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Response of soil micro-ecology to different levels of cadmium in alkaline soil
Ecotoxicology and Environmental Safety 166 (2018) 116–122
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Response of soil micro-ecology to different levels of cadmium in alkaline soil Bin Wu, Siyu Hou, Dinghua Peng, Ying Wang, Can Wang, Fei Xu, Heng Xu
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Key Laboratory of Bio-Resource and Eco-Evironment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Micro-ecology Cadmium Bacterial diversity Fungal diversity Soil
Although the effect of heavy metal on soil microbial diversity was widely studied, the interaction among microecological environment in heavy metal contaminated soil was less known. In this study, we systematically investigated the influence of cadmium (Cd) on soil micro-ecological environment (pH, nutrient content, soil enzyme activities, microbial biomass, bacterial and fungal diversities). Results showed that pH values slightly decreased with the Cd level increase, whereas the nutrient content including of Olsen-P (OSP), Alkeline-N (ALN), Olsen-K (OSK) and organic matter (OM) did not show significant difference in different treatments. In contrast to physicochemical properties, the biochemical qualities were easily influenced by Cd pollutant, resulting in soil microbial numbers and enzyme activities significantly decreased. High-throughput sequencing showed that microbial community composition was significantly affected by heavy metal. For bacteria, Actinobacteria abundance significantly decreased in Cd treated soil, corresponding to Proteobacteria and Firmicutes increased. For fungi, the most dominant phyla member (Ascomycota) was significantly decreased whereas Zygomycota significantly increased with Cd addition. These results further revealed the integral interrelation of microecology environmental players under the stress of different Cd levels.
1. Introduction Soil ecological environment has been seriously threatened by heavy metal, which has possibly caused the alteration of soil main properties. In the previous reports, the main nutrient elements (Nitrogen, Phosphorus, Potassium and Organic matter) varied among different heavy metal contaminated soil, which might have a relation with the heavy metal pollution (Khan et al., 2017; Chaffei et al., 2004). More importantly, soil is one of the largest microbial libraries on earth and contains large of microbial diversity with an estimated 107–109 distinct bacterial species and 1.5 million fungi taxa worldwide (NarendrulaKotha and Nkongolo, 2017). The diversity and abundance of microbes are closely related with regional distribution, environmental conditions and human activities. In recent years, many studies found that the microbial structure and composition were directly and indirectly influenced by heavy metal. Among the varieties of heavy metal pollutants, Cadmium (Cd), a non-essential trace element, could cause toxic reactions even in low concentrations (Khan et al., 2015). The enrichment of Cd in soil caused by natural and anthropogenic activities has been considered to be a great concern, which has threated the ecology and food safety (Pan et al., 2016). Therefore, the Cd was chosen to comprehensively understand the effect of Cd on microbial diversity, which would be also benefit for the remediation of Cd contaminated
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soil. With the studies between soil and contaminant going further, many researches have focused on the connection between indigenous microorganism and contaminant (Bouskill et al., 2010; Sheeba et al., 2017). Nowadays, high-throughput sequencing techniques have been widely applied to study the microbial diversity and abundance (S. Li et al., 2017; Luo et al., 2017; Wang et al., 2015). Although many works have been done on the understanding of the interaction between heavy metals and microbes, there was no consensus on the effect of heavy metals on microbial diversity and abundance. For example, Gołębiewski et al., (2014) reported the bacterial diversity was decreased in Zn contaminated soil. However, some reports showed that the bacterial diversity in forest soil was not influenced by heavy metal (Chodak et al., 2013). Because the energy and nutrient cycling remained unclear, few studies revealed the interactions of microorganisms among heavy metal and other compounds, especially in alkaline soil. In addition, studies of fungal diversity on heavy metal contaminated soil are very lacking. More importantly, not only the microbial diversity should be studied, but the whole micro-ecology under Cd stress should be evaluated. In this study, we systematically investigated the influence of Cd at different levels on micro-ecology in alkaline soil. The physicochemical and biochemical properties including of pH, Olsen-P (OSP), Alkeline-N (ALN), Olsen-K (OSK), organic matter (OM), urease, invertase,
Corresponding author. E-mail address: [email protected] (H. Xu).
https://doi.org/10.1016/j.ecoenv.2018.09.076 Received 10 May 2018; Received in revised form 16 September 2018; Accepted 18 September 2018 0147-6513/ © 2018 Elsevier Inc. All rights reserved.
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was measured at 578 nm and the urease activity was expressed as μg NH4+/(g soil·24 h). Invertase activity was assayed at 37 ℃ for 24 h with the mixture of 1 g soil, 3 mL 8% sucrose solution, 1 mL phosphate buffer (pH = 5.5) and the supernatant was measured at 508 nm. The invertase activity was expressed as μg glucose/(g soil·24 h).
dehydrogenase, bacterial and fungi diversities were determined to discuss the interaction of main environmental factors under Cd stress. 2. Material and methods 2.1. Soil preparation
2.4. Microbial diversity analysis Soil samples were collected from the campus of Sichuan University, Chengdu, China. The soil samples were air dried, sieved through 2 mm mesh, and then carefully mixed with cadmium chloride solution. The Cd levels added into soil were NCS (0 mg/kg), CS1 (5 mg/kg), CS2 (10 mg/kg), CS3 (15 mg/kg) and CS4 (20 mg/kg), which were conducted with three replicates. After the soil adequately mixed with Cd, the soil was sieved again to pass 2 mm mesh and then placed into pots (2 kg soil/pot). The soil was wetted with deionized water to approximately 65% soil field water capacity and then covered with plastic foil. Finally, the soil contaminated Cd was incubated for 60 days.
To analysis the diversities of active bacteria and fungi in soil, the microbial RNA was extracted from 0.5 g fresh soil with OMG soil RNA Kit (Vazyme Biotech Co.,Ltd) according to the manufacturer's instructions. After that, the cDNA gene was obtained by RNA inverse transcription with HiScript Q RT SuperMix for qPCR (Vazyme Biotech Co.,Ltd). For amplification of bacterial 16S rRNA, PCR was performed using primer pair 530F (5′ GTG CCA GCM GCN GCG G) and 1100R (5′ GGG TTN CGN TCG TTR). For amplification of fungal internal transcribed spacer (ITS), PCR was performed using primer pair ITSF (5′ TCC GTA GGT GAA CCT GCG G) and ITSR (5′ TCC TCC GCT TAT TGA TAT GC). PCR reactions were performed in triplicate 20 μL mixture containing 4 μL of 5x FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase and 10 ng of template DNA. The resulted PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™ST (Promega, USA) according to the manufacturer's protocol. Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego,USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Raw fastq files were qualityfiltered by Trimmomatic and merged by FLASH with the following criteria: (i) The reads were truncated at any site receiving an average quality score < 20 over a 50 bp sliding window. (ii) Sequences whose overlap being longer than 10 bp were merged according to their overlap with mismatch no more than 2 bp. (iii) Sequences of each sample were separated according to barcodes (exactly matching) and Primers (allowing 2 nucleotide mismatching), and reads containing ambiguous bases were removed. Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/) with a novel ‘greedy’ algorithm that performs chimera filtering and OTU clustering simultaneously. The taxonomy of each 16S rRNA gene sequence was analyzed by RDP Classifier algorithm (http://rdp.cme. msu.edu/) against the Silva (SSU123) 16S rRNA database using confidence threshold of 70% (X. Li et al., 2017).
2.2. Soil properties analysis After 60 days incubation, soil pH was measured by the pH meter （METTLER-S220）with a soil/water ratio of 5 g/25 mL. OSP, ALN and OSK of soil were measured as the method ascribed by Liu et al. (2017). Soil organic matter was determined according to the method ascribed by Walz et al. (2017). In addition, the available Cd was measured by the TCLP method (Xu et al., 2017). BCR sequential extraction method was used to analysis the Cd formation according to Wu et al. (2016). The sequential extraction method was conducted as follows: (1) 1 g of the sieved soil was shaken at 25 ℃, 150 rpm for 16 h with 40 mL 0.11 M CH3COOH and then centrifuged for 5 min at 8000 rpm to acquire supernatant; (2) The above mentioned residue was shaken at 25 ℃, 250 rpm for 16 h with a 40 mL mixture of 0.5 M NH2OH·HCl and 0.05 M HNO3. Then the mixture was centrifuged for 5 min at 8000 rpm and the supernatant was collected for assay. (3) The above mentioned residue was mixed with 10 mL 30% H2O2 (pH = 2.5) and placed in a bath at 85 ℃ for about 1 h until the volume of liquid was less than 3 mL, then the residue was extracted with 10 mL 30% H2O2. Finally, 50 mL 1.0 M CH3COONH4 (pH = 2) was added and the mixture was centrifuged for assay when the volume of above liquid was less than 1 mL; (4) the above residual soil was digested by using a microwave digestion method that 0.2 g above residual soil was mixed with 6 mL HNO3, 5 mL HClO4 and 4 mL HF, then the mixture was respectively heated at meddle-low, meddle and meddle-high temperature for 5 min. The method partitioned metal into HOAc extractable, reducible, oxidizable and residual fractions. Finally, the Cd concentration was measured by atomic absorption spectroscopy (AAS; VARIAN, SpecterAA-220Fs).
2.5. Statistical analysis In this study, mean and standard deviation values of three replicates were calculated. Statistical significance was performed using SPSS 18.0 package, and means values were considered to be different when P < 0.05 using least significant difference (LSD). All statistics were performed using Origin 8.0 (USA).
2.3. Soil microbial biomass and enzyme analysis Soil microbial biomass was determined according the method described by Cheema et al. (2009). Aqueous extracts of 3 g soil samples were serially diluted and spread on nutrient agar for bacteria and streptomycin-rose bengal agar for fungi through the plate spread method. The total number of microbes was counted after 3–5 days cultivation at 28 °C in the dark. The soil enzyme activities were determined according to the method ascribed by Wu et al. (2016). Dehydrogenase activity was assayed at 37 ℃ for 24 h by incubation of the mixture containing 1 g soil, 4 mL Tris-HCl buffer (pH = 7.6) and 2 mL 0.5% TTC. After that, 10 mL methanol added into the mixture. The above mixture was centrifuged at 4500 rpm for 5 min and the supernatant was determined spectrophotometrically at 492 nm. Dehydrogenase activity was evaluated by triphenylformazan (TPF) and expressed as μg TPF/(g soil·24 h). Urease activity was assayed at 37 ℃ for 24 h with the mixture of 1 g soil, 200 μL methylbenzene, 2 mL 10% urea solution and 4 mL of citrate buffer (pH 6.7). The urease activity was evaluated by the NH4+ color complex that
3. Results and discussion 3.1. Soil main properties analysis The main properties of soil before treatment with Cd were showed in Table S1. The soil was sandy loam texture and alkaline (pH 8.89). The organic matter was low with a value of 12.53 g/kg, whereas the total N, P and K were 0.65, 1.39, 23.15 g/kg, respectively. After treatment with Cd, the main characteristics of soil were weakly influenced and the results were showed in Table 1. As illustrated in Table 1, the pH values of contaminated soil weakly decreased with Cd increase. The OM, OSK, ALN were 12.15–12.45, 39.55–45.30, 42.00–44.50 mg/kg, respectively, which were not obviously influenced in different Cd 117
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Table 1 The soil main properties in different treatments. Values in each column followed with different lowercase letters indicated significant (P ≤ 0.05) difference among different treatments. Treatments
pH
NCS CS1 CS2 CS3 CS4
8.82 8.68 8.61 8.58 8.41
OSP (mg/kg) ± ± ± ± ±
0.14b 0.08 ab 0.10ab 0.06ab 0.05a
24.35 21.60 23.80 30.10 24.70
± ± ± ± ±
ALN (mg/kg) 1.23a 0.87a 1.05a 1.42b 1.30ab
42.50 42.00 44.00 44.50 42.50
Fig. 1. The available heavy metal content and percent by TCLP extraction method in different treatments. Error bars represent standard deviations, and means with different letters are significantly different from each other (P < 0.05) according to the LSD test (n = 3).
± ± ± ± ±
OSK (mg/kg) 3.43a 3.85a 3.25a 4.04a 3.28a
40.35 39.55 40.60 45.30 40.05
± ± ± ± ±
OM (g/kg) 2.78a 2.64a 3.05a 3.43a 2.98a
12.45 12.15 12.15 13.30 12.45
± ± ± ± ±
1.24a 1.32a 1.02a 1.64a 0.86a
Fig. 2. The fractions of heavy metal by BCR extraction method in different treatments.
contaminated soil, while there was not significant difference among the Cd contaminated soil with different levels. e However, the fungal number was significantly decreased in the Cd contaminated soil. Compared with non-contaminated soil, the fungal numbers decreased 21.52–54.43% in Cd contaminated soil. These results illustrated that Cd showed a severe toxicity for microorganism and thus decreased the microbial numbers. Similar to the microbial numbers, the soil enzyme activities were also significantly influenced by Cd pollutant. In this study, dehydrogenase, urease and invertase activities were determined to reflect the soil biological quality (Fig. 3). As shown in Fig. 3a, dehydrogenase activity in Cd contaminated soil was 7.00–23.68% lower than noncontaminated soil, whereas it did not show difference in CS2 and CS3 treatments. Urease activity (Fig. 3b) was significantly impacted in the Cd treatment, which showed a minimum in the CS2 treatment, about 27.41% lower than control. In addition, the invertase activity (Fig. 3c) also decreased in the Cd contaminated soil and the lowest value was found in CS4 treatment, about 45.02% lower than control. These results showed that the influence of different enzyme activities was varied at different Cd levels, while the enzyme activities integrally decreased in Cd contaminated soil that might related to the reduce of soil microbial numbers. Heavy metals have been well known to be toxic to soil microorganisms when presented in excessive concentrations. The detrimental effect on microorganisms by Cd could be exerted through their capability of combining with biomolecules and may act as potent enzyme inhibitors hindering biochemical processes and compromising DNA and cell membrane integrity (Kaur et al., 2006; Poli et al., 2009). Moreover, some research demonstrated that soil microorganisms might cost more additional energy under Cd stress, which could lead to a decrease in the amount of substrate that is available for microorganisms growth (Muhammad et al., 2005). Our results were consistent with previous studies that the microbial biomass was inhibited in heavy metal contaminated soil. The soil enzymes were mainly secreted from
contaminated soil. Meanwhile, the OSP was not significantly changed, except in CS3 treatment, which suggested that the main nutrient matters in soil could not be notably influenced in the Cd contaminating process. 3.2. Soil Cd fractions analysis As shown in Fig. 1a, the TCLP extraction method suggested that the available Cd in soil was about 37.05–62.25% of total Cd after 60 days incubation. In general, the available Cd in alkaline soil showed a lower percent than neutral and acidic soil and the active fractions may provide some indication of heavy metal that were available for microorganisms (Nkongolo et al., 2013). Compared with CS1 and CS2, CS3 and CS4 showed higher percentage of available Cd with the increase of Cd addition. In addition, the translation of Cd fractions was determined by BCR extraction method and the results were showed in Fig. 1b. With the addition of Cd increase, the percentage of HOAc extractable Cd slightly increased whereas the percentage of reducible Cd significantly increased. Compared with CS1 treatment, the percentage of reducible Cd in CS2, CS3 and CS4 treatments increased 45.59%, 54.48% and 22.95%, respectively. Nevertheless, compared with CS1 treatment, the percentage of oxidizable Cd significantly decreased in CS2, CS3 and CS4 treatments. The Cd fractions showed that HOAc extractable Cd and reducible Cd were main forms in the different Cd levels. 3.3. Soil microbial numbers and enzymes analysis The influence of different Cd levels on soil microbial numbers and enzyme activities were determined and these results were shown in Fig. 2. The soil microbial numbers decreased after treatment with different concentrations of Cd. It was observed that bacterial numbers in Cd contaminated soil were 32.86–44.00% lower than non118
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Fig. 3. Bacterial and fungal numbers (a), dehydrogenase activity (b), invertase activity (c) and urease activity (d) in different treatments. Error bars represent standard deviations, and means with different letters are significantly different from each other (P < 0.05) according to the LSD test (n = 3).
treatments, the OTU numbers were significantly decreased compared to other treatments (NCS, CS1 and CS2). Meanwhile, the Chao1 index values were decreased in Cd contaminated soil, especially in CS3 treatment. No difference between different soil types was observed for Simpons index values, while Shannon diversity index in CS2 treatment was slightly increased. For fungi, the OTU numbers were also significantly influenced by the Cd addition. In the CS3 treatment, the OTU number decreased to 177, about 51.24% lower than NCS. In addition, the Chao1 index values in Cd contaminated soil were lower than noncontaminated soil. Our results are similar with some other reports, which found that the heavy metal caused the community structure change in many terrestrial ecosystems, such as sediments and neutral mine drainage contaminated soils (Yin et al., 2015). In this study, the OTUs, Chao1, Shannon and Simpson index were used to determine bacterial and fungal diversity. OUTs are observations of microorganisms and the number of individuals present in the community, which was widely used to estimate bacterial and fungal species (Edgar, 2013). Our reports showed that the microbial OTU numbers and compositions of OTUs
microorganisms in this study. Therefore, the enzyme activities were significantly decreased in heavy metal contaminated soil mostly because of the decrease of microbial biomass. In addition, reduced enzyme activities may be partly caused by binding of Cd2+ to sulphydryl groups (Sanadi, 1982). 3.4. Soil microbial diversity analysis In this study, the total of RNA gene was extracted from soil to obtain the cDNA that was used for amplification of 16S rRNA and ITS by PCR procedure, which could truly reflect the changes of the diversity of active microorganisms under the heavy metal stress within months. A total of 141,740 high quality 16s rRNA gene reads and 193,410 high quality ITS gene reads were obtain from different treated samples, which were clustered into 1243 and 649 OTUs. As shown in Fig S1, the rarefaction curves of bacteria and fungi both tended to plain, which illustrated the sample numbers were sufficient to downstream analyses. Alpha-diversity indexes calculated from OTU relative abundance were analyzed and showed in Table 2. For bacteria, in the CS3 and CS4
Table 2 Alpha-diversity indexes calculated from OTU relative abundance of different treatments. Values in each column followed with different lowercase letters indicated significant (P ≤ 0.05) difference among different treatments. H: Shannon diversity index, SIE: Simpson index of evenness and OTU: observed OTU number. Bacterial alpha-diversity indexes H NCS CS1 CS2 CS3 CS4
5.66 5.47 5.99 5.68 5.12
SIE ± ± ± ± ±
0.11bc 0.07ab 0.06c 0.10bc 0.08a
0.01 0.01 0.01 0.01 0.02
Fungal alpha-diversity indexes Chao1
± ± ± ± ±
0.00a 0.00a 0.00a 0.00a 0.00a
1200 1176 1193 997 1033
± ± ± ± ±
OTU 48b 39b 45b 32a 52a
1126 1124 1151 964 936
H ± ± ± ± ±
34b 21b 27b 33a 22a
119
2.98 3.15 4.11 2.21 2.74
SIE ± ± ± ± ±
0.24b 0.19b 0.28c 0.12a 0.31b
0.15 0.12 0.04 0.13 0.23
Chao1 ± ± ± ± ±
0.02b 0.03b 0.00a 0.03b 0.05c
377 380 272 182 205
± ± ± ± ±
OTU 22c 26c 18b 13a 16a
363 371 271 177 203
± ± ± ± ±
18b 21b 15b 11a 15ab
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Fig. 4. Relative abundance of bacterial phyla (a) and fungal phyla (b) in different treatments. Heat map of bacterial phyla (c) and fungal phyla (d) in different treatments.
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and diversity were considerable important since fungi constituted the major proportion in soil and they were deeply involved in material cycle of soil ecosystem. In the previous reports, it was observed that the fungal radial growth decreased after exposure to Cd, Cu, Zn and Ni (Azevedo and Cássio, 2010). Some studies also showed that metals such as Cd and Zn could inhibit the conidial production in some fungal species (Chiapello et al., 2015). In brief, studies have shown that metal toxicity varies with fungal species, metal type, metal contrition, and even soil environment including of pH, nutrient content and plant diversity (Azevedo and Cássio, 2010; O'Brien et al., 2005; Goupil et al., 2015). Among the main fungal phyla identified in this study, Ascomycota was dominant in soil with different treatment, especially in noncontaminated and low contaminated soil. The proportion of Ascomycota is always over 70% in soil and classified as litter/wood decomposers, lichen forming fungi, saprotrophs, plant parasites, saprotrophs and few edible mushroom (Alemu, 2013). Zygomycota was another phylum identified in both soil samples but their abundance was significantly lower than Ascomycota. Zygomycota represents approximately 1% of fungal groups and are commonly found in terrestrial ecosystems. Mortierella sp., Mortierellales sp., and Mucoromycotina sp. belonging to the Zygomycota phylum have been reported as common fungi in soils (Donnell, 2004). Rathnayake et al. reported that soil microorganisms could develop various mechanisms to resist contamination, even in toxic metal environment (Rathnayake et al., 2011). The mechanisms contribute to microbial resilience to metals such as Cd, Ni, Zn and As have been explained that include transfer of heavy metal tolerance genes in microbial communities through substitution of metal-sensitive strains and proportion of heavy bioavailability (Sobecky and Coombs, 2009; Griffiths and Philippot, 2013). In general, the development of resistant bacteria and fungi is an indication of deterioration of ecosystems.
changed, especially in heavily contaminated soil, which illustrated that heavy metal caused the bacterial diversity and community structure change. Chao1 is commonly used as a species richness indicator used on the number of OTUs found in samples (Chao, 1984). The values of Chao index reveled higher bacterial species richness compared to fungi species richness in different treatment and the results of Chao index in both bacterial and fungal diversity were consistent with OTUs, which further suggested that the microorganisms were influenced under Cd stress. Moreover, Shannon and Simpson indexes are also widely used to analyze community diversity (Gotelli, 2008). Shannon index values for fungal diversity was low compared to bacterial diversity and the slight difference among different treatments was observed. The values of species evenness for bacteria and fungi were low, which illustrated that communities were dominated by one and two species rather than distributed equally among species (Narendrula-Kotha and Nkongolo, 2017). The relative abundance of bacteria and fungi in different treatments was illustrated in Fig. 4. For bacteria distribution (Fig. 4a), the dominant groups across all samples were Actinobacteria (30.06–54.58%) and Proteobacteria (29.20–41.49%). In Cd treated soil, Actinobacteria abundance significantly decreased. Especially in CS2 and CS3 treatments, the percentages of relative abundance were 44.91% and 43.68% lower than control. However, the percentages of relative abundance of Proteobacteria in different Cd treatments increased by 14.89–29.62% compared with control. Although the percentages of Firmicutes, Bacteroidetes, Acidobacteria, Chloroflexi and Gemmatimonadetes were lower than 1%, they were the least abundance of the five phyla in soil samples. For fungi distribution (Fig. 4b), the most dominant phyla member was Ascomycota and the percentage of relative abundance reach to 74.25–96.71% in different treatments. In the CS3 treatment, the percentage of Ascomycota relative abundance decreased to minimum, about 25.12% lower compared to control. However, the percentage of Zygomycota relative abundance increased in Cd treatment, which reached to maximum in CS3 treatment. what's more, top abundance genera of bacteria and fungi in this study was chose to analysis microbial community through heat map of relative abundance genera (Fig. 4c-d), which showed that bacterial and fungal abundance between NCS and CS1 was more closer than other treatments. These results indicated that the bacterial and fungal diversities were more likely to change in heavily polluted soil. In the present study, the percentage of Actinobacteria abundance decreased to 30.06% in 10 mg/kg Cd contaminated soil, whereas it slightly increased in the higher Cd contaminated soil. Actinobacteria is an important phylum for metal remediation due to its metabolic function and ability for fast colonization of selective substrates (NarendrulaKotha and Nkongolo, 2017). Proteobacteria is another prevalent phylum and the percentage of relative abundance significantly increased in Cd contaminated soil. The phylum Proteobacteria was found to be abundant in many heavy metal contaminated environments, such as mine drainage, mine sediments, heavy metal polluted soil and water (Gołębiewski et al., 2014; Zhang et al., 2014; Halter et al., 2011; Serkebaeva et al., 2013). The changes of Proteobacteria varied in different studies. Our results similar with some reports, which found the Proteobacteria was tolerant to Cd pollutant and the abundance of Proteobacteria increased (Lorenz et al., 2006; Sandaa et al., 2001). Whereas Yin et al. (2015) found the abundance of Proteobacteria slightly decreased in heavy metal soil. The variation of the response of Proteobacteria to heavy metal may due to Proteobacteria exhibits a complex lifestyle and can utilize various of organic matters as carbon, nitrogen, and energy sources, which make Proteobacteria can be adapt to different environmental change (Bouskill et al., 2010). Acidobacteria was another phylum with high abundance in the studied samples, which has shown to possess ability to resist heavy metal, acidic and other extreme environment (Barns et al., 2007). Although few studies reported the effect of heavy metal on fungal diversity, studying and understanding the fungal community structure
4. Conclusions Our results showed that the micro-ecological environment of soil significantly changed after treatment with Cd. In Cd contaminated soil, the pH values slight decreased whereas OSP, ALN, OSK and OM were not significantly influenced. However, the microbial biomass and enzyme activities were significantly reduced with the addition of Cd increase. High-throughput sequencing suggested that the microbial diversity and community structure were influenced by heavy metal, especially in higher contaminated soil. The percentages of relative abundance for bacteria and fungi were both similar between non-contaminated soil and contaminated soil with 5 mg/kg Cd. However, in heavily contaminated soil, a remarkable decrease of Actinobacteria abundance for bacteria and Ascomycota abundance for fungi occurred, while Proteobacteria and Zygomycota were significantly increased. Community heatmap of bacteria and fungi further demonstrated that the microorganisms suffered severely stress and microbial diversity was more easily influenced in heavily contaminated soil. These results provided a comprehensive evaluation for the influence of Cd on microecological environment based on physicochemical and biochemical analyses. Acknowledgements This study was financially supported by the Key Research and Development Program of Sichuan Province (2017SZ0181). The authors also wish to thank Professor Guanglei Cheng from Sichuan University for the technical assistance. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2018.09.076. 121
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Contents lists available at ScienceDirect
Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Response of soil micro-ecology to different levels of cadmium in alkaline soil Bin Wu, Siyu Hou, Dinghua Peng, Ying Wang, Can Wang, Fei Xu, Heng Xu
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T
Key Laboratory of Bio-Resource and Eco-Evironment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Micro-ecology Cadmium Bacterial diversity Fungal diversity Soil
Although the effect of heavy metal on soil microbial diversity was widely studied, the interaction among microecological environment in heavy metal contaminated soil was less known. In this study, we systematically investigated the influence of cadmium (Cd) on soil micro-ecological environment (pH, nutrient content, soil enzyme activities, microbial biomass, bacterial and fungal diversities). Results showed that pH values slightly decreased with the Cd level increase, whereas the nutrient content including of Olsen-P (OSP), Alkeline-N (ALN), Olsen-K (OSK) and organic matter (OM) did not show significant difference in different treatments. In contrast to physicochemical properties, the biochemical qualities were easily influenced by Cd pollutant, resulting in soil microbial numbers and enzyme activities significantly decreased. High-throughput sequencing showed that microbial community composition was significantly affected by heavy metal. For bacteria, Actinobacteria abundance significantly decreased in Cd treated soil, corresponding to Proteobacteria and Firmicutes increased. For fungi, the most dominant phyla member (Ascomycota) was significantly decreased whereas Zygomycota significantly increased with Cd addition. These results further revealed the integral interrelation of microecology environmental players under the stress of different Cd levels.
1. Introduction Soil ecological environment has been seriously threatened by heavy metal, which has possibly caused the alteration of soil main properties. In the previous reports, the main nutrient elements (Nitrogen, Phosphorus, Potassium and Organic matter) varied among different heavy metal contaminated soil, which might have a relation with the heavy metal pollution (Khan et al., 2017; Chaffei et al., 2004). More importantly, soil is one of the largest microbial libraries on earth and contains large of microbial diversity with an estimated 107–109 distinct bacterial species and 1.5 million fungi taxa worldwide (NarendrulaKotha and Nkongolo, 2017). The diversity and abundance of microbes are closely related with regional distribution, environmental conditions and human activities. In recent years, many studies found that the microbial structure and composition were directly and indirectly influenced by heavy metal. Among the varieties of heavy metal pollutants, Cadmium (Cd), a non-essential trace element, could cause toxic reactions even in low concentrations (Khan et al., 2015). The enrichment of Cd in soil caused by natural and anthropogenic activities has been considered to be a great concern, which has threated the ecology and food safety (Pan et al., 2016). Therefore, the Cd was chosen to comprehensively understand the effect of Cd on microbial diversity, which would be also benefit for the remediation of Cd contaminated
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soil. With the studies between soil and contaminant going further, many researches have focused on the connection between indigenous microorganism and contaminant (Bouskill et al., 2010; Sheeba et al., 2017). Nowadays, high-throughput sequencing techniques have been widely applied to study the microbial diversity and abundance (S. Li et al., 2017; Luo et al., 2017; Wang et al., 2015). Although many works have been done on the understanding of the interaction between heavy metals and microbes, there was no consensus on the effect of heavy metals on microbial diversity and abundance. For example, Gołębiewski et al., (2014) reported the bacterial diversity was decreased in Zn contaminated soil. However, some reports showed that the bacterial diversity in forest soil was not influenced by heavy metal (Chodak et al., 2013). Because the energy and nutrient cycling remained unclear, few studies revealed the interactions of microorganisms among heavy metal and other compounds, especially in alkaline soil. In addition, studies of fungal diversity on heavy metal contaminated soil are very lacking. More importantly, not only the microbial diversity should be studied, but the whole micro-ecology under Cd stress should be evaluated. In this study, we systematically investigated the influence of Cd at different levels on micro-ecology in alkaline soil. The physicochemical and biochemical properties including of pH, Olsen-P (OSP), Alkeline-N (ALN), Olsen-K (OSK), organic matter (OM), urease, invertase,
Corresponding author. E-mail address: [email protected] (H. Xu).
https://doi.org/10.1016/j.ecoenv.2018.09.076 Received 10 May 2018; Received in revised form 16 September 2018; Accepted 18 September 2018 0147-6513/ © 2018 Elsevier Inc. All rights reserved.
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was measured at 578 nm and the urease activity was expressed as μg NH4+/(g soil·24 h). Invertase activity was assayed at 37 ℃ for 24 h with the mixture of 1 g soil, 3 mL 8% sucrose solution, 1 mL phosphate buffer (pH = 5.5) and the supernatant was measured at 508 nm. The invertase activity was expressed as μg glucose/(g soil·24 h).
dehydrogenase, bacterial and fungi diversities were determined to discuss the interaction of main environmental factors under Cd stress. 2. Material and methods 2.1. Soil preparation
2.4. Microbial diversity analysis Soil samples were collected from the campus of Sichuan University, Chengdu, China. The soil samples were air dried, sieved through 2 mm mesh, and then carefully mixed with cadmium chloride solution. The Cd levels added into soil were NCS (0 mg/kg), CS1 (5 mg/kg), CS2 (10 mg/kg), CS3 (15 mg/kg) and CS4 (20 mg/kg), which were conducted with three replicates. After the soil adequately mixed with Cd, the soil was sieved again to pass 2 mm mesh and then placed into pots (2 kg soil/pot). The soil was wetted with deionized water to approximately 65% soil field water capacity and then covered with plastic foil. Finally, the soil contaminated Cd was incubated for 60 days.
To analysis the diversities of active bacteria and fungi in soil, the microbial RNA was extracted from 0.5 g fresh soil with OMG soil RNA Kit (Vazyme Biotech Co.,Ltd) according to the manufacturer's instructions. After that, the cDNA gene was obtained by RNA inverse transcription with HiScript Q RT SuperMix for qPCR (Vazyme Biotech Co.,Ltd). For amplification of bacterial 16S rRNA, PCR was performed using primer pair 530F (5′ GTG CCA GCM GCN GCG G) and 1100R (5′ GGG TTN CGN TCG TTR). For amplification of fungal internal transcribed spacer (ITS), PCR was performed using primer pair ITSF (5′ TCC GTA GGT GAA CCT GCG G) and ITSR (5′ TCC TCC GCT TAT TGA TAT GC). PCR reactions were performed in triplicate 20 μL mixture containing 4 μL of 5x FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase and 10 ng of template DNA. The resulted PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™ST (Promega, USA) according to the manufacturer's protocol. Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego,USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Raw fastq files were qualityfiltered by Trimmomatic and merged by FLASH with the following criteria: (i) The reads were truncated at any site receiving an average quality score < 20 over a 50 bp sliding window. (ii) Sequences whose overlap being longer than 10 bp were merged according to their overlap with mismatch no more than 2 bp. (iii) Sequences of each sample were separated according to barcodes (exactly matching) and Primers (allowing 2 nucleotide mismatching), and reads containing ambiguous bases were removed. Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/) with a novel ‘greedy’ algorithm that performs chimera filtering and OTU clustering simultaneously. The taxonomy of each 16S rRNA gene sequence was analyzed by RDP Classifier algorithm (http://rdp.cme. msu.edu/) against the Silva (SSU123) 16S rRNA database using confidence threshold of 70% (X. Li et al., 2017).
2.2. Soil properties analysis After 60 days incubation, soil pH was measured by the pH meter （METTLER-S220）with a soil/water ratio of 5 g/25 mL. OSP, ALN and OSK of soil were measured as the method ascribed by Liu et al. (2017). Soil organic matter was determined according to the method ascribed by Walz et al. (2017). In addition, the available Cd was measured by the TCLP method (Xu et al., 2017). BCR sequential extraction method was used to analysis the Cd formation according to Wu et al. (2016). The sequential extraction method was conducted as follows: (1) 1 g of the sieved soil was shaken at 25 ℃, 150 rpm for 16 h with 40 mL 0.11 M CH3COOH and then centrifuged for 5 min at 8000 rpm to acquire supernatant; (2) The above mentioned residue was shaken at 25 ℃, 250 rpm for 16 h with a 40 mL mixture of 0.5 M NH2OH·HCl and 0.05 M HNO3. Then the mixture was centrifuged for 5 min at 8000 rpm and the supernatant was collected for assay. (3) The above mentioned residue was mixed with 10 mL 30% H2O2 (pH = 2.5) and placed in a bath at 85 ℃ for about 1 h until the volume of liquid was less than 3 mL, then the residue was extracted with 10 mL 30% H2O2. Finally, 50 mL 1.0 M CH3COONH4 (pH = 2) was added and the mixture was centrifuged for assay when the volume of above liquid was less than 1 mL; (4) the above residual soil was digested by using a microwave digestion method that 0.2 g above residual soil was mixed with 6 mL HNO3, 5 mL HClO4 and 4 mL HF, then the mixture was respectively heated at meddle-low, meddle and meddle-high temperature for 5 min. The method partitioned metal into HOAc extractable, reducible, oxidizable and residual fractions. Finally, the Cd concentration was measured by atomic absorption spectroscopy (AAS; VARIAN, SpecterAA-220Fs).
2.5. Statistical analysis In this study, mean and standard deviation values of three replicates were calculated. Statistical significance was performed using SPSS 18.0 package, and means values were considered to be different when P < 0.05 using least significant difference (LSD). All statistics were performed using Origin 8.0 (USA).
2.3. Soil microbial biomass and enzyme analysis Soil microbial biomass was determined according the method described by Cheema et al. (2009). Aqueous extracts of 3 g soil samples were serially diluted and spread on nutrient agar for bacteria and streptomycin-rose bengal agar for fungi through the plate spread method. The total number of microbes was counted after 3–5 days cultivation at 28 °C in the dark. The soil enzyme activities were determined according to the method ascribed by Wu et al. (2016). Dehydrogenase activity was assayed at 37 ℃ for 24 h by incubation of the mixture containing 1 g soil, 4 mL Tris-HCl buffer (pH = 7.6) and 2 mL 0.5% TTC. After that, 10 mL methanol added into the mixture. The above mixture was centrifuged at 4500 rpm for 5 min and the supernatant was determined spectrophotometrically at 492 nm. Dehydrogenase activity was evaluated by triphenylformazan (TPF) and expressed as μg TPF/(g soil·24 h). Urease activity was assayed at 37 ℃ for 24 h with the mixture of 1 g soil, 200 μL methylbenzene, 2 mL 10% urea solution and 4 mL of citrate buffer (pH 6.7). The urease activity was evaluated by the NH4+ color complex that
3. Results and discussion 3.1. Soil main properties analysis The main properties of soil before treatment with Cd were showed in Table S1. The soil was sandy loam texture and alkaline (pH 8.89). The organic matter was low with a value of 12.53 g/kg, whereas the total N, P and K were 0.65, 1.39, 23.15 g/kg, respectively. After treatment with Cd, the main characteristics of soil were weakly influenced and the results were showed in Table 1. As illustrated in Table 1, the pH values of contaminated soil weakly decreased with Cd increase. The OM, OSK, ALN were 12.15–12.45, 39.55–45.30, 42.00–44.50 mg/kg, respectively, which were not obviously influenced in different Cd 117
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Table 1 The soil main properties in different treatments. Values in each column followed with different lowercase letters indicated significant (P ≤ 0.05) difference among different treatments. Treatments
pH
NCS CS1 CS2 CS3 CS4
8.82 8.68 8.61 8.58 8.41
OSP (mg/kg) ± ± ± ± ±
0.14b 0.08 ab 0.10ab 0.06ab 0.05a
24.35 21.60 23.80 30.10 24.70
± ± ± ± ±
ALN (mg/kg) 1.23a 0.87a 1.05a 1.42b 1.30ab
42.50 42.00 44.00 44.50 42.50
Fig. 1. The available heavy metal content and percent by TCLP extraction method in different treatments. Error bars represent standard deviations, and means with different letters are significantly different from each other (P < 0.05) according to the LSD test (n = 3).
± ± ± ± ±
OSK (mg/kg) 3.43a 3.85a 3.25a 4.04a 3.28a
40.35 39.55 40.60 45.30 40.05
± ± ± ± ±
OM (g/kg) 2.78a 2.64a 3.05a 3.43a 2.98a
12.45 12.15 12.15 13.30 12.45
± ± ± ± ±
1.24a 1.32a 1.02a 1.64a 0.86a
Fig. 2. The fractions of heavy metal by BCR extraction method in different treatments.
contaminated soil, while there was not significant difference among the Cd contaminated soil with different levels. e However, the fungal number was significantly decreased in the Cd contaminated soil. Compared with non-contaminated soil, the fungal numbers decreased 21.52–54.43% in Cd contaminated soil. These results illustrated that Cd showed a severe toxicity for microorganism and thus decreased the microbial numbers. Similar to the microbial numbers, the soil enzyme activities were also significantly influenced by Cd pollutant. In this study, dehydrogenase, urease and invertase activities were determined to reflect the soil biological quality (Fig. 3). As shown in Fig. 3a, dehydrogenase activity in Cd contaminated soil was 7.00–23.68% lower than noncontaminated soil, whereas it did not show difference in CS2 and CS3 treatments. Urease activity (Fig. 3b) was significantly impacted in the Cd treatment, which showed a minimum in the CS2 treatment, about 27.41% lower than control. In addition, the invertase activity (Fig. 3c) also decreased in the Cd contaminated soil and the lowest value was found in CS4 treatment, about 45.02% lower than control. These results showed that the influence of different enzyme activities was varied at different Cd levels, while the enzyme activities integrally decreased in Cd contaminated soil that might related to the reduce of soil microbial numbers. Heavy metals have been well known to be toxic to soil microorganisms when presented in excessive concentrations. The detrimental effect on microorganisms by Cd could be exerted through their capability of combining with biomolecules and may act as potent enzyme inhibitors hindering biochemical processes and compromising DNA and cell membrane integrity (Kaur et al., 2006; Poli et al., 2009). Moreover, some research demonstrated that soil microorganisms might cost more additional energy under Cd stress, which could lead to a decrease in the amount of substrate that is available for microorganisms growth (Muhammad et al., 2005). Our results were consistent with previous studies that the microbial biomass was inhibited in heavy metal contaminated soil. The soil enzymes were mainly secreted from
contaminated soil. Meanwhile, the OSP was not significantly changed, except in CS3 treatment, which suggested that the main nutrient matters in soil could not be notably influenced in the Cd contaminating process. 3.2. Soil Cd fractions analysis As shown in Fig. 1a, the TCLP extraction method suggested that the available Cd in soil was about 37.05–62.25% of total Cd after 60 days incubation. In general, the available Cd in alkaline soil showed a lower percent than neutral and acidic soil and the active fractions may provide some indication of heavy metal that were available for microorganisms (Nkongolo et al., 2013). Compared with CS1 and CS2, CS3 and CS4 showed higher percentage of available Cd with the increase of Cd addition. In addition, the translation of Cd fractions was determined by BCR extraction method and the results were showed in Fig. 1b. With the addition of Cd increase, the percentage of HOAc extractable Cd slightly increased whereas the percentage of reducible Cd significantly increased. Compared with CS1 treatment, the percentage of reducible Cd in CS2, CS3 and CS4 treatments increased 45.59%, 54.48% and 22.95%, respectively. Nevertheless, compared with CS1 treatment, the percentage of oxidizable Cd significantly decreased in CS2, CS3 and CS4 treatments. The Cd fractions showed that HOAc extractable Cd and reducible Cd were main forms in the different Cd levels. 3.3. Soil microbial numbers and enzymes analysis The influence of different Cd levels on soil microbial numbers and enzyme activities were determined and these results were shown in Fig. 2. The soil microbial numbers decreased after treatment with different concentrations of Cd. It was observed that bacterial numbers in Cd contaminated soil were 32.86–44.00% lower than non118
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Fig. 3. Bacterial and fungal numbers (a), dehydrogenase activity (b), invertase activity (c) and urease activity (d) in different treatments. Error bars represent standard deviations, and means with different letters are significantly different from each other (P < 0.05) according to the LSD test (n = 3).
treatments, the OTU numbers were significantly decreased compared to other treatments (NCS, CS1 and CS2). Meanwhile, the Chao1 index values were decreased in Cd contaminated soil, especially in CS3 treatment. No difference between different soil types was observed for Simpons index values, while Shannon diversity index in CS2 treatment was slightly increased. For fungi, the OTU numbers were also significantly influenced by the Cd addition. In the CS3 treatment, the OTU number decreased to 177, about 51.24% lower than NCS. In addition, the Chao1 index values in Cd contaminated soil were lower than noncontaminated soil. Our results are similar with some other reports, which found that the heavy metal caused the community structure change in many terrestrial ecosystems, such as sediments and neutral mine drainage contaminated soils (Yin et al., 2015). In this study, the OTUs, Chao1, Shannon and Simpson index were used to determine bacterial and fungal diversity. OUTs are observations of microorganisms and the number of individuals present in the community, which was widely used to estimate bacterial and fungal species (Edgar, 2013). Our reports showed that the microbial OTU numbers and compositions of OTUs
microorganisms in this study. Therefore, the enzyme activities were significantly decreased in heavy metal contaminated soil mostly because of the decrease of microbial biomass. In addition, reduced enzyme activities may be partly caused by binding of Cd2+ to sulphydryl groups (Sanadi, 1982). 3.4. Soil microbial diversity analysis In this study, the total of RNA gene was extracted from soil to obtain the cDNA that was used for amplification of 16S rRNA and ITS by PCR procedure, which could truly reflect the changes of the diversity of active microorganisms under the heavy metal stress within months. A total of 141,740 high quality 16s rRNA gene reads and 193,410 high quality ITS gene reads were obtain from different treated samples, which were clustered into 1243 and 649 OTUs. As shown in Fig S1, the rarefaction curves of bacteria and fungi both tended to plain, which illustrated the sample numbers were sufficient to downstream analyses. Alpha-diversity indexes calculated from OTU relative abundance were analyzed and showed in Table 2. For bacteria, in the CS3 and CS4
Table 2 Alpha-diversity indexes calculated from OTU relative abundance of different treatments. Values in each column followed with different lowercase letters indicated significant (P ≤ 0.05) difference among different treatments. H: Shannon diversity index, SIE: Simpson index of evenness and OTU: observed OTU number. Bacterial alpha-diversity indexes H NCS CS1 CS2 CS3 CS4
5.66 5.47 5.99 5.68 5.12
SIE ± ± ± ± ±
0.11bc 0.07ab 0.06c 0.10bc 0.08a
0.01 0.01 0.01 0.01 0.02
Fungal alpha-diversity indexes Chao1
± ± ± ± ±
0.00a 0.00a 0.00a 0.00a 0.00a
1200 1176 1193 997 1033
± ± ± ± ±
OTU 48b 39b 45b 32a 52a
1126 1124 1151 964 936
H ± ± ± ± ±
34b 21b 27b 33a 22a
119
2.98 3.15 4.11 2.21 2.74
SIE ± ± ± ± ±
0.24b 0.19b 0.28c 0.12a 0.31b
0.15 0.12 0.04 0.13 0.23
Chao1 ± ± ± ± ±
0.02b 0.03b 0.00a 0.03b 0.05c
377 380 272 182 205
± ± ± ± ±
OTU 22c 26c 18b 13a 16a
363 371 271 177 203
± ± ± ± ±
18b 21b 15b 11a 15ab
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Fig. 4. Relative abundance of bacterial phyla (a) and fungal phyla (b) in different treatments. Heat map of bacterial phyla (c) and fungal phyla (d) in different treatments.
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and diversity were considerable important since fungi constituted the major proportion in soil and they were deeply involved in material cycle of soil ecosystem. In the previous reports, it was observed that the fungal radial growth decreased after exposure to Cd, Cu, Zn and Ni (Azevedo and Cássio, 2010). Some studies also showed that metals such as Cd and Zn could inhibit the conidial production in some fungal species (Chiapello et al., 2015). In brief, studies have shown that metal toxicity varies with fungal species, metal type, metal contrition, and even soil environment including of pH, nutrient content and plant diversity (Azevedo and Cássio, 2010; O'Brien et al., 2005; Goupil et al., 2015). Among the main fungal phyla identified in this study, Ascomycota was dominant in soil with different treatment, especially in noncontaminated and low contaminated soil. The proportion of Ascomycota is always over 70% in soil and classified as litter/wood decomposers, lichen forming fungi, saprotrophs, plant parasites, saprotrophs and few edible mushroom (Alemu, 2013). Zygomycota was another phylum identified in both soil samples but their abundance was significantly lower than Ascomycota. Zygomycota represents approximately 1% of fungal groups and are commonly found in terrestrial ecosystems. Mortierella sp., Mortierellales sp., and Mucoromycotina sp. belonging to the Zygomycota phylum have been reported as common fungi in soils (Donnell, 2004). Rathnayake et al. reported that soil microorganisms could develop various mechanisms to resist contamination, even in toxic metal environment (Rathnayake et al., 2011). The mechanisms contribute to microbial resilience to metals such as Cd, Ni, Zn and As have been explained that include transfer of heavy metal tolerance genes in microbial communities through substitution of metal-sensitive strains and proportion of heavy bioavailability (Sobecky and Coombs, 2009; Griffiths and Philippot, 2013). In general, the development of resistant bacteria and fungi is an indication of deterioration of ecosystems.
changed, especially in heavily contaminated soil, which illustrated that heavy metal caused the bacterial diversity and community structure change. Chao1 is commonly used as a species richness indicator used on the number of OTUs found in samples (Chao, 1984). The values of Chao index reveled higher bacterial species richness compared to fungi species richness in different treatment and the results of Chao index in both bacterial and fungal diversity were consistent with OTUs, which further suggested that the microorganisms were influenced under Cd stress. Moreover, Shannon and Simpson indexes are also widely used to analyze community diversity (Gotelli, 2008). Shannon index values for fungal diversity was low compared to bacterial diversity and the slight difference among different treatments was observed. The values of species evenness for bacteria and fungi were low, which illustrated that communities were dominated by one and two species rather than distributed equally among species (Narendrula-Kotha and Nkongolo, 2017). The relative abundance of bacteria and fungi in different treatments was illustrated in Fig. 4. For bacteria distribution (Fig. 4a), the dominant groups across all samples were Actinobacteria (30.06–54.58%) and Proteobacteria (29.20–41.49%). In Cd treated soil, Actinobacteria abundance significantly decreased. Especially in CS2 and CS3 treatments, the percentages of relative abundance were 44.91% and 43.68% lower than control. However, the percentages of relative abundance of Proteobacteria in different Cd treatments increased by 14.89–29.62% compared with control. Although the percentages of Firmicutes, Bacteroidetes, Acidobacteria, Chloroflexi and Gemmatimonadetes were lower than 1%, they were the least abundance of the five phyla in soil samples. For fungi distribution (Fig. 4b), the most dominant phyla member was Ascomycota and the percentage of relative abundance reach to 74.25–96.71% in different treatments. In the CS3 treatment, the percentage of Ascomycota relative abundance decreased to minimum, about 25.12% lower compared to control. However, the percentage of Zygomycota relative abundance increased in Cd treatment, which reached to maximum in CS3 treatment. what's more, top abundance genera of bacteria and fungi in this study was chose to analysis microbial community through heat map of relative abundance genera (Fig. 4c-d), which showed that bacterial and fungal abundance between NCS and CS1 was more closer than other treatments. These results indicated that the bacterial and fungal diversities were more likely to change in heavily polluted soil. In the present study, the percentage of Actinobacteria abundance decreased to 30.06% in 10 mg/kg Cd contaminated soil, whereas it slightly increased in the higher Cd contaminated soil. Actinobacteria is an important phylum for metal remediation due to its metabolic function and ability for fast colonization of selective substrates (NarendrulaKotha and Nkongolo, 2017). Proteobacteria is another prevalent phylum and the percentage of relative abundance significantly increased in Cd contaminated soil. The phylum Proteobacteria was found to be abundant in many heavy metal contaminated environments, such as mine drainage, mine sediments, heavy metal polluted soil and water (Gołębiewski et al., 2014; Zhang et al., 2014; Halter et al., 2011; Serkebaeva et al., 2013). The changes of Proteobacteria varied in different studies. Our results similar with some reports, which found the Proteobacteria was tolerant to Cd pollutant and the abundance of Proteobacteria increased (Lorenz et al., 2006; Sandaa et al., 2001). Whereas Yin et al. (2015) found the abundance of Proteobacteria slightly decreased in heavy metal soil. The variation of the response of Proteobacteria to heavy metal may due to Proteobacteria exhibits a complex lifestyle and can utilize various of organic matters as carbon, nitrogen, and energy sources, which make Proteobacteria can be adapt to different environmental change (Bouskill et al., 2010). Acidobacteria was another phylum with high abundance in the studied samples, which has shown to possess ability to resist heavy metal, acidic and other extreme environment (Barns et al., 2007). Although few studies reported the effect of heavy metal on fungal diversity, studying and understanding the fungal community structure
4. Conclusions Our results showed that the micro-ecological environment of soil significantly changed after treatment with Cd. In Cd contaminated soil, the pH values slight decreased whereas OSP, ALN, OSK and OM were not significantly influenced. However, the microbial biomass and enzyme activities were significantly reduced with the addition of Cd increase. High-throughput sequencing suggested that the microbial diversity and community structure were influenced by heavy metal, especially in higher contaminated soil. The percentages of relative abundance for bacteria and fungi were both similar between non-contaminated soil and contaminated soil with 5 mg/kg Cd. However, in heavily contaminated soil, a remarkable decrease of Actinobacteria abundance for bacteria and Ascomycota abundance for fungi occurred, while Proteobacteria and Zygomycota were significantly increased. Community heatmap of bacteria and fungi further demonstrated that the microorganisms suffered severely stress and microbial diversity was more easily influenced in heavily contaminated soil. These results provided a comprehensive evaluation for the influence of Cd on microecological environment based on physicochemical and biochemical analyses. Acknowledgements This study was financially supported by the Key Research and Development Program of Sichuan Province (2017SZ0181). The authors also wish to thank Professor Guanglei Cheng from Sichuan University for the technical assistance. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2018.09.076. 121
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