microorganisms with Cd, Pb, Zn, and Hg.
Fig. 3. Heatmap analysis of the dominant genera distribution in the soil samples. The shift of the bacterial community compositions are depicted by the color intensity ranged from 4 to −4.
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Fig. 4. Principal component analysis (PCA) plot based on the 16S rRNA sequencing genes from soil samples. The scatter plot is of principal coordinate 1 (PC1) vs principal coordinate 2(PC2).
Environmental parameters such as nutrient availability and cation solubility are often related to soil pH (Brady and Weil, 1996) and these factors may cause changes in pH that affect the microbial community composition.
(2006) reported predominance of Proteobacteria (39%) in healthy soil samples, followed by Acidobacteria and Actinobacteria (20% and 13%); the remaining 28% of the community was composed of Verrucomicrobia, Bacteroidetes, Chloroflexi, Planctomycetes, and Gemmatimonadetes, whose individual proportions ranged from 2% to 7%. The microbial community of healthy soil reported by Janssen was different from the community found in our soil samples. Proteobacteria in our samples accounted for the majority of the population (37.56% L, 43.31% G), followed by Acidobacteria (16.86% L, 11.54% G), Bacteroidetes (8.63% L, 8.19% G) and Actinobacteria (6.26% L, 8.83% G). The microbial community structure of heavy metal contaminated soil was
4.2. Microbial community structure Microbial communities exposed to heavy metals pollution change, those microorganisms that are susceptible to toxins abruptly decrease while resistant organisms adapt to the altered conditions, thereby altering microbial community structure (Ranjard et al., 2006). Janssen
Fig. 5. Canonical correspondence analysis (CCA) of 16S rRNA gene data and environmental parameters. Arrows indicate the direction and magnitude of environmental parameters associated with bacterial community structure.
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Fig. 6. Spearman rank correlation to study the environmental factors and microbial species richness. The corresponding value of heat map is Spearman correlation coefficient where “r” value is between −1 and 1, r < 0 is negative correlation, r > 0 is positive correlation, marked * indicates significance test p < 0.05, marked ** indicates significance test p < 0.01.
environmental pollution control and biotechnology (Kahng et al., 2000; Bucheli-Witschel et al., 2009; Fava et al., 1995; Bruins et al., 2000). In our study, we found that the abundance of Ralstonia was negatively correlated with Cd, Zn, Pb and Hg in contaminated soils. Seriously heavy metals contaminated soil used in our experiment, which might surpass the maximum resistant level of Ralstonia, and certain members of the Ralstonia genus are not as resistant as the famous Ralstonia. In brief, there is no proper pathway which can explain the accumulation of heavy metals in bacteria. The structural complexity of the cell and the complexity of the interaction between the heavy metals and microorganism make it difficult to better understand the basic mechanism. Thus, it needs more efforts to deeply explore this mechanism for future research work.
significantly different from that of healthy soil, especially Acidobacteria and Actinobacteria. Compared with healthy soils, soil samples in this study have undergone significant changes in the composition of microbial communities under long-term heavy metal pollution stress conditions. These changes may be explained by the selection of tolerant groups, while sensitive ones were clearly reduced (Singh et al., 2014). Adaptation to heavy metal-rich environments of these groups was attributed to different activity of biosorption, bioprecipitation, extracellular precipitation and chelation (Haferburg and Kothe, 2008). 4.3. Bacterial groups identified in long-term heavy metal polluted soil Several studies have found that most bacteria living in extremely polluted soil belong to Proteobacteria (Diels and Mergeay, 1990; Nies et al., 2000). In our study, Sphingomonas was more abundant than other genera of Proteobacteria. Tangaromsuk et al. (2002) concluded that Sphingomonas sp. has the capacity to adsorb Cd, but more research reported that Sphingomonas can degrade a variety of aromatic chemical pollutant (Baraniecki et al., 2002). In general, there were few reports on the heavy metal resistance of Sphingomonas, and the mechanism of resistance to heavy metals was hardly reported. It can be concluded from the previous literature and our current study that Sphingomonas has a greater potential in environmental protection and thus it should be a good choice for future study. At present, domestic and foreign researchers have screened a large number of heavy metal-resistant microorganisms, where most of the bacteria studied was Ralstonia sp. (Nies, 2000). Previous studies have shown that Ralstonia sp. have a wide range of applications in the field of
5. Conclusion In summary, we described the behavior of microbial communities in heavy metal contaminated soils based on high throughput sequencing, physicochemical parameters and statistical analysis. We came to the conclusion that the microbial community of contaminated soil has changed compared with healthy soil, and environmental factors (SOM, pH, Zn, Cd, Pb and H2O) had a significant effect on microbial community structure. Ralstonia, Gemmatimonas, Rhodanobacter and Mizugakiibacter were highly sensitive groups to Cd, Pb, Zn and Hg, whereas unidentified-Nitrospiraceae, Blastocatella and unidentifiedAcidobacteria were highly tolerant groups to these heavy metals. The relationship between microorganisms and SOM, pH was similar to that of most soil microorganisms with Cd, Pb, Zn and Hg. These tolerant and 305
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