Resilience and Assemblage of Soil Microbiome in Response toChemical Contamination Combined with Plant Growth

Shuo Jiao,a,b Weimin Chen,a Gehong Weia

aState Key Laboratory of Crop Stress Biology in Arid Areas, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, People’s Republic of ChinabCollege of Urban and Environmental Sciences, Peking University, Beijing, People’s Republic of China

ABSTRACT A lack of knowledge of the microbial responses to environmental changeat the species and functional levels hinders our ability to understand the intrinsic mech-anisms underlying the maintenance of microbial ecosystems. Here, we present resultsfrom temporal microcosms that introduced inorganic and organic contaminants intoagro-soils for 90 days, with three common legume plants. Temporal dynamics and as-semblage of soil microbial communities and functions in response to contamination un-der the influence of growth of different plants were explored via sequencing of the 16SrRNA amplicon and by shotgun metagenomics. Soil microbial alpha diversity and struc-ture at the taxonomic and functional levels exhibited resilience patterns. Functional pro-files showed greater resilience than did taxonomic ones. Different legume plants im-posed stronger selection on taxonomic profiles than on functional ones. Network andrandom forest analyses revealed that the functional potential of soil microbial communi-ties was fostered by various taxonomic groups. Betaproteobacteria were important pre-dictors of key functional traits such as amino acid metabolism, nucleic acid metabolism,and hydrocarbon degradation. Our study reveals the strong resilience of the soil micro-biome to chemical contamination and sensitive responses of taxonomic rather thanfunctional profiles to selection processes induced by different legume plants. This is piv-otal to develop approaches and policies for the protection of soil microbial diversity andfunctions in agro-ecosystems with different response strategies from global environmen-tal drivers, such as soil contamination and plant invasion.

IMPORTANCE Exploring the microbial responses to environmental disturbances is acentral issue in microbial ecology. Understanding the dynamic responses of soil mi-crobial communities to chemical contamination and the microbe-soil-plant interac-tions is essential for forecasting the long-term changes in soil ecosystems. Neverthe-less, few studies have applied multi-omics approaches to assess the microbialresponses to soil contamination and the microbe-soil-plant interactions at the taxo-nomic and functional levels simultaneously. Our study reveals clear succession andresilience patterns of soil microbial diversity and structure in response to chemicalcontamination. Different legume plants exerted stronger selection processes on tax-onomic than on functional profiles in contaminated soils, which could benefit plantgrowth and fitness as well as foster the potential abilities of hydrocarbon degrada-tion and metal tolerance. These results provide new insight into the resilience andassemblage of soil microbiome in response to environmental disturbances in agro-ecosystems at the species and functional levels.

KEYWORDS functional reassembly, metagenomics, microbial resilience, plantinvasion, response strategy, soil contamination, taxonomic levels

Agricultural ecosystems are experiencing increasing anthropogenic pressure andenvironmental perturbation, such as climate oscillation, plant invasion, and the

accumulation of pollutants, pesticides, and antibiotics in soil (1–3). In particular, long-

Citation Jiao S, Chen W, Wei G. 2019.Resilience and assemblage of soil microbiomein response to chemical contaminationcombined with plant growth. Appl EnvironMicrobiol 85:e02523-18. https://doi.org/10.1128/AEM.02523-18.

Editor Isaac Cann, University of Illinois atUrbana-Champaign

Copyright © 2019 American Society forMicrobiology. All Rights Reserved.

Address correspondence to Weimin Chen,chenwm029@nwsuaf.edu.cn, or Gehong Wei,weigehong@nwsuaf.edu.cn.

Received 15 October 2018Accepted 3 January 2019

Accepted manuscript posted online 18January 2019Published

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standing contamination is often associated with substantial changes in soil biodiversity(4, 5). In the terrestrial ecosystem, soil microorganisms play critical roles in driving theglobal biogeochemical cycles of carbon, nitrogen, and other inorganic elements (6),and they respond rapidly to environmental change caused by contamination (7, 8).Given their importance in ecosystem functioning and services, it is vital to determinethe temporal dynamics of soil microbial communities and their functions in agro-ecosystems in response to such contamination. This realm of investigation couldprovide valuable insight into the restoration of polluted ecosystems and environmentalmanagement, and yet it remains understudied.

Plant invasion is an important biotic and environmental perturbation that can altersoil nutrient cycling at the global scale (9). Plant invasion may also influence thestability of soil microbial biodiversity by introducing colonized arbuscular mycorrhizalfungi, ectomycorrhizal fungi, and soilborne pathogens (10). Plant roots provide aconsiderable amount of nutrients to surrounding environments via their release ofexudates and mucilage, which modify the local soil’s physiochemical properties, tosubsequently shape the microbial communities residing in the soil (11–14). Moreover,specific microbes are able to assemble into plant-associated communities that influ-ence terrestrial carbon and nutrient cycling, as well as host plants’ growth and health(15–17). Recently, host plant effects on the assembly of root-associated microbiomeswere documented in Arabidopsis spp. (18, 19), rice (20), and some legumes (21).Microbes have evolved genes enabling them to adapt to plant environments; forexample, more carbohydrate metabolism functions and fewer mobile elements char-acterized the plant-associated bacteria than with taxonomically related yet non-plant-associated ones (22). In addition, previous studies have demonstrated that soil con-tamination could alter the plant rhizosphere metatranscriptome, and genes related tohydrocarbon degradation were generally more expressed in contaminated soils; how-ever, the exact complements of genes induced were different between bulk andrhizosphere soils (23, 24). Distinguishing them from other plants, legumes form nodulesvia symbiosis with N2-fixing rhizobia, generating specific associations between legumesand their root-associated microbiomes (21). A recent study showed that soybean rootsimposed clear microbial selection at both the taxonomic and functional levels (25); thefunctional selection was related to the metabolism of nitrogen, iron, phosphorus, andpotassium, all of which were enriched in the rhizosphere, thus potentially promotingplant growth and nutrition (25). Investigating the joint influence of soil and plantcharacteristics on the microbial community assemblage at the functional level is helpfulfor better understanding complex microbe-soil-plant interactions in agro-ecosystems,particularly in those with chemical contamination.

In the face of shifting soil conditions, microbes exhibit remarkable stability, largelydue to their high degree of metabolic flexibility and physiological tolerance; their highabundances, widespread dispersal, and high growth rates; and their evolutionaryadaptation via horizontal gene transfer (26, 27). Generally, the stability of microbialecosystems is attributed to three mechanisms, resistance, resilience, and functionalredundancy (27). In the first case, some microorganisms display a high degree oftolerance to disturbances; for example, those bacteria with resistance to heavy-metal oroil contamination (8). When the microbial community is changed by a disturbance yetrapidly recovers to its initial or alternative stable state, that is resilience (28, 29). Whenperturbed, the ecosystem processes retain similarity to those of the original state, evenif the community is substantially altered without recovery, and this is then attributed tomicrobial functional redundancy (27). Soil functions show resilience to environmentalchange via acclimatization, as driven by three potential underlying mechanisms, asfollows: (i) generalists with broad adaptations to new conditions are functionallydominant members of the community, (ii) specialists with distinct functional traitsbecome active and predominate, and (iii) functionally dominant microbial memberswith rapid microbial adaptations are recruited under environmental disturbances (30).However, broad research interests in the microbial responses to environmental changelargely focus on the whole-soil or community level, so we still lack much knowledge

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about species-level microbial responses and how they scale up to the functional level(30, 31).

The major aim of this study was to reveal the temporal dynamics of the soilmicrobial community in an agro-ecosystem in response to chemical contamination andto determine the microbial community assemblage occurring under the influence ofdifferent legume plants. We selected phenanthrene, n-octadecane, and cadmium as thepollutants, since they are prevalent in contaminated agro-soils. Three common le-gumes, Robinia pseudoacacia (woody), Medicago sativa (herbaceous), and Vicia villosa(herbaceous), were used in this study because of their wide distribution and suitabilityfor a wide range of environments. We applied 16S rRNA gene amplicon sequencing toanalyze the community structure and used the total DNA shotgun sequencing ap-proach to assess their functional profiles. Our results demonstrated a strong resilienceof the soil microbiome to chemical contamination and sensitive responses of taxo-nomic rather than functional profiles to selection processes induced by differentlegume plants, which provide valuable information for better understanding microbialresilience and assemblage patterns to environmental change at species and moredeeply functional levels.

RESULTSGeneral characteristics of the sequencing data. In total, we obtained 21 soil

samples contaminated with phenanthrene, n-octadecane, and CdCl2, including fourtime points (0, 10, 30, and 90 days), in three legume plant treatments with 90 days ofgrowth (Table 1). Overall, the 16S rRNA gene amplicon sequencing yielded 1,045,206high-quality sequences. The average number of sequences per sample (n � 21) was49,772 (maximum, 59,291 sequences; minimum, 42,994 sequences; standard deviation[SD], 4,569 sequences). The total operational taxonomic unit (OTU) number was 4,577,defined at 97% sequence similarity. Proteobacteria (40.9%), Actinobacteria (27.7%),Acidobacteria (8.77%), and Chloroflexi (6.28%) accounted for the largest proportion ofsequences. Meanwhile, the metagenomic shotgun sequencing yielded 256.79 Gb ofdata. After the quality trimming, a total of 164,030,284 sequences were obtained. Theconstructed metagenomic libraries were dominated by bacteria (80.98%), but therewere also sequences matching to archaea (2.21%), viruses (1.22%), and eukaryotes(0.25%).

Temporal dynamics of soil microbial taxonomic and functional traits in re-sponse to contamination. During the incubation period, the alpha diversity for the

TABLE 1 Microbial alpha-diversity characteristics for the 16S rRNA gene amplicon and the metagenomic shotgun sequencing data sets ofsoil samples

Treatment

16S rRNA data set Metagenomic data set

No. of high-quality reads

Alpha-diversitymeasures

No. of high-quality reads

Alpha-diversitymeasures

Richness

Shannon-Wienerindex Richness

Shannon-Wienerindex

Avg SD Avg SD Avg SD Avg SD Avg SD Avg SD

Without plantDay 0 50,097 1,343 2,305 48 6.07 0.10 2,481,503 296,839 262,002 2,069 12.09 0.05Day 10 53,802 5,333 1,304 306 2.87 0.68 12,180,988 1,073,569 408,848 1,366 11.36 0.06Day 30 46,778 1,916 1,868 132 5.66 0.26 11,857,296 1,844,246 593,721 9,520 11.81 0.22Day 90 51,798 6,766 2,015 158 6.08 0.11 6,437,238 1,707,960 609,994 63,985 12.48 0.23

With plant at day 90Robinia 46,716 2,301 2,069 127 5.97 0.12 7,658,247 457,603 637,942 10,014 12.34 0.07Alfalfa 47,570 4,438 2,101 215 5.98 0.20 6,958,994 629,300 629,069 17,152 12.36 0.13Vetch 51,642 5,784 1,975 153 5.84 0.08 7,102,496 1,089,311 617,532 18,630 12.34 0.18

Total 1,045,206 4,577 164,030,284 1,023,953

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taxonomic and functional traits showed clear successional patterns in response to thecontamination, first decreasing and then increasing to basically the same as the day 0samples, except for richness of functional traits (Fig. 1A and B). For beta diversity, aprincipal-coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity revealed that

FIG 1 General patterns of microbial alpha and beta diversity in response to soil contamination and plantgrowth. The alpha diversity of richness and Shannon-Wiener index and the beta diversity based on Bray-Curtisdistance between the samples were estimated. (A and B) Temporal changes in alpha diversity during incubationwith organic and inorganic pollutants (phenanthrene � n-octadecane � cadmium) are shown for thetaxonomic (A) and functional (B) traits. (C and D) PCoA of beta diversity among the samples of differentincubation time points for the taxonomic (C) and functional (D) traits. (E and F) Dissimilarities of beta diversitybetween samples of day 0 and other time points for the taxonomic (E) and functional (F) traits, as estimatedby the fitted quadratic OLS models. (G and H) PCoA of beta diversity among the samples of different plantsgrown in soil for the taxonomic (G) and functional (H) traits.

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the samples for different incubation time points formed distinct clusters in the ordi-nation space (Fig. 1C and D), with significant differences found at both the taxonomic(RANOSIM � 1, P � 0.001; R2

ADONIS � 0.8994, P � 0.001) and functional (RANOSIM �

0.9506, P � 0.001; R2ADONIS � 0.8133, P � 0.001) levels. Furthermore, to explore

whether the soil microbial community recovered under disturbance from contamina-tion, we estimated the dissimilarities in the taxonomic and functional profiles betweenthe samples taken at the initial (day 0) and other time points (Fig. 1E and F). Thedissimilarities significantly decreased from the day 10 to day 90 samples, which variedmore rapidly for the functional than taxonomic profiles, as determined by the fittedquadratic ordinary least squares (OLS) models. In addition, the dissimilarities betweenthe day 0 and day 90 samples were significantly lower for the functional than taxo-nomic profiles (P � 0.001), indicating a greater recovery of the functional profiles.

For the taxonomic profiles (Fig. 2), OTUs with an average relative abundance of�0.005% at all time points were classified into two clusters. Cluster 1 included 48 OTUsfor which the abundance levels were first increased and then decreased; in contrast,cluster 2 included 884 OTUs that first decreased but then increased in abundance.Interestingly, the OTUs in both of these two clusters showed a trend of resiliency, withthe lowest difference in abundance found between the day 0 and day 90 samples. Thetaxonomic distribution analysis showed that the OTUs in cluster 1 were mainly assignedto the genera Massilia, Lysobacter, Pseudoduganella, and Bacillus, while the generaGaiella, Perlucidibaca, Sphingomonas, Nocardioides, and Aeromicrobium dominated incluster 2.

Similar to the taxonomic observations, two clusters were classified for the functionaltraits (see Fig. S1 and S2 in the supplemental material). In total, 855 Kyoto Encyclopediaof Genes and Genomes (KEGG) orthology annotations (KOs) and 16 KEGG level 2

FIG 2 Temporal dynamics of the microbial communities at the species level during incubation under soil contamination. This analysis wasperformed using the maSigPro method based on operational taxonomic units (OTUs) with an average relative abundance of �0.005%.(A) Temporal dynamic visualization of the significant OTUs was based on a cluster analysis that grouped OTUs with similar profiles andconveyed here in a heatmap. Each row in the heatmap has been standardized (to a mean of zero and a standard deviation of one), withits color intensity proportional to the standardized relative abundances of the taxa. The taxonomic distributions of the significant OTUswere estimated at the genus level for cluster 1 (B) and cluster 2 (C).

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pathways were classified into cluster 1, whereas 322 KOs and 18 KEGG level 2 pathwayswere assigned to cluster 2. In cluster 1, the functional traits were primarily associatedwith signal transduction, energy metabolism, environmental adaptation, and the im-mune system. In cluster 2, the dominant functions were related to cell motility, somehuman diseases, amino acid metabolism, and the biodegradation and metabolismxenobiotics. Furthermore, we classified the KOs to their functional ontology as providedby Functional Ontology Assignments for Metagenomes (FOAM), which were relevant toenvironmental microorganisms. Based on FOAM level 2, totals of 55 and 57 traits wererespectively grouped into clusters 1 and 2 (Fig. 3 and Table S1). The functions relatedto cellular response to stress, nucleic acid metabolism, saccharide and derivatedsynthesis, fatty acid oxidation, and the Embden-Meyerhof-Parnas (EMP) pathway weremore prevalent in cluster 1 (Fig. 3A and B). Meanwhile, the functions related totransporters, hydrolysis of polymers, carbohydrate-active enzymes, and hydrocarbondegradation were most dominant in cluster 2 (Fig. 3A and C).

Effect of plant selection processes on soil microbial taxonomic and functionaltraits. In the initial experimental design, we compared the day 10 and day 30 plantedsoils with the unplanted soils, finding no significant differences in microbial communitycomposition (Fig. S3 and Table S2; by analysis of similarity [ANOSIM] and Adonis tests,P � 0.05). Therefore, we did not include day 10 and day 30 planted soils in thesubsequent experiment and analysis. After 90 days of incubation, samples with andwithout plants formed distinct clusters in the ordination space (Fig. 1G and H), withsignificant differences in their taxonomic (RANOSIM � 0.6258, P � 0.003; R2

ADONIS �

0.2571, P � 0.002) and functional (RANOSIM � 0.85, P � 0.005; R2ADONIS � 0.2284,

P � 0.003) levels. Furthermore, the beta diversity for the taxonomic profiles was sig-

FIG 3 Temporal dynamics of the microbial communities at the functional level during incubation under soil contamination. This analysis was conducted usingthe maSigPro method based on the functional traits of FOAM level 2. (A) Temporal dynamic visualization of the significant OTUs was based on a cluster analysisthat grouped OTUs with similar profiles and conveyed here in a heatmap. Each row in the heatmap has been standardized (to a mean of zero and a standarddeviation of one), with its color intensity proportional to the standardized relative abundances of the taxa. (B and C) Functional distributions (Funs) of thesignificant traits were estimated at the FOAM level 1 for cluster 1 (B) and cluster 2 (C). Detailed information on the functional traits of FOAM level 2 is shownin Table S1.

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nificantly different among the planted samples with different legumes (RANOSIM �

0.5391, P � 0.002; R2ADONIS � 0.3825, P � 0.002), while there were no significant

differences detected among their functional profiles (RANOSIM � 0.1029, P � 0.197;R2

ADONIS � 0.2859, P � 0.098). In addition, the alpha-diversity indices for taxonomic andfunctional traits were similar between the samples with and without plants after90 days of incubation (P � 0.05).

To test whether the microbial populations were affected by the treatments, weidentified the microbial taxa (OTUs) significantly enriched in each plant treatmentrelative to the unplanted soil using linear models. A bipartite association networkvisualized the associations between the significant OTUs and different plant species(Fig. 4). In clusters 1 to 3, a total of 62, 52, and 72 OTUs were associated with robinia,alfalfa, and vetch, respectively. In clusters 4 to 6, we found 46 OTUs simultaneouslyassociated with two plant species. In cluster 7, there were 21 OTUs associated with

FIG 4 The bipartite association network depicting the associations between the significantly enriched OTUs and the different plant speciestreatments (estimated via linear statistics in the “limma” package for R). Node sizes represent the relative abundance levels of the OTUs.Edges represent the association patterns of individual OTUs with different plant treatments. Circle-shaped nodes represent those OTUsonly associated with one treatment. Diamond-shaped nodes represent the OTUs associated with two treatments. Triangle-shaped nodesrepresent the cross-combination OTUs associated with all three treatments. The number of OTUs and relative abundance levels areprovided for each cluster (1 to 6), as are the taxonomic distributions of the dominant OTUs per cluster.

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three plant species. These results indicated a distinctly separate effect by differentplants on the microbial communities, confirming the significant difference for betadiversity in the taxonomic profiles. Furthermore, Sphingopyxis, Phaselicystis, and Me-sorhizobium were enriched in soil planted with robinia; Lysobacter, Flavisolibacter,Hydrogenophaga, and Bacillus dominated in soil planted with alfalfa; and Aeromicro-bium, Marmoricola, Nitratireductor, and Rhizobium were more predominant in soil withvetch. The OTUs belonging to Pseudoxanthomonas, Devosia, and Chryseolinea wereenriched by all the plant treatments.

Some significant differences between the samples with and without plants wereobserved for the functional traits of FOAM level 1 (Fig. 5). The functions related tosaccharide and derivative synthesis and the EMP pathway were more prevalent in theplanted soils, while it was amino acid utilization biosynthesis metabolism, hydrocarbondegradation, and methanogenesis that were significantly overrepresented in the un-planted soils. A more detailed investigation was pursued for the functional traits basedon FOAM level 2 (Fig. S4). Here, we found that functions associated with the responseto nitrosative stress, the cellular response to oxidative stress, and pyrimidine and purinemetabolisms were enriched in the planted soils, while phenylalanine, tyrosine andtryptophan biosynthesis, and glycosyltransferase functions predominated in the un-planted soils.

Potential associations between soil microbial taxonomic and functional traits.To disentangle the potential associations between the microbial taxa and severalimportant functional traits, we applied the network analysis based on the SpiecEasimethod. At FOAM level 1, the network consisted of 335 nodes and 492 edges (Fig. S5).Most of these nodes belonged to Proteobacteria (n � 109), Actinobacteria (n � 94), andAcidobacteria (n � 39). The maximum number of edges (n � 42) occurred betweenProteobacteria and Acidobacteria, and only four edges connected Actinobacteria toAcidobacteria. Nodes belonging to Proteobacteria were mostly connected with otherphyla (138 edges), and the within-phylum associations were maximal for Actinobacteria,with 99 edges, followed by Proteobacteria, with 89 edges.

To explore the associations between microbial taxonomic and functional traits, wegenerated a subnetwork that only consisted of the connections involving functional

FIG 5 Effects of plant growth on the soil microbial community functional profile. (A) Relative abundance of the functional traits (FOAM database level 1)between samples with and without plants growing in the soil. The error bars show standard errors. Blue asterisks (*) indicate those categories significantly moreabundant in samples with plant growth (P � 0.05, Wilcoxon rank-sum test) and orange asterisks (*) indicate the categories that were significantly moreabundant in samples without plant growth. (B) PCoA plots of the functional genes classified into particular categories of FOAM database level 1. Similarity valuesbetween the samples with and without plant growth were examined via the ANOSIM test, which are shown in each plot. Only significant categories aredisplayed. The dashed ellipses in blue and orange represent the clustering of samples with and without plant growth, respectively.

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traits (Fig. S6). Overall, 45 nodes (this included 24 OTUs and 21 functional traits) and 52edges were selected for this subnetwork. There were 28 edges between the taxonomicand functional traits. For example, positive correlations were found for Limnobacter andBlastocatella and the tricarboxylic acid (TCA) cycle, Nocardioides and the EMP pathway,Solirubrobacterales and transporters; and Phenylobacterium and fatty acid oxidation. Incontrast, Massilia was negatively correlated with the utilization of sugar and thiosulfatemetabolism.

To further investigate the environmentally associated functions, we selected afunctional subset (FOAM level 2) of cellular response to stress, hydrocarbon degrada-tion, transporters, and the nitrogen cycle and examined their associations with themicrobial taxa in soils (Fig. 6). Some interesting results were obtained. For example,Massilia was positively correlated with the major facilitator superfamily; Gaiellales waspositively correlated with the ABC transporters; Gemmatimonadaceae was positivelycorrelated with polycyclic aromatic hydrocarbon degradation; Gaiella and Acidobacteriawere positively correlated with the regulation of translational initiation in response toosmotic stress; and Actinoplanes, Brevundimonas, Sphingomonadales, and Acidobacteriawere all positively correlated with cellular response to cation stress.

Furthermore, we applied the random forest (RF) analysis to identify the mainmicrobial predictors of the functional traits in FOAM level 1 (Fig. S7 to S10). Differenttaxonomic phyla contributed to the important variable for distinct functional traits. TheBetaproteobacteria were the most important variable for predicting key functional traits,including utilization of sugar (P � 0.05), amino acid utilization biosynthesis metabolism(P � 0.01), nucleic acid metabolism (P � 0.05), hydrocarbon degradation (P � 0.01),

FIG 6 Disentangling the potential associations between the soil microbial taxa and functional traits via a network analysis. This analysis was based on theSpiecEasi method. Functional traits were selected as a functional subset (FOAM level 2) of the cellular response to stress, hydrocarbon degradation, transporters,and nitrogen cycle. Microbial taxa were the OTUs with a relative abundance of �0.05%. Yellow nodes represent functional traits; other nodes were coloredaccording to phylum. The size of each node is proportional to relative abundance. The taxonomic distributions of these OTUs are also given. BTEX, benzene,toluene, ethylbenzene, and xylene.

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carbohydrate-active enzymes (P � 0.01), transporters (P � 0.01), and cellular responseto stress (P � 0.05). Other important variables for predicting key functional traits werethe Parcubacteria for fermentation (P � 0.01); the Bacteroidetes for the superpathway ofthiosulfate metabolism, the EMP pathway, and gluconeogenesis (P � 0.01); the Chlo-roflexi for homoacetogenesis, fatty acid oxidation, the nitrogen cycle, and hydrogenmetabolism (P � 0.01); the Actinobacteria for the TCA cycle (P � 0.01); the Thermomi-crobia for sulfur compound metabolism and the hydrolysis of polymers (P � 0.05); andthe Armatimonadetes for saccharide and derivative synthesis (P � 0.01).

DISCUSSION

Elucidating the mechanisms of microbial succession and their resilience in responseto environmental disturbances, including contamination from chemical pollutants, isone of the central issues in microbial ecology (8, 32). It is challenging to robustlycharacterize how microbial temporal dynamics are affected by contamination at thespecies and more deeply functional levels (30, 31). Taxonomic and functional profilingin the present study was used to evaluate the differences in the structure andfunctional potential of the soil microbial community in response to disturbances.Herein, our results revealed distinct patterns of resilience in microbial diversity andspecific traits at both the taxonomic and functional levels in response to inorganic andorganic soil pollutants. Additionally, plants imposed stronger selection processes on thetaxonomic profiles than on the functional profiles of the soil microbial community.

Microbes are able to respond rapidly to local environmental change, and pollutantsoften have a great influence on microbial community structure (33, 34). In temporalmicrocosms, microbial diversity significantly decreased throughout soil enrichmentsubcultures treated with pollutants (7, 8, 35). In the present study, soil microbial alphadiversity showed a clear succession pattern in response to contamination, whichdecreased at first and then recovered. This suggests the importance of resilience of soilmicrobiomes for the stability of microbial ecosystems. The rapid increase in functionalrichness in the early stage of contamination revealed the high influx of new functionaltraits, which may be attributed to the rapid evolutionary adaptation of soil microbes toenvironmental disturbances (27). The significant decrease in dissimilarities in themicrobial community between samples of the initial and later stages also suggested theresilience of soil microbiomes while contaminated. In particular, the dissimilaritiesbetween the day 0 and day 90 samples were significantly lower for the functional thanfor taxonomic profiles, and the former varied more rapidly than the latter. This resultsuggested that functional traits exhibited more resilience than taxonomic ones. Mi-crobes have high growth rates and undergo rapid evolutionary adaptation via hori-zontal gene transfer; therefore, their functioning could have shifted while they them-selves did not change under environmental disturbances (26, 27). The resilience of soilfunction is attributable to acclimatization, and functional microbial members capable ofrapid adaptation could quickly become abundant under environmental disturbances(30). Previous studies have demonstrated that the reshaping and constraining of soilfunctional responses to altered environments were driven by microbial communitycomposition and their influence on the rates of ecosystem processes (36, 37).

Although the microbial alpha diversity apparently recovered in contaminated soils,substantial shifts in taxonomic and functional structure still occurred; this indicates thatthe recovery of soil microbial alpha diversity to a stable state was largely influenced bythe pollutants we applied. The resilience of a community may also be defined andviewed as the recovery process following a disturbance leading to an alternative stablestate (28, 29). Most of the traits were classified into two clusters, both of whichexhibited a trend of resiliency, with the lowest difference in abundance levels foundbetween the day 0 and day 90 samples. The OTUs belonging to Massilia, Lysobacter,Pseudoduganella, and Bacillus increased first but then decreased, pointing to theirpotential abilities of hydrocarbon degradation and metal tolerance. Massilia spp. arereportedly phenanthrene degraders (38), and Bacillus spp. were dominant in then-octadecane degradation consortia (7, 39). In contrast, many more OTUs (n � 884)

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were classified into cluster 2, which decreased first and then increased. This result maybe explained if most of the microbes had suffered immediate toxicity of heavy metal atthe early stage of contamination, yet they could later adapt to this stress condition viahigh growth rates and evolutionary acclimatization during the incubation process. Ourresults are consistent with those from a prior study demonstrating that those microbialtaxa which responded positively to disturbance eventually decrease in abundance toreturn to their original composition, allowing other negatively impacted taxa to thenrecover in abundance (27). Additionally, we also found that the functions related tocellular response to stress increased first and then decreased, thus indicating themicrobes’ rapid functional responses to the selection pressure from inorganic pollut-ants (e.g., cadmium). In particular, the functions associated with hydrocarbon degra-dation showed opposing trends, which may be explained by members with the abilityof hydrocarbon degradation being suppressed by the heavy-metal toxicity. Overall,these observations confirm the high resilience in the structure and functions of soilmicrobiomes in response to environmental contamination at not just the whole-community level but also at the species-specific level.

The selective effect of plant roots on the soil microbial community structure hasbeen reported in many other studies and could be attributable to plant species identity(16, 25). Disturbance from pollutants reduces the number of active microbial taxa toonly those that are pollutant tolerant; therefore, microorganisms that are generallyassociated with a particular plant species in uncontaminated soils may no longer be arelevant component of the community in an aged contaminated environment (40). Inthe present study, we observed no significant difference in microbial communitycompositions between the planted and unplanted samples after 10 or 30 days ofincubation, while significant differences in the microbial taxonomic and functionalprofiles were detected after 90 days of incubation. Our results indicated that theplant-induced differences in the microbial community only occur after several months,whereas the effects of chemical contamination occur very quickly. This confirms thatplants exerted a selection process on both taxonomic and functional traits of the soilmicrobial community in a contaminated environment. This also implies that plantsgrowing in contaminated agro-soils have a significant impact on how the soil microbialcommunity reassembles and recovers over time. Yet, our results showed that thedifferent legume plants drove the distinct assembly of species composition rather thanfunctional traits. There are two possible reasons for this result. First, different microbesin the distinct community may function similarly, thus resulting in the same soilfunctional profiling. Second, the microbial taxa selected by different plants may actuallybe functionally redundant, so that the soil microbial functions as a whole would notchange when combined and considered at the community level. Prior studies havefound that plant roots can exert significantly selective effects on microbial communitystructure, but to a lesser extent, or not at all, on the functional profiles (25, 41).

In contaminated soil environments, the influence of plant traits on the microbialcommunity assemblage at the taxonomic and functional levels remains unclear. Here,we found that the enriched microbial taxa in soils with different plants were selectivelyassembled, and this might in turn benefit plant growth and health. These potentiallybeneficial microbes may support nutrient acquisition for promoting plant growth andare typically referred to as plant growth-promoting bacteria. For example, Mesorhizo-bium spp. enriched in soils planted with Robinia spp. have been mainly isolated fromthe black locust nodule (42). Rhizobium was the main rhizobial genus to form noduleswith hairy vetch plants (43), and it was abundant in the vetch-grown soils. Ensifer spp.could perform nitrogen fixation for legumes, solubilize inorganic phosphate, produceindole acetic acid and siderophores, and induce systemic resistance to collectivelypromote plant growth (44, 45). Novosphingobium spp. can produce the phytohormonessalicylic acid, gibberellins, indole-3-acetic acid, and abscisic acid (46). That said, in ourstudy, the enrichment processes in the planted soils selected microbial functionalgenes specifically related to saccharide and derivated synthesis, the EMP pathway, andthe response to nitrosative and oxidative stress. These particular functions appear to be

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relevant for interactions with the plants, some of which have been shown to beimportant in root-associated microbial communities (25, 41). For example, functionsparticipating in the response to nitrosative and oxidative stress were crucial for theadaptation of plants to abiotic and biotic stresses (47). In particular, the presence ofboth organic pollutants and heavy metal (CdCl2) could function as environmentalstresses for the selection of these bacteria with potential abilities of hydrocarbondegradation and metal tolerance. Indeed, Ensifer spp. have been shown to be capableof degrading phenanthrene (PHE) (48). In addition, a few genera enriched in soilsplanted with legumes, such as Novosphingobium, Nocardia, Sphingobium, Hydrog-enophaga, and Sphingopyxis, were previously detected in microcosms where organiccontaminants served as the sole sources of energy and carbon in the presence of CdCl2(7, 8). Hence, our results regarding both species composition and functional traitssuggest that plants may exert selection pressure on the soil microbial community basedon its particular functional traits in the organic- and inorganic-contaminated environ-ments. This selection process could benefit plant growth and fitness while fostering thepotential abilities of hydrocarbon degradation and metal tolerance.

Network analysis based on SpiecEasi method provided an integrated understandingof the microbial community taxonomic and functional traits. Our analysis contained allpossible positive and negative correlations between the taxonomic and functionaltraits, which could better reflect the many complex interaction relationships amongspecies and potential functions of soil microbial community (25). In general, Proteo-bacteria had the most external connections, which indicated their adaptation across awide range of ecological niches (49). The low interactions of Actinobacteria with othertaxa may be due to their antibiotic activity (50). Furthermore, we estimated theassociations of specific microbial taxa to certain environmental functional groups,including the cellular response to stress, hydrocarbon degradation, transporters, andthe nitrogen cycle. Massilia spp. and Gaiellales were positively correlated with the majorfacilitator superfamily and ABC transporters, respectively, which encode importantproteins with heavy-metal transportation ability (51, 52); this finding indicates thepotential roles played by these two taxa in heavy-metal detoxification. Additionally, ourresults suggested that the bacteria belonging to Tumebacillus, Gemmatimonadaceae,and Acidobacteria may participate in hydrocarbon degradation, consistent with resultsfrom previous studies (53). Functions of cellular response to stress were associated withdefending against threats from the environment (54). In our study, Gaiella spp.,Actinoplanes spp., Brevundimonas spp., Sphingomonadales, and Acidobacteria all exhib-ited strong relationships with these functions, thus indicating their contributions tocounteracting the stress from organic pollutants and heavy metal in agricultural soil.

Different taxonomic phyla likely explained the distinct functional traits of soilmicrobial communities. Betaproteobacteria, which are widely distributed across differ-ent ecological niches (49), are important predictors for many important functional traits,such as amino acid utilization biosynthesis metabolism, nucleic acid metabolism, andhydrocarbon degradation, highlighting their crucial roles in soil ecosystem processes.Parcubacteria (candidate phylum OD1) was found to predict fermentation functions;this is supported by other work inferring that Parcubacteria fermented various sugars toorganic acids, with some species having the capacity to degrade complex carbonsources (55, 56). Bacteroidetes and Thermomicrobia contributed most to the functionsrelated to sulfur cycling, and some of their members are known to be associated withsulfur metabolism (57). Overall, the combined network and RF analyses suggest thatvarious taxonomic groups jointly contributed to the functional potential of the soilmicrobial community we studied, although specific phyla were the most importantpredictors for distinctive functional traits.

In this study, we observed clear succession and resilience patterns of soil microbialdiversity and structure at the taxonomic and functional levels in response to soilcontamination. In addition, different legume plants exerted stronger selection pro-cesses on taxonomic than functional profiles of soil microbiome in the organic- andinorganic-contaminated environments; these processes were potentially beneficial to

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the growth and health of plants with abilities of hydrocarbon degradation and metaltolerance. Importantly, the functional potential of the soil microbial community re-flected contributions made by not one, but various cooccurring taxonomic groups,which did not preclude some phyla being the important predictors for specific func-tional traits. Overall, the results presented here provide valuable information for betterunderstanding microbial resilience patterns to environmental contamination, not onlyon the community as a whole but also at the species and more deeply functional levels,as well as the selection processes acting on the taxonomic and functional profiles asinduced by different plant hosts. Further work should investigate a range of land usetypes, environmental conditions, and soil properties to confirm the general and globalapplications of these findings. Ideally, such work could be extended to identifying theconsequences of microbial temporal resilience on ecosystem functions and services.

MATERIALS AND METHODSExperimental setup and sampling. In July 2014, 20 kg of soil samples (0 to 20 cm) was collected

from a cornfield located in Yangling, Shaanxi Province, Northwest China (108°4=51�E, 34°17=31�N). Thissoil was of a sandy loam texture, with pH 8.16. Other soil properties were as follows: available nitrogen,14.2 mg kg�1; total nitrogen, 1,013.9 mg kg�1; available potassium, 169.1 mg kg�1; available phosphorus,18.2 mg kg�1; total phosphorus, 960.7 mg kg�1; and organic matter, 22.1 g kg�1. A subset of soils werestored at �80°C until the microbial analysis (referred to as day 0 of soil communities). The remaining soilswere sieved (5-mm mesh size) to remove any plant debris and large clods. To prepare the contaminatedsoils, subsamples of the sieved soils were spiked with a mixture of phenanthrene and n-octadecane indichloromethane at 1,000 mg/kg, plus cadmium chloride (CdCl2) in water at 50 mg/kg. The dichloro-methane solution containing the organic pollutants was first mixed with 200 g of soil. After the completeevaporation of the dichloromethane under a fume hood, it was thoroughly mixed with a further 800 gof soil and CdCl2 solution.

In total, four time points for temporal microcosms without plants, 0, 10, 30, and 90 days, wereselected to estimate the succession of soil microbiome in response to chemical contamination. Threelegume plant treatments were sampled after 90 days of growth in the contaminated soils and used todetermine the microbial community assemblage occurring under the influence of different legumeplants. For all treatments, the contaminated soils (�1 kg) were filled into pots (10-cm diameter) that hada depth of 10 cm. For the plant growth treatments, seeds of R. pseudoacacia (robinia), M. sativa (alfalfa),and V. villosa (vetch) were surface-sterilized and germinated at 28°C for 36 h under aseptic conditions.Five robinia plants, 20 alfalfa, and 20 vetch seedlings (each 1 cm in length) were sown per pot. Pots withand without plants were incubated in a greenhouse (16-h day [25°C]/8-h night [20°C]) for 90 days. Potswere given sterile water three times per week to maintain their soil moisture at �15%. Pots assigned tothe different treatments were arranged randomly and rotated regularly throughout the 90-day period.Nine replicate pots were maintained for each of the four time points without plant (0, 10, 30, and 90 days)and for each of the three legume species at 90 days.

At the designated time points, the soils in pots without any plants were collected from a depth of2 to 10 cm, which adopted destructive sampling to avoid disturbance. After the 90-day incubation, soilsin the plant-grown pots were collected following the same procedure. Plant roots with soil attached wereremoved, and the rest of the soil without roots was mixed and collected. Although the rhizosphereconsists of the soil most affected by plants, the sample volume collected here was too small. Therefore,we focused on the soil microbiome on a larger scale, that is, in the root zone impacted by plant growth.In total, 63 soil samples were obtained, and the three replications were pooled in groups, leaving 21 soilsamples contaminated with phenanthrene, n-octadecane, and CdCl2, with three biological replicates forfour time points (0, 10, 30, and 90 days), and three legume plant treatments for 90-day growth. All thesesoil samples were stored at �80°C for future use. At the end of the experiment, the remainingcontaminated soils were collected and sent to an environmental protection company for processing viaa chemical method.

DNA extraction and 16S rRNA gene amplicon and metagenomic sequencing. Genomic DNA wasextracted from each of the 21 soil samples. The V4-V5 region of the 16S rRNA gene was amplified byusing the primer pair 515F/907R. Sequencing was performed on a HiSeq 2500 (250-bp paired-end reads)platform (Illumina, Inc., San Diego, CA, USA). Metagenomic analysis of the 21 soil DNA samples wasconducted with the sequencing libraries generated after implementing the NEBNext Ultra DNA libraryprep kit for Illumina (NEB, Ipswich, MA, USA). This sequencing was performed on a HiSeq 4000 platform(Illumina, Inc.). More detailed information is given in the supplemental material.

For the analysis of metabolism pathways, the KEGG database (58) was used, and amino acidalignment against the KEGG database was performed using BLASTP (E value � 1 10�5) in DIAMOND(59). To annotate the set of genes related to environmental microorganisms, the identified gene families(specified by KEGG Orthology groups) were screened against the Functional Ontology Assignments forMetagenomes (FOAM) database and then grouped into different FOAM levels (60).

Statistical analyses. All statistical analyses were conducted using R version 3.2.2 (http://www.r-project.org), unless otherwise stated. For the taxonomic data sets, a subsample with a minimum of42,994 sequences (according to the sample size) from each sample was used to eliminate all potentialinaccuracies. The alpha diversity (richness and Shannon-Wiener index) and beta diversity (Bray-Curtis

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distance) were calculated for the taxonomic and functional traits of the soil microbial community basedon their corresponding OTUs and open reading frames, respectively.

The dynamic patterns of the taxonomic and functional traits during the 90-day incubation periodwere identified using maSigPro (61) in the Bioconductor package, as described previously (8). Atwo-step regression approach was performed to select the traits with statistically significant stagechanges (using false-discovery rate-corrected P values of �0.05). The visualization of significanttraits was based on a cluster analysis for those group traits sharing a similar profile. The microbialtaxa (i.e., OTUs) significantly enriched for each plant treatment relative to the soils lacking plants(day 90) were identified by linear statistics with the “limma” package. Specifically, a linear model wasfitted for each OTU, and differentially abundant OTUs were estimated using moderated t tests. Themoderated t test used a Bayesian model to shrink or expand the standard error of each OTU towarda common value, thereby borrowing information on the variance of other OTUs. This approach isespecially suitable when the number of measurements per sample is large but the sample sizes aresmall. The resulting P values were adjusted for multiple-hypothesis testing by applying theBenjamini-Hochberg correction method. A bipartite association network was then used to visualizethe associations between the significantly enriched OTUs and the different plant samples, which wasgenerated in Cytoscape.

Network analyses were carried out to obtain a better understanding of the potential associationsbetween the microbial taxa and functional traits of FOAM levels 1 and 2. Correlations among theOTUs with a relative abundance of more than 0.05% and the functional traits were calculated bysparse inverse covariance estimation for ecological association inference (SpiecEasi) using theMeinshausen and Bühlmann algorithm. Networks were visualized in the Cytoscape platform. Themain microbial predictors for potential functions were identified by a classification RF analysis. Inthe RF models, the main microbial phyla served as predictors for all functional traits of FOAM level1 (n � 21). The significance of the models and the cross-validated R2 values were assessed with 5,000permutations of the response variable by using the A3 package. Similarly, the significance of theimportance measures of each predictor on a given response variable (i.e., functional trait) wasassessed by using the rfPermute package.

Data availability. The 16S rRNA gene amplicon sequencing and metagenomics data reported inthis paper have been deposited in the Genome Sequence Archive in the Beijing Institute ofGenomics (BIG) Data Center, Chinese Academy of Sciences, under accession number PRJCA001123(http://bigd.big.ac.cn/bioproject/browse/PRJCA001123).

SUPPLEMENTAL MATERIALSupplemental material for this article may be found at https://doi.org/10.1128/AEM

.02523-18.SUPPLEMENTAL FILE 1, PDF file, 1.8 MB.SUPPLEMENTAL FILE 2, XLSX file, 0.02 MB.

ACKNOWLEDGMENTSThis work was supported by the National Science Foundation of China (grants

31570493, 31270529, and 41807030), the Cheung Kong Scholars Program (grantT2014208), the National Postdoctoral Program for Innovative Talents of China (grantBX201700005), and the China Postdoctoral Science Foundation (grant 2018M630041).

S.J., W.C., and G.W. conceived and designed the experiments, and S.J. performed theexperiments, analyzed the data, and wrote the paper.

We declare no conflicts of interest.

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