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Article

Transcriptomic Analysis of Sodium-Silicate-Induced Resistance against Rhizoctonia solani AG-3 in Potato

by
Yayan Feng
1,
Jianjun Hao
2,
Dongmei Zhang
1,3,
Hongli Huo
1,3,
Lele Li
1,
Zhijun Xiu
1,3,
Chunfang Yang
1,3 and
Xiaoyu Zhang
1,*
1
Key Laboratory of Biopesticide Creation and Resource Utilization in Inner Mongolia Autonomous Region, College of Horticulture and Plant Protection, Inner Mongolia Agricultural University, Hohhot 010019, China
2
School of Food and Agriculture, University of Maine, Orono, ME 04469, USA
3
Plant Protection Institute, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(6), 1207; https://doi.org/10.3390/agronomy14061207
Submission received: 30 April 2024 / Revised: 23 May 2024 / Accepted: 27 May 2024 / Published: 3 June 2024
(This article belongs to the Section Pest and Disease Management)
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Abstract

:
Stem canker and black scurf of potatoes, caused by Rhizoctonia solani, are economically important diseases. Although the field application of sodium silicate has been shown to improve potato’s resistance against R. solani, the underlying mechanism remains unclear. In this study, we examined this resistance using transcriptomic analysis. Potato stems inoculated with R. solani were treated with sodium silicate, while a control group received no sodium silicate treatment. The plants were grown under natural environmental conditions at the farm of Inner Mongolia Agricultural University. Potato stems were sampled 4, 8, and 12 days after treatment. Total RNA was extracted using the TRIzol reagent and transformed into cDNA. The cDNA was sequenced, the reads were aligned, and the expression levels of genes were quantified and compared between the treated and control groups. A total of 1491 genes were identified as differentially expressed genes (DEGs). Furthermore, these DEGs were found to be involved in hydrolase activity, plant–pathogen interactions, hormone signal transduction, and the phenylpropanoid biosynthesis pathway. To confirm the up- and down-regulation of DEGs, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on randomly selected genes. The results showed that the application of sodium silicate induces a complex defense network in potato plants involving physical barriers, innate immunity, phytohormone signaling, and various phenylpropanoid compounds to combat R. solani infection. This study provides valuable insights into the molecular mechanisms underlying sodium-silicate-induced resistance and its potential for reducing stem canker and black scurf in potato crops.

1. Introduction

The potato (Solanum tuberosum L.) is the fourth most cultivated field crop and is recognized as important due to the abundance of essential minerals, vitamins, and antioxidants; moreover, it is a staple food crop [1,2]. However, the production and quality of potatoes are threatened by various fungal diseases, including Rhizoctonia solani, which causes stem canker and black scurf, resulting in substantial marketable yield losses of up to 30% [3,4,5].
Rhizoctonia solani constitutes a species complex with 13 genotypes referred to as anastomosis groups (AGs) [6]. AGs show various degrees of host specificity, with R. solani AG3 being the predominant type associated with potato infection [7]. Rhizoctonia solani infects potatoes with either pseudosclerotia or hypha [8] through wounds or natural openings such as the lenticels and stomata [9]. The most recognizable symptoms of stem canker and black scurf are the appearance of pseudosclerotia on the surface of potato tubers [10] as well as the development of brown, dry, and sunken lesions on the stems, stolon, and roots. This infection delays shoot emergence, reduces the number of stems, increases height variation, and leads to stolon and sprout pruning [7,11].
The current methods for controlling R. solani include the use of biocontrol agents, treating seed tubers with fungicides, treating soils with fungicides, and implementing crop rotation [7]. However, these strategies are not entirely satisfactory or compliant with safety requirements [12,13]. Thus, searching for and developing effective means and techniques to protect potato crops and increase yields is a crucial strategy.
One emerging strategy is the application of sodium silicate in the field to alleviate R. solani infections by inducing plant resistance [14,15]. Various hypotheses have been proposed regarding the mechanisms by which sodium silicate induces plant resistance. For example, sodium silicate enhances the mechanical barrier [16,17]. The apoplast of a plant is the initial site of the fungal effector and plant receptor interactions [18,19]. The deposition of sodium silicate in the apoplast creates a physical barrier that hinders the interaction, resulting in the prevention of further infection [20,21,22]. In addition to physical barriers, sodium silicate is also believed to induce plants to activate various signaling pathways, produce secondary metabolic products, and engage in cross-talk to enhance plant disease resistance. Sodium silicate is well known for imparting disease resistance in sodium-silicate-accumulating plant species like cucumber, wheat, rice, and other crops. For example, sodium silicate induces the production of fungitoxic flavonoid compounds in cucumbers [23,24]. It also increases the expression of enzymes like peroxidase (POD) and polyphenol oxidase (PPO), and the accumulation of polyphenolic compounds, which contribute to the defense against Pythium ultimum [25]. Similarly, sodium silicate treatment induces the accumulation of electron-dense phenolic material, which protects plants against Blumeria graminis f. sp. tritici in wheat [26], and it increases the accumulation of momilactone phytoalexins in leaves, enhancing resistance against Magnaporthe grisea in rice [27,28].
Sodium silicate also induces tomato’s resistance against pathogens such as Pseudomonas syringae, Colletotrichum gloeosporioides [29,30,31], and R. solani through the key genes for ethylene (ET) and jasmonic acid (JA) and reactive oxygen species (ROS) signaling pathways [32] such as JAZ, PYR/PYL, PP2C, and so on. Furthermore, sodium silicate has shown inhibitory effects against root pathogens [21] such as Pythium aphanidermatum in the roots of bitter gourd (through symplastic deposition) [33], Fusarium in tomato [34], and R. solani in potato [35]. Our previous results showed that sodium silicate deficiency can enhance the resistance of potatoes to Rhizoctonia solani AG-3 by increasing the activity of POD and catalase (CAT) [36], although the specific resistance mechanisms remain unclear [37].
The objectives of this study were to identify key genes and potential metabolic pathways of potato resistance against R. solani AG-3 induced by sodium silicate using high-throughput transcriptomic sequencing and quantitative PCR, which laid the foundation for studying the functions of these genes. Through a comprehensive analysis of gene expression, this research aimed to enhance our understanding of the resistance induced by sodium silicate in potatoes against R. solani. Furthermore, the findings of this study will contribute to the development of disease-resistant potato cultivars, offering an effective and environmentally friendly strategy for disease control.

2. Materials and Methods

2.1. Potato and Pathogen

The virus-free potato ‘Atlantic’ was obtained through tissue culture and maintained at the Research Center of Potato Breeding at Inner Mongolia Agricultural University, Hohhot, China. This cultivar is known to be highly susceptible to R. solani (strain PR11, AG3). Rhizoctonia solani AG-3 strain PR11 was isolated from an infected potato tuber collected in Wuchuan county of Inner Mongolia, which was confirmed to be highly pathogenic on stems and tubers of the ‘Atlantic’ potato. The culture was stored on potato sucrose agar (PSA, containing potato 200 g, sucrose 20 g, agar 15 g, distilled water 1000 mL) at 4 °C.

2.2. Potato Inoculation

The potato ‘Atlantic’ was initially grown on Murashige and Skoog (MS) under conditions of a photo flux density of 3000 to 4000 Lx, 50 to 60% relative humidity, and a 16 h day and 8 h night cycle at 25 °C for 30 d. Subsequently, the plants were transferred to pots (18 cm × 20 cm) and grown under natural environmental conditions at the farm of Inner Mongolia Agricultural University for an additional 30 d.
Rhizoctonia solani was cultivated on PSA plates at 25 °C for 5 d. Culture disks from 5-day-old R. solani were then transferred to the center of a new PSA plate and incubated for 7 d at 25 ± 1 °C. The resulting mycelium was scraped from the plates using sterile toothpicks and transferred into a sterile tube for grinding. The mycelial suspension was diluted to 1 × 107 mycelium/mL for inoculation.
The potato subterraneous stems were inoculated with 3 mL of mycelial suspension (1 × 107 mycelium/mL) and covered with soil. For sodium silicate treatments, 500 mL of 3.02 g/L Na2SiO3·9H2O in MS nutrient solution was applied once a week. As for the control group, mock potato plants were treated with MS nutrient solution only. Potato subterraneous stems were taken at 4, 8, and 12 days dpa, with three replicates per treatment. The samples were quickly frozen in liquid nitrogen and stored at −80 °C for further analysis.

2.3. RNA Sequencing and Library Construction

Total RNA was extracted from the stored mycelia using the MiniBEST Plant RNA Extraction Kit according to the manufacturer’s instructions (Takara Biomedical Technology, Beijing, China). RNA integrity, purity, and concentrations were assessed using 1% agarose gels and a NanoPhotometer® spectrophotometer (IMPLEN, Calabasas, CA, USA), Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA), and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA).
Messenger RNA (mRNA) was subjected to purification and subsequent construction of complementary DNA (cDNA) libraries. This construction process included cDNA end repair, adapter ligation, and cDNA amplification following the methodologies for preparing Illumina RNA-seq libraries of Novogene Bioinformatics Technology Co., Ltd. (Beijing, China). To quantify the final libraries, their concentrations were determined. The libraries were then sequenced on an Illumina HiSeqTM 4000 platform, generating paired-end reads of 125/150 bp. This sequencing method allowed for the generation of high-quality and comprehensive data for further analysis.

2.4. Sequence Alignment

To ensure the quality of the reads obtained from sequencing, initial quality control analysis was conducted using FastQC (v0.19.7). Low-quality sequences (Qphred ≤ 20), as well as sequences containing adapter and poly-N contamination, were identified and filtered out. Only high-quality reads were retained for subsequent analyses. This step ensured that only high-quality reads were retained for further analyses. The reference genome was indexed using Hisat2 (v2.0.5). The paired-end reads were then aligned to the reference genome using Hisat2 (v2.0.5) to accurately map the reads. Hisat2 was utilized for counting the number of reads mapped to each gene.
Subsequently, the mapped reads from each sample were assembled by StringTie (v1.3.3b) in a reference-based approach. FeatureCounts (v1.5.0-p3) was used to count the reads mapped to each gene. FPKM (fragments per kilobase of transcript per million mapped reads) values were calculated for each gene based on its length and the corresponding read count. Read alignment and expression quantification were performed separately for each sample. Genes meeting the criteria of having an FPKM value > 4 and low variation across three biological replicates (coefficient of variation < 30%) were considered reliable and included in subsequent analyses. This stringent selection ensured the inclusion of only robust and consistent genes for further analysis.

2.5. Identification and Functional Enrichment Analysis of Differentially Expressed Genes

The identification of DEGs was performed using the DESeq2 R package (1.16.1), which provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting p-values were adjusted using the Benjamini and Hochberg’s approach to control the false discovery rate (FDR). Genes with an adjusted p-value < 0.05, as determined by DESeq2, were considered differentially expressed. This threshold ensured a reasonable balance between the sensitivity and specificity of identifying significant changes in gene expression levels.

2.6. Gene Ontology and KEGG Pathways

To gain insights into the functional significance of the differentially expressed genes (DEGs), gene ontology (GO) enrichment analysis was performed using the clusterProfiler R package, which corrects for gene length bias. GO terms with corrected p-values below 0.05 were deemed significantly enriched among the DEGs.
KEGG pathway analysis was conducted to understand the high-level functions and utilities of the biological system. The clusterProfiler R package was utilized to test the statistical enrichment of differentially expressed genes in KEGG pathways (KEGG: Kyoto Encyclopedia of Genes and Genomes). This analysis provides a broader understanding of the biological pathways that may be influenced by the observed gene expression changes, thereby revealing potential functional implications. Both GO enrichment analysis and KEGG pathway analysis enable the interpretation of the biological significance and underlying mechanisms associated with the DEGs.

2.7. Real-Time qPCR Analysis

To validate the results obtained from RNA-seq, reverse-transcriptase quantitative PCR (RT-qPCR) was performed using the same biological replicates. RNA extraction and cDNA synthesis were performed according to the manufacturer’s instructions (TaKaRa, Beijing, China). Quantitative real-time PCR (qRT-PCR) was carried out using Applied Biosystems equipment (Thermo Fisher Scientific, Inc., Waltham, MA, USA). A total of 16 genes were selected for qPCR validation of the RNA-seq results, with the potato EF-1α gene serving as an internal control. The primer sequences used for quantitative real-time PCR are shown in Table S14. These specific primer sequences enabled the amplification and quantification of the target genes, providing valuable information for confirming the RNA-seq results.
The PCR amplification was performed in a 20 µL reaction volume, containing 10 µL of 2 × SYBR Green I Master (Roche Diagnostics GmbH, Mannheim, Germany), 0.4 µL of 10 µM each primer, 1 µL of 10-fold diluted cDNA, and 8.2 µL of PCR grade water. A negative control without target cDNA was included in each PCR run. The thermal cycling procedure involved an initial step at 95 °C for 5 min, followed by 45 cycles of 95 °C for 10 s, 58 °C for 10 s, and 72 °C for 10 s. This was followed by a melting curve analysis with a procedure of 95 °C for 5 s, annealing temperature for 1 min, and 97 °C continuous monitoring to determine the specificity of PCR amplification. Relative mRNA expression levels were calculated following the modified 2−∆∆CT method [37] and expressed as mean ± standard deviation (S.D.). The amplification efficiency of all genes was determined using quantitative real-time PCR with 10-fold serial diluted cDNA as a template. Statistical analysis was conducted using SPSS v. 20.0 software, allowing for the evaluation of the significance of the observed differences.

3. Results

3.1. Prediction of Novel Transcripts

To investigate the mechanism of plant resistance, R. solani was inoculated on potato subterraneous stems, followed by sodium silicate treatment (Supplementary Figure S1). Illumina’s next-generation sequencing technology (Illumina, San Diego, CA, USA) was used to analyze the transcriptomes in subterraneous potato stems (Supplementary Table S1).
A total of 771.98 MB of reads was obtained with an average of 42.89 MB for each library. After stringent quality filtration, high-quality reads were obtained ranging from 39.42 to 48.66 MB in size, and these reads were mapped to the potato genome for further analysis (Supplementary Table S2). The raw sequencing reads have been deposited in the NCBI SRA database under accession number PRJNA905064. In addition, the expression levels of 65,569 genes were quantified across all eighteen plant samples (Supplementary Table S3).

3.2. Global Gene Expression Profiling and Differentially Expressed Genes

The high repeatability and reliability of the experimental replicates were confirmed by the R2 values, which ranged from 0.831 to 0.93 for the six replicated groups (Supplementary Figure S2), suggesting high repeatability and reliability. This result provides assurance of the reliability of the differences in the gene expression analyses between the treated and control groups.
To explore the transcriptomic differences between the sodium silicate treatment and non-treated control at 4, 8, and 12 days post application (dpa), pairwise comparisons were performed.
A total of 1491 genes were identified as DEGs, exhibiting a |log2 fold change| > 2.0 and an adjusted p-value < 0.05 (Figure 1A, Supplementary Tables S4–S7). Among these DEGs, 566 genes were identified in SS4 vs. CK4, with 263 up-regulated and 303 down-regulated; 253 genes were identified in SS8 vs. CK8, with 103 up-regulated and 150 down-regulated; and 759 genes were identified in SS12 vs. CK12, with 658 up-regulated and 101 down-regulated (Figure 1A). Additionally, 502, 205, and 700 DEGs were found to be unique to SS4 vs. CK4, SS8 vs. CK8, and SS12 vs. CK12, respectively, while three genes exhibited consistent regulation across all three time points. Furthermore, 81 genes were commonly expressed between at least two time points, with 25 genes shared between 4 dpa and 8 dpa, 20 genes shared between 8 dpa and 12 dpa, and 36 genes shared between 4 dpa and 12 dpa (Figure 1B). Notably, the number of DEGs decreased at 8 dpa and then increased at 12 dpa, with three unique DEGs identified at each time point.
To better understand the transcriptome changes underlying sodium-silicate-induced resistance in potatoes against R. solani, expression patterns of all DEGs were analyzed. The expression patterns across the three application time points indicated that the DEGs exhibited both induction and repression, with a notable increase in the abundance of DEGs at 12 dpa (Figure 2).

3.3. Gene Ontology and Kyoto Encyclopedia Analysis of Differentially Expressed Genes

Functional analysis of all DEGs was conducted using gene ontology (GO) annotation. In the SS4-CK4 group, out of the total 556 DEGs, 228 (93 up-regulated and 135 down-regulated) were assigned to biological process (BP), 153 (62 up-regulated and 91 down-regulated) were assigned to cellular component (CC), and 271 (109 up-regulated and 162 down-regulated) were assigned to molecular function (MF). Similarly, in the SS8-CK8 group, 116 (58 up-regulated and 58 down-regulated) were assigned to BP, 76 (33 up-regulated and 43 down-regulated) were assigned to CC, and 134 (63 up-regulated and 71 down-regulated) were assigned to molecular function (MF). In the SS12-CK12 group, 322 (271 up-regulated and 51 down-regulated) of 759 DEGs were assigned to BP, 222 (178 up-regulated and 44 down-regulated) were assigned to CC, and 363 (303 up-regulated and 60 down-regulated) were assigned to molecular function (MF).
At 4 dpa, up-regulated DEGs were primarily involved in the nucleoside monophosphate metabolic process, 37 genes were assigned as being involved in mitochondrion, 21 genes were assigned as being involved in NADH dehydrogenase activity; down-regulated genes were mainly (two hundred and thirty-six) assigned as being involved in the carbohydrate metabolic process, and inside genes are encoding cell-wall-degrading enzymes, including several different hydrolases acting on cellulase or pectinesterase (Figure 3, Supplementary Table S8). At 8 dpa, five up-regulated DEGs were involved in the defense response, and sixty-one DEGs were associated with enzyme regulator activity, including five genes involved in defense response monooxygenase activity (Figure 4, Supplementary Table S9). At 12 dpa, 165 up-regulated DEGs were mainly associated with secondary metabolic processes, while down-regulated DEGs (thirty-seven) encoded endopeptidase activity (Figure 5, Supplementary Table S10).
To identify the biochemical pathways in which the DEGs were involved, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed targeting potato (organism code ‘sot’). The analysis revealed that the common DEGs were associated with both primary and secondary metabolisms, including pathways such as amino sugar and nucleotide sugar metabolism (sot 00520), carbon metabolism (sot 01200), biosynthesis of amino acids (sot 01230), steroid biosynthesis (sot 00100), plant hormone signal transduction (sot 04075), glycerophospholipid metabolism (sot 00564), pentose and glucuronate interconversions (sot 00040), and phenylpropanoid biosynthesis (sot 04070). Moreover, several pathways related to disease defense response were also enriched in application. Three of these pathways are detailed below.

3.4. Differentially Expressed Genes Involved in the Plant Hormone Signal Transduction Pathway

The application of sodium silicate induced the activation of signal transduction pathways associated with several main plant hormones such as auxin, abscisic acid (ABA), ethylene (ET), brassinosteroids, jasmonic acid (JA), and salicylic acid (SA). A total of 13 DEGs involved in the plant hormone signal transduction pathway were identified (Figure 6, Supplementary Table S11).
In the Auxin signaling transduction pathway, the DEGs indole-3-acetic acid-amido synthetase (GH3) and auxin-responsive protein SAUR32 (SAUR) were up-regulated at 4 dpa and 12 dpa but down-regulated at 8 dpa. In the abscisic acid signal transduction pathway, the DEG abscisic acid receptor (PYL4) was up-regulated at 4 dpa and down-regulated at 8 dpa and 12 dpa. The protein phosphatase 2C (PP2C) showed a continuous increase over time, while the abscisic acid-insensitive 5-like protein (ABF) was down-regulated at 4 dpa and up-regulated at 8 dpa and 12 dpa. In the brassinosteroid transduction pathway, the DEG xyloglucan endotransglucosylase/hydrolase protein 24-like (TCH4) exhibited a continuous increase over time. In the JA transduction pathway, the DEG protein TIFY 10A (JAZ) was up-regulated at 4 dpa and 12 dpa but down-regulated at 8 dpa. In the SA transduction pathway, the DEG pathogenesis-related protein 1A1 (PR-1) was up-regulated at 4 dpa but down-regulated at 8 dpa and 12 dpa. In the ET transduction pathway, the DEGs ethylene receptor 2-like (ETR) and ethylene-responsive transcription factor 2-like (ERF1/2) were up-regulated at 4 dpa and 12 dpa but down-regulated at 8 dpa.

3.5. Differentially Expressed Genes Involved in the Plant–Pathogen Interaction Pathway

According to the KEGG pathway analysis, nine DEGs in potato induced by sodium silicate were associated with the plant–pathogen interaction pathway (Figure 7, Supplementary Table S12). To defend against pathogens, plants have developed a multi-layered immune system. One crucial defense layer involves the recognition of pathogen-associated molecular patterns (PAMPs) by cell surface pattern recognition receptors (PRRs), which triggers a basal defense response known as PAMP/PRR-triggered immunity (PTI) [38]. Calcium signaling is essential in plant perception of PAMPs. Calcium is transmitted by calcium-dependent protein kinases (CDPKss) and calmodulin (CaM)/calmodulin-like proteins (CML) to regulate plant immune responses, including the production of reactive oxygen species (ROS) and nitric oxide (NO), as well as transcriptional reprogramming of immune genes [39]. In this study, we found that the up-regulation of cyclic nucleotide-gated channels (CNGCs), CDPK, and CaM/CML after sodium silicate application at 4 dpa and 12 dpa occurred, while their expression was down-regulated at 8 dpa. The activation of molecular signaling mechanisms following the perception of pathogen-associated molecular patterns (PAMPs) has been extensively studied in pattern recognition receptors (PRRs). Fagellin-sensing 2 (FLS2), like PRRs in most higher plants, can induce a series of defense responses [40]. We observed that the expression of FLS2 was down-regulated after sodium silicate application at 4 and 8 dpa but up-regulated at 12 dpa.
Pathogens can successfully secrete effectors into the plant cells, thereby suppressing PAMP-triggered immunity (PTI). In response to this, plants have evolved resistance proteins (R proteins) that recognize these effectors inside the cell, which results in the initiation of a second level of defense called effector-triggered immunity (ETI) [41]. In this study, we identified that ETI gene PRM1-interacting protein 4 (RIN4) was up-regulated at 4 dpa and 12 dpa but down-regulated at 8 dpa.

3.6. Differentially Expressed Genes Involved in the Phenylpropanoid Biosynthesis Pathway

The phenylpropanoid biosynthesis pathway, responsible for the production of secondary metabolites, plays a primary role in plant resistance [42]. There were nine DEGs involved in the phenylpropanoid biosynthesis pathway in response to sodium silicate treatment (Table 1, Supplementary Table S13). Among the three identified PAL transcripts, two were up-regulated at 4 dpa and 12 dpa but down-regulated at 8 dpa. The remaining PAL transcript was down-regulated at 4 dpa and 8 dpa and up-regulated at 12 dpa. Six DEGs encoded lignin biosynthesis enzymes, including transcripts encoding 4-coumarate-CoA ligase (4CL), peroxidase (PO), and cytochrome P450 (CYP450). These enzymes are associated with lignin production and are important for plant defense mechanisms.

3.7. Validation of the Gene Expression of Differentially Expressed Genes by qRT-PCR

To ensure the reliability of the RNA sequencing data, we conducted qRT-PCR assays to validate the relative expression levels of these 15 genes at 4, 8, and 12 dpa (Figure 8; Supplementary Table S14). These genes were randomly chosen for the validation analysis. Results were compared with the expression levels obtained from our RNA-seq data. The comparison demonstrated a consistent pattern of expression for the selected genes, confirming the reliability of the transcriptome data.

4. Discussion

Rhizoctonia solani can cause stem canker and black scurf of potatoes. Both above- and below-ground parts of potatoes can be infected. Above-ground symptoms include brown lesions on the stem base of potatoes that lead to stem canker, leaf curling, root detrition, and falling off the plant. The most obvious symptoms are brown-to-black pseudosclerotia on potato tubers. Rhizoctonia solani secretes toxins and cell-wall-degrading enzymes to attack potato tissues, resulting in cell necrosis and nutrient acquisition in the plant [43]. To defend against pathogen infection, the plant cell wall acts as a physical barrier [28,44]. Sodium silicate deposition in the potato cell wall, as observed in our study, hinders the growth of R. solani by down-regulating genes encoding cell-wall-degrading enzymes, such as hydrolases acting on cellulase or pectinesterase. This is consistent with previous findings in rice, where sodium silicate fortified the cell wall and enhanced resistance to R. solani [45].
In the plant–pathogen interaction, plants have developed a multi-layered immune system. One crucial defense layer involves the recognition of pathogen-associated molecular patterns (PAMPs) by cell surface pattern recognition receptors (PRRs), which triggers a basal defense response known as PAMP/PRR-triggered immunity (PTI) [38]. Pathogens can secrete effectors into the plant cells, thereby suppressing PTI. In response to this, plants have evolved resistance proteins (R proteins) that recognize these effectors inside the cell, which results in the initiation of a second level of defense called effector-triggered immunity (ETI) [41]. We have found that Fagellin-sensing 2 (FLS2), a well-known pattern recognition receptor (PRR) [46], which can induce a series of defense responses [40], was down-regulated at 4 and 8 dpa.
The role of Calcium in recognizing PAMPs is vital as it functions as a crucial intracellular second messenger in various plant signaling pathways by activating proteins like calcium-dependent protein kinase (CDPK) and respiratory burst oxidase homologue (Rboh), leading to the production of reactive oxygen species (ROS) and nitric oxide (NO) production and the reprogramming of immune genes [32,47]. Our research indicates that sodium silicate influences calcium signaling, with a noticeable up-regulation of CDPK and CAM/CML genes, and a subsequent cascade of defense responses enhancing resistance in potatoes. This process is corroborated by the observed up-regulation of the receptor protein RIN4, integral to defense signaling pathways, which aligns with the activation of effector-triggered immunity (ETI) upon R. solani infection. The genes were up-regulated at 4 and 12 dpa, suggesting that sodium silicate modulates Ca2+ levels in potato cells, triggering a series of defense responses and enhancing plant resistance.
Plant secondary metabolites, such as plant hormones, function as defense or signaling molecules in biotic and abiotic stress responses [48]. Not surprisingly, we observed that differentially expressed genes (DEGs) were enriched in plant hormone signal transduction pathways, including auxin, ABA, ET, brassinosteroid, JA, and SA signaling pathways. This indicates that sodium silicate application may induce these pathways and contribute to the potato’s response to R. solani infection.
In signal transduction pathways, auxin signaling can have different or even opposite regulatory roles in plant immunity in necrotrophic fungal pathogens [49]. Auxin response genes, such as GH3 and SAUR, act as key effectors in regulating plant immunity in response to hormonal and environmental signals [50,51]. Rhizoctonia solani might fall into this category as it is a necrotrophic pathogen [52].
ET and JA signaling pathways synergistically interact with auxin signaling to confer necrotrophic resistance [53,54,55]. JAZ proteins, when combined with jasmonoyl-isoleucine, activate the expression of the JA signaling pathway to regulate plant immunity [56]. Sodium silicate application has been shown to enhance tomato resistance against Ralstonia solanacearum by regulating the JA signaling pathway [32]. SA is primarily involved in defense against biotrophic pathogens [57]. The ABA phytohormone binds to the family of ABA receptors (PYR/PYL/RCAR) and activates downstream genes, such as PP2C and ABF, triggering plant responses to biotic stress [58]. Brassinosteroid genes exhibit changes in expression levels in response to environmental stimuli [59]. TCH4 encodes a xyloglucan endotransglucosylase/hydrolase. This change in expression may reflect recruitment of cell-wall-modifying activity in response to environmental stress [60].
The expression patterns of ETR and ERF1/2 were like those of auxin signaling. All DEGs detected in the auxin transduction pathway were up-regulated at 4 and 12 dpa. PR1 was up-regulated at 4 dpa and down-regulated at 8 and 12 dpa, suggesting that sodium silicate induces SA signaling primarily in the early infection stage. PP2C was up-regulated at all stages, while PYR/PYC was only up-regulated at 4 dpa, and ABF was only up-regulated at 12 dpa, indicating that these genes, induced by sodium silicate application, may be involved in different infection stages. The TCH4 protein was up-regulated all the time according to KEGG analysis, and several DEGs were enriched in hydrolase function, indicating that the application of sodium silicate could change the activity of the cell wall and increase resistance to R. solani.
The phenylpropanoid biosynthesis pathway, responsible for the production of secondary metabolites, plays a primary role in plant resistance [42]. Phenylpropanoid metabolism encompasses various compounds such as flavonoids, stilbenes, monolignols, and phenolic acids, which play a vital role in the synthesis of secondary resistance metabolites like phytoalexins, lignin, and phenolic compounds [61,62,63]. Lignin plays a vital role in plant defense against pathogens [64]. We observed that most of the genes encoding PAL, peroxidase, and enzymes involved in lignin biosynthesis were up-regulated after sodium silicate application. These findings are consistent with a previous study, demonstrating that sodium silicate application induces regulatory mechanisms leading to disease resistance, including the activation of glucanase, peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase and the accumulation of antimicrobial glycosylated phenolics and diterpenoid phytoalexins [65].
We have found a significant number of DEGs related to nucleoside phosphate metabolic processes were enriched at 4 dpa, suggesting that sodium silicate application enhances potato resistance through the modulation of nucleoside phosphate metabolic processes. This is supported by previous research showing the involvement of nucleotides in plant immune responses, where cyclic GMP (cGMP) and cyclic nucleotide monophosphates (cNMPs) act as essential secondary messengers [66,67].

5. Conclusions

In this research, we have analyzed transcriptomics of two groups; the potato stems inoculated with R. solani were treated with sodium silicate, while a control group received no sodium silicate treatment. There was a total of 1491 DEGs, with 566 (4 dpa), 253 (8 dpa), and 759 (12 dpa) genes showing differential expression compared to the control. DEGs were enriched in hydrolase activity, plant–pathogen interactions, hormone signal transduction, and phenylpropanoid biosynthesis pathways. Our results suggest that sodium silicate application induces the activation of physical barriers, innate immunity, phytohormone signaling, and phenylpropanoid compounds, which collectively form a complex defense network in plants, in response to R. solani infection. This study provides valuable insights into the application of sodium-silicate-induced resistance against R. solani in potato and enhances our understanding of the underlying mechanisms. These findings will contribute to accelerating research on sodium-silicate-induced resistance against R. solani in potatoes and provide valuable insights into the defense responses of plants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14061207/s1, Figure S1: Potato subterraneous stems at 12 days post application with sodium silicate; Figure S2: Pearson correlation between different potato subterraneous stems treated with sodium silicate at different time points, including 4, 8, and 12 days post application; Table S1: Data summary of RNA-seq; Table S2: Summary of clean reads mapped to potato genome; Table S3: Transcripts prediction and expression level quantification (FPKM values); Table S4: FPKM values for DEGs of potato after Silicon application; Table S5: DEGs list of pairwise comparisons between 4 dpa Silicon-applied treatment group (SS4) and the non-application group (CK4); Table S6: DEGs list of pairwise comparisons between 8 dpa Silicon-applied treatment group (SS8) and the non-application group (CK8); Table S7: DEGs list of pairwise comparisons between 12 dpi sodium silicate-ap-plied treatment group (SS12) and the non-application group (CK12); Table S8: Gene ontology classification of DEGs at 4 dpa; Table S9: Gene ontology classification of DEGs at 8 dpa; Table S10: Gene ontology classification of DEGs at 12 dpa; Table S11: Genes involved in the plant hormone signal transduction pathway; Table S12: Genes involved in the plant–pathogen interaction pathway; Table S13: Genes involved in the phenylpropanoid biosynthesis pathway; Table S14: Differentially expressed genes used for RT-qPCR verification.

Author Contributions

Methodology, H.H.; formal analysis, D.Z., H.H. and C.Y.; writing—original draft preparation, Y.F.; writing—review and editing, J.H., D.Z., H.H., L.L., Z.X., C.Y. and X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The research work was partially supported by the Natural Science Foundation of Inner Mongolia (2021MS03053), Inner Mongolia Science and Technology Project (2022YFYZ0008), Special Breed Breeding Public Relations Joint Project of Inner Mongolia Autonomous Region (YZ2023006).

Data Availability Statement

The raw sequencing reads have been deposited in the NCBI SRA database under accession number PRJNA905064 (https://www.ncbi.nlm.nih.gov/sra/PRJNA905064, release date 5 December 2024).

Conflicts of Interest

The plant collection and use were in accordance with all the relevant guidelines. The authors declare that they have no competing interests.

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Agronomy 14 01207 g001
Figure 1. Identification of differentially expressed genes (DEGs) between sodium-silicate-treated and non-treated ‘Atlantic’ potato, analyzed using pairwise comparisons of eighteen transcriptomes: (A) The up- and down-regulated DEGs at 4 (SS4-CK4), 8 (SS8-CK8), and 12 (SS12-CK12) days post application (dpa). (B) Venn diagram displaying the distribution of the DEGs at different time points of sodium silicate treatments.
Figure 1. Identification of differentially expressed genes (DEGs) between sodium-silicate-treated and non-treated ‘Atlantic’ potato, analyzed using pairwise comparisons of eighteen transcriptomes: (A) The up- and down-regulated DEGs at 4 (SS4-CK4), 8 (SS8-CK8), and 12 (SS12-CK12) days post application (dpa). (B) Venn diagram displaying the distribution of the DEGs at different time points of sodium silicate treatments.
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Figure 2. A heat map of differentially expressed genes (DEGs) between sodium-silicate-treated and non-treated ‘Atlantic’ potato stems at each time point, including 4 (a4 and b4), 8 (a8 and b8), and 12 (a12 and b12) days post application (dpa).
Figure 2. A heat map of differentially expressed genes (DEGs) between sodium-silicate-treated and non-treated ‘Atlantic’ potato stems at each time point, including 4 (a4 and b4), 8 (a8 and b8), and 12 (a12 and b12) days post application (dpa).
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Figure 3. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 4 days post application (dpa) of sodium silicate.
Figure 3. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 4 days post application (dpa) of sodium silicate.
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Figure 4. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 8 days post application (dpa) of sodium silicate.
Figure 4. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 8 days post application (dpa) of sodium silicate.
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Figure 5. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 12 days post application (dpa) of sodium silicate.
Figure 5. Gene ontology (GO) term distribution of differential genes for biological process (BP), molecular function (MF), and cellular component (CC) at 12 days post application (dpa) of sodium silicate.
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Figure 6. Differentially expressed genes (DEGs) involved in the plant hormone signal transduction pathway in response to sodium silicate treatment enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
Figure 6. Differentially expressed genes (DEGs) involved in the plant hormone signal transduction pathway in response to sodium silicate treatment enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
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Figure 7. Differentially expressed genes (DEGs) involved in the plant–pathogen interaction pathway in potato with sodium silicate applied based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
Figure 7. Differentially expressed genes (DEGs) involved in the plant–pathogen interaction pathway in potato with sodium silicate applied based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
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Figure 8. The relative expression level change of 15 selected genes from DEGs by quantitative real-time PCR. The left vertical coordinate is FPKM (fragments per kilobase of transcript per million mapped reads) of RNA-seq; the right vertical coordinate is the relative expression level of qRT-PCR.
Figure 8. The relative expression level change of 15 selected genes from DEGs by quantitative real-time PCR. The left vertical coordinate is FPKM (fragments per kilobase of transcript per million mapped reads) of RNA-seq; the right vertical coordinate is the relative expression level of qRT-PCR.
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Table 1. Differentially expressed genes (DEGs) involved in the phenylpropanoid biosynthesis (PB) pathway of ‘Atlantic’ potato after 4, 8, and 12 days of application of sodium silicate.
Table 1. Differentially expressed genes (DEGs) involved in the phenylpropanoid biosynthesis (PB) pathway of ‘Atlantic’ potato after 4, 8, and 12 days of application of sodium silicate.
Gene IDKEGG EntryKO IDTimes Identified as a DEG aGene Name (Predicted)KEGG DefinitionGene Description
PGSC0003DMG400003155102581073K01904124CL34-coumarate-CoA ligase4-coumarate:CoA ligase
PGSC0003DMG400007178102578747K0975412CYP4505-O-(4-coumaroyl)-D-quinate 3′-monooxygenaseP-coumaroyl quinate/shikimate 3′-hydroxylase
PGSC0003DMG400010465102605677K004308peroxidase 21peroxidasePeroxidase
PGSC0003DMG400023458102582618K1077512PALphenylalanine ammonia-lyasePhenylalanine ammonia-lyase
PGSC0003DMG400024967102592844K004304peroxidase 51peroxidasePeroxidase
PGSC0003DMG400031457102596891K107754PALphenylalanine ammonia-lyasePhenylalanine ammonia-lyase 1
PGSC0003DMG401021549102596343K1077512PALphenylalanine ammonia-lyasePhenylalanine ammonia-lyase
PGSC0003DMG402014734102590830K0975412CYP4505-O-(4-coumaroyl)-D-quinate 3′-monooxygenaseP-coumaroyl quinate/shikimate 3′-hydroxylase
PGSC0003DMG402025083102585990K0043012peroxidase 12peroxidasePeroxidase
a Times a gene is identified as a DEG in three comparisons (SS4-CK4, SS8-CK8, and SS12-CK12).
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Feng, Y.; Hao, J.; Zhang, D.; Huo, H.; Li, L.; Xiu, Z.; Yang, C.; Zhang, X. Transcriptomic Analysis of Sodium-Silicate-Induced Resistance against Rhizoctonia solani AG-3 in Potato. Agronomy 2024, 14, 1207. https://doi.org/10.3390/agronomy14061207

AMA Style

Feng Y, Hao J, Zhang D, Huo H, Li L, Xiu Z, Yang C, Zhang X. Transcriptomic Analysis of Sodium-Silicate-Induced Resistance against Rhizoctonia solani AG-3 in Potato. Agronomy. 2024; 14(6):1207. https://doi.org/10.3390/agronomy14061207

Chicago/Turabian Style

Feng, Yayan, Jianjun Hao, Dongmei Zhang, Hongli Huo, Lele Li, Zhijun Xiu, Chunfang Yang, and Xiaoyu Zhang. 2024. "Transcriptomic Analysis of Sodium-Silicate-Induced Resistance against Rhizoctonia solani AG-3 in Potato" Agronomy 14, no. 6: 1207. https://doi.org/10.3390/agronomy14061207

APA Style

Feng, Y., Hao, J., Zhang, D., Huo, H., Li, L., Xiu, Z., Yang, C., & Zhang, X. (2024). Transcriptomic Analysis of Sodium-Silicate-Induced Resistance against Rhizoctonia solani AG-3 in Potato. Agronomy, 14(6), 1207. https://doi.org/10.3390/agronomy14061207

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