
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12633-3
10.1016/j.heliyon.2024.e36602
e36602
Research Article
The response of root-zone soil bacterial community, metabolites, and soil properties of Sanyeqing medicinal plant varieties to anthracnose disease in reclaimed land, China
Li Xuqing a
Lu Qiujun b
Hafeez Rahila c
Ogunyemi Solabomi Olaitan c
Ibrahim Ezzeldin c
Ren Xiaoxu d
Tian Zhongling zltian@zjsru.edu.cn
e⁎
Ruan Songlin d
Mohany Mohamed f
Al-Rejaie Salim S. f
Li Bin c
Yan Jianli yanjianli00@gmail.com
a⁎⁎
a Institute of Vegetable, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
b Hangzhou Agricultural and Rural Affairs Guarantee Center, Hangzhou, China
c State Key Laboratory of Rice Biology and Breeding, Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, China
d Institute of Crop and Ecology, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
e Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou, China
f Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
⁎ Corresponding author. zltian@zjsru.edu.cn
⁎⁎ Corresponding author. yanjianli00@gmail.com
20 8 2024
30 8 2024
20 8 2024
10 16 e3660220 12 2023
19 8 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Objectives

To enhance the utilization of reclaimed land, Sanyeqing (SYQ) has been extensively cultivated in Zhejiang province, China. However, the prevalence of anthracnose has significantly hindered SYQ growth, emerging as a primary obstacle to its production. This study aimed to elucidate SYQ's responses to anthracnose in reclaimed land environments by comprehensively analyzing root-zone bacterial community structure, metabolites, and soil properties.

Methods

The experiment was conducted on reclaimed land in Chun'an, China. In order to evaluate the responses of SYQ to anthracnose, the fresh and dry weight of SYQ tubes, the soil properties, the high-throughput sequencing, and metabolomics assay were carried out.

Results

Significant differences were observed between an anthracnose-resistant variety (A201714) and an anthracnose-susceptibile variety (B201301). Fresh and dry weight increased 131.53 % and 144.82 % for A201714 compared to B201301.Lacibacterium (39.85 %), Gp6 (21.83 %), Gp5 (21.49 %), and Sphingomonas (18.84 %) were more prevalent, whereas Gp3 (22.71 %), WPS-1 (18.88 %), Gp4 (15.60 %), Subdivision3 (14.70 %), Chryseolinea (14.37 %), and Nitrospira (0.76 %) were less prevalent in A201714 than B201301. A total of 24 bacterial biomarkers were detected in all soil samples, while the network suggests a more stable soil bacterial community in A201714 than in B201301. Eight differentially expressed metabolites (DEMs) that belonged to lipids and lipid-like molecules, organic acids and derivatives, benzenoids, nucleosides, nucleotides, and analogues were found between two soil samples, and all these eight DEMs were downregulated in A201714 and had a strong correlation with 12 genera of bacteria. Moreover, the data from the redundancy analysis indicated that the main variables affecting changes in the bacterial communities were pH, available phosphorus (AP), available potassium (AK), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN).

Conclusion

This research offers new insights into the SYQ response to anthracnose in reclaimed land and provides valuable recommendations for the high-quality SYQ cultivation and production.

Keywords

SYQ
Anthracnose
Reclaimed land
Bacterial communities
Metabolites
Soil properties
==== Body
pmc1 Introduction

Sanyeqing (SYQ), also called Tetrastigma hemsleyanum Diels & Gilg, is a member of the Vitaceae family. It is a Chinese herbal medicine plant that is native to China, and mostly found in the provinces of Zhejiang, Fujian, Guangxi, Jiangsu, and so on [[1], [2], [3]]. Since 2018, SYQ has been included in the “new eight famous herbal drugs in Zhejiang”, genuine and pure medicinal materials [4]. Where, the entire plant or some of its parts (especially the tubers) is widely used against chronic diseases including fever and inflammation because they contain many effective compounds [[1], [2], [3]]. However, it is widely used in Chinese medication and health products, such as Jinsidijia capsule, Jieshikang capsule, and Huashixuanfeiheji (formulated in Zhejiang Province to combat COVID-19) [[5], [6], [7]], wild plants of SYQ are now facing extinction [8]. Therefore, it is very urgent to promote the artificial planting of SYQ trees to reduce dependence on wild plants. In recent years, large areas of reclaimed land in Zhejiang Province have been used for SYQ cultivation. Although reclaimed land is soil acidity, low nutrient and high gravel content, SYQ can grow well to form better-developed root systems and achieve high yield under rocky soils and poor nutrients conditions [[9], [10], [11], [12], [13]]. But unfortunately, artificial planting of SYQ is frequently affected by various biotic stresses in reclaimed land. Thereinto, anthracnose is one of the most devastating diseases to SYQ, which can cause irregular spots on leaves and water-soaked lesions on stem, ultimately leading to serious losses (sometimes exceed 80 %) in yield and forcing to abandon or replace the plants [14].

Anthracnose, a disease caused by the fungus Colletotrichum gloeosporioides (Penz.) Penz. & Sacc., significantly impact a wide range of susceptible plants in warm and humid environments. This disease can result in substantial losses (30–80 %), both in the field during post-harvest handling [[15], [16], [17], [18]]. Whereas, changes in environmental temperature, light, soil fertility and host can have a direct effect on disease occurrence and severity [19]. Organisms have evolved a wide variety of complex defensive systems to deal with anthracnose stress. Jeyaraj et al. [20], revealed that plant trigger two layers of immunity, produce a combination of phytohormones, transcription factors, and secondary metabolites in response to Colletotrichum stress. Additionally, prior research has demonstrated that there are complex interactions and symbiotic relationships between soil microbes and plants [21], and these interactions help plants improve resistance to pathogens [22,23]. The soil microbial diversity and abundance of beneficial microbes result in lower disease incidence in plants [24,25]. Moreover, studies in soil ecosystems have also revealed that microbes can produce diverse secondary metabolites, such as antibiotics, antifungals, and siderophores, and these metabolites can mediate interactions, communication, and competition with plant pathogens and the environment [26,27], while soil properties might also be a reason for variations among plant rhizosphere microbes or metabolites [28]. In order to establish connections between soil characteristics, microbial populations, metabolites, and plants, further research is critically required. In other words, more attention should be paid on soil sustainable management intensification in future.

Here, in view of anthracnose causing serious losses (usually 30 %, sometimes exceed 80 %) on SYQ during 2019–2021, we postulated that, while growing SYQ cultivation on reclaimed land, the root-zone bacterial communities and metabolites play crucial part in response to anthracnose. Through a comprehensive examination of soil characteristics, microbial populations, and metabolites, this research attempted to discover SYQ processes in reclaimed land subjected to anthracnose stress. The results may avail as a scientific basis for understanding the resistance of SYQ to anthracnose and provide good practice guidelines for SYQ production in reclaimed land.

2 Materials and methods

2.1 SYQ materials and experimental setup

The randomized experiment was conducted from August 4, 2019 to February 14, 2023 in reclaimed land of Chun'an country, Zhejiang Province, China. The soil is classified as Terric Anthrosols according to the World Reference Base for Soil Resources (WRB) [29]. Each plot covered an area of 12 m2 (length = 24 m, width = 50 cm, height = 30 cm) with 30 cm between ridges. The SYQ cutting seedlings of A201714 (anthracnose-resistant variety) and B201301 (anthracnose-susceptible variety), were procured from Hangzhou Academy of Agricultural Sciences (Hangzhou, China) planted in a single row on the ridges with 30 cm space between each plant. In detail, on August 4, 2019, sheep manure (2.25 kg/m2) and plant ash (0.15 kg/m2) were mixed the upper-soil layer (0–20 cm) in the experimental fields before planting, increasing the Nitrogen (N, 0.71 kg), Phosphorus (P, 0.63 kg), Potassium (K, 1.58 kg), Carbon (C, 8.64 kg) contents in each plot. Cutting seedlings of A201714 and B201301 were subsequently planted in the experimental field, with each treatment replicated three times.

2.2 Assessment of tuber and soil characteristics

Tubers of three randomly selected plants from each plot of both cultivars were harvested using hoes. After removing the donut-bound soil with tap water, the fresh weight of the tubers was calculated by a digital scale (Shanghai Precision Instrument Co., Ltd., Shanghai, China). The length and width of tubers were determined by digital tape measure (Ningbo Great Wall Precision Industrial Co., Ltd, Yuyao, China). After three months of air drying, tubers were weighed. The total flavonoid contents (TFC) of dry tubers were estimated as described by Bao et al. [30].

The quartering method yielded 1.0 kg of fresh root-zone soil (5–20 cm depth) for three plants of both cultivars [31]. It was stored in a refrigerator at 4 °C through a 2 mm sieve, air-dried at room temperature, and passed through a 0.45 mm gauze to remove stones and root debris. After that, soil pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), alkaline hydrolysis nitrogen (AHN) using air-dried soil samples, while microbial biomass nitrogen (MBN) and microbial biomass carbon (MBC) using fresh soil samples, were estimated in this soil. Where, soil pH was determined using a pH meter (FE28, MettlerToledo, Zurich, Switzerland) with a 1:5 (soil/water, w/v) solution [32]. The SOM was calculated using K2Cr2O7 oxidation heating method [33]. The TN was determined using spectrophotometric method [34], while AHN was analyzed through conductometric titration [35]. The AK and AP was extracted using ammonium lactate solution and analyzed through spectrophotometry and flame photometry respectively [36]. Lastly, MBC and MBN were determined using the chloroform fumigation-extraction method [37,38].

2.3 Genome sequencing

Ten grams of fresh root-zone soil for both SYQ cultivars were used for genome sequencing experiments following the procedure of Li et al. [98]. Each soil sample was subjected to DNA extraction using the E.Z.N.A™ Mag-Bind Soil DNA Kit (OMEGA, GA, USA). DNA quality was assessed spectrophotometrically with NanoDrop 1000 (Thermo Fisher Scientific, MA, USA).

The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified via PCR using the primers (341F, 5′-CCTACGGGNGGCWGCAG-3′) and (805R, 5′-GACTACHVGGGTATCTAATCC-3′) [39]. DNA template (1 μl), general primers (10 μM, each 1 μl), ddH2O (12 μl), and 2 × Hieff® Robust PCR Master Mix (15 μl) were all included in the PCR reaction mix. The PCR thermal cycle comprised 25 cycles of denaturation (95 °C, 30 s), annealing (55 °C, 30 s), and extension (72 °C, 30 s), followed by a final extension (72 °C, 5 min). PCR products were purified using Hieff NGSTM DNA selection beads (Yeasen, China) and subsequently pooled in equimolar amounts for paired-end sequencing (2 × 250 bp) on an Illumina MiSeq platform (Shanghai Sangon Biotechnology Co., Ltd., Shanghai, China).

As described in our previous study, bioinformatics analysis was accomplished [11,98]. Indeed, after paired-end reads were merged, quality controlled, and trimmed [40,41], clean reads were analyzed and clustered by USEARCH (v11.0.667) into operational taxonomic units (OTUs) at 97 % resemblance [42,43]. Using QIIME (v2020.06) to choose a sample read from each OTU, all reads were then taxonomically categorized using BLAST against the RDP database with a confidence threshold of 90 % [44].

2.4 Metabolomics assay via liquid chromatography-mass spectrometry (LC-MS)

For metabolomic profiling, 10 g soil samples from the root zone of each plant were collected on February 14, 2023, during tuber harvest, and stored at −80 °C. Following our previous study [45,98], metabolic analysis was performed by LC-MS system (Vanquish, Thermo Fisher Scientific) connected to an Orbitrap Exploris 120 mass spectrometer (Orbitrap MS, Thermo). A pooled quality control sample, made by mixing equal volumes of all sample supernatants, was included to assess data reliability and reproducibility. Raw data was converted to mzXML format using ProteoWizard and processed with R using the XCMS package. Metabolite annotation was performed against an in-house MS2 database (Sangon) with a confidence threshold of 0.3.

2.5 Data analysis

The data were analyzed using SPSS software version 16.0 (SPSS Inc., Chicago, IL, USA) for single-factor analysis of variance. Preliminary data management was conducted using Excel 2007. Chao1, Shannon, and Simpson indices were calculated using Origin (v2023) (Hampton, MA, USA) based on OTU data to assess microorganisms' abundance and alpha diversity in root-zone soil bacterial communities. To assess structural changes in the soil bacterial community within the root zone, principal component analysis (PCA) was conducted using Bray-Curtis distance. Linear discriminant analysis effect size (LEfSe) was employed to identify bacterial taxa differentially abundant between groups [46]. To evaluate the effect of varieties of SYQs on bacterial competition, the Sparcc connectivity coefficient between SYQ root-zone soil bacterial communities was calculated based on the relative abundance (RA) of bacterial OTUs for the two SYQs. OTUs with a RA> 1 %, p < 0.01, and Sparcc correlation coefficient N > 0.5 or < −0.5 were included in network construction [47]. To identify SYQ variety effects on metabolite accumulation patterns, MetaboAnalyst 4.0 was used to platform, orthogonal projections to latent structures discriminant analysis (OPLS-DA), volcano plots, matchstick analyses, and heat maps. The criteria used to select differentially expressed metabolites (DEMs) were p < 0.05 and variable importance in the projection (VIP) > 1. By computing the Pearson correlation coefficient, a correlation heat map was created to look at the correlations between the DEMs [48]. Similarly, to observe the correlations between DEMs and related bacteria, Spearman correlation coefficients among the significant DEMs (the largest VIP, p < 0.05) and SYQ root-zone soil bacteria with high RA (top 30 bacteria at genus level) were determined by clustering a heat map [49]. Furthermore, redundancy discriminant analysis (RDA) was performed using Origin to identify significant environmental variables (pH, SOM, MBC, TN, AP, AK, AHN, and MBN) that influence bacterial communities and soil metabolites [28,50,98].

3 Results

3.1 Tuber biomass and quality under anthracnose stress

Tuber biomass of SYQ varieties was assessed approximately 42 months post-planting to evaluate their response to anthracnose in reclaimed land. Symptoms of anthracnose infection, characterized by irregular spots and water-soaked lesions, were observed on the leaves and stems of B201301, while A201714 exhibited no visible disease symptoms (Fig. 1a and b). A201714 exhibited significant increases in all tuber growth parameters compared to the control (Table 1; Fig. 1c and d). Indeed, compared to B201301, the tubers of the A201714 plantation resulted in a 131.53 % (fresh weight), 144.82 % (dry weight), 7.40 % (length), and 20.98 % (width), 1.45 % (TFC of SYQ tubers) increased, respectively. These results clearly demonstrate that A201714 outperformed B201301 under anthracnose stress conditions.Fig. 1 Field performance and tuber biomass of A201714 (a,c) and B201301 (b,d) plants grown on reclaimed land.

Fig. 1

Table 1 Tuber biomass and quality of A201714 and B201301.

Table 1Treatments	Fresh weight	Dry weight	Length	Width	TFC (%)	
A201714	103.31 ± 3.92a	27.20 ± 3.47a	31.19 ± 3.26a	16.61 ± 1.43a	4.21 ± 0.27	
B201301	44.62 ± 2.76	11.11 ± 1.38	29.04 ± 2.80	13.73 ± 1.61	4.15 ± 0.47	
TFC: total flavonoid contents. Means are averages ± standard deviations (SD).

a represents significant increases compared to B201301 (p < 0.05).

3.2 Soil bacterial diversity and communities under anthracnose stress

The bacterial community assembly in the root-zone soil of SYQ was investigated using high-throughput sequencing technology. From the soil samples collected in the root zones of A201714 and B201301, a total of 271,686 raw 16S rRNA gene sequences were obtained. Among these sequences, the low-quality reads were filtered, and 118,928 high-quality sequence reads (accounting for 43.77 %) were obtained (16,672 to 23,638 per sample). Total of 6206 bacterial OTUs were identified at a 97 % similarity threshold (Fig. 2a). On average, A201714 and B201301 samples contained 1008 (range: 959-1039) and 1060 (range: 1027–1077) OTUs, respectively. To assess bacterial richness and alpha diversity within all soil samples, Chao1, Shannon, and Simpson indices were intended (Fig. 2b–d). The Chao1 indexes were 1081 (1057–1094) and 1124 (1104–1137), Shannon indexes were 5.90 (5.86–5.93) and 5.98 (5.93–6.02), and Simpson indexes were 0.006 (0.006–0.006) and 0.005 (0.005–0.006) for A201714 and B201301, respectively. Consequently, the bacterial OTUs and Chao1 index was significantly lower (4.90 %, 3.80 %) in the A201714 communities, along with lower Shannon index (1.46 %) but higher Simpson index (12.60 %) than in the B201301 communities. In other words, the bacterial richness and alpha diversity of A201714 and B201301 root-zone soil bacterial communities were different, and B201301 showed greater root-zone bacterial richness and alpha diversity in reclaimed land.Fig. 2 Abundance of operational taxonomic units (OTUs) (a) and alpha diversity metrics (b, Chao1; c, Shannon; d, Simpson) of SYQ root-zone soil bacterial communities based on OTUs. Significant differences (p < 0.05) between groups indicated by distinct lowercase letters.

Fig. 2

To explore the differences further in beta diversity of bacterial communities between A201714 and B201301, PCA based on OTU levels was performed (Fig. 3a). Results showed that the four replicates of A201714 and B201301 formed two different groups, and there was overlapping between the groups. The first and second principal component (PC1, PC2) explained 33.29 % and 20.66 % of the variations in the bacterial community, respectively. Permutation multivariate analysis of variance (PERMANOVA) performed on all samples also showed that different experiments (two SYQ varieties) explained 20.83 % of the variation (p = 0.085). Overall, no significant differences in bacterial community composition were observed between A201714 and B201301.

Furthermore, the bacterial community compositions of A201714 and B201301 root-zone soils were compared. The RAs of the top 10 bacterial phyla and genera within the entire soil dataset was determined (Fig. 4). The results revealed marked discrepancies in the bacterial community makeup of A201714 and B201301 root-zone soils within the reclaimed soil. Compared to B201301, the RAs of Actinobacteria (28.97 %), Acidobacteria (15.98 %), and Proteobacteria (10.03 %) phyla were significantly higher in root-zone soils of A201714, while the RAs of Planctomycetes (32.09 %), Bacteroidetes (24.15 %), WPS-1 (18.88 %), Planctomycetes (17.83 %), Gemmatimonadetes (17.63 %), Verrucomicrobia (16.32 %), and Nitrospirae (0.76 %) bacterial phyla were lower (Fig. 4a). Furthermore, plantation of A201714 led to significantly higher RA of Lacibacterium (39.85 %), Gp6 (21.83 %), Gp5 (21.49 %), and Sphingomonas (18.84 %) genera and lower RA of Gp3 (22.71 %), WPS-1 (18.88 %), Gp4 (15.60 %), Subdivision3 (14.70 %), Chryseolinea (14.37 %) and Nitrospira (0.76 %) genera compared to B201301 (Fig. 4b). All of these findings suggested that the varying degrees of resistance to anthracnose stress due to the variations in the quantity of certain bacteria between the root-zone soils of A201714 and B201301.

3.3 Soil microbiome and biomarkers under anthracnose stress

To discover the biomarkers in SYQ root-zone soil bacterial communities between A201714 and B201301, LEfSe (linear discriminant analysis LDA >3, p < 0.05) was carried out (Fig. 3b). A total 24 bacterial biomarkers were found in all soil samples. Among them, 13 and 11 bacterial biomarkers were in root-zone soil of A201714 and B201301, respectively. Additionally, heat maps visually illustrated the differences in relative abundance composition (at both phylum and genus levels) between the root-zone soil bacterial communities of A201714 and B201301 (Fig. 5). At the phylum level, A201714 soil samples were enriched with Actinobacteria, but the RA of Gemmatimonadetes, Chloroflexi, Verrucomicrobia, Planctomycetes, and Bacteroidetes (p < 0.05) was lower (Fig. 5a). At the genus level, A201714 soil samples were enriched with Lacibacterium, Gp3, Sphingomonas and Gp5, but had a lower RA of WPS-1, Chryseolinea, Gp4 and Subdivision3 (p < 0.05) (Fig. 5b). The results suggest that different SYQ varieties influence the RA of specific bacterial species, leading to variations in the SYQ root-zone soil community structure. This variation may play a crucial role in modulating SYQ growth under anthracnose stress.Fig. 3 SYQ root-zone bacterial communities' principal component analysis (PCA) based on OTU abundances compositional variance (a). Eclipses indicating sample groups at a 0.95 confidence limit. LEfSe identified key bacterial taxa differentiating A201714 and B201301 soils (b) LDA >3, p < 0.05.

Fig. 3

Fig. 4 Histograms of relative abundance of the top 10 bacteria at the phylum (a) and genus levels (b) in the taxonomy.

Fig. 4

Fig. 5 Analysis of hierarchical clustering and heat map for the phylum (a) and genus (b). A tree plot represents a cluster of the top 10 bacteria at the phylum and genus levels, based on their Person correlation coefficient matrix and RA.

Fig. 5

3.4 Bacterial co-occurrence networks patterns under anthracnose stress

Co-occurrence network analysis provides new insight into microbial relationships and their responses to various stress [51]. As depicted in Fig. 6, the microbial network characteristics varies between A201714 and B201301. The network of A201714 consisted of 163 nodes and 202 edges (85 positive, 117 negatives), with an average degree of 2.479, and a modularity of −2.651. In contrast, the B201301 network comprised 159 nodes and 202 edges (92 positive, 110 negatives), with an average degree of 2.541 and a modularity of −6.150. While the number of nodes and edges was similar between A201714 and B201301, A201714 displayed higher modularity, indicating that its bacterial community structure was more stable than that of B201301.Fig. 6 Patterns of soil bacterial community co-occurrence in A201714 and B201301. At the OTU level, networks were built. The bacteria's relative abundance was shown by the size of the nodes (OTUs). The color of the nodes indicated a different phylum. The line color indicated the positive (red) and negative (green) correlation coefficients. Spearman's correlation coefficients (r > 0.5 and p < 0.01) were used for network construction. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

3.5 Soil metabolomics analysis under anthracnose stress

A total of 20,028 peaks were analyzed across all the root-zone soils of A201714 and B201301, leading to the identification of 453 metabolites by LC-MS. In addition, a score map was generated using OPLS-DA to identify variables distinguishing the two groups (Fig. 7a). The analysis revealed that A201714 and B201301 samples were distinctly separated in the positive and negative regions of t [1]P, correspondingly, with values of R2X (cum) = 0.391, R2Y (cum) = 0.991, and Q2 (cum) = 0.300. This indicates significant differences in the metabolites of A201714 and B201301 soils. These findings were further corroborated by a volcano plot (p < 0.05) (Fig. 7b).Fig. 7 Score map of OPLS-DA for the root-zone soil metabolomes of A201714 and B201301 (a). Volcano plot displaying the DEMs between the soils of A201714 and B201301 (b). Pie plot illustrating the classification and proportion of metabolites (c). Number of DEMs in A201714 and B201301 soils (d). Matchstick analysis of DEMs between A201714 and B201301 soils (e). The size of the dot represents the VIP value, and *represents p < 0.05. Heat map depicting DEMs and metabolome clustering among A201714 and B201301 soils (f).

Fig. 7

The 453 metabolites were predominantly composed of lipids and lipid-like molecules (42.35 %), followed by organic acids and derivatives (12.71 %), benzenoids (7.29 %), organic oxygen compounds (7.29 %), and phenylpropanoids and polyketides (5.56 %) (Fig. 7c), with 304 metabolites upregulated and 149 downregulated (Fig. 7d). Further, metabolites with significant difference between A201714 and B201301 were normalized and visualized by matchstick plot and hierarchical cluster heat map (Fig. 7e and f). Compared to B201301, eight DEMs were significantly downregulated (4.48−60.62 %) in A201714 (Fig. 7e and f; Table S1), indicating that these metabolites could play a significant role in SYQ's response to anthracnose stress.

3.6 Correlations among environmental factors, soil bacteria, and metabolites under anthracnose stress

A correlation heat map and a clustering heat map were generated to further investigate the relationships between the DEMs and the bacterial community (Fig. S1). Results revealed that the relationships among all eight DEMs were positive (Fig. S1a). Meanwhile, there was a distinct correlation between eight DEMs and 12 genera of bacteria (Fig. S1b). These findings collectively suggested that complex interactions existed between bacteria and DEMs in SYQ soil, which could play vital roles in protecting SYQ under anthracnose stress.

Furthermore, the field experiment indicated significant differences existed in the AHN, AP, AK, MBC, and MBN between A201714 and B201301. However, no substantial changes were observed in soil pH, SOM, and TN (Table S2). Compared to B201301, the soil from A201714 had a significant higher content of AHN (10.33 %), AP (31.17 %), and AK (140.78 %), but a significant lower content of MBC (18.82 %) and MBN (30.20 %). To further analyze the relations between soil properties and root-zone bacterial communities (or DEMs) in all soil samples, RDA was performed. Results indicated that environmental factors not only affected the bacterial community composition at the genus level but also the distribution of metabolites at the DEMs level. Indeed, AP (r2 = 0.96, p = 0.002), MBC (r2 = 0.93, p = 0.006), AK (r2 = 0.86, p = 0.030), MBN (r2 = 0.83, p = 0.044), and pH (r2 = 0.78, p = 0.004) found to be the most significant factors in explaining the variation in the composition of the bacterial community, with axes 1 (48.85 %) and axes 2 (23.45 %) accounting for 72.30 % of total variations (Fig. 8a). Chryseolinea was found to be significantly and positively correlated with MBC, MBN, and pH, while significant negative correlations with other properties. Nitrospira and Sphingomonas had positive correlations with SOM, TN, AHN, AK, and AP. Meanwhile, MBN (r2 = 0.89, p = 0.006), AK (r2 = 0.88, p = 0.016), AP (r2 = 0.87, p = 0.015), AHN (r2 = 0.83, p = 0.024), MBC (r2 = 0.82, p = 0.028), and pH (r2 = 0.80, p = 0.021) were the most significant factors for explaining the variation in soil metabolites, with axes 1 (69.03 %) and axes 2 (8.89 %) accounting for 77.92 % of total variations (Fig. 8b). All eight DEMs exhibited significant positive correlations with pH, MBC, and MBN and negative correlations with the others. In general, soil environmental factors (including pH and nutrient elements) obviously influence the soil bacterial communities and metabolites.Fig. 8 RDA (redundancy discriminant analysis) comparing soil characteristics to bacterial populations at the genus level (a). Metabolites at the DEM level (b). Subdivision3, Chr, Chryseolinea, Nit, Nitrospira, Lac, Lacibacterium, Sph, Sphingomonas, Ade, Adenosine, Gin, Gingerol. Pal, palmitoylethanolamide; Met, N-Methylsalsolinol; Oct, Octadecanamide; Doc, 8-Hydroxy-6-docosanone; Die, (10E,12Z)-(9S)-9-Hydroperoxyoctadeca-10,12-dienoic acid; Gan, acid ganoderic N. Soil organic matter (SOM); total nitrogen (TN); alkaline hydrolysis nitrogen (AHN); available potassium (AK); available phosphorus (AP); microbial biomass carbon (MBC); and microbial biomass nitrogen (MBN) are among the variables. The direction and amplitude of the soil variables linked to the various bacterial genera, or DEMs, are indicated by arrows.

Fig. 8

4 Discussion

4.1 Variations in SYQ biomass and quality under anthracnose stress

In this study, A201714 demonstrated significantly greater resistance to anthracnose compared to B201301. Additionally, under anthracnose stress, A201714 exhibited higher tuber biomass and TFC than B201301. These findings align with previous research, which also showed that disease outbreaks can severely impact the yield and quality of crops, such as wheat [52,53]. Although balancing yields, quality, and disease resistance is a great challenge in plant breeding due to the negative relationships among these traits [54], cultivation and application of super plants with high yields, good quality, and high disease resistance is a general trend [55,56]. For example, Yuenongsimiao, a high yield, good quality, and disease-resistant rice variety, has been successively bred and used these years [57,58].

4.2 Root-zone microbiomes were different under anthracnose stress

Considering the ecological significance of soil microbial communities for plant health, soil microbial diversity is a crucial indicator [59,60]. Alpha and beta diversity were used to analyze the richness and diversity of bacterial communities in soils A201714 and B201301. Results indicated greater root-zone bacterial richness and alpha diversity in B201301, while no notable difference in bacterial community structure was observed between A201714 and B201301. Previous studies showed that the microbial community diversity in resistant plant rhizosphere soil was higher than that of susceptible plants [25,61]. However, the bacterial diversity in GS (tobacco susceptible to bacterial wilt) rhizosphere soil was instead higher than that in KS (tobacco resistant mutant to bacterial wilt) in Luzhou, China [24]. It was probably because the differences in plant genotypes led to colonization, and absence of some microbes, while some microbes retained ecological niche homeostasis [24].

Furthermore, high-throughput 16S rRNA gene sequencing was used to examine the bacterial populations in the root zones of A201714 and B201301 soil. Notable differences were observed in the root zones of A201714 and B201301 soil under anthracnose stress. A total of 24 bacterial indicators were found in all soil samples. Meanwhile, plantations of A201714 led to significantly higher RA of Actinobacteria, Acidobacteria, and Proteobacteria phyla than B201301. Also, the RA genera Lacibacterium, Gp6, Gp5, Gp3, and Sphingomonas in A201714 were significantly higher than B201301. Previous studies reported that rhizosphere microbial community composition of plant could vary by plants genotype. For example, significant differences were observed in sugarcane rhizosphere bacterial communities with different genotypes [62]. The soil bacterial and fungal community structure of mulberry (resistance to bacterial wilt) was more stable than that of susceptible ones [63]. According to Shi et al. [24], the KS and GS exhibited different bacterial community abundances when considering the top 15 bacterial taxa. Zuo et al. [64] noted that the bacterial populations in the Dendrobium rhizosphere were diverse, with Proteobacteria, Actinobacteria, and Bacteroidetes being the dominant phyla. Therefore, it is necessary to conduct further research on significantly distinct microorganisms under anthracnose stress. Actinobacteria can produce different kinds of antibiotics (such as gentamicin, oxytetracycline, streptomycin, and tetracycline), which are linked to disease suppression and beneficial for agricultural soils [[65], [66], [67]]. Acidobacteria may have the ability of using nitrite as N source, responding to soil nutrients and acidity, expressing several active transporters [68]. Proteobacteria predominates ecosystems, especially in soil systems, as the microbes of this phylum are significant for C and N cycles [69,70]. Additionally, Lacibacterium can remove pyrene from co-contaminated soils [71]. Sphingomonas promote soil ecosystems and plant development, and some may boost agricultural plant growth under drought, heavy metals, and salt stress [72]. Thus, anthracnose stress may change root-zone bacterial communities by altering soil bacteria abundances and diversities surrounding SYQ in reclaimed areas. Co-occurrence network analysis offers novel insights into the intricate structure of microbial communities, while enhancing and broadening the information obtained via alpha and beta diversity measurements [73]. It was used to analyze co-occurrences and interactions between A201714 and B201301 OTUs (Sparcc correlation N > 0.5 or < −0.5, p < 0.01). Both number of network nodes and edges showed no significant differences between A201714 and B201301, but modularity was higher in A201714 than B201301. High modularity indicates network stability, while more nodes and edges imply complexity [74,75]. This suggests that A201714 had a more stable soil bacterial community structure than B201301. These results confirm earlier findings that the co-occurrence network visualizes a lot of microbial information [76].

4.3 Differential root-zone metabolites under anthracnose stress

Understanding the relationships that exist between soil, plants, and microbes can be gained through the study of soil metabolomics [[77], [78], [79]]. It has been reported previously that benzoic acid, lauric acid, and mercaptoacetic acid can promote or inhibit the occurrence of soil-borne disease [80,81]. The total number of metabolomic peaks extracted from the soil samples taken from the SYQ root-zone was 20,028. Based on the OPLS-DA and volcano plot of metabolite profiles, it was observed that the soil metabolite compositions were significantly different in A201714 and B201301. Specifically, 304 metabolites belonging to categories such as organoheterocyclic compounds, benzenoids, organic acids and derivatives, lipids and lipid-like molecules, and organooxygen compounds were found to be elevated, while 149 metabolites were downregulated out of a total of 453 metabolites. Compared to B201301, A201714 showed a significant downregulation of eight DEMs. These differential metabolites contained various bioactive components and secondary metabolites, indicating their potential role in several activities related to SYQ growth. It is well reported that lipids are major components of cellular membranes and are usually used as an energy source for seeds to germinate [82]. Similarly, organic acids are at the core of cellular metabolism, and it has been demonstrated that organic acid exudation at the root-soil interface is involved in many plant responses to soil stress, which can promote soil nutrient acquisition and tolerance to toxic metals [83]. Rapid production of benzenoids has been witnessed in plants or microbes as a defense against pathogens, insects, or stress [84,85].

4.4 Correlation analysis of soil properties, microbiome, and metabolites under anthracnose stress

Various environmental factors can influence the structure of soil bacterial communities in response to external stresses [86,98]. Soil environmental factors (including pH and nutrient elements) could greatly influence the soil bacterial metabolites. To evaluate whether soil properties influenced the composition of bacteria and metabolites dispersing into root-zone soil under anthracnose stress, RDA was carried out. Results showed that AP, MBC, AK, MBN, and pH were the main variables of bacterial communities in the soils of A201714 and B201301. Conversely, MBN, AK, AP, AHN, MBC, and pH were observed as the main variables controlling the soil metabolites. As shown by previous studies, soil pH, AP, and TN, soil moisture content, organic matter, AN, Cu, Pb, MBN were the major factors influencing the soil bacterial communities [[87], [88], [89]]. The increased RA of some microbial species has been connected to soil parameters (high pH and AP) associated with the organization of the microbial community [90]. A sugarcane-peanut intercropping system showed substantial and positive correlations between TP, TK, and pH and metabolites like adenine and adenosine [91]. The TN and organic matter could accumulate amino acids and derivatives, nucleotides and derivatives, alkaloids but inhibit organic acids, vitamins biosynthesis [92].

Heat maps were utilized to analyze the relationships between different metabolites and the bacterial community. The results indicated that all eight metabolites exhibited positive connections. However, the interactions between these metabolites and the twelve bacterium species in SYQ soil were intricate. Seven metabolites showed positive connections with five bacterial taxa, while all eight metabolites displayed negative correlations with the remaining seven bacterial taxa. In other words, these eight DEMs might have critical roles in coordinating the activities of root-zone bacteria of SYQ as well as protecting SYQ from anthracnose in reclaimed land. Similarly, some metabolites have also been reported to be correlated with microbial abundance [93], for example, the tobacco soil microbial community was found to be significantly shaped by lauric acid, benzoic acid, 4-hydroxy-3 methoxybenzaldehyde, and mercaptoacetic acid [94]. In addition, chemical interactions in the rhizosphere active zones promoted interactions between plant roots and soil microbes [95] while soil metabolic profiles were significantly altered by soil microbial communities, which indicated that certain metabolic pathways were mediated by soil microbial communities to adapt to environmental stress [96]. Moreover, significant effects of secondary metabolites on the microbial community were reported; some were antibiotic and pharmaceutically relevant, while others were disease interaction-relate [97].

5 Conclusions

Overall, this study detected substantial differences in the soil bacterial community, metabolic diversity, and physico-chemical characteristics between the anthracnose-resistant SYQ variety (A201714) and the anthracnose-susceptible variety (B201301) under anthracnose stress. In reclaimed land, SYQ may be protected against anthracnose stress by having a more consistent bacterial community structure, specific microbes, metabolites, and higher levels of AP, MBN, AK, MBC, AHN, and pH. This study can provide new insights into the defense mechanisms of SYQ involved in anthracnose stress when grown on reclaimed land. It can also offer good practice guidelines for the high-quality production of SYQ. Integrating soil optimization techniques into agricultural operations may enhance crop resilience by promoting the growth of beneficial microbes. Examining the relationship between the soil microbiome and anthracnose resistance could offer valuable insights for future breeding projects that seek to develop new SYQ cultivars with enhanced disease resistance.

Funding

This research was funded by Hangzhou Science and Technology Development Plan Project (20231203A05 ), Zhejiang Province Key Research and Development Program of China (2019C02035 ) and Science and Technology Innovation and Promotion Demonstration Project of Hangzhou Academy of Agricultural Sciences (2024HNCT-01 , 2022HNCT-07 ), and Researchers Supporting Project number of 10.13039/501100002383 King Saud University , Riyadh, Saudi Arabia (RSPD2024R758 ).

Data availability statement

The data presented in this study can be found in an online repository (CNGBdb) under the accession numbers CNP0004696 and CNP0004698.

CRediT authorship contribution statement

Xuqing Li: Writing – original draft, Software, Methodology, Formal analysis, Data curation, Conceptualization. Qiujun Lu: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Rahila Hafeez: Writing – review & editing, Validation. Solabomi Olaitan Ogunyemi: Visualization, Resources, Formal analysis. Ezzeldin Ibrahim: Writing – review & editing, Validation. Xiaoxu Ren: Validation, Software, Methodology, Investigation, Data curation. Zhongling Tian: Visualization, Supervision, Resources, Project administration, Funding acquisition, Formal analysis. Songlin Ruan: Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition. Mohamed Mohany: Resources, Funding acquisition. Salim S. Al-Rejaie: Resources, Investigation. Bin Li: Supervision, Project administration, Funding acquisition. Jianli Yan: Visualization, Validation, Supervision, Project administration, Funding acquisition, Formal analysis.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Author [Bin Li] is an AE of this journal. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgements

The authors extend their appreciation for Researchers Supporting Project number (RSPD2024R758), 10.13039/501100002383 King Saud University , Riyadh, Saudi Arabia.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36602.
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References

1 Xiang Q. Hu S. Ligaba-Osena A. Yang J. Tong F. Guo W. Seasonal variation in transcriptomic profiling of Tetrastigma hemsleyanum fully developed tuberous roots enriches candidate genes in essential metabolic pathways and phytohormone signaling Front. Plant Sci. 12 2021 659645 10.3389/fpls.2021.669645
2 Yin S. Cui H. Zhang L. Yan J. Qian L. Ruan S. Transcriptome and metabolome integrated analysis of two ecotypes of Tetrastigma hemsleyanum reveals candidate genes involved in chlorogenic acid accumulation Plants 10 2021 1288 10.3390/plants10071288 34202839
3 Zhu R. Xu X. Ying J. Cao G. Wu X. The phytochemistry, pharmacology, and quality control of Tetrastigma hemsleyanum Diels & Gilg in China: a review Front. Pharmacol. 11 2020 550497 10.3389/fphar.2020.550497
4 Liu Y. Pan J. Ni S. Xing B. Cheng K. Peng X. Transcriptome and metabonomics combined analysis revealed the defense mechanism involved in hydrogen-rich water-Regulated cold stress response of Tetrastigma hemsleyanum Front. Plant Sci. 13 2022 889726 10.3389/fpls.2022.889726
5 Hu W. Xia P. Liang Z. Molecular cloning and structural analysis of key enzymes in Tetrastigma hemsleyanum for resveratrol biosynthesis Int. J. Biol. Macromol. 190 19 2021 32 10.1016/j.ijbiomac.2021.08.178
6 Hu W. Zheng Y. Xia P. Liang Z. The research progresses and future prospects of Tetrastigma hemsleyanum Diels et Gilg: A valuable Chinese herbal medicine J. Ethnopharmacol. 271 2021 113836 10.1016/j.jep.2021.113836
7 Peng X. Ji Q. Liang Y. Zhang Y. Lou T. Research progress in utilization of Tetrastigma hemsleyanum germplasms Zhongguo Xiandai Zhongyao 18 2016 1088 1092
8 Peng X. Ji Q. Fan S. Zhang Y. Zhang J. Genetic diversity in populations of the endangered medicinal plant Tetrastigma hemsleyanum revealed by ISSR and SRAP markers: implications for conservation Genet. Resour. Crop Evol. 62 2015 1069 1078 10.1007/s10722-014-0210-6
9 Jiang C. Liang Z. Xie X. Priming for saline-alkaline tolerance in rice: current knowledge and future challenges Rice Sci. 30 2023 417 425 10.1016/j.rsci.2023.05.003
10 Ganapati R.K. Naveed S.A. Zafar S. Wang W. Xu J. Saline-alkali tolerance in rice: physiological response, molecular mechanism, and QTL identification and application to breeding Rice Sci. 29 2022 412 434 10.1016/j.rsci.2022.05.002
11 Li X. Li D. Jiang Y. Xu J. Ren X. Zhang Y. Wang H. Lu Q. Yan J. Ahmed T. Li B. Guo K. The effects of microbial fertilizer based Aspergillus brunneoviolaceus HZ23 on pakchoi growth, soil properties, rhizosphere bacterial community structure, and metabolites in newly reclaimed land Front. Microbiol. 14 2023 1091380 10.3389/fmicb.2023.1091380
12 Fu L. Zhao L. Lyu H. Yan M. Zheng Y. Liu Q. Jin L. Cheng J. Lu T. Wang L. Effects of nitrogen level on growth of Tetrastigma hemsleyanum and phytochemical content and antioxidant activity in stems and leaves Zhongguo Zhongyao Zazhi 44 2019 696 702 10.19540/j.cnki.cjcmm.20181204.006 30989881
13 Hong C. Shao Q. Qin W. Zhang J. Wei B. Shen D. Zheng B. Guo H. Bacterial communities are associated with the tuber size of Tetrastigma hemsleyanum in stony soils Biol. Fert. Soils 57 2021 373 388 10.1007/s00374-020-01530-4
14 Li X. Yan J. Ruan S. Identification and biological characteristics of anthracnose pathogen on Tetrastigma hemsleyanum Acta Agric. Zhejiangensis 32 2020 2009 2019 10.3969/j.issn.1004-1524.2020.11.11
15 Chung P.C. Wu H.Y. Wang Y.W. Ariyawansa H.A. Hu H.P. Hung T.H. Tzean S.S. Chung C.L. Diversity and pathogenicity of Colletotrichum species causing strawberry anthracnose in Taiwan and description of a new species, Colletotrichum miaoliense sp. nov Sci. Rep. 10 2020 14664 10.1038/s41598-020-70878-2
16 Jayawardena R.S. Hyde K.D. Jeewon R. Li X.H. Liu M. Yan J. Mycosphere Essay 6: why is it important to correctly name Colletotrichum species Mycosphere 7 2016 1076 1092 10.5943/mycosphere/si/2c/1
17 Khodadadi F. González J.B. Martin P.L. Giroux E. Bilodeau G.J. Peter K.A. Doyle V.P. Aćimović S.G. Identification and characterization of Colletotrichum species causing apple bitter rot in New York and description of C. noveboracense sp. nov Sci. Rep. 10 2020 11043 10.1038/s41598-020-66761-9
18 Martinez J. Gomez A. Ramirez C. Gil J. Durangoa D. Controlling anthracnose by means of extracts, and their major constituents, from Brosimum rubescens Taub Biotechnol. Rep. (Amst) 25 2020 e00405 10.1016/j.btre.2019.e00405
19 Peralta-Ruiz Y. Rossi C. Grande-Tovar C.D. Chaves-López C. Green management of postharvest anthracnose caused by Colletotrichum gloeosporioides J. Fungi (Basel) 9 2023 623 10.3390/jof9060623 37367558
20 Jeyaraj A. Elando T. Chen X. Zhuang J. Wang Y. Li X. Advances in understanding the mechanism of resistance to anthracnose and induced defence response in tea plants Mol. Plant Pathol. 24 2023 1330 1346 10.1111/mpp.13354 37522519
21 Zhalnina K. Louie K.B. Hao Z. Mansoori N. da Rocha U.N. Shi S. Cho H. Karaoz U. Loqué D. Bowen B.P. Firestone M.K. Northen T.R. Brodie E.L. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly Nat. Microbiol. 3 2018 470 480 10.1038/s41564-018-0129-3 29556109
22 Kwak M.J. Kong H.G. Choi K. Kwon S.K. Song J.Y. Lee J. Lee P.A. Choi S.Y. Seo M. Lee H.J. Jung E.J. Park H. Roy N. Kim H. Lee M.M. Rubin E.M. Lee S.W. Kim J.F. Rhizosphere microbiome structure alters to enable wilt resistance in tomato Nat. Biotechnol. 36 2018 1100 1109 10.1038/nbt.4232
23 Van Elsas J.D. Chiurazzi M. Mallon C.A. Elhottova D. Krisstufek V. Salles J.F. Microbial diversity determines the invasion of soil by a bacterial pathogen Proc. Natl. Acad. Dci. U.S.A 109 2012 1159 1164 10.1073/pnas.1109326109
24 Shi H. Xu P. Wu S. Yu W. Cheng Y. Chen Z. Yang X. Yu X. Li B. Ding A. Wang W. Sun Y. Analysis of rhizosphere bacterial communities of tobacco resistant and non-resistant to bacterial wilt in different regions Sci. Rep. 12 2022 18309 10.1038/s41598-022-20293-6
25 Zhang Y. Hu A. Zhou J. Zhang W. Li P. Comparison of bacterial communities in soil samples with and without tomato bacterial wilt caused by Ralstonia solanacearum species complex BMC Microbiol. 20 2020 89 10.1186/s12866-020-01774-y 32290811
26 Charlop-Powers Z. Owen J.G. Reddy B.V.B. Ternei M.A. Brady S.F. Chemical-biogeographic survey of secondary metabolism in soil Proc. Natl. Acad. Dci. U.S.A 111 2014 3757 3762 10.1073/pnas.1318021111
27 Hibbing M.E. Fuqua C. Parsek M.R. Peterson S.B. Bacterial competition: surviving and thriving in the microbial jungle Nat. Rev. Microbiol. 8 2010 15 25 10.1038/nrmicro2259 19946288
28 Huang W. Sun D. Chen L. An Y. Integrative analysis of the microbiome and metabolome in understanding the causes of sugarcane bitterness Sci. Rep. 11 2021 6024 10.1038/s41598-021-85433-w 33727648
29 IUSS Working Group WRB World Reference Base for Soil Resources. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps fourth ed. 2022 International Union of Soil Sciences (IUSS) Vienna, Austria 234 https://www3.ls.tum.de/boku/?id=1419
30 Bao Y. Li J. Zheng L. Li H. Antioxidant activities of cold-nature Tibetan herbs are signifcantly greater than hot-nature ones and are associated with their levels of total phenolic components Chin. J. Nat. Med. 13 2015 609 617 10.1016/S1875-5364(15)30057-1 26253494
31 Campos-M M. Campos-C R. Application of quartering method in soils and foods Int.J. Eng. Res. Appl. 7 2017 35 39 10.9790/9622-0701023539
32 Rathje Jackson M.L. Soil chemical analysis. Verlag: prentice Hall, Inc., Englewood Cliffs, NJ J. Plant Nutr. Soil Sci. 85 1959 251 252 1958, 498 S. DM 39.40 https://api.semanticscholar.org/CorpusID:96801453
33 Nelson D.W. Sommers L.E. Total carbon, organic carbon, and organic matter Methods of soil analysis: Part 3 Chemical methods 34 1996 961 1010 10.2136/sssabookser5.3.c34
34 Koistinen J. Sjöblom M. Spilling K. Total nitrogen determination by a spectrophotometric method Methods Mol. Biol. 1980 2020 81 86 10.1007/7651_2019_206 30734162
35 Chen B. Yang H. Song W. Liu C. Xu J. Zhao W. Zhou Z. Effect of N fertilization rate on soil alkali-hydrolyzable N, subtending leaf N concentration, fiber yield, and quality of cotton Crop J 4 2016 323 330 10.1016/j.cj.2016.03.006
36 Tian H. Qiao J. Zhu Y. Jia X. Shao M. Vertical distribution of soil available phosphorus and soil available potassium in the critical zone on the Loess Plateau, China Sci. Rep. 11 2021 3159 10.1038/s41598-021-82677-4 33542419
37 Brookes P.C. Landman A. Pruden G. Jenkinson D.S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil Soil Biol. Biochem. 17 1985 837 842 10.1016/0038-0717(85)90144-0
38 Vance E.D. Brookes P.C. Jenkinson D.S. An extraction method for measuring soil microbial biomass C Soil Biol. Biochem. 19 1987 703 707 10.1016/0038-0717(87)90052-6
39 Xi H. Shen J. Qu Z. Yang D. Liu S. Nie X. Zhu L. Effects of long-term cotton continuous cropping on soil microbiome Sci. Rep. 9 2019 18297 10.1038/s41598-019-54771-1
40 Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet journal 17 2011 10 12 10.14806/ej.17.1.200
41 Zhang J. Kobert K. Flouri T. Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR Bioinformatics 30 2014 614 620 10.1093/bioinforatics/btt593 24142950
42 Edgar R.C. UPARSE: highly accurate OTU sequences from microbial amplicon reads Nat. Methods 10 2013 996 998 10.1038/nmeth.2604 23955772
43 Edgar R.C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences bioRxiv 2016 10.1101/074161
44 Wang Q. Garrity G.M. Tiedje J.M. Cole J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy Appl. Environ. Microbiol. 73 2007 5261 5267 10.1128/AEM.00062-07 17586664
45 Li X. Wang D. Lu Q. Tian Z. Yan J. Effects of SMOF on soil properties, root-zone microbial community structure, metabolites, and maize (Zea mays L.) response on a reclaimed barren mountainous land Front. Microbiol. 14 2023 1181245 10.3389/fmicb.2023.1181245
46 Segata N. Izard J. Waldron L. Gevers D. Miropolsky L. Garrett W.S. Huttenhower C. Metagenomic biomarker discovery and explanation Genome Biol. 12 2011 1 18 10.1186/gb-2011-12-6-r60
47 Yu F.M. Jayawardena R.S. Thongklang N. Lv M.L. Zhu X.T. Zhao Q. Morel production associated with soil nitrogen-fixing and nitrifying microorganisms J. Fungi 8 2022 299 10.3390/jof8030299
48 Stewart K. Pearson’s correlation coefficient, Encyclopedia Britannica 31 Jul. 2024 https://www.britannica.com/topic/Pearsons-correlation-coefficient Accessed 9 August 2023
49 Hollander M. Wolfe D.A. Chicken E. Nonparametric statistical methods Biostatistics and Microbiology: A Survival Manual 2008 Springer New York, NY 121 162 10.1007/978-0-387-77282-0_7
50 Xu H. Huang Y. Xiong X. Zhu H. Lin J. Shi J. Tang C. Xu J. Changes in soil Cd contents and microbial communities following Cd-containing straw return Environ. Pollut. 330 2023 121753 10.1016/j.envpol.2023.121753
51 Guo B. Zhang L. Sun H. Gao M. Yu N. Zhang Q. Mou A. Liu Y. Microbial co-occurrence network topological properties link with reactor parameters and reveal importance of low-abundance genera npj Biofilms Microbiomes 8 2022 3 10.1038/s41522-021-00263-y 35039527
52 Bunta Gh Toma I. Gabriela G. Pîțu S. The relationship between genotypes, diseases attack, yield and quality in winter wheat in western Romania J. Hortic. For. Biotechnol. 19 2015 24 33 https://api.semanticscholar.org/CorpusID:90271680
53 Oerke E.-C. Dehne H.-W. Safeguarding production—losses in major crops and the role of crop protection Crop Protect. 23 2004 275 285 10.1016/j.cropro.2003.10.001
54 Xiao N. Pan C. Li Y. Wu Y. Cai Y. Lu Y. Wang R. Yu L. Shi W. Kang H. Zhu Z. Huang N. Zhang X. Chen Z. Liu J. Yang Z. Ning Y. Li A. Genomic insight into balancing high yield, good quality, and blast resistance of japonica rice Genome Biol. 22 2021 283 10.1186/s13059-021-02488-8 34615543
55 Li C. Li H. Zhang X. Yang Z. A pleiotropic drug resistance family protein gene is required for rice growth, seed development and zinc homeostasis Rice Sci. 30 2023 127 137 10.1016/j.rsci.2023.01.005
56 Wing R.A. Purugganan M.D. Zhang Q. The rice genome revolution: from an ancient grain to Green Super Rice Nat. Rev. Genet. 19 2018 505 517 10.1038/s41576-018-0024-z 29872215
57 Luo H. He L. Du B. Pan S. Mo Z. Yang S. Zou Y. Tang X. Epoxiconazole improved photosynthesis, yield formation, grain quality and 2-acetyl-1-pyrroline biosynthesis of fragrant rice Rice Sci. 29 2022 189 196 10.1016/j.rsci.2022.01.007
58 Lu Z. Fang Z. Liu W. Lu D. Wang X. Wang S. Xue J. He X. Grain quality characteristics analysis and application on breeding of Yuenongsimiao, a high-yielding and disease-resistant rice variety Sci. Rep. 13 2023 6335 10.1038/s41598-022-21030-9 37072409
59 Ren H. Wang H. Qi X. Yu Z. Zheng X. Zhang S. Wang Z. Zhang M. Ahmed T. Li B. The damage caused by decline disease in bayberry plants through changes in soil properties, rhizosphere microbial community structure and metabolites Plants 10 2021 2083 10.3390/plants10102083 34685892
60 Zhang Y. Dong S. Gao Q. Liu S. Zhou H. Ganjurjav H. Wang X. Climate change and human activities altered the diversity and composition of soil microbial community in alpine grasslands of the Qinghai-Tibetan Plateau Sci. Total Environ. 526 2016 353 363 10.1016/j.scitotenv.2016.03.221
61 Shiomi Y. Nishiyama M. Onizuka T. Marumoto T. Comparison of bacterial community structures in the rhizoplane of tomato plants grown in soils suppressive and conducive towards bacterial wilt Appl. Environ. Microbiol. 65 1999 3996 4001 10.1128/AEM.65.9.3996-4001.1999 10473407
62 Hamonts K. Trivedi P. Garg A. Janitz C. Grinyer J. Holford P. Botha F.C. Anderson I.C. Singh B.K. Field study reveals core plant microbiota and relative importance of their drivers Environ. Microbol. 20 2018 124 140 10.1111/1462-2920.14031
63 Dong Z. Guo Y. Yu C. Zhu Z. Mo R. Deng W. Li Y. Hu X. The dynamics in rhizosphere microbial communities under bacterial wilt resistance by mulberry genotypes Arch. Microbiol. 203 2021 1107 1121 10.1007/s00203-020-02098-1 33165874
64 Zuo J. Zu M. Liu L. Song X. Yuan Y. Composition and diversity of bacterial communities in the rhizosphere of the Chinese medicinal herb Dendrobium BMC Plant Biol. 21 2021 127 10.1186/s12870-021-02893-y 33663379
65 Fu L. Ruan Y. Tao C. Li R. Shen Q. Continous application of bioorganic fertilizer induced resilient culturable bacteria community associated with banana Fusarium wilt suppression Sci. Rep. 6 2016 27731 10.1038/srep27731
66 Polti M.A. Role of Actinobacteria in bioremediation Surajit D. Microbial Biodegradation and Bioremediation 2014 Elsevier 269 286 10.1016/B978-0-12-800021-2.00011-X
67 Sanguin H. Sarniguet A. Gazengel K. Moënneel Y. coz Grundmann G.L. Rhizosphere bacterial communities associated with disease suppressiveness stages of takemuni decline in wheat monoculture New Phytol. 184 2009 694 707 10.1111/j.1469-8137.2009.03010.x 19732350
68 Kielak A.M. Barreto C.C. Kowalchuk G.A. van Veen J.A. Kuramae E.E. The ecology of Acidobacteria: moving beyong genes and genomes Front. Microbiol. 7 2016 744 10.3389/fmicb.2016.00744 27303369
69 Dai Z. Su W. Chen H. Barberán A. Zhao H. Yu M. Yu L. Brookes P.C. Schadt C.W. Chang S.X. Xu J. Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteria in agro-ecosystems across the globe Global Change Biol. 24 2018 3452 3461 10.1111/gcb.14163
70 Quoreshi A.M. Suleiman M.K. Kumar V. Manuvel A.J. Sivadasan M.T. Islam M.A. Khasa D.P. Untangling the bacterial community composition and structure in selected Kuwait desert soils Appl. Soil Ecol. 138 2019 1 9 10.1016/j.apsoil.2019.02.006
71 Li G. Wang Z. Lv Y. Jia S. Chen F. Liu Y. Huang L. Effect of culturing ryegrass (Lolium perenne L.) on CD and pyrene removal and bacteria variations in co-contaminated soil Environ. Technol. Innov. 24 2021 101963 10.1016/j.et.2021.101963
72 Asaf S. Numan M. Khan A.L. Al-Harrasi A. Sphingomonas: from diversity and genomics to functional role in environmental remediation and plant growth Crit. Rev. Biotechnol. 40 2020 138 152 10.1080/07388551.2019.1709793 31906737
73 Jiang H. Lv L. Ahmed T. Jin S. Shahid M. Noman M. Osman H. Wang Y. Sun G. Li X. Li B. Nanoparticles-mediated amelioration of tomato bacterial wilt disease by modulating the rhizosphere bacterial community Int. J. Mol. Sci. 23 2022 414 10.3390/ijms23010414
74 Ma W. Yang Z. Liang L. Ma Q. Wang G. Zhao T. Characteristics of the fungal communities and co-occurrence networks in hazelnut tree root endospheres and rhizosphere soil Front. Plant Sci. 12 2021 749871 10.3389/fpls.2021.749871
75 Freundt S. Emergence in a complex network with two types of directed edges–a numerical investigation Results Phys. 30 2021 104819 10.1016/j.rinp.2021.104819
76 Chaffron S. Rehrauer H. Pernthaler J. Von Mering C. A global network of coexisting microbes from environmental and whole-genome sequence data Genome Res. 20 2010 947 959 10.1101/gr.104521.109 20458099
77 Hayden H.L. Rochfort S.J. Ezernieks V. Savin K.W. Mele P.M. Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS Sci. Total Environ. 651 2019 1627 1638 10.1016/j.scitotenv.2018.09.249 30360288
78 Ros M. Almagro M. Fernández J.A. Egea-Gilabert C. Faz Á. Pascual J.A. Approaches for the discrimination of suppressive soils for Pythium irregulare disease Appl. Soil Ecol. 147 2020 103439 10.1016/j.apsoil.2019.103439
79 Withers E. Hill P.W. Chadwick D.R. Jones D.L. Use of untargeted metabolomics for assessing soil quality and microbial function Soil Biol. Biochem. 143 2020 107758 10.1016/j.soilbio.2020.107758
80 Li S. Xu C. Wang J. Guo B. Yang L. Chen J. Ding W. Cinnamic, myristic and fumaric acids in tobacco root exudates induce the infection of plants by Ralstonia solanacearum Plant Soil 412 2017 381 395 10.1007/s11104-016-3060-5
81 Liu Y. Li X. Cai K. Cai L. Lu N. Shi J. Identification of benzoic acid and 3-phenylpropanoic acid in tobacco root exudates and their role in the growth of rhizosphere microorganisms Appl. Soil Ecol. 93 2015 78 87 10.1016/j.apsoil.2015.04.009
82 Kim H.U. Lipid metabolism in plants Plants 9 2020 871 10.3390/plants9070871 32660049
83 Panchal P. Miller A.J. Giri J. Organic acids: versatile stress-response roles in plants J. Exp. Bot. 72 2021 4038 4052 10.1093/jxb/erab019 33471895
84 Herrmann K.M. The shikimate pathway: early steps in the biosynthesis of aromatic compounds Plant Cell 7 1995 907 915 10.1105/tpc.7.7.907 12242393
85 Pu F. Ren J. Qu X. Nucleobases, nucleosides, and nucleotides: versatile biomolecules for generating functional nanomaterials Chem. Soc. Rev. 47 2018 1285 1306 10.1039/c7cs00673j 29265140
86 Pajares S. Campo J. Bohannan B.J.M. Etchevers J.D. Environmental controls on soil microbial communities in a seasonally dry tropical forest Appl. Environ. Microbiol. 84 2018 e00342 10.1128/AEM.00342-18
87 Sun R. Zhang X.X. Guo X. Wang D. Chu H. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw Soil Biol. Biochem. 88 2015 9 18 10.1016/j.soilbio.2015.05.007
88 Feng X. Sun X. Li S. Zhang J. Hu N. Relationship study among soils physicochemical properties and bacterial communities in urban green space and promotion of its composition and network analysis Agron. J. 113 2021 515 526 10.1002/agj2.20460
89 Kong J. He Z. Chen L. Yang R. Du J. Efficiency of biochar, nitrogen addition, and microbial agent amendments in remediation of soil properties and microbial community in Qilian Mountains mine soils Ecol. Evol. 11 2021 9318 9331 10.1002/ece3.7715 34306624
90 Wang R. Zhang H. Sun L. Qi G. Chen S. Zhao X. Microbial community composition is related to soil biological and chemical properties and bacterial wilt outbreak Sci. Rep. 7 2017 343 10.1038/s41598-017-00472-6 28336973
91 Tang X. He Y. Zhang Z. Wu H. He L. Jiang J. Meng W. Huang Z. Xiong F. Liu J. Zhong R. Han Z. Wan S. Tang R. Benefical shift of shizosphere soil nutrients and metabolites under a sugarcane/peanut intercropping system Front. Plant Sci. 13 2022 1018727 10.3389/fpls.2022.1018727
92 Sun Z. Wang L. Zhang G. Yang S. Zhong Q. Pepino (Solanum muricatum) metabolic profiles and soil mutrient association analysis in three growing sites on the loess plateau of Northwestern China Metabolites 12 2022 885 10.3390/metabo12100885 36295787
93 Song Y. Li X. Yao S. Yang X. Jiang X. Correlations between soil metabolomics and bacterial community structures in the pepper rhizosphere under plastic greenhouse cultivation Sci. Total Environ. 728 2020 138439 10.1016/j.scitotenv.2020.138439
94 Hu Y. Zhao W. Li X. Feng J. Li C. Yang X. Guo Q. Wang L. Chen S. Li Y. Yang Y. Integrated biocontrol of tobacco bacterial wilt by antagonistic bacteria and marigold Sci. Rep. 11 2021 16360 10.1038/s41598-021-95741-w
95 van Dam N.M. Bouwmeester H.J. Metabolomics in the rhizosphere: tapping into belowground chemical communication Trends Plant Sci. 21 2016 256 265 10.1016/j.tplants.2016.01.008 26832948
96 Zhao Y. Yao Y. Xu H. Xie Z. Guo J. Qi Z. Jiang H. Soil metabolomics and bacterial functional traits revealed the responses of rhizosphere soil bacterial community to long-term continuous cropping of Tibetan barley PeerJ 10 2022 e13254 10.7717/peerj.13254
97 Fox E.M. Howlett B.J. Secondary metabolism: regulation and role in fungal biology Curr. Opin. Microbiol. 11 2008 481 487 10.1016/j.mib.2008.10.007 18973828
98 X Li Ren X. Su Y. Zhou X. Wang Y. Ruan S. Yan J. Li B. Guo K. Differential effects of winter cold stress on soil bacterial communities, metabolites, and physicochemical properties in two varieties of Tetrastigma hemsleyanum Diels & Gilg in reclaimed land Microbiol. Spectr. 12 2024 e02425 10.1128/spectrum.02425-23
