MAP kinase and plant–pathogen interactions govern male Zizania latifolia responses to Ustilago esculenta during the early stages of infection

Male Z. latifolia plants at the five-leaf stage were injected with the resulting U. esculenta cell suspension (TR), while male Jiaobai plants injected with potato dextrose liquid medium served as controls (CK). Inoculation and sampling were performed as described previously in the studies of Zhou (2021) and Xu et al. (2023). Male Z. latifolia leaves at 3 hr, 12 hr and 24 hr (TR-3 hr, TR-12 hr and TR-24 hr) after inoculation and controls (CK) were frozen in liquid nitrogen. Total RNA was extracted using an RNAprep Pure Plant Kit (Tiangen Biotech [Beijing] Co. Ltd., Beijing, China), and mRNA was purified by magnetic beads with Oligo dT. Transcriptome sequencing was conducted by Beijing Biomarker Technologies Co. Ltd. (Beijing, China). To ensure the accuracy of the analysis, strict quality control was exercised in relation to the original data. Reads containing joints, reads with a proportion of N >10%, reads with a mass value of Q ≤10, and alkali bases accounting for >50% of the whole read were removed. The remaining reads were aligned against the Z. latifolia genome database (http://ibi.zju.edu.cn/ricerelativesgd/download.php). The HSAT2 system was used for comparison, and StringTie software (https://ccb.jhu.edu/software/stringtie/index.shtml) was used to assemble and quantify the data. Three biological replicates were included, and r (Spearman’s correlation coefficient) was used to evaluate the correlation of biological replicates. The closer r² is to 1, the higher the correlation between biological replicates, and the more reliable the results. DEGs among sample groups (CK-3 hr vs. TR-3 hr, CK-12 hr vs. TR-12 hr and CK-24 hr vs. TR-24 hr) were analysed using DESeq2 software (Love et al., 2014). Among the comparisons, CK-3 hr vs. TR-3 hr represents the comparison between the treatment group (TR-3 hr) and the control group (CK-3 hr) at 3 hr after inoculation. DEGs were screened using the criteria of a fold change in expression level of ≥2 and p ≤ 0.05 for up- and downregulated genes.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to clarify the biological functions of DEGs. The Goseq R package (Young et al., 2010) was used for GO enrichment analysis of DEGs, and KOBAS software (Bu et al., 2021) was used to assess pathways enriched in DEGs. The plant TF database (PlantTFDB, http://planttfdb.gao-lab.org/index.php) was used to identify TFs among DEGs, and the number of TFs and their families were obtained.

Twenty DEGs were selected and real-time fluorescence quantitative PCR (qPCR) was used to confirm the reliability of the transcriptome data. Total RNA from male Z. latifolia leaves of the same batch was extracted with ReverTra Ace qPCR RT Master Mix (Code No. FSQ-201: Toyobo [Shanghai] Biotechnology Co., Ltd., Shanghai, China) and reverse-transcribed into cDNA. Specific primers were designed with Primer Premier 5.0 (Kakhki et al., 2019). Sequences of primers are shown in Table S1 in Supplementary Materials. A CFX96 Real-time System (BIO-RAD, CA, USA) and TB Green Premix Ex Taq II fluorescent reagent (Takara, Dalian City, China) were used for detection following the manufacturer’s instructions, and ZlActin2 served as an internal reference gene (Wang et al., 2017). The 2^-ΔΔCq method (Zhou et al., 2020), where ΔCq = Cq (gene of interest) – Cq (Actin2) and ΔΔCq = ΔCq (treatment, inoculated with U. esculenta) – ΔCq (CK, inoculated with potato dextrose liquid medium), was used to estimate the relative expression levels of DEGs.

WGCNA was used to cluster differential genes with common expression and high correlations into the same module, then select target modules; KEGG enrichment analysis was then performed on the target module to identify important metabolic pathways, the complete metabolic pathway was drawn and relevant DEGs were screened. DEGs were detected by qPCR using the primers shown in Table 1. Combined with the transcriptome results, candidate genes of male Z. latifolia involved in response to infection by U. esculenta were determined.

Microsoft Excel 2016 software (Microsoft Corp., Redmond, WA, USA) was used for basic processing of test data, IBM SPSS statistics 25 software (Jackson and Ukwe, 2022) was used for analysis of variance (p ≤ 0.05 indicated statistical significance), and SigmaPlot 14.0 (Khata et al., 2019) and Origin 2019b 32bit (Rusnac et al., 2021) were used for mapping.

Twenty DEGs were selected, specific primers were designed and the reliability of transcriptome data was assessed by qPCR. The results showed that expression of 19 differential genes was consistent with the transcriptome data, with Zlat_10020427 being the only exception (Figures 2A and 2B). This indicates that the transcriptome data were reliable and could be used for subsequent experimental analysis. Meanwhile, further study is needed to ascertain whether the Zlat_10020427 gene plays an important role in the response of Z. latifolia to U. esculenta infection.

Figure 2.

Pathogens invading plants are attacked by the plant immune system. Firstly, signal networks such as the MAPK cascade can help plants identify foreign invaders and quickly trigger immune responses to prevent infection (Cheng et al., 2012). Secondly, plant hormones also play an important role in helping plants respond to adverse environments (Verma et al., 2016). For example, gibberellin (GA), abscisic acid (ABA), indole acetic acid (IAA) and cytokinin hormones are related to plant defence signal pathways, and play an important role in regulating plant defence response to pathogen infection (Bari and Jones, 2009). In this study, DEGs were mainly associated with the metabolic pathways of auxins, cytokinins, gibberellins, abscisic acid, brassinosteroids and jasmonic acid. Among them, brassinosteroid pathways accounted for the most (12) DEGs (9 upregulated, 3 downregulated), followed by auxin (2 upregulated, 5 downregulated) and jasmonic acid pathway (6 upregulated, 1 downregulated), but further investigation is needed to ascertain whether these genes participate in the response process. Additionally, phenylpropane derivatives participate in the biosynthesis of lignin or flavonoids, and can also enhance the resistance of plants to pathogens (Dong and Lin, 2021). In the present study, 1,226 DEGs were identified at different timepoints after inoculation of Z. latifolia with U. esculenta. KEGG enrichment analysis revealed that most DEGs appeared at 3 hr after inoculation, most were largely enriched in ‘plant–pathogen interaction’ and ‘MAPK signalling pathway-plant’ pathways, and most were significantly upregulated. This indicates that male Z. latifolia plants might have already begun to respond to U. esculenta infection at 3 hr after inoculation, by activating the expression of related stress resistance genes.

TFs are important participants in plant responses to biological stress that can enhance the ability of plants to resist insect attack and pathogen infection (Amorim et al., 2017). As regulatory proteins, TFs play an important role in transcription reprogramming (Bordenave et al., 2013). Members of WRKY, NAC, MYB and other TF families are involved in the regulation of plant response to biological stress (Feller et al., 2011; Amorim et al., 2017; Li et al., 2017). Cui et al. (2019) found that the GmWRKY40 gene could positively regulate the response of soybean to Phytophthora infestans by regulating hydrogen peroxide accumulation and JA signalling. The AtMYB30 gene is a positive regulator of Arabidopsis defence and related cell death responses (Raffaele and Rivas, 2013). Overexpression of the NAC family ATAF1 gene can enhance the tolerance of plants to Botrytis cinerea and Pseudomonas syringae (Christianson et al., 2010). In the present study, 139 TF genes were identified, mainly enriched in 31 families including WRKY, MYB, NAC, bHLH and other TF families. Among them, TF genes enriched in the CK-3 hr vs. TR-3 hr comparison mainly belonged to the WRKY family. Studies have shown that the WRKY family is involved in the early regulation of plant resistance to pathogen infection, and members play a crucial role in plant responses to biological stress (Zheng et al., 2006; Chi et al., 2013; Fu et al., 2022).

With the rapid development of sequencing technology, the cost of genome detection has been greatly reduced, and transcriptome sequencing has become a popular choice to study differential gene expression. However, RNA-seq still faces many challenges in terms of data processing and analysis. Studies have shown that only 85% of results are typically consistent with qPCR results. Through analysis of all inconsistent genes, it was found that they were usually small in length, with fewer exons, and relatively scarce in the transcriptome (Everaert et al., 2017). In the present study, when the transcriptome was verified by qPCR, 19 of 20 DEGs were consistent with the transcriptome sequencing results. Subsequently, screening criteria can be optimised, and highly expressed DEGs can be selected for further analysis.

During evolution, plants have evolved various defence mechanisms to resist infection by pathogens. These include the nonspecific pattern-triggered immunity (PTI) mechanism induced by pathogen-related molecular patterns (PAMPs) mediated by pattern recognition receptors (PRRs), and effector-triggered immunity (ETI), a specific defence mechanism induced by effector proteins secreted by pathogens (Liu, 2018; Zhang et al., 2018). Studies have shown that the MAPK pathway transmits extracellular signals to downstream response factors through receptors on the cell membrane via MAPKKK, MAPKK and MAPK, and plays an important role in PTI and ETI defence responses (Wang et al., 2019). In plant nonspecific defences, FLS2 combines with the receptor kinase BAK1 to activate immune responses (Chinchilla et al., 2007). In the present study, at 3 hr after inoculation with U. esculenta, both FLS2 and BAK1 genes were significantly upregulated in male Z. latifolia, implying involvement in the infection process of male Z. latifolia in response to U. esculenta. Studies have shown that Arabidopsis FLG22 can upregulate MAPK3/4/6/11, thereby improving resistance to pathogens (Roux et al., 2011), consistent with the results of the present study. MPK3/MPK6 can enhance the stability of proteins and promote the production of ET by inhibiting the ubiquitination and degradation of ACS6, thereby enhancing the resistance of plants to pathogens (Joo et al., 2008; Han et al., 2010). MAPKs can also regulate transcription reprogramming by phosphorylating some WRKY TFs. When Arabidopsis is infected by pathogenic bacteria, WRKY33 forms a complex with MPK and MKS1, and phosphorylated WRKY33 promotes the synthesis of antitoxins, thereby improving the immune response of Arabidopsis (Qiu et al., 2008). In this study, 16 candidate genes were screened in ‘plant–pathogen interaction’ and ‘MAPK signalling pathway-plant’ pathways, and these candidate genes might participate in the responses of male Z. latifolia to U. esculenta infection. The functions of these candidate genes need to be studied in the future.