Effect of biocontrol agent Bacillus amyloliquefaciens SN16-1 and plant pathogen Fusarium oxysporum on tomato rhizosphere bacterial community composition

Accepted Manuscript Effect of biocontrol agent Bacillus amyloliquefaciens SN16-1 and plant pathogen Fusarium oxysporum on tomato rhizosphere bacterial community composition Tingting Wan, Huihui Zhao, Wei Wang PII: DOI: Reference:

S1049-9644(17)30116-0 http://dx.doi.org/10.1016/j.biocontrol.2017.05.014 YBCON 3598

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Biological Control

Received Date: Revised Date: Accepted Date:

9 March 2017 23 May 2017 30 May 2017

Please cite this article as: Wan, T., Zhao, H., Wang, W., Effect of biocontrol agent Bacillus amyloliquefaciens SN16-1 and plant pathogen Fusarium oxysporum on tomato rhizosphere bacterial community composition, Biological Control (2017), doi: http://dx.doi.org/10.1016/j.biocontrol.2017.05.014

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Effect of biocontrol agent Bacillus amyloliquefaciens SN16-1 and plant pathogen Fusarium oxysporum on tomato rhizosphere bacterial community composition

Tingting Wan, Huihui Zhao, Wei Wang*

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China

*Corresponding author: Wei Wang Tel: +86 21 64253707 Fax: +86 21 64253707 E-mail: [email protected]

Co-authors: Tingting Wan (e-mail: [email protected]); Huihui Zhao (e-mail: [email protected])

Abstract The fungus Fusarium oxysporum causes tomato plant wilt, which lead to economic losses worldwide. Bacillus spp. has been widely used as a biological control agent against tomato wilt; in a previous study, Bacillus amyloliquefaciens SN16-1 showed great potential for controlling F. oxysporum f. sp. lycopersici (FOL). However, little is known about the effects of SN16-1 and FOL on tomato rhizosphere bacterial community composition. To address this issue, the present study investigated changes in community diversity following SN16-1 and FOL treatment by Illumina MiSeq sequencing of the 16S rRNA V4 region. The sequences were used to establish operational taxonomic units and were classified into 22 phyla and 323 genera, respectively. Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, and Bacteroidetes were the most highly represented phyla in each sample. Pseudomonas and Massilia were more abundant in the SN16-1 group, whereas Chthoniobacter and Haloferula were more highly represented in the FOL group. Diversity indices and principal component analysis indicated that treatment with SN16-1 and FOL had transient effects on the rhizosphere bacterial community. In summary, these results combined with the functional analysis of the changed genera provide safe and effective measure associated with using B. amyloliquefaciens SN16-1 for the control of tomato wilt. Keywords: Bacillus amyloliquefaciens SN16-1; Fusarium oxysporum f. sp. lycopersici; bacterial diversity; Illumina MiSeq; rhizosphere community

1. Introduction The soil-borne ascomycete Fusarium oxysporum invades the roots of at least 100 different plant species, causing plant yellowing, wilting, and even death through colonization of xylem vessels (Gawehns et al., 2015). Strategies for controlling Fusarium-associated wilt disease include crop rotation, application of chemical fungicides, breeding resistant crops, and biological control (Rongai et al., 2016; Shen et al., 2015). However, Fusarium spp. can persist for long periods in the soil and have a broad host range; moreover, chemical treatments have resulted in the emergence of resistant species and are a source of environmental pollution (Boyd et al., 2013; Kim et al., 2015; Lopez-Berges et al., 2013; Prasanna et al., 2013; Vitullo et al., 2012). For these reason, biological control agents (BCAs) are increasingly favored as an effective and environmental safe approach to managing plant wilt (Yin et al., 2013). Bacillus spp. is abundant in soil and has been investigated for their potential application as a BCA against plant disease (Zhao et al., 2014). Their biocontrol activities include secretion of secondary metabolites ( such as cyclic lipopeptides and volatile organic compounds), competition for nutrients, parasitism, and induction of systemic resistance in crops (Kim et al., 2015; Mandal et al., 2009; Shang et al., 2016; Vitullo et al., 2012; Wang et al., 2012). However, BCAs may also exert these effects on non-target microorganisms (Chowdhury et al., 2013; Krober et al., 2014; Wu et al., 2016), which could have detrimental effects on plant growth. Rhizosphere bacterial communities play an important role in plant disease suppression (She et al., 2016). Various approaches have been used to evaluate the diversity of bacteria in soil, such as culture-dependent methods and DNA fingerprinting (Kim et al., 2010; Vink et al.,

2014; Wu et al., 2015); however, these can only reflect changes at the community and not the individual level (Chen et al., 2016). Application of next generation sequencing approaches provide powerful tool to obtain information about the microbial diversity, structure and composition from environment soil samples. Illumina (San Diego, CA, USA) has adopted a sequencing by synthesis approach (Quail et al., 2012) that is more cost effective than 454 pyrosequencing and can be used to evaluate microbial diversity (Bokulich et al., 2013b; Degnan and Ochman, 2012; Zhou et al., 2011). What is more, the use of Illumina MiSeq sequencing technology can clearly analyze the microbial population in soil and its relative abundance, and get variation of microbial population and structure after the condition changed, which is a powerful method in the field of plant pathology and biological control. Bacillus amyloliquefaciens SN16-1, which was isolated from the soil in our laboratory, has demonstrated the ability to inhibit tomato wilt. In addition, Pseudomonas fluorescens SN15-2 has been identified to have a strong antagonistic activity against a variety of plant pathogens , and acted as the control of biocontrol bacterial SN16-1 in this assay. However, it is unclear what effect SN16-1 has on indigenous soil microorganisms. To address this issue, the present study investigated the effects of SN16-1 and Fusarium oxysporum f. sp. lycopersici (FOL) on tomato rhizosphere microbial community composition using Illumina MiSeq for amplification and analysis of the 16S rRNA V4 region of bacterial genomes.

2. Materials and methods 2.1. Bacterial and fungal strains The BCAs B. amyloliquefaciens SN16-1 and P. fluorescens SN15-2 were preserved in our

laboratory, and grown in Luria Broth (LB) medium and Kings’ B (KB) medium at 30°C, 200 r/min for 12h, respectively. After centrifugation at 12,000g for 10 min, the precipitate was suspended in distilled water. The pathogen FOL was kindly provided by Dr. Chulong Zhang (Zhejiang University, ZJ, China) and was cultured in Potato Dextrose Broth (PDB) medium at 28°C and 200r/min for 14 days. After removal of mycelium, spores were suspended in distilled water again.

2.2. Greenhouse experiment design Soil samples were obtained from potted plants of tomato (Solanum lycopersicum). Tomato seed surface was sterilized by soaking in 2% NaClO for 3 min, followed by several rinses with sterile water. The seeds were placed in polyvinyl chloride pots each containing 350 g soil and 10 g perlite (six seeds per pot). The soil was inoculated with FOL (10 6 spores/g soil) that was cultured with shaking for 14 days. Water was used as a control. The pots were placed in a greenhouse at 25°C under a 16/8 h light/dark cycle for 7 days, then the soil was inoculated with the BCAs SN16-1 and SN15-2 (108 cfu/g soil), with water used for the control group. The pots were divided into the following six treatment groups: no treatment (control)， inoculation with SN16-1, SN15-2, SN16-1 and FOL, SN15-2 and FOL, and FOL only. The above pot experiment was repeated three times.

2.3. Soil sampling and DNA extraction Samples (36 in total, i.e., three for each group) were collected at 10 and 40 days after BCAs inoculation. Soil clinging to the root of the tomato plant was stored at −20°C until analysis.

Genomic DNA was extracted from 250 mg of rhizosphere soil using the PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, USA) according to the manufacturer’s instructions. DNA concentration and quality were determined using an ultraviolet spectrometer (Eppendorf, Hamburg, Germany).

2.4. PCR amplification The bacterial primers 520F (5′-barcode+GCACCTAAYTGGGYDTAAAGNG-3′) and 802R (5′-TACNVGGGTATCTAATCC-3′) were used to amplify 16S rRNA V4 region, which was suitable for distinguishing bacterial species at the genus level (Kozich et al., 2013). The barcode in the pre-primer was a 7-base oligonucleotide sequence that was used to distinguish different samples from the same library. The 25-µl PCR reaction contained Q5 high-fidelity DNA polymerase (0.25 µl), 5× reaction buffer (5 µl), 5× high GC buffer (5 µl), dNTP (10 mM, 0.5 µl), template DNA (1 µl), and primers (10 µM, 1 µl each). The cycling conditions were 98°C for 30 s; 27 cycles of 98°C for 15 s, 50°C for 30 s, and 72°C for 30 s; and 72°C for 5 min. Amplicons were verified by 2% agarose gel electrophoresis, and fragments were recovered using a gel recovery kit (Axygen, Union City, CA, USA).

2.5. cDNA Library construction and sequencing PCR products were quantified with the Quant-iT PicoGreen dsDNA Assay kit on a microplate reader (FLx800; BioTek, Winooski, VT, USA), and a cDNA library was constructed with TruSeq Nano DNA LT Sample Prep Kit (Illumina, San Diego, CA, USA). After end repair, addition of the 3ꞌ poly-A tail, and labeling, the cDNA was purified using AMPure XP beads (Beckman-Coulter, Brea, CA, USA). The library was analyzed with the Agilent High

Sensitivity DNA kit and bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and quantified with the Quant-iT PicoGreen dsDNA Assay kit using QuantiFluor (Promega, Madison, WI, USA). The library was screened by 2×300 bp paired-end sequencing using the Illumina MiSeq platform.

2.6. Data analysis MiSeq paired-end raw data were stored in FASTQ format and have been deposited in the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra/; accession no. SRP095308). The sliding window method was used for quality verification from the 5′ end of the first base. Reads for which average base quality was >Q20 (sequencing accuracy rate ≥ 99%) with interceptive length >150 bp and no ambiguous bases (N) were conserved. Coincident screened reads were pair-connected using FLASH v.1.2.7 software (http://ccb.jhu.edu/software /FLASH/) (Magoc and Salzberg, 2011) according to overlapping bases. Index sequences were classified for corresponding samples and effective sequences of each sample were obtained. Target sequences were identified using Quantitative Insights Into Microbial Ecology (QIIME v.1.8.0 software (http://qiime.org/) (Derakhshani et al., 2016), and chimera sequences were removed using USEARCH v.5.2.236 (http://www.drive5.com/usearch/). The UCLUST sequence alignment tool was used to merge and divide operational taxonomic units (OTUs) according to 97% sequence similarity (Edgar, 2010). The SILVA database (release 115; http://www.arb-silva.de) (Huang et al., 2009) was used to obtain the corresponding taxonomic classification, and the abundance of OTUs (<0.001%) were removed before further analysis (Bokulich et al., 2013a). The rarefaction

curve corresponding to observed OTUs at different sequencing depths was examined using QIIME software to determine whether the depth was reasonable. Chao 1 and abundance-based coverage estimator (ACE) indices were used to calculate the evenness of each sample (Bokulich et al., 2013b), while the Shannon and Simpson indices were determined as a measure of diversity (Hong et al., 2015). The beta diversity among samples was determined by principal component analysis (PCA) using R software (https://www.r-project.org/). Significant differences in microbial community composition between paired samples were determined using the Metastats analysis and Mothur program by counting taxa in five classified levels (Lu et al., 2016).

3. Results 3.1. Raw data and OTU classification Illumina sequencing produced 129,013 effective sequences for each sample with an average length of 225 ± 2 bp. After trimming and filtering, 113,582 high-quality reads were obtained, accounting for 88.17% of total sequences. Those with 97% similarity were merged into the same OTU, yielding 4708, 4708, 4508, 4124, 3281, and 2603 OTUs at the phylum, class, order, family, genus, and species levels, respectively (Table 1).

3.2. α-Diversity analysis of rhizosphere soil The sequencing depth was analyzed to identify new taxa. The rarefaction curve for evaluating depth did not reach saturation, indicating that a greater sequencing depth was needed (Fig. S1). The richness and diversity of different treatments and sampling times were calculated using

Chao1, ACE, Simpson, and Shannon indices (Table 2). Chao1 and ACE indices showed that the SN15-2 group had the lowest whereas the FOL group had the highest number of OTUs at 10 days. In addition, the two BCA treatment groups showed the lowest Shannon and Simpson diversity indices, which ranged from 8.57 to 9.72; there were significant differences among the six groups (P < 0.05). At 40 days, there were no differences in the four indices between any of the group relative to control (Table 2).

3.3. Bacterial community composition The top 20 taxa from phylum to genus were ranked in order to identify the predominant species (Fig. 1). The average number of microbial groups at phylum, class, order, family, genus, and species levels were 33, 94, 169, 261, 369, and 371, respectively (Table S1). The samples showed similar phylum compositions, but differed in terms of the relative abundance of various groups (Fig. 2). The most highly represented phyla were Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, and Bacteroidetes, with average relative abundances of 32%, 17%, 12%, 11%, and 8%, respectively. Verrucomicrobia, Chloroflexi, Planctomycetes, Nitrospirae, and Firmicutes had relative abundances between 1% and 6%. The relative abundances of the remaining 23 phyla were far less than 1%. The rhizosphere bacterial community of 10 days altered drastically, but not significantly at 40 days. In addition, PCA and diversity analyses obtained the same results. Therefore, the sampling time of 10 days was used for further evaluation of the genera alteration responding to the SN16-1 and FOL application. Almost 51% of genera (323 in total) were annotated using the SILVA database and relative abundances over 0.1% were listed (Table S2). The

total bacterial population was both increased after SN16-1 and FOL inoculation, especially FOL. The total relative abundance of annotated genera in SN16-1 treatment was higher than control, while FOL group with little change. The genera with relative abundance > 1% are shown in Table 3 and Figure S2. The 10, 12, 14, 13, 11, and 10 most abundant genera were represented in the control, SN16-1, SN15-2, SN16-1 and FOL, SN15-2 and FOL, and FOL groups, respectively. Massilia, Sphingomonas, Pseudomonas, and Arthrobacter were more highly represented in SN16-1 than in the control group, with relative abundances ranging from 3.89%–8.25%, 1.21%–2.55%, 1.03%–9.02%, and 0.50%–1.86%, respectively. In contrast, Arenimonas, Brevundimonas, and Nocardioides were less represented in SN16-1 group relative to the control, with abundances ranging from 2.46%–1.54%, 3.22%–2.29%, and 7.44%–1.54%, respectively. In the FOL group, the relative abundances of Adhaeribacter, Flexibacter, Flavisolibacter, Gemmatimonas, Chthoniobacter, and Haloferula increased from 0.60% to 1.26%, 0.12% to 1.98%, 0.35% to 1.45%, 2.31% to 3.32%, 0.13% to 2.02%, and 0.03% to 2.75%, respectively, whereas the abundances of Nocardioides, Brevundimonas, and Massilia decreased from 7.44% to 3.50%, 3.22% to 0.89%, and 3.89% to 1.35%, respectively.

3.4. Bacterial community structure PCA was used to compare soil bacterial communities in different treatment groups and at various sampling times (Sun et al., 2013; Zhao et al., 2016). The first two principle components could explain 52.58% and 16.91% of the total variation; the similarity of different samples was measured by Euclidean distance (Fig. 3). All treatment groups—especially SN16-1 and SN15-2—differed significantly with respect to the control,

with the greatest distance at 10 days. At 40 days, the treatment groups were similar to the control group. The relative abundance of predominant genera in each sample was reflected in the heatmap analysis (Fig. 4).

4. Discussion Rhizosphere microbial communities play important roles in plant health and disease prevention (Dudenhöffer et al., 2016; Gu et al., 2016). We analyzed the diversity of rhizosphere soil according to richness (Chao 1 and ACE) and diversity (Simpson and Shannon) indices, which showed marked changes at 10 days, with much lower values (especially for Shannon and Simpson indices) in the SN16-1 and SN15-2 groups than in the control. This indicated that these two groups negatively affected soil bacterial diversity, possibly because of their broad-spectrum resistance. However, the bacterial communities eventually recovered after SN16-1 and FOL treatment, as evidenced by the increase in richness and diversity indices at 40 days. The relative abundances of microorganism populations in rhizosphere soil can be perturbed by biotic and abiotic factors (Ding et al., 2014; Huang et al., 2016). For instance, the introduction of additional bacteria and fungi can change native community structure (Karpouzas et al., 2011). Here we found that bacterial composition was initially altered relative to the control by the various treatments, although these differences eventually disappeared. This indicated that the presence of SN16-1 had a transient influence on the rhizosphere community, in accordance with a report that rhizosphere bacterial communities were not significantly altered by treatment with B. amyloliquefaciens FZB42 (Krober et al.,

2014). The 10 most abundant sequences accounted for over 98% of total sequences at the phylum level. Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, and Gemmatimonadetes are the predominant phyla in most rhizosphere soils (Huang et al., 2016; Zhu et al., 2013). Proteobacteria is the most highly represented phylum in various soils (Han et al., 2016; Shang et al., 2016) ; it members typically undergo rapid growth by absorbing root-associated carbon substrates—i.e., their abundance is positively associated with carbon availability (Cleveland et al., 2006; Fierer et al., 2007). We found that the relative abundance of Proteobacteria was elevated in SN16-1 groups and reduced in the FOL group. Thus, increasing the abundance of Proteobacteria may be useful for enhancing the stimulatory effects of SN16-1 on plant growth. Acidobacteria, the second abundant phylum in this study, comprises over 20 subgroups; of these, subgroups 3, 4, and 6 were predominant in whole samples (data not shown) and are known to be enriched in different soil types (Huang et al., 2014; Rokunuzzaman et al., 2016), as they are versatile aerobic heterotrophs that can adapt oligotrophic conditions (Tebo et al., 2015). The abundance of Acidobacteria is negatively correlated with nutrient availability and is not associated with denitrification or nitrogen fixation (Lu et al., 2015). This could explain why SN16-1 treatment reduced Acidobacteria numbers relative to the FOL group. The classification of bacterial populations is essential for understanding the function and structure of bacterial communities (Ofek et al., 2012). Nocardioides and Massilia were the most abundant genera in all treated samples, although their relative abundances varied markedly. Massilia (Betaproteobacteria, Proteobacteria), a rhizosphere and root-colonizing

bacterium associated with many plant species, was more abundant in the SN16-1 than in the FOL group. It has been reported that this microorganism suppresses soil-borne disease (Cretoiu et al., 2013); thus, their induction by SN16-1 treatment may synergistically protect tomato plants against wilt. Nocardioides (Actinobacteria, Actinobacteria), which has oxidase and catalase activities (Kim et al., 2013), was widely distributed in agricultural soils. Both Massilia and Nocardioides were more abundant in the SN16-1 and FOL as compared to the FOL only group, indicating that inoculation of SN16-1 can act synergistically with FOL to exert biocontrol effects. Pseudomonas (Gammaproteobacteria, Proteobacteria) was highly represented in the SN16-1 group. This genus has demonstrated the ability to control various pathogens via mechanisms such as antimicrobial production, induction of systemic resistance, promotion of plant growth, siderophore production, and completion for nutrients (Kong et al., 2016; Li et al., 2012; Ma et al., 2016). A previous study reported that Pseudomonas colonizes tomato roots and secretes acylated homoserine lactones into the rhizosphere, which is important for quorum sensing and pathogen resistance (Choudhary and Johri, 2009). The high representation of Pseudomonas in the SN16-1 group suggests that this genus is a key factor in the development of antimicrobial activity induced by SN16-1. The relative abundances of Arthrobacter (Actinobacteria, Actinobacteria), Sphingomonas (Alphaproteobacteria, Proteobacteria), Ramlibacter (Betaproteobacteria, Proteobacteria) spp. were also elevated by SN16-1 treatment. These genera may promote plant growth by increasing plant biomass, inhibiting pathogenic strains, and inducing phosphate solubilization (Banerjee et al., 2010; Velázquez-Becerra et al., 2010; White et al., 1996). Phosphorous is an

essential macronutrient required by plants for photosynthesis, protein and nucleic acid synthesis, and nitrogen fixation. Solubilized phosphorous stimulates lateral root development (Ortíz-Castro et al., 2014). Thus, phosphorus-solubilizing rhizosphere bacteria that promote root growth may be induced by SN16-1. Soil bacterial are critical for suppressing soil-borne disease (She et al., 2016) and possibly inducing resistance mechanisms in plants. Upon FOL treatment, the abundances of the Chthoniobacter, Haloferula, Flexibacter, and Flavisolibacter genera were increased. This was especially true of Haloferula (Verrucomicrobiae, Verrucomicrobia) which was previously identified as a root endophyte of halophytes (Okazaki et al., 2014) that solubilizes phosphate, produces siderophores, and stimulates plant growth (Bibi et al., 2011). Thus, Haloferula may protect plants against pathogen infection. Chthoniobacter (Spartobacteria, Verrucomicrobia) metabolizes many saccharide components of plant biomass (Brewer et al., 2016); Flavisolibacter (Sphingobacteriia, Bacteroidetes), which is uniquely associated with tomato plants (Kim et al., 2006), is a cellulose consumer (Kramer et al., 2016); and Flexibacter (Cytophagia, Bacteroidetes) is a plant and animal pathogen (Zhang et al., 2016). The increased abundance of these three genera may suppress plant growth and promote pathogen infection. In conclusion, our results demonstrate that SN16-1 and FOL treatments exerted certain pressures on the rhizosphere community composition. In particular, SN16-1 treatment increased the abundance of beneficial bacteria of the Pseudomonas, Sphingomonas, and Massilia genera, whereas the numbers of Chthoniobacter and Haloferula were increased in response to FOL treatment. Over time, rhizosphere bacterial community composition

gradually returned to the baseline. These findings provide insight into the risk associated with B. amyloliquefaciens SN16-1 for the biocontrol of tomato wilt.

Acknowledgments This study was supported by the National Key Research and Development Program of China (

) and Key Research Projects of Science and Technology Commission of Shanghai

Municipality, China (no. 15391901500).

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Table captions Table 1. OTU classification and corresponding numbers in with various treatments and at different sampling times Table 2. Diversity indices with various different treatments and at different sampling times Table 3. Most abundant genera (%) in different treatment groups at 10 days

Figure captions

Figure 1. Tree diagram of total sample classification generated using GraPhlAn showing the top 20 most abundant groups at different levels (phylum to genus). Node sizes correspond to the average abundance of the taxon. Inner to outer rings correspond to phylum, class, order, family, and genus levels, respectively.

Figure 2. Relative abundance at the phylum level for different treatments and sampling times (a, b, 10 days; c, d, 40 days). Bars represent the standard deviation of three replicates of each sample.

Figure 3. Principal component analysis of control (A), B. amyloliquefaciens SN16-1(B), P.

fluorescens SN15-2(C), SN16-1 and pathogen FOL (D), SN15-2 and FOL (E), and FOL (F) groups at 10 and 40 days. Red and blue colors represent sampling times of 10 and 40 days, respectively. Different shapes represent different treatments.

Figure 4. Heatmap of bacterial communities based on the levels of the 50 most abundant genera in each sample. Green and red represent low and high relative abundances, respectively.

Table 1.

Treatment

Phylum

Class

Order

Family

Genus

A1

4609±230

4609±230

4407±222

4021±208

3156±175

B1

4689±268

4689±268

4501±251

4107±238

3238±195

C1

4519±232

4519±232

4327±222

3974±195

3148±158

D1

4823±87

4823±87

4608±79

4210±74

3341±60

E1

4455±344

4455±344

4287±327

3935±303

3151±251

F1

4842±176

4842±176

4646±159

4244±162

3368±126

A4

4587±277

4587±277

4412±251

4055±231

3256±181

B4

4821±74

4821±74

4599±71

4205±64

3345±51

C4

4614±237

4614±237

4414±223

4041±205

3232±174

D4

4705±182

4705±182

4513±161

4135±152

3329±118

E4

4878±25

4878±25

4654±22

4250±9

3369±9

F4

4949±266

4949±266

4727±254

4316±233

3435±179

A, B, C, D, E, and F represent control, Bacillus amyloliquefaciens SN16-1, Pseudomonas fluorescens SN15-2, SN16-1 and pathogen Fusarium oxysporum f. sp. lycopersici (FOL), SN15-2 and FOL, and FOL, respectively. Numbers 1 and 4 indicate sampling times of 10 and 40 days, respectively. Data were calculated from three replicates of each treatment and are shown as mean ± standard deviation.

Table 2.

Richness index

Diversity index

Treatment Chao1

ACE

Simpson

Shannon

A1

3644.33±218.60b

4203.47±419.59ab

0.9933±0.00048c

9.2592±0.03657c

B1

3468.67±328.09bc

4255.53±769.65ab

0.9898±0.00051d

8.9109±0.02925d

C1

3206.00±184.01c

3821.71±338.03b

0.9873±0.00114e

8.5734±0.11629e

D1

3552.00±387.71bc

4333.22±647.68ab

0.9937±0.00070bc

9.2675±0.12218c

E1

3626.67±283.90b

4081.50±666.54b

0.9946±0.00024b

9.4206±0.05268b

F1

4044.33±64.35a

4981.32±422.94a

0.9958±0.00027a

9.7222±0.04669a

A4

3736.67±157.07a

4437.79±394.55a

0.9951±0.00060ab

9.5226±0.09568bc

B4

3908.67±91.87a

5059.44±90.76a

0.9943±0.00006b

9.3544±0.04590c

C4

3852.33±83.51a

4678.76±373.70a

0.9940±0.00082b

9.3716±0.11683c

D4

3852.99±121.50a

4778.08±488.10a

0.9945±0.00037b

9.4388±0.05591c

E4

3686.33±262.17a

4283.18±481.13a

0.9949±0.00048b

9.5849±0.03999b

F4

3947.00±186.39a

4591.95±377.64a

0.9960±0.00036a

9.7814±0.05850a

Data were calculated from three replicates of each treatment and are shown as mean ± standard deviation. Different letters indicate significant differences according to Fisher’s least significant difference and Duncan tests (P < 0.05).

Table 3.

Genus

A

B

C

D

E

F

Phylum

Nocardioides

7.44±0.78b

4.61±0.25cd

12.06±1.25a

7.50±1.23b

5.56±0.41c

3.50±0.12d

Actinobacteria

Gemmatimonas

2.31±0.35b

2.15±0.03b

1.88±0.39b

2.33±0.14b

2.87±0.13a

3.32±0.37a

Gemmatimonadetes

Massilia

3.89±0.11bc

8.25±0.54a

9.26±0.29a

5.48±0.57b

2.86±0.62cd

1.35±0.19d

Proteobacteria

Arenimonas

2.46±0.76a

1.54±0.05b

1.66±0.21ab

1.90±0.18ab

1.70±0.90ab

1.94±0.27ab

Proteobacteria

Thermomonas

1.41±0.44b

1.34±0.04b

3.25±0.42a

1.77±0.14b

2.08±0.10b

1.23±0.12b

Proteobacteria

Brevundimonas

3.22±0.39b

2.29±0.14c

3.99±0.50a

1.38±0.15d

1.19±0.02d

<1.00±0.00

Proteobacteria

Sphingomonas

1.21±0.50b

2.55±0.19a

1.28±0.32b

1.77±0.11b

<1.00±0.00

<1.00±0.00

Proteobacteria

Ramlibacter

<1.00±0.00

1.46±0.09a

<1.00±0.00

1.15±0.08a

<1.00±0.00

<1.00±0.00

Proteobacteria

Pseudomonas

1.03±0.36d

9.02±0.21a

4.30±0.65b

3.40±0.20c

<1.00±0.00

<1.00±0.00

Proteobacteria

Lysobacter

1.43±0.47b

1.28±0.01b

2.47±0.40a

1.16±0.03b

1.15±0.59b

<1.00±0.00

Proteobacteria

Xanthomonas

1.07±0.29a

1.31±0.04a

1.42±0.25a

1.40±0.13a

<1.00±0.00

<1.00±0.00

Proteobacteria

Chthoniobacter

<1.00±0.00

<1.00±0.00

<1.00±0.00

<1.00±0.00

<1.00±0.00

2.02±0.26

Verrucomicrobia

Haloferula

<1.00±0.00

<1.00±0.00

<1.00±0.00

2.30±0.45b

2.29±0.05b

2.75±0.40a

Verrucomicrobia

Arthrobacter

<1.00±0.00

1.86±0.38a

1.81±0.52a

<1.00±0.00

<1.00±0.00

<1.00±0.00

Actinobacteria

Adhaeribacter

<1.00±0.00

<1.00±0.00

1.16±0.21a

<1.00±0.00

1.42±0.32a

1.26±0.32a

Bacteroidetes

Flexibacter

<1.00±0.00

<1.00±0.00

<1.00±0.00

<1.00±0.00

1.84±0.46a

1.98±0.49a

Bacteroidetes

Flavisolibacter

<1.00±0.00

<1.00±0.00

1.39±0.44a

1.45±0.18a

2.44±0.52a

1.45±0.31a

Bacteroidetes

Bacillus

<1.00±0.00

<1.00±0.00

1.07±0.06

<1.00±0.00

<1.00±0.00

<1.00±0.00

Firmicutes

Genera with relative abundance > 1% are shown. Data were calculated from three replicates of each 33

treatment and are presented as mean ± standard deviation. Different letters indicate significant differences according to Fisher’s least significant difference and Duncan tests (P < 0.05).

34

Graphical abstract

Tree diagram of total sample classification generated using GraPhlAn showing the top 20 most abundant groups at different levels (phylum to genus). Node sizes correspond to the average abundance of the taxon. Inner to outer rings correspond to phylum, class, order, family, and genus levels, respectively.

35

Highlights •

Fusarium oxysporum f. sp. lycopersici (FOL) causes tomato wilt.

•

FOL-induced tomato wilt can be suppressed by Bacillus amyloliquefaciens SN16-1.

•

Bio-control agent SN16-1 and pathogen FOL affect tomato rhizosphere bacterial.

•

SN16-1 inoculation conferred biocontrol capacity to tomato rhizosphere bacterial.

36