Screening of Differentially Expressed Genes in Tomato Defense against Meloidogyne incognita Based on RNA-seq and Expression Characteristics Analysis of RPP13 Gene

doi:10.13560/j.cnki.biotech.bull.1985.2025-0657

Abstract

Abstract:

Objective By conducting RNA-seq and weighted gene co-expression network analysis on resistant and susceptible tomato materials inoculated with root-knot nematodes (RKNs) ( Meloidogyne incognita), we aim to focus on the signaling pathways and genes related to disease resistance, verify the root expression patterns of target genes, and provide theoretical basis for subsequent molecular function research on these genes. Method The resistant lines ‘18060’ and susceptible lines ‘Moneymaker’ were used for inoculation, and root samples were collected for transcriptome sequencing and WGCNA at the time points of no inoculation, 2 d and 4 d after inoculation The main pathways and regulatory genes that might be involved in disease resistance were screened, and the determination of the tissue-specific expression and root expression of these target genes were completed. Result As the inoculation time prolonged, the number of differentially expressed genes (DEGs) gradually increased. In the comparison between the resistant and susceptible groups at 2 dpi, 1 900 DEGs were screened out, and at 4 dpi the number of DEGs increased to 3 012. KEGG analysis showed that the “plant-pathogen interaction” was significantly enriched in both the within-group and between-group comparisons. In the between-group comparison, the DEGs enriched in this pathway mainly encoded receptor-like proteins/kinase, calcium signaling-related proteins, heat shock proteins, and transcription factors, etc. Among which 5 R genes encoding RPP13 protein were also screened out. Focusing on the DEGs during the inoculation period, the significant module MEbrown was screened out via the WGCNA, and the top 10 nodes based on the in degree and out degree were selected as hub genes based on the in degree and out degree. Two DEGs encoding RPP13 were found among them. The tissue-specific expression and root expression pattern identification further indicated that Solyc07g039410.3 might play a key role in tomato defense against the invasion of M. incognita. Conclusion Six RPP13 genes potentially involved in the process of resisting M. incognita are screened out. Among them, Solyc07g039420.1 and Solyc07g039410.3 demonstrate specific expression characteristics in the roots. At 4 dpi, the hub gene Solyc07g039410.3 is significantly induced and its expression increases by 6.23 times, indicating this gene plays a critical role in the defense of tomato against M. incognita.

Key words: tomato, root-knot nematode, disease resistance, RPP13 gene, RNA-seq, WGCNA, root expression pattern

FENG Cheng-hao, DANG Yu-le, WANG Zhi-ze, NIE Wei-dan, YANG Zhong-min, DU Chong. Screening of Differentially Expressed Genes in Tomato Defense against Meloidogyne incognita Based on RNA-seq and Expression Characteristics Analysis of RPP13 Gene[J]. Biotechnology Bulletin, 2026, 42(4): 141-152.

Figures/Tables 12

Fig. 1 Statistics of root galls numbers after inoculation of disease-resistant tomato '18060' and disease-susceptible tomato 'Moneymaker'A: The staining results of the root parts of the tested tomato materials after inoculation with acid fuchsin solution. B: Number statistics of root-knots on the roots of 10 tested tomato plants for both resistant and susceptible materials

Table 1 Statistics and quality assessment of transcriptome sequencing data

样本Sample	干净数据Clean data （Gb）	Q30 （%）	GC含量 GC content （%）	总比对率Total mapped reads	唯一映射的读段Unique mapped reads	基因区间Intergenic （%）	外显子Exon（%）
MM-0d-1	5.88	96.29	42.35	97.11	94.01	5.95	88.38
MM-0d-2	6.16	95.50	42.19	97.14	94.26	6.21	87.17
MM-0d-3	5.74	96.37	42.37	97.38	94.42	5.69	88.99
MM-2d-1	6.08	95.65	42.19	96.96	94.18	5.97	87.66
MM-2d-2	5.98	95.62	42.34	97.16	94.55	5.86	87.97
MM-2d-3	6.54	96.54	42.33	97.58	94.94	5.41	89.42
MM-4d-1	5.87	96.32	42.33	96.60	93.88	5.72	89.04
MM-4d-2	5.76	96.50	42.27	97.60	95.10	5.55	89.74
MM-4d-3	6.05	96.17	42.32	97.03	94.51	5.89	89.19
XH-0d-1	6.03	96.50	42.30	95.98	93.72	6.10	88.70
XH-0d-2	6.10	96.10	42.29	95.87	93.65	6.18	88.21
XH-0d-3	6.07	96.27	42.22	94.94	92.70	6.31	87.35
XH-2d-1	6.21	95.78	42.36	95.29	93.21	6.39	87.50
XH-2d-2	6.29	95.56	42.17	94.96	92.97	6.58	86.90
XH-2d-3	5.98	95.89	42.26	95.30	92.81	6.16	88.18
XH-4d-1	6.43	96.39	42.35	96.58	94.26	5.90	88.63
XH-4d-2	6.42	96.04	41.95	95.90	93.85	6.14	87.90
XH-4d-3	6.10	95.95	42.20	96.21	93.99	5.95	88.32

Fig. 2 PCA distribution of 18 samples of the tested tomatoes based on RNA-seqThe different coordinates refer to different principal components, and the percentages refer to the contribution value of the corresponding principal component to the sample’s variance. Each point indicate a sample, and samples in different groups are marked by different colors and shapes

Fig. 3 Statistical analysis of DEGs under different comparison strategies (A) and analysis of the co-expression trend within the resistant and susceptible groups (B)

Fig. 4 KEGG enrichment pathway of DEGs in different comparison groups

Fig. 5 Cluster analysis of FPKM values of DEGs enriched in the plant-pathogen interaction pathway in the XH groupThe heatmap shows the FPKM values of DEGs in the XH group at different treatment stages, the colors of the modules indicate the size of FPKM, and the green arrows indicate the DEGs that were jointly selected

Fig. 6 Cluster analysis of log2FC values for DEGs enriched in the plant-pathogen interaction pathway during inter-group comparisons (MM_vs_XH)The cluster heatmap indicates the statistical log2FC values of DEGs in the MM_vs_XH group comparison at different inoculation stages, the colors and sizes of the circles indicate the numerical values of FPKM, and the green arrows indicate the DEGs that are jointly selected

Fig. 7 Hierarchical clustering and module divisionA: Selection of the optimal soft threshold. B: The system clustering tree of DEGs and the division of gene modules, with different colors indicating different modules

Fig. 8 Correlation analysis between the module and different samplesThe horizontal axis indicates different materials and different inoculation periods, the vertical axis indicates the module name, and the darker the color of the module, the stronger the correlation

Fig. 9 Construction diagram of co-expression networkThe darker the color of a node, the greater the number of in degree. The larger the circle, the greater the number of out degree. The nodes within the dotted box indicate the top ten hub genes in terms of in degree and out degree respectively

Fig. 10 Analysis of the tissue-specific expressions of the RPP13 genesns: Significant difference (one-way ANOVA, P>0.05), *:P<0.05, **P<0.01, *** P<0.001. The same below

Fig. 11 Identification of the root expression pattern of the RPP13 genes

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