Next Article in Journal
Physicochemical Marker for Determination of Value-Adding Component in Over-Ripe Thai Mango Peels
Previous Article in Journal
Agronomic, Biochemical, and Physiological Responses to Magnesium (Mg) Application in Tomato (Solanum lycopersicum L.) under Greenhouse Conditions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Horticulturae
Volume 10
Issue 10
10.3390/horticulturae10101035
horticulturae-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Analysis of the Effects of Crude Metabolic Extracts of Three Biocontrol Bacteria on Microbial Community Structure Provides a New Strategy for the Biological Control of Apple Replant Disease

by
Jinhui Lv
1,
Weitao Jiang
1,
Zihui Xu
1,
Gongshuai Wang
2,
Xiaoxuan Li
1,
Xinyu Wu
1,
Fengxia Ding
3,
Yusong Liu
1,
Xuesen Chen
1,
Chengmiao Yin
1,* and
Zhiquan Mao
1,*
1
College of Horticulture Science, Engineering Shandong Agricultural University, Tai’an 271018, China
2
College of Agricultural Science and Technology, Shandong Agriculture and Engineering University, Jinan 251100, China
3
Wudi County Jieshi Shan Town Agricultural Comprehensive Service Center, Binzhou 251910, China
*
Authors to whom correspondence should be addressed.
Horticulturae 2024, 10(10), 1035; https://doi.org/10.3390/horticulturae10101035
Submission received: 5 August 2024 / Revised: 20 September 2024 / Accepted: 26 September 2024 / Published: 29 September 2024
(This article belongs to the Section Plant Pathology and Disease Management (PPDM))
Browse Figures
Versions Notes

Abstract

:
The crude metabolic extract from plant biocontrol bacteria plays a very important role in sustainable agricultural production. These extracts help maintain healthy plants and have very important application prospects in biotechnology related to alleviating apple replant disease (ARD). In this study, Bacillus velezensis XC1 (T1), Bacillus amyloliquefaciens QSB-6 (T2), and Lactobacillus reuteri LBR (T3) were examined to characterize the ability of their crude metabolic extracts to alleviate ARD. The high-throughput sequencing data of the soil microbial community structure were analyzed in relation to LBR crude metabolic extracts, and an extensive untargeted metabolomic analysis of UHPLC-Qex active components was performed. Active LC-MS/MS revealed that the main secondary metabolites involved in the biological control exerted by L. reuteri included 3-hydroxypropionaldehyde, extracellular polysaccharides (EPS), p-hydroxybenzoic acid, and azelaic acid. These crude metabolic extracts significantly inhibited the growth of soil pathogenic fungi, reduced the abundance of Fusarium, promoted the abundance of beneficial bacteria such as Pseudomonas, and optimized the soil microbial community structure. Improved modern extraction and purification technologies will be able to offer additional insights into the mechanism of action of these secondary metabolites and enable them to be used in biological preparations to prevent and control ARD in the future, as well as to allow harmful chemical fumigants to be discontinued.

Graphical Abstract

1. Introduction

China is the world’s largest producer and consumer of apples [1]. Currently, China’s apple industry is undergoing a significant structural transformation because more than 10 hectares of apple orchards are aging, underproducing, and in urgent need of renewal and reconstruction [2]. Due to the shortage of land resources, the repeated cultivation of apple trees cannot be avoided when renewing old orchards, resulting in the common occurrence of apple replant disease (ARD). The specific symptoms of ARD include slow growth of apple trees, reduced yield and quality, and, in some cases, no fruit production and even tree death [3,4]. Hence, ARD causes serious economic losses to fruit farmers.
The causative factors of ARD are very complex and mainly include biotic (e.g., unbalanced soil microbial community structure and increase in harmful fungi) and abiotic (e.g., deterioration of soil physicochemical properties and accumulation of phenolic acid substances) factors [5,6]. Fusarium is generally regarded as the main fungal pathogen causing ARD in China [7]. The results of a large-scale study in the main apple-producing regions in China in the early stages of our project showed that the imbalance in the soil microbial community caused by the increase in the relative abundance of Fusarium was the main causal factor of ARD [8,9]. Among them, F. proliferatum MR5 was identified as a pathogen of ARD that aggressively invaded apple rootstocks [9]. Certain measures—such as soil chemical fumigation, agronomic practices (e.g., crop rotation, intercropping, and mixed cropping), resistant rootstock selection, and the application of organic materials—have been used to control ARD [10,11,12,13]. Chemical fumigants such as methyl bromide are usually broad-spectrum fungicides that kill both harmful and beneficial soil flora indiscriminately, destroy the soil flora structure, pollute the environment, and are harmful to the human body, and thus their use has been gradually restricted [14].
Biological control is a green, safe, efficient, and economical control method that uses beneficial bacteria to suppress pathogens in the soil or interfere with the infection process of host plants caused by pathogens. Biological control to combat ARD is a promising strategy [15,16]. The effectiveness of biological control on soil-borne diseases is closely related to the abundance of viable biocontrol bacteria and the concentrations of the active substances produced by them [17,18]. Bacillus, Rhizobium, Corynebacterium, Streptomyces, and Pseudomonas have been reported as candidate biocontrol bacteria against various plant diseases [19,20,21,22]. The organic compounds related to Pseudomonas aeruginosa effectively inhibited ARD and improved the survival rate of apple plantation rootstocks [15]. Our previous research found that the application of the Bacillus amyloliquefaciens QSB-6 biofertilizer effectively promoted the growth of replanted Malus hupehensis Rehd. seedlings and apple roots [21]. The XC1 compound from the Bacillus velezensis biological agent effectively inhibited the relative abundance of soil pathogenic fungi, promoted the growth of replanted apple seedlings, and alleviated ARD [22]. Zhang found that Lactobacillus reuteri LBR and Penicillium had a synergistic effect on the growth of replanted apple seedlings. L. reuteri is a biocontrol bacterium that can promote the below-ground growth of apple seedlings and reduce the relative abundance of Fusarium in the soil [23]. We have found that the above three biocontrol bacteria, XC1, QSB-6, and LBR, can be used as a biofertilizer to lower the severity of ARD occurrence. However, the disease prevention capacity and effectiveness of biofertilizers are affected by the effectiveness of microbial colonization as well as the soil environment [24]. An increasing amount of evidence has shown that secondary metabolites produced by beneficial rhizosphere bacteria directly inhibit the proliferation of pathogenic fungi, as well as weaken their virulence. Hence, secondary metabolites have a high potential for reducing soil-borne biotic obstacles and have become a hot topic in the field of microbial ecology [25]. However, both the effect of the crude metabolic extracts produced by XC1, QSB-6, and LBR in relieving ARD and their underlying mechanisms are unknown. Therefore, this study used XC1, QSB-6, and LBR as models to explore the composition of biocontrol crude metabolic extracts and their effectiveness in controlling ARD. High-throughput sequencing and untargeted metabolomics were used to obtain the relevant data. The most effective biocontrol bacteria were also screened, and their metabolites and mechanisms of action were further examined in order to deliver a greener prevention strategy against ARD.

2. Materials and Methods

2.1. Experimental Materials

The test soil was taken from a 33-year-old apple orchard (scion/rootstock combination: Fuji/Malus × robusta Rehder) soil in Songjiazhuang Village, Tai’an City, Shandong Province, China (36.1° N, 117.1° E). The soil was randomly sampled at multiple points and mixed. Healthy tissue-cultured M9T337 plants with uniform growth and no pests or diseases were selected as apple rootstock for the pot experiment. When the seedlings had grown five to six true leaves, they were transplanted into mud pots (top diameter 25 cm, bottom diameter 17 cm, and height 18 cm) containing 6.5 kg of soil from different treatments.
The field sites were selected in Cary Liu Village (PL) in Penglai District, Yantai City (120.8° N, 37.8° E), Shilibao (QX) in Qixia City, Yantai City (120.8° N, 37.3° E), and Xiaoguanzhuang Village (TA) in Daiyue District, Tai’an City (117.1° N, 36.1° E), Shandong Province.
The test strains Bacillus velezensis XC1 and Bacillus amyloliquefaciens QSB-6 belonged to strains preserved in the laboratory. Lactobacillus reuteri LBR was purchased from the China National Institute of Food and Fermentation Research (CIFAR) and was cataloged under the conservation number CICC6118.

2.2. The Preparation of Crude Metabolic Extracts

Firstly, seed cultures of XC1, QSB-6, and LBR were prepared by inoculating a single colony of bacteria into 100 mL of LB broth (tryptone 10 g, yeast extract 5 g, NaCl 10 g, pH 7.0) and incubating at 37 ℃ overnight with shaking at 180 rpm.
The fermentation process was carried out in 500 mL Erlenmeyer flasks containing 200 mL of lysogeny broth (LB) inoculated with 3% seed (v/v) culture and incubated at 37 °C and 180 rpm for 4 d and then centrifuged at 12,000 rpm for 5 min at 4 ℃. The supernatant was passed through a 0.22 μm Nylon66 microporous membrane to obtain a cell-free culture filtrate (CFCF).

2.3. Antagonism Tests of Cell-Free Culture Filtrates

In order to verify the antibacterial effect of the CFCFs from the three biocontrol bacteria, the cell-free culture filtrates from XC1 (T1), QSB-6 (T2), and LBR (T3) were first divided into concentration gradients of 5%, 10%, and 15% (v/v), and were then added to a potato dextrose agar (PDA) medium (55 °C). After the culture medium had solidified, a circular hyphal disk (d = 5 mm) of the pathogenic fungus was inoculated into the center of the amended media and cultured at 25 ℃ until the negative control growth treatment had covered the whole surface of the plate. Each treatment was repeated three times. The PDA plates without culture filtrate were used as negative controls. The growth inhibition of the pathogen was measured according to the method of Wang et al. [13].

2.4. Potting Experiment

In May 2022, the pot experiment was conducted at the National Apple Engineering Experiment Center of the Horticultural Science and Engineering College of Shandong Agricultural University (117.1° N, 36.1° E). There were five treatments: 33-year-old orchard soil (CK), 33-year-old orchard soil fumigated with methyl bromide (CK1), XC1 CFCF treatment (T1), QSB-6 CFCF treatment (T2), and LBR CFCF treatment (T3), with 20 replicates per treatment. Each repetition consisted of a pot of plants containing both plants and soil. The soil was treated one week after the potted seedlings were planted by applying 100 mL CFCF to each pot. CK and CK1 were treated with 100 mL LB broth as controls. All experiments were performed using normal irrigation and manure management practices. Sampling was carried out on 1 August, 1 September, and 1 October 2022. Three pots were randomly taken from each treatment as the three replicates.
The soil at the surface and around the pot was removed, the rhizosphere soil was collected and filtered through a 0.85 mm sieve to remove impurities, and a composite soil sample was obtained by mixing the sieved soil. The soil samples were divided and stored in three sealed bags: one sealed bag was stored in a refrigerator at 4 °C to determine the number of culturable microorganisms in the soil, one sealed bag was stored at −80 °C for soil DNA extraction and qPCR analysis, and a third sealed bag was air-dried and used to determine the soil-related indicators. Three seedlings were harvested from each treatment to determine their biomass.

2.4.1. Biomass

The plant height and stem diameter of plant seedlings were measured using conventional methods, such as with millimeter scales and vernier calipers. After cleaning the plant seedlings with tap water, the surface moisture was dried and the plants’ fresh weights were measured using an electronic balance. Next, the seedlings were transferred to an envelope, baked at 105 °C for 30 min, and then dried at 65 °C to measure their dry weight.

2.4.2. Soil DNA Extraction

The soil DNA was extracted using the FastDNA SPIN Kit (MP Biomedicals, Santa Ana, CA, USA). The DNA was extracted from 1 g of mixed soil sample (three replicates mixed), and then the extracted genomic DNA was assessed via 1% agarose gel electrophoresis. The quality and quantity of DNA was determined using an EPPENDORF BioPhotometer nuclei acid and protein analyzer (Eppendorf, Hamburg, Germany).

2.4.3. Quantitative PCR (qPCR) Analysis

The extracted soil fungal DNA was quantitatively tested for the presence of four pathogenic Fusarium strains using the CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad, USA) according to the method of Zhao et al. [26]. The primers used for the experimental strains F. oxysporum, F. solani, F. proliferatum and F. moniliforme came from the previous laboratory research [8]. The primers and standard curves are shown in Supplementary Table S2. Each PCR reaction contained 1 μL of the target DNA, 12.5 μL of SYBR Green premix Ex Taq, 1 μL of each primer, and 9.5 μL sterile distilled water. Each sample was processed in triplicate, and the results were obtained by taking the Cq value as y and bringing it into the PCR standard curve, resulting in the logarithmic value of the gene copy number of the soil samples.

2.4.4. Analysis of Soil Microbial Community Structure

The extracted DNA was sent to Shanghai Majorbio Biomedical Technology Co., Ltd. for high-throughput sequencing. The data were analyzed on the Majorbio Cloud Platform (www.majorbio.com, accessed on 21 February 2023). The amplification of fungi was performed using ITS1F: CTTGGTCATTTAGAGGAAGTAA and ITS2R: GCTGCGTTCTTCATCGATGC. The amplificatiom of bacteria was performed using 338F: ACTCCTACGGGAGGCAGCAG and 806R: GGACTACHVGGGTWTCTAAT. The PCR instrument used was the ABI GeneAmp® 9700. The PCR reaction parameters were as follows: pre-denaturation at 95 ℃ for 3 min, followed by 95 °C for 30 s, 60 °C for 30 s, 72 °C for 45 s, and holding at 72 °C for 10 min until the reaction was completed, followed by 10 ℃. The PCR products were quantified using the QuantiFluor™-ST Blue Fluorescence Quantification System (Promega, Tokyo, Japan), followed by Illumina library preparation and Illumina sequencing.

2.5. Field Experiment

The field experiment was set up with four treatments: XC1 CFCF (T1), QSB-6 CFCF (T2), LBR CFCF (T3), and 33-year-old orchard soil (CK), with 20 replicates per treatment. Each repeat was a plant. The soil was treated one week after the apple seedings were planted by applying 800 mL CFCF to each tree. The CK treatment added 800 mL water as the control. The apple seedlings used in the experiment were two-year-old grafted seedlings purchased from Laizhou Natural Horticulture Technology Co., Ltd. (Shandong, China). The rootstock and panicle combination was Yanfu 3/M9T337. The stem diameter of the grafted seedlings was about 10 mm, and the fixed stem was 1.4 m. The spacing between the rows of plants was 1.5 m × 4 m, and the trees were pruned into a spindle shape. In October, the plant height, stem diameter, and number of branches on the grafted seedlings were measured.

2.6. UHPLC-Q Exactive LC-MS/MS Extensive Untargeted Metabolism Analysis

The extracted CFCF of Lactobacillus reuteri was sent to Shanghai Maibo Biomedical Technology Co., Ltd. (Shanghai, China) for UHPLC-Q Exactive LC-MS/MS extensive targeted metabolism analysis. This method and its specific parameters are described in Supplementary Method S1.

2.7. Effects of Three Major Secondary Metabolites on Apple Seedlings and Pathogenic Fusarium

2.7.1. Growth-Promoting Effect

The components of the top three LBR crude metabolic extracts—namely 3-hydroxypropionaldehyde, exopolysaccharides, and p-hydroxybenzoic acid—were selected for pot experiments to verify their growth-promoting effects. 3-hydroxypropionaldehyde and p-hydroxybenzoic acid were purchased from Shanghai McLean Biochemical Technology Co., Ltd. (Shanghai, China). The extraction method for extracellular polysaccharide is described in Supplement Method S2. Three treatments were set up: the control consisted of sterile distilled water-inoculated sterilized soil, “Low” consisted of sterilized soil inoculated with 10 μM 3-hydroxypropionaldehyde (R1), 10 μM EPS (R2), or 10 μM p-hydroxybenzoic acid (R3), and “High” consisted of sterilized soil inoculated with 10 mM 3-hydroxypropionaldehyde, 10 mM EPS, or 10 mM p-hydroxybenzene formic acid. The amount of compound added was 5 mL per pot. The seedlings were then planted in plastic pots (7 × 7 cm) and grown at 28 °C with 16 h light/8 h dark. Each pot contained one M9T337 seedling, all pots were randomly arranged, and each treatment was replicated 15 times. The experiment was replicated three times. The growth-promoting effect was judged by measuring the dry and fresh weights of the M9T337 plants.

2.7.2. Antifungal Effects

First, 3-hydroxypropionaldehyde (R1), EPS (R2), and p-hydroxybenzoic acid (R3) were divided into concentration gradients of 10% (v/v) and added to a PDA medium (55 °C). After the culture medium had solidified, a circular hyphal disk (d = 5 mm) of the pathogenic fungus was placed onto the center of the amended media and cultured at 25 °C until the negative control growth had covered the whole surface of the plate. Each treatment was replicated three times. PDA plates without culture filtrate were used as controls. The growth inhibition of the pathogen was then measured.

2.8. Statistical Analysis

All data were expressed as mean ± standard deviation of the triplicates. Data were processed using Microsoft Excel 2013 (Microsoft Corporation, Redmond, WA, USA), plotted using Graphpad prism 8.0 (GraphPad software, Inc., New York, NY, USA), and analyzed using ANOVA in IBM SPSS 20.0 (IBM SPSS Statistics, IBM Corporation, Armonk, NY, USA). One-way ANOVA was used to assess whether there were significant differences between samples. p < 0.05 was considered as a statistically significant difference between samples. Shannon, ACE, and Chao indices of α diversity in the quantitative data analysis of the samples were all analyzed via Mothur (version v.1.30.2 https://mothur.org/wiki/calculators, accessed on 21 February 2023) on the Meiji biological cloud platform, and the similarity level of OTU used for index evaluation was 97% (0.97). Alpha diversity index difference test between groups were conducted using R packages (boot1.3.18 and stats3.3.1). PCoA analysis was conducted using R for PCoA analysis and mapping. Co-occurrence network analysis was performed by calculating the Spearman correlations among the bacterial genera in R (vers. 4.2.2) and visualizing the network in Gephi (version 0.7).

3. Results

3.1. Effect of CFCF on the Growth of Apple Seedlings in the Pot Experiment

As shown in Figure 1A, the exogenous addition of the crude metabolic extracts from XC1 (T1), QSB-6 (T2), and LBR (T3) significantly promoted the growth of the M9T337 seedlings. The treatment effects of LBR (T3) and methyl bromide fumigation (CK1) were the most significant. The plant height in the T3 treatment in September and October were significantly higher than those in the control treatment (CK), having increased by 17.30% and 28.31%, respectively. The dry and fresh weights of the apple rootstock seedlings in October effectively increased by adding the crude metabolic extracts from biocontrol bacteria (Figure 1C). Among them, the T3 treatment had the most significant effect on the dry and fresh weights of the above-ground and subsurface parts of the plants. Compared with the CK treatment, T3 increased the fresh weight of above-ground, fresh weight of below-ground, dry weight of above-ground, and dry weight of below-ground plant parts by 43.78%, 27.01%, 46.04%, and 34.13%, respectively, which were significant and positive effects on the growth of the replanted apple seedlings.

3.2. Effect of CFCF on the Growth of Grafted Seedlings in the Field Experiment

Adding CFCFs from different strains significantly promoted the growth and increased the number of branches on the grafted seedlings (Figure 1B). The LBR treatment (T3) had the strongest effect among the three treatments. In the PL orchard, the plant height, stem diameter, and number of branches in the T3 treatment increased by 41.94%, 29.01%, and 59.09%, respectively, compared with CK. In the QX orchard, they increased by 39.75%, 26.51%, and 63.61%, respectively, and in the TA orchard, they increased by 40.34%, 30.64%, and 61.13%, respectively, compared with CK.

3.3. Antifungal Effect of CFCF on Fusarium oxysporum

The antifungal test results of different CFCFs on the mycelial growth of F. oxysporum found in the soil samples showed that as the amount of CFCF added increased, its antifungal effect on mycelial growth became stronger (Figure 2A,B). XC1 (T1) had the best antifungal effect, with an inhibition rate of 35.8% and 48.6% after adding 10% and 15%, respectively, and LBR (T3) had the second best effect, with an antifungal rate of 33.8% and 37.9%, respectively.

3.4. Effect of CFCF on the Number of Gene Copies of Four Fusarium Species in the Soil

Compared with CK, the addition of different bacterial CFCF and the CK1 treatments significantly reduced the number of gene copies of four Fusarium species in the soil (Figure 2C). The number of gene copies of F. oxysporum, F. solani, F. proliferatum, and F. moniliforme treated with T3 decreased by 83.31%, 88.43%, 69.20%, and 67.14%, respectively, while their number of gene copies decreased when treated with T2 by 73.53%, 68.12%, 66.87%, and 51.10%, respectively, and decreased when treated with T1 by 76.5%, 73.5%, and 74.9%, respectively. It can be observed that the T3 treatment had the best antifungal effect, while T1 and T2 exhibited the second highest antifungal effects.

3.5. Effects of Different CFCFs on the Soil Microbial Community Structure

The Shannon, ACE, and Chao indices of the fungal communities treated with different CFCFs all increased (Table 1). Among them, the LBR (T3) treatment had the most significant effect on diversity. The Simpson index, as opposed to the interpretation of the other indices, where lower numbers indicate greater diversity, was 40% lower for XC1 (T1) than that of the control. The rhizosphere bacteria under different CFCF treatments showed an α diversity that was consistent with the trends observed among the fungi (Table 1). The Shannon, ACE, and Chao indices of T3 increased significantly by 9.11%, 10.96%, 10.56% compared with CK, followed by QSB-6 (T2). The Simpson index showed that the XC1 (T1) treatment significantly reduced the rhizosphere bacterial diversity, which decreased by 50.00% compared with CK.
The exogenous addition of CFCFs had a significant impact on the structure of the rhizosphere bacterial and fungal communities at the phylum level (Figure 3B and Figure 4B). Compared with CK, the relative abundance of Chlorophyta was significantly lower after CFCF treatment, and LBR (T3) significantly increased the relative abundance of Actinomycetes and Basidiomycotina in the rhizosphere. At the genus level (Figure 4C), the relative abundance of Emericella, Exserohilum, Alternaria, and Fusarium was significantly lower in the CFCF treatment compared with CK. The relative abundance of the genus Emericella was significantly different among different treatments, with the highest abundance in the QSB-6 treatment (p < 0.01). The relative abundance of the genus Mortierella increased significantly in the QSB-6 (p < 0.01) and LBR (p < 0.05) treatments.
Among rhizosphere bacteria (Figure 3C), compared with the CK, the relative abundance of Bacteroides in the LBR treatment and that of Pseudoarthrobacter (p < 0.05), Sphingomonas (p < 0.05), and Bacillus in the CFCF treatments (p < 0.001) were all significantly higher. The relative abundance of Tolypothrix and Lyngbya in the XC1 and QSB-6 treatments was significantly higher than that observed in the CK and CK1 treatments.

3.6. PCoA Analysis of the Soil Microbial Communities

The PC1 and PC2 values of the rhizosphere fungi were 33.56% and 19.20%, respectively (Figure 4A). These two axes together explained 52.76% of the diversity difference. The XC1 (T1) and LBR (T3) treatments were highly separated from CK in the ordination space, which indicated that the T1 and T3 treatments significantly changed the diversity of the rhizosphere fungal communities. QSB-6 (T2) was also highly separated from the other treatments, indicating that its community was significantly different from the other treatments. From the PCoA analysis of soil rhizosphere bacteria (Figure 3A), the PC1 and PC2 axes explained a total of 49.96% of the diversity difference. The CFCF treatment was highly separated from CK, indicating that the CFCF treatment significantly changed the diversity of the rhizosphere bacterial communities.

3.7. Co-Occurrence Network Analysis of the Soil Microorganisms Treated with Crude Metabolic Extracts from Biocontrol Bacteria

The results of the co-occurrence network analysis of the root microbial communities treated with crude metabolic extracts from biocontrol bacteria revealed that (Figure 5A) different genera belonging to the same phylum appeared in different co-occurrence networks, where they had different relative abundances and played different biological roles. Among all the treatments, the CK treatment accounted for the largest proportion of negative associations in the network, and the network connectivity in the T1, T2, and T3 treatments was higher than that in the CK treatment, among which the T3 treatment had the largest network density and average degree. Therefore, the T3 treatment complicated the bacterial co-occurrence network to some extent, as well as promoted the formation of rhizosphere bacterial communities and co-occurrence networks and optimized the soil environment. Among them, the relative abundance ratios of Bacillus, Massilia, and Streptomyces between the T3 and CK treatments (>1%) showed that these genera were highly important in the co-occurrence network of rhizosphere bacteria after treatment (Supplementary Figure S1B). The proportion of positive edges in the T3 treatment was 27.56% higher than that of the negative edges, while the proportion of negative edges in the CK treatment was 11.78% higher than that of the positive edges (Supplementary Figure S1A). In the fungal co-occurrence network (Figure 5A), the calculated center coefficients of the keystone nodes of the co-occurrence network, Mortierella, Emericella, and Trichoderma, were relatively high, and the relative abundance ratio (> 1%) played a key role in fungal interactions. The proportion of positive edges in the rhizosphere fungal co-occurrence network treated by the three crude metabolic extracts from biocontrol bacteria increased significantly (Supplementary Figure S1A).

3.8. Correlation Analysis between the Plant Seedling Biomass and Key Microorganisms in the Rhizosphere Soil

The correlation analysis between the relative abundance of key soil bacteria and the plant height and diameter of M9T337 (Figure 5B) revealed positive correlations between the plant height and stem diameter, and the relative abundance of Bacillus and Streptomyces. The plant height and the relative abundance of Pseudoarthrobacter were negatively correlated. There was no significant relationship between the plant biomass and the relative abundance of other key microorganisms. In the correlation analysis of the relative abundance of key fungi, the plant height and stem diameter were positively correlated with the relative abundance of Mortierella (Figure 5B). The stem diameter was negatively correlated with the relative abundance of Fusarium, and the plant height was negatively correlated with that of Emericella.

3.9. LC-MS/MS Broadly Untargeted Metabolomic Analysis of L. reuteri

According to the previous research results, the crude metabolic extract from L. reuteri promoted the above-ground and below-ground growth of the M9T337 seedlings, inhibited the growth of soil pathogenic Fusarium, and optimized the community structure of rhizosphere microorganisms. These observations showed that the best way to prevent and control ARD was to use the LBR crude metabolic extract, among the crude metabolic extracts of three biological control bacteria. Therefore, LBR crude metabolic extract were selected for untargeted metabolomics to study the key components of preventing and controlling ARD.
A qualitative assessment of L. reuteri CFCF and multivariate data analysis were conducted using an untargeted metabolomics approach and the UHPLC-Q Exactive LC-MS/MS. The high overlap among the TIC curves for the different QC samples indicated that the repeatability and reliability of the evaluation results were good (Figure 6A,B). The QC samples were tightly distributed and clustered in the center, indicating that high reproducibility and reliability of the results had been achieved. Figure 6A,B shows the multi-peak detection plot of the sample metabolites in MRM mode. The metabolites in the samples were characterized by mass spectrometry, using the in-house Metware database and public databases. A total of 167 metabolites were detected. The top three categories of crude metabolic extracts were lipids and lipid-like molecules (26.34%), organic acids and derivatives (21.26%), and organoheterocyclic compounds (19.30%; Figure 6D). Next, the top 20 substances were selected (Figure 6C) and sorted from low to high. The top three substances with the highest content were extracellular polysaccharides, 3-hydroxybenzoic acid, and p-hydroxybenzoic acid, and the top 20 components also contained sugars, amino acids, and lipids.

3.10. Effects of Three Main Secondary Metabolite Compounds on the Growth of M9T337

Table 2 shows that the exogenous addition of the three main compounds significantly promoted seedling growth. Both 3−-hydroxypropionaldehyde (R1) and p-hydroxybenzoic acid (R3) significantly promoted seedling growth within the normal concentration range, and inhibited the growth at high concentrations. As the amount of exopolysaccharides (R2) increased, their growth-promoting effect on the seedlings also increased. After adding low and high concentrations, the dry and fresh weights of the seedlings increased by 27.41% and 62.15% for the dry weight and 31.71% and 70.27% for the fresh weight, respectively, compared with the control.

3.11. Inhibitory Effects of Three Main Secondary Metabolite Compounds on F. oxysporum

Figure 7 illustrates that the three main compounds significantly inhibited the mycelial growth of F. oxysporum. The inhibition rate of F. oxysporum by 3-hydroxypropionaldehyde at low and high concentrations was 25.34% and 50.54%, respectively. The inhibition rate by the extracellular polysaccharides was 20.45% and 38.30% (Figure 8), and that of p-hydroxybenzoic acid was 50.00% and 74.21%, respectively (Table 3).

4. Discussion

Replant disease is a common phenomenon in agricultural production. At present, an increasing number of studies have found that the accumulation of harmful microorganisms and the imbalance of the microbial community structure are the main reasons leading to replant disease. For example, a significant increase in the abundance of Fusarium in the soil and the concentration of phenolic acids in the soil has caused continuously cropped strawberries to collapse [27]. The single planting of cucumber often leads to an increase in the relative abundance of Fusarium, Dactylonectria, Alternaria, and Gibberella in the soil, which leads to obstacles in continuous cucumber cropping [28]. Numerous fungi that have been identified in orchards have been shown to be implicated in ARD, but among these, only a few pathogenic Pythium, Phytophthora, Cylindrocarpon, and Fusarium species have been reported worldwide [29,30]. According to the previous studies in our laboratory, Fusarium was the main causal agent of ARD in China, displaying strong pathogenicity in the main apple-producing regions of the country [8,9,31]. In addition, F. proliferatum MR5 was first reported as an ARD-specific pathogen, with high disease incidence on apple roots [9]. Therefore, both reducing the abundance of pathogenic fungi in replanted soil and building a more diverse microbial community structure play important roles in improving disease resistance in soil. In our study, we found that the relative abundance of Fusarium was high in the replanted treatment, and the addition of crude metabolic extracts from biocontrol bacteria reduced the relative abundance of Fusarium and significantly increased plant biomass.
Biocontrol bacteria can effectively control pathogenic fungi through interactions like antagonism and niche competition [32]. Secondary metabolites can be used to effectively complement the various functions of biocontrol bacteria [29,30]. Fenugreek bacterium Priestia endophytica strain SK1 produces hydrogen cyanide (HCN) and has an antagonistic response against F. oxysporum in dual plate assays, thereby demonstrating that it can effectively prevent and control Fusarium wilt disease in the fenugreek plant [19]. Streptomyces rochei strain JK1 produces macrolide secondary metabolites, which can effectively inhibit oomycete-caused diseases [19,33]. Using untargeted metabolomic compound detection, L. reuteri was shown to secrete a variety of bacteriocins, antifungal substances, and intermediates of various plant metabolic pathways, including p-hydroxybenzoic acid, erythromycin, cycloheximide, azelaic acid, and other broad-spectrum antifungal substances (Figure 6). In this study, the crude metabolic extracts from XC1, QSB-6, and LBR all had strong inhibitory effects on pathogenic fungi (Figure 2A). The pot treatments containing the crude metabolic extracts from the three biocontrol strains in this experiment also resulted in the significant inhibition of soil Fusarium and lowered the number of gene copies, effectively reducing the number of pathogenic Fusarium in apple-replanted soil. Low concentrations of p-hydroxybenzoic acid, a common root exudate and secondary metabolite produced by microorganisms, effectively inhibit the germination of Fusarium chlamydospores and the production of conidia [34]. Exopolysaccharides produced by Porphyridium can induce the defense response in Arabidopsis thaliana against Fusarium and activate the salicylic acid pathway [35]. In this experiment, the crude metabolic extract of LBR, which had a better effect on preventing and controlling ARD, was detected via untargeted metabolomics. As a result, a variety of antibacterial substances, including salicylic acid, p-hydroxybenzoic acid and exopolysaccharides, were found. Therefore, the existence of these broad-spectrum antifungal substances may be one of the reasons behind the significant decrease in the number of pathogenic Fusarium after soil treatment.
Rhizosphere microorganisms are links between soil and plants and have a stable community structure, which is beneficial for plant growth and resistance against pathogens [36]. The abundance of soil beneficial bacteria and the complexity of the rhizosphere microbial community structure are directly related to the healthy growth of plants [37]. Long-term replanting often provokes microbial communities in the plant rhizosphere to become imbalanced. Single cropping of peanuts often leads to changes in the soil microbial community structure, which, in turn, reduces the relative abundance of potentially beneficial microorganisms and increases the relative abundance of potentially pathogenic fungi [38]. The most prevalent means of communication among interacting microorganisms is via secreted small molecules, known as secondary metabolites [39]. The secondary metabolites from biocontrol bacteria can be thought of as words in the microbial lexicon, and these words, or secondary metabolites, convey the instructions to prevent and control replant disease. The microbial community structure thrives under complex conditions. Bacillus cereus inocula have been reported to effectively optimize the microbial community structure in the rhizosphere, promote the growth of poplar, and alleviate the obstacles related to continuous cropping [40]. The fermentation products of the mixed bacteria on agave Bacillus agave C-9 and Novosphingobium A1 increased the abundance of beneficial bacteria and alleviated cotton verticillium wilt [41]. Pseudomonas sivasensis inocula effectively changed the microbial community structure, promoted the relative abundance of beneficial microorganisms, and significantly promoted the growth of rapeseed [42]. The combined analysis of LBR’s crude metabolic extract using untargeted metabolomic analysis and high-throughput sequencing in our study showed that the application of the crude metabolic extract from LBR could effectively inhibit the relative abundance of harmful fungi in the rhizosphere, as well as increase the relative abundance of local beneficial bacteria, such as Bacillus and Pseudomonas. Local microorganisms are the research focus of biological control in recent years. Previous studies believed that the mobilization of local microorganisms could effectively resist plant stress and promote plant growth under adverse conditions [43]. Local microorganisms were able to enhance the effective interaction between root microorganisms and maintain the stability of soil community structure [44]. In addition, the application of LBR’s crude metabolic extract significantly increased the complexity of the interactions among the rhizosphere bacterial community members, as well as improved the direct interaction among microorganisms and the stability of the soil microbial community structure. When soil microorganisms interact, the relative abundance of key biocontrol bacteria always plays a role in linking plant health with soil environmental conditions [42,45]. In this study, we found that the relative abundance of Pseudomonas was positively correlated with the biomass index of potted plants. Pseudomonas was shown to be a key member of the soil microbial community treated with the crude metabolic extract from LBR. The crude metabolic extract from LBR mobilized local Pseudomonas, effectively optimized the rhizosphere microbial community structure, and alleviated ARD.

5. Conclusions

The crude metabolic extracts from the biocontrol bacteria XC1, QSB-6, and LBR effectively alleviated ARD, and LBR was the most effective strain for combating ARD. LBR’s metabolites contained a large amount of organic acids, sugars, and amino acids (3-hydroxypropionaldehyde, exopolysaccharides, p-hydroxybenzoic acid) that effectively inhibited the relative abundance of Fusarium, improved the relative abundance of beneficial microorganisms—such as Pseudomonas and Bacillus—and optimized the microbial community structure, thereby fulfilling its role in alleviating ARD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10101035/s1, Supplementary Method S1. UHPLC-Q Exactive LC-MS/MS Extensive targeted metabolism analysis. Supplementary Method S2. Extraction of extracellular polysaccharide. Table S1. Oligonucleotide primers used in this experiment. Figure S1. Co-occurrence network analysis of the soil microorganisms treated with secondary metabolites.

Author Contributions

Writing—review & editing, J.L.; Data curation, W.J.; Formal analysis, G.W.; Resources, X.L. and Z.X.; Supervision, X.W.; Visualization, Y.L. and F.D.; Conceptualization, X.C., C.Y. and Z.M. All authors have read and agreed to the published version of the manuscript.

Funding

The work was funded by the China Agriculture Research System of MOF and MARA (CARS-27); the National Natural Science Foundation of China (32072510); Taishan Scholar Funded Project (NO. ts20190923, NO. tsqn202408119); Key R&D program of Shandong Province (2022TZXD0037); National Key Research and Development Program (2023YFD2301003).

Data Availability Statement

Data is contained within the article and supplementary material.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Na, W.; Wolf, J.; Fusuo, Z. Towards sustainable intensification of apple production in China—Yield gaps and nutrient use efficiency in apple farming systems. J. Integr. Agric. 2016, 15, 716–725. [Google Scholar] [CrossRef]
  2. Sun, Y.; Lu, Y.; Wang, Z.; Li, M. Production efficiency and change characteristics of China’s apple industry in terms of planting scale. PLoS ONE 2021, 16, e0254820. [Google Scholar] [CrossRef] [PubMed]
  3. Lidia, N.; Heribert, I.; Pertot, I.; Blaž, S. Reanalysis of microbiomes in soils affected by apple replant disease (ARD): Old foes and novel suspects lead to the proposal of extended model of disease development. Appl. Soil Ecol. 2018, 129, 24–33. [Google Scholar] [CrossRef]
  4. Traud, W.; Kornelia, S.; Wulf, A.; Gerhard, B.; Gisela, G.-S.; Xorla, K.; Rainer, M.; Stefanie, R.; Michaela, S.; Doris, V.; et al. Apple replant disease: Causes and mitigation strategies. Curr. Issues Mol. Biol. 2019, 30, 89–106. [Google Scholar] [CrossRef]
  5. Chen, Y.; Du, J.; Li, Y.; Tang, H.; Yin, Z.; Yang, L.; Ding, X. Evolutions and managements of soil microbial community structure drove by continuous cropping. Front. Microbiol. 2022, 13, 839494. [Google Scholar] [CrossRef]
  6. Liu, X.; Xu, S.; Wang, X.; Xin, L.; Wang, L.; Mao, Z.; Chen, X.; Wu, S. MdBAK1 overexpression in apple enhanced resistance to replant disease as well as to the causative pathogen Fusarium oxysporum. Plant Physiol. Biochem. 2022, 179, 144–157. [Google Scholar] [CrossRef]
  7. Wang, G.; Yin, C.; Pan, F.; Wang, X.; Xiang, L.; Wang, Y.; Wang, J.; Tian, C.; Chen, J.; Mao, Z. Analysis of the fungal community in apple replanted soil around Bohai Gulf. Hortic. Plant J. 2018, 4, 175–181. [Google Scholar] [CrossRef]
  8. Xu, X.; Jiang, W.; Wang, G.; Ding, F.; Li, Q.; Wang, R.; Chen, X.; Shen, X.; Yin, C.; Mao, Z. Analysis of soil fungal community in aged apple orchards in Luochuan County, Shaanxi Province. Agriculture 2022, 13, 63. [Google Scholar] [CrossRef]
  9. Duan, Y.; Jiang, W.; Zhang, R.; Chen, R.; Chen, X.; Yin, C.; Mao, Z. Discovery of Fusarium proliferatum f. sp. Malus domestica causing apple replant disease in China. Plant Dis. 2022, 106, 2958–2966. [Google Scholar] [CrossRef]
  10. Alicia, B.-S.; Maik, L.; Doris, V.; Søren, J.S.; Traud, W.; Kornelia, S.; Samuel, J. Exploring microbial determinants of apple replant disease (ARD): A microhabitat approach under split-root design. FEMS Microbiol. Ecol. 2020, 96, fiaa211. [Google Scholar] [CrossRef]
  11. Mark, M.; Manici, L. Apple replant disease: Role of microbial ecology in cause and control. Annu. Rev. Phytopathol. 2012, 50, 45–65. [Google Scholar] [CrossRef]
  12. Milan, P.; Samuel, C.H.; Fulya, B.-G. Methods for management of soilborne diseases in crop production. Agriculture 2020, 10, 16. [Google Scholar] [CrossRef]
  13. Wang, H.; Tang, W.; Mao, Y.; Ma, S.; Chen, X.; Shen, X.; Yin, C.; Mao, Z. Isolation of Trichoderma virens 6PS-2 and its effects on Fusarium proliferatum f. sp. Malus domestica MR5 related to apple replant disease (ARD) in China. Hortic. Plant J. 2022, in press. [Google Scholar] [CrossRef]
  14. Julia, R.B. Getting the drift-methyl bromide application and adverse birth outcomes in an agricultural area. Environ. Health Perspect. 2013, 121, a198. [Google Scholar] [CrossRef]
  15. Ravinder, S.; Joginder, P.; Sheetal, R.; Mohinder, K. Suppression of soil borne fungal pathogens associated with apple replant disease by cyclic application of native strains of Pseudomonas aeruginosa. J. Appl. Nat. Sci. 2017, 9, 2105–2109. [Google Scholar] [CrossRef]
  16. Hausmann, B.; Knorr, K.H.; Schreck, K.; Tringe, S.G.; del Rio, T.G.; Loy, A.; Pester, M. Consortia of low-abundance bacteria drive sulfate reduction-dependent degradation of fermentation products in peat soil microcosms. ISME J. 2016, 10, 2365–2375. [Google Scholar] [CrossRef]
  17. Abdel-Gayed, M.A.; Abo-Zaid, G.A.; Mohamed, M.S.R.; Hafez, E.E. Fermentation, formulation and evaluation of PGPR Bacillus subtilis isolate as a bioagent for reducing occurrence of peanut soil-borne diseases. J. Integr. Agric. 2019, 18, 2080–2092. [Google Scholar] [CrossRef]
  18. Ramalingam, R.; Abeer, H. Bacillus: A Biological Tool for Crop Improvement through Bio-Molecular Changes in Adverse Environments. Front. Physiol. 2017, 8, 667. [Google Scholar] [CrossRef]
  19. Komal, S.; Neha, C.; Anukool, V.; Virendra Singh, R.; Sanjiv, S. Fenugreek associated bacterium Priestia endophytica SK1 induces defense response against fusarium wilt by accumulation of secondary metabolites. S. Afr. J. Bot. 2023, 160, 229–234. [Google Scholar] [CrossRef]
  20. Duan, Y.; Chen, J.; He, W.; Chen, J.; Pang, Z.; Hu, H.; Xie, J. Fermentation optimization and disease suppression ability of a Streptomyces ma. FS-4 from banana rhizosphere soil. BMC Microbiol. 2020, 20, 24. [Google Scholar] [CrossRef]
  21. Duan, Y.; Chen, R.; Zhang, R.; Jiang, W.; Chen, X.; Yin, C.; Mao, Z. Isolation, identification, and antibacterial mechanisms of Bacillus amyloliquefaciens QSB-6 and its effect on plant roots. Front. Microbiol. 2021, 12, 746799. [Google Scholar] [CrossRef] [PubMed]
  22. Geng, W.; Lv, Y.; Duan, Y.; Wang, H.; Jiang, W.; Zhang, R.; Chen, R.; Chen, X.; Shen, X.; Yin, C.; et al. Preparation of composite microbial culture and its biocontrol effect on apple replant disease. Sci. Hortic. 2022, 303, 111236. [Google Scholar] [CrossRef]
  23. Zhang, R.; Huang, J.; Duan, Y.; Wang, H.; Wang, M.; Chen, X.; Shen, X.; Yin, C.; Mao, Z. The fermentation products of Penicillium D12 and Lactobacillus reuteri promoted the growth of Pingyi sweet tea seedlings and improved the soil biological environment of continuous cultivation. J. Plant Nutr. Fertil. 2022, 28, 344–356. [Google Scholar]
  24. Gu, Y.; Meng, D.; Yang, S.; Xiao, N.; Li, Z.; Liu, Z.; Li, L.; Zeng, X.; Zeng, S.; Yin, H. Invader-resident community similarity contribute to the invasion process and regulate biofertilizer effectiveness. J. Clean. Prod. 2019, 241, 118278. [Google Scholar] [CrossRef]
  25. Wang, J.; Raza, W.; Jiang, G.; Yi, Z.; Fields, B.; Greenrod, S.; Friman, V.P.; Jousset, A.; Shen, Q.; Wei, Z. Bacterial volatile organic compounds attenuate pathogen virulence via evolutionary trade-offs. ISME J. 2023, 17, 443–452. [Google Scholar] [CrossRef] [PubMed]
  26. Zhao, L.; Jiang, W.; Chen, R.; Wang, H.; Duan, Y.; Chen, X.; Shen, X.; Yin, C.; Mao, Z. Quicklime and superphosphate alleviating apple replant disease by improving acidified soil. ACS Omega 2022, 7, 7920–7930. [Google Scholar] [CrossRef]
  27. Chen, P.; Wang, Y.; Liu, Q.; Zhang, Y.; Li, X.; Li, H.; Li, W. Phase changes of continuous cropping obstacles in strawberry (Fragaria × ananassa Duch.) production. Appl. Soil Ecol. 2020, 155, 103626. [Google Scholar] [CrossRef]
  28. Ali, A.; Elrys, A.S.; Liu, L.; Iqbal, M.; Zhao, J.; Huang, X.; Cai, Z. Cover plants-mediated suppression of Fusarium Wilt and root-knot incidence of cucumber is associated with the changes of rhizosphere fungal microbiome structure-under plastic shed system of north China. Front. Microbiol. 2022, 13, 697815. [Google Scholar] [CrossRef]
  29. Manici, L.; Markus, K.; Franke-Whittle, I.H.; Thomas, R.; Gerhard, B.; Nicoletti, F.; Caputo, F.; Topp, A.; Heribert, I.; Andreas, N. Relationship between root-endophytic microbial communities and replant disease in specialized apple growing areas in Europe. Appl. Soil Ecol. 2013, 72, 207–214. [Google Scholar] [CrossRef]
  30. Yared Tesfai, T.; Mark, M.; Iwan, F.L.; McLeod, A. A multi-phasic approach reveals that apple replant disease is caused by multiple biological agents, with some agents acting synergistically. Soil Biol. Biochem. 2021, 43, 1917–1927. [Google Scholar] [CrossRef]
  31. Wang, X.; Wang, G.; Liu, Y.; Chen, X.; Shen, X.; Yin, C.; Mao, Z. Correlation analysis of apple replant disease and soil fungal community structure in the Northwest Loess Plateau area. Acta Hortic. Sin. 2018, 45, 855–864. [Google Scholar] [CrossRef]
  32. Senka, Č.; Manupriyam, D.; Marian, M.; Guillem, S.; Vladimir, S.; Nicolas, C.; Hans-Joachim, R.; Shinichi, S.; van der Peter, M. Niche availability and competitive loss by facilitation control proliferation of bacterial strains intended for soil microbiome interventions. Nat. Commun. 2024, 15, 2557. [Google Scholar] [CrossRef]
  33. Zhou, D.; Wang, X.; Anjago, W.M.; Li, J.; Li, W.; Li, M.; Jiu, M.; Zhang, Q.; Zhang, J.; Deng, S.; et al. Borrelidin-producing and root-colonizing Streptomyces rochei is a potent biopesticide for two soil-borne oomycete-caused plant diseases. Biol. Control 2024, 188, 105411. [Google Scholar] [CrossRef]
  34. Evans, W.; Jochen, S.; Altus, V.; Frank, R. Phenolics mediate suppression of Fusarium oxysporum f. sp. cubense TR4 by legume root exudates. Rhizosphere 2022, 21, 100459. [Google Scholar] [CrossRef]
  35. Drira, M.; Jihen, E.; Hajer Ben, H.; Faiez, H.; Christine, G.; Christophe, R.; Didier Le, C.; Philippe, M.; Slim, A.; Imen, F. Optimization of exopolysaccharides production by Porphyridium sordidum and their potential to induce defense responses in Arabidopsis thaliana against Fusarium oxysporum. Biomolecules 2021, 11, 282. [Google Scholar] [CrossRef]
  36. Wogene, S.; Tibor, J.; Zoltán, M. Unveiling the significance of rhizosphere: Implications for plant growth, stress response, and sustainable agriculture. Plant Physiol. Biochem. 2024, 206, 108290. [Google Scholar] [CrossRef]
  37. Hu, Q.; Tan, L.; Gu, S.; Xiao, Y.; Xiong, X.; Zeng, W.; Feng, K.; Wei, Z.; Deng, Y. Network analysis infers the wilt pathogen invasion associated with non-detrimental bacteria. NPJ Biofilms Microbiomes 2020, 6, 8. [Google Scholar] [CrossRef]
  38. Li, H.; Li, C.; Song, X.; Liu, Y.; Gao, Q.; Zheng, R.; Li, J.; Zhang, P.; Liu, X. Impacts of continuous and rotational cropping practices on soil chemical properties and microbial communities during peanut cultivation. Sci. Rep. 2022, 12, 2758. [Google Scholar] [CrossRef]
  39. Yifan, Z.; Étienne, G.; Jong-Duk, P.; Mohammad, R.S. The small-molecule language of dynamic microbial interactions. Annu. Rev. Microbiol. 2022, 76, 641–660. [Google Scholar] [CrossRef]
  40. Sui, J.; Yang, J.; Li, C.; Zhang, L.; Hua, X. Effects of a microbial restoration substrate on plant growth and rhizosphere microbial community in a continuous cropping poplar. Microorganisms 2023, 11, 486. [Google Scholar] [CrossRef]
  41. Cheng, F.; Li, G.; Peng, Y.; Wang, A.; Zhu, J. Mixed bacterial fermentation can control the growth and development of Verticillium dahliae. Biotechnol. Biotechnol. Equip. 2020, 34, 58–69. [Google Scholar] [CrossRef]
  42. Joanna, Ś.; Agnieszka, K.; Attila, S.; Maria Swiontek, B. Pseudomonas sivasensis 2RO45 inoculation alters the taxonomic structure and functioning of the canola rhizosphere microbial community. Front. Microbiol. 2023, 14, 1168907. [Google Scholar] [CrossRef]
  43. Wang, F.; Wei, Y.; Yan, T.; Wang, C.; Chao, Y.; Jia, M.; An, L.; Sheng, H. Sphingomonas sp. Hbc-6 alters physiological metabolism and recruits beneficial rhizosphere bacteria to improve plant growth and drought tolerance. Front. Plant Sci. 2022, 13, 1002772. [Google Scholar] [CrossRef] [PubMed]
  44. Jiang, M.; Delgado-Baquerizo, M.; Yuan, M.M.; Ding, J.; Yergeau, E.; Zhou, J.; Crowther, T.W.; Liang, Y. Home-based microbial solution to boost crop growth in low-fertility soil. New Phytol. 2023, 239, 752–765. [Google Scholar] [CrossRef]
  45. Xiong, W.; Jousset, A.; Guo, S.; Karlsson, I.; Zhao, Q.; Wu, H.; Kowalchuk, G.A.; Shen, Q.; Li, R.; Geisen, S. Soil protist communities form a dynamic hub in the soil microbiome. ISME J. 2017, 12, 634–638. [Google Scholar] [CrossRef]
Horticulturae 10 01035 g001
Figure 1. Effects of different crude metabolic extracts on the biomass of M9T337 seedlings. (A). Effects of the crude metabolic extracts of biocontrol bacteria on the biomass of M9T337 seedlings. (B). Effects of the crude metabolic extracts of biocontrol bacteria on the biomass indexes of young M9T337 trees. (C). Effects of the crude metabolic extracts of biocontrol bacteria on the dry and fresh weight of M9T337 seedlings. T1: XC1 CFCF treatment; T2: QSB-6 CFCF treatment; T3: LBR CFCF treatment; CK: replanted soil treatment; and CK1: methyl bromide fumigation. PL: Penglai, Yantai, Shandong. QX: Qixia, Yantai, Shandong. TA: Tai‘an, Shandong. Lowercase letters above the columns indicate a significant difference at p < 0.05. Values are mean ± SE.
Figure 1. Effects of different crude metabolic extracts on the biomass of M9T337 seedlings. (A). Effects of the crude metabolic extracts of biocontrol bacteria on the biomass of M9T337 seedlings. (B). Effects of the crude metabolic extracts of biocontrol bacteria on the biomass indexes of young M9T337 trees. (C). Effects of the crude metabolic extracts of biocontrol bacteria on the dry and fresh weight of M9T337 seedlings. T1: XC1 CFCF treatment; T2: QSB-6 CFCF treatment; T3: LBR CFCF treatment; CK: replanted soil treatment; and CK1: methyl bromide fumigation. PL: Penglai, Yantai, Shandong. QX: Qixia, Yantai, Shandong. TA: Tai‘an, Shandong. Lowercase letters above the columns indicate a significant difference at p < 0.05. Values are mean ± SE.
Horticulturae 10 01035 g001
Horticulturae 10 01035 g002
Figure 2. Antifungal effect of different CFCFs on Fusarium. (A). Antifungal effect of different CFCFs on F. oxysporum. T1: B. velezensis XC1 CFCF; T2: B. amyloliquefaciens QSB-6 CFCF; T3: L. reuteri LBR CFCF; CK: blank control. (B). Antifungal rates of different CFCF treatments on F. oxysporum. (C). Effect of different treatments on the copy number of Fusarium. Lowercase letters above the columns indicate a significant difference at p < 0.05. T1: XC1 CFCF treatment; T2: QSB-6 CFCF treatment; T3: LBR CFCF treatment; CK: replant soil treatment; and CK1: Methyl bromide treatment.
Figure 2. Antifungal effect of different CFCFs on Fusarium. (A). Antifungal effect of different CFCFs on F. oxysporum. T1: B. velezensis XC1 CFCF; T2: B. amyloliquefaciens QSB-6 CFCF; T3: L. reuteri LBR CFCF; CK: blank control. (B). Antifungal rates of different CFCF treatments on F. oxysporum. (C). Effect of different treatments on the copy number of Fusarium. Lowercase letters above the columns indicate a significant difference at p < 0.05. T1: XC1 CFCF treatment; T2: QSB-6 CFCF treatment; T3: LBR CFCF treatment; CK: replant soil treatment; and CK1: Methyl bromide treatment.
Horticulturae 10 01035 g002
Horticulturae 10 01035 g003
Figure 3. Effects of bacterial CFCFs on rhizosphere bacterial community structures. (A). PCoA analysis. (B). Relative abundances of bacterial taxa at the phylum level. (C). Relative abundances of bacterial taxa at the genus level. (D). Significance analysis of bacterial genus level community differences across sample groups.
Figure 3. Effects of bacterial CFCFs on rhizosphere bacterial community structures. (A). PCoA analysis. (B). Relative abundances of bacterial taxa at the phylum level. (C). Relative abundances of bacterial taxa at the genus level. (D). Significance analysis of bacterial genus level community differences across sample groups.
Horticulturae 10 01035 g003
Horticulturae 10 01035 g004
Figure 4. Effects of bacterial CFCFs on rhizosphere fungal community structures. (A). PCoA analysis. (B). Relative abundances of fungal taxa at the phylum level. (C). Relative abundances of fungal taxa at the genus level. (D). Significance analysis of fungal genus level community differences among sample groups.
Figure 4. Effects of bacterial CFCFs on rhizosphere fungal community structures. (A). PCoA analysis. (B). Relative abundances of fungal taxa at the phylum level. (C). Relative abundances of fungal taxa at the genus level. (D). Significance analysis of fungal genus level community differences among sample groups.
Horticulturae 10 01035 g004
Horticulturae 10 01035 g005
Figure 5. Effect of the crude metabolic extracts from biocontrol bacteria on the rhizosphere microbial co-occurrence network and correlation analysis between the relative abundance of key microorganisms and biomass. (A). Co-occurrence network analysis of the rhizosphere microorganisms. (B). Correlation analysis of the biomass and abundance of key microorganisms in apple seedlings. × indicates that there is no significant correlation between the two factors, and no markings indicate significant correlations.
Figure 5. Effect of the crude metabolic extracts from biocontrol bacteria on the rhizosphere microbial co-occurrence network and correlation analysis between the relative abundance of key microorganisms and biomass. (A). Co-occurrence network analysis of the rhizosphere microorganisms. (B). Correlation analysis of the biomass and abundance of key microorganisms in apple seedlings. × indicates that there is no significant correlation between the two factors, and no markings indicate significant correlations.
Horticulturae 10 01035 g005
Horticulturae 10 01035 g006
Figure 6. The composition of L. reuteri CFCF was analyzed using broadly targeted metabolomics with the UPLC-ESI-Q TRAP-MS/MS. (A). ESI (−) total ion mobility chart. (B). ESI (+) total ion flux chart. The ordinate represents the ionic current intensity of the metabolite, and the abscissa represents the retention time. Each peak in the graph represents a detected metabolite. (C). Top 20 substances. (D). Proportion of substance types among the crude metabolic extracts.
Figure 6. The composition of L. reuteri CFCF was analyzed using broadly targeted metabolomics with the UPLC-ESI-Q TRAP-MS/MS. (A). ESI (−) total ion mobility chart. (B). ESI (+) total ion flux chart. The ordinate represents the ionic current intensity of the metabolite, and the abscissa represents the retention time. Each peak in the graph represents a detected metabolite. (C). Top 20 substances. (D). Proportion of substance types among the crude metabolic extracts.
Horticulturae 10 01035 g006
Horticulturae 10 01035 g007
Figure 7. Effects of three secondary metabolite compounds on the growth of M9T337 seedlings. Note: R1: 3-hydroxypropionaldehyde; R2: exopolysaccharides; and R3: p-hydroxybenzoic acid. Low: 10 μM, High: 10 mM.
Figure 7. Effects of three secondary metabolite compounds on the growth of M9T337 seedlings. Note: R1: 3-hydroxypropionaldehyde; R2: exopolysaccharides; and R3: p-hydroxybenzoic acid. Low: 10 μM, High: 10 mM.
Horticulturae 10 01035 g007
Horticulturae 10 01035 g008
Figure 8. Effects of three secondary metabolite compounds on the growth of F. oxysporum. Note: R1: 3-hydroxypropionaldehyde; R2: exopolysaccharides; and R3: p-hydroxybenzoic acid. Low: 10 μM, High: 10 mM.
Figure 8. Effects of three secondary metabolite compounds on the growth of F. oxysporum. Note: R1: 3-hydroxypropionaldehyde; R2: exopolysaccharides; and R3: p-hydroxybenzoic acid. Low: 10 μM, High: 10 mM.
Horticulturae 10 01035 g008
Table 1. Diversity analysis of soil bacteria and fungi. Lowercase letters above the columns indicate a significant difference at p < 0.05.
Table 1. Diversity analysis of soil bacteria and fungi. Lowercase letters above the columns indicate a significant difference at p < 0.05.
Microbial
Species		TreatmentShannonSimpsonACEChao
FungiT13.75 ± 0.14 bc0.09 ± 0.01 c506.47 ± 9.26 c573.85 ± 13.61 bc
T23.57 ± 0.09 c0.12 ± 0.01 b592.40 ± 22.87 b594.86 ± 19.44 b
T34.07 ± 0.07 b0.05 ± 0.00 d755.84 ± 32.66 a685.31 ± 43.43 a
CK4.45 ± 0.08 a0.04 ± 0.00 d559.07 ± 25.68 bc647.67 ± 13.99 ab
CK12.96 ± 0.04 d0.19 ± 0.02 a486.69 ± 18.99 c508.34 ± 7.20 c
BacteriaT16.20 ± 0.05 b0.05 ± 0.01 a3235.27 ± 184.56 c3157.08 ± 125.27 c
T26.04 ± 0.14 b0.01 ± 0.00 b4013.57 ± 52.35 ab3456.98 ± 76.87 b
T36.81 ± 0.06 a0.01 ± 0.00 b4178.50 ± 72.46 a3862.23 ± 53.86 a
CK6.19 ± 0.05 b0.02 ± 0.00 b3720.47 ± 44.58 b3454.59 ± 30.55 b
CK15.45 ± 0.18 c0.05 ± 0.00 a2939.46 ± 31.21 c3308.99 ± 58.72 bc
Table 2. Effects of three secondary metabolite compounds on the growth of M9T337 seedlings.
Table 2. Effects of three secondary metabolite compounds on the growth of M9T337 seedlings.
TreatmentFresh Weight (g)Dry Weight (g)
LowHighLowHigh
R12.54 ± 0.04 b2.41 ± 0.05 b0.45 ± 0.02 b0.40 ± 0.05 b
R22.70 ± 0.01 a2.87 ± 0.02 a0.65 ± 0.02 a0.74 ± 0.03 a
R32.39 ± 0.04 b2.13 ± 0.07 c0.48 ± 0.02 b0.34 ± 0.03 b
CK1.96 ± 0.08 c1.96 ± 0.08 d0.22 ± 0.02 c0.22 ± 0.02 c
Note: R1: 3-hydroxypropionaldehyde treatment; R2: exopolysaccharides treatment; and R3: p-hydroxybenzoic acid treatment. Low: 10 μM, High: 10 mM. Lowercase letters above the columns indicate a significant difference at p < 0.05.
Table 3. Effects of three secondary metabolite compounds on the growth inhibition of F. oxysporum.
Table 3. Effects of three secondary metabolite compounds on the growth inhibition of F. oxysporum.
TreatmentAntifungal Effects (%)
LowHigh
R125.34 ± 0.19 b50.54 ± 0.19 b
R210.45 ± 0.17 c38.30 ± 0.23 c
R340.00 ± 0.23 a74.21 ± 0.09 a
Note: R1: 3-hydroxypropionaldehyde; R2: exopolysaccharides; and R3: p-hydroxybenzoic acid. Low: 10 μM, High: 10 mM. Lowercase letters above the columns indicate a significant difference at p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lv, J.; Jiang, W.; Xu, Z.; Wang, G.; Li, X.; Wu, X.; Ding, F.; Liu, Y.; Chen, X.; Yin, C.; et al. Comparative Analysis of the Effects of Crude Metabolic Extracts of Three Biocontrol Bacteria on Microbial Community Structure Provides a New Strategy for the Biological Control of Apple Replant Disease. Horticulturae 2024, 10, 1035. https://doi.org/10.3390/horticulturae10101035

AMA Style

Lv J, Jiang W, Xu Z, Wang G, Li X, Wu X, Ding F, Liu Y, Chen X, Yin C, et al. Comparative Analysis of the Effects of Crude Metabolic Extracts of Three Biocontrol Bacteria on Microbial Community Structure Provides a New Strategy for the Biological Control of Apple Replant Disease. Horticulturae. 2024; 10(10):1035. https://doi.org/10.3390/horticulturae10101035

Chicago/Turabian Style

Lv, Jinhui, Weitao Jiang, Zihui Xu, Gongshuai Wang, Xiaoxuan Li, Xinyu Wu, Fengxia Ding, Yusong Liu, Xuesen Chen, Chengmiao Yin, and et al. 2024. "Comparative Analysis of the Effects of Crude Metabolic Extracts of Three Biocontrol Bacteria on Microbial Community Structure Provides a New Strategy for the Biological Control of Apple Replant Disease" Horticulturae 10, no. 10: 1035. https://doi.org/10.3390/horticulturae10101035

APA Style

Lv, J., Jiang, W., Xu, Z., Wang, G., Li, X., Wu, X., Ding, F., Liu, Y., Chen, X., Yin, C., & Mao, Z. (2024). Comparative Analysis of the Effects of Crude Metabolic Extracts of Three Biocontrol Bacteria on Microbial Community Structure Provides a New Strategy for the Biological Control of Apple Replant Disease. Horticulturae, 10(10), 1035. https://doi.org/10.3390/horticulturae10101035

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop