1. Introduction
Grapevine (Vitis L.) is an economically significant fruit crop that is extensively used to produce wine, grape juice, and table grapes. However, the majority of cultivars derived from V. vinifera are highly susceptible to various pathogens [1]. Among these, grape ripe rot, caused by Colletotrichum viniferum, a notorious disease, causes fruit rot and is one of the most economically significant diseases worldwide, resulting in considerable losses in the quality of grape production [2]. Potentially, it causes losses with severities up to 67% or 37% in the Mid-Atlantic U.S. and northeast China, respectively [3]. This fungus predominantly infects mature fruits [4,5]. Moreover, the impact of Colletotrichum on the chemical properties of fruits and wine is well-documented [6]. The typical symptoms are lesions, which are found on berries, stalks, and leaves in warm and wet conditions [2,7].
Currently, this fungal disease is largely controlled using fungicides in viticulture. However, the use of synthetic fungicides is detrimental to both the environment and human health and may enhance the resistance of diseases to fungicides. Therefore, breeding grapes so that they gain resistance to ripe rot might be an ecologically friendly and efficient preventative method in the current agriculture. The unfavorable impact of fungicides in the prevention of the spread of this disease in ripe-rot-susceptible grape cultivars has increased the interest in breeding ripe-rot-resistant table grapes through interspecific cross breeding between American and European hybrids [8]. Two hundred and thirty-five Vitis and six Muscadinia grapevine cultivars and selections were evaluated for ripe rot resistance, and fifty cultivars or selections were classified as highly resistant [8]. Accordingly, it was recently reported that a grape-ripe-rot-resistance locus (Cgr1) has been identified from V. amurensis [9]. However, the molecular mechanism of grapevines in C. viniferum infection is largely unknown.
As one of the centers of grape production, China is home to a number of native ripe-rot-resistant grape genotypes. V. davidii, for instance, has a relatively high tolerance to rot, making it a desirable source of material in ripe-rot-resistance investigation into classical cross breeding or molecular mechanisms. In the present study, a ripe-rot-tolerant Vitis species, V. davidii, was inoculated with C. viniferum, and then, the transcriptomic analysis of the differentially expressed genes and pathways was carried out, thereby providing ripe-rot-resistant gene resources for the molecular improvement of susceptible cultivars.
2. Materials and Methods
2.1. Plant Materials and Treatments
Well-cultivated V. davidii (accession FuAn) grapevines were planted in XiTa village (27°04′55.61″ N/119°31′29.11″ E), MuYun town, FuAn city, Fujian Province, China. Three biological repeats were conducted. For one biological repeat per treatment, forty-two healthy, ripe grape berries (130 days after anthesis) were plucked from three grape plants (fourteen berries per plant). A C. viniferum isolate was collected and purified from diseased grapevine berries and cultured on potato dextrose agar medium (PDA). Four-millimeter-diameter disks of C. viniferum PDA were removed with a puncher and were then placed on scratched grapevine berries using a needle. Uninfected berries at day zero were used as control samples (CK), an inoculation was made at day zero, and the berries were subsequently analyzed at 1 day, 3 days, and 7 days post inoculation (dpi). All the samples (CK, 1 dpi, 3 dpi, and 7 dpi) were collected for further transcriptome analysis. The infected pericarps from 42 berries in one biological repeat per treatment were mixed as one sample and stored at −80 °C.
2.2. RNA Extraction and Transcriptome Analysis
Three biological replicates of grape berries in each infection stage were used for the transcriptome analysis. Total RNA was extracted using lithium chloride precipitation [10], and the quality of the RNA was determined using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). mRNA was enriched using oligo (dT) magnetic beads and fragmented with endonuclease. Then, fragments were ligated with unique adaptors, which was followed by PCR amplification to generate libraries for RNA-seq. Libraries were sequenced with an Illumina HiseqTM 2000 System by Genedenovo Co., Ltd. (Guangzhou, China). Clean data filtered from raw sequencing data were used for assembly and RPKM value calculation. First, we mapped transcriptome data with Tophat [11] to the reference genome of “Pinot Noir” [12]. The expression value of genes was calculated using the reads per kb per million reads method [13]. We used expressed genes to blastx search against the NCBI Non-redundant Protein database, the SwissProt database, the KEGG database, and the COG/KOG database with an E-value of 0.00001. The functional annotations of the expressed genes were definite, and the gene ontologies of the expressed genes were annotated using Blast 2 GO [14].
2.3. Functional Annotation of Differentially Expressed Genes (DEGs)
EdgeR was used to identify DEGs with raw count data [15]. Raw p-values were tested with the false-discovery rate (FDR) [16,17]. Genes with fold change >2.0 and FDR < 0.05 were considered to be DEGs. Gene ontology (GO) enrichment was conducted with web-based agriGO [18], and corrected p-values < 0.05 were regarded as significant GO terms. DEGs were mapped to the KEGG database [19] to identify significant KEGG pathways. In addition, a Q-value was used to multi-test the p-value, and a Q-value < 0.05 was set as the threshold for a significant KEGG pathway.
2.4. Co-Expression Analysis
The WGCNA package (Los Angeles, CA, USA) was used for the weighted correlation network analysis of the identified genes [20]. Genes were clustered into different blocks based on their correlation coefficient. Then, we filtered genes that belonged to specific categories using a self-written Perl script. Finally, the co-expression network was constructed using Cytoscape v3.5.1 (Seattle, WA, USA) [21].
2.5. Quantitative RT-PCR (qRT-PCR) Analysis
Total RNA was extracted from samples in the same way that it was extracted for RNA-seq. RNA samples (2 μg) were treated with RNase-free DNase I to remove residual genomic DNA, and then, they were used for cDNA synthesis with a PrimeScript™ RT reagent Kit with a gDNA Eraser (TaKaRa). Reaction mixtures were diluted 1:40 with distilled water and used as templates for qRT-PCR. Information regarding primers is listed in Supplementary Table S1. Quantitative assays were performed in a Bio-Rad iQ5 system (Bio-Rad, Hercules, CA, USA) in triplicate on each cDNA sample, and the transcript levels were calculated relative to the level of grapevine GAPDH (CB973647) using the 2−ΔΔCt method [22]. All data are presented as mean ± SD (n = 3).
3. Results
3.1. Statistics of Infected Berries Inoculated with Colletotrichum Viniferum
Healthy, ripe grape berries (susceptible: V. vinifera × V. labrusca, cv. Kyoho; resistant: V. davidii accession FuAn) were washed with sterile water several times before microbial infection was carried out. Each berry was scratched with a needle, and sclerotium was then applied to the wound. The rates of infection were examined at 1 dpi, 3 dpi, and 7 dpi. At 1 dpi, there were no lesions at the wound site; however, at 3 dpi, there were slight lesions at the wound site; and at 7 dpi, the lesions were larger than those at 3 dpi (Figure 1 and Figure S1). We chose V. davidii samples (CK, 1 dpi, 3 dpi, and 7 dpi) for transcriptome analysis in an effort to understand the dynamic changes from the gene response to the disease-resistant gene expression.
3.2. Transcriptome Sequencing and Data Statistics
RNA-seq was performed on an Illumina Hiseq2000, and each sample had at least 20 million filtered pair-end reads (Table 1). We mapped transcriptome data to the genome reference [12]. The total mapped reads, uniquely mapped reads, and mapping ratio were examined (Table 1). The max and min total mapped reads were 23,577,067 and 17,292,774, respectively. Moreover, the maximum and minimum uniquely mapped reads were 21,660,483 and 15,925,781, respectively. The lowest mapping ratio was 75.81% (infected samples at 7 dpi), while the highest mapping ratio was 87.13% (infected samples at 3 dpi).
3.3. DEGs of V. davidii after Infection
To understand the dynamic change in the gene expression profiles after infection, the FPKM value of each gene was calculated (Supplementary Table S2), and DEGs of V. davidii were identified by comparing infected samples at different time points to control samples (Supplementary Tables S3–S5). For the V. davidii, there were 1315 up-regulated genes and 495 down-regulated genes (Figure 2). In detail, there were 10, 17 and 1288 up-regulated genes and 2, 5, and 488 down-regulated genes at 1 dpi, 3 dpi, and 7 dpi (Figure 2B). In general, these genes could be clustered into two groups. One group represented genes that were up-regulated at the 7-dpi stage, while the other group represented genes that were down-regulated at the 7-dpi stage (Supplementary Table S5).
3.4. GO Functional Analysis of the DEGs
We conducted a GO functional analysis of up-regulated and down-regulated DEGs at the 7-dpi stage. They were clustered into three categories, namely “biological process”, “molecular function”, and “cellular component”. In addition, we conducted a GO enrichment analysis of up-regulated and down-regulated genes at the 7-dpi stage (Supplementary Table S6). The enriched GO categories of up-regulated genes are shown in Figure 3A. In detail, there were 28 enriched GO categories of up-regulated genes. MAPK cascade, calcium ion binding, and protein serine/threonine kinase activity were enriched (Figure 3A and Figure S2). In terms of the down-regulated genes, there were 14 enriched GO categories. From these GO terms, disaccharide metabolic process, motor activity, and protein kinase activity were enriched, and these gene ontologies were mostly engaged in carbohydrate metabolisms and cytoskeleton structures (Figure 3B and Figure S3).
3.5. KEGG Pathway Enrichment Analysis of the DEGs
To explore the linked pathways of DEGs at the 7-dpi stage, we enriched the KEGG pathway of the DEGs to understand the potential pathways involved in C. viniferum infection (Supplementary Table S7). The top 20 enriched pathways are shown in Figure 4, in which the top five most significant enriched pathways were stilbenoid, diarylheptanoid, and gingerol biosynthesis; fatty acid elongation in mitochondria; fructose and mannose metabolism; phenylpropanoid biosynthesis; and phenylalanine metabolism (Figure 4).
3.6. DEGs Involved in Primary Metabolism
Significant effects of biotic and abiotic stress on the primary metabolism were observed [23,24,25]. In the present study, the starch and sucrose metabolisms as well as the fatty acid metabolism were altered in C. viniferum-infected grapes (Supplementary Table S8). The activity of beta-glucosidase (VIT_01s0011g00760), acid beta-fructofuranosidase (VIT_02s0154g00090), and sucrose-phosphate synthase (VIT_18s0089g00410) was down-regulated (Figure 5A), suggesting that the conversion of both polysaccharide and sucrose into glucose was inhibited. However, the activity of pectin esterase (VIT_13s0047g00230 and VIT_15s0048g00510) was enhanced, while the relationship between pectin esterase and pathogen resistance remained complex [26]. All the DEGs (VIT_04s0044g01110, VIT_16s0039g00320, VIT_18s0001g00380, and VIT_18s0001g15410) involved in fatty acid metabolism were up-regulated (Figure 5A), and the DEGs belonged to the alcohol dehydrogenase gene family.
3.7. DEGs Involved in Signal Transduction
In the plant–pathogen interaction, signal transduction plays a crucial role [27]. We detected dynamic changes in genes associated with plant hormone signal transduction, the MAPK cascade, and calcium-mediated signaling (Figure 5B). In plant hormone signal transduction, BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 (VIT_12s0055g01160) and ethylene receptor (VIT_05s0049g00090 and VIT_06s0004g05240) were up-regulated, while auxin-induced genes (VIT_04s0023g03230, VIT_15s0048g02860 and VIT_13s0067g00330) were inhibited (Figure 5B and Supplementary Table S8). In the DEGs of the MAPK cascade, the up-regulated genes were mitogen-activated protein kinase 7 (VIT_04s0023g02420), LRR receptor-like serine/threonine-protein kinase (VIT_17s0000g06710), and receptor-like protein kinase 5 (VIT_11s0016g03080), whereas the down-regulated genes were receptor-like cytosolic serine/threonine-protein kinase RBK1 (VIT_00s0768g00040) and cysteine-rich receptor-like protein kinase 2 (VIT_04s0079g00020) (Figure 5B and Supplementary Table S8). CBL-interacting protein kinase 08 (VIT_08s0058g01090) was the only DEG in the calcium-mediated signaling pathway (Figure 5B and Supplementary Table S8).
3.8. DEGs Involved in Pathogen-Resistance Genes
We identified four clusters of pathogen-resistance genes, including plant–pathogen interaction, response to biotic stimulus, peroxisome, and cytochrome P450 (Figure 5C, Figure 6 and Supplementary Table S8). In terms of plant–pathogen interaction, several calcium-binding proteins were observed, namely VIT_04s0023g01100, VIT_08s0007g05790, VIT_14s0006g01400, and VIT_18s0122g00180. Additionally, in the terms of response to biotic stimulus, we identified five Mildew Locus O (MLO)-like proteins (VIT_13s0019g04060, VIT_07s0031g02240, VIT_10s0003g00410, VIT_08s0040g02180, and VIT_08s0040g02170) and four pathogenesis-related proteins (VIT_05s0077g01560, VIT_05s0077g01670, VIT_05s0077g01690, and VIT_05s0077g01550). The DEGs (VIT_00s0698g00010 and VIT_04s0044g00020) that belonged to peroxisome were down-regulated during C. viniferum infection, while for VIT_19s0093g00510, it was first up-regulated at 3 dpi and then decreased at 7 dpi. In addition, two cytochrome P450 enzymes (VIT_11s0065g00350 and VIT_11s0078g00290) were up-regulated upon 7 dpi with C. viniferum (Figure 5C, Figure 6 and Supplementary Table S8).
3.9. DEGs Involved in Phenylalanine Metabolism Pathway
As three out of five enriched pathways were related with the phenylalanine metabolism pathway, we filtered DEGs in this pathway, and genes encoding three key enzymes were identified, as shown in Figure 6. The expression profiles of phenylalanine ammonia-lyase genes, stilbene synthase genes, and the UDP-glucose: flavonoid 3-O-glucosyltransferase gene were significantly up-regulated at 7 dpi, while in the early infection stages, little change was observed (Figure 6 and Supplementary Table S8). Nine phenylalanine ammonia-lyase genes were up-regulated, and the number of genes in the phenylalanine ammonia-lyase gene family was thirteen in V. vinifera. In addition, we observed that the synthesis of nine stilbene synthase genes, which catalyzed the resveratrol, a phytoalexin, was up-regulated. Interestingly, anthocyanin accumulation was also positively regulated as the UDP-glucose: flavonoid 3-O-glucosyltransferase gene (VIT_12s0034g00030) was significantly up-regulated (Figure 6).
3.10. Co-Expression Analysis of Differentially Expressed Transcription Factors and Target Functional Genes
After annotating the DEGs, we identified numerous transcription factors that were differentially expressed in C. viniferum-infected grapes. The most important transcription factor genes were WRKY transcription factors. Fourteen WRKY TFs were involved in the infection process. Additionally, the expression patterns of 10 ERF TFs were prominently changed. Moreover, four MYB TFs were found to be up-regulated upon pathogen infection (Supplementary Table S8). To generate an overview of the regulatory network of genes resistant to C. viniferum, the correlation coefficients of all the genes without missing values in this study were calculated using WGCNA (Figure S4), and we obtained the node-to-node interaction relationships of transcript factor genes with their target genes (Figure 7 and Supplementary Table S9). Several WRKY and ERF transcription factors were centered in this co-expression network, surrounded by primary-metabolism-, signal-transduction-, pathogen-resistance-, and phenylalanine-metabolism-related genes (Figure 7).
3.11. Validation of Differentially Expressed Genes
To verify the transcriptomic results, 10 genes, including LRR-RLK (VIT_17s0000g06710), MYB4 (VIT_05s0049g01020), WRKY22 (VIT_15s0046g02190), WRKY31 (VIT_19s0090g00840), WRKY33 (VIT_06s0004g07500), ERF4 (VIT_12s0028g03270), ERF5 (VIT_16s0013g01120), ERF14 (VIT_18s0001g03240), ERF23 (VIT_18s0089g01030), and ERF113 (VIT_01s0150g00120), which were differentially expressed, were analyzed using qRT-PCR. The transcriptome and qRT-PCR results are compared in Figure 8. Most of the selected genes analyzed using qRT-PCR showed an expression profile consistent with the transcriptomic data. Two genes, MYB4 and ERF113, showed inconsistent regulation between the qRT-PCR and transcriptome results (Figure 8). This discrepancy may be attributed to the different sensitivities of the qRT-PCR and transcriptome analytical methods.
4. Discussion
Currently, it has been accepted that 190 Colletotrichum species infect various plants worldwide [28,29]. At least 10 Colletotrichum species have been identified in grape [5,30,31,32,33,34]. In our early study, we identified three pathogens of grape ripe rot in southern China based on multi-gene data and morphology, among which C. viniferum was mainly responsible for cases of ripe rot in Fujian Province and Guizhou Province [35]. Pathogenicity tests revealed that the typical symptoms of ripe rot in V. davidii initially developed 3 days after C. viniferum infection, and then, the spots grew larger and were surrounded by narrow, reddish-brown to black margins at 7 dpi (Figure 1), which was also observed in the susceptible V. vinifera cv. Kyoho [35]. This result revealed that C. viniferum was able to infect the resistant cultivar V. davidii.
To determine the resistance mechanism in response to C. viniferum infection in the resistant cultivar, all DEGs at 1 dpi, 3 dpi, and 7 dpi were assessed. Compared with the control, 12 DEGs were identified at 1 dpi during the early phase of infection (Figure 2B). This phenomenon persisted for 3 days, with 22 DEGs being identified. These results were in accordance with the observations that only three transcripts were responsible for powdery mildew fungus infection in Erysiphe necator-resistant V. aestivalis “Norton” [36]. However, 1776 DEGs were involved in the response to C. viniferum infection at the 7-dpi stage. Diverse defense-related genes and functional pathways have been enriched in response to Colletotrichum infection in grape, lentil, bean, tea and chili pepper [37,38,39,40,41]. Based on our GO analysis, these DEGs were enriched in 42 (28 up-regulated genes/14 down-regulated genes) terms, including 5 (2 up-regulated genes/3 down-regulated) biological process, 2 (2 up-regulated genes/0 down-regulated) cellular components, and 35 (24 up-regulated genes/11 down-regulated) molecular functions (Figure 3). In total, 28 up-regulated GO categories participated in signal transduction, and 14 down-regulated GO categories were repressed in catalytic activity. Furthermore, these DEGs were enriched in 20 functional pathways, including stilbenoid, diarylheptanoid, and gingerol biosynthesis; plant–pathogen interaction; and plant hormone signal transduction, which were involved in the defense against C. viniferum infection. The overall changes in C. viniferum-induced gene expression were consistent with the phenotype in berries, indicating that defense-oriented transcriptional modifications are triggered late in the infection process in resistant grapes.
It was revealed that the carbohydrate and fatty acid metabolisms are important in disease resistance [41,42,43]. Four out of sixteen DEGs involved in the starch and sucrose metabolisms were up-regulated in order to prevent the degradation of pectin, hence enhancing plant disease resistance [44] (Figure 5). As signaling molecules, an increase in the levels of these sugars may contribute to plant immunity [42,45]. After invading plant cells, pathogenic fungi are able to exploit the plant’s fatty acid production system to enhance infection [43]. Interestingly, four DEGs implicated in fatty acid biosynthesis initiation were increased in response to Colletotrichum infection (Figure 5). However, the role of fatty acids in the grape–Colletotrichum interaction is still unknown. Secondary metabolites derived from phenylpropanoids are used in antimicrobial compounds to improve plant disease resistance [46,47,48]. In this study, the expression levels of nine phenylalanine ammonia-lyase (PAL) and two trans-cinnamate 4-monooxygenase (Cytochrome P450 family) genes were up-regulated to participate in phenylalanine metabolism and phenylpropanoid biosynthesis (Figure 6). Nine stilbene synthase (STS) genes were significantly increased in response to C. viniferum infection at 7 dpi. These findings revealed that the stilbene biosynthetic pathway was implicated in the defense against ripe rot in the resistant cultivar, which was consistent with the rapid accumulation of stilbenes in response to downy mildew in grapevines [49,50].
MAPK cascades and Ca2+ signaling pathways are meditated by the R gene, which activates the expression of PCD-related genes to initiate defense against C. fructicola in tea plants [41]. In our study, one receptor-like protein kinase 5, one LRR receptor-like serine/threonine-protein kinase, one mitogen-activated protein kinase 7 (MAPK7), and one CBL-interacting protein kinase 08 were induced by C. viniferum (Figure 5B). MAPK cascades and Ca2+ signaling may also be the most important defensive signaling pathways in grapes regarding the activation of the downstream defense response against pathogen invasion [51,52]. Interestingly, DEGs implicated in biotic stimulus and plant–pathogen interaction were enriched (Figure 5C). Two DEGs encoding PR10.3 and one encoding PR10.8 were up-regulated by more than ten-fold relative to the control. These PR proteins may have a role in plant defense against Colletotrichum infection. Moreover, plant hormones may play a role in plant defense against ripe rot (Figure 5B). One gene encoding ethylene receptor 2 protein was up-regulated by more than 2-fold, indicating that ethylene signaling was involved in the regulation of both berry maturation and plant defensive responses [52].
The analysis of the expression of the ripe-rot-resistance-related DEGs and transcription factor genes (TFs) may shed light on how plants recruit “guards” to activate defensive responses. The construction of a co-expression network between the selected DEGs revealed considerable positive connections between transcription factors and genes involved in primary and secondary metabolites, signal transduction, biotic stimulation, and plant–pathogen interaction (Figure 7). Three types of TFs (including eight WRKYs, five ERFs, and two MYBs) were identified as co-expressing within and between other DEGs. For example, WRKY65 shared strong positive correlations with ERF98, MYB24, WRKY17, WRKY40, and WRKY69, and these TFs were also co-expressed with five PALs, nine STSs, and two PR proteins. Consistent with the reports by Wong and Matus [53] and Vannozzi et al. [54], multi-TFs may regulate these genes involved in ripe rot resistance. Our data are novel in suggesting the regulatory roles of multi-TF families in phytoalexin synthesis and PR protein expression to enhance resistance to grape ripe rot. More research is needed in the future to clarify these functions.
5. Conclusions
Grapes have evolved a sophisticated defensive system to combat numerous pathogens. Colletotrichum appressoria was unable to penetrate the cells of berries of the resistant material V. davidii during the early stage of infection. However, late in the infection process, appressoria invaded plant cells. Several biological processes were drastically altered in berries infected with Colletotrichum. Colletotrichum produced a variety of virulence factors and triggered signaling pathways, such as MAPK cascades and Ca2+ signaling. The activated WRKY-, ERF-, and MYB-type TFs directly regulated the expression of genes involved in primary and secondary metabolites, biotic stimulus, and plant–pathogen interaction. Subsequently, the production of stilbene phytoalexin and the expression of PR protein enhanced the resistance to grape ripe rot (Figure 9). The precise and efficient defensive responses inhibited the growth of grape ripe rot.
Q.C. conceived the research; Y.L. and X.Y. performed the study and analyzed the data; T.C., Y.Y., X.L. and X.T. helped prepare the plant and pathogen materials; Y.L. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are available in the article and
The authors declare no conflict of interest.
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Enlarge this image.Figure 1. Symptom development in Vitis davidii Föex berries upon pathogen attack. (A) Uninfected berries at day zero were used as control samples (CK), an inoculation was made at day zero, and the berries were subsequently analyzed at 1 day, 3 days, and 7 days post inoculation (dpi). (B) Typical symptoms of berries. Arrows represent inoculation sites.
Enlarge this image.Figure 2. DEGs of different incubation time points of Vitis davidii Föex berries. (A) Venn diagram analysis of gene expression at different incubation time points. (B) Number of different gene expressions at different incubation time points. Orange bar represents up-regulated genes; blue bar represents down-regulated genes.
Enlarge this image.Figure 3. GO enrichment of DEGs of Vitis davidii Föex berries with Colletotrichum viniferum infection at 7 dpi. (A) GO enrichment of up-regulated genes; (B) GO enrichment of down-regulated genes.
Enlarge this image.Figure 4. KEGG pathways of differentially expressed genes of Vitis davidi Föex berries infected with Colletotrichum viniferum at 7 dpi.
Enlarge this image.Figure 5. Differentially expressed genes in different pathways. (A) Expression profiles of primary metabolism genes in Vitis davidii Föex berries infected with Colletotrichum viniferum. (B) Expression profiles of signal transduction genes in Vitis davidii Föex berries infected with Colletotrichum viniferum. (C) Expression profiles of pathogen-resistance genes in Vitis davidii Föex berries infected with Colletotrichum viniferum. Heat maps of genes were constructed with transcriptome quantitative data. The genes that increased and decreased are displayed in red and blue, respectively.
Enlarge this image.Figure 6. Differentially expressed genes in phenylalanine metabolism pathway that promote stilbene and anthocyanin accumulation in Vitis davidii Föex berries incubated with Colletotrichum viniferum. PAL, phenylalanine ammonia-lyase; STS, stilbene synthase; UFGT, UDP-glucose: flavonoid 3-O-glucosyltransferase. Heat maps of genes were constructed with transcriptome quantitative data. The genes that increased and decreased are displayed in red and blue, respectively.
Enlarge this image.Figure 7. Co-expression network of transcript factor genes with their target genes in Vitis davidii Föex berries incubated with Colletotrichum viniferum.
Enlarge this image.Figure 8. Validation and expression analysis of 10 genes in response to Colletotrichum viniferum. Gene expression analysis was carried out with qRT-PCR using cDNA from berries of Vitis davidii Föex after Colletotrichum viniferum inoculation for different lengths of time. Color panels above the bars symbolize log2-transformed mean expression values obtained in the RNA-seq (see reference color bar). All data are shown as the mean ± SD (n = 3).
Enlarge this image.Figure 9. Model of the Colletotrichum viniferum recognition signal transduction pathway.
Statistics of RNA-seq data of Vitis davidii Föex berries incubated with Colletotrichum viniferum.
| Sample | Clean Data (bp) | Q30 (%) | All Reads Num | Unique Mapped Reads | Mapping Ratio |
|---|---|---|---|---|---|
| CK-1 | 4,018,242,900 | 94.71% | 26,705,700 | 21,281,489 | 86.92% |
| CK-2 | 3,243,124,800 | 92.76% | 21,536,268 | 16,703,124 | 84.22% |
| CK-3 | 3,717,704,100 | 92.20% | 24,716,638 | 19,629,622 | 85.51% |
| 1 dpi-1 | 4,110,842,400 | 92.49% | 27,270,040 | 21,660,483 | 86.46% |
| 1 dpi-2 | 3,827,514,000 | 92.69% | 25,395,464 | 19,739,443 | 84.27% |
| 1 dpi-3 | 3,105,880,500 | 92.77% | 20,600,182 | 15,925,781 | 83.94% |
| 3 dpi-1 | 3,842,454,600 | 94.52% | 25,584,844 | 20,578,578 | 87.13% |
| 3 dpi-2 | 3,693,470,400 | 92.63% | 24,576,840 | 19,248,684 | 85.01% |
| 3 dpi-3 | 4,030,943,700 | 92.71% | 26,802,666 | 21,077,493 | 85.21% |
| 7 dpi-1 | 3,517,864,800 | 92.85% | 23,405,352 | 17,628,147 | 82.03% |
| 7 dpi-2 | 3,743,716,200 | 92.99% | 24,914,150 | 18,468,163 | 80.75% |
| 7 dpi-3 | 4,558,513,200 | 94.94% | 30,292,856 | 21,269,288 | 75.81% |
Note: Uninfected berries at day zero were used as control samples (CK).
Supplementary Materials
The following supporting information can be downloaded at:
References
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1 Fruit Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350013, China; College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2 College of Horticulture, Northwest A&F University, Yangling 712100, China
3 Fruit Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350013, China
4 College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China