Tobacco plant (K326 cultivar) and R. solani AG3 were used in this study. All plants were grown in the greenhouse at 25°C and 70% relative humidity with a 12/12h of day/night light. The fifth and sixth leaves from the lower part of a nine-leaf–stage potted tobacco plant were collected for friction inoculation. A mycelium-containing agar disk (diameter, 5 mm) was inoculated at the inoculation point, with a friction area of approximately 4 mm². The mycelial plug was inoculated on the tobacco leaf according to the above-mentioned method for the control; six inoculation points were selected on each tobacco leaf. The plant was grown in a greenhouse at 25°C and 90% relative humidity. The fungal cake was removed 2 d after inoculation. The infection status was analyzed at the inoculation points after 6, 12, 24, 48, 72, 96, and 120 h. Symptomatic leaves were obtained from the infected symptomatic tobacco plants. Samples from each of the three tobacco plants were harvested during each treatment and pooled as a mixed line. Samples were stored at -80°C until use for RNA-seq. In total, 21 samples were analyzed: seven time points × three replicates each.

Total RNA was extracted as previously described (Peng et al., 2021). Briefly, approximately 0.1 g of symptomatic leaves per sample was ground into a homogenate in liquid nitrogen, and 1 mL of TRIzol (TransGen, Beijing, China) was added to each sample. The total RNA–containing supernatant was washed with an equivalent volume of chloroform; the total RNA was precipitated using isopropanol and dissolved in RNase-free water with RNase inhibitors. The total RNA quality and quantity were assessed using a SpectraMax NanoDrop system (Thermo Fisher Scientific, MA, USA). All samples had RNA integrity numbers higher than 8.0, indicating relatively intact and protein-free RNA. The RNA-seq library was prepared using 4 μg total RNA, with the KAPA Stranded mRNA-Seq Library Preparation Kit (Kapa Biosystems, Roche, Basel, Switzerland). RNA-seq libraries were used for cluster formation with the HiSeq X PE Cluster Kit V2.5 on an Illumina cBOT cluster generation system (Illumina) and sequenced in one lane by using the Illumina HiSeq 2500 platform in the 100 bp paired-end mode.

Raw data were separated by barcodes by using the Illumina pipeline (CASAVA, version 1.8.2). Illumina adapters were removed using the Cutadapt software (version 1.4.1), and base calls with average phred scores of less than 20 and lengths below 50 bp were filtered out using the Trimmomatic software (version 0.39). The filtered and trimmed reads in each library were aligned by using TopHat2 version 2.1.0. GO enrichment was functionally annotated using Blast2GO version 2.5 against the non-redundant protein sequence database of the National Center for Biotechnology Information (NCBI). Differentially expressed genes (DEGs) were identified by mapping each gene to the KEGG database for pathway enrichment by using clusterProfiler3 version 4.0. KEGG pathway enrichment analysis of the correlation was performed using p-values adjusted using Benjamini–Hochberg correction for the false discovery rate.

To observe ROS accumulation, the tobacco leaves were harvested at 72 hours after inoculated with R. solani AG3. The harvested tobacco leaves were stained with (2 mg/ml, pH 4) 3,3’-Diaminobenzidine (DAB) in the dark overnight, then add decolorizing solution (ethanol/acetic acid/glycerol 3:1:1) to decolorize the leaves at 95°C for 15 min. To evaluate the elimination effect of ROS, the pYBA1143-based constructs of Perox genes were transformed into the Agrobacterium tumefaciens strain EHA105 and co-injected with apoptosis regulator BAX. The BAX and pYBA1143 vector served as a positive and negative control, respectively.

Samples were collected under identical conditions and at similar time points for real-time PCR (qPCR) analysis. The experiment was performed in triplicates and repeating three times. Reverse transcription and qPCR assays were performed as previously described (Peng et al., 2022), with minor modifications. RNA (1 μg) was reverse-transcribed to cDNA by using HiScript III All-in-one RT SuperMix Perfect (Vazyme, Nanjing, China), following the manufacturer’s protocols. All the primers were designed using the online GenScript Real-time PCR Primer Design software and checked for specificity by using the NCBI Primer-BLAST bioinformatics tool. Primers are provided in Table S2 . qPCR was performed using NovoStart SYBR qPCR SuperMix Plus (Novoprotein, Suzhou, China), according to the manufacturer’s protocol, on a C1000 Touch Thermal Cycler with a CFX96 Real-Time System (Bio-Rad, CA, USA).

The tobacco leaf symptoms noted after R. solani AG3 inoculation are shown in Figure 1 . At 12 hpi, no obvious change was noted at the inoculation site. At 24 hpi, the wounds gradually expanded and started turning yellow at their edges. At 48 hpi, the inoculation site showed obvious yellowing, and the abaxial side exhibited occasional aerial mycelia. At 72 hpi, the inoculation site developed water-soaked circular or elliptical spots and the lesion site exhibited remarkable chlorosis. The color of the lesion gradually deepened, and it showed two-tiered brown and gray concentric rings at 96 hpi. At 120 hpi, the lesion eventually developed three layers of concentric whorls and showed partial necrosis.

To investigate the changes in the tobacco transcript levels at different stages of infection by R. solani, we used high-throughput sequencing technology to assess gene expression in tobacco after inoculation. Transcriptomic analysis of the inoculation experiment results involved analysis at seven time points, with three biological repeats at each time point; thus, a total of 21 samples were tested. The raw RNA-seq data for the 21 tobacco samples were submitted to the NCBI database under accession number SRP400587: SRR21777789 - SRR21777809. After the filtration process, a total of 215.32 Gb clean data were acquired from 21 samples; the total clean reads of each sample varied from 49.85 to 78.58 M. The Q20 and Q30 values for each sample were greater than 99% and 95%, respectively ( Supplementary Table S1 ).

To identify which gene expression pattern had changed and to determine the time points at which these alterations occurred, we investigated the number of DEGs at various time points (12, 24, 48, 72, 96, and 120 h) after the tobacco plant was inoculated with R. solani AG3 ( Figure 2A ). The lowest number of DEGs (974) was found at 12 hpi; 576 transcripts were differentially induced, while 398 genes were differentially reduced. The highest number of DEGs (1205) was identified at 120 hpi; 661 genes were found to be differentially induced, while 544 were differentially reduced. A total of 1090 DEGs (723 upregulated and 367 downregulated) were identified at 24 h, 1024 DEGs (650 upregulated and 374 downregulated) at 36 h, and 1071 DEGs (690 upregulated and 381 downregulated) at 96 hpi. Among these DEGs, 378 were continuously expressed at all time points after inoculation ( Figure 2B ).

Queries were run against the GO database for each DEG in order to categorize and annotate the possible functions of these DEGs at different infection stages ( Figure 3A ). GO enrichment analysis showed that the common DEGs were significantly enriched in metabolism and biosynthesis categories, included “carbohydrate metabolic process,” “polysaccharide metabolic process,” and “amide biosynthetic process.” GO enrichment analysis showed that the GO terms exhibited distinct expression patterns in the different infection stages, and common DEGs were significantly enriched in metabolism and biosynthesis categories. The number of DEGs associated with “pectin catabolic process” gradually increased from 12 to 48 hpi and reached the highest level at 72 hpi; subsequently, slow decline was noted at 96 and 120 hpi but the number remained higher than that at 24 hpi. In addition, the cell wall macromolecule catabolic process and cell wall polysaccharide catabolic process began at 12 hpi and peaked at 48 hpi, while the cell wall polysaccharide metabolic process and macromolecule metabolic process began at 48 hpi and peaked at 96 hpi. The findings suggest that these time points were critical for successful R. solani infestation. KEGG enrichment analysis was performed to further investigate the specific R. solani pathways involved during plant infection ( Figure 3B ). The findings showed that “pentose and glucuronate interconversions,” “starch and sucrose metabolism,” and “cytochrome P450” were the common significantly enriched pathways during the process of R. solani infection. The complete GO and KEGG results are shown in Supplementary Tables S3 , S4 .

On the basis of the GO and KEGG analyses, we extracted expression data for pectin-degrading (PD) and cytochrome P450 enzymes, considering their key roles in pathogenesis. We found 34 genes potentially encoding cytochrome P450 enzymes exhibited two synchronous but diametrically opposite expression patterns at different time points. These results indicate that the cytochrome P450 gene family might competitively antagonize the functional response at different infection stages ( Figure 4A ). The expression of 31 PD enzyme–encoding genes was analyzed and showed a consistent pattern throughout infection. The expression abundance of the entire PD gene family increased to different degrees, suggesting that PD enzymes play a key role during plant infection ( Figure 4B ). To determine whether PD can enhance the pathogenicity of R. solani, PD8 was transiently overexpressed in N. benthamiana. We found there were significantly big lesions in PD8–overexpression plants leaves infected by R. solani than wild-type ( Figure 5A, B ).

In addition, we found that reactive oxygen species (ROS) are produced in large quantities in tobacco leaves during R. solani infection ( Figure 6A ). Correspondingly, R. solani express dedicated detoxification mechanisms in order to counteract host ROS and survive oxidative stress. To elucidate the mechanism of detoxification of H₂O₂, we analyzed the relative expression of FAD/NAD binding oxidoreductase (FNBO) and peroxidase (Perox) gene family, which play an important role in reducing oxidative damage. Our results showed that four FNBO genes and three Perox genes have sustained upregulation at different infection stages ( Figure 6B ). Next, we performed a ROS stain experiment by co-injecting Perox3 and apoptosis regulator BAX. The result showed that none of pYBA1143 empty vector and pYBA1143-Perox3 were able to produce ROS, and apoptosis regulator BAX can induce the production of large amounts of ROS. In contrast, co-injection of Perox3 and apoptosis regulator BAX resulted in a significant decrease of ROS production and reveal that Perox3 has a great capacity for ROS scavenging ( Figure 6C ).

To investigate the evolutionary relationship of R. solani PD members, 31 aligned PD protein sequences from R. solani were used to construct a phylogenetic tree using RAxML software and the Maximum Likelihood method ( Figure 7A ). The evolutionary tree indicated that the PD family classified into four subfamilies (Named I to IV). The subfamilies with the most members were subclass III (18) and those with the fewest members were subfamilies II (3) whereas PD24 form a separate clade. To further disclose the potential interactions between pectin degrading enzymes proteins, the pectin degrading enzymes protein family interaction network was generated based on the String server ( Figure 7B ). As a result, 32 nodes and 70 edges were obtained in the protein-protein interaction network. PD13 was predicted to interact with 18 pectin degrading enzymes proteins and other enzymes proteins and PD4, PD5, PD8, PD30 were predicted to interact with eight proteins. These results suggested that PD were the key pathogenicity-related genes in R. solani.

To verify the RNA-seq results, 12 genes were chosen for qPCR on the basis of their RNA-seq results to further examine their expression patterns ( Figure 8 ). These genes are mainly involved in ROS responses and plant defense responses (Sasaki et al., 2002). This analysis included genes that were significantly up- or downregulated in the infected samples at various time points. The expression patterns of these selected genes were validated using qPCR, and the relative fragments per kilobase million (FPKM) transcript levels from the transcriptome exhibited strong correlation, indicating that the RNA-seq results were reliable. Furthermore, we note that the expression patterns of PD4 and PD8 were in good agreement, though the degree of expression varied, which indicated that they may be participated in similar regulation mechanisms as well as similar biological functions.