Rhizosphere secondary metabolites quinolones and benzoxazinoid alleviate continuous cropping obstacles of strawberry

doi:10.21203/rs.3.rs-4948384/v1

Alterations to the rhizosphere microenvironment following the continuous cropping of strawberry can result in substantial declines in yield and quality. Plant rhizosphere produces a wide variety of secondary metabolites, which play important roles in regulating plant growth and development. According to the chemical structure and biosynthesis pathways, secondary metabolites can be devided into different groups, and some of these metabolites have been demonstrated to hold ecological significance and responsing to biotic and abiotic stresses. But how this type of feedback affects plant growth is unknown. In this research, strawberry cultivar ‘Santa’ under continuous cropping for 10 years at different cultivation media were used. We assessed the pH value and conductivity of medium solution, dry weight of roots and analyzed the secondery metabolites using ultra performance liquid chromatography (UPLC). After adding cow dung, we detected a significant lower conductivity of medium solution. In addition, after adding goat manure, we detected a significant heigher dry weight of roots. A total of 736 metabolites from 11 classes were detected across all samples. The 20 most significant differentially accumulated metabolites with variable importance in projection scores greater than 1 in each treatment included 17 terpenoids, 5 organic acids, 5 nucleotides and derivatives, 4 lipids, 2 alkaloids, 2 flavonoids, 1 phenolic acid, and 4 others. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that these metabolites were significantly enriched in the following pathways: metabolic pathways, nucleotide metabolism, and biosynthesis of secondary metabolites. Statistical approach showed that the rhizosphere secondary metabolites quinolones and benzoxazinoid were two key compounds that alleviate continuous cropping obstacles of strawberry. If this phenomenon holds true across different soils and environments, this strategy could be a powerful and tractable application to alleviate continuous cropping obstacles.

Biological sciences/Plant sciences/Secondary metabolism

Biological sciences/Physiology/Metabolism

Strawberry

Continuous cropping obstacles

Quinolones

Benzoxazinoid

A total of 25 species of strawberry (Rosaceae: Fragaria) have been described to date (Amil-Ruiz et al. 2011; Shulaev et al. 2008), including 13 diploid, 5 tetraploid, 1 pentaploid, 1 hexaploid, 3 octoploid, and 2 decaploid species (Cappelletti et al. 2015; G.j. 2019; Hummer et al. 2009; Oosumi et al. 2006). Strawberry is an economically important small fruit crop of global significance. Fragaria chiloensis has become highly popular among consumers for its pleasant aroma, exotic white-pink color, and high phenolic content (Morales-Quintana & Ramos 2019). Previous studies have indicated that the consumption of strawberry fruit can provide various benefits, including decreasing the risk of cardiovascular, neurodegenerative, and other diseases in humans; strawberry consumption has also been suggested to have positive effects on diseases of aging, obesity, and various types of cancer (Pinto Mda et al. 2010; Zhang et al. 2008). For this reason, several strawberry varieties have been developed in the temperate zones of the world via different breeding programs and cultivation regimes (Amil-Ruiz et al. 2011). Global strawberry production reached approximately 125.65 million tons in 2021 (FAOSTAT Agriculture Data, http://faostat.fao.org/). Strawberry provides a useful model for the development of basic genomic tools and facility cultivation systems because of its broad horticultural importance (Amil-Ruiz et al. 2011). The development of the strawberry industry accelerated rapidly in China after the 1980s. The area of land on which strawberry is cultivated and the economic efficiency of strawberry production have increased substantially in recent decades, and facility cultivation is becoming increasingly used for the cultivation of strawberry (Zhang et al. 2016). Limitations in the availability of arable land and increases in the demand for strawberries have motivated increases in the use of continuous cropping in facility agriculture systems. However, long-term continuous cropping can have negative effects on the yield and quality of crops (Cheng et al. 2010) (Zou et al. 2022). Some of the deleterious effects of continuous cropping are caused by changes in the physical and chemical properties of soil, self-allelopathy, and the deterioration of the biological environment of soil (Pang et al. 2021). Increases in soil acidity promote the growth of fungi and slow the reproduction of bacteria and actinomycetes (Bai et al. 2015), and this results in the inhibition of crop growth, reductions in yield, and significant economic losses, which impedes the sustainable development of modern agriculture (Bai et al. 2015; Santhanam et al. 2015). Continuous cropping for several years can have deleterious effects on soil physicochemical properties, enzyme activity, the number of microorganisms, salinity, community structure, and rhizosphere metabolites (Xu 2009). Changes in the microenvironment, especially secondary metabolites, can affect the growth of plants via feedback mechanisms (Sui et al. 2022). Rhizosphere microbes can delay the flowering of Arabidopsis (Lu et al. 2018). Plants attract beneficial rhizosphere communities to enhance plant growth after the aboveground parts of plants are exposed to pathogens (Tan et al. 2017). To alleviate or even overcome continuous cropping obstacles, crop rotation schemes have been used for centuries (van der Putten et al. 2013). As to facilities soilless cultivation, the cultivation medium should be changed yearly to overcome some of the deleterious effects of continuous cropping (Zou et al. 2022); however, this inevitably leads to increases in costs. How do plants alter the rhizosphere microenvironment? Although multiple mechanisms are likely at play, the release of small molecular weight compounds, including primary and secondary metabolites, is considered as a major determinant of rhizosphere microenvironment (Pang et al. 2021). The deleterious effects of continuous cropping in crops have been shown to be related to the dispersion and accumulation of root exudates in the rhizosphere (Bonanomi et al. 2018; Chen et al. 2011; Li & Wu 2018; Wolinska et al. 2018). Changes in patterns of metabolite accumulation can alter the microenvironment of the rhizosphere and thereby modulate growth and defense status of plants through feedback pathway.

We have recently shown that the degree of salinity of the rhizosphere in which strawberries have been planted for 10 years decreases following the addition of cow dung, suggesting that there might be some metabolites in cow dung that mitigate salinity. These metabolites could thus be useful for alleviating some of the deleterious effects of continuous cropping.

Here, we studied the accumulation patterns of rhizosphere metabolites in strawberry using metabolomics analysis to identify the metabolites responsible for alleviating the deleterious effects of continuous cropping. We used multi-variate data analysis (MVDA) to screen various types of metabolites. Our findings provided a bright insight for future research aimed at overcoming the challenges associated with continuous cropping and strawberry production.

Plant materials, treatments, and growth conditions

The strawberry cultivar ‘Santa’ was used in this experiment. The strawberries were continuously cropped in the experimental station of Hebei Agricultural University for 10 years, and the experiment was conducted from Jan. to Apr. 2023. The experiment comprised three treatments: T1 (soil:medium = 1:1), T2 (cow dung:medium = 1:1), and T3 (goat manure:medium = 1:1). T1 was used as the control. All treatments comprised three parallel rows with 20 cm between each row; 30 strawberry plants were grown in racks 1.2 m off the ground in each row. The day temperature ranged from 25°C to 28°C and the temperature at night was 20°C. In each experiment, five plants were collected.

Determination of pH and salinity

pH was determined following the method described in a previous study (Qiu 2022). The conductivity is positively correlated with the salinity of a solution within a certain concentration range. Thus, conductivity was analyzed relative to medium salinity levels following the method described in a previous study (Shi 2022).

Determination of root growth

Root dry weight was determined following a previously described method (Xu 2022). Briefly, five whole roots from each treatment were collected, and the average was used to calculate the fresh weight. Before taking weight measurements, the roots were cleaned with distilled water, and the surface was dried with absorbent paper. The roots were then stored in a kraft paper bag and placed in a dryer at 105℃ for 15 min, followed by 80℃. The dry weight was measured when a constant root weight was achieved.

Sample preparation and extraction for metabolomics analysis

Samples were prepared and extracted at the State Key Laboratory of North China Crop Improvement and Regulation at Hebei Agricultural University. Prior to sample collection, the sampling apparatus should be disinfected. The extraction should be performed via several steps. First, a rhizosphere matrix 20 cm deep was collected from the sampling point shown in Fig. 1; the sample was then filtered through a 20-mesh sieve to remove plant roots, animal residues, and other impurities; and 3–5 g of the filtered sample was placed in a sterile centrifuge tube (50 ml). The samples were quickly frozen in liquid nitrogen for 15 min; they were then sealed and stored at -80℃. 2) Samples were removed from the − 80℃ refrigerator and thawed on ice; they were then vortexed for 10 s to mix well. 3) A portion (100 µL) of the sample was added to a 2.0 mL centrifuge tube. 4) Methanol internal standard extract (100 µL) was added, and the sample was vortexed for 15 min. 5) The sample was then centrifuged at 12,000 r/min for 3 min at 4℃. 6) The supernatant was removed, filtered through a microporous membrane (0.22 µm), and stored in an injection bottle for liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection.

Ultra performance liquid chromatography (UPLC) conditions

A UPLC tandem mass spectrometry (UPLC-ESI-MS/MS) system (UPLC, ExionLC™ AD, https://sciex.com.cn/) and tandem mass spectrometry system (https://sciex.com.cn/) were used to analyze sample extracts. The analytical conditions are described below.

UPLC was conducted using an Agilent SB-C18 column (1.8 µm, 2.1 mm * 100 mm). The mobile phase comprised solvent A (pure water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). Gradient elution was performed with the following steps: initial conditions of A:B = 95%:5%; within 9 min, a linear gradient to A:B = 5%:95%, which was held for 1 min; and within 1.1 min, A:B = 95%:5%, which was held for 2.9 min. The flow velocity was set to 0.35 mL per minute, the column oven was set to 40°C, and the injection volume was 4 µL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap mass spectrometer.

Principal component analysis (PCA)

Unsupervised PCA was performed on unit variance-scaled data using R (www.r-project.org).

Selection of differentially accumulated metabolites (DAMs)

DAMs were determined according to the following criteria: variable importance in projection (VIP) > 1 and |log₂(fold change)| ≥ 1.0. The results of orthogonal partial least squares discriminant analysis (OPLS-DA) were used to obtain VIP values, as well as generate score plots and permutation plots using the R package MetaboAnalystR. The data were log-transformed (log₂) and mean-centered prior to OPLS-DA. A permutation test (200 permutations) was conducted to avoid overfitting.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

The metabolites identified were annotated using the KEGG database (http://www.kegg.jp/kegg/compound/); annotated metabolites were then mapped to the KEGG pathway database (http://www.kegg.jp/kegg/pathway.html). Metabolite set enrichment analysis was conducted on these pathways, and the significance of the pathways was determined according to hypergeometric tests.

Effects of different media on pH and salinity

We analyzed the pH and salinity of the media in each treatment. No differences in pH were observed among the three treatments (Fig. 2). However, the salinity was significantly lower in T2 than in T1.

Effects of different media on the dry weight of the roots

Measurements of the dry weight of the roots were taken to evaluate the effects of different media on root growth. The dry weight of the roots was 2.41 g, 2.57 g, and 3.20 g in T1, T2, and T3, respectively (Fig. 3). The dry weight of the roots was significantly higher in T3 than in T1 and T2.

Qualitative metabolite data

Qualitative data on metabolites were used to analyze changes in metabolites and characterize differences among treatments (Supplementary File 1 and Fig. S1). Across all samples, 736 metabolites from 11 classes were detected (Fig. 4). A total of 128 lipids (17.39%), 96 phenolic acids (13.04%), 79 terpenoids (10.73%), 73 organic acids (9.92%), 70 alkaloids (9.51%), 69 amino acids and derivatives (9.38%), 42 flavonoids (5.71%), 32 nucleotides and derivatives (4.35%), 20 lignans and coumarins (2.72%), 5 quinones (0.68%), and 122 other metabolites (16.58%) were identified. As shown in Fig. S1, all 736 metabolites identified in the samples changed among three treetments. In addition, T2 treatment had a stronger impact on the content of terpenoids, organic acids, alkaloids, phenolic acids and flavonoids.

Principal component analysis (PCA)

Clear separation was observed among all treatments in a PCA score plot of the metabolite data (Fig. 5). Principal component 1 (PC1) explained 25.00% of the total variance in the data, and principal component 2 (PC2) explained 18.56% of the total variance in the data. Although the reproducibility of the data was high, as indicated by the clustering of the metabolite data within each treatment in the PCA score plot, the clear separation among treatments indicates that the content of metabolites differed among treatments.

Quality control (QC) of the PCA data

QC samples were used to evaluate the robustness of the PCA data. Generally, the quality of the data is considered adequate when the PC1 scores are within ±3 standard deviations (SD). In Fig. 6, each point corresponds to a sample, and the horizontal axis indicates the order in which samples were detected. The PC1 scores of all the samples were within ±2 SD, suggesting that the quality of the PC1 data was adequate.

Orthogonal partial least squares discriminant analysis (OPLS-DA)

OPLS-DA can be used to analyze weakly correlated variables. Generally, greater similarity in the distribution of sample points indicates higher similarity in the composition and content of the compounds in the samples. OPLS-DA was used to analyze the effects of different media on the accumulation of 736 metabolites. An effective data classification algorithm is shown in Fig. 7; the sample points in the different groups were clearly separated, suggesting that the composition and content of metabolites varied among media.

Variable importance in projection (VIP)

The VIP score based on the OPLS-DA model was calculated to evaluate the ability of the variables to be discriminated (Mi et al. 2020). In T2, 296 metabolites with VIP > 1 were detected. In T3, 332 metabolites with VIP > 1 were detected. A total of 186 common metabolites in T2 and T3 with VIP > 1 were detected, including 36 lipids, 20 organic acids, 23 phenolic acids, 34 terpenes, 11 alkaloids, 5 amino acids and derivatives, 13 nucleotides and derivatives, and 44 flavonoids, quinones, lignans, coumarins, and other metabolites (Supplementary file 2).

Analysis of differences in the content of metabolites

Fold change values were ordered from largest to smallest to visualize metabolomic differences. We labeled the 10 most significant up-regulated and down-regulated metabolites. The results are shown in Table 1 and Fig. 8. In T2 and T3 treatment, the content of certain alkaloinds, organic acids, phenolic acids, terpenoids, flavonoids, lignans and coumarins changed, which was basically consistent with that in Fig. S1.

Table 1 Dynamic analysis of the difference of metabolite content

	T2		T3
	Compounds	Class I	Compounds	Class I
Up-regulated metabolites	2,4-Dihydroxyquinoline	Alkaloids	2,4-Dihydroxyquinoline	Alkaloids
	3-Hydroxy-3-Methyl-2-Oxopentanoic Acid	Organic acids	3-Hydroxy-3-Methyl-2-Oxopentanoic Acid	Organic acids
	Fortuneanoside A	Phenolic acids	Fortuneanoside A	Phenolic acids
	2,3,23-Trihydroxyolean-12-en-28-oic acid methyl ester	Terpenoids	trans-Aconitic acid	Organic acids
	2,3,19-Trihydroxyurs-12-en-28-oic acid (Tormentic acid)	Terpenoids	4-Nitrophenol	Phenolic acids
	Adenine	Nucleotides and derivatives	2-Hydroxy-4-methyl-3-tridecanoyloxypentanoic acid methyl ester	Lipids
	7-Hydroxyflavanone	Flavonoids	Meso-Erythritol	Others
	LysoPE 16:1(2n isomer)	Lipids	Choline Alfoscerate	Lipids
	Histidinol	Alkaloids	LysoPE 16:0(2n isomer)	Lipids
	LysoPE 16:0(2n isomer)	Lipids	Syringetin-7-O-glucoside	Flavonoids
Down-regulated metabolites	Cirsimaritin (4',5-dihydroxy-6,7-dimethoxyflavone)	Flavonoids	2-Hydroxy-4-methyl-3-undecanoyloxypentanoic acid methyl ester	Lipids
	Aldovibsanin A	Terpenoids	Thymidine	Nucleotides and derivatives
	Lathyrol	Terpenoids	6-(((S)-1-carboxyethyl)amino)-4-hydroxyhexanoicacid	Amino acids and derivatives
	Siegesbeckic acid	Terpenoids	Cyclo(L-Phe-trans-4-hydroxy-L-Pro)	Amino acids and derivatives
	Lambertianic acid	Terpenoids	Phylloquinone (Vitamin K1)	Others
	6 beta-Hydroxymethandrostenolone	Others	8-Hydroxy-1,2,6-trimethoxyxanthone	Others
	8-Hydroxy-1,2,6-trimethoxyxanthone	Others	Glucopyranose 6-Hydroxydecanoate	Others
	Peucedanol 7-O-glucoside	Lignans and Coumarins	Methyl 3-O-Methyl Gallate	Phenolic acids
	Costunolide(iso-03)	Terpenoids	O-MethylNaringenin-8-C-arabinoside	Flavonoids
	7-Oxodehydroabietic acid(ISO-1)	Terpenoids	Hesperetin-7-O-rutinoside (Hesperidin)	Flavonoids

Screening of DAMs

The 20 metabolites with the highest VIP values are shown in Fig. 9. The details of the DAMs are shown in the figure legend. These DAMs included 17 terpenoids, 5 organic acids, 5 nucleotides and derivatives, 4 lipids, 2 alkaloids, 2 flavonoids, 1 phenolic acid, and 4 others. Up-regulated DAMs included 3-hydroxy-3-methyl-2-oxopentanoic acid, 2,4-dihydroxyquinoline, 2,3,23-trihydroxyolean-12-en-28-oic acid methyl ester, 3-hydroxy-3-methyl-2-oxopentanoic acid, and LysoPC 16:1(2n isomer); all other DAMs were down-regulated.

In Fig. 9a, the metabolites (from up to bottom) are: 7-Oxodehydroabietic acid(ISO-1) (Terpenoids), 8-Hydroxy-1,2,6-trimethoxyxanthone (Others), Costunolide(iso-03) (Terpenoids), 4-Oxoretinoic acid (Organic acids), Siegesbeckic acid (Terpenoids), Cirsimaritin (4',5-dihydroxy-6,7-dimethoxyflavone) (Flavonoids), Dihydroxy-dimethoxyflavone (Flavonoids), Pisiferic acid (Terpenoids), 2,3,23-Trihydroxyolean-12-en-28-oic acid methyl ester (Terpenoids), Methyl abieta (Terpenoids), Lathyrol (Terpenoids), Methyl neoabietate (Terpenoids), Lambertianic acid (Terpenoids), 3-Hydroxy-3-Methyl-2-Oxopentanoic Acid (Organic acids), 9(10)-EpOME;(9R,10S)-(12Z)-9,10-Epoxyoctadecenoic acid (Lipids), phyllanflexoid A (Terpenoids), 6beta-Hydroxymethandrostenolone (Others), LysoPC 16:1(2n isomer) (Lipids), 3-Acetoxy-9,13-epoxy-16-hydroxy-labda-15,16-olide(ISO-1) (Terpenoids), Calliphyllin (Terpenoids).

In Fig. 9b, the metabolites (from up to bottom) are: 3-Hydroxy-3-Methyl-2-Oxopentanoic Acid (Organic acids), Glucopyranose 6-Hydroxydecanoate (Others), 8-Hydroxy-1,2,6-trimethoxyxanthone (Others), Cordycepin (3'-Deoxyadenosine)* (Nucleotides and derivatives), 2'-Deoxyadenosine* (Nucleotides and derivatives), Methyl 3-O-Methyl Gallate (Phenolic acids), 2,4-Dihydroxyquinoline (Alkaloids), Methyl neoabietate* (Terpenoids), Methyl abieta* (Terpenoids), 5'-Deoxyadenosine* (Nucleotides and derivatives), 9,10,13-Trihydroxy-11-Octadecenoic Acid (Lipids), 5(S),15(S)-DiHETE; 5,15-Dihydroxy-6,8,11,13-Eicosatetraenoic Acid (Lipids), Tianshic acid (Organic acids), Siegesbeckic acid (Terpenoids), Calliphyllin (Terpenoids), 2-Aminopurine (Nucleotides and derivatives), Thymine (Nucleotides and derivatives), Lathyrol (Terpenoids), 4-Oxoretinoic acid (Organic acids), 2(3H)-Benzothiazolone (Alkaloids).

KEGG pathway enrichment analysis of DAMs

A KEGG pathway enrichment analysis was conducted on the DAMs (Fig. 10). A total of 22 KEGG pathways were identified. Metabolic pathways, nucleotide metabolism, and biosynthesis of secondary metabolites were essential pathways. Given that secondary metabolites can affect the rhizosphere microenvironment, these KEGG pathways might play a key role in biological processes essential for rhizosphere metabolism.

Secondary metabolites play diverse roles in different parts of plants (Singh et al. 2021). Plants synthesize various secondary metabolites that protect plants against toxic microbes (Zaynab et al. 2018). Some secondary metabolites can facilitate communication between plants and other organisms (Schafer & Wink 2009). These secondary metabolites thus play key roles in plant growth and development (Rosenthal 1986). Secondary metabolites have long been suggested to engage in communication with pathogenic organisms (Hartmann 2008). Several secondary metabolites have been shown to play an important role in stress responses in plants and increase plant fitness (Chamarthi et al. 2011). Flavonoids are natural phenolic compounds that are ubiquitous in plants and play important roles in growth, development, resistance to biotic and abiotic stress, and interactions with other organisms; they are also important components of animal antioxidant systems because of their large numbers of double bonds (Shen et al. 2022; Singh et al. 2021). Flavonoids can contain chemical information that affects the outcomes of large numbers of biological interactions (Del Valle et al. 2020). Flavonoids have been shown to promote nutrient uptake in plants (Singh et al. 2021). A total of 17 terpenoids were identified among the 40 metabolites with the highest VIP scores (Fig. 9). Terpenoids (isoprenoids) comprise the largest and most diverse class of chemicals among the myriad compounds produced by plants (Tholl 2015). Terpenoids are produced through the shikimic acid pathway and enhance plant defenses (Zaynab et al. 2018); they are used to perform various basic functions that promote plant growth and development. The toxicity of phenols is reduced because of their polar nature (Wink 2008). The phenolic content has been shown to be positively related to antioxidant activity in many plant species, such as oak (Quercus robur), pine (Pinus maritima), and cinnamon (Cinnamomum zeylanicum) (Dudonne et al. 2009). Organic acids are present in most agricultural soils and play important roles in various biological processes; for example, they promote the growth of specific microbial communities, induce the mineralization and solubilization of insoluble nutrients, and increase crop yields (Dakora & Phillips 2002; Iram et al. 2021; Oburger et al. 2009; Weng et al. 2021). The release of organic acids in the soil and rhizosphere can lead to the acidification of the rhizosphere (Arcand & Schneider 2006). Secondary metabolites can alter the three-dimensional structure of proteins through the formation of covalent bonds, which can result in the loss of function of these proteins. For example, polyphenols can interact with proteins via the formation of hydrogen bonds and strong ionic bonds (Zaynab et al. 2018). Alkaloids show various biological activities, which are likely associated with their diverse structures (Erharuyi et al. 2014; Moloudizargari et al. 2013). Phenolics have been shown to alter the cell permeability of microbes and induce structural and functional deformations in membrane proteins (de Oliveira et al. 2011). Some plant terpenes have important industrial and medicinal implications (Bohlmann & Keeling 2008); flavonoids have been shown to have high antioxidant activity (Agbafor et al. 2011).

Alkaloids is the largest group of nitrogenous basic compounds. They are bioactive components in various crops (Zou et al. 2022) that play key roles in various processes, such as insecticide and sterilization with no poisonousness to plants (Machowinski et al. 2006) and immunoregulation, antitumor, neuroprotective, hepatoprotective, antioxidant, anti-radiation, and anti-mutation activities to animals (Chen et al. 2020; Soccol et al. 2016; Xie et al. 2020). In addition, alkaloids and some other secondary metabolites,such as terpenoids and isoflavonoids, can act as phytoalexins that can affect the performance of herbivores (Nuessly et al. 2007). As the result shown, in this experiment, no significant variation in pH was detected in the experiment (Fig. 2). We suspect that changes in alkaloids are responsible for the lack of observed pH change. The content of 2,4-Dihydroxyquinoline were upregulated in both T2 and T3 treatments. The quinolones with active group are widely used bacteriostat (Liu 2017). Beyond that, 2(3H)-benzothiazolone is another important alkaloids that can restrain insect herbivore and increase the germination, tillering and yield of wheat (Gfeller et al. 2023). These may be two important alkaloids compounds that works in this experiment. To verify the result, further search should be carried out.

The aim of this study was to find out the key regulatory metabolites that alleviate continuous cropping obstacles. The content of certain alkaloinds, organic acids, phenolic acids, terpenoids, flavonoids, lignans and coumarins changed and KEGG pathway enrichment analysis revealed that the DAMs were mainly involved in metabolic pathways. Two kinds of alkaloids compounds were screened by statistical approach, they are 2,4-Dihydroxyquinoline, a quinolones and 2(3H)-benzothiazolone, a benzoxazinoid.

UPLC: Ultra performance liquid chromatography

MVDA: Multi-variate data analysis

PCA: Principal component analysis

DAMs: Differentially accumulated metabolites

KEGG: Kyoto Encyclopedia of Genes and Genomes

Funding：This manuscript is supported by China Agriculture Research System of MOF and MARA (grant number CARS-24-G-03), earmarked fund for Province-Ministry Joint Collaborative Innovation Center for Green & Efficient Vegetable Industry of Hebei (grant number HBCT2023100212), Natural Science Foundation of Hebei Province (C2023204123).

Competing interests: The authors declare they have no financial interests.

Authors’ contribution：Bingbing Cai, Yike Liu, lina Yang and Zihan Xu operated the experiment and wrote the manuscript, Qingyun Li and Zhanjun Xue edited the manuscript.

Data availability: Data related to this paper can be accessed in supplementary information files.

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No competing interests reported.

Rhizosphere secondary metabolites quinolones and benzoxazinoid alleviate continuous cropping obstacles of strawberry

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Plant materials, treatments, and growth conditions

Determination of pH and salinity

Determination of root growth

Sample preparation and extraction for metabolomics analysis

Ultra performance liquid chromatography (UPLC) conditions

Principal component analysis (PCA)

Selection of differentially accumulated metabolites (DAMs)

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1