- Open Access
Assessment of reference gene stability in Rice stripe virus and Rice black streaked dwarf virus infection rice by quantitative Real-time PCR
© Fang et al. 2015
Received: 11 June 2015
Accepted: 16 October 2015
Published: 24 October 2015
Stably expressed reference gene(s) normalization is important for the understanding of gene expression patterns by quantitative Real-time PCR (RT-qPCR), particularly for Rice stripe virus (RSV) and Rice black streaked dwarf virus (RBSDV) that caused seriously damage on rice plants in China and Southeast Asia.
The expression of fourteen common used reference genes of Oryza sativa L. were evaluated by RT-qPCR in RSV and RBSDV infected rice plants. Suitable normalization reference gene(s) were identified by geNorm and NormFinder algorithms.
UBQ 10 + GAPDH and UBC + Actin1 were identified as suitable reference genes for RT-qPCR normalization under RSV and RBSDV infection, respectively. When using multiple reference genes, the expression patterns of OsPRIb and OsWRKY, two virus resistance genes, were approximately similar with that reported previously. Comparatively, by using single reference gene (TIP41-Like), a weaker inducible response was observed.
We proposed that the combination of two reference genes could obtain more accurate and reliable normalization of RT-qPCR results in RSV- and RBSDV-infected plants. This work therefore sheds light on establishing a standardized RT-qPCR procedure in RSV- and RBSDV-infected rice plants, and might serve as an important point for discovering complex regulatory networks and identifying genes relevant to biological processes or implicated in virus.
Rice viral diseases are major threats to rice production and have been distributed worldwide across regions depending on rice cultivation . Two of the most prevalent rice viruses are RSV and RBSDV, which were transmitted by a small brown planthopper (SBPH, Laodelphax striatellus Fallen) [2–4]. When infected with RSV at the seedling stage, normally, rice plants grow poorly and often develop folded and twisted leaves, with the central leaves yellowing and withering; and plant growth may terminate and ultimately the plant will die [5–7]. In China, rice stripe is very serious, especially in Jiangsu province, where about 0.6 M ha per year of rice were infected by RSV during the period of 2000 to 2003, increasing to 1 M ha in 2004. In heavily infected fields, rice yield is reduced by 30–50 %, and in some of the most severely infected fields, no harvest is possible . In RBSDV infected rice always develops stunted stems, dark green, twisted leaves, and white waxy swellings along veins on the abaxial surface of the leaves [3, 9, 10]. The disease caused severely damage on rice in most parts of eastern China with due to widespread release of susceptible cultivars. Since the infection damage was very severe in China and Southeast Asia, the understanding of the responses of rice to viral infection, especially gene expression analysis, is very important for developing strategies for disease control [8, 11].
The widely used method to measure transcript abundance is RT-qPCR compared to reverse transcription-polymerase chain reaction (RT-PCR) and northern blot [12–14]. Besides being a powerful tool, RT-qPCR suffers from certain pitfalls, most important being the normalization with a reference gene [15–17]. In recent years, the reference genes, such as those encoding actin (Actin), tubulin (TUB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and 18S rRNA, are often separately chosen for the normalization in RT-qPCR because of their constant expression levels in living organisms . Nevertheless, different studies sometimes proved different or even opposing results of these reference genes, and it was demonstrated that the transcript levels of these genes actually vary under different experimental conditions [18–21]. For example, different expression levels normalized by a different reference gene could be approximately 100 folds . Furthermore, Myzus persicae’s actin and GAPDH protein were found to interaction with Beet western yellows virus in vitro  and some A.pisumwere’s genes (Actin and GAPDH) considered to be potentially related to the transmission of Peaenation mosaic virus and Soybean dwarf virus . Thus, it is important and necessary to select suitable reference gene(s) for different experimental paradigms, particularly in RSV and RBSDV infection conditions which those appropriate internal reference(s) were not identified [25–27].
In this study, we reported the validation of reference genes to identify the most suitable internal control gene(s) for the normalization of RT-qPCR data upon viral infection in rice plants. Using statistical algorithms geNorm and Norm Finder [28, 29], the stability of 14 candidate reference genes (Actin, UBC, 18S rRNA, EF-1α, UBQ 5, GAPDH, α-TUB, β-TUB, eIF-4α, Actin1, UBQ 10, TIP41-like, EXP and Os AOC) was examined and compared. Two best reference genes were identified more stably expressed than traditional ones in RSV- and RBSDV-infected treatments. Our results further indicated that the combination of these two reference genes provides a good starting point for gene expression analysis in rice viral infection plants by RT-qPCR.
Rice infectivity assay
Identification of candidate reference genes
Candidate reference genes and their primer sequences used in this study
Primer sequence (5′ → 3′)
Amplicon length (bp)
Ubiquitin-conjugating enzyme E2
18S ribosomal RNA
Eukaryotic elongation factor1-alpha
Eukaryotic-initiation factor 4α
TIP41-like family protein
Allene oxide cyclase
Because the transcript levels of reference genes actually varied under different experimental conditions [18, 21], we analyzed if virus infection (RSV and RBSDV infection) altered the expression of any of the 14 candidate genes. The Ct values obtained for each gene were compared in RSV- and RBSDV-infected against virus-free treatment. In this study, our results showed that each candidate reference gene expressed constantly in different treatments (Fig. 3b). Variation in transcript quantity of RSV- and RBSDV-infected samples revealed that each tested gene approximately exhibited the similar transcript levels. Therefore, above 14 references genes were selected in the subsequent analyses.
Comparison of the expression stability of housekeeping genes by geNorm and NormFinder
The expression stability of abovementioned candidate reference genes could be assessed by different software programs. To date, the most popular and useful method is geNorm algorithm [22, 28]. The geNorm is a statistical algorithm which determines the gene stability measure (M) of all the genes under investigation, based on the geometric averaging of multiple control genes and mean pairwise variation of a gene from all other control genes in a given set of samples . It relies on the principle that the expression ratio of two ideal internal control genes is identical in all the samples, regardless of the experimental condition and cell-type. Genes with the lowest M values have the most stable expression.
In general, it is not sufficient by using only one most stable reference gene to obtain an accurate and reliable result. Therefore, the next question is how many reference genes should be included for RT-qPCR normalization. The geNorm software also calculated the pairwise variations (V n/n+1 ) between two sequential normalization factors to determine the necessity of adding further reference gene(s). As suggested by Vandesompele et al. , 0.15 is a cutoff V value, below which the inclusion of an additional reference gene is not required. But this proposed value can not be taken as an absolute rule and might depend on the data. It is advisable to add additional reference genes to the normalization factor until the added gene has no significant effect [28, 30].
The pairwise variation analyses showed that all of V values were less than 0.15 in this set of samples (Fig. 4c and d). Moreover, the V 3/4 values were higher than that of V 2/3 and V 4/5. Although V 4/5 values were much lower than that of V 3/4 and even lower values were obtained by adding more reference genes, considering practical applications, two reference genes was optimal in our experimental conditions.
Comparison of single and multiple reference gene(s) in quantitative Real-time PCR normalization
RSV and RBSDV are two significant rice viruses that threat rice production. To understand the mechanism of viral transmission and discover some resistance genes, a reliable quantitation method is particularly needed. RT-qPCR is still the most commonly used technique. However, an accurate and reliable gene expression analyzed by RT-qPCR highly requires stably expressed reference genes for normalization. In fact, no one gene can act as a universal reference under different experimental conditions, and the normalization of gene expression with a single reference gene can usually lead to relatively errors [28, 30]. Thus, two or more stably reference genes for normalizing are essential.
In this study, the suitable reference genes for normalizing gene expression in RSV- and RBSDV-infected rice plants were identified. We first assessed the integrity of RNA samples (Fig. 2c). Meanwhile, the specificity of the RT-qPCR primer pairs was confirmed by agarose gel electrophoresis (Fig. 2b) and melting curves analysis (Fig. 2a). The relative abundance of candidate reference genes was further identified (Fig. 3). The results of these indicated that the candidate reference genes could be used for the subsequent analyses.
To date, the commonly used methods to assess the stability of reference genes are geNorm , NormFinder  and BestKeeper . As BestKeeper cannot test more than 10 candidates, it was not used in this study. By using geNorm and NormFinder, algorithms, 14 reference genes (Table 1) were evaluated. Our findings revealed that UBQ 10 and GAPDH were overall the most stable genes in RSV-infected rice (Figs. 4a and 5a), and Actin1 and UBC ranked the best candidate genes under RBSDV infection (Figs. 4b and 5b). Interestingly, the suitable reference genes for RSV- and RBSDV-infected rice plants were different, it is indicated that the expression of the reference genes can vary under given situations . Therefore, it is vital to choose suitable reference gene(s) for normalization of gene expression. Moreno et al.  showed that in different Cassava brown streak virus (CBSV)-infected tissues, PP2A, UBQ10 and GTPb appeared to be the most stable genes. In infected tomato plants, GAPDH and UBQ indicated as the most appropriate internal standards both in leaves and root tissues, and Actin was also stably expressed in the infected plants . When infected with four commonly known tomato viral pathogens, ACT, CAC and EF1-α were considered as the most suitable reference genes in the studies of host–virus interactions . Therefore, the selected multiple reference genes (UBQ 10 + GAPDH for RSV, and UBC + Actin1 for RBSDV) could be used for the normalization of gene expression pattern in RSV- and RBSDV-infected rice plants.
Normally, EF1-α and 18S rRNA were used as the reference genes in RT-qPCR experiments. Previous results showed that EF1-α was expressed stably in potato during biotic and abiotic stress , and 18S rRNA was identified to be suitable for normalisation in Barley yellow dwarf virus-infected cereals . In our study, the performance of above two genes was dissatisfactory. These were similar to the earlier studies in Cicer arietinum and virus-infected tomato, showing the considerable alterations in the transcript levels of EF1-α and 18S rRNA [42, 46]. Thus, the two traditional reference genes might not be the optimal choices for quantitating transcript level in rice during RSV- and RBSDV-infection. Meanwhile, TUB was widely used as reference gene for gene expression analysis. However, due to the potential regulation in various physiological states, the suitability as internal control has been questioned [45, 47]. In our experimental conditions, α-TUB and β-TUB (in particularly) were not stable either, especially in RBSDV-infected plants. Taken together, our data demonstrated that α-TUB and β-TUB were unsuitable for the normalization of gene expression levels in viral infected rice.
To further investigate the suitability of the selected reference genes, the expression levels of two virus resistant response genes, the gene expression of OsPR1b and OsWRKY, was compared by using single and multiple reference gene(s) for normalization. In the earlier studies [38, 39], for example, the expression of OsPR1b gene was strongly induced by virus infection. Our results further showed that when using TIP41-Like gene for normalization, the induction patterns of OsPR1b and OsWRKY transcripts were not stronger than those using two reference genes (Fig. 6). Thus, the application of multiple reference genes is a better choice for gene expression analysis under virus infection situation.
In conclusion, we evaluated 14 candidate reference genes, and identified UBQ 10 + GAPDH and UBC + Actin1 as the most stably expressed reference genes in RSV- and RBSDV-infected rice, respectively. These genes will enable more accurate and reliable normalization of RT-qPCR analysis for gene expression studies to get insight into complex regulatory networks and will most probably lead to the identification of genes relevant to new biological processes, in RSV- and RBSDV-infected plants.
Plant materials, virus isolates and inoculation assay
Rice (Oryza sativa L. cv. Nipponbare) seeds were used throughout the experiments. After the disinfection with 0.1 % HgCl2 for 1 h and thorough washing with RO (reverse-osmosis) water, seeds were soaked overnight in RO water and incubation at 25 °C for 1 day. Seedlings were then grown on plastic chambers using Kimura B nutrient solution , with a 14/10 h (day/night) regimes at 28 ± 1 °C.
Rice plants infected with RSV and RBSDV, were collected from Jianhu county, Jiangsu Province in July 2014. Young instar nymphs of SBPHs were fed RSV- and RBSDV-infected rice plants for 2 days to acquire the virus, respectively. The virus was maintained by SBPHs in an insect-rearing room at a temperature of 25 °C. Virus-free SBPHs were also used for the control inoculation. Viruliferous or virus-free SBPHs were reared on rice seedlings (Oryza sativa L. cv. Wuyujing No. 3) in glass vessels at 22 °C under alternating photoperiods of 14 h of light and 10 h of dark. 14-day-old seedlings were inoculated with 10 viruliferous nymphs per plant and were kept in a growth chamber. After the incubation for 3 days, planthoppers were removed and plants were transferred to field. Virus-free SBPHs were also used for control inoculation. Leaf tissues were collected after 7, 14 and 21 days of virus treatment and immediately frozen in liquid nitrogen and stored at −80 °C until further analyses.
According to previous report , RT-PCR was carried out to detect RSV and RBSDV in rice plants. Primer RSRB-R (5′-CCYATCACAAASAAATMAAAAT-3′) paired with primer RSV-F (5′-AGATCCAGAGAGAGTCACGGAAG-3′) was used to amplify a specific 1114 bp fragment for detection of RSV. Primers RSRB-R and RBSDV-F (5′-GTTCAAAGACAATACACTCAAAA-3′) were used to amplify a 414 bp product, which was specific for RBSDV.
Preparation and quantification of DNA-free total RNA
Total RNA was extracted by Trizol reagent (Invitrogen, Gaithersburg, MD, USA) according to the manufacturer’s instructions. The RNA was dissolved in DNase-treated distilled water. The concentration and quality of isolated RNA were determined using the NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Only the RNA samples with 260/280 ratio (an indication of protein contamination) between 1.9 and 2.1 and 260/230 ratio (an indication of reagent contamination) greater than 2.0, were used for the analyses [50, 51]. The integrity of RNA samples was assessed by agarose gel electrophoresis. All RNA samples were adjusted to the same concentration and measured again to homogenize RNA for the subsequent experiments.
Reverse transcription and quantitative Real-time PCR assay
cDNA was synthesized from 2 μg of total RNA using an oligo (dT) primer and M-MLV reverse transcriptase (BioTeke, Beijing, China). RT-qPCR was performed using the SsoFastTM Eva Green® Supermix (Bio-Rad) with the Bio-Rad iQ5 RT-qPCR system. Combined with all internal control genes used recently [25, 27, 28], we further selected 14 genes as the candidate reference genes in this study (Table 1). Additionally, the specific primers 5′-ACGCCTTCACGGTCCATAC-3′ and 5′-AAACAGAAAGAAACAGAGGGAGTAC-3′ were used for OsPR1b (AK107926); 5′-TCAGTGGAGAAGCGGGTGGTG-3′ and 5′-GGGTGGTTGTGCTCGAAGGAG-3′ were used for OsWRKY (EF143611). The efficiency and specificity of all the primers were checked by melting curve analysis, similar to previous report .
Real-time PCR primers of virus genes (RSV and RBSDV) used in this study
Primer sequence (5′ → 3′)
Amplicon length (bp)
Each quantitative Real-time PCR was performed using 0.5 μL cDNA, 10 μL of SsoFastTM Eva Green® Supermix and 0.2 μM forward and reverse primers were used in a total volume of 20 μL. All tubes were subjected to denaturation for 10 min at 95 °C, followed by 40 cycles of 95 °C for 10 s, and 56 °C for 20 s. SYBR Green absorbance was detected at 56 °C. All reactions were conducted in triplicate. Amplicon dissociation curves (melting curves), were recorded after cycle 40 by heating from 60 °C to 95 °C at a ramp speed of 1.9 °C ⁄min.
The expression stability of the candidate reference genes were analyzed by using geNorm  and NormFinder . Expression levels were assessed based on the number of amplification cycles needed to reach a specific threshold (cycle threshold; Ct) in the exponential phase of RT-qPCR. For both programs, raw Ct values of each gene were converted into relative quantities before inputting into software. The relative expression levels of corresponding genes were calculated relative to the maximum abundance in different samples. The highest relative expression for each gene was set to 1.0. The geNorm algorithm  derives a stability measure (M). Via a stepwise exclusion of the least stable gene, it creates a stability ranking. It also estimates the number of genes required to calculate a robust normalization factors, and performs a stepwise analysis (more stable to less stable genes) to calculate the pairwise variation (V n/n+1 ) between two sequential normalization factors containing an increasing number of genes. NormFinder algorithm  used an ANOVA-based model to estimate intra- and inter-group variation. It combines these results to provide a direct measurement of the variation in the expression for each gene . Statistical significance of Ct differences between treatments was calculated by the Mann–Whitney t test using the GraphPad Prism 5 software.
This study was financially supported by grants from the National Key Basic Research and Development Program (973 Program) of China (2013CBA01403), the National Natural Science Foundation (31101412), the Special Fund for Agro-scientific Research in the Public Interest (201303021), the Jiangsu Agricultural Scientific Self-innovation Fund (No.cx1053) and the Jiangsu Province Science and Technology Support Project (BE2013301).
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.
- Hibino H. Biology and epidemiology of rice viruses. Annu Rev Phytopathol. 1996;34:249–74.View ArticlePubMedGoogle Scholar
- Kuribayashi K. On the relationship between rice stripe disease and Delphacodes striatella Fallen. J Plant Prot. 1931;18:565–71. 636–640.Google Scholar
- Shikata E, Kitagawa Y. Rice black-streaked dwarf virus: its properties, morphology and intracellular localization. Virology. 1977;77:826–42.View ArticlePubMedGoogle Scholar
- Falk BW, Tsai JH. Biology and molecular biology of viruses in the genus Tenuivirus. Annu Rev Phytopathol. 1998;36:139–63.View ArticlePubMedGoogle Scholar
- Kiso A, Yamamoto T. Infection and symptom in rice stripe disease with special reference to disease-specific protein other than virus. Rev Plant Prot Res. 1973;6:75–100.Google Scholar
- Shinkai A. Studies on insect transmission of rice virus diseases in Japan. Bull Nat Inst Agric Sci Ser C. 1962;14:1–12.Google Scholar
- Iida TT, Shinkai A. Transmission of dwarf, yellow dwarf, stripe and black-streaked dwarf. In: RF Chandler, editor. The virus diseases of the rice plant. International Rice Research Institute, the Johns Hopkins Press: Baltimore, MD; 1969. p. 125–129.Google Scholar
- Sun DZ, Jiang L. Research on the inheritance and breeding of rice stripe resistance. Chin Agric Sci Bull. 2006;22:318–22.Google Scholar
- Fang S, Yu J, Feng J, Han C, Li D, Liu Y. Identification of rice black-streaked dwarf fijivirus in maize with rough dwarf disease in China. Arch Virol. 2001;146:167–70.View ArticlePubMedGoogle Scholar
- Isogai M, Uyeda I, Lee BC. Detection and assignment of proteins encoded by rice black streaked dwarf fijivirus S7, S8, S9 and S10. J Gen Virol. 1998;79:1487–94.View ArticlePubMedGoogle Scholar
- Dong GK, Wang EG, Luo GL, Lin LW, Guan MP, Zhang ZD, et al. Occurrence of rice black-streaked dwarf disease in late season hybrid rice and its control strategy. Acta Agric Zhejiangensis. 1999;11:364–7.Google Scholar
- Gachon C, Mingam A, Charrier B. Real-time PCR: what relevance to plant studies? J Exp Bot. 2004;55:1445–54.View ArticlePubMedGoogle Scholar
- Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR–a perspective. J Mol Endocrinol. 2005;34:597–601.View ArticlePubMedGoogle Scholar
- Dekkers BJW, Willems L, Bassel GW, van Bolderen-Veldkamp RP, Ligterink W, Hilhorst HWM, et al. Identification of reference genes for RT-qPCR expression analysis in Arabidopsis and tomato seeds. Plant Cell Physiol. 2012;53:28–37.View ArticlePubMedGoogle Scholar
- Bustin SA, Nolan T. Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15:155–66.PubMed CentralPubMedGoogle Scholar
- Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6:279–84.View ArticlePubMedGoogle Scholar
- Skern R, Frost P, Nilsen F. Relative transcript quantification by quantitative PCR: roughly right or precisely wrong? BMC Mol Biol. 2005;6:10.PubMed CentralView ArticlePubMedGoogle Scholar
- Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005;139:5–17.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee PD, Sladek R, Greenwood CMT, Hudson TJ. Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res. 2002;12:292–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A. Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 2004;313:856–62.View ArticlePubMedGoogle Scholar
- Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999;75:291–5.View ArticlePubMedGoogle Scholar
- Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefebvre JF, Louvet R, et al. The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J. 2008;6:609–18.View ArticlePubMedGoogle Scholar
- Seddas P, Boissinot S, Strub JM, Van Dorsselaer A, Van Regenmortel MH, Pattus F. Rack-1, GAPDH3, and actin: proteins of Myzus persicae potentially involved in the transcytosis of beet western yellows virus particles in the aphid. Virology. 2004;325:399–412.View ArticlePubMedGoogle Scholar
- Tamborindeguy C, Monsion B, Brault V, Hunnicutt L, Ju HJ, Nakabachi A, et al. genomic analysis of transcytosis in the pea aphid, Acyrthosiphon pisum, a mechanism involved in virus transmission. Insect Mol Biol. 2010;19:259–72.View ArticlePubMedGoogle Scholar
- Brenndörfer M, Boshart M. Selection of reference genes for mRNA quantification in Trypanosoma brucei. Mol Biochem Parasitol. 2010;172:52–5.View ArticlePubMedGoogle Scholar
- Lord JC, Hartzer K, Toutges M, Oppert B. Evaluation of quantitative PCR reference genes for gene expression studies in Tribolium castaneum after fungal challenge. J Microbiol Methods. 2010;80:219–21.View ArticlePubMedGoogle Scholar
- Radonic A, Thulke S, Bae HG, Muller M, Siegert W, Nitsche A. Reference gene selection for quantitative real-time PCR analysis in virus infected cells: SARS corona virus, Yellow fever virus, Human Herpesvirus-6, Camelpox virus and Cytomegalo virus infections. Virol J. 2006;2:7.View ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:7. resrarch0034–resrarch0034.11.View ArticleGoogle Scholar
- Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–50.View ArticlePubMedGoogle Scholar
- Gutierrez L, Mauriat M, Pelloux J, Bellini C, Van Wuytswinkel O. Towards a systematic validation of references in real-time RT-PCR. Plant Cell. 2008;20:1734–5.PubMed CentralView ArticlePubMedGoogle Scholar
- Hao ZN, Wang LP, He YP, Liang JG, Tao RX. Expression of defense genes and activities of antioxidant enzymes in rice resistance to rice stripe virus and small brown planthopper. Plant Physiol Biochem. 2011;49:744–51.View ArticlePubMedGoogle Scholar
- Satoh K, Kondoh H, De Leon T, Macalalad RJA, Cabunagan RC, Cabauatan PC, et al. Gene expression responses to Rice tungro spherical virus in susceptible and resistant near-isogenic rice plants. Virus Res. 2013;171:111–20.View ArticlePubMedGoogle Scholar
- Lee KJ, Kim K. The rice serine/threonine protein kinase OsPBL1 (ORYZA SATIVA ARABIDOPSIS PBS1- LIKE 1) is potentially involved in resistance to rice stripe disease. Plant Growth Regul. 2015, 1–9. doi: 10.1007/s10725-015-0036-z.
- Qiu D, Xiao J, Ding X, Xiong M, Cai M, Cao Y, et al. OsWRKY13 mediates rice disease resistance by regulating defense-related genes in salicylate- and jasmonate-dependent signaling. Mol Plant Microbe Interact. 2007;20:492–9.View ArticlePubMedGoogle Scholar
- Bai FW, Yan J, Qu ZC, Zhang HW, Xu J, Ye MM, et al. Phylogenetic analysis reveals that a dwarfing disease on different cereal crops in China is due to rice black streaked dwarf virus (RBSDV). Virus Genes. 2002;25:201–6.View ArticlePubMedGoogle Scholar
- Catoni M, Miozzi L, Fiorilli V, Lanfranco L, Accotto GP. Comparative analysis of expression profiles in shoots and roots of tomato systemically infected by Tomato spotted wilt virus reveals organ-specific transcriptional responses. Mol Plant Microbe Interact. 2009;22:1504–13.View ArticlePubMedGoogle Scholar
- Tao Z, Liu H, Qiu D, Zhou Y, Li X, Xu C, et al. A pair of allelic WRKY genes play opposite roles in rice–bacteria interactions. Plant Physiol. 2009;151:936–48.PubMed CentralView ArticlePubMedGoogle Scholar
- Satoh K, Kondoh H, Sasaya T, Shimizu T, Choi IR, Omura T, et al. Selective modification of rice (Oryza sativa) gene expression by rice stripe virus infection. J Gen Virol. 2010;91:294–305.View ArticlePubMedGoogle Scholar
- Zheng WJ, Ma L, Zhao JM, Li ZQ, Sun FY, Lu XU. Comparative transcriptome analysis of two rice varieties in response to rice stripe virus and small brown planthoppers during early interaction. PLoS One. 2013;8:e82126.PubMed CentralView ArticlePubMedGoogle Scholar
- Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26:509–15.View ArticlePubMedGoogle Scholar
- Moreno I, Gruissem W, Vanderschuren H. Reference genes for reliable potyvirus quantitation in cassava and analysis of Cassava brown streak virus load in host varieties. J Virol Methods. 2011;177:49–54.View ArticlePubMedGoogle Scholar
- Mascia T, Santovito E, Gallitelli D, Cillo F. Evaluation of reference genes for quantitative reverse-transcription polymerase chain reaction normalization in infected tomato plants. Mol Plant Pathol. 2010;11:805–16.View ArticlePubMedGoogle Scholar
- Wieczorek P, Wrzesińska B, Obrępalska-Stęplowska A. Assessment of reference gene stability influenced by extremely divergent disease symptoms in Solanum lycopersicum L. J Virol Methods. 2013;194:161–8.View ArticlePubMedGoogle Scholar
- Wan H, Zhao Z, Qian C, Sui Y, Malik AA, Chen J. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem. 2010;399:257–61.View ArticlePubMedGoogle Scholar
- Jarošová J, Kundu J. Validation of reference genes as internal control for studying viral infections in cereals by quantitative real-time RT-PCR. BMC Plant Biol. 2010;10:146.PubMed CentralView ArticlePubMedGoogle Scholar
- Castro P, Roman B, Rubio J, Die JV. Selection of reference genes for expression studies in Cicer arietinum L.: analysis of cyp81E3 gene expression against Ascochyta rabiei. Mol Breed. 2012;29:261–74.View ArticleGoogle Scholar
- Mafra V, Kubo KS, Alves-Ferreira M, Ribeiro-Alves M, Stuart RM, Boava LP, et al. Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS One. 2012;7:e31263.PubMed CentralView ArticlePubMedGoogle Scholar
- Ma JF, Goto S, Tamai K, Ichii M. Role of root hairs and lateral roots in silicon uptake by rice. Plant Physiol. 2011;127:1773–80.View ArticleGoogle Scholar
- Li S, Wang X, Xu JX, Ji YH, Zhou YJ. A simplified method for simultaneous detection of Rice stripe virus and Rice black-streaked dwarf virus in insect vector. J Virol Methods. 2015;211:32–5.View ArticlePubMedGoogle Scholar
- Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1:1559–82.View ArticlePubMedGoogle Scholar
- Kumar K, Muthamilarasan M, Prasad M. Reference genes for quantitative real-time PCR analysis in the model plant foxtail millet (Setaria italica L.) subjected to abiotic stress conditions. Plant Cell Tiss Org. 2013;115:13–22.View ArticleGoogle Scholar
- Chi XY, Hu RB, Yang QL, Zhang XW, Pan LJ, Chen N, et al. Validation of reference genes for gene expression studies in peanut by quantitative real-time RT-PCR. Mol Genet Genomics. 2012;287:167–76.View ArticlePubMedGoogle Scholar