Assessment of reference gene stability in Rice stripe virus and Rice black streaked dwarf virus infection rice by quantitative Real-time PCR
Identifieur interne : 000349 ( Pmc/Corpus ); précédent : 000348; suivant : 000350Assessment of reference gene stability in Rice stripe virus and Rice black streaked dwarf virus infection rice by quantitative Real-time PCR
Auteurs : Peng Fang ; Rongfei Lu ; Feng Sun ; Ying Lan ; Wenbiao Shen ; Linlin Du ; Yijun Zhou ; Tong ZhouSource :
- Virology Journal [ 1743-422X ] ; 2015.
Abstract
Stably expressed reference gene(s) normalization is important for the understanding of gene expression patterns by quantitative Real-time PCR (RT-qPCR), particularly for
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.
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.
The online version of this article (doi:10.1186/s12985-015-0405-2) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12985-015-0405-2
PubMed: 26497487
PubMed Central: 4619528
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PMC:4619528Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Assessment of reference gene stability in <italic>Rice stripe virus</italic> and <italic>Rice black streaked dwarf virus</italic> infection rice by quantitative Real-time PCR</title><author><name sortKey="Fang, Peng" sort="Fang, Peng" uniqKey="Fang P" first="Peng" last="Fang">Peng Fang</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Lu, Rongfei" sort="Lu, Rongfei" uniqKey="Lu R" first="Rongfei" last="Lu">Rongfei Lu</name><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Sun, Feng" sort="Sun, Feng" uniqKey="Sun F" first="Feng" last="Sun">Feng Sun</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Lan, Ying" sort="Lan, Ying" uniqKey="Lan Y" first="Ying" last="Lan">Ying Lan</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Shen, Wenbiao" sort="Shen, Wenbiao" uniqKey="Shen W" first="Wenbiao" last="Shen">Wenbiao Shen</name><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Du, Linlin" sort="Du, Linlin" uniqKey="Du L" first="Linlin" last="Du">Linlin Du</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Yijun" sort="Zhou, Yijun" uniqKey="Zhou Y" first="Yijun" last="Zhou">Yijun Zhou</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Tong" sort="Zhou, Tong" uniqKey="Zhou T" first="Tong" last="Zhou">Tong Zhou</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26497487</idno><idno type="pmc">4619528</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619528</idno><idno type="RBID">PMC:4619528</idno><idno type="doi">10.1186/s12985-015-0405-2</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000349</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000349</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Assessment of reference gene stability in <italic>Rice stripe virus</italic> and <italic>Rice black streaked dwarf virus</italic> infection rice by quantitative Real-time PCR</title><author><name sortKey="Fang, Peng" sort="Fang, Peng" uniqKey="Fang P" first="Peng" last="Fang">Peng Fang</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Lu, Rongfei" sort="Lu, Rongfei" uniqKey="Lu R" first="Rongfei" last="Lu">Rongfei Lu</name><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Sun, Feng" sort="Sun, Feng" uniqKey="Sun F" first="Feng" last="Sun">Feng Sun</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Lan, Ying" sort="Lan, Ying" uniqKey="Lan Y" first="Ying" last="Lan">Ying Lan</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Shen, Wenbiao" sort="Shen, Wenbiao" uniqKey="Shen W" first="Wenbiao" last="Shen">Wenbiao Shen</name><affiliation><nlm:aff id="Aff3">College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</nlm:aff></affiliation></author><author><name sortKey="Du, Linlin" sort="Du, Linlin" uniqKey="Du L" first="Linlin" last="Du">Linlin Du</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Yijun" sort="Zhou, Yijun" uniqKey="Zhou Y" first="Yijun" last="Zhou">Yijun Zhou</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author><author><name sortKey="Zhou, Tong" sort="Zhou, Tong" uniqKey="Zhou T" first="Tong" last="Zhou">Tong Zhou</name><affiliation><nlm:aff id="Aff1">Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2">Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</nlm:aff></affiliation></author></analytic><series><title level="j">Virology Journal</title><idno type="eISSN">1743-422X</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Stably expressed reference gene(s) normalization is important for the understanding of gene expression patterns by quantitative Real-time PCR (RT-qPCR), particularly for <italic>Rice stripe virus</italic> (RSV) and <italic>Rice black streaked dwarf virus</italic> (RBSDV) that caused seriously damage on rice plants in China and Southeast Asia.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p><italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> 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 <italic>OsPRIb</italic> and <italic>OsWRKY</italic>, two virus resistance genes, were approximately similar with that reported previously. Comparatively, by using single reference gene (<italic>TIP41-Like</italic>), a weaker inducible response was observed.</p></sec><sec><title>Conclusions</title><p>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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12985-015-0405-2) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Hibino, H" uniqKey="Hibino H">H Hibino</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kuribayashi, K" uniqKey="Kuribayashi K">K Kuribayashi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shikata, E" uniqKey="Shikata E">E Shikata</name></author><author><name sortKey="Kitagawa, Y" uniqKey="Kitagawa Y">Y 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Virol J</journal-id><journal-id journal-id-type="iso-abbrev">Virol. J</journal-id><journal-title-group><journal-title>Virology Journal</journal-title></journal-title-group><issn pub-type="epub">1743-422X</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26497487</article-id><article-id pub-id-type="pmc">4619528</article-id><article-id pub-id-type="publisher-id">405</article-id><article-id pub-id-type="doi">10.1186/s12985-015-0405-2</article-id><article-categories><subj-group subj-group-type="heading"><subject>Methodology</subject></subj-group></article-categories><title-group><article-title>Assessment of reference gene stability in <italic>Rice stripe virus</italic> and <italic>Rice black streaked dwarf virus</italic> infection rice by quantitative Real-time PCR</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Fang</surname><given-names>Peng</given-names></name><address><email>2011116138@njau.edu.cn</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref><xref ref-type="aff" rid="Aff3"></xref></contrib><contrib contrib-type="author"><name><surname>Lu</surname><given-names>Rongfei</given-names></name><address><email>2014116011@njau.edu.cn</email></address><xref ref-type="aff" rid="Aff3"></xref></contrib><contrib contrib-type="author"><name><surname>Sun</surname><given-names>Feng</given-names></name><address><email>sunfeng1201@gmail.com</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author"><name><surname>Lan</surname><given-names>Ying</given-names></name><address><email>lanying0918@hotmail.com</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author"><name><surname>Shen</surname><given-names>Wenbiao</given-names></name><address><email>wbshenh@njau.edu.cn</email></address><xref ref-type="aff" rid="Aff3"></xref></contrib><contrib contrib-type="author"><name><surname>Du</surname><given-names>Linlin</given-names></name><address><email>dulinlin12@126.com</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Yijun</given-names></name><address><email>yjzhou@jaas.ac.cn</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Zhou</surname><given-names>Tong</given-names></name><address><phone>+86-025-84390539</phone><email>zhoutong@jaas.ac.cn</email></address><xref ref-type="aff" rid="Aff1"></xref><xref ref-type="aff" rid="Aff2"></xref></contrib><aff id="Aff1"><label></label>Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China</aff><aff id="Aff2"><label></label>Scientific Observing and Experimental Station of Crop Pests in Nanjing, Ministry of Agriculture, China, Nanjing, 210014 China</aff><aff id="Aff3"><label></label>College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095 China</aff></contrib-group><pub-date pub-type="epub"><day>24</day><month>10</month><year>2015</year></pub-date><pub-date pub-type="pmc-release"><day>24</day><month>10</month><year>2015</year></pub-date><pub-date pub-type="collection"><year>2015</year></pub-date><volume>12</volume><elocation-id>175</elocation-id><history><date date-type="received"><day>11</day><month>6</month><year>2015</year></date><date date-type="accepted"><day>16</day><month>10</month><year>2015</year></date></history><permissions><copyright-statement>© Fang et al. 2015</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), 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 (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Stably expressed reference gene(s) normalization is important for the understanding of gene expression patterns by quantitative Real-time PCR (RT-qPCR), particularly for <italic>Rice stripe virus</italic> (RSV) and <italic>Rice black streaked dwarf virus</italic> (RBSDV) that caused seriously damage on rice plants in China and Southeast Asia.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p><italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> 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 <italic>OsPRIb</italic> and <italic>OsWRKY</italic>, two virus resistance genes, were approximately similar with that reported previously. Comparatively, by using single reference gene (<italic>TIP41-Like</italic>), a weaker inducible response was observed.</p></sec><sec><title>Conclusions</title><p>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.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12985-015-0405-2) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Gene expression</kwd><kwd>RT-qPCR</kwd><kwd>Reference genes</kwd><kwd>RSV- and RBSDV-infected rice</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2015</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p>Rice viral diseases are major threats to rice production and have been distributed worldwide across regions depending on rice cultivation [<xref ref-type="bibr" rid="CR1">1</xref>]. Two of the most prevalent rice viruses are RSV and RBSDV, which were transmitted by a small brown planthopper (SBPH, <italic>Laodelphax striatellus Fallen</italic>) [<xref ref-type="bibr" rid="CR2">2</xref>–<xref ref-type="bibr" rid="CR4">4</xref>]. 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 [<xref ref-type="bibr" rid="CR5">5</xref>–<xref ref-type="bibr" rid="CR7">7</xref>]. 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 [<xref ref-type="bibr" rid="CR8">8</xref>]. 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 [<xref ref-type="bibr" rid="CR3">3</xref>, <xref ref-type="bibr" rid="CR9">9</xref>, <xref ref-type="bibr" rid="CR10">10</xref>]. 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 [<xref ref-type="bibr" rid="CR8">8</xref>, <xref ref-type="bibr" rid="CR11">11</xref>].</p><p>The widely used method to measure transcript abundance is RT-qPCR compared to reverse transcription-polymerase chain reaction (RT-PCR) and northern blot [<xref ref-type="bibr" rid="CR12">12</xref>–<xref ref-type="bibr" rid="CR14">14</xref>]. Besides being a powerful tool, RT-qPCR suffers from certain pitfalls, most important being the normalization with a reference gene [<xref ref-type="bibr" rid="CR15">15</xref>–<xref ref-type="bibr" rid="CR17">17</xref>]. In recent years, the reference genes, such as those encoding actin (<italic>Actin</italic>), tubulin (<italic>TUB</italic>), glyceraldehyde-3-phosphate dehydrogenase (<italic>GAPDH</italic>), and <italic>18S rRNA</italic>, are often separately chosen for the normalization in RT-qPCR because of their constant expression levels in living organisms [<xref ref-type="bibr" rid="CR16">16</xref>]. 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 [<xref ref-type="bibr" rid="CR18">18</xref>–<xref ref-type="bibr" rid="CR21">21</xref>]. For example, different expression levels normalized by a different reference gene could be approximately 100 folds [<xref ref-type="bibr" rid="CR22">22</xref>]. Furthermore, <italic>Myzus persicae’s</italic> actin and GAPDH protein were found to interaction with <italic>Beet western yellows virus</italic> in vitro [<xref ref-type="bibr" rid="CR23">23</xref>] and some <italic>A.pisumwere’s</italic> genes (<italic>Actin</italic> and <italic>GAPDH</italic>) considered to be potentially related to the transmission of <italic>Peaenation mosaic virus</italic> and <italic>Soybean dwarf virus</italic> [<xref ref-type="bibr" rid="CR24">24</xref>]. 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 [<xref ref-type="bibr" rid="CR25">25</xref>–<xref ref-type="bibr" rid="CR27">27</xref>].</p><p>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 [<xref ref-type="bibr" rid="CR28">28</xref>, <xref ref-type="bibr" rid="CR29">29</xref>], the stability of 14 candidate reference genes (<italic>Actin</italic>, <italic>UBC</italic>, <italic>18S rRNA</italic>, <italic>EF-1α</italic>, <italic>UBQ 5</italic>, <italic>GAPDH</italic>, <italic>α-TUB</italic>, <italic>β-TUB</italic>, <italic>eIF-4α</italic>, <italic>Actin1</italic>, <italic>UBQ 10</italic>, <italic>TIP41-like</italic>, <italic>EXP</italic> and <italic>Os AOC</italic>) 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.</p></sec><sec id="Sec2" sec-type="results"><title>Results</title><sec id="Sec3"><title>Rice infectivity assay</title><p>We characterized the phenotype of RSV- and RBSDV-infected rice plants, and the symptoms were allowed to develop under the controlled environmental conditions. RSV-infected rice developed folded and twisted leaves, with the central leaves yellowing and withering. Meanwhile in RBSDV-infected rice plants, it developed stunted stems and white waxy swellings along veins on the abaxial surface of the leaves (Fig. <xref rid="Fig1" ref-type="fig">1a</xref>). RSV and RBSDV were respectively detected in inoculated rice, using RT-PCR (Fig. <xref rid="Fig1" ref-type="fig">1b</xref>).<fig id="Fig1"><label>Fig. 1</label><caption><p>RSV and RBSDV infection in rice plants. <bold>a</bold> Disease symptoms developed on rice during virus infection. From left to right, non-inoculated plant, RSV- and RBSDV-infected rice plants. <bold>b</bold> Agarose gel (2 %) showing RSV and RBSDV in inoculated rice (1–3 means three replications)</p></caption><graphic xlink:href="12985_2015_405_Fig1_HTML" id="MO1"></graphic></fig></p></sec><sec id="Sec4"><title>Identification of candidate reference genes</title><p>In order to evaluate the expression stability of reference genes, the candidates should be identified first. In our test, primers were designed for the fourteen commonly used reference genes, identified by BLASTN and TBLASTN searches at National Center for Biotechnology Information (NCBI). The gene names, accession numbers, gene description and primer sequences were all provided in Table <xref rid="Tab1" ref-type="table">1</xref>. First, melting curve for four representative genes (Dissociation curves for all other genes with single peak were not shown) and agarose gel analysis confirmed that each primer set gave a single amplified product of desired size (Fig. <xref rid="Fig2" ref-type="fig">2a</xref> and <xref rid="Fig2" ref-type="fig">b</xref>). Therefore, these genes were selected for RT-qPCR validation. It is also important to accurately quantitate the quality of RNA before reverse transcription. The concentration and quality of isolated RNA were determined using the NanoDrop 2000 spectrophotometer. Agarose gel electrophoresis assay also confirmed the integrity of RNA samples (Fig. <xref rid="Fig2" ref-type="fig">2c</xref>).<table-wrap id="Tab1"><label>Table 1</label><caption><p>Candidate reference genes and their primer sequences used in this study</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Gene name</th><th>Accession number</th><th>Gene description</th><th>Primer sequence (5′ → 3′)</th><th>Amplicon length (bp)</th></tr></thead><tbody><tr><td rowspan="2"><italic>Actin</italic></td><td rowspan="2">AK058421</td><td rowspan="2">Actin</td><td>F-CAGCCACACTGTCCCCATCTA</td><td rowspan="2">86</td></tr><tr><td>R-AGCAAGGTCGAGACGAAGGA</td></tr><tr><td rowspan="2"><italic>UBC</italic></td><td rowspan="2">AK059694</td><td rowspan="2">Ubiquitin-conjugating enzyme E2</td><td>F-CCGTTTGTAGAGCCATAATTGCA</td><td rowspan="2">76</td></tr><tr><td>R-AGGTTGCCTGAGTCACAGTTAAGTG</td></tr><tr><td rowspan="2"><italic>18S rRNA</italic></td><td rowspan="2">AK059783</td><td rowspan="2">18S ribosomal RNA</td><td>F-CTACGTCCCTGCCCTTTGTACA</td><td rowspan="2">65</td></tr><tr><td>R-ACACTTCACCGGACCATTCAA</td></tr><tr><td rowspan="2"><italic>EF-1α</italic></td><td rowspan="2">AK061464</td><td rowspan="2">Eukaryotic elongation factor1-alpha</td><td>F-TTTCACTCTTGGTGTGAAGCAGAT</td><td rowspan="2">103</td></tr><tr><td>R-GACTTCCTTCACGATTTCATCGTAA</td></tr><tr><td rowspan="2"><italic>UBQ 5</italic></td><td rowspan="2">AK061988</td><td rowspan="2">Ubiquitin 5</td><td>F-ACCACTTCGACCGCCACTACT</td><td rowspan="2">69</td></tr><tr><td>R-ACGCCTAAGCCTGCTGGTT</td></tr><tr><td rowspan="2"><italic>GAPDH</italic></td><td rowspan="2">AK064164</td><td rowspan="2">Glyceraldehyde-3-Phosphate dehydrogenase</td><td>F-AAGCCAGCATCCTATGATCAGATT</td><td rowspan="2">79</td></tr><tr><td>R-CGTAACCCAGAATACCCTTGAGTTT</td></tr><tr><td rowspan="2"><italic>α-TUB</italic></td><td rowspan="2">AK067721</td><td rowspan="2">Alpha-tubulin</td><td>F-GGAAATACATGGCTTGCTGCTT</td><td rowspan="2">89</td></tr><tr><td>R-TCTCTTCGTCTTGATGGTTGCA</td></tr><tr><td rowspan="2"><italic>β-TUB</italic></td><td rowspan="2">AK072502</td><td rowspan="2">Beta-tubulin</td><td>F-GCTGACCACACCTAGCTTTGG</td><td rowspan="2">82</td></tr><tr><td>R-AGGGAACCTTAGGCAGCATGT</td></tr><tr><td rowspan="2"><italic>eIF-4α</italic></td><td rowspan="2">AK073620</td><td rowspan="2">Eukaryotic-initiation factor 4α</td><td>F-TTGTGCTGGATGAAGCTGATG</td><td rowspan="2">76</td></tr><tr><td>R-GGAAGGAGCTGGAAGATATCATAGA</td></tr><tr><td rowspan="2"><italic>Actin1</italic></td><td rowspan="2">AK100267</td><td rowspan="2">Actin1</td><td>F-CTCCCCCATGCTATCCTTCG</td><td rowspan="2">67</td></tr><tr><td>R-TGAATGAGTAACCACGCTCCG</td></tr><tr><td rowspan="2"><italic>UBQ 10</italic></td><td rowspan="2">AK101547</td><td rowspan="2">Ubiquitin 10</td><td>F-TGGTCAGTAATCAGCCAGTTTGG</td><td rowspan="2">65</td></tr><tr><td>R-GCACCACAAATACTTGACGAACAG</td></tr><tr><td rowspan="2"><italic>TIP41-Like</italic></td><td rowspan="2">AK103511</td><td rowspan="2">TIP41-like family protein</td><td>F-GTTTGGATGAACCCCGCAA</td><td rowspan="2">75</td></tr><tr><td>R-GGCAACAAGGTCAATCCGATC</td></tr><tr><td rowspan="2"><italic>EXP</italic></td><td rowspan="2">Os06g11070</td><td rowspan="2">Expressed protein</td><td>F-AGGCTGGTCGAGGAGTCCAT</td><td rowspan="2">84</td></tr><tr><td>R-TTCTCCTCCCTAGCGAACACCT</td></tr><tr><td rowspan="2"><italic>Os AOC</italic></td><td rowspan="2">AI493664</td><td rowspan="2">Allene oxide cyclase</td><td>F-CCACCATCACAGATCGGATCTT</td><td rowspan="2">78</td></tr><tr><td>R-GCGGTCAGAGCGAAAGTAGCTA</td></tr></tbody></table></table-wrap><fig id="Fig2"><label>Fig. 2</label><caption><p>The specificity of real time PCR amplification. <bold>a</bold> Dissociation curves for four representative genes with single peak obtained from three technical replicates. <bold>b</bold> Agarose gel (2 %) showing amplification of a specific PCR product of expected size for each reference gene tested in the study; <bold>c</bold> Agarose gel showing the quality of RNA (0, 7, 14 and 21 means days after RSV- and RBSDV-infection)</p></caption><graphic xlink:href="12985_2015_405_Fig2_HTML" id="MO2"></graphic></fig></p><p>To give an overview picture of the relative abundance of candidate reference genes, the calculated cycle threshold (Ct) values were determined for each gene across all the tested RSV- and RBSDV-infected samples (Fig. <xref rid="Fig3" ref-type="fig">3a</xref>). RT-qPCR analysis indicated that 14 candidate reference genes exhibited different levels of abundance. The Ct values ranged from 14 to 30, with the most lying between 22 and 28. <italic>18S rRNA</italic> and <italic>UBQ 10</italic> were more abundantly transcribed than others, while <italic>β-TUB</italic> was the least expressed gene across all the tested samples.<fig id="Fig3"><label>Fig. 3</label><caption><p>Expression levels of candidate reference genes. <bold>a</bold> Average cycle threshold (Ct) values for the 14 candidate reference genes used in this study; <bold>b</bold> Comparison of the expression levels of candidate reference genes in different treatments (Con: virus-free SBPHs infection; RSV: RSV SBPHs infection; RBSDV: RBSDV SBPHs infection). 14-day-old rice seedlings were inoculated with or without viruliferous nymphs (RSV and RBSDV) for 3 days. Total RNA was extracted from RSV- and RBSDV-infected seedlings, respectively. Values were given in the form of RT-qPCR quantification cycle numbers across all tested samples. Bars indicate standard error of the mean</p></caption><graphic xlink:href="12985_2015_405_Fig3_HTML" id="MO3"></graphic></fig></p><p>Because the transcript levels of reference genes actually varied under different experimental conditions [<xref ref-type="bibr" rid="CR18">18</xref>, <xref ref-type="bibr" rid="CR21">21</xref>], 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. <xref rid="Fig3" ref-type="fig">3b</xref>). 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.</p></sec><sec id="Sec5"><title>Comparison of the expression stability of housekeeping genes by geNorm and NormFinder</title><p>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 [<xref ref-type="bibr" rid="CR22">22</xref>, <xref ref-type="bibr" rid="CR28">28</xref>]. 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 [<xref ref-type="bibr" rid="CR28">28</xref>]. 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.</p><p>In our experimental conditions, geNorm analysis showed that <italic>GAPDH</italic> and <italic>UBQ 10</italic> were ranked as the best reference genes in RSV-infected plants, followed by <italic>EXP</italic>, while <italic>UBC</italic> and <italic>18S rRNA</italic> were the least stable genes (Fig. <xref rid="Fig4" ref-type="fig">4a</xref>). When considering RBSDV-infected treatments, the average expression stability value (M) of <italic>Actin1</italic> and <italic>UBC</italic> were two most stable genes, followed by <italic>UBQ 10</italic>, and those of <italic>β-TUB</italic> and <italic>TIP41-like</italic> were the highest two (Fig. <xref rid="Fig4" ref-type="fig">4b</xref>).<fig id="Fig4"><label>Fig. 4</label><caption><p>Average expression stability values (M) and pairwise variation (<italic>V</italic>) analyses of candidate reference genes under virus-infected conditions by geNorm. 14 candidate reference genes were amplified in cDNA samples from RSV- and RBSDV-infected seedlings. A lower M value indicated more stable expression. Mean expression stability following stepwise exclusion of the least stable gene across samples from RSV-infected treatments (<bold>a</bold>) or RBSDV-infected samples (<bold>b</bold>), respectively. The pairwise variation (<italic>V</italic><sub>n/n+1</sub>) measured the effect of adding additional reference genes on the normalization factor (The dash line denotes 0.15 cut-off <italic>V</italic> value) for these treatments (<bold>c</bold> and <bold>d</bold>). Calculations were performed as described in “Materials and Methods” section</p></caption><graphic xlink:href="12985_2015_405_Fig4_HTML" id="MO4"></graphic></fig></p><p>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 (<italic>V</italic><sub><italic>n/n+1</italic></sub>) between two sequential normalization factors to determine the necessity of adding further reference gene(s). As suggested by Vandesompele et al. [<xref ref-type="bibr" rid="CR28">28</xref>], 0.15 is a cutoff <italic>V</italic> 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 [<xref ref-type="bibr" rid="CR28">28</xref>, <xref ref-type="bibr" rid="CR30">30</xref>].</p><p>The pairwise variation analyses showed that all of <italic>V</italic> values were less than 0.15 in this set of samples (Fig. <xref rid="Fig4" ref-type="fig">4c</xref> and <xref rid="Fig4" ref-type="fig">d</xref>). Moreover, the <italic>V</italic><sub>3/4</sub> values were higher than that of <italic>V</italic><sub>2/3</sub> and <italic>V</italic><sub>4/5</sub>. Although <italic>V</italic><sub>4/5</sub> values were much lower than that of <italic>V</italic><sub>3/4</sub> and even lower values were obtained by adding more reference genes, considering practical applications, two reference genes was optimal in our experimental conditions.</p><p>Different algorithm methods sometimes may produce different results from the same data set. Hence, all of the data were reassessed by NormFinder to avoid introducing unnecessary bias. NormFinder is a mathematical model which performs separate analysis of sample subgroups. It estimates intra- and inter-group variations and combines both results into a consistent value for each investigated gene [<xref ref-type="bibr" rid="CR29">29</xref>]. Interestingly, we found that the ranking generated by this approach (Fig. <xref rid="Fig5" ref-type="fig">5</xref>) was approximately similar with those determined by geNorm (Fig. <xref rid="Fig4" ref-type="fig">4a</xref> and <xref rid="Fig4" ref-type="fig">b</xref>). <italic>UBC</italic> and <italic>18S rRNA</italic> were sill ranked higher than other reference genes in RSV-infected samples. In the RBSDV-infected samples, <italic>β-TUB</italic> and <italic>TIP41-Like</italic> were also ranking the highest. Although in RSV-infected samples, the rank order of <italic>EXP</italic> and <italic>GAPDH</italic> were slightly altered in NormFinder analysis in comparison with those in geNorm analyses, the stability of <italic>UBQ 10</italic> + <italic>GAPDH</italic> was high enough for reliable normalization compared to those of <italic>UBC</italic> and <italic>18S rRNA</italic>. Also, the similar result was obtained in the RBSDV-treated samples, showing that <italic>Actin1</italic> + <italic>UBC</italic> were suitable for normalization in RT-qPCR analysis.<fig id="Fig5"><label>Fig. 5</label><caption><p>The ranking of candidate reference genes based on stability values calculated by NormFinder under virus-infected conditions. 14 candidate reference genes were amplified in cDNA samples from RSV- and RBSDV-infected seedlings. Relative quantifications of RSV-infected (<bold>a</bold>) or RBSDV-infected (<bold>b</bold>) seedlings, were performed respectively, as described in “Materials and Methods” section</p></caption><graphic xlink:href="12985_2015_405_Fig5_HTML" id="MO5"></graphic></fig></p></sec><sec id="Sec6"><title>Comparison of single and multiple reference gene(s) in quantitative Real-time PCR normalization</title><p>In order to demonstrate the usefulness of the above validated reference genes in RT-qPCR, two important genes in virus resistant response, <italic>OsPR1b</italic> [<xref ref-type="bibr" rid="CR31">31</xref>–<xref ref-type="bibr" rid="CR33">33</xref>] and <italic>OsWRKY</italic> [<xref ref-type="bibr" rid="CR34">34</xref>–<xref ref-type="bibr" rid="CR37">37</xref>], were selected and applied as target genes (Fig. <xref rid="Fig6" ref-type="fig">6</xref>). In RSV- and RBSDV-infected samples, <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> were used as multiple reference genes, respectively. Meanwhile, <italic>TIP41-Like</italic> was used as single reference gene in our experimental conditions. As expected, in comparison with single reference gene, the normalization of <italic>OsPR1b</italic> using <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic>, respectively, resulted in significant increase in transcript levels under virus infection conditions (Fig. <xref rid="Fig6" ref-type="fig">6a</xref> and <xref rid="Fig6" ref-type="fig">b</xref>). The result was similar with previous studies [<xref ref-type="bibr" rid="CR38">38</xref>, <xref ref-type="bibr" rid="CR39">39</xref>]. Meanwhile, the expression levels of <italic>OsWRKY</italic> were progressively increased in the early stage of infection and then decreased (Fig. <xref rid="Fig6" ref-type="fig">6c</xref> and <xref rid="Fig6" ref-type="fig">d</xref>). By contrast, when <italic>TIP41-Like</italic> was used as single reference gene, the increasing transcript patterns of <italic>OsPR1b</italic> and <italic>OsWRKY</italic> were differentially lower than those normalized by multiple reference genes.<fig id="Fig6"><label>Fig. 6</label><caption><p>Relative expression levels of <italic>OsPR1b</italic> and <italic>OsWRKY</italic> using single or multiple reference gene(s) for normalization during RSV- (<bold>a</bold> and <bold>c</bold>) and RBSDV- (<bold>b</bold> and <bold>d</bold>) infection. 14-day-old rice seedlings were inoculated with viruliferous nymphs (RSV and RBSDV) for 3 days. Total RNA was extracted from RSV- and RBSDV-infected seedlings, respectively. <italic>TIP41-Like</italic> was used as a single reference gene, while <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> were used multiple reference genes under RSV- and RBSDV-infection plants, respectively, in our experimental conditions. Bars indicate standard error of the mean</p></caption><graphic xlink:href="12985_2015_405_Fig6_HTML" id="MO6"></graphic></fig></p><p>We also detect the virus gene expression in plants during viral infection. According to the results, the expression patterns of the virus gene are consistent with the expression level of virus resistant gene (Fig. <xref rid="Fig7" ref-type="fig">7</xref>). The expression level normalized by multiple reference genes were better than those by single reference, therefore multiple reference genes were suitable for normalization in RT-qPCR analysis.<fig id="Fig7"><label>Fig. 7</label><caption><p>Relative expression levels of virus gene<italic>s</italic> (<italic>CP</italic> and <italic>SP</italic> for RSV, <italic>P5-1</italic> and <italic>P9-1</italic> for RBSDV) using single or multiple reference gene(s) for normalization during RSV- (<bold>a</bold> and <bold>c</bold>) and RBSDV- (<bold>b</bold> and <bold>d</bold>) infection. 14-day-old rice seedlings were inoculated with viruliferous nymphs (RSV and RBSDV) for 3 days. Total RNA was extracted from RSV- and RBSDV-infected seedlings, respectively. <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> were used multiple reference genes under RSV- and RBSDV-infection plants, respectively</p></caption><graphic xlink:href="12985_2015_405_Fig7_HTML" id="MO7"></graphic></fig></p></sec></sec><sec id="Sec7" sec-type="discussion"><title>Discussion</title><p>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 [<xref ref-type="bibr" rid="CR28">28</xref>, <xref ref-type="bibr" rid="CR30">30</xref>]. Thus, two or more stably reference genes for normalizing are essential.</p><p>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. <xref rid="Fig2" ref-type="fig">2c</xref>). Meanwhile, the specificity of the RT-qPCR primer pairs was confirmed by agarose gel electrophoresis (Fig. <xref rid="Fig2" ref-type="fig">2b</xref>) and melting curves analysis (Fig. <xref rid="Fig2" ref-type="fig">2a</xref>). The relative abundance of candidate reference genes was further identified (Fig. <xref rid="Fig3" ref-type="fig">3</xref>). The results of these indicated that the candidate reference genes could be used for the subsequent analyses.</p><p>To date, the commonly used methods to assess the stability of reference genes are geNorm [<xref ref-type="bibr" rid="CR28">28</xref>], NormFinder [<xref ref-type="bibr" rid="CR29">29</xref>] and BestKeeper [<xref ref-type="bibr" rid="CR40">40</xref>]. 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 <xref rid="Tab1" ref-type="table">1</xref>) were evaluated. Our findings revealed that <italic>UBQ 10</italic> and <italic>GAPDH</italic> were overall the most stable genes in RSV-infected rice (Figs. <xref rid="Fig4" ref-type="fig">4a</xref> and <xref rid="Fig5" ref-type="fig">5a</xref>), and <italic>Actin1</italic> and <italic>UBC</italic> ranked the best candidate genes under RBSDV infection (Figs. <xref rid="Fig4" ref-type="fig">4b</xref> and <xref rid="Fig5" ref-type="fig">5b</xref>). 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 [<xref ref-type="bibr" rid="CR21">21</xref>]. Therefore, it is vital to choose suitable reference gene(s) for normalization of gene expression. Moreno et al. [<xref ref-type="bibr" rid="CR41">41</xref>] showed that in different <italic>Cassava brown streak virus</italic> (CBSV)-infected tissues, <italic>PP2A</italic>, <italic>UBQ10</italic> and <italic>GTPb</italic> appeared to be the most stable genes. In infected tomato plants, <italic>GAPDH</italic> and <italic>UBQ</italic> indicated as the most appropriate internal standards both in leaves and root tissues, and <italic>Actin</italic> was also stably expressed in the infected plants [<xref ref-type="bibr" rid="CR42">42</xref>]. When infected with four commonly known tomato viral pathogens, <italic>ACT</italic>, <italic>CAC</italic> and <italic>EF1-α</italic> were considered as the most suitable reference genes in the studies of host–virus interactions [<xref ref-type="bibr" rid="CR43">43</xref>]. Therefore, the selected multiple reference genes (<italic>UBQ 10</italic> + <italic>GAPDH</italic> for RSV, and <italic>UBC</italic> + <italic>Actin1</italic> for RBSDV) could be used for the normalization of gene expression pattern in RSV- and RBSDV-infected rice plants.</p><p>Normally, <italic>EF1-α</italic> and <italic>18S rRNA</italic> were used as the reference genes in RT-qPCR experiments. Previous results showed that <italic>EF1-α</italic> was expressed stably in potato during biotic and abiotic stress [<xref ref-type="bibr" rid="CR44">44</xref>], and <italic>18S rRNA</italic> was identified to be suitable for normalisation in <italic>Barley yellow dwarf virus</italic>-infected cereals [<xref ref-type="bibr" rid="CR45">45</xref>]. In our study, the performance of above two genes was dissatisfactory. These were similar to the earlier studies in <italic>Cicer arietinum</italic> and virus-infected tomato, showing the considerable alterations in the transcript levels of <italic>EF1-α</italic> and <italic>18S rRNA</italic> [<xref ref-type="bibr" rid="CR42">42</xref>, <xref ref-type="bibr" rid="CR46">46</xref>]. Thus, the two traditional reference genes might not be the optimal choices for quantitating transcript level in rice during RSV- and RBSDV-infection. Meanwhile, <italic>TUB</italic> 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 [<xref ref-type="bibr" rid="CR45">45</xref>, <xref ref-type="bibr" rid="CR47">47</xref>]. In our experimental conditions, <italic>α-TUB</italic> and <italic>β-TUB</italic> (in particularly) were not stable either, especially in RBSDV-infected plants<italic>.</italic> Taken together, our data demonstrated that <italic>α-TUB</italic> and <italic>β-TUB</italic> were unsuitable for the normalization of gene expression levels in viral infected rice.</p><p>To further investigate the suitability of the selected reference genes, the expression levels of two virus resistant response genes, the gene expression of <italic>OsPR1b</italic> and <italic>OsWRKY</italic>, was compared by using single and multiple reference gene(s) for normalization. In the earlier studies [<xref ref-type="bibr" rid="CR38">38</xref>, <xref ref-type="bibr" rid="CR39">39</xref>], for example, the expression of <italic>OsPR1b</italic> gene was strongly induced by virus infection<italic>.</italic> Our results further showed that when using <italic>TIP41-Like</italic> gene for normalization, the induction patterns of <italic>OsPR1b</italic> and <italic>OsWRKY</italic> transcripts were not stronger than those using two reference genes (Fig. <xref rid="Fig6" ref-type="fig">6</xref>). Thus, the application of multiple reference genes is a better choice for gene expression analysis under virus infection situation.</p><p>In conclusion, we evaluated 14 candidate reference genes, and identified <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> 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.</p></sec><sec id="Sec8" sec-type="materials|methods"><title>Methods</title><sec id="Sec9"><title>Plant materials, virus isolates and inoculation assay</title><p>Rice (<italic>Oryza sativa</italic> L. cv. Nipponbare) seeds were used throughout the experiments. After the disinfection with 0.1 % HgCl<sub>2</sub> 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 [<xref ref-type="bibr" rid="CR48">48</xref>], with a 14/10 h (day/night) regimes at 28 ± 1 °C.</p><p>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 (<italic>Oryza sativa</italic> 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.</p><p>According to previous report [<xref ref-type="bibr" rid="CR49">49</xref>], 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.</p></sec><sec id="Sec10"><title>Preparation and quantification of DNA-free total RNA</title><p>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 [<xref ref-type="bibr" rid="CR50">50</xref>, <xref ref-type="bibr" rid="CR51">51</xref>]. 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.</p></sec><sec id="Sec11"><title>Reverse transcription and quantitative Real-time PCR assay</title><p>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 SsoFast<sup>TM</sup> Eva Green® Supermix (Bio-Rad) with the Bio-Rad iQ5 RT-qPCR system. Combined with all internal control genes used recently [<xref ref-type="bibr" rid="CR25">25</xref>, <xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR28">28</xref>], we further selected 14 genes as the candidate reference genes in this study (Table <xref rid="Tab1" ref-type="table">1</xref>). Additionally, the specific primers 5′-ACGCCTTCACGGTCCATAC-3′ and 5′-AAACAGAAAGAAACAGAGGGAGTAC-3′ were used for <italic>OsPR1b</italic> (AK107926); 5′-TCAGTGGAGAAGCGGGTGGTG-3′ and 5′-GGGTGGTTGTGCTCGAAGGAG-3′ were used for <italic>OsWRKY</italic> (EF143611). The efficiency and specificity of all the primers were checked by melting curve analysis, similar to previous report [<xref ref-type="bibr" rid="CR51">51</xref>].</p><p>Using the RSV <italic>CP</italic>, <italic>SP</italic> gene and RBSDV <italic>P5-1, P9-1</italic> gene<italic>,</italic> the dynamics of viral infection in rice plants was measured by reverse-transcription real-time PCR. The primer and products were shown in Table <xref rid="Tab2" ref-type="table">2</xref>.<table-wrap id="Tab2"><label>Table 2</label><caption><p>Real-time PCR primers of virus genes (RSV and RBSDV) used in this study</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Gene name</th><th>Primer sequence (5′ → 3′)</th><th>Amplicon length (bp)</th><th>Target genes</th></tr></thead><tbody><tr><td><italic>CP-F</italic></td><td>TGCAGAAGGCAATCAATGACAT</td><td rowspan="2">150</td><td>RSV NCP</td></tr><tr><td><italic>CP-R</italic></td><td>TGTCACCACCTTTGTCCTCAA</td><td></td></tr><tr><td><italic>SP-F</italic></td><td>CCTGTTAGGAGGTGAAGATGATGA</td><td rowspan="2">180</td><td>RSV SP</td></tr><tr><td><italic>SP-R</italic></td><td>GCTCTCAGCCTTAGCCATCTTG</td><td></td></tr><tr><td><italic>P5-1-F</italic></td><td>GTTTACGGTGGTGCAATTTTCA</td><td rowspan="2">150</td><td>RBSDV P5-1</td></tr><tr><td><italic>P5-1-R</italic></td><td>AGGCTTTCCTTCACTAACTTCTGACT</td><td></td></tr><tr><td><italic>P9-1-F</italic></td><td>TGGTGCTTCTCGTCAAACTGTCT</td><td rowspan="2">100</td><td>RBSDV P9-1</td></tr><tr><td><italic>P9-1-R</italic></td><td>GCCAACAATTCGTGTCCTGAA</td><td></td></tr></tbody></table></table-wrap></p><p>Each quantitative Real-time PCR was performed using 0.5 μL cDNA, 10 μL of SsoFast<sup>TM</sup> 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.</p></sec><sec id="Sec12"><title>Data processing</title><p>The expression stability of the candidate reference genes were analyzed by using geNorm [<xref ref-type="bibr" rid="CR28">28</xref>] and NormFinder [<xref ref-type="bibr" rid="CR29">29</xref>]. 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 [<xref ref-type="bibr" rid="CR28">28</xref>] 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 <italic>(V</italic><sub><italic>n/n+1</italic></sub><italic>)</italic> between two sequential normalization factors containing an increasing number of genes. NormFinder algorithm [<xref ref-type="bibr" rid="CR29">29</xref>] 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 [<xref ref-type="bibr" rid="CR52">52</xref>]. Statistical significance of Ct differences between treatments was calculated by the Mann–Whitney <italic>t</italic> test using the GraphPad Prism 5 software.</p></sec></sec></body><back><app-group><app id="App1"><sec id="Sec13"><title>Additional file</title><p><media position="anchor" xlink:href="12985_2015_405_MOESM1_ESM.doc" id="MOESM1"><label>Additional file 1: Figure S1.</label><caption><p>Relative expression levels of <italic>OsPR1b</italic> and <italic>OsWRKY</italic> using single or multiple reference gene(s) for normalization during RSV- (A and C) and RBSDV- (B and D) infection. 14-day-old rice seedlings were inoculated with viruliferous nymphs (RSV and RBSDV) for 3 days. Total RNA was extracted from RSV- and RBSDV-infected seedlings, respectively. In our experimental conditions, <italic>UBQ 10</italic> + <italic>GAPDH</italic> and <italic>UBC</italic> + <italic>Actin1</italic> were used multiple reference genes under RSV- and RBSDV-infection plants. (DOC 303 kb)</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Competing interests</bold></p><p>The authors declare that they have no competing interests.</p></fn><fn><p><bold>Authors’ contributions</bold></p><p>Conceived and designed the experiments: TZ, YZ, WS. Performed the experiments: PF, RL, FS, YL, LD. Analysed the data: PF. Wrote the paper: PF, WS, TZ, YZ. All authors read and approved the final manuscript.</p></fn></fn-group><ack><p>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.cx[15]1053) and the Jiangsu Province Science and Technology Support Project (BE2013301).</p></ack><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hibino</surname><given-names>H</given-names></name></person-group><article-title>Biology and epidemiology of rice viruses</article-title><source>Annu Rev Phytopathol</source><year>1996</year><volume>34</volume><fpage>249</fpage><lpage>274</lpage><pub-id pub-id-type="doi">10.1146/annurev.phyto.34.1.249</pub-id><pub-id pub-id-type="pmid">15012543</pub-id></element-citation></ref><ref id="CR2"><label>2.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kuribayashi</surname><given-names>K</given-names></name></person-group><article-title>On the relationship between rice stripe disease and Delphacodes striatella Fallen</article-title><source>J Plant Prot</source><year>1931</year><volume>18</volume><fpage>565</fpage><lpage>571</lpage></element-citation></ref><ref id="CR3"><label>3.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shikata</surname><given-names>E</given-names></name><name><surname>Kitagawa</surname><given-names>Y</given-names></name></person-group><article-title>Rice black-streaked dwarf virus: its properties, morphology and intracellular localization</article-title><source>Virology</source><year>1977</year><volume>77</volume><fpage>826</fpage><lpage>842</lpage><pub-id pub-id-type="doi">10.1016/0042-6822(77)90502-5</pub-id><pub-id pub-id-type="pmid">855190</pub-id></element-citation></ref><ref id="CR4"><label>4.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Falk</surname><given-names>BW</given-names></name><name><surname>Tsai</surname><given-names>JH</given-names></name></person-group><article-title>Biology and molecular biology of viruses in the genus <italic>Tenuivirus</italic></article-title><source>Annu Rev Phytopathol</source><year>1998</year><volume>36</volume><fpage>139</fpage><lpage>163</lpage><pub-id pub-id-type="doi">10.1146/annurev.phyto.36.1.139</pub-id><pub-id pub-id-type="pmid">15012496</pub-id></element-citation></ref><ref id="CR5"><label>5.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kiso</surname><given-names>A</given-names></name><name><surname>Yamamoto</surname><given-names>T</given-names></name></person-group><article-title>Infection and symptom in rice stripe disease with special reference to disease-specific protein other than virus</article-title><source>Rev Plant Prot Res</source><year>1973</year><volume>6</volume><fpage>75</fpage><lpage>100</lpage></element-citation></ref><ref id="CR6"><label>6.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shinkai</surname><given-names>A</given-names></name></person-group><article-title>Studies on insect transmission of rice virus diseases in Japan</article-title><source>Bull Nat Inst Agric Sci Ser C</source><year>1962</year><volume>14</volume><fpage>1</fpage><lpage>12</lpage></element-citation></ref><ref id="CR7"><label>7.</label><mixed-citation publication-type="other">Iida TT, Shinkai A. 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