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Diversity of bacteria and archaea in the groundwater contaminated by chlorinated solvents undergoing natural attenuation
Environmental Research 185 (2020) 109457
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Diversity of bacteria and archaea in the groundwater contaminated by chlorinated solvents undergoing natural attenuation
T
Decai Jina,d, Fengsong Zhangb,∗, Yi Shic, Xiao Konga,d, Yunfeng Xiec,∗∗, Xiaoming Duc, Yanxia Lie, Ruiyong Zhangf a
Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China c State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China d College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China e State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China f Federal Institute for Geosciences and Natural Resources (BGR), Hannover, 30655, Germany b
A R T I C LE I N FO
A B S T R A C T
Keywords: Chlorinated solvents Groundwater Microbial ecology Dichlorination Illumina sequencing
Chlorinated solvents (CS)-contaminated groundwater poses serious risks to the environment and public health. Microorganisms play a vital role in efficient remediation of CS. In this study, the microbial community (bacterial and archaeal) composition of three CS-contaminated groundwater wells located at an abandoned chemical factory which covers three orders of magnitude in concentration (0.02–16.15 mg/L) were investigated via 16S rRNA gene high-throughput sequencing. The results indicated that Proteobacteria and Thaumarchaeota were the most abundant bacterial and archaeal groups at the phylum level in groundwater, respectively. The major bacterial genera (Flavobacterium sp., Mycobacterium sp. and unclassified Parcubacteria taxa, etc.) and archaeal genera (Thaumarchaeota Group C3, Miscellaneous Crenarchaeotic Group and Miscellaneous Euryarchaeotic Group, etc.) might be involved in the dechlorination processes. In addition, Pearson's correlation analyses showed that alpha diversity of the bacterial community was not significantly correlated with CS concentration, while alpha diversity of archaeal community greatly decreased with the increased contamination of CS. Moreover, partial Mantel test indicated that oxidation-reduction potential, dissolved oxygen, temperature and methane concentration were major drivers of bacterial and archaeal community composition, whereas CS concentration had no significant impact, indicating that both indigenous bacterial and archaeal community compositions are capable of withstanding elevated CS contamination. This study improves our understanding of how the natural microbial community responds to high CS-contaminated groundwater.
1. Introduction
worldwide (Bhattacharjee and Ghoshal, 2018). As most CS are known or suspected human carcinogens or mutagens (Brisson et al., 2012; Houde et al., 2015), these compounds are designated as priority pollutants by the U.S. Environmental Protection Agency (EPA). Therefore, it is necessary to understand their behavior in the environment for their control and remediation. Over the past several years, natural attenuation has become increasingly accepted as a remediation strategy for chlorinated solvents in groundwater (Kawabe and Komai, 2019; Scow and Hicks, 2005; Wright et al., 2017). The term “natural attenuation” refers to naturally-occurring processes in soil and groundwater which reduce the mass, toxicity, mobility, volume, or concentration of contaminants without human
Chlorinated solvents (CS), such as trichloroethylene (TCE) and tetrachloroethylene (PCE), chloroform and 1,1,1-trichloroethane are used for a variety of commercial and industrial purposes, including dry cleaning, metal cleaning, degreasing, automotive aerosols, printing, paper and textile industries, paint removal, furniture industry (Mortan et al., 2017). Contamination of groundwater with CS is frequently observed in industrial areas as a result of improper handling or accidental leakages. In addition, CS can be released into the environment during production, storage, transport and use processes (Bhatt et al., 2007). Currently, CS are the most frequently detected organic contaminants
∗
Corresponding author. Corresponding author. E-mail addresses: [email protected] (F. Zhang), [email protected] (Y. Xie).
∗∗
https://doi.org/10.1016/j.envres.2020.109457 Received 11 September 2019; Received in revised form 27 March 2020; Accepted 27 March 2020 Available online 29 March 2020 0013-9351/ © 2020 Elsevier Inc. All rights reserved.
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at three different time points within 24 h. The depth of groundwater samples for wells BJ2, BJ12 and BJ20 were 16.70 m, 16.42 m and 16.17 m, respectively. Electrical conductivity (EC), pH, temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were directly measured using WTW Multi 350i sensors (WTW, Weilheim, Germany). Concentrations of methane and ethylene in water were determined using U.S. EPA method RSK 175 (RSKSOP175, 2004). All the other environmental parameters were analyzed by Pony Testing International Group (Beijing, China). Briefly, the concentrations of total nitrogen, total ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, chloride, sulfate were determined by “Sanitary Standard for Drinking Water Testing Method” (GB/T 5750.5–2006). Total organic carbon in water was analyzed by using thermal combustion-ion chromatography. Samples were preserved on ice in the field and transport to the laboratory immediately.
intervention. These processes include physical, chemical and biological transformations (Mulligan and Yong, 2004). Under conditions found in the majority of groundwater, biodegradation is believed to be the major process for the oxidation or reduction of contaminants. A variety of microorganisms able to utilize CS have been reported, including the polychlorinated aliphatic alkanes-dehalogenating bacterium Dehalogenimonas lykanthroporepellens (Moe et al., 2009), chlorinated alkane dehalogenating bacterium Dehalogenimonas alkenigignens (Bowman et al., 2013), dichloromethane-degrading bacterium Candidatus Dichloromethanomonas elyunquensis (Kleindienst et al., 2017), chloroanilines-dehalogenating Dehalococcoides mccartyi strain CBDB1 and Dehalobacter strain 14DCB1 (Zhang et al., 2017), chlorinated alkanerespiring bacterium Dehalogenimonas formicexedens (Key et al., 2017), etc. As only a minor proportion of environmental microorganisms can be cultivated, the majority of bacterial strains involved in CS biotransformation cannot be fully described using culture dependent methods. In addition, complete dichlorination of highly substituted CS such as TCE and TeCA to nontoxic ethylene involves a mixed consortium of organisms (Manchester et al., 2012; Mortan et al., 2017). In the past two decades, the biotransformation of CS by indigenous microbial community has been well documented. For example, in the early 1990s, a total of 104 (94 bacterial and 10 archaeal) sequence types were determined by using the restriction fragment length polymorphisms (RFLP) and clone library to investigate the microbial communities associated with an aquifer contaminated with hydrocarbons and chlorinated solvents (Dojka et al., 1998). Later, the community composition of microbial cultures for chlorinated ethene-degrading was studied by using PCR-denaturing gradient gel electrophoresis (PCRDGGE) and quantitative PCR method (Duhamel and Edwards, 2006), the results showed that Dehalococcoides populations were the dominant phylotypes. Recently, studies on the microbial communities associated with the dechlorination process have been remarkably promoted by the 16S rRNA gene amplicon sequencing approach with metagenomic sequencing (Brisson et al., 2012; David et al., 2015; Delgado et al., 2017; Kao et al., 2016; Liu et al., 2018; Simsir et al., 2017; Wright et al., 2017; Yu et al., 2016a). Most of these previous researches were based on data in the laboratory and in pilot field tests, but study in the natural environment could obtain more authentic information on the changes of microbial community composition in the contaminated environments. Wright et al. (2017) reported that dichloromethane degrading organisms, such as the Desulfosporosinus, thrived within the most heavily contaminated groundwater samples. However, to date, the available information about the microbial community involved in CS biotransformation in the in situ environment are still not understood in detail. Additionally, relatively little is known regarding the effect of CS contamination and physio-chemical parameters on the microbial communities at field sites undergoing natural attenuation. In the present study, CS contaminated groundwater showing natural attenuation was selected. The objectives of our study were 1) to evaluate the microbial community response to CS contamination; 2) to explore the native structure of the bacterial and archaeal communities involved in the CS biotransformation process; and 3) to better understand the relationship between the microbial community and environmental parameters, and their correlation with groundwater contamination.
2.2. DNA extraction and DNA sequencing Approximately 1.0 L of water samples were filtered through sterile 0.22 μm cellulose filters (Millipore, Billerica, MA, USA). The membranes were then sheared and total DNA was extracted by using the FastDNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, USA). Fusion primers consisting of an adapter, a 6 bp barcode, and the templatespecific sequence were used to amplify the 16S rRNA gene of bacteria and archaea. Specifically, bacterial 16S rRNA genes were amplified using the primers 338F (5′-GGACTACHVGGGTWTCTAAT-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Dennis et al., 2013) targeting the V3–V4 high variable region of the 16S rRNA gene. The V4 region of the archaeal 16S rRNA genes were amplified using the primers U519F ( 5′-GTGCCAGCMGCCGCGG-3′) and 806R (5′-CCGTCAATTCMTTTRAGTTT-3′) (Shehab et al., 2013). The 50 μL PCR reaction mixture consisted of 5 μL of 10 × PCR buffer, 4 μL of dNTPs, 1.5 μL of 10 μM each primer, 0.5 μL of Taq DNA polymerase (TaKaRa Biotech, Beijing, China) and 20–30 ng of the DNA template. The PCR reaction was processed in a thermal cycler (Bio-Rad Laboratories, Hercules, CA) using the following protocol: 94 °C for 5 min, then 30 cycles of 20 s at 94 °C, 25 s at 57 °C, 45 s at 72 °C, and a final extension of 10 min at 72 °C. The PCR products were visualized on a 1.8–2.0% agarose gel. The PCR amplicon bands were excised from the gels and then purified with E. Z.N.A.™ Gel Extraction Kit (Omega Bio-tek, Norcross, GA, USA). Concentration of purified PCR products were determined using the Qubit hs-DS-DNA kit (Invitrogen, Carlsbad, CA, USA) on a Tecan Infinite F200 Pro plate reader, and then pooled in equimolar concentration. The highthroughput sequencing was performed on Illumina MiSeq instrument by Beijing Fixgene Technoly Co., Ltd (Beijing, China). The sequences have been deposited at the NCBI Sequence Read Archive under the project PRJNA508863. 2.3. Bioinformatics and statistical analyses The reads from the original DNA fragments were merged using FLASH (V1.2.7, http://ccb.jhu.edu/software/FLASH/), and then processed with Mothur 1.36.1 (Schloss et al., 2009) using a MiSeq standard operating procedure (SOP) (Kozich et al., 2013). Briefly, sequences shorter than 200 bp and those with a homopolymer longer than 8 bp were removed from the dataset, then trimmed sequences were aligned (align.seqs) with the SILVA database (silva.seed_v123) (Quast et al., 2013). Followed by Mothur,s pipeline procedure including filter. seqs, unique. seqs, and pre. cluster, chimeras were removed using the UCHIME algorithm with command “chimera.uchime” (Edgar et al., 2011). Sequences were then taxonomically classified using the Naive Bayesian Classifier with 80% confidence threshold by using (classify.seqs) command (Wang et al., 2007). Sequences that were classified as Chloroplasts, Mitochondria, Eukaryota, or unknown were eliminated using (remove.lineage) command. For the bacterial sequence set, sequences classified as archaeal were also removed, conversely, bacterial
2. Materials and methods 2.1. Sample collection The contaminated site was located at an abandoned chemical factory in the east of Beijing, China. During its operation, various CS were imported as raw materials or produced as final products, resulting in the site's groundwater pollution. Groundwater samples were taken using a PTFE bailer (Cole-Parmer, Chicago, 1 L, USA) from three wells contaminated with CS, three water samples were taken from each well 2
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Table 1 Diversity indices from the three groundwater water samples. The Sobs, Chao1, Ace, Shannon and Invsimpson parameters are presented for a dissimilarity of 3. Samples
BJ2 BJ12 BJ20
Bacterial OTUs (Resample 5769 sequences)
Archaeal OTUs (Resample 13,151 sequences)
Sobs
Chao1
Ace
Shannon
Invsimpson
Sobs
Chao1
Ace
Shannon
Invsimpson
1762 1331 1067
7344 6127 4583
15,058 13,458 9894
5.99 5.05 4.22
97.66 24.99 12.95
1844 1068 1527
5029 2932 4239
8718 5344 7909
5.28 3.66 4.63
44.38 13.17 29.49
obtained for bacterial and archaeal 16S rRNA gene sets with 221,596 and 394,774 high quality sequences remaining after quality control for analysis. Briefly, 5,768 to 40,255 bacterial and 14,464 to 49,100 archaeal 16S rRNA gene sequence reads per sample were obtained. After random resampling, 5,769 and 13,151 sequences per sample remained for bacteria and archaea, respectively. The coverage index (greater than 76% and 92% for bacterial and archaeal, respectively) indicated that all of the communities were well captured at the sequencing depth. Bacterial and archaeal community richness (Observed species richness: Sobs, Chao1 and Ace) and diversity (Shannon and Invsimpson) index values were compared for the three wells (Table 1). According to richness indexes (Sobs, Chao1 and Ace), bacterial richness from the highest to lowest was BJ2 > BJ12 > BJ20, and the diversity indexes (Shannon and Invsimpson) follow by the same trend. The trend of both richness and diversity indexes was not in accordance with well CS concentrations, which were from the lowest to highest BJ2 < BJ20 < BJ12. Archaeal diversity was lower than the bacterial one, with the highest diversity found in BJ2. However, we observed archaeal richness increased (Sobs, Chao1 and Ace indexes) as CS concentrations decreased, and this trend was also supported by Shannon diversity index. Overall, the diversity indices showed that both the bacterial and archaeal communities in wells with lower CS concentrations were significantly more diverse than those with higher concentrations. Based on Pearson correlation coefficient between environment parameters and αdiversity for the bacterial community (Table S4), the results showed that DO and ORP were positively correlated with all richness and diversity indexes, while methane was negatively correlated with all richness and diversity indexes. No significant associations were found between the other environment parameters and α-diversity indices. Compared with bacterial community, parameters like DO, ORP and methane were found to be significantly positively correlated with all archaeal richness and diversity indexes, while EC, AN, NN, chloride, sulfate and TN, ethylene, Mn, TOC and CS were negatively correlated with all richness and diversity indices (Table S5), a significant positive linear correlation was also found between these ten environment parameters.
sequences were removed for the archaeal sequence set. After removal of contaminant sequences, VSEARCH was used to cluster sequences into operational taxonomic units (OTUs) with 97% similarity (Rognes et al., 2016). The number of reads per sample was normalized by using (sub.sample) command. The alpha diversity (α-diversity) and beta diversity analyses were based on the normalized data. The number of sequences of each OTU was determined using (make.shared) command, and the taxonomy for each OTU was specified using (classify.otu) command. Rarefaction curves and community richness and diversity indices were performed by using (rarefaction.single) command and (summary.shared) command, respectively. Principal coordinates analysis (PCoA) was performed with the R package using a Bray-Curtis or Jaccard dissimilarity matrix to visualize dissimilarities in bacterial and archaeal community structures. To identify which prokaryotic taxa contributed to the Bray-Curtis distance, SIMPER method (Clarke, 1993) was applied to identifies the OTUs contributing to similarity within and dissimilarity between groups and rank their contribution. Pearson's correlation analysis was employed to determine the correlation between the microbial community with the environmental factors using the SPSS statistical software (IBM, Armonk, New York, USA), the level of statistical significance was set at p < 0.05. The function annotation for the 16S rRNA gene was performed by Functional Annotation of Prokaryotic Taxa (FAPROTAX) (Louca et al., 2016). To determine the correlations between microbial communities and environmental variables, Mantel, partial Mantel tests, and canonical correspondence analysis (CCA) were used. Based on the variance inflation factors (VIF) values, redundant variables with VIF > 20 were removed from the CCA model. 3. Results 3.1. Physio-chemical parameters of multiple wells As shown in Table S1, total CS concentration spanned three orders of magnitude (0.02–16.15 mg/L) with the highest levels detected in BJ12. No distinct differences in measured water chemical parameters were observed between groundwater samples collected in the wells BJ2 and BJ20. However, significant differences were found between BJ12 and the other two wells. There were 4, 11 and 8 different organic chlorinated compounds in BJ2, BJ12 and BJ20, respectively (Table S2), with 1,2-dichlorobenzene the dominant pollutant in BJ2, and vinyl chloride in wells BJ12 and BJ20. A small amount of ethylene was observed in two wells (BJ2 and BJ20), while a higher level (42.73 mg/L) was detected in BJ12. The pH of the samples ranged from 6.43 to 7.22, the lowest pH value was in BJ2. Except for pH, all of the detected physio-chemical parameters in the other two wells (BJ2 and BJ20) were significantly lower than that of BJ12. Pearson correlation analysis was used to analyze the relationship between physio-chemical factors and the concentration of total CS. As shown in Table S3, the total CS was positively correlated to electrical conductivity, ammonia nitrogen (AN), nitrate nitrogen (NN), chloride, sulfate, total nitrogen (TN), ethylene, Fe, Mn and total organic carbon (TOC) (P < 0.01).
3.3. Characterization of groundwater microbiome Based on the Illumina platform analysis, the bacterial sequences were classified into 40 different phyla. Among these detected phyla, Bacteroidetes, Proteobacteria, Actinobacteria, Parcubacteria, Chloroflexi, Deinococcus-Thermus, Nitrospirae, Firmicutes, Saccharibacteria, Chlorobi, Planctomycetes and Candidate division OP3 had relative abundance greater than 1% (Fig. 1A). As shown in Fig. 1, the dominant phyla in three wells were different, Proteobacteria sequences were predominant and represented 30.2% in the BJ2, 21.5% in the BJ12 and 23.2% in the BJ20, respectively. While Parcubacteria and Bacteroidetes were predominant in BJ2 and BJ20, represented 23.4% and 57.4%, respectively. The most abundant bacterial genera in all samples were Flavobacterium, unclassified Parcubacteria taxa, Mycobacterium and Paludibacter (Fig. 1B). In BJ2 Flavobacterium was predominant 5.80%, followed by unclassified Parcubacteria (5.66%) and Mycobacterium (5.39%). Unclassified Parcubacteria taxa (23.40%) and Mycobacterium (16.78%) were most abundant genera in BJ12, and both were far higher than other genera in this
3.2. Diversities of bacterial and archaeal communities A total of 509,099 and 760,987 raw sequences, respectively, were 3
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Fig. 1. Taxonomic classification of bacterial community at the phylum (A) and genus (B). To simplify the figure, only taxonomic groups in the phylum and genus levels with > 1.0% and > 2.0% sequence abundance in at least one sample are represented, respectively. The values shown are the means of three replicates.
and archaeal genera. The results showed that the greatest contribution was by temperature, as 12 of top 20 bacterial genera showed significant correlation with temperature, while six genera were found to have significant correlation with CS concentration (data not shown). Temperature also exhibited significant positive/negative correlation with 6 of the 10 top archaeal genera, which was much higher than other environmental factors. The FAPROTAX showed that 49 categories were classified within the samples, and the dominant functional groups were chemoheterotrophy, aerobic chemoheterotrophy, aerobic nitrite oxidation, nitrification, fermentation, ureolysis, aromatic compound degradation and hydrocarbon degradation, etc. (Fig. S1). Among these functional groups, we found the ureolysis microbial functional cluster was significantly positively correlated with the CS concentration. Principal coordinate analysis based on Bray–Curtis dissimilarity metric was applied to evaluate the overall differences in microbial community structure. The PCoA plots (Fig. 3) showed that bacterial and archaeal communities both clustered by well of sample origin. This was supported by a permutational multivariate analysis (PERMANOVA) with a p value of 0.009 and 0.004, respectively, for bacterial and
well. A similar trend was observed in BJ20, in which Flavobacterium and Paludibacter accounted for 31.05% and 20.39% of total sequences, respectively. Compared to bacterial diversity, the archaeal population was relatively lower in all wells, with only ten phyla detected in all the samples combined (Fig. 2A), Unclassified Archaea were dominant in BJ2, while Thaumarchaeota were dominant in BJ12 and BJ20, representing 36.79%, 40.59% and 55.33% of sequences, respectively. Interestingly, the abundance of unclassified Miscellaneous Crenarchaeotic Group (MCG) decreased with increasing total CS concentration. The dynamic profiles of the archaeal community at the genus level were significantly different in the three wells (Fig. 2B). For example, unclassified Archaea taxa was the most dominant group in BJ2, while its proportions in BJ12 and BJ20 were 25.39% and 9.75%, respectively; Thaumarchaeota Group C3 was the most dominant group in BJ12, while its proportion in BJ2 and BJ20 were 6.43% and 11.78%, respectively. The relative abundance of Thaumarchaeota Group C3 was found to increase as CS concentration increased. Pearson correlation analysis was used to examine the relationships between measured environmental parameters and the main bacterial
Fig. 2. Taxonomic classification of archaeal community at the phylum (A) and genus (B). To simplify the figure, only taxonomic groups with > 0.5% abundance at the genus level in at least one sample are represented. The values shown are the means of three replicates. 4
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Fig. 3. Principal coordinates analysis presents the spatial variations of bacterial (A) and archaeal communities (B) according to the Bray-Curtis distance.
were negatively correlated with axis 1 for both bacterial and archaeal community structure while DO and ORP had positive correlations. As indicated by VPA, the contribution of methane, DO and ORP explained 9.94%, 8.65% and 6.84%, respectively, of bacterial community changes and 10.63%, 9.49% and 8.51% of the archaeal community changes.
archaeal communities at 999 permutations. According to the PCoA analysis, the first two principal coordinates PC1 and PC2 of bacterial PCoA explained 51.40% and 25.73% of total variation. In comparison, the variation of archaeal communities was explained similar by the first and second axes, together accounting for 70.25% of the total variance.
4. Discussion
3.4. Relationships between microbial community compositions and environmental variables
Microorganisms play important roles in the self-remediation activity and their compositions that are largely influenced by the external pollutants (Liang et al., 2019). In the present study, the microbial communities of three different wells contaminated with organic CS, with concentrations spanned three orders of magnitude, were studied by using Illumina MiSeq sequencing. Bacteroidetes, Proteobacteria, Actinobacteria and Parcubacteria were found to be the most abundant in all wells. This was in agreement with previous finding that focused on microbial communities in CS contaminated environments. For example, Proteobacteria were found to be prevalent in groundwater samples polluted by chlorinated ethenes undergoing natural attenuation (Nemecek et al., 2017). A similar dominance by Proteobacteria has also been identified at other CS contaminated groundwater sites (Matturro et al., 2018). Wright et al. (2017) reported that Proteobacteria were the most abundant phylum across groundwater samples with dichloromethane contamination ranging from 0.89 to 9,800,000 μg/L, whereas Firmicutes, Bacteroidetes and Chlamydiae varied as the second most abundant group in different samples. In microcosms established creek sediments receiving CS, the majority of bacteria were found to be the phyla Proteobacteria, Bacteroidetes and Chloroflexi (Simsir et al., 2017). Actinobacteria have also been found as a dominant bacterial phylum in PCB-contaminated environment (Yu et al., 2016a). Interestingly, sequences affiliated with Parcubacteria were found at high abundance (23.4%) in the BJ12, which had the highest contamination. Similar results were obtained by Matturro et al. (2018) at another polluted groundwater site. As shown in Fig. 1, the relative abundance of the dominant bacterial genera (total abundance > 2% in all samples) accounted for 57%, 67% and 78% of sequences in BJ2, BJ12 and BJ20, respectively. Most of these genera are well-known for their ability to degrade chlorinated organic pollutants. Flavobacterium, which usually associated with sulfur-oxidizing processes, has been suggested to coexist with dechlorination process (Long et al., 2018), as accounts for up to 31% of sequences in BJ20. The genus Mycobacterium was found to be the second most abundant group across all the samples. This genus has been reported to be able to readily cooxidize vinyl chloride (Jin et al., 2010), and has been identified in various chlorinated compounds contaminated sites (Liu and Mattes, 2016; Nemecek et al., 2017). Sphingobium fuliginis HC3, has the ability to completely mineralize biphenyl and PCBs without dead-end
Because of the strong collinearity between environmental variables, the partial Mantel test based on Bray–Curtis distance was carried out to analyze the relationship between the microbial community structure and environmental factors (Table 2). We found that both microbial communities were most significantly correlated with ORP (ρ = 0.80; P < 0.01), followed by temperature and methane concentration for the bacterial and archaeal communities, respectively. The CS concentration was not the important factor influencing the microbial community structure. Based on the partial Mantel test results and VIF values, four environmental factors along were selected for CCA to further identify parameters which significantly explain variance in the microbial community (Fig. 4). According to the CCA profiles, these variables significantly (P = 0.005) explained 45.62% and 43.79% of the variation of the bacterial and archaeal community structures, respectively. The temperature and methane Table 2 The partial Mantel tests demonstrate the correlations between individual environmental factors and beta-diversity. Significant correlations with |ρ| ≥ 0.5 and P ≤ 0.05 are highlighted with bold type. Environmental factors
pH EC DO Temp ORP AN NN Chloride Sulfate TN Fe Methane Ethylene Mn TOC CS
Bacterial
Archaeal
ρ
P
ρ
P
−0.3574 −0.568 0.3927 0.51 0.8045 −0.7262 −0.4104 −0.4447 −0.5546 −0.4905 −0.3793 0.2604 −0.3854 −0.5161 −0.405 −0.4339
0.969 0.992 0.027 0.001 0.004 0.997 0.967 0.979 0.996 0.981 0.958 0.04 0.967 0.992 0.975 0.98
−0.5846 −0.6726 0.6226 0.3787 0.8023 −0.5472 −0.6264 −0.6331 −0.6218 −0.6612 −0.6052 0.5532 −0.6125 −0.6354 −0.6135 −0.6395
1 0.997 0.004 0.009 0.002 0.991 0.998 0.997 0.995 0.996 0.995 0.009 0.995 0.996 0.996 0.995
5
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Fig. 4. Canonical correspondence analysis biplot of bacterial (A) and archaea (B) communities and four significant environment factors.
Ace, Invsimpson and Shannon metrics (P < 0.01), while the presence of methane was significantly negatively correlated to all diversity metrics. It is worth to note that bacterial α-diversity was not significantly correlated to CS contamination, this finding was consistent with the results reported by Wright et al. (2017), who found that bacterial αdiversity did not change significantly between DCM concentration groupings. In contrast, archaeal α-diversity decreased with increased CS concentrations and was significantly negatively correlated with raised CS concentrations (P < 0.01). However, no significant correlation was observed between archaeal α-diversity and any of the environmental factors which showed significant correlation to bacterial αdiversity. These results indicated that archaeal community α-diversity was more sensitive to CS contamination than that of bacterial community. The mechanism of contradictory correlation in bacterial and archaeal with the environmental factors remains to be demonstrated. Despite the microbial community response to chlorinated compounds contamination has been studied extensively (Matturro et al., 2018; Simsir et al., 2017; Wright et al., 2017; Xu et al., 2017), little information is available on the relationships between environmental factors and microbial community composition at sites contaminated by chlorinated compounds, especially on archaea. Here, we found that both bacterial and archaeal community composition and were strongly correlated to DO, temperature, ORP and methane (partial Mantel test, P ≤ 0.05 or 0.01). ORP was the most important environmental factor to influence the microbial community composition, a possible explanation for this result is that most of biochemical processes must occur within a specific redox range, especially for anaerobic microorganisms. DO was also an important factor affecting the investigated microbial communities and its influence on microbial community composition in various ecosystems has been noted by others (Andrei et al., 2015; Kalwasinska et al., 2018). These studies have shown that different DO conditions have a direct impact on community composition. Additionally, temperature had a very significant impact on both bacterial and archaeal community structure. A number of studies have shown that the microbial community structure was closely related to the environmental temperature (Liu et al., 2017; Wang et al., 2012). We found that microbial community structure was also significantly correlated with methane concentration (bacterial, P = 0.04; archaeal, P = 0.009). It is likely that the presence of methane could have strong influence on the microbial functional groups such as methane-oxidizing bacteria or archaea (Brazelton et al., 2006). This was further confirmed by the Pearson's correlation coefficients of methane concentration and the main bacteria and archaeal genera, indicating many abundant genera were significant correlation with methane. Previous studies have reported that methane concentrations and ORP were significant correlations (P < 0.01) with dechlorination potential (Lee et al., 2016), and DO was also an important factor affects dechlorinating activity as many anaerobic microorganism were sensitive to oxygen (Atashgahi et al.,
intermediates (Hu et al., 2015). Interestingly, Parcubacteria was the most abundant in BJ12, a similar phenomenon was also observed by Lhotsky et al. (2017) and Matturro et al. (2018) in CS polluted groundwater or wells. Although Parcubacteria has frequently been identified in a broad range of environments polluted with chlorinated compounds, the role of this group in biodegradation of those compounds is still unclear. It is worth noting that Paludibacter, which is a Gram-negative, strictly anaerobic, and chemoorganotrophic genus, was most abundance in BJ20, however, no strains from this genus have been reported to possess dechlorinating abilities. Unclassified Chloroflexi taxa, unclassified Anaerolineaceae taxa, unclassified SAR202 clade and unclassified Dehalococcoidia taxa and unclassified Candidate division OP3 all belonging to the phylum Chloroflexi which is associated with reductive dichlorination, were also detected in all samples. Unclassified Dehalococcoidia taxa and unclassified SAR202 clade taxa from class Dehalococcoidia were present at very low abundance (0.18%–1.72% and 0.29%–1.24%, respectively). Although Dehalococcoides is well known for its complete anaerobic dechlorination of chlorinated ethylenes into ethene, none were found in our study, even though ethylene was detected in all contaminated wells, especially in BJ12 (42.42 mg/L), which indicates the occurrence of complete dechlorination of PCE/TCE. These results indicate that non-Dehalococcoides may play an important role in the microbial dechlorination process, a similar explanation was also made by Yu et al. (2016b) and Patil et al. (2014) in a microbial electrochemical system. Compared to bacterial taxa, few archaeal phyla were detected in these samples, with the majority of sequences belonging to the Thaumarchaeota, followed by unclassified Archaeal and Woesearchaeota. This result was different from the studies by Simsir et al. (2017), where Euryarchaeota and the Crenarchaeota were the dominant phyla. At the genus level, Thaumarchaeota Group C3 abundance increased from 6.43% in BJ2 to 32.67% in BJ12 as CS concentrations increased. Conversely, the relative abundance of ammonia oxidizers MCG decreased as CS concentrations increased. These results indicated that the involvement of Thaumarchaeota Group C3 in the biodegradation of CS, while MCG was sensitive to CS pollution. An unclassified Archaeal genus was the most abundant in BJ2, and accounted for 25.39% and 9.75% in BJ12 and BJ20, respectively. Pearson correlation analysis showed significant correlations between this unclassified Archaeal genus and DO, ORP and methane, indicating it plays an important role in methane oxidation or production. Miscellaneous Euryarchaeotic Group (MEG) has been reported to possess highest activity during pentachlorophenol reductive dichlorination (Xu et al., 2017), which was similar to our results that high percentage of MEG were present in the BJ12 (25.0%). Pearson correlation coefficients for environmental factors and αdiversity were calculated in this study. For the bacterial community, DO and ORP methane all showed significant positive correlation with Sobs, 6
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2013), so changing these environment factors might have great potential for improving remediation efficiency of CS. Interesting, although the CS concentrations vary widely in the three wells, it had no significant relationship to the changes of the microbial community structure, indicating that microbial community composition could withstand high concentration of CS contamination.
compounds-a review. Crit. Rev. Environ. Sci. Technol. 37, 165–198. Bhattacharjee, S., Ghoshal, S., 2018. Optimal design of sulfidated nanoscale zerovalent iron for enhanced trichloroethene degradation. Environ. Sci. Technol. 52, 11078–11086. Bowman, K.S., Nobre, M.F., da Costa, M.S., Rainey, F.A., Moe, W.M., 2013. Dehalogenimonas alkenigignens sp. nov., a chlorinated-alkane-dehalogenating bacterium isolated from groundwater. Int. J. Syst. Evol. Microbiol. 63, 1492–1498. Brazelton, W.J., Schrenk, M.O., Kelley, D.S., Baross, J.A., 2006. Methane- and sulfurmetabolizing microbial communities dominate the Lost City hydrothermal field ecosystem. Appl. Environ. Microbiol. 72, 6257–6270. Brisson, V.L., West, K.A., Lee, P.K., Tringe, S.G., Brodie, E.L., Alvarez-Cohen, L., 2012. Metagenomic analysis of a stable trichloroethene-degrading microbial community. ISME J. 6, 1702–1714. Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. 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Epoxyalkane: coenzyme M Transferase gene diversity and distribution in groundwater samples from chlorinated-ethene-contaminated sites. Appl. Environ. Microbiol. 82, 3269–3279. Liu, X., Wu, Y., Wilson, F.P., Yu, K., Lintner, C., Cupples, A.M., Mattes, T.E., 2018. Integrated methodological approach reveals microbial diversity and functions in
5. Conclusion The microbial community (bacterial and archaeal) composition of three different CS-contaminated groundwater wells were analyzed by using high-throughput 16S rRNA gene analysis. The most abundant bacterial phyla in three wells were Bacteroidetes, Proteobacteria, Actinobacteria and Parcubacteria, while Thaumarchaeota, Unclassified Archaeal and Woesearchaeota were the most abundant archaeal phyla. Some genera may be involved in microbial oxidation or reduction of CS were also identified (i.e. Mycobacterium, Flavobacterium, Miscellaneous Euryarchaeotic Group and Miscellaneous Crenarchaeotic Group). In addition, α-diversity of archaeal community was more sensitive to CS than that of bacterial community. The partial Mantel test results showed that both the bacterial and archaeal communities variance correlated strongly with the same four environment factors (ORP, DO, temperature and methane concentration), while the CS pollution has no influence on the on the microbial community composition. These results could be useful to understand the role of microbial community structure in the bioremediation of CS-contaminated groundwater. More attention should be paid to archaeal community that may contribute to biotransformation of CS. Funding This study was financially supported by the National Natural Science Foundation of China (Nos. 41977122, 21577007, 31500083 and 31601642), the National Key Research and Development Program of China (No. 2017YFD0800102), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23010400). Author statement D.C.J., F.S.Z., Y.F.X., and X.M.D. planned and designed the research. X.K., Y.S., Y.X.L., and R.Y.Z. performed experiments and analyzed data. D.C.J. wrote the main manuscript text and all the authors contributed to the revision of the manuscript and approved the version to be published. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relavtionships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envres.2020.109457. References Andrei, A.S., Robeson, M.S., Baricz, A., Coman, C., Muntean, V., Ionescu, A., Etiope, G., Alexe, M., Sicora, C.I., Podar, M., Banciu, H.L., 2015. Contrasting taxonomic stratification of microbial communities in two hypersaline meromictic lakes. ISME J. 9, 2642–2656. Atashgahi, S., Maphosa, F., Dogan, E., Smidt, H., Springael, D., Dejonghe, W., 2013. Small-scale oxygen distribution determines the vinyl chloride biodegradation pathway in surficial sediments of riverbed hyporheic zones. FEMS Microbiol. Ecol. 84, 133–142. Bhatt, P., Kumar, M.S., Mudliar, S., Chakrabarti, T., 2007. Biodegradation of chlorinated
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aerobic groundwater microcosms adapted to vinyl chloride. FEMS Microbiol. Ecol. 94. Long, M., Ilhan, Z.E., Xia, S., Zhou, C., Rittmann, B.E., 2018. Complete dechlorination and mineralization of pentachlorophenol (PCP) in a hydrogen-based membrane biofilm reactor (MBfR). Water Res. 144, 134–144. Louca, S., Parfrey, L.W., Doebeli, M., 2016. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. Manchester, M.J., Hug, L.A., Zarek, M., Zila, A., Edwards, E.A., 2012. Discovery of a transdichloroethene-respiring Dehalogenimonas species in the 1,1,2,2-tetrachloroethanedechlorinating WBC-2 consortium. Appl. Environ. Microbiol. 78, 5280–5287. Matturro, B., Pierro, L., Frascadore, E., Petrangeli Papini, M., Rossetti, S., 2018. Microbial community changes in a chlorinated solvents polluted aquifer over the field scale treatment with poly-3-hydroxybutyrate as amendment. Front. Microbiol. 9, 1664. Moe, W.M., Yan, J., Nobre, M.F., Costa, M.S.d., Rainey, F.A., 2009. Dehalogenimonas lykanthroporepellens gen. nov., sp. nov., a reductively dehalogenating bacterium isolated from chlorinated solvent-contaminated groundwater. Int. J. Syst. Evol. Microbiol. 59, 2692–2697. Mortan, S.H., Martin-Gonzalez, L., Vicent, T., Caminal, G., Nijenhuis, I., Adrian, L., Marco-Urrea, E., 2017. Detoxification of 1,1,2-trichloroethane to ethene in a bioreactor co-culture of Dehalogenimonas and Dehalococcoides mccartyi strains. J. Hazard Mater. 331, 218–225. Mulligan, C.N., Yong, R.N., 2004. Natural attenuation of contaminated soils. Environ. Int. 30, 587–601. Nemecek, J., Dolinova, I., Machackova, J., Spanek, R., Sevcu, A., Lederer, T., Cernik, M., 2017. Stratification of chlorinated ethenes natural attenuation in an alluvial aquifer assessed by hydrochemical and biomolecular tools. Chemosphere 184, 1157–1167. Patil, S.S., Adetutu, E.M., Rochow, J., Mitchell, J.G., Ball, A.S., 2014. Sustainable remediation: electrochemically assisted microbial dechlorination of tetrachloroethenecontaminated groundwater. Microb. Biotechnol. 7, 54–63. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glockner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. Rognes, T., Flouri, T., Nichols, B., Quince, C., Mahe, F., 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584. Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B.,
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Diversity of bacteria and archaea in the groundwater contaminated by chlorinated solvents undergoing natural attenuation
T
Decai Jina,d, Fengsong Zhangb,∗, Yi Shic, Xiao Konga,d, Yunfeng Xiec,∗∗, Xiaoming Duc, Yanxia Lie, Ruiyong Zhangf a
Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China c State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China d College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China e State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China f Federal Institute for Geosciences and Natural Resources (BGR), Hannover, 30655, Germany b
A R T I C LE I N FO
A B S T R A C T
Keywords: Chlorinated solvents Groundwater Microbial ecology Dichlorination Illumina sequencing
Chlorinated solvents (CS)-contaminated groundwater poses serious risks to the environment and public health. Microorganisms play a vital role in efficient remediation of CS. In this study, the microbial community (bacterial and archaeal) composition of three CS-contaminated groundwater wells located at an abandoned chemical factory which covers three orders of magnitude in concentration (0.02–16.15 mg/L) were investigated via 16S rRNA gene high-throughput sequencing. The results indicated that Proteobacteria and Thaumarchaeota were the most abundant bacterial and archaeal groups at the phylum level in groundwater, respectively. The major bacterial genera (Flavobacterium sp., Mycobacterium sp. and unclassified Parcubacteria taxa, etc.) and archaeal genera (Thaumarchaeota Group C3, Miscellaneous Crenarchaeotic Group and Miscellaneous Euryarchaeotic Group, etc.) might be involved in the dechlorination processes. In addition, Pearson's correlation analyses showed that alpha diversity of the bacterial community was not significantly correlated with CS concentration, while alpha diversity of archaeal community greatly decreased with the increased contamination of CS. Moreover, partial Mantel test indicated that oxidation-reduction potential, dissolved oxygen, temperature and methane concentration were major drivers of bacterial and archaeal community composition, whereas CS concentration had no significant impact, indicating that both indigenous bacterial and archaeal community compositions are capable of withstanding elevated CS contamination. This study improves our understanding of how the natural microbial community responds to high CS-contaminated groundwater.
1. Introduction
worldwide (Bhattacharjee and Ghoshal, 2018). As most CS are known or suspected human carcinogens or mutagens (Brisson et al., 2012; Houde et al., 2015), these compounds are designated as priority pollutants by the U.S. Environmental Protection Agency (EPA). Therefore, it is necessary to understand their behavior in the environment for their control and remediation. Over the past several years, natural attenuation has become increasingly accepted as a remediation strategy for chlorinated solvents in groundwater (Kawabe and Komai, 2019; Scow and Hicks, 2005; Wright et al., 2017). The term “natural attenuation” refers to naturally-occurring processes in soil and groundwater which reduce the mass, toxicity, mobility, volume, or concentration of contaminants without human
Chlorinated solvents (CS), such as trichloroethylene (TCE) and tetrachloroethylene (PCE), chloroform and 1,1,1-trichloroethane are used for a variety of commercial and industrial purposes, including dry cleaning, metal cleaning, degreasing, automotive aerosols, printing, paper and textile industries, paint removal, furniture industry (Mortan et al., 2017). Contamination of groundwater with CS is frequently observed in industrial areas as a result of improper handling or accidental leakages. In addition, CS can be released into the environment during production, storage, transport and use processes (Bhatt et al., 2007). Currently, CS are the most frequently detected organic contaminants
∗
Corresponding author. Corresponding author. E-mail addresses: [email protected] (F. Zhang), [email protected] (Y. Xie).
∗∗
https://doi.org/10.1016/j.envres.2020.109457 Received 11 September 2019; Received in revised form 27 March 2020; Accepted 27 March 2020 Available online 29 March 2020 0013-9351/ © 2020 Elsevier Inc. All rights reserved.
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at three different time points within 24 h. The depth of groundwater samples for wells BJ2, BJ12 and BJ20 were 16.70 m, 16.42 m and 16.17 m, respectively. Electrical conductivity (EC), pH, temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were directly measured using WTW Multi 350i sensors (WTW, Weilheim, Germany). Concentrations of methane and ethylene in water were determined using U.S. EPA method RSK 175 (RSKSOP175, 2004). All the other environmental parameters were analyzed by Pony Testing International Group (Beijing, China). Briefly, the concentrations of total nitrogen, total ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, chloride, sulfate were determined by “Sanitary Standard for Drinking Water Testing Method” (GB/T 5750.5–2006). Total organic carbon in water was analyzed by using thermal combustion-ion chromatography. Samples were preserved on ice in the field and transport to the laboratory immediately.
intervention. These processes include physical, chemical and biological transformations (Mulligan and Yong, 2004). Under conditions found in the majority of groundwater, biodegradation is believed to be the major process for the oxidation or reduction of contaminants. A variety of microorganisms able to utilize CS have been reported, including the polychlorinated aliphatic alkanes-dehalogenating bacterium Dehalogenimonas lykanthroporepellens (Moe et al., 2009), chlorinated alkane dehalogenating bacterium Dehalogenimonas alkenigignens (Bowman et al., 2013), dichloromethane-degrading bacterium Candidatus Dichloromethanomonas elyunquensis (Kleindienst et al., 2017), chloroanilines-dehalogenating Dehalococcoides mccartyi strain CBDB1 and Dehalobacter strain 14DCB1 (Zhang et al., 2017), chlorinated alkanerespiring bacterium Dehalogenimonas formicexedens (Key et al., 2017), etc. As only a minor proportion of environmental microorganisms can be cultivated, the majority of bacterial strains involved in CS biotransformation cannot be fully described using culture dependent methods. In addition, complete dichlorination of highly substituted CS such as TCE and TeCA to nontoxic ethylene involves a mixed consortium of organisms (Manchester et al., 2012; Mortan et al., 2017). In the past two decades, the biotransformation of CS by indigenous microbial community has been well documented. For example, in the early 1990s, a total of 104 (94 bacterial and 10 archaeal) sequence types were determined by using the restriction fragment length polymorphisms (RFLP) and clone library to investigate the microbial communities associated with an aquifer contaminated with hydrocarbons and chlorinated solvents (Dojka et al., 1998). Later, the community composition of microbial cultures for chlorinated ethene-degrading was studied by using PCR-denaturing gradient gel electrophoresis (PCRDGGE) and quantitative PCR method (Duhamel and Edwards, 2006), the results showed that Dehalococcoides populations were the dominant phylotypes. Recently, studies on the microbial communities associated with the dechlorination process have been remarkably promoted by the 16S rRNA gene amplicon sequencing approach with metagenomic sequencing (Brisson et al., 2012; David et al., 2015; Delgado et al., 2017; Kao et al., 2016; Liu et al., 2018; Simsir et al., 2017; Wright et al., 2017; Yu et al., 2016a). Most of these previous researches were based on data in the laboratory and in pilot field tests, but study in the natural environment could obtain more authentic information on the changes of microbial community composition in the contaminated environments. Wright et al. (2017) reported that dichloromethane degrading organisms, such as the Desulfosporosinus, thrived within the most heavily contaminated groundwater samples. However, to date, the available information about the microbial community involved in CS biotransformation in the in situ environment are still not understood in detail. Additionally, relatively little is known regarding the effect of CS contamination and physio-chemical parameters on the microbial communities at field sites undergoing natural attenuation. In the present study, CS contaminated groundwater showing natural attenuation was selected. The objectives of our study were 1) to evaluate the microbial community response to CS contamination; 2) to explore the native structure of the bacterial and archaeal communities involved in the CS biotransformation process; and 3) to better understand the relationship between the microbial community and environmental parameters, and their correlation with groundwater contamination.
2.2. DNA extraction and DNA sequencing Approximately 1.0 L of water samples were filtered through sterile 0.22 μm cellulose filters (Millipore, Billerica, MA, USA). The membranes were then sheared and total DNA was extracted by using the FastDNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, USA). Fusion primers consisting of an adapter, a 6 bp barcode, and the templatespecific sequence were used to amplify the 16S rRNA gene of bacteria and archaea. Specifically, bacterial 16S rRNA genes were amplified using the primers 338F (5′-GGACTACHVGGGTWTCTAAT-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Dennis et al., 2013) targeting the V3–V4 high variable region of the 16S rRNA gene. The V4 region of the archaeal 16S rRNA genes were amplified using the primers U519F ( 5′-GTGCCAGCMGCCGCGG-3′) and 806R (5′-CCGTCAATTCMTTTRAGTTT-3′) (Shehab et al., 2013). The 50 μL PCR reaction mixture consisted of 5 μL of 10 × PCR buffer, 4 μL of dNTPs, 1.5 μL of 10 μM each primer, 0.5 μL of Taq DNA polymerase (TaKaRa Biotech, Beijing, China) and 20–30 ng of the DNA template. The PCR reaction was processed in a thermal cycler (Bio-Rad Laboratories, Hercules, CA) using the following protocol: 94 °C for 5 min, then 30 cycles of 20 s at 94 °C, 25 s at 57 °C, 45 s at 72 °C, and a final extension of 10 min at 72 °C. The PCR products were visualized on a 1.8–2.0% agarose gel. The PCR amplicon bands were excised from the gels and then purified with E. Z.N.A.™ Gel Extraction Kit (Omega Bio-tek, Norcross, GA, USA). Concentration of purified PCR products were determined using the Qubit hs-DS-DNA kit (Invitrogen, Carlsbad, CA, USA) on a Tecan Infinite F200 Pro plate reader, and then pooled in equimolar concentration. The highthroughput sequencing was performed on Illumina MiSeq instrument by Beijing Fixgene Technoly Co., Ltd (Beijing, China). The sequences have been deposited at the NCBI Sequence Read Archive under the project PRJNA508863. 2.3. Bioinformatics and statistical analyses The reads from the original DNA fragments were merged using FLASH (V1.2.7, http://ccb.jhu.edu/software/FLASH/), and then processed with Mothur 1.36.1 (Schloss et al., 2009) using a MiSeq standard operating procedure (SOP) (Kozich et al., 2013). Briefly, sequences shorter than 200 bp and those with a homopolymer longer than 8 bp were removed from the dataset, then trimmed sequences were aligned (align.seqs) with the SILVA database (silva.seed_v123) (Quast et al., 2013). Followed by Mothur,s pipeline procedure including filter. seqs, unique. seqs, and pre. cluster, chimeras were removed using the UCHIME algorithm with command “chimera.uchime” (Edgar et al., 2011). Sequences were then taxonomically classified using the Naive Bayesian Classifier with 80% confidence threshold by using (classify.seqs) command (Wang et al., 2007). Sequences that were classified as Chloroplasts, Mitochondria, Eukaryota, or unknown were eliminated using (remove.lineage) command. For the bacterial sequence set, sequences classified as archaeal were also removed, conversely, bacterial
2. Materials and methods 2.1. Sample collection The contaminated site was located at an abandoned chemical factory in the east of Beijing, China. During its operation, various CS were imported as raw materials or produced as final products, resulting in the site's groundwater pollution. Groundwater samples were taken using a PTFE bailer (Cole-Parmer, Chicago, 1 L, USA) from three wells contaminated with CS, three water samples were taken from each well 2
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Table 1 Diversity indices from the three groundwater water samples. The Sobs, Chao1, Ace, Shannon and Invsimpson parameters are presented for a dissimilarity of 3. Samples
BJ2 BJ12 BJ20
Bacterial OTUs (Resample 5769 sequences)
Archaeal OTUs (Resample 13,151 sequences)
Sobs
Chao1
Ace
Shannon
Invsimpson
Sobs
Chao1
Ace
Shannon
Invsimpson
1762 1331 1067
7344 6127 4583
15,058 13,458 9894
5.99 5.05 4.22
97.66 24.99 12.95
1844 1068 1527
5029 2932 4239
8718 5344 7909
5.28 3.66 4.63
44.38 13.17 29.49
obtained for bacterial and archaeal 16S rRNA gene sets with 221,596 and 394,774 high quality sequences remaining after quality control for analysis. Briefly, 5,768 to 40,255 bacterial and 14,464 to 49,100 archaeal 16S rRNA gene sequence reads per sample were obtained. After random resampling, 5,769 and 13,151 sequences per sample remained for bacteria and archaea, respectively. The coverage index (greater than 76% and 92% for bacterial and archaeal, respectively) indicated that all of the communities were well captured at the sequencing depth. Bacterial and archaeal community richness (Observed species richness: Sobs, Chao1 and Ace) and diversity (Shannon and Invsimpson) index values were compared for the three wells (Table 1). According to richness indexes (Sobs, Chao1 and Ace), bacterial richness from the highest to lowest was BJ2 > BJ12 > BJ20, and the diversity indexes (Shannon and Invsimpson) follow by the same trend. The trend of both richness and diversity indexes was not in accordance with well CS concentrations, which were from the lowest to highest BJ2 < BJ20 < BJ12. Archaeal diversity was lower than the bacterial one, with the highest diversity found in BJ2. However, we observed archaeal richness increased (Sobs, Chao1 and Ace indexes) as CS concentrations decreased, and this trend was also supported by Shannon diversity index. Overall, the diversity indices showed that both the bacterial and archaeal communities in wells with lower CS concentrations were significantly more diverse than those with higher concentrations. Based on Pearson correlation coefficient between environment parameters and αdiversity for the bacterial community (Table S4), the results showed that DO and ORP were positively correlated with all richness and diversity indexes, while methane was negatively correlated with all richness and diversity indexes. No significant associations were found between the other environment parameters and α-diversity indices. Compared with bacterial community, parameters like DO, ORP and methane were found to be significantly positively correlated with all archaeal richness and diversity indexes, while EC, AN, NN, chloride, sulfate and TN, ethylene, Mn, TOC and CS were negatively correlated with all richness and diversity indices (Table S5), a significant positive linear correlation was also found between these ten environment parameters.
sequences were removed for the archaeal sequence set. After removal of contaminant sequences, VSEARCH was used to cluster sequences into operational taxonomic units (OTUs) with 97% similarity (Rognes et al., 2016). The number of reads per sample was normalized by using (sub.sample) command. The alpha diversity (α-diversity) and beta diversity analyses were based on the normalized data. The number of sequences of each OTU was determined using (make.shared) command, and the taxonomy for each OTU was specified using (classify.otu) command. Rarefaction curves and community richness and diversity indices were performed by using (rarefaction.single) command and (summary.shared) command, respectively. Principal coordinates analysis (PCoA) was performed with the R package using a Bray-Curtis or Jaccard dissimilarity matrix to visualize dissimilarities in bacterial and archaeal community structures. To identify which prokaryotic taxa contributed to the Bray-Curtis distance, SIMPER method (Clarke, 1993) was applied to identifies the OTUs contributing to similarity within and dissimilarity between groups and rank their contribution. Pearson's correlation analysis was employed to determine the correlation between the microbial community with the environmental factors using the SPSS statistical software (IBM, Armonk, New York, USA), the level of statistical significance was set at p < 0.05. The function annotation for the 16S rRNA gene was performed by Functional Annotation of Prokaryotic Taxa (FAPROTAX) (Louca et al., 2016). To determine the correlations between microbial communities and environmental variables, Mantel, partial Mantel tests, and canonical correspondence analysis (CCA) were used. Based on the variance inflation factors (VIF) values, redundant variables with VIF > 20 were removed from the CCA model. 3. Results 3.1. Physio-chemical parameters of multiple wells As shown in Table S1, total CS concentration spanned three orders of magnitude (0.02–16.15 mg/L) with the highest levels detected in BJ12. No distinct differences in measured water chemical parameters were observed between groundwater samples collected in the wells BJ2 and BJ20. However, significant differences were found between BJ12 and the other two wells. There were 4, 11 and 8 different organic chlorinated compounds in BJ2, BJ12 and BJ20, respectively (Table S2), with 1,2-dichlorobenzene the dominant pollutant in BJ2, and vinyl chloride in wells BJ12 and BJ20. A small amount of ethylene was observed in two wells (BJ2 and BJ20), while a higher level (42.73 mg/L) was detected in BJ12. The pH of the samples ranged from 6.43 to 7.22, the lowest pH value was in BJ2. Except for pH, all of the detected physio-chemical parameters in the other two wells (BJ2 and BJ20) were significantly lower than that of BJ12. Pearson correlation analysis was used to analyze the relationship between physio-chemical factors and the concentration of total CS. As shown in Table S3, the total CS was positively correlated to electrical conductivity, ammonia nitrogen (AN), nitrate nitrogen (NN), chloride, sulfate, total nitrogen (TN), ethylene, Fe, Mn and total organic carbon (TOC) (P < 0.01).
3.3. Characterization of groundwater microbiome Based on the Illumina platform analysis, the bacterial sequences were classified into 40 different phyla. Among these detected phyla, Bacteroidetes, Proteobacteria, Actinobacteria, Parcubacteria, Chloroflexi, Deinococcus-Thermus, Nitrospirae, Firmicutes, Saccharibacteria, Chlorobi, Planctomycetes and Candidate division OP3 had relative abundance greater than 1% (Fig. 1A). As shown in Fig. 1, the dominant phyla in three wells were different, Proteobacteria sequences were predominant and represented 30.2% in the BJ2, 21.5% in the BJ12 and 23.2% in the BJ20, respectively. While Parcubacteria and Bacteroidetes were predominant in BJ2 and BJ20, represented 23.4% and 57.4%, respectively. The most abundant bacterial genera in all samples were Flavobacterium, unclassified Parcubacteria taxa, Mycobacterium and Paludibacter (Fig. 1B). In BJ2 Flavobacterium was predominant 5.80%, followed by unclassified Parcubacteria (5.66%) and Mycobacterium (5.39%). Unclassified Parcubacteria taxa (23.40%) and Mycobacterium (16.78%) were most abundant genera in BJ12, and both were far higher than other genera in this
3.2. Diversities of bacterial and archaeal communities A total of 509,099 and 760,987 raw sequences, respectively, were 3
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Fig. 1. Taxonomic classification of bacterial community at the phylum (A) and genus (B). To simplify the figure, only taxonomic groups in the phylum and genus levels with > 1.0% and > 2.0% sequence abundance in at least one sample are represented, respectively. The values shown are the means of three replicates.
and archaeal genera. The results showed that the greatest contribution was by temperature, as 12 of top 20 bacterial genera showed significant correlation with temperature, while six genera were found to have significant correlation with CS concentration (data not shown). Temperature also exhibited significant positive/negative correlation with 6 of the 10 top archaeal genera, which was much higher than other environmental factors. The FAPROTAX showed that 49 categories were classified within the samples, and the dominant functional groups were chemoheterotrophy, aerobic chemoheterotrophy, aerobic nitrite oxidation, nitrification, fermentation, ureolysis, aromatic compound degradation and hydrocarbon degradation, etc. (Fig. S1). Among these functional groups, we found the ureolysis microbial functional cluster was significantly positively correlated with the CS concentration. Principal coordinate analysis based on Bray–Curtis dissimilarity metric was applied to evaluate the overall differences in microbial community structure. The PCoA plots (Fig. 3) showed that bacterial and archaeal communities both clustered by well of sample origin. This was supported by a permutational multivariate analysis (PERMANOVA) with a p value of 0.009 and 0.004, respectively, for bacterial and
well. A similar trend was observed in BJ20, in which Flavobacterium and Paludibacter accounted for 31.05% and 20.39% of total sequences, respectively. Compared to bacterial diversity, the archaeal population was relatively lower in all wells, with only ten phyla detected in all the samples combined (Fig. 2A), Unclassified Archaea were dominant in BJ2, while Thaumarchaeota were dominant in BJ12 and BJ20, representing 36.79%, 40.59% and 55.33% of sequences, respectively. Interestingly, the abundance of unclassified Miscellaneous Crenarchaeotic Group (MCG) decreased with increasing total CS concentration. The dynamic profiles of the archaeal community at the genus level were significantly different in the three wells (Fig. 2B). For example, unclassified Archaea taxa was the most dominant group in BJ2, while its proportions in BJ12 and BJ20 were 25.39% and 9.75%, respectively; Thaumarchaeota Group C3 was the most dominant group in BJ12, while its proportion in BJ2 and BJ20 were 6.43% and 11.78%, respectively. The relative abundance of Thaumarchaeota Group C3 was found to increase as CS concentration increased. Pearson correlation analysis was used to examine the relationships between measured environmental parameters and the main bacterial
Fig. 2. Taxonomic classification of archaeal community at the phylum (A) and genus (B). To simplify the figure, only taxonomic groups with > 0.5% abundance at the genus level in at least one sample are represented. The values shown are the means of three replicates. 4
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Fig. 3. Principal coordinates analysis presents the spatial variations of bacterial (A) and archaeal communities (B) according to the Bray-Curtis distance.
were negatively correlated with axis 1 for both bacterial and archaeal community structure while DO and ORP had positive correlations. As indicated by VPA, the contribution of methane, DO and ORP explained 9.94%, 8.65% and 6.84%, respectively, of bacterial community changes and 10.63%, 9.49% and 8.51% of the archaeal community changes.
archaeal communities at 999 permutations. According to the PCoA analysis, the first two principal coordinates PC1 and PC2 of bacterial PCoA explained 51.40% and 25.73% of total variation. In comparison, the variation of archaeal communities was explained similar by the first and second axes, together accounting for 70.25% of the total variance.
4. Discussion
3.4. Relationships between microbial community compositions and environmental variables
Microorganisms play important roles in the self-remediation activity and their compositions that are largely influenced by the external pollutants (Liang et al., 2019). In the present study, the microbial communities of three different wells contaminated with organic CS, with concentrations spanned three orders of magnitude, were studied by using Illumina MiSeq sequencing. Bacteroidetes, Proteobacteria, Actinobacteria and Parcubacteria were found to be the most abundant in all wells. This was in agreement with previous finding that focused on microbial communities in CS contaminated environments. For example, Proteobacteria were found to be prevalent in groundwater samples polluted by chlorinated ethenes undergoing natural attenuation (Nemecek et al., 2017). A similar dominance by Proteobacteria has also been identified at other CS contaminated groundwater sites (Matturro et al., 2018). Wright et al. (2017) reported that Proteobacteria were the most abundant phylum across groundwater samples with dichloromethane contamination ranging from 0.89 to 9,800,000 μg/L, whereas Firmicutes, Bacteroidetes and Chlamydiae varied as the second most abundant group in different samples. In microcosms established creek sediments receiving CS, the majority of bacteria were found to be the phyla Proteobacteria, Bacteroidetes and Chloroflexi (Simsir et al., 2017). Actinobacteria have also been found as a dominant bacterial phylum in PCB-contaminated environment (Yu et al., 2016a). Interestingly, sequences affiliated with Parcubacteria were found at high abundance (23.4%) in the BJ12, which had the highest contamination. Similar results were obtained by Matturro et al. (2018) at another polluted groundwater site. As shown in Fig. 1, the relative abundance of the dominant bacterial genera (total abundance > 2% in all samples) accounted for 57%, 67% and 78% of sequences in BJ2, BJ12 and BJ20, respectively. Most of these genera are well-known for their ability to degrade chlorinated organic pollutants. Flavobacterium, which usually associated with sulfur-oxidizing processes, has been suggested to coexist with dechlorination process (Long et al., 2018), as accounts for up to 31% of sequences in BJ20. The genus Mycobacterium was found to be the second most abundant group across all the samples. This genus has been reported to be able to readily cooxidize vinyl chloride (Jin et al., 2010), and has been identified in various chlorinated compounds contaminated sites (Liu and Mattes, 2016; Nemecek et al., 2017). Sphingobium fuliginis HC3, has the ability to completely mineralize biphenyl and PCBs without dead-end
Because of the strong collinearity between environmental variables, the partial Mantel test based on Bray–Curtis distance was carried out to analyze the relationship between the microbial community structure and environmental factors (Table 2). We found that both microbial communities were most significantly correlated with ORP (ρ = 0.80; P < 0.01), followed by temperature and methane concentration for the bacterial and archaeal communities, respectively. The CS concentration was not the important factor influencing the microbial community structure. Based on the partial Mantel test results and VIF values, four environmental factors along were selected for CCA to further identify parameters which significantly explain variance in the microbial community (Fig. 4). According to the CCA profiles, these variables significantly (P = 0.005) explained 45.62% and 43.79% of the variation of the bacterial and archaeal community structures, respectively. The temperature and methane Table 2 The partial Mantel tests demonstrate the correlations between individual environmental factors and beta-diversity. Significant correlations with |ρ| ≥ 0.5 and P ≤ 0.05 are highlighted with bold type. Environmental factors
pH EC DO Temp ORP AN NN Chloride Sulfate TN Fe Methane Ethylene Mn TOC CS
Bacterial
Archaeal
ρ
P
ρ
P
−0.3574 −0.568 0.3927 0.51 0.8045 −0.7262 −0.4104 −0.4447 −0.5546 −0.4905 −0.3793 0.2604 −0.3854 −0.5161 −0.405 −0.4339
0.969 0.992 0.027 0.001 0.004 0.997 0.967 0.979 0.996 0.981 0.958 0.04 0.967 0.992 0.975 0.98
−0.5846 −0.6726 0.6226 0.3787 0.8023 −0.5472 −0.6264 −0.6331 −0.6218 −0.6612 −0.6052 0.5532 −0.6125 −0.6354 −0.6135 −0.6395
1 0.997 0.004 0.009 0.002 0.991 0.998 0.997 0.995 0.996 0.995 0.009 0.995 0.996 0.996 0.995
5
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Fig. 4. Canonical correspondence analysis biplot of bacterial (A) and archaea (B) communities and four significant environment factors.
Ace, Invsimpson and Shannon metrics (P < 0.01), while the presence of methane was significantly negatively correlated to all diversity metrics. It is worth to note that bacterial α-diversity was not significantly correlated to CS contamination, this finding was consistent with the results reported by Wright et al. (2017), who found that bacterial αdiversity did not change significantly between DCM concentration groupings. In contrast, archaeal α-diversity decreased with increased CS concentrations and was significantly negatively correlated with raised CS concentrations (P < 0.01). However, no significant correlation was observed between archaeal α-diversity and any of the environmental factors which showed significant correlation to bacterial αdiversity. These results indicated that archaeal community α-diversity was more sensitive to CS contamination than that of bacterial community. The mechanism of contradictory correlation in bacterial and archaeal with the environmental factors remains to be demonstrated. Despite the microbial community response to chlorinated compounds contamination has been studied extensively (Matturro et al., 2018; Simsir et al., 2017; Wright et al., 2017; Xu et al., 2017), little information is available on the relationships between environmental factors and microbial community composition at sites contaminated by chlorinated compounds, especially on archaea. Here, we found that both bacterial and archaeal community composition and were strongly correlated to DO, temperature, ORP and methane (partial Mantel test, P ≤ 0.05 or 0.01). ORP was the most important environmental factor to influence the microbial community composition, a possible explanation for this result is that most of biochemical processes must occur within a specific redox range, especially for anaerobic microorganisms. DO was also an important factor affecting the investigated microbial communities and its influence on microbial community composition in various ecosystems has been noted by others (Andrei et al., 2015; Kalwasinska et al., 2018). These studies have shown that different DO conditions have a direct impact on community composition. Additionally, temperature had a very significant impact on both bacterial and archaeal community structure. A number of studies have shown that the microbial community structure was closely related to the environmental temperature (Liu et al., 2017; Wang et al., 2012). We found that microbial community structure was also significantly correlated with methane concentration (bacterial, P = 0.04; archaeal, P = 0.009). It is likely that the presence of methane could have strong influence on the microbial functional groups such as methane-oxidizing bacteria or archaea (Brazelton et al., 2006). This was further confirmed by the Pearson's correlation coefficients of methane concentration and the main bacteria and archaeal genera, indicating many abundant genera were significant correlation with methane. Previous studies have reported that methane concentrations and ORP were significant correlations (P < 0.01) with dechlorination potential (Lee et al., 2016), and DO was also an important factor affects dechlorinating activity as many anaerobic microorganism were sensitive to oxygen (Atashgahi et al.,
intermediates (Hu et al., 2015). Interestingly, Parcubacteria was the most abundant in BJ12, a similar phenomenon was also observed by Lhotsky et al. (2017) and Matturro et al. (2018) in CS polluted groundwater or wells. Although Parcubacteria has frequently been identified in a broad range of environments polluted with chlorinated compounds, the role of this group in biodegradation of those compounds is still unclear. It is worth noting that Paludibacter, which is a Gram-negative, strictly anaerobic, and chemoorganotrophic genus, was most abundance in BJ20, however, no strains from this genus have been reported to possess dechlorinating abilities. Unclassified Chloroflexi taxa, unclassified Anaerolineaceae taxa, unclassified SAR202 clade and unclassified Dehalococcoidia taxa and unclassified Candidate division OP3 all belonging to the phylum Chloroflexi which is associated with reductive dichlorination, were also detected in all samples. Unclassified Dehalococcoidia taxa and unclassified SAR202 clade taxa from class Dehalococcoidia were present at very low abundance (0.18%–1.72% and 0.29%–1.24%, respectively). Although Dehalococcoides is well known for its complete anaerobic dechlorination of chlorinated ethylenes into ethene, none were found in our study, even though ethylene was detected in all contaminated wells, especially in BJ12 (42.42 mg/L), which indicates the occurrence of complete dechlorination of PCE/TCE. These results indicate that non-Dehalococcoides may play an important role in the microbial dechlorination process, a similar explanation was also made by Yu et al. (2016b) and Patil et al. (2014) in a microbial electrochemical system. Compared to bacterial taxa, few archaeal phyla were detected in these samples, with the majority of sequences belonging to the Thaumarchaeota, followed by unclassified Archaeal and Woesearchaeota. This result was different from the studies by Simsir et al. (2017), where Euryarchaeota and the Crenarchaeota were the dominant phyla. At the genus level, Thaumarchaeota Group C3 abundance increased from 6.43% in BJ2 to 32.67% in BJ12 as CS concentrations increased. Conversely, the relative abundance of ammonia oxidizers MCG decreased as CS concentrations increased. These results indicated that the involvement of Thaumarchaeota Group C3 in the biodegradation of CS, while MCG was sensitive to CS pollution. An unclassified Archaeal genus was the most abundant in BJ2, and accounted for 25.39% and 9.75% in BJ12 and BJ20, respectively. Pearson correlation analysis showed significant correlations between this unclassified Archaeal genus and DO, ORP and methane, indicating it plays an important role in methane oxidation or production. Miscellaneous Euryarchaeotic Group (MEG) has been reported to possess highest activity during pentachlorophenol reductive dichlorination (Xu et al., 2017), which was similar to our results that high percentage of MEG were present in the BJ12 (25.0%). Pearson correlation coefficients for environmental factors and αdiversity were calculated in this study. For the bacterial community, DO and ORP methane all showed significant positive correlation with Sobs, 6
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2013), so changing these environment factors might have great potential for improving remediation efficiency of CS. Interesting, although the CS concentrations vary widely in the three wells, it had no significant relationship to the changes of the microbial community structure, indicating that microbial community composition could withstand high concentration of CS contamination.
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5. Conclusion The microbial community (bacterial and archaeal) composition of three different CS-contaminated groundwater wells were analyzed by using high-throughput 16S rRNA gene analysis. The most abundant bacterial phyla in three wells were Bacteroidetes, Proteobacteria, Actinobacteria and Parcubacteria, while Thaumarchaeota, Unclassified Archaeal and Woesearchaeota were the most abundant archaeal phyla. Some genera may be involved in microbial oxidation or reduction of CS were also identified (i.e. Mycobacterium, Flavobacterium, Miscellaneous Euryarchaeotic Group and Miscellaneous Crenarchaeotic Group). In addition, α-diversity of archaeal community was more sensitive to CS than that of bacterial community. The partial Mantel test results showed that both the bacterial and archaeal communities variance correlated strongly with the same four environment factors (ORP, DO, temperature and methane concentration), while the CS pollution has no influence on the on the microbial community composition. These results could be useful to understand the role of microbial community structure in the bioremediation of CS-contaminated groundwater. More attention should be paid to archaeal community that may contribute to biotransformation of CS. Funding This study was financially supported by the National Natural Science Foundation of China (Nos. 41977122, 21577007, 31500083 and 31601642), the National Key Research and Development Program of China (No. 2017YFD0800102), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23010400). Author statement D.C.J., F.S.Z., Y.F.X., and X.M.D. planned and designed the research. X.K., Y.S., Y.X.L., and R.Y.Z. performed experiments and analyzed data. D.C.J. wrote the main manuscript text and all the authors contributed to the revision of the manuscript and approved the version to be published. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relavtionships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envres.2020.109457. References Andrei, A.S., Robeson, M.S., Baricz, A., Coman, C., Muntean, V., Ionescu, A., Etiope, G., Alexe, M., Sicora, C.I., Podar, M., Banciu, H.L., 2015. Contrasting taxonomic stratification of microbial communities in two hypersaline meromictic lakes. ISME J. 9, 2642–2656. Atashgahi, S., Maphosa, F., Dogan, E., Smidt, H., Springael, D., Dejonghe, W., 2013. Small-scale oxygen distribution determines the vinyl chloride biodegradation pathway in surficial sediments of riverbed hyporheic zones. FEMS Microbiol. Ecol. 84, 133–142. Bhatt, P., Kumar, M.S., Mudliar, S., Chakrabarti, T., 2007. Biodegradation of chlorinated
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