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. 2025 Apr 9;25:446. doi: 10.1186/s12870-025-06509-7

Uncovering the key miRNA-target network of tea plants in resistance to sooty mold disease

Shuangshuang Wang 1, Ran Zhang 3, Litao Sun 1, Xiuxiu Xu 1, Jiazhi Shen 1, Xiaojiang Li 1, Chaoling Wei 2, Zhaotang Ding 1,✉, Shengrui Liu 2,✉
PMCID: PMC11980228  PMID: 40200131

Abstract

Background

Sooty mold (SM) disease severely threatens tea plant health, reducing yield and quality. Driven by climate change and intensive farming practices, SM prevalence in China has surged, causing significant economic losses and forcing farmers to rely on chemical fungicides, which compromise environmental sustainability. Despite its impact, the molecular mechanisms underlying tea plant defenses against SM remain unclear.

Results

Integrated transcriptomic, sRNAome, and degradome analyses revealed that differentially expressed genes (DEGs) exhibited infection-level-dependent expression patterns. Post-transcriptional regulation by miRNAs was identified through sRNAome-degradome mapping, with six miRNA-target defense pairs validated by 5′ RLM-RACE and qRT-PCR. Co-expression network analysis showed that two miRNA-target pairs, PC-5p-33681_128-auxin response factor (CsARF) and ppe-MIR535b-p3-1ss12TC-aldehyde dehydrogenase (CsALDH), play crucial roles in responding to SM infection. Furthermore, 5′ RLM-RACE and dual-luciferase assays revealed that the PC-5p-33681_128 and ppe-MIR535b-p3-1ss12TC could regulate the expression of CsARF and CsALDH by mRNA cleavage, respectively.

Conclusion

This study elucidates miRNA-mediated defense networks in tea plants against SM, offering actionable targets for breeding SM-resistant cultivars via genetic engineering or marker-assisted selection. Implementing these strategies could reduce yield losses, stabilize farmer incomes, and minimize environmental harm from fungicide overuse. This work advances climate-resilient practices for the global tea industry by linking molecular insights to sustainable agriculture.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-06509-7.

Keywords: Tea plants, Sooty mold disease, Defense response, Integration analysis, miRNA-target circuits

Introduction

Sooty mold (SM) is a plant disease characterized by the appearance of a dark, sooty, velvety coating on the surfaces of leaves and fruits, particularly in tropical and subtropical climates [1, 2]. The SM can thrive on the honeydew excreted by sap-feeding insects such as aphids, whiteflies, and scale insects, which secrete a sugar-rich substance that the SM uses as a nutrient source [3, 4]. Sooty mold can significantly inhibit photosynthesis, affecting plant growth and development, and reducing the aesthetic and economic value of crops [5]. Cladosporium species, along with other fungi such as Capnodium theae, Alternaria spp., Ulocladium spp., are major pathogens that cause SM disease in many plants [2, 6]. These fungi exhibit diverse lifestyles, functioning as either saprophytes, which thrive on organic matter, or parasites, which directly absorb nutrients from host plants [7]. In tea plants, SM infection is characterized by a distinctive crust-like appearance on the surface of leaves, which severely impacts plant health and yield. This visible symptom serves as a diagnostic marker for SM and underscores the need for further research into the molecular mechanisms underlying the infection process and host responses.

Plants have evolved a sophisticated innate immune system to defend against pathogen attacks. This system relies on the recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) on the plant cell surface, triggering PAMP-triggered immunity (PTI) [8, 9]. PTI involves activating defense-related genes, producing reactive oxygen species (ROS), and reinforcing cell walls through the deposition of callose and lignin. When pathogens overcome PTI, plants deploy effector-triggered immunity (ETI), which is mediated by resistance (R) proteins that recognize specific pathogen effectors, leading to a stronger and more specific immune response, often accompanied by programmed cell death at the infection site [10]. In addition to these immune responses, plants produce a wide array of secondary metabolites that play critical roles in disease resistance. For instance, many secondary metabolites produced by tea plants are considered to represent the quality of tea and have been found to enhance resistance to infection [11–13]. Many genes related to the phenylpropanoid catabolic process are involved in plant resistance to disease. For example, phenylalanine ammonia-lyase (PAL) is a key enzyme that regulates primary metabolism flow into second metabolism, producing anti-microbial phytoalexins and response to pathogen attacks [14, 15]. Plants utilize the metabolites produced by the phenylpropane metabolism pathway, such as lignin, pollen, anthocyanins, and organic acids, which play essential roles in regulating plant immunity [16]. The accumulation of lignin, reinforces defensive structures, such as the cell wall by thickening lignin layers [17]. Disease resistance also involves the induction of several transcription factors (such as ARF, WRKY, and ERF), protective enzymes, and resistance proteins as key regulators of immunity [18–20].

MicroRNAs (miRNAs) serve as critical post-transcriptional regulators of gene expression through their ability to guide mRNA translation and modulate complex biological processes in response to various biotic and abiotic stressors [21–25]. Emerging evidence from Camellia sinensis research reveals sophisticated miRNA-mediated defense mechanisms against phytopathogens [19, 24]. For example, miR477 exerts negative regulation on the immune response to gray blight disease by targeting cinnamate 4-hydroxylase [24]. However, miR530b targets ethylene-responsive factor 96 to reduce ROS levels, and miRn211 targets the thaumatin-like protein to increase ROS levels, both modulating the tea plant’s immune response to gray blight [19]. Furthermore, miR828a negatively regulates Lasiodiplodia theobromae resistance through the CsMYB28-CsRPP13 regulatory module [26]. Parallel investigations in rice demonstrate conserved regulatory mechanisms, exemplified by the miR172a-SNB module coordinating disease resistance via MYB30-mediated lignin biosynthesis [27]. Despite these advancements, the functional landscape of miRNAs in tea plant immunity remains incomplete, particularly regarding their diversity and specific roles in response to SM infection, warranting comprehensive exploration.

To investigate the resistance mechanism and identify molecular interactions between tea plants and SM, we conducted transcriptome, sRNAome, and degradome sequencing on leaves with varying levels of SM infection. Our results revealed a regulatory gene network containing two key miRNA-target pairs, PC-5p-33681_128-CsARF and ppe-MIR535b-p3-1ss12TC-CsALDH, which are crucial in responding to SM infection. The 5′ RLM–RACE and dual luciferase reporter gene assays showed that PC-5p-33681_128 and ppe-MIR535b-p3-1ss12TC could regulate the tea plant’s immunity by repressing their corresponding target genes.

Materials and methods

Plant materials and sampling

Three-year-old tea plants (Camellia sinensis cv. Huangshanzhong) were collected from Chunxi tea plantation in Linyi (N 36°65′, E 117°26′), Shandong Province, China. The mature tea leaves were collected on September 20, 2023. To ensure the representativeness of the samples, we collected leaves from the middle layer of the tea plant canopy, which are the main photosynthetic organs and are more likely to be affected by pathogens. These leaves were at the same growth stage, with similar leaf ages.

Three groups of samples were collected based on infection severity: healthy (Ale_L, with no visible signs of infestation), moderate (Ale_M, with scattered black mold spots covering 10–30% of the leaf surface), and severe (Ale_S, with thick crust-like black coatings covering > 70% of the leaf surface). To confirm the presence of Cladosporium species and identify the specific species involved, fungal DNA was extracted from infected leaves and subjected to ITS sequencing. Sampling standards are following our previous study [26]. All leaves were wiped with 75% ethanol to remove external contaminants and immediately placed in liquid nitrogen, then stored at -80 °C until RNA extraction.

Sequencing of transcriptome and data processing

Total RNA was extracted by an RNAprep Pure Plant kit (DP441, Tiangen, China) and used to construct the transcriptome library. Transcriptome sequencing was performed as described in our previous study [26]. Briefly, mRNA was enriched from total RNA using NEBNext Oligo(dT)25 Magnetic Beads (S1419S, New England Biolabs, USA), and reverse-transcribed into cDNA. Sequencing libraries were sequenced on the Illumina NovaSeq 6000 platform (150 bp paired-end reads). The fragments per kilobase of transcript per million mapped reads (FPKM) method was employed to estimate the gene expression levels, with a threshold of log2 (Fold change) ≥|2| and FDR < 0.05.

Small RNAome and degradome library sequencing and data analysis

About 1 µg of total RNA per sample was selected to prepare for sRNAome library construction as described in our previous study [19]. Raw reads were subjected to an in-house program, ACGT101-miR (v4.2, LC Sciences, USA) to remove adapter dimers, junk, low complexity, common RNA families (rRNA, tRNA, snRNA, snoRNA), and repeats. Subsequently, unique sequences with lengths in 18 ~ 25 nucleotide were mapped to specific species precursors in miRBase 22.1 (http://www.mirbase.org/) by BLAST search to identify known and novel 3p- and 5p- derived miRNAs. MicroRNAs not mapped to genes in miRBase but mapped to the tea genome without a mismatch were identified as novel miRNA candidates. The DEmiRNAs were analyzed using Student’s t-test. The threshold for the differential expression of miRNAs in individual samples was set at P-value ≤ 0.05.

The total RNA was mixed to construct one degradome library. ACGT101-DEG (v.4.0, LC Sciences, USA) and CleaveLand (v.4.0) were used to detect possible targets of the miRNAs. All targets were divided into five categories (0, 1, 2, 3, and 4) based on the abundance of the target.

5’ RLM-RACE for identification of target cleavage sites

The cleavage sites of miRNAs in the target genes were verified by 5’ RLM-RACE using the RLM-RACE Kit (AM1700, Invitrogen, USA) as described previously [19]. All PCR products were cloned into the pEASY-T1 vector (CT101, TransGen Biotech, China), and ten clones were sequenced. The primers used to amplify the cleavage products of miRNA are listed in Table S1.

Validation of gene expression by qRT-PCR

For qRT-PCR, total RNA (500 ng) was reverse transcribed with Primer Script RT reagent Kit (RR037A, TaKaRa, Japan) according to the manufacturer’s instructions, with specific stem-loop RT primer for miRNA and oligo-dT primer for target genes. PCR amplification was performed using SYBR Green Master Mix (04887352001, Roche, Switzerland). The relative expression of miRNAs and target genes was calculated using the 2−ΔΔCt method. Technical replicates comprised three repeated measures of each biological replicate. The references for miRNAs and their target genes were CsU6 and CsGAPDH. The primers are listed in Table S1.

Co-expression analysis

The R package WGCNA was used for co-expression network analysis. A gene expression adjacency matrix was developed to analyze the network topology described in the previous study [26]. Cytoscape v.3.9.1 was used to visualize the gene co-expression networks.

Transient dual-luciferase assay in Nicotiana benthamiana

The precursor stem-loop sequences of PC-5p-33681_128 and ppe-MIR535b-p3_1ss12TC were inserted separately into pGrennII 62-SK effector vectors. The CDS sequences of CsARF (CSS0031673) and CsALDH (CSS0002426) were inserted into the reporter vector pGreenII 0800-LUC. The recombinant plasmids were co-transformed into Nicotiana benthamiana leaves by Agrobacterium-mediated transient transformation, and an in vivo imaging analysis system was used for luminescence detection (Fusion FX7, VILBER, France). Renilla luciferase (REN) was used as an internal control for activity normalization. Transient expression levels of promoter activity were shown as the LUC/REN ratio. At least 20 leaves were infiltrated for each construct in each independent experiment. The primers used are listed in Table S1.

Results

Field observations and symptoms

From September to October 2023, there was a large outbreak of sooty mold disease in tea plantations across Shandong Province. Compared with healthy leaves, the initial symptoms manifested as small, circular, or irregular black spots on the leaf surfaces, which progressively enlarged over time (Fig. 1A, B). In severe cases, the surface of the leaves is almost completely covered by black coal powder-like substances, forming a thick layer of mold. Disease spots not only cover the leaves, but also spread to small branches, stems, or even the entire tea plant (Fig. 1C).

Fig. 1.

Fig. 1

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Typical symptoms of sooty mold disease on tea plants in the field. A: the healthy tea leaves; the (B) moderate- and (C) severe-infected tea leaves

Transcriptome sequencing and analysis

The sequencing yielded an average of 47.5 million clean reads per sample, with a Q20 base percentage exceeding 97.45% (Table S2). The principal component analysis (PCA) analysis indicated that the data were high quality and repeatable (Fig. S1). A total of 760 DEGs were identified, with numbers ranging from 312 (210 upregulated, 102 downregulated) in Ale_S and Ale_M to 545 (261 upregulated, 284 downregulated) in Ale_S and Ale_L (Fig. 2A). The number of downregulated DEGs was higher than the upregulated DEGs in both Ale_L vs. Ale_M and Ale_L vs. Ale_S samples, suggesting suppressed gene expression in infected leaves. Gene expression overlap revealed unique DEGs per comparison: Ale_L vs. Ale_S had the most (149), while Ale_M vs. Ale_S had the fewest (73) (Fig. 2B).

Fig. 2.

Fig. 2

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Overview of DEGs responding to sooty mold disease. A: The DEG number in different treatments; B: Venn diagram of three treatments; Scatter plot of the most enriched GO terms (C) and KEGG pathways (D) of all DEGs

To further explore the functions of DEGs in response to sooty mold disease, Gene Ontology (GO) annotation was conducted. The most significantly enriched terms were lignin biosynthetic and metabolic process, phenylpropanoid catabolic process, and plant-type cell wall biogenesis (Fig. 2C). An analysis of the enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that the most enriched pathways were metabolic pathways, biosynthesis of specialized metabolites, and isoflavonoid biosynthesis (Fig. 2D).

Different expression patterns of DEGs

Under pathogen infection, DEGs exhibit diverse expression patterns in tea plants. A heatmap with GO enrichment revealed pronounced differential expression in Ale_L samples, primarily enriched in cell wall biogenesis, glucuronoxylan and xylan biosynthesis, and metabolic pathways (Fig. 3). However, the number of DEGs in Ale_M samples was significantly lower than in other treatments. The genes involved in flavonoid biosynthetic and metabolic processes, positive regulation of transferase activity, and hydrolase activity were observed in Ale_M/S in Cluster II. In severely infected samples, numerous genes associated with the specialized metabolic process, phenylpropanoid and lignin catabolism, and defense response to fungus exhibited induced expression in Cluster III, indicating heightened immune responses. Notably, UDP-glucosyltransferase and oxidoreductase activities were universally observed across all samples, underscoring their critical roles in SM response.

Fig. 3.

Fig. 3

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Cluster analysis of DEGs. The clustering was performed based on the FPKM for each gene. The genes showing similar expression patterns were grouped into three clusters (Clusters I–III). The original expression values of DEGs were normalized by Z-score normalization

Identification of SM-regulated MiRNAs by deep sequencing

A total of 63.3 million valid reads were obtained for further analysis after filtering out about 17.3 million reads that were < 18 nucleotides (nt) or > 25 nt (Table S4). A total of 451 mature miRNAs were classified in all samples, and these were split into four groups (gp1, gp2a, gp2b, and gp3), all of which were annotated in the miRBase database (Fig. S2, Table S5). These known miRNAs were classified into 39 miRNA families, with miR171 being the largest family (22 members), followed by miR167 (18 members). Additionally, after removing the miRNAs that did not meet the plant miRNA standard, 1,619 novel mature miRNAs (grouped as gp4) were identified, which were not annotated in the miRbase database (Fig. S2, Table S5).

Differentially expressed MiRNAs during SM infection

To investigate miRNA roles in SM infection, differentially expressed miRNAs (DEmiRNAs) were identified between healthy and infected leaves, totaling 197 (97 known, 100 novel). The number of DEmiRNAs ranged from 51 (22 upregulated; 29 downregulated) at Ale_L/S to 151 (91 upregulated; 60 downregulated) at Ale_L/M (Fig. S3).

A heatmap of selected DEmiRNAs revealed diverse expression patterns across infection levels (Fig. 4). Compared with healthy leaves, several new miRNAs (PC-5p-64738_64, PC-3p-36261_119, PC-5p-33681_128) were highly induced, while PC-5p-100271_37, PC-3p-124953_27, and PC-3p-22835_183 were reduced in moderate infection leaves. In severe infections, known miRNAs, such as ath-miR8175, mtr-miR2592, and peu-miR2916, were induced compared with healthy leaves. While the expression levels of ath-miR166, ath-miR396, and ath-miR166 family members (e.g., ath-miR166-5p_1ss4CA, ath-miR166a-3p_L + 3) were significantly upregulated in severe infection leaves compared with that in moderate infection leaves.

Fig. 4.

Fig. 4

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A heatmap analysis of the selected differential expressed miRNAs. The clustering was performed based on log2 fold change for each miRNA in comparing different infected degree samples

Target prediction and degradome analysis

Through degradome sequencing, 1095 target genes for 194 known and 219 novel miRNAs were verified (Table S6). Twenty significantly enriched GO terms related to SM infection were visualized in Fig. S4A. The most enriched terms were chloroplast, cytosol, response to auxin, and response to light stimulus. The most enriched KEGG pathways were closely associated with pathogen response, including plant hormone signal translation, photosynthesis, and glyoxylate and dicarboxylate metabolism (Fig. S4B).

Correlational analysis of SM-responsive MiRNA and the target expression profile

The DEmiRNAs and their target gene expression profiles were combined to investigate the roles of miRNAs under SM infection for different infection degrees. There were 72 differentially expressed targets for 36 DEmiRNAs, and 37 miRNA–target pairs showed reverse expression patterns (Table S7). For instance, novel miRNA PC-5p-33681_128 exhibited an 8.62 log2 fold change, whereas its target gene CSS0031673 (Auxin-responsive protein) showed a -1.61 log2 fold change in severe infections. A similar expression profile was observed for the ath-miR858a_L-1R + 1-MYB5e (CSS0028845) pair.

An RNA ligase-mediated rapid amplification of 5 cDNA ends (5′ RLM-RACE) was developed to validate the degradome sequencing results (Fig. 5A). These target genes have been tested for cleavage by miRNAs. To verify the relationship between miRNAs and target genes, we tested their expression profiles using quantitative reverse transcription (qRT)-PCR (Fig. 5B). The results indicated that the outcomes of qRT-PCR were in line with the sequencing data.

Fig. 5.

Fig. 5

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Validation of degradome and high-throughput sequencing. (A) Validation of degradome through 5′ RLM-RACE, the numbers represent the cleavage frequency. The arrows represent the cleavage sites. (B) validation of sRNAome and transcriptome through qRT-PCR

Gene co-expression network analysis

To comprehensively explore the miRNA-target pairs for regulating tea plants’ immunity under SM infection, the transcriptome, sRNAome, and degradome data were used for co-expression analysis. WGCNA analysis revealed nine distinct module clusters containing 6218 unigenes (Fig. S5). We focused on the DEmiRNA and their differentially expressed target, and two target genes, namely CsARF (targeted by PC-5p-33681_128) and CsALDH (targeted by ppe-MIR535b-p3-1ss12TC) were selected (Fig. 6). Finally, CsARF and CsALDH were directly associated with 88 and 72 edges in this network, respectively. Of these, 61 edges were co-regulated by CsARF and CsALDH, indicating that these two hub genes were regulated via this gene network. Of the network, genes were clustered in three groups, which linked to the biosynthesis of specialized metabolites, plant-pathogen interaction, and plant hormone signal transduction (Fig. 6 and Table S8).

Fig. 6.

Fig. 6

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Co-expression subnetwork of CsARF (CSS0031673) and CsALDH (CSS0002426). The dotted arrows indicate miRNA–target regulation paris: PC-5p-33681_128-CsARF and ppe-MIR535b-p3-1ss12TC- CsALDH. Green circles represent target genes in plant hormone signal transduction pathways; blue circles denote genes involved in plant-pathogen interaction; and reddish-brown circles signify genes associated with the biosynthesis of specialized metabolites. Grey arrows indicate the interactions between network edges and CsARF and CsALDH nodes

Validation of the interactions between MiRNAs and hub genes

The 5′ RLM-RACE was employed to validate the degradome sequencing data, and the results showed that CsARF is cleaved by PC-5p-33681_128 and CsALDH is cleaved by ppe-MIR535b-p3_1ss12TC (Fig. 7A). To investigate the relationships, we further examined the interactions of the miRNAs and hub genes through qRT-PCR analysis (Fig. 7B). The negative correlations between miRNA–target pairs in the expression levels suggest targets post-transcriptional repression may occur through their corresponding miRNAs.

Fig. 7.

Fig. 7

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Expression correlations between miRNAs and their targets. A: The miRNA targets were identified through 5′ RLM-RACE. The numbers represent the cleavage frequency. The arrows represent the cleavage sites. B: Quantitative expression analysis of miRNA and targets via qRT-PCR. The transient luciferase complementation imaging assay between PC-5p-33681_128- CsARF (C) and ppe-MIR535b-p3_1ss12TC- CsALDH pairs (D). Data are represented as the mean ± SD of three independent experiments. Asterisks and different letters indicate significant differences, as determined by one-tail Student’s t-test (**P < 0.01, *P < 0.05)

We employed the transient dual-luciferase assay in tobacco leaves to explore the interactions between miRNAs and target genes. The CDS sequences of CsARF and CsALDH were inserted into the pGreenII 0800-LUC vector, fused to a LUC reporter gene. The precursor sequences of PC-5p-33681_128 and ppe-MIR535b-p3_1ss12TC were fused to the pGreen 62-SK vector as an effector. For the PC-5p-33681_128- CsARF pair, leaves inoculated with empty vector and 35 S::CsARF showed a similar phenotype (Fig. 7C). However, co-expression of 35 S::PC-5p-33681_128 with 35 S::CsARF resulted in remarkably weaker luminescence intensity. Furthermore, the luminescence detection assay results agreed with the tobacco leave observations. The ppe-MIR535b-p3_1ss12TC- CsALDH pair exhibited a similar repression relationship in tobacco (Fig. 7D). These findings showed that CsARF is the direct target of PC-5p-33681_128, and CsALDH is the direct target of ppe-MIR535b-p3_1ss12TC, and post-transcriptional repression of the targets may be regulated by their corresponding miRNAs.

Discussion

SM disease is a severe foliar fungal disease that causes significant damage to tea production. In the year of the outbreak of SM, not only does the tea yield severely decrease and the quality decline, but severe cases can also lead to the withering of diseased plants. Herein, we employed transcriptome, sRNAome, and degradome to gain insight into crucial miRNA-target pairs related to the response of tea plants to SM infection.

A total of 761 genes demonstrated differential expression patterns, and it was seen that a significant number of novel genes were also a part of these DEGs. It is worth noting that many defense genes are significantly expressed in healthy leaves (Fig. 3). Several studies have demonstrated the crucial role of phenolic compounds in providing chemical resistance, and these compounds can eliminate harmful microorganisms and the substances produced by them after an infection has occurred [27, 28]. Lignin is one of the most significant phenolic compounds that support organs, transmits sap through lignified parts of the plant’s vascular system, and serves as a defensive compound [29, 30]. Under SM infection, the DEGs related to lignin biosynthetic and metabolic process, phenylpropanoid catabolic process, and plant-type cell wall biogenesis were upregulated in severe infection leaves compared with moderate infection leaves (Fig. 3). The results suggest that tea plants resisted the invasion of pathogens by increasing the cell wall thickness in severely SM-infected leaves, consistent with previous studies’ findings [19, 27, 28].

Several studies have demonstrated the regulatory role of miRNAs in tea plant development and stress responses [22, 24, 31–33]. However, their involvement in SM infection remains unexplored. Here, we identified ​197 DEmiRNAs​ under SM infection, including ​100 novel miRNAs​ predominantly enriched in moderately infected leaves (Ale_M/Ale_L). This finding expands the repertoire of SM-responsive miRNAs and suggests their unique contribution to tea immunity, aligning with prior reports of novel miRNA functions in plant-pathogen interactions [21, 34]. Furthermore, the known miRNAs also play important roles in responding to SM infection in tea plants. For example, compared to healthy leaves, gma-miR5368 family members exhibited 6.93- and 5.23-fold changes in moderate and severe infected leaves, respectively (Fig. 4). In addition, the gma-miR5368 was predicted to regulate genes involved in the monoterpenoid and sesquiterpenoid synthesis [35]. The functions of miRNAs can be executed by regulating their targets. Degradome analysis showed that the targets were closely associated with pathogen response. For instance, miR394 targets UDP-glycosyltransferase, which participates in flavonoid biosynthesis and is associated with the plant defense response [36]. Similarly, the novel miRNA ​PC-5p-33681_128​ inversely regulated its target CsARF, suggesting phytohormone crosstalk during infection. These results collectively demonstrate that miRNAs orchestrate tea immunity through both conserved and species-specific regulatory networks.

Construction and analysis of a co-expression regulatory network revealed that CsARF and CsALDH might play significant roles in regulating SM defense. The qRT-PCR results indicated that CsARF and CsALDH may negatively regulate tea plants’ response to SM infection. Furthermore, the dual-luciferase assay suggested that PC-5p-33681_128 and ppe-MIR535b-p3_1ss12TC could repress the expression of CsARF and CsALDH in vivo, respectively. Previous studies have shown that CsARF4 shows differential expressions in shoots and roots when treated with hormones like auxin, salicylic acid, and 6-benzyladenine [37]. It participates in multiple hormone signaling pathways and is crucial for the growth of tea plants and their response to external stimuli. When tea plants are infected with Colletotrichum gloeosporioides, CsmiR160c negatively regulates CsARF5 and related target genes are involved in the tea plant’s immunity [18].

Conclusions

Our study reveals a novel miRNA-target network involved in tea plant responses to SM infection, shedding light on the complex regulatory mechanisms underlying plant-pathogen interactions. The defense response is complex, beginning with insect attacks that activate several specialized metabolisms and the biogenesis of the host cell wall. In moderately infected samples, the immune response weakened, while in severely infected samples, tea plants reactivated immunity through regulated specialized metabolism and plant-pathogen interactions (Fig. 8). Subsequent co-expression assays revealed two hub genes significantly associated with SM response. Combined qRT-PCR and LUC assays demonstrate that PC-5p-33681_128 directly targets CsARF, while ppe-MIR535b-p3_1ss12TC binds to CsALDH via mRNA cleavage to participate in the tea plant immune response to SM. These findings enhance our understanding of the molecular basis of tea plant immunity and provide potential targets for improving tea plant resistance.

Fig. 8.

Fig. 8

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A hypothetical scheme showing the summary of the physiological and biochemical events during the interaction of sooty mold disease with tea plants. The heat maps indicate the up-regulation (pink) and down-regulation (olive) of gene/miRNA in Ale_L, Ale_M, and Ale_S samples. Genes specifically expressed at each treatment were selected for the Gene Ontology (GO) enrichment analysis. Representative genes from significantly enriched GO terms and some specifically expressed defense genes from each treatment are depicted as a heat map. The expression values were normalized by Z-score normalization

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (7MB, docx)
Supplementary Material 2 (1,022.4KB, xlsx)

Abbreviations

SM

Sooty mold

DEGs

Differentially expressed genes

CsARF

Auxin response factor

CsALDH

Aldehyde dehydrogenase

ROS

Reactive oxygen species

PAL

Phenylalanine ammonia-lyase

miRNAs

MicroRNAs

FPKM

Fragments per kilobase of transcript per million mapped reads

REN

Renilla luciferase

PCA

Principal component analysis

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

DEmiRNAs

Differentially expressed miRNAs

qRT

Quantitative reverse transcription

vsRNAs

Virus-derived small RNAs

SLCMV

Sri Lankan Cassava Mosaic Virus

Author contributions

SSW, ZTD, and SRL proposed ideas, designed experiments, and wrote the initial manuscript. SSW, RZ, and LTS analyzed data and performed experiments. XXX, JZS, and XJL participated in the materials collection. All authors reviewed the manuscript.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (ZR2021QC092) and the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization (SKLTOF20220104).

Data availability

The transcriptome, sRNAome, and degradome data are available at NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra), and the BioProject ID is PRJNA1107820.

Declarations

Ethics approval and consent to participate

The landowners permitted us to access the land, collect samples, and conduct necessary analyses. All methods were carried out according to relevant guidelines and regulations.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Zhaotang Ding, Email: dzttea@163.com.

Shengrui Liu, Email: liushengrui@ahau.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (7MB, docx)
Supplementary Material 2 (1,022.4KB, xlsx)

Data Availability Statement

The transcriptome, sRNAome, and degradome data are available at NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra), and the BioProject ID is PRJNA1107820.

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