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Article

A Combined mRNA and microRNA Transcriptome Analysis of B. oleracea Response to Plasmodiophora brassicae Infection

by
Min Wang
†,
Xiaowei Zhu
†,
Xiang Tai
,
Jinxiu Chen
* and
Tianyue Bo
*
Shanghai Key Laboratory of Protected Horticultural Technology, Horticultural Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2024, 10(10), 1013; https://doi.org/10.3390/horticulturae10101013
Submission received: 17 July 2024 / Revised: 5 September 2024 / Accepted: 18 September 2024 / Published: 24 September 2024
(This article belongs to the Section Biotic and Abiotic Stress)
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Abstract

:
Clubroot disease, caused by the pathogen Plasmodiophora brassicae, is a serious disease that poses a critical threat to cabbage production. However, the molecular mechanism of the microRNAs (miRNAs) involved in the cabbage’s response to P. brassicae infection remains to be elucidated. Here, the mRNA and miRNA expression profiles of cabbage in response to a P. brassicae infection were analyzed. In the transcriptome analysis, 2217 and 5552 differentially expressed genes (DEGs) were identified at 7d and 21d after inoculation, which were enriched in MAPK signaling, plant–pathogen interaction, plant hormone signal transduction, and phenylpropanoid biosynthesis pathways. BolC02g057640.2J, BolC09g006890.2J, BolC02g013230.2J, BolC06g006490.2J, BolC03g052660.2J, BolC07g052580.2J, and BolC04g044910.2J were predicted to be significantly involved in the defense response or plant–pathogen interaction through co-expression network analysis. Small RNA data analysis identified 164 miRNAs belonging to 51 families. miR1515, miR166, miR159, and miR9563 had the greatest number of members among the miRNA families. Integrated analysis revealed 23 miRNA–mRNA interactions related to a P. brassicae infection. The target genes of differentially expressed miRNAs (DEMs) revealed the NAC, ARF, TCP, and SPL transcription factor members that probably participate in the defense response. This study provided new insights into the miRNA-involved regulatory system during the process of disease infection with P. brassicae in cabbage.

1. Introduction

Clubroot disease, known as “cruciferous cancer”, is a soil-borne disease caused by Plasmodiophora brasicae, which usually causes a large number of tumors in the roots of crops, thus preventing crops from absorbing nutrients and water from the soil, or even leading to the death of the whole plant [1]. Cabbage (Brassica oleracea var. capitata L.) belonging to the Brassica species, is one of the most important leafy vegetables in the world. In recent years, the rapid development of clubroot disease in China has caused serious losses to cabbage vegetables. In B. oleracea, a few clubroot-resistant (CR) loci have been identified, such as Pb-Bo1 [2], pb-Bo (Anju)1 [3], CRQTL-GN_1, CRQTL-GN_2, CRQTL-YC [4], qCRc7-2, qCRc7-3, and qCRc7-4 [5]. There was a CR gene (Rcr7), which conferred strong resistance to clubroot disease, identified in B. oleracea [6]. At present, several transcriptomic studies have focused on identifying critical genes responsive to the P. brassicae infection. Comparative transcriptome analysis between a clubroot-resistant line (XG) and a clubroot-susceptible line (JF) revealed most DEGs involved in plant–pathogen interaction, biosynthesis of secondary metabolites, and plant hormone transduction [7]. Zhang et al. [8] confirmed that genes related to SA signal transduction, NBS-LRR proteins, phytoalexins biosynthesis, cell wall and chitinase, RBOH proteins, and Ca2+ signals were associated with the defense response to clubroot disease.
miRNAs are a kind of endogenous and noncoding small RNAs with a length of 20–24 nt, which regulate gene expression by degrading or inhibiting translation of their target mRNAs. miRNAs play crucial roles in the regulation of various biological processes including development, secondary metabolism, signal transduction, and stress responses [9,10,11]. miRNAs also play an important role in the regulation of plant-disease resistance, which can target and regulate disease resistance/susceptibility genes [12,13,14]. Arabidopsis miR393 was the first miRNA involved in pathogen-associated molecular pattern (PAMP) triggered immunity (PTI) [15]. MiR393 suppressed auxin signaling, thereby preventing the inhibition of SA, increasing the level of glucosinolates, and reducing camalexin levels, then enhancing the resistance of Arabidopsis to P. syringae [16]. MiR393 was also a positive regulator of a soybean’s defense against Phytophthora sojae [17]. Overexpression of Arabidopsis miR398b and miR773 had been shown to negatively regulate PAMP-induced callose deposition and bacterial resistance, whereas miR160a had the opposite effect [18]. Overexpression of miR160a or miR398b in rice can enhance resistance to the Magnaporthe oryzae disease [19]. miR169 played a negative regulatory role in rice immunity against the blast fungus M. oryzae by inhibiting the expression of nuclear factor Y-A (NF-YA) genes [20]. miR528 negatively regulates viral resistance in rice by cleaving L-ascorbate oxidase (AO) messenger RNA, thereby, reducing the AO-mediated accumulation of the reactive oxygen species [21]. Malus hupehensis miR168, targeting MhAGO1, performed a positive role in the regulation of an apple’s resistance to Botryosphaeria dothidea infection [22]. Integrated transcriptome, miRNAs, degradome, and phytohormone analyses showed that miRNA-targets bra-miR164-NAC1/4, bra-miR319-TCP10, and bra-miR167-ARF8 were closely related to the plant hormones, which are associated with clubroot symptom development in Chinese cabbage [23]. Here, to reveal the gene regulation of cabbage infected by P. brassicae, an integrated analysis of mRNA sequencing and miRNA sequencing of cabbage roots was conducted in this study. These results provide insights into the interactions that occur in transcriptional and post-transcriptional regulation during the clubroot-disease infection.

2. Materials and Methods

2.1. Plant Materials

The pathogen of P. brassicae used in this study was collected from the Xiannv mountain vegetable base in Wulong, Chongqing. This P. brassicae was identified as physiological race 4 in the Williams authentication system. The concentration of spore suspension was adjusted to 1 × 107/mL. The inoculated material was a high-generation inbred line “ZF” of cabbage, which is susceptible to P. brassicae. The seeds were sown in a nutrient bowl and placed in an artificial climate chamber with a light cycle of 16 h/dark 8 h and a temperature of 24/22 °C. When seedlings had two real leaves, 4mL of resting-spore suspension was injected at the stem bottom of each plant using a transferpettor [24]. According to the dynamics of root infection by P. brassicae [25,26], cabbage roots were collected 7 days after inoculation (peak of primary infection) and 21 days after inoculation (later secondary infection) (7 days after inoculation, 7 DAI, F7I, and 21 days after inoculation, 21 DAI, F21I). The roots without inoculation at 7d and 21d (F7CK and F21CK) were also harvested as a control. Two biological replications were conducted, and ten plants were pooled together as one replicate. The root tissues of “ZF” were harvested and washed with distilled water, then these samples were immediately frozen in liquid nitrogen and stored at −80 °C for RNA extraction.

2.2. RNA Extraction and Sequencing

Total RNA was extracted from the root samples using the Trizol Kit (Promega, Beijing, China) according to the manufacturer’s instructions. RNA concentration and purity were measured using the NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA). Sequencing libraries were generated using the NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, MA, USA) following the manufacturer’s recommendations. After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated. For the miRNA-Seq, the library preparations were sequenced on an Illumina platform and single-end reads were generated.

2.3. mRNA and miRNA Sequence Alignment and Annotation

The raw data (raw reads) in the FASTQ format were first processed through in-house Perl scripts. Clean reads were obtained by removing reads containing adapters and reads containing ploy-N and low-quality reads from the raw data. Gene function was annotated based on the following databases: Nr (NCBI non-redundant protein sequences); Nt (NCBI non-redundant nucleotide sequences); Pfam (protein family); KOG/COG (clusters of orthologous groups of proteins); SWISS-PROT (a manually annotated and reviewed protein sequence database); KO (KEGG ortholog database); and GO (gene ontology).
The miRNAs of reads were trimmed and cleaned by removing the sequences smaller than 18 nt or longer than 30 nt. All the downstream analyses were based on clean data with high quality. Using the Bowtie 1.0.0 software, the clean reads were aligned, respectively, with the Silva database, GtRNAdb database, Rfam database, and Repbase database sequence alignment, filter ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), and other ncRNA and repeats. The remaining reads were used to detect known miRNAs and novel miRNAs predicted by comparison with the cabbage genome and known miRNAs from miRBase. Randfold 2.0 software was used for novel miRNA secondary-structure prediction.

2.4. Functional Analysis of Differentially Expressed Unigenes

Gene expression levels were estimated by fragments per kilobase of transcript per million fragments mapped (FPKM). Differential expression analysis of two conditions/groups was performed using the DESeq2. Genes with an adjusted p-value < 0.01 found by DESeq2 were assigned as differentially expressed. Differential expression analysis of two samples was performed using edgeR. The FDR <0.01 & Fold Change ≥2 was set as the threshold for significantly differential expression. Gene ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages based on Wallenius’ noncentral hypergeometric distribution [27], which can adjust for gene length bias in DEGs. The KOBAS [28] software was used to test the statistical enrichment of differential expression genes in KEGG pathways. Regarding miRNA, differential expression analysis of two conditions/groups was performed using the DESeq2 R package (1.10.1). miRNA with |log2(FC)| ≥ 0.58; p-value ≤ 0.05 found by DESeq2 were assigned as differentially expressed.

2.5. Identification and Classification of NBS-LRR Gene Family in Cabbage

The complete genome data of B. oleracea (JZS_v2) was downloaded from the BRAD database (http://brassicadb.cn, accessed on 19 October 2023). The Hidden Markov Model (HMM) profile of the NBS domain (PF00931) was used as a query to screen and identify the NBS-encoding genes in cabbage with threshold E-values of 10−2 by HMMER 3.0 and simple HMM search of TBtools (v2.096) [29]. Then all candidate protein sequences were submitted to the NCBI-CDD website (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi, accessed on 25 June 2024), the SMART website (http://smart.embl.de, accessed on 27 June 2024), and the IntePro website (https://www.ebi.ac.uk/interpro/, accessed on 28 June 2024) for further structural domain prediction. The NBS-LRR proteins of the A. thaliana genome were downloaded from the TAIR database (https://www.arabidopsis.org, accessed on 3 July 2024). The neighbor-joining method (Bootstrap: 1000 with p-distance model, pairwise deletion) was used to construct a phylogenetic tree of the NBS-LRR family protein sequences from B. oleracea and A. thaliana.

2.6. Co-Expression Networks

The Pearson’s correlation coefficient (PCC) was calculated by R package Hmisc 3.16-0 for all pairwise relationships between the 532 DEGs in different pathways. The optimal threshold of the PCC was set at 0.9. The genes labeled with names in the network represent more than 150 interacting genes with that gene (including 150), with a total of 527 nodes and 15,343 edges. The constructed co-expression network was visualized using Cytoscape 3.10.2 software.

2.7. miRNA Target Prediction

Based on the known miRNAs, newly predicted miRNAs, and the corresponding gene-sequence information of cabbage, the TargetFinder 1.6 software was used to predict target genes. Then the predicted target-gene sequences were compared with NR, SWISS-PROT, GO, COG, KEGG, KOG, and Pfam databases using BLAST 2.2.26 software to obtain the annotation information of the target genes.

2.8. Quantitative Real-Time PCR Validation

The synthesis of miRNA first strand cDNA was completed using the Poly (A) tail addition method (Sangon Biotech, Shanghai, China) following the manufacturer’s instructions. The qRT-PCR was performed on Quant Studio Q5 (ABI, Invitrogen Trading (Shanghai) Co., Ltd, Shanghai, China) using the microRNAs qPCR kit (SYBR Green Method) (Sangon Biotech, Shanghai, China). The U6 was used as the reference gene. For differently expressed target genes, reverse transcription and qRT-PCR reactions were carried out using PrimeScript™ RT Master Mix (Perfect Real Time) (TaKaRa, Beijing, China) and TB Green® Premix Ex Taq™ (Tli RNaseH Plus) (TaKaRa, Beijing, China), respectively. The actin gene of cabbage serves as an internal reference gene [30]. All primers used in this experiment are listed in Table S1. The relative expression level of selected genes and miRNAs was calculated by the 2−ΔΔCt method [31].

3. Results

3.1. RNA Sequencing and Differentially Expressed Genes (DEGs)

In order to investigate the gene expression profile regulated by clubroot-disease infection, eight RNA sequencing libraries were constructed from roots at two different infection stages. In total, 75.03 GB of clean data were obtained, and the clean data of each library reached 6.34 GB. The Q30 base percentage was 91.51% or more. The comparison results of the sequencing data and reference genome of the eight samples are shown in Table S2. From the comparison results, the comparison efficiency of the reads and reference genome of each sample is between 77.87% and 88.79%. There were 62,691 unigene sequences; 61,073 unigene sequences (97.4%) matched with the nr (non-redundant protein sequences) database, while 36,521 unigene sequences matched the SWISS-PROT database (58.3%). In addition, 30,803 (49.1%) and 38,051 (60.7%) unigene sequences could be annotated into the KGO and KEGG databases, respectively.
The expression level of all genes was calculated using the FPKM method. The software DESeq2 was used to analyze the differential expression between the treated samples and the control. During the detection of differentially expressed genes, Fold Change ≥ 2 and FDR (False Discovery Rate) < 0.01 were used as screening criteria. There were 2217 differentially expressed genes, including 1606 upregulated genes and 611 downregulated genes in F7CK vs F7I. However, 5552 differentially expressed genes including 1999 upregulated genes and 3553 downregulated genes were identified in F21CK vs. F21I (Figure S1). More differentially expressed genes were identified at 21 DAI than at 7 DAI.

3.2. GO and KEGG Pathway Enrichment Analyses

The KEGG pathways analysis of DEGs was performed to identify the candidate pathways during the cabbage–clubroot interaction process. The first 20 pathways with the lowest significant Q value are shown in Figure 1. Among the two comparisons (F7CK vs F7I, F21CK vs F21I), the plant–pathogen interaction (KEGG term ko04626), plant hormone signal transduction (ko04075), MAPK signaling (ko04016), and phenylpropanoid biosynthesis (ko00940) pathways were significantly enriched (p-value < 0.05). The plant–pathogen infection pathway contains the largest number of DEGs. Go enrichment analysis was also performed to explore the functions of DEGs. In the comparison of F7CK vs F7I (Figure 2), the most significantly enriched molecular functions of DEGs included transcription factor activity, protein serine/threonine kinase activity, peroxidase activity, and phenylalanine ammonia-lyase activity. While in the comparison of F21CK vs F21I, the most significantly enriched molecular functions of DEGs included ATP binding, transcription factor activity, protein kinase activity, and peroxidase activity.

3.3. The Expression of DEGs in Important Signaling Pathways

In the plant–pathogen interaction pathway, the majority of DEGs were annotated as a leucine-rich repeat (LRR) receptor-like protein (50 out of 256), a protein kinase domain (32 out of 256), and a WRKY transcription factor family (25 out of 256). There were 10 genes and 41 genes with a fold change >2 at 7 DAI and 21 DAI, respectively. BolC04g064700.2J (WRKY transcription factor) and BolC06g041510.2J (LRR receptor-like serine/threonine-protein kinase) were detected as the top two highest and most highly expressed genes at 7 DAI. BolC04g035730.2J (WRKY transcription factor) and BolC06g042870.2J (LRR receptor-like serine/threonine-protein kinase) were highly expressed at 21 DAI (Figure 3A, Table S3). In terms of the hormone signal transduction pathway (Figure 3B), the auxin-related genes account for the largest proportion, followed by JA-related genes, which also contain TIFY, GRAS, and MYB transcription factors. The JA signal pathway is an important component of the plant-disease resistance-response signal network. Two JAZ motif proteins were observed, namely BolC02g005510.2J and BolC03g006340.2J, which were upregulated after P. brassicae infection. While in the MAPK signaling pathway (Figure 3C), seven mitogen-activated protein kinase kinase kinase, two mitogen-activated protein kinase kinase, and three mitogen-activated protein kinase were upregulated at 7 DAI. Phenylpropane metabolism is one of the important metabolic pathways in the secondary metabolism of plant-disease resistance. In most species, phenylalanine ammonia-lyase (PAL), 4-hydroxylase (C4H), and 4-coumarate: coenzyme A ligase (4CL) constitute a common pathway for phenylpropane metabolism. In the study, seven PAL genes (BolC03g009160.2J, BolC08g058800.2J, BolC05g029110.2J, BolC03g083530.2J, BolC04g032580.2J, BolC04g014160.2J, and BolC03g056670.2J) and four cytochrome P450 genes (BolC08g033240.2J, BolC03g066690.2J, BolC09g052380.2J, and BolC03g066680.2J) were screened from the DEGs and they were upregulated at 7d or 21d after clubroot-disease infection. Interestingly, lignin production and anthocyanin accumulation were also positively regulated as the six cinnamoyl-CoA reductase (CCR1) and five flavonoid 3-O-glucosyltransferase (UFGT) genes were markedly upregulated. In addition, 44 peroxidase genes showed different expression patterns in response to clubroot-disease infection (Figure 3D).

3.4. Expression of NBS-LRR Genes and Transcription Factors

In our study, 169 NBS-LRR genes were identified in the cabbage genome. Among them, 75 genes contained specific domains at the N-terminal, of which 21 genes contained CC domains, 45 genes contained TIR domains, and 9 genes contained RPW8 domains. In total, 88 genes contained LRR domains at the C-terminal. The cabbage NBS-LRR genes were divided into eight classes according to the protein structural domains (Table S4). Three classes, N, NL, and TNL, have many genes, containing 52, 42, and 32, respectively. The number of members in all other classes was less than 15. A phylogenetic tree was constructed based on 163 BoNBS-LRR and 41 AtNBS-LRR protein sequences (Figure S2). All NBS-LRR protein sequences were categorized into three subfamilies: TNL, CNL, and RNL. We investigated the expression patterns of 108 NBS-LRR genes during clubroot-disease infection, nearly half of them were differentially expressed after P. brassicae inoculation (Table S5). Among them, 28 members showed relatively high expression (Figure S3). The expression of three genes (BolC06g044410.2J, BolC09g025110.2J, and BolC09g010300.2J) were highly expressed at 7 DAI. While at 21 DAI, six genes were strongly expressed. In particular, BolC04g013520.2J has the highest expression level, which is 4.7 times higher than the control. BolC09g050330.2J was highly expressed throughout the entire pathogen infection process.
Transcription factors play an important role in plant-disease resistance, numerous transcription factors were differentially expressed in clubroot-infected roots, mainly including WRKY, NAC, bZIP, MYB, and bHLH TFs (Figure 4, Table S6). Fifty WRKY TFs participated during the infection process. Eight WRKY genes (BolC07g047560.2J, BolC08g040340.2J, BolC05g029850.2J, BolC02g46670.2J, BolC04g056510.2J, BolC04g035730.2J, BolC09g006060.2J, and BolC09g006890.2J) showed very high expression at 21 DAI. Additionally, the transcription level of 19 MYB TF members was prominently changed. In total, 14 NAC genes had different expression patterns in response to P. brassicae infection. BolC01g011530.2J and BolC03g011450.2J were significantly high expressed at 21 DAI. There were 11 bHLH genes expressed. BolC02g015780.2J showed high expression at 7 DAI and 21 DAI. Moreover, six bZIP genes (BolC02g004670.2J, BolC04g064920.2J, BolC03g053580.2J, BolC09g061470.2J, BolC05g048460.2J, and BolC06g004110.2J) were found to be remarkably upregulated when infected with a pathogen at 21 DAI.

3.5. Co-Expression Network

To gain more insight into the regulatory relationships of cabbage-resistance-related genes, 532 genes involved in defense response were selected and an effective PCC threshold of 0.9 was trained to generate the co-expression networks. The correlation was calculated by R package Hmisc (Hmisc_3.16-0), and the network was drawn using Cytoscape. There were 527 nodes and 15343 edges in this network. The genes, which have more than 150 interacting genes, were marked by a gene ID. Two WRKY genes (BolC02g057640.2J and BolC09g006890.2J), two LRR (BolC02g013230.2J and BolC06g006490.2J), one disease resistance protein (BolC03g052660.2J), one MAPK (BolC07g052580.2J), and one NAC (BolC04g044910.2J) showed high connectivity in the network, suggesting their prominent roles in cabbage response against P. brassicae infection (Figure 5).

3.6. Identification of miRNAs in Cabbage Roots in Response to P. brassicae

In order to explore the expression dynamics of B. oleracea miRNAs during the infection process of clubroot disease, eight miRNA sequencing libraries were constructed. After filtration, a total of 90.09 M clean reads were retained. The Q30 values of the eight samples were greater than 96%, displaying data reliably. After removing rRNA, snRNA, snoRNA, tRNA, and repeat-associated sRNA, the remaining unannotated reads were used for conserved miRNA identification and novel miRNA prediction. After analysis, 269 miRNAs were obtained from all samples, including 8 known miRNAs and 261 newly predicted miRNAs. The six known miRNAs were classified into five subfamilies, only the MIR172 family possessed two members, while the remaining families contained one member. These 164 novel miRNAs were divided into 51 subfamilies, among them, miR159, miR166, miR1515, and miR9563 families contained more than 10 members (Figure 6A). Of all the known miRNAs, 21 nt-miRNAs were the most abundant in the eight libraries (Figure 6B). The length distribution of the novel miRNAs ranged from 20 nt to 24 nt, with 21 nt and 24 nt miRNAs being the most numerous (Figure 6C).
In order to obtain insight into the possible role of miRNA during the clubroot-disease infection, differential expression was analyzed by comparing F7CK vs F7I and F21CK vs F21I. There were 11 upregulated and 4 downregulated miRNAs found at 7 d after inoculation. In total, seven and six miRNAs were upregulated or downregulated at 21d after infection (Figure 7A). Twelve miRNAs (novel-miR163, -194, -131, -200, -218, -25, -219, -245, -126, -141, -22 and bol-miR9411) were upregulated during P. brassicae infection. The 12 upregulated DEMs included one known miRNA (bol-miR9411), two miRNAs from the MIR7724 and MIR818 families, and eight novel miRNAs. Three conserved miRNAs from the miR159 and miR171 families and one novel miRNA were downregulated. Two miRNAs (novel-miR55 and novel-miR80) were upregulated at 7 DAI while downregulated at 21 DAI. novel-miR129 was downregulated at 7 DAI and upregulated at 21 DAI. The other eight miRNAs (novel-miR9, -49, -50, -58, -139, -160, -174, -181) were found downregulated during P. brassicae infection (Figure 7B).

3.7. Identification of miRNA-mRNA Pairs Associated with P. brassicae Infection

TargetFinder software was used to target gene prediction. In total, 3260 target genes of 223 miRNAs were identified. Pathway enrichment analysis showed the predicted target genes of DEMs in F7CK vs F7I were enriched in cutin, suberine, and wax biosynthesis, phenylpropanoid biosynthesis, and starch and sucrose metabolism. While in F21CK vs F21I, the predicted DEM target genes were enriched in the biosynthesis of amino acids, spliceosome, MAPK signaling pathway, and plant hormone signal transduction (Figure S4). By combining the analyses of miRNA and mRNA, 22 targets for seven DEMs were identified (Table 1). Among them, ten miRNA-mRNA negative correlation pairs were found during P. brassicae infection, including novel_miR58 (MIR171_1) targets BolC08g051460.2J, novel_miR_160 (MIR164) targets BolC04g044910.2J and BolC05g053690.2J, novel_miR_129 (MIR319) targets BolC04g018790.2J, BolC03g071340.2J, and BolC05g027880.2J, novel_miR181 targets BolC05g014870.2J and BolC09g009060.2J, novel_miR139 targets BolC02g08640.2J and BolC04g045360.2J. Three miRNA-target pairs, novel_miR_160/BolC02g060420.2J, novel_miR181/BolC07g025060.2J, and novel_miR181/BolC02g054180.2J, showed a similar expression tendency. Then, five miRNA target-gene pairs showed a reverse expression pattern in the F7CK vs F7I and F21CK vs F21I comparison groups, respectively. Furthermore, most miRNAs had more than one possible target gene, while different miRNAs could regulate the same targets. Notably, among them, novel_miR80/BolC05g028200.2J and novel_miR139/BolC02g008640.2J probably participated in the disease response of cabbage because the target mRNAs were homologous to the known genes related to the defense response.

3.8. Validation of the DEGs and DEMs Data Using RT-qPCR

To verify the accuracy and reliability of RNA-seq data and miRNA data, a qRT-PCR was performed (Figure 8). The NAC domain protein (BolC02g008640.2J) and cation efflux family gene (BolC04g045360.2J) were highly expressed at 7d and 21d after inoculation, which was consistent with the RNA-seq data. BolC02g054180.2J was found (SANT/Myb-like domain protein) to be downregulated at 7 DAI and 21 DAI. The expression level of two leucine-rich receptor protein kinases (BolC09g018620.2J and BolC09g018650.2J) were upregulated at 7 DAI, while downregulated at 21 DAI. BolC03g017770.2J (SBP domain protein) was also confirmed to have differential expression patterns in response to P. brassicae infection. Meanwhile, three miRNAs were randomly selected for qRT-PCR analysis. Overall, these results showed that six mRNAs and three miRNAs showed similar expression patterns compared to DEGs and DEMs analysis, which indicated that the RNA-Seq and small RNA data were reliable.

4. Discussion

Clubroot disease, triggered by P. brassicae, is one of the most harmful diseases in the agricultural production of cruciferous crops such as cabbage. Previously, comparative transcriptome studies have been conducted on cabbage clubroot disease. However, the role of miRNAs in the roots of cabbage in response to clubroot disease has not been reported yet. Here, we employed mRNA and small RNA sequencing to identify DEGs and DEMs under control and stress conditions to study the interaction mechanism between cabbage and clubroot disease.
Hormones play important roles in diverse growth and developmental processes as well as various biotic and abiotic stress responses in plants [32]. The phytohormones, salicylic acid (SA) and jasmonic acid (JA), are the main participants in plant immunity. Some TIFY proteins belonging to the JAZ subfamily in Brassica oleracea were activated after a Fusarium oxysporum infection [33]. Two TIFY proteins (BolC02g005510.2J and BolC03g006340.2J), belonging to the JAZ subfamily, were highly upregulated at 7d after clubroot-disease infection. P. brassicae and F. oxysporum pathogens are both soil-borne diseases. These results indicated that JA pathway genes may play a crucial role in soil-borne disease. Transcription factors (TFs) play a crucial role in controlling plant defense and development by controlling the expression of various downstream target genes. Here, a total of 149 TFs belonging to five different families, were differentially expressed during clubroot infection, including WRKY, MYB, NAC, MYB, bZIP, and bHLH. As one of the largest transcription factor families, the WRKY proteins take part in many stress-response defense pathways. In B. cinerea, BcWRKY33A directly activates the expression of BcMYB51-3 and downstream indolic glucosinolates’ (IGSs) biosynthetic genes, thereby improving the B. cinerea tolerance of non-heading Chinese cabbage (NHCC) plants [34]. Transgenic B. napus plants overexpressing BnWRKY33 showed markedly enhanced resistance to Sclerotinia sclerotiorum [35]. In Arabidopsis, AtWRKY46, AtWRKY70, and AtWRKY53 positively regulated basal resistance to P. syringae [36]. In our study, most of the WRKY members were enriched in the MAPK signaling and plant–pathogen interaction pathway. The WRKY gene (BolC04g035730.2J), which was orthologous to AtWRKY70, was especially highly expressed at 21d after clubroot-disease infection.
miRNAs are small noncoding RNAs that participate in the regulation of plant growth and development, and stress resistance through post-transcriptional regulation of target genes. A total of 269 miRNAs were identified from cabbage, among which 8 miRNAs were known miRNAs previously reported, and 261 were novel miRNAs. There were 164 novel miRNAs classified into 51 miRNA families. A total of 21 differentially expressed clubroot-responsive miRNAs were identified in the two comparisons, such as MIR159 (novel_miR9 and novel_miR49), MIR167 (novel_miR55 and novel_miR80), and MIR171 (novel_miR58), which were found to play a role in the cabbage–clubroot-disease interaction. These miRNA families were also reported to involve plant–pathogen interactions previously. miR159a were involved in the poplar species’ disease resistance to different fungi and bacteria [37]. The expression levels of miR166 and miR159 in cotton infected with Verticillium dahliae showed an upregulation trend, causing specific silencing of fungal hyphae. At the same time, it was found that fungal virulence genes Clp-1 and HiC-15 can be recognized and degraded by miRNA [38]. Arabidopsis miR167 was differentially expressed in response to the bacterial pathogen Pseudomonas syringae, and overexpression of miR167 conferred a very high level of resistance [39]. The miR171a-SCL6 module in cotton regulated Verticillium wilt resistance through the post-transcriptional process [40].
Target-gene analysis of miRNAs provides useful information on the complex miRNA-mediated regulatory network of cabbage’s response to clubroot disease. These targets were associated with several transcription factors modulating transcriptional programs controlling plant response to environmental stresses. NAC transcription factors have become key participants in a plant’s biological stress responses, particularly in defending against pathogen infections and insect pests [41]. In Arabidopsis, miR164 negatively regulated NAC4 and promoted pathogen-induced cell death [42]. The miR164-GhNAC100 module participated in the cotton plant’s resistance against Verticillium dahliae [43]. After rice was infected with M. oryzae, miR164a in rice was downregulated, targeting OsNAC60 transcription factor upregulation, initiating programmed cell death, accumulation of reactive oxygen species, and deposition of callus, thereby enhancing immune defense against the pathogen. Overexpression of miR164a inhibits the immune response of rice to M. oryzae [44]. Previous studies have shown that the miRNA-target pair miR164-NAC1/4 was closely related to plant hormones, which are associated with clubroot symptom development in B. rapa [23]. In this study, novel miR160 (miR164 family) was downregulated at 7d and 21d after clubroot-disease inoculation, while the targets BolC04g044910.2J and BolC05g053690.2J were upregulated during the inoculation period. Considering the negative interaction existing between novel miR160 and these two NAC genes, we deduced that novel miR160 might play a vital role in the clubroot-disease response. Interestingly, BolC02g008640.2J (NAC gene) was also predicted to be the target gene of novel miR139, and a negative correlation occurred between them. BolC02g008640.2J was highly orthologous to BnNAC87, which plays an active regulatory role in ROS metabolism and cell death [45].
We found that BolC04g018790.2J (TCP transcription factor) was targeted by novel_miR129 (MIR319). The miR319 family is an ancient miRNA group that encodes plant phenotype regulatory factors targeting TCP genes. Overexpressing BramiR319a plants were more sensitive to S. sclerotiorum infection than wild-type plants, whereas TCP4-overexpressing plants were more resistant to the pathogen [46]. In our analysis, the expression of novel_miR129 was downregulated at 7d and upregulated at 21d after inoculation. The expression of its target genes BolC04g018790.2J, BolC03g071340.2J, and BolC05g027880.2J were upregulated at 7d and downregulated at 21d after inoculation. The novel_miR129 and its targets also showed negative regulation. Two putative targets of novel_miR245 (miR818), leucine-rich repeat receptor-like kinases (LRR-RLKs), is the largest transmembrane receptor kinase subfamily in plants, widely regulating plant development and disease resistance responses [47]. Additionally, other annotated genes were identified as targets of differentially expressed miRNAs, such as the SacI homology domain protein (BolC08g051460.2J) as the target of novel_miR58 (MIR171), the CASP-like protein (BolC05g014870.2J) as the target of novel_miR181, the Ribosomal protein S28e (BolC09g009060.2J) as the target of novel_miR181, and the cation efflux family gene (BolC04g045360.2J) as the target of novel_miR139.

5. Conclusions

Our transcriptome study emphasizes the role of DEGs, miRNAs, and their putative target genes associated with the defense response of cabbage to clubroot disease. Functional and signaling pathway analyses of DEGs display that hormone signal transduction, MAPK signaling, phenylpropanoid biosynthesis, plant–pathogen interaction, and transcription factors (WRKY, NAC, bHLH, MYB, bZIP) were associated with cabbage in response to P. brassicae infection. In addition, we also identified 8 known microRNAs and 164 novel miRNAs belonging to 51 subfamilies. Integrated analysis revealed 23 miRNA-mRNA interactions related to P. brassicae infection. Notably, novel_miR80/BolC05g028200.2J and novel_miR139/BolC02g008640.2J might participate in the disease response. In short, combining mRNA transcriptome and miRNA data will be helpful and guide us to better understand the regulatory mechanisms in cabbage’s defense against P. brassicae.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10101013/s1, Figure S1: Comparative analysis of differentially expressed genes. A: Venn diagram showing the overlap of DEGs among the four samples; B: Numbers of DEGs compared between two samples (F7CK vs F7I, F21CK vs F21I, F7I vs F21I, F7CK vs F21CK). Figure S2: Phylogenetic tree of NBS-LRR proteins from B. oleracea and A. thaliana. Red, blue, and purple correspond to CNL, TNL, and RNL subfamily. Figure S3: The expression of BoNBS-LRR genes in response to P. brassicae infection. Figure S4: Scatter plot of enriched KEGG pathway of differentially expressed miRNA target genes. Table S1: Primer sequences of DEGs and DEMs used in qRT-PCR experiments. Table S2: Summary statistics of the transcriptome sequencing data. Table S3: The expression level of DEGs in different KEGG pathways. Table S4: Clusters of cabbage NBS-LRR genes. Table S5: The expression level of NBS-LRR genes of cabbage. Table S6: The expression level of different transcription factors. Table S7: Number of identified miRNAs in known miRNA families. Table S8: The expression level of DEMs during clubroot-disease infection.

Author Contributions

Conceptualization, M.W. and T.B.; software, M.W. and X.Z.; investigation, M.W. and J.C.; resources, X.T.; data curation, M.W.; writing—original draft preparation, M.W.; writing—review and editing, M.W., X.Z. and T.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the “14th Five-Year Plan” National Key Research and Development Program (Grant No. 2023YFD1200005-1) and the Excellent Team Project (Nongkezhuo 2022(007)).

Data Availability Statement

The original data presented in this study are available on the NCBI SRA database (PRJNA1138485, accessed on 18 July 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Javed, M.A.; Schwelm, A.; Zamani-Noor, N.; Salih, R.; Silvestre Vañó, M.; Wu, J.; González García, M.; Heick, T.M.; Luo, C.; Prakash, P.; et al. The clubroot pathogen Plasmodiophora brassicae: A profile update. Mol. Plant Pathol. 2023, 24, 89–106. [Google Scholar] [CrossRef] [PubMed]
  2. Rocherieux, J.; Glory, P.; Giboulot, A.; Boury, S.; Barbeyron, G.; Thomas, G.; Manzanares-Dauleux, M.J. Isolate-specific and broad-spectrum QTLs are involved in the control of clubroot in Brassica oleracea. Theor. Appl. Genet. 2004, 108, 1555–1563. [Google Scholar] [CrossRef]
  3. Nagaoka, T.; Doullah, M.A.; Matsumoto, S.; Kawasaki, S.; Ishikawa, T.; Hori, H.; Okazaki, K. Identification of QTLs that control clubroot resistance in Brassica oleracea and comparative analysis of clubroot resistance genes between B. rapa and B. oleracea. Theor. Appl. Genet. 2010, 120, 1335–1346. [Google Scholar] [CrossRef]
  4. Lee, J.; Izzah, N.K.; Choi, B.S.; Joh, H.J.; Lee, S.C.; Perumal, S.; Seo, J.; Ahn, K.; Jo, E.J.; Choi, G.J.; et al. Genotyping-by-sequencing map permits identification of clubroot resistance QTLs and revision of the reference genome assembly in cabbage (Brassica oleracea L.). DNA Res. 2016, 23, 29–41. [Google Scholar] [CrossRef] [PubMed]
  5. Ce, F.; Mei, J.; He, H.; Zhao, Y.; Hu, W.; Yu, F.; Li, Q.; Ren, X.; Si, J.; Song, H.; et al. Identification of Candidate Genes for Clubroot-Resistance in Brassica oleracea Using Quantitative Trait Loci-Sequencing. Front. Plant Sci. 2021, 12, 703520. [Google Scholar] [CrossRef] [PubMed]
  6. Dakouri, A.; Zhang, X.; Peng, G.; Falk, K.C.; Gossen, B.D.; Strelkov, S.E.; Yu, F. Analysis of genome-wide variants through bulked segregant RNA sequencing reveals a major gene for resistance to Plasmodiophora brassicae in Brassica oleracea. Sci. Rep. 2018, 8, 17657. [Google Scholar] [CrossRef]
  7. Ning, Y.; Wang, Y.; Fang, Z.; Zhuang, M.; Zhang, Y.; Lv, H.; Liu, Y.; Li, Z.; Yang, L. Comparative transcriptome analysis of cabbage (Brassica oleracea var. capitata) infected by Plasmodiophora brassicae reveals drastic defense response at secondary infection stage. Plant Soil 2019, 443, 167–183. [Google Scholar]
  8. Zhang, X.; Liu, Y.; Fang, Z.; Li, Z.; Yang, L.; Zhuang, M.; Zhang, Y.; Lv, H. Comparative Transcriptome Analysis between Broccoli (Brassica oleracea var. italica) and Wild Cabbage (Brassica macrocarpa Guss.) in Response to Plasmodiophora brassicae during Different Infection Stages. Front. Plant Sci. 2016, 7, 1929. [Google Scholar]
  9. Li, H.; Sun, X.; Ma, C.; Meng, H.; Chen, Q. Identification and characterization of microRNAs during common buckwheat (Fagopyrum esculentum) seed development. Int. J. Agric. Biol. 2020, 24, 1115–1124. [Google Scholar]
  10. D’Ario, M.; Griffiths-Jones, S.; Kim, M. Small RNAs: Big impact on plant development. Trends Plant Sci. 2017, 22, 1056–1068. [Google Scholar] [CrossRef]
  11. Chen, C.H.; Zhong, Y.D.; Yu, F.X.; Xu, M. Deep sequencing identifies miRNAs and their target genes involved in the biosynthesis of terpenoids in Cinnamomum camphora. Ind. Crop. Prod. 2020, 145, 111853. [Google Scholar] [CrossRef]
  12. Shivaprasad, P.V.; Chen, H.M.; Patel, K.; Bond, D.M.; Santos, B.A.; Baulcombe, D.C. A microRNA superfamily regulates nucleotide binding site-leucine-rich repeats and other mRNAs. Plant Cell 2012, 24, 859–874. [Google Scholar] [CrossRef] [PubMed]
  13. Gupta, O.P.; Sharma, P.; Gupta, R.K.; Sharma, I. Current status on role of miRNAs during plant-fungus interaction. Physiol. Mol. Plant Pathol. 2014, 85, 1–7. [Google Scholar] [CrossRef]
  14. Mishra, R.; Mohanty, J.N.; Chand, S.K.; Joshi, R.K. Can-miRn37a mediated suppression of ethylene response factors enhances the resistance of chilli against anthracnose pathogen Colletotrichum truncatum L. Plant Sci. 2018, 267, 135–147. [Google Scholar] [CrossRef]
  15. Navarro, L.; Dunoyer, P.; Jay, F.; Arnold, B.; Dharmasiri, N.; Estelle, M.; Voinnet, O.; Jones, J.D. A plant miRNA contributes to antibac-terial resistance by repressing auxin signaling. Science 2006, 312, 436–439. [Google Scholar] [CrossRef]
  16. Robert-Seilaniantz, A.; MacLean, D.; Jikumaru, Y.; Hill, L.; Yamaguchi, S.; Kamiya, Y.; Jones, J.D. The microRNA miR393 re-directs secondary metabolite biosynthesis away from camalexin and towards glucosinolates. Plant J. 2011, 67, 218–231. [Google Scholar] [CrossRef]
  17. Wong, J.; Gao, L.; Yang, Y.; Zhai, J.; Arikit, S.; Yu, Y.; Duan, S.; Chan, V.; Xiong, Q.; Yan, J.; et al. Roles of small RNAs in soybean defense against Phytophthora sojae infection. Plant J. 2014, 79, 928–940. [Google Scholar] [CrossRef]
  18. Li, Y.; Zhang, Q.; Zhang, J.; Wu, L.; Qi, Y.; Zhou, J.M. Identification of microRNAs involved in pathogen-associated molecular pattern-triggered plant innate immunity. Plant Physiol. 2020, 152, 2222–2231. [Google Scholar] [CrossRef] [PubMed]
  19. Li, Y.; Lu, Y.G.; Shi, Y.; Wu, L.; Xu, Y.J.; Huang, F.; Guo, X.Y.; Zhang, Y.; Fan, J.; Zhao, J.Q.; et al. Multiple rice microRNAs are involved in immunity against the blast fungus Magnaporthe oryzae. Plant Physiol. 2014, 164, 1077–1092. [Google Scholar] [CrossRef]
  20. Li, Y.; Zhao, S.L.; Li, J.L.; Hu, X.H.; Wang, H.; Cao, X.L.; Xu, Y.J.; Zhao, Z.X.; Xiao, Z.Y.; Yang, N.; et al. Osa-miR169 Negatively Regulates Rice Immunity against the Blast Fungus Magnaporthe oryzae. Front. Plant Sci. 2017, 8, 2. [Google Scholar] [CrossRef]
  21. Wu, J.; Yang, R.; Yang, Z.; Yao, S.; Zhao, S.; Wang, Y.; Li, P.; Song, X.; Jin, L.; Zhou, T.; et al. ROS accumulation and antiviral defence control by microRNA528 in rice. Nat. Plants 2017, 3, 16203. [Google Scholar] [CrossRef] [PubMed]
  22. Yu, X.; Hou, Y.; Chen, W.; Wang, S.; Wang, P.; Qu, S. Malus hupehensis miR168 Targets to ARGONAUTE1 and Contributes to the Resistance against Botryosphaeria dothidea Infection by Altering Defense Responses. Plant Cell Physiol. 2017, 58, 1541–1557. [Google Scholar] [CrossRef] [PubMed]
  23. Wei, X.; Liao, R.; Zhang, X.; Zhao, Y.; Xie, Z.; Yang, S.; Su, H.; Wang, Z.; Zhang, L.; Tian, B.; et al. Integrative Transcriptome, miRNAs, Degradome, and Phytohormone Analysis of Brassica rapa L. in Response to Plasmodiophora brassicae. Int. J. Mol. Sci. 2023, 24, 2414. [Google Scholar] [CrossRef]
  24. Yuan, Y.; Qin, L.; Su, H.; Yang, S.; Wei, X.; Wang, Z.; Zhao, Y.; Li, L.; Liu, H.; Tian, B.; et al. Transcriptome and Coexpression Network Analyses Reveal Hub Genes in Chinese Cabbage (Brassica rapa L. ssp. pekinensis) During Different Stages of Plasmodiophora brassicae Infection. Front Plant Sci. 2021, 12, 650252. [Google Scholar]
  25. Kageyama, K.; Asano, T. Life Cycle of Plasmodiophora brassicae. J. Plant Growth Regul. 2009, 28, 203–211. [Google Scholar] [CrossRef]
  26. Liu, L.; Qin, L.; Zhou, Z.; Hendriks, W.G.H.M.; Liu, S.; Wei, Y. Refining the Life Cycle of Plasmodiophora brassicae. Phytopathology 2020, 110, 1704–1712. [Google Scholar] [CrossRef]
  27. Young, M.D.; Wakefield, M.J.; Smyth, G.K.; Oshlack, A. Gene ontology analysis for RNA-seq: Accounting for selection bias. Genome Biol. 2010, 11, R14. [Google Scholar] [CrossRef] [PubMed]
  28. Mao, X.; Cai, T.; Olyarchuk, J.G.; Wei, L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 2005, 21, 3787–3793. [Google Scholar] [CrossRef]
  29. Chen, C.; Chen, H.; Zhang, Y.; Thomas, H.R.; Frank, M.H.; He, Y.; Xia, R. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol. Plant 2020, 13, 1194–1202. [Google Scholar] [CrossRef]
  30. Zhang, W.; Wang, S.; Yu, F.; Tang, J.; Shan, X.; Bao, K.; Yu, L.; Wang, H.; Fei, Z.; Li, J.; et al. Genome-wide characterization and expression profiling of SWEET genes in cabbage (Brassica oleracea var. capitata L.) reveal their roles in chilling and clubroot disease responses. BMC Genom. 2019, 20, 93. [Google Scholar]
  31. Livak, K.J.; Schmittgen, T. Analysis of relative gene expression data using real-time quantitative PCR and the 2−∆∆CT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  32. Bari, R.; Jones, J.D. Role of plant hormones in plant defence responses. Plant Mol. Biol. 2009, 69, 473–488. [Google Scholar] [CrossRef] [PubMed]
  33. Liu, X.; Zhao, C.; Yang, L.; Zhuang, M.; Zhang, Y.; Wang, Y.; Fang, Z.; Lv, H. A time-resolved dual transcriptome analysis reveals the molecular regulating network underlying the compatible/incompatible interactions between cabbage (Brassica oleracea) and Fusarium oxysporum f. sp. conglutinans. Plant Soil 2020, 448, 455–478. [Google Scholar] [CrossRef]
  34. Wang, H.; Zheng, Y.; Xiao, D.; Li, Y.; Liu, T.; Hou, X. BcWRKY33A Enhances Resistance to Botrytis cinerea via Activating BcMYB51-3 in Non-Heading Chinese Cabbage. Int. J. Mol. Sci. 2022, 23, 8222. [Google Scholar] [CrossRef] [PubMed]
  35. Wang, Z.; Fang, H.; Chen, Y.; Chen, K.; Li, G.; Gu, S.; Tan, X. Overexpression of BnWRKY33 in oilseed rape enhances resistance to Sclerotinia sclerotiorum. Mol. Plant Pathol. 2014, 15, 677–689. [Google Scholar] [CrossRef]
  36. Hu, Y.; Dong, Q.; Yu, D. Arabidopsis WRKY46 coordinates with WRKY70 and WRKY53 in basal resistance against pathogen Pseudomonas syringae. Plant Sci. 2012, 185–186, 288–297. [Google Scholar] [CrossRef]
  37. Yang, X.; Fu, T.; Yu, R.; Zhang, L.; Yang, Y.; Xiao, D.; Wang, Y.; Wang, Y.; Wang, Y. miR159a modulates poplar resistance against different fungi and bacteria. Plant Physiol. Biochem. 2023, 201, 107899. [Google Scholar] [CrossRef]
  38. Zhang, T.; Zhao, Y.L.; Zhao, J.H.; Wang, S.; Jin, Y.; Chen, Z.Q.; Fang, Y.Y.; Hua, C.L.; Ding, S.W.; Guo, H.S. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nat. Plants 2016, 2, 16153. [Google Scholar] [CrossRef]
  39. Caruana, J.C.; Dhar, N.; Raina, R. Overexpressio of Arabidopsis microRNA167 induces salicylic acid-dependent defense against Pseudomonas syringae through the regulation of its targets ARF6 and ARF8. Plant Direct 2020, 4, e00270. [Google Scholar] [CrossRef]
  40. Hu, G.; Ge, X.; Wang, P.; Chen, A.; Li, F.; Wu, J. The cotton miR171a-SCL6 module mediates plant resistance through regulating GhPR1 expression. Plant Physiol. Biochem. 2023, 202, 107995. [Google Scholar] [CrossRef]
  41. Masri, R.; Kiss, E. The role of NAC genes in response to biotic stresses in plants. Physiol. Mol. Plant Pathol. 2023, 126, 102034. [Google Scholar] [CrossRef]
  42. Lee, M.H.; Jeon, H.S.; Kim, H.G.; Park, O.K. An Arabidopsis NAC transcription factor NAC4 promotes pathogen-induced cell death under negative regulation by microRNA164. New Phytol. 2017, 214, 343–360. [Google Scholar] [CrossRef] [PubMed]
  43. Hu, G.; Lei, Y.; Liu, J.; Hao, M.; Zhang, Z.; Tang, Y.; Chen, A.; Wu, J. The ghr-miR164 and GhNAC100 modulate cotton plant resistance against Verticillium dahlia. Plant Sci. 2020, 293, 110438. [Google Scholar] [CrossRef] [PubMed]
  44. Wang, Z.; Xia, Y.; Lin, S.; Wang, Y.; Guo, B.; Song, X.; Ding, S.; Zheng, L.; Feng, R.; Chen, S.; et al. Osa-miR164a targets OsNAC60 and negatively regulates rice immunity against the blast fungus Magnaporthe oryzae. Plant J. 2018, 95, 584–597. [Google Scholar] [CrossRef] [PubMed]
  45. Yan, J.; Tong, T.; Li, X.; Chen, Q.; Dai, M.; Niu, F.; Yang, M.; Deyholos, M.K.; Yang, B.; Jiang, Y.Q. A Novel NAC-Type Transcription Factor, NAC87, from Oilseed Rape Modulates Reactive Oxygen Species Accumulation and Cell Death. Plant Cell Physiol. 2018, 59, 290–303. [Google Scholar] [CrossRef]
  46. Dong, W.; Ren, W.; Wang, X.; Mao, Y.; He, Y. MicroRNA319a regulates plant resistance to Sclerotinia stem rot. J. Exp. Bot. 2021, 72, 3540–3553. [Google Scholar] [CrossRef]
  47. Cho, H.; Lee, J.; Oh, E. Leucine-Rich Repeat Receptor-Like Proteins in Plants: Structure, Function, and Signaling. J. Plant Biol. 2023, 66, 99–107. [Google Scholar] [CrossRef]
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Figure 1. KEGG pathway enrichment analysis of F7CK vs F7I and F21CK vs F21I. The x-axis represents the rich factor, and the y-axis indicates the enriched KEGG pathway.
Figure 1. KEGG pathway enrichment analysis of F7CK vs F7I and F21CK vs F21I. The x-axis represents the rich factor, and the y-axis indicates the enriched KEGG pathway.
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Figure 2. The top 20 enriched GO enrichment analyses of the differentially expressed genes in F7CK vs. F7I and F21CK vs F21I.
Figure 2. The top 20 enriched GO enrichment analyses of the differentially expressed genes in F7CK vs. F7I and F21CK vs F21I.
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Figure 3. Heatmaps of the differentially expressed genes at different time points (7 and 21 DAI) in different signaling pathways. Expression profiles of plant–pathogen interaction (A), plant hormone signal transduction (B), MAPK signaling pathway (C), and phenylpropane metabolic pathway (D) in cabbage infected with P. brassicae.
Figure 3. Heatmaps of the differentially expressed genes at different time points (7 and 21 DAI) in different signaling pathways. Expression profiles of plant–pathogen interaction (A), plant hormone signal transduction (B), MAPK signaling pathway (C), and phenylpropane metabolic pathway (D) in cabbage infected with P. brassicae.
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Figure 4. Heatmaps of the differentially expressed transcription factors at different time points (7 DAI and 21 DAI).
Figure 4. Heatmaps of the differentially expressed transcription factors at different time points (7 DAI and 21 DAI).
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Figure 5. Co-expression network analysis identifies hub genes associated with clubroot-disease response. The yellow circles represent phenylpropanoid metabolism pathway genes, the green circles represent transcription factors, the red circles represent disease resistance genes, the blue circles represent MAPK signaling pathway genes, and the purple circles represent plant hormone pathway genes.
Figure 5. Co-expression network analysis identifies hub genes associated with clubroot-disease response. The yellow circles represent phenylpropanoid metabolism pathway genes, the green circles represent transcription factors, the red circles represent disease resistance genes, the blue circles represent MAPK signaling pathway genes, and the purple circles represent plant hormone pathway genes.
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Figure 6. The miRNA subfamily distribution (A). The length distribution of new miRNAs (B) and conserved miRNAs (C).
Figure 6. The miRNA subfamily distribution (A). The length distribution of new miRNAs (B) and conserved miRNAs (C).
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Figure 7. Number of differentially expressed miRNAs at 7d and 21d after P. brassicae infection (A). The clustering diagram of differential expression miRNA, cluster based on log10(TPM) values (B). With red indicating high expression of miRNAs and blue indicating low expression of miRNAs.
Figure 7. Number of differentially expressed miRNAs at 7d and 21d after P. brassicae infection (A). The clustering diagram of differential expression miRNA, cluster based on log10(TPM) values (B). With red indicating high expression of miRNAs and blue indicating low expression of miRNAs.
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Figure 8. Quantitative real-time reverse transcription PCR (qRT-PCR) validation of DEGs and DEMs expression in cabbage roots after P. brassicae infection vs. by RNA-Seq. Blue bars: qRT-PCR expression, red lines: transcriptomics RNA-Seq and miRNA-Seq analysis.
Figure 8. Quantitative real-time reverse transcription PCR (qRT-PCR) validation of DEGs and DEMs expression in cabbage roots after P. brassicae infection vs. by RNA-Seq. Blue bars: qRT-PCR expression, red lines: transcriptomics RNA-Seq and miRNA-Seq analysis.
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Table 1. Differentially expressed miRNAs with their target genes.
Table 1. Differentially expressed miRNAs with their target genes.
miRNAFamilyGene IDGene AnnotationF7CK VS F7IF21CK VS F21I
Log2FC ofLog2FC ofLog2FC ofLog2FC of
miRNAmRNAmiRNAmRNA
novel_miR_58MIR171_1BolC08g051460.2JSacI homology domain protein−0.8461.444−1.6291.349
novel_miR_80MIR167BolC05g028200.2JAuxin response factor0.325−0.526−2.379−0.792
novel_miR_160
MIR164		BolC04g044910.2JNAC domain protein−0.6870.678−0.4182.205
BolC05g053690.2JNAC domain protein−0.6870.693−0.4181.256
BolC02g003130.2JNAC domain protein−0.6870.207−0.418−1.162
BolC02g060420.2JNAC domain protein−0.687−0.326−0.418−3.979
BolC03g086720.2JNAC domain protein−0.6870.696−0.418−0.902
novel_miR_129
MIR319		BolC04g018790.2JTCP transcription factor −0.6870.6161.209−2.123
BolC03g071340.2JTCP transcription factor−0.6870.3381.209−1.537
BolC05g027880.2JTCP transcription factor−0.6870.0251.209−2.025
novel_miR_245MIR818BolC09g018620.2JLeucine-rich receptor protein kinase1.6591.2421.282−4.861
BolC09g018650.2JLeucine-rich receptor kinase1.6590.9311.282−3.723
novel_miR_181 BolC02g054180.2JSANT/Myb-like domain protein−0.506−1.395−1.844−1.78
BolC03g017770.2JSBP domain protein−0.506−0.154−1.8442.11
BolC03g074680.2JNB-ARC domain protein−0.5060.253−1.844−1.433
BolC05g014870.2JCASP-like protein−0.5060.702−1.8442.144
BolC07g025060.2JSBP domain protein−0.506−0.289−1.844−1.274
BolC08g008660.2JLeucine-rich repeats protein−0.5060.147−1.844−1.179
BolC09g009060.2JRibosomal protein S28e−0.5060.031−1.8441.076
novel_miR_139 BolC02g008640.2JNAC domain protein−1.1650.536−2.81.278
BolC03g017770.2JSBP domain protein−1.165−0.154−2.82.11
BolC04g045360.2JCation efflux family−1.1650.195−2.81.455
BolC05g024810.2JSBP domain protein−1.165−1.369−2.80.436
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MDPI and ACS Style

Wang, M.; Zhu, X.; Tai, X.; Chen, J.; Bo, T. A Combined mRNA and microRNA Transcriptome Analysis of B. oleracea Response to Plasmodiophora brassicae Infection. Horticulturae 2024, 10, 1013. https://doi.org/10.3390/horticulturae10101013

AMA Style

Wang M, Zhu X, Tai X, Chen J, Bo T. A Combined mRNA and microRNA Transcriptome Analysis of B. oleracea Response to Plasmodiophora brassicae Infection. Horticulturae. 2024; 10(10):1013. https://doi.org/10.3390/horticulturae10101013

Chicago/Turabian Style

Wang, Min, Xiaowei Zhu, Xiang Tai, Jinxiu Chen, and Tianyue Bo. 2024. "A Combined mRNA and microRNA Transcriptome Analysis of B. oleracea Response to Plasmodiophora brassicae Infection" Horticulturae 10, no. 10: 1013. https://doi.org/10.3390/horticulturae10101013

APA Style

Wang, M., Zhu, X., Tai, X., Chen, J., & Bo, T. (2024). A Combined mRNA and microRNA Transcriptome Analysis of B. oleracea Response to Plasmodiophora brassicae Infection. Horticulturae, 10(10), 1013. https://doi.org/10.3390/horticulturae10101013

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