This study was conducted in an abandoned factory (42° 03′N, 123° 29′E) in Shenyang, Liaoning Province, China. This region typically has a north temperate continental monsoon climate, characterized by a dry and cold winter, mild and rainy summer, and strong winds in spring and autumn. Two land types (uncontaminated and polluted) and three depths (0–20, 20–40, and 40–60 cm) were used in exploring the effects of Cr(VI) on soil bacterial communities. Using the five-point sampling approach and soil samplers, we collected 18 soil samples (50 g each). The samples were separated into two portions after homogenization and then placed in the dark. One portion was stored at 4°C; and the other, at room temperature. Each land type had three replicated sites (Table 1).

Microwave digestion and inductively coupled plasma-mass spectrometry (ICP-MS) were conducted. In brief, soils (0.2500 g, dried, filtered through a 0.15-mm nylon screen) were digested in a disintegration tank containing 11 mL of a 6:3:2 mixture of nitric, hydrochloric, and hydrofluoric acid. Then, the mixture was supplemented with ultrapure water and heated again to discharge acid. Finally, the solution was placed in a 50 mL volumetric flask, and the volume was determined. The chromium levels of the soils were analyzed through ICP-MS (Hewlett-Packard, HP-4500, Avondale, PA, USA) according to previous methods (17).

Soil microbial genomic DNA was isolated using an E.Z.N.A. soil DNA kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's instructions. DNA extracts were checked through 1% (w/v) agarose gel electrophoresis and stored for PCR amplification. The different regions of the 16S rRNA were amplified using special primers (V3–V4 regions for forward primers containing the sequence 5′-ACTCCTACGGGAGGCAGCAG-3′ and reverse primers 5′-GGACTACHVGGGTWTCTAAT-3′). The PCR protocol was as follows: 3 min denaturation at 95°C for the initial amplification, followed by 27 cycles at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, and a final step at 72°C for 10 min. PCR products were purified using the AxyPrep DNA gel extraction kit (Axygen Biosciences, USA) according to the manufacturer's instructions and were quantified using the QuantiFluor-ST sensitive fluorometer (Promega, USA). High-throughput sequencing was performed on an Illumina MiSeq sequencing platform (Illumina, San Diego, USA) according to standard protocols. The obtained raw sequences were filtered according to quality.

The catalase (CAT) and nitrate reductase (NR) levels of the soils were determined using commercially available kits (Cat: ml076929 and ml076879, Mlbio, Shanghai, China). Other procedures were performed by strictly following the manufacturer's instructions.

Operational taxonomical units (OTUs) with 97% similarity were clustered using the algorithm UPARSE version 7.1 (http://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. Alpha indices were acquired via Mothur 1.30.2 (https://www.mothur.org/wiki/Download_mothur). Tables for each taxonomy and beta diversity distance calculation were analyzed with Quiime 1.9.1 (http://qiime.org/install/index.html). The data were analyzed on the online platform of Majorbio Cloud Platform (www.majorbio.com) (18–21). The results are presented as the mean ± standard deviation and were checked by one-way ANOVA or student's t-test (t-test) using Statistical Package for the Social Sciences (version 19.0; SPSS Inc., Chicago, IL, USA). P < 0.05 indicated statistical significance.

The measured soil chromium contents varied substantially between the two land types (uncontaminated and polluted) and among the three depths (0–20, 20–40, and 40–60 cm). The concentrations of chromium increased in polluted soils at all depths compared with those in reference soils. Meanwhile, chromium content decreased with increasing depth (Table 2).

Rank abundance curves were obtained by ranking the number of species (or OTUs) at a certain taxonomic level as the horizontal coordinate and the relative percentages of the number of species at that taxonomic level as the vertical coordinates. Low-quality reads were removed from the OTUs, and investigation was conducted using rank abundance curves for the analysis of bacterial abundance and evenness (Figure 1). The curve width represented bacterial abundance, and the curve shape indicated the evenness of soil bacteria. Changes in total species and core species richness were described through Pan–Core species analysis (Figures 2A, B).

Alpha (α) diversity measurements were calculated to describe microbial diversity, such as OTU Sobs, Chao, Ace, Shannon, Simpson's, and coverage diversity estimators (Table 3). Initially, the coverage diversity indicator of each sample was over 98%, thereby indicating that the identified 16s rDNA sequences can represent the majority of bacteria present in the samples. Similarly, the uncontaminated samples had the highest Sobs, Shao, Ace, and Shannon diversity estimators but had the lowest Simpson's diversity estimator in all depths compared with polluted samples. Table 3 shows that the bacterial microbiomes ranged from 1,062 to 1,777 in the Sobs diversity estimator, 1,253.3 to 2,190.3 in the Chao diversity estimator, 5.04 to 6.22 Shannon diversity estimator, and 0.00501 to 0.0288 in the Simpson diversity estimator.

Rarefaction curve analysis was used in comparing the OTU indicators at different sequencing depths and taxon richness, and whether the acquired sequence amounts were sufficiently quantified was determined. The rarefaction curves in Figures 2C, D showed similar patterns for all soil samples, showing that soils were equally sampled and sufficiently sequenced.

As shown in Figure 3A, the Venn diagram used to evaluate out distribution in different treatments at the genus level reflected that 263 OTUs were common in all soil samples. In addition, up to 400 and 433 OTUs belong to uncontaminated and polluted soils, respectively. C1 and T1 had 366 OTUs, C2 and T2 had 377, and C3 and T3 had 367. However, C1, C2, C3, T1, T2, and T3 had 8, 4, 23, 10, 11, and 15 unique OTUs, respectively. At the genus levels, Acidobacteria was the dominant bacterium in all soil groups (C1–C3 and T1–T3, 8.06%, Figure 3B), control groups (C1–C3, 10.26%, Figure 3C), C1 and T1 groups (6.84%, Figure 3E), C2 and T2 groups (5.03%, Figure 3F), and C3 and T3 groups (7.80%, Figure 3G). However, Silanimonas was the dominant bacterium in the polluted soil groups (T1–T3, 8.28%, Figure 3D).

The composition and structure of bacterial communities in the samples were analyzed at different taxonomic levels. Uncontaminated and contaminated groups showed analogous bacterial diversity but differed in terms of abundance to a certain extent (Figures 4, 6). Bacterial community analysis revealed relative abundance at the phylum level (Figure 4). Proteobacteria was the predominant phylum, and Actinobacteria was the secondary phylum. The relative abundance of bacterial phyla changed in the two soil types. In the C1 samples, Proteobacteria was 28.95% of the total bacteria, and the percentages of Chloroflexi in C1, C2, C3, T1, T2, and T3 were 21.55, 17.78, 22.24, 8.50, 6.37, and 4.97%, respectively. Acidobacteria and Chloroflexi decreased, whereas Proteobacteria and Deinococcus-Thermus were enriched in the Cr(VI)-polluted soils. At the phylum level, significance analysis of the relative abundance indicated that the significance levels of Actinobacteria, Gemmatimonadetes, Firmicutes, Deinococcus-Thermus, Cyanobacteria, and Nitrospirae in the chromium groups were higher than those in the control groups, whereas the significance levels of Chloroflexi, Acidobacteria, Bacteroidetes, and Planctomycetes were lower than those of the control groups. Additionally, Latescibacteria and Ignavibacteriae were unique in the control groups (Figure 5).

The abundance of relative bacterial community at the genus level is illustrated in Figure 6. Norank Acidobacteria, norank Anaerolineaceae, and norank Nitrosomonadaceae were the dominant genera in the control groups (C1–C3). However, the dominant genera in polluted soils were diverse when the contaminants were input. Specifically, Lysobacter (8.33%), Luteimonast (4.36%), and norank Longimicrobiaceae (4.04%) were dominant in T1; Silanimonas (15.94%), norank Gemmatimonadetes (6.71%), and Nesterenkonia (5.45%) were dominant in T2; and Silanimonas (8.27%), Truepera (7.28%), and Nesterenkonia (6.52%) were dominant in T3.

A heatmap of microbial communities was further analyzed for the identification of the similarity and differences among bacterial community structures in the soil samples. The genera of the control groups (C1–C3) had a cluster pattern of bacterial community composition different from the pattern observed in polluted soils (T1–T3; Figure 7). At the genus level, the relative abundance of Methylotenera, Rivibacter, Nitriliruptoraceae, Wenzhouxiangella, Luteimonas, Silanimonas, Truepera, and Nesterenkonia in the chromium groups were higher than those in the control groups, whereas the relative abundance of Rhodocyclaceae, 43F-1404R, and H16 were lower than those in the control groups.

Bacterial community composition varied by chromium content and soil depth. Therefore, the samples were separated by principal coordinate analysis (PCoA) and hierarchical clustering analysis, as illustrated in Figure 8B. At the OTU level, the PCoA indicated the clear separation of the bacterial communities in different groups. The microbiota of the T1, T2, and T3 samples was distinct from that of the C1, C2, and C3 samples. The first axis explained the 58.91% cumulative percentage variance of species, and the second axis explained 15.83%. The two axes explained a total of 74.74% variance of species. Meanwhile, further analysis of microbial communities was executed through a hierarchical clustering tree, which was used in identifying similarities and differences among bacterial community structures (Figure 8A). The microbial community structures in uncontaminated and polluted soils substantially differed, and these structures were divided into two clusters.

A Kruskal-Wallis H-test was used for detecting richness exhibited by different samples of microbial communities at the phylum level. Proteobacteria, Actinobacteria, Chloroflexi, and Acidobacteria were the dominant bacterial phyla in all samples, but the C1–C3 and T1–T3 samples differed in the abundance of these bacteria (Figure 9). In addition, LDA effect size analysis (LEfSe) was used to discriminate between microbial communities in uncontaminated soils and those in polluted soils. Figures 10, 11 show differences in bacterial taxa. The dominant bacterial phyla were Bacteroidetes, Verrucomicrobia, Armatimonadetes, Chlorobi, and WS2 in C1; Planctomycetes and Latescibacteria in C2; Chloroflexi, Acidobacteria, and Nitrospirae in C3; Proteobacteria, Cyanobacteria, and BRC1 in T1; Gemmatimonadetes, BJ_169, M6__Dependentiae, and Parcubacteria in T2; and Actinobacteria, Deinococcus_Thermus, and Firmicutes in T3.

The correlation of environmental factors, soil samples, and bacterial floras were further assessed by redundancy analysis (RDA) (Figure 12). RDA results illustrated that the sequence of contributors to the diversification was as follows: bacterial community's chromium content > NR > depth > CAT (P < 0.05). The factors involved in chromium content, soil depths, and enzyme levels explained 85.79% diversity of the bacterial communities. The RDA1 axis explained 59.52% of the total variance separating C1–C3 groups from T1–T3 groups.

The content of antioxidant enzymes (NR and CAT) decreased in Cr(VI)-polluted soils at all depths compared with those in C1–C3. Moreover, the NR and CAT content increased with depth (Figure 13).