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Article

Structure and Assembly Mechanism of Archaeal Communities in Deep Soil Contaminated by Chlorinated Hydrocarbons

by
Yanling Fan
1,2,
Zengjun Liu
2,
Hefeng Xu
3 and
Hongqi Wang
1,*
1
College of Water Sciences, Beijing Normal University, Beijing 100875, China
2
Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, China
3
Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing 100085, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11511; https://doi.org/10.3390/su151511511
Submission received: 26 May 2023 / Revised: 12 July 2023 / Accepted: 21 July 2023 / Published: 25 July 2023
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Abstract

:
Chlorinated hydrocarbons are typical organic pollutants in contaminated sites, and microbial remediation technology has attracted more and more attention. To study the structural characteristics and assembly mechanism of the archaeal community in chlorinated hydrocarbon-contaminated soil, unsaturated-zone soil within 2~10 m was collected. Based on high-throughput sequencing technology, the archaeal community was analyzed, and the main drivers, environmental influencing factors, and assembly mechanisms were revealed. The results showed that chlorinated hydrocarbon pollution altered archaeal community structure. The archaeal community composition was significantly correlated with trichloroethylene (r = 0.49, p = 0.001), chloroform (r = 0.60, p = 0.001), pH (r = 0.27, p = 0.036), sulfate (r = 0.21, p = 0.032), and total carbon (r = 0.23, p = 0.041). Under pollution stress, the relative abundance of Thermoplasmatota increased to 25.61%. Deterministic processes increased in the heavily polluted soil, resulting in reduced species richness, while positive collaboration among surviving species increased to 100%. These results provide new insights into the organization of archaeal communities in chlorinated hydrocarbon-contaminated sites and provide a basis for remediation activities.

1. Introduction

As an important organic solvent, chlorinated hydrocarbons (CAHs) are extensively used in various industrial processes and improper disposal methods [1]. They can cause occupational trichloroethylene drug rash dermatitis, liver and kidney tumors, and cardiovascular system damage, posing a serious threat to the health of living organisms [2,3]. CAHs significantly reduced the survival percentage of rice seedlings, resulting in a decrease in grain yield [4]. High density, low interfacial tension, and viscosity enable them to rapidly penetrate the subsurface, contaminating unsaturated soils and creating persistent sources of pollution [5]. As a result, in sites with a lengthy history of production, the depth of chlorinated hydrocarbon contamination is often greater [6,7]. However, most microbial studies focused on the topsoil layer and ignored the deep soil where chlorinated hydrocarbons migrate and diffuse [8,9]. Therefore, little is known about the responses of soil microbial communities across the soil profile to chlorinated hydrocarbon pollution.
Although most environments are dominated by bacteria [10], archaea are more dominant under extreme conditions [11]. In an aquifer system contaminated with chlorinated aliphatic compounds, archaea were included in the top three microbial phyla of all samples [12]. The gene types and metabolic characteristics of archaea are different from those of bacteria [13,14]. As an important component of soil microbial communities, archaea play a regulatory role in many ecological processes [15]. It was reported that a variety of combinations of bacteria and archaeal species can effectively remove 1,1-DCE [16]. However, we know very little about archaeal changes in response to chlorinated hydrocarbon pollution.
The structure and function of microbial communities are shaped by interactions between microorganisms [17]. In the past, candidate species for bioremediation were selected based on significant differences in abundance between polluted and unpolluted environments [8,11,18]. However, in attempting to use these bacterial strains for remediation, the desired results are often not obtained. This is because remediation functions are not solely carried out by individual species but rather by a community of interacting organisms that collectively contribute to the functions. Zhuang et al. reported that soil microbial network complexity increased with petroleum pollution levels [8]. Liu et al. reported that the species composition of each ecological cluster was distinct and that the importance of environmental factors varied greatly from module to module under arsenic pollution [18].
So, what kind of rules drive the aggregation of species in a community? The generation of soil microbial community diversity and functionality, referred to as the community assembly process, reflects the spatiotemporal processes that determine community composition [19,20]. Microbial assembly is determined by the combined effects of deterministic and stochastic processes [21]. Niche theory assumes that community structure formation is a deterministic process controlled by species characteristics, interspecific interactions, and environmental conditions [22,23]. Neutral theory assumes that all species or individuals are functionally equivalent in ecological function and that species dynamics are shaped by random processes such as birth, death, migration, species formation, and diffusion limitation [24]. Understanding the microbial community assembly mechanism is important for maintaining the stability of soil functions.
This study examines and compares the structure of archaeal communities in soil profiles ranging from 2 to 10 m deep at a site contaminated with chlorinated hydrocarbons in northern China. The objective is to investigate the driving factors and assembly mechanisms that shape microbial communities, ultimately providing references for the remediation of chlorinated hydrocarbon-contaminated sites.

2. Materials and Methods

2.1. Sample Collection

Soil samples were collected from an industrial site in northern China. Since the 1990s, the site has successively built processing plants for equipment processing, solder auxiliaries, mechanical and electrical materials, electronic materials, etc. According to the results of the previous contaminated site survey, the soil was mainly polluted by CAHs. The unsaturated zone of the site is mainly composed of fill soil, loamy clay, and sandy loam. In October 2022, an area was selected in the most polluted area, according to the site investigation report. Three wells are arranged in a triangle in the area, spaced one meter apart. Soil samples were collected from each of the three boreholes at depths of 2 m, 4 m, 6 m, 8 m, and 10 m. Soil samples were collected using sterile shovels and volatile organic compounds (VOCs) soil samplers. Two samples were taken, with one transferred to a sterile centrifuge tube and transported with dry ice to the laboratory for microbial testing, while the other was separated into two portions and placed into 40 mL and wide-mouthed amber bottles, respectively. These samples were then sent to the laboratory under low-temperature conditions to test for pollutant content and soil physicochemical properties. The soil types were judged by technical personnel according to GB 50021-2001 [25].

2.2. Soil Physical and Chemical Analysis

Soil VOC content was measured using purge and trap gas chromatography/mass spectrometry. 5 g of soil were weighed in a pre-weighed 40 mL colorless sample bottle. Quickly add 10.0 mL of methanol, cover the bottle, and shake for 2 min. After settling, use a disposable Pasteur glass pipette to transfer about 1 mL of the extract into 2 mL brown glass bottles. Measure 10.0–100 μL of extraction solution, 10.0 μL of internal standard solution, and 10.0 μL of substitute standard solution with a microinjector into 5.0 mL of blank reagent water measured with an airtight injector as sample material, put it into a 40 mL sample bottle, and measure according to the instrument reference conditions specified in HJ605-2011 [26].
The redox potential was measured directly by the FJA-6 ORP Depolarization Automatic Analyzer (Nanjing Chuan-Di lnstrument & Equipment Co., Ltd., Nanjing, China) in the soil core box immediately after the soil was collected from the subsurface. Τhe percentage of soil moisture content was determined gravimetrically, by weight difference, by drying 20 g of each soil sample at 105 °C (±5) for 24 h [27]. Soil pH was determined by a pH monitor with a fresh soil-to-water ratio of 2.5:1 according to the standard HJ 962-2018 [28]. Soil organic matter (OM) was determined using the potassium dichromate method according to NY/T1 121.6-2006 [29]. Soil water-soluble sulfate was determined gravimetrically according to HJ 635-2012 [30]. Soil total carbon (TC) was determined using the dry combustion method according to ISO 10649-1995 [31]. Soil total nitrogen (TN) was determined by the modified Kjeldahl method according to HJ 717-2014 [32]. Soil total phosphorus (TP) was determined by the fusion-Mo-Sb anti-spectrophotometric method according to HJ 632-2011 [33].

2.3. DNA Extraction and Amplification, Illumina MiSeq Sequencing, and Data Analysis

Total DNA was extracted from 0.5 g of soil from each sample according to the E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, USA) instructions. DNA extraction quality was measured by 1% agarose gel electrophoresis. DNA concentration and purity were determined using the NanoDrop2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, NC, USA). For archaea, 524F (5′-TGYCAGCCGCCGCGGTAA-3′) and 958R (5′-YCCGGCGTTGAVTCCAATT-3′) were used to amplify the V4-V5 variable region of the 16S rRNA gene by an ABI GeneAmp® 9700 PCR thermocycler (ABI, Los Angeles, CA, USA). The PCR mixtures contain 5 × TransStart FastPfu buffer (4 μL), 2.5 mM dNTPs (2 μL), forward primer (5 μM) 0.8 μL, reverse primer (5 μM) 0.8 μL, TransStart FastPfu DNA Polymerase 0.4 μL, template DNA (10 ng), and finally ddH2O up to 20 μL. PCR reactions were performed in triplicate. The PCR product was extracted from a 2% agarose gel, purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions, and quantified using Quantus™ Fluorometer (Promega, Madison, WI, USA).
Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).
Quality control on the original sequencing sequence was performed using fastp [34] (https://github.com/OpenGene/fastp, version 0.20.0, accessed on 20 November 2022). Splicing was conducted using FLASH [35] (http://www.cbcb.umd.edu/software/flash, version 1.2.7, accessed on 20 November 2022). Using UPARSE software [36] (http://drive5.com/uparse/, version 7.1, accessed on 20 November 2022), perform OTU clustering on sequences based on 97% similarity and remove chimeras [36,37]. RDP classifier [38] (http://rdp.cme.msu.edu/, version 2.2, accessed on 20 November 2022) was used to annotate species classification for each sequence, compare it with the Silva 16S rRNA database (version 138), and set the alignment threshold to 70%.

2.4. Statistical Analysis

To investigate the drivers of community structure differences, hierarchical clustering analysis was conducted with hclust() from the Vegan package in R [39,40]. The analysis is based on Bray-Curtis distance, and the agglomeration method to be used is ward.D2. A dendrogram was plotted using the ggplot2 package to visualize the results of this analysis. PCoA analysis was performed through the cloud platform of Shanghai Meiji Biomedical Technology Co., Ltd., Shanghai, China (https://cloud.majorbio.com, accessed on 1 March 2023), while Adonis analysis was utilized to test for group differences.
Alpha diversity analysis was performed through the cloud platform of Shanghai Meiji Biomedical Technology Co., Ltd. (https://cloud.majorbio.com, accessed on 1 March 2023). Sobs reflects species richness, with larger values indicating greater richness; Shannoneven reflects species evenness, with higher values indicating a more even distribution of species; and Shannon is a comprehensive measure of richness and evenness, reflecting total diversity, with higher values indicating greater diversity. PD reflects phylogenetic diversity, with higher values representing greater evolutionary distances among species [41]. The Kruskal–Wallis rank sum test was utilized to assess group differences in alpha diversity indices.
Community barplot analysis, Venn diagrams, and Endemic species diagrams were performed through the cloud platform of Shanghai Meiji Biomedical Technology Co., Ltd. (https://cloud.majorbio.com, accessed on 1 March 2023).
To examine the relationship between species and environmental factors, Mantel analysis was performed with mantel_test() from the ggcor package in R [40]. To identify biomarkers that exhibit statistically significant differences between two or more groups, LEfSe analysis was performed through the cloud platform of Shanghai Meiji Biomedical Technology Co., Ltd. (https://cloud.majorbio.com, accessed on 1 March 2023), with the LDA score threshold set to 3.
To analyze the mechanisms of species interactions, co-occurrence network analysis was conducted with graph.adjacency() from the igraph package in R [40]. Correlations with a coefficient greater than 0.6 and a p-value less than 0.01 were selected to construct an igraph network. A co-occurrence network was visualized using Gephi, and the network’s topological properties were then calculated.
The iCAMP and ape packages in R were utilized to calculate the βNTI and RCbray values, enabling us to determine the relative contributions of homogeneous selection, homogenizing dispersal, undominated dispersal, dispersal limitation, and heterogeneous selection [42].

3. Result

3.1. Soil Pollution and Physicochemical Properties Analysis

The concentrations of pollutants detected in unsaturated zone soil samples are shown in Table 1. The soil samples were found to be polluted by TCE and chloroform. The standard permissible limits of TCE and chloroform in both soil and groundwater according to EPA Regional Screening Levels (RLS) [43] are shown in Table 2. The concentrations of TCE and chloroform were found to be higher in soil samples H06, H08, and H10 than in H02 and H04. The results of soil physical properties are shown in Table 3.

3.2. Driving Factors of Archaeal Community Difference

The hierarchical clustering results of the samples (Figure 1A) have divided all the samples into two groups: the low concentration area (2 m and 4 m) and the high concentration area (6 m, 8 m, and 10 m). The samples were divided into two groups based on pollutant concentration levels: the low-concentration group (D) and the high-concentration group (G). The results of the PCoA analysis are shown in Figure 1B, and there was a significant differentiation between the D and G groups. The cumulative explained variance for PC1 and PC2 was 68.32%. Permutation multivariate analysis of variance (PERMANOVA) yielded an R2 value of 0.4225 and a p-value of 0.003, indicating that the differences between the D and G groups were significantly greater than those within each group.

3.3. Effects of Pollution Stress on Archaeal Community Structure

A total of 15 samples were subjected to high-throughput sequencing, and a total of 716 OTUs were obtained, which were classified into 12 phyla, 23 families, 34 orders, 51 families, 82 genera, and 176 species.
The results of the significant difference test in the diversity index of archaeal communities between groups D and G are presented in Figure 2. In Group D, the sobs index was 296, which decreased to 77 in the high concentration group, indicating that CAH pollution stress inhibited most species (Figure 2A). The shannoneven index of Group D was notably lower than that of Group G (Figure 2B), indicating a more uniformly distributed tolerant species under CAH stress. There was no significant difference observed in the shannon index between Groups D and G (Figure 2C), suggesting no significant variation in diversity overall. However, the significant decline in the pd index in the high concentration group reveals a closer phylogenetic relationship among the tolerant species under CAH stress (Figure 2D).
The community structure of archaea at phylum level in the D and G samples is displayed in Figure 3A, with species accounting for less than 1% of the relative abundance classified as “other”. In Group D, the dominant phyla were Crenarchaeota, Thermoplasmatota, Euryarchaeota, Halobacterota, and Aenigmarchaeota, with relative abundances of 85.72%, 2.73%, 6.16%, 4.82%, and 0.02%, respectively. In Group G, the quantities of Crenarchaeota, Euryarchaeota, and Halobacterota decreased, with their relative abundances dropping to 64.20%, 2.66%, and 3.63%, respectively. On the other hand, the quantities of Thermoplasmatota and Aenigmarchaeota increased, with their relative abundances increasing to 25.61% and 1.40%, respectively.
Venn analysis was conducted at the genus level to compare archaea species under various pollution stressors (Figure 3B). The results revealed that Group D had 29 endemic species, while Group G had 5. Additionally, there were 48 shared species between groups D and G. Endemic species in Group D include norank Thermoplasmata, Methanocalculus, unclassified Halobacterota, Rice Cluster l, Candidatus Nitrosopelagicus, Methanocella, Methanospirillum, norank Nitrosopumilaceae, norank Marine Benthic Group D, and DHVEG-1, Methanomassiliicoccus et al. (Figure 3C). Endemic species in Group G include Candidatus Nitrosotalea, Methanothermobacter, norank Hydrothermarchaeales, Methanoregula, et al. (Figure 3D).
Species with significantly different abundances were identified between Group D and Group G through LEfSe analysis (Figure 4A). At the phylum level, Crenarchaeota and Micrarchaeota were enriched in Group D, whereas Thermoplasmatota was enriched in Group G. At the class level, Nitrososphaeria, Methanocellales, and Deep_Sea_Euryarchaeotic_GroupDSEG were enriched in Group D, while Thermoplasmata was enriched in group G. LDA score results (Figure 4B) showed that Thermoplasmata, Methanomassiliicoccales, and Nitrosopumilaceae had higher values in Group G, indicating their tolerance to CAH pollution.

3.4. Interaction Relationships of Archaea under Different Pollution Stress

A co-occurrence network was built for samples from the low concentration group (Figure 5A) and the high concentration group (Figure 5B). The network topological parameters are shown in Table 4. There were 84 nodes and 63 links in the co-occurrence network of the low concentration group, which decreased to 11 nodes and 11 links in the high concentration group, indicating that most archaeal species were inhibited under pollution stress. The node degree represents the number of nodes directly connected to this node. In Group G, an increase in the node degree indicates enhanced connectivity among species. The positive correlation increased from 84.13% to 100.00%, indicating a symbiotic relationship between species in Group G. Modularity refers to modular classification, with a default resolution of 1. The higher the value, the fewer modules are obtained. In group G, modularity decreased from 0.92 to 0.41, indicating the formation of more sub-modules and stronger network stability. As the number of sub-modules increases, the risk of collapse caused by a single module will decrease for the entire network.

3.5. Environmental Factors Affecting Archaeal Community

The results of the correlation analysis between Archaea species composition and environmental factors are shown in Figure 6. Mantel tests identified that TCE was positively correlated with chloroform and negatively correlated with soil pH. TCE (r = 0.49, p = 0.001) and Chloroform (r = 0.60, p = 0.001) were the dominant factors correlating with archaeal community structure. PH levels (r = 0.27, p = 0.036), sulfate (r = 0.21, p = 0.032), and TC (r = 0.23, p = 0.041) were also influencing the archaeal community composition.

3.6. Assemblage Processes of the Archaeal Community under Different Pollution Stress

Deterministic processes include Homogeneous Selection and Heterogeneous Selection, while stochastic processes include Homogenizing Dispersal, Drift, and Dispersal Limitation. The assembly mechanism of the archaeal community at different pollution levels is shown in Figure 7. In Group D, the assembly of archaeal communities is mainly dominated by stochastic processes, with a diffusion limitation of 20.0% and a drift of 80.0%. In high-concentration conditions, stochastic processes decrease to 83.3%, with a diffusion limitation of 41.6% and a drift of 41.6%. Deterministic processes account for 16.7% and are all due to Heterogeneous selection.

4. Discussion

Archaea play important roles in soil biogeochemical cycles and are often predominant in the most extreme conditions [44]. Our results show that Crenarchaeota, Thermoplasmatota, and Euryarchaeota are the dominant species in both groups D and G. Crenarchaeota, Thermoplasmatota, and Euryarchaeota have been widely reported in a variety of ecosystems, such as oceans [45], soils [46], sludge [47], and wetlands [48] et al. Many Crenarchaea are anaerobic heterotrophs, utilizing proteins and sugars, while others are sulfur (oxidation and reduction)-cycling chemolithoautotrophs [49]. Euryarchaeotes are commonly involved in sulfur, nitrogen, and iron cycling [49].
In this study, the abundance of Thermoplasmatota increased under chlorinated hydrocarbon stress (Figure 3A), which has previously been reported to be related to the degradation of microplastics [50] and aromatics [51]. LEfSe analysis shows that Thermoplasmata, Methanomassiliicoccales, and Nitrosopumilaceae were enriched in the high concentration group (Figure 4A). Thermoplasmata has been reported to encode novel copper membrane monooxygenases that perform the first and rate-limiting, step in aerobic oxidation of ammonia, methane, or other simple hydrocarbons [52]. Methanomassiliicoccales has been reported to perform methyl-dependent hydrogenotrophic methanogenesis [53]. Cheng et al. reported that Nitrosopumilaceae may play fundamental roles in obtaining energy under oligotrophic conditions and thus maintaining the stability of the cave ecosystem [54].
Hierarchical clustering and PCoA results show that chlorinated hydrocarbon pollution stress was the main driving factor of archaeal community structure. A field investigation has revealed that chlorinated aliphatic compounds can decrease microbial population richness and influence the composition and diversity of microbial communities [12]. In this study, hierarchical clustering and PCoA results show that chlorinated hydrocarbon pollution stress was the main driving factor of archaeal community structure, which is consistent with previous reports. Several studies have reported that pH is the main factor affecting archaeal diversity [55]. However, our study found that the influence of pH tends to decrease under chlorinated hydrocarbon contamination stress. Mantel test results showed that TCE and Chloroform were the main influencing factors affecting the composition of archaeal species, followed by pH, sulfate, and TC.
Under the stress of chlorinated hydrocarbons, the tolerant species that survived showed a more uniform distribution. Furthermore, based on the analysis of their phylogenetic relationships, the evolutionary distance between these species was observed to be smaller. The co-occurrence network analysis demonstrated a high level of positive interaction among the tolerant species with a strength of 100%, resulting in a smaller modularity and indicating a greater tendency to form sub-modules and improve network stability. This can be attributed to the fact that an increased number of sub-modules can minimize the risk to the entire network in the event that any individual module collapses. It was previously reported that archaea have stronger connections in alpine steppes with oligotrophic conditions and a high soil pH [56]. The result is consistent with our findings, indicating that environmental filtering leads to increased correlations between species.
Under low-concentration conditions, the assembly of archaeal communities is a completely random process, whereas under high-concentration conditions, the deterministic process increases to 16.7%. Previous studies have reported that when environmental conditions are adequate for most species to thrive, random processes dominate [57]. The neutral process increases with the increase of species richness, diversity, and generation rate [58,59,60,61]. Xun et al.’s study suggested that the diversity of microbial communities is related to the assembly process; as soil bacterial richness and functional diversity decrease, the assembly process of bacterial communities changes from a random process to a deterministic process [19]. Similarly, Lan et al. found that the assembly of soil bacterial communities in forest ecosystems is mainly driven by random processes. However, as environmental conditions become more severe, deterministic processes become dominant, and dominant bacterial species occupy the main positions [62].

5. Conclusions

In this study, CAHs altered archaeal community structure and assembly mechanisms in deep soil. CAH stress decreases the richness and phylogenetic diversity of archaea. Under CAH stress, Thermoplasmatota was enriched, while Crenarchaeota and Micrarchaeota were inhibited. TCE and chloroform play a major role in altering the structure of archaea, along with pH, sulfate, and TC. In high-pollution soil, deterministic processes increase from 0 to 16.7%, and the positive correlation between surviving species increases from 84.13% to 100.00%.
This study explored the archaeal community’s response to CAHs at contaminated sites. In order to understand the response more comprehensively, rich species and rare species should be further distinguished, and more analytical methods should be included.

Author Contributions

Y.F.: formal analysis, investigation, data curation, and writing—original draft preparation and revision. Z.L.: resources and funding acquisition. H.X.: sample collection, figure drawing, and reference citation sorting. H.W.: supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Foundation Project of the Beijing Municipal Research Institute of Eco-Environmental Protection, grant number Y2020-003.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request: contact Yanling Fan (flylinger@163.com).

Acknowledgments

The authors extend their appreciation to the Foundation Project of the Beijing Municipal Research Institute of Eco-Environmental Protection (Y2020-003).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

CAHs, chlorinated hydrocarbons; 1,1-DCE, 1,1-Dichloroethene; TCE, trichloroethylene; VOCs, volatile organic compounds; ORP, Oxido Reduction Potential; OM, organic matter; TC, total carbon; TN, total nitrogen; TP, total phosphorus; TCE, trichloroethylene; USA, United States of America; GA, Georgia; CA, California; Co., Ltd., ABI, Applied Biosystems; Company Limited; PCR, Polymerase Chain Reaction; OTU, operational taxonomic units; PD, phylogenetic diversity; LEfSe, Linear discriminant analysis Effect Size; LDA, Linear discriminant analysis; βNTI, Beta Nearest Taxon Index; RLS, Regional Screening Levels.

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Figure 1. Driving factors analysis of archaeal communities in deep soil contaminated by chlorinated hydrocarbons. (A): Hierarchical clustering analysis to study the similarity or difference in community structure of different samples from different depths; the Tree diagram shows clustering based on Bray–Curtis distances. Samples H02, H04, H06, H08, and H10 represent the soils collected at 2, 4, 6, 8, and 10 m, respectively; _1, _2, and _3 represent the 3 boreholes, respectively. (B): Principal co-ordinates analysis (PCoA) analysis comparing differences in the archaeal community composition in the low-concentration soil samples (D) and the high concentration ones (G).
Figure 1. Driving factors analysis of archaeal communities in deep soil contaminated by chlorinated hydrocarbons. (A): Hierarchical clustering analysis to study the similarity or difference in community structure of different samples from different depths; the Tree diagram shows clustering based on Bray–Curtis distances. Samples H02, H04, H06, H08, and H10 represent the soils collected at 2, 4, 6, 8, and 10 m, respectively; _1, _2, and _3 represent the 3 boreholes, respectively. (B): Principal co-ordinates analysis (PCoA) analysis comparing differences in the archaeal community composition in the low-concentration soil samples (D) and the high concentration ones (G).
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Figure 2. Archaeal diversity index under different pollution stresses. (A): Wilconxon rank-sum test for sobs index of OTU level under different pollution; (B): Wilconxon rank-sum test for Shannoneven index of OTU level under different pollution; (C): Wilconxon rank-sum test for Shannon index of OTU level under different pollution; (D): Wilconxon rank-sum test for Pd index of OTU level under different pollution; Box plots showing different diversity indexes measured under different pollution stresses; the bars represent the standard errors. D: the low-concentration soil samples; G: the high-concentration soil samples. Significance: blank, not significant; **, p < 0.05;.
Figure 2. Archaeal diversity index under different pollution stresses. (A): Wilconxon rank-sum test for sobs index of OTU level under different pollution; (B): Wilconxon rank-sum test for Shannoneven index of OTU level under different pollution; (C): Wilconxon rank-sum test for Shannon index of OTU level under different pollution; (D): Wilconxon rank-sum test for Pd index of OTU level under different pollution; Box plots showing different diversity indexes measured under different pollution stresses; the bars represent the standard errors. D: the low-concentration soil samples; G: the high-concentration soil samples. Significance: blank, not significant; **, p < 0.05;.
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Figure 3. Changes in archaea community structure under pollution stress. (A): Relative abundance of the ten most abundant archaeal phyla in soil samples at different pollution levels. Others include the archaeal phyla with the largest relative abundance <1% in each sample; (B): Venn diagram at genus level; (C): Endemic species in Group D at genus level; (D): Endemic species in Group G at genus level. In all the legends, D represents the low concentration soil samples, and G represents the high-concentration soil samples.
Figure 3. Changes in archaea community structure under pollution stress. (A): Relative abundance of the ten most abundant archaeal phyla in soil samples at different pollution levels. Others include the archaeal phyla with the largest relative abundance <1% in each sample; (B): Venn diagram at genus level; (C): Endemic species in Group D at genus level; (D): Endemic species in Group G at genus level. In all the legends, D represents the low concentration soil samples, and G represents the high-concentration soil samples.
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Figure 4. LEfSe analysis of archaeal species under different pollution stressors (A) and the corresponding LDA values of differentially abundant species (B). The red and blue areas in cladogram represent different groups. The nodes from inside to outside represent phylum, class, order, family, and genus, respectively. Yellow indicates undifferentiated species, red nodes in the branches indicate species enriched in the low concentrations group, and blue nodes in the branches indicate species enriched in the high concentrations group.
Figure 4. LEfSe analysis of archaeal species under different pollution stressors (A) and the corresponding LDA values of differentially abundant species (B). The red and blue areas in cladogram represent different groups. The nodes from inside to outside represent phylum, class, order, family, and genus, respectively. Yellow indicates undifferentiated species, red nodes in the branches indicate species enriched in the low concentrations group, and blue nodes in the branches indicate species enriched in the high concentrations group.
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Figure 5. The co-occurrence network structure of archaeal community at OTU level. (A): low concentration group; (B): high concentration group. Nodes represent OTUs colored according to modules, and the size represents connectivity. The line between the nodes represents a significant correlation (∣r∣ > 0.6, p < 0.01), with a positive correlation in red and a negative correlation in green.
Figure 5. The co-occurrence network structure of archaeal community at OTU level. (A): low concentration group; (B): high concentration group. Nodes represent OTUs colored according to modules, and the size represents connectivity. The line between the nodes represents a significant correlation (∣r∣ > 0.6, p < 0.01), with a positive correlation in red and a negative correlation in green.
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Figure 6. Correlation analysis of environmental factors with archaeal community compositions. The color gradients and box sizes represent Spearman’s correlation coefficients, and red indicates a positive correlation and purple indicates a negative correlation. Bacterial community compositions are related to each environmental factor by Mantel tests. Line width corresponds to Mantel’s r statistic for the corresponding distance correlations, and line color indicates the statistical significance based on 999 permutations.
Figure 6. Correlation analysis of environmental factors with archaeal community compositions. The color gradients and box sizes represent Spearman’s correlation coefficients, and red indicates a positive correlation and purple indicates a negative correlation. Bacterial community compositions are related to each environmental factor by Mantel tests. Line width corresponds to Mantel’s r statistic for the corresponding distance correlations, and line color indicates the statistical significance based on 999 permutations.
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Figure 7. Assembly mechanism of archaeal community at different pollution levels. The relative contribution of homogeneous selection, homogenizing dispersal, drift, dispersal limitation, and heterogeneous selection to assembly mechanism of archaeal communities at different pollution levels. D: the low concentration soil samples; G: the high-concentration soil samples.
Figure 7. Assembly mechanism of archaeal community at different pollution levels. The relative contribution of homogeneous selection, homogenizing dispersal, drift, dispersal limitation, and heterogeneous selection to assembly mechanism of archaeal communities at different pollution levels. D: the low concentration soil samples; G: the high-concentration soil samples.
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Table
Table 1. Contaminant concentrations in soil samples.
Table 1. Contaminant concentrations in soil samples.
Sample NumberTCE (mg/kg)Chloroform (mg/kg)
H021.71 ± 0.150.36 ± 0.21
H042.70 ± 1.130.79 ± 0.51
H069.56 ± 2.069.89 ± 0.49
H087.00 ± 2.405.98 ± 2.78
H108.23 ± 6.685.23 ± 3.39
Note: The number after plus or minus signs represents SD.
Table
Table 2. Standard permissible limits of TCE and Chloroform in soil/groundwater.
Table 2. Standard permissible limits of TCE and Chloroform in soil/groundwater.
Soil/Groundwater Use ScenarioTCEChloroform
Resident Soil0.94 mg/kg0.32 mg/kg
Industrial Soil6 mg/kg1.4 mg/kg
maximum contaminant levels5 μg/L80 μg/L
Table
Table 3. Physicochemical properties of soil samples.
Table 3. Physicochemical properties of soil samples.
ParameterH02H04H06H08H10
pH8.7 ± 0.18.6 ± 0.18.4 ± 0.28.6 ± 0.18.7 ± 0.3
Moisture (%)17.1 ± 1.421.1 ± 1.821.1 ± 0.515.2 ± 1.519.8 ± 2.9
ORP (mV)364.6 ± 47.4390.6 ± 52.7454.0 ± 103.5433.7 ± 92.5447.6 ± 80.7
Organic matter (g/kg)7.3 ± 3.75.2 ± 3.14.0 ± 0.72.7 ± 0.83.1 ± 0.6
Sulfate (mg/kg)447.3 ± 87.3376.0 ± 131.3513.3 ± 78.0182.0 ± 47.6154.0 ± 18.5
TC (g/kg)8.7 ± 0.18.6 ± 0.18.4 ± 0.28.6 ± 0.18.7 ± 0.3
TN (mg/kg)409.3 ± 72.3470.3 ± 186.7316.3 ± 16.4194.7 ± 67.6198.3 ± 52.6
TP (mg/kg)472.3 ± 135.0437.7 ± 163.3307.0 ± 65.4415.7 ± 23.3480.0 ± 53.0
Table
Table 4. Topological parameters of archaeal community co-occurrence network.
Table 4. Topological parameters of archaeal community co-occurrence network.
Topological ParametersDG
Nodes8411
Edges6311
Positive correlation84.13%100.00%
Degree1.52.0
Modularity0.920.41
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Fan, Y.; Liu, Z.; Xu, H.; Wang, H. Structure and Assembly Mechanism of Archaeal Communities in Deep Soil Contaminated by Chlorinated Hydrocarbons. Sustainability 2023, 15, 11511. https://doi.org/10.3390/su151511511

AMA Style

Fan Y, Liu Z, Xu H, Wang H. Structure and Assembly Mechanism of Archaeal Communities in Deep Soil Contaminated by Chlorinated Hydrocarbons. Sustainability. 2023; 15(15):11511. https://doi.org/10.3390/su151511511

Chicago/Turabian Style

Fan, Yanling, Zengjun Liu, Hefeng Xu, and Hongqi Wang. 2023. "Structure and Assembly Mechanism of Archaeal Communities in Deep Soil Contaminated by Chlorinated Hydrocarbons" Sustainability 15, no. 15: 11511. https://doi.org/10.3390/su151511511

APA Style

Fan, Y., Liu, Z., Xu, H., & Wang, H. (2023). Structure and Assembly Mechanism of Archaeal Communities in Deep Soil Contaminated by Chlorinated Hydrocarbons. Sustainability, 15(15), 11511. https://doi.org/10.3390/su151511511

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