American ginseng (Panax quingquefolius L.) is a perennial herbaceous plant of Panax genus in the family of Araliaceae, which is a traditional Chinese medicine (Wang and Wang, 2019). It is widely used in the fields of food and medicine because of medicinal properties. At present, China has become the largest consumer of Panax quingquefolius in the world, and the market demand of Panax quingquefolius continues to rise (Farh et al., 2018). However, due to the serious continuous cropping obstacles in agricultural production, and the current rotation cycle is long, which seriously restricts the development of its industry and affects the income of farmers (Zhang et al., 2018). Continuous cropping obstacle is a key problem to be solved urgently in Panax quingquefolius industry.

Current studies have shown that the imbalance of crop soil nutrition, root exudation allelochemicals and rhizosphere microorganisms caused by continuous cropping are the main factors leading to continuous cropping obstacles (He et al., 2019). Among these three factors, they can interact with each other. For example, the allelochemicals secreted by roots into rhizosphere soil can lead to the imbalance of rhizosphere soil nutrients and microorganisms (Zhang et al., 2007). Therefore, many scholars believe that the isolation and identification of specific allelochemicals secreted and accumulated by different crops to rhizosphere soil is the primary task to explain the mechanism of continuous cropping obstacles (Yang et al., 2015). Our research group found that continuous cropping of Panax quingquefolius can easily lead to the occurrence of root rot, and in the rhizosphere soil of the Panax quingquefolius with root rot disease, the soil microbial community will change significantly (Jiang et al., 2019). Therefore, whether the changes of rhizosphere soil microorganisms are caused by allelochemicals secreted by Panax quinquefolium, and by which allelochemicals are worthy of further study.

The content of root exudates is low, and its composition is complex. Root exudates can be directly used as a substrate by soil microorganisms, so its composition and content vary greatly with soil properties, which makes it very difficult to study root exudates (Vives-Peris et al., 2020). At present, there is no unified and authoritative method for collection, separation and identification of root exudates in the reported research work, and most of them adopt traditional methods (Li and Duan, 2013), which also restricts the development of this field. In this study, the metabolomics method based on gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) was used to analyze the differential metabolites in rhizosphere soil of healthy ginseng (HS) and root rot ginseng (RS), and to analyze the possible relationship between these differential metabolites and the occurrence of RS, to provide reference for the occurrence of Panax quinquefolius with root rot diseases and allelochemicals.

1 Results and Analysis

1.1 Pretreatment of raw data

A total of 956 peaks were detected in 12 experimental samples (Figure 1). We can see that the similarity of chromatograms between groups is high, but there are also some differences. The single peak was filtered to remove noise, and the missing values in the raw data are simulated. Internal standard (IS) was used for normalization, and 932 peaks were retained after pretreatment. No significant peak was found in the detection of blank samples (Figure 2), indicating that there was no cross-contamination between samples.

1.2 Principal component analysis of soil samples

The PCA method was used for multidimensional statistical analysis of the data of 12 samples (Figure 3A). The 12 samples were all within the 95% confidence interval (Hotelling’s T-squared ellipse). The PCA scatter points of the samples in the control group (HS) and the root rot group (RS) were well separated, indicating that the data processing of the two groups of samples was reliable, and the difference between groups was obvious. The samples in the group had good aggregation, but there were also differences in individual sample groups.

The OPLS-DA model can enhance the separation between groups observed in PCA analysis (Figure 3B). The 12 samples were all within the 95% confidence interval. The samples of the control group (HS) and the root rot group (RS) were significantly separated along the t[1]P axis, and the samples between groups were more dispersed than those of PCA, indicating that the difference in the level of metabolites between groups was significant. At the same time, the samples in the group were more clustered. Selecting some samples as interactive verification, it is found that the original model R²Y is very close to 1, indicating that the modeling conforms to the real situation of sample data. Q² represents the predictability of the model, and Q² is about 0.5, indicating that there are significant differences between the two groups of samples. At the same time, the intercept between the regression line and the longitudinal axis of Q is below zero, which shows that the original model has good stability (Figure 4).

1.3 Screening of differential metabolites between rhizosphere soil groups

One-way analysis of variance (ANOVA) was performed for all 932 metabolites to finish the volcano map (Figure 5). Each point of the volcano map represents a metabolite. Compared with the control group (HS), there are 103 differential metabolites in rhizosphere soil of root rot group (RS), of which 98 are significantly increased (p<0.05) and 5 are significantly decreased (p<0.05).

OPLS-DA method was used to analyze the HS group and the RS group to further determine the differences of metabolites in rhizosphere soil of HS and RS. Combined with VIP value (>1), independent sample t test (p<0.05), and the matching degree with the substances in the standard library was more than 80% to find differential metabolites, 13 metabolites with significant differences (p<0.05) were screened (Table 1). The results showed that there were 9 organic acids: lignoceric acid, palmitic acid, cerotinic acid, benzoic acid, oleic acid, heptadecanoic acid, azelaic acid, salicylic acid and 3,4-dihydroxybenzoic acid; three saccharides: D-Talose, mannose and N-Acetyl-D-galactosamine; one terpene: phytol.

1.4 Analysis of metabolism pathway of different metabolites in rhizosphere soil

KEGG pathway enrichment analysis showed that 13 differentially expressed metabolites were enriched to 11 metabolic pathways (Table 1). These metabolic pathways mainly involve biosynthesis of unsaturated fatty acids, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, degradation of aromatic compounds, phenylalanine metabolism, galactose metabolism and biosynthesis of phenylpropanoids (Table 2).

2 Discussion

Organic acids are important secondary metabolites produced by plant metabolism, which are secreted into rhizosphere soil through roots. It has been reported that organic acids have allelopathic autotoxicity to many medicinal plants (Zhang et al., 2018; Kochan et al., 2019). Compared with HS group, lignoceric acid, palmitic acid, cerotinic acid, benzoic acid, oleic acid, heptadecanoic acid, azelaic acid, salicylic acid and 3,4-dihydroxybenzoic acid level was significantly increased (p<0.05) in RS group. It has been reported that the extract from the rhizosphere soil of continuous cropping ginseng has a significant allelopathic inhibitory effect on the growth of hypocotyls and radicles of ginseng seeds. Cerotinic acid, benzoic acid, oleic acid and other compounds were identified in the extract (Li et al., 2008). It was found that palmitic acid or benzoic acid at the concentration of 1.00 mg/L showed strong allelopathic inhibition on the growth of radicle or hypocotyl of ginseng seeds (Huang et al., 2009), and significantly inhibited the growth of ginseng callus at the concentration of 10 mg/L. Oleic acid also inhibited the growth of ginseng callus (Yang et al., 2017). The incidence and disease severity index of ginseng rust rot treated with the 5 phenolic acids such as salicylic acid, benzoic acid and cinnamic acid increased significantly. Trichoderma harzianum Tri41 could reduce these 5 phenolic acids. After treatment with Trichoderma harzianum Tri41, the incidence and disease severity index of ginseng rust rot were significantly reduced (Li et al., 2016). Studies have shown that the 3,4-dihydroxybenzoic acid can promote the growth of root rot pathogen of Atractylodes macrocephala Koidz, with a promotion rate of 1.5% (Fang et al., 2011). Co-culture of phenolic acids with the pathogen of Panax quinquefolium showed that phenolic acids could promote the growth of root rot fungi at appropriate concentration (Yang and Gao, 2009). The nine organic acids in this study are all compounds widely found in traditional Chinese medicine (Wang et al., 2017). The effects of lignoceric acid, heptadecanoic acid and azelaic acid in rhizosphere soil on crops have not been reported. Their accumulation in rhizosphere soil may also inhibit the growth of Panax quinquefolium, which needs further study. It is speculated that with the increase of planting years, these organic acids are secreted into the rhizosphere soil through ginseng roots to inhibit the normal growth and development of ginseng roots, and when they reach a certain concentration, they may promote the growth of pathogens and lead to the occurrence of ginseng root disease.

Saccharides are the main carbon source of microorganisms, and there are great differences in carbon source preferences among different kinds of microorganisms (Salcedo-Vite et al., 2019). It was found that saccharides were the sensitive carbon source for the change of soil microbial community under organic acid treatment, and organic acid could improve the metabolic ability of soil microbial carbon source (Kong et al., 2017). The results showed that the levels of D-Talose, mannose, N-Acetyl-D-galactosamine and phytol in the RS were significantly lower than those of HS (p<0.05). Studies have shown that Atractylodes macrocephaia Koidz with root rot diseases can use mannose as a carbon source, and mannose has a significant role in promoting the growth of mycelium of root rot disease (Fang et al., 2011), which may promote the growth of Panax quinquefolius L. with root rot diseases. D-Talose and N-Acetyl-D-galactosamine are present in plants and bacteria, but have not been reported in rhizosphere soil. It is speculated that D-Talose and N-Acetyl-D-galactosamine may be secreted into the soil and accumulated by the root system, which is utilized by the carbon source of pathogenic microorganism crops, resulting in a decrease in their levels. Phytol is a chain of diterpenoid oxygen-containing compounds, which is almost insoluble in water and widely distributed in plants. The effect of phytol on crop growth has not been reported and needs further study. Microorganisms such as bacteria and fungi in soil play an important role in soil nutrient transformation and soil fertility formation. Microorganisms and chemical components isolated from soil not only affect plant growth, but also interact with each other. In this process, some chemicals promote the growth of pathogenic microorganisms in soil, or inhibit the growth of beneficial microorganisms, or inhibit the growth of beneficial microorganisms as well in two ways, thereby reducing the number of beneficial microorganisms in soil and increasing the number of harmful microorganisms. At the same time, pathogenic microorganisms stimulate root secretion of more toxic substances. It was speculated that with the increase of planting years, the accumulation of organic acids in rhizosphere soil of Panax quinquefolius promoted the growth of pathogenic microorganisms, accelerated the utilization of soil carbon source by pathogenic microorganisms, led to the decrease of carbon source level in soil and inhibited the growth of beneficial microorganisms. At the same time, carbon source also promoted the growth of pathogenic microorganisms, which led to the occurrence of Panax quinquefolius root disease. In addition, the number of pathogenic microorganisms in rhizosphere soil of Panax quinquefolius was increased, and more toxic substances were secreted by roots.

In this study, KEGG pathway analysis showed that differential metabolites were mainly enriched in 10 metabolic pathways, including metabolic pathways, biosynthesis of unsaturated fatty acids, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, degradation of aromatic compounds, fatty acid biosynthesis, biosynthesis of plant secondary metabolites, phenylalanine metabolism, galactose metabolism and biosynthesis of phenylpropanoids. KEGG analysis further indicated that known differential metabolites were involved in multiple metabolic pathways.

The differential metabolites in rhizosphere soil of HS and RS were detected and screened, and the metabolic pathways involved in these compounds were analyzed to provide reference for the further study of soil chemical composition and the changes of key allelopathy substances leading to continuous cropping obstacles of Panax quinquefolius. At the same time, it may provide reference for other Chinese medicinal materials of Panax and other crops with continuous cropping obstacles. This also laid a foundation for our next study on the autotoxicity experiment of target allelochemicals and their relationship with the changes of soil microbial community, and the screening of detoxifying bacteria.

3 Materials and Methods

3.1 Collection of rhizosphere soil samples

On August 12, 2017, the rhizosphere soil samples of Panax quinquefolius L. were collected from the plantation of Panax quinquefolius cultivated for four years. The plantation is in Zhakoushi Panax quinquefolius planting base (33°38'N, 106°43'E) in Liuba County, Hanzhong City, Shaanxi Province, with the altitude of 1 701 m, the temperature of 25.8°C, and the humidity of 56.0%. The specific collection method refers to the diagonal collection method of Jiang et al. (2019). At the depth of 20 cm near the Panax quinquefolius, excavated the plants. Based on the root rot symptoms of Panax quinquefolius, the RS and HS were finally determined. Then the root soil was shaken off, which was regarded as the rhizosphere soil of HS and the RS. Six samples were collected in each group as the test repetition. They were loaded into the sample collection bag, and put into the refrigerator to bring back to the laboratory, and stored at 4°C.

3.2 Extraction of soil metabolites

Each of the 12 samples was taken 1 g±1 mg. The extract was successively added with methanol water (volume ratio 3:1), ethyl acetate and 2-Chloro-L-phenylalanine, respectively. Concentrated in vacuum after ultrasonic centrifugation, methoxamine salt reagent was added to the dried metabolites, mixed and incubated in the oven at 80℃. 40 μL bis(trimethylsilyl) trifluoroacetamide (BSTFA) containing 1% (φ) chlorotrimethylsilane (TMCS) was added to each sample, and the mixture was incubated at 70℃ for 1.5 h and then detected in random order.

3.3 Detection of soil metabolites by GC-TOF-MS

Instrument: Agilent 7890; Column: Agilent DB-5MS (30 m×250 μm×0.25 μm). The specific analysis conditions (Table 3).

3.4 Data analysis

SIMCA software (V14.1, Sartorius Stedim Data Analytics AB, Umea, Sweden) was used for multivariate statistical analysis. Carried out principal component OPLS-DA modeling analysis of the data, cross-verification (7-fold cross validation). And then the RY obtained after cross-verification was used to evaluate the validity of the model. Finally, the model validity was further tested by permutation test. The volcano map was drawn by ANOVA (p-value) analysis to screen the differential metabolites quickly. Substances that match the differential metabolites was searched in Fiehn database. KEGG pathway analysis was used to analyze the pathway enrichment of differential metabolites.

Authors’ Contributions

LL is the experimental designer and executor of this study, completing data analysis and writing the first draft of the paper. JJL participated in the experimental design and analyzed the experimental results. LL is the designer and director of the project, guiding experimental design, data analysis, paper writing and revision. Both authors read and approved the final manuscript.

Acknowledgments

This study was supported by the Science and Technology Research Fund of Shaanxi University of Technology (SLG1909) and the Fund for Research Team on Catalytic Conversion of Syngas of Shaanxi University of Technology

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