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Evaluation of aphid resistance on different rose cultivars and transcriptome analysis in response to aphid infestation

BMC Genomics volume 25, Article number: 232 (2024) Cite this article

Abstract

Background

The rose is one of the most important ornamental flowers in the world for its aesthetic beauty but can be attacked by many pests such as aphids. Aphid infestation causes tremendous damage on plant tissues leading to harmed petals and leaves. Rose cultivars express different levels of resistance to aphid infestation yet the information remains unclear. Not only that, studies about the transcriptional analysis on defending mechanisms against aphids in rose are limited so far.

Results

In this study, the aphid resistance of 20 rose cultivars was evaluated, and they could be sorted into six levels based on the number ratio of aphids. And then, a transcriptome analysis was conducted after aphid infestation in one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar. In open environment the majority of rose cultivars had the highest aphid number at May 6th or May 15th in 2020 and the resistance to infestation could be classified into six levels. Differential expression analysis revealed that there were 1,626 upregulated and 767 downregulated genes in the R cultivar and 481 upregulated and 63 downregulated genes in the S cultivar after aphid infestation. Pathway enrichment analysis of the differentially expressed genes revealed that upregulated genes in R and S cultivars were both enriched in defense response, biosynthesis of secondary metabolites (phenylpropanoid, alkaloid, and flavonoid), carbohydrate metabolism (galactose, starch, and sucrose metabolism) and lipid processing (alpha-linolenic acid and linolenic acid metabolism) pathways. In the jasmonic acid metabolic pathway, linoleate 13S-lipoxygenase was specifically upregulated in the R cultivar, while genes encoding other crucial enzymes, allene oxide synthase, allene oxide cyclase, and 12-oxophytodienoate reductase were upregulated in both cultivars. Transcription factor analysis and transcription factor binding search showed that WRKY transcription factors play a pivotal role during aphid infestation in the R cultivar.

Conclusions

Our study indicated the potential roles of jasmonic acid metabolism and WRKY transcription factors during aphid resistance in rose, providing clues for future research.

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Background

As one of the most important ornamental flowers in the world, roses (Rosa chinensis L.) are attractive for their long flowering period, beautiful appearance, and a tremendous number of varieties; 30,000–35,000 cultivars are bred throughout the world [1]. However, due to their high carbohydrate and sugar content, roses are attacked by many pests, including rose aphid, Macrosiphum rosae (L.) [2].

There are 4,000 known aphid species worldwide, of which approximately 100 have successfully exploited the agricultural environment and pose a serious threat to crop production [3]. Aphids are phloem-feeding insects that cause direct and indirect damage to plants: direct damage is due to acquiring phloem nutrients necessary for plant growth, resulting in plant wilting and yield loss; and indirect damage is usually through honeydew excretion, saliva injection, and the transmission of plant fungal and viral pathogens [4]. Various insecticides have been used to inhibit aphid production, which may result in acquired resistance in pest species against these insecticides [5], as well as pollution to the ecological environment [6]. Therefore, the breeding of plant genotypes with strong resistance is a radical, yet inexpensive, and environmentally safe way to control aphids, which requires selection and evaluation of aphid resistance on different rose cultivars, and a full understanding of the mechanism of plant resistance to aphids.

There are three types of resistance to aphids, namely antixenosis, which is rejection of a plant when a choice is possible; antibiosis, which causes adverse effects on the phytophage viability during feeding; and tolerance [7]. Different types of resistance are usually found to be controlled by different genes [8], and several gene loci were simultaneously involved in antixenosis and antibiosis in some crop genotypes [9, 10], resulting in a complex mechanism of resistance to aphids.

To elucidate the changes in gene expression in response to aphids on a genomic scale, many “-omics” research studies have been conducted in several crops, including wheat (Triticum aestivum L.) [11, 12], maize (Zea mays L.) [13], celery (Apium graveolens cv. Dulce) [14], Brassica juncea [15], and soybean (Glycine max L. Merr) [16]. These studies have identified many genes involved in the plant response to aphids, including those involved in signal transduction; transcriptional regulation; reactive oxygen species; protein synthesis, modification, and degradation; maintenance of cell structure and homeostasis; and secondary metabolism [17]. However, only a few genes have been cloned and confirmed to provide resistance to aphids. Two nucleotide-binding-site leucine-rich repeat (NBS-LLR) proteins, Mi-1 and virus aphid transmission (Vat), confer resistance to Macrosiphum euphorbiae in tomato [18] and Aphis gossypii in melon [19], respectively. In Arabidopsis, a small heat shock-like protein, sieve element-lining chaperone 1 (SLI1), confers resistance to the tobacco aphid M. persicae nicotianae, the cabbage aphid Brevicoryne brassicae, and the cabbage whitefly Aleyrodes proletella [20, 21], implying a broad-spectrum resistance to phloem-feeding insects.

It has been shown that phytohormone, jasmonic acid (JA), and salicylic acid (SA) signal transduction pathways were associated with aphid resistance in plants. The JA synthesis-related genes LOX, AOS, and AOC were significantly upregulated in aphid-feeding sites in wheat [22], while exogenous application of JA effectively decreased aphid reproduction in cucumber leaves [23]. Additionally, SA content in the leaves of barley plant increased with aphid infestation [24], and exogenous SA improved the resistance of wheat to the grain aphid [25]. However, the molecular mechanism of resistance to aphids in plants is yet to be clarified.

In this study, 20 rose cultivars, which were commonly used for ornamental flowers in North China, were selected for evaluation of aphid resistance. And then, a comparative transcriptomic analysis after aphid infestation in aphid-resistant (R) and -susceptible (S) rose cultivars was carried out to identify the aphid resistance on different rose cultivars, to discovery aphid-resistance–related genes in rose and to characterize the potential roles of phytohormone and transcription factors (TFs) during aphid resistance in rose.

Results

Evaluation of aphid resistance on 20 rose cultivars

Generally, the number of aphids increased firstly and then decreased from April 29th to May 29th in most of rose cultivars, except for tengbenyueji and Gräfin von Hardenberg. The number of aphids on these two cultivars decreased continuously, with a highest aphid number on April 29th. Additionally, the density of aphids of 8 rose cultivars, including Dortmund, Garden Fun, Jayne Austin, Agnes Schilliger, Shizuku, Mary Ann, Harmonie, and Parkdirektor Riggers, peaked on May 6th; the density of aphids of 9 rose cultivars, including Ramukan, Carefree wonder, My Choice, Caramella, Souvenir de Louis, Louise Odier, Fancy Ruffle, Highgrove, and Mozart, peaked on May 15th; and one rose cultivar, bel canto, maintained the highest density of aphids on May 22nd (Table 1).

Table 1 Resistance levels to aphids of 20 rose cultivars
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Based on the number ratio of aphids, the aphid resistance of 20 rose cultivars could be sorted into six levels, in which 5 cultivars, Dortmund, Carefree Wonder, tengbenyueji, Gräfin von Hardenberg, and Mozart were high susceptibility to aphid (RL > 1.25); 3 cultivars, Jayne Austin, Agnes Schilliger, and Souvenir de Louis were moderate susceptibility to aphid (RL = 1.01–1.25); 3 cultivars, Ramukan, Garden Fun, and Mary Ann were susceptibility to aphid (RL = 0.76–1.00); 1 cultivar, Parkdirektor Riggers was resistance to aphid (RL = 0.51–0.75); 4 cultivars, My Choice, Caramella, Shizuku, and Highgrove were moderate resistance to aphid (RL = 0.26–0.50); 4 cultivars, Louise Odier, bel canto, Harmonie, and Fancy Ruffle were high resistance to aphid (RL = 0.01–0.25) (Table 1).

Transcriptome profiles of Rosa chinensis treated with aphids

To investigate the underlying mechanisms of aphid resistance in rose, two cultivars exhibiting diverse levels of resistance to aphid infestation, the R cultivar (Harmonie) for aphid resistance and the S cultivar (Carefree Wonder) for aphid susceptibility, were chosen and infested by aphids. Deep RNA-seq sequencing of the R and S cultivars was performed, with and without infestation, and each with three biological repeats. A total of 549.96 million filtered high-quality reads from 12 libraries (Q30 ranged from 95.1% to 95.6%) were obtained. The basic sequencing statistics are shown in Table S1. Previously, two research groups had successfully conducted full genome sequencing of the homozygous cultivar ‘Old Blush’. Clean reads were aligned to both reference genomes, Rosa chinensis Whole Genome v1.0 (OBDH-1.0) and Rosa chinensis Old Blush homozygous Genome v2.0 (RchiOBHm-V2), and the alignment rates are shown in Table 2. The overall alignment rate was slightly higher in OBDH-1.0 than in RchiOBHm-V2, with an average value of 86.26 and thus was used as the reference genome. RNA-seq read coverage showed that the results met the characteristics of normal RNA sequencing (Fig. S1).

Table 2 Alignment Rate comparison of two reference genomes
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Expression levels of genes in the R and S cultivars

The expression level and density of all genes were normalized as FPKM (Fig. S2A, B; Table S2). Using the FPKM value in the samples > 1 as a threshold, a total of 32,930 expressed genes filtered from 44,481 mapped genes were identified.

Principal component analysis (PCA) and Spearman correlation coefficient (SCC) were used to gain a global overview of the transcriptomic differences (Fig. 1A, B). Correlations among samples in the same cultivar showed relatively higher values than those between two cultivars with or without the aphid infestation. PCA showed that cultivars R and S were separated by PC1 (60.96% variation). Overall, these results indicated that the R and S cultivars showed distinct responses to aphid infestation.

Fig. 1
figure 1

Global view of gene expression profiles and changes of two rose cultivars under aphid infestation. A Spearman correlation coefficient (SCC) of gene expression profiles between samples. RC, RT, SC, and ST are samples of two cultivars (R and S) with (T) or without (C) aphid infestation. The size and color of each circle indicate the coefficient value between each sample. B Principal component analysis (PCA) of samples distinguished by different colors with three biological repeats. C Number of differentially expressed genes (DEGs) compared between different samples. D Cross-comparison Venn diagram showing the number of DEGs following aphid infestation in the R and S cultivars

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Transcriptome changes in the R and S cultivars after aphid infestation

DEGs were detected using an adjusted P-value (padj) < 0.05 and |log2 foldchange|> 1 as thresholds. When infested by aphids, there were 1,626 upregulated and 767 downregulated genes in the R cultivar and 481 upregulated and 63 downregulated genes in the S cultivar, while a large number of DEGs (> 10,000) were also found between the R and S cultivars whether or not they were infested by aphids (Fig. 1C). A Venn diagram showed common or uniquely regulated genes between the two cultivars infested by aphids, and only 8.7% of DEGs were common (Fig. 1D). When infested by aphids, the log2 foldchange of DEGs was mainly found in the interval [1, 2] (Fig. S2C, D). Volcano plots showed gene IDs with restricted cut-offs (|log2 foldchange|> 2, − log10padj > 9) (Fig. S2E, F). These results revealed an interspecific difference between the R and S cultivars. The mechanisms confronting aphids may be partially identical between the two cultivars.

Expression clusters by k-means

All DEGs between the R and S cultivars following aphid infestation were further categorized into four clusters based on k-means clustering analysis (Fig. 2A). The optimal k value (4) was determined by the elbow method (Fig. S3). Of the four clusters, C1 (725) and C3 (1,345) represented genes that were upregulated with aphid infestation, whereas C2 (373) and C4 (467) represented genes that were downregulated. In upregulated clusters, genes in C3 were specifically highly expressed in the R cultivar, and genes in C1 were specifically highly expressed in the S cultivar.

Fig. 2
figure 2

Cluster analysis of DEGs based on the k-means method. A Four clusters (C1–C4) based on the k-means algorithm. The Y-axis stands for scaled FPKM. Gene expression profiles in the line plots are shown in gray, and the mean values are shown in red for each cluster. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation. B Enriched GO terms of genes in the four clusters. Terms were ranked by the number of genes. C KEGG pathway enrichment analysis of three clusters. P-value levels are indicated as − log10 (P value), and the values are represented by the asterisks near the bar. No pathways were found enriched in Cluster C2

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GO and KEGG enrichment of DEGs and expression clusters

The significant DEGs were then annotated based on functional GO from GDR using Fisher's precision probability test (Table S3). Using GO enrichment analysis, DEGs were divided into three major enrichment categories: molecular functions (MF), cellular components (CC), and biological processes (BP). At most 10 enriched terms of each category were listed in bar plots. There were fewer enriched terms in MF compared to CC and BP in the R cultivar. Terms enriched in CC showed that upregulated genes in the R cultivars were enriched in transcription regulator activity, DNA-binding transcription factor activity, and sequence-specific DNA binding, while the terms enriched in BP revealed that upregulated genes in the R and S cultivars were both enriched in defense response, response to biotic stimulus, and sugar, chitin, and aminoglycan metabolism. The analysis also showed that downregulated genes in the R cultivar were enriched in photosynthesis, while several metabolic processes of carbohydrates were suppressed in the S cultivar (Fig. 3).

Fig. 3
figure 3

Bar plots showing the most enriched GO terms in the two cultivars after aphid infestation. Upregulated and downregulated DEGs in the two cultivars were analyzed by GO enrichment separately. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation. GO terms were subclassified into three categories distinguished by different colors: biological processes (BP), cellular components (CC), and molecular function (MF). Terms were primarily ranked by the number of genes participating in the relevant pathway. Values at the bottom of each bar represent the adjusted P-value (padj) attributed to the enrichment of the relevant pathway

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KEGG enrichment analysis were further conducted using a local KofamKOALA database (Table S4). DEGs of two cultivars were assigned to significant (p < 0.05) KEGG pathways (Fig. 4). This showed that biosynthesis of secondary metabolites (phenylpropanoid, alkaloid, and flavonoid), carbohydrate metabolism genes (galactose, starch, and sucrose metabolism), and lipid processing (alpha-linolenic acid and linolenic acid metabolism) were enriched in upregulated genes in both the R and S cultivars, while there were no enriched terms of downregulated genes in the S cultivar.

Fig. 4
figure 4

Distribution of KEGG pathways in the two cultivars after aphid infestation. Upregulated and downregulated DEGs in the two cultivars were analyzed by KEGG enrichment separately. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation. Enriched terms are visualized by bar plots with P-value levels indicated as − log10 (P value). The values are represented by the asterisks near the bar

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Next, we conducted GO and KEGG enrichment analyses for clusters based on gene expression patterns to differentiate genes linked with aphid resistance more clearly (Fig. 2B, C). Cluster C1 consisted of genes with the highest expression after aphid infestation in the S cultivar. Enrichment analysis showed that this cluster was enriched in chitin metabolic processes, cell wall catabolic processes, and carbohydrate metabolism. Cluster C3 consisted of genes with the highest expression in the R cultivar, in which lipid metabolism and biosynthesis of secondary metabolites, such as phenylpropanoid and flavonoid biosynthesis, were enriched. Since anabolism of JA and its derivatives is part of alpha-linolenic metabolism and phenylpropanoid is a precursor to SA, these results indicated that plant hormones, especially JA and SA, may play crucial roles in aphid resistance, and that the divergent expression levels of their related metabolism between the R and S cultivars may explain the distinct levels of resistance. Compared to genes in Cluster C3, genes in Cluster C1 may represent background mechanisms of pest infestations, such as cell wall and chitin metabolic process.

In the alpha-linolenic metabolism pathway depicted by the R package pathview (Fig. S4), genes encoding linoleate 13S-lipoxygenase (LOX, EC1.13.11.12) were specifically upregulated in the R cultivar, while genes encoding other crucial enzymes for generating the basic structure of JA, including allene oxide synthase (AOS, EC4.2.1.92), allene oxide cyclase (AOC, EC5.3.99.6), and 12-oxophytodienoate reductase (OPR, EC1.3.1.42), were upregulated in both the R and S cultivars. It is noteworthy that the expression level of the gene encoding JA carboxyl methyltransferase (JMT, EC 21.1.141) was higher in the S cultivar than in the R cultivar. Taken together, the results showed that JA biosynthesis-related genes were induced by aphid infestation, implying the potential roles of JA in aphid resistance.

TFs identified in the response to aphid infestation

To determine the transcription factors involved in the aphid infestation response in each cultivar and cluster, all DEGs were compared to the iTAK database based on HMMER. Of the 1,745 TFs identified by the iTAK database, 159, 30, 31, 23, 88, and 26 predicted TFs from 49 distinct families were present in RT vs RC, ST vs SC, C1, C2, C3, and C4, accounting for 6.01%, 6.17%, 6.54%, 5.57%, 6.64%, and 5.51% of each subset, respectively. We found that APETALA2/ethylene-responsive factor (AP2/ERF), basic/helix-loop-helix (bHLH), MYB, NAC, and WRKY were more abundant in C3 and RTvsRC. Significantly enriched analysis using Fisher’s exact test (padj < 0.05) was then performed, and we found that WRKY and AP2/ERF were enriched in both RTvsRC and C3.

Considering the expression pattern and GO analysis of DEGs in Cluster C3, C3 may explain the different resistance levels between the R and S cultivars. Therefore, the upstream 1,000 base pairs of DEGs in C3 were used as queries and analyzed using the PlantPAN database. Using transcription factor binding search (TFBS), five TF binding sites were identified in more than 1,000 DEG promoters. They were bZIP, bHLH, C2H2, WRKY40, and NAC (Table S5). We noticed that the description of WRKY (TFmatrixID_0445, “tgGTCAAt”) was related to the pathogen-induced transcription factor, and the expression pattern of its homologous gene in rose (RC2G0412700) was the same as that in C3 (Fig. 5). Considering its biochemical function in biotic stress, RC2G0412700 may play a vital role in pest resistance to aphids.

Fig. 5
figure 5

The identification of transcription factors (TFs) and the abundant binding sites (TFBS). A, B Distribution of TF families in the four clusters and two cultivars. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation. The color represents the number of genes in each TF family. C, D Significantly overrepresented TF families within Cluster C3 and the R cultivar in response to aphid infestation. E The seqlogo of abundant TFBS (TFmatrixID_0445) in Cluster C3. F The expression pattern of gene RC2G0412700 based on FPKM

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DEGs involved in phytohormone metabolism during aphid infestation

Phytohormones play indispensable roles in orchestrating biotic plant defenses. Proteins expressed by genes involved in phytohormone metabolic pathways, including abscisic acid (ABA), auxin (IAA), brassinosteroid (BR), cytokinin (CK), ethylene (ET), gibberellin (GA), JA, SA, and strigolactone (SL) were analyzed through MapMan together with the PlantCyc databases. We found that the expression levels of genes associated with the phytohormone metabolic pathways in the R and S cultivars were different in normal states. When infested with aphids, genes involved in the JA and SA pathways were significantly upregulated in both the R and S cultivars (Fig. 6). Gene expression related to phytohormones identified by MapMan is shown in a heatmap (Fig. S5).

Fig. 6
figure 6

Expression patterns of genes involved in different phytohormone metabolism pathways. A The differences in phytohormone metabolism between the two cultivars in a normal state. B, C Dynamic changes in phytohormone metabolism in the R and S cultivars infested by aphids. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation

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PPI network among the DEGs

PPI analysis of DEGs involved in the hormone process was performed to identify cross-talk among different hormones. The network was built using Cytoscape and only genes with relatively strong physical interactions are shown (Fig. S6). Ten genes with the highest ranking are listed (Table 3). There were genes participating in various hormone metabolic processes, suggesting a mixed hormonal regulation of the plant biotic defense response.

Table 3 Top10 genes with highest degree in PPI (Hormone)
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We also analyzed PPI relationships of DEGs in C3 to obtain a better understanding of the molecular mechanism of aphid resistance (Fig. S6). The building process was the same as described above, and 10 genes with the highest ranking were identified (Table 4). We found three WRKY transcription factors (RC4G0344000, RC6G0452500, and RC2G0412700) and a probable linoleate 9S-lipoxygenase 5 related to linolenic acid metabolism.

Table 4 Top10 genes with highest degree in PPI (Cluster C3)
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Gene Expression Validation by qRT-PCR

Quantitative real-time PCR was conducted to further validate the reliability of the RNA-seq data with 4 clustered expression patterns as well as the PPI analysis results. So, we selected 4 DEGs which have relative higher degree in PPI analysis (GA20ox, WRKY75, WRKY40 and MYB108) and 1 random non-differential expression gene (GA30ox) for verification. The results of qRT-PCR were basically consistent with the RNA-seq results (Fig. 7A). GA20ox, WRKY75, WRKY40 and MYB108 were dramatically induced in R cultivar, implying these genes may be involved in aphid resistance in rose.

Fig. 7
figure 7

Verification of RNA-seq by qRT-PCR and the correlation analysis. A Expression patterns of 5 selected genes measured using the 2−ΔΔCT method. The histograms in cyan represented RT-PCR results with Error bars showing the means ± SEM. The orange lines represented the average values of FPKM for each sample. RC, RT, SC, and ST are samples of one high resistance (R, Harmonie) and one highly susceptibility (S, Carefree Wonder) rose cultivar with (T) or without (C) aphid infestation. B Correlation analysis for 5 selected genes between RNA-Seq and qRT-PCR

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We further analyzed the correlation between RNA-Seq and qRT-PCR results. The high correlation (R = 0.88, p = 5.7e-07) supports the reliability of the transcriptome results (Fig. 7B).

Discussion

As one of most popular ornamental plant, rose are usually attacked by aphids, which can reduce the flower growth and quality. In this study, it indicated that the rose cultivars were vary in resistance to aphids (Table 1), consistent with other report on 10 rose cultivars [26]. The highest density of aphids was found on May 6th or May 15th on most of rose cultivars, which may be useful for developing an integrated pest management in rose plants. Additionally, 4 rose cultivars, Harmonie, bel canto, Louise Odier and Fancy Ruffle were found to be high resistance to aphid, which can be important germplasms for aphid resistance breeding in rose.

RNA-seq is a standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions [27]. Comparative transcriptome analyses focusing on aphid infestations were conducted in sorghum [28], wheat [29], soybean [16], maize [13], and rose [30], in which hundreds of genes were found to be differentially expressed after aphid infestation. In this study, aphid infestation triggered the expression of many genes (Fig. 1A). Interestingly, more DEGs after aphid infestation can be observed in R cultivar than S cultivar, implying a stronger response to aphids can be triggered at the transcriptional level in the R cultivar, which may contribute to high resistance.

TFs play vital roles in regulating the gene expression involved in both biotic and abiotic defense responses [31]. In our study, GO analysis showed that C3 and DEGs upregulated in R cultivar were enriched in transcription regulator activity, DNA-binding transcription factor activity, and sequence-specific DNA binding (Figs. 2, 3). It suggested that transcriptional regulation of downstream genes was very important for rose to resist aphids.

As one of the largest families of TFs in plants, WRKY transcription factors modulate many plant processes [32], including defense signaling [33]. Previous reports suggested that WRKY TFs played pivotal roles in aphid resistance. In tomato, the SlWRKY70 transcript level was inducible in response to aphid infestation, and silencing SlWRKY70 attenuated Mi-1-mediated resistance against aphids [34]. The overexpression of CmWRKY48 inhibited the aphid population growth capacity in chrysanthemum [35]. Importantly, RlWRKY10 and RlWRKY14 in rose (Rosa longicuspis) were positive regulators in aphid resistance [36]. However, TaWRKY53 in wheat [37], CmWRKY53 in chrysanthemum [38], and AtWRKY22 in Arabidopsis [39] negatively regulated the resistance to aphids. In this study, it can be observed that WRKY TFs were enriched in both RTvsRC and C3 (Fig. 5), and the binding sites of WRKY can be found in the promoter of DEGs in R cultivar after aphid infestation (Table S5), implying the potential roles of WRKY TFs in aphid resistance in rose, which may be the regulators for transcriptional regulation of downstream genes.

The phytohormone JA contributed to plant defense against biotic stresses, including insect attacks [40]. JA and its cyclopentanone derivatives are also involved in inducing a defense against aphids. For example, exogenous application of JA or JA-Ile significantly enhanced the aphid resistance in potato [41], cucumber [23], and soybean [42], and JA biosynthesis was enhanced after aphid infestation [42]. In our results, KEGG analysis showed that DEGs upregulated in R cultivar were enriched in alpha-Linolenic acid metabolism (Fig. 4), which was a key step for JA biosynthesis. It can be also observed that genes encoding crucial enzymes for generating the basic structure of JA, including 13-LOX that was specifically upregulated in R cultivar, and AOS, AOC, and OPR that were upregulated in both the R and S cultivars (Fig. S4), suggested the important role of JA in aphid resistance in rose, in which LOX may be a key regulator. However, although the JA level was transiently increased at the early stages of aphid feeding on an aphid-resistant sorghum cultivar, exogenous application of JA promoted improved aphid feeding and colonization [43], which indicated a dichotomous role of JA in aphid resistance. Interestingly, the expression levels of LOX genes, which catalyze the first committed step of JA biosynthesis, are generally regulated by WRKY TFs [44, 45], which were up-regulated by aphid infestation in this study (Fig. 5; Table 4). The precise regulatory mechanism of JA in aphid resistance in rose need more research to reveal.

SA also plays a crucial role in resistance to biotic stresses. It has shown that the functions of SA in resistance to aphids varied in plant species. SA can enhance defense response to Russian wheat aphid (RWA) in wheat [46], reduced plant damage and suppressed sugarcane aphid (SCA) population growth and fecundity in sorghum [47]. However, overexpression of Armet, an effector protein, can induce a fourfold increase in SA accumulation and enhance the plants’ resistance to bacterial pathogen Pseudomonas syringae, but had no detectable adverse effects on aphid survival or reproduction [48]. In this study, KEGG analysis showed that DEGs upregulated in R cultivar were enriched in phenylpropanoid biosynthesis (Fig. 4), which is related with SA biosynthesis, while induced PAL expression is associated with SA accumulation in plants [49, 50]. Considering the complexity between SA and aphid resistance, the function of SA in resistance to aphids in rose needs further confirmation.

Conclusions

In summary, our study evaluated the aphid resistance of 20 rose cultivars, which could be sorted into six levels based on the number ratio of aphids. Transcriptome analysis in response to aphid infestation characterized several genes triggered by aphid infestation, which may be likely regulated by the WRKY transcription factor, and associated with JA or/and SA biosynthesis or signal transduction. Our work is of great significance for screening of aphid-resistant rose germplasm and the functional identification of aphid-resistant genes.

Materials and methods

Evaluation of aphid resistance on different rose cultivars

The aphid resistance on 20 rose cultivars were evaluated, including Dortmund, Ramukan, Carefree wonder, Garden Fun, Jayne Austin, Agnes Schilliger, My Choice, Caramella, Shizuku, tengbenyueji, Gräfin von Hardenberg, Souvenir de Louis, Mary Ann, Louise Odier, bel canto, Harmonie, Parkdirektor, Riggers Fancy Ruffle, Highgrove, and Mozart. In October 2018, the scions of the 20 tested rose cultivars with robust and consistent growth state were selected for cutting propagation and rooting in a greenhouse. On April 30, 2019, they were planted in an open environment at our field experiment station in Baoding City, Hebei Province, China. One rose cultivar was planted with an area of 1.8 m × 1.4 m, row spacing of 45 cm and plant spacing of 20 cm, in a random block arrangement with three repeats. Conventional water and fertilizer were used without pesticide during the experiment period.

The number of aphids was counted in an open environment on April 29th, May 6th, May 15th, May 22nd, and May 29th in 2020. Ten rose plants were selected randomly for every cultivar, and the number of aphids was counted within 10 cm of stem tip. The resistance level to aphid of one rose cultivar was represented using a number ratio of aphids, in which the resistance of rose cultivars to aphids could be sorted into six levels (Table 5).

Table 5 The resistance levels based on number ratio of aphids
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Number Ratio of aphids = the number of aphids in one rose cultivar/the average number of aphids in all rose cultivars.

Aphid infestation for transcriptome analysis

Based on the identification of aphid resistance, two rose cultivars, Harmonie (high resistance to aphid) and Carefree Wonder (high susceptibility to aphid) were planted in a greenhouse and selected for transcriptome analysis. The rose plants were challenged with 20 aphids. Leaf tissues were collected after 72 h from aphid-treated and control plants (RC, control plants for aphid-resistant cultivar; RT, aphid-treated plants for aphid-resistant cultivar; SC, control plants for aphid-susceptible cultivar; ST, aphid-treated plants for aphid-susceptible cultivar).

RNA extraction, Illumina library construction, and sequencing

Samples were ground in separate RNase-free mortars filled with liquid nitrogen. Total RNA was extracted using an RNAprep Pure Plant kit (Tiangen, Beijing, China) according to the manufacturer’s instructions. An RNA Nano 6000 assay kit, part of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA), was used to assess the RNA integrity. Then, mRNA was extracted by VAHTS mRNA capture beads (Vazyme Biotech, Nanjing, China) following the manufacturer’s protocol. To generate the sequencing libraries, an NEBNext Ultra RNA library prep kit (NEB, USA) was used. After PCR product purification and library quality assessment, RNA sequencing was subsequently performed on an Illumina Novaseq platform (Illumina, USA) by Novogene Corporation (Beijing, China), and 150 bp paired-end reads were generated.

Quality control, trimming, and mapping of reads

Sequenced raw reads in a fastq format were filtered with FASTP v0.23 [51] to remove low-quality reads and reads containing adapter and N bases using the default parameters. Paired-end clean reads were then mapped to the reference genome Rosa chinensis Genome v1.0 (assessed on February 2018) [52] with the parameters “–new-summary –dta” after building the genome index by HISAT2 v2.2.1 [53]. SAM files were converted into BAM files using SAMtools v1.10 [54], and Qualimap v2.2.1 [55] was used to evaluate the sequencing alignment data. To count the read numbers in each gene model shown in the gff file, the R package FeatureCounts v1.5.0 [56] was used, and the expression level of each gene was normalized as fragments per kilobase of transcript per million mapped reads (FPKM). To remove low-expression genes, genes were filtered with the threshold FPKM deg > 1. Visualized results of principal component analysis (PCA) using the R package PCAtools v2.8.0 [57] and correlation analysis based on the Spearman correlation coefficient method were used for quality analysis. The R package factoextra v1.0.7 [58] was used to analyze the hierarchical relationship by building a dendrogram.

Identification and functional annotation of DEGs

Differentially expressed genes (DEGs) between any two samples were identified by the R package DESeq2 v1.20.0 [59]. The P-value was adjusted by the Benjamini and Hochberg method, and a P-value (padj) < 0.05 and |log2 foldchange|> 1 were used as the cut-off criteria for screening significant DEGs. An analysis of common and unique DEGs between different samples was conducted by visualizing results acquired from the R package VennDiagram [60]. A k-means cluster analysis was performed based on the R package factoextra [58]. The k number was chosen using the elbow method for the most optimal cluster number. Functional annotations of DEGs were generated based on Mercator4 [61] with MapMan and Plant Metabolic Network metabolic pathway databases (https://plantcyc.org/).

GO and KEGG pathway enrichment analysis

Gene Ontology (GO) terms analyzed by InterProScan [62] and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyzed through the KEGG automatic annotation server (KAAS) [63] of Rosa chinensis based on its transcripts are available on Genome Database for Rosaceae (GDR) (https://www.rosaceae.org/) [64]. After building the R package OrgDB of Rosa chinensis using the R package AnnotationForge [65], GO and KEGG pathway enrichment analyses of DEGs were implemented by the R package clusterProfiler with padj < 0.05 as the threshold [66].

TF identification and PPI analysis

Plant TFs were predicted through iTAK v1.5 [67], a TF database based on PlnTFDB [68] and PlantTFDB [69]. Full-length protein sequences were used in iTAK as queries to acquire the composition of each TF family in the Rosa chinensis genome, against which different DEG groups were aligned. Significant enrichment of TF families was analyzed using the R package clusterProfiler [66] with P-value < 0.05 set as the cut-off.

Information on rose proteins in the STRING database (https://cn.string-db.org/) was obtained and then used as a BLAST db. After sequence alignment to the local BLAST database by BLAST [70], STRING v11.5 [71] was used to predict protein–protein interactions (PPIs), and the resulting network was visualized through Cytoscape v3.9 [72].

Validation of transcriptome results using quantitative real-time PCR

The Quantitative Real-time PCR was carried out on a 7500 Fast Real-Time PCR System (Applied Biosystems, Foster, CA, USA) using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) with the following procedure: 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s, 60 °C for 30 s, and a melt curve stage of 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 30 s. RcActin was used as an internal control for its consistent expression. The relative expression level of genes was calculated by the method of 2−ΔΔCT [73]. Each treatment has triplicate biological replicates which was performed with three technical repeats. Gene-specific primers were listed in Table S6. The correlation analysis between RNA-Seq and qPCR results was conducted under R based on the Pearson method. The plot was drawn by R package ggpubr.

Availability of data and materials

The transcriptomic data presented in this study are openly available on the National Center for Biotechnology Information (NCBI) BioProject PRJNA968003. The experimental materials, 20 rose cultivars were obtained from and planted by the Institute of Biotechnology and Food Science, Hebei Academy of Agriculture and Forestry Sciences (HAAFS). All databases in this study are available to the public.

References

  1. Roberts A, et al. Encyclopedia of Rose Science. 2003.

    Google Scholar 

  2. Muneer S, et al. Proteomic analysis of aphid-resistant and -sensitive rose (Rosa Hybrida) cultivars at two developmental stages. Proteomes. 2018;6(2):25.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Emden HV, Harrington R. Aphids as Crop Pests. 2017.

    Book  Google Scholar 

  4. Goggin FL. Plant-aphid interactions: molecular and ecological perspectives. Curr Opin Plant Biol. 2007;10(4):399–408.

    Article  CAS  PubMed  Google Scholar 

  5. Edwards OR, et al. Insecticide resistance and implications for future aphid management in Australian grains and pastures: a review. Aust J Exp Agr. 2008;48:1523–30.

    Article  CAS  Google Scholar 

  6. Dedryver CA, Le Ralec A, Fabre F. The conflicting relationships between aphids and men: a review of aphid damage and control strategies. CR Biol. 2010;333(6–7):539–53.

    Article  Google Scholar 

  7. Radchenko EE, Abdullaev RA, Anisimova IN. Genetic resources of cereal crops for aphid resistance. Plants (Basel). 2022;11(11):1490.

    Article  CAS  PubMed  Google Scholar 

  8. Castro AM, et al. Identification of wheat chromosomes involved with different types of resistance against greenbug (Schizaphis graminum, Rond.) and the Russian wheat aphid (Diuraphis noxia, Mordvilko). Euphytica. 2001;118:321–30.

    Article  Google Scholar 

  9. Zhu LC, Smith CM, Reese JC. Categories of resistance to greenbug (Homoptera: Aphididae) biotype K in wheat lines containing Aegilops tauschii genes. J Econ Entomol. 2005;98(6):2260–5.

    Article  CAS  PubMed  Google Scholar 

  10. Weng YQ, et al. Phenotypic mechanisms of host resistance against greenbug (Homoptera: Aphididae) revealed by near isogenic lines of wheat. J Econ Entomol. 2004;97(2):654–60.

    Article  PubMed  Google Scholar 

  11. Reddy SK, et al. Transcriptomics of induced defense responses to greenbug aphid feeding in near isogenic wheat lines. Plant Sci. 2013;212:26–36.

    Article  CAS  PubMed  Google Scholar 

  12. Batyrshina ZS, et al. Comparative transcriptomic and metabolic analysis of wild and domesticated wheat genotypes reveals differences in chemical and physical defense responses against aphids. BMC Plant Biol. 2020;20(1):19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tzin V, et al. Dynamic maize responses to aphid feeding are revealed by a time series of transcriptomic and metabolomic assays. Plant Physiol. 2015;169:1727–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Divol F, et al. Systemic response to aphid infestation by Myzus persicae in the phloem of Apium graveolens. Plant Mol Biol. 2005;57(4):517–40.

    Article  CAS  PubMed  Google Scholar 

  15. Duhlian L, et al. Comparative transcriptomics revealed differential regulation of defense related genes in Brassica juncea leading to successful and unsuccessful infestation by aphid species. Sci Rep. 2020;10(1):10583.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  16. Lee S, et al. Transcriptomic dynamics in soybean near-isogenic lines differing in alleles for an aphid resistance gene, following infestation by soybean aphid biotype 2. BMC Genomics. 2017;18(1):472.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Smith CM, Boyko EV. The molecular bases of plant resistance and defense responses to aphid feeding: current status. Entomol Exp Appl. 2007;122(1):1–16.

    Article  CAS  Google Scholar 

  18. Rossi M, et al. The nematode resistance gene Mi of tomato confers resistance against the potato aphid. Proc Natl Acad Sci. 1998;95:9750–4.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  19. Dogimont C. Molecular diversity at the Vat/Pm-W resistance locus in melon. 2008.

    Google Scholar 

  20. Kloth KJ, et al. SIEVE ELEMENT-LINING CHAPERONE1 restricts aphid feeding on Arabidopsis during heat stress. Plant Cell. 2017;29(10):2450–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kloth KJ, et al. SLI1 confers broad-spectrum resistance to phloem-feeding insects. Plant Cell Environ. 2021;44(8):2765–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu X, et al. Wheat gene expression is differentially affected by a virulent Russian wheat aphid biotype. J Chem Ecol. 2011;37(5):472–82.

    Article  CAS  PubMed  Google Scholar 

  23. Qi X, et al. Jasmonic acid, ethylene and ROS are involved in the response of cucumber (Cucumis sativus L.) to aphid infestation. Sci Hortic. 2020;269:109421.

    Article  CAS  Google Scholar 

  24. Chaman ME, Copaja SV, Argandon VH. Relationships between salicylic acid content, phenylalanine ammonia-lyase (PAL) activity, and resistance of barley to aphid infestation. J Agric Food Chem. 2003;51:2227–31.

    Article  CAS  PubMed  Google Scholar 

  25. Feng JL, et al. Exogenous salicylic acid improves resistance of aphid-susceptible wheat to the grain aphid, Sitobion avenae (F.) (Hemiptera: Aphididae). Bull Entomol Res. 2021;111(5):544–52.

    Article  CAS  PubMed  Google Scholar 

  26. Golizadeh A, et al. Population growth parameters of rose aphid, Macrosiphum rosae (Hemiptera: Aphididae) on different rose cultivars. Neotrop Entomol. 2017;46(1):100–6.

    Article  CAS  PubMed  Google Scholar 

  27. Pertea M, et al. Transcript-level expression analysis of RNA-seq experiments with HISAT. StringTie and Ballgown Nat Protoc. 2016;11(9):1650–67.

    Article  CAS  PubMed  Google Scholar 

  28. Serba DD, et al. Comparative transcriptome analysis reveals genetic mechanisms of sugarcane aphid resistance in grain sorghum. Int J Mol Sci. 2021;22(13):7129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang Y, et al. Transcriptome analysis reveals rapid defence responses in wheat induced by phytotoxic aphid Schizaphis graminum feeding. BMC Genomics. 2020;21(1):339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gao P, et al. Transcriptomic and metabolomic changes triggered by Macrosiphum rosivorum in rose (Rosa longicuspis). BMC Genomics. 2021;22(1):885.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ng DW, Abeysinghe JK, Kamali M. Regulating the regulators: the control of transcription factors in plant defense signaling. Int J Mol Sci. 2018;19(12):3737.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rushton PJ, et al. WRKY transcription factors. Trends Plant Sci. 2010;15(5):247–58.

    Article  CAS  PubMed  Google Scholar 

  33. Eulgem T, Somssich IE. Networks of WRKY transcription factors in defense signaling. Curr Opin Plant Biol. 2007;10(4):366–71.

    Article  CAS  PubMed  Google Scholar 

  34. Atamian HS, Eulgem T, Kaloshian I. SlWRKY70 is required for Mi-1-mediated resistance to aphids and nematodes in tomato. Planta. 2012;235(2):299–309.

    Article  CAS  PubMed  Google Scholar 

  35. Li P, et al. The over-expression of a chrysanthemum WRKY transcription factor enhances aphid resistance. Plant Physiol Biochem. 2015;95:26–34.

    Article  CAS  PubMed  ADS  Google Scholar 

  36. Zhu Z, et al. Functional analysis of aphid resistance genes RlWRKY10 and RlWRKY14. Physiol Mol Plant P. 2022;122:101922.

    Article  CAS  Google Scholar 

  37. Van Eck L, et al. Virus-induced gene silencing of WRKY53 and an inducible phenylalanine ammonia-lyase in wheat reduces aphid resistance. Plant Biotechnol J. 2010;8(9):1023–32.

    Article  PubMed  Google Scholar 

  38. Zhang W, et al. Chrysanthemum CmWRKY53 negatively regulates the resistance of chrysanthemum to the aphid Macrosiphoniella sanborni. Hortic Res. 2020;7(1):109.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kloth KJ, et al. AtWRKY22 promotes susceptibility to aphids and modulates salicylic acid and jasmonic acid signalling. J Exp Bot. 2016;67(11):3383–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang J, et al. Jasmonate action in plant defense against insects. J Exp Bot. 2019;70(13):3391–400.

    Article  CAS  PubMed  Google Scholar 

  41. Cooper WR, Goggin FL. Effects of jasmonate-induced defenses in tomato on the potato aphid. Macrosiphum euphorbiae Entomol Exp Appl. 2005;115:107–15.

    Article  CAS  Google Scholar 

  42. Yates-Stewart AD, et al. Jasmonic acid-isoleucine (JA-Ile) Is involved in the host-plant resistance mechanism against the soybean aphid (Hemiptera: Aphididae). J Econ Entomol. 2020;113(6):2972–8.

    Article  CAS  PubMed  Google Scholar 

  43. Grover S, et al. Dichotomous role of jasmonic acid in modulating sorghum defense against aphids. Mol Plant Microbe Interact. 2022;35(9):755–67.

    Article  CAS  PubMed  Google Scholar 

  44. Jiao C, et al. CALMODULIN1 and WRKY53 function in plant defense by negatively regulating the jasmonic acid biosynthesis pathway in Arabidopsis. Int J Mol Sci. 2022;23(14):7718.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Qiao H, et al. TaWRKY13-A serves as a mediator of jasmonic acid-related leaf senescence by modulating jasmonic acid biosynthesis. Front Plant Sci. 2021;12:717233.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Mohase L, van der Westhuizen AJ. Salicylic acid is involved in resistance responses in the Russian wheat aphid-wheat interaction. J Plant Physiol. 2002;159(6):585–90.

    Article  CAS  Google Scholar 

  47. Pant S, Huang Y. Genome-wide studies of PAL genes in sorghum and their responses to aphid infestation. Sci Rep. 2022;12(1):22537.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  48. Cui N, et al. Armet, an aphid effector protein, induces pathogen resistance in plants by promoting the accumulation of salicylic acid. Philos Trans R Soc Lond B Biol Sci. 2019;374(1767):20180314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shine MB, et al. Cooperative functioning between phenylalanine ammonia lyase and isochorismate synthase activities contributes to salicylic acid biosynthesis in soybean. New Phytol. 2016;212(3):627–36.

    Article  CAS  PubMed  Google Scholar 

  50. Kim DS, Hwang BK. An important role of the pepper phenylalanine ammonia-lyase gene (PAL1) in salicylic acid-dependent signalling of the defence response to microbial pathogens. J Exp Bot. 2014;65(9):2295–306.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen S, et al. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hibrand Saint-Oyant L, et al. A high-quality genome sequence of Rosa chinensis to elucidate ornamental traits. Nat Plants. 2018;4(7):473–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kim D, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Li H, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Garcia-Alcalde F, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28(20):2678–9.

    Article  CAS  PubMed  Google Scholar 

  56. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013;30(7):923–30.

    Article  PubMed  Google Scholar 

  57. Blighe, K. and A. Lun. PCAtools: Everything Principal Components Analysis. 2021; Available from: https://github.com/kevinblighe/PCAtools.

  58. Kassambara, A. and F. Mundt. factoextra : Extract and Visualize the Results of Multivariate Data Analyses. 2020; Available from: https://rpkgs.datanovia.com/factoextra/index.html.

  59. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011;12:35.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Bolger M., R. Schwacke, and B. Usadel, MapMan visualization of RNA-Seq data using Mercator4 functional annotations. Methods Mol Biol. 2021.

  62. Jones P, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Moriya Y, et al. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182–5.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Jung S, et al. 15 years of GDR: New data and functionality in the Genome Database for Rosaceae. Nucleic Acids Res. 2019;47:D1137–45.

    Article  PubMed  Google Scholar 

  65. Carlson, M. and H. Pagès. AnnotationForge: Tools for building SQLite-based annotation data packages. 2022; Available from: https://github.com/Bioconductor/AnnotationForge.

  66. Yu G, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zheng Y, et al. iTAK: a program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators, and protein kinases. Mol Plant. 2016;9(12):1667–70.

    Article  CAS  PubMed  Google Scholar 

  68. Perez-Rodriguez P, et al. PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic Acids Res. 2010;38:D822–7.

    Article  CAS  PubMed  Google Scholar 

  69. Jin J, et al. PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 2014;42:D1182–7.

    Article  CAS  PubMed  Google Scholar 

  70. Camacho C, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Szklarczyk D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    Article  CAS  PubMed  Google Scholar 

  72. Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25(4):402–8.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the HAAFS Agriculture Science and Technology Innovation Project (2022KJCXZX-SSS-4) and Hebei Provincial Key Natural Science Foundation and Key Basic Research Projects of Basic Research Program (18962901D).

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Authors and Affiliations

  1. Hebei Key Laboratory of Plant Genetic Engineering, Institute of Biotechnology and Food Science, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050051, China

    Wenqi Dong, Lei Sun, Bo Jiao, Pu Zhao, Chunhong Ma & Shuo Zhou

  2. Department of Ornamental Horticulture, State Key Laboratory of Agrobiotechnology, Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, College of Horticulture, China Agricultural University, Beijing, 100193, China

    Wenqi Dong, Junping Gao & Shuo Zhou

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  1. Wenqi Dong
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Contributions

S.Z. and J.G. designed the experiment. W.D. did the field trial experiments. W.D., P.Z. and C.M. made the methodology on evaluating the resistance and helped the statistical analysis. L.S. analyzed the transcriptomic data. S.Z, W.D. and L.S. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shuo Zhou.

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Dong, W., Sun, L., Jiao, B. et al. Evaluation of aphid resistance on different rose cultivars and transcriptome analysis in response to aphid infestation. BMC Genomics 25, 232 (2024). https://doi.org/10.1186/s12864-024-10100-z

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