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The epidemic occurrence of decline disease in bayberry trees altered plant and soil related microbiome and metabolome

Environmental Microbiome volume 19, Article number: 79 (2024) Cite this article

Abstract

Background

In China, decline disease with unknown etiology appeared as an epidemic among bayberry trees in the southern area of the Yangtze River. Furthermore, the use of beneficial microbes has been reported to be able to reduce the incidence of this disease, emphasizing the association of this disease with microorganisms. Therefore, it has become critical to uncover the microbiome’s function and related metabolites in remodeling the immunity of bayberry trees under biotic or abiotic stresses.

Results

The amplicon sequencing data revealed that decline disease significantly altered bacterial and fungal communities, and their metabolites in the four distinct niches, especially in the rhizosphere soils and roots. Furthermore, the microbial communities in the four niches correlated with the metabolites of the corresponding niches of bayberry plants, and the fungal and bacterial networks of healthy trees were shown to be more complex than those of diseased trees. In addition, the role of microbiome in the resistance of bayberry trees to the occurrence of decline disease was justified by the isolation, identification, and characterization of important microorganisms such as significantly enriched Bacillus ASV804, Pseudomonas ASV815 in healthy plants, and significantly enriched Stenotrophomonas ASV719 in diseased plants.

Conclusion

Overall, our study revealed that the occurrence of decline disease altered the microbiome and its metabolites in four ecological niches in particular rhizosphere soils and roots of bayberry, which provides new insight into the control of bayberry decline disease.

Background

Bayberry decline disease has been widespread in the south of the Yangtze River in China in recent years, which is becoming a major economic disease in orchards due to serious negative effects on new shoots outgrowth, photosynthetic rate, and death of plants [1]. Although many research works have been carried out on this disease, the exact causal agent of the bayberry decline disease is still unclear, and no pathogen has been found or effectively isolated from the diseased bayberry plants. According to our recent research, the disease incidence may be highly associated with inappropriate fertilization and poor management. For example, Ren et al. [2] reported that three types of bio fertilizers could promote vegetative development and fruit grade of diseased bayberry trees by improving the nutrient status of soils and plants, which provide alternates for the control of decline disease.

The incidence of decline disease in bayberry plants is often linked with the alternations in the microbial community of rhizosphere soils and different plant tissues such as roots, stems, and leaves [3,4,5]. Indeed, plant-microbiome interactions have a significant impact on host function and adaptation [6,7,8,9,10]. It is well known that there is a close link between the ecology of the rhizosphere microbiome and the health of plants. Recently, a new definition “holobiont” was proposed to describe the coevolution between plants and their related microbiomes [11,12,13]. For example, plants respond to various biotic or abiotic stressors such as pathogen infection by altering associated microbial assembly [14, 15]. Additionally, the suppressive microorganisms had reportedly been identified as being crucial in maintaining the health of plants, which contributes to raising resistance against phytopathogens [16, 17].

Plants can secrete different metabolic compounds, such as organic acids, amino acids, sugars, etc. into the rhizosphere, which provide plants with nutrients and signaling molecules responsible for maintaining plant rhizosphere homeostasis [18]. On the other hand, the colonization of some bacteria in the rhizosphere needs the presence of aromatic organic acids. These processes happened due to the coevolution between plants and their microbiome [19]. In particular, some metabolites have been found to be able to defend against pathogens invasion by modulating beneficial microbes [20,21,22]. Conversely, the structure of plant metabolites was mainly affected by the kind of soils and species of the host plants [23]. For this reason, it is urgent to identify these metabolites, which significantly altered the interactions between plants and their microbiome.

In general, the current study states the relationship between microbial assembly and its metabolites on the occurrence of bayberry decline disease in Zhejiang Province, China by investigating the changes between healthy and diseased plants in both soil and different plant parts. Furthermore, the enrichment of specific bacteria was justified by isolation, identification, and functional characterization. The result of this study will provide new insights to alleviate the epidemic occurrence of bayberry decline disease by modulation of specific microbial species.

Methods

Sampling

During the growing season in December 2020, two kilograms of soil samples around the rhizosphere were gathered from the attached soil particles to the root branches. Five samples were obtained at different areas around the canopy of both healthy and diseased bayberry trees besides 1.5 m distance of the irrigation dripping lines at orchard in Xianju county, Zhejiang Province, China. Furthermore, at 5–10 cm below the ground, root samples were collected. The soil samples of the rhizosphere were pulverized and then passed through 0.45 mm sieve based on the quadrat method. Stem and leaf samples were prepared by collecting a branch attached to the central trunk with a diameter of 5–8 cm and a height of approximately 50 cm from each tree, and then cutting each section 1.0 cm from the base to the top of each branch as stem samples and gathering all leaves from the base to the top of each branch as leaf samples. Each treatment had six trees, and each replicate contained one tree.

Sample processing, DNA extraction, and sequencing

Sample processing was carried out by the method of Beckers et al. [24]. To sum up, rhizosphere soils were prepared by shaking (20 min, 120 rpm) on a platform and then discarding roots from soil particles. Plant samples were prepared by disinfecting with 75% alcohol and then cleaning via sterilized water. The different parts of the plant were separated into tiny pieces; six duplicates of each homogenized plant sample (1.5 ml) from each bayberry tree was preserved at -80 °C for DNA harvesting. DNA extraction kit Foregene (Chengdu Fujifilm Biotechnology Co., LTD., Chengdu, China) was applied to harvest genomic DNA from the soil samples with 6 replicates. The bacterial assembly was examined by amplifying the 16 S rRNA V3-V4 region using 27 F and 1492R primers, while the fungal assembly was examined by amplifying the ITS1 and ITS4 region using ITS1 (5’⁃TCCGTAGGTGAACCTGGG-3’) and ITS4 (5’⁃TCCTCCGCTTATTGATATGC-3’) primers.

The purification of PCR products was carried out through AGCOURT AMPure XP beads (Beckman Coulter Co., Chaska, MN, USA). According to the primary results of electrophoresis quantification, the PCR products were quantified and homogenized equally with the volume that is essential for sequencing of each sample via using QuantiFluor™-ST Blue fluorescence quantification system (Promega, Inc.). TruSeq DNA PCR kit (Illumina, USA) was utilized to build the sequencing library, and then sequenced through the Illumina NovaSeq® 6000 sequencer (Illumina, Inc., California, USA). The sequencing of the paired-end reads was performed using Illumina after the separation of the samples. The sequential noise reduction method (DADA2) was performed to optimize the data processing of the control quality. The ASVs (Amplicon Sequence Variants) were used for sequencing and abundance of information. Bacterial sequences were matched using Silva database with lengths between 200 and 550 bp, while the sequences of the fungi were compared using unite8.0 database with lengths between 140 and 550 bp. The resulting homogenization data were used for subsequent diversity analysis.

GC-MS investigation

To examine the metabolic alternations in healthy and diseased bayberry plants, gas chromatography-mass spectrometry (GC-MS) and multivariate statistical assay were carried out in this study. Furthermore, the metabolic characteristics of the different four groups were visualized and distinguished based on the correlation, OPLS-DA, and KEGG analyses. In addition, the Wilcoxon rank sum test and Kruskal-Wallis H test were used to differentiate the metabolite’s prosperity between the four groups via pair comparisons.

Co-occurrence network analysis

In our investigation, the microbiological networks of both healthy and infected bayberry plants were respectively constructed. Correlations between microorganisms and communities were measured using R-packet psych. ASVs with significant relative abundance (> 0.1%) and associations that are statistically relevant (P < 0.05, SparCC correlation > 0.6 or < -0.6) were included in the network analysis. The conversion of the network file in the adjacency matrix format was obtained above into an adjacency list of the network using the R package graph. The collaborative principle of Gephi was used to observe the network. In the network of microbiome, nodes represent distinct microbial species, and edges represent paired correlations between nodes, showing physiological or biochemical interaction between them.

Isolation, purification, and functional analysis of plant-associated bacteria

After disinfection of the sample tissues, bacteria were isolated, and cultivated in NA and PDA, respectively, while some representative isolates were selected to test their antagonistic activity against bayberry twig blight pathogen P. versicolor XJ27 that frequently occur simultaneously with decline disease due to that no pathogen has been obtained for bayberry decline disease. P. versicolor XJ27 was cultured into the PDA plate in its center, with the bacterial isolates spotted at equidistant from the center. PDA plate inoculated with only P. versicolor was kept as control. Each plate was carried out three times. After six days of incubation at 25 °C, the diameter of the inhibitory area was scaled, and the inhibitory rate was calculated. The ability of bacterial isolates to fix nitrogen Bacteria was determined by inoculating them on Azube’s solid medium and then culturing at 35 °C for seven days [25]. The ability of bacterial isolates to solubilize organic and inorganic phosphorus was determined by culturing them on a Montana solid medium and then recording the diameter of the clear zone [26].

The tender leaves of bayberry were immersed in a suspension of the selected antagonistic bacterium isolated from the healthy bayberry, which was inoculated into NB liquid broth and then shaken at 25 °C with 200 rpm for 24 h (OD = 1.6). After incubation in a culture dish for 48 h at 25 °C, to quantify the amount of plant hormones and the activity of enzymes, the treated leaves were removed, ground in liquid nitrogen, and then kept at -80 °C. The control was treated with NB liquid broth. Furthermore, the activity of antioxidant enzymes such as phenylalanine ammonia- lyase (PAL), catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), polyphenol oxidase (PPO), and ascorbate peroxidase (APX) was determined by using the appropriate kits from Suzhou Comin Biotechnology Co., China, which were carried out according to the instructions for use. Additionally, using the method of Pan et al. [27], the concentration of plant hormones such as auxin (IAA), gibberellins (GA3), and abscisic acid (ABA) were ascertained. Each treatment contains 3 leaves, and the assay was repeated thrice.

Statistical analysis

The R Foundation for Statistical Computing (R 4.2.1) was used for statistical analysis. To verify that the data had a normal distribution, the Shapiro-Wilk test was used. Based on the distribution of the estimated parameters, significant differences in parameter variances were identified using analysis of variance or the Kruskal-Wallis rank sum test. The pair Wilcoxon rank sum test or Tukey’s honest test of significant difference was used for postmortem comparisons. ANOVA was used to assess the effect of several plant compartment niches (rhizosphere soils, roots, stems, and leaves) on the abundance of microbial populations. Diversity in R Vegan was used to construct the alpha diversity index. The Bray-Curtis dissimilarity matrix calculation was used to generate principal coordinate analysis (PCoA). The substantial differences between different treatments in β-diversity of microbial communities were tested using PERMANOVA.

Results

The impacts of niches on the distribution and structure of bayberry–associated microbiomes

According to 16 S rRNA and ITS amplicon sequencing analysis, the fungal and bacterial community structure on the four niches (rhizosphere soils, roots, stems, and leaves) of healthy and infected bayberry in the Xianju area were examined in this study. The respective roles of habitats and disease in shaping the construction of the microbiota associated with bayberry plants by using Principal Coordinate Analysis (PCoA) and Permutation Multivariate Analysis of Variance (PERMANOVA). The findings demonstrated that niches (bacteria: R2 = 37.35%, P < 0.001; fungi: R2 = 38.16%, P < 0.001) and decline disease (fungi: R2 = 6.61%, P < 0.001) were the primary factors explaining the variance between the bacterial and fungal populations (Fig. 1A). PCoA of the microbiome of each niche showed that the decline disease had a significant (PERMANOVA; P < 0.01) influence on the construction of bacterial and fungal communities in rhizosphere soils and roots, but did not substantially (PERMANOVA; P > 0.01) alter the bacterial community structure of stems and leaves (Fig. 1A, S1A, S1B). In contrast, this disease has a role on the Chao1 and ASVs diversity index of bacterial and fungal communities in the rhizosphere soils (ANOVA, P < 0.05). The three alpha diversity indices including the Shannon index, the Chao1 richness, and the ASV index depend on the type of microbe (bacterial or fungal community structure), niches, and disease (Figure S2).

Fig. 1
figure 1

Microbiomes associated with bayberry trees assembling as a community. (A) Bray–Cutis dissimilarity matrices using principal coordinate analysis (PCoA) are used to illustrate how different conditions and declining disease affect the bacterial community structure of the bayberry rhizosphere (soil, root, and stem) microbiomes. (B) The bacterial communities’ Shannon diversity index in the bayberry-associated microbiomes of both healthy and unhealthy plants Significant differences are indicated by asterisks (**P < 0.001). (C) Based on relative abundance data, a taxonomic analysis reveals the composition of bacterial genus in the four niches occupied by healthy and diseased plants. D is diseased; H is healthy

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A taxonomic study revealed that there was a significant change in bacterial and fungal community structures between the four distinct niches, mainly in the rhizosphere soils and roots, which has been regarded as a consequence of decline disease infection (Fig. 1C, S3). The findings of this study showed that decline disease causes a great reduction in the relative abundance of Fusarium, but a great increase in the relative abundance of Penicillium in the rhizosphere soils and roots (Figure S3). Furthermore, the occurrence of decline disease resulted in a considerable reduction in the relative abundance of numerous possible beneficial bacteria, including Bacillus, Bradyrhizobium, Sphingosinomonas, Stenotrophomonas, and Pseudomonas. On the other hand, decline disease has considerably increased the relative abundance of Acidothermus in rhizosphere soils of bayberry plants (Fig. 1C).

Alternations in microbial community composition

The two-tailed Wilcoxon rank sum test result indicated that there was a significant change between different niches in relative abundance of bacterial and fungal community at the genus level, which was consistent with the result of PCoA analysis (Tables 1 and 2). Indeed, there were 133 differential bacterial genera between healthy and diseased groups of rhizosphere soils (P < 0.05), which was much larger than 39, 4 and 5 differential bacterial genera in roots, stems and leaves, respectively. There were 140 differential fungal genera between healthy and diseased groups in rhizosphere soils (P < 0.05), which was much larger than the 15, 26 and 11 differential fungal genera in roots, stems and leaves, respectively. These findings imply that microbial communities’ structure in rhizosphere soils is significantly influenced by the infection of decline disease. Additionally, 93 bacterial genera were distinguished between healthy roots and healthy rhizosphere soils, 66 bacterial genera were distinguished between diseased roots and diseased rhizosphere soil, and 140 bacterial genera were distinguished between healthy stems and healthy rhizosphere soils. These results suggest that the infection of declined disease may reduce the variance of bacterial community between rhizosphere soils and roots.

Table 1 The numbers of high significant genera of bacteria under different conditions utilized by the test of two-tailed Wilcoxon (P < 0.05)
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Table 2 The numbers of high significant genera of fungi under different conditions utilized by the test of two-tailed Wilcoxon (P < 0.05)
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The Manhattan map demonstrated that the primary ASVs in rhizosphere soils and roots that distinguished between healthy and infected bayberry plants belonged to the genus with abundances larger than 1% (Fig. 2A and C). The rhizosphere microbiota was impacted by decline disease at the genus level, according to the results of the STAMP study. A two-tailed Wilcoxon test with a significance level of P < 0.05 was found in the rhizosphere soils between healthy and diseased plants in 11 bacterial genera (Fig. 2B). Similarly, a two-tailed Wilcoxon test with a significance level of P < 0.05 was found in the roots of healthy and diseased plants in Frankia and Sphingomonas (Fig. 2D). Among these microbial populations, the rhizosphere soils of healthy plants had a significant enrichment of Bacillus ASV804 and Pseudomonas ASV815 with an abundance > 0.1%, whereas the rhizosphere soils of unhealthy plants had a relevant enrichment of Stenotrophomonas ASV719 with an abundance > 0.1% (Fig. 2A). Additionally, Fig. 2C shows that Pantoea ASV6021 was considerably concentrated in the roots of infected plants with an abundance > 0.1%, while Sphingomonas ASV5906 was strongly enriched in the roots of healthy plants with an abundance > 0.1%.

Fig. 2
figure 2

Taxonomic distinctions in the bacterial communities between healthy and unhealthy rhizosphere soils and roots, respectively (A, C) Manhattan plots illustrated the significance or non-significance of ASVs in the bacterial microbiota of the rhizosphere soils and root, respectively, in the diseased samples. A single ASV is represented by each circle. Circles that are unfilled or filled indicate whether ASVs are significant or non-significant in the diseased samples (ASV abundance > 0.005%, P < 0.05). (B, D) absolute variations in the relative abundance of bacterial species in rhizosphere soils and roots, respectively between healthy and diseased plants (Welch’s t test; P < 0.05). The red-colored bacteria are Bacillus, Pseudomonas, and Stenotrophomonas. Standard deviations are represented by error bars

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Decline disease modifies the molecular ecology networks of microbiomes linked with bayberry

The influence of the decline disease on the bayberry microbial community in this research was determined by assessing the bacterial-bacterial and fungal-fungal intra-kingdom networks. The higher the average degree, the more complex the network. The findings showed that there was a greater complexity in bacterial and fungal networks of healthy trees than those of diseased trees, while the fungal network contains more nodes and edges compared to the bacterial network (Fig. 3; Table 3). In bacterial networks, hub nodes with high and near centrality values tended to have favorable correlations with other nodes, particularly in cases of decline disease. The fungal network graph consisted of a majority of positively linked edges (Fig. 3; Table 3). Moreover, the complexity of bacterial and fungal network was significantly impacted by the decline disease (Fig. 3A, S4). Indeed, the bacterial richness and network complexity in healthy bayberry plants was presented as rhizosphere soils (17.08), roots (14.75), stems (3.43), and leaves (3.08) (Figure S4A), while the bacterial richness and network complexity (2.91) in unhealthy plants were presented as rhizosphere soils (16.66), stems (4.64), roots (3.00), and leaves (16.66). Furthermore, the lowest fungus richness and network complexity was observed in root (3.57 in healthy bayberry; 2 in diseased bayberry), while the greatest fungus richness and network complexity was observed in the rhizosphere soils (20.03 in healthy bayberry; 16.204 in diseased bayberry) (Figure S4B). In addition, there was a difference in the taxonomic structure of the soils and plant compartments networks, with more proteobacteria found in plant niches and more acid bacteria found in soil niches (Figure S4A).

Fig. 3
figure 3

Co-occurrence networks within a kingdom. (A) Intra-kingdom networks between bacteria and fungus. The bacterial phylum and fungal class are represented by the colors of the nodes. The size of the node shows how connected it is. The edge color indicates the correlations that are positive (pink) and negative (blue). (B) A comparison of the node-level topological features in panel A reveals that the hub taxa have high degree and closeness centrality values; (C) A comparison of the degree of bacterial and fungal taxa indicates that the healthy bacterial network is more complex than the diseased bacterial network, and the fungal networks exhibit a similar pattern. Using the Wilcoxon test, the significance of the difference was ascertained

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Table 3 Relations and topological characteristics of bacterial and fungal microbial communities networks in healthy and diseased bayberry trees
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GC-MS analysis of metabolic profiles in four different niches of healthy and diseased bayberry plants

Five hundred and two metabolites were detected from healthy and diseased plants at four niches, while these metabolites were classified into eight groups, including carbohydrates, organic acids, lipids, nucleic acids, peptides, steroids, as well as hormones and neurotransmitters, vitamins and cofactors (Fig. 4, S5, S6). Significant changes were observed between healthy and diseased bayberry plants based on OPLS-DA analysis of metabolites (Fig. 4A). Indeed, compared to healthy plants, the majority of carbohydrates such as D-saccharic acid, cellotetraose, sucrose, rhamnose, D-mannitol, and lactobionic acid, and organic acids such as malonic acid, D-malic acid, 4-hydroxybenzoic acid, tartronic acid, O-phosphocolamine, ellagic acid and lactobionic acid, phosphoric acid, and gluconic acid were up-regulated, while the majority of amino acids such as L-valine, pipecolic acid, L-glutamic acid (dehydrated) were down-regulated in diseased plants.

Fig. 4
figure 4

A comparative metabolome investigation of four distinct niches between healthy and unhealthy bayberry trees. (A) OPLS-DA models; (B) VIP values in the top 25 and 30 rhizosphere soils and roots values (VIP > 1.0, P < 0.05); (C) A map of the metabolic pathway enrichment of various metabolites in the rhizosphere soils and roots of both healthy and unhealthy bayberry plants. The size of the point indicates the quantity of metabolites, and the color mark on the right indicates the P value’s size, ranging from orange to green

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Among the differentially expressed metabolites between healthy and diseased bayberry plants, thirty-one significantly increased and nineteen significantly reduced metabolites were found in rhizosphere soils (Fig. 4B; Table S1); nineteen significantly increased and thirty-three significantly reduced metabolites were found in roots (Fig. 4B; Table S2); twenty-nine significantly increased and thirty significantly reduced metabolites in stems (Figure S5A, S6A; Table S3); forty-one significantly increased metabolites and twelve significantly reduced metabolites in leaves (Figure S5B, S6B; Table S4). Therefore, it can be inferred that the differentially expressed metabolites may be involved in the response of bayberry plants to decline disease.

Based on the enrichment analysis of metabolic pathways using KEGGID of differential metabolites, the bubble map displayed the top 20 pathways in roots, stems, leaves, and soils that were statistically significant (P < 0.05) (Fig. 4C, S6). The findings demonstrated that the pathways involved in the manufacture of tryptophan, aminoyl-TrNA, glucoside, phenylalanine metabolism, and ABC transporters were the primary areas of enrichment for the divergent metabolites found in roots. The primary metabolic pathways that were enriched in the differential metabolites in stems were those for tyrosine metabolism, aminoacyl-TrNA production, niacin and niacinamide metabolism, and ABC transporters. The metabolism of alanine, glutamic acid, and aspartic acid, glutathione metabolism, and the tyrosine metabolism pathway were the primary areas of enrichment for the differential metabolites found in leaves. The breakdown route of p-aminobenzoate, the flavonoid biosynthesis pathway, and the phenylpropanol biosynthesis pathway were the key areas of enrichment for the differential metabolites in rhizosphere soils. In general, amino acid biosynthesis pathways and ABC transporters had a major enrichment in the differential metabolites of roots, stems, and leaves.

Metabolites induce the enrichment of potentially beneficial microbes in diseased bayberry trees

The correlation between the microbiome assembly in the four niches of the bayberry tree and the metabolites of the corresponding niches was obtained by Pearson’s correlation analysis. In rhizosphere soils, the microorganisms from the genera Bacillus, Pseudomonas, and Fusarium were positively correlated with organic heterocyclic compounds, hormones and transmitters, and carbohydrates, but negatively correlated with flavonoids (Fig. 5). In roots, the microorganisms from the genus Bacillus, Pseudomonas, and Stenotrophomonas were positively correlated with aromatic compounds, organic acids and their derivatives, while the microorganisms from the genus Penicillium showed an inverse relationship with them. However, in the stems, microorganisms from the genus Bacillus and Pseudomonas were negatively correlated with aromatic compounds, phenylpropanes, polyketides, and organic heterocyclic compounds, and positively correlated with lipids, lipid molecules, and carbohydrates (Fig. 5). Notably, in leaves, the microorganisms from the genus Bacillus and Stenotrophomonas had inverse relationship with phenylpropionic acids and polyketides, alcohols with polyols, carbohydrates, organic acids and their derivatives (Fig. 5).

Fig. 5
figure 5

The differential microorganisms and differential metabolites in rhizosphere soils (A), roots (C), stems (B), and leaves (D) were analyzed using Pearson’s correlation heatmap analysis, with a P value of 0.05 (after FDR correction)

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Enrichment of specific bacteria was justified by isolation, identification, and functional characterization

Three hundred and forty-four single bacterial colonies were randomly selected from the rhizosphere soils and roots of both diseased and healthy bayberry plants in order to further describe ASV719, 804, and 815. Eight isolates of these bacteria exhibited strong antagonistic activity with a plate test antagonism rate of more than 50% against P. versicolor XJ27. Moreover, the phylogenetic tree of eight antagonistic bacteria constructed based on analysis of 16 S rRNA gene sequences revealed that five isolates were related to the Bacillus genus, two isolates were related to the Pseudomonas genus, and one isolate was related to the Stenotrophomonas genus (Fig. 6).

Fig. 6
figure 6

The eight bacterial strains’ suppressive actions against P. versicolor XJ27 were seen in the rhizosphere soils and roots, two distinct habitats. (A) Neighbor-Joining phylogenetic analysis based on 16 S RNA sequence; (B) Antagonistic behaviors in dual culture experiments. Placing a P. versicolor XJ27 mycelial plug in the middle of the medium served as the antagonist test. Only the control plates were infected with P. versicolor XJ27. Diseased soil (DS), healthy root (HR), and diseased root (DR)

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The findings also showed that the eight bacterial isolates differed in plant growth promoting characters, including phosphate solubilization, nitrogen fixation, and IAA production (Table 4). Indeed, IAA could be synthesized by four bacterial isolates (DR5B6, DR5B23, DSB10, and DSB41), with isolate DR5B23 obtaining the highest secretion (9.92 mg/l). Six bacterial isolates (DR5B6, DR5B16, HB4B6, DSB10, DSB41, and DS1B19) showed the capacity to solubilize phosphorus, with isolate DR5B6 achieving the highest SI index of 2.66. Seven bacterial isolates (DR5B5, DR5B6, DR5B16, DR5B23, HR4B6, DSB10, and DS1B19) possessed a reasonably steady nitrogen fixation capacity by inoculating these isolates on Ashube’s solid or liquid media and observing their growth.

Table 4 Classification and functional identification of 8 antagonistic bacteria
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In agreement with the antagonistic activity and nitrogen fixation capacity, antagonistic isolate HR4B6 was also able to influence the production of phytohormones and the activity of enzymatic antioxidants in bayberry leaves (Table 5). Indeed, compared to leaves inoculated with sterile water (control group), inoculation with isolate HR4B6 significantly (P < 0.05) increased the antioxidant enzyme activities of APX, PAL, PPO, and SOD, but significantly (P < 0.05) decreased the activities of CAT and POD. Furthermore, it was also found that inoculation with isolate HR4B6 considerably (P < 0.05) increased the content of ABA and IAA, but significantly (P < 0.05) decreased the level of plant hormones in bayberry leaves compared to those inoculated with sterile water (control group). Thus, it can be inferred that the resistance of bayberry plants to biotic and abiotic stresses may be partially attributed to changes in plant hormones and antioxidant enzyme activity.

Table 5 Effect of antagonistic bacterium HR4B6 on enzymatic antioxidants activity and phytohormones content in bayberry leaves
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Discussion

It is still unclear the causal agent of bayberry decline disease, but few studies have revealed that the healthy growth of bayberry plants are highly associated with phyllosphere and rhizosphere microbiome although no detailed studies outlined the correlation between bayberry decline disease and the metabolome or microbiome. The current study determined the impact of bayberry decline disease on the structure and assembly of both bacterial and fungal communities in the rhizosphere soils, roots, stems, and leaves by metagenomics sequencing, metabolome analysis, and biological assays of some antagonistic microbes (Fig. 1A, S1). In addition, the rhizosphere soils showed the highest assembly of bacteria and fungi compared with other niches (leaves, stems, and roots) with 133 differential bacterial genera and 140 differential fungal genera between healthy and diseased bayberry plants via using the Wilcoxon rank sum test.

Compared to various parts of plants, the rhizosphere soils exhibit a greater number between healthy and diseased bayberry plants in differential bacterial and fungal genera. This may be partially due to the least variation among different replicates of the rhizosphere soil samples based on PCoA analysis of the microbial communities, which depicts differences in the taxonomic composition of bacterial and fungal communities among the independent replicates. Furthermore, the variation among different replicates may be also able to be attributed to the function of the microbial community. Indeed, earlier studies have outlined that root exudates drive the assembly of the microbiome in the rhizosphere soils or roots [28, 29], while the microbiome in plants have been found to be involved in a series of various cellular processes such as the interaction between bacteria and host plants, production of cell wall degradation enzymes, and the expression of genes related to chemotactic action, the pili and flagella formation [7, 30,31,32].

In agreement with the result of this study, it has been well known that soil microbial communities including bacteria, fungi, and archaea can impact soil and plant health by involving various essential processes such as nutrient cycling, soil structure, organic matter decomposition, and disease suppression, which is critical for maintaining soil fertility and supporting plant growth [33, 34]. For example, certain microbial species exhibit the ability of nitrogen fixation, making this essential nutrient more readily available to plants. Moreover, some microbial species exhibit the ability to suppress plant pathogens, enhancing plant health and reducing the need for chemical pesticides. Additionally, certain microbial species exhibit the ability to decompose organic matters, which can release essential nutrients such as nitrogen, phosphorus, and sulfur, making them available for plant uptake.

In this study, alpha diversity indices (ASVs, Chao1, Shannon) of both bacterial and fungal community in the roots and rhizosphere soils of healthy bayberry plants were higher than those in diseased bayberry plants. Plant immunity towards disease has been mainly linked to the diversity of soil microorganism’s assembly [2], while the stability is possibly enhanced by the high assembly of soil microbes [35,36,37]. For this reason, it has been noticed that there is a high number of microbes in a complex structure in healthy plants. Additionally, previous studies outlined the stimulating of systemic resistance together with strong microbial networks interactions towards pathogens engulfment [38, 39]. In contrast, the result of this study revealed a weaker microbial network interaction in diseased bayberry plants in both bacterial and fungal microbiota compared to healthy bayberry plants.

Microbial co-occurrence network analysis has been proposed to be a useful approach to examine the bacterial and fungal co-occurrence relationship, which provide novel insights into the complex microbial communities and the interactions within microbial communities. Consistent with the findings of our investigation, Shi et al. [40] discovered that a severe case of potato common scab led to a decrease in the complexity of the microbiome network. This could be because the network became less stable due to a decrease in microbial community organization, which in turn led to weakened microbial relationships [41, 42]. Compared to highly connected co-occurrence networks, simple networks reduce the adaptability of microbes to environmental shocks, which are more unfavorable to plant survival and growth.

Similar to soil microbiota, plant-associated microbiota have also been distinguished to be a secondary microbial layer for the resistance of plants to biotic and abiotic stresses [43, 44]. It was noticed in this study when compared to healthy plants, the community diversity of fungal microbiota in the roots, stems, and leaves of diseased plants was much lower, suggesting that only a limited number of fungal microbiota were involved in the response to the decline growth disorder of bayberry. Furthermore, in the rhizosphere soils and roots of diseased trees, notably elevated levels were observed on the potentially pathogenic fungal taxon Penicillium spp., which has been well known to be an opportunistic pathogen parasite on extremely susceptible disease plants.

The most abundant bacteria in terms of relative quantity were shown to be Burkholderia-Caballeronia-Paraburkholderia, which was markedly enhanced in roots and rhizosphere soils of diseased bayberry. According to previous studies, the assembled bacteria in Panax notoginseng rhizosphere soils are called Burkholderia-Caballeronia-Paraburkholderia, which can break down the plant’s autotoxic saponins and fight off the protobacteria that cause root rot [45]. Also, the Burkholderia-Caballeronia-Paraburkholderia can rapidly colonize and grow during in situ bioremediation in contaminated soil or water [46]. In addition, Pantoea was significantly enriched in root compartments of diseased trees compared to healthy trees. Interestingly, various roles such as plant pathogen, antagonist, and plant growth promoter have been played by bacteria from the genus of Pantoea [47], while the extremely significant abundance of Pantoea in the roots of diseased bayberry may be involved in the responses of the bayberry to decline growth disorder. However, no viable bacteria corresponding to Burkholderia-Caballeronia-Paraburkholderia and Pantoea were isolated from the samples of the bayberry trees.

Manhattan map and STAMP assays outlined that Bacillus and Pseudomonas were relevantly assembled in rhizosphere soils of healthy bayberry trees, while Stenotrophomonas was relevantly assembled in rhizosphere soils of diseased bayberry trees. Hence, Bacillus, Pseudomonas, and Stenotrophomonas have been screened for their antagonistic activities against P. versicolor XJ27. Therefore, Stenotrophomonas can be thought of as a “recruited microbe” in the bayberry plants that suffer from the decline disease. In agreement with the enrichment in healthy bayberry trees, previous studies have shown that various species of Pseudomonas and Bacillus have an important role in inhibiting plant pathogens by colonizing different compartment niches of plants [10, 44, 48, 49].

The result of this study revealed that bayberry plants respond to decline disease by recruiting Stenotrophomonas ASV719. In agreement with the data in this study, previous studies have shown that numerous bacteria that are attracted to plants under different stresses are beneficial to their development and health. For instance, the leaf defense mechanism is activated and the rhizosphere is particularly enriched with three types of beneficial bacteria that stimulate the growth and development of plants and cause disease resistance of Arabidopsidis to the infection of downy mildew pathogen Hyaloperonospora arabidopsidis [50]. Furthermore, the enrichment of Chitinobacteriaceae and Flavobacteriaceae in beetroot led to the activation of inhibitory functions [51], while soil-borne pathogen Fusarium pseudograminearum was reduced by recruiting beneficial rhizosphere microbe SR80 in wheat plants [44]. In addition, citrus plants responded to the attack of the pathogen D. citri by recruiting Pantoea and Methylobacterium in the leaf [38].

In agreement with the increased accumulation of Stenotrophomonas by bayberry decline disease in the rhizosphere soils microbiome assembly in this study, Stenotrophomonas has been found to be significantly increased in the root of capsicum after white fly infected the leaf tissue of capsicum [52], and its specific microbial groups were also abundant in the unhealthy plants. These results indicate that Stenotrophomonas is usually affected by different plant biological stresses and can promote the defense activation of plants. In addition, Stenotrophomonas has been reported to inhibit numerous pathogenic fungi by producing volatiles [53, 54]. In our current investigation, the high abundance of Stenotrophomonas in decline disease can be justified by the successful isolation of strain DS1B19, which exhibited strong inhibitory effect on twig blight pathogen P. versicolor XJ27 that frequently occurred simultaneously with decline disease due to that no pathogen has been obtained for bayberry decline disease.

Significant changes in metabolites were observed in all parts of diseased bayberry trees compared to healthy bayberry trees. It has been shown that metabolic compounds such as terpenoids and flavonoids are essential for fine-tuning the structure and function of plant microbiome assembly [55, 56]. However, the majority of research has focused on the importance of the inter-root secretions, which play an important role in modulating the alternations in inter-root microbiome communities as a result of plant resistance [57,58,59,60]. For example, terpenes, aromatic compounds, and amino acids have been outlined to modulate Pseudomonas populations in pathological systems [61,62,63]. As a response of pathogen invasion, a significant amount of malic acid was secreted from roots of Arabidopsis in order to recruit Bacillus [21]. In the current study, we found that heterocyclic compounds especially the organic ones were relevantly abundant, while carbohydrates were significantly decreased in the rhizosphere around the soils of diseased bayberry. It has been found that the assembly of Pseudomonas has been down-regulated in the presence of carbohydrates, but relevantly up-regulated by flavonoid accumulation. Aromatic compounds and organic acids content reduced relevantly in diseased roots which were mainly linked to Pseudomonas, Bacillus, and Stenotrophomonas assembly. Heterotrophs could defend themselves by using the aromatic metabolites, which are secreted as carbon sources or signaling molecules [64]. Interestingly, the accumulation of amino acids and organic acids promotes the assembly of Pseudomonas in the leaves of diseased bayberry.

Previous studies outlined that organic acids and their derivatives could promote the immunity of plants towards pathogens in wilt disease and suppress pathogens by recruiting some favorable microbes [65, 66]. It has been observed that the presence of Pseudomonas, Bacillus, and Stenotrophomonas in bayberry stems was enhanced significantly as a result of the reduction of alkaloid levels. Alkaloids are considered to be secondary metabolites, which are secreted by plants for protection from pathogens [67, 68]. These results revealed that microbial diversity and plant growth can be modulated via the accumulation of some metabolic compounds such as amides, amino acids, aromatic acids, fatty acids, and phenolic compounds [69], which can recruit different beneficial microbes to promote the resistance of plants to biotic and abiotic stresses [10, 21, 50, 70]. In agreement with the result of previous studies, our results showed that response of plants to different stressors may be partially due to the secretion of different metabolic compounds in diseased bayberry trees, which stimulates the recruitment of three different beneficial bacteria (Pseudomonas, Bacillus, and Stenotrophomonas).

The result of this study showed that the associated bacteria have a positive correlation with the health of bayberry plants by possessing the ability of nitrogen fixation, phosphorus solubilization, and production of active antifungal metabolites, and phytohormones (IAA and ABA), as well as and the induction of plant defense response such as the enhanced activities of the enzymes responsible for the antioxidant action and the reduction of ROS amount in plant cells. In agreement with our findings, the relationships of bayberry trees and their associated bacteria have received widespread attention. For example, the increase in ABA content is correlated to stomatal closure, which prevents pathogens from infection through the stomatal pathway [71]. Furthermore, previous studies showed that pathogen invasion may be a key inducer of the microbial community in the plants, which trigger one new strategy of “cry for help” to make plants stimulate soil aid for maintaining different biological stresses [41, 51].

Conclusions

Ultimately, the obtained results from our study provided an evidence for the alternations between healthy and diseased bayberry plants in the microbiome of the soil and different plant niches. Furthermore, our findings revealed that there was a significant change in the accumulation of the metabolic compounds between healthy and diseased bayberry plants, which subsequently remodeled the microbial assembly. In addition, the importance of both microbiome and metabolome in the response of bayberry plants to decline disease can be further elucidated by the isolation, identification, and characterization of key microorganisms and metabolites that were significantly regulated in diseased bayberry trees. Overall, the result of this study provide a new insight in controlling decline disease and maintaining healthy growth of bayberry plants.

Data availability

The datasets supporting the results of this article are included within the article and its additional files. Access to the citations for these Sequence Read Archive metadata: CRA012187 in https://ngdc.cncb.ac.cn/gsa/. The 16 S RNA nucleotide sequences of strains DR5B16, DS1B19, DSB10, DSB41, DR5B5, DR5B6, DR5B23, HR4B6 generated in this study were deposited at: https://www.ncbi.nlm.nih.gov/genbank/under the accession numbers PQ285118- PQ285125.

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Acknowledgements

We thank Shanghai Major Biological Co., Ltd (Shanghai, China) for high throughput sequencing service and bioinformatics support. The authors extend their appreciation to the Researchers Supporting Project (RSPD2024R758), King Saud University, Riyadh, Saudi Arabia.

Funding

This work was supported by Zhejiang Province “San Nong Jiu Fang” Science and Technology Cooperation Plan (2024SNJF073; 2023SNJF041; 2023SNJF040), Zhejiang Major Agricultural Technology Collaborative Promotion Plan Project (2022XTTGGP04), the Key R & D projects in Zhejiang Province (2021C02009), Hangzhou Science and Technology Development Plan Project (20231203A05) and the Researchers Supporting Project (RSPD2024R758), King Saud University, Riyadh, Saudi Arabia.

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  1. State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Institute of Horticulture, Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China

    Haiying Ren, Xingjiang Qi, Zheping Yu & Gang Li

  2. State Key Laboratory of Rice Biology and Breeding, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China

    Xuefang Huang, Yasmine Abdallah, Solabomi Olaitan Ayoade & Bin Li

  3. College of Plant Protection, China Agricultural University, Beijing, 100193, China

    Zhenshuo Wang & Qi Wang

  4. Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 55760, Riyadh, 11451, Saudi Arabia

    Mohamed Mohany & Salim S. Al-Rejaie

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Contributions

HR, XH, and ZW performed sampling and data collection; YA, SOO, XQ, ZY, and QW performed bioinformatics and statistical analyses; HR, XH, ZW, XQ, ZY, QW, MM, SSA, BL and GL planned the experiment, obtaining grants, and validate data; HR, XH and ZW drafted the manuscript; XQ, ZY, MM, SSA and QW preformed data visualization; HR, XH, ZW, YA, SOO, GL and BL proofread the final version and all the authors helped reviewing and editing the manuscript.

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Ren, H., Huang, X., Wang, Z. et al. The epidemic occurrence of decline disease in bayberry trees altered plant and soil related microbiome and metabolome. Environmental Microbiome 19, 79 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-024-00618-w

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  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-024-00618-w

Keywords

Environmental Microbiome

ISSN: 2524-6372