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2023 Volume 2
Article Contents
Abstract
Introduction
Materials and methods
Plant materials
Protein extraction
Trypsin digestion
Affinity enrichment of ubiquitinated peptides
LC-MS/MS and data analysis
Bioinformatics methods
Results
Overview of the ubiquitomic analysis
Properties of ubiquitinated proteins and motifs
A temperature-regulated rice endosperm ubiquitome
Functions for differentially expressed ubiquitinated proteins
Discussion
Ubiquitination of heat shock proteins and transcription factors under high-temperature stress
Sucrose and starch metabolism are modified by ubiquitination under high-temperature stress
Ubiquitination affects sucrose hydrolysis
Ubiquitination of ADP-glucose pyrophosphorylase (AGPase)
Ubiquitination regulates amylose biosynthesis
Ubiquitination regulates amylopectin biosynthesis
Ubiquitination affects ribosomal systems under high-temperature stress
Author contributions
Data availability
Acknowledgments
Conflict of interest
Supplementary information
Rights and permissions
References
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ARTICLE   Open Access    

Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation

  • 1.

    Jilin Agricultural University, Co-construction State Key Laboratory Base of Ecological Restoration and Ecosystem Management, Changchun, Jilin 130118, China

  • 2.

    Changchun Customs Technology Center, Changchun, Jilin 130118, China

  • 3.

    Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210014, China

  • 4.

    Economic Botany Research Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin 130118, China

More Information
  • The fungal species of Fusarium solani (F. solani) is closely associated with the soil borne root rot disease of Panax ginseng, leading to a decline in the quality of the medicinal materials. In this study, we established experimental conditions of two PCR-based microbiological assays, a droplet digital PCR (ddPCR) method and a RT-qPCR method, and tested their efficacy of quick and quantitative detection of F. solani DNA in ginseng soil. These methods were further evaluated in quantifying F. solani for rhizosphere samples of ginseng plants with different status of pathogenic disease infection. The results showed that the two methods were both highly specific and reproducible in detecting and quantifying the gene of F. solani, without any observable cross-reaction with other species. Compared to the RT-qPCR method in this study, ddPCR method exerted a higher sensitivity of detection (0.92 copies μL−1) than RT-qPCR method (920 copies μL−1), and free of reliance on a standard calibration curve. With the ddPCR method, the absolute quantification of nucleic acid samples is made possible by changing the detection standard from fluorescence intensity to the presence or absence of fluorescence, and the accuracy of detection has been significantly improved. The abundance of F. solani were 0−2,100 copies g−1 dws, 927.5−8,470 copies g−1 dws and 10,605−43,697 copies g−1 dws respectively in uncultured ginseng soil, soil from healthy ginseng and the soil from infected ginseng. The quantitative abundance of the pathogenic fungal species infected by the diseases but also track the dynamic change in the gene abundance of F. solani in disease monitoring with ginseng growth. While the gene abundance of F. solani was highly accumulated in mid-August and decreased in September, the pathogenic fungal gene abundance tested was closely correlated to the incidence rate of ginseng root rot. Apparently, this pathogenic fungus is exists widely in ginseng soil even with the healthy plants in September, indicating a potentially great risk of high disease incidence for ginseng cultivation. Therefore, the developed ddPCR assay could be reliable and feasible, potentially applied in early diagnosis of the infection by the pathogenic fungal of F. solani in ginseng cultivation and in routine assessment of the disease occurrence with ginseng growth.

  • Introduction

    Rice (Oryza sativa L.) is a world staple crop that feeds over half of the world's population[1,2]. In recent years, high-temperature events are becoming more frequent and intensive as a result of global warming, which can severely affect rice grain yield and quality[3,4]. During flowering and grain filling stages, high-temperature stress can result in a significant reduction in seed setting rate and influence amylose content, starch fine structure, functional properties and chalkiness degree of rice[5−7]. Transcriptome and proteome analysis in rice endosperm have also been used to demonstrate the differences in high-temperature environments at gene and protein expression levels[8−10]. In addition, as an important post-translational modification, protein phosphorylation has proven to be involved in the regulation of starch metabolism in response to high-temperature stress[11]. However, little is known about whether protein ubiquitination regulates seed development under high-temperature stress.

    Ubiquitination is another form of post-translational modification that plays key roles in diverse cellular processes[12]. Several reports have described the functions of ubiquitination in rice defense responses based on ubiquitome analysis. Liu et al.[13] investigated relationships between ubiquitination and salt-stress responses in rice seedlings using a gel-based shotgun proteomic analysis and revealed the potential important role of protein ubiquitination in salt tolerance in rice. Xie et al.[14] identified 861 peptides with ubiquitinated lysines in 464 proteins in rice leaf cells by combining highly sensitive immune affinity purification and high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). These ubiquitinated proteins regulated a wide range of processes, including response to stress stimuli. A later study revealed the relationships between ubiquitination status and activation of rice defense responses, and generated an in-depth quantitative proteomic catalog of ubiquitination in rice seedlings after chitin and flg22 treatments, providing useful information for understanding how the ubiquitination system regulates the defense responses upon pathogen attack[15]. Although many studies have shown that ubiquitination plays improtant roles in the heat response of plant[16,17], there has been little systematic discussion on the ubiquitome of rice endosperm in the context of global climate change.

    In this study, we examine the high-temperature induced ubiquitination change in two rice varieties with different starch qualities, through a label-free quantitative ubiquitome analysis. This study provides a comprehensive view of the function of ubiquitination in high-temperature response of rice developing seed, which will shed new light on the improvement of rice grain quality under heat stress.

    Materials and methods

    Plant materials

    Two indica rice varieties with different starch quality, 9311 and Guangluai4 (GLA4), were used as materials. 9311 is a heat-sensitive variety, which displays low amylose content with good starch quality; while GLA4 is known to be the parental variety of HT54, an indica breeding line with heat tolerance, and thus GLA4 is possibly heat tolerant, which shows high amylose content with poor starch quality[18,19]. Rice growth conditions, sample treatment and collection were conducted as previously described[11].

    Protein extraction

    Husk, pericarp and embryo were detached from immature rice grains on ice[20]. Rice endosperm was then ground with liquid nitrogen, and the cell powder was sonicated 10 rounds of 10 s sonication and 15 s off-sonication on ice in lysis buffer (6 M Guanidine hydrochloride, pH 7.8−8.0, 0.1% Protease Inhibitor Cocktail) using a high intensity ultrasonic processor. Following lysis, the suspension was centrifuged at 14,000 g for 40 min at 4 °C to remove the debris. The supernatant was collected, and the protein concentration was estimated using BCA assay (Pierce BCA Protein assay kit, Thermo Fisher Scientific, Waltham, MA, USA) before further analysis.

    Trypsin digestion

    The protein mixture was reduced by DTT with the final concentration of 10 mM at 37 °C for 1 h, alkylated by iodoacetamide with a final concentration of 50 mM at room temperature in the dark for 0.5 h, and digested by trypsin (1:50) at 37 °C for 16 h. Then the sample was diluted by adding trifluoroacetic acid (TFA) to the final concentration of 0.1%. The enzymatic peptides were desalted on a Sep-Pak C18 cartridge (Waters, Milford, MA, USA), concentrated by lyophilization and reconstituted in precooled IAP buffer (50 mM MOPS-NaOH PH 7.2, 10 mM Na2HPO4, 50 mM NaCl) for further analysis.

    Affinity enrichment of ubiquitinated peptides

    The peptides solution was incubated with prewashed K-ε-GG antibody beads (PTMScan Ubiquitin Remnant Motif (K-ε-GG) Kit), and gently shaken at 4 °C for 1.5 h. The suspension was centrifuged at 2,000 g for 30 s, and the supernatant was removed. The Anti-K-ε-GG antibody beads were washed with IAP Buffer three times and with ddH2O three times. The peptides were eluted from the beads with 0.15% trifluoroacetic acid (TFA). Finally, the eluted fractions were combined and desalted with C18 Stage Tips.

    LC-MS/MS and data analysis

    LC-MS/MS analysis were performed using the methods of Pang et al.[11]. Raw mass spectrometric data were analyzed with MaxQuant software (version 1.3.0.5) and were compared with the indica rice protein sequence database (Oryza sativa subsp. indica-ASM465v1). Parameters were set according to Pang et al.[11]. All measurements were obtained from three separate biological replicates.

    Bioinformatics methods

    Quantification of the modified peptides was performed using the label-free quantification (LFQ) algorithm[11]. Differentially ubiquitinated sites (proteins) in response to high-temperature were identified by Student's t-test (p < 0.05, log2(fold-change) > 1) with at least two valid values in any condition or the ubiquitination sites that exhibited valid values in one condition (at least two of three replicates) and none in the other.

    Subcellular localization was performed using CELLO database (http://cello.life.nctu.edu.tw). Gene Ontology (GO) annotation proteome was derived from the AgriGO (http://bioinfo.cau.edu.cn/agriGO/). The differential metabolic profiles were visualized with MapMan software (version 3.6.0RC1). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation was performed by using KEGG Automatic Annotation Server (KAAS) software. A p-value of < 0.05 was used as the threshold of significant enrichment. SWISS-MODEL was used to generate the tertiary structure of GBSSI (SWISS-MODEL, http://swissmodel.expasy.org/). The figures were annotated with Adobe Illustrator (Adobe Systems, San Jose, CA, USA).

    Results

    Overview of the ubiquitomic analysis

    To elucidate how high-temperature stress influences rice developing endosperm at the ubiquitination level, a label-free analysis was performed to quantify ubiquitome from two indica rice varieties under normal (9311-C and GLA4-C) and high-temperature conditions (9311-H and GLA4-H). The distribution of mass error of all the identified peptides was near zero and most of them (71.5%) were between -1 and 1 ppm, suggesting that the mass accuracy of the MS data fits the requirement (Fig. 1a). Meanwhile, the length of most peptides distributed between 8 and 42, ensuring that sample preparation reached standard conditions (Fig. 1b).

    Figure 1.  Characteristics of the ubiquitinated proteome of rice endosperm and QC validation of MS data. (a) Mass error distribution of all identified ubiquitinated peptides. (b) Peptide length distribution. (c) Frequency distribution of ubiquitinated proteins according to the number of ubiquitination sites identified.
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    In all endosperm samples, a total of 437 ubiquitinated peptides were identified from 246 ubiquitinated proteins, covering 488 quantifiable ubiquitinated sites (Supplemental Table S1). Among the ubiquitinated proteins, 60.6% had only one ubiquitinated lysine site, and 18.7%, 8.1%, 5.3%, or 7.3% had two, three, four, or five and more ubiquitinated sites, respectively. In addition, four proteins (1.6%, BGIOSGA004052, BGIOSGA006533, BGIOSGA006780, BGIOSGA022241) were ubiquitinated at 10 or more lysine sites (Fig. 1c, Supplemental Table S1). The proteins BGIOSGA008317 had the most ubiquitination sites with the number of 16. It was noted that besides ubiquitin, two related ubiquitin-like proteins NEDD8 and ISG15 also contain C-terminal di-Gly motifs generated by trypsin cleavage, and the modifications of these three proteins cannot be distinguished by MS[21]. Here, the di-Gly-modified proteome therefore represents a composite of proteins modified by these three proteins. However, the sites from NEDD8 or ISG15 modifications were limited because they mediate only a few reactions in cells[21].

    Properties of ubiquitinated proteins and motifs

    To better understand the lysine ubiquitome changes in rice endosperm induced by high-temperature, we performed a Gene Ontology (GO) functional annotation analysis on all identified ubiquitinated proteins (Fig. 2a). In the biological process GO category, 'metabolic process' and 'cellular process' were mainly enriched, accounting for 75.1% and 74.1% of ubiquitinated proteins, respectively. In addition, 34.6% proteins were associated with 'response to stimulu', emphasizing the regulatory role of ubiquitination modification in response to high-temperature stress. From the cellular component perspective, ubiquitinated proteins were mainly associated with 'cellular anatomical entity' (99.4%), 'intracellular' (84.4%) and 'protein-containing complex' (29.9%). The molecular function category result suggested that these proteins were largely involved in 'binding' (62.7%), 'catalytic activity' (43.4%) and 'structural molecule activity' (16.5%). Furthermore, subcellular location annotation information indicated that 34.7%−39.4% proteins were located in the cytoplasm, and other were mostly located in the nucleus (23.5%−27.7%), plasma membrane (9.4%−11.4%), and chloroplast (9.6%−12.8%) (Fig. 2b). It is noteworthy that the ubiquitinated proteins located in the cytoplasm were decreased in high-temperature environments in both varieties.

    Figure 2.  Analysis of ubiquitinated proteins and motifs. (a) Gene ontology (GO) functional characterization of ubiquitinated proteins. (b) Subcellular localization of ubiquitinated proteins. From the inside out, the ring represents 9311-C, 9311-H, GLA4-C and GLA4-H, respectively. (c) Motif enrichment analysis of ubiquitinated proteins.
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    The following two significantly enriched motifs from all of the identified ubiquitinated sites were identified using MoDL analysis: [A/S]xKub and Kubxx[E/Q/R/V]x[E/G/L/P/Q/R/Y], which covered 84 and 100 sequences, respectively (Fig. 2c). Further analysis showed that the conserved alanine (A) and glutamic acid (E) were included in upstream and downstream of the ubiquitinated lysine sites in rice endosperm. A similar phenomenon also occurred in rice leaf[14], wheat leaf[22], and petunia[23], indicating that alanine (A) and glutamic acid (E) were likely to be the specific amino acids in conserved ubiquitination motifs in plants. Additionally, serine (S) was enriched at the position -2 (upstream) of the ubiquitinated lysine, while various amino acids such as arginine (R), glutamic acid (E), glutamine (Q), valine (V) were found at positions +3 and +5 (downstream).

    A temperature-regulated rice endosperm ubiquitome

    To detect possible changes in rice endosperm ubiquitome attributable to high-temperature stress, we then performed LFQ analysis on all quantifiable ubiquitination sites within our dataset. As shown in Fig. 3a, more ubiquitinated proteins, peptides and sites were detected in the treatment groups (9311-H and GLA4-H), suggesting that exposure to high-temperature stress may increase the ubiquitination events in rice endosperm. Only 282 common ubiquitinated sites in 158 proteins were quantifiable for all sample groups due to reversible ubiquitination induced by high-temperature (Fig. 3b). Principal component analysis (PCA) showed that three repeats of each sample clustered together, and four groups were clearly separated (Fig. 3c). Furthermore, the differentially expression profiles of ubiquitination sites (proteins) in 9311 and GLA4 under high-temperature stress were depicted to further understand the possible changes (Fig. 3d). Where LFQ values were missing, the data were filtered to identify those ubiquitination sites with a consistent presence/absence expression pattern. These analyses yielded 89 ubiquitination sites that were only present in 9311-H and six that were only present in 9311-C (Fig. 3d, Supplemental Table S2). Similarly, 51 differentially expressed ubiquitination sites were present in GLA4-H and 13 ubiquitination sites only occurred in GLA4-C (Fig. 3d & Supplemental Table S3). Beyond that, a total of 113 and 50 significantly changed ubiquitination sites (p < 0.05, log2(fold-change) >1) were screened out in 9311 and GLA4, respectively (Fig. 3d, Supplemental Tables S4 & S5). For subsequent comparative analysis, the ubiquitination expression profiles with consistent presence/absence and ubiquitination sites with significant differences in statistical testing were combined and named as 9311-Up, 9311-Down, GLA4-Up, and GLA4-Down, respectively (Fig. 3d). The number of significantly up-regulated ubiquitination sites was far greater than down-regulated ubiquitination sites in both 9311 and GLA4 varieties. These findings indicated that high temperature not only induced the occurrence of ubiquitination sites, but also significantly upregulated the intensity of ubiquitination. Beyond that, the magnitude of the up-regulation in 9311 was higher than that in GLA4 (Fig. 3b & d), indicating that the ubiquitination modification of heat-sensitive varieties was more active than heat-resistant varieties in response to high-temperature stress.

    Figure 3.  A temperature regulated rice endosperm ubiquitome. (a) The number of ubiquitinated proteins, peptides and sites detected in four group samples. (b) Venn diagram of ubiquitination sites (proteins) detected in four group samples. (c) PCA based on ubiquitination intensity across all four sample groups with three biological repetitions. (d) Differentially expression profiles of ubiquitination sites (proteins) in 9311 and GLA4 under high-temperature stress. The expression profiles of selected ubiquitination sites (p < 0.05, log2(fold-change) >1) were normalized using the Z-score and presented in a heatmap.
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    To further investigate the ubiquitination regulatory pattern under high temperature stress in two varieties, four groups with significantly regulated sites were analyzed. There were 37 ubiquitination sites showed the same regulatory trend in 9311 and GLA4, accounting for 17.8% and 32.5% of the total differentially expressed sites in 9311 and GLA4, respectively. Among them, 36 ubiquitination sites were upregulated and one site was downregulated (Fig. 4a). In addition, 159 upregulated ubiquitination sites and three downregulated sites were only present in 9311, while 53 upregulated sites and 15 downregulated sites were only present in GLA4. Moreover, nine ubiquitination sites showed opposite regulatory trends in 9311 and GLA4. A similar regulatory trend of ubiquitination proteins is shown in Fig. 4b. It is noted that some proteins had both upregulated and downregulated ubiquitination sites (Supplemental Tables S6 & S7), indicating that significant differences in ubiquitination were, to some extent, independent of protein abundance.

    Figure 4.  Comparison of differentially ubiquitinated sites and proteins in 9311 and GLA4 under high-temperature stress.
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    Functions for differentially expressed ubiquitinated proteins

    To understand the function of ubiquitination in response to the high-temperature stress of rice endosperm, we conducted GO enrichment-based clustering analysis of the differentially ubiquitinated proteins in 9311 and GLA4 at high temperature, respectively (Fig. 5). In the biological process category of 9311, proteins were relatively enriched in the carbohydrate metabolic process, polysaccharide metabolic process, starch biosynthetic process, cellular macromolecule localization, protein localization, intracellular transport, and phosphorylation (Fig. 5). For the molecular function analysis, we found that the proteins related to kinase activity, nucleotidyltransferase activity, phosphotransferase activity, and nutrient reservoir activity were enriched (Fig. 5). The two principal cellular components were intrinsic component of membrane and integral component of membrane (Fig. 5). There was no significantly enriched GO term in the GLA4 group due to the dataset containing relatively few proteins, and thus, further enrichment analysis was conducted on the proteins that were common to both varieties. The results showed that proteins were over-represented in carbon metabolism, including starch biosynthesis and metabolism, glucan biosynthesis and metabolism, and polysaccharide biosynthesis and metabolism (Fig. 5), indicating the importance of carbohydrate synthesis and metabolism in the ubiquitination regulatory network.

    Figure 5.  Enrichment analysis of differentially expressed ubiquitinated proteins based on Gene Ontology (GO) terms.
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    To identify pathways which were differentially ubiquitinated under high-temperature stress, the KEGG pathway-based clustering analysis was conducted. The results showed that the differentially ubiquitinated proteins in both 9311 and GLA4 were mostly abundant in the pathways of carbohydrate metabolism, starch and sucrose metabolism, folding, sorting and degradation, translation, ribosome, and protein processing in endoplasmic reticulum (Fig. 6a). In the 9311 group, the pathways of carbohydrate metabolism, starch and sucrose metabolism, glycosyltransferases, glycolysis, and energy metabolism were enriched in the differentially ubiquitinated proteins (Fig. 6b); while there was no significantly enriched KEGG pathway in the GLA4 group. We further found the proteins that were common to both varieties were only significantly enriched in the starch and sucrose metabolism pathways (p = 0.04). The ubiquitination proteins involved in the starch and sucrose metabolism mainly include: sucrose hydrolysis (SUS, FK, UGPase), and starch synthesis (AGPase, GBSSI, BEI, BEIIb, PUL, PHO1), which are discussed below.

    Figure 6.  KEGG classification and enrichment analysis of differentially ubiquitinated proteins. (a) Number of differentially ubiquitinated proteins based on KEGG classification in 9311 and GLA4. (b) KEGG enrichment analysis of differentially ubiquitinated proteins in 9311.
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    Discussion

    Although many reports have described specific examples of ubiquitination in rice defense responses[13,15,16], our knowledge on global changes in the developing endosperm ubiquitome under high-temperature stress is still lacking. In this study, a label-free quantitative proteomic analysis of ubiquitination was applied to examine the high-temperature induced ubiquitination change of two indica rice varieties (9311 and GLA4) with distinct starch quality. We identified many new lysine modification sites on proteins involved in various pathways, highlighting the complexity of the ubiquitination-mediated regulatory system in high-temperature stress responses in rice.

    Ubiquitination of heat shock proteins and transcription factors under high-temperature stress

    Heat shock proteins accumulate under various stresses and play important roles in plant defenses against abiotic stresses[24,25]. Research has shown that a number of heat shock proteins were prominent in the rice ubiquitome network, of which OsHSP71.1, and OsHSP82A showed increased ubiquitination levels under chitin and flg22 treatment[15]. Here, seven lysine residues on five heat shock proteins possessed ubiquitination modification in rice endosperm. Three sites (BGIOSGA011420-K78, BGIOSGA026764-K99, BGIOSGA029594-K106) showed significant up-regulation in 9311 under high-temperature stress, while in GLA4, the ubiquitination level of BGIOSGA011420-K78 was down-regulated. This differential ubiquitination of heat-tolerant and heat-sensitive varieties provided a basis for studying the regulation of post-translational modification of heat shock proteins under high-temperature stress, despite the regulatory role of those heat shock proteins being still unclear.

    Transcription factors (TFs) play an essential role in the regulation of gene expression. A total of three transcription factors were identified in the ubiquitination dataset, of which two were NAC family members. As one of the largest plant-specific TF families, NAC is involved in the responses to abiotic and biotic stresses[26]. The ubiquitination modification of K173 in BGIOSGA018048 was specifically expressed in the 9311-H group, which may affect the stress resistance level in high-temperature environments. In addition, two sites K148 and K149 of ERF TF family member BGIOSGA024035 was downregulated in GLA4 under high-temperature stress. This differential ubiquitination was likely to affect the expression of related genes regulated by this transcription factor.

    Sucrose and starch metabolism are modified by ubiquitination under high-temperature stress

    Ubiquitination affects sucrose hydrolysis

    The results of GO and KEGG enrichment analysis of differential ubiquitination proteins indicated that the sucrose and starch metabolic pathway was largely affected by ubiquitination regulation under high-temperature stress (Figs 5 & 6). The ubiquitination sites involved in sucrose and starch metabolism are listed in Table 1. To assess how high-temperature stress affects the crucial pathway, the significantly differential ubiquitination sites in 9311 and GLA4 were displayed in the heatmap of specific proteins (Fig. 7).

    Table 1.  Ubiquitination sites related to sucrose and starch metabolism in rice endosperm.
    Gene nameAnnotationProtein entryModification site(s)
    SUS1Sucrose synthase 1BGIOSGA010570K172, K177
    SUS2Sucrose synthase 2BGIOSGA021739K160, K165, K176, K804
    SUS3Sucrose synthase 3BGIOSGA026140K172, K177, K541, K544, K588
    FKFructokinaseBGIOSGA027875K143
    UGPaseUDP-glucose pyrophosphorylaseBGIOSGA031231K27, K150, K303, K306
    AGPS1ADP-glucose pyrophosphorylase small subunit 1BGIOSGA030039K94, K464, K484
    AGPS2ADP-glucose pyrophosphorylase small subunit 2BGIOSGA027135K106, K132, K385, K403, K406, K476, K496
    AGPL2ADP-glucose pyrophosphorylase large subunit 2BGIOSGA004052K41, K78, K134, K191, K227, K254, K316, K338, K394, K396, K463, K508, K513
    AGPL3ADP-glucose pyrophosphorylase large subunit 3BGIOSGA017490K509
    GBSSIGranule bound starch synthase IBGIOSGA022241K130, K173, K177, K181, K192, K258, K371, K381, K385, K399, K462, K517, K530, K549, K571, K575
    BEIStarch branching enzyme IBGIOSGA020506K103, K108, K122
    BEIIbStarch branching enzyme IIbBGIOSGA006344K134
    PULStarch debranching enzyme:PullulanaseBGIOSGA015875K230, K330, K431, K736, K884
    PHO1Plastidial phosphorylaseBGIOSGA009780K277, K445, K941
     | Show Table
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    Figure 7.  Sucrose and starch pathway at the ubiquitination levels in rice endosperm under high-temperature stress.
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    In cereal endosperm, sucrose is the substrate for the biosynthesis of starch. The formation of glucose 1-phosphate (G1P, used in starch synthesis, see below) from sucrose requires a series of enzymes[27]. Here, we found that sucrose synthase 1 (SUS1), SUS2, SUS3, fructokinase (FK), and UDP-glucose pyrophosphorylase (UGPase) were ubiquitinated among all sample groups (Table 1, Fig. 7). In the ubiquitome of seedling and leaf in japonica rice, ubiquitination sites have been found in SUS1, SUS2, UGPase, and FK, which were related to sucrose hydrolysis[14,15]. SUS catalyzed the process of cleaving sucrose into UDP-glucose (UDPG) and fructose. Two ubiquitination sites, K172 and K177, were identified in SUS1 in rice endosperm, which were also found in rice leaves[14]. A total of four ubiquitination sites were identified in SUS2, two of which were also reported in rice seedling and leaf, indicating the conservation of the lysine residues in different rice tissues. It was noted that all four ubiquitination sites in SUS2 were upregulated in high-temperature environments, although the regulated sites of 9311 and GLA4 were different. In 9311, the ubiquitination levels of K160, K174, and K804 were increased, while GLA4 was only upregulated in K176. The ubiquitination sites K541, K544, and K588 in SUS3 were screened from developing rice seeds for the first time. In addition, SUS3 had two completely overlapping sites K172 and K177 with SUS1, and it was difficult to determine which enzymes the two sites belonged to. The ubiquitination levels of SUS3-K541 and SUS3-K544 in 9311 significantly increased in high-temperature environments, while there was no significant difference in the ubiquitination level of SUS3 in GLA4. Overall, the ubiquitination sites of SUS in rice endosperm were located in the functional domain except for SUS2-K804, reflecting the importance of ubiquitination regulation in SUS.

    UGPase catalyses the conversion of glucose 1-phosphate and UTP into UDPG[28]. Research has shown that the mutation of UGPase gene lead to chalky endosperm[29]. As shown in Table 1, four ubiquitination sites K27, K150, K303, and K306 were identified in rice endosperm, which were completely inconsistent with the seven ubiquitination sites in rice seedlings and two in leaves[14,15], reflecting the tissue specificity. We speculated that the UGPase with different modification sites may play different regulatory roles in metabolic pathways in different tissues. Under high-temperature stress, the ubiquitination level of UGPase-K27 was 8.1-fold up-regulated. Liao et al.[10] demonstrated that the expression of UDPase was down-regulated in both heat-tolerant and heat-sensitive rice lines under high temperature conditions, which could reasonably explain the significant up-regulation of UGPase-K27 ubiquitination level. The ubiquitination site K143 of FK was also reported in seedling tissues[15].

    Ubiquitination of ADP-glucose pyrophosphorylase (AGPase)

    The AGPase reaction represents the first committed step of starch biosynthesis[27]. A total of 22 lysine ubiquitination sites were identified in four AGPase subunits (AGPL2, AGPL3, AGPS1, AGPS2). AGPL2 had 13 ubiquitination sites, of which six were located in NTP_transferase domain, including K254, K338, K191, K134, K227, and K316. High-temperature stress resulted in an increase in the ubiquitination level of K254 in both 9311 and GLA4, and significant upregulation of K508 and K513 in 9311, as well as K191, K227, and K316 in GLA4. In contrast, AGPL2-K394 were significantly downregulated in GLA4. AGPL3 contained one ubiquitination site K509, and the modification level of AGPL3-K509 was up-regulated in high-temperature environments in 9311. AGPS1 had one specific ubiquitination site K464 and another two sites K94 and K484 that completely overlapped with AGPS2-K106 and AGPS2-K496, respectively. The modification levels of K464 and K484 significantly increased in high-temperature environments in 9311, and K94 was significantly up-regulated in both varieties. There were seven ubiquitination sites in AGPS2 in rice endosperm, which were different with the sites found in rice leaves[14]. In addition to the two sites that overlapped with AGPS1, AGPS2 had another two ubiquitination sites (K406 and K132) that upregulated in high-temperature environments.

    Ubiquitination regulates amylose biosynthesis

    Amylose content is one of the key determinants that strongly influence rice grain quality[30]. The biosynthesis of amylose requires the catalytic effect of granule-bound starch synthase I (GBSSI)[30,31]. Here, a total of 16 ubiquitination sites were identified in GBSSI (Table 1, Fig. 8a). Among these ubiquitination sites, six lysine residues (K130, K173, K177, K181, K192, K258) were located in glycosyltransferase 5 (GT5) domain, and three sites (K399, K462, K517) were located in GT1 domain (Fig. 6), indicating the important role of ubiquitination regulation of GBSSI. Under high-temperature stress, the ubiquitination levels of six sites (K130, K177, K399, K381, K385, K549) increased in two indica rice varieties, while one sites (K258) showed downregulation in 9311 (Fig. 8a). Numerous studies had described that the amylose content was reduced under high-temperature stress in rice[5,7], which might be due to the degradation of GBSSI proteins caused by the increased significantly up-regulated ubiquitination sites. These ubiquitination sites identified in rice GBSSI with significant differences under high-temperature stress were expected to become a new breakthrough point for the improvement of starch quality.

    Figure 8.  Structure of GBSSI. (a) Domain structure of GBSSI and ubiquitination sites with significant differences in response to high-temperature stress. (b) 3D model of GBSSI and the relationship between ubiquitination sites K462 and ADP, SO4 (salt bridge or hydrogen bond).
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    To further determine the regulatory role of the ubiquitination sites in GBSSI, SWISS-MODEL was used to predict 3D structural model. As shown in Fig. 8b, GBSSI had three SO4 (sulfate ions) and one ADP ligand. These ligands interact with GBSSI through hydrogen bonds and salt bridges. Three sites, K447, R458, and K462, were associated with SO4 through salt bridges, while G100, N265, Q412, K462, and Q493 interact with the hydrogen bonds of ADP in GBSSI[32,33]. Based on this finding, it can be reasonably inferred that the K462 site with ubiquitination modification located in the GT1 domain played an important role in the interaction between GBSSI, SO4, and ADP. An in-depth investigation was necessary to gain a more comprehensive understanding of the regulatory function of ubiquitination modification at GBSSI-K462, although there was no significant difference in the ubiquitination level under high-temperature stress.

    Ubiquitination regulates amylopectin biosynthesis

    Amylopectin, the major component of starch, is synthesized by the coordinated action of multiple enzymes including soluble starch synthase (SSs), starch branching enzyme (BEs), starch debranching enzyme (DBEs), and phosphorylases (PHOs or Phos) with ADPG as a substrate. In this study, ubiquitination sites were detected in BEs, DBEs, and Phos.

    BEs, covering two isoforms, BEI and BEII, are responsible for catalyzing the formation of α-1,6-glucosidic linkages of amylopectin[34]. There were three ubiquitination sites (K103, K108, and K122) identified in BEI (Fig. 9a). K122 was the first amino acid in the carbohydrate-binding module 48 (CBM48) domain. Sequence alignment analyses of BEs from eight plants revealed K122 was conserved among all plants' BEI (Fig. 9a), suggesting a high probability of the functional effects of ubiquitination modification at this site. In high-temperature environments, ubiquitination levels of K108 and K122 were significantly up-regulated in 9311, while no significantly regulated ubiquitination sites of BEI were observed in GLA4. Only one ubiquitination site, K134, was found in BEIIb (Fig. 9a). The ubiquitination levels showed a slightly upward trend with no significant differences in high-temperature environments in both varieties. These changes could be one of the reasons for increased gelatinization temperature and relative crystallinity of rice starch in response to high-temperature[5].

    Figure 9.  Domain structure of (a) BEs, (b) PUL and (c) Pho1 as well as their ubiquitination sites with significant differences in response to heat stress. Residues in red indicate the ubiquitination site. Non-ubiquitinated residues are shown in dark grey.
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    DBEs consists of isoamylase (ISA) and pullulanase (PUL) with catalytic function for hydrolyzing α-1,6-glucosic linkages[35]. In the present study, we found that only PUL was ubiquitinated in rice endosperm (Fig. 9b). Among five ubiquitination sites (K230, K330, K432, K736, and K884) identified in PUL, K230 was located in the PULN2 domain, while K330 was in the CBM48 domain. Under high-temperature stress, K330 showed completely opposite regulatory trends in two cultivars. In addition, the ubiquitination level of K884, located in the DUF3372 domain, was significantly up-regulated in 9311. Previous study has reported that the expression of PUL was significantly up-regulated in 9311 under high-temperature stress, while GLA4 showed down-regulation in PUL abundance[11]. Consequently, there might be two possible functions of these ubiquitination sites. One possibility is that ubiquitination sites were unrelated to protein degradation; instead, they regulated the biosynthesis of amylopectin by affecting other functions of the protein. Secondly, ubiquitination sites were associated with protein degradation, and the levels of ubiquitination modification were based on protein abundance, resulting in a completely consistent regulation of ubiquitination modification and protein abundance under high-temperature stress.

    PHOs, including two types, Pho1/PHO1 and Pho2/PHO2, are responsible for the transfer of glucosyl units from Glc-1-P to the non-reducing end of a-1,4-linked glucan chains[36]. Pho1 is a temperature-dependent enzyme and considered crucial not only during the maturation of amylopectin but also in the initiation process of starch synthesis[37,38]. The three ubiquitination sites (K277, K445, K941) identified in Pho1 were located in two phosphorylase domains. We found that two sites, Pho1-K277 and Pho1-K445, were only ubiquitinated in high-temperature environments in 9311 and GLA4, respectively. Pang et al.[11] has demonstrated that the protein abundance of Pho1 decreased under high-temperature stress, especially in GLA4. Satoh et al.[38] reported that the functional activity of Pho1 was weakened under conditions of high temperature and its function might be complement by one or more other factors. Hence, these ubiquitination modifications that specifically occurred in high-temperature environments might be related to the degradation of Pho1 proteins.

    Ubiquitination affects ribosomal systems under high-temperature stress

    As a factory for protein synthesis in cells, the ribosome is an extremely crucial structure in the cell[39]. It has been proven that multiple ribosomal subunits were abundantly ubiquitinated in Arabidopsis and wheat[22]. In the present study, 57 ubiquitination sites involving 33 ribosome subunits were identified in 40S and 60S ribosome complexes in rice. Under high-temperature stress, the ubiquitination levels of some sites were significantly upregulated or downregulated, implying that ubiquitination of ribosomal proteins is likely to be an important regulatory mechanism in high-temperature response in rice endosperm. The results of GO and KEGG enrichment analysis indicated that the ribosome system was one of the most active systems for ubiquitination regulation under high-temperature stress. We speculated that the ubiquitin-proteasome system might be involved in the removal of subunits or entire ribosomes that were improperly folded in high-temperature environments. As shown in Fig. 10, the S10e, L18Ae, S27Ae, L9e, S3e, S28e, S20e, and S2e subunits were significantly up-regulated in 9311, while L13e subunits showed a completely opposite regulatory trend at the ubiquitination sites K81 and K88. In GLA4, the ubiquitination levels of S10e, S27Ae, L10Ae, L9e, S3e, S2e, and L4e showed a significant increase, while the ubiquitination level of L17e was significantly down-regulated under high-temperature stress. A total of seven ubiquitination sites involving S10e, S27Ae, L9e, S3e, and S2e subunits were jointly up-regulated in both two varieties. These sites might be related to the degradation of improperly folded ribosome subunits under high-temperature stress, while other ubiquitination sites with variety specificity might be associated with ribosomal function.

    Figure 10.  Ribosome system at the ubiquitination levels in rice endosperm under high-temperature stress. Grey shadings represent ubiquitinated proteins with no significant differences under heat stress. Red and blue shadings indicate up-regulated and down-regulated ubiquitinated proteins, respectively. Orange shading displays a combination of up- and down-regulated ubiquitinated sites in the same ubiquitinated protein.
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    In conclusion, this study provides the first comprehensive view of the ubiquitome in rice developing endosperm, and demonstrated that ubiquitination has diverse functions in the high-temperature response of rice endosperm by modulating various cellular processes, especially the sucrose and starch metabolism. Comparative analysis of the temperature-induced ubiquitination status revealed some similarities and more interesting differences between 9311 and GLA4. These differences might be the reason for the different qualities formation of the two indica rice varieties, which could provide potential genetic resources for the improvement of the heat resistance in rice. Considering the diversity of ubiquitination modification, it is worthwhile to further validate and explore the function and regulatory mechanism of the key targets and key pathways. The findings provide valuable insights into the role of ubiquitination in response to high-temperature stress and lay a foundation for further functional analysis of lysine ubiquitination in rice.

    Author contributions

    The authors confirm contribution to the paper as follows: study conception and design: Bao J, Pang Y; data collection: Pang Y; analysis and interpretation of results: Pang Y; draft manuscript preparation: Ying Y; Revised manuscript preparation: Ying Y, Pang Y, Bao J. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

    Acknowledgments

    This work was financially supported by the AgroST Project (NK2022050102) and Zhejiang Provincial Natural Science Foundation (LZ21C130003).

  • The authors declare that they have no conflict of interest.

  • Supplemental Fig. S1 Healthy ginseng(a), initial infection ginseng (b), aboveground part (c) and underground part(d) of ginseng infected with F. solani in pot experiment.
    Supplemental Fig. S2 Disease symptoms of Root rot(1a, 2a), Rust rot(1b, 2b), Gray mold(1c, 2c) and Black spot(1d, 2d) on root, leaf and seedling in ginseng field.

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  • Cite this article

    Yang L, Zhou S, Nie D, Liu C, Yu L, et al. 2023. Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation. Soil Science and Environment 2:7 doi: 10.48130/SSE-2023-0007
    Yang L, Zhou S, Nie D, Liu C, Yu L, et al. 2023. Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation. Soil Science and Environment 2:7 doi: 10.48130/SSE-2023-0007
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Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation

  • 1.

    Jilin Agricultural University, Co-construction State Key Laboratory Base of Ecological Restoration and Ecosystem Management, Changchun, Jilin 130118, China

  • 2.

    Changchun Customs Technology Center, Changchun, Jilin 130118, China

  • 3.

    Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210014, China

  • 4.

    Economic Botany Research Institute, Jilin Academy of Agricultural Sciences, Changchun, Jilin 130118, China

  • Received: 24 May 2023
  • Accepted: 19 August 2023
  • Published online: 21 September 2023
Soil Science and Environment  2,  Article number: 7  (2023)  |  Cite this article

Abstract: The fungal species of Fusarium solani (F. solani) is closely associated with the soil borne root rot disease of Panax ginseng, leading to a decline in the quality of the medicinal materials. In this study, we established experimental conditions of two PCR-based microbiological assays, a droplet digital PCR (ddPCR) method and a RT-qPCR method, and tested their efficacy of quick and quantitative detection of F. solani DNA in ginseng soil. These methods were further evaluated in quantifying F. solani for rhizosphere samples of ginseng plants with different status of pathogenic disease infection. The results showed that the two methods were both highly specific and reproducible in detecting and quantifying the gene of F. solani, without any observable cross-reaction with other species. Compared to the RT-qPCR method in this study, ddPCR method exerted a higher sensitivity of detection (0.92 copies μL−1) than RT-qPCR method (920 copies μL−1), and free of reliance on a standard calibration curve. With the ddPCR method, the absolute quantification of nucleic acid samples is made possible by changing the detection standard from fluorescence intensity to the presence or absence of fluorescence, and the accuracy of detection has been significantly improved. The abundance of F. solani were 0−2,100 copies g−1 dws, 927.5−8,470 copies g−1 dws and 10,605−43,697 copies g−1 dws respectively in uncultured ginseng soil, soil from healthy ginseng and the soil from infected ginseng. The quantitative abundance of the pathogenic fungal species infected by the diseases but also track the dynamic change in the gene abundance of F. solani in disease monitoring with ginseng growth. While the gene abundance of F. solani was highly accumulated in mid-August and decreased in September, the pathogenic fungal gene abundance tested was closely correlated to the incidence rate of ginseng root rot. Apparently, this pathogenic fungus is exists widely in ginseng soil even with the healthy plants in September, indicating a potentially great risk of high disease incidence for ginseng cultivation. Therefore, the developed ddPCR assay could be reliable and feasible, potentially applied in early diagnosis of the infection by the pathogenic fungal of F. solani in ginseng cultivation and in routine assessment of the disease occurrence with ginseng growth.

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    Introduction

    • Panax ginseng is a perennial herb, which is widely used in medicine in Asia and Europe. It has always been one of the most valuable traditional medicinal herbs in China though mostly cultivated for medicinal demand in the commercial market for the past two decades(Jin et al., 2022). However, the cultivation of ginseng in farmlands is often challenged by soil borne fungal diseases, including ginseng root rot disease, ginseng rusty root rot and ginseng black spot disease, seriously threating root yield and quality (Eo et al., 2013; Liu et al., 2022). Causing a root yield loss of up to 80%, the ginseng root rot disease appears with high incidence, is contagious and hard to detect (Lee, 2004; Kim, 2012; Ryu et al., 2014; Deng et al., 2023). This disease is caused by infection of fungal pathogens such as Fusarium solani, Cylindrocarpon destructans, Phytophthora cactorum and Pythium ultimum (Lee, 2004; Eo et al., 2013; Wang et al., 2016), frequently identified in infected ginseng root or the rhizosphere, particularly in replanted fields (Lee, 2004). While much attention has been given to the origin and infection in cultivated ginseng production, developing quantitative detection and assessment of the pathogenic microbes is becoming urgent for safeguarding ginseng production.

      So far, a number of laboratory protocols have been reported such as the plate count method (Deng et al., 2023), machine vision (Harakannanavar et al., 2022), infrared thermal imaging technology (Zhu et al., 2018), spectroscopy method (Shin et al., 2023) and molecular biological identification method (Li et al., 2020; Almario et al., 2013; Zhang et al., 2023). Among these, the plate count method is used widely for classical direct and quantitative identification of the pathogenic fungal species. However, the quantification was impacted by the experiment proficiency of the lab operators and the variability of the soil and field conditions (Li et al., 2022). Moreover, plate cultivation with artificial medium often fails to recognize specific soil microorganisms potentially associated with the disease (Chen, 2013). To overcome such shortcomings, polymerase chain reaction (PCR) could detect nucleic acids of target genes without cultivation of pathogens and thus allow reliable quantification of the pathogenic microbes (Renvoisé et al., 2013; Kreitmann et al., 2023). Accordingly, novel real-time quantitative polymerase-chain-reaction (RT-qPCR) has been widely used in pathogen detection and biomedical diagnostics (Pabinger et al., 2014). As a new generation of absolute quantitative PCR technique, droplet-based digital PCR (ddPCR), independent of standard curve, has been proven to be of high sensitivity, precision, and reproducibility (Hindson et al., 2013; Hou et al., 2023). Compared with RT-qPCR, ddPCR decreases interference from non-target nucleic acid fragments, and the results are less affected by amplification efficiency and PCR inhibitors (Hou et al. 2023). From the perspective of practical application, ddPCR has more application value for early diagnosis and ongoing monitoring of diseases.

      In this study, we developed a specific ddPCR method to detect and quantify the gene abundance of F. solani. By testing a number of samples, we compare the sensitivity and accuracy with that of the classic RT-qPCR method. In addition, we assess the applicability of the method, by statistical correlation of the root rot disease incidence of ginseng plants with the abundance of the pathogens quantified for the root-zone soil. The objectivity is to provide a reliable and feasible tool for the detection of F. solani in ginseng cultivation, so as to provide a quick and precise diagnosis of pathogen infection for early warning of ginseng diseases.

    Materials and methods

      Fungal strains

    • The positive strain used was Fusarium solani while the negative strains included Sclerotinia ginseng, Ilyonectria robusta, Alternaria panax, Botrytis cinerea, Fusarium oxysporum, Fusarium graminearum and Fusarium moniliforme.

      Ginseng seedlings (3-year old) and soil

    • The ginseng seedlings (3-year old) were provided by the ginseng seedling breeding base with Jilin Shenwang Plant Protection Technology Co. Ltd (Jilin, China). Used for pot experiment was ginseng-harvested topsoil (brown loam) collected from the Experiment Base of ginseng planting of Jilin Agricultural University, located in Choushui Village, Fusong County, Jilin Province, China (127°5′12.08″ N, 42°23′48.16″ W). After the harvest of ginseng, the soil was plowed twice at a depth of 30 to 40 cm, each interval was 7−10 d, and the missing ginseng roots and weeds were removed during the ploughing process. The soil was left for a winter, then taken for experiment after thawing in April of the following year. Its basic properties were as follows: soil pH of 5.57, available nitrogen of 29.05 mg·kg−1, Olsen-phosphorus of 107.53 mg·kg−1 and available potassium of 170.56 mg·kg−1 as well as organic carbon content of 3.17%.

      Primers and probe

    • The ITS sequence was used as the target genetic location for detecting only in F. solani. Primers and probe sequences were generated by Primer Express® Software (Ver 2.0, PE Applied Biosystems). The amplify fragments was consistent with the conserved sequences from GenBank when compared by Vector NTI software. Theoretical specificity of the primers was examined by Primer-BLAST on the National Center for Biotechnology Information (NCBI) website (www.ncbi.nlm.nih.gov/tools/primer-blast/), the comparison results showed that the primers in this study could only amplify F. solani, and the probe was labeled with Fluorescein (FAM). Primers and probes were synthetized by Sangon Biotech, Shanghai China (Table 1). The same primers and probe were used in both ddPCR and RT-qPCR reactions.

      Table 1.  Detailed information of primers and probes used in this study.

      Geneprimer/
      probe      		Sequence (5′-3′)Product
      size (bp)
      ITSforwardGGAACAGACGGCCCTGTAA149
      reverseTTTCGCTGCGTTCTTCATCG
      probeCCGCCAGAGGACCCCTAACTCTGTT

      Optimization of the ddPCR method

    • The ddPCR method was performed in accordance with the manufacturer's instructions (Solarbio, China)，in a 20 μL reaction volume included 10 μL of QX200TMddPCRTMEvGreen Supermix, 0.1 μL of each forward and reverse primer (10 μM), 0.1 μL of probe (10 μM), 9.2 μL of ddH2O and 0.5 μL of DNA template. The reactions were optimized for probe concentration (1,000, 750, 500 and 250 nM). The reaction mixture (20 μL) for each sample was loaded into a well of a disposable DG8™ cartridge (Bio-Rad, USA) and 70 μL of Droplet Generation Oil (Bio-Rad, USA) were placed into each of the adjacent oil wells in the cartridge (Bio-Rad, USA). Droplets were produced in each well using a QX200™ droplet generator (Bio-Rad, USA). The droplets were then transferred to a 96-well PCR plate (Bio-Rad, USA). To differentiate the amplitude between the negative and positive droplets and to reduce the background of the negative droplets, we performed a temperature gradient in the annealing step. The ddPCR amplifications were performed with an initial step of 94 °C for 5 min, followed by 50 cycles of 95 °C for 30 s, 64 °C for 60 s and 1 cycle of 98 °C for 10 min, with a final hold at 4 °C. To achieve the best results for the method, a range of annealing temperatures (57 to 64 °C) was tested.

      After reaction, the microdroplets from each well were read individually using a QX200 Droplet Reader (Bio-Rad, CA, USA). A threshold was set between the positive and negative microdroplet clusters and the copy number of each well was evaluated with QuantaSoft™ version 1.7.

      Optimization of the RT-qPCR method

    • The RT-qPCR was performed using 2× Taqman PCR MasterMix Kit (Solarbio, China) in a volume of 20 μL containing 10 μL of Fast Probe Mixture, 0.1 μL of each forward and reverse primer (10 μM), 0.1 μL of probe (10 μM) and 9.2 μL ddH2O and 0.5 μL of DNA template. The reactions were set up for the ABI7500 (Thermofisher), and amplification programmes were as follows: an initial heating 94 °C for 5 min; followed by 40 cycles of denaturation at 95 °C for 30 s, primer annealing at 64 °C and elongation at 64 °C for 1 min. To achieve the best results for the method, a range of annealing temperatures (57 to 64 °C) were tested and optimized for probe concentration (1,000, 750, 500 and 250 nM).

      Analytical assessment of the method proficiency

    • To determine the specificity for F. solani and the cross-reactivity with other microbes, DNA from F. solani, S. ginseng, I. robusta, A. panax, B. cinerea, F. oxysporum, F. graminearum and F. moniliforme were tested. Double-distilled water was used as the negative control.

      To evaluate the sensitivity, a 10-fold serial dilution of the DNA (10 to 10−5 ng·μL−1) from the positive sample was used with the assays performed using the optimized conditions as described above. The detection limit was determined as the last serial dilution that gave a positive result.

      For evaluating inter-assay reproducibility, each dilution was tested in triplicate and in three independent runs with the RT-qPCR method, and in octuplicates and in eight independent runs with the ddPCR method.

      Quantitative analysis in artificially infected samples

      Preparation the spore suspension of F. solani

    • The strain of F. solani was stored at −80 °C. After rejuvenescence of the strain, its mycelium was inoculated separately in PDA culture-medium under aseptic conditions. The plates were incubated in artificial climatic chamber at 25 ± 3 °C until the plate was covered with strain. Mycelium were collected and transferred to bottle with a sodium carboxymethylcellulose solution (6 g·L−1), kept shaking for 20 min. The solution was filtered and diluted to a concentration of 3 × 104 spores mL−1 with sterile deionized water.

      Quantitative analysis

    • To draw the correlation of the disease incidence of cultivated ginseng with the quantified abundance of F. solani in the soil, a pot experiment with the artificially infected pathogens was conducted in the shade greenhouse within the campus of the Jilin Agricultural University. Three randomly selected ginseng seedlings (3-year old) were transplanted in a pot (23 cm in diameter and 21 cm in height each, with a total of 150 pots) filled with the collected ginseng soil on May 3, 2020. Following growth for 60 d, 90 pots with similar sized ginseng plants were selected and divided into three groups (30 pots each group) subject to further treatments. For Group A with disease induction only, the spore suspension above mentioned was added in the pot soil once at a dosage of 50 ml per plant; for Group B with pesticide control on induced pathogens, a suspension of carbendazim diluted 250 times was further added at a dosage of 50 ml per plant, 3 d following the pathogen induction as for Group A. In addition, Group C was set up as the control, where neither pathogens nor pesticides added. The pot experiment was consistently managed with the standard protocol (GB/T 34789-2017) for ginseng cultivation (Jilin Ginseng and Pilose Antler Management Office et al., 2017) for weed control, irrigation and shading.

      A soil sample (3−5 g) of the ginseng root zone in a pot was collected respectively at 3 d, 5 d, 10 d, 20 d, 30 d and 40 d following the treatment on July 15, 2020. These samples were sealed in ice boxes and stored at −80 °C prior to the molecular assays. While soil sampling, the incidence of ginseng disease was observed and recorded based on the symptoms of yellowing and wilting above-ground parts, and the dark brown, hollow and rot roots. The incidence status of ginseng is generally expressed with morbidity, which is the percentage of the number of plants with the symptoms to the total number of plants in a pot.

      Quantitative analysis in field samples

    • For evaluating efficacy of the developed method of ddPCR, 40 rhizosphere samples under varying incidence conditions were collected, as per the method described by Liu (Liu et al., 2022), from the ginseng planting base of the Choushui Township, Fusong County, Jilin Province, China. The rhizosphere samples covered healthy ginseng plant and infected plant with root rot, rust rot, black spot or gray mold with a disease severity index above 3 (Fang, 1998; Jilin Ginseng and Pilose Antler Management Office et al., 2017). In addition, four independent topsoil samples for each category of disease infection were collected respectively in August and September when ginseng is actively growing. All samples stored in sampling kits were shipped to the laboratory and tested with the ddPCR protocol described above within 24 hr following sampling. Each sample was analyzed in triplicate.

      Statistical analysis

    • All data were expressed as the means plus/minus one standard deviation of a treatment plot and processed with DPS 9.50 and Origin 2018 software. Statistical analysis of variance was performed with ANOVA, LSD methord using SPSS software (Version 20.0). A difference among treatments or a correlation between analyzed parameters was defined as significant at p < 0.05.

    Results

      Test conditions and the efficacy of the F. solani detecting methods

    • Specific primers and probes were used in the RT-qPCR protocol. For the first step, different probe concentrations (250, 500, 750 and 1,000 nM) were assessed using the RT-qPCR method. The results showed that the optimal concentration was 500 nM. The optimal annealing temperature for the RT-qPCR method was identified by testing temperatures of 64, 63, 62, 61, 60, 59, 58, and 57 °C. As shown by the results, an annealing temperature of 64 °C gave the best amplification effect.

      Finally, an optimal probe concentration of 500 nM and an annealing temperature of 64 °C were the operation conditions for the RT-qPCR method established for F. solani, and the condition was applied to the ddPCR method.

      To evaluate the specificity of the ddPCR and RT-qPCR method, DNA from F. solani, S. ginseng, I. robusta, A. panax, B. cinerea, F. oxysporum, F. graminearum and F. moniliforme were tested. As shown in Fig. 1a, the DNA primers were tested for amplification with repeated cycles up to 40 with the RT-qPCR method. Whereby, only F. solani DNA samples appeared positive with the fluorescence amplification (dR) whilst the negative control and samples containing all the other pathogens tested negative response with amplification. The amplified products were sequenced by Sangon Biotech, Shanghai China, and compared with the known species sequences in NCBI database, the results showed, that this 149 bp length gene sequence appears only in F. solani and is 100% homologous to the target gene sequence. In Fig. 1b of the ddPCR method, F. solani DNA samples exhibited concentrated Cha amplitude signals across A04-C04 while those of negative strains displayed few but scattered signals across D10~H11. Clearly, both the ddPCR and qPCR methods were shown to be specific for the detection of F. solani.

      Figure 1. 

      Specific detection of primers used for the RT-qPCR and ddPCR methods. (a) Fluorescence intensity in response to amplification cycles with RT-qPCR methods. The curve with amplification was for A4 (F. solani) and that without amplification was for the negative strains (A6~D6). (b) Ch1 amplitude in response to event number using the ddPCR method. The blue signals concentrated across A04, B04, and C04 reflected the DNA of F. solani while those scattered across D10~H11 represented the DNA of the negative strain, including S. ginseng (D10, E10), I. robusta (F10, G10), A. Panax (H10, A11), B. cinerea (B11, C11), F. oxysporum (D11, E11), F. graminearum (F11, G11), and F. moniliforme (H11).

      Based on the data shown in Fig. 2a, the relative standard deviation (RSD) reached 0.31% for the tests among the three independent runs in the RT-qPCR method. Comparably, a RSD of 0.35% was obtained for the tests among the eight runs with the ddPCR method (Fig. 2b). These results indicated a high precision of the determination of both assays, being mutually comparable in quantitative determination of F. solani in rhizosphere.

      Figure 2. 

      The test for reproducibility of the methods. (a) Florescence response to repeated cycle of amplification with RT-qPCR method. Note that the curves of three independent runs are almost consistent. (b) Ch1 amplitude response to event number.

      Based on the curves in Fig. 3a, sharp fluorescence response was observed at a DNA concentration of 10−2 ng·μL−1 (the third curve on the left), relevant to a gene abundance of 920 copies μL−1. With the ddPCR method, whereas, discernible amplitude signals could appear at a concentration as low as 10−5 ng·μL−1 (relevant to a gene abundance of 0.92 copies μL−1) though very strong at concentrations above 10−3 ng·μL−1. The established F. solani-specific ddPCR method was able to detect very low concentrations of template DNA, being much more sensitive than the RT-qPCR method.

      Figure 3. 

      Sensitivity test of the methods. (a) Amplification curve with RT-qPCR method of a concentration of 10, 10−1, 10−2, 10−3, 10−4, 10−5 ng·μL−1 respectively from left to right. (b) Ch1 amplitude response to event number with the ddPCR method. The black signals represented the positive strain (A03, C03, E03, G03) while the blue signals in the regions represented the concentration respectively at 10 (C04, F04), 10−1 (A05, C05, E05, G05), 10−2 (A06, C06, F06), 10−3 (A07, C07, E07, G07), 10−4 (A08, C08, E08, G08), 10−5 ng Μl−1 (A09, C09, E09, G09).

      Quantification of F. solani and disease incidence with Ginseng in pot culture

    • With the established ddPCR protocol, the dynamic abundance of F. solani in the soil of the pot experiment was quantitatively traced (Fig. 4, Supplemental Fig. S1). For the control group (Fig. 4a), the gene abundance of F. solani gradually increased from 927.5 to 8,470 copies g−1 over the whole 60 d growth. Whereas, a bi-model abundance dynamic was found for the disease induction group, with which the first peak was 23,467.5 copies g−1 at 10 d after transplantation while the second peak was 62,765.5 copies g−1 at 40 d after transplantation respectively. With the disease induction plus pesticide treatment, the gene abundance of F. solani was similar to the control though significantly higher at 10 d after transplantation than the control. In other words, the gene abundance of the pathogenic fungus was seen decreased by 38% following the pesticide addition over that before the pesticide addition. The established ddPCR method thus allowed a clear but sharp differentiation in the gene abundance between the disease treatments in the pot experiment.

      Figure 4. 

      Quantitative dynamics of F. solani in ginseng root zone. (a) Observed with the developed ddPCR method and (b) the disease incidence rate (%) of ginseng plants in the pot experiment. Green line, control; Red line, the disease induction treatment; Gray line, disease induction plus pesticide treatment.

      The disease incidence dynamics was portrayed in Fig. 4b. The disease incidence of the ginseng plants with disease induction treatment appeared at 10 d following the transplantation, when no incidence occurring in the control treatment. At 110 d after transplantation (40 d after root disease incidence treatment), the incidence of the pathogenic disease reached 83.3%, 75.0% and 66.7% respectively for disease induction treatment, disease induction plus pesticide treatment and the control. This was correlated significantly (r = 0.634, p < 0.05) to gene abundance of the pathogenic fungus in the soil respectively for the treatments. It is worthy to note that there was high gene abundance of F. solani in the cultivated ginseng soil even under the control in this experiment, probably from external sources. Conventional practice using pesticides, for instance carbendazim in this study, had little impact in preventing disease incidence.

      Quantification of F. solani and disease incidence in ginseng fields

    • The gene abundance of F. solani in soil under healthy ginseng and unhealthy ginseng associated with four respective categories of pathogenic disease, including root rot, rust rot, black spot or gray mold, are displayed in Table 2 and Supplemental Fig. S2. In the soil with healthy ginseng plants, the fungal species of F. solani was not observed in August but detected in an abundance of 2,100 copies g−1 in September. In the soil with unhealthy plants, however, the pathogenic fungal in the rooted soil was found with gene abundances in a range of 1.7 × 103 copies g−1 to 43.7 × 103 copies g−1 in August and of 0 to 10.6 × 103 copies g−1 in September. The pathogenic fungal abundance was all higher (by 40%−100 %) in August with more active growth than in September, during which ginseng plant tended to wilt as the temperature in the mountainous area became lower. And, the gene abundance of the rooted soil was generally in an order of root rot diseased plants > gray mold diseased plant and rust rot diseased plant > black spot diseased plant, with clear root symptoms observed only on the root rot diseased plants. Apparently, this pathogenic fungus exists widely in ginseng soil even with the healthy plants in September, indicating a potential risk of high disease incidence for ginseng cultivation.

      Table 2.  Abundance of F. solani in the rooted soil with plants infected by different diseases.

      Soil under ginsengGene abundance of F. solani
      (103 copies g−1)
      AugustSeptember
      Healthy plantn.d. d2.10 ± 0.19b
      Root rot plants43.7 ± 1.91a10.6 ± 0.96a
      Rust rot plants4.48 ± 0.97c2.76 ± 0.29b
      Gray mold disease plants29.9 ± 1.33b1.85 ± 1.28b
      Black spot disease plants1.71 ± 0.43dn.d. d
      n.d., Not detected as below the detection limit of the ddPCR prorocol. Lowercase letters indicate the difference is significant (p < 0.05).
    Discussion

    • Molecular detection is the best method for diagnosis of the spread of soilborne plant diseases (Farh et al., 2019). Specific primers are arguably the most critical single components of any PCR assay for their properties control the exquisite specificity and sensitivity that make this method uniquely powerful, and achieve quantitative analysis of the target species. Interspecies discrimination can be achieved with well-designed specific primers (Shi et al., 2023). Plant rhizosphere soils have a high microbial diversity and specific primers are the basis for developing pathogen detection methods for complex soil microbiome. While ITS is one of the genes used for the discrimination of F. solani (Mesapogu et al., 2011), in this study, we developed the method with specific ITS primers along with four strains of pathogenic fungi with ginseng root disease and three strains of related species of F. solani as negative strains. The developed method using the primers allowed a reliable differentiation of F. solani from other Fusarium species and other fungal genera.

      Both ddPCR and RT-qPCR methods have been used as a specific and sensitive molecular detection method for Penicillium citrinum, Priestia megaterium, Tomato brown rugose fruit virus and Fusarium asiaticum (Chow et al., 2018; Kaminsky et al., 2022; Vargas-Hernández et al., 2022; Wang et al., 2023). In the study, we developed a ddPCR protocol for detecting F. solani with the specific primers based on the ITS gene. Our tests proved that the ddPCR protocol can detect pathogenic bacteria effectively in soil, with strong specificity, good reproducibility and high sensitivity, similar to or better than the RT-qPCR method in quantitative detection of the fungal species associated with the ginseng diseases. When compared with other molecular detection methods, ddPCR is advantageous as it is sensitive, accurate and does not require an external standard curve (Kiselinova et al., 2014). When compared with the previously reported results, the sensitivity obtained in this study was better than that of PCR (103 pg) (Omori et al., 2018), qRT-PCR (1 pg·μL−1) (Farh et al., 2019) Compared to the RT-qPCR method in this study, ddPCR method exerted a much lower limit of detection (down to 0.92 copies μL−1) , potentially applicable to early diagnosis of ginseng disease with very weak pathogeny (Nishimura et al., 2022). We anticipate that the ddPCR method could be developed into a powerful, accurate, sensitive and precise tool for assessing the surveillance of F. solani in ginseng cultivation.

      Furthermore, the ddPCR method also showed better reproducibility than the RT-qPCR method. Previous studies have shown that there is no established linear relationship between the occurrence of disease and the abundance of pathogens though there was positive correlations between the disease index and the number of pathogens (Lu et al., 2019). The incidence of Fusarium wilt is closely related to the gene abundance of Fusarium oxysporum present in the soil, when the number of F. oxysporum in the soil reached more than 103 cfu g−1, it was likely to result in an outbreak of Fusarium wilt (Liang et al., 2013). In this study, the gene abundance of F. solani was in a range of 1−8.5 thousands copies g−1 in ginseng cultured soil, indicating a wide occurrence of the pathogenic fungal species in ginseng soil, and highly accumulated in mid-August and decreased in September. This phenomenon may be related to the close relationship between the growth of microorganisms such as fungi in the soil and the biological and abiotic factors in the environment. According to the biological characteristics of F. solani (Shen et al., 2012), the optimal growth temperature is 30 °C, and its sporulation quantity and conidium germination rate are 2.70 × 107 / dish，100%. When the ambient temperature is 15 °C, the sporulation quantity and conidium germination rate drop to 0.03 × 107 / dish and 4.4%, with a decrease of 98.9% and 95.6%, respectively. When the ambient temperature was reduced to 10 °C, there was no spore production and germination. The average temperature in September is 5~18 °C of Fusong (China), so the temperature drop may be an important reason for the decrease in the number of F. solani. Second, Yaw (Akosah et al., 2021) found that Fusarium was among the dominant genera of the rhizosphere and rhizoplane during flowering, while at senescence they were replaced by the closely related genus Monographella, FUN Guild analysis confirms the role of the potato plant in assembling fungal taxa and guilds. Dalvinder (Singh et al., 2009) studied the interactions of temperature and water potential in displacement of Fusarium pseudograminearum (Fp) by fungal antagonists, they found that the ability to displace Fp under cool dry conditions appears to be critical. It can be seen that the dominance of different fungal populations in soil is related to the growth stage, and there are dynamic changes in the dominance of different fungal populations under the comprehensive action of environmental factors such as moisture and temperature. However, the population with direct succession relationship with F. solani in rhizosphere soil of ginseng remains to be further studied.

      Both with the treated soil in pot experiments and the field ginseng soil, the abundance of the disease-inducing species peaked at the time of active growth of ginseng plant, being consistent with the disease emergence observed in the field in August. This was partly explained with the correlation of root rot disease incidence with the fungal abundance, which was reported with a biochar soil improvement experiment for ginseng disease control (Liu et al., 2022). Such well-established correlation may value its potential application in diagnosis and assessment of ginseng disease spread in ginseng cultivation (Marta et al., 2015). Overall, this method could be used to quantitatively characterize the potential incidence of ginseng root disease in the monitoring and prevention of root rot in ginseng cultivation.

      Acknowledgments

      • This study was funded partly by Jilin Provincial Science Technology Department under a grant number of 20230204022YY, 20210204184YY. We thank Prof. Dr. Changtian Li and Dr. Zheliang Lv for their kind assistance in field work of sampling and disease observation. The advice and comments for the work by Dr. Cuijin Liu are also acknowledged.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Supplementary information
      • Supplemental Fig. S1 Healthy ginseng(a), initial infection ginseng (b), aboveground part (c) and underground part(d) of ginseng infected with F. solani in pot experiment.
      • Supplemental Fig. S2 Disease symptoms of Root rot(1a, 2a), Rust rot(1b, 2b), Gray mold(1c, 2c) and Black spot(1d, 2d) on root, leaf and seedling in ginseng field.
      Rights and permissions
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (4)  Table (2) References (40)
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    Cite this article
    Yang L, Zhou S, Nie D, Liu C, Yu L, et al. 2023. Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation. Soil Science and Environment 2:7 doi: 10.48130/SSE-2023-0007
    Yang L, Zhou S, Nie D, Liu C, Yu L, et al. 2023. Developing PCR-based novel molecular assays to quantitatively detect Fusarium solani in ginseng soil for assessing soil health in ginseng cultivation. Soil Science and Environment 2:7 doi: 10.48130/SSE-2023-0007
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  • Introduction
  • Materials and methods

  • Fungal strains

  • Ginseng seedlings (3-year old) and soil

  • Primers and probe

  • Optimization of the ddPCR method

  • Optimization of the RT-qPCR method

  • Analytical assessment of the method proficiency

  • Quantitative analysis in artificially infected samples

  • Preparation the spore suspension of F. solani
  • Quantitative analysis

  • Quantitative analysis in field samples

  • Statistical analysis

  • Results

  • Test conditions and the efficacy of the F. solani detecting methods

  • Quantification of F. solani and disease incidence with Ginseng in pot culture

  • Quantification of F. solani and disease incidence in ginseng fields

  • Discussion
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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