Skip to main content

The role of WRKY transcription factors, FaWRKY29 and FaWRKY64, for regulating Botrytis fruit rot resistance in strawberry (Fragaria × ananassa Duch.)



The cultivated strawberry (Fragaria × ananassa Duch.) is one of the most economically important horticultural crops worldwide. Botrytis fruit rot (BFR) caused by the necrotrophic fungal pathogen Botrytis cinerea is the most devasting disease of cultivated strawberries. Most commercially grown strawberry varieties are susceptible to BFR, and controlling BFR relies on repeated applications of various fungicides. Despite extensive efforts, breeding for BFR resistance has been unsuccessful, primarily due to lack of information regarding the mechanisms of disease resistance and genetic resources available in strawberry.


Using a reverse genetics approach, we identified candidate genes associated with BFR resistance and screened Arabidopsis mutants using strawberry isolates of B. cinerea. Among the five Arabidopsis T-DNA knockout lines tested, the mutant line with AtWRKY53 showed the greatest reduction in disease symptoms of BFR against the pathogen. Two genes, FaWRKY29 and FaWRKY64, were identified as orthologs in the latest octoploid strawberry genome, ‘Florida Brilliance’. We performed RNAi-mediated transient assay and found that the disease frequencies were significantly decreased in both FaWRKY29- and FaWRKY64-RNAi fruits of the strawberry cultivar, ‘Florida Brilliance’. Furthermore, our transcriptomic data analysis revealed significant regulation of genes associated with ABA and JA signaling, plant cell wall composition, and ROS in FaWRKY29 or FaWRKY64 knockdown strawberry fruits in response to the pathogen.


Our study uncovered the foundational role of WRKY transcription factor genes, FaWRKY29 and FaWRKY64, in conferring resistance against B. cinerea. The discovery of susceptibility genes involved in BFR presents significant potential for developing resistance breeding strategies in cultivated strawberries, potentially leveraging CRISPR-based gene editing techniques.

Peer Review reports


The cultivated strawberry (Fragaria × ananassa Duch.) is one of the most economically important horticultural crops worldwide [1]. Strawberries are grown on 389,665 hectares and produced about 9.18 million tons worldwide in 2021 according to the Food and Agriculture Organization ( The modern cultivated strawberry is an allo-octoploid (2n = 8x = 56) species. The first chromosome-scale genome assembly of Fragaria × ananassa was released from the cultivar ‘Camarosa’ in 2019 [2]. Recently, two high-quality haplotype-phased genomes from ‘Royal Royce’ [3] and ‘Florida Brilliance’ [4] are published and available at the Genome Database of Rosaceae ( The size of the phased octoploid strawberry genome is approximately 1.6 Gb containing 115,000 genes.

Strawberries are prone to various biotic stressors such as fungi, bacteria, viruses, and nematodes [5, 6]. Among these diseases, Botrytis fruit rot (BFR) disease caused by Botrytis cinerea is a major problematic issue for cultivated strawberries. Despite the application of fungicides, BFR can cause severe reduction (more than 50%) in commercial strawberry yields [7, 8]. B. cinerea infects both flowers and fruits, and it is often difficult to control under favorable environmental conditions. The development of BFR on strawberry fruit is a primary reason for fruit rejection by growers and consumers alike [6]. While the management of BFR typically relies on repeated applications of various groups of fungicides, the efficacy of these chemical treatments has significantly decreased due to the occurrence of fungicide-resistant isolates. Consequently, alternatives such as developing B. cinerea resistant cultivars are in high demand to mitigate the economic losses caused by BFR disease.

Several studies have been conducted to improve resistance against the devastating B. cinerea in cultivated strawberries [9,10,11]. The β–glucosidases (BGs), which play crucial roles in plant development and pathogen defense, are known to regulate abscisic acid (ABA) homeostasis and fruit ripening-related gene expression, and thus are significant players in BFR resistance [11]. In FaBG3-RNAi fruit, ABA content and gene expression related to cell wall catabolism (FaPG1FaEXP4FaXYL1, and FaGAL1) were substantially decreased, resulting in enhanced resistance against B. cinerea. A two-pore K+ channel gene (FaTPK1) regulating fruit ripening has been identified as a Botrytis-susceptible gene [10]. Resistance against Botrytis pathogen was found to decrease in FaTPK1-overexpressing fruit, while it increased in FaTPK1-RNAi fruit. It was also suggested that FaWRKY11 is a positive regulator of plant resistance against B. cinerea, controlling genes related to phytohormone metabolism, including ABA and jasmonic acid (JA), and disease-resistance transcription factors (TFs) [9]. Resistance against B. cinerea was significantly increased in FaWRKY11-overexpressing fruit, while it slightly decreased in FaWRKY11-RNAi fruit.

Arabidopsis T-DNA knockout mutant lines have been widely utilized to study the gene functions for plant disease resistance [12, 13]. Screening of Arabidopsis mutants and identifying candidate genes for B. cinerea resistance can provide valuable insights into the mechanisms of BFR resistance in strawberries. The candidate genes identified in Arabidopsis can be tested for gene functions in octoploid strawberries by transient overexpression and RNA interference knockdown assays. Several genes have been reported for the resistance against B. cinerea in Arabidopsis. T-DNA insertion Atmyb46 mutant lines exhibited enhanced resistance against B. cinerea in Arabidopsis leaves and transcriptional reprogramming of genes related to cell wall proteins and enzymes, including extracellular type III peroxidases [14]. An ethyl methanesulfonate-derived Atocp3 mutant line also showed enhanced resistance against B. cinerea in Arabidopsis leaves, along with hydrogen peroxide (H2O2) accumulation and the transcriptional expression of marker genes, such as Glutathione S-transferase1 and Plant Defensin 1.2 (PDF1.2) [15]. Furthermore, CRISPR/Cas9-derived VvWRKY52 (an ortholog gene of AtWRKY53) knockout mutants showed enhanced resistance against B. cinerea in grape leaves [16].

Unripe fruits infected with Botrytis cinerea usually lead to quiescent infections, while B. cinerea aggressively infects ripened fruits [17]. Plant hormones play a variety of roles in both strawberry fruit ripening and plant defense responses against B. cinerea. ABA is an inducer of fruit ripening, with ABA levels increasing gradually while auxin levels decrease gradually during fruit ripening. ABA signaling is involved in reactive oxygen species (ROS) production after B. cinerea infection [18,19,20,21]. The highest JA content is found in big green fruits, which drastically decreases in white fruits. JA and ET signaling are involved in plant defenses against necrotrophic pathogens including B. cinerea. External application of JA to red fruits has been shown to improve resistance against B. cinerea [5, 22]. Cell wall serves as a mechanical barrier against B. cinerea infection, and fruit ripening is associated with cell wall depolymerization and cell wall solubilization, leading to fruit softening that can facilitate B. cinerea infection [23]. Cell wall degradation not only makes it easier for B. cinerea to invade fruit cells but it also increases the content of fruit sugar, which B. cinerea utilizes [6]. Changes in secondary metabolites such as phenylpropanoids, flavonoids, hydrolysable tannins, and benzoic acids occur during fruit ripening and can also affect resistance against B. cinerea [24, 25].

WRKY transcription factors (TFs) play a significant role in regulating plant developmental processes, particularly in biotic and abiotic stress responses in plants, including Arabidopsis, rice, and tomato [26, 27]. WRKY TFs have been reported as modulators of plant immune responses against various pathogens, including B. cinerea [28,29,30]. Camellia oleifera WRKY78 was significantly low in expression in anthracnose-resistant cultivars. Reduced anthracnose resistance was observed in CoWRKY78 overexpression tobacco transgenic plants, exhibiting lower superoxide dismutase and peroxidase activities [28]. The promoter region of Morus indica WRKY53 is induced by salicylic acid (SA). Overexpression of MiWRKY53 in Arabidopsis altered not only leaf morphology, but also resistance against Pseudomonas syringae PstDC3000 [29]. AtWRKY70 is associated with the regulation of the mutually antagonistic cross-talk between SA- and JA-dependent plant defense mechanisms. AtWRKY70 expression is activated by external SA treatment and plays a role as an activator of SA-inducible genes but is repressed by external methyl jasmonate (MeJA) treatment and as a repressor of JA-inducible genes. After inoculation of the necrotrophic fungal pathogen, Alternaria brassicicola, the transcripts of the JA-responsive marker PDF1.2 were highly accumulated in the Atwrky70 mutant [31, 32]. AtWRKY53 expression is strongly correlated with AtWRKY70 expression after systemic acquired resistance induced by SA with the transcription cofactor NPR1. Both AtWRKY53 and AtWRKY70 have partially overlapping roles as positive regulators of plant basal defense responses against virulent Pseudomonas syringae [33]. AtWRKY41 expression is immediately activated with AtWKRY53 by flagellin treatment derived from P. syringae. Overexpression of AtWRKY41 increases resistance to the hemi-biotrophic pathogen P. syringae but decreases resistance to the necrotrophic pathogen Erwinia carotovora [34]. AtWRKY33 is a critical transcriptional regulator of plant immune responses, upregulated at early stages of B. cinerea infection in Arabidopsis [30, 35, 36]. Loss of function of AtWRKY33 resulted in increased susceptibility to B. cinerea, while overexpression of the gene decreased susceptibility. AtWRKY33 was found to regulate hormone signaling, redox homeostasis, and camalexin biosynthesis induced by the pathogen. AtWRKY33 also affected the upstream regulation of NCED3/NCED5, negatively regulating ABA biosynthesis. Disruption of AtWRKY33 function resulted in accumulation of ABA, which interacts with other plant defense hormones, such as jasmonic acid/ethylene (JA/ET) and SA. The antagonistic effect of ABA on JA was found to be a key regulator of resistance against B. cinerea, which is controlled by AtWRKY33. Additionally, single and double mutants of AtWRKY3 and AtWRKY4 were found to exhibit enhanced B. cinerea susceptibility, while AtWRKY8 was found to physically interact with AtVQ10 [37].

In addition to Arabidopsis, WRKY TFs in strawberries have been found to regulate plant defense responses to B. cinerea [5, 9]. It has been reported that FaWRKY11 is involved in enhancing the resistance against B. cinerea in octoploid strawberry fruits [9]. Transient overexpression of FaWRKY11 increased B. cinerea resistance in fruit, whereas transient knockdown of FaWRKY11 decreased resistance against B. cinerea. Following B. cinerea infection, FaJAZ1, FaJAZ4, and FaMAPK19 were highly expressed in FaWRKY11-overexpressing fruits. On the contrary, FaWRKY25, classified as group I WRKY, was found to be a susceptibility gene to B. cinerea in octoploid strawberries [5]. Transient overexpression of FaWRKY25 increased B. cinerea susceptibility in strawberry fruits, while transient knockdown of FaWRKY25 enhanced B. cinerea resistance. FaWRKY25 expression gradually increased after B. cinerea infection. External JA application to fruits resulted in a decrease in FaWRKY25 expression, leading to enhanced B. cinerea resistance. Genes related to JA biosynthesis and metabolism, such as FaLOX, FaAOS, FaAOC, and FaOPR3, were highly expressed in FaWRKY25 knockdown fruits during fruit maturation. Following B. cinerea infection, FaCOI1, FaMYC2, and FaJAZ12 were highly expressed in FaWRKY25 knockdown fruits compared to mock fruits.

In this study, we identified and investigated the role of two WRKY genes, FaWRKY29 and FaWRKY64, both of which belong to the strawberry WRKY group III, in conferring resistance to BFR in cultivated strawberries. Recently, 64 WRKY TFs of Fragaria vesca and 257 WRKY TFs of Fragaria × ananassa (cv. ‘Camarosa’) were renamed by Garrido-Gala et al. [38], respectively, and we followed the nomenclature shown in the publication. Transient knockdown experiments on each of two genes, FaWRKY29 and FaWRKY64, were performed to determine their effect on B. cinerea resistance in strawberry fruits, and analyzed transcriptome data to identify differentially expressed genes. Our findings indicate that FaWRKY29 and FaWRKY64 genes are B. cinerea susceptible genes for developing B. cinerea resistant strawberries through CRISPR/Cas9-mediated mutagenesis.


Identification of candidate genes for Botrytis cinerea resistance in octoploid strawberry

From a search of public database and previous publications, we identified several candidate genes, AtDND1 (At5g15410) [39], AtMPK3 (At3g45640) [40], AtOCP3 (At5g11270) [15], AtMYB46 (At5G12870) [14], VvWRKY52 (AtWRKY53, At4g23810) [16]. These genes have been reported to be involved in plant defense responses against BFR in plants. Arabidopsis mutants obtained from the Arabidopsis Information Resource (TAIR, were inoculated with strawberry field isolates of B. cinerea (Table S1). In this study, we identified three candidate genes, Arabidopsis OVEREXPRESSOR OF CATIONIC PEROXIDASE 3 (OCP3), MYB46, and WRKY53, potentially associated with the resistance of BFR in strawberry. T-DNA inserted Atmyb46 mutants and ethyl methanesulfonate-derived Atocp3 mutants exhibited enhanced B. cinerea resistance in Arabidopsis [14, 15]. CRISPR/Cas9-derived VvWRKY52 (an ortholog gene of AtWRKY53) knockout mutants showed enhanced B. cinerea resistance in grape [16]. Homozygous seeds of T-DNA insertion mutant lines for each of the three genes were obtained from TAIR. The homozygous plants were confirmed by PCR (Fig. S1). After B. cinerea inoculation, necrosis symptoms in Arabidopsis leaves were notably less evident in mutants of AtWRKY53 (SALK_034157), AtMYB46 (SALK_088514), and AtOCP3 (SALK_003729) compared to wild type (WT) (Fig. 1A). Two days after B. cinerea inoculation, the disease area was significantly reduced in SALK_034157 and SALK_088514 compared to WT, with the smallest area observed in SALK_034157 (Fig. 1B). Three days after B. cinerea inoculation, the disease area was significantly reduced in SALK_003729, SALK_034157, SALK_088514, and SALK_100993 compared to WT, with the smallest area also observed in SALK_034157 (Fig. 1C). The SALK_034157 mutant has T-DNA insertion on AtWRKY53, and the ortholog of this gene was used for further validation of resistance against B. cinerea in strawberry fruits. With the amino acid sequence of AtWRKY53, we identified two ortholog genes, FaWRKY29 on chromosome 5 and FaWRKY64 on chromosome 7, both of which are classified into WRKY group III. The gene names of FaWRKY29 and FaWRKY64 were followed by the nomenclature of strawberry WRKY TFs described by Garrido-Gala et al. [38]. The cDNA sequences of FaWRKY29 or FaWRKY64 were used to identify their homoeologous sequence in the telomere-to-telomere quality reference genome of octoploid strawberry, ‘Florida Brilliance’ (FaFB1) [4]. In total, we identified four homoeologs both for FaWRKY29 and FaWRKY64 in the reference genome of FaFB1, respectively (Fig. 2A and B). Using our previous transcriptome data available from ‘Florida Brilliance’, the expression patterns of FaWRKY29 and FaWRKY64 were determined in six different stages of fruit development (Small Green, SG; Medium Green, MG; Large Green, LG; White, W; Turning Red, TR; and Red, R) (Fig. 2C and D). It was found that all homoeologous copies of the FaWRKY29 and FaWRKY64 were constitutively expressed in the main stages of fruit development – MG, LG, W, and TR. Two homoeologous copies of FaWRKY29 (Fxa5Ag1276230 and Fxa5Cg1231820) and three for FaWRKY64 (Fxa7Ag358230, Fxa7Bg1533970, and Fxa7Dg973830) were dominantly expressed in all fruit development stages. All copies of FaWRKY29 and FaWRKY64 showed their highest expression levels at stage W (fruit stage used for Agrobacterium infiltration for RNAi gene silencing) and slightly decreased at stage TR. RNAi constructs targeting all the homoeologs of FaWRKY29 or FaWRKY64 were specifically designed, avoiding the WRKY conserved domain.

Fig. 1
figure 1

Botrytis cinerea inoculation to Arabidopsis T-DNA knockout lines. Botrytis cinerea was inoculated to 19 – 24 leaves from 4-week-old Arabidopsis plants for each homozygous mutant line. A Botrytis cinerea inoculated leaf pictures were taken one day after inoculation from wild type (WT), SALK_034157, and SALK_088514. The disease area was measured two days after inoculation (B) and three days (C) using the ImageJ program. Col-0 was used as WT. The experiment was repeated two times. Error bars indicate standard error. Asterisks indicate significant differences from the WT determined by the Student’s t-test (* p < 0.05, ** p < 0.01, and *** p < 0.001)

Fig. 2
figure 2

Multiple alignments of amino acid sequences for WRKY29 and WKRY64 in Fragaria vesca and Fragaria × ananassa (cv. ‘Florida Brilliance’). A AtWRKY53 (At4g23810), FvWRKY29 (FvH4_5g04370), and FaWRKY29 homoeologs. B AtWRKY53, FvWRKY64 (FvH4_7g31050), and FaWRKY64 homoeologs. Red bars indicate RNAi targeting regions. Blue bars indicate WRKY domains. Expression profile of homoeologous copies of FaWRKY29 (C) and FaWRKY64 (D) in six different fruit developmental stages of ‘Florida Brilliance’; Small Green (SG), Medium Green (MG), Large Green (LG), White (W), Turning Red (TR), and Red (R). The gene expression levels were represented as log2-transformed depth-normalized counts (CPM+1). To visualize the gene expression profile, a heatmap was generated using TBtools

Enhanced resistance against B. cinerea in strawberry fruits with knockdown of FaWRKY29 or FaWRKY64

A Botrytis cinerea infection assay was performed to examine whether transient knockdown of FaWRKY29 or FaWRKY64 could enhance resistance in strawberry fruits. White stage fruits were infiltrated with Agrobacterium harboring the RNAi vectors (FaWRKY29-RNAi or FaWRKY64-RNAi) or an empty vector (EV). Five days after Agrobacterium infiltration (0 days after B. cinerea inoculation), the expression of FaWRKY29 and FaWRKY64 was significantly decreased in FaWRKY29-RNAi and FaWRKY64-RNAi fruits compared to the EV (Fig. S2A and B). Four days after B. cinerea inoculation, the expression levels of FaWRKY29 and FaWRKY64 were generally reduced in EV fruits and the corresponding RNAi fruits compared to those of 0 days after B. cinerea inoculation. Disease frequency was calculated by dividing the number of BFR developed fruits by the number of B. cinerea inoculated fruits. Five days after B. cinerea infection in fruits, disease frequencies were significantly decreased in both FaWRKY29 and FaWRKY64 knockdown fruits compared to fruits containing the EV (Fig. 3A). Delayed disease occurrence was observed in fruits with transiently knockdown of FaWRKY29 or FaWRKY64 at all time points. Five days after inoculation (DAI) of B. cinerea, strawberry fruits were severely infected with B. cinerea, and mycelia were visible on the surface of the fruits treated with each RNAi or the EV (Fig. 3B). A significant reduction in disease area was observed in FaWRKY64 knockdown fruits compared to the EV at 5-DAI, but not in FaWRKY29 knockdown fruits (Fig. 3C). Taken together with the B. cinerea inoculation results, transient knockdown of FaWRKY29 or FaWRKY64 led to decreased disease symptoms and reduced growth of B. cinerea in the infected fruits.

Fig. 3
figure 3

Botrytis cinerea inoculation assay on Agrobacterium infiltrated strawberry fruits. A Red strawberry fruits were infected with Botrytis cinerea five days after Agrobacterium infiltration. Disease frequency was calculated four, five, and six days after inoculation (DAI) with Botrytis cinerea. B Disease symptoms on strawberry fruits were taken pictures at 5-DAI. C Disease area was measured using the ImageJ program at 4- and 5-DAI. The experiment was repeated four times. Error bars indicate standard error. Asterisks indicate significant differences from the EV at zero days determined by the Student’s t-test (*p < 0.05, ** p < 0.01)

Differentially expressed genes in response to B. cinerea in strawberry fruits with knockdown of FaWRKY29 or FaWRKY64

Five days after agroinfiltration, FaWRKY29-RNAi, FaWRKY64-RNAi, EV, or mock agroinfiltrated fruits were inoculated with B. cinerea. For transcriptome data analysis, fruit samples were collected 0-DAI or 4-DAI. RNA sequencing data were mapped to the octoploid strawberry ‘Camarosa’ reference genome [2]. Approximately 91.4% of RNA reads were successfully mapped to the reference genome (Table S2, NCBI BioProject number: PRJNA946145). Differentially expressed genes (DEGs) were determined by comparing normalized transcript abundances using a threshold (≥ threefold with p-value ≤ 0.05) between the RNA-seq samples of EV vs. mock control, FaWRKY29-RNAi vs. EV, and FaWRKY64-RNAi vs. EV at 0-DAI and 4-DAI of B. cinerea (Table S3). The term DEG indicates a gene with different expression levels, exhibiting a fold change of three or more between the compared samples. We applied a fold change of three or more to emphasize genes that exhibit drastic expression change rather than a fold change of two or more.

At 0-DAI, a total of 1,337 (633 downregulated and 704 upregulated) in FaWRKY29-RNAi and 1,265 genes (652 downregulated and 613 upregulated) in FaWRKY64-RNAi were differentially expressed compared to the EV (Fig. 4A and B). In addition, 305 genes were identified in both FaWRKY29-RNAi and FaWRKY64-RNAi compared to the EV. At 4-DAI, a total of 1,787 (1,022 downregulated and 765 upregulated) in FaWRKY29-RNAi and 2,864 genes (1,750 downregulated and 1,114 upregulated) in FaWRKY64-RNAi were differentially expressed compared to the EV (Fig. 4E and F). In addition, 622 genes were simultaneously identified in FaWRKY29-RNAi and FaWRKY64-RNAi compared to the EV.

Fig. 4
figure 4

Differentially expressed genes (DEGs) and GO terms in Botrytis cinerea infected strawberry fruits. Venn diagram depiction among three pairwise libraries including EV vs. Mock, FaWRKY29-RNAi vs. EV, and FaWRKY64-RNAi vs. EV at 0-DAI (A) and 4-DAI (E). Histogram representing the number of DEGs detected on pairwise comparison zero (B) and four (F) days after B. cinerea infection. GO terms assignment for the strawberry transcriptome on pairwise FaWRKY29-RNAi 0-DAI vs. EV 0-DAI (C), FaWRKY64-RNAi 0-DAI vs. EV 0-DAI (D), FaWRKY29-RNAi 4-DAI vs. EV 4-DAI (G), and FaWRKY64-RNAi 4-DAI vs. EV 4-DAI (H). Results are summarized into three main GO categories of cellular component, molecular function and biological process. The left y-axis represents the percentage of specific category of genes present in each main category whereas, the right y-axis indicates the gene number in the same category

Global analyses of FaWRKY29 or FaWRKY64 knockdown fruits DEGs were performed to provide an overview of gene expression before and after B. cinerea inoculation. Enrichment of gene ontology terms was analyzed, and DEGs were classified by cellular components, molecular function, and biological process (Fig. 4). As a transcription factor, knockdown of FaWRKY29 or FaWRKY64 principally altered gene expressions related to ‘binding’ and ‘catalytic activity’. In molecular function, ‘binding’ and ‘catalytic activity’ were the two most enriched groups. At 0-DAI, 455 (66.72%) DEGs and 433 (66.11%) DEGs were grouped into ‘binding’ in FaWRKY29-RNAi fruit and FaWRKY64-RNAi fruit, respectively (Fig. 4C and D). At 0-DAI, 337 (49.41%) DEGs and 328 (50.08%) DEGs were grouped into ‘catalytic activity’ in FaWRKY29-RNAi fruit and FaWRKY64-RNAi fruit, respectively. The ratios of DEGs grouped into ‘binding’ at 4-DAI were similar to those of 0-DAI, but the numbers of DEGs were increased at 4-DAI compared to 0-DAI (Fig. 4G and H).

Mapman analyses were performed to characterize the biological relevance of DEGs resulting from FaWRKYs knockdown and B. cinerea infection (Fig. 5 and Fig. S3). The FaWRKY29-RNAi-specific DEGs (727 in Fig. 4A and 859 in Fig. 4E), the FaWRKY64-RNAi-specific DEGs (643 in Fig. 4A and 1,353 in Fig. 4E) were classified in the ‘biotic stress’ through Mapman analyses. Within the ‘biotic stress’ category, most of the DEGs classified in ‘Redox state’, ‘Secondary metabolites’, ‘Cell wall’, and ‘ABA’ for both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 0-DAI (Fig. S3). DEGs involved in ‘JA’ were observed in both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 0-DAI, but those related to ‘SA’ were not observed. DEG involved in ‘Ethylene’ was observed in FaWRKY29-RNAi DEGs, but not in FaWRKY64-RNAi DEGs. At 4-DAI, DEGs classified in the ‘biotic stress’ category showed similar distribution patterns to those at 0-DAI for both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs (Fig. 5). Most DEGs were classified under ‘Redox state’, ‘Secondary metabolites’, ‘Cell wall’, and ‘ABA’ for both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 4-DAI. DEGs related to ‘JA’ were observed in both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 4-DAI, but those related to ‘SA’ were not observed. DEGs involved in ‘Ethylene’ were observed in FaWRKY29-RNAi DEGs but not in FaWRKY64-RNAi DEGs. There were more DEGs in the ‘biotic stress’ category for FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 4-DAI than at 0-DAI, respectively. More DEGs were observed in ‘Signaling’ for both FaWRKY29-RNAi DEGs and FaWRKY64-RNAi DEGs at 4-DAI than at 0-DAI. A greater number of DEGs were observed in the ‘Abiotic stress’ category for FaWRKY64-RNAi DEGs at 4-DAI compared to 0-DAI.

Fig. 5
figure 5

MapMan overview map at four days after inoculation (DAI). Differentially expressed genes specific to FaWRKY29-RNAi fruits (A) and FaWRKY64-RNAi fruits (B). Red: upregulation and Blue: downregulation of genes

Following B. cinerea infection (4-DAI), numerous DEGs involved in plant cell wall and ROS were identified in the ‘Redox state’, ‘Secondary metabolites’, and ‘Cell Wall’ categories (Table 1) [41,42,43,44,45,46,47,48]. Genes regulating cell wall composition and stiffening were identified in FaWRKY29-RNAi DEGs and/or FaWRKY64-RNAi DEGs in response to B. cinerea. Two pectin methylesterase (PME, Fxa1Dg717060, and Fxa7Ag354550) genes were identified in FaWRKY64-RNAi-specific DEGs and six PME (Fxa4Cg522800, Fxa4Cg522810, Fxa6Bg1778560, Fxa7Ag336960, Fxa7Ag714250, and Fxa7Cg2153620) genes were identified in FaWRKY29-RNAi-specific DEGs. Fxa7Ag354550 in FaWRKY64-RNAi-specific DEGs and Fxa7Cg2153620 and Fxa7Ag336960 in FaWRKY29 are functionally associated with the resistance against B. cinerea [41]. Cytochrome P450-dependent fatty acyl omega-hydroxylase (Fxa5Ag1274450, Fxa5Cg1230200, and Fxa5Cg1230210) genes were identified in FaWRKY29-RNAi-specific DEGs. In addition, genes that regulate ROS production and degradation were identified in FaWRKY29-RNAi DEGs and/or FaWRKY64-RNAi DEGs (Table 1). NADPH-oxidase (Rboh, Fxa5Cg1230750), M-type thioredoxin (Fxa6Dg750700), and atypical 2-Cys peroxiredoxin (PrxQ, Fxa1Bg2233150) genes were identified in FaWRKY64-RNAi-specific DEGs. H-type thioredoxin (Fxa6Cg2410140), O-type thioredoxin (Fxa3Dg2298420), mRNA-binding regulatory factor (TZF, Fxa1Ag149970), and PrxQs (Fxa1Cg1501580 and Fxa1Dg714640) genes were identified in FaWRKY29-RNAi-specific DEGs. H-type thioredoxin (Fxa2Bg966150) and M-type thioredoxin (Fxa6Cg2434130) genes were identified in both FaWRKY29- and FaWRKY64-RNAi DEGs.

Table 1 Differentially expressed genes related to cell wall composition and reactive oxygen species in response to B. cinerea in FaWRKY29 or FaWRKY64 knockdown strawberry fruits after B. cinerea infection


Botrytis cinerea has a wide range of host plants, and the pathogen causing BFR in strawberry fruits can also cause disease symptoms in Arabidopsis [49]. In this study, we investigated candidate genes for Botrytis resistance in public databases and screened Arabidopsis mutant lines with strawberry field isolates of B. cinerea. As shown in Fig. 1, the Atwrky53 mutant showed enhanced disease resistance at 1-DAI. The disease area was the smallest in the Atwrky53 mutant at both 2-DAI and 3-DAI. It has been known that most of the group III Arabidopsis WRKY TFs participate in plant-pathogen interactions [50]. AtWRKY53 plays an important functional role in the senescence regulatory network, connecting with plant hormones such as SA and JA, ROS, and other TFs [51]. It has also been reported that AtWRKY53 is highly upregulated by ROS and associated with the regulation of the mutually antagonistic cross-talk between SA and JA in response to biotic stresses. Interestingly, a recent study showed that CRISPR/Cas9-derived VvWRKY52 mutants showed increased resistance in B. cinerea infected leaves [16]. However, the mode of action for VvWRKY52-mediated resistance against B. cinerea in grapes remains unknown. Another study demonstrated that the expression of VqWRKY52 was strongly activated by SA, and ectopic overexpression of VqWRKY52 in Arabidopsis increased susceptibility to B. cinerea [52].

Our results showed that the knockdown of two strawberry orthologs of AtWRKY53, FaWRKY29 or FaWRKY64, led to a decrease in disease frequency and disease area after B. cinerea inoculation and induced changes in the expression of multiple defense-related genes, indicating their potential role in the regulation of plant immunity against B. cinerea in strawberry. In this study, we performed Mapman analyses on DEGs to better understand the biological relevance of these genes and B. cinerea resistance (Fig. 5 and Fig. S3). Results showed that after B. cinerea infection, the plant stress hormones ABA and JA were over-represented in the DEGs from FaWRKY29-RNAi and FaWRKY64-RNAi. The plant stress hormones ABA, JA, SA, and ET play a crucial role in controlling plant defense mechanisms against pathogens [53]. JA and ET are typically involved in plant defense against necrotrophic pathogens such as B. cinerea, but the signaling pathways for these hormones can interact reciprocally with the SA signaling pathway [17]. Generally, ABA can repress plant resistance by antagonistically regulating the SA- and JA/ET-dependent defense pathways. However, ABA has also been reported to positively impact JA and SA signaling components [36]. Our results suggest that the knockdown of FaWRKY29 and FaWRKY64 primarily altered the expression of genes related to ABA and JA signaling, while genes related to ET and SA signaling might be regulated in an antagonistic manner by ABA and/or JA signaling.

A strawberry mRNA-binding regulatory factor (TZF, Fxa1Ag149970) gene, involved in JA signaling and ROS, was identified in FaWRKY29-RNAi-specific DEGs (Table 1). AtOZF1 (an Arabidopsis ortholog) expression was highly induced by JA, and AtOZF1 positively regulated the expression of genes in JA signaling such as AtPDF1.2, AtVSP2, AtTHI2.1, and AtORA59 [54]. AtOZF1 was involved in both NON-EXPRESSOR of PR1 (NPR1)-dependent and NPR1-independent SA-signaling. AtOZF1 plays a role in SA-JA cross-talk like AtNPR1. Loss-of-function of OZF1 enhanced susceptibility to B. cinerea in Atozf1 mutants. After B. cinerea infection, the strawberry FaTZF was upregulated (fold change 4.57) in FaWRKY29 knockdown fruits, which can reduce B. cinerea susceptibility in infected fruits.

Changes in the expression of plant hormone-related genes (ABA and JA) appear to affect plant cell wall composition and ROS production. Knockdown of FaWRKY29 and FaWRKY64 genes resulted in altered expression of genes associated with cell walls and ROS in B. cinerea infected fruits (Table 1). Plants defend against pathogens through a combination of mechanisms, including recognition, signaling, cell wall remodeling, and cell death. The cuticle and cell wall serve as the primary barriers to pathogens and are penetrated by fungal pathogens through physical or chemical means. Necrotrophic plant pathogens like B. cinerea attack plant cuticles and cell walls by releasing hydrolyzing enzymes such as cutinases, PMEs, and polygalacturonases [17, 55, 56].

PMEs are associated with pectin remodeling and disassembly of cell walls and play an important role in multiple biotic stresses like B. cinerea infection [41]. Several strawberry PMEs were identified in the DEGs related to cell wall composition in response to B. cinerea (Table 1). Three strawberry PMEs (Fxa7Ag336960, Fxa7Ag354550, and Fxa7Cg2153620) were orthologs of AtPME17 (AT2G45220), which is highly expressed after B. cinerea infection [41]. ABA, JA, SA, and ET signaling networks were involved in AtPME17 expression during B. cinerea infection. Lesion area was significantly increased in B. cinerea inoculated leaf tissues in Atpme17 mutants, suggesting the role of AtPME17 in plant defense signaling responses. Additionally, a significant decrease in Plant Defensin 1.2 (PDF1.2, a marker for the JA/ET signaling pathways) expression was observed in Atpme17 mutants. AtPME17 highly contributes to B. cinerea resistance in Arabidopsis by activating JA/ET-dependent PDF1.2 expression. In this study, three strawberry PMEs were upregulated by B. cinerea infection, which may reduce B. cinerea susceptibility in both FaWRKY29 and FaWRKY64 knockdown fruits by pectin remodeling.

ROS plays numerous important roles in plant physiology, development, and cellular signaling. In general, high ROS levels trigger detrimental effects such as lipid peroxidation in cellular membranes, DNA damage, and protein denaturation in plant cells. At the same time, ROS is an important signaling molecule and plays an essential role in response to various biotic stresses [57]. Pathogen attack by B. cinerea triggers plants to produce large amounts of ROS in the early plant-pathogen interaction, known as the oxidative burst. The oxidative burst can induce one of the plant defense mechanisms, hypersensitive cell death [58]. The major types of ROS are H2O2, superoxide (O2), and hydroxyl radicals (OH•). Scavenging of extra ROS can be conducted by antioxidant enzymes such as superoxide dismutase, catalase, Prx, and ascorbate peroxidase [59]. Cell death caused by excessive ROS production promotes B. cinerea infection. Thus, the inhibition of ROS formation in infected tissue can improve resistance against B. cinerea [60]. Overexpression of BnaWGR1, an oilseed rape ortholog of AtWRKY53, increased ROS levels and induced cell death in Arabidopsis leaves [61].

Strawberry NADPH-oxidase (Rboh, Fxa5Cg1230750) gene related to ROS was identified in FaWRKY64-RNAi-specific DEGs (Table 1), and it was an ortholog of AtRBOHF (AT1G64060). AtRBOHF is necessary to accumulate ROS by an apoplastic oxidative burst in the plant defense responses [62]. In addition, AtRBOHD and AtRBOHF are involved in ROS-dependent ABA signaling, resulting in ABA-induced ROS production in guard cell regulation [20]. After B. cinerea infection, the strawberry FaRboh was downregulated (fold change -4.81) in FaWRKY64 knockdown fruits. The downregulation of FaRboh can reduce ABA-induced ROS production in infected fruit, which can contribute to the enhancement of B. cinerea resistance in FaWRKY64 knockdown fruits.

Our transcriptome results showed that the enhanced resistance against B. cinerea in FaWRKY29 and FaWRKY64 knockdown fruits appears to be modulated by complex interactions between ABA signaling, JA signaling, and ROS accumulation. As shown in Fig. 6, we suggest a hypothetical model of the enhancement of B. cinerea resistance in FaWRKY29 or FaWRKY64 knockdown fruits in response to B. cinerea based on our transcriptome analysis. The knockdown of FaWRKY29 or FaWRKY64 leads to the regulation of genes involved in both JA biosynthesis (AOS, AOC4, and OPR3) and JA signaling (Cul1 and ERF1). Additionally, it triggers the activation of PME17 and OZF1 gene expression, which positively regulate JA/ET‐dependent PDF1.2 expression. Consequently, FaWRKY29 or FaWRKY64 knockdown plays a significant role in enhancing B. cinerea resistance through the JA/ET plant defense pathways. Furthermore, the knockdown of FaWRKY29 or FaWRKY64 also influences the expression of genes associated with ABA biosynthesis (NCED1, NCED4, and CYP707A1), along with ABA signaling genes (PYL2, PYL4, and PP2C2). Also, it negatively regulates RBOHF expression, involved in ABA-induced ROS production. The reduction of ROS in infected fruit could contribute to enhanced B. cinerea resistance.

Fig. 6
figure 6

A hypothetical model representing the enhancement of resistance against Botrytis cinerea in the FaWRKY29 or FaWRKY64 knockdown strawberry fruits. Differentially expressed genes identified in FaWRKY29 and/or FaWRKY64 knockdown fruits in response to B. cinerea were represented. Dotted lines indicate FaWRKY29, while solid lines indicate FaWRKY64. The red color represents upregulation upon FaWRKY29 or FaWRKY64 knockdown, whereas the blue color signifies downregulation

A low level of ABA can enhance B. cinerea resistance and induce early and strong H2O2 accumulation in B. cinerea infected tissues [63]. After B. cinerea infection, strawberry genes that might be involved in pectin and cuticle development (such as PMEs and fatty acyl omega-hydroxylases) were modulated by the knockdown of FaWRKY29 and/or FaWRKY64. Since it is known that AtPME17, AtT1, and AtLCR have critical roles in proper pectin and cuticle development [64,65,66]. Moreover, upregulation of AtPME17 and AtOZF1 could enhance the resistance against B. cinerea, as the genes positively regulate JA/ET‐dependent PDF1.2 expression [41, 54]. Strawberry genes involved in ROS production and ROS scavenging (such as FaRboh, FaTZF, thioredoxins, and peroxiredoxins) were also regulated by knockdown of FaWRKY29 and/or FaWRKY64. FaWRKY64 knockdown fruits showed relatively stronger B. cinerea resistance than FaWRKY29 knockdown fruits. The total number of DEGs classified in ‘Biotic stress’ was higher in FaWRKY64-RNAi-specific DEGs than in FaWRKY29-RNAi-specific DEGs (Fig. 5).


In this study, we employed a reverse genetics approach to screen Arabidopsis T-DNA knockout mutants reported in the plant defense pathway against B. cinerea. The mutants for AtWRKY53 showed enhanced resistance in response to B. cinerea infection. Two orthologs of this gene, FaWRKY29 and FaWRKY64, were identified in octoploid strawberries, and transient knockdown of either FaWRKY29 or FaWRKY64 led to enhanced resistance against B. cinerea in fruits. Genes involved in ABA and JA signaling, as well as genes related to plant cell wall composition, decomposition (PME and fatty acyl omega-hydroxylases), and ROS (Rboh and PrxQ), were over-represented in FaWRKY29- and FaWRKY64-RNAi DEGs after B. cinerea infection. This suggests that the knockdown of FaWRKY29 and FaWRKY64 enhances resistance against B. cinerea in strawberries by triggering ABA and JA signaling. In conclusion, our results demonstrated that the knockdown of strawberry WRKY TF genes can increase resistance against B. cinerea in strawberries. FaWRKY29 and FaWRKY64, as B. cinerea susceptible genes, could be promising candidates for developing new, CRISPR/Cas9-mediated, B. cinerea-resistant varieties of octoploid strawberries.


Plant materials and inoculations

The homozygous Arabidopsis T-DNA knockout lines (SALK_003729, SALK_034157, SALK_088514, SALK_100993, and SALK_201649) were obtained from TAIR (Table S1). PCR was performed with gene-specific primers and a T-DNA-specific primer to identify homozygous lines. The list of primers is available in Table S4. The Arabidopsis seeds with the Col-0 ecotype as control were surface sterilized with 70% EtOH followed by three times rinse with autoclaved water. Seeds were germinated on half-strength of the Murashige and Skoog (MS) medium supplemented with 1.5% (w/v) sucrose at 22°C (± 1) with a 16-h photoperiod (16 h light/8 h dark). Arabidopsis seedlings were transferred to soil and grown at 25°C (± 1) with a 14-h photoperiod for further inoculation test. The strawberry cultivar ‘Florida Brilliance’ was grown in the strawberry field at the Gulf Coast Research and Education Center (GCREC) in Balm, FL, USA, during the 2021–2022 winter season. Plants were managed according to the Florida strawberry industry standards. Fruits at the white stage were harvested for inoculation test. Five field isolates of B. cinerea (12–65, 12–221, 12–255, 12–332, and 12–355) collected from commercial strawberry fields in Florida were used in this study [67]. Each of the B. cinerea isolates was grown on HA agar plates (10 g l−1 malt extract, 4 g l−1 glucose, 4 g l−1 yeast extract, 15 g l−1 agar, pH 5.5) at 22°C (± 1) a 16‐h photoperiod. For Arabidopsis leaf inoculation with B. cinerea, rosette leaves collected from 4-week-old Arabidopsis plants were transferred to transparent square petri dishes containing 0.8% agar, inserting the petiole in the medium. The mycelia of B. cinerea were scraped from the surface of the isolates and spores were diluted to a concentration of 5 × 105 spores ml−1 with potato dextrose broth. Arabidopsis leaves were inoculated by placing 5 µl of the spore suspension on the middle of the vein. The square petri dish containing inoculated leaves was covered with a plastic lid to keep moisture. The experiment was repeated two times and 19—24 leaves were used in each batch for each treatment. For strawberry fruit inoculation with B. cinerea, red fruits were prepared in plastic egg cartons five days after Agrobacterium infiltration with FaWRKY29- or FaWRKY64-RNAi vector. Spores were diluted to a concentration of 1 × 106 spores ml−1 with potato dextrose broth. Strawberry fruits were inoculated by placing 20 µl of the spore suspension on the middle of the fruit. Every two egg cartons containing inoculated fruits were kept in a plastic box with a water dish to keep moisture. The experiment was repeated four times. A total of 22—30 fruits were used in each batch for each treatment. After B. cinerea inoculation to Arabidopsis leaves or strawberry fruits, disease development progress was taken pictures every day. The disease area was calculated using the ImageJ program.

RNAi-mediated fruit transient assay

To identify ortholog sequences of AtWRKY53 and VvWRKY52 in octoploid strawberries, BLAST search was done using amino acid sequences from the Strawberry GARDEN ( and GDR ( Homologous and homoeologous copies of FaWRKY29 and FaWRKY64 were searched using BLAST analysis with the octoploid reference genomes from ‘Camarosa’ and ‘Florida Brilliance’. Multiple sequence alignments of FaWRKY29 or FaWRKY64 amino acid sequences were performed using GeneDoc. The pssRNAit program ( was utilized to design specific RNAi sequences targeting FaWRKY29 or FaWRKY64 [68]. FaWRKY29-RNAi and FaWRKY64-RNAi were synthesized (Genscript, NJ, USA) and cloned into pDONRTM221 vector and subsequently the pK7GWIWG2(i) binary vector by Gateway cloning system (Invitrogen, MA, USA).

RNAi vectors were introduced to Agrobacterium strain EHA105 by a freeze–thaw method [69]. For a transient assay, Agrobacterium was cultured at 28°C until OD600 of 1.0 in LB media containing 50 mg l−1 spectinomycin and 10 mg l−1 rifampicin. Bacterial pellets were resuspended with MS containing 10 mM 2-(4-morpholino) ethanesulfonic acid, 200 μM acetosyringone, and 10 mM MgCl2 (pH 5.5). The Agrobacterium with FaWRKY29-RNAi and FaWRKY64-RNAi was incubated for about 2 h in a rotary shaker (28°C, 150 rpm) before fruit infiltration. ‘Florida Brilliance’ plants were grown in the GCREC strawberry field of the University of Florida (Wimauma, FL, USA). White stage fruits were detached from plants and surface sterilized with 1% (v/v) commercial bleach, followed by rinsing three times with distilled water. About three ml of Agrobacterium suspension was evenly injected into fruits using a 5 ml sterile syringe until runoff. Every two egg cartons containing Agrobacterium infiltrated fruits were kept in a plastic box with a water dish to keep moisture. B. cinerea inoculation was conducted five days after Agrobacterium infiltration. The experiment was repeated four times, and approximately 30 fruits were used in each independent experiment.

RNA extraction and quantitative real-time PCR

RNA extractions from fruits of mock control, EV, pK7GWIWG2(i)-WRKY29-RNAi, or pK7GWIWG2(i)-WRKY64-RNAi were conducted using the Spectrum™ Plant Total RNA Kit (Sigma–Aldrich, MO, USA) five days after Agrobacterium infiltration. After DNaseI treatment, the cDNA synthesis was performed using LunaScript® RT SuperMix Kit (New England Biolabs, MA, USA). The quantitative real-time PCR was performed using the Forget-Me-Not™ EvaGreen qPCR Master Mix (Biotium, CA, USA) with an internal reference gene of FaGAPDH2 using LightCycler® 480 system (Roche, Basel, Switzerland). The primer sequences used this gene expression assay are FaWRKY29_F (5’-GGAGATCATTGAAGGGATGGAG-3’), FaWRKY29_R (5’-GAGGTCAATATCCTCTGCACTAAA-3’), FaWRKY64_F (5’-CACCTTCGCTAAATGGGAGT-3’), FaWRKY64_R (5’-TTACTTGTTTGGTCCACCGT-3’), FaGAPDH2_F (5’-CCCAAGTAAGGATGCCCCCATGTTCG-3’), and FaGAPDH2_R (5’-TTGGCAAGGGGAGCAAGACAGTTGGTAG-3’). Three biological and technical replications were used. The relative gene expression levels were calculated using the 2−ΔΔCT.

Analysis of RNA sequencing and differentially expressed genes

RNA sequencing was performed with fruits (mock control, EV, pK7GWIWG2(i)-WRKY29-RNAi, and pK7GWIWG2(i)-WRKY64-RNAi) four days after B. cinerea infection and without the pathogen inoculation. Each RNA sample was quantified with the Qubit™ RNA high sensitivity (HS) Assay Kit (Invitrogen, MA, USA). Four individual samples were pooled equally with the same RNA concentration of 40 ng/µl. The RNA sequencing was conducted at Novogene (San Diego, CA, USA). RNA sequencing libraries were prepared with NEBNext® Ultra RNA Library Prep Kit for Illumina® (Boston, MA, USA) using the standard manufacturer’s protocol. The RNA libraries were sequenced using NovaSeq 6000 (San Diego, CA, USA) sequencer with 2 × 150 bp paired-end sequences. The Illumina RNA-Seq data are available in NCBI (BioProject number PRJNA946145). The eight cDNA libraries (mock control, EV, pK7GWIWG2(i)-WRKY29-RNAi, pK7GWIWG2(i)-WRKY64-RNAi at 0- and 4-DAI) were analyzed by FASTQC 0.11.4 ( to assess read quality. After removing adapter sequences using CLC Genomics Workbench 11.0 (, reads with poor quality and ambiguous sequences were trimmed. The trimmed short reads were mapped to full coding region sequences of the octoploid strawberry reference genome [2] using CLC Genomics Workbench 11.0. The stringent mapping parameters were employed with a length fraction of 0.8, similarity fraction of 0.9, insertion cost = 3, deletion cost = 3, and mismatch cost = 2. Unmapped reads were discarded for further downstream analysis. Gene expression data was normalized by computing the Reads Per Million mapped reads [70]. Raw expression values were transformed by adding a constant of 5, as well as normalized by a scaling method [71]. For DEG analysis, Baggerly’s test was implemented to compare a group with another [72]. This Baggerly’s statistical test relates the mean expression value between two samples. Bonferroni correction and FDR p-value correction were employed [73]. The DEGs were called only if (a) the normalized fold change was ≥ threefold and (b) the p-value was ≤ 0.05. Pairwise evaluations between the RNA-seq samples of: EV vs. mock control, FaWRKY29-RNAi vs. EV, and FaWRKY64-RNAi vs. EV at zero and four DAI. For MapMan analysis [74], all fasta files of reference ‘Camarosa’ [2] were analyzed with the Mercator webtool ( for Bincode mapping. The fold-change value of DEGs on each pairwise library was analyzed using MapMan [74] to identify affected metabolic pathways. Heatmap of common DEGs between FaWRKY29-RNAi vs. EV and FaWRKY64-RNAi vs. EV were visualized using TBtools software [75]. To explore the gene expression of WRKY TFs, RNA sequencing data were extracted from our previous study [4]. ‘Florida Brilliance' strawberry fruits were collected at six different development stages. The fruit sampling stages were categorized to Small Green (SG), Medium Green (MG), Large Green (LG), White (W), Turning Red (TR), and Red (R).

Functional annotation of genes differentially expressed

DEGs were annotated by the BLASTX program available in the BLAST+ package [76]. The sequences were annotated with an E-value cut-off of 1e-5 against non-redundant protein database available at the genome database for Rosaceae ( The highest high-scoring segment pairs were retrieved from the corresponding database. To find the functions associated with DEGs from this study, a homology against non-redundant protein database was searched at NCBI [77]. Set enrichment analysis was performed using the singular enrichment analysis tool using the webtool AgriGO [77]. Gene ontology distribution of each pairwise library across categories was plotted using WEGO [78].

Availability of data and materials

The supporting datasets and supplemented materials are included within the article as additional files. RNA sequencing data for strawberry is available in NCBI SRA database with accession numbers (SRR23906067-SRR23906074 of BioProject number PRJNA946145,, and the data will be shared on reasonable request of the corresponding author.



Abscisic acid




Botrytis fruit rot


Days after inoculation


Differentially expressed gene




Empty vector


Murashige and Skoog


Overexpressor of cationic peroxidase


Plant defensin


Pectin methylesterase




Respiratory burst oxidase homolog


Reactive oxygen species


Salicylic acid


The Arabidopsis information resource


Two-pore K + channel


Transcription factor



ild type


  1. An X, Li Z, Zude-Sasse M, Tchuenbou-Magaia F, Yang Y. Characterization of textural failure mechanics of strawberry fruit. J Food Eng. 2020;282:110016.

    Google Scholar 

  2. Edger PP, Poorten TJ, VanBuren R, Hardigan MA, Colle M, McKain MR, et al. Origin and evolution of the octoploid strawberry genome. Nat Genet. 2019;51:541–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Hardigan MA, Feldmann MJ, Pincot DDA, Famula RA, Vachev MV, Madera MA, et al. Blueprint for phasing and assembling the genomes of heterozygous polyploids: application to the octoploid genome of strawberry. bioRxiv.

  4. Han H, Barbey CR, Fan Z, Verma S, Whitaker VM, Lee S. Telomere-to-telomere and haplotype-phased genome assemblies of the heterozygous octoploid ‘Florida Brilliance’ strawberry (Fragaria × ananassa). bioRxiv.

  5. Jia S, Wang Y, Zhang G, Yan Z, Cai Q. Strawberry FaWRKY25 transcription factor negatively regulated the resistance of strawberry fruits to Botrytis cinerea. Genes. 2020;12:56.

    PubMed  PubMed Central  Google Scholar 

  6. Petrasch S, Knapp SJ, van Kan JAL, Blanco-Ulate B. Grey mould of strawberry, a devastating disease caused by the ubiquitous necrotrophic fungal pathogen Botrytis cinerea. Mol Plant Pathol. 2019;20:877–92.

    PubMed  PubMed Central  Google Scholar 

  7. Cordova LG, Amiri A, Peres NA. Effectiveness of fungicide treatments following the Strawberry Advisory System for control of Botrytis fruit rot in Florida. Crop Prot. 2017;100:163–7.

    CAS  Google Scholar 

  8. Bolívar-Anillo HJ, Garrido C, Collado IG. Endophytic microorganisms for biocontrol of the phytopathogenic fungus Botrytis cinerea. Phytochem Rev. 2020;19:721–40.

    Google Scholar 

  9. Wang Y, Zhao F, Zhang G, Jia S, Yan Z. FaWRKY11 transcription factor positively regulates resistance to Botrytis cinerea in strawberry fruit. Sci Hortic. 2021;279:109893.

    CAS  Google Scholar 

  10. Wang S, Song M, Guo J, Huang Y, Zhang F, Xu C, et al. The potassium channel FaTPK1 plays a critical role in fruit quality formation in strawberry (Fragaria × ananassa). Plant Biotechnol J. 2018;16:737–48.

    CAS  PubMed  Google Scholar 

  11. Li Q, Ji K, Sun Y, Luo H, Wang H, Leng P. The role of FaBG3 in fruit ripening and B. cinerea fungal infection of strawberry. Plant J. 2013;76:24–35.

    CAS  PubMed  Google Scholar 

  12. Ren X, Chen Z, Liu Y, Zhang H, Zhang M, Liu Q, et al. ABO3, a WRKY transcription factor, mediates plant responses to abscisic acid and drought tolerance in Arabidopsis. Plant J. 2010;63:417–29.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Sheng Y, Yan X, Huang Y, Han Y, Zhang C, Ren Y, et al. The WRKY transcription factor, WRKY13, activates PDR8 expression to positively regulate cadmium tolerance in Arabidopsis. Plant Cell Environ. 2019;42:891–903.

    CAS  PubMed  Google Scholar 

  14. Ramírez V, Agorio A, Coego A, García-Andrade J, Hernandez MJ, Balaguer B, et al. MYB46 Modulates Disease Susceptibility to Botrytis cinerea in Arabidopsis. Plant Physiol. 2011;155:1920–35.

    PubMed  PubMed Central  Google Scholar 

  15. Coego A, Ramirez V, Gil MJ, Flors V, Mauch-Mani B, Vera P. An Arabidopsis Homeodomain Transcription Factor, OVEREXPRESSOR OF CATIONIC PEROXIDASE 3, Mediates Resistance to Infection by Necrotrophic Pathogens. Plant Cell. 2005;17:2123–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang X, Tu M, Wang D, Liu J, Li Y, Li Z, et al. CRISPR/Cas9-mediated efficient targeted mutagenesis in grape in the first generation. Plant Biotechnol J. 2018;16:844–55.

    CAS  PubMed  Google Scholar 

  17. Haile ZM, Nagpala-De Guzman EG, Moretto M, Sonego P, Engelen K, Zoli L, et al. Transcriptome profiles of strawberry (Fragaria vesca) fruit interacting with Botrytis cinerea at different ripening stages. Front Plant Sci. 2019;10:1131.

    PubMed  PubMed Central  Google Scholar 

  18. Given NK, Venis MA, Gierson D. Hormonal regulation of ripening in the strawberry, a non-climacteric fruit. Planta. 1988;174:402–6.

    CAS  PubMed  Google Scholar 

  19. Jia HF, Chai YM, Li CL, Lu D, Luo JJ, Qin L, et al. Abscisic acid plays an important role in the regulation of strawberry fruit ripening. Plant Physiol. 2011;157:188–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kwak JM, Mori IC, Pei ZM, Leonhard N, Angel Torres M, Dangl JL, et al. NADPH oxidase AtrbohD and AtrbohF genes function in ROS-dependent ABA signaling in Arabidopsis. EMBO J. 2003;22:2623–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Qi J, Wang J, Gong Z, Zhou JM. Apoplastic ROS signaling in plant immunity. Curr Opin Plant Biol. 2017;38:92–100.

    CAS  PubMed  Google Scholar 

  22. Berrocal-Lobo M, Molina A, Solano R. Constitutive expression of ETHYLENE-RESPONSE-FACTOR1 in Arabidopsis confers resistance to several necrotrophic fungi. Plant J. 2002;29:23–32.

    CAS  PubMed  Google Scholar 

  23. Rosli HG, Civello PM, Martínez GA. Changes in cell wall composition of three Fragaria x ananassa cultivars with different softening rate during ripening. Plant Physiol Biochem. 2004;42:823–31.

    CAS  PubMed  Google Scholar 

  24. Nagpala EG, Guidarelli M, Gasperotti M, Masuero D, Bertolini P, Vrhovsek U, et al. Polyphenols Variation in Fruits of the Susceptible Strawberry Cultivar Alba during Ripening and upon Fungal Pathogen Interaction and Possible Involvement in Unripe Fruit Tolerance. J Agric Food Chem. 2016;64:1869–78.

    CAS  PubMed  Google Scholar 

  25. Li Z, Wei Y, Cao Z, Jiang S, Chen Y, Shao X. The jasmonic acid signaling pathway is associated with terpinen-4-ol-induced disease resistance against Botrytis cinerea in strawberry fruit. J Agric Food Chem. 2021;69:10678–87.

    CAS  PubMed  Google Scholar 

  26. Bai Y, Sunarti S, Kissoudis C, Visser RGF, van der Linden CG. The role of tomato WRKY genes in plant responses to combined abiotic and biotic stresses. Front Plant Sci. 2018;9:801.

    PubMed  PubMed Central  Google Scholar 

  27. Wu KL, Guo ZJ, Wang HH, Li J. The WRKY family of transcription factors in rice and Arabidopsis and their origins. DNA Res. 2005;12:9–26.

    CAS  PubMed  Google Scholar 

  28. Li J, Xiong C, Ruan D, Du W, Li H, Ruan C. Identification of Camellia oleifera WRKY transcription factor genes and functional characterization of CoWRKY78. Front Plant Sci. 2023;14:1110366.

    PubMed  PubMed Central  Google Scholar 

  29. Negi N, Khurana P. A salicylic acid inducible mulberry WRKY transcription factor, MiWRKY53 is involved in plant defence response. Plant Cell Rep. 2021;40:2151–71.

    CAS  PubMed  Google Scholar 

  30. Wang D, Xu H, Huang J, Kong Y, AbuQamar S, Yu D, et al. The Arabidopsis CCCH protein C3H14 contributes to basal defense against Botrytis cinerea mainly through the WRKY33-dependent pathway. Plant Cell Environ. 2020;43:1792–806.

    CAS  PubMed  Google Scholar 

  31. Li J, Brader G, Palva ET. The WRKY70 transcription factor: a node of convergence for jasmonate-mediated and salicylate-mediated signals in plant defense. Plant Cell. 2004;16:319–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Li J, Brader G, Kariola T, Tapio PE. WRKY70 modulates the selection of signaling pathways in plant defense. Plant J. 2006;46:477–91.

    CAS  PubMed  Google Scholar 

  33. Wang D, Amornsiripanitch N, Dong X. A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants. PLoS Pathog. 2006;2:e123.

    PubMed  PubMed Central  Google Scholar 

  34. Higashi K, Ishiga Y, Inagaki Y, Toyoda K, Shiraishi T, Ichinose Y. Modulation of defense signal transduction by flagellin-induced WRKY41 transcription factor in Arabidopsis thaliana. Mol Genet Genomics. 2008;279:303–12.

    CAS  PubMed  Google Scholar 

  35. Liu S, Ziegler J, Zeier J, Birkenbihl RP, Somssich IE. Botrytis cinerea B05.10 promotes disease development in Arabidopsis by suppressing WRKY33-mediated host immunity. Plant Cell Environ. 2017;40:2189–206.

    CAS  PubMed  Google Scholar 

  36. Liu S, Kracher B, Ziegler J, Birkenbihl RP, Somssich IE. Negative regulation of ABA Signaling By WRKY33 is critical for Arabidopsis immunity towards Botrytis cinerea 2100. Elife. 2015;4:e07295.

    PubMed  PubMed Central  Google Scholar 

  37. Lai Z, Vinod K, Zheng Z, Fan B, Chen Z. Roles of Arabidopsis WRKY3 and WRKY4 transcription factors in plant responses to pathogens. BMC Plant Biol. 2008;8:1–13.

    Google Scholar 

  38. Garrido-Gala J, Higuera JJ, Rodríguez-Franco A, Muñoz-Blanco J, Amil-Ruiz F, Caballero JL. A comprehensive study of the WRKY transcription factor family in strawberry. Plants. 2022;11:1585.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Govrin EM, Rachmilevitch S, Tiwari BS, Solomon M, Levine A. An elicitor from Botrytis cinerea induces the hypersensitive response in Arabidopsis thaliana and other plants and promotes the gray mold disease. Phytopathology. 2006;96:299–307.

    CAS  PubMed  Google Scholar 

  40. Galletti R, Ferrari S, de Lorenzo G. Arabidopsis MPK3 and MPK6 Play Different Roles in Basal and Oligogalacturonide- or Flagellin-Induced Resistance against Botrytis cinerea. Plant Physiol. 2011;157:804–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. del Corpo D, Fullone MR, Miele R, Lafond M, Pontiggia D, Grisel S, et al. AtPME17 is a functional Arabidopsis thaliana pectin methylesterase regulated by its PRO region that triggers PME activity in the resistance to Botrytis cinerea. Mol Plant Pathol. 2020;21:1620.

    PubMed  PubMed Central  Google Scholar 

  42. Lionetti V, Raiola A, Camardella L, Giovane A, Obel N, Pauly M, et al. Overexpression of Pectin Methylesterase Inhibitors in Arabidopsis Restricts Fungal Infection by Botrytis cinerea. Plant Physiol. 2007;143:1871–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Bessire M, Chassot C, Jacquat AC, Humphry M, Borel S, Petétot J, et al. A permeable cuticle in Arabidopsis leads to a strong resistance to Botrytis cinerea. EMBO J. 2007;26:2158–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Hématy K, Höfte H. Novel receptor kinases involved in growth regulation. Curr Opin Plant Biol. 2008;11:321–8.

    PubMed  Google Scholar 

  45. Li X, Zhang H, Tian L, Huang L, Liu S, Li D, et al. Tomato SLRbohB, a member of the NADPH oxidase family, is required for disease resistance against Botrytis cinerea and tolerance to drought stress. Front Plant Sci. 2015;6:1–14.

    Google Scholar 

  46. AbuQamar S, Chen X, Dhawan R, Bluhm B, Salmeron J, Lam S, et al. Expression profiling and mutant analysis reveals complex regulatory networks involved in Arabidopsis response to Botrytis infection. Plant J. 2006;48:28–44.

    CAS  PubMed  Google Scholar 

  47. Sun L, Ren H, Liu R, Li B, Wu T, Sun F, et al. An h-type thioredoxin functions in tobacco defense responses to two species of viruses and an abiotic oxidative stress. MPMI. 2010;23:1470–85.

    CAS  PubMed  Google Scholar 

  48. Kiba A, Nishihara M, Tsukatani N, Nakatsuka T, Kato Y, Yamamura S. A peroxiredoxin Q homolog from gentians is involved in both resistance against fungal disease and oxidative stress. Plant Cell Physiol. 2005;46:1007–15.

    CAS  PubMed  Google Scholar 

  49. Denby KJ, Kumar P, Kliebenstein DJ. Identification of Botrytis cinerea susceptibility loci in Arabidopsis thaliana. Plant J. 2004;38:473–86.

    CAS  PubMed  Google Scholar 

  50. Kalde M, Barth M, Somssich IE, Lippok B. Members of the Arabidopsis WRKY Group III transcription factors are part of different plant defense signaling pathways. Mol Plant Microbe Interact. 2003;16:295–305.

    CAS  PubMed  Google Scholar 

  51. Zentgraf U, Doll J. Arabidopsis WRKY53, a node of multi-layer regulation in the network of senescence. Plants. 2019;8:578.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang X, Guo R, Tu M, Wang D, Guo C, Wan R, et al. Ectopic expression of the wild grape WRKY transcription factor vqWRKY52 in Arabidopsis thaliana enhances resistance to the biotrophic pathogen powdery mildew but not to the necrotrophic pathogen Botrytis cinerea. Front Plant Sci. 2017;8:97.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Blanco-Ulate B, Vincenti E, Powell ALT, Cantu D. Tomato transcriptome and mutant analyses suggest a role for plant stress hormones in the interaction between fruit and Botrytis cinerea. Front Plant Sci. 2013;4:142.

    PubMed  PubMed Central  Google Scholar 

  54. Singh N, Nandi AK. AtOZF1 positively regulates JA signaling and SA-JA cross-talk in Arabidopsis thaliana. J Biosci. 2022;47:1–7.

    Google Scholar 

  55. Curvers K, Seifi H, Mouille G, de Rycke R, Asselbergh B, van Hecke A, et al. Abscisic acid deficiency causes changes in cuticle permeability and pectin composition that influence tomato resistance to Botrytis cinerea. Plant Physiol. 2010;154:847–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. van Kan JAL. Licensed to kill: the lifestyle of a necrotrophic plant pathogen. Trends Plant Sci. 2006;11:247–53.

    PubMed  Google Scholar 

  57. Liu Y, He C. Regulation of plant reactive oxygen species (ROS) in stress responses: learning from AtRBOHD. Plant Cell Rep. 2016;35:995–1007.

    CAS  PubMed  Google Scholar 

  58. Shi H, Cui R, Hu B, Wang X, Zhang S, Liu R, et al. Overexpression of transcription factor AtMYB44 facilitates Botrytis infection in Arabidopsis. Physiol Mol Plant Pathol. 2011;76:90–5.

    CAS  Google Scholar 

  59. Azarabadi S, Abdollahi H, Torabi M, Salehi Z, Nasiri J. ROS generation, oxidative burst and dynamic expression profiles of ROS-scavenging enzymes of superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) in response to Erwinia amylovora in pear (Pyrus communis L). Eur J Plant Pathol. 2017;147:279–94.

    CAS  Google Scholar 

  60. Govrin EM, Levine A. The hypersensitive response facilitates plant infection by the necrotrophic pathogen Botrytis cinerea. Curr Biol. 2000;10:751–7.

    CAS  PubMed  Google Scholar 

  61. Yang L, Ye C, Zhao Y, Cheng X, Wang Y, Jiang YQ, et al. An oilseed rape WRKY-type transcription factor regulates ROS accumulation and leaf senescence in Nicotiana benthamiana and Arabidopsis through modulating transcription of RbohD and RbohF. Planta. 2018;247:1323–38.

    CAS  PubMed  Google Scholar 

  62. Torres MA, Dangl JL, Jones JDG. Arabidopsis gp91phox homologues AtrbohD and AtrbohF are required for accumulation of reactive oxygen intermediates in the plant defense response. Proc Natl Acad Sci U S A. 2002;99:517–22.

    CAS  PubMed  Google Scholar 

  63. Asselbergh B, Curvers K, França SC, Audenaert K, Vuylsteke M, van Breusegem F, et al. Resistance to Botrytis cinerea in sitiens, an abscisic acid-deficient tomato mutant, involves timely production of hydrogen peroxide and cell wall modifications in the epidermis. Plant Physiol. 2007;144:1863–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Mang HG, Laluk KA, Parsons EP, Kosma DK, Cooper BR, Park HC, et al. The Arabidopsis RESURRECTION1 gene regulates a novel antagonistic interaction in plant defense to Biotrophs and Necrotrophs. Plant Physiol. 2009;151:290–305.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Schnurr J, Shockey J, Browse J. The Acyl-CoA Synthetase Encoded by LACS2 Is Essential for Normal Cuticle Development in Arabidopsis. Plant Cell. 2004;16:629–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Cominelli E, Sala T, Calvi D, Gusmaroli G, Tonelli C. Over-expression of the Arabidopsis AtMYB41 gene alters cell expansion and leaf surface permeability. Plant J. 2008;53:53–64.

    CAS  PubMed  Google Scholar 

  67. Amiri A, Heath SM, Peres NA. Resistance to Fluopyram, Fluxapyroxad, and Penthiopyrad in Botrytis cinerea from Strawberry. Plant Dis. 2014;98:532–9.

    CAS  PubMed  Google Scholar 

  68. Ahmed F, Senthil-Kumar M, Dai X, Ramu VS, Lee S, Mysore KS, et al. pssRNAit: A Web Server for Designing Effective and Specific Plant siRNAs with Genome-Wide Off-Target Assessment. Plant Physiol. 2020;184:65–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen H, Nelson RS, Sherwood JL. Enhanced recovery of transformants of Agrobacterium tumefaciens after freeze-thaw transformation and drug selection. Biotechniques. 1994;16:664–8.

    CAS  PubMed  Google Scholar 

  70. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8.

    CAS  PubMed  Google Scholar 

  71. Bolstad BM, Irizarry RA, Åstrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19:185–93.

    CAS  PubMed  Google Scholar 

  72. Baggerly KA, Deng L, Morris JS, Aldaz CM. Differential expression in SAGE: accounting for normal between-library variation. Bioinformatics. 2003;19:1477–83.

    CAS  PubMed  Google Scholar 

  73. Dudoit S, Shaffer JP, Boldrick JC. Multiple hypothesis testing in microarray experiments. Statist Sci. 2003;18:71–103.

    Google Scholar 

  74. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, et al. mapman: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37:914–39.

    CAS  PubMed  Google Scholar 

  75. Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant. 2020;13:1194–202.

    CAS  PubMed  Google Scholar 

  76. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:1–9.

    Google Scholar 

  77. Du Z, Zhou X, Ling Y, Zhang Z, Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010;38:W64-70.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Ye J, Zhang Y, Cui H, Liu J, Wu Y, Cheng Y, et al. WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucleic Acids Res. 2018;46:W71–5.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank Adrian I Zuniga and Natalia Peres at the Gulf Coast Research and Education Center for providing B. cinerea isolates, and Yoon Jeong Jang for technical assistance.


This research is supported by grants from the United Stated Department of Agriculture National Institute of Food and Agriculture (NIFA) Specialty Crops Research Initiative (#2022–51181-38328).

Author information

Authors and Affiliations



MBL and SL contributed conception and design of the study. MBL performed the B. cinerea infection assay using Arabidopsis and strawberry. HH made all the bioinformatic analysis from raw RNA-seq to gene mapping, identification, and statistical analysis. MBL and SL wrote the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

Corresponding author

Correspondence to Seonghee Lee.

Ethics declarations

Ethics approval and consent to participate

Arabidopsis T-DNA mutant lines were obtained from the Arabidopsis Information Resource (TAIR, The collection and handling of plants were in accordance with all the relevant guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Additional file 1:

Table S1. List of Botrytis resistant Arabidopsis T-DNA knockout lines.

Additional file 2:

Table S2. Total reads number and mapped rate.

Additional file 3:

Table S3. List of DEGs. The DEGs were called if the normalized fold change was ≥ 3-fold and the p-value was ≤ 0.05. Pairwise evaluations between the RNA-seq samples of: EV vs. mock control, FaWRKY29-RNAi vs. EV, and FaWRKY64-RNAi vs. EV at zero and four days after Botrytis cinerea inoculation.

Additional file 4:

Table S4. List of primers to detect homozygous lines in Arabidopsis T-DNA insertion lines.

Additional file 5:

Fig. S1. Identification of Arabidopsis homozygous T-DNA insertion lines. T-DNA-specific primer and gene-specific primers were used for PCR to detect homozygous lines.

Additional file 6:

Fig. S2. Real-time PCR was performed using RNA samples from EV, FaWRKY29-RNAi fruits and FaWRKY64-RNAi fruits collected zero days and four days after Botrytis cinerea inoculation.

Additional file 7:

Fig. S3. MapMan overview map at zero days after inoculation (DAI) of Botrytis cinerea.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, M.B., Han, H. & Lee, S. The role of WRKY transcription factors, FaWRKY29 and FaWRKY64, for regulating Botrytis fruit rot resistance in strawberry (Fragaria × ananassa Duch.). BMC Plant Biol 23, 420 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: