{"paper_id":"e10c4702-10d8-4c3f-b32e-92ceda8bf3ad","body_text":"Transcriptional Responses of Sodium-Silicate-Induced Potato Resistance Against Rhizoctonia solani AG-3 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptional Responses of Sodium-Silicate-Induced Potato Resistance Against Rhizoctonia solani AG-3 YaYan Feng, jianjun hao, dongmei zhang, hongli huo, lele li, zhijun xiu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3978878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Stem canker and black scurf of potatoes, caused by Rhizoctonia solani , is a highly destructive worldwide. In controlling the disease, the application of sodium silicate in potato fields has shown promise in improving potato resistance against R. solani , although the underlying mechanism remains unclear. In this study, we used RNA sequencing analysis to examine the transcriptome of potato subterraneous stems of potato plants. These stems were both inoculated with R. solani and treated with sodium silicate, while a control group received no sodium silicate treatment. Transcriptome analysis was performed at 4, 8, and 12 days post-application (Group SS) and compared with the control (Group CK). A total of 1491 genes were identified as differentially expressed genes (DEGs). Furthermore, these DEGs are involved in hydrolase activity, plant-pathogen interactions, hormone signal transduction, and the phenylpropanoid biosynthesis pathway. These findings suggest that the application of sodium silicate induces a complex defense network in 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. Solanum tuberosum L. stem canker and black scurf differentially expressed genes RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The potato ( Solanum tuberosum L.) is the fourth most-cultivated field crop, which is recognized to be important due to their abundance of essential minerals, vitamins, and antioxidants, serve as a source of food (Ali et al, 2021 ; Tian et al., 2016 ). However, the production and quality of potatoes are threatened by various fungal diseases, including Rhizoctonia solani causing stem canker and black scurf, resulting in substantial marketable yield losses, sometimes up to 30% (Banville, 1989 ; Johnson et al., 2015; Yang et al., 2015 ). Rhizoctonia solani constitutes a species complex with various genotypes called as anastomosis groups (AGs), and 13 AGs of R. solani have been described (Ajayi-Oyetunde et al., 2018). These AGs show various degrees of host specificity, with R. solani AG3 being the predominant type associated with potatoes (Tsror, 2010 ). R. solani infects potatoes with either sclerotia or hyphae (Balali et al., 2007 ), through wounds or natural openings, such as lenticels and stomata (Yang et al., 2017 ). The most recognizable symptoms of stem canker and black scurf are the appearance of sclerotia on the surface of potato tubers (Carling et al., 1989 ), as well as the development of brown, dry, and sunken lesions on stems, stolon, and roots. This infection delays shoot emergence, reduces the number of stems, increases height variation, and leads to stolon and sprout pruning (Tsror, 2010 ;Read et al., 1989 ). 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 (Tsror, 2010 ). However, these strategies are not entirely satisfied or compliant with safety requirements (Bohinc et al., 2019 ; Pierre et al., 2013 ). Thus, searching or 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 field to alleviate R. solani infections by inducing plant resistance (Sabes et al., 2020 ; Saludares et al., 2011 ). Various hypotheses have been proposed regarding the mechanisms by which sodium silicate induces plant resistance. For example, sodium silicate enhances the mechanical barrier (Mandlik et al., 2020 ; Zhang et al., 2013 a). The apoplast of plants is the initial site of fungal effector and plant receptor interactions (Bozkurt et al., 2012 ; Wang et al., 2018). The deposition of sodium silicate in the apoplast creates a physical barrier that hinders the interaction, resulting in the prevention for further infection (Thorsten et al., 2005; Rasoolizadeh et al., 2020 ; Zheng et al., 2013 ). In addition to the physical barriers, sodium silicate is also believed to induce plants to activate various signaling pathways, produce secondary metabolic products, and engage in cross-talks 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 (Fawe et al., 1998 ; Samuels et al., 1991 ). 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 (Cherif et al., 1992). Similarly, sodium silicate treatment induces the accumulation of the electron-dense phenolic material that protects plants against Blumeria graminis f. sp. tritici in wheats (Bélanger et al., 2003 ), and increases the accumulation of momilactone phytoalexins in leaves, enhancing resistance against Magnaporthe grisea in rice (Rodrigues et al., 2004 ; Cai et al., 2008 ). Sodium silicate also induces disease resistance in plants, particularly in tomato that do not accumulate sodium silicate. Silicon treatment increases resistance against pathogens such as Pseudomonas syringae and Colletotrichum gloeosporioides (Resende et al., 2013 ; Dannon et al., 2004; David et al., 2012), and improves resistance against R. solani in tomato through ethylene (ET), jasmonic acid (JA), and reactive oxygen species (ROS) signaling pathways (Ghareeb et al., 2011 ). Furthermore, sodium silicate has shown inhibitory effects against root pathogens (Rasoolizadeh et al., 2020 ), such as Pythium aphanidermatum spread in the roots of bitter gourd through symplastic deposition (Heine et al., 2007 ), Fusarium in tomato (Cheng et al., 2011 )d solani in potato (Huo, 2018 ), although the specific resistance mechanisms remain unclear. (Vulavala, et al., 2016 ). The objectives of this study were to examine the resistance enhanced by sodium silicate, determine the mechanisms of the induced resistance, and identify the genes associated with the defense responses in potato during the R. solani infection. Through a comprehensive analysis of gene expression, this research aims to enhance our understanding of the resistance induced by sodium silicate in potato 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. Results Sequencing data summary and prediction of novel transcripts R. solani success fully infected potato Subterraneous stems and applied sodium silicate resulted in a decrease in lesion area (Supplementary Fig. S1 ). However, the underlying defense mechanism of sodium silicate against fungal diseases, particularly against R. solani , remains poorly understood. In order to gain insights into the response of sodium silicate-treated potato subterraneous stems during R. solani infection, we used Illumina next-generation sequencing technology (Illumina, San Diego, CA, USA) to analyze the transcriptomes inoculated potato subterraneous stems with and without sodium silicate-treated at 4, 8, and 12 dpa. There were three biological replicates, resulting in eighteen samples (Supplementary Table S1 ). In total, we obtained 771.98 MB reads with an average of 42.89 MB for each library. After stringent quality filtration, the high-quality reads ranged from 39.42 to 48.66 MB in size, and these reads were then 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, we quantified the expression levels of 65, 569 genes across all eighteen plant samples (Supplementary Table S3). Global gene expression profiling analysis and identification of differentially expressed genes (DEGs) The high repeatability and reliability of the experimental replicates were confirmed by the R 2 values, which ranged 0.831 to 0.93 for the six replicate groups (Supplementary Fig. S2 ). These results suggest that the replicate samples exhibited high repeatability and reliability. This result provide assurance of obtaining reliable differences in gene expression analyses between Group SS and Group CK. To explore the transcriptomic differences between Group SS (sodium silicate-applied treatments) and Group CK(Group control) at 4, 8, and 12 dpa, pairwise comparisons were performed separately. A total of 1491 genes were identified as DEGs, exhibiting a |log2 fold change | > 2.0 and an adjusted P-value < 0.05 (Fig. 1 A, Supplementary Tables S4-S7). Among these DEGs, 566 genes (263 up-regulated and 303 down-regulated), 253 genes (103 up-regulated and 150 down-regulated), and 759 genes (658 up-regulated and 101 down-regulated) were identified in SS4 vs CK4, SS8 vs CK8 and SS12 vs CK12, respectively (Fig. 1 A). 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 (Fig. 1 B). 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 potato 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 (Fig. 2). Figure 2. A heat map of differentially expressed genes (DEGs) between sodium silicate-treated and non-treated potato ‘Atlantic’ stems at each time point, including 4 (a4 and b4), 8 (a8 and b8), and 12 (a12 and b12) days post application (dpa). Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes analysis of DEGs Functional analysis of all DEGs was conducted using gene ontology (GO) annotation to gain functional insights into their functional categories. 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 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 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 (one hundred and twenty-seven) involved in nucleoside monophosphate metabolic process, thirty-seven genes are assigned as being involved mitochondrion, twenty-one genes are assigned as being involved NADH dehydrogenase activity; down-regulated genes are mainly (two hundred and thirty-six) assigned as being involved in carbohydrate metabolic process, inside genes are encoding cell wall degrading enzymes including several different hydrolases acting on cellulase or pectinesterase (Fig. 3 , Supplementary Table S8). At 8 dpa, five up-regulated DEGs were involved in defense response, and sixty-one DGEs were associated with enzyme regulator activity, including five genes involved in defense response monooxygenase activity (Fig. 4 , Supplementary Table S9). At 12 dpa, up-regulated DEGs (one hundred and sixty-five) were mainly associated with secondary metabolic process, while down-regulated DEGs (thirty-seven) encoded endopeptidase activity (Fig. 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 for 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. DEGs involved in the plant hormone signal transduction pathway Plant hormones play a crucial role in plant defense responses (Bari et al., 2009), and in this study, 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 (Fig. 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 12dpa, 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. DEGs involved in the plant-pathogen interaction pathway According to the KEGG pathway analysis, 9 DEGs in potato induced by sodium silicate were associated with the plant-pathogen interaction pathway (Fig. 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 patterns recognition receptors (PRRs), which triggers a basal defense response known as PAMP / PRR-triggered immunity (PTI) (Monaghan et al., 2012). Calcium signaling is essential in plant perception of PAMPs. Calcium is transmitted by calcium-dependent protein kinase (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 (Gao et al., 2014 ). 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 while their expression was down-regulated at 8 dpa. The activation of molecular signaling mechanisms following perception of pathogen associated molecular patterns (PAMPs) has been extensively studied in patterns recognition receptors (PRRs). Fagellin-sensing 2 (FLS2), as PRRs in most higher plants, can induce a series of defense responses (Lee et al., 2011 ). 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. Calcium signaling is essential in plant perception of PAMPs. Calcium is transmitted by calcium-dependent protein kinase (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 (Gao et al., 2014 ). 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 while their expression was down-regulated at 8 dpa. The activation of molecular signaling mechanisms following perception of pathogen associated molecular patterns (PAMPs) has been extensively studied in patterns recognition receptors (PRRs). Fagellin-sensing 2 (FLS2), as PRRs in most higher plants, can induce a series of defense responses (Lee et al., 2011 ). 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 successful 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) (Jones, et al., 2006). In this study, we identified ETI gene PRM1-interacting protein 4 (RIN4) was up-regulated at 4 dpa and 12 dpa while down-regulated at 8 dpa. DEGs involved in the phenylpropanoid biosynthesis pathway The phenylpropanoid biosynthesis pathway, responsible for the production of secondary metabolites, plays a primary role in plant resistance (Taheri et al., 2010). We identified 9 DEGs involved in the phenylpropanoid biosynthesis pathway in response to sodium silicate application (Table 1 , Supplementary Table S13). Among these DEGs, phenylalanine ammonia-lyase (PAL) is the key enzyme that initiates the phenylpropanoid pathway and is known to respond to pathogen infections. We have found three PAL transcripts, with two being up-regulated at 4 dpa and 12 dpa, while down-regulated at 8 dpa. The remaining PAL transcript was down-regulated at 4 dpa and 8 dpa, and up-regulated at 12 dpa. Furthermore, lignin, a well-characterized component, plays vital role in defense against pathogens (Li, et al., 2020 ). In our data, six DEGs encoding 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. Table 1 Differentially expressed genes (DEGs) involved in the phenylpropanoid biosynthesis (PB) pathway of potato ‘Atlantic’ under sodium silicate treatments, measured after 4, 8, and 12 days post application. gene ID KEGG entry KO ID Times identified as a DEG a Gene name (Predicted) KEGG definition Gene description PGSC0003DMG400003155 102581073 K01904 12 4CL3 4-coumarate–CoA ligase 4-coumarate:CoA ligase PGSC0003DMG400007178 102578747 K09754 12 CYP450 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase P-coumaroyl quinate/shikimate 3'-hydroxylase PGSC0003DMG400010465 102605677 K00430 8 peroxidase 21 peroxidase Peroxidase PGSC0003DMG400023458 102582618 K10775 12 PAL phenylalanine ammonia-lyase Phenylalanine ammonia-lyase PGSC0003DMG400024967 102592844 K00430 4 peroxidase 51 peroxidase Peroxidase PGSC0003DMG400031457 102596891 K10775 4 PAL phenylalanine ammonia-lyase Phenylalanine ammonia-lyase 1 PGSC0003DMG401021549 102596343 K10775 12 PAL phenylalanine ammonia-lyase Phenylalanine ammonia-lyase PGSC0003DMG402014734 102590830 K09754 12 CYP450 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase P-coumaroyl quinate/shikimate 3'-hydroxylase PGSC0003DMG402025083 102585990 K00430 12 peroxidase 12 peroxidase Peroxidase a-Times of a gene identified as a DEG in three comparations (SS4-CK4, SS8-CK8 and SS12-CK12). Validation of the gene expression of DEGs by qRT-PCR To ensure the reliability of the RNA sequencing (RNA-seq) data, we conducted qRT-PCR analysis to validate the relative expression levels of these 16 genes at 4, 8, and 12 dpa (Fig. 8 ; supplementary Table S14). These genes were randomly chosen for the validation analysis. The qRT-PCR 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. Discussion Through RNA-seq analysis, we have identified a total of 1491 differentially expressed genes (DEGs) in response to sodium silicate treatment. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed enrichment in pathways associated with primary metabolism, secondary metabolism, and defense responses. These findings indicate that sodium silicate triggers a wide range of mechanisms in potato plants. Rhizoctonia solani secretes toxins and cell wall-degrading enzymes to attack plant tissues and induces host cell necrosis and nutrient acquisition (Mengiste, 2012 ). To defend the pathogen infection, the plant cell wall acts as a physical barrier (Cai et al., 2008 ; Hayasaka et al., 2008 ). 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 fortifies the cell wall and enhances resistance to R. solani (Zhang et al., 2006 ). In addition to physical barriers, plants have evolved complex immune systems, including pattern-triggered immunity (PTI) and effector-triggered immunity (ETI), as the front line of defense against pathogens (Jones et al., 2006). We found FLS2, a well-known pattern recognition receptor (PRR) (Yuan et al., 2021 ), was up regulated at 4 and 8 days post-application (dpa) of sodium silicate. This suggests that sodium silicate triggers the pattern recognization in potato cells. Calcium (Ca2 + ), a versatile intracellular second messenger involved in various signaling pathways by activating proteins such as calcium-dependent protein kinase (CDPK) and respiratory burst oxidase homologue (Rboh), leading to reactive oxygen species (ROS) production (Johnson et al., 2014 ). Sodium silicate has been shown to mediate defense against Ralstonia solanacearum in tomato through ROS signaling (Ghareeb et al., 2011 ). In our study, CDPK and CAM / CML 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. Additionally, we observed up-regulation of the receptor protein RIN4, which plays a role in defense signaling pathways (Li et al., 2014 ), further supporting the activation of ETI in response to R. solani infection (Jones et al., 2006). Plant secondary metabolites, such as plant hormones function as defense or signaling molecules in biotic and abiotic stress responses (Pane et al., 2020 ). 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. Signal transduction pathways have extensively been studied. Auxin signaling can have different or even opposing regulatory roles in plant immunity depending on pathogen lifestyles, which has been associated with necrotrophic fungi (Zhang, et al., 2018 a; Llorente, et al., 2008). Auxin response genes, such as GH3 and SAUR, act as key effectors in regulating plant immunity in response to hormonal and environmental signals (Hong et al., 2015; Domingo et al., 2009 ). Because R. solani is a necrotrophic pathogen (Zrenner et al., 2021 ), we believe it would fall into this category. ET and JA signaling pathways synergistically interact with auxin signaling to confer necrotrophic resistance (Yang et al., 2013 ; Glazebrook, 2005 ; Velivelli et al., 2015 ). JA, primarily induced by necrotrophic pathogens, involves in plant defense (Zrenner et al., 2021 ). JAZ proteins, when combined with jasmonoyl-isoleucine, activate the expression of the JA signaling pathway to regulate plant immunity (Liu, et al., 2019 ). Sodium silicate application has been shown to enhance tomato resistance against Ralstonia solanacearum by regulating the JA signaling pathway (Ghareeb, et al., 2011 ). SA is primarily involved in defense against biotrophic pathogens (Halder et al., 2019 ). 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 (Infantes et al., 2022 ). Brassinosteroid genes exhibit changes in expression levels in response to environmental stimuli (Xu et al., 1995 ). TCH4 encodes a xyloglucan endotransglucosylase / hydrolase. This change in expression may reflect a recruitment of cell wall modifying activity in response to environmental stress (Iliev et al., 2002 ). In this study, we have demonstrated that 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 pda 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. TCH4 protein were up-regulated all the time according KEGG analysis, a number of DEGs were enriched in hydrolase function, that indicting application sodium silicate could change the activity of cell wall and increasing resistance to R. solani . 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 (Zhang et al., 2017 ; Zhang et al., 2016 ; Taheri et al., 2011). In our analysis of the phenylpropanoid biosynthesis pathway, 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, phenylalanine ammonia-lyase, and the accumulation of antimicrobial glycosylated phenolics and diterpenoid phytoalexins (Jonas et al., 2013 ). The GO analysis revealed that 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 (Gehring et al., 2017; Isner et al., 2018). Conclusions In conclusion, we have identified DEGs in the subterraneous stems of potato plants that were inoculated with R. solani and simultaneously treated with sodium silicate. 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. Methods Potato and pathogen The virus-free potato ‘Atlantic’ was obtained through tissue culture and maintained at the Research Center of Potato Breeding in Inner Mongolia Agricultural University, Hohhot, China. This cultivar is known to be highly susceptible to R. solani (strain PR11, AG3). R. solani AG-3 strain PR11 was isolated from an infected potato tuber 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 (PSA, containing potato 200 g, sucrose 20 g, agar 15 g, distilled water 1000 mL)] at 4°C in the Department of Plant Pathology, Inner Mongolia Agricultural University. Potato planting and inoculation The potato was initially grown on Murashige and Skoog (MS) medium 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 additional 30 d. R. solani was cultivated on PSA at 25℃ for 5 d. Disks from 5-day-old R. solani cultures were then transferred to the center of a new PSA plate and incubated for 7 d at 25 ± 1℃. The resulting mycelium was scraped from the plates use sterile toothpicks, and the mycelium was put into sterile tube, and then rapidly frozen in liquid nitrogen to grind. The mycelium suspension was diluted to 1 × 10 7 mycelium / mL for inoculation. The potato subterraneous stems were inoculated with 3 mL of mycelial suspension (1 × 10 7 mycelium/mL) and covered with soil. For sodium silicate treatments (Group SS), 500 mL of MS nutrient solution containing NaSiO 3 · 9H 2 O (concentration 3.02 kg / L) was applied once a week. As for the control group (Group CK), mock potato plants were treated with MS nutrient solution following the same procedure. To assess the effects of the treatments, potato subterraneous stems were taken at 4, 8, and 12 days post application (dpa), with three replicates per treatment. The sample were rapidly frozen in liquid nitrogen and stored at -80℃ for further analysis. There were three replicates in each treatment. Library construction and RNA-seq For RNA extraction, an equal amount of collected tissues from each replicate at each time point (4, 8 and 12 dpa) was used. Total RNA was extracted 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, CA, USA), Qubit 2.0 Fluorometer (Life Technologies, CA, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, 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. 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. Identification and functional enrichment analysis of differentially expressed genes (DEGs) 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. GO and KEGG pathway To gain insights into the functional significance of the differentially expressed genes (DEGs), gene ontology (GO) enrichment analysis was performed using the clusterProfler 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. 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. 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 conducted according to the manufacturer’s instructions (TaKaRa, Beijing, China). Quantitative real-time PCR was carried out using Applied Biosystems equipment (Thermo Fisher, Massachusetts, 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 were 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 were performed in a 20 µL reaction volume, containing 10 µL of 2 × SYBR Green I Master (Roche, Switzerland), 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℃ for 5 min, followed by 45 cycles of 95℃ for 10 s, 58℃ for 10 s, and 72℃ for 10 s. This was followed by a melting curve analysis with a procedure of 95℃ for 5 s, annealing temperature for 1 min, and 97℃ continuous monitoring to determine the specificity of PCR amplification. Relative mRNA expression levels were calculated following modified 2 −∆∆CT method (Livak, 2001) and expressed as mean ± standard deviation (S.D.). Amplification efficiency of all genes was determined using quantitative real-time PCR with 10-fold serial diluted cDNA as template. Statistical analysis was conducted using SPSS v. 20.0 software, allowing for the evaluation of the significance of the observed differences. Ethics approval and consent to participate (Not applicable) The plant collection and use was in accordance with all the relevant guidelines. The virus-free potato ‘Atlantic’ was obtained through tissue culture and maintained at the Research Center of Potato Breeding in Inner Mongolia Agricultural University, Hohhot, China. This cultivar is known to be highly susceptible to R. solani (strain PR11, AG3). R. solani AG-3 strain PR11 was isolated from an infected potato tuber in Wuchuan county of Inner Mongolia, which was confirmed to be highly pathogenic on stems and tubers of the ‘Atlantic’ potato. Above work were did by prof. xiaoyu zhang. Our lab have permission to collect Potato plant. Declarations Consent for publication We confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to Scientific reports. Availability of supporting data The raw sequencing reads have been deposited in the NCBI SRA database under accession number PRJNA905064. The plant collection and use was in accordance with all the relevant guidelines Competing interests The authors declare that they have no competing interests. Funding The research work was partially supported by the Inner Mongolia Science and Technology Project (2022YFYZ0008), and the projects of National Natural Science Foundation of China (31460468). Author Contribution YaYan Feng, Dongmei Zhang and Hongli Huo conceived and designed research. YaYan Feng, Lele Li and Zhijun Xiu conducted experiments. YaYan Feng and Chunfang Yang contributed new reagents or analytical tools. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3978878\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":278149383,\"identity\":\"23e70877-62e1-4064-983f-36fe0fbeab43\",\"order_by\":0,\"name\":\"YaYan Feng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"YaYan\",\"middleName\":\"\",\"lastName\":\"Feng\",\"suffix\":\"\"},{\"id\":278149384,\"identity\":\"d09ea422-a1ad-4709-9ee6-a21e91e9b0ce\",\"order_by\":1,\"name\":\"jianjun hao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Maine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"jianjun\",\"middleName\":\"\",\"lastName\":\"hao\",\"suffix\":\"\"},{\"id\":278149385,\"identity\":\"4096b4ff-634c-4c53-a7de-04525b08081c\",\"order_by\":2,\"name\":\"dongmei zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Academy of Agricultural \\u0026 Animal Husbandry Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"dongmei\",\"middleName\":\"\",\"lastName\":\"zhang\",\"suffix\":\"\"},{\"id\":278149386,\"identity\":\"89dff765-57a9-49a6-94d6-4b907250553f\",\"order_by\":3,\"name\":\"hongli huo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Academy of Agricultural \\u0026 Animal Husbandry Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"hongli\",\"middleName\":\"\",\"lastName\":\"huo\",\"suffix\":\"\"},{\"id\":278149387,\"identity\":\"122996da-e91c-4049-a45d-aa1b654de71a\",\"order_by\":4,\"name\":\"lele li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"lele\",\"middleName\":\"\",\"lastName\":\"li\",\"suffix\":\"\"},{\"id\":278149388,\"identity\":\"a2ee7156-54a8-4eb3-8daf-8bdff500c9b8\",\"order_by\":5,\"name\":\"zhijun xiu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"zhijun\",\"middleName\":\"\",\"lastName\":\"xiu\",\"suffix\":\"\"},{\"id\":278149389,\"identity\":\"7ad84cfd-c9e8-40f3-9e8d-86a04c56c317\",\"order_by\":6,\"name\":\"chunfang yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"chunfang\",\"middleName\":\"\",\"lastName\":\"yang\",\"suffix\":\"\"},{\"id\":278149390,\"identity\":\"e03beefc-3e83-4238-ac52-ae4ac68b1dd6\",\"order_by\":7,\"name\":\"Xiaoyu Zhang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYHACxgcJBhJybOzNB4jWwmzwocLGmI/nWALRWtgkZ5xJS5wnkaNAnHr5GTkG0rxth9PbGHIYGH5UbCOshbHnjIExUEtuG8PZA0DObcJamNl7DJLBWhj7EpgZ24jQwsbMY3AY5DAQgzgtPOw9ho1A7yewsRGrRYLnWDEDMJAN23jYEg4S5Rf5GcnbfwCjUl5+/uODD35UEKGFgYHDAM48QIx6IGB/QKTCUTAKRsEoGLEAAD+XOU5rPAv1AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Agricultural University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xiaoyu\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-02-22 14:50:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3978878/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3978878/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":52482250,\"identity\":\"8c6f6e61-492b-4734-bd26-db65ba8e488d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:18:00\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":130730,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIdentification of differentially expressed genes (DEGs) between sodium silicate-treated and non-treated potato ‘Atlantic,’ 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.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/fa804c40ce937513dcfcc2ba.png\"},{\"id\":52481893,\"identity\":\"09073f5c-1418-4266-8aa4-006f6444ce43\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:00\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":58709,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA heat map of differentially expressed genes (DEGs) between sodium silicate-treated and non-treated potato ‘Atlantic’ stems at each time point, including 4 (a4 and b4), 8 (a8 and b8), and 12 (a12 and b12) days post application (dpa).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/548f2cd325c87276e4c7fbb9.png\"},{\"id\":52482252,\"identity\":\"81e871f9-b3bb-4169-8c53-c6a0844f348a\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:18:00\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":459411,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGene ontology (GO) term distribution of differentially genes for biological process (BP), molecular function (MF), and cellular component (CC) at 4 days post application (dpa) with sodium silicate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/2490de5da5664e8c6bc59c1f.png\"},{\"id\":52481891,\"identity\":\"4ba1a9d0-fa80-4391-ad2f-51822fe26b6d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:00\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":300560,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGene ontology (GO) term distribution of differentially genes for biological process (BP), molecular function (MF), and cellular component (CC) at 8 days post application (dpa) with sodium silicate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/46f2f5cac78d6191b486b084.png\"},{\"id\":52481894,\"identity\":\"330a5bb5-1ad8-4578-a375-9c20292575ef\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:00\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":437231,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGene ontology (GO) term distribution of differentially genes for biological process (BP), molecular function (MF), and cellular component (CC) at 12 days post application (dpa) with sodium silicate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/7c86d58532c17d0acd5f58d6.png\"},{\"id\":52482251,\"identity\":\"742da2e0-5601-478f-b8ba-b5298e47103c\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:18:00\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":224717,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferentially 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.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/4d7196883dc15b06eb8b114a.png\"},{\"id\":52482253,\"identity\":\"05e01e96-54ad-4209-8c57-c5641dbcfdb2\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:18:01\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":178766,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferentially expressed genes (DEGs) involved in the plant-pathogen interaction pathway in potato applied with sodium silicate based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/ea9db0999dbdf23ff82bc1af.png\"},{\"id\":52481897,\"identity\":\"fc746971-9086-4de1-b86d-e290e3539d5d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:00\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1313142,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe relative expression level change of 16 selected genes form DEGs by quantitative real-time PCR. Left vertical coordinate is FPKM (fragments per kilobase of transcript per million mapped reads) of RNA-Seq; right vertical coordinate is relative expression level of qRT-PCR.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/ecc8176a792213e4464eb9c9.png\"},{\"id\":54158419,\"identity\":\"106adda7-a1bf-4d1d-968e-9042d2f129ec\",\"added_by\":\"auto\",\"created_at\":\"2024-04-05 12:41:22\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1662784,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/bb774209-a8c8-4ed4-904c-96f8f0699a0c.pdf\"},{\"id\":52481900,\"identity\":\"836dd6ff-7026-4214-a641-56cee560fbb7\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:01\",\"extension\":\"doc\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14104011,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"3SupplementaryFigure.doc\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/f6b45a368556ded8de58a7ef.doc\"},{\"id\":52481899,\"identity\":\"0d01cc46-8d4f-4173-aca2-184b43326f37\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 06:10:01\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11377609,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"4SupplementaryTable.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3978878/v1/e2fcd7800d900f56445efa1f.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Transcriptional Responses of Sodium-Silicate-Induced Potato Resistance Against Rhizoctonia solani AG-3\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe potato (\\u003cem\\u003eSolanum tuberosum\\u003c/em\\u003e L.) is the fourth most-cultivated field crop, which is recognized to be important due to their abundance of essential minerals, vitamins, and antioxidants, serve as a source of food (Ali et al, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Tian et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). However, the production and quality of potatoes are threatened by various fungal diseases, including \\u003cem\\u003eRhizoctonia solani\\u003c/em\\u003e causing stem canker and black scurf, resulting in substantial marketable yield losses, sometimes up to 30% (Banville, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1989\\u003c/span\\u003e; Johnson et al., 2015; Yang et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eRhizoctonia solani\\u003c/em\\u003e constitutes a species complex with various genotypes called as anastomosis groups (AGs), and 13 AGs of \\u003cem\\u003eR. solani\\u003c/em\\u003e have been described (Ajayi-Oyetunde et al., 2018). These AGs show various degrees of host specificity, with \\u003cem\\u003eR. solani\\u003c/em\\u003e AG3 being the predominant type associated with potatoes (Tsror, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). \\u003cem\\u003eR. solani\\u003c/em\\u003e infects potatoes with either sclerotia or hyphae (Balali et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), through wounds or natural openings, such as lenticels and stomata (Yang et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The most recognizable symptoms of stem canker and black scurf are the appearance of sclerotia on the surface of potato tubers (Carling et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e1989\\u003c/span\\u003e), as well as the development of brown, dry, and sunken lesions on stems, stolon, and roots. This infection delays shoot emergence, reduces the number of stems, increases height variation, and leads to stolon and sprout pruning (Tsror, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e;Read et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e1989\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCurrent methods for controlling \\u003cem\\u003eR. solani\\u003c/em\\u003e include the use of biocontrol agents, treating seed tubers with fungicides, treating soils with fungicides, and implementing crop rotation (Tsror, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). However, these strategies are not entirely satisfied or compliant with safety requirements (Bohinc et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Pierre et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Thus, searching or developing effective means and techniques to protect potato crops and increase yields is a crucial strategy.\\u003c/p\\u003e \\u003cp\\u003eOne emerging strategy is the application of sodium silicate in field to alleviate \\u003cem\\u003eR. solani\\u003c/em\\u003e infections by inducing plant resistance (Sabes et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Saludares et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Various hypotheses have been proposed regarding the mechanisms by which sodium silicate induces plant resistance. For example, sodium silicate enhances the mechanical barrier (Mandlik et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003ea). The apoplast of plants is the initial site of fungal effector and plant receptor interactions (Bozkurt et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Wang et al., 2018). The deposition of sodium silicate in the apoplast creates a physical barrier that hinders the interaction, resulting in the prevention for further infection (Thorsten et al., 2005; Rasoolizadeh et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zheng et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). In addition to the physical barriers, sodium silicate is also believed to induce plants to activate various signaling pathways, produce secondary metabolic products, and engage in cross-talks 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 (Fawe et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e; Samuels et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e). 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 \\u003cem\\u003ePythium ultimum\\u003c/em\\u003e (Cherif et al., 1992). Similarly, sodium silicate treatment induces the accumulation of the electron-dense phenolic material that protects plants against \\u003cem\\u003eBlumeria graminis\\u003c/em\\u003e f. sp. \\u003cem\\u003etritici\\u003c/em\\u003e in wheats (B\\u0026eacute;langer et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e), and increases the accumulation of momilactone phytoalexins in leaves, enhancing resistance against \\u003cem\\u003eMagnaporthe grisea\\u003c/em\\u003e in rice (Rodrigues et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Cai et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSodium silicate also induces disease resistance in plants, particularly in tomato that do not accumulate sodium silicate. Silicon treatment increases resistance against pathogens such as \\u003cem\\u003ePseudomonas syringae\\u003c/em\\u003e and \\u003cem\\u003eColletotrichum gloeosporioides\\u003c/em\\u003e (Resende et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Dannon et al., 2004; David et al., 2012), and improves resistance against \\u003cem\\u003eR. solani\\u003c/em\\u003e in tomato through ethylene (ET), jasmonic acid (JA), and reactive oxygen species (ROS) signaling pathways (Ghareeb et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Furthermore, sodium silicate has shown inhibitory effects against root pathogens (Rasoolizadeh et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), such as \\u003cem\\u003ePythium aphanidermatum\\u003c/em\\u003e spread in the roots of bitter gourd through symplastic deposition (Heine et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), \\u003cem\\u003eFusarium\\u003c/em\\u003e in tomato (Cheng et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e)d \\u003cem\\u003esolani\\u003c/em\\u003e in potato (Huo, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), although the specific resistance mechanisms remain unclear. (Vulavala, et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe objectives of this study were to examine the resistance enhanced by sodium silicate, determine the mechanisms of the induced resistance, and identify the genes associated with the defense responses in potato during the \\u003cem\\u003eR. solani\\u003c/em\\u003e infection. Through a comprehensive analysis of gene expression, this research aims to enhance our understanding of the resistance induced by sodium silicate in potato against \\u003cem\\u003eR. solani.\\u003c/em\\u003e 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.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSequencing data summary and prediction of novel transcripts\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003eR. solani\\u003c/em\\u003e success fully infected potato Subterraneous stems and applied sodium silicate resulted in a decrease in lesion area (Supplementary Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). However, the underlying defense mechanism of sodium silicate against fungal diseases, particularly against \\u003cem\\u003eR. solani\\u003c/em\\u003e, remains poorly understood. In order to gain insights into the response of sodium silicate-treated potato subterraneous stems during \\u003cem\\u003eR. solani\\u003c/em\\u003e infection, we used Illumina next-generation sequencing technology (Illumina, San Diego, CA, USA) to analyze the transcriptomes inoculated potato subterraneous stems with and without sodium silicate-treated at 4, 8, and 12 dpa. There were three biological replicates, resulting in eighteen samples (Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn total, we obtained 771.98 MB reads with an average of 42.89 MB for each library. After stringent quality filtration, the high-quality reads ranged from 39.42 to 48.66 MB in size, and these reads were then mapped to the potato genome for further analysis (Supplementary Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). The raw sequencing reads have been deposited in the NCBI SRA database under accession number PRJNA905064. In addition, we quantified the expression levels of 65, 569 genes across all eighteen plant samples (Supplementary Table S3).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGlobal gene expression profiling analysis and identification of differentially expressed genes (DEGs)\\u003c/h2\\u003e \\u003cp\\u003eThe high repeatability and reliability of the experimental replicates were confirmed by the R\\u003csup\\u003e2\\u003c/sup\\u003e values, which ranged 0.831 to 0.93 for the six replicate groups (Supplementary Fig. \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). These results suggest that the replicate samples exhibited high repeatability and reliability. This result provide assurance of obtaining reliable differences in gene expression analyses between Group SS and Group CK.\\u003c/p\\u003e \\u003cp\\u003eTo explore the transcriptomic differences between Group SS (sodium silicate-applied treatments) and Group CK(Group control) at 4, 8, and 12 dpa, pairwise comparisons were performed separately. A total of 1491 genes were identified as DEGs, exhibiting a |log2 fold change | \\u0026gt; 2.0 and an adjusted P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA, Supplementary Tables S4-S7). Among these DEGs, 566 genes (263 up-regulated and 303 down-regulated), 253 genes (103 up-regulated and 150 down-regulated), and 759 genes (658 up-regulated and 101 down-regulated) were identified in SS4 vs CK4, SS8 vs CK8 and SS12 vs CK12, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). 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 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). Notably, the number of DEGs decreased at 8 dpa and then increased at 12 dpa, with three unique DEGs identified at each time point.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo better understand the transcriptome changes underlying sodium silicate-induced resistance in potato against \\u003cem\\u003eR. solani\\u003c/em\\u003e, 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 (Fig.\\u0026nbsp;2).\\u003c/p\\u003e \\u003cp\\u003eFigure 2. A heat map of differentially expressed genes (DEGs) between sodium silicate-treated and non-treated potato \\u0026lsquo;Atlantic\\u0026rsquo; stems at each time point, including 4 (a4 and b4), 8 (a8 and b8), and 12 (a12 and b12) days post application (dpa).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGene Ontology classification and Kyoto Encyclopedia of Genes and Genomes analysis of DEGs\\u003c/h2\\u003e \\u003cp\\u003eFunctional analysis of all DEGs was conducted using gene ontology (GO) annotation to gain functional insights into their functional categories. 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 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 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 (one hundred and twenty-seven) involved in nucleoside monophosphate metabolic process, thirty-seven genes are assigned as being involved mitochondrion, twenty-one genes are assigned as being involved NADH dehydrogenase activity; down-regulated genes are mainly (two hundred and thirty-six) assigned as being involved in carbohydrate metabolic process, inside genes are encoding cell wall degrading enzymes including several different hydrolases acting on cellulase or pectinesterase (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Supplementary Table S8). At 8 dpa, five up-regulated DEGs were involved in defense response, and sixty-one DGEs were associated with enzyme regulator activity, including five genes involved in defense response monooxygenase activity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, Supplementary Table S9). At 12 dpa, up-regulated DEGs (one hundred and sixty-five) were mainly associated with secondary metabolic process, while down-regulated DEGs (thirty-seven) encoded endopeptidase activity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, Supplementary Table S10).\\u003c/p\\u003e \\u003cp\\u003eTo identify the biochemical pathways in which the DEGs were involved, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed for potato (organism code \\u0026lsquo;sot\\u0026rsquo; ). 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDEGs involved in the plant hormone signal transduction pathway\\u003c/h2\\u003e \\u003cp\\u003ePlant hormones play a crucial role in plant defense responses (Bari et al., 2009), and in this study, 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 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, Supplementary Table S11).\\u003c/p\\u003e \\u003cp\\u003eIn 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 12dpa, 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDEGs involved in the plant-pathogen interaction pathway\\u003c/h2\\u003e \\u003cp\\u003e According to the KEGG pathway analysis, 9 DEGs in potato induced by sodium silicate were associated with the plant-pathogen interaction pathway (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e, 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 patterns recognition receptors (PRRs), which triggers a basal defense response known as PAMP / PRR-triggered immunity (PTI) (Monaghan et al., 2012).\\u003c/p\\u003e \\u003cp\\u003eCalcium signaling is essential in plant perception of PAMPs. Calcium is transmitted by calcium-dependent protein kinase (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 (Gao et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). 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 while their expression was down-regulated at 8 dpa. The activation of molecular signaling mechanisms following perception of pathogen associated molecular patterns (PAMPs) has been extensively studied in patterns recognition receptors (PRRs). Fagellin-sensing 2 (FLS2), as PRRs in most higher plants, can induce a series of defense responses (Lee et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). 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. Calcium signaling is essential in plant perception of PAMPs. Calcium is transmitted by calcium-dependent protein kinase (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 (Gao et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). 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 while their expression was down-regulated at 8 dpa. The activation of molecular signaling mechanisms following perception of pathogen associated molecular patterns (PAMPs) has been extensively studied in patterns recognition receptors (PRRs). Fagellin-sensing 2 (FLS2), as PRRs in most higher plants, can induce a series of defense responses (Lee et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). 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.\\u003c/p\\u003e \\u003cp\\u003ePathogens can successful 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) (Jones, et al., 2006). In this study, we identified ETI gene PRM1-interacting protein 4 (RIN4) was up-regulated at 4 dpa and 12 dpa while down-regulated at 8 dpa.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDEGs involved in the phenylpropanoid biosynthesis pathway\\u003c/h2\\u003e \\u003cp\\u003eThe phenylpropanoid biosynthesis pathway, responsible for the production of secondary metabolites, plays a primary role in plant resistance (Taheri et al., 2010). We identified 9 DEGs involved in the phenylpropanoid biosynthesis pathway in response to sodium silicate application (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Supplementary Table S13). Among these DEGs, phenylalanine ammonia-lyase (PAL) is the key enzyme that initiates the phenylpropanoid pathway and is known to respond to pathogen infections. We have found three PAL transcripts, with two being up-regulated at 4 dpa and 12 dpa, while down-regulated at 8 dpa. The remaining PAL transcript was down-regulated at 4 dpa and 8 dpa, and up-regulated at 12 dpa. Furthermore, lignin, a well-characterized component, plays vital role in defense against pathogens (Li, et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In our data, six DEGs encoding 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDifferentially expressed genes (DEGs) involved in the phenylpropanoid biosynthesis (PB) pathway of potato \\u0026lsquo;Atlantic\\u0026rsquo; under sodium silicate treatments, measured after 4, 8, and 12 days post application.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003egene ID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKEGG entry\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKO ID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTimes identified as a DEG\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGene name (Predicted)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eKEGG definition\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eGene description\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400003155\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102581073\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK01904\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4CL3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4-coumarate\\u0026ndash;CoA ligase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4-coumarate:CoA ligase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400007178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102578747\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK09754\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCYP450\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-coumaroyl quinate/shikimate 3'-hydroxylase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400010465\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102605677\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK00430\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eperoxidase 21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eperoxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePeroxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400023458\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102582618\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK10775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePAL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ephenylalanine ammonia-lyase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePhenylalanine ammonia-lyase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400024967\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102592844\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK00430\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eperoxidase 51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eperoxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePeroxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG400031457\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102596891\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK10775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePAL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ephenylalanine ammonia-lyase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePhenylalanine ammonia-lyase 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG401021549\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102596343\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK10775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePAL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ephenylalanine ammonia-lyase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePhenylalanine ammonia-lyase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG402014734\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102590830\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK09754\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCYP450\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-coumaroyl quinate/shikimate 3'-hydroxylase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePGSC0003DMG402025083\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102585990\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eK00430\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eperoxidase 12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eperoxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePeroxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ea-Times of a gene identified as a DEG in three comparations (SS4-CK4, SS8-CK8 and SS12-CK12).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eValidation of the gene expression of DEGs by qRT-PCR\\u003c/h2\\u003e \\u003cp\\u003eTo ensure the reliability of the RNA sequencing (RNA-seq) data, we conducted qRT-PCR analysis to validate the relative expression levels of these 16 genes at 4, 8, and 12 dpa (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e; supplementary Table S14). These genes were randomly chosen for the validation analysis. The qRT-PCR 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThrough RNA-seq analysis, we have identified a total of 1491 differentially expressed genes (DEGs) in response to sodium silicate treatment. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed enrichment in pathways associated with primary metabolism, secondary metabolism, and defense responses. These findings indicate that sodium silicate triggers a wide range of mechanisms in potato plants.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eRhizoctonia solani\\u003c/em\\u003e secretes toxins and cell wall-degrading enzymes to attack plant tissues and induces host cell necrosis and nutrient acquisition (Mengiste, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). To defend the pathogen infection, the plant cell wall acts as a physical barrier (Cai et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Hayasaka et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Sodium silicate deposition in the potato cell wall, as observed in our study, hinders the growth of \\u003cem\\u003eR. solani\\u003c/em\\u003e 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 fortifies the cell wall and enhances resistance to \\u003cem\\u003eR. solani\\u003c/em\\u003e (Zhang et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn addition to physical barriers, plants have evolved complex immune systems, including pattern-triggered immunity (PTI) and effector-triggered immunity (ETI), as the front line of defense against pathogens (Jones et al., 2006). We found FLS2, a well-known pattern recognition receptor (PRR) (Yuan et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), was up regulated at 4 and 8 days post-application (dpa) of sodium silicate. This suggests that sodium silicate triggers the pattern recognization in potato cells.\\u003c/p\\u003e \\u003cp\\u003eCalcium (Ca2\\u003csup\\u003e+\\u003c/sup\\u003e), a versatile intracellular second messenger involved in various signaling pathways by activating proteins such as calcium-dependent protein kinase (CDPK) and respiratory burst oxidase homologue (Rboh), leading to reactive oxygen species (ROS) production (Johnson et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Sodium silicate has been shown to mediate defense against \\u003cem\\u003eRalstonia solanacearum\\u003c/em\\u003e in tomato through ROS signaling (Ghareeb et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). In our study, CDPK and CAM / CML genes were up regulated at 4 and 12 dpa, suggesting that sodium silicate modulates Ca2\\u003csup\\u003e+\\u003c/sup\\u003e levels in potato cells, triggering a series of defense responses and enhancing plant resistance. Additionally, we observed up-regulation of the receptor protein RIN4, which plays a role in defense signaling pathways (Li et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), further supporting the activation of ETI in response to \\u003cem\\u003eR. solani\\u003c/em\\u003e infection (Jones et al., 2006).\\u003c/p\\u003e \\u003cp\\u003ePlant secondary metabolites, such as plant hormones function as defense or signaling molecules in biotic and abiotic stress responses (Pane et al., \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). 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 \\u003cem\\u003eR. solani\\u003c/em\\u003e infection.\\u003c/p\\u003e \\u003cp\\u003eSignal transduction pathways have extensively been studied. Auxin signaling can have different or even opposing regulatory roles in plant immunity depending on pathogen lifestyles, which has been associated with necrotrophic fungi (Zhang, et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003ea; Llorente, et al., 2008). Auxin response genes, such as GH3 and SAUR, act as key effectors in regulating plant immunity in response to hormonal and environmental signals (Hong et al., 2015; Domingo et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Because \\u003cem\\u003eR. solani\\u003c/em\\u003e is a necrotrophic pathogen (Zrenner et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), we believe it would fall into this category.\\u003c/p\\u003e \\u003cp\\u003eET and JA signaling pathways synergistically interact with auxin signaling to confer necrotrophic resistance (Yang et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Glazebrook, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Velivelli et al., \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). JA, primarily induced by necrotrophic pathogens, involves in plant defense (Zrenner et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). JAZ proteins, when combined with jasmonoyl-isoleucine, activate the expression of the JA signaling pathway to regulate plant immunity (Liu, et al., \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Sodium silicate application has been shown to enhance tomato resistance against \\u003cem\\u003eRalstonia solanacearum\\u003c/em\\u003e by regulating the JA signaling pathway (Ghareeb, et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). SA is primarily involved in defense against biotrophic pathogens (Halder et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). 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 (Infantes et al., \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Brassinosteroid genes exhibit changes in expression levels in response to environmental stimuli (Xu et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e). TCH4 encodes a xyloglucan endotransglucosylase / hydrolase. This change in expression may reflect a recruitment of cell wall modifying activity in response to environmental stress (Iliev et al., \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn this study, we have demonstrated that 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 pda 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. TCH4 protein were up-regulated all the time according KEGG analysis, a number of DEGs were enriched in hydrolase function, that indicting application sodium silicate could change the activity of cell wall and increasing resistance to \\u003cem\\u003eR. solani\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003ePhenylpropanoid 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 (Zhang et al., \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Taheri et al., 2011). In our analysis of the phenylpropanoid biosynthesis pathway, 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, phenylalanine ammonia-lyase, and the accumulation of antimicrobial glycosylated phenolics and diterpenoid phytoalexins (Jonas et al., \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe GO analysis revealed that 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 (Gehring et al., 2017; Isner et al., 2018).\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, we have identified DEGs in the subterraneous stems of potato plants that were inoculated with \\u003cem\\u003eR. solani\\u003c/em\\u003e and simultaneously treated with sodium silicate. 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 \\u003cem\\u003eR. solani\\u003c/em\\u003e infection. This study provides valuable insights into the application of sodium silicate-induced resistance against \\u003cem\\u003eR. solani\\u003c/em\\u003e in potato and enhances our understanding of the underlying mechanisms.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003ch2\\u003ePotato and pathogen\\u003c/h2\\u003e\\u003cp\\u003eThe virus-free potato ‘Atlantic’ was obtained through tissue culture and maintained at the Research Center of Potato Breeding in Inner Mongolia Agricultural University, Hohhot, China. This cultivar is known to be highly susceptible to \\u003cem\\u003eR. solani\\u003c/em\\u003e (strain PR11, AG3). \\u003cem\\u003eR. solani\\u003c/em\\u003e AG-3 strain PR11 was isolated from an infected potato tuber 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 (PSA, containing potato 200 g, sucrose 20 g, agar 15 g, distilled water 1000 mL)] at 4°C in the Department of Plant Pathology, Inner Mongolia Agricultural University.\\u003c/p\\u003e\\u003ch2\\u003ePotato planting and inoculation\\u003c/h2\\u003e\\u003cp\\u003eThe potato was initially grown on Murashige and Skoog (MS) medium 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 additional 30 d.\\u003c/p\\u003e\\u003cp\\u003e \\u003cem\\u003eR. solani\\u003c/em\\u003e was cultivated on PSA at 25℃ for 5 d. Disks from 5-day-old \\u003cem\\u003eR. solani\\u003c/em\\u003e cultures were then transferred to the center of a new PSA plate and incubated for 7 d at 25 ± 1℃. The resulting mycelium was scraped from the plates use sterile toothpicks, and the mycelium was put into sterile tube, and then rapidly frozen in liquid nitrogen to grind. The mycelium suspension was diluted to 1 × 10\\u003csup\\u003e7\\u003c/sup\\u003e mycelium / mL for inoculation.\\u003c/p\\u003e\\u003cp\\u003eThe potato subterraneous stems were inoculated with 3 mL of mycelial suspension (1 × 10\\u003csup\\u003e7\\u003c/sup\\u003e mycelium/mL) and covered with soil. For sodium silicate treatments (Group SS), 500 mL of MS nutrient solution containing NaSiO\\u003csub\\u003e3\\u003c/sub\\u003e · 9H\\u003csub\\u003e2\\u003c/sub\\u003eO (concentration 3.02 kg / L) was applied once a week. As for the control group (Group CK), mock potato plants were treated with MS nutrient solution following the same procedure.\\u003c/p\\u003e\\u003cp\\u003eTo assess the effects of the treatments, potato subterraneous stems were taken at 4, 8, and 12 days post application (dpa), with three replicates per treatment. The sample were rapidly frozen in liquid nitrogen and stored at -80℃ for further analysis. There were three replicates in each treatment.\\u003c/p\\u003e\\u003ch2\\u003eLibrary construction and RNA-seq\\u003c/h2\\u003e\\u003cp\\u003eFor RNA extraction, an equal amount of collected tissues from each replicate at each time point (4, 8 and 12 dpa) was used. Total RNA was extracted 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, CA, USA), Qubit 2.0 Fluorometer (Life Technologies, CA, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA).\\u003c/p\\u003e\\u003cp\\u003eMessenger 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.\\u003c/p\\u003e\\u003ch2\\u003eSequence alignment\\u003c/h2\\u003e\\u003cp\\u003eTo 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.\\u003c/p\\u003e\\u003cp\\u003eThe 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.\\u003c/p\\u003e\\u003cp\\u003eSubsequently, 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 \\u0026gt; 4 and low variation across three biological replicates (coefficient of variation \\u0026lt; 30%) were considered reliable and included in subsequent analyses. This stringent selection ensured the inclusion of only robust and consistent genes for further analysis.\\u003c/p\\u003e\\u003ch2\\u003eIdentification and functional enrichment analysis of differentially expressed genes (DEGs)\\u003c/h2\\u003e\\u003cp\\u003eThe 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 \\u0026lt; 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.\\u003c/p\\u003e\\u003ch2\\u003eGO and KEGG pathway\\u003c/h2\\u003e\\u003cp\\u003eTo gain insights into the functional significance of the differentially expressed genes (DEGs), gene ontology (GO) enrichment analysis was performed using the clusterProfler R package, which corrects for gene length bias. GO terms with corrected p-values below 0.05 were deemed significantly enriched among the DEGs.\\u003c/p\\u003e\\u003cp\\u003eKEGG 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. 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.\\u003c/p\\u003e\\u003ch2\\u003eReal-time qPCR analysis\\u003c/h2\\u003e\\u003cp\\u003eTo 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 conducted according to the manufacturer’s instructions (TaKaRa, Beijing, China). Quantitative real-time PCR was carried out using Applied Biosystems equipment (Thermo Fisher, Massachusetts, 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 were 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.\\u003c/p\\u003e\\u003cp\\u003eThe PCR amplification were performed in a 20 µL reaction volume, containing 10 µL of 2 × SYBR Green I Master (Roche, Switzerland), 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℃ for 5 min, followed by 45 cycles of 95℃ for 10 s, 58℃ for 10 s, and 72℃ for 10 s. This was followed by a melting curve analysis with a procedure of 95℃ for 5 s, annealing temperature for 1 min, and 97℃ continuous monitoring to determine the specificity of PCR amplification. Relative mRNA expression levels were calculated following modified 2\\u003csup\\u003e−∆∆CT\\u003c/sup\\u003e method (Livak, 2001) and expressed as mean ± standard deviation (S.D.). Amplification efficiency of all genes was determined using quantitative real-time PCR with 10-fold serial diluted cDNA as template. Statistical analysis was conducted using SPSS v. 20.0 software, allowing for the evaluation of the significance of the observed differences.\\u003c/p\\u003e\\u003ch2\\u003eEthics approval and consent to participate (Not applicable)\\u003c/h2\\u003e\\u003cp\\u003e The plant collection and use was in accordance with all the relevant guidelines. The virus-free potato ‘Atlantic’ was obtained through tissue culture and maintained at the Research Center of Potato Breeding in Inner Mongolia Agricultural University, Hohhot, China. This cultivar is known to be highly susceptible to \\u003cem\\u003eR. solani\\u003c/em\\u003e (strain PR11, AG3). \\u003cem\\u003eR. solani\\u003c/em\\u003e AG-3 strain PR11 was isolated from an infected potato tuber in Wuchuan county of Inner Mongolia, which was confirmed to be highly pathogenic on stems and tubers of the ‘Atlantic’ potato. Above work were did by prof. xiaoyu zhang. Our lab have permission to collect Potato plant.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eConsent for publication\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to Scientific reports.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eAvailability of supporting data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe raw sequencing reads have been deposited in the NCBI SRA database under accession number PRJNA905064.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The plant collection and use was in accordance with all the relevant guidelines\\u003c/p\\u003e\\n\\u003cp skip=\\\"true\\\"\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe research work was partially supported by the Inner Mongolia Science and Technology Project (2022YFYZ0008), and the projects of National Natural Science Foundation of China (31460468).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Author Contribution\\u003c/p\\u003e\\n\\u003cp\\u003eYaYan Feng, Dongmei Zhang and Hongli Huo conceived and designed research. YaYan Feng, Lele Li and Zhijun Xiu conducted experiments. YaYan Feng and Chunfang Yang contributed new reagents or analytical tools. YaYan Feng and Jianjun Hao analyzed data. YaYan Feng and Xiaoyu Zhang wrote the manuscript. All authors read and approved the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAli, M.R.; Parmar, A.; Niedbała, G.; Wojciechowski, T.; Abou El-Yazied, A.; El-Gawad, H.G.A.; Nahhas, N.E.; Ibrahim, M.F.M.; El-Mogy, M.M. Improved shelf-life and consumer acceptance of fresh-cut and fried potato strips by an edible coating of garden cress seed mucilage. \\u003cem\\u003eFoods. \\u003c/em\\u003e\\u003cstrong\\u003e2021\\u003c/strong\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e, 1536. https://doi.org/10.3390/ foods10071536 \\u003c/li\\u003e\\n\\u003cli\\u003eBanville, G. J. 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M. cGMP signalling in plants: from enigma to main stream. \\u003cem\\u003eFunct. plant biol.\\u003c/em\\u003e. \\u003cstrong\\u003e2018\\u003c/strong\\u003e. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Solanum tuberosum L., stem canker and black scurf, differentially expressed genes, RNA sequencing\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3978878/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3978878/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eStem canker and black scurf of potatoes, caused by \\u003cem\\u003eRhizoctonia solani\\u003c/em\\u003e, is a highly destructive worldwide. In controlling the disease, the application of sodium silicate in potato fields has shown promise in improving potato resistance against \\u003cem\\u003eR. solani\\u003c/em\\u003e, although the underlying mechanism remains unclear. In this study, we used RNA sequencing analysis to examine the transcriptome of potato subterraneous stems of potato plants. These stems were both inoculated with \\u003cem\\u003eR. solani\\u003c/em\\u003e and treated with sodium silicate, while a control group received no sodium silicate treatment. Transcriptome analysis was performed at 4, 8, and 12 days post-application (Group SS) and compared with the control (Group CK). A total of 1491 genes were identified as differentially expressed genes (DEGs). Furthermore, these DEGs are involved in hydrolase activity, plant-pathogen interactions, hormone signal transduction, and the phenylpropanoid biosynthesis pathway. These findings suggest that the application of sodium silicate induces a complex defense network in plants, involving physical barriers, innate immunity, phytohormone signaling, and various phenylpropanoid compounds, to combat \\u003cem\\u003eR. solani\\u003c/em\\u003e 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.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Transcriptional Responses of Sodium-Silicate-Induced Potato Resistance Against Rhizoctonia solani AG-3\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-12 06:09:55\",\"doi\":\"10.21203/rs.3.rs-3978878/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"66715ddd-057f-4194-9319-2ef6e1e49b8e\",\"owner\":[],\"postedDate\":\"March 12th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-05T12:33:10+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-03-12 06:09:55\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3978878\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3978878\",\"identity\":\"rs-3978878\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}