Transcriptome responses to Ralstonia solanacearum infection in the tetraploid potato

doi:10.21203/rs.3.rs-3291585/v1

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Transcriptome responses to Ralstonia solanacearum infection in the tetraploid potato

https://doi.org/10.21203/rs.3.rs-3291585/v1

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Potato (Solanum tuberosum L.) is a vital global food source, but its growth can be severely impacted by Ralstonia solanacearum (R. s) infection. Despite extensive research, the molecular mechanisms of potato resistance to this pathogen are not fully understood. Huashu No.12, a tetraploid potato genotype, has been identified as highly resistant to R. s. In this study, Huashu No.12 and Longshu No.7 were inoculated with R. s to evaluate disease resistance. Results showed that Huashu No.12 exhibited significantly higher resistance to R. s compared to Longshu No.7, with increased reactive oxygen species (ROS) and lignin content, as well as an abundance of corpus callosum and strong autofluorescence in the phloem sieve tube. Related enzymes, such as SOD, CAT, POD, PAL, and PPO, were also found to contribute to Huashu No.12's resistance to R. s disease. Transcriptome sequencing revealed 659 differentially expressed genes between resistant and susceptible materials, with the ERF family having the most differentially expressed genes. GO and KEGG analyses provided further insights into the genetic basis and molecular mechanisms underlying plant defense against R. sdisease. Overall, this study highlights the crucial role of related enzymes and differential gene expression in the resistance of Huashu No.12 to R. s, providing valuable insights into potato breeding for disease resistance.

Potato

Ralstonia solanacearum

RNA-seq

Potato (Solanum tuberosum L.) is the world's third most important food crop. Unfortunately, it is vulnerable to infection by Ralstonia solanacearum, one of the most aggressive bacterial pathogens affecting potato crops. This bacterium is responsible for causing R. s, a devastating disease that results in enormous losses in food production (Wang et al. 2019). R. s has a wide host range and can infect more than 450 plants in 54 families. In China, R. s disease has been reported in 30 provinces, with more in southern and eastern areas than northern and western areas (Jiang et al. 2017). Due to its complex physiology and adaptability, the bacterium can survive in the soil for several years, making it difficult to eradicate (Liu et al. 2017). R. s is a vascular disease that enters plants through wounds in their roots and rapidly colonizes the xylem, producing excess exopolysaccharides that prevent the flow of water. As a result, the plant wilts and eventually dies, no effective prevention measures have been found for this disease (Boschi et al. 2017). Breeding of crop cultivars with suitable resistance is regarded as a key approach for integrated management of bacterial wilt (Deng et al. 2014).

When pathogens invade plants, the cell wall functions as the primary barrier to prevent pathogen invasion. In the early stages of infection, the plant synthesizes lignin and callose, which limit the spread of the pathogen within plant tissues. Concurrently, the activities of PAL, POD, PPO, CAT, and SOD change, promoting the lignification of the plant cell wall and inhibiting pathogen invasion (Mellersh and Heath 2001). Reactive oxygen species (ROS) signals from chloroplasts may be one of the key regulators of intracellular signal transduction because they regulate both retrograde and systemic signals during stress conditions (SUZUKI et al. 2012).

One approach for addressing the challenges posed by plant-pathogen interactions is to integrate omics data with other information to better understand the control mechanisms involved. Transcriptome sequencing, in particular, has become increasingly widely used in recent years to study plant disease resistance mechanisms (Woo, Masclaux-Daubresse and Lim 2018). The high late blight resistant potato genotype SD20 was subjected to exogenous ethylene (ET) application for gene expression profiling, revealing specific transcriptional responses to the treatment. The analysis of differentially expressed genes (DEGs) identified a total of 1226 ET-specific DEGs that included significantly differentially expressed transcription factors, kinases, defense enzymes, and disease resistance-related genes (Yang et al. 2020). Employed a NaCl treatment and time-course RNA sequencing to identify salt stress response genes in Longshu No. 5, providing valuable insights into the molecular mechanisms underlying salt tolerance in potatoes (Li et al. 2020).

Wild-type potato S. commersonii has been shown to exhibit resistance to both biotic and abiotic stressors (Andino et al. 2021). Microarray analysis was performed to investigate the response of a resistant its genotype against R. s, revealing that at an early stage of infection, the photosynthetic process was greatly inhibited and several genes encoding proteins related to ROS production showed significant differential expression (Narancio et al. 2013). RNA-seq analysis was used to investigate its transcriptional response to R. s, revealing that the number of differentially expressed genes was significantly greater than observed in susceptible controls. Moreover, approximately half of the up-regulated genes were found to be involved in plant-pathogen interactions(Zuluaga et al. 2015). While much of the research on potato bacterial wilt has focused on wild-type materials in recent years, relatively few studies have been conducted on cultivated materials, largely due to low levels of resistance in tetraploid varieties.

In this study, the tetraploid potato Huashu No.12, which has been shown to exhibit high resistance to bacterial wilt, was used to investigate the regulatory factors underlying disease resistance through determination of physiological indices and transcriptome analysis. The aim of this study was to provide valuable insights into the molecular mechanisms of the interaction between potato and R. s by identifying defense-related pathways relevant to this process.

Plant materials and bacterial strain

Two individuals from tetraploid potato showing significant resistance to R.solanacearum were used (Longshu No.7, susceptible and Huashu No.12, resistant). The seedlings were grown to 9–10 leaf stage in a greenhouse at about (22 ± 2) °C and photoperiod of 16 h for inoculation of R. s.

The strain of R. s P2 (phylotype Ⅰ) was isolated from infected potato plants in western Guangdong, China, and used to inoculate test plants. Bacteria were grown overnight in nutrient broth medium containing glucose (10 g/L), peptone (5 g/L), and beef extract (3 g/L) at pH 7.0 and 28°C with shaking at 200 rpm. Prior to plant inoculation, the bacterial concentration was adjusted to 10⁷ colony-forming units cfu/ml. Control plants were inoculated with sterile water. Each test group consisted of 10 plants, with three replications conducted for each bacterial resistance test.

Plant inoculation and disease rating

To investigate the incidence and severity of R. s infection, plants were inoculated using the root injury method as described by Hayward (Hayward 2010). Inoculations were performed with the same bacterial concentration and at a consistent time each day, and incidence was monitored daily for a total of 21 days. The plants were assessed for disease symptoms using a disease score (DS) ranging from 0 to 9 (0:no wilted leaves;1:1–2 leaves wilting;3:1/3 leaves of the plant wilted;5:1/2 leaves of the plant wilt;7:2/3 leaves of the plant wilted;9:all leaves of plants wilt or die)(Kim-Lee et al. 2005).

Calculate disease index: DI = Σ (number of the plants with a specific DI×DI)/ (total number of inoculated plants×9) ×100%. Calculating the relative value of the disease: RV= (DI of susceptible control-DI of the tested plant material)/ (DI of susceptible control) ×100%. Resistance is divided into 5 grades according to relative value of disease index: grade1, high resistance (RV > 75%), grade3, medium resistance (75%≥RV > 55%), grade5, resistance (55%≥RV > 35%), grade 7, susceptibility (35%≥RV > 15%), grade9, high susceptibility (RV < 15%)(Chen et al. 2013).

Differential staining of cross section of stem base

ROS Detection

At four different time points during the experiment (0, 24, 48 and 72 hours post-infection, or hpi), a 1 cm section of stem located 1 cm from the base was harvested and sliced. The slices were then placed at a temperature of 25℃ for 15 min. Cross sections of the stem, with a thickness of 1 mm, were subsequently submerged in a solution of 5 mL 50 mmol/L DHR123(dihydrorhodamine) for 30 minutes while kept in darkness, following the protocol described by Jones and Dangl (Jones and Dangl 2006). Confocal images were captured using a Zeiss LSM800 laser scanning fluorescence microscope equipped with two-color imaging capability at wavelengths of 480 nm and 510 nm. These images were used to assess the extent and location of fluorescence produced by the DHR123 stain, which has been shown to be correlated with the presence of ROS in cells.

Lignin Detection

At two time points during the experiment (0 and 7 days post-infection, or dpi), a 1 cm section of the stem base was removed and sliced. The slices were then immersed in a solution of 5% phloroglucinol in 95% ethanol for 2–5 min, and subsequently treated with concentrated hydrochloric acid for 3–6 min. The slices were then rinsed with water and observed under a microscope, with images captured for later analysis. This approach enables researchers to visualize lignin deposition in the plant stems, which is a key component of the plant's defense response (Bernards and Razem 2001).

Corpus callosum detection

At 2 dpi, sections of the plant stem were treated with aniline blue stain, a fluorescent dye that binds to chitin, a component of fungal cell walls. After staining, the sections were immediately observed under an ultraviolet fluorescence microscope (Ferreira et al. 2017).

Enzyme activity determination

At four different time points during the experiment (0, 24, 48, and 72 hpi), a 1 cm section of the stem located 1 cm from the root was harvested, washed with distilled water, and immediately placed in liquid nitrogen before being stored at -80°C. To ensure statistical rigor, five plants were selected for each time point, with three biological replicates performed. To extract proteins from the frozen stem tissue, the stems were ground into a fine powder using liquid nitrogen, and then mixed with nine times the volume of phosphate buffer (0.1 mol/L, pH 7-7.4). The mixture was centrifuged at 4°C for 15 min at 12,000 rpm, and the resulting supernatant was collected for subsequent testing.

To assess the activities of various enzymes involved in the plant's defense response, reactions were carried out according to the manufacturer's instructions provided with the commercial assay kit (Nanjing Jiancheng Bioengineering Co., Ltd.). The reaction solution was measured using both an ultraviolet spectrophotometer and Tecan Spark microplate reader, with absorbance values substituted into a formula to calculate the activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), phenylalanine ammonia-lyase (PAL), and polyphenol oxidase (PPO). CAT enzymatic activity is expressed as the amount of enzyme required to decompose 1 µmol hydrogen peroxide per minute under pH 7.0 conditions, and is presented in U/mg protein. PAL enzymatic activity is expressed as an increase of 0.01 at 290 nm per minute, and is presented in U/g. POD enzymatic activity is expressed as an increase of 0.01 at 470 nm per minute, and is presented in U/g. PPO enzymatic activity is expressed as an increase of 0.01 absorbance units at 398 nm per minute, and is presented in U/g. SOD activity is expressed as the amount of enzyme required to inhibit 50% of NBT photochemical reduction, and is presented in U/g.

Transcriptome analysis

RNA extraction was performed using commercial kits (Beijing Huayueyang Biotechnology Co., Ltd), with the purity, concentration, and integrity of RNA samples assessed using multiple methods, including Nanodrop, Qubit 2.0, and Aglient 2100. Reverse transcription of cDNA was carried out using a commercially available kit (Novizan Biological Co., Ltd). Data analysis was conducted using the bioinformatics pipeline provided by BMKCloud (www.biocloud.net).

Raw data obtained from the sequencing platform was subjected to offline filtering to obtain clean data for subsequent analysis. Clean data was then compared to a specified reference genome sequence to generate mapped data, which was then used to perform library quality assessments such as insert fragment length tests and random inspections. Structural level analysis, including alternative splicing analysis, new gene mining, and gene structure optimization, were performed on the mapped data.

Differential expression analysis was conducted using gene expression levels in different sample groups, followed by functional annotation and enrichment analysis of differentially expressed genes (DEGs).

qRT-PCR validation

To confirm the accuracy and reliability of the sequencing results, 10 differentially expressed genes were selected and subjected to quantitative real-time polymerase chain reaction (qRT-PCR) validation. The relative expression levels of each target gene were calculated using the 2^−ΔΔCt method. To perform qRT-PCR, primers for both the reference gene 18S rRNA (actin gene) and target genes were synthesized by Sangon Biotech Co., Ltd. qRT-PCR amplification was then conducted using the Real Universal Color Fluorescence Quantitative Premix Kit (Tiangen Biotech Co., Ltd.), following the manufacturer's instructions.

All primers utilized in this study were designed using the software Primer Premier 5.0. By confirming the differential expression of selected genes through qRT-PCR analysis, researchers can validate the accuracy and reliability of the RNA sequencing data, ensuring that any subsequent analysis is based on a solid foundation of high-quality data.

Statistical analysis

Statistical analysis was performed using Microsoft Excel 2019 and IBM SPSS Statistics 26 software. Graphs were generated using Origin 2022 and Adobe Illustrator 2021, allowing for clear visualization of the data and facilitating a more thorough understanding of the experimental results.

Identification of R. s resistance inoculation

The bacterial strain P2 was inoculated into the root systems of two different potato cultivars, Huashu No.12 and Longshu No.7, using the root injury method. Disease progression was monitored over a period of 21 days, and DI was calculated as an indicator of the severity of R. s infection. Results showed that at 16 dpi, the DI of Longshu No.7 reached its maximum value of 100%, while the DI of Huashu No.12 was only 6.22%. By 18 dpi, the DI of Huashu No.12 had increased to 7.97% (Fig. 1A). The relative value (RV) of the DI was calculated to be 92.03%, indicating that Huashu No.12 is a potato cultivar with high resistance to R. s.

At 4 dpi, some Longshu No.7 samples had already exhibited grade 1 wilting symptoms, as shown in Fig. 1C. In contrast, leaves of Huashu No.12 did not exhibit any signs of wilting at this time (Fig. 1B). By 6 dpi, one-third of Longshu No.7 leaves were wilted, reaching grade 3 of the DI scale, while no wilting was observed in Huashu No.12. By 8 dpi, two-thirds of Longshu No.7 leaves were partially wilted, and the DI had reached grade 7, whereas no leaf wilting was observed in Huashu No.12. At 12 dpi, all the leaves of Longshu No.7 were wilted, resulting in a DI of grade 9, while some Huashu No.12 plants still had a DI of grade 1. At 18 dpi, wilting symptoms were observed in one-third of Huashu No.12 leaves, reaching a DI of grade 3 (Fig. 1A). Collectively, these findings suggest that Huashu No.12 exhibits a strong resistance to R. s infection compared to Longshu No.7.

Histological effects of R. s infection in potato plants

When observed under laser confocal microscopy at 480 nm and 510 nm wavelengths, a large amount of strong yellow fluorescence appeared in the stem base of Huashu No.12 at 24 hpi (Fig. 2). This yellow fluorescence weakened at 48 hpi and was almost gone by 72 hpi. In contrast, Longshu No.7 did not exhibit any fluorescence after infection with the P2 strain (Fig. 2). These results suggest that Huashu No.12 produces a large amount of ROS at the base of the stem within 24 hours of inoculation in response to R. s infection, which may prevent the pathogen from spreading to the top of the plant. The content of ROS then decreased gradually over time until it disappeared by 72 hpi. In contrast, Longshu No.7 did not produce any ROS in response to R. s infection. Confirm the ROS as hydrogen peroxide through comparison.

Both Huashu No.12 and Longshu No.7 have a certain amount of lignin when not inoculated with R. s, but Huashu No.12 has a higher lignin content than Longshu No.7 (Fig. 3). At 7 dpi, the lignin content of both varieties increased, but the increase was greater in Huashu No.12 than in Longshu No.7. This suggests that Huashu No.12 may have a stronger ability to produce lignin in response to R. s infection, which is consistent with its stronger resistance to this pathogen.

The corpus callosum was stained with aniline blue solution at 2 dpi, and Huashu No.12 exhibited an abundance of corpus callosum located in the sieve tube of phloem tissue (Fig. 4A, C, E). In contrast, Longshu No.7 showed callosal padding filling the sieve tubes in the cell communication area between phloem tissue and cortical parenchyma cells (Fig. 4B, D, F). After inoculation with R. s, the defense response of Huashu No.12 increased, leading to an increase in the number of catheter tissues exhibiting strong autofluorescence in lignified tissues (Fig. 4E). The unique location and abundance of corpus callosum in Huashu No.12 may contribute to its stronger resistance to R. s infection, potentially by enhancing its ability to transport defense-related molecules through the phloem tissue.

Comparative analysis of enzyme activity

The activities of CAT, PAL, POD, PPO, and SOD as detected in Huashu No.12 and Longshu No.7 at different time points (0 hpi, 24 hpi, 48 hpi, and 72 hpi) in response to R. s infection. The activity of CAT enzyme decreased at 24 hpi in both varieties, but the decrease rate was higher in Longshu No.7 than in Huashu No.12. The CAT activity then increased from 24–48 hpi, and the increase rate was significantly higher in Huashu No.12 than in Longshu No.7. The CAT activity then decreased again from 48–72 hpi, but the decrease rate was slower. Overall, the CAT activity was higher in Huashu No.12 than in Longshu No.7, except for 0 hpi (Fig. 5A).

The activities of PAL and POD increased within 24 hpi and then decreased in both varieties, with Huashu No.12 having a higher activity for both enzymes compared to Longshu No.7 (Fig. 5B, C).

The PPO enzyme activity initially showed a downward trend within 24 hpi, with the decrease rate being higher in Huashu No.12 than in Longshu No.7. It then showed an upward trend, with the increase rate of Longshu No.7 being significantly higher than that of Huashu No.12 within 24–48 hpi, and then the increase rate of Huashu No.12 being slightly higher than that of Longshu No.7 (Fig. 5D).

The SOD enzyme activity increased within 48 hpi and then decreased, with Huashu No.12 having a higher activity compared to Longshu No.7 throughout the entire time course. These results suggest that Huashu No.12 may have a stronger ability to regulate the activities of these enzymes in response to R. s infection, which is consistent with its stronger resistance to this pathogen (Fig. 5E).

RNA‑sequencing and mapping

Based on SBS technology, we sequenced 18 samples using the Illumina high-throughput sequencing platform. We employed FASTQ format to assess the sequencing quality and obtained high-quality Clean Data by removing any artefacts and low-quality reads from our data. In total, we generated 125.24 Gb Clean Data across all 18 samples, with each sample yielding Clean Data of 5.87 Gb in size and a Q30 bases percentage of 90.62% or more (Supplementary Table 1). Next, we aligned the Clean Reads of each sample against the potato reference genome v4.03 and achieved an alignment efficiency ranging from 83.61–86.86%. Finally, the percentage of Reads in the Clean Reads ranged from 80.94–84.02%.

Differential expression analysis

We utilized DESeq2 (1.6.3) to perform differential expression analysis between sample groups in order to obtain a set of differentially expressed genes (DEGs) between the two biological conditions. For DEG detection, we used Fold Change (FC) ≥ 2 and false discovery rate (FDR) < 0.01 as screening criteria, which were obtained by correcting the p-value. The number of DEGs in each set is shown in Fig. 6. Our results demonstrate that four DEGs were down-regulated in Huashu No.12 compared to 24 hpi. Meanwhile, there were 199 DEGs at 24 hpi and 48 hpi, of which 44 were up-regulated and 155 were down-regulated. We also identified 272 DEGs between 0 hpi and 48 hpi, of which 63 were up-regulated and 209 were down-regulated (Fig. 6). Comparing with the DEGs identified at 24 hpi, Longshu No.7 exhibited a much greater differential expression pattern with a total of 3736 DEGs detected, of which 2026 were up-regulated and 1710 were down-regulated. In detail, we found 72 DEGs at 24 hpi and 48 hpi, of which 18 were up-regulated and 54 were down-regulated, and identified 1769 DEGs at 0 hpi and 48 hpi, of which 854 were up-regulated and 915 were down-regulated (Fig. 6, Supplementary Table 2).

Moreover, our Venn diagram analysis showed that a total of 5858 DEGs were detected in both Huashu No.12 and Longshu No.7, among which 659 DEGs were common at 0 hpi, 24 hpi, and 48 hpi (Fig. 7).

GO Enrichment of DEGs

To identify the significantly enriched Gene Ontology (GO) terms for the differentially expressed genes (DEGs), we used R/topGO (2.18.0) and set the parameter First Sig Nodes = 10. Our analysis covered three branches of GO, namely biological process, cellular component, and molecular function.

Most of the enriched biological process GO terms were linked to cellular process (GO: 0009987, 1146 DEGs), metabolic process (GO: 0008152, 1116 DEGs) and single-organism process (GO: 0044699, 1096 DEGs). In the cellular component class, a large number of the DEGs were annotated to cell (GO: 0005623, 1217 DEGs), cell part (GO: 0044464, 1217 DEGs) and organelle (GO: 0043226, 958 DEGs). Most of the enriched molecular function GO terms were related to binding (GO: 0005488, 925 DEGs), catalytic activity (GO: 0003824, 856 DEGs) and transporter activity (GO: 0005215, 119 DEGs) (Fig. 8, Supplementary Table 3).

KEGG Enrichment of DEGs

Kyoto Encyclopedia of Genes and Genomes orthologs (KEGG) was used to analyze the DEGs in each treatment. 6392 DEGs had KEGG annotation and were assigned to 342 KEGG pathways. The results of KEGG pathway enrichment analysis of differentially expressed genes are as follows (Fig. 9, Supplementary Table 4).

A large number of DEGs were annotated to plant-pathogen interaction, MAPK signaling pathway-plant, plant hormone signal system, carbon metabolism and phenylpropanoid biosynthesis (Fig. 9A). Synthesis and degradation of ketone bodies, glycosphingolipid biosynthesis-ganglio series, nicotinate and nicotinamide metabolism, ABC transporters, lipoic acid metabolism were the most significantly enriched in DEGs (Fig. 9B).

Differential expression of transcription factors in response to R. s

Among the 5858 genes differentially regulated by R. s, 419 encoded transcription factors (TFs) belonging to 63 families (Supplementary Table 5). Most TFs were ERF, followed by MYB, bHLH, WRKY, C2H2, bZIP, MYB-related, NAC, GRAS, HB-HD-ZIP, GNAT and others (Fig. 10).

qRT-PCR Verification of DEGs

In order to verify the validity of transcriptome sequencing results, 10 DEGs were randomly selected for qRT-PCR validation (Supplementary Table 6). The log₂Fold Change value verified by qRT-PCR was basically consistent with the transcriptome sequencing results (Fig. 11).

The tetraploid potato genotype Huashu No.12 is an excellent material for R. s resistance

In field evaluations aimed at screening potato materials for R. s resistance, somatic hybrids created by crossing S. commersonii and S. tuberosum displayed phenotypes more similar to those of cultivated potato, and the resulting F₂ progeny showed promising transfer of beneficial traits from S. commersonii into the cultivated background (Cardi, Mazzei and Frusciante 2002). In a recent study, SSR alleles were identified for bacterial wilt resistance breeding in S. chacoense-somatic hybrids and backcross progenies up to the BC3 generation (Chen et al. 2016). However, despite efforts to utilize somatic hybrid material, no registered cultivars have been produced thus far (Tiwari et al. 2017). The results of this study revealed that Huashu No.12 exhibited high resistance to phylotype Ⅰ R. s, as determined by the DI investigation conducted over 21 days (Fig. 1). Based on these findings, it can be concluded that Huashu No.12 represents an excellent candidate for the development of tetraploid potato varieties with enhanced resistance to R. s.

Histological effects of R. s infection in potato plants

The quick generation of ROS has been associated with the innate immunity of plants(Saeed and Trujillo 2022, Kobayashi et al. 2012). Several findings indicate that the resistance or susceptibility of late blight may be governed by the equilibrium between the activation of ROS/antioxidants (both in terms of intensity and timing) and the trophic stage of P. infestans (El_Komy et al. 2020). To gain insights into the biochemical factors underlying tomato plant's response to R. s, it is crucial to investigate ROS generation, antioxidative mechanisms, and cell wall reinforcement in two distinct cultivars, Arka Meghali and BT-10, the results suggest that enhanced ROS production, along with an efficient antioxidative system, reduced lipid peroxidation rate, and increased lignin deposition in the cell wall, could play a key role in the resistance of tomato plants against R. s (Mandal, Das and Mishra 2011). In the present study, we investigated the dynamics of ROS generation in two potato cultivars, Huashu No.12 and Longshu No.7, following inoculation with R. s. Our results showed that at 24 hpi, Huashu No.12 exhibited the highest ROS content, which subsequently decreased over time. In contrast, Longshu No.7 did not produce any detectable ROS, indicating the involvement of ROS in the mechanism of potato resistance to bacterial wilt caused by R. s.

Previous studies have shown that secondary metabolites, including phenylpropanoids and biosynthesis related to cell wall synthesis, participate in the defense responses of potato against P. infestans by synthesizing lignin and strengthening the cell wall (Yogendra et al. 2017, Kashyap et al. 2020). The lignin content of the resistant potato variety Orion significantly increased after infection compared to the susceptible variety Majestic. The increased activity of PAL and lignin content improved the resistance of potato tubers to P. infestans and Fusarium sulphureum (HENDERSON and Friend 1978, Fan et al. 2021). In this study, the lignin content of Huashu No.12 increased more than that of Longshu No.7, it shows that lignin is involved in potato resistance to R. s related reactions.

Inoculation of plants with endophytic bacteria has been shown to confer a protective effect against pathogenic bacteria. This protection is thought to be mediated through several mechanisms, including cell wall reinforcement, increased lignification and callose deposition, which hinder the successful colonization of pathogenic bacteria (Rodriguez et al. 2019, Kawa and Brady 2022). In this study, we observed that in Huashu No.12, the corpus callosum is located within the sieve tube of phloem tissue, and in Longshu No.7, it is situated in the region of cellular communication between the phloem tissue and cortical parenchyma cells. Notably, the corpus callosum liner was found to fill the sieve tube of Huashu No.12. Our results suggest that this difference in the position of the corpus callosum between the two cultivars may be attributed to variations in the expression of corpus callosum synthase gene following infection with R. s. This disparity could play a crucial role in conferring resistance to R. s infection in Huashu No.12.

Effects of R. s on activities of several potato defense enzymes

Plants have evolved sophisticated mechanisms to defend themselves against pathogenic microorganisms, and increasing the activity of resistance-related enzymes is one such strategy. In the field of plant disease resistance, CAT is the most studied enzyme (Bereika et al. 2020). Previous research has demonstrated that susceptible plant varieties tend to exhibit higher levels of CAT activity than resistant varieties in the absence of R. s inoculation (El-Argawy and A. Adss 2016), a finding that is consistent with the results of our study. In our investigation, while the CAT activity of Huashu No.12 was initially lower than that of Longshu 7 at 0 hpi, it surpassed the CAT activity of Longshu No.7 at all other time points.

PAL is a critical enzyme involved in the phenylpropanoid metabolic pathway, which plays a crucial role in the biosynthesis of secondary metabolites such as lignin and phenols. These metabolites are known to be involved in various physiological and defense-related processes in plants (Xie et al. 2017). As such, PAL is regarded as a vital defensive enzyme, essential for conferring protection against biotic and abiotic stresses in plants. Our study found that the PAL activity of Huashu No.12 was consistently higher than that of Longshu No.7 at each time point following inoculation with R. s, with the greatest disparity observed at 24 hpi. This suggests that 24 hpi may be a critical period for potato resistance to bacterial wilt, and highlights the central role played by PAL in conferring resistance to R. s infection in potato plants.

PPO and POD are two commonly studied defense-related enzymes in plants that play a critical role in the plant's immune response against pathogenic microorganisms. These enzymes act as biocontrol agents, increasing the plant's immune response by catalyzing the production of ROS and other signaling molecules that help to activate and regulate the plant's defense mechanisms (Moussa et al. 2022, Narasimha Murthy, Uzma and Chitrashree 2014, Cantos, Espín and Tomás-Barberán 2001). Therefore, PPO and POD are considered essential defense enzymes that can enhance the plant's resistance against diseases and other stress factors. Our study showed that the activity of POD initially increased and then decreased following inoculation with R. s, with the amplitude of increase being greater in Huashu No.12 than in Longshu No.7. Conversely, the activity of PPO first decreased and then increased, with the decrease rate being greater in Huashu No.12 than in Longshu No.7. These findings suggest that the regulation of POD and PPO activities varies between potato cultivars in response to R. s infection, highlighting the importance of these defense-related enzymes in conferring resistance against bacterial wilt in potato plants.

SOD plays a crucial role in plant cells' ability to resist viral infections. As such, it is widely regarded as an essential enzyme in the defense machinery of plants (Shafi et al. 2017). The SOD activity of Huashu No.12 was higher than that of Longshu No.7, which increased first and then decreased.

Transcriptomic investigation of the impact of R. s on enhancing potato resistance against bacterial wilt

In recent years, there has been a growing use of transcriptome sequencing in the study of the mechanisms underlying plant disease resistance, indicating its increasing applicability and utility (Cho et al. 2016, Sade et al. 2015, Cao et al. 2020, Sajeevan et al. 2023, Balotf et al. 2022).

A systematic investigation of the transcriptomic dynamics of Qingshu No.9 infected with P. infestans to identify resistance-related genes. Many DEGs, including transcription factor genes, were found to be significantly enriched in biosynthesis, plant-pathogen interaction, and photosynthesis pathways. Several genes related to disease resistance also showed differential expression during infection (He et al. 2021).

R. s infection in potato plants can trigger changes at the transcriptional level, leading to alterations in gene expression and subsequent modulation of various physiological processes that underlie the plant's defense response. We utilized DESeq2 (1.6.3) software to identify a set of DEGs between two biological conditions, using FC ≥ 2 and (FDR < 0.01 as the screening criteria. Our Venn diagram analysis revealed that a total of 5858 DEGs were detected in both conditions. GO annotation analysis showed that the DEGs were mainly involved in cellular processes, metabolic processes, single-organism processes, cell, and organelle. KEGG pathway enrichment analysis indicated that the DEGs were mainly associated with plant-pathogen interaction, MAPK signaling pathway-plant, plant hormone signal system, carbon metabolism, and phenylpropanoid biosynthesis. Furthermore, we identified 420 TFs belonging to 50 families that regulate these DEGs, with ERF, MYB, and bHLH being the most common families.

Author contributions

Conceptualization, Jin Hui; investigation, Zhu Xi and Zhang Yu; data curation, Shao Shunwei; writing—original draft preparation, Chen Zhuo; writing—review and editing, Jin Hui, Zhu Xi and Chen Zhuo. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Hainan Province (Approval No. 323QN296).

The Conflict of Interest

“The authors declare no conflict of interest.”

Data Availability Statement

Additional data are made available in supplementary tables of this manuscript.

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SupplementaryTable1.xlsx
Supplemental Table 1 Basic summary of RNA-sequencing results
SupplementaryTable2.xlsx
Supplemental Table 2 The total DEGs between R and S
SupplementaryTable3.xlsx
Supplemental Table 3 GO analysis of differential genes
SupplementaryTable4.xlsx
Supplemental Table 4 KEGG Enrichment of DEGs
SupplementaryTable5.xlsx
Supplemental Table 5 Number of TFs in different families differentially expressed
SupplementaryTable6.xlsx
Supplemental Table 6 Sequences of primers used for qRT-PCR

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Transcriptome responses to Ralstonia solanacearum infection in the tetraploid potato

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Abstract

Figures

Introduction

Materials and methods

Plant materials and bacterial strain

Plant inoculation and disease rating

Differential staining of cross section of stem base

ROS Detection

Lignin Detection

Corpus callosum detection

Enzyme activity determination

Transcriptome analysis

qRT-PCR validation

Statistical analysis

Results

Comparative analysis of enzyme activity

RNA‑sequencing and mapping

Differential expression analysis

GO Enrichment of DEGs

KEGG Enrichment of DEGs

qRT-PCR Verification of DEGs

Discussion

Declarations

References

Supplementary Files

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