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Article

Dodder Parasitism Leads to the Enrichment of Pathogen Alternaria and Flavonoid Metabolites in Soybean Root

by
Wen Luo
1,
Yuanli Li
1,
Ruiping Luo
2,
Gehong Wei
1,
Yongxin Liu
3,* and
Weimin Chen
1,*
1
State Key Laboratory of Crop Stress Biology in Arid Areas, Shaanxi Key Laboratory of Agricultural and Environmental Microbiology, College of Life Sciences, Northwest A&F University, Xianyang 712100, China
2
Crop Research Institute of Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan Comprehensive Experimental Station of National Soybean Industry Technology System, Yinchuan 750105, China
3
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(6), 1571; https://doi.org/10.3390/agronomy13061571
Submission received: 13 May 2023 / Revised: 4 June 2023 / Accepted: 8 June 2023 / Published: 9 June 2023
(This article belongs to the Special Issue Metagenomic Analysis for Unveiling Agricultural Microbiome)
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Abstract

:
Dodders (Cuscuta chinensis) are rootless and holoparasitic herbs that can infect a variety of host plants, including the vitally important economic and bioenergy crop soybean (Glycine max). Although dodder parasitism severely affects the physiology of host plants, little is known about its effects on fungal communities and root secondary metabolites in hosts. In this study, variations in root-associated fungal communities and root metabolites of soybean under different parasitism conditions were investigated using ITS rRNA gene sequencing and UPLC–MS/MS metabolome detection technologies. The results showed that dodder parasitism significantly altered the composition and diversity of the fungal communities in the rhizosphere and endosphere of soybean. The relative abundance of the potential pathogenic fungus Alternaria significantly increased in the root endosphere of dodder-parasitized soybean. Furthermore, correlation analysis indicated that the fungal community in the root endosphere was susceptible to soil factors under dodder parasitism. Meanwhile, the content of soil total nitrogen was significantly and positively correlated with the relative abundance of Alternaria in the rhizosphere and endosphere of soybean. Metabolomic analysis indicated that dodder parasitism altered the accumulation of flavonoids in soybean roots, with significant upregulation of the contents of kaempferol and its downstream derivatives under different parasitism conditions. Taken together, this study highlighted the important role of dodder parasitism in shaping the fungal communities and secondary metabolites associated with soybean roots, providing new insights into the mechanisms of multiple interactions among dodder, soybean, microbial communities and the soil environment.

1. Introduction

Parasitic plants are heterotrophs that obtain resources for growth and reproduction from host plants [1]. They attach to the host root system or stem through a specialized organ called the haustorium. The life history of parasitic plants severely affects the growth of host plants [2]. In particular, the holoparasitic dodder is completely dependent on the host plant to complete its life history [3] and is considered a common noxious weed that can reduce crop and forage production in agroecosystems [4]. In contrast to parasitic plants, beneficial root-associated microbes have significantly positive effects on their host plants. Numerous studies demonstrated the important role of beneficial microbes in promoting plant growth or stress resistance [5,6,7] and even in alleviating the damage caused by parasitism [8]. Therefore, exploring the host microbial community under plant parasitism is essential to the management of parasitic plants and their hosts.
The rhizosphere is a critical link in soil nutrient cycling and crop nutrient acquisition in agricultural fields [9]. In the narrow rhizosphere environment, close interactions between roots and microbes can strongly promote the nutrient acquisition processes of plants [10,11]. In particular, the rhizosphere harbors various functional guilds of fungi with varying trophic patterns, such as endophytes, saprophytes or pathogens [12]. Environmental factors, soil characteristics and cultivation practices can affect the diversity and composition of the rhizosphere fungal community [13,14,15]. Consequently, these factors also alter plant growth, thereby jointly regulating the rhizosphere microbial community [9]. These studies also demonstrate that the growth status of plants plays an important role in influencing rhizosphere microbial communities [14,16]. Dodder parasitism, as an important biotic stress, has serious negative impacts on host growth by also acquiring fixed carbon, water and mineral resources [17]. To tolerate detrimental parasitism, host plants need to modify their physiological or metabolic activities and attract beneficial microbes from their surrounding environment into the rhizosphere and even the root endosphere [18,19,20]. Some studies investigated the effects of parasitic plants on root-associated bacterial communities in host plants [21,22,23]. However, little is known about the effect of parasitic plant, such as dodder, on host root-associated fungal communities.
Root metabolites of plants have essential associations with the surrounding fungal communities. Metabolomic analyses indicate that plant root tissues and exudates contain hundreds of secondary metabolites [24]. Some of these secondary metabolites can act as signaling molecules, nutrient sources or toxins toward the fungal community in the rhizosphere and root endosphere [25]. For example, studies demonstrated that flavonoids produced by plant roots can enhance mycorrhizal symbioses by promoting the germination of fungal spores, increasing fungal colonization of the roots and stimulating hyphal growth [26,27]. Benzoxazinoids and herbicolin A have a significant effect on pathogenic fungal taxa in the maize [28] and wheat [29]. Biotic stress imposed by parasitic plants may change the metabonomic characteristics of the host. Mistletoe (Phoradendron perrottetii) infection had negative effects on the soluble carbohydrates in branches of Tapirira guianensis [30]. Parasitism by dodder increased the levels of phenolic acids and flavonols in the leaves of cranberry cultivars [31]. Biotic stress not only induces defense response metabolites in aboveground plant parts but also may influence chemical substances in the roots [32]. Nonetheless, there is limited knowledge on how the accumulation of these secondary metabolites within host roots is affected by dodder parasitism.
Soybean (Glycine max) is an important crop for addressing global food insecurity, providing a valuable source of plant protein, oil and biofuel [33]. However, increasing challenges, including rapid human population growth, soil degradation and climate change, threaten global food production [34]. The holoparasitic plant dodder (Cuscuta chinensis), a parasitic plant that usually infests soybean crops, can severely disrupt soybean growth and yield [35,36]. Dodder and soybean are regarded as an excellent model system for studying the interactions between parasitic plants and hosts [37,38]. We conducted this research in a major soybean-growing region in northwestern China, which is persistently infected with dodder. This research could be beneficial for understanding the impact of the interaction between dodder and soybean on the composition of root-associated fungal communities and the root metabolite profiling in soybean. We identified the composition of the root-associated fungal communities and root metabolites from dodder-parasitized and nonparasitized soybeans. There were three objectives: (1) to investigate the impact of dodder parasitism on the composition and recruitment of root-associated fungal communities in soybean, (2) to assess how key soil factors influence the composition and specific taxa of root-associated fungal communities in soybean under dodder parasitism and (3) to determine the effect of dodder parasitism on the metabolite profiles and the accumulation of specific metabolites in soybean roots.

2. Materials and Methods

2.1. Sample Collection

The study area was located in Yinchuan, Ningxia Hui Autonomous Region, China. The dominant soil type was Anthrosol. The area was in an semi-arid region, which belongs to the temperate continental climate. Soybean (Glycine max) was one of the major economic crops in the region, and suffered heavy infestation by the holoparasitic plant dodder (Cuscuta chinensis). Soybeans were parasitized by dodder for more than 10 years in some localized areas, as dodder is also considered an economically important herbal medicine. Samples were collected from three different sites, including S1 (38°6′55″ N, 106°10′45″ E), S2 (38°12′56″ N, 106°11′54″ E) and S3 (38°9′44″ N, 106°12′25″ E) [39]. Based on the growth status (Figure S1), two types of soybeans with dodder parasitism were selected: soybeans with slightly inhibited growth (P1) and soybeans with significantly inhibited growth (P2). Soybeans that were not subjected to dodder parasitism were used as controls. At each sampling site, five random plots (3 × 3 m) were utilized as replicates. Fifteen plants of each soybean type were randomly collected and mixed as a replicate sample in each plot. Bulk soil (BS) samples were collected from 0 to 20 cm depth and 20 cm away from the main stems of the plants. The root compartment samples were collected according to a previous method [40]: the entire soybean plant was carefully removed from the soil, and its root system was gently shaken to collect loose soil adhering to the roots. The loose soil was used as the root zone soil (ZS) sample. Then, the tightly adhered soil (<1 mm) was washed from the roots with 1 M phosphate-buffered solution and used as the rhizosphere soil (RS) sample. Finally, ultrasonic treatment was performed on the roots two or more times to remove root surface microbes, and the remaining roots were preserved as root endosphere (RE) samples. The BS and ZS samples were separated into two subsamples. One subsample was kept for the subsequent analysis of soil physicochemical properties, while the other subsample and the RS and RE samples were stored at −80 °C for microbial analysis.

2.2. DNA Extraction, Sequencing and Bioinformatics Analysis

According to the manufacturers’ instructions, the genomic DNA was extracted from soil and plant samples using the FastDNA SPIN Kit for Soil (MP, Santa Ana, CA, USA) and the Power Plant Pro DNA kit (MB, Santa Ana, CA, USA). The primers 1737F (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and 2043R (5′-GCTGCGTTCTTCATCGATGC-3′) were used to amplify the sequence of the fungal ITS1 region [41]. The polymerase chain reaction (PCR) amplifications were performed in triplicate for each sample, and the PCR products were mixed in equidensity ratios. The obtained PCR products were checked on a 2.0% agarose gel and purified using the GeneJET Gel Recovery Kit from Thermo Scientific. DNA libraries were constructed using the Ion Plus Fragment Library Kit from Thermo Fisher Scientific. The DNA library quality was assessed using the Qubit@ 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Finally, the library was sequenced on Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) for high-throughput double-end sequencing at Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).
Raw sequences were analyzed using DADA2 (version 1.8.0) [42], and unique microbial taxa were inferred based on amplicon sequence variants (ASVs). The DADA2 pipeline mainly included: sequence trimming and filtering, removal of redundant sequences, removal of chimeric sequences, generation of ASV abundance tables and fungal taxonomic annotation (UNITE database) [43]. To minimize the effect of sequencing artifacts, singletons were removed. Considering the differences in sequencing depth per sample, the data were normalized with the minimal sequence method and were then subjected to downstream analysis. Fungal functional annotation library FUNGuild was used to predict the potential environmental functions of fungal communities [12].

2.3. Determination of Physical and Chemical Properties of Soils

Sixteen soil factors were measured using standard testing methods [44,45], including soil pH, soil organic matter (SOM), total nitrogen (TN), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), total phosphorus (TP), available phosphorus (AP), total potassium (TK), available potassium (AK), soluble sodium (Na), soluble potassium (K), soluble calcium (Ca), soluble magnesium (Mg), percentage of clay, sand and silt.

2.4. Widely-Targeted Metabolomic Analysis

For the widely-targeted metabolomic analysis, a set of three biological samples were randomly chosen as replicates. The sample preparation and extraction process was carried out as described below: (1) plant root samples were freeze-dried using a vacuum freeze-dryer (Scientz-100F, Scientz, Ningbo, China); (2) the freeze-dried samples were ground into a fine powder using a mixer mill (MM 400, Retsch, Haan, Germany) at 30 Hz for 1.5 min; (3) 100 mg of the powder was weighed and dissolved in 1.2 mL of a 70% methanol solution; (4) the sample was vortexed for 30 s every 30 min, for a total of 6 times, and then stored in a refrigerator at 4 °C overnight; (5) the sample was subjected to centrifugation at 12,000 rpm for 10 min and then filtered through a microporous membrane (0.22 μm) before being stored for UPLC–MS/MS analysis.
UPLC–MS/MS (UPLC, SHIM-PACK UFLC Shimadzu CBM30A system; MS, Applied Biosystems 4500 QTRAP) was used to perform extensive targeted metabolomics on the prepared samples (Metware, Wuhan, China). The operation procedure was as follows: a Waters ACQUITY UPLC HSS T3 C18 column (1.8 μm, 2.1 mm × 100 mm) was used, and the mobile phase consisted of solvent A (water containing 0.04% acetic acid) and solvent B (acetonitrile containing 0.04% acetic acid). The linear gradient elution program was as follows: phase B increased from 5% to 95% during the first 9.0 min and was maintained at 95% for another 1.0 min, followed by 2.9 min of re-equilibrium (phase A/phase B: 95%/5%). The column temperature was set at 40 °C, and the injection volume was 4 μL.
In the present study, an electrospray ionization (ESI)-triple quadrupole-linear ion trap (QTRAP)-mass spectrometer (AB 4500 Q TRAP UPLC/MS/MS System) was utilized to perform linear ion trap (LIT) and triple quadrupole (QQQ) scans. An ESI turbo ion–spray interface was equipped and executed in the positive/negative ion mode. The scans were controlled by Analyst 1.6.3 software (AB Sciex). The ESI source operating parameters were as follows: ion source, turbo spray, source temperature 550 °C; ion spray voltage (IS): 5500 V (positive ion mode)/−4500 V (negative ion mode); ion source gas I (GSI), 50 psi; ion source gas II (GSII), 60 psi; curtain gas (CUR), 25 psi; the collision-induced ionization parameter, high. The QQQ scan was obtained by multiple reaction monitoring (MRM) experiments with the collision gas (nitrogen) set to medium. After further optimization of the collision energy (CE) and declustering potential (DP), a specific set of MRM pairs was monitored for each period according to the metabolites eluted within the period. Data acquisition and processing were performed as described previously [46]. Metabolites were annotated using the Metware in-house MS2 spectral tag (MS2T) library (Wuhan Metware Biotechnology Co., Ltd.; http://www.metware.cn, accessed on 8 May 2022, Wuhan, China).

2.5. Statistical Analysis

All statistical analyses were performed with R software (3.5.0, http://www.r-project.org, accessed on 1 April 2023). Unless stated, visualization of data relied on the ggplot2 package [47]. The significance was tested by one-way analysis of variance (ANOVA) and Tukey’s HSD post hoc test in the stats and multcomp packages. Constrained principal coordinates analysis (CAP) based on Bray–Curtis distance was performed to visualize the relationships among samples using the vegan package [48]. Multivariate permutation analysis of variance (PERMANOVA/Adonis) was employed to determine differences in microbial communities between treatments by the vegan package. The correlations between the matrices of the microbial communities and soil properties were examined using the Mantel test in the ggClusterNet package [49]. Spearman correlation and ordinary least squares linear regression were used to analyze the relationship between dominant taxa and specific soil nutrients.
The metabolomic data were processed by several multivariate statistical analysis methods. Unsupervised principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to determine the overall metabolite differences among different groups by the vegan package and ComplexHeatmap package [50], respectively. Supervised multiple regression orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted to discriminate the differentially expressed metabolites based on the variable importance in projection (VIP) value in the test model by the MetaboAnalystR package [51]. The threshold VIP value ≥ 1 and fold change ≥2 (upregulated) or ≤0.5 (downregulated) were used for screening the differential metabolites. Differential metabolites identified in the OPLS-DA were used for further k-means clustering analysis to investigate variations in different clusters of the metabolites. Metabolites were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http://www.kegg.jp/kegg/pathway.html, accessed on 8 May 2022) to determine pathway associations. Pathway enrichment analysis was performed using Metabolite Sets Enrichment Analysis (MSEA; http://www.msea.ca, accessed on 8 May 2022). The significance was determined by Bonferroni-corrected p values.

3. Results

3.1. Alpha and Beta Diversity of the Fungal Community

PERMANOVA analysis revealed that the root-associated fungal community of soybean was significantly affected by sampling site (R2 = 0.16, p = 0.001), root compartment (R2 = 0.12, p = 0.001) and parasitism condition (R2 = 0.02, p = 0.032) (Figure 1A and Figure S2A). Additionally, both the sampling site and parasitism condition had a greater influence on the community in RS than in ZS and RE of soybean (Figure 1B). To elucidate the effect of parasitism on the root-associated fungal communities, further analysis was conducted to determine the variations in the fungal communities among different parasitism conditions at each sampling site. Parasitism significantly affected the alpha diversity in different root compartments (p < 0.05) and increased the richness index of the RE community at sampling site S3 (Figure S2B). Furthermore, paired PERMANOVA tests showed that different parasitism effects (P1 vs. control, P2 vs. control and P1 vs. P2) significantly affected the fungal community structure at each sampling site. Although different parasitism effects on fungal community composition varied with sampling site, the RE community composition was significantly affected by parasitism across all sampling sites (p < 0.05) (Figure S2C).

3.2. Fungal Community Composition and the Enrichment of Dominant Taxa

According to the community abundance analysis, the dominant phyla in different root compartments were Ascomycota (BS: 57.25%, RZ: 60.33%, RS: 70.22%, RE: 63.60%) and Basidiomycota (BS: 10.81%, RZ: 8.19%, RS: 1.44%, RE: 8.09%), and their relative abundances varied with parasitism condition (Figure 2A). Specifically, the relative abundance of Ascomycota was higher under P1 and P2 conditions than under control condition in ZS and RE. The relative abundance of Ascomycota under P2 condition was higher than that under P1 condition in all root compartments. At the genus level, Fusarium (RZ, 15.83%; RS, 21.82%, RE, 22.29%) and Alternaria (RZ, 5.41%; RS, 17.05%, RE, 26.10%) were the dominant taxa in all root compartments. Compared with under the control condition, the relative abundance of Alternaria was significantly higher under P1 condition in RE (p < 0.05) and under P2 condition in RS (p < 0.05) (Figure 2B). Furthermore, functional prediction analysis of the fungal community showed that the relative abundance of plant pathogens and parasites significantly increased under P1 condition in RE (p < 0.05) and under P2 condition in RS (p < 0.05) compared to under control condition. These results suggest that dodder parasitism led to the enrichment of potentially pathogenic fungi in the host root system.

3.3. Relationship between Fungal Communities and Soil Factors

Community dissimilarity analysis was used to investigate the effect of parasitism on the variation in the fungal community among different sampling sites. The results showed that fungal community dissimilarity in ZS and RS significantly increased under P1 and P2 conditions (p < 0.05) compared with under control condition (Figure S3). However, the RE community under the P1 and P2 conditions showed the opposite trend. The Mantel test further showed the correlations between fungal communities and different soil factors. More soil factors were significantly associated with the RE fungal community by parasitism (control: 6; P1: 10, P2: 13). In addition, the effect of important soil factors on the fungal communities was different among parasitism conditions (Figure 3A). Under control condition, fungal communities in ZS, RS and RE were mainly affected by silt (r = 0.47, p = 0.001), silt (r = 0.65, p = 0.001) and soluble K (r = 0.46, p = 0.001), respectively. Under P1 condition, these fungal communities were mainly influenced by soluble K (r = 0.60, p = 0.001), soluble Ca (r = 0.51, p = 0.001) and TN (r = 0.67, p = 0.001). Under P2 condition, these fungal communities were mainly affected by soluble Na (r = 0.60, p = 0.001), AP (r = 0.60, p = 0.001) and NH4+-N (r = 0.74, p = 0.001). In general, the RE fungal community under P1 and P2 conditions exhibited strong environmental sensitivity, especially to soil nitrogen.
Spearman correlation analysis was performed to examine the relationship between soil factors and dominant fungal taxa. The results showed that dodder parasitism altered the correlations between soil factors and the top five fungal taxa in each root compartment. Notably, more soil nutrients were significantly correlated (p < 0.05) with the relative abundance of Alternaria in RS and RE under P1 and P2 conditions (Figure 3B). In the RS, the soil NH4+-N content was positively correlated with the relative abundance of Alternaria under the two parasitism conditions. In RE, the contents of TC, TN, TP, TK and soluble Na were positively correlated with the relative abundance of Alternaria under the two parasitism conditions. These results indicated close relationships between soil nitrogen (TN and NH4+-N) and Alternaria under dodder parasitism. The ordinary least square regression analysis further confirmed the strong and positive correlation between soil TN content and the relative abundance of Alternaria in RS and RE under P2 condition (Figure 3C,E and Figure S4).

3.4. Accumulation of Metabolites in Soybean Roots

The microbial analysis showed that dodder parasitism had a significant effect on the fungal communities in both the rhizosphere and root endosphere at sampling site S1 (Figure S2C). Consequently, soybean root samples from this site were chosen for metabolomic analysis to gain a better understanding of how parasitism affects the metabolites. A total of 956 metabolites were detected in soybean roots, with flavonoids being the most abundant class (18.9%), followed by lipids (14.4%), phenolic acids (3.9%) and terpenoids (10.2%) (Figure 4A). Additionally, PCA showed that the metabolites exhibited a clear separation between different parasitism conditions based on the first principal component (PC1, explained 54.92% of the total variance) and the second principal component (PC2, explained 16.79% of the total variance) (Figure 4B). This result indicated that the total metabolites were greatly changed by dodder parasitism. HCA analysis classified the metabolites with the same characteristics into a group to identify the variation in the content of metabolites among different parasitism conditions. The heatmap showed that the metabolites were clearly divided into three distinct profiles by parasitism, further confirming the strong effect of dodder parasitism on the metabolites (Figure 4C). Differential metabolite analysis showed that a total of 474 metabolites significantly varied among different parasitism conditions, with flavonoids being the most abundant class (21.3%), followed by lipids (15.4%), phenolic acids (13.5%), and amino acids and their derivatives (9.7%) (Figure 4D).
OPLS-DA was used to compare the metabolic characteristics of soybeans under different parasitism conditions. The results showed a total of 150 metabolites that significantly differed between control condition and P1 condition, of which 120 metabolites were upregulated under P1 condition (Figure 5A). In contrast, a total of 411 metabolites were significantly different between the control condition and P2 condition, of which 201 were upregulated under the P2 condition (Figure 5B). To study the change trends of differential metabolites among different sample groups, k-means cluster analysis was then performed. These differential metabolites were divided into five subclasses (Figure 5C). Notably, the standardized relative content of 63 metabolites in subclass 3 was elevated in soybean roots under P1 and P2 conditions, with the highest levels under P2 condition. In contrast, the standardized relative content of 92 metabolites in subclass 5 sharply varied in the opposite way, showing a decreasing trend under both P1 and P2 conditions, with the lowest level under P2 condition.
The differential enrichment analysis yielded 82 core metabolites coenriched in soybean roots under both P1 and P2 conditions, mainly composed of flavonoids (35.4%), followed by phenolic acids (12.2%), alkaloids (9.8%) and lipids (9.8%) (Figure 6A). Similarly, the metabolites in subclass 3 were mainly composed of flavonoids (41.3%) (Figure 6B). To identify core metabolites that are sensitive to parasitism, the coenriched metabolites and subclass 3 metabolites were compared. Twenty-nine core flavonoids were further identified and were mainly kaempferol, luteolin, quercetin and their derivatives (Table S1). Specifically, the contents of kaempferol (3,5,7,4′-tetrahydroxyflavone), hesperetin-5-O-glucoside, isorhamnetin-7-O-glucoside and neptin-7-O-alloside exhibited greater changes than other metabolites (Figure 6C).

3.5. Flavonoid Metabolic Reprogramming Induced by Dodder in Soybean

To deeply investigate the impact of dodder parasitism on the accumulation of flavonoid metabolites in soybean roots, the metabolic dataset was analyzed using the KEGG database in a point-by-point manner. The flavonoid metabolites that were significantly altered due to parasitism were assigned to a common metabolic pathway and subsequently interpreted in a flavonoid biosynthesis metabolite network (Figure 7). The results showed that many downstream metabolites of naringenin (a key intermediate and precursor in the flavonoid biosynthesis pathway) were significantly activated by dodder parasitism (p < 0.05), such as kaempferol, luteolin, quercetin and their metabolite derivatives. Under P1 and P2 conditions, neohesperidin, sakuranetin, scolymoside, kaempferol and astragalin metabolites were upregulated in soybean roots, while dihydrokaempferol was the opposite. Meanwhile, upstream (dihydrokaempferol) and downstream (quercetin) metabolites of kaempferol were downregulated, while its own and derived metabolites (astragalin) were upregulated under two parasitism conditions. Moreover, more metabolites, such as luteolin, isovitexin, isoquercitrin and rutin, were upregulated in soybean roots under P2 condition than under P1 condition.

4. Discussion

4.1. Dodder Parasitism Led to the Enrichment of Potential Pathogenic Fungus Alternaria in Soybean Root

Our results indicated that although the effects of soil factors, plant site and parasitism on the fungal communities of soybean were low (R2 < 0.4), the effects were significant. The results were similar to some studies on plant root-associated microbial communities [21,52,53]. Thus, such findings can not be ignored. The possible reason might be that plants have a strong selective effect on root-associated microbes through metabolites such as root secretions [7] and plants can supply a stable environment to maintain the ecological niche of microbes [54]. Therefore, different factors may be difficult to strongly affect plant-associated microbes. Dodder parasitism had the least significant impact on the root-associated fungal community. This might be related to indirect regulation by dodder through the host plant. Previous studies demonstrated that the influence of the host plant genotype on plant-associated microbial communities was weaker than that of soil sources [53,55]. Compared with the rhizosphere and root zones, the fungal community in the root endosphere was more susceptible to different parasitism effects of dodder across different soil environments, indicating a close relationship between the cascading effect induced by parasitism and the root fungal community in the host. However, this potential cascading effect on the root fungal community might be negative for plants. Differential abundance analysis indicated that dodder parasitism resulted in the significant enrichment of potential pathogenic fungi of the genus Alternaria in soybean roots. There could be two potential explanations for this observation: on the one hand, it is possible that parasitic stress by dodder leads to disruption of the physiological metabolism and immune system of the host plant [4,56]; on the other hand, dodder parasitism may lead to the accumulation of nutrient resources (e.g., metabolites) in soybean roots, reducing the competition between microbes and favoring the persistence of a larger number of microbes. Interestingly, previous studies suggested the isolation of Alternaria from dodder for use as a dodder-resistant herbicide [57]. Whether the invasion of Alternaria in host plants is associated with dodder-resistance needs to be further investigated in future work.

4.2. Dodder Parasitism Strengthened the Relationship between Soil Nitrogen and the Genus Alternaria in Soybean Root

The distribution of fungal communities associated with plant roots is known to be closely linked to soil environmental factors [58]. The present study demonstrated that parasitism by dodder further strengthened this association in the host soybean. This is similar to previous research, indicating that external stress can alter the response of plant microbial communities to environmental changes [59]. External stress can stimulate the systemic immune response of the host, leading to the accumulation of metabolites in the host roots that can impact the recruitment and colonization of host root microbes [60]. In the case of soybean parasitized by dodder, this effect may be amplified, making the host root-associated microenvironment more unstable and vulnerable to environmental disturbances. Our results revealed that the fungal communities within the roots of soybean parasitized by dodder were affected by a greater number of soil factors, particularly total nitrogen. Nitrogen is known to have an inhibitory effect on the formation of soybean root nodules [61], which could significantly impact soybean growth. Moreover, we observed a positive correlation between total soil nitrogen content and the abundance of Alternaria in soybean, indicating that while biological nitrogen fixation in soybean may be compromised, pathogenic fungi may take advantage of the opportunity to invade. This raises serious concerns for the healthy growth of soybean and coping with dodder parasitism, underscoring the need for additional trade-offs in future management of soybean and dodder in agricultural systems.

4.3. Dodder Parasitism Promote the Accumulation of Flavonoid Metabolites in Soybean Root

Phenylpropanoids are known to play a crucial role in plant defense against biotic and abiotic stresses, including pathogen and pest attacks [62]. Flavonoids are a key product of the phenylpropanoid pathway. Our metabolomic analysis revealed that flavonoids were induced in soybean roots under dodder parasitism. The flavonoid synthesis pathway plays multiple roles in plant roots, including as a signaling molecule for symbiotic microorganisms and resistance to pathogenic microorganisms. Flavonoids can act as signaling molecules in the root zone to recruit rhizobia and promote the development of nodules, which help the host plant to grow [63]. It is noteworthy that the secondary metabolite kaempferol and its derivatives in the roots of soybean were highly sensitive to dodder parasitism, and their contents accumulate actively in response. A previous study indicated that the flavonol kaempferol acted as an auxin transport regulator and was critical to the nodulation of Medicago truncatula [64]. Furthermore, flavonoids are known to have fungicidal activity against potential pathogenic fungi [65,66]; for example, the parasitism-induced flavonoid rutin exhibited a potent inhibitory effect on the growth of Alternaria [67]. Hence, the accumulation of flavonoids in soybean roots under dodder parasitism could be beneficial in establishing systemic defense in soybean, potentially inhibiting pathogenic fungi and enhancing soybean resistance to the parasitic plant.

5. Conclusions

In this study, we investigated the impact of dodder parasitism on the root-associated fungal communities and root secondary metabolites in soybean, as well as the correlation between the microbial communities and soil properties. Our results demonstrated a significant effect of dodder parasitism on the fungal community in the soybean root endosphere across different soil environments. Additionally, the genus Alternaria was found to be enriched in soybean roots and was closely associated with soil nitrogen under dodder parasitism. These findings suggest that dodder parasitism increased the risk of potential pathogen invasion into soybean roots while also enhancing the environmental sensitivity of the root fungal community. Furthermore, our metabolomic analysis indicated that dodder parasitism significantly induced the accumulation of flavonoid metabolites in soybean roots. Overall, our study revealed the collaborative response of the root-associated fungal communities and root secondary metabolites in soybeans to dodder, potentially providing a new perspective for the management of dodder and soybean.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13061571/s1, Figure S1: The sketch of soybeans with three parasitism status (Control, P1, P2) and three rhizocompartments (root zone soil, rhizosphere soil and root endosphere); Figure S2: The effect of dodder parasitism on the root-associated fungal community of host in each sampling site; Figure S3: The variation in the dissimilarity (Bray–Curtis distance) of the fungal community in each root compartment among different parasitism statuses; Figure S4: The relationship between soil NH4+-N content and the relative abundance of Alternaria; Table S1: The core flavonoid metabolites that significantly enriched under both P1 and P2 conditions based on the differential enrichment analysis and the K-means clustering analysis.

Author Contributions

Investigation, W.L. and R.L.; data curation, Y.L. (Yuanli Li); writing—original draft preparation, W.L.; conceptualization, G.W.; writing—review and editing, W.C. and Y.L. (Yongxin Liu) supervision and funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China, grant number 42177106 and 31870476.

Data Availability Statement

Raw sequence data are available in the GenBank database under the project accession number PRJNA967994.

Acknowledgments

We warmly thank Jiao Xi for providing the guidance and yard for this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. General response patterns of root-associated fungal community diversities in soybeans to parasitism. (A) Constrained analysis of principal coordinates (CAP) based on Bray–Curtis distances, showing the compositional variation explained by compartment and parasitism. (B) Bar chart showing the compositional variation of the root-associated fungal communities explained by parasitism condition and sampling site (PERMANOVA test, * p < 0.05, ** p < 0.01, and *** p < 0.001). BS, Bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
Figure 1. General response patterns of root-associated fungal community diversities in soybeans to parasitism. (A) Constrained analysis of principal coordinates (CAP) based on Bray–Curtis distances, showing the compositional variation explained by compartment and parasitism. (B) Bar chart showing the compositional variation of the root-associated fungal communities explained by parasitism condition and sampling site (PERMANOVA test, * p < 0.05, ** p < 0.01, and *** p < 0.001). BS, Bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
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Figure 2. Composition and potential functions of fungal communities under different parasitism conditions. (A) The distribution of the fungal communities in different root compartments. (B) Variation in dominant taxa and their potential functions among different soybeans. (* p < 0.05). BS, bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
Figure 2. Composition and potential functions of fungal communities under different parasitism conditions. (A) The distribution of the fungal communities in different root compartments. (B) Variation in dominant taxa and their potential functions among different soybeans. (* p < 0.05). BS, bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
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Figure 3. Effect of parasitism on the relationships between soil properties and the root-associated fungal communities of soybean. (A) Correlation analysis between soil properties and the root-associated fungal communities (Bray–Curtis distances) based on the Mantel test. The color of the line represents the significance of the differences (p values). The thickness of the line represents correlation coefficients (Mantel’s r). The number of significant soil factors and the strongest soil factors are displayed under each compartment. (B,D) Spearman correlation analysis between soil factors and dominant taxa. (* p < 0.05, ** p < 0.01, and *** p < 0.001). (C,E) The relationship between soil TN content and the relative abundance of Alternaria. The line represents the linear regression of the ordinary least squares model. BS, bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
Figure 3. Effect of parasitism on the relationships between soil properties and the root-associated fungal communities of soybean. (A) Correlation analysis between soil properties and the root-associated fungal communities (Bray–Curtis distances) based on the Mantel test. The color of the line represents the significance of the differences (p values). The thickness of the line represents correlation coefficients (Mantel’s r). The number of significant soil factors and the strongest soil factors are displayed under each compartment. (B,D) Spearman correlation analysis between soil factors and dominant taxa. (* p < 0.05, ** p < 0.01, and *** p < 0.001). (C,E) The relationship between soil TN content and the relative abundance of Alternaria. The line represents the linear regression of the ordinary least squares model. BS, bulk soil; ZS, root zone soil; RS, rhizosphere soil; RE, root endosphere.
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Figure 4. Qualitative and quantitative analysis of the metabolomics data of soybean roots under different parasitism conditions. (A) Bar chart showing the categories of identified metabolites. (B) PCA of metabolites identified from soybean roots. (C) HCA of the metabolites identified from soybean roots. (D) Bar chart showing the categories of the metabolites that significantly changed under different parasitism conditions.
Figure 4. Qualitative and quantitative analysis of the metabolomics data of soybean roots under different parasitism conditions. (A) Bar chart showing the categories of identified metabolites. (B) PCA of metabolites identified from soybean roots. (C) HCA of the metabolites identified from soybean roots. (D) Bar chart showing the categories of the metabolites that significantly changed under different parasitism conditions.
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Figure 5. K-means analysis of all differential metabolites of soybean roots among different parasitism conditions. Volcano plots showing the expression levels of different metabolites in soybean roots under P1 (A) and P2 (B) conditions. (C) K-means clustering analysis of differential metabolites based on the fuzzy C-means algorithm (Mfuzz).
Figure 5. K-means analysis of all differential metabolites of soybean roots among different parasitism conditions. Volcano plots showing the expression levels of different metabolites in soybean roots under P1 (A) and P2 (B) conditions. (C) K-means clustering analysis of differential metabolites based on the fuzzy C-means algorithm (Mfuzz).
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Figure 6. The distribution of coenriched metabolites (A) and K-means cluster metabolites (B). (C) Volcano plot of the core flavonoid metabolites that were significantly enriched under both P1 and P2 conditions and sensitive to parasitism. Metabolites with more than 10-fold change were labeled on the left or top of the dot in volcano plot.
Figure 6. The distribution of coenriched metabolites (A) and K-means cluster metabolites (B). (C) Volcano plot of the core flavonoid metabolites that were significantly enriched under both P1 and P2 conditions and sensitive to parasitism. Metabolites with more than 10-fold change were labeled on the left or top of the dot in volcano plot.
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Figure 7. Regulation of the flavonoid metabolite biosynthesis network in soybean roots by parasitism. The yellow and green boxes represent metabolites that exhibit significant changes under P1 and P2 conditions, respectively. The red- and blue-colored arrows indicate the metabolites that were significantly upregulated and downregulated (p < 0.05), respectively.
Figure 7. Regulation of the flavonoid metabolite biosynthesis network in soybean roots by parasitism. The yellow and green boxes represent metabolites that exhibit significant changes under P1 and P2 conditions, respectively. The red- and blue-colored arrows indicate the metabolites that were significantly upregulated and downregulated (p < 0.05), respectively.
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Luo, W.; Li, Y.; Luo, R.; Wei, G.; Liu, Y.; Chen, W. Dodder Parasitism Leads to the Enrichment of Pathogen Alternaria and Flavonoid Metabolites in Soybean Root. Agronomy 2023, 13, 1571. https://doi.org/10.3390/agronomy13061571

AMA Style

Luo W, Li Y, Luo R, Wei G, Liu Y, Chen W. Dodder Parasitism Leads to the Enrichment of Pathogen Alternaria and Flavonoid Metabolites in Soybean Root. Agronomy. 2023; 13(6):1571. https://doi.org/10.3390/agronomy13061571

Chicago/Turabian Style

Luo, Wen, Yuanli Li, Ruiping Luo, Gehong Wei, Yongxin Liu, and Weimin Chen. 2023. "Dodder Parasitism Leads to the Enrichment of Pathogen Alternaria and Flavonoid Metabolites in Soybean Root" Agronomy 13, no. 6: 1571. https://doi.org/10.3390/agronomy13061571

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