Bacillus amyloliquefaciens B1408 suppresses Fusarium wilt in cucumber by regulating the rhizosphere microbial community

Applied Soil Ecology 136 (2019) 55–66

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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil

Bacillus amyloliquefaciens B1408 suppresses Fusarium wilt in cucumber by regulating the rhizosphere microbial community

T

⁎

Lingjuan Hana, Zeyu Wangc, Na Lia, Yonghong Wanga,b, , Juntao Fenga,b, Xing Zhanga,b a

Shaanxi Research Center of Biopesticide Engineering and Technology, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, China Key Laboratory of Plant Protection Resources and Pest Management of Ministry of Education, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, China c School of Food and Biological Engineering, Shaanxi University of Science & Technology, Weiyang University Campus, Xi'an, Shaanxi 710021, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Bacillus amyloliquefaciens B1408 Rhizosphere microbial community Illumina sequencing Fusarium wilt Cucumber

Fusarium wilt of cucumber caused by the fungus Fusarium oxysporum f. sp. cucumerinum (FOC) is one of the most destructive soil-borne diseases throughout the world. Bacillus amyloliquefaciens B1408, isolated from the rhizosphere soil of cucumber plants, can effectively suppress the pathogen invasion. In this study, we evaluated the effects of strain B1408 application on mycelial morphology, cucumber growth and rhizosphere microbial communities. Rhizosphere microbial communities were accessed by Illumina sequencing, and the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was applied to predict gene functions. The results showed that B1408 altered the hyphal morphology of FOC by causing malformation, distortion and extravasation of cytoplasm. The application of B1408 reduced the incidence of Fusarium wilt disease by 59.0% and promoted plant growth. Compared with the FOC treatment, the bacterial diversity significantly increased, whereas the presence of the fungi was significantly reduced following the B1408 application. More specifically, the B1408 application reduced the relative abundance of Fusarium, and promoted that of Acidovorax, Rhodanobacter, Sediminibacterium, Dongia, Streptomyces, Rhizobium, Mesorhizobium, BurkolderiaParaburkholderia, Asticcacaulis and Rhizoscyphus genera. Moreover, the disease index was significant negatively correlated with the abundance of beneficial taxa, including Rhodanobacter, Sediminibacterium, Streptomyces, Mesorhizobium, Bradyrhizobium, Rhizoscyphus and Penicillium. The PICRUSt data predicted an array of bacterial functions, with most differences being apparent in the combined inoculation treatment (B1408 + FOC). This study suggests that B. amyloliquefaciens B1408 may promote plant growth and alleviate FOC-induced damage by changing the microbial community composition in the cucumber rhizosphere.

1. Introduction Cucumber (Cucumis sativus L.), an important commercial vegetable, is widely grown worldwide. However, this crop is severely threatened by Fusarium wilt disease caused by Fusarium oxysporum f. sp. cucumerinum (FOC), a destructive soil-borne fungal disease that leads to plant death and serious economic loss (Ahn et al., 1998). To control this disease, some previous studies have revealed that the application of resistant varieties and chemical products has some effect, but these strategies are not economical, available or environmentally friendly (Cao et al., 2012; Chen et al., 2014). Currently, the biological control involving plant growth-promoting rhizobacteria (PGPR) has been used as a potential effective and sustainable alternative approach (Zhang et al., 2017). These PGPR strains

have exhibited the capacity to promote plant growth and suppress soilborne pathogens, such as Pseudomonas spp., Trichoderma spp., Streptomyces spp. and Bacillus spp.. Previous studies have demonstrated that Bacillus amyloliquefaciens species are particularly efficient biocontrol agents to fight a wide range of plant diseases (Lin et al., 2018; Yamamoto et al., 2015; Kulimushi et al., 2017a). The primary mechanism probably involves the synthesis of different antimicrobial compounds, competition for nutrients and biological space, and induction of systemic resistance (Martinezviveros et al., 2010; Chen et al., 2014). Furthermore, disease-suppression has also been attributed to diverse rhizospheric microbial communities, including bacteria, fungi and protozoa, which affect pathogen survival, soil enzyme activity and root colonization (Penton et al., 2014; Raza et al., 2015; Cha et al., 2016; Rania et al., 2016; Wu et al., 2016a). Shi et al. (2017) reported

⁎ Corresponding author at: Shaanxi Research Center of Biopesticide Engineering and Technology, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, China. E-mail address: [email protected] (Y. Wang).

https://doi.org/10.1016/j.apsoil.2018.12.011 Received 15 September 2018; Received in revised form 12 December 2018; Accepted 14 December 2018 Available online 29 December 2018 0929-1393/ © 2018 Elsevier B.V. All rights reserved.

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block design with three replications (eighty seedlings per replication in all treatments) for each treatment. A disease assessment was conducted 30 days after inoculation with FOC. The disease severity was rated based on a scale of 0–4 (0 = healthy; 1 = < 25% of leaves wilted; 2 = 25–50% of leaves wilted; 3 = 50–75% of leaves wilted; 4 = 75–100% of leaves wilted) (Peltier and Grau, 2008). The disease severity index (DSI) was calculated using the formula: disease index = [∑(rating × number of plants rated)/(total number of plants × highest rating)] × 100 (Shi et al., 2017).

that cucumber Fusarium wilt disease was significantly suppressed after Paenibacillus polymyxa NSY50 application by changing the soil enzyme activities, phy-chemical properties and microbial community structure. In our previous study, we found that B. amyloliquefaciens B1408, originally isolated from the rhizosphere of cucumber plants, exhibited a broad-spectrum defense against different fungal pathogens and was a potential biocontrol agent (Han et al., 2018). However, the mechanism of the microbial community involved suppressing disease after B1408 application is currently unknown. Recently, new generation metagenomics has been applied to comprehensively analyze the structure of the soil microbial community based on updated high-throughput sequencing technology (Shi et al., 2017). This method could rapidly provide accurate high-volume production of sequence data and offer an opportunity to achieve a high throughput and deeper insight into the effects of different treatments on the composition of rhizosphere microbial communities (Binladen et al., 2007; Shen et al., 2015). Therefore, the objectives of this study were (1) to analyze the effects of B1408 on the suppression of FOC and growth of cucumber plants, (2) to compare the differences in the composition of the rhizospheric microbial community after challenge with B1408 and FOC using high-throughput sequencing technology, (3) to explore the potential correlations between the beneficial microflora and disease suppression.

2.4. Assays of growth indices Plant height (cm), stem diameter (mm), root length (cm), and shoot and root fresh weights (g) were measured after 30 days of inoculation. Three seedlings were randomly selected from each treatment. In brief, the shoot and root fresh weights were measured on an electronic scale after washing with sterile distilled water and blot-dried with a paper towel. 2.5. Soil sample collection and DNA extraction Thirty days after inoculation with FOC, rhizosphere soils from the three biological replicate pots were collected and sieved (2 mm). Briefly, the roots were lightly shaken to remove loosely attached soil. Then, the soil still tightly adhering to the roots was harvested as rhizosphere soil and frozen at −80 °C for DNA extraction (Wu et al., 2016b). The total soil DNA was extracted using a MoBioPowerSoil™ DNA Isolation Kit (Mo Bio Laboratories Inc., USA) according to the manufacturer’s instructions. The genomic DNA concentration and purity were measured using an Eppendorf Biophotometer plus (Eppendorf, Germany).

2. Materials and methods 2.1. Microbial cultivation B. amyloliquefacien B1408 was grown on a Luria-Bertani (LB) medium at 30 °C for 12 h under a rotary shaker at 180 r/min, then inoculated in 100 mL of an LB medium at 2% inoculum and cultured at 30 °C and 180 r/min for 48 h; afterwards, it was centrifuged at 5000 r/ min for 10 min to collect the cells (Han et al., 2018). The harvested cells were resuspended in distilled water to generate 2.0 × 108 colonyforming units (CFU)/mL for inoculation. The pathogen FOC inoculum was prepared by incubation in PDB (potato dextrose broth) under a rotary shaker (180 r/min) for 7 days at 28 °C. The liquid culture was then filtered through two layers of sterile gauze and adjusted to 1.0 × 108 CFU/mL with sterile distilled water.

2.6. PCR amplification and Illumina MiSeq sequencing The bacterial community composition was assessed by sequencing the V4-V5 region of the 16S rRNA gene using the PCR primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT-3′) (Walters et al., 2016). The fungal ITS region was amplified using the primer set ITS1 (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′) (Mueller et al., 2014). The PCR reactions were carried out in a 50 μL reaction mixture containing 1.5 μL each primer, 1 μL dNTP, 10 μL Buffer, 0.2 μL Q5 HighFidelity DNA Polymerase, 10 μL High GC Enhancer and 40 ng soil DNA template. The PCR conditions for bacteria were initiated at 95 °C for 3 min, followed by 25 cycles of denaturation at 95 °C for 45 s, annealing at 50 °C for 45 s, and extension at 68 °C for 90 s, followed by a final elongation at 68 °C for 7 min, and then at 4 °C hold. The PCR conditions for fungi were initiated at 98 °C for 2 min, followed by 30 cycles of denaturation at 98 °C for 30 s, annealing at 50 °C for 30 s, and extension at 72 °C for 1 min, followed by a final elongation at 72 °C for 5 min, and then at 4 °C hold. The PCR products were pooled and visualized on 1.8% agarose gels, purified using a MinElute® PCR Purification Kit according to the manufacturer’s instructions, and quantified using QuantiFluorTM-ST (Promega, US). High-throughput sequencing was carried out on the Illumina MiSeq platform (BioMarker Technologies Co. Ltd, China). After pyrosequencing, raw sequences were processed with Prinseq (PRINSEQlite 0.19.5) to remove low-quality data and improve the syncretic rates of the subsequent sequence. Split sequences for each sample were merged using FLASH V1.2.7 (Magoc and Salzberg, 2011). Using UCLUST (version 1.2.22) with a cut-off of 97% similarity, the OTUs were clustered and the taxonomic classification was performed using RDP Classifier (Version 2.2, based on Bergey’s taxonomy) with the classification threshold set at 0.8. The sequences were taxonomically identified by a

2.2. Scanning electron microscopy (SEM) The fungal pathogen was cultured at 28 °C for 5 days in a PDA medium in the presence or absence of the strain B1408. The margin of the inhibition zone (10 mm × 10 mm) was cut and observed under a scanning electron microscope (Tao et al., 2014). 2.3. Pot experiment Seeds of cucumber (C. sativus L., cv. Jinchun 2) were germinated on a moist filter paper in darkness at 28 °C for 24 h and were then transferred into plastic pots (10 × 10 × 9 cm) containing a mixed substrate (peat and vermiculite 2:1, v/v) and cultivated in a growth chamber with a photoperiod of 16 h (600 μmol m−2·s−1 at 28 °C) at 28/18 °C (day/night) and 75% relative humidity. Each plastic pot contained one seedling. At the stage of two true leaves, plants were subjected to inoculation. Four treatments were designed as follows: (1) CK, untreated plants (control), (2) B1408, plants were inoculated with 20 mL of B1408, (3) FOC, plants were inoculated with 20 mL of FOC, and (4) B1408 + FOC, plants were inoculated with 20 mL of B1408, and three days later, plants were inoculated with 20 mL of FOC. All treatments were conducted by the root-irrigation method. The control treatments consisted of an equivalent volume of sterile distilled water, and the whole growth process was supplemented weekly with half-strength Hoagland’s nutrient solution. The experiment was arranged in a randomized complete 56

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3.3. Sequencing and microbial diversity analysis

BLASTn search of a curated NCBI database.

A total of 872,017 16S rRNA gene sequences and 728,377 fungal ITS sequences were analyzed across 12 rhizosphere soil samples, with an average of 72,668 ± 622 sequences per soil sample for bacteria and 60,698 ± 18,879 sequences per soil sample for fungi (Table S1). Based on a threshold of 97% nucleotide sequence identity, these sequences were grouped into 899 bacterial and 324 fungal OTUs (Fig. S2). A rarefaction curve analysis at 3% dissimilarity for the bacterial and fungal communities revealed that the curves reached plateaus, implying that the sampling was sufficient and reasonable. The bacterial and fungal richness (ACE and Chao1) and community diversity (Shannon index) were estimated (Table 2). We observed significantly higher bacterial richness and community diversity in the rhizosphere soil of the B1408 + FOC treatment compared with the values of rhizosphere soil of the FOC treatment. However, for fungi, the ACE and Chao1 indices showed the opposite results.

2.7. Functional gene prediction Putative bacterial metagenomic functions were imputed using a phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) on the 16S rRNA gene abundance data (Langille et al., 2013). Using functions within the PICRUSt pipeline, the OTU-table was normalized and used for metagenome inference of Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs). The predicted functions were then collapsed into hierarchical KEGG pathways using the categorize_by_function step in the PICRUSt pipeline (Wilkinson et al., 2017).

2.8. Statistical analysis All data and Spearman correlation analysis were analyzed using the SPSS 20.0 program (SPSS Inc., USA), and the significance between treatments was assigned at p < 0.05 using a one-way analysis of variance (ANOVA) with Duncan’s test. For the diversity analysis, a rarefaction curve was generated to compare the relative levels of bacterial and fungal OTU diversity across all soil samples using the Mothur software (Shi et al., 2017). Permutational multivariate analysis of variance (PERMANOVA) was performed to evaluate the significant differences in bacterial and fungal community structures between the four treatments. A linear discriminant analysis (LDA) effect size (LEfSe) was performed to identify the bacterial and fungal taxa differentially represented between the FOC and B1408 + FOC treatments (Segata et al., 2011). Pearson’s correlation network analyses, which visually reflect the correlation between species at a certain classification level (phyla, class, order, family, genus, species) between selected samples, were implemented in Python (Friedman and Alm, 2012). A Statistical Analysis of Metagenomic Profiles (STAMP) was employed to analyze the PICRUSt data (Parks et al., 2014).

3.4. Microbial community structure A principal coordinate analysis (PCoA) based on the Bray-Curtis algorithm clearly revealed that the soil microbial community structures varied among treatments (Fig. 3). For both fungal and bacterial communities, all treatments were clearly separated from each other (PERMANOVA: R2 = 0.928, p = 0.001 for bacteria; R2 = 0.943, p = 0.001 for fungi). Of the total variance in the dataset, the first two principal components together explained 85.18% and 91.96% of the total bacterial and fungal communities, respectively. In addition, the first principal component (PC1) was the most important, accounting for 65.38% and 78.33% of the total variation of the bacterial and fungal communities, respectively. 3.5. Effects of the B1408 challenge on rhizosphere microbial taxonomic composition The top 10 most abundant bacterial and fungal phyla were selected to monitor changes in the rhizosphere microbial community in samples from different treatments (Fig. 4). Proteobacteria, followed by Bacteroidetes, Verrucomicrobia, Planctomycetes, Actinobacteria and Acidobacteria, were the six most abundant phyla across all samples among the classified bacterial phyla, accounting for > 80% of all bacterial sequences. Among the most abundant phyla, the relative abundance of Actinobacteria was lowest, while that of Bacteroidetes was highest in the FOC treatment compared with the control (CK), B1408 and B1408 + FOC treatments. Firmicutes and Chloroflexi were relatively more abundant in the B1408 treatment compared with the control treatment (CK). Moreover, the rhizosphere soils in the B1408 + FOC treatment were found to result in a lower abundance of Proteobacteria and Actinobacteria compared with the soils from the control treatment (CK). In addition, the relative abundances of Proteobacteria, FBP and Firmicutes were significantly increased in the B1408 + FOC treatment compared with the FOC treatment. Of the classified fungal phyla, Ascomycota and Basidiomycota were the most abundant phyla in all treatments. Interestingly, the abundance of Basidiomycota increased significantly in the B1408 treatment, while that of Ascomycota was the highest in the FOC treatment. At the genus level, the comparison of the relative abundances of top 20 classified genera revealed significant differences with respect to all treatments (Fig. 5). Among the top 20 classified bacterial genera, the B1408 + FOC treatment significantly enriched the genera Acidovorax, Rhodanobacter, Sediminibacterium, Dongia, Streptomyces, Rhizobium, Mesorhizobium, Burkolderia-Paraburkholderia and Asticcacaulis compared with the rhizosphere soils treated with FOC. Moreover, the relative abundances of nine genera, including Rhodanobacter, Sediminibacterium and Mesorhizobium, were significantly higher in the B1408 treatment than in the control treatment (CK). However, the abundances of ten

3. Results 3.1. Effect of B1408 on mycelial morphology of FOC The ultrastructure of FOC mycelia treated with B1408 was observed by SEM. After being treated with B1408, the mycelia suffered severe changes. The control displayed the characteristic morphology, namely, typical tapered apices and a smooth surface (Fig. 1A and B), while in the treatment with B1408 (Fig. 1C and D), the prominent morphological changes appeared as malformation, distortion and extravasation of cytoplasm.

3.2. Biocontrol effect and plant growth The effects of B1408 application on disease suppression were investigated in cucumber plants (Fig. 2). Compared to the FOC treatment, the pretreatment with B1408 significantly reduced the incidence of cucumber Fusarium wilt disease from 83.7% to 24.7%. In addition, the B1408 treatment significantly showed an increased impact on the growth of cucumbers compared to the other treatments (Table 1). However, the FOC treatment seriously decreased the growth, height, stem diameter, root length, and shoot and root fresh weight of the plants, which were significantly suppressed compared with the control treatment (CK). Moreover, the B1408 + FOC treatment exhibited a 2.06- and 1.55-fold increase in shoot and root fresh weight, respectively, compared with the FOC treatment alone. The common symptoms caused by the FOC treatment were lower leaf yellowing and wilt (Fig. S1). 57

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Fig. 1. Scanning electron micrograph of F. oxysporum hyphae. A and B: hyphae of the control treatment; C and D: hyphae treated with B1408.

Table 2 The mean of the ACE, Chao 1 and Shannon indices of rhizosphere soil challenged with B1408 and FOC at 97% similarity. Treatment

Community characteristics Bacterial community

CK B1408 FOC B1408 + FOC

Fungal community

ACE

Chao1

Shannon

ACE

Chao1

Shannon

878c 881b 876c 886a

874c 885b 875c 889a

5.28b 5.4a 5.11c 5.23b

276a 256b 252b 224c

276a 257b 255b 227c

3.55a 3.59a 1.29b 1.27b

Different letters in each column indicate statistically significant differences based on Duncan’s test (p < 0.05).

genera, including Rhizomicrobium, Acidovorax and Rhodanobacter, decreased significantly in the FOC treatment compared with the control treatment (CK). Among the top 20 classified fungal genera, the relative abundances of Rhizoscyphus, Cryptococcus, Scytalidium, Arthrobotrys, and Clitopilus increased significantly in the B1408 treatment compared with the control treatment (CK). However, the relative abundances of fifteen genera, including Rhizoscyphus, Chrysosporium, Penicillium and Scytalidium, decreased significantly in the FOC treatment compared with the control treatment (CK). Compared with the FOC treatment, the fungal genera of Rhizoscyphus, Chrysosporium, Penicillium, Chaetomium and Cryptococcus were more abundant in the B1408 + FOC treatment. Fusarium was the hyper dominant genus, accounting for 79.37% and 74.02% of the total fungal sequences in the FOC and B1408 + FOC

Fig. 2. Effects of different treatments on the incidence of Fusarium wilt of cucumber seedlings at 30 days after FOC inoculation. Different treatments: CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. Each histogram represents the mean ± SE of three independent biological experiments (n = 3). Different letters above the bars indicate statistically significant differences by Duncan’s test (p < 0.05).

Table 1 Effects of different treatments on the growth indices of the cucumbers. Treatment

Plant height (cm)

Stem diameter (mm)

Root length (cm)

Shoot fresh mass (g)

Root fresh mass (g)

CK B1408 FOC B1408 + FOC

23.32 27.54 11.49 16.22

6.14 6.75 4.41 5.04

12.52 ± 0.47b 14.98 ± 0.37a 8.48 ± 0.15c 8.45 ± 0.08c

13.66 ± 0.20b 17.14 ± 0.35a 3.04 ± 0.36d 6.26 ± 0.30c

0.61 0.78 0.22 0.34

± ± ± ±

1.22b 0.89a 1.02d 1.05c

± ± ± ±

0.16b 0.09a 0.11d 0.11c

± ± ± ±

0.07b 0.06a 0.04d 0.03c

CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. Data were expressed as the mean ± SE of three replicates. Different letters indicate significant differences at p < 0.05, according to Duncan’s test. 58

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Fig. 3. Principal component analysis based on the distance matrix calculated using the Bray-Curtis algorithm for soil samples collected from different treatments. Different treatments: CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/ mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. (A) bacteria; (B) fungi.

of Bacillus was positively correlated with 26 classified genera, such as Acidicapsa, Acidovorax, Dongia and Rhizobium. Among the fungal community network, 28 classified genera were detected, while the relative abundance of Fusarium was negatively correlated with genera of Penicillium, Rhizoscyphus and Chaetomium (Fig. 7B).

treatments, respectively. Bacterial and fungal taxa with significantly different abundances were detected by using LEfSe between the FOC and B1408 + FOC treatments (Fig. 6). The most differentially abundant bacterial taxa in the B1408 + FOC rhizosphere soils belong to the Proteobacteria phylum (Fig. 6A). Moreover, 33 genera out of the top 54 bacterial genera were overrepresented in B1408 + FOC rhizosphere soil samples, such as Acidicapsa, Streptomyces, Sediminibacterium, Bacillus and Mestorhizobium. With respect to fungi, Ascomycota was more abundant in the FOC rhizosphere soils, whereas the genera Rhizoscyphus, Chrysosporium, Penicillium, Chaetomium and Cryptococcus were more abundant in the B1408 + FOC rhizosphere soils (Fig. 6B).

3.7. Correlation between microbial community structure and Fusarium wilt disease suppression The correlation of the top 10 abundant phyla associated with the bacterial and fungal communities with Fusarium wilt disease incidence was assessed (Table S2). The relative abundances of Proteobacteria and Actinobacteria were shown negative correlation with the disease index (p < 0.01). Additionally, the relative abundance of Ascomycota was positively correlated with disease index (p < 0.01). At genus level, the top 20 abundant genera were shown in Table S3. Among the bacterial genera involved in disease suppression, Spearman correlation analysis showed that the relative abundances of Rhizomicrobium, Rhodanobacter, Sediminibacterium, Streptomyces, Planctomycete, Mesorhizobium and Bradyrhizobium were negatively correlated with

3.6. Microbial community networks Network analyses, using significant Pearson’s correlations, identified the correlation between species at the genus level of the bacterial and fungal communities in the FOC and B1408 + FOC treatments (Fig. 7). The resulting bacterial community network consisted of 50 correlated classified genera (Fig. 7A). Moreover, the relative abundance

Fig. 4. The relative abundance of the top 10 classified bacterial phyla (A) and fungal phyla (B) for soil samples collected from different treatments. Different treatments: CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. The relative abundance was based on the proportional frequencies of those DNA sequences classified at the phylum level. Different numbers (1, 2, 3) after the letters of all treatments indicate the three replications, and the letter B in the graph replaces B1408 to avoid confusion. 59

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Fig. 5. The relative abundance of the main bacterial genera (A) and fungal genera (B) for the soil samples collected from different treatments. Different treatments: CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. Each histogram represents the mean ± SE of three independent biological experiments (n = 3).

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Fig. 6. Comparison of microbial variations based on the LEfSe analysis in the FOC treatment (plants challenged with FOC (1.0 × 108 CFU/mL)) and B1408 + FOC treatment (plants challenged with B1408 (2.0 × 108 CFU/mL) for 3 days, and then with FOC (1.0 × 108 CFU/mL)). Differences are represented by the color of the taxa (green indicating the B1408 + FOC treatment, red indicating the FOC treatment). (A) bacteria; (B) fungi. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.8. Effects of B1408 challenge on rhizosphere microbial community function

disease index. Among the fungi genera, the cucumber Fusarium wilt disease was caused by the pathogen FOC and the results showed that abundance of Fusarium exhibited a significantly positive correlation with disease index (p < 0.01). Besides, the results also indicated that the abundances of Rhizoscyphus, Chrysosporium, Penicillium, Rhizophlyctis, Cladosporium, Chaetomium, Scytalidium, Aspergillus, Capronia, Cryptococcus and Exophiala were significantly negative correlation with disease index (p < 0.01).

Predictions of the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs) and pathways were performed on the 16S rRNA gene soil bacterial composition data by using the PICRUSt (Fig. 8). The pathways related to cell motility, the endocrine system, signal transduction and membrane transport were overrepresented in the B1408 treatment compared with the control treatment (CK) (Fig. 8A), while 12 pathways, including cell motility, nucleotide 61

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Fig. 7. Pearson’s correlation network analyses of microbial communities in FOC treatment (plants challenged with FOC (1.0 × 108 CFU/mL)) and B1408 + FOC treatment (plants challenged with B1408 (2.0 × 108 CFU/mL) for 3 days, and then with FOC (1.0 × 108 CFU/mL)). Nodes represent genera, with size reflecting the relative abundance. Edges between nodes represent correlations between the nodes they connect, with edge width indicating the correlation magnitude and orange and green colors indicating positive and negative correlations, respectively. (A) bacteria; (B) fungi. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

metabolism, metabolism of cofactors and vitamins, biosynthesis of other secondary metabolites, energy metabolism, glycan biosynthesis and metabolism and signal transduction, were overrepresented in the B1408 + FOC treatment after inoculation with the pathogen (Fig. 8B).

Development of environmentally friendly alternatives to replace the extensive use of chemical pesticides is one of the biggest ecological challenges at present. The use of beneficial microorganisms is considered one of the most promising methods for more rational and safer 62

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Fig. 8. Metagenomes predicted by PICRUSt show significant differences in the functionality in the soil samples collected from different treatments. Different treatments: CK, untreated plants (control); B1408, plants challenged with B1408 (2.0 × 108 CFU/mL); FOC, plants challenged with FOC (1.0 × 108 CFU/mL); B1408 + FOC: plants challenged with B1408 for 3 days, and then with FOC. (A) Significant comparisons between CK and soils treated with B1408; (B) Significant comparisons between FOC and soils treated with B1408 + FOC.

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properties and other microbial communities, to maintain an optimal environment to suppress disease (Shi et al 2017). Previous studies have also revealed higher abundances of Proteobacteria in suppressive soil, which was known for species that produce high levels of secondary metabolites to inhibit pathogens (Mendes et al., 2011; Rosenzweig et al., 2012). Thus, enrichment of Actinobacteria and Proteobacteria in disease-suppressive soils may associate with higher disease suppression ability. In particular, the genera Acidovorax, Rhodobacter, Sediminibacterium, Dongia, Streptomyces, Rhizobium, Mesorhizobium and BurkolderiaParaburkholderia, identified as the dominant genera in B1408 + FOCtreated soil, have been frequently reported to be responsible for suppressing soil-borne pathogens (Mahaffee and Kloepper, 1997; Ahemad and Kibret, 2014; Li et al., 2014; Xue et al., 2015). However, our results showed that Rhizobium had a positive correlation with the disease index. This may be relation with the shifts in soil properties, including enzyme activities and phy-chemical properties, may affect the microbial community, thus influencing the interaction between plants and beneficial microbes (Shi et al 2017). Furthermore, recent studies have indicated that indigenous rhizosphere microbial communities, especially PGPRs, are often directly, or via production and secretion of various regulatory chemicals, in the vicinity of the rhizosphere, consequently impacting crop growth (Kloepper et al., 2004; Haichar et al., 2008; Bhattacharyya and Jha, 2012; Ahemad and Kibret, 2014). In our study, the relative abundance of the majority of well-recognized PGPRs (e.g., Rhizomicrobium sp., Rhodanobacter sp., Sediminibacterium sp., Caulobacter sp., Burkholderia sp., and Bradyrhizobium sp.) was significantly higher in the B1408 treatment than in the control treatment (CK). Moreover, the relative abundance of Bacillus was positively correlated with many PGPRs, which were significant negatively correlated with disease index (Fig. 7A and Table S3). Therefore, our results, combined with previous research, indicated that the biocontrol agent B1408 may promote the growth of the PGPRs, and consequently improve the development of cucumbers and the suppression of pathogens. The use of B1408 in the suppression of cucumber Fusarium wilt also altered the composition of the fungal community. In this study, Basidiomycota and Ascomycota were the most abundant fungal phyla in all treatments. Moreover, the abundance of Ascomycota decreased significantly under B1408-challenged treatments and showed a positively correlation with the incidence of Fusarium wilt disease. Previous studies demonstrated that the Ascomycota phylum included some fungal pathogens (Marcos et al., 2003; Sprague et al., 2007). In addition, Shen et al. (2015) reported that the depletion of Ascomycota in the banana rhizosphere was correlated with Fusarium wilt disease suppression. For the fungal genera, B1408 application significantly enriched the abundance of Rhizoscyphus which was significantly negatively correlated with disease index (r = −0.914). Previous study has shown that Rhizoscyphus sp. can significantly increase C. barbinervis seedling growth by promoting the uptake of the essential macronutrient K (Yamaji et al., 2016). Previous studies have also demonstrated that Chrysosporium, Penicillium, Chaetomium and Cryptococcus can alleviate the disease (Chand-Goyal and Spotts, 1996; Li et al., 2017; Shi et al., 2017; Soytong, 1992). Interestingly, these results also confirmed by the Spearman correlation analysis in our research (Table S3). Our study also found that the relative abundance of Fusarium was negatively correlated with genera of Penicillium, Rhizoscyphus and Chaetomium (Fig. 7B). Although Fusarium was the hyper dominant genus after FOC inoculation, the B1408 + FOC treatment significantly decreased the disease incidence. Maybe, it was related to the pathogenicity ability of pathogen, in our study, B1408 severely changed the mycelial morphology of FOC. Moreover, the FOC populations include pathogenic and non-pathogenic FOC, and they could not be separately quantified (Shi et al., 2017; Sutherland et al., 2013). These results indicated that B1408 application can be considered to be an effective strategy to control cucumber Fusarium wilt. Understanding the function of the microbiome is key for

crop management (Chen et al., 2014). Members of the Bacillus genus, widely used in the biocontrol of plant pathogens, are often considered to be important biocontrol agents (Chowdhury et al., 2015; Jiang et al., 2015). Therefore, understanding the biocontrol mechanisms will help us evaluate and improve the biological control of soil-borne pathogens in agriculture. This study determined the effect of B. amyloliquefaciens B1408 against F. oxysporum for the inhibition of mycelial growth. It was observed that B1408 caused hyphal deformities, such as malformation, distortion and extravasation of cytoplasm, in the test pathogen selected for the study, which may be due to the production of antifungal secondary metabolites. This is in line with a previous study which showed the swelling of mycelial tips due to antagonistic effect of Bacillus towards Curvularia lunata (Basha and Ulaganathan, 2002). Mycelial deformities and inhibition of conidiation in several phytopathogenic fungi, such as S. sclerotiorum and M. phaseolina, by Burkholderia cepacia have been shown due to the production of metabolites in vitro (Upadhyay and Jayaswal, 1992; Minaxi and Saxena, 2010). B1408 application significantly stimulated the growth of cucumber plants; the plant height, stem diameter, root length, and shoot and root fresh weight were all increased to different degrees compared to the CK treatment in the pot experiment. However, FOC added to cucumber seedlings exerted significantly negative effects on growth (Table 1). This finding is in accord with previous reports that various diseases adversely affect plant growth and development (Zhang et al., 2001; Nadeem et al., 2014; Shi et al., 2016). Furthermore, the disease incidence was significantly suppressed after B1408 application. The results were roughly in agreement with many previously published works showing that B. amyloliquefaciens species may be efficiently applied as biocontrol agents to reduce the impact of pathogen infection in the field and to promote plant growth (Kulimushi et al., 2017b; Abdallah et al., 2018; Wan et al., 2018). Among the microbial diversity analysis, the higher OTU numbers for the bacterial 16S rRNA gene sequences than those for the fungal ITS sequences were observed in the all treatments. Moreover, the alpha diversity indices for bacteria, including the ACE richness, Chao1 index and Shannon diversity, were higher in the rhizosphere samples from the soil of the B1408 + FOC treatment compared with the FOC treatment. However, contrasting results were observed for fungi. These results confirmed the findings of previous studies, in which greater bacterial diversity in suppressive soils promotes resistance to Fusarium wilt of cucumber (Shen et al., 2015; Shi et al., 2017). The microbial community composition and structural analysis revealed that all the treatments harbored structurally distinct microbial communities. Proteobacteria, Bacteroidetes, Verrucomicrobia, Planctomycetes, Actinobacteria and Acidobacteria, were the most abundant bacterial phyla in all treatments. The B1408 application increased the abundance of Chloroflexi and Firmicutes, which was in agreement with a previous study on microflora in a biofertilizer soil (Shen et al., 2015). Compared with the control treatment (CK), the FOC-challenged treatments reduced the enrichment of the Actinobacteria phylum. Moreover, the abundance of Actinobacteria was negatively (r = −0.885) associated with disease index. This finding was consistent with earlier reports, in which the Actinobacteria phylum was consistently associated with disease suppression, since they have higher abundances in many disease-suppressive soils than in disease-conducive soils (Fu et al., 2016; Hunter et al., 2006; Sanguin et al., 2010). Compared with the FOC treatment, the B1408 + FOC treatment significantly enriched the relative abundance of the phyla Proteobacteria, FBP and Firmicutes. Furthermore, the abundance of Proteobacteria was positively correlated with disease suppression (r = −0.873), while the abundance of Firmicutes was not correlated to Fusarium wilt disease incidence. On the contrary, the FBP phylum had a positive relationship with the disease index. This may be because biocontrol agents and pathogens can produce a wide range of responses to affect the soil environment, such as altering enzyme activities, phy-chemical 64

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understanding their interrelationships with the environment. Inferring function based on the diversity of bacteria present can be difficult, because the bacteria often transfer genes and show a high degree of reliance and redundancy (Allison and Martiny, 2008). The relatively recently developed PICRUSt program has proved to be effective at obtaining functional predictions from 16S rRNA taxonomic data (Langille et al., 2013). Therefore, in an attempt to gain functional insight into the rhizosphere microbial community, we used PICRUSt. The main observations from the PICRUSt function data were that B1408 application significantly affected the function of the rhizosphere microbiome. The function of membrane transport in the soybean rhizosphere may be related to benefits to the plant, such as growth promotion and nutrition (Mendes et al., 2014). Other studies have indicated that energy metabolism and signal transduction can enhance resistance to Fusarium wilt in banana (Berg et al., 2007). Plant growth-promoting rhizobacteria can produce secondary metabolites antagonistic to several soil-borne pathogens. In our study, there was a marked increase of biosynthesis of other secondary metabolites in the B1408 + FOC treatment following FOC infection.

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