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Article

Comparative Transcriptome Analysis of Different Mulberry Varieties to Reveal Candidate Genes and Small Secreted Peptides Involved in the Sclerotiniose Response

by
Li Liu
1,2,
Zixuan Guo
1,
Xiaoru Kang
1,
Shan Li
1,
Shuai Huang
1,
Longyan Zheng
1,
Rumeng Fu
1,
Keermula Yidilisi
1 and
Nan Chao
1,2,*
1
Jiangsu Key Laboratory of Sericultural Sericulture and Animal Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
2
Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, The Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang 212100, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(7), 1126; https://doi.org/10.3390/f15071126
Submission received: 16 May 2024 / Revised: 21 June 2024 / Accepted: 26 June 2024 / Published: 28 June 2024
(This article belongs to the Section Genetics and Molecular Biology)
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Abstract

:
Mulberry sclerotiniose is a devastating fungal disease of mulberry fruit and has been a limitation for the utility of mulberry fruits and the diversified development of sericulture. In the present study, we presented a workflow for screening candidate sclerotiniose-resistance genes and small secreted peptides (SSPs) based on a genome-wide annotation of SSPs and comparative transcriptome analysis of different mulberry varieties. A total of 1088 SSPs with expression evidence were identified and annotated in mulberry. A comprehensive analysis of the sclerotiniose-related RNA sequencing datasets showed that photosynthesis, plant hormone signaling, and metabolic pathways were the main pathways involved in the response to sclerotiniose. Fifty-two candidate sclerotiniose-response genes (SRGs), including 15 SSPs, were identified based on comparative transcriptome analysis. These SRGs are mainly involved in the hormone signaling pathway and cell wall biogenesis. Transient overexpression in tobacco and the knock-down of five SRGs affected the resistance against Ciboria shiraiana. MaMYB29, MaMES17, and MaSSP15 were primarily determined as negative regulators of plant resistance to C. shiraiana infection. Our results provide a foundation for controlling sclerotiniose in mulberry using genetic engineering and biological approaches such as spraying antifungal peptides.

1. Introduction

Mulberry (Morus spp., Moraceae) is an economically important plant that is globally distributed in tropical and temperate areas [1]. It is the primary food for domesticated silkworms and has been one of the vital components of the sericulture industry in China for thousands of years [2,3,4,5]. In addition, mulberry fruits have long had edible and medicinal value, especially in Asia, and are believed to have antioxidant, antimicrobial, and anti-inflammatory properties [2,6]. Products containing mulberry are becoming increasingly popular in the food industry [7].
Mulberry sclerotiniose is a devastating fungal disease affecting mulberry fruit [8,9]. Mulberry fruits with sclerotiniose lose their color and flavor and turn pale instead of ripening. Sclerotiniose thus severely impacts mulberry fruit quality and yield. Although a recent report indicated that Sclerotinia sclerotiorum can infect mulberry, three fungi in the Sclerotiniaceae family, namely Ciboria carunculoides, Ciboria shiraiana, and Scleromitrula shiraiana, were reported to be the causal agents of mulberry sclerotial disease [10,11,12,13,14]. Devastating plant diseases worldwide caused by pathogens from the Sclerotiniaceae family lead to huge economic losses each year. The annual losses caused by Sclerotinia sclerotiorum in the United States have exceeded $200 million [15]. Scleromitrula shiraiana was reported as a causal agent of reduced sorosis sclerotiniose, and C. carunculoides is a dominant causal agent resulting in small-particle sorosis sclerotiniose. Ciboria shiraiana is the dominant causal agent of mulberry sclerotiniose in China, and it results in hypertrophy sorosis sclerotiniose, which causes huge economic losses in many mulberry-growing areas every year, especially in southwest China [7,16]. C. shiraiana and C. carunculoides are the dominant pathogens of mulberry sclerotial disease in China and many other countries, while S. shiraiana is found only in a relatively small region of southwest China [17].
It is necessary to develop novel therapeutic strategies based on an understanding of the plant innate immune response process against sclerotiniose in mulberry. Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are the two main reaction processes of plant innate immunity [18]. A genome sequencing analysis of S. shiraiana suggested that there were fewer genes encoding cell-wall-degrading enzymes (CWDEs) and effector proteins in S. shiraiana than in S. sclerotiorum and B. cinerea, as well as many other ascomycetes responsible for its narrow host plant range [7]. A genome sequence analysis of S. scleroterum indicated that its genome contains a large number of genes that encode secretory effector proteins that may be involved in the interaction between S. scleroterum and the host [11]. Omics-based (transcriptome, proteome, and metabolome) screening of candidate genes involved in the response to mulberry sclerotiniose has been performed in different mulberry varieties using the Morus notabilis genome as a reference genome [5]. Chitase-family genes were screened as candidate genes for the improvement of plant S. scleroterum resistance, and several pathology-related (PR) proteins were also annotated as S. scleroterum response proteins in Morus laevigata [11]. The accumulation of phytohormones such as salicylic acid (SA), jasmonic acid (JA), and ethylene is also an important plant defense signal for the activation of the plant defense response [19]. A previous study showed that plant hormone signal transduction, calcium-mediated defense signaling, transcription factors, and secondary metabolites were stimulated, whereas photosynthesis and cellular-growth-related metabolism were suppressed, following C. carunculoides infection in Morus atropurpurea [20]. Another study on mulberry infected with C. shiraiana also indicated that secondary metabolism and defense-related hormone pathways were involved in the response to mulberry sclerotiniose. Moreover, SA and JA play major roles in plant defense by activating SA signaling and inhibiting JA signaling in mulberry fruit infected with C. carunculoides. [7]. As for mulberry infected with C. shiraiana’ the phenylpropanoid biosynthesis pathway was reported to be involved in the response to C. shiraiana infection in Morus alba [8]. However, further research is still required to screen specific genes or pathways as targets for developing therapeutic strategies and determining whether it is possible to identify target genes that can respond to different fungal infections in Morus.
Plant small secreted peptides (SSPs) have been reported as an important class of regulatory molecules involved in plant growth, development, stress tolerance, and pathogen defense [21,22,23,24,25]. SSPs are typically encoded within preproteins of 100–250 amino acids, which are subsequently processed into shorter bioactive peptides of 5–50 residues [23,26,27]. To date, the genome-wide annotation of SSPs has been reported in Arabidopsis and Medicago truncatula [28,29]. However, there are few reports on SSPs in mulberry. Given the roles of SSPs in the pathogen response, it is necessary to perform a genome-wide analysis of SSPs in mulberry and identify candidate SSPs that are possibly involved in the response to mulberry sclerotiniose.
A chromosome-level genome of M. alba has been released, providing us with a complete reference genome for performing the genome-wide annotation of SSPs and comparative transcriptome analysis [4]. We first annotated genome-wide SSPs in mulberry and established a local mulberry database with both genes and SSP annotations. RNA sequencing (RNA-seq) data obtained from two different mulberry varieties, namely Zs5801 (‘Zhongsang5801′), which is susceptible to mulberry sclerotiniose, and K (‘Morus atropurpurea variety K’), which is tolerant to mulberry sclerotiniose, as well as published transcriptome datasets consisting of data on mulberry sclerotiniose caused by C. carunculoides, were analyzed together with our annotation files and the M. alba reference genome. The comparative transcriptome analysis results provided 52 target genes and SSPs that may modify mulberry resistance to mulberry sclerotiniose. Hormone signaling pathway genes and cell-wall-related genes were implied to play important roles in the resistance to mulberry sclerotiniose. In addition, genes involved in photosynthesis were significantly inhibited in infected mulberry fruits. Our results provide an available gene source for controlling sclerotiniose in mulberry using genetic engineering and primary insights for mulberry defense mechanism under sclerotiniose pathogen infection.

2. Materials and Methods

2.1. Plant Materials

Healthy fruits and diseased fruits of Zs5801 and K1 were collected from the National Mulberry Genebank (NMGB) plantation, Zhenjiang, Jiangsu Province, China, on 8 May 2022. The Zs5801 and K1 plants were planted nearby with a row spacing of 0.8 m and plant spacing of 0.8 m in fields. The collected fruits were immediately frozen in liquid nitrogen and stored at −80 °C until use. Ciboria shiraiana was provided by Professor Zhao and was cultured in potato dextrose agar (PDA) medium. Seedlings of the M. alba variety ‘Fengchi’ and tobacco were planted in pots and grown in a chamber at 22 °C with a 16/8 day/night cycle and 40%–60% humidity.

2.2. Data Collection and Genome-Wide Annotation of SSPs

Morus alba genome information and gene annotation were provided by Professor Jiao. The annotation of SSPs was performed according to the reported protocols and the codes provided in GitHub with link of https://github.com/ZhaoBioinformaticsLab/PlantSSPProtocols (accessed on 14 May 2023) [28,29]. A total of 73 published RNA-seq datasets were used to validate the expression levels of annotated SSPs (Table S1). The annotation of SSPs was performed according to the protocols using MtSSPdb (https://mtsspdb.noble.org, accessed on 14 May 2023) [28,29]. The transcriptome-validated SSPs were organized as a local SSP database for mulberry and were used for further analysis in the present study.

2.3. RNA-seq Analysis

All RNA-seq datasets were analyzed using the chromosome-level M. alba genome as the reference genome for alignment using bowtie2 (version 2.3.2) with parameters used for pair-end sequencing [30]. Samtools was used to sort and index the bam files. The genome annotation file included the SSPs that were used for calculating the expression matrix using StringTie v2.15 [31]. Differentially expressed genes (DEGs) were obtained using DEseq2 by comparing the expression levels of sample pairs according to the reference manual [32]. A weighted correlation network analysis (WGCNA) was performed to screen the co-expressed DEGs involved in the response to mulberry sclerotiniose according to published tutorials [33]. The estimated soft threshold (power) was 14 in this study. R version 4.1.2 was used for R-package-based analyses.

2.4. Workflow for Screening Sclerotiniose-Response Genes (SRGs) in Mulberry

The workflow for this study is shown in Figure 1. Datasets used for comparative transcriptome analysis included the RNA-seq dataset of the M. alba cultivar ‘Hongguo II’ infected with C. carunculoides, which was deposited in the National Genomics Data Center (NGDC) repository with accession number CRA003673; the RNA-seq dataset of M. atropurpurea infected with C. carunculoides, which was deposited in the National Center for Biotechnology Information (NCBI) database under accession number GSE111319; and the RNA-seq dataset of Zs5801 and K infected with C. shiraiana, which was deposited in the NGDC repository with accession number CRA006034.

2.5. Isolation of RNA and cDNA Synthesis

Samples including mulberry fruits, leaves, and tobacco leaves were ground individually with liquid nitrogen, and total RNA was extracted using a Plant RN53 Kit (Aidlab, Beijing, China) according to the manual. The quality of RNA was evaluated using both a Nanodrop one instrument (Thermo Scientific, Waltham, MA, USA) and electrophoresis. cDNA was synthesized with the PC54-TRUEscript RT kit (Aidlab, Beijing, China) according to the manufacture’s protocol.

2.6. RT-qPCR Analysis

Quantitative real-time PCR (qRT-PCR) was performed using an ABI StepOnePlus™ Real-Time PCR System (Foster City, CA, USA). The primers are listed in Table S2. Actin was used as the reference gene [34]. GraphPad Prism8.0 was used to visualize the qRT-PCR results. SPSS19.0 was used to perform t-tests and ANOVA, with p < 0.05 considered significant. Three biological replicates and three technical replicates were performed for qRT-PCR.

2.7. Obtaining Transient Transgenic Nicotiana Benthamiana

Nimble cloning was adopted to construct recombinant plasmids according to a published protocol [35]. The primers with adaptors used for nimble cloning are listed in Table S2. The recombinant plasmids pNC-1304-35s:SRGs and empty vector pNC-1304-35s:GFP as the negative control were transformed into Agrobacterium tumefaciens GV3101 and then transferred into N. benthamiana leaves via Agrobacterium-mediated transient transformation. Infiltration experiments were performed on 4–6-week-old tobacco plants using needleless syringes, as described previously [36]. Transgenic plants were determined using qRT-PCR by comparing the expression levels of target genes in transgenic plants with those in the negative controls.

2.8. Obtaining VIGS Transgenic Mulberry

Virus-induced gene silencing (VIGS) was used to obtain candidate SRGs that were down-regulated in transgenic mulberry according to our previous report [37]. Nimble cloning was performed to construct pTRV2-MaSRGs according to a published protocol [35]. The recombinant plasmids pTRV2-MaSRG, pTRV1, and pTRV2 (negative control) were transformed into Agrobacterium tumefaciens GV3101 using the freeze–thaw method [38]. Three-week-old M. alba ‘Fengchi’ seedlings were used for the treatment. The knock-down efficiency of the MaSRGs was determined by qRT-PCR by comparing the transgenic plants with the negative controls.

2.9. Estimation of Plant Resistance to C. shiraiana Infection

C. shiraiana was cultivated on potato dextrose agar medium (Coolaber, Beijing, China) at 26 °C for 4–5 days and then cut into small discs and applied to lightly wounded leaves that had been sterilized with 75% ethanol for 2 min and washed with ddH2O. Cell death symptoms and the growth condition of C. shiraiana were recorded to estimate the resistance of transgenic plants to C. shiraiana infection [7,39]. Ciboria shiraiana was inoculated at 2 d after infiltration in tobacco and at 10 d after infiltration in mulberry. The cell death symptoms were photographed after inoculation until the sclerotia appeared. The results are representative of at least three biological replicates.

3. Results

3.1. Genome-Wide Annotation of SSPs in Mulberry

According to reported protocols [28,29], we successfully identified 1309 SSPs. The parent gene length mainly ranged from 100 bp to 1000 bp (Figure 2A). Further transcriptome analysis of these SSPs showed that 1088 of the 1309 annotated SSPs had detected expression levels in at least one sample using 73 published RNA-seq datasets (Figure 2B, Table S1). The annotation of the 1088 RNA-seq-validated SSPs showed that 997 SSPs could be annotated as known SPPs, and these SSPs were classified into 37 classes (Table 1). The top two classes of SSPs in mulberry were Clavata/Embryo Surrounding Region (CLE) and Rapid Alkalinization Factor (RALF), with 137 and 106 annotated SSPs, respectively. There were four new annotated classes of SSPs in mulberry (marked with a blue shadow in Table 1) compared with the reported classes in Medicago truncatula.

3.2. RNA-seq of Mulberry Varieties Susceptible and Resistant to Mulberry Sclerotiniose

The numbers of diseased fruits of Zs5801 and K were observed and recorded, and the diseased fruits had symptoms of hypertrophy sorosis sclerotiniose (Figure 3A–F). A significant difference in the diseased fruit ratio between Zs5801, the susceptible mulberry variety, and the resistant mulberry variety K, was observed. Diseased fruits per hundred fruits (DFPH) with symptoms of hypertrophy sorosis sclerotiniose in Zs5801 were 92.79 ± 2.27, while the DFPH value in K was only 2.56 ± 2.00 (Figure 3A–D). PCR amplification using primers ITS1/4, followed by sequencing, further validated that C. shiraiana was the causal agent for these diseased fruits (Figure S1). Correlation analysis using a gene expression matrix and principal component analysis (PCA) using the DEGs of the sequencing samples were performed to evaluate the repeatability of the replicates and the differences among different sample sets (Figure 4). The results showed that the biological replicates clustered together, and the different sample sets could be distinguished clearly using the DEG expression matrix (Figure 4B).

3.3. Comparative Transcriptome Analysis Revealed the Candidate Genes Involved in the Response to Mulberry Sclerotiniose

The workflow for identifying candidate genes involved in the response to mulberry sclerotiniose is shown in Figure 1. First, a total of 2555 DEGs were obtained in different sample pairs (L vs. S, L vs. K, and K vs. S). Primarily, the DEGs in L vs. S and K vs. S were thought to be candidates involved in the response to mulberry sclerotiniose. The overlapping DEGs in L vs. K were identified as genes involved in the resistance to mulberry sclerotiniose. A total of 368 of 507 (72.58%) DEGs in L vs. K were mulberry-sclerotiniose-related genes (Figure 5A), and thus, the major DEGs in susceptible (L) and resistant (K) mulberry varieties were involved in the response to mulberry sclerotiniose. Therefore, we classified these 368 DEGs as primary sclerotiniose-response gene cluster 1 (PSRG1). There were 81 annotated SSPs in PSRG1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that genes in PSRG1 mainly participated in photosynthesis, the plant hormone signaling pathway, and the metabolic pathway (Figure 5B).
The DEGs obtained from healthy fruits and infected fruits of Zs5801 together with the DEGs obtained using published transcriptome datasets consisting of data on mulberry sclerotiniose caused by C. carunculoides were analyzed to explore the candidate genes involved in the response to sclerotiniose with either fungal agent. Based on the Venn diagram, we obtained 183 DEGs that were identified as DEGs in at least two experiments, and we classified these 183 DEGs as primary sclerotiniose-response gene cluster 2 (PSRG2) (Figure 5C). The GO and KEGG pathway enrichment analyses showed that genes in PSRG2 mainly participated in photosynthesis, response to fungus, the plant hormone signaling pathway, the metabolic pathway, and linoleic acid metabolism (Figure 5D). The WGCNA using the PSRGs from both PSRG1 and PSRG2 showed that genes in the turquoise module were significantly positively correlated with healthy fruits and significantly negatively correlated with severely diseased fruits (Fruits at S3 stage) (Figure 5E). There were 51 common DEGs, including 39 genes and 12 SSPs, in the three independent experiments with different fungal agents. Combining PSRG1 and PSRG2, an additional seven new SSPs were found in both clusters. Among these 58 common genes and SSPs—39 common genes and 19 (12 + 7) common SSPs—52 genes and SSPs also belonged to the turquoise module (Figure 5F). Therefore, we ultimately assigned these 52 genes and SSPs as sclerotiniose-response genes (SRGs) in mulberry.

3.4. Functional Characterization of SRGs in Mulberry

The expression patterns of the 52 SRGs were strictly identical in both experiments (Table 2 and Table 3). Among the 37 functional genes and transcription factors, 12 of the 19 down-regulated SRGs in the diseased fruits were up-regulated in the resistant mulberry variety K, and 9 of the 18 up-regulated SRGs showed down-regulated expression levels in K (Table 2). The functional annotation of these SRGs showed that they were mainly involved in plant hormone biosynthesis and signaling pathways and in cell wall and fruit development, which also corresponds with the GO and KEGG pathway enrichment analyses of PSRG1 and PSRG2. Nineteen sclerotiniose-response-related SSP genes were also annotated, and these SSPs belonged to nine signal classes, namely, CLE, GASA, ns-LTP, RALF, STIG, CAPE, PSK, PIP, and DVL/RTFL (Table 3).
Five SRGs (M.alba_G0010603, MaPPO; maker-Chr4-est_gff_StringTie-gene-90.0, MaSSP15; M.alba_G0005265, MaMYB29; M.alba_G0003453, MaMES17; and M.alba_G0012049, MaSWEET1) were first selected as target genes to perform functional analyses in mulberry to test their roles in the resistance to sclerotiniose. qRT-PCR validated the results of the RNA-seq, and these SRGs involved in the response to sclerotiniose and corresponding expression level changes were observed by both qRT-PCR and RNA-seq (Figure 6A). Among these selected SRGs, MaSWEET1 has been validated as negative regulator for mulberry resistance to sclerotiniose [40]. The overexpression of the selected SRGs in tobacco showed that the expression level of SRGs affected tobacco resistance to C. shiraiana infection (Figure 6B,C and Figures S2–S5). The cell death symptoms were more severe in plants overexpressing MaMYB29, MaMES17, and MaSSP15 at 5 d after inoculation, while the overexpression of MaPPO resulted in fungal growth inhibition and the alleviation of cell death symptoms in tobacco. The knock-down of the above SRGs by VIGS in mulberry showed corresponding results, and the decreased expression levels of MaMYB29′ MaMesh17, and MaSSP15 resulted in the alleviation of cell death symptoms at 2 d after inoculation compared with the controls (Figure 6D,E and Figures S2–S5).

4. Discussion

SSPs are thought to play various roles in plants, especially in development and stress response. Studies on SSPs are unevenly distributed in terms of plant species and SSP classes, and there is little information available for SSPs in mulberry. We presented a genome-wide annotation of 1088 transcriptome-level validated SSPs in mulberry for the first time herein, which provided us with a foundation for exploring the function of SSPs in mulberry. Several SSP gene families have been well studied in plants. For example, the CLAVATA3/EMBRYO SURROUNDING REGION-RELATED (CLE) genes, which were annotated as the largest class of SSPs in mulberry, encode a large family of extracellular signaling peptides. CLEs are well studied in Arabidopsis and are reported to stimulate receptor-mediated signal transduction cascades to modulate diverse developmental and physiological processes [41]. Previous studies identified 32 CLE genes in Arabidopsis, 52 CLE genes in Medicago truncatula, and 107 CLE genes in wheat [28,41,42], and we reported 137 CLE genes in mulberry in the present study. Further functional characterization should be performed to explicitly characterize the roles of these CLEs in mulberry. Gibberellic Acid Stimulated Arabidopsis (GASA) proteins have been shown to play important roles in various developmental programs, including seed germination, fruit development, and ripening. Some GASAs have been reported to be involved in cell elongation and cell division [43,44,45]. In the present study, we also identified two GASA genes, namely MaSSP2 and MaSSP15, as sclerotiniose-response genes in mulberry, indicating their possible roles in the response to sclerotiniose (Table 3). The overexpression and knock-down of MaSSP15 indicated that the expression level of MaSSP15 could affect plant resistance to C. shiraiana infection in both tobacco and mulberry (Figure 6 and Figure S5). The SSP database provides a resource for exploring functional SSPs and new targets for molecular breeding in mulberry.
Multi-omics technology has been used in studies of mulberry sclerotiniose since the release of the M. notabilis genome. Transcriptomics in combination with proteomics and metabolomics has been used to evaluate sclerotiniose pathogen infection in mulberry, including infection by C. carunculoides and C. shiraiana [7,8,20]. A comparative transcriptome analysis of mulberry variety K, which showed high resistance to sclerotiniose, and Zs5801, which is susceptible to sclerotiniose, indicated the possible genes involved in the response to mulberry sclerotiniose. Three RNA-seq datasets of mulberry fruits infected with C. carunculoides or C. shiraiana were analyzed together using our workflow (Figure 1). The analysis revealed a narrow SRG cluster involved in the response to infection by both pathogens. Our results indicated that photosynthesis, the plant hormone signaling pathway, and the metabolic pathway represent the main response pathways by which mulberry responds to mulberry sclerotiniose (Figure 5). Similar results were also reported in previous studies [7,8,20].
Five selected SRGs were validated and functionally characterized for their roles in the response to C. shiraiana infection. MaPPO (M.alba_G0010603), annotated as polyphenol oxidase gene (PPO), which is an important enzyme involved in defense, showed a positive influence on plant resistance to C. shiraiana infection (Figure 5) [46]. V-myb myeloblastosis viral oncogene homolog (MYB) proteins have been identified as important regulators that work as activators or repressors in diverse processes, including development, stress responses, and metabolism [47]. MaMYB29 is an R2R3-MYB, and it was identified as an important regulator in the response to mulberry sclerotiniose. The overexpression and knock-down of MaYB29 influenced plant resistance to C. shiraiana infection in both tobacco and mulberry (Figure 6 and Figure S2). These results provided us with definite targets for modification in future molecular breeding studies. Further exploration of the molecular mechanisms of these SRGs in the response to mulberry sclerotiniose would enrich our understanding of the defense mechanisms against pathogen infection in the plant kingdom.

5. Conclusions

In conclusion, we firstly annotated genome-wide SSPs in mulberry and established a local mulberry database with both genes and SSP annotations. Fifty-two target genes and SSPs that may modify mulberry resistance to mulberry sclerotiniose were identified based on comparative transcriptome analysis results, and five of them were primarily characterized. Our results also indicated that hormone signaling pathway genes and cell wall-related genes play important roles in the resistance to mulberry sclerotiniose.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15071126/s1: Table S1 Public RNA-seq data information used in this study; Table S2 Primers used in this study; Figure S1 Phylogenetic analysis of the fungi based on rDNA sequences. The rDNA sequences isolated by PCR from diseased Zs5801 fruits are marked with a red box. The maximum-likelihood phylogenetic tree was constructed using MEGA 7.0 with default parameters and assessed by bootstrapping using 1000 replicates.; Figure S2 Cell death symptoms in MaMYB29 transgenic tobacco and mulberry. CK, control plants infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids; VIGS, knocked-down mulberry with A. tumefaciens GV3101 containing recombinant plasmids and pTRV1; Figure S3 Cell death symptoms in MaMes17 transgenic tobacco and mulberry. CK, control plants infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids; VIGS, knocked-down mulberry with A. tumefaciens GV3101 containing recombinant plasmids and pTRV1; Figure S4 Cell death symptoms in MaSSP15 transgenic tobacco and mulberry. CK, control plants infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids; VIGS, knocked-down mulberry with A. tumefaciens GV3101 containing recombinant plasmids and pTRV1; Figure S5 Cell death symptoms in MaPPO transgenic tobacco. CK, control plants infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids.

Author Contributions

L.L. and N.C. guided the work and provided advice; N.C., X.K., Z.G., L.L., L.Z., R.F., S.H., S.L. and K.Y. performed the experiments and analyzed the data; N.C. and L.L. organized the figures and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was jointly supported by the National Natural Science Foundation (NSF 32201526 to Nan Chao), the Crop Germplasm Resources Protection Project of the Ministry of Agriculture and Rural Affairs of the People’s Republic of China (19221879), the National Infrastructure for Crop Germplasm Resources (NCGRC-2022-041), and the China Agriculture Research System of MOF and MARA (CARS-18).

Data Availability Statement

Data is contained within the article and Supplementary Materials.

Acknowledgments

Great thanks to Jiao, who provided us the genome annotation file of Morus alba.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

References

  1. Vijayan, K.; Tikader, A.; Weiguo, Z.; Nair, C.V.; Ercisli, S.; Tsou, C.-H. Morus. In Wild Crop Relatives: Genomic and Breeding Resources; Springer: Berlin/Heidelberg, Germany, 2011; pp. 75–95. [Google Scholar] [CrossRef]
  2. Wen, P.; Hu, T.; Linhardt, R.J.; Liao, S.; Wu, H.; Zou, Y. Mulberry: A review of bioactive compounds and advanced processing technology. Trends Food Sci. Technol. 2019, 83, 138–158. [Google Scholar] [CrossRef]
  3. Wang, D.; Dong, Z.; Zhang, Y.; Guo, K.; Guo, P.; Zhao, P.; Xia, Q. Proteomics Provides Insight into the Interaction between Mulberry and Silkworm. J. Proteome Res. 2017, 16, 2472–2480. [Google Scholar] [CrossRef] [PubMed]
  4. Jiao, F.; Luo, R.; Dai, X.; Liu, H.; Yu, G.; Han, S.; Lu, X.; Su, C.; Chen, Q.; Song, Q.; et al. Chromosome-level reference genome and population genomic analysis provide insight into the evolution and improvement of domesticated mulberry (Morus alba L.). Mol. Plant 2020, 13, 1001–1012. [Google Scholar] [CrossRef] [PubMed]
  5. He, N.; Zhang, C.; Qi, X.; Zhao, S.; Tao, Y.; Yang, G.; Lee, T.; Wang, X.; Cai, Q.; Li, D. Draft genome sequence of the mulberry tree Morus notabilis. Nat. Commun. 2013, 4, 2445. [Google Scholar] [CrossRef] [PubMed]
  6. Yuan, Q.; Zhao, L. The Mulberry (Morus alba L.) Fruit—A Review of Characteristic Components and Health Benefits. J. Agric. Food Chem. 2017, 65, 10383–10394. [Google Scholar] [CrossRef] [PubMed]
  7. Lv, Z.; Hao, L.; Ma, B.; He, Z.; Luo, Y.; Xin, Y.; He, N. Ciboria carunculoides Suppresses Mulberry Immune Responses Through Regulation of Salicylic Acid Signaling. Front. Plant Sci. 2021, 12, 658590. [Google Scholar] [CrossRef] [PubMed]
  8. Bao, L.; Gao, H.; Zheng, Z.; Zhao, X.; Zhang, M.; Jiao, F.; Su, C.; Qian, Y. Integrated Transcriptomic and Un-Targeted Metabolomics Analysis Reveals Mulberry Fruit (Morus atropurpurea) in Response to Sclerotiniose Pathogen Ciboria shiraiana Infection. Int. J. Mol. Sci. 2020, 21, 1789. [Google Scholar] [CrossRef] [PubMed]
  9. Lü, R.; Zhao, A.; Yu, J.; Wang, C.; Liu, C.; Cai, Y.; Yu, M. Biological and epidemiological characteristics of the pathogen of hypertrophy sorosis scleroteniosis, Ciboria shiraiana. Wei Sheng Wu Xue Bao= Acta Microbiol. Sin. 2017, 57, 388–398. [Google Scholar]
  10. Lv, Z.; He, Z.; Hao, L.; Kang, X.; Ma, B.; Li, H.; Luo, Y.; Yuan, J.; He, N. Genome Sequencing Analysis of Scleromitrula shiraiana, a Causal Agent of Mulberry Sclerotial Disease with Narrow Host Range. Front. Microbiol. 2020, 11, 603927. [Google Scholar] [CrossRef] [PubMed]
  11. Jiang, H.; Jin, X.; Shi, X.; Xue, Y.; Jiang, J.; Yuan, C.; Du, Y.; Liu, X.; Xie, R.; Liu, X.; et al. Transcriptomic Analysis Reveals Candidate Genes Responsive to Sclerotinia scleroterum and Cloning of the Ss-Inducible Chitinase Genes in Morus laevigata. Int. J. Mol. Sci. 2020, 21, 8358. [Google Scholar] [CrossRef] [PubMed]
  12. Wolf, W. The Cup Fungus, Ciboria carunculoides, Pathogenic on Mulberry Fruits. Mycologia 1945, 37, 476–491. [Google Scholar]
  13. Hong, S.K.; Wan, G.K.; Sung, G.B.; Nam, S.H. Identification and Distribution of Two Fungal Species Causing Sclerotial Disease on Mulberry Fruits in Korea. Mycobiology 2007, 35, 87–90. [Google Scholar] [CrossRef]
  14. Zhu, P.; Kou, M.; Liu, C.; Zhang, S.; Lu, R.; Xia, Z.; Yu, M.; Zhao, A. Genome Sequencing of Ciboria shiraiana Provides Insights into the Pathogenic Mechanisms of Hypertrophy Sorosis scleroteniosis. Mol. Plant Microbe Interact. 2021, 34, 62–74. [Google Scholar] [CrossRef] [PubMed]
  15. Bolton, M.D.; Thomma, B.P.H.J.; Nelson, B.D. Sclerotinia sclerotiorum (Lib.) de Bary: Biology and molecular traits of a cosmopolitan pathogen. Mol. Plant Pathol. 2005, 7, 1–16. [Google Scholar] [CrossRef]
  16. Lü, R.; Zhao, A.; Jin, X.; Du, Y.; Wu, W.; Wang, X.; Yu, M. A primary experiment on the control of mulberry fruit sclerotiniosis using herbicide glyphosate. Sci. Seric. 2011, 37, 907–913. [Google Scholar]
  17. Lu, Z.; Kang, X.; Xiang, Z.; He, N. Laccase Gene Sh-lac Is Involved in the Growth and Melanin Biosynthesis of Scleromitrula shiraiana. Phytopathology 2017, 107, 353–361. [Google Scholar] [CrossRef] [PubMed]
  18. Jones, J.; Dangl, J.L. The plant immune system. Nature 2006, 444, 323–329. [Google Scholar] [CrossRef] [PubMed]
  19. Yu, X.; Feng, B.; He, P.; Shan, L. From Chaos to Harmony: Responses and Signaling Upon Microbial Pattern Recognition. Annu. Rev. Phytopathol. 2017, 55, 109. [Google Scholar] [CrossRef] [PubMed]
  20. Dai, F.; Wang, Z.; Li, Z.; Luo, G.; Wang, Y.; Tang, C. Transcriptomic and proteomic analyses of mulberry (Morus atropurpurea) fruit response to Ciboria carunculoides. J. Proteom. 2019, 193, 142–153. [Google Scholar] [CrossRef] [PubMed]
  21. Jourquin, J.; Fukaki, H.; Beeckman, T. Peptide-Receptor Signaling Controls Lateral Root Development. Plant Physiol. 2020, 182, 1645–1656. [Google Scholar] [CrossRef] [PubMed]
  22. Fukuda, H.; Hardtke, C.S. Peptide Signaling Pathways in Vascular Differentiation. Plant Physiol. 2020, 182, 1636–1644. [Google Scholar] [CrossRef]
  23. Takahashi, F.; Hanada, K.; Kondo, T.; Shinozaki, K. Hormone-like peptides and small coding genes in plant stress signaling and development. Curr. Opin. Plant Biol. 2019, 51, 88–95. [Google Scholar] [CrossRef] [PubMed]
  24. Campos, M.L.; de Souza, C.M.; de Oliveira, K.B.S.; Dias, S.C.; Franco, O.L. The role of antimicrobial peptides in plant immunity. J. Exp. Bot. 2018, 69, 4997–5011. [Google Scholar] [CrossRef] [PubMed]
  25. Chen, Y.L.; Lee, C.Y.; Cheng, K.T.; Chang, W.H.; Huang, R.N.; Nam, H.G.; Chen, Y.R. Quantitative peptidomics study reveals that a wound-induced peptide from PR-1 regulates immune signaling in tomato. Plant Cell 2014, 26, 4135–4148. [Google Scholar] [CrossRef] [PubMed]
  26. Schuster, M.; van der Hoorn, R.A.L. Plant Biology: Distinct New Players in Processing Peptide Hormones during Abscission. Curr. Biol. 2020, 30, R715–R717. [Google Scholar] [CrossRef] [PubMed]
  27. Motomitsu, A.; Sawa, S.; Ishida, T. Plant peptide hormone signalling. Essays Biochem. 2015, 58, 115–131. [Google Scholar] [PubMed]
  28. Boschiero, C.; Dai, X.; Lundquist, P.K.; Roy, S.; Christian de Bang, T.; Zhang, S.; Zhuang, Z.; Torres-Jerez, I.; Udvardi, M.K.; Scheible, W.R.; et al. MtSSPdb: The Medicago truncatula Small Secreted Peptide Database. Plant Physiol. 2020, 183, 399–413. [Google Scholar] [CrossRef] [PubMed]
  29. Boschiero, C.; Lundquist, P.K.; Roy, S.; Dai, X.; Zhao, P.X.; Scheible, W.R. Identification and Functional Investigation of Genome-Encoded, Small, Secreted Peptides in Plants. Curr. Protoc. Plant Biol. 2019, 4, e20098. [Google Scholar] [CrossRef] [PubMed]
  30. Langdon, B.W. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Min. 2015, 8, 1. [Google Scholar] [CrossRef] [PubMed]
  31. Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015, 33, 290–295. [Google Scholar] [CrossRef]
  32. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Nat. Preced. 2010, 5. [Google Scholar] [CrossRef]
  33. Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 2008, 9, 559. [Google Scholar] [CrossRef] [PubMed]
  34. Shukla, P.; Reddy, R.A.; Ponnuvel, K.M.; Rohela, G.K.; Shabnam, A.A.; Ghosh, M.K.; Mishra, R.K. Selection of suitable reference genes for quantitative real-time PCR gene expression analysis in Mulberry (Morus alba L.) under different abiotic stresses. Mol. Biol. Rep. 2019, 46, 1809–1817. [Google Scholar] [CrossRef] [PubMed]
  35. Yan, P.; Zeng, Y.; Shen, W.; Tuo, D.; Li, X.; Zhou, P. Nimble Cloning: A Simple, Versatile, and Efficient System for Standardized Molecular Cloning. Front. Bioeng. Biotechnol. 2019, 7, 460. [Google Scholar] [CrossRef] [PubMed]
  36. Sharma, R.; Liang, Y.; Lee, M.Y.; Pidatala, V.R.; Mortimer, J.C.; Scheller, H.V. Agrobacterium-mediated transient transfor-mation of sorghum leaves for accelerating functional genomics and genome editing studies. BMC research notes 2020, 13, 1–7. [Google Scholar] [CrossRef] [PubMed]
  37. Li, R.; Liu, L.; Dominic, K.; Wang, T.; Fan, T.; Hu, F.; Wang, Y.; Zhang, L.; Li, L.; Zhao, W. Mulberry (Morus alba) MmSK gene enhances tolerance to drought stress in transgenic mulberry. Plant Physiol. Biochem. 2018, 132, 603–611. [Google Scholar] [CrossRef] [PubMed]
  38. Chen, H.; Nelson, R.S.; Sherwood, J.L. Enhanced recovery of transformants of Agrobacterium tumefaciens after freeze-thaw transformation and drug selection. BioTechniques 1994, 16, 664–668, 670. [Google Scholar] [PubMed]
  39. Zhu, P.; Zhang, S.; Li, R.; Liu, C.; Fan, W.; Hu, T.; Zhao, A. Host-Induced Gene Silencing of a G Protein α Subunit Gene CsGpa1 Involved in Pathogen Appressoria Formation and Virulence Improves Tobacco Resistance to Ciboria shiraiana. J. Fungi 2021, 7, 1053. [Google Scholar] [CrossRef] [PubMed]
  40. Kang, X.; Huang, S.; Feng, Y.; Fu, R.; Tang, F.; Zheng, L.; Li, P.; Chao, N.; Liu, L. SWEET transporters and their potential roles in response to abiotic and biotic stresses in mulberry. Beverage Plant Res. 2023, 3, 1–13. [Google Scholar] [CrossRef]
  41. Fletcher, J.C. Recent Advances in Arabidopsis CLE Peptide Signaling. Trends Plant Sci. 2020, 25, 1005–1016. [Google Scholar] [CrossRef] [PubMed]
  42. Li, Z.; Liu, D.; Xia, Y.; Li, Z.; Niu, N.; Ma, S.; Wang, J.; Song, Y.; Zhang, G. Identification and Functional Analysis of the CLAVATA3/EMBRYO SURROUNDING REGION (CLE) Gene Family in Wheat. Int. J. Mol. Sci. 2019, 20, 4319. [Google Scholar] [CrossRef] [PubMed]
  43. Zhong, C.; Xu, H.; Ye, S.; Wang, S.; Li, L.; Zhang, S.; Wang, X. AtGASA6 Serves as an Integrator of Gibberellin-, Abscisic Acid- and Glucose-Signaling during Seed Germination in Arabidopsis. Plant Physiol. 2015, 169, 15–00858. [Google Scholar] [CrossRef] [PubMed]
  44. Moyano-Canete, E.; Bellido, M.L.; Garcia-Caparros, N.; Medina-Puche, L.; Amil-Ruiz, F.; Gonzalez-Reyes, J.A.; Caballero, J.L.; Munoz-Blanco, J.; Blanco-Portales, R. FaGAST2, a strawberry ripening-related gene, acts together with FaGAST1 to determine cell size of the fruit receptacle. Plant Cell Physiol. 2013, 54, 218–236. [Google Scholar] [CrossRef]
  45. de la Fuente, J.I.; Amaya, I.; Castillejo, C.; Sanchez-Sevilla, J.F.; Quesada, M.A.; Botella, M.A.; Valpuesta, V. The strawberry gene FaGAST affects plant growth through inhibition of cell elongation. J. Exp. Bot. 2006, 57, 2401–2411. [Google Scholar] [CrossRef] [PubMed]
  46. Zhang, J.; Sun, X. Recent advances in polyphenol oxidase-mediated plant stress responses. Phytochemistry 2021, 181, 112588. [Google Scholar] [CrossRef] [PubMed]
  47. Dubos, C.; Stracke, R.; Grotewold, E.; Weisshaar, B.; Martin, C.; Lepiniec, L. MYB transcription factors in Arabidopsis. Trends Plant Sci. 2010, 15, 573–581. [Google Scholar] [CrossRef]
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Figure 1. Workflow for screening sclerotiniose-response genes (SRGs) in mulberry. PSRG1, sclerotiniose-response gene cluster 1; PSRG2, sclerotiniose-response gene cluster 2.
Figure 1. Workflow for screening sclerotiniose-response genes (SRGs) in mulberry. PSRG1, sclerotiniose-response gene cluster 1; PSRG2, sclerotiniose-response gene cluster 2.
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Figure 2. Statistic summary of annotated secreted peptides (SSPs) in mulberry. (A) SSP gene length and distribution in mulberry. (B) Expression levels of RNA-seq-detected SSPs in mulberry.
Figure 2. Statistic summary of annotated secreted peptides (SSPs) in mulberry. (A) SSP gene length and distribution in mulberry. (B) Expression levels of RNA-seq-detected SSPs in mulberry.
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Figure 3. Infection ratio and growth condition of different mulberry varieties in the mulberry sclerotiniose outbreak period. (A,B) Diseased fruits in Zs5801 in a plantation; diseased fruits per hundred fruits (DFPH) with symptoms of hypertrophy sorosis sclerotiniose are shown in red text. (C,D) Diseased fruits in K in plantations; diseased fruits per hundred fruits (DFPH) with symptoms of hypertrophy sorosis sclerotiniose are shown in red text. (E) The sporocarp growing under mulberry plants in a plantation. (F) Growth condition of Ciboria shiraiana in PDA medium. (G) Mulberry fruits with symptoms of hypertrophy sorosis sclerotiniose. Red arrows indicate the diseased area in fruits.
Figure 3. Infection ratio and growth condition of different mulberry varieties in the mulberry sclerotiniose outbreak period. (A,B) Diseased fruits in Zs5801 in a plantation; diseased fruits per hundred fruits (DFPH) with symptoms of hypertrophy sorosis sclerotiniose are shown in red text. (C,D) Diseased fruits in K in plantations; diseased fruits per hundred fruits (DFPH) with symptoms of hypertrophy sorosis sclerotiniose are shown in red text. (E) The sporocarp growing under mulberry plants in a plantation. (F) Growth condition of Ciboria shiraiana in PDA medium. (G) Mulberry fruits with symptoms of hypertrophy sorosis sclerotiniose. Red arrows indicate the diseased area in fruits.
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Figure 4. The correlation of different samples. (A) Heatmap of the correlations of different samples based on an expression matrix. (B) PCA analysis of different samples. L, healthy Zs5801 fruits; K, healthy K fruits; S, diseased 5801 fruits. Each sample set contained three biological replicates.
Figure 4. The correlation of different samples. (A) Heatmap of the correlations of different samples based on an expression matrix. (B) PCA analysis of different samples. L, healthy Zs5801 fruits; K, healthy K fruits; S, diseased 5801 fruits. Each sample set contained three biological replicates.
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Figure 5. Comprehensive analysis of DEGs for screening sclerotiniose-response genes in mulberry. (A) Venn diagram of DEGs in Zs5801 and K; L, healthy Zs5801 fruits; K, healthy K fruits; S, diseased 5801 fruits; primary sclerotiniose-response gene cluster 1(PSRG1) is indicated by a red box. (B) GO and KEGG enrichment analyses of DEGs in PSRG1. (C) Venn diagram of DEGs from three independent RNA-seq datasets (This study, Dai et al. (2019) [20] and Lv et al. (2020) [10]); primary sclerotiniose-response gene cluster 2 (PSRG2) is indicated by a red box. (D) GO and KEGG enrichment analyses of DEGs in PSRG2. (E) Heatmap of module–trait associations based on the WGCNA. (F) Venn diagram of PSRGs in the turquoise module and common DEGs of three independent RNA-seq datasets.
Figure 5. Comprehensive analysis of DEGs for screening sclerotiniose-response genes in mulberry. (A) Venn diagram of DEGs in Zs5801 and K; L, healthy Zs5801 fruits; K, healthy K fruits; S, diseased 5801 fruits; primary sclerotiniose-response gene cluster 1(PSRG1) is indicated by a red box. (B) GO and KEGG enrichment analyses of DEGs in PSRG1. (C) Venn diagram of DEGs from three independent RNA-seq datasets (This study, Dai et al. (2019) [20] and Lv et al. (2020) [10]); primary sclerotiniose-response gene cluster 2 (PSRG2) is indicated by a red box. (D) GO and KEGG enrichment analyses of DEGs in PSRG2. (E) Heatmap of module–trait associations based on the WGCNA. (F) Venn diagram of PSRGs in the turquoise module and common DEGs of three independent RNA-seq datasets.
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Figure 6. Functional characterization of selected SRGs in tobacco and mulberry. (A) Expression levels of selected SRGs in healthy fruits (L) and diseased fruits (S) of Zs5801 and healthy fruits (K) of K by qRT-PCR. (B) Overexpression lines determined based on qRT-PCR of the target genes. (C) Representative replicates of cell death symptoms in inoculated tobacco leaves; CK, control tobacco infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids; the locations for Ciboria shiraiana inoculation are shown as a sketch map on the left. (D) Knock-down of mulberry lines determined based on qRT-PCR of the target genes. (E) Representative replicates of cell death symptoms in inoculated mulberry leaves; CK, control mulberry infiltrated with A. tumefaciens GV3101 containing the empty vector pTRV2 and pTRV1; VIGS, knocked-down mulberry with A. tumefaciens GV3101 containing recombinant plasmids and pTRV1. More cell death symptoms in at least three biological replicates are shown in Figures S2–S5.
Figure 6. Functional characterization of selected SRGs in tobacco and mulberry. (A) Expression levels of selected SRGs in healthy fruits (L) and diseased fruits (S) of Zs5801 and healthy fruits (K) of K by qRT-PCR. (B) Overexpression lines determined based on qRT-PCR of the target genes. (C) Representative replicates of cell death symptoms in inoculated tobacco leaves; CK, control tobacco infiltrated with Agrobacterium tumefaciens GV3101 containing an empty vector; OE, tobacco overexpressing A. tumefaciens GV3101 containing recombinant plasmids; the locations for Ciboria shiraiana inoculation are shown as a sketch map on the left. (D) Knock-down of mulberry lines determined based on qRT-PCR of the target genes. (E) Representative replicates of cell death symptoms in inoculated mulberry leaves; CK, control mulberry infiltrated with A. tumefaciens GV3101 containing the empty vector pTRV2 and pTRV1; VIGS, knocked-down mulberry with A. tumefaciens GV3101 containing recombinant plasmids and pTRV1. More cell death symptoms in at least three biological replicates are shown in Figures S2–S5.
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Table 1. Classes of annotated SSPs in mulberry.
Table 1. Classes of annotated SSPs in mulberry.
SSP Family NameDescriptionClassMode of ActionNumber of Genes in M. alba
2SA2S AlbuminCys-richAntimicrobial0
401p11401p11sORFSignal0
908p11908p11sORFSignal0
ARACINARACINCys-richAntimicrobial10
BBPIBowman–Birk Peptidase InhibitorCys-richPeptidase inhibitor0
BRAZZEINBRAZZEINCys-richAntimicrobial0
CAPECAP-derived PeptideFunctional PrecursorSignal42
CEPC-terminally Encoded PeptidePTMSignal31
CIFCasparian Strip Integrity FactorPTMSignal0
CLEClavata/Embryo Surrounding RegionPTMSignal137
CTLACytotoxic T-lymphocyte antigen-2 alphanon-Cys, non-PTMPeptidase inhibitor0
CYCCYCLOTIDE (CYC)Cys-richAntimicrobial; oeptidase inhibitor1
EALEgg Appartus-Like (EAL)Cys-richSignal0
ECEgg Cell 1-LikeCys-richSignal19
ECAEgg Cell Appartus 50
ENOD40Early Nodulin 40sORFSignal0
EPFLEpidermal Patterning Factor-LikeCys-richSignal39
ESEmbryo Sac (ES)Cys-richSignal; antimicrobial2
ESFEmbryo Surrounding Factor (ESF)Cys-richSignal5
GASAGibberellic Acid Stimulated in ArabidopsisCys-richSignal68
GLVGolven/Root Growth FactorPTMSignal32
GmPEPPlant Elicitor Peptidesnon-Cys, non-PTMSignal2
HAIRPININalfa-HAIRPININ (HAIRPININ)Cys-richAntimicrobial; peptidase inhibitor3
HEVEINHEVEINCys-richAntimicrobial7
HypSysHydroxyproline-Rich Systemin (HypSys)PTMAntimicrobial; Signal2
Ib-AMPIb-AMPCys-richAntimicrobial2
IDAInflorescence Deficient in AbscissionPTMSignal43
IMAIron Man (IMA) 0
KazKazal family inhibitorsCys-richPeptidase inhibitor0
KNOTTIN/LURE/PDFKNOTTIN/LURE/Plant DefensinCys-richAntimicrobial; peptidase inhibitor31
KODKiss of Death (KOD)sORFSignal0
KunitzKunitz-P trypsin inhibitorCys-richPeptidase inhibitor0
LAT52-POELAT52/Pollen Ole e 1 AllergenCys-richSignal14
LCRLow-molecular weight Cys-richCys-richUnknown40
LeginLeginsulinCys-richSignal0
LPLEED..PEEDnon-Cys, non-PTMUnknown0
MBP-1Antimicrobial Peptide MBP-1 (MBP-1)Cys-richAntimicrobial0
MEGMaternally Expressed GeneCys-richSignal0
MTIMustard Trypsin Inhibitor (MTI)Cys-richPeptidase inhibitor0
MtPEPPlant Elicitor Peptidesnon-Cys, non-PTMSignal0
MtSUBPEPSubtilisin-embedded Plant Elicitor PeptideFunctional PrecursorSignal2
N26Nodulin26Cys-richSignal0
NCR-ANodule-specific Cysteine Rich Group ACys-richUnknown0
NCR-BNodule-specific Cysteine Rich Group BCys-richUnknown0
NodGRPNodule-specific Glycine-rich Proteinnon-Cys, non-PTMUnknown0
nsLTPNon-specific Lipid Transfer ProteinCys-richSignal47
OSIP108Oxidative Stress-Induced Peptide 108 (OSIP108)sORFAntimicrobial0
PCP-APollen Coat Protein Group A (PCP-A)Cys-richSignal0
PCP-BPollen Coat Protein Group B (PCP-B)Cys-richSignal0
PCYPlantcyanin/ChemocyaninCys-richSignal0
PDLPlant Defensin-likeCys-richAntimicrobial0
PDPPawS-derived Peptide (PDP)Functional Precursor; cyclotidePeptidase inhibitor17
PhyCysPhytocystatinnon-Cys, non-PTMPeptidase inhibitor0
PIPPAMP-induced Secreted PeptidePTMSignal27
PLSPolaris (PLS)sORFSignal0
PNPPlant Natriuretic Peptidenon-Cys, non-PTMSignal14
PRP485Pro-rich Protein Group 485 (PRP485)non-Cys, non-PTM-0
PRP669Pro-rich Protein Group 669non-Cys, non-PTMUnknown0
PSKPhytosulfokinePTMSignal19
PSYPlant Peptide Containing Sulfated TyrosinePTMSignal47
RALFRapid Alkalinization FactorCys-richSignal106
RCRoot CapCys-richSignal0
RTFL/DVLRotundifolia/DevilsORFSignal16
SCRS-locus Cysteine Rich (SCR)Cys-richSignal0
SCRLS-Locus Cysteine Rich-LikeCys-richSignal21
STIG-GRIStigma1/GRICys-richSignal46
SubInSubtilisin inhibitornon-Cys, non-PTMPeptidase inhibitor0
SYSSystemin (SYS)non-Cys, non-PTMSignal2
T2SPIPotato type II proteinase inhibitorCys-richPeptidase inhibitor0
TAXTaximinCys-richSignal16
THIONINTHIONINCys-richAntimicrobial13
THLThionin-likeCys-richAntimicrobial0
TPDTapetum Determinant 1Cys-richSignal16
TPDLTapetum Determinant 1-likeCys-richSignal8
Unkown 91
Total 1088
Four new annotated classes of SSPs in mulberry were highlighted with blue shadows.
Table 2. Expression matrix and functional annotation of SRGs.
Table 2. Expression matrix and functional annotation of SRGs.
Dai et al. (2019) [20]Lv et al. (2020) [10]This Study
Functional AnnotationGene IDsModulesHomolog in ArabidopsisLog2FC (S1/CK)Log2FC (S2/CK)Log2FC (S1/CK)Log2FC (S2/CK)Log2FC (S3/CK)Log2FC (S/CK)Log2FC (K/CK)
ABAM.alba_G0017378TurquoiseAT4G38970−3.306−5.4950.324−1.662−1.489−3.0791.398
ABAM.alba_G0004617TurquoiseAT5G59320−1.814−8.4670.561−2.507−1.365−4.499−0.515
ABAM.alba_G0006018TurquoiseAT4G37050−2.575−12.3850.347−2.121−1.131−4.498−0.088
JAM.alba_G0015820TurquoiseAT3G45140−3.296−8.9820.933−2.025−2.141−5.3360.032
JAM.alba_G0014496TurquoiseAT1G55020−2.847−5.8380.375−2.636−2.125−6.4630.686
JAM.alba_G0017173TurquoiseAT5G42650−2.820−5.6590.508−1.886−2.935−5.3990.472
JA-FUNGUSM.alba_G0010603TurquoiseNone−2.469−9.3190.220−1.745−2.432−5.0011.042
PhotosynthesisM.alba_G0003694TurquoiseAT2G39730−3.271−2.9430.029−1.224−1.873−2.4500.617
PhotosynthesisM.alba_G0007619TurquoiseAT5G38410−2.911−3.3270.179−1.514−1.668−2.2330.846
PhotosynthesisM.alba_G0004494TurquoiseAT3G08940−2.900−2.8190.419−2.267−2.684−1.8071.625
PhotosynthesisM.alba_G0001233TurquoiseAT3G26650−2.883−3.6900.071−1.642−1.433−2.7971.290
PhotosynthesisM.alba_G0008333TurquoiseAT5G35630−2.804−3.7830.106−1.898−1.601−2.2781.550
PhotosynthesisM.alba_G0010904TurquoiseAT2G34430−2.318−2.9640.087−2.371−1.955−2.2432.160
PhotosynthesisM.alba_G0002406TurquoiseAT1G06680−2.291−1.9280.026−1.359−1.240−1.6281.423
PhotosynthesisM.alba_G0006044TurquoiseAT5G66570−2.035−1.9800.172−1.126−1.330−1.5071.007
Fruit developmentM.alba_G0020219TurquoiseAT1G02065−1.832−5.1350.333−1.289−1.124−3.1210.141
Fruit developmentM.alba_G0012049GreyAT1G21460−1.268−2.0160.984−0.653−0.268−1.028−0.696
Cell wallM.alba_G0005265TurquoiseAT1G63910−4.453−2.8462.7690.157−1.335−3.923−0.683
Cell wallM.alba_G0001161TurquoiseAT2G44480−2.172−8.6271.059−2.144−2.460−3.6360.387
alpha/beta-HydrolasesM.alba_G0013391TurquoiseAT1G56630−1.101−6.6930.333−2.408−1.406−4.6680.187
EthyleneM.alba_G0011799TurquoiseAT1G050102.3492.901−1.468−0.271−0.0562.122−0.715
EthyleneM.alba_G0013301TurquoiseAT5G251904.2953.8060.0302.3122.3593.4090.816
IAAM.alba_G0003453TurquoiseAT3G108701.8851.9080.2422.0362.6671.5531.236
Nutritional immunityM.alba_G0002433TurquoiseAT1G053001.4850.452−1.0521.7431.8141.272−0.278
Fruit developmentM.alba_G0009820TurquoiseAT3G526003.6416.0361.2875.1024.2324.238−0.898
AntifungalM.alba_G0011399TurquoiseAT2G197602.2212.457−0.0390.6061.5583.506−0.265
Cell wallM.alba_G0016996TurquoiseAT4G023303.4162.584−1.0272.3123.0362.5870.245
Cell wallM.alba_G0013029TurquoiseAT1G201903.5943.4310.3972.1462.4593.448−1.805
alpha/beta-HydrolasesM.alba_G0008183TurquoiseAT4G159602.2112.428−0.1133.4103.4312.978−2.447
M.alba_G0013851TurquoiseNone0.8541.038−0.1651.4872.1131.960−0.546
M.alba_G0005835TurquoiseNone1.3922.630−0.3121.2811.6892.995−0.696
M.alba_G0012361TurquoiseAT1G721601.3961.530−0.0681.2141.4541.561−0.863
M.alba_G0013424TurquoiseAT3G161501.8172.490−1.2461.5581.6512.0960.451
M.alba_G0011456TurquoiseAT1G730101.8531.995−0.4990.9821.5904.0460.827
M.alba_G0008070TurquoiseNone1.8752.9730.3771.3560.8331.956−0.378
ABAM.alba_G0012076TurquoiseAT3G119452.6012.780−0.6892.4621.5592.157−0.567
M.alba_G0009086GreyAT3G099252.8100.387−0.956−1.125−1.529−3.0170.072
M.alba_G0012741TurquoiseAT1G099103.6664.4930.3022.3822.8712.530−0.888
M.alba_G0000303TurquoiseAT5G547604.5454.793−1.0401.9433.1182.965−1.757
The sclerotiniose-response genes (SRGs) are marked as turquoise modules. Colors indicated the up or down-regulation: Red, upregulated genes, blue, down-regulated genes.
Table 3. Expression matrix and annotation of SSPs.
Table 3. Expression matrix and annotation of SSPs.
Dai et al. (2019) [20]Lv et al. (2020) [10]This Study
SymbolsGene IDsModulesClassLog2FC (S1/CK)Log2FC (S2/CK)Log2FC (S1/CK)Log2FC (S2/CK)Log2FC (S3/CK)Log2FC (S/CK)Log2FC (K/CK)Log2FC (K/S)
SSP1maker-Chr12-exonerate_protein2genome-gene-102.32TurquoiseCLE−0.727−11.867−0.027−3.690−4.089−6.6230.6107.234
SSP2maker-Chr5-est_gff_StringTie-gene-35.3TurquoiseGASA−0.545−11.311−1.898−4.865−4.746−6.5020.9527.454
SSP3maker-Chr12-est_gff_StringTie-gene-47.1Turquoisens-LTP−2.032−13.7150.594−2.335−1.185−5.147−0.8684.278
SSP4maker-Chr12-est_gff_StringTie-gene-142.0TurquoiseNA−4.798−3.0152.9290.473−1.072−4.295−1.1433.152
SSP5maker-Chr14-exonerate_protein2genome-gene-46.38TurquoiseCLE−3.745−8.2710.076−1.877−2.462−4.0701.1365.206
SSP6maker-Chr12-est_gff_StringTie-gene-122.1TurquoiseNA−0.319−10.777−0.257−1.504−1.659−3.587−0.3813.206
SSP7h26356.03TurquoiseRALF−3.282−10.6640.594−1.390−1.518−2.1840.8783.062
SSP8h60346.02TurquoiseSTIG−2.817−3.409−0.221−1.770−1.476−2.1770.8673.044
SSP9maker-Chr6-est_gff_StringTie-gene-51.4TurquoiseCAPE−1.383−2.6250.461−0.240−0.220−1.6511.2382.889
SSP10maker-Chr5-est_gff_StringTie-gene-4.6GreyPSK−1.576−1.5220.729−0.494−0.244−1.132−1.1060.025
SSP11maker-Chr14-est_gff_StringTie-gene-10.7TurquoiseRALF1.0350.6440.0440.8271.3011.270−1.153−2.423
SSP12maker-Chr13-exonerate_protein2genome-gene-49.4GreyGASA0.875−9.4210.8374.2273.3901.432−1.092−2.523
SSP13maker-Chr8-exonerate_protein2genome-gene-129.4TurquoiseCLE2.9964.732−1.3490.5091.1391.864−1.739−3.602
SSP14maker-Chr8-exonerate_protein2genome-gene-3.26TurquoisePSK1.9813.301−1.966−1.432−1.0201.980−1.355−3.335
SSP15maker-Chr4-est_gff_StringTie-gene-90.0TurquoiseGASA4.0974.695−0.3932.5753.1702.222−0.878−3.099
SSP16h55705.01BlueCAPE2.1232.509−2.2260.399−0.6752.4082.205−0.202
SSP17h7280.01TurquoiseDVL/RTFL1.9900.7690.0450.2021.4162.5350.727−1.808
SSP18maker-Chr4-exonerate_protein2genome-gene-224.17TurquoisePIP−8.7645.3710.2964.2353.7025.5770.754−4.822
SSP19maker-Chr5-est_gff_StringTie-gene-118.1GreyGASA2.2103.8491.0920.536−0.0185.6953.828−1.867
The sclerotiniose-response genes (SRGs) are marked as turquoise models. Colors indicated the up or down-regulation: Red, upregulated genes, blue, down-regulated genes.
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Liu, L.; Guo, Z.; Kang, X.; Li, S.; Huang, S.; Zheng, L.; Fu, R.; Yidilisi, K.; Chao, N. Comparative Transcriptome Analysis of Different Mulberry Varieties to Reveal Candidate Genes and Small Secreted Peptides Involved in the Sclerotiniose Response. Forests 2024, 15, 1126. https://doi.org/10.3390/f15071126

AMA Style

Liu L, Guo Z, Kang X, Li S, Huang S, Zheng L, Fu R, Yidilisi K, Chao N. Comparative Transcriptome Analysis of Different Mulberry Varieties to Reveal Candidate Genes and Small Secreted Peptides Involved in the Sclerotiniose Response. Forests. 2024; 15(7):1126. https://doi.org/10.3390/f15071126

Chicago/Turabian Style

Liu, Li, Zixuan Guo, Xiaoru Kang, Shan Li, Shuai Huang, Longyan Zheng, Rumeng Fu, Keermula Yidilisi, and Nan Chao. 2024. "Comparative Transcriptome Analysis of Different Mulberry Varieties to Reveal Candidate Genes and Small Secreted Peptides Involved in the Sclerotiniose Response" Forests 15, no. 7: 1126. https://doi.org/10.3390/f15071126

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