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Article

Combined Application of High-Throughput Sequencing and Metabolomics to Evaluate the Microbial Mechanisms of Plant-Growth-Promoting Bacteria in Enhancing the Remediation of Cd-Contaminated Soil by Hybrid Pennisetum

by
Shan-Shan Gao
1,
Ying-Jun Zhang
1,
Yang Shao
1,
B. Larry Li
2,
Han Liu
1,
Yu-Ying Li
2,
Xue-Min Ren
1,* and
Zhao-Jin Chen
2,*
1
College of Water Resource and Modern Agriculture, Nanyang Normal University, Nanyang 473061, China
2
Overseas Expertise Introduction Center for Discipline Innovation of Watershed Ecological Security in the Water Source Area of the Mid-Line Project of South-to-North Water Diversion, Nanyang 473061, China
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(10), 2348; https://doi.org/10.3390/agronomy14102348
Submission received: 28 August 2024 / Revised: 3 October 2024 / Accepted: 7 October 2024 / Published: 11 October 2024
(This article belongs to the Special Issue Innovations and Obstacles: Microbial Communities in the Journey of Soil Remediation)
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Abstract

:
The contamination of soil with the heavy metal cadmium (Cd) is increasingly prominent and severely threatens food security in China. Owing to its low cost, suitable efficacy, and ability to address the shortcomings of plant remediation by enhancing the ability of plants to take up Cd, plant–microbe combination remediation technology has become a research hotspot in heavy metal pollution remediation. A pot experiment was performed to examine the effects of inoculation with the plant-growth-promoting bacterium Brevibacillus sp. SR-9 on the biomass, Cd accumulation, and soil nutrients of hybrid Pennisetum. The purpose of this study was to determine how Brevibacillus sp. SR-9 alleviates stress caused by heavy metal contamination. High-throughput sequencing and metabolomics were used to determine the effects of inoculation on the soil bacterial community composition and microbial metabolic functions associated with hybrid Pennisetum. The results suggest that mutation of Brevibacillus sp. SR-9 effectively alleviates Cd pollution stress, leading to increased biomass and accumulation of Cd in hybrid Pennisetum. The aboveground biomass and the root weight increased by 12.08% and 27.03%, respectively. Additionally, the accumulation of Cd in the aboveground sections and roots increased by 21.16% and 15.50%, respectively. Measurements of the physicochemical properties of the soil revealed that the strain Brevibacillus sp. SR-9 slightly increased the levels of available phosphorus, total nitrogen, total phosphorus, and available potassium. High-throughput DNA sequencing revealed that Brevibacillus sp. SR-9 implantation modified the composition of the soil bacterial community by increasing the average number of Actinobacteria and Bacillus. The total nitrogen content of the soil was positively correlated with the Actinobacteria abundance, total phosphorus level, and available phosphorus level. Metabolomic analysis revealed that inoculation affected the abundance of soil metabolites, and 59 differentially abundant metabolites were identified (p < 0.05). Among these, 14 metabolites presented increased abundance, whereas 45 metabolites presented decreased abundance. Fourteen metabolic pathways were enriched in these metabolites: the folate resistance pathway, the ABC transporter pathway, D-glutamine and D-glutamic acid metabolism, purine metabolism, and pyrimidine metabolism. The abundance of the metabolites was positively correlated with the levels of available phosphorus, total potassium, total phosphorus, and total nitrogen. According to correlation analyses, the development of hybrid Pennisetum and the accumulation of Cd are strongly associated with differentially abundant metabolites, which also impact the abundance of certain bacterial populations. This work revealed that by altering the makeup of microbial communities and their metabolic processes, bacteria that promote plant development can mitigate the stress caused by Cd. These findings reveal the microbiological mechanisms through which these bacteria increase the ability of hybrid Pennisetum to take up the Cd present in contaminated soils.

1. Introduction

Soil heavy metal contamination has worsened due to economic factors, urbanization, and the rapid growth of agricultural inputs [1,2,3]. In China, the total area of arable land affected by heavy metal pollution has reached 20 million hectares [4]. Cadmium (Cd) is one of the most hazardous heavy metals on the planet [5] and interferes with the absorption and transport of essential nutrients in plants, disrupts plant metabolism, and inhibits plant growth [6,7]. Cd treatment dramatically decreased the root tip number, surface area, and root length of chili peppers [8]. Additionally, Cd can enter animals and humans through the food chain, and long-term exposure can lead to kidney deposition, ultimately causing kidney failure, lung disease, and bone fragility [9]. Thus, it is critical to create economical and ecologically sound techniques for remediating soils polluted with Cd [10].
Currently, in soil heavy metal pollution remediation research, phytoremediation is regarded as a very promising green technology because of its cost-effectiveness, simplicity, and environmentally friendly nature [11]. Hybrid Pennisetum species have strong stress resistance, wide adaptability, and high biomass yield. Additionally, recent studies have shown that hybrid Pennisetum species have strong Cd tolerance and moderate Cd accumulation capacity and can extract a considerable total amount of Cd from the soil [12,13]. Additionally, hybrid Pennisetum has application value in anaerobic digestion for biogas production, direct combustion for power generation, pulp and paper production, and the manufacture of regenerated cellulose films [14]. However, in practical remediation, issues such as slow plant growth, low biomass, long remediation cycles, poor heavy metal tolerance, and difficulty in recycling contaminated plants significantly limit the effectiveness of remediation [15].
Plant growth promoting bacteria (PGPB) are bacteria that can directly or indirectly increase plant growth [16]. In the field of heavy metal remediation, by increasing metal accumulation and improving heavy metal tolerance, PGPB can increase the ability of plants to adapt to their surroundings and, therefore, increase phytoremediation effectiveness [17]. Kong et al. [18] and Mohammadzadeh et al. [19] reported that PGPB could affect the phytotoxic effects of heavy metals and enhance the absorption of heavy metals by plants. Soil microorganisms are essential for the soil ecosystem and strongly affect plant development in soils polluted with heavy metals. They also play a vital role in the biogeochemical cycling of heavy metals [20,21]. The rhizosphere is the site of interactions among plants, microbes, and soil, the rhizosphere microbial community structure and diversity are regarded as important indicators of soil health [22]. Studies have shown that inoculation with PGPB can alter the rhizosphere microbial community structure to promote plant growth [23]. Research has also shown that under heavy metal stress, PGPB can increase the complexity and connectivity of rhizosphere bacterial community network modules, suggesting that PGPB may improve network stability and thereby promote plant growth [24]. Similarly, rhizosphere metabolites—comprising root exudates and small molecules produced by microbial metabolism in the rhizosphere—play a vital role in the collaborative resistance of plants and microbes to heavy metal stress [25]. Zuluaga et al. [26] and Faten et al. [27] reported that inoculation with PGPB changed the metabolism and function of the plant rhizosphere, improved plant resistance to stress, and increased plant biomass.
High-throughput sequencing technology is now commonly utilized in soil, rhizosphere, and environmental studies [28,29,30]. High-throughput sequencing can be employed to investigate the impact of heavy metals on plants, offering useful knowledge for the remediation of heavy metal contamination. Using high-throughput technologies, Sheng et al. [31] investigated how chromium (Cr) affects the bacterial population of Iris species, illuminating the mechanisms underlying the stress response of Iris plants to Cr heavy metal stress. Metabolomic techniques are now widely used to elucidate the physiological effects of various pollutants on exposed organisms. For example, Navarro-Reig et al. [32] used untargeted metabolomics to perform a comprehensive LC–MS analysis of metabolites extracted from the aboveground tissues of rice plants under Cd and copper (Cu) stress, identifying 97 different metabolites. Compared with Cu, Cd had a more significant effect on rice, primarily affecting secondary metabolism and amino acid, purine, and carbon and glycerolipid metabolism pathways. Ghate et al. [33] studied the oxidative stress response of rice to arsenic (As) and potassium salt exposure using both untargeted and targeted metabolomics via LC–MS. The results highlighted two key metabolites identified as response metabolites to As. Currently, there is limited research on how PGPB alter the microbial composition and metabolism of renewable energy plants such as hybrid Pennisetum for the remediation of soil heavy metal contamination. The following questions were addressed in this study: 1. Can the plant-growth-promoting bacterium Brevibacillus sp. SR-9 promote the growth of hybrid Pennisetum and increase the heavy metal content in Cd-contaminated soils, thereby improving the phytoremediation efficiency? 2. Does the plant-growth-promoting bacterium Brevibacillus sp. SR-9 promote hybrid Pennisetum growth by altering its microbial community composition and metabolism? Is there an interaction between the makeup of the microbial population in the rhizosphere and the metabolism of hybrid Pennisetum that influences the phytoremediation efficiency?

2. Materials and Methods

2.1. Experimental Materials

The tested soil was collected from a garden near a pomegranate orchard at Nanyang Normal University. The material was filtered to eliminate contaminants such as leaves and stones. Afterward, it was air-dried and subsequently passed through a sieve with a mesh size of 20 for further analysis. The seeds of hybrid Pennisetum used in the experiments were purchased from Shuyang County Tuojing Horticulture Co., L (Anhui, China). The bacteria, which were chosen for their ability to promote plant development and withstand heavy metals, were identified as members of the Brevibacillus genus and given the name Brevibacillus sp. SR-9. The strain Brevibacillus sp. SR-9 (SUB11374180) [34] was obtained from Cd-contaminated soil used for farming in Nanyang, China. Brevibacillus sp. SR-9 bacteria are highly tolerant to heavy metals and are proficient in the production of iron carriers and indole-3-acetic acid (IAA) and the solubilization of phosphorus and potassium.

2.2. Experimental Methods

The Brevibacillus sp. SR-9 strain was introduced into LB liquid medium and incubated at 30 °C with agitation at a speed of 180 revolutions per minute for 24 h to prepare the Brevibacillus sp. SR-9 bacterial suspension. After incubation, the bacterial cells were collected via centrifugation and washed with deionized water. The suspension was then diluted to achieve an optical density (OD) value of 1.0–1.5 for further use.
Pot experiment: We measured and portioned the soil and then added analytical grade CdSO₄·8H₂O to attain a concentration of 20 milligrams per kilogram (mg/kg) of Cd ions in the soil. The combination was completely blended and allowed to reach a state of equilibrium for 30 days. The dirt that had been prepared was placed into pots, with each pot containing 0.75 kg of soil. The experiment included two treatments: a control group (CK) without bacterial inoculation with a Cd concentration of 20 mg/kg and an experimental group inoculated with Brevibacillus sp. SR-9 at the same Cd concentration of 20 mg/kg. Hybrid Pennisetum seeds were evenly sown in plastic pots and grown outdoors. The pots were kept under adequate light, and their positions were rotated randomly every 4 days to provide uniform and stable growth conditions. Thinning was carried out after the hybrid Pennisetum seedlings grew to a height of 5 cm, with only 3 seedlings remaining in each pot. The test bacterium was inoculated into the experimental group at 30, 50, and 70 days after germination. The control group was administered an equal amount of tap water at the same intervals. The plants were grown for 95 days before harvesting and sample collection.

2.3. Soil Cd Content and Physicochemical Property Measurements

Digestion with hydrochloric acid, nitric acid, hydrofluoric acid, and perchloric acid was performed, and the Cd content in different plant tissues was determined via an inductively coupled plasma–optical emission spectrometer (ICP–OES) (Optima 2100 DV, Perkin Elmer, USA) after digestion. The soil pH was determined via a potentiometric approach with a soil-to-water ratio of 1:5. The indophenol blue colorimetric technique was employed to determine the total nitrogen (TN) concentration in the soil samples. The total phosphorus (TP) content in the soil samples was determined via the sodium hydroxide fusion–molybdenum antimony colorimetric method. The flame photometry technique was used to assess the available potassium (AK) concentration in the soil samples. The 0.5 mol·L−1 NaHCO₃ extraction–molybdenum antimony colorimetric technique was used to evaluate the available phosphorus (AP) concentration in the soil samples.

2.4. High-Throughput Sequencing

For the purpose of harvesting and sequencing DNA, the soil samples were transported to Maggi Biomedical Technology Co., Ltd. (Shanghai, China) The purity of the isolated genomic DNA was assessed via 1% agarose gel electrophoresis after the extraction process. PCR detection was performed via a TransGen AP 221-02 and TransStart Fastpfu DNA polymerase (MP Biomedicals, Irvine, CA, USA) in a 20 µL reaction. PCR was conducted with an ABI GeneAmp® 9700 instrument (Illumina Inc., San Diego, CA, USA), which utilizes particular primers that are synthesized with barcodes corresponding to the specified sequencing areas. All the materials were processed via conventional experimental methods, with three replicates per sample. The PCR products obtained from identical samples were combined and subjected to analysis via 2% agarose gel electrophoresis. Following Tris-HCl elution and the use of an AxyPrep DNA Gel Extraction Kit (AXYGE, Silicon Valley, CA, USA), the PCR products were recovered and reassessed via 2% agarose gel electrophoresis. A QuantiFluor™-ST Blue Fluorescence Quantification System (Promega, Shanghai, China) was used to quantify the PCR products on the basis of the electrophoresis results. The samples were subsequently mixed at the necessary ratios on the basis of the sequencing volume specifications before Illumina sequencing was conducted. Illumina sequencing produces paired-end (PE) reads, which are initially assembled via overlap relationships. The quality of each sequence was subsequently evaluated and refined. Operational taxonomic unit (OTU) clustering analysis and taxonomic categorization were carried out following sample separation. Numerous diversity indices were examined, and the sequencing depth was evaluated via the OTU data. In addition, the community structure was subjected to statistical analysis at various taxonomic levels via categorization information. On the basis of the aforementioned investigations, numerous comprehensive statistical and visual analyses, multivariate analyses, and significance tests were carried out on the phylogenetic data and community compositions of many samples.

2.5. Metabolomic Analysis

One gram of each soil sample was weighed, and 20 microliters of internal standard and 1 milliliter of a solution containing 50% methanol were added. A centrifuge was used to separate and collect the remaining liquid after sedimentation. The liquid above the sediment was passed through a filter with a pore size of 0.22 µm via a sterile syringe. All samples were stored at −80 °C for LC–MS analysis. The untargeted metabolomics analysis in this experiment was carried out by employing an ultrahigh-performance liquid chromatography system (UHPLC-TOF MS) (Agilent Technologies Inc., CA, USA) for separation. Instrument calibration was conducted as needed to ensure accurate mass spectrometry data. The appropriate injection mode and injection volume were selected on the basis of the sample properties and analytical requirements. The analysis was initiated, and the instrument was controlled according to the preset conditions. After sample separation, MS analysis was performed using an Agilent 6538 Ultra-High-Resolution Q-TOF (Jungkap Park, Santa Clara, CA, USA) mass spectrometer in both positive and negative ion modes. The raw data were processed to extract metabolite information and convert it into an analyzable format through baseline correction, peak alignment, peak identification, missing value imputation, data filtering, and normalization. The techniques of partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to obtain insights into the variations in metabolites across different groups. Metabolites that exhibited notable differences across groups were chosen on the basis of variable importance in projection (VIP) values obtained from the OPLS-DA in conjunction with a t-test (p < 0.05). The identified differentially abundant metabolites were mapped to corresponding metabolic pathways via the KEGG database.

2.6. Data Analysis

SPSS 17.0 (IBM, Armonk, NY, USA) was used to analyze the heavy metal concentrations, high-throughput sequencing, and soil physicochemical parameter data. To assess group differences, one-way ANOVA and t-tests were employed. The transfer factor (TF) was calculated as the aboveground Cd content/root Cd content [35].

3. Results

3.1. Effects of Brevibacillus sp. SR-9 on Hybrid Pennisetum Growth, Cd Content, and Cd Accumulation under Cd-Induced Stress

As shown in Table 1, the biomass results of hybrid Pennisetum indicate that, compared with those of the control group (CK), the biomass of the aerial parts increased by 12.08%, and the root dry weight significantly increased by 27.03% (p < 0.01) in the group treated with Brevibacillus sp. SR-9. These findings suggest that the introduction of Brevibacillus sp. SR-9 increased the ability of hybrid Pennisetum roots to withstand the presence of Cd. Compared with that in the control group, which was not inoculated with Brevibacillus sp. SR-9, the accumulation of Cd in the aerial portions and roots of hybrid Pennisetum increased by 21.16% and 15.50%, respectively, in the group treated with Brevibacillus sp. SR-9. Thus, inoculation with Brevibacillus sp. SR-9 can increase Cd accumulation in various tissues of hybrid Pennisetum. The transport coefficient increased from 0.14 to 0.16, representing a 19.94% increase compared with that of the CK group. These findings indicate that inoculation with Brevibacillus sp. SR-9 enhances the ability of the plant to transport heavy metals from the soil into the plant, thereby increasing the remediation capacity of the plant.

3.2. Effects of Brevibacillus sp. SR-9 on the Physicochemical Properties of Soil Contaminated with Cd

Table 2 shows that, compared with those in the control group, the total nitrogen content, total phosphorus content, available phosphorus content, and available potassium content in the microbial treatment group significantly increased (p < 0.05) by 6.25%, 3.46%, 12.00%, and 2.49%, respectively, with the increase in available phosphorus being particularly notable. Under the influence of Brevibacillus sp. SR-9, soil nutrients increased, leading to increased growth of hybrid Pennisetum.

3.3. Impact of Brevibacillus sp. SR-9 on Soil Bacterial Communities

At the OTU level, diversity index analysis revealed that (Figure 1), compared with the CK, the inclusion of Brevibacillus sp. SR-9 resulted in decreases in the ACE index, Chao1 index, and Sobs index of 3.1%, 0.65%, and 1.4%, respectively. The application of microbial therapy resulted in a decrease in the overall abundance of bacteria and caused changes in the composition of the bacterial population. Nevertheless, there were no substantial alterations in the Shannon or Simpson indices.
Venn diagram analysis revealed the differences in the OTU compositions and unique species of rhizosphere soil bacterial communities under the different treatments. A total of 15,123 bacterial OTUs were identified across both treatments, with 6545 OTUs shared between them. The CK group had 1134 unique OTUs, accounting for 7.50% of the total OTUs, whereas the microbial treatment group had 899 unique OTUs, representing 5.95% of the total OTUs. As shown in Figure 2, PLS-DA analysis at the OTU level revealed that the nonmicrobial treatment group and the microbial treatment group were positioned at opposite sides of the plot, clustering separately. These findings indicate that there were differences between the two treatment groups and the Brevibacillus sp. SR-9 treatment altered the composition of the soil bacterial community.
On the basis of high-throughput sequencing of the soil bacterial 16S rDNA gene and clustering at 97% similarity, a total of 39 phyla and 958 genera were identified. On the basis of the phylum-level analysis (Figure 3A) of the microbial populations residing in the rhizosphere of hybrid Pennisetum under various therapeutic regimens, the dominant phyla were Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Firmicutes. Together, these phyla accounted for more than 80% of the total bacterial abundance in the samples. In the CK treatment group, the relative abundances of the dominant phyla were 26.7% for Actinobacteria, 24.9% for Proteobacteria, 15.5% for Acidobacteria, 11.3% for Chloroflexi, and 4.5% for Firmicutes. In the microbial treatment group, the relative abundances were 27.7% for Actinobacteria, 24.4% for Proteobacteria, 15.2% for Acidobacteria, 11.2% for Chloroflexi, and 4.7% for Firmicutes. Compared with the CK group, the microbial treatment group presented a 1% increase in the relative abundance of Actinobacteria, whereas the relative abundance of Acidobacteria decreased by 0.3%. Furthermore, the abundance of Proteobacteria was greater in the CK group than in the microbial treatment group. As shown in Figure 3B, at the genus level, the four most dominant bacterial genera based on relative abundance were Sphingomonas, RB41, Bacillus, and Gaiella. Compared with that in the nonmicrobial treatment group, the relative abundance of the Bacillus genus in hybrid Pennisetum rhizosphere soil increased by 0.3% with Brevibacillus sp. SR-9 treatment. Compared with the microbial treatment group, the CK group presented a substantial increase in the relative abundance of Sphingomonas, although the relative abundances of RB41 and Gaiella did not significantly differ between the two groups. The differences and changes in the phylum- and genus-level bacterial communities between the two treatment groups indicate that inoculation with Brevibacillus sp. SR-9 affects the rhizosphere microbial communities.
The symbiotic relationships between bacteria under the two different treatments were explored via co-occurrence network analysis, resulting in a phylum-level co-occurrence network diagram of the rhizosphere soil (Figure 4). The results reveal that the bacterial co-occurrence networks differed between treatments. The CK group had 387 nodes and 5879 edges, whereas the microbial treatment group had 378 nodes and 9110 edges. The average degree, network density, and clustering coefficient in the microbial treatment group were greater than those in the CK group. Nevertheless, the microbial treatment group presented a reduced average network distance. Additionally, the microbial treatment group had a lower modularity (0.277) than the CK group (0.353) (Table 3). These findings indicate that the microbial treatment group had a more complex and compact bacterial network than the CK group.
Redundancy analysis (RDA) was used to examine the relationships between the physicochemical parameters of the soil in hybrid Pennisetum and the composition of the bacterial population at the phylum level in the rhizosphere. As shown in Figure 5, the results reveal that the soil physicochemical parameters accounted for 70.2% of the variance in the organization of the bacterial population. The initial two principal components explained 54.66% and 15.54% of the total variation, respectively. Among these factors, pH, total nitrogen, available potassium, and available phosphorus were identified as important factors influencing disparities in the hybrid Pennisetum microbial populations present in the soil. Among the five most abundant microbial phyla in hybrid Pennisetum soil, Acidobacteria was directly related to soil pH, while the remaining three phyla were inversely related to the pH of the soil. Moreover, the presence of Actinobacteria was positively correlated with total nitrogen, total phosphorus, and available phosphorus in the soil. At the genus level, the soil physicochemical properties explained 54.86% of the variation, with the first two canonical axes accounting for 38.49% and 16.37% of the variance, respectively. The abundance of the Bacillus genus was directly related to the available phosphorus, available potassium, and pH, but was negatively correlated with the total nitrogen and total phosphorus. In contrast, Sphingomonas exhibited the opposite pattern, with an inverse relationship with AP, AK, and pH and a positive correlation with TN and TP.

3.4. Impact of Brevibacillus sp. SR-9 on Metabolic Functions

To investigate the impact of Brevibacillus sp. SR-9 inoculation on the metabolic profiles of hybrid Pennisetum rhizosphere soil under Cd stress, PLS-DA and OPLS-DA score analyses were performed, with 200-cycle cross-validation to assess the model stability. As shown in Figure 6, in both the cationic and anionic modes, the soil samples from the microbial treatment group and the nonmicrobial treatment group were well separated, indicating that there were differences in the soil metabolites between the two treatments. These findings suggest that Brevibacillus sp. SR-9 inoculation affects the rhizosphere soil metabolite profile. Selected sample cross-validation revealed that the R²Y values of the original model in both the cationic and anionic modes were close to 1, indicating that the model accurately represented the sample data. The Q² value of approximately 0.5 suggests a clear differentiation between the two sample groups. Additionally, the intercept of the Q regression line with the y-axis below zero reflects the model’s stability, as shown in Figure 7.
A total of 613 metabolites were detected across both sample groups, with 299 metabolites common to both groups. The CK samples contained 306 metabolites, whereas the microbial treatment samples contained 307 metabolites. A volcano plot of the 613 metabolites was created (Figure 8). In the plot, each point represents a metabolite, with larger scatter points indicating higher VIP values. This means that metabolites with larger scatter points are considered more reliably differentially abundant. To further identify differences in metabolites between the microbial treatment and control soils, OPLS-DA was performed with the criteria of p < 0.05 and VIP > 1. The results revealed 59 differentially abundant metabolites (p < 0.05), with the abundance of 14 metabolites increasing and the abundance of 45 metabolites decreasing in the microbial treatment group. These differentially abundant metabolites include petasitenine, 8,13-dihydroxy-9,11-octadecadienoic acid, 3-hydroxy-10′-apo-b,y-carotenal, and hypoxanthine. The metabolites whose abundance increased included 3-hydroxy-10′-apo-b, y-carotenal, petasitenine, and margaroylglycine. This indicates that inoculation with Brevibacillus sp. SR-9 affects the differential abundance of metabolites present in the rhizosphere soil of hybrid Pennisetum under the influence of Cd-induced stress.
To further understand the effects of Brevibacillus sp. SR-9 on metabolites in hybrid Pennisetum soil and explore the mechanisms behind metabolite changes, a KEGG pathway enrichment analysis was conducted on the selected differentially abundant metabolites. On the basis of the KEGG database, the KEGG IDs of the differentially abundant metabolites were used to obtain pathway enrichment results. Pathways significantly enriched among the differentially expressed metabolites were identified via hypergeometric testing. This technique facilitated the investigation of metabolic pathway enrichment for the differentially abundant metabolites. The analysis identified three main pathways: environmental information processing, human diseases, and metabolism. Among these, the metabolism category had the most enriched pathways. The main pathways included lipid metabolism, metabolism of cofactors and vitamins, metabolism of other amino acids, nucleotide metabolism, and terpenoid and polyketide metabolism. The metabolic pathway bubble plot (Figure 9) revealed that the pathways significantly enriched in differentially abundant metabolites were the folate resistance pathway, the ABC transporter pathway, d-glutamine and d-glutamic acid metabolism, purine metabolism, and pyrimidine metabolism (p < 0.05). Notably, the purine metabolism and pyrimidine metabolism pathways were enriched in a greater number of metabolites. These pathways play important physiological roles in plant stress responses.
To investigate the correlation between differentially abundant metabolites and soil nutrients, a correlation analysis was conducted between the physicochemical properties of hybrid Pennisetum rhizosphere soil and the metabolites whose abundances increased in both groups (Figure 10). The results indicate that the abundances of MG (0:0/16:0/0:0) and Cis-4-hydroxycyclohexylacetic acid were positively correlated with the available potassium. Moreover, the abundance of methyl dihydrojasmonate was significantly positively correlated with the available phosphorus. The abundance of propofol glucuronide and kiwiionoside was significantly negatively correlated with soil pH.

3.5. Correlation Analysis

Brevibacillus sp. SR-9 altered both the composition and concentration of metabolites in the rhizosphere and affected the abundances of certain rhizosphere bacterial species. Correlation analysis between the differentially abundant metabolites and the abundance of rhizosphere bacteria revealed which bacterial populations were influenced by the differentially abundant metabolites. Spearman correlation analysis was used to examine the relationships between bacterial communities and differentially abundant metabolites (Figure 11), and the findings revealed a substantial positive correlation between Subgroup_10 and C17 sphinganine (p = 0.036), with a correlation coefficient of 0.74. Sphingomonas was also significantly positively correlated with 3,4,5-trihydroxy-6-(8-hydroxy-2-oxo-2H-furo[2,3-h] chromen-4-yl}oxy)oxane-2-carboxylic acid (p = 0.02), with a 0.79 correlation coefficient.
Sphingomonas was significantly negatively correlated with Cis-4-hydroxycyclohexylacetic acid (p = 0.01), with a correlation coefficient of -0.83. At the genus level, Bacillus was primarily positively correlated with farnesyl acetone, hypoxanthine, and C17 sphinganine. Additionally, the effects of various bacterial populations and metabolites on hybrid Pennisetum growth indices, Cd accumulation, and soil physicochemical parameters were analyzed via linear regression and Mantel testing (Figure 12). The findings indicate that the abundance of Sphingomonas is positively correlated with the Cd content in the aboveground part (r = 0.52, p = 0.05) and with the DTPA-Cd content (r = 0.91, p = 0.03). The abundance of 3-hydroxy-10′-apo-b,y-carotenal was positively correlated with the belowground dry weight (r = 0.42, p = 0.01), the Cd content in the aboveground part(r = 0.76, p = 0.04), and the DTPA-Cd content (r = 0.59, p = 0.05). The abundance of 8,13-dihydroxy-9,11-octadecadienoic acid was positively correlated with the Cd content in the aboveground part (r = 0.56, p = 0.03) and exhibited a substantial positive association with the DTPA-Cd content (r = 0.97, p = 0.008).

4. Discussion

Hybrid Pennisetum is a nonfood energy crop with high biomass potential that shows promise for soil heavy metal pollution remediation in China [11]. Nevertheless, when subjected to intense heavy metal pressure, the biomass of hybrid Pennisetum diminishes, resulting in decelerated development and diminished effectiveness in remediation [36]. PGPB significantly affect soil remediation by secreting metabolites such as IAA, siderophores, enzymes, and organic acids. These substances promote plant growth and increase the ability of plants to accumulate heavy metals [37]. Bacillus is an important PGPB. Shah [38] reported that treating tomatoes with Bacillus can improve seed germination rates, seedling vigor, and several growth characteristics, including plant height, root length, and fresh weight. In this study, Brevibacillus sp. SR-9 was inoculated into soil with a Cd concentration of 20 mg/kg, and a pot cultivation experiment was conducted with hybrid Pennisetum. The findings demonstrate that the introduction of Brevibacillus sp. SR-9 resulted in a 12.08% increase in the aboveground biomass of hybrid Pennisetum, thus facilitating its development. Studies such as showing that Pseudomonas aeruginosa CPSB1 promotes wheat growth under Cd stress [37] and showing that SaMR12 increases Cd accumulation in Sedum alfredii report similar findings to those of this experiment [39]. This result may be related to the IAA and siderophores secreted by Brevibacillus sp. SR-9. For example, siderophores produced by Streptomyces tendae significantly increase the growth of sunflowers (Helianthus annuus) and their uptake of Fe and Cd [40]. Ma et al. [41] reported that siderophores produced by Bacillus pumilus E2S2, which was isolated from Sedum plumbizincicola, increased the growth of the host plant. Chen et al. [42] demonstrated that under Cd treatment, inoculation with SaMR12 enhanced the tolerance of Sedum alfredii to Cd, mitigated Cd-induced oxidative stress, and thus promoted plant growth and increased Cd absorption. These conclusions align with the outcomes of this investigation. The concentrations of Cd in the aboveground portions and roots increased by 21.16% and 15.50%, respectively. The translocation factor increased by 19.94% compared with that of the noninoculated group. These findings indicate that inoculation with Brevibacillus sp. SR-9 promotes the proliferation of hybrid Pennisetum, effectively alleviates Cd stress, and enhances plant remediation efficiency. According to Wu et al. [13], the presence of Bacillus BM18-2 resulted in an 83.1% increase in the biomass of hybrid Pennisetum and a 28.6% increase in Cd accumulation. Moreira et al. [43] reported that inoculation with Ralstonia eutropha and Chryseobacterium humi increased Cd accumulation in maize (Zea mays) roots by 18.6%. Moreover, heavy metal accumulation in soil leads to alterations in its physicochemical characteristics, which in turn affect the forms of heavy metals. These changes in metal forms determine their mobility and bioavailability [44]. Sofia Houida et al. [45] reported that introducing PGPB into soil had a substantial positive effect on plant development and resulted in increased levels of nitrogen, phosphate, and potassium. These findings suggested that these bacteria increase the availability of nutrients in the soil. Similarly, this study revealed that the introduction of Brevibacillus sp. SR-9 resulted in a 6.25% increase in the overall nitrogen content of the soil, a 3.46% increase in its overall phosphorus content, a 12.00% increase in its available phosphorus content, and a 2.49% increase in its available potassium content. These findings suggest that the Brevibacillus sp. SR-9 strain can increase plant nutrient uptake by secreting beneficial substances or converting less accessible soil components into forms that plants can absorb, thereby increasing the utilization of diverse nutrients [46,47].
The introduction of PGPB into the rhizosphere has dual effects: it directly influences plants and has an impact on the microbial population in the soil around roots [48]. The introduction of PGPB by inoculation can increase plant growth and enhance the effectiveness of remediation by changing the bacterial composition in the rhizosphere [49]. α diversity is a key indicator reflecting the diversity and abundance of bacterial populations [50]. In this study, the richness of rhizosphere soil bacteria decreased after inoculation with PGPB. This outcome aligns with the findings of Pongsilp [51], possibly because inoculation with plant growth-promoting microorganisms lowered the soil pH and increased the total nitrogen content [3]. This study employed partial least squares discriminant analysis (PLS-DA) at the operational taxonomic unit (OTU) level and revealed that the introduction of Brevibacillus sp. SR-9 affected the structure of the soil microbial community. This result is similar to those of Pongsilp et al. [51] and may be due to the properties of Brevibacillus sp. SR-9, such as the ability to secrete IAA, siderophores, and phosphate solubilizers, along with its heavy metal resistance, which allows for it to effectively colonize the rhizosphere and impact the composition of microorganisms in the rhizosphere of hybrid Pennisetum. It is also possible that soil microorganisms compete with exogenous PGPB for nutrients and ecological niches, leading to changes in the bacterial community structure. Habibollahi et al. [52] reported that the dominant phyla within soils polluted with high concentrations of metallic elements included Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes. Similarly, our high-throughput sequencing results revealed that at the phylum level, the predominant bacterial phyla in the rhizosphere soil of hybrid Pennisetum were Actinobacteria, Proteobacteria, and Acidobacteria. Notably, after SR-9 inoculation, the relative abundance of Proteobacteria significantly increased. Proteobacteria, a dominant bacterial phylum, includes groups such as the chemolithotrophic Acidithiobacillus species involved in the soil iron and sulfur cycles. These groups can survive under heavy metal contamination and enhance Cd chelation and precipitation through processes such as iron oxidation, thereby reducing the movement of heavy metals in soil and reducing the harmful effects of Cd on plants [53]. Additionally, Proteobacteria are closely linked to carbon and nitrogen cycles, which increase soil fertility in the plant rhizosphere and provide plants with essential nutrients. Acidobacteria are associated mainly with soil metal speciation and mineral weathering. This group, which is often acidophilic, can lead to soil acidification and aggregation issues, reflecting its role in altering soil conditions [54]. In this study, the reduction in Acidobacteria by 0.3% after Brevibacillus sp. SR-9 inoculation suggested that the strain may lower the bioavailability of Cd in the soil. Indeed, Acidobacteria may serve as an important indicator of Cd bioavailability in Cd-contaminated soils of hybrid Pennisetum. Studies have also confirmed that Actinobacteria, among other phyla, are typically among the most abundant phyla in soil and are also known to be resistant to heavy metals [55]. At the genus level, the most prevalent genera were Sphingomonas, RB41, Bacillus, and Gaiella. Chen et al. reported that Sphingomonas SaMR12 significantly increased the uptake of Cd by Sedum alfredii [56]. Additionally, Bacillus spp. produce various antibiotics, including bacillomycin, bacilysin, and mycosubtilin, which inhibit pathogens and are well documented for their heavy metal resistance, typically present in soils that are polluted with various heavy metals [57,58]. Co-occurrence network analysis of microbial community members reveals the interactions between different taxa, elucidates network characteristics, and identifies key taxa that play significant roles in community interactions and organization. This approach has been widely used to explore complex interaction networks and ecological rules in microbial communities across various environments [59,60]. In a co-occurrence network, the clustering coefficient measures the level of clustering within a network’s nodes, reflecting how nodes tend to form clusters within the network. The average path length is the count of edges in the shortest path connecting any two nodes in the network, indicating the level of distance between nodes [61]. Modularity measures the degree of modularity within a network, indicating that species within the same module interact more frequently with each other than with species in different modules [62,63]. This study revealed that although the modularity index decreased after treatment, the mean network distance decreased, and the clustering coefficient increased, suggesting that bacterial injection improved the diversity of the soil microbial community. Therefore, the arrangement of bacterial communities in rhizosphere soil is characterized by distinct modules rather than being haphazard [64]. Soil physicochemical conditions significantly influence microbial community composition, with the adaptability of microorganisms to these factors determining their community structure [65]. This study revealed that pH, total nitrogen, available potassium, and available phosphorus were important factors influencing the differences in the bacterial community composition in the soil of Leymus chinensis. The Actinobacteria phylum and Bacillus genus are related to soil physicochemical properties. PGPB stimulate the activation and increase the availability of soil mineral nutrients, thus facilitating plant development.
Root rhizosphere metabolomics is a recent high-throughput omics screening technique focused on rhizosphere metabolites. This study explored the relationships between rhizosphere metabolites and crop physiology–soil nutrient changes, revealing the physiological and ecological functions within the system. This approach has been widely applied in microbiological research and other fields [66]. Rhizosphere soil metabolites are a range of organic and inorganic compounds, as well as signaling molecules, that are secreted by both plant roots and soil microorganisms during plant–soil interactions. These metabolites include primary and secondary metabolites derived from various photosynthetic products [67]. LC–MS analysis was conducted to investigate the differential changes in rhizosphere soil metabolites of Artemisia argyi following treatment with Brevibacillus sp. SR-9. OPLS-DA revealed 59 differentially abundant metabolites, with 14 showing increased abundance and 45 showing decreased abundance. These results suggest that the introduction of the PGPB Brevibacillus sp. SR-9 affected the composition of metabolites in the rhizosphere soil, which led to changes in microbial activity. This, in turn, alleviated Cd stress and promoted the growth of Artemisia argyi. This outcome aligns with the discoveries made by Zuluaga et al. [26]. According to Wang et al. [68], Cd treatment has a considerable effect on the expression levels of many metabolites associated with amino acid, lipid, carbohydrate, and nucleotide metabolism. These changes are attributed to the plant’s response to Cd stress, in which it produces and regulates key metabolites such as carbohydrates, amino acids, and lipids to maintain cellular homeostasis. Similarly, in this study, after Brevibacillus sp. SR-9 treatment, the metabolites whose abundance increased included mainly lipids, amino acids, carbohydrates, organic acids, flavonoids, and other metabolites. Among these, carbohydrates serve as mediators of the mutualistic association between crops and PGPB, playing a role in the regulatory processes of PGPBin plants [69]. Flavonoids derived from plants have the ability to impede free radical reactions and diminish oxidative stress [70]. Lipid compounds may delay leaf senescence in plants [71]. Tan et al. [72] reported that in the presence of Cd stress, amino acids and their derivatives have the ability to create compounds with heavy metal ions by utilizing their carboxyl and amino groups. This process ultimately enhances the ability of plants to withstand the harmful effects of heavy metals. In addition, organic acids can modify the chemical structure of heavy metals, hence decreasing their level of toxicity [73]. The KEGG enrichment analysis identified many pathways that showed substantial enrichment in the differentially abundant metabolites. These pathways include the folate resistance pathway, the ABC transporter pathway, d-glutamine and d-glutamic acid metabolism, purine metabolism, and pyrimidine metabolism. Plants rely on purine and pyrimidine nucleotides for a wide range of metabolic reactions. They play crucial roles in nucleic acid production, function as energy sources, and serve as building blocks for the production of fundamental substances such as sucrose, polysaccharides, and phospholipids, as well as secondary compounds [74]. Hence, the process of creating and breaking down nucleotides is vital for the growth and development of plants. Research has confirmed that ABC transporters play essential roles in plant heavy metal detoxification. For example, ABC transporters can increase the resistance of black wheat by transporting PC-Cd or GSH-Cd from the cytoplasm to the vacuole, thereby facilitating detoxification. Additionally, ABC transporters can export intracellular Cd across membranes to the extracellular space, thus increasing plant tolerance to Cd [75]. The study also revealed that differentially abundant metabolites such as alkaloids, organic acids, lipids, and lipid-like molecules were positively correlated with the available potassium, available phosphorus, total phosphorus, and total nitrogen. This may be because the root-secreted metabolites affect the soil nutrient composition, which in turn impacts plant growth. The composition and concentration of rhizosphere metabolites can alter the structure of the rhizosphere microbial community. Changes in microbial community structure imply shifts in microbial community functions, which can have an impact on the development of plants. These findings indicate that after immunization with Brevibacillus sp. SR-9, soil metabolites not only directly or indirectly provide nutrients to hybrid Pennisetum but also indirectly govern the community and organization of microorganisms in the rhizosphere. This regulation, in turn, affects hybrid Pennisetum growth and ultimately alleviates Cd stress. Soil metabolites serve as a reflection of changes in soil microbial communities, as alterations at the organismal and enzymatic levels manifest as modified metabolite profiles [76]. Under external pressures, plants drive the adjustment of microbial distribution to ensure optimal metabolic activity [77]. Therefore, metabolic communication is a crucial mechanism for plant–microbe interactions [68].

5. Conclusions

This work revealed that inoculating hybrid Pennisetum with the PGPB Brevibacillus sp. SR-9 greatly altered the bacterial composition and absorption of Cd and reduced the content of Cd in the soil. The contents of total nitrogen, total phosphorus, available phosphorus, and available potassium in the soil increased, alleviating Cd stress and thereby promoting the formation and proliferation of hybrid Pennisetum biomass to a certain degree. Moreover, the Brevibacillus sp. SR-9 strain altered the composition of the bacterial population in the root soil under Cd stress, affecting the proportions of Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, and Bacilli and increasing the relative abundance of bacterial genera in the root soil of hybrid Pennisetum. Furthermore, following the introduction of the Brevibacillus sp. SR-9 strain, there was a noticeable alteration in the proportion of metabolites present in the soil samples. This shift had an impact on many metabolic pathways, including the folate resistance pathway, the ABC transporter pathway, d-glutamine and d-glutamate metabolism, purine metabolism, and pyrimidine metabolism. This, in turn, increased the ability of hybrid Pennisetum to respond to Cd stress. This study deepens our understanding of the biochemical processes occurring in the rhizosphere and presents novel opportunities for the using microorganisms to assist plants in restoring soils polluted with heavy metals.

Author Contributions

Z.-J.C. and X.-M.R. designed the experiments; Z.-J.C., X.-M.R. and S.-S.G. participated in writing the paper; S.-S.G., Y.-J.Z., Y.S. and H.L. performed the experiments and analyzed the data; Z.-J.C., X.-M.R., B.L.L. and Y.-Y.L. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. U2004145), the Program for Science & Technology Innovation Talents in the Universities of Henan Province (No. 23HASTIT018), the Key Research and Development Projects of Henan Province (Grant Nos. 221111520600 and 231111113000), and the Key Scientific and Technological Project of Henan Province (Grant Nos. 242102521067 and 232102320252).

Data Availability Statement

The data that support the findings of this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. Assessment of the bacterial community diversity in different samples. (A) Comparison of the Chao index between groups, (B) comparison of the ACE index between groups, (C) comparison of the coverage index between groups, (D) comparison of the Shannon index between groups, (E) comparison of the Sobs index between groups, and (F) comparison of the Simpson index between groups. Green indicates the CK group, and orange indicates the SR-9 group.
Figure 1. Assessment of the bacterial community diversity in different samples. (A) Comparison of the Chao index between groups, (B) comparison of the ACE index between groups, (C) comparison of the coverage index between groups, (D) comparison of the Shannon index between groups, (E) comparison of the Sobs index between groups, and (F) comparison of the Simpson index between groups. Green indicates the CK group, and orange indicates the SR-9 group.
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Figure 2. PLS-DA of the bacterial community composition.
Figure 2. PLS-DA of the bacterial community composition.
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Figure 3. The relative abundance of communities in different treatment groups at the phylum (A) and genus (B) levels.
Figure 3. The relative abundance of communities in different treatment groups at the phylum (A) and genus (B) levels.
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Figure 4. Co-occurrence patterns of bacterial community networks in the CK (A) and SR-9 (B) groups. The figure is divided into 7 sections comprising the bacterial community networks under each treatment group. Within each square, the groups and the top 7 significant bacterial communities of the co-occurrence network are enumerated.
Figure 4. Co-occurrence patterns of bacterial community networks in the CK (A) and SR-9 (B) groups. The figure is divided into 7 sections comprising the bacterial community networks under each treatment group. Within each square, the groups and the top 7 significant bacterial communities of the co-occurrence network are enumerated.
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Figure 5. RDA ordination biplot between bacterial communities and environmental factors at the phylum (A) and genus (B) levels.
Figure 5. RDA ordination biplot between bacterial communities and environmental factors at the phylum (A) and genus (B) levels.
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Figure 6. PLS-DA analysis chart of cationic (A) and anionic (B) metabolites in the rhizosphere soil of hybrid Pennisetum.
Figure 6. PLS-DA analysis chart of cationic (A) and anionic (B) metabolites in the rhizosphere soil of hybrid Pennisetum.
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Figure 7. Permutation test of OPLS-DA models in the CK group (A) and Brevibacillus sp. SR-9 (B) group. The abscissa represents the displacement retention of the displacement test, the ordinate represents the values of the R2 (dot) and Q2 (triangular) displacement tests, and the two dotted lines represent the regression lines of R2 and Q2.
Figure 7. Permutation test of OPLS-DA models in the CK group (A) and Brevibacillus sp. SR-9 (B) group. The abscissa represents the displacement retention of the displacement test, the ordinate represents the values of the R2 (dot) and Q2 (triangular) displacement tests, and the two dotted lines represent the regression lines of R2 and Q2.
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Figure 8. Volcano plot showing differentially abundant metabolites between groups. Each dot in the graph represents a specific metabolite, and the size of the dot represents the VIP value. The default red dots indicate the metabolites with significantly increased abundance, whereas the blue dots indicate the metabolites with significantly decreased abundance. The gray dots indicate nonsignificantly differentially abundant metabolites.
Figure 8. Volcano plot showing differentially abundant metabolites between groups. Each dot in the graph represents a specific metabolite, and the size of the dot represents the VIP value. The default red dots indicate the metabolites with significantly increased abundance, whereas the blue dots indicate the metabolites with significantly decreased abundance. The gray dots indicate nonsignificantly differentially abundant metabolites.
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Figure 9. Bubble map of the KEGG enrichment analysis results. The size of the bubble in the figure represents the number of differentially abundant metabolites that were enriched in that pathway, and the color of the bubble represents the size of the p-value of different enriched pathways.
Figure 9. Bubble map of the KEGG enrichment analysis results. The size of the bubble in the figure represents the number of differentially abundant metabolites that were enriched in that pathway, and the color of the bubble represents the size of the p-value of different enriched pathways.
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Figure 10. Correlation analysis diagram of the differentially abundant metabolites and soil physicochemical properties of hybrid Pennisetum. The abscissa shows the physical and chemical properties of the soil, the ordinate shows the metabolites whose abundances increased, and a larger circle size and deeper color indicate higher values. * Indicates significance.
Figure 10. Correlation analysis diagram of the differentially abundant metabolites and soil physicochemical properties of hybrid Pennisetum. The abscissa shows the physical and chemical properties of the soil, the ordinate shows the metabolites whose abundances increased, and a larger circle size and deeper color indicate higher values. * Indicates significance.
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Figure 11. Correlation analysis of the differentially abundant metabolites and bacterial communities in hybrid Pennisetum. The abscissa represents the bacterial community at the genus level, and the ordinate represents the top 20 differentially abundant metabolites, and a larger circle size and deeper color indicate higher values.
Figure 11. Correlation analysis of the differentially abundant metabolites and bacterial communities in hybrid Pennisetum. The abscissa represents the bacterial community at the genus level, and the ordinate represents the top 20 differentially abundant metabolites, and a larger circle size and deeper color indicate higher values.
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Figure 12. Relationships between hybrid Pennisetum growth indices and soil physicochemical properties, bacterial communities, and differentially abundant metabolites. Hybrid Pennisetum growth indices are shown in a butterfly plot, and the physicochemical properties of the soil and bacterial communities and differentially abundant metabolites are shown on the right.
Figure 12. Relationships between hybrid Pennisetum growth indices and soil physicochemical properties, bacterial communities, and differentially abundant metabolites. Hybrid Pennisetum growth indices are shown in a butterfly plot, and the physicochemical properties of the soil and bacterial communities and differentially abundant metabolites are shown on the right.
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Table 1. Effects of inoculation with Brevibacillus sp. SR-9 on biomass, Cd content, Cd accumulation, and translocation factor (TF) in different parts of hybrid Pennisetum.
Table 1. Effects of inoculation with Brevibacillus sp. SR-9 on biomass, Cd content, Cd accumulation, and translocation factor (TF) in different parts of hybrid Pennisetum.
GroupAboveground Biomass (g)Root Dry Weight (g)Aboveground Cd Content (mg/kg)Root Cd Content (mg/kg)Aboveground Cd Accumulation (ug)Root Cd Accumulation (ug)TF
CK5.38 ± 0.760.74 ± 0.192.38 ± 0.3217.48 ± 2.9813.23 ± 2.7612.84 ± 4.300.14
SR-96.03 ± 0.500.94 ± 0.162.55 ± 0.6115.68 ± 2.6916.03 ± 2.8814.83 ± 1.620.16
Table 2. Effects of inoculation with Brevibacillus sp. SR-9 on the physicochemical properties of hybrid Pennisetum rhizosphere soil.
Table 2. Effects of inoculation with Brevibacillus sp. SR-9 on the physicochemical properties of hybrid Pennisetum rhizosphere soil.
GrouppHTotal Nitrogen Content (mg/kg)Total Phosphorus Content (mg/kg)Available Phosphorus Content (mg/kg)Available Potassium Content (mg/kg)
CK7.12 ± 0.101.76 ± 0.09242.07 ± 8.080.025 ± 0.001107.33 ± 2.17
SR-97.05 ± 0.051.87 ± 0.11250.44 ± 15.040.028 ± 0.003111.00 ± 2.74
Table 3. Topological parameters of the microbial co-occurrence networks.
Table 3. Topological parameters of the microbial co-occurrence networks.
GroupCKSR-9
Nodes387378
Edges58799110
Positive correlation33255248
Negative correlation25543862
Average degree30.382448.2011
Average weight degree21.229233.9104
Average path length1.51661.3958
Network diameter2.63023.1889
Network density0.07870.1279
Clustering coefficient0.4060.4906
Betweenness centralization0.00790.0063
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Gao, S.-S.; Zhang, Y.-J.; Shao, Y.; Li, B.L.; Liu, H.; Li, Y.-Y.; Ren, X.-M.; Chen, Z.-J. Combined Application of High-Throughput Sequencing and Metabolomics to Evaluate the Microbial Mechanisms of Plant-Growth-Promoting Bacteria in Enhancing the Remediation of Cd-Contaminated Soil by Hybrid Pennisetum. Agronomy 2024, 14, 2348. https://doi.org/10.3390/agronomy14102348

AMA Style

Gao S-S, Zhang Y-J, Shao Y, Li BL, Liu H, Li Y-Y, Ren X-M, Chen Z-J. Combined Application of High-Throughput Sequencing and Metabolomics to Evaluate the Microbial Mechanisms of Plant-Growth-Promoting Bacteria in Enhancing the Remediation of Cd-Contaminated Soil by Hybrid Pennisetum. Agronomy. 2024; 14(10):2348. https://doi.org/10.3390/agronomy14102348

Chicago/Turabian Style

Gao, Shan-Shan, Ying-Jun Zhang, Yang Shao, B. Larry Li, Han Liu, Yu-Ying Li, Xue-Min Ren, and Zhao-Jin Chen. 2024. "Combined Application of High-Throughput Sequencing and Metabolomics to Evaluate the Microbial Mechanisms of Plant-Growth-Promoting Bacteria in Enhancing the Remediation of Cd-Contaminated Soil by Hybrid Pennisetum" Agronomy 14, no. 10: 2348. https://doi.org/10.3390/agronomy14102348

APA Style

Gao, S. -S., Zhang, Y. -J., Shao, Y., Li, B. L., Liu, H., Li, Y. -Y., Ren, X. -M., & Chen, Z. -J. (2024). Combined Application of High-Throughput Sequencing and Metabolomics to Evaluate the Microbial Mechanisms of Plant-Growth-Promoting Bacteria in Enhancing the Remediation of Cd-Contaminated Soil by Hybrid Pennisetum. Agronomy, 14(10), 2348. https://doi.org/10.3390/agronomy14102348

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