Metabolic response of peanut (Arachis hypogaea L.) to Sclerotium rolfsii Sacc. in root exudates system

ABSTRACT

In the current study the metabolic response of peanut root exudates to Sclerotium rolfsii Sacc. infection was analyzed in plant-pathogen interaction systems. The analytical methodology employed in this study entailed the utilization of ultra-high-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) in relation to metabolomics. The experimental findings have demonstrated that pathogen infection induces significant variations in both the composition and abundance of peanut root exudates. A total of 322 metabolites were identified in peanut root exudates, among which 19 metabolites showed significant differences under pathogen infection. The results of the orthogonal partial least squares score discriminant analysis (OPLS) and hierarchical cluster analysis (HCA) plots clearly demonstrated that the metabolic profiling effectively distinguished between peanut plants inoculated with S. rolfsii Sacc. and the healthy control plants that were not inoculated. Pathogen infection is associated with five metabolic pathways, the most correlated being arginine and proline metabolism and ABC transporters.

KEYWORDS:

• Untargeted metabolomics
• UHPLC-Q-TOF-MS
• stem rot
• ABC transporters

Introduction

Stem rot, also referred to as white mold, is a highly destructive disease of peanut plants (Arachis hypogaea L.) that is caused by the soilborne pathogen S. rolfsii Sacc. This disease has a significant global impact (Li et al. 2023), and results in significant reductions in crop production in China (Yan et al. 2021). Recent findings have demonstrated that the pathogen exerts its influence on plant growth and pod yields by means of infecting the lower stems, pegs, pods, and roots (Sconyers et al. 2005;Standish et al. 2019; Safari Motlagh et al. 2022). Under favorable conditions, the fungal organism will persistently expand its presence within the plant's tissues over the course of the season, leading to the decomposition of stems, pods, and roots (Backman and Brenneman 1997; Ayed et al. 2018). The release of root exudates by plant roots is a crucial process that serves as a vital mediator, facilitating interactions between the plant and various soil microbes (Yuan et al. 2018; Olanrewaju et al. 2019). As a result of being invaded by harmful microorganisms, plants are unable to produce and accumulate substances released by their roots (Baetz and Martinoia 2014; Song et al. 2021). Root exudates have various functions, including the ability to attract or repel microorganisms through chemical signals, as well as promoting or inhibiting their growth (Bais et al. 2006; Feng et al. 2021), in addition to impacting the colonization and activation of pathogens that infect plant roots (Singh et al. 2004; Návarová et al. 2012). Hence, root exudates play a crucial role in the subterranean plant defense mechanism (Williams and de Vries 2020). Root exudates’ importance as belowground defensive chemicals has been underestimated, perhaps because they are buried. Due to metabolomics, plant root exudate analysis and differential exudate identification have been addressed in the last decade. Metabolomics, a branch of systems biology, studies and quantifies all small molecule metabolites of organisms, organs, or single cells in a certain physiological phase, providing new research ideas (Liu et al. 2021; Akyol et al. 2023). The method is currently essential for studying plant–pathogen interactions since it detects pathogen infection and plant defense metabolites. Deciphering regulating systems and stimuli that affect root exudate mixes has also advanced, highlighting the intricacy and precision of plants’ belowground defense system. Metabolomics analyses of fungal pathogen–plant interactions have mostly focused on Fusarium graminearum, Magnaporthe oryzae, Ustilago maydis, Rhizoctonia solani, Botrytis cinerea, and Sclerotinia sclerotiorum and their hosts. (Chen et al. 2019; Castro-Moretti et al. 2020). Paranidharan et al. (2008) studied wheat's F. graminearum resistance using GC-MS. After inoculation with F. graminearum and Fusarium toxin DON, GC-MS detected 117 metabolites. Five antifungal phenylpropanoids were induced by root-derived aromatic exudates in barley attacked by F. graminearum, according to Lanoue et al. (2010). Jones et al. (2011) employed GC-MS/MS, LC-MS/MS, and NMR to analyze rice at various time periods following infection by compatible and incompatible M. oryzae strains. Doehlemann et al. (2010) studied metabolites that cause tumors in sensitive maize hosts. The flavonoid and shikimate pathways were engaged during tumor growth, increasing metabolites such phenylpropionic acid, tyrosine, and shikimic acid. Similarly metabolic analysis of B. cinerea-infected strawberry discovered putative biomarkers early in disease development, when symptoms were not obvious, which may aid early detection (Hu et al. 2019). Tugizimana et al. (2019) examined the metabolic changes of three sorghum cultivars in response to Colletotrichum sublineolum using a UHPLC-HDMS analytical platform. Their findings revealed key biochemical mechanisms and offered crop breeding insights. The biochemical and cellular interactions of S. rolfsii Sacc. with its host cells and peanut resistance to infection are unknown (Guclu et al. 2020; Zhang et al. 2021). Ultra-high-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) is an updated technique in analyzing the metabolites. Moreover, QTOF-MS offers a high resolving power that reduces the occurrence of false positives when analyzing similar elements (Saito-Shida et al. 2018). QTOF-MS offers a wide range of analytes that can be simultaneously investigated, along with high sensitivity and the ability to conduct retrospective analysis. Hence, QTOF-MS is becoming more and more recognized as an extremely valuable instrument (Diallo et al. 2022; Kottadiyil et al. 2023).

This study aims to increase our understanding of plant defense responses and metabolic interactions in peanut plants infected with S. rolfsii Sacc. We conducted a comprehensive analysis of metabolite changes in peanut root exudates using advanced analytical techniques ultra-high performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) chromatograms with unsupervised and supervised data mining methods. The results provide valuable insights into the effects of S. rolfsii Sacc. infection on peanut metabolism, which can be used to develop strategies for managing this pathogen and breeding resistant peanut cultivars.

Materials and methods

Plant growth and S. rolfsii Sacc. inoculation of plants

The Peanut Scientific Research Centre at Shenyang Agricultural University has successfully acquired a highly pathogenic strain of S. rolfsii Sacc. from a naturally infected peanut plant using the tissue segment method on potato dextrose agar (PDA). The pure culture was obtained using the hyphal tip method and preserved on potato dextrose agar. Oat grains were put into a conical flask, soaked in distilled water for 4 h, then poured away, and autoclaved at 121°C for 20 min. The mycelium of S. rolfsii Sacc. was inoculated in sterilized oat grains and incubated at 30 °C for 15 days. The inoculation conical flask used for inoculation was shaken 2–3 times a day until all oat grains had mycelium growth. The YueYou No. 7 peanut seeds were soaked in water for hydration. These moistened seeds were carefully planted in plastic pots, with one seed per container. Sterilized vermiculite filled these containers to provide a proper growing environment for seedlings. The plants were grown in a controlled glasshouse at 28˚C with a 14-hour photoperiod. Wheat straw covered the potted plants. At 70 days, we selected plants to test their reaction to S. rolfsii Sacc. The pathogen-infected oat grain inoculums were carefully placed near each plant's main stem. We properly quantified the inoculums per pot at 2 grams. The control plants received infection-free oat grain. Controlled settings in the glasshouse maintained a 28˚C temperature and 70–80% humidity. These settings best promote pathogen multiplication and infection. The experiment used a randomized complete block design (RCBD) with infected and control plants. Five replicates in the un-infected control treatment and seven in the inoculated treatment yielded 12 plants.

Peanut root exudates collection and sample preparation

After a week of pathogen (S. rolfsii Sacc.) inoculation, a total of twelve plants were gently taken out from their plastic pots. Among them, seven plants were inoculated with the pathogen while the remaining five plants were not. This procedure was intended to collect root exudates from the plants. The roots were rinsed with sterile distilled water (SDW) and gently dried with sterile tissue paper to collect root exudates. Each plant was carefully placed in a 750-mL BKMAM BIO polypropylene container from Changde, Hunan, China. These bottles contained 500 mL of pure sterile distilled water (SDW) to keep the plants clean. These bottles also maintained plant growth temperatures. After seven days in SDW, sterile root exudates were collected. The exudates were filtered using 2.5 µm filter paper from Whatman™ GE Healthcare UK Limited in Amersham Place, UK. The filtered exudates were kept at −20 °C until metabolite extraction. Root exudate samples were carefully collected from all 12 Arachis hypogaea plants in the controlled pot study.

Metabolite extraction

Root exudates were extracted using the Sangster et al. (2006) and Want et al. (2012) methods. The cryopreserved root exudates were thawed at 4 °C before lyophilization. UHPLC-Q-TOF-MS, a well-known biotechnology approach, identified metabolites in dried samples. The reputed Shanghai-based Personal Biotechnology Co., Ltd. performed this study. The specimens were re-dissolved in a 1:1 volumetric ratio of 100 μL of aqueous solution with acetonitrile and water. The mixture was vortexed and centrifuged for 15 min at 14,000 g and 4 °C. The supernatant was collected and analyzed.

Ultra-high performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) analysis

An ultra-high performance liquid chromatography (UHPLC) system (1290 Infinity LC, Agilent Technologies, Palo Alto, CA, USA) and quadrupole time-of-flight mass spectrometer (AB Sciex TripleTOF 6600, AB Sciex, Framingham, MA, USA) were used to analyze peanut root exudate phytochemical responses to S. rolfsii Sacc. infection. UHPLC-Q-TOF-MS was used to profile the metabolites of peanut plants inoculated with S. rolfsii Sacc. or uninoculated. Hydrophilic interaction liquid chromatography (HILIC) used a 2.1 mm-diameter, 100-mm-long column to analyze materials. For this purpose, Waters in Ireland created an ACQUIY UPLC BEH column with a particle size of 1.7 µm for this purpose. The mobile phase in both electrospray ionization (ESI) positive and negative modes was an aqueous solution with A = 25 mM ammonium acetate, 25 mM ammonium hydroxide, and B = acetonitrile. The elution profile started at 85% B for 1 min and then linearly decreased to 65% B over 11 min. The gradient composition was then reduced to 40% B in 0.1 min and sustained for 4 min. In 0.1 min, a rapid rise to 85% B occurred in 0.1 min. For system stability and equilibrium, a 5-minute re-equilibration time was included. The gradient flow rate was maintained at 0.5 mL/min, while column temperatures were kept constant at 25 °C. Each sample was injected with a 2 µL aliquot. The samples were kept at 4 °C in an automated sampling system throughout the analytical process. For uninterrupted sample analysis, a randomized sequence was used to reduce instrumental detection signal variations. QC samples were strategically placed in the sample queue to carefully evaluate the system's stability and experimental data reliability.

The electrospray ionization (ESI) source experimental conditions were: The experimental setup used 60-unit ion sources, Gas1 and Gas2. There were 30 units of curtain gas (CUR) pressure was 30 units. A high source temperature of 600 °C was maintained for ionization. The ion spray voltage float (ISVF) was adjusted to ±5500 V for maximum ionization efficiency. MS data collection was set to acquire data in the 60–1000 Da mass-to-charge ratio (m/z) range. The time-of-flight (TOF) MS scan took 0.20 s per spectrum. The analytical apparatus was set to acquire data from 25–1000 Da during auto MS/MS acquisition. The researchers set the product ion scan accumulation time at 0.05 s per spectrum. Information dependent acquisition (IDA) and high sensitivity mode were used to acquire the product ion scan. The experimental conditions were: maintaining a constant collision energy (CE) of 35 V with a variation of ±15 eV; setting the declustering potential (DP) at 60 V in positive mode and −60 V in negative mode; excluding isotopes within 4 Da; and monitoring 10 candidate ions per cycle.

Data processing and statistical analysis

The ProteoWizard MSConvert utility converted MS data to MzXML, which was imported into the publicly available XCMS software. Peak picking was performed using the following parameters were used for peak picking: The CentWave program detected mass spectrometry peak data. To accurately identify peaks, the m/z tolerance was set at 25 ppm to accurately identify peaks. For further research, peaks with widths between 10 and 60 units were given a range of c (10, 60) for further research. Prefiltering with a range of c (10, 100) excluded peaks with intensities outside of this range. Peak grouping is achieved with bw = 5, mzwid = 0.025, and minfrac = 0.5. CAMERA (Collection of Algorithms of Metabolite pRofile Annotation) helped annotate isotopes and adducts in the context. Only variables with above-50% non-zero measurement values in at least one group were preserved for ion feature extraction. Metabolites were identified by carefully assessing m/z readings with a tolerance of fewer than 25 parts per million. The MS/MS spectra were also rigorously examined in an internally generated database of legitimate standards. The data was analyzed using R (ropls) after normalization to overall peak intensity. The data were then analyzed using multivariate methods such Pareto-scaled PCA, orthogonal partial least-squares discriminant analysis (OPLS-DA), and hierarchical cluster analysis. The model's robustness was tested using 7-fold cross-validation and response permutation. We used the variable importance in the projection (VIP) value to evaluate each variable in the OPLS-DA model during differential metabolite screening. This VIP value indicates the variable's categorization impact. To evaluate each metabolite's relevance, a univariate Student's t-test was performed on those with VIP values larger than 1. P-values < 0.05 indicated statistical significance for metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism database was used to build metabolic pathways for differing metabolites. Under consideration biological specimens. To assess the system's inherent stability and experimental data dependability, QC samples were deliberately added to the sample queue.

A systematic technique established the electrospray ionization (ESI) source in experimental settings. The experimental design used ion sources Gas1 (Gas1) and Gas2 (Gas2) at 60 units each to facilitate the inquiry. The bioinformatics protocols set curtain gas (CUR) at 30.

Results and discussion

Metabolic profiling of peanut root exudates

In this comprehensive investigation, a total of 322 metabolites were meticulously acquired, encompassing 219 metabolites in the positive ion mode and 103 metabolites in the negative ion mode, following rigorous data pre-processing and meticulous annotation procedures. Based on the chemical composition of the metabolites, the root exudates of peanut have been categorized into eleven prominent groups (Figure 1). These groups encompass various organic acids, including carboxylic acid, hydroxy acid, cinnamic acid, and keto acid. Additionally, lipids such as fatty acyls, steroids, glycerophospholipids, and sphingolipids are present (Supplementary file, Table S3). Root exudation involves the release of various carbon-containing primary metabolites like sugars and amino acids, as well as more complex secondary compounds. This process plays a role in nutrient and water uptake, plant defense, and interactions with other soil organisms (Canarini et al. 2019). The exudates also contain organoheterocyclic compounds like benzene, diazines, pyridines, indoles, azoles, and pteridines. Furthermore, nucleosides, nucleotides, and analogues, such as purine nucleosides, pyrimidine nucleosides, purine nucleotides, and pyrimidine nucleotides, are detected. Other constituents include organic oxygen compounds, benzenoids, phenylpropanoids, polyketides, and various other metabolites. The composition and characteristics of root exudates are influenced by various factors, including the host species, phases of plant growth, soil physio-chemical properties, and the diversity of microbial communities (Hu et al. 2018; Singh et al. 2022).

Figure 1. Classification of the root metabolites of the peanut.

Root differential metabolite analysis of peanut in response to pathogen infection

White mold, commonly known as Stem rot, in peanuts, causes apparent symptoms in the aerial sections of the plant a long time after infection. The first sign of a plant infection is dark-brown stem lesions at or near the soil surface. According to Punja Z. K. (1985), leaf discoloration gradually progresses to withering and yellowing. This work investigated the notion that peanut plant metabolomes may identify S. rolfsii Sacc. from uninfected plants during the latent phase. The metabolome composition reveals the host plant-pathogenic microbe metabolic interaction (Gupta et al. 2022).

Pattern recognition analysis was performed on the 322 peanut root exudates from the S. rolfsii Sacc. inoculated and un-inoculated control groups using PCA and OPLS-DA. The CK and BJ sample points somewhat overlap in the principal component analysis (PCA) score plot (Figure 2). This overlap is due to treatment group repeatability and discrimination. This shows that some samples have very similar root exudate composition and content. According to (Williams et al. 2021) it was observed that the root exudate profiles during the shortest recovery period (0 days) exhibited resemblances to the extracts obtained from damaged roots.

Figure 2. PCA scores plots of the peanut root metabolites under S. rolfsii Sacc. inoculated and un-inoculated control (a: positive ion mode; b: negative ion mode).

The principal component analysis (PCA) model was employed to assess the explained variance in the sample under both positive and negative ion modes. The R₂X value, ranging from 0.544–0.579, exceeded the threshold of 0.500. Furthermore, all samples fell within the 95% Hotelling's T-squared ellipse, suggesting the absence of any abnormal data. A comparative analysis of OPLS-DA results was performed to determine the most significant differences between exudates from peanut roots under un-inoculated control and pathogen-inoculated control. Peanut root exudates are widely believed to serve a key role in suppressing soil-borne diseases (Li et al. 2013).

Figure 3 shows a clustering pattern of data points from the same treatment group in the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) score plot. In contrast, sample points from various treatments are separated along the t[1] axis. The OPLS-DA model seems to capture and describe the differences across treatments. The findings show that metabolite profiles provide important metabolic process information. These profiles are effective in distinguishing diseased plants from healthy ones. In recent Shuangqian et al. (2023) discussed the importance of identifying specific metabolites in plants that are found in different ecological environments and when exposed to different stresses. They also explored how metabolomics can be used to identify functional genes, understand metabolic pathways, and assist in breeding programs by analyzing plant populations with diverse genetic variations.

Figure 3. OPLS-DA scores plots of the peanut root metabolites under S. rolfsii Sacc. inoculated and un-inoculated control (a: positive ion mode; b: negative ion mode).

Metabolomics uses differential metabolites to reveal organisms’ metabolic processes and physiological and pathological causes. In this work, 19 DEMs (Differential expression metabolites) were discovered using OPLS-DA VIP > 1 and p-value < 0.05 as screening criteria. These DEMs were found in both positive and negative ion modes (Table 1). The analysis of metabolite distribution was subjected to hierarchical cluster analysis (HCA), a method that effectively elucidates the distinctions among various treatments by discerning the differentially expressed metabolites (DEMs) they encompass. The generated hierarchical cluster analysis (HCA) plots depicted in Figure 4 have revealed that the presence of pathogen infection has induced alterations in the metabolic processes occurring within the root system of the peanut plant. The intricate interplay between host, microbiota, and pathogens gives rise to metabolic processes that can exert either beneficial or detrimental effects on the host's ability to survive infection (Kanwar and Jha 2019).

Figure 4. Two-way hierarchical clustering analysis visualized using a dendrogram combined with a heat map. The heat map represents the intensities of the DEMs in the roots of S. rolfsii Sacc. inoculated and un-inoculated control (a: positive ion mode; b: negative ion mode).

Table 1. Differential expression metabolites of different treatments in both positive and negative ion mode.

Ion mode	Category	Name	VIP	p-value^a	FC^b
Positive	Organic acid	Creatinine	2.03	0.002**	0.57
		Betaine	1.19	0.004**	0.68
		L-Proline	1.52	0.02*	0.23
		DL-3-Hydroxybutyric acid	1.11	0.017*	1.61
	Lipids	trans-Vaccenic acid	1.51	0.019*	0.84
		Acetylcarnitine	1.47	0.001**	0.58
	Organic nitrogen compounds	Triethanolamine	4.17	0.031*	0.55
	Organic oxygen compounds	Pantothenate	1.77	0.004**	1.69
	Organoheterocyclic compounds	Diethylcarbamazine	3.32	0.022*	2.46
		Nicotine	3.12	0.027*	0.48
	Nucleosides, nucletides	Cytidine	4.45	0.042*	0.62
	undefined	beta-Homoproline	1.13	0.007**	0.45
Negative	Benzenoids	Vanillic acid	1.13	0.038*	0.55
	Lipids	Stearic acid	1.4	0.023*	0.43
	Organic oxygen compounds	Xylitol	3.81	0.002**	1.71
	undefined	Fludrocortisone acetate	1.03	0.003**	1.89
		1-Palmitoyl-2-linoleoyl-sn-glycero-3-phosphate	5.39	0.031*	0.64
		Tetrahydrocorticosterone	1.19	0.02*	0.62
		Norethindrone acetate	6.86	0.046*	1.18

^a p-values calculated using the student t-test showing significant differences between the S. rolfsii Sacc. inoculated and un-inoculated control. The asterisks indicate significant differences between the two treatments (* p ≤ 0.05; ** p ≤ 0.01). ^b Fold change column showed the amount of the metabolites content changed in samples incubated with S. rolfsii Sacc compared to without ones. The numbers greater than 1 represent the upregulation of metabolites and less than 1 represent the downregulation of metabolites.

The observed data reveals distinct groupings of repeated measurements within the same treatment condition, with the exception of one particular repeat (CK2) in the negative ion mode. Furthermore, the various treatment conditions exhibit significant separation in both positive and negative ion modes, suggesting notable disparities in metabolic alterations between CK and BJ. Notably, metabolites that cluster within different groups exhibit diverse expression patterns, potentially indicating disparate functionalities or involvement in distinct metabolic processes or cellular pathways. A strong correlation between metabolite signatures and phenotypic information is useful in predicting diagnosis and prognosis as well as monitoring treatment efficacy (Qiu et al. 2023).

Figure 5 depicts the observed alterations in metabolite levels of peanut root in response to inoculation treatments with S. rolfsii Sacc., specifically highlighting the phenomena of up-regulation and down-regulation. In the positive ion mode, it was observed that three metabolites exhibited up-regulation, while nine metabolites displayed down-regulation in the BJ sample when compared to the CK sample. In the context of negative ion mode, it was observed that three metabolites exhibited an up-regulated expression pattern, while four metabolites displayed a down-regulated expression pattern. Through the utilization of fold change calculations, an in-depth analysis was conducted on the alterations observed in the differential metabolites of the peanut root across various treatments, as outlined in Table 1. In the positive ion mode, discernible variations in the organic acid composition were observed, indicating that distinct treatments exert diverse impacts on the exudation of organic acids in the subterranean structures of Arachis hypogaea. The presence of a pathogenic agent has been observed to impede the excretion of creatinine, betaine, and L-proline in the root system of the peanut plant. The accumulation of proline and its oxidative metabolism are important mechanisms that allow proline to provide protective benefits to organisms (Christgen and Becker 2019). Consequently, the quantities of these substances were found to be significantly diminished, reaching levels approximately 0.57, 0.68, and 0.23 times lower than the control group, respectively. Concurrently, the application of BJ treatment elicited a notable upregulation in the exudation of DL-3-Hydroxybutyric acid within the root system, resulting in a substantial augmentation of 1.61-fold compared to the control group (CK). 3-HB is a crucial metabolite found in animals, bacteria, and plants. Within plants, 3-HB serves as a regulatory molecule that is believed to impact the expression of genes related to DNA methylation, leading to changes in DNA methylation levels (Mierziak et al. 2021).

Figure 5. Fold change analysis of the DEMs in the roots of S. rolfsii Sacc. inoculated and un-inoculated control (a: positive ion mode; b: negative ion mode).

The findings presented in Table 1 demonstrate a notable inhibition of partial lipid secretion in peanut root systems as a direct consequence of pathogen infection. In comparison to the control group (CK), the levels of acetylcarnitine and trans-vaccenic acid were found to be significantly diminished under the experimental conditions (BJ). Specifically, the concentrations of acetylcarnitine and trans-vaccenic acid in BJ were determined to be 0.58 and 0.84 times that of CK, respectively. Furthermore, it is noteworthy to mention that the levels of triethanolamine, nicotine, cytidine, and beta-homoproline were observed to undergo a substantial reduction of approximately 0.55, 0.48, 0.62, and 0.45 times, respectively, upon the application of the BJ treatment. The levels of pantothenate and diethylcarbamazine exhibited a substantial increase of 1.69-fold and 2.46-fold, respectively, in the BJ group when compared to the CK group. In the negative ion mode, the abundance of vanillic acid was observed to decrease by a factor of 0.55, while stearic acid exhibited a decrease of 0.43. Conversely, xylitol demonstrated a significant increase of 1.71 times, and fludrocortisone acetate exhibited a notable increase of 1.89 times when comparing the results obtained from BJ to those of CK. The metabolites under consideration have been identified as putative biomarkers that exhibit a strong association with the infection caused by the fungal pathogen S. rolfsii Sacc. Prior research conducted by Gu et al. in 2016 has demonstrated that the presence of pathogens can lead to the activation of defense-related compounds and the enhancement of constitutive root exudation processes, including synthesis, accumulation, and release.

In addition to the compounds that have been tentatively identified as being involved in defense mechanisms, this study has also detected other compounds in the root exudates that remain unidentified. Therefore, further investigation using advanced analytical techniques such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) is necessary. This investigation aims to identify both the newly discovered compounds that are consistently secreted and those that are induced in response to pathogenic microorganisms in the rhizosphere. Additionally, the ion features that distinguish between the control group without inoculation and the group inoculated with S. rolfsii Sacc. will also be identified.

Metabolic pathway analysis of differential metabolites in peanut under pathogen infection

The intricate metabolic processes and their regulatory mechanisms in organisms are not executed in isolation, but rather orchestrated by intricate pathways and networks comprised of diverse genes and proteins. The dynamic interplay and intricate control mechanisms between various biological components ultimately culminate in the orchestrated alterations observed in the metabolome. Through a comprehensive examination of the metabolic pathways associated with distinct metabolites in the subterranean structures of Arachis hypogaea, we can ascertain the specific metabolic pathway that is intricately linked to the process of pathogenic invasion. In this investigation, the utilization of KEGG annotation analysis was employed to ascertain the metabolic pathways implicated in differential metabolites. Subsequently, a comprehensive analysis of the metabolic pathways was conducted, encompassing enrichment analysis and topological analysis, utilizing the MetPA database. Prior to the annotation and analysis of the KEGG pathway, the differential metabolites obtained from both positive and negative ion modes were merged. In this study, a comprehensive analysis was conducted to identify and characterize the metabolic pathways present in the roots of peanut. A total of 31 distinct metabolic pathways were detected, as outlined in Table S1. These pathways were derived from the root exudates of the peanut plant, providing valuable insights into the intricate biochemical processes occurring within the root system. To elucidate the pivotal pathways exhibiting the most pronounced correlation with pathogen infection, an investigation was conducted to probe their involvement in the response to S. rolfsii Sacc. inoculation in the context of peanut. Key pathways were determined by selecting metabolic pathways with a p-value below 0.05.

Figure 6 illustrates the impact of pathogen infection on cellular metabolism, revealing the involvement of five crucial metabolic pathways. These pathways encompass ABC transporters, the synthesis and degradation of ketone bodies, arginine and proline metabolism, as well as phototransduction in flies and prodigiosin biosynthesis. The obtained outcome suggests that the involvement of these five metabolic pathways could potentially be significant in the peanut plant's response to S. rolfsii Sacc. inoculation. A recent in-depth exploration was conducted by Hu et al. (2019) to shed light on the metabolic reaction of strawberry plants when inoculated with Botrytis cinerea, a fungal pathogen.

Figure 6. Quantities of DEMs in Enriched KEGG pathway.

Compared with CK, xylitol was significantly accumulated in ABC transporters, L-proline, betaine and cytidine were significantly reduced under S. rolfsii Sacc. inoculation. Creatinine and L-proline were significantly decreased in arginine and proline metabolism. Only DL-3-Hydroxybutyric acid was significantly increased in lipid metabolism (synthesis and degradation of ketone bodies), while stearic acid and L-proline were significantly reduced in phototransduction – fly and prodigiosin biosynthesis respectively. The results showed that under S. rolfsii Sacc. inoculation, arginine and proline metabolism, phototransduction – fly and prodigiosin biosynthesis were suppressed, synthesis and degradation of ketone bodies (lipid metabolism) was promoted, and significant effects on ABC transporters were detected. Pathogens exert significant impact on the physiological, molecular, biochemical, and metabolic attributes of the host plants (Duan et al. 2013).

Conclusion

The present study found that peanut root exudates exhibited metabolic responses to Sclerotium rolfsii Sacc. infection. The instantaneous reaction emphasizes the crucial role of instantly recognizing an invading pathogen and subsequently triggering a swift and efficient response from the host's defense mechanisms, which is a vital process in the realm of plants. In this investigation, the presence of pathogens had a discernible impact on the composition and quantity of exudates released by plant roots. During the infection caused by S. rolfsii Sacc., several DEMs compounds were detected in the exudates released by the roots. These compounds include L-proline, xylitol, cytidine, betaine, vanillic acid, stearic acid, and DL-3-hydroxybutyric acid. The identification of the amino acid metabolism pathway, specifically arginine and proline metabolism, along with the membrane transport pathway involving ABC transporters, has revealed their significance in relation to pathogen infection. This study could provide an understanding of the peanut plant's resistance to the S. rolfsii Sacc. pathogen.

Ethics approval and consent to participate

N/A.

Consent for publication

Not applicable.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw data collected during the current study are not publicly available before publication but are available from the corresponding author on reasonable request.

Additional information

Funding

This work was supported by Basic scientific research projects of colleges and universities in Liaoning Province: [Grant Number LJKMZ20221044].

Notes on contributors

Wenrui Wang

Wen Rui Wang, Master, Educational Background, Graduated from Liaoning University of Traditional Chinese Medicine with a bachelor’s degree, Master’s degree at Shenyang Agricultural University, The main research direction is secondary metabolites.

Chuang Liu

Chuang Liu, Master, Educational Background, Bachelor’s degree from Shenyang Polytechnic University, Master’s degree at Shenyang Agricultural University, Main research interests are plant protection of peanut crops, control effect of different fungicides on peanut disease and field application.

Sitong Du

Si Tong Du, Master, Educational Background, Bachelor’s degree from Shenyang Agricultural University, Master’s degree at Shenyang Agricultural University, Main research interests are biological control of plant diseases; Extraction of metabolic compounds from Bacillus velezensis.

Chao-Qun Zang

Dr. Chao Qun Zang, Senior Research Fellow, Educational Background, Bachelor’s degree from Huaibei Coal Normal University, PhD, Shenyang Agricultural University, Research Expertise, Dr. Chao Qun Zang specializes in the management of continuous cropping obstacles in peanuts and research on biological control. Relevant research achievements have been published as the first author or corresponding author in professional journals such as the European Journal of Plant Pathology and Applied Sciences.

Yu-Qian Huang

Dr. Yu Qian Huang, Associate Professor, Educational Background, Bachelor of Science, Shenyang Agricultural University, Master of Science, Shenyang Agricultural University, PhD, Shenyang Agricultural University, Visiting Scholar, Cornell University, Research Expertise, Dr. Yu Qian Huang specializes in agricultural sciences with a focus on the management of obstacles and diseases associated with peanut continuous cropping systems. His extensive research has contributed significantly to the understanding and development of sustainable agricultural practices for peanut cultivation. The relevant achievements have been published as the first author or corresponding author in international professional journals such as Journal of Advanced Research BMC Plant Biology and Biological Control.