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- Open Access
Transcriptome analysis of woodland strawberry (Fragaria vesca) response to the infection by Strawberry vein banding virus (SVBV)
© The Author(s). 2016
Received: 15 May 2016
Accepted: 4 July 2016
Published: 13 July 2016
Woodland strawberry (Fragaria vesca) infected with Strawberry vein banding virus (SVBV) exhibits chlorotic symptoms along the leaf veins. However, little is known about the molecular mechanism of strawberry disease caused by SVBV.
We performed the next-generation sequencing (RNA-Seq) study to identify gene expression changes induced by SVBV in woodland strawberry using mock-inoculated plants as a control.
Using RNA-Seq, we have identified 36,850 unigenes, of which 517 were differentially expressed in the virus-infected plants (DEGs). The unigenes were annotated and classified with Gene Ontology (GO), Clusters of Orthologous Group (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The KEGG pathway analysis of these genes suggested that strawberry disease caused by SVBV may affect multiple processes including pigment metabolism, photosynthesis and plant-pathogen interactions.
Our research provides comprehensive transcriptome information regarding SVBV infection in strawberry.
Strawberry vein banding virus (SVBV) is one of the major viruses infecting strawberries. It has been reported worldwide, from North America to Australia to Belgium to Japan [1, 2]. In China, SVBV has been found in Henan, Hebei, Jilin and Zhejiang provinces causing serious loss of strawberry production . Woodland strawberry infected with SVBV shows such characteristic symptoms as yellowing along the major leaf veins, shorter stolons, reduced plant growth and smaller fruit, as well as significant reduction in berry yield and quality. SVBV is transmitted either by grafting or by aphids in a semi-persistent manner . SVBV is a member of the genus Caulimovirus, and has a circular dsDNA genome with one single stranded discontinuity on each DNA strand. The full-length of SVBV genome is ~8 kilobase pairs-long and encodes seven proteins .
Investigation of the pathogenic mechanism of SVBV in strawberry is important for better design of the disease control strategies. Woodland strawberry (family Rosaceae) is an important experimental plant for studying the mechanisms of virus-plant interactions and a facile model for investigating gene expression changes in response to pathogen infections [6, 7]. Because such response involves multiple physiological and metabolic processes, genome-wide expression profiling is a method of choice for studying these processes at the transcriptional level. Next generation sequencing techniques, such as RNA-Seq, have provided a powerful approach for studying the entire transcriptomes. RNA-Seq is used to measure gene expression, identify the mRNAs, non-coding RNAs and small RNAs, as well as to detect alternative splicing patterns .
Previous studies using RNA-Seq have greatly extended our understanding of the mechanisms underlying plant diseases caused by a wide range of pathogens. The transcriptome analysis of Nicotiana tabacum infected with Cucumber mosaic virus (CMV) suggested that, the changes in pigment metabolism pathway may be directly responsible for the disease development . Comparative transcriptome analysis of two rice varieties in response to Rice stripe virus (RSV) infection showed that the lignification and cell wall strengthening play a critical role in resistance to RSV, providing a foundation for breeding for resistance to rice stripe disease . Transcriptome gene expression analysis of Chenopodium amaranticolor in response to virus-induced hypersensitive response (HR) identified a number of candidate genes such as RIN4, RPS2, PR1 and COI1, that are essential for a jasmonate-regulated defense. Each of these genes was specifically regulated during either TMV or CMV infection at both early and late stages of the HR .
In this study, we analyzed the response of strawberry plants to infection with SVBV using RNA-Seq and investigated the alterations in gene expression between the healthy and infected plants. We found that the genes involved in in many biological processes, such as plant pigment metabolism, photosynthesis and plant-pathogen interactions, were differentially expressed. These results will help to understand the regulatory mechanisms involved in strawberry response to SVBV infection.
Results and discussion
Illumina sequencing and reads assembly
Summary statistics for strawberry genes based on the RNA-Seq data
Global patterns of gene expression in response to SVBV infection
Further analysis revealed that 517 unigenes showed differential expression as compared to mock-inoculated control. Among these 517 differentially expressed genes (DEGs), 280 were up regulated, and 237 were down regulated. BLAST results were obtained for these DEGs against five protein databases, namely the non-redundant (nr, NCBI) protein database, Swiss-Prot database, Clusters of Orthologous Groups of proteins (COG, NCBI) database, the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Among them, 498 DEGs had nr annotation, 388 DEGs had Swiss-Prot annotation, 195 DEGs had COG annotations, 425 DEGs had GO annotations and 79 DEGs had KEGG functional annotations.
Analysis of important KEGG pathways
Numbers of DEGs of top 18 KEGG pathways
DEGs with pathway annotation (55)
6 (10.9 %)
5 (9.09 %)
3 (5.45 %)
3 (5.45 %)
2 (3.63 %)
Cysteine and methionine metabolism
2 (3.63 %)
2 (3.63 %)
2 (3.63 %)
2 (3.63 %)
Starch and sucrose metabolism
2 (3.63 %)
Nucleotide excision repair
2 (3.63 %)
2 (3.63 %)
Alanine, aspartate and glutamate metabolism
2 (3.63 %)
Plant hormone signal transduction
2 (3.63 %)
2 (3.63 %)
Ubiquitin mediated proteolysis
2 (3.63 %)
Protein processing in endoplasmic reticulum
2 (3.63 %)
2 (3.63 %)
Glutamic acid decarboxylase (GAD) gene was significantly up-regulated by SVBV infection. GAD has been reported to be involved in “taurine and hypotaurine metabolism” of plant pigment metabolism and is the key enzyme in γ-aminobutyric acid (GABA) synthesis. In response to the biotic stress, plants rapidly increase GAD expression and accumulate quantity of GABA [12, 13], leading to reduce other free amino acid content and inhibiting the growth of stems . The carotenoid biosynthesis pathway that mainly related to the photosynthesis also has a certain degree of enrichment, which might be highly associated with strawberry regulatory network in response to SVBV infection. The pathway might be highly associated with strawberry regulatory network in response to SVBV infection. Abscisic acid-8'-hydroxylase was related to “carotenoid biosynthesis” upon the pathogens and environmental stresses, the content of ABA caused by abscisic acid-8'-hydroxylase rise so rapidly, ABA depresses entire plant growth and vitro organs by functioning as a strong plant growth inhibitor .
Plant WRKY transcription factors and respiratory burst oxidase homolog protein were involved in plant-pathogen interaction, the up-regulation of factors is the embodiment of the plant immune responses . Anthocyanin reductase and dihydroflavonol 4-reductase were both related to “flavonoid biosynthesis”. Anthocyanins, the plant secondary metabolites, play a key role in the process of fruit mature of strawberry .
Confirmation of expression patterns by qRT-PCR
Materials and methods
Strawberry plants (F. vesca) were grown in a greenhouse on a cycle of 16 h light at 30 °C and 8 h dark at 25 °C. SVBV infectious clone, pBIN-1.5SVBV, constructed by insertion of 1.5-mer SVBV cDNA into the plant expression vector pBINPLUS were used for inoculation (unpublished data). Agrobacterium tumefaciens (EHA105) containing pBIN-1.5SVBV was inoculated onto the top two leaves of Fragaria vesca plants. For southern blot, total DNA was extracted from strawberry leaves, transferred onto a Hybond-N+ membrane (GE Healthcare), and immersed into the buffer containing radioactively labeled DNA. The radioactivity has been detected using X-ray film according to manufacturer’s instructions.
Mock-inoculated (T1) and SVBV-infected strawberry leaves (T2) were harvested at 40 dpi. In order to account for the variation between individual plants, three leaves from three different plants were used to prepare each RNA sample. Total RNAs were extracted from leaf tissues using TRIzol Reagent following the manufacturer’s instructions (Invitrogen). RNA concentration and integrity were analyzed on an Agilent 2100 Bioanalyzer (Agilent Technologies).
Illumina sequencing and CDS analysis
cDNA library preparation and sequencing were conducted by the Biomarker Technology Company, Beijing, China. The cDNA library was sequenced on the Illumina Cluster Station and Illumina Genome Analyzer platform. The Trinity method was used for de novo assembly of reads. The transcripts were clustered by similarity of correct match length beyond the 80 % of longer transcripts or 90 % of shorter transcripts using the multiple sequence alignment tool BLAST . The raw sequence data of two samples were uploaded to NCBI (http://trace.ncbi.nlm.nih.gov/Traces/sra_sub/sub.cgi), and the accession numbers are SRR1930099 for SVBV-infected sample and SRR1930097 for mock-inoculated control, respectively.
The coding sequences (CDS) of the unigenes were predicted by ‘getorf’ model of EMBOSS (http://emboss.sourceforge.net/apps/cvs/emboss/apps/getorf.html/). The complete CDS sequences were compared with the CDS sequences of Nipponbare (Os-Nipponbare-Reference-IRGSP-1.0) (http://rapdb.dna.affrc.go.jp/download/irgsp1.html).
Functional annotation and digital gene expression analysis
We annotated unigenes based on a set of sequential BLAST searches designed to find the most descriptive annotation for each sequence . The assembled unigenes were searched against sequences stored in the online databases NR, NT, Swiss-Prot, KEGG, COG and GO. The Blast2GO program was used to obtain GO annotations for the unigenes, and WEGO software was then used for GO functional classification .
Gene expression levels were measured in RNA-Seq (Invitrogen) analyses as numbers of reads and were normalized with RPKM . IDEG6 software was used to identify differentially expressed genes in pair-wise comparison , and the results of all statistical tests were corrected for multiple testing with the Benjamini–Hochberg false discovery rate (FDR < 0.01).
Quantitative RT-PCR (qRT-PCR) analysis
To validate the results of pyrosequencing analysis, we determined the expression levels of 10 DEGs by qRT-PCR. Total RNAs from each sample were extracted using TRIzol reagent (Invitrogen) and qRT-PCR was performed using SYBR® Premix Ex Tap™ II (TaKaRa) according to the manufacturer’s instructions. qRT-PCR cycles were carried out on a Step One Real-Time PCR system (ABI) as follows: 30 s at 95 °C for denaturation, followed by 40 cycles of 5 s at 95 °C for denaturation, 30 s at 60 °C for annealing. Fluorescence data was collected at 60 °C. A melting curve was performed from 60 °C to 95 °C (held for 1 s per 0.1 °C increase) to examine the specificity of the amplified product. Primers used in qRT-PCR for validation of differentially expressed genes are shown in Additional file 1: Table S1, an actin gene from woodland strawberry was selected as the reference gene. Expression quantification and data analysis were performed in accordance with Bustin et al. .
This research was supported by the only Grant (No. 31371915) from National Natural Science Fund of China.
Availability of data and materials
The raw sequence data of two samples were uploaded to NCBI (http://trace.ncbi.nlm.nih.gov/Traces/sra_sub/sub.cgi). The accession numbers are SRR1930099 for SVBV-infected sample and SRR1930097 for mock-inoculated control, respectively. The coding sequences (CDS) of the unigenes were predicted by ‘getorf’ model of EMBOSS (http://emboss.sourceforge.net/apps/cvs/emboss/apps/getorf.html/). The complete CDS sequences were compared with the CDS sequences of Nipponbare (Os-Nipponbare-Reference-IRGSP-1.0) (http://rapdb.dna.affrc.go.jp/download/irgsp1.html).
Tong Jiang and Jing Chen conceived and designed the study and wrote the paper. Jing Chen, Hanping Zhang and Mingfeng Feng performed the experiments and data analysis. All authors read and approved the final manuscript.
The authors declare that they have no competing interest.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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