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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (<italic>Corchorus capsularis</italic>) under stress treatments</title><author><name sortKey="Niu, Xiaoping" sort="Niu, Xiaoping" uniqKey="Niu X" first="Xiaoping" last="Niu">Xiaoping Niu</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Qi, Jianmin" sort="Qi, Jianmin" uniqKey="Qi J" first="Jianmin" last="Qi">Jianmin Qi</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Zhang, Gaoyang" sort="Zhang, Gaoyang" uniqKey="Zhang G" first="Gaoyang" last="Zhang">Gaoyang Zhang</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>College of Life Sciences, Shangrao Normal University</institution><country>Shangrao, China</country></nlm:aff></affiliation></author><author><name sortKey="Xu, Jiantang" sort="Xu, Jiantang" uniqKey="Xu J" first="Jiantang" last="Xu">Jiantang Xu</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Tao, Aifen" sort="Tao, Aifen" uniqKey="Tao A" first="Aifen" last="Tao">Aifen Tao</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Fang, Pingping" sort="Fang, Pingping" uniqKey="Fang P" first="Pingping" last="Fang">Pingping Fang</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Su, Jianguang" sort="Su, Jianguang" uniqKey="Su J" first="Jianguang" last="Su">Jianguang Su</name><affiliation><nlm:aff id="aff3"><institution>Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences</institution><country>Changsha, China</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26528312</idno><idno type="pmc">4604321</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604321</idno><idno type="RBID">PMC:4604321</idno><idno type="doi">10.3389/fpls.2015.00848</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000085</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (<italic>Corchorus capsularis</italic>) under stress treatments</title><author><name sortKey="Niu, Xiaoping" sort="Niu, Xiaoping" uniqKey="Niu X" first="Xiaoping" last="Niu">Xiaoping Niu</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Qi, Jianmin" sort="Qi, Jianmin" uniqKey="Qi J" first="Jianmin" last="Qi">Jianmin Qi</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Zhang, Gaoyang" sort="Zhang, Gaoyang" uniqKey="Zhang G" first="Gaoyang" last="Zhang">Gaoyang Zhang</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>College of Life Sciences, Shangrao Normal University</institution><country>Shangrao, China</country></nlm:aff></affiliation></author><author><name sortKey="Xu, Jiantang" sort="Xu, Jiantang" uniqKey="Xu J" first="Jiantang" last="Xu">Jiantang Xu</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Tao, Aifen" sort="Tao, Aifen" uniqKey="Tao A" first="Aifen" last="Tao">Aifen Tao</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Fang, Pingping" sort="Fang, Pingping" uniqKey="Fang P" first="Pingping" last="Fang">Pingping Fang</name><affiliation><nlm:aff id="aff1"><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></nlm:aff></affiliation></author><author><name sortKey="Su, Jianguang" sort="Su, Jianguang" uniqKey="Su J" first="Jianguang" last="Su">Jianguang Su</name><affiliation><nlm:aff id="aff3"><institution>Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences</institution><country>Changsha, China</country></nlm:aff></affiliation></author></analytic><series><title level="j">Frontiers in Plant Science</title><idno type="eISSN">1664-462X</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>To accurately measure gene expression using quantitative reverse transcription PCR (qRT-PCR), reliable reference gene(s) are required for data normalization. <italic>Corchorus capsularis</italic>, an annual herbaceous fiber crop with predominant biodegradability and renewability, has not been investigated for the stability of reference genes with qRT-PCR. In this study, 11 candidate reference genes were selected and their expression levels were assessed using qRT-PCR. To account for the influence of experimental approach and tissue type, 22 different jute samples were selected from abiotic and biotic stress conditions as well as three different tissue types. The stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper programs, and the comprehensive rankings of gene stability were generated by aggregate analysis. For the biotic stress and NaCl stress subsets, <italic>ACT7</italic> and <italic>RAN</italic> were suitable as stable reference genes for gene expression normalization. For the PEG stress subset, <italic>UBC</italic>, and <italic>DnaJ</italic> were sufficient for accurate normalization. For the tissues subset, four reference genes <italic>TUB</italic>β, <italic>UBI, EF1</italic>α, and <italic>RAN</italic> were sufficient for accurate normalization. The selected genes were further validated by comparing expression profiles of <italic>WRKY15</italic> in various samples, and two stable reference genes were recommended for accurate normalization of qRT-PCR data. Our results provide researchers with appropriate reference genes for qRT-PCR in <italic>C</italic>. <italic>capsularis</italic>, and will facilitate gene expression study under these conditions.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Alam, M M" uniqKey="Alam M">M. M. Alam</name></author><author><name sortKey="Sharmin, S" uniqKey="Sharmin S">S. Sharmin</name></author><author><name sortKey="Nabi, Z" uniqKey="Nabi Z">Z. Nabi</name></author><author><name sortKey="Mondal, S I" uniqKey="Mondal S">S. I. Mondal</name></author><author><name sortKey="Islam, M S" uniqKey="Islam M">M. S. Islam</name></author><author><name sortKey="Bin Nayeem, S" uniqKey="Bin Nayeem S">S. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Plant Sci</journal-id><journal-id journal-id-type="iso-abbrev">Front Plant Sci</journal-id><journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type="epub">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26528312</article-id><article-id pub-id-type="pmc">4604321</article-id><article-id pub-id-type="doi">10.3389/fpls.2015.00848</article-id><article-categories><subj-group subj-group-type="heading"><subject>Plant Science</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (<italic>Corchorus capsularis</italic>) under stress treatments</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Niu</surname><given-names>Xiaoping</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/263356/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Qi</surname><given-names>Jianmin</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zhang</surname><given-names>Gaoyang</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Jiantang</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Tao</surname><given-names>Aifen</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Fang</surname><given-names>Pingping</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Su</surname><given-names>Jianguang</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="author-notes" rid="fn002"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>Key Laboratory for Genetics, Breeding and Multiple Utilization of Crops, Fujian Agriculture and Forestry University</institution><country>Fuzhou, China</country></aff><aff id="aff2"><sup>2</sup><institution>College of Life Sciences, Shangrao Normal University</institution><country>Shangrao, China</country></aff><aff id="aff3"><sup>3</sup><institution>Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences</institution><country>Changsha, China</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Ashraf El-Kereamy, University of California Cooperative Extension, Kern county, USA</p></fn><fn fn-type="edited-by"><p>Reviewed by: Qingshun Quinn Li, Western University of Health Sciences, USA; M. Teresa Sanchez-Ballesta, Institute of Food Science, Technology and Nutrition - Spanish National Research Council, Spain</p></fn><corresp id="fn001">*Correspondence: Jianmin Qi <email xlink:type="simple">qijm863@163.com</email>;</corresp><corresp id="fn002">Jianguang Su <email xlink:type="simple">jgsu@vip.163.com</email></corresp><fn fn-type="other" id="fn003"><p>This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type="epub"><day>14</day><month>10</month><year>2015</year></pub-date><pub-date pub-type="collection"><year>2015</year></pub-date><volume>6</volume><elocation-id>848</elocation-id><history><date date-type="received"><day>07</day><month>8</month><year>2015</year></date><date date-type="accepted"><day>28</day><month>9</month><year>2015</year></date></history><permissions><copyright-statement>Copyright © 2015 Niu, Qi, Zhang, Xu, Tao, Fang and Su.</copyright-statement><copyright-year>2015</copyright-year><copyright-holder>Niu, Qi, Zhang, Xu, Tao, Fang and Su</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>To accurately measure gene expression using quantitative reverse transcription PCR (qRT-PCR), reliable reference gene(s) are required for data normalization. <italic>Corchorus capsularis</italic>, an annual herbaceous fiber crop with predominant biodegradability and renewability, has not been investigated for the stability of reference genes with qRT-PCR. In this study, 11 candidate reference genes were selected and their expression levels were assessed using qRT-PCR. To account for the influence of experimental approach and tissue type, 22 different jute samples were selected from abiotic and biotic stress conditions as well as three different tissue types. The stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper programs, and the comprehensive rankings of gene stability were generated by aggregate analysis. For the biotic stress and NaCl stress subsets, <italic>ACT7</italic> and <italic>RAN</italic> were suitable as stable reference genes for gene expression normalization. For the PEG stress subset, <italic>UBC</italic>, and <italic>DnaJ</italic> were sufficient for accurate normalization. For the tissues subset, four reference genes <italic>TUB</italic>β, <italic>UBI, EF1</italic>α, and <italic>RAN</italic> were sufficient for accurate normalization. The selected genes were further validated by comparing expression profiles of <italic>WRKY15</italic> in various samples, and two stable reference genes were recommended for accurate normalization of qRT-PCR data. Our results provide researchers with appropriate reference genes for qRT-PCR in <italic>C</italic>. <italic>capsularis</italic>, and will facilitate gene expression study under these conditions.</p></abstract><kwd-group><kwd>jute (<italic>Corchorus capsularis</italic>)</kwd><kwd>qRT-PCR</kwd><kwd>reference genes</kwd><kwd>gene expression</kwd><kwd>abiotic and biotic stress</kwd></kwd-group><funding-group><award-group><funding-source id="cn001">the 948 project of the Agricultural Department of China</funding-source><award-id rid="cn001">2013–Z70</award-id></award-group><award-group><funding-source id="cn002">the National Bast Fiber Germplasm Resources Project of China</funding-source><award-id rid="cn002">K47NI201A</award-id></award-group><award-group><funding-source id="cn003">the National Bast Fiber Research System of China</funding-source><award-id rid="cn003">CARS–19–E06</award-id></award-group><award-group><funding-source id="cn004">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></funding-source><award-id rid="cn004">31471549</award-id></award-group></funding-group><counts><fig-count count="4"></fig-count><table-count count="5"></table-count><equation-count count="0"></equation-count><ref-count count="58"></ref-count><page-count count="12"></page-count><word-count count="9106"></word-count></counts></article-meta></front><body><sec sec-type="intro" id="s1"><title>Introduction</title><p>With the increase of global environmental awareness, more and more people are actively purchasing goods made from ecologically friendly materials. Unfortunately, many of these materials are intrinsically unrecyclable, including many of the predominantly used polymers. However, many natural fibers such as jute and kenaf possess properties that are comparable to more traditional composites, including stiffness, flexibility, impact resistance, and elasticity (Sydenstricker et al., <xref rid="B49" ref-type="bibr">2003</xref>). The overlap between natural fiber properties and those of traditional, reinforced composites are underscored by their environmentally friendly nature. For instance, fiber components are biodegradable, renewable, and result in low energy consumption. Collectively, these characteristics lend natural fibers to being logical substitutes for non-renewable synthetic fibers (Oksman et al., <xref rid="B39" ref-type="bibr">2003</xref>; Corrales et al., <xref rid="B10" ref-type="bibr">2007</xref>).</p><p>Jute (<italic>Corchorus capsularis</italic> L.) is an annual herbaceous fiber crop. It is found predominantly in Southeast Asia and is the second cheapest and most commercially available fiber crop, whereby it provides a biodegradable and renewable lignocellulose fiber. Jute is a promising and featured fiber material among many value-added industrial products, due in large part to its high luster, moisture absorption properties, ability for rapid water loss, and easy breakdown. Furthermore, jute has much potential for use in the industrial production of packaging materials (Sydenstricker et al., <xref rid="B49" ref-type="bibr">2003</xref>). With the recent and increasing interest in jute, work has been conducted to better understand its physiological and biochemical properties (Corrales et al., <xref rid="B10" ref-type="bibr">2007</xref>; del Río et al., <xref rid="B14" ref-type="bibr">2009</xref>; Defoirdt et al., <xref rid="B13" ref-type="bibr">2010</xref>). At a molecular level, several studies have also been done to examine relevant molecular markers (e.g., SSR, ISSR, RAPD, and AFLP) (Qi et al., <xref rid="B43" ref-type="bibr">2003a</xref>,<xref rid="B42" ref-type="bibr">b</xref>; Basu et al., <xref rid="B4" ref-type="bibr">2004</xref>; Roy et al., <xref rid="B46" ref-type="bibr">2006</xref>; Mir et al., <xref rid="B35" ref-type="bibr">2008</xref>, <xref rid="B34" ref-type="bibr">2009</xref>), ESTs, stress response factors (Alam et al., <xref rid="B1" ref-type="bibr">2010</xref>), and a transformation system (Chattopadhyay et al., <xref rid="B7" ref-type="bibr">2011</xref>; Zhang et al., <xref rid="B55" ref-type="bibr">2013</xref>). However, many of these previous experiments used single or multiple gene expression analysis via classical RT-PCR and/or Northern blot (Alam et al., <xref rid="B1" ref-type="bibr">2010</xref>). Only a handful of studies have used quantitative reverse transcription PCR (qRT-PCR) to determine the expression pattern of functional jute genes (Zhang et al., <xref rid="B55" ref-type="bibr">2013</xref>, <xref rid="B56" ref-type="bibr">2014</xref>).</p><p>As of this writing, qRT-PCR provides the most efficient, sensitive, low cost, and reproducible method for accurate and rapid detection and quantification of mRNA transcription levels for a given gene of interest (Bustin, <xref rid="B5" ref-type="bibr">2002</xref>). However, several factors including RNA integrity, reverse transcription efficiency, cDNA quality, sample amount, and/or extraneous tissue and cell activities can significantly influence the accuracy of gene expression (Bustin, <xref rid="B5" ref-type="bibr">2002</xref>; Huggett et al., <xref rid="B21" ref-type="bibr">2005</xref>). To lessen these problems, one or more reference genes are required to account for the variance between samples and/or reactions. To this end, the transcription level of an ideal reference gene should remain constant across different tissues, treatments, and developmental stages (Gutierrez et al., <xref rid="B19" ref-type="bibr">2008</xref>). A number of commonly used housekeeping genes (e.g., β-actin, elongation factor 1α, 18S ribosomal RNA, and polyubiquitin) have been used, but results indicated that their expression levels are somewhat unstable. Thus, their use as internal reference genes should be taken with some caution under a given set of experimental conditions (Gutierrez et al., <xref rid="B19" ref-type="bibr">2008</xref>). Taken together, these inconsistencies highlight the need to evaluate the stability of candidate reference genes under the relevant experimental conditions prior to using for gene expression normalization by qRT-PCR.</p><p>In recent years, an increasing number of stable reference genes have been studied in several systems, including eukaryotic elongation factor 1-alpha (<italic>EF1</italic>α) and F-box family protein (<italic>FBX</italic>) in <italic>Arabidopsis thaliana</italic>; 18S ribosomal RNA (18S <italic>rRNA</italic>), ubiquitin5 (<italic>UBQ5</italic>), and eukaryotic elongation factor 1-alpha (<italic>eEF1</italic>α) in <italic>Oryza sativa</italic> (Czechowski et al., <xref rid="B12" ref-type="bibr">2005</xref>; Jain et al., <xref rid="B24" ref-type="bibr">2006</xref>; Remans et al., <xref rid="B45" ref-type="bibr">2008</xref>; Narsai et al., <xref rid="B36" ref-type="bibr">2010</xref>); catalytic subunit of protein phosphatase 2A (<italic>PP2A</italic>), actin 4 (<italic>ACT4</italic>), ubiquitin 14 (<italic>UBQ14</italic>), F-box family protein (<italic>FBX6</italic>), clathrin adaptor complexes medium subunit family protein (<italic>MZA</italic>), and polypyrimidine tract-binding protein homolog (<italic>PTB</italic>) in <italic>Gossypium hirsutum</italic> (Artico et al., <xref rid="B3" ref-type="bibr">2010</xref>). Furthermore, Zhang et al. and Saraiva et al. both found that <italic>EF1</italic>α could also be used as a reference gene in <italic>Triticum aestivum</italic> (Paolacci et al., <xref rid="B40" ref-type="bibr">2009</xref>; Long et al., <xref rid="B31" ref-type="bibr">2010</xref>) as well as in <italic>Glycine max</italic> (Jian et al., <xref rid="B25" ref-type="bibr">2008</xref>; Libault et al., <xref rid="B27" ref-type="bibr">2008</xref>). Additionally, suitable reference genes for use in gene expression analysis have also been methodically identified for a variety of species, such as <italic>Saccharum officinarum</italic> (Ling et al., <xref rid="B29" ref-type="bibr">2014</xref>), <italic>Litchi chinensis</italic> (Zhong et al., <xref rid="B57" ref-type="bibr">2011</xref>), <italic>Litsea cubeba</italic> (Lin et al., <xref rid="B28" ref-type="bibr">2013</xref>), <italic>Citrus sinensis</italic> (Mafra et al., <xref rid="B33" ref-type="bibr">2012</xref>), <italic>Coffea</italic> ssp. (Cruz et al., <xref rid="B11" ref-type="bibr">2009</xref>), <italic>Solanum tuberosum</italic> (Nicot et al., <xref rid="B37" ref-type="bibr">2005</xref>), <italic>Musa acuminata</italic> (Chen et al., <xref rid="B8" ref-type="bibr">2011</xref>), <italic>Vitis vinifera</italic> (Reid et al., <xref rid="B44" ref-type="bibr">2006</xref>), <italic>Helianthus annuus</italic> (Fernandez et al., <xref rid="B18" ref-type="bibr">2011</xref>), <italic>Nicotiana tabacum</italic> (Schmidt and Delaney, <xref rid="B47" ref-type="bibr">2010</xref>; Liu et al., <xref rid="B30" ref-type="bibr">2012</xref>), and <italic>Linum usitatissimum</italic> (Huis et al., <xref rid="B22" ref-type="bibr">2010</xref>). However, no systematic validation of reference genes has been performed in jute (<italic>Corchorus capsularis</italic>), which prevents further studies on this species at the functional gene expression and transcriptome profile analysis levels.</p><p>In the present study, 11 genes were selected as candidate reference genes for evaluation expression stability in jute: 18S ribosomal RNA (18S <italic>rRNA</italic>), actin (<italic>ACT</italic>), actin 7 (<italic>ACT7</italic>), chaperone protein dnaJ (<italic>DnaJ</italic>), eukaryotic elongation factor 1-alpha (<italic>EF1</italic>α), ras-related small GTP-binding protein (<italic>RAN</italic>), alpha-tubulin (<italic>TUB</italic>α), beta-tubulin (<italic>TUB</italic>β), ubiquitin-conjugating enzyme like protein (<italic>UBC</italic>), ubiquitin extension protein (Imai et al., <xref rid="B23" ref-type="bibr">2014</xref>) and ubiquitin (<italic>UBQ</italic>). We then sought to reveal which reference gene(s) were the best option for gene expression qRT-PCR analysis of jute under various experimental treatments using three available statistical algorithms, geNorm (Vandesompele et al., <xref rid="B51" ref-type="bibr">2002</xref>), NormFinder (Andersen et al., <xref rid="B2" ref-type="bibr">2004</xref>), and BestKeeper (Pfaffl et al., <xref rid="B41" ref-type="bibr">2004</xref>). Finally, we validated the expression levels of the transcription factor <italic>CcWRKY15</italic> using the selected reference gene(s). The results from this work will facilitate future studies on gene expression as well as foster a better understanding of how novel genes function in the molecular mechanisms of jute biological and/or physiological processes.</p></sec><sec sec-type="materials|methods" id="s2"><title>Materials and methods</title><sec><title>Plant materials and treatments</title><p>Jute (<italic>Corchorus capsularis</italic> L.) variety Huangma 108 was used for all experimental treatment groups. To ensure disease-free materials, seeds were rinsed under running water for 10 min and sterilized with 5% sodium hypochlorite for 10 min. They were then washed three times with sterile water before germinated on filter paper that had been saturated with water in complete darkness at 28°C. After 3 days, seedlings were grown in the greenhouse in 1/4 Hoagland solution under a 16/8-h light/dark cycle at 30/26°C (day/night). Seedlings were assessed at the 3–5 leaf stage and the most consistent were used for (i) the abiotic and biotic stress groups or for (ii) harvesting of different plant tissues (e.g., root, stem, and leaf).</p><p>For the salinity and drought treatments, seedlings were subjected to 200 mM sodium chloride and 15% (w/v) PEG6000, respectively, and harvested at 0, 2, 4, 6, 8, 12, and 24 h. For the biotic treatment, seedlings were inoculated with 2 ml (10<sup>6</sup> spores per ml) of <italic>Colletotrichum siamense</italic> spores in suspension previously described by Ma et al. (<xref rid="B32" ref-type="bibr">2009</xref>) and then sampled at 0, 1, 6, 12, 24, 48, 72, and 96 h. Tissues from the roots, stems, and leaves were collected from plants in the 3–5 leaf stage that had grown well under the experimental conditions. All samples were harvested from three replicate plants, giving a total of 25 samples comprised of 14 abiotic and eight biotic stress treatment samples and three tissue-specific samples. Samples were immediately frozen in liquid nitrogen and stored at −80°C until RNA extraction.</p></sec><sec><title>Total RNA extraction and cDNA synthesis</title><p>Total RNA was extracted from approximately 100 mg fresh leaf tissue using the OMEGA isolation kit (R6827-01, USA) according to the manufacturer's instructions. The genomic DNA contamination was eliminated using RNase-free DNase I (TaKaRa, Japan) and RNA sample quality was then determined using the NanoDrop 2000 spectrophotometer (NanoDrop, Thermo Scientific). RNA integrity was assessed by 2.0% agarose gel electrophoresis. Finally, RNA samples with an A<sub>260</sub>/A<sub>280</sub> ratio between 1.9 and 2.1 and an A<sub>260</sub>/A<sub>230</sub> ratio greater than 2.0 were used for further analysis. Subsequently, the first-strand cDNA was synthesized from 1 μg total RNA in a volume of 20 μl using the PrimeScript® RT reagent kit (TaKaRa, Japan) by following the manufacturer's protocol. All final cDNA samples were diluted 10-fold for subsequent quantitative real-time RT-PCR reactions. Samples were stored at −20°C until use.</p></sec><sec><title>Primer design, verification of PCR products, and qRT-PCR</title><p>A total of 11 candidate reference genes, including 18S <italic>rRNA</italic> (FJ527599), <italic>ACT</italic> (GU207477), <italic>ACT7</italic> (GH985158), <italic>DnaJ</italic> (GR463675), <italic>EF1</italic>α (GH985217), <italic>RAN</italic> (FK826494), <italic>TUB</italic>α (GH985177), <italic>TUB</italic>β (JK743820), <italic>UBC</italic> (GR463733), <italic>UBI</italic> (GH985256), and <italic>UBQ</italic> (FK826547), were identified after performing a TBLASTN among the jute expressed sequence tags (ESTs) database (<ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov/nucest/?term=jute">http://www.ncbi.nlm.nih.gov/nucest/?term=jute</ext-link>). Primers were designed according to these potential reference gene sequences using Primer 3 (<ext-link ext-link-type="uri" xlink:href="http://bioinfo.ut.ee/primer3-0.4.0/primer3/">http://bioinfo.ut.ee/primer3-0.4.0/primer3/</ext-link>) and based on the following criteria: GC content 45−80%, melting temperatures 58−62°C, primer lengths 18−24 bp, and amplicon lengths 100−250 bp (See Table <xref ref-type="table" rid="T1">1</xref> for detailed primer information).</p><table-wrap id="T1" position="float"><label>Table 1</label><caption><p><bold>Primer sequences and amplicon characteristics of the 11 candidate internal control genes</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene description</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold><italic>Arabidopsis</italic> ortholog locus</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Primer sequence F/R (5′–3′)</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Product size(bp)</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Efficiency (%)</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>R2</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Mean Ct</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV (%)</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S ribosomal RNA</td><td valign="top" align="left" rowspan="1" colspan="1">AT3G41768</td><td valign="top" align="left" rowspan="1" colspan="1">CTACGTCCCTGCCCTTTGTA</td><td valign="top" align="center" rowspan="1" colspan="1">175</td><td valign="top" align="center" rowspan="1" colspan="1">90.1</td><td valign="top" align="center" rowspan="1" colspan="1">0.997</td><td valign="top" align="center" rowspan="1" colspan="1">16.85</td><td valign="top" align="center" rowspan="1" colspan="1">0.78</td><td valign="top" align="center" rowspan="1" colspan="1">4.63</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GGTTCACCTACGGAAACCTTG</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Actin</td><td valign="top" align="left" rowspan="1" colspan="1">AT3G12110</td><td valign="top" align="left" rowspan="1" colspan="1">CATTACCATTGGGGCAGAAC</td><td valign="top" align="center" rowspan="1" colspan="1">168</td><td valign="top" align="center" rowspan="1" colspan="1">113.7</td><td valign="top" align="center" rowspan="1" colspan="1">0.991</td><td valign="top" align="center" rowspan="1" colspan="1">25.75</td><td valign="top" align="center" rowspan="1" colspan="1">1.31</td><td valign="top" align="center" rowspan="1" colspan="1">5.09</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GAGCCACCACTGAGGACAAT</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Actin 7</td><td valign="top" align="left" rowspan="1" colspan="1">AT5G09810</td><td valign="top" align="left" rowspan="1" colspan="1">ACAATTGGAGCAGAGCGTTT</td><td valign="top" align="center" rowspan="1" colspan="1">166</td><td valign="top" align="center" rowspan="1" colspan="1">99.52</td><td valign="top" align="center" rowspan="1" colspan="1">0.997</td><td valign="top" align="center" rowspan="1" colspan="1">22.24</td><td valign="top" align="center" rowspan="1" colspan="1">0.92</td><td valign="top" align="center" rowspan="1" colspan="1">4.14</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">TAGACCCACCGCTAAGCACT</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Chaperone</td><td valign="top" align="left" rowspan="1" colspan="1">AT3G07590</td><td valign="top" align="left" rowspan="1" colspan="1">TGTATGCACCGAGGAAAATG</td><td valign="top" align="center" rowspan="1" colspan="1">154</td><td valign="top" align="center" rowspan="1" colspan="1">104.1</td><td valign="top" align="center" rowspan="1" colspan="1">0.999</td><td valign="top" align="center" rowspan="1" colspan="1">22.22</td><td valign="top" align="center" rowspan="1" colspan="1">0.75</td><td valign="top" align="center" rowspan="1" colspan="1">3.38</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">protein dnaJ</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GTGGAAAAATCGTTGGCAAT</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">Elongation</td><td valign="top" align="left" rowspan="1" colspan="1">AT1G07940</td><td valign="top" align="left" rowspan="1" colspan="1">GAAGAAGGACCCATCTGGTG</td><td valign="top" align="center" rowspan="1" colspan="1">130</td><td valign="top" align="center" rowspan="1" colspan="1">104.2</td><td valign="top" align="center" rowspan="1" colspan="1">0.990</td><td valign="top" align="center" rowspan="1" colspan="1">18.28</td><td valign="top" align="center" rowspan="1" colspan="1">0.94</td><td valign="top" align="center" rowspan="1" colspan="1">5.14</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">factor 1-alpha</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">TCCACAAAACCGCAATGTAA</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Ras-related small</td><td valign="top" align="left" rowspan="1" colspan="1">AT5G59840</td><td valign="top" align="left" rowspan="1" colspan="1">GCCATGCCGATAAGAACATT</td><td valign="top" align="center" rowspan="1" colspan="1">167</td><td valign="top" align="center" rowspan="1" colspan="1">99.52</td><td valign="top" align="center" rowspan="1" colspan="1">0.996</td><td valign="top" align="center" rowspan="1" colspan="1">26.37</td><td valign="top" align="center" rowspan="1" colspan="1">1.29</td><td valign="top" align="center" rowspan="1" colspan="1">4.89</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GTP-binding protein</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GTGAAGGCAGTCTCCCACAT</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">Alpha-tubulin</td><td valign="top" align="left" rowspan="1" colspan="1">AT4G14960</td><td valign="top" align="left" rowspan="1" colspan="1">AATGCTTGCTGGGAGCTTTA</td><td valign="top" align="center" rowspan="1" colspan="1">213</td><td valign="top" align="center" rowspan="1" colspan="1">98.21</td><td valign="top" align="center" rowspan="1" colspan="1">0.988</td><td valign="top" align="center" rowspan="1" colspan="1">28.43</td><td valign="top" align="center" rowspan="1" colspan="1">2.15</td><td valign="top" align="center" rowspan="1" colspan="1">7.56</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">GTGGAATAACTGGCGGTACG</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1">Beta-tubulin</td><td valign="top" align="left" rowspan="1" colspan="1">AT2G29550</td><td valign="top" align="left" rowspan="1" colspan="1">CTGGTTCCTCTTCCTCACCA</td><td valign="top" align="center" rowspan="1" colspan="1">201</td><td valign="top" align="center" rowspan="1" colspan="1">108.9</td><td valign="top" align="center" rowspan="1" colspan="1">0.999</td><td valign="top" align="center" rowspan="1" colspan="1">22.40</td><td valign="top" align="center" rowspan="1" colspan="1">1.07</td><td valign="top" align="center" rowspan="1" colspan="1">4.78</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">ACAAGATGTTCAGGCGTGTG</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Ubiquitin-conjugating</td><td valign="top" align="left" rowspan="1" colspan="1">AT3G52560</td><td valign="top" align="left" rowspan="1" colspan="1">CTGCCATCTCCTTTTTCAGC</td><td valign="top" align="center" rowspan="1" colspan="1">150</td><td valign="top" align="center" rowspan="1" colspan="1">108.6</td><td valign="top" align="center" rowspan="1" colspan="1">0.992</td><td valign="top" align="center" rowspan="1" colspan="1">21.20</td><td valign="top" align="center" rowspan="1" colspan="1">1.13</td><td valign="top" align="center" rowspan="1" colspan="1">5.33</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">enzyme like protein</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">CGAGTGTCCGTTTTCATTCA</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Ubiquitin extension</td><td valign="top" align="left" rowspan="1" colspan="1">AT2G47110</td><td valign="top" align="left" rowspan="1" colspan="1">CCACTCTCCACCTTGTCCTC</td><td valign="top" align="center" rowspan="1" colspan="1">158</td><td valign="top" align="center" rowspan="1" colspan="1">108.9</td><td valign="top" align="center" rowspan="1" colspan="1">0.989</td><td valign="top" align="center" rowspan="1" colspan="1">21.44</td><td valign="top" align="center" rowspan="1" colspan="1">0.62</td><td valign="top" align="center" rowspan="1" colspan="1">2.89</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">protein</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">CAGCCTCTGAACCTTTCCAG</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1">Ubiquitin</td><td valign="top" align="left" rowspan="1" colspan="1">AT5G20620</td><td valign="top" align="left" rowspan="1" colspan="1">TCTTTGCAGGGAAGCAACTT</td><td valign="top" align="center" rowspan="1" colspan="1">219</td><td valign="top" align="center" rowspan="1" colspan="1">96.49</td><td valign="top" align="center" rowspan="1" colspan="1">0.997</td><td valign="top" align="center" rowspan="1" colspan="1">20.13</td><td valign="top" align="center" rowspan="1" colspan="1">1.54</td><td valign="top" align="center" rowspan="1" colspan="1">7.65</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">CTGCATAGCAGCAAGCTCAC</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>WRKY15</italic></td><td valign="top" align="left" rowspan="1" colspan="1">DNA-binding</td><td valign="top" align="left" rowspan="1" colspan="1">AT2G23320</td><td valign="top" align="left" rowspan="1" colspan="1">CTTGGACAGCGTTTTCTTCC</td><td valign="top" align="center" rowspan="1" colspan="1">128</td><td valign="top" align="center" rowspan="1" colspan="1">99.46</td><td valign="top" align="center" rowspan="1" colspan="1">0.996</td><td valign="top" align="center" rowspan="1" colspan="1">–</td><td valign="top" align="center" rowspan="1" colspan="1">–</td><td valign="top" align="center" rowspan="1" colspan="1">–</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">Protein WRKY15</td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">TGAATGGTTTTGGTGCAGAC</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr></tbody></table><table-wrap-foot><p><italic>R<sup>2</sup>, regression coefficient; SD, standard deviation; CV, co-variance</italic>.</p></table-wrap-foot></table-wrap><p>To check the specificity of the amplicon, all primer pairs were initially tested via standard RT-PCR using the Premix Ex Taq (TaKaRa, Japan) and the amplification product of each gene was verified by electrophoresis on a 2% agarose gel. Real-time amplification reactions were performed with the Applied Biosystems 7500 Real-Time PCR System using SYBR® Premix Ex Taq™ (TaKaRa, Japan). Reactions were prepared in a 20 μl volume containing the following: 2 μl cDNA template, 0.4 μl of each amplification primer, 0.4 μl ROX Reference Dye II, 10 μl 2x SYBR Premix Ex Taq™, and 6.8 μl dH<sub>2</sub>O. Amplifications were performed with an initial 30 s step of 95°C followed by 40 denaturation cycles at 95°C for 5 s and primer annealing at 60°C for 34 s. The melting curve ranged from 60 to 95°C and temperature was increased in increments of 0.2°C every 10 s for all PCR products. ABI Prism Dissociation Curve Analysis Software was used to confirm the occurrence of specific amplification peaks. All qRT-PCR reactions were carried out in triplicate with template-free negative controls being performed in parallel.</p></sec><sec><title>Statistical analyses of gene expression stability</title><p>To select a suitable reference gene, three publicly available software packages, geNorm (version 3.5), NormFinder and BestKeeper, were used to analyze the stability of each reference gene. All analyses using these packages occurred according to the manufacturers' instructions. For geNorm and NormFinder algorithms, the raw Ct values from the qRT-PCR were transformed into the relative expression levels using the following formula: E<sup>−Δ<italic>Ct</italic></sup>, where ΔCt equaled each corresponding Ct value minus the minimum Ct value. Then, the relative expression values were imported into geNorm and NormFinder to analysis gene expression stability. For BestKeeper analysis, the Ct value was used as input data to calculate the coefficient of variation (CV) and the standard deviation (SD). The comprehensive rankings of the best reference genes were obtained by integrating the results of three algorithms. To validate the reliability of the selected reference genes, we analyzed the relative expression levels of the transcription factor <italic>CcWRKY15</italic> in all tested samples. Additionally, a standard curve was generated from a 10-fold dilution of cDNA in a qRT-PCR assay using Microsoft Excel 2003. The PCR efficiency (E) and the regression coefficient (<italic>R</italic><sup>2</sup>) were calculated using the slope of the standard curve according to the equation E = [10<sup>−(1∕<italic>slope</italic>)</sup> − 1] × 100%. All other multiple comparisons were performed using the statistical analysis software SPSS 22.0 (SPSS Inc., USA).</p></sec></sec><sec sec-type="results" id="s3"><title>Results</title><sec><title>qRT-PCR data of candidate reference genes</title><p>A total of 11 candidate reference genes, including 18S <italic>rRNA, ACT, ACT7, DnaJ, EF1</italic>α, <italic>RAN, TUB</italic>α, <italic>TUB</italic>β, <italic>UBC, UBI</italic>, and <italic>UBQ</italic>, were identified and assessed under abiotic stress (NaCl stress and drought stress), biotic stress (<italic>Colletotrichum siamense</italic>) and in different tissues in this study. For each gene, the specificity of the designed primers was verified using agarose gel electrophoresis and the subsequent presence of a single band with the expected size (Figure <xref ref-type="supplementary-material" rid="SM1">S1</xref>), and further confirmed by the presence of a single peak in the melting curve analysis, which was done prior to performing qRT-PCR (Figure <xref ref-type="supplementary-material" rid="SM2">S2</xref>). As described in Table <xref ref-type="table" rid="T1">1</xref>, the amplicon size ranged from 130 to 219 bp. The PCR efficiency (E) was greater than 90% and varied from 90.1% (18S <italic>rRNA</italic>) to 113.7% (<italic>ACT</italic>), and the regression coefficient (<italic>R</italic><sup>2</sup>) ranged from 0.988 (<italic>TUB</italic>α) to 0.999 (<italic>DnaJ</italic> and <italic>TUB</italic>β) (Table <xref ref-type="table" rid="T1">1</xref>).</p><p>To evaluate stability of the reference genes across all experimental samples, the transcript abundances of the 11 candidate reference genes were detected by their mean Ct values. The Ct values of these candidates varied from 15.22 to 31.28, with the majority falling between 20.13 and 24.40 (Figure <xref ref-type="fig" rid="F1">1</xref>). Across all samples, 18S <italic>rRNA</italic> was the most abundantly expressed gene, with the lowest average Ct ± SD (16.85 ± 0.78), followed by <italic>EF1</italic>α (18.28 ± 0.94), <italic>UBQ</italic> (20.13 ± 1.54), <italic>UBC</italic> (21.20 ± 1.13), <italic>UBI</italic> (21.44 ± 0.62), <italic>DnaJ</italic> (22.22 ± 0.75), <italic>ACT7</italic> (22.24 ± 0.92), and <italic>TUB</italic>β (22.40 ± 1.07). <italic>TUB</italic>α was found to have the lowest level of expression of any of the genes tested, with a mean Ct ± SD of 28.43 ± 2.15, followed by <italic>RAN</italic> (26.37 ± 1.29) and <italic>ACT</italic> (25.75 ± 1.31) (Table <xref ref-type="table" rid="T1">1</xref>). Small co-variance (CV) of the Ct value indicates that a given gene is more stably expressed. Among these 11 candidate reference genes, <italic>UBQ</italic> showed the greatest variation with CV value of 7.65%, whereas both <italic>UBI</italic> (2.89%) and <italic>DnaJ</italic> (3.38%) showed the least variation in their expression levels across all tested samples. The ranking of gene stability by CV was as follows: <italic>UBI</italic> > <italic>DnaJ</italic> > <italic>ACT7</italic> > 18S <italic>rRNA</italic> > <italic>TUB</italic>β > <italic>RAN</italic> > <italic>ACT</italic> > <italic>UBC</italic> > <italic>TUB</italic>α > <italic>UBQ</italic> (Table <xref ref-type="table" rid="T1">1</xref>). Collectively, these results indicate that the transcript levels of the candidate reference genes varied across different experimental samples. Thus, it is essential to screen the most appropriate reference genes in jute in order to normalize gene expression analysis.</p><fig id="F1" position="float"><label>Figure 1</label><caption><p><bold>Expression levels of 11 candidate reference genes across all experimental samples</bold>. The box graph indicates the interquartile range, the median, and maximum/minimum values.</p></caption><graphic xlink:href="fpls-06-00848-g0001"></graphic></fig></sec><sec><title>Stability analysis of reference genes by genorm</title><p>A geNorm-based analysis was carried out to determine which candidate reference gene(s) would be optimal in each of the tested samples sets. As shown in Figure <xref ref-type="fig" rid="F2">2</xref>, genes were ranked according to their M values. Since a lower M value indicates increased stability, <italic>RAN</italic>, and <italic>ACT7</italic> were determined to be the most stable reference genes in total samples. In contrast, <italic>TUB</italic>α and <italic>ACT</italic> were the least stable reference genes. For each subset of the treatment, the top two reference genes for qRT-PCR normalization were <italic>ACT7</italic> and <italic>RAN</italic> in biotic stress subset, <italic>TUB</italic>β, and <italic>UBI</italic> in different tissues, <italic>EF1</italic>α and <italic>RAN</italic> for NaCl stress, and <italic>EF1</italic>α and <italic>UBC</italic> for drought stress. In general, the most stable genes across all experimental samples were <italic>RAN</italic> and <italic>ACT7</italic>, while <italic>TUB</italic>α, <italic>ACT</italic>, and 18S <italic>rRNA</italic> were the least stable (Figure <xref ref-type="fig" rid="F2">2</xref>).</p><fig id="F2" position="float"><label>Figure 2</label><caption><p><bold>Expression stability of 11 candidate genes in jute as calculated by geNorm</bold>. Mean expression stability (M) was calculated following stepwise exclusion of the least stable gene in biotic stress samples, tissue samples, NaCl- and PEG-treated samples and all samples. The least stable genes are on the left and the most stable genes on the right.</p></caption><graphic xlink:href="fpls-06-00848-g0002"></graphic></fig><p>The pairwise variation (V<sub>n</sub>) between normalization factors (NF<sub>n</sub>) calculated by the geNorm algorithm also determines the optimal number of reference genes for accurate normalization. A cut-off value of V<sub>n∕n+1</sub> < 0.15 (Vandesompele et al., <xref rid="B51" ref-type="bibr">2002</xref>) indicates that an additional reference gene makes no significant contribution to the normalization. As depicted in Figure <xref ref-type="fig" rid="F3">3</xref>, the V<sub>2∕3</sub> values in biotic stress, NaCl stress, and PEG stress subsets were below 0.15, which indicated that two reference genes (<italic>ACT7</italic> and <italic>RAN</italic> for biotic subset; <italic>EF1</italic>α and <italic>RAN</italic> for NaCl subset; and <italic>EF1</italic>α and <italic>UBC</italic> for drought subset) were sufficient for accurate normalization. In the tissue subset, four reference genes (<italic>TUB</italic>β, <italic>UBI, EF1</italic>α, and <italic>RAN</italic>) were needed for accurate normalization, as the V<sub>4∕5</sub> value was lower than 0.15. When the total samples were taken into account, the V3/4 value (0.132) was lower than the cutoff value of 0.15, which indicated that three genes (<italic>ACT7, RAN</italic>, and <italic>DnaJ</italic>) were suitable for all samples in this study (Figure <xref ref-type="fig" rid="F3">3</xref>).</p><fig id="F3" position="float"><label>Figure 3</label><caption><p><bold>Determination of the optimal number of reference genes for normalization by pairwise variation (V) using geNorm</bold>. The pairwise variation (V<sub>n</sub>/V<sub>n+1</sub>) was calculated between normalization factors NF<sub>n</sub> and NF<sub>n+1</sub> by geNorm to determine the optimal number of reference genes for qRT-PCR data normalization.</p></caption><graphic xlink:href="fpls-06-00848-g0003"></graphic></fig></sec><sec><title>Stability analysis of reference genes by normfinder</title><p>The NormFinder approach was used to determine the stability of reference genes based on inter- and intra-group variance in expression. As shown in Table <xref ref-type="table" rid="T2">2</xref>, similar results were generated by NormFinder, which predicted <italic>ACT7</italic> and <italic>RAN</italic> to be the two most stably expressed normalization factors in both biotic and total subsets. For the NaCl and PEG treated samples, <italic>ACT7</italic> and <italic>RAN</italic> performed as the best reference genes by the NormFinder analysis, while <italic>ACT7</italic> was ranked the second in NaCl subset, and <italic>ACT7</italic> and <italic>RAN</italic> the fifth and third in PEG subset by the geNorm analysis. For the tissue subset, the four most stable genes, <italic>TUB</italic>β, <italic>EF1</italic>α, <italic>RAN</italic>, and <italic>UBI</italic> determined by NormFinder were ranked the first, second, third and first by geNorm algorithm, respectively (Table <xref ref-type="table" rid="T2">2</xref>; Figure <xref ref-type="fig" rid="F2">2</xref>).</p><table-wrap id="T2" position="float"><label>Table 2</label><caption><p><bold>Expression stability of candidate reference genes as calculated by Normfinder</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Rank</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Biotic</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Tissue</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>NaCl</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>PEG</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Total</bold></th></tr><tr><th rowspan="1" colspan="1"></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Stability</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Stability</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Stability</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Stability</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Stability</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">1</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.142</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.112</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.040</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.053</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.116</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">2</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.357</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.126</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.167</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.173</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.262</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">3</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.499</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.136</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.190</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.206</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.349</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">4</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.545</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.320</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.236</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.262</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.410</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">5</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.583</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.383</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.325</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.264</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.487</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">6</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.650</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.603</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.366</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.292</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.492</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">7</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.734</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.673</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.366</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.376</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.616</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">8</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.869</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.846</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.472</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.404</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.660</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.882</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.992</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.490</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.543</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.749</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">1.079</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.882</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.744</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.557</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.830</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">11</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.257</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">2.409</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.288</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.702</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.262</td></tr></tbody></table></table-wrap></sec><sec><title>Stability analysis of reference genes by bestkeeper</title><p>The BestKeeper program was used to evaluate the stabilities of reference genes based on the coefficient of variance (CV) and the standard deviation (SD) of the average Ct values. The most stable genes were identified as those that exhibit the lowest CV and SD (CV ± SD), and genes with SD greater than 1 were considered unacceptable and should be excluded (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>; Xiao et al., <xref rid="B54" ref-type="bibr">2014</xref>). In the biotic stress subset, 18S <italic>rRNA</italic> (2.96 ± 0.51) and <italic>UBI</italic> (2.39 ± 0.52) had lowest CV ± SD values, and showed remarkably stable expression. In the NaCl- and PEG-treated subset, <italic>TUB</italic>α had the lowest CV ± SD values of 0.79 ± 0.24 and 0.96 ± 0.29, respectively, and showed the most stable expression. In the total samples subset, <italic>UBI</italic> (2.32 ± 0.50) and 18S <italic>rRNA</italic> (3.76 ± 0.63) were identified as the two best reference genes for normalization. These results are inconsistent with those acquired from the geNorm and NormFinder methods (Figure <xref ref-type="fig" rid="F2">2</xref>; Tables <xref ref-type="table" rid="T2">2</xref>, <xref ref-type="table" rid="T3">3</xref>). In the tissue samples subset, the most stable reference genes identified by BestKeeper were <italic>UBI</italic> (1.98 ± 0.42) and <italic>TUB</italic>β (2.03 ± 0.43). This result is consistent with the results obtained from geNorm and NormFinder analyses (Figure <xref ref-type="fig" rid="F2">2</xref>; Tables <xref ref-type="table" rid="T2">2</xref>, <xref ref-type="table" rid="T3">3</xref>).</p><table-wrap id="T3" position="float"><label>Table 3</label><caption><p><bold>Expression stability of candidate reference genes as calculated by BestKeeper</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Rank</bold></th><th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Biotic</bold></th><th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Tissue</bold></th><th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;" rowspan="1"><bold>NaCl</bold></th><th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;" rowspan="1"><bold>PEG</bold></th><th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Total</bold></th></tr><tr><th rowspan="1" colspan="1"></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>CV</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">1</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.51</td><td valign="top" align="center" rowspan="1" colspan="1">2.96</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.42</td><td valign="top" align="center" rowspan="1" colspan="1">1.98</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.24</td><td valign="top" align="center" rowspan="1" colspan="1">0.79</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.29</td><td valign="top" align="center" rowspan="1" colspan="1">0.96</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.50</td><td valign="top" align="center" rowspan="1" colspan="1">2.32</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">2</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.52</td><td valign="top" align="center" rowspan="1" colspan="1">2.39</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.43</td><td valign="top" align="center" rowspan="1" colspan="1">2.03</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.27</td><td valign="top" align="center" rowspan="1" colspan="1">1.30</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.31</td><td valign="top" align="center" rowspan="1" colspan="1">1.44</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.63</td><td valign="top" align="center" rowspan="1" colspan="1">3.76</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">3</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.56</td><td valign="top" align="center" rowspan="1" colspan="1">2.47</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.59</td><td valign="top" align="center" rowspan="1" colspan="1">2.60</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.33</td><td valign="top" align="center" rowspan="1" colspan="1">1.58</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.35</td><td valign="top" align="center" rowspan="1" colspan="1">1.71</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.64</td><td valign="top" align="center" rowspan="1" colspan="1">2.89</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">4</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.59</td><td valign="top" align="center" rowspan="1" colspan="1">2.58</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.6</td><td valign="top" align="center" rowspan="1" colspan="1">2.24</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.41</td><td valign="top" align="center" rowspan="1" colspan="1">1.59</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.40</td><td valign="top" align="center" rowspan="1" colspan="1">1.87</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.72</td><td valign="top" align="center" rowspan="1" colspan="1">3.22</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">5</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.61</td><td valign="top" align="center" rowspan="1" colspan="1">2.18</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.65</td><td valign="top" align="center" rowspan="1" colspan="1">3.23</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.43</td><td valign="top" align="center" rowspan="1" colspan="1">2.25</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.4</td><td valign="top" align="center" rowspan="1" colspan="1">2.41</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.72</td><td valign="top" align="center" rowspan="1" colspan="1">3.92</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">6</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.66</td><td valign="top" align="center" rowspan="1" colspan="1">2.98</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.95</td><td valign="top" align="center" rowspan="1" colspan="1">4.66</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.44</td><td valign="top" align="center" rowspan="1" colspan="1">2.41</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.41</td><td valign="top" align="center" rowspan="1" colspan="1">1.86</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.78</td><td valign="top" align="center" rowspan="1" colspan="1">3.48</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">7</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.75</td><td valign="top" align="center" rowspan="1" colspan="1">4.01</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.97</td><td valign="top" align="center" rowspan="1" colspan="1">4.11</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">0.47</td><td valign="top" align="center" rowspan="1" colspan="1">2.09</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.42</td><td valign="top" align="center" rowspan="1" colspan="1">2.17</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.81</td><td valign="top" align="center" rowspan="1" colspan="1">3.81</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">8</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.86</td><td valign="top" align="center" rowspan="1" colspan="1">3.25</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td><td valign="top" align="center" rowspan="1" colspan="1">5.70</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.48</td><td valign="top" align="center" rowspan="1" colspan="1">2.18</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.43</td><td valign="top" align="center" rowspan="1" colspan="1">1.68</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.94</td><td valign="top" align="center" rowspan="1" colspan="1">3.64</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.06</td><td valign="top" align="center" rowspan="1" colspan="1">4.79</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.03</td><td valign="top" align="center" rowspan="1" colspan="1">6.28</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.55</td><td valign="top" align="center" rowspan="1" colspan="1">2.48</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.48</td><td valign="top" align="center" rowspan="1" colspan="1">1.87</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.15</td><td valign="top" align="center" rowspan="1" colspan="1">4.35</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">1.13</td><td valign="top" align="center" rowspan="1" colspan="1">4.32</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.13</td><td valign="top" align="center" rowspan="1" colspan="1">5.27</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.59</td><td valign="top" align="center" rowspan="1" colspan="1">3.43</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.49</td><td valign="top" align="center" rowspan="1" colspan="1">2.19</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.32</td><td valign="top" align="center" rowspan="1" colspan="1">6.54</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">11</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="center" rowspan="1" colspan="1">1.17</td><td valign="top" align="center" rowspan="1" colspan="1">5.04</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">1.50</td><td valign="top" align="center" rowspan="1" colspan="1">6.37</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="center" rowspan="1" colspan="1">0.81</td><td valign="top" align="center" rowspan="1" colspan="1">3.15</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">0.56</td><td valign="top" align="center" rowspan="1" colspan="1">3.09</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="center" rowspan="1" colspan="1">2.66</td><td valign="top" align="center" rowspan="1" colspan="1">9.37</td></tr></tbody></table></table-wrap></sec><sec><title>Comprehensive stability analysis of reference genes</title><p>To acquire a consensus result of the best reference genes, three algorithms rankings of the stability were integrated, generating a comprehensive ranking according to the geometric mean of three rankings (Xiao et al., <xref rid="B54" ref-type="bibr">2014</xref>). The comprehensive rankings were shown in Table <xref ref-type="table" rid="T4">4</xref>: <italic>RAN</italic> and <italic>ACT7</italic> were ranked as the top two stable reference genes in the biotic stress subset, NaCl stress subset and total samples subset; <italic>UBC</italic> and <italic>DnaJ</italic> were the two most stable genes in the PEG stress subset; <italic>TUB</italic>β and <italic>UBI</italic> were the most stable genes, followed by <italic>EF1</italic>α and <italic>RAN</italic> in the tissue subset (Table <xref ref-type="table" rid="T4">4</xref>). Taken the number of reference genes to use suggested by geNorm and the comprehensive rankings into consideration, the most stable and least stable combination of reference genes in each subset was shown in Table <xref ref-type="table" rid="T5">5</xref>.</p><table-wrap id="T4" position="float"><label>Table 4</label><caption><p><bold>Expression stability ranking of the 11 candidate reference genes</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Method</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>1</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>2</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>3</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>4</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>5</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>6</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>7</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>8</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>9</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>10</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>11</bold></th></tr></thead><tbody><tr><td valign="top" align="left" colspan="12" style="background-color:#bbbdc0" rowspan="1"><bold>(A) RANKING ORDER UNDER BIOTIC STRESS (BETTER-GOOD-AVERAGE)</bold></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">NormFinder</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">geNorm</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic>/<italic>RAN</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">BestKeeper</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Comprehensive ranking</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td></tr><tr><td valign="top" align="left" colspan="12" style="background-color:#bbbdc0" rowspan="1"><bold>(B) RANKING ORDER UNDER DIFFERENT TISSUES (BETTER-GOOD-AVERAGE)</bold></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">NormFinder</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">geNorm</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β/<italic>UBI</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">BestKeeper</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Comprehensive ranking</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" colspan="12" style="background-color:#bbbdc0" rowspan="1"><bold>(C) RANKING ORDER UNDER NACL STRESS (BETTER-GOOD-AVERAGE)</bold></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">NormFinder</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">geNorm</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α/<italic>RAN</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">BestKeeper</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Comprehensive ranking</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" colspan="12" style="background-color:#bbbdc0" rowspan="1"><bold>(D) RANKING ORDER UNDER PEG STRESS (BETTER-GOOD-AVERAGE)</bold></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">NormFinder</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">geNorm</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α/<italic>UBC</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">BestKeeper</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Comprehensive ranking</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" colspan="12" style="background-color:#bbbdc0" rowspan="1"><bold>(E) RANKING ORDER UNDER TOTAL SAMPLES (BETTER-GOOD-AVERAGE)</bold></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">NormFinder</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">geNorm</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic>/<italic>RAN</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">BestKeeper</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Comprehensive ranking</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBQ</italic></td><td valign="top" align="left" rowspan="1" colspan="1">18S <italic>rRNA</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td></tr></tbody></table></table-wrap><table-wrap id="T5" position="float"><label>Table 5</label><caption><p><bold>Best combination of reference genes based on the geNorm and comprehensive rankings in each subset</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Biotic</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Tissue</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>NaCl</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>PEG</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Total</bold></th></tr><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Most</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Least</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Most</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Least</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Most</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Least</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Most</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Least</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Most</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Least</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>β</td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBC</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>TUB</italic>α</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>UBI</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>ACT7</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>EF1</italic>α</td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>DnaJ</italic></td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1"><italic>RAN</italic></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td></tr></tbody></table></table-wrap></sec><sec><title>Reference genes validation</title><p>To validate the selected reference genes, the relative expression levels of the target gene, <italic>CcWRKY15</italic> under different experimental conditions were evaluated using qRT-PCR. <italic>CcWRKY15</italic> is a homolog of <italic>AtWRKY15</italic>, which is known to be a central regulator in the response to oxidative stress and pathogenic infection (Vanderauwera et al., <xref rid="B50" ref-type="bibr">2012</xref>). Thus, it would be expected to have similar expression patterns as <italic>AtWRKY15</italic> under abiotic and biotic stress conditions. However, a substantial divergence can occur in its relative transcript abundance when normalized to different kinds of reference genes. We therefore used the most stable reference genes found in each subset (<italic>ACT7</italic> and <italic>RAN</italic> for Biotic stress and NaCl stress; <italic>UBC</italic> and <italic>DnaJ</italic> for PEG stress) either singly or in combination, and the least stable reference gene (<italic>ACT</italic> or <italic>TUB</italic>α), to perform a qRT-PCR analysis. Results showed that in accordance with the behavior of <italic>AtWRKY15</italic> previously described in <italic>A. thaliana</italic>, the <italic>CcWRKY15</italic> functions as a negative regulator and was induced by salt-stress, oxidative-stress, and pathogenic infections (<italic>C</italic>. <italic>siamense</italic>) in jute (Figure <xref ref-type="fig" rid="F4">4</xref>). In addition, we also examined the reference gene (<italic>EF1</italic>α and <italic>UBC</italic> for abiotic stress; <italic>TUB</italic>β for fungal stress) selected by Ferdou et al. (<xref rid="B17" ref-type="bibr">2015</xref>) in NaCl stress, PEG stress, and fungal stress subsets. The results showed that <italic>EF1</italic>α could sever as a stable reference gene for normalization but <italic>UBC</italic> unstable under NaCl stress condition (Figure <xref ref-type="fig" rid="F4">4A</xref>). For the PEG stress subset, the relative expression folds of <italic>CcWRKY15</italic> normalized by <italic>EF1</italic>α were slightly decreased compared to the stable genes <italic>UBC</italic> and <italic>DnaJ</italic> (Figure <xref ref-type="fig" rid="F4">4B</xref>). For the biotic stress subset, reference gene <italic>TUB</italic>β was not as stable as that described by Ferdou et al. (<xref rid="B17" ref-type="bibr">2015</xref>), conversely, similar to the expression pattern of the worst gene <italic>TUB</italic>α (Figure <xref ref-type="fig" rid="F4">4C</xref>). Our tissue type analysis revealed that the transcript abundance of <italic>CcWRKY15</italic> was the highest in the leaf, followed by the stem and then the root (<italic>p</italic> < 0.01) (Figure <xref ref-type="fig" rid="F4">4D</xref>). Contrastingly, when the least stable reference gene was used as normalization factor, the expression level of <italic>CcWRKY15</italic> was significantly overestimated (<italic>p</italic> < 0.01). For example, the relative expression folds of <italic>CcWRKY15</italic> were approximately eight-fold (<italic>p</italic> < 0.01) higher than that of <italic>ACT7, RAN</italic>, or their combination at 4 h under NaCl stress, when <italic>ACT</italic> was used as normalization factor (Figure <xref ref-type="fig" rid="F4">4A</xref>).</p><fig id="F4" position="float"><label>Figure 4</label><caption><p><bold>Relative quantification of <italic>CcWRKY15</italic> expression using the validated reference gene(s)</bold>. The results are represented as mean fold changes in relative expression when compared to the first sampling stage (0 h). cDNA samples were taken from the same subset used for gene expression stability analysis. <bold>(A,B)</bold> Leaves were collected from 3-week-old seedlings subjected to salt- and PEG-stress after 0, 2, 4, 6, 8, 12, and 24 h of treatment. <bold>(C)</bold> Leaves were collected from 3-week-old seedlings subjected to biotic stress (<italic>C</italic>. <italic>siamense</italic>) after 0, 6, 12, 24, 48, 72, and 96 h of treatment. <bold>(D)</bold> Different tissue types were collected from 3-week-old seedlings. <sup>*</sup> indicates statistically significant (<italic>p</italic> < 0.05); <sup>**</sup> indicates greatly statistically significant (<italic>p</italic> < 0.01).</p></caption><graphic xlink:href="fpls-06-00848-g0004"></graphic></fig></sec></sec><sec sec-type="discussion" id="s4"><title>Discussion</title><p>There has been a surge in interest for environment-friendly materials and jute fiber—used as reinforcement component—has been widely used in the textile, papermaking, automotive, and aerospace industries. Given its popularity, the need for the application of new genomic tools has become increasingly important (Sydenstricker et al., <xref rid="B49" ref-type="bibr">2003</xref>; Corrales et al., <xref rid="B10" ref-type="bibr">2007</xref>). New technologies such as qRT-PCR now make it possible to understand the molecular mechanisms underpinning the commercially important traits of jute. qRT-PCR is an essential tool that can be used in studies of target gene expression patterns. Although past studies have used 18S <italic>rRNA</italic> as a reference gene for examining the functions of UDP-glucose pyrophosphorylase and caffeoyl-CoA 3-O-methyltransferase in jute (<italic>C</italic>. <italic>capsularis</italic>) (Zhang et al., <xref rid="B55" ref-type="bibr">2013</xref>, <xref rid="B56" ref-type="bibr">2014</xref>), and in the most recent study were 7 reference genes tested under abiotic and biotic stress in the other jute species <italic>Corchorus olitorius</italic> (Ferdou et al., <xref rid="B17" ref-type="bibr">2015</xref>), little information is available on the systematic exploration and validation of a set of suitable reference genes in jute (<italic>C</italic>. <italic>capsularis</italic>). In addition, it has been reported that ribosomal RNA genes are not adequate reference genes due to their high transcription abundance. Ultimately, this could lead to experimental error when normalizing genes with weak expression (Jain et al., <xref rid="B24" ref-type="bibr">2006</xref>; Niu et al., <xref rid="B38" ref-type="bibr">2014</xref>). Comparing our results to that of Ferdou et al. (<xref rid="B17" ref-type="bibr">2015</xref>), the stability values of three out of seven reference genes (<italic>EF1</italic>α and <italic>UBC</italic> for abiotic stress; <italic>TUB</italic>β for biotic stress) were significantly lower in our experiment. For instance, when <italic>UBC</italic> was used as reference gene, <italic>CcWRKY15</italic> had a 5.0-fold higher expression value at 24 h under NaCl stress condition (Figure <xref ref-type="fig" rid="F4">4A</xref>); when <italic>TUB</italic>β as an internal reference, <italic>CcWRKY15</italic> had a 6.0 times higher at 24 h after inoculation (Figure <xref ref-type="fig" rid="F4">4C</xref>). Additionally, <italic>TUB</italic>β was found to be one of the least stable genes in our study. These expression stability differences might be a result of different species/cultivars analyzed in the compared experiment conditions. It is consistent with the previous studies that the stability of reference genes is not only cultivar/species specific but may also be tissue specific and influenced by the different experimental treatments (Nicot et al., <xref rid="B37" ref-type="bibr">2005</xref>; Štajner et al., <xref rid="B48" ref-type="bibr">2013</xref>; Zhuang et al., <xref rid="B58" ref-type="bibr">2015</xref>). This is also the primary reason for validating the stability of reference genes for a specific genotype and/or experimental treatment.</p><p>In this study, we used three publicly available programs, geNorm (Vandesompele et al., <xref rid="B51" ref-type="bibr">2002</xref>), NormFinder (Andersen et al., <xref rid="B2" ref-type="bibr">2004</xref>), and BestKeeper (Pfaffl et al., <xref rid="B41" ref-type="bibr">2004</xref>), to evaluate the expression stability of 11 candidate reference genes in a total of 25 jute samples taken from different tissues and experimental treatment groups. We found different rankings for the selected genes after comparison to the ranking of the candidates generated by the three algorithms (Figure <xref ref-type="fig" rid="F2">2</xref>; Tables <xref ref-type="table" rid="T2">2</xref>, <xref ref-type="table" rid="T3">3</xref>). This apparent divergence probably reflects the discrepancies in the three statistical algorithms to calculate stability. NormFinder takes the inter- and intra-group variations into account, and combines them into a stability value, and finally ranks the top genes with minimal inter- and intra-group variation. In contrast, geNorm identifies two reference genes with the highest degree of similarity in expression profile and the lowest intra-group variation (Andersen et al., <xref rid="B2" ref-type="bibr">2004</xref>; Jian et al., <xref rid="B25" ref-type="bibr">2008</xref>; Cruz et al., <xref rid="B11" ref-type="bibr">2009</xref>; Liu et al., <xref rid="B30" ref-type="bibr">2012</xref>). As for BestKeeper, this program determines the stability ranking of the reference genes based on the coefficient of variance (CV) and the standard deviation (SD) values. The most stable genes are identified as those that exhibit the lowest CV ± SD values (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>; Xiao et al., <xref rid="B54" ref-type="bibr">2014</xref>). Similar methods have been used in previous studies of different species, such as <italic>Salicornia europaea</italic> (Huang et al., <xref rid="B20" ref-type="bibr">2014</xref>; Xiao et al., <xref rid="B54" ref-type="bibr">2014</xref>), <italic>Populus euphratica</italic> (Wang et al., <xref rid="B53" ref-type="bibr">2014</xref>), and <italic>Cynodon dactylon</italic> (Chen et al., <xref rid="B9" ref-type="bibr">2014</xref>). Thus, referring to the previous studies (Štajner et al., <xref rid="B48" ref-type="bibr">2013</xref>; Xiao et al., <xref rid="B54" ref-type="bibr">2014</xref>), the integrated results were obtained from three programs, leading to a more comprehensive ranking and better accuracy for each gene (Table <xref ref-type="table" rid="T4">4</xref>).</p><p>Of the top three reference genes defined by three algorithms in the total samples subset, <italic>ACT7</italic> and <italic>RAN</italic> were found to be the best candidates for the biotic stress and NaCl stress subsets. The strong performance of <italic>ACT7</italic> in jute (<italic>C</italic>. <italic>capsularis</italic>) was consistent with the results obtained in the developmental stage series of <italic>G</italic>. <italic>max</italic> (Jian et al., <xref rid="B25" ref-type="bibr">2008</xref>), during grape berry development of <italic>V</italic>. <italic>vinifera</italic> (Reid et al., <xref rid="B44" ref-type="bibr">2006</xref>), and across different tissues and cold-treated samples of <italic>Platycladus orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>). However, this gene performed poorly in studies of <italic>O</italic>. <italic>sativa</italic> (Jain et al., <xref rid="B24" ref-type="bibr">2006</xref>), <italic>S</italic>. <italic>officinarum</italic> (Ling et al., <xref rid="B29" ref-type="bibr">2014</xref>), and <italic>S</italic>. <italic>tuberosum</italic> (Nicot et al., <xref rid="B37" ref-type="bibr">2005</xref>), suggesting that the expression levels of reference genes are variable among different species. <italic>RAN</italic> was the other most stable reference gene found in the present study. It has also been shown to be the best performer across different tissue and hormone treatment samples of <italic>M</italic>. <italic>acuminata</italic> (Chen et al., <xref rid="B8" ref-type="bibr">2011</xref>) as well as in the leaf or plant growth regulator treatment samples of <italic>C</italic>. <italic>melo</italic> (Kong et al., <xref rid="B26" ref-type="bibr">2014</xref>). <italic>DnaJ</italic> showed relatively stable expression and ranked third across all samples (Figure <xref ref-type="fig" rid="F2">2</xref>; Tables <xref ref-type="table" rid="T3">3</xref>, <xref ref-type="table" rid="T4">4</xref>). This gene has also been recommended as the best reference gene in different tissues and NaCl-treated samples of <italic>P</italic>. <italic>orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>).</p><p>However, the data presented show that the candidate reference genes <italic>EF1</italic>α, <italic>UBC, UBI, UBQ</italic>, and <italic>TUB</italic>β were moderately expressed and had variable rankings in this study. For example, <italic>EF1</italic>α was ranked first by geNorm analysis and was stably expressed in NaCl- and PEG-treated samples, but was shown to be the fourth in NaCl stress subset analyzed by NormFinder; the fifth in PEG stress subset by NormFinder and Comprehensive ranking. Previously, <italic>EF1</italic>α showed stable expression during abiotic and biotic stress in <italic>O</italic>. <italic>sativa</italic> (Jain et al., <xref rid="B24" ref-type="bibr">2006</xref>), <italic>L</italic>. <italic>chinensis</italic> (Zhong et al., <xref rid="B57" ref-type="bibr">2011</xref>), <italic>S</italic>. <italic>tuberosum</italic> (Nicot et al., <xref rid="B37" ref-type="bibr">2005</xref>), and <italic>C</italic>. <italic>sativus</italic> (Wan et al., <xref rid="B52" ref-type="bibr">2010</xref>), but was shown as an unsatisfactory reference gene in <italic>T</italic>. <italic>aestivum</italic> (Paolacci et al., <xref rid="B40" ref-type="bibr">2009</xref>), <italic>G</italic>. <italic>max</italic> (Jian et al., <xref rid="B25" ref-type="bibr">2008</xref>), <italic>N</italic>. <italic>benthamiana</italic> (Liu et al., <xref rid="B30" ref-type="bibr">2012</xref>), and <italic>P</italic>. <italic>orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>). <italic>UBC</italic>, an ubiquitin-conjugating enzyme gene, was ranked first in the PEG stress subset, which was also the optimal reference gene under NaCl- and PEG-treated conditions in <italic>P. orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>) and <italic>C. olitorius</italic> (Ferdou et al., <xref rid="B17" ref-type="bibr">2015</xref>); however, it was not the best choice for normalization in the different tissues of bamboo (Fan et al., <xref rid="B16" ref-type="bibr">2013</xref>). <italic>UBQ</italic> were relatively weakly expressed across all the experimental samples according to the three programs and comprehensive analyses, but were the optimal reference genes for developmental stages and under NaCl- and PEG-treated conditions in <italic>P</italic>. <italic>orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>). As previously noted, <italic>TUB</italic>β was the most stable reference gene across various developmental stages of <italic>G</italic>. <italic>max</italic> (Jian et al., <xref rid="B25" ref-type="bibr">2008</xref>), leaf senescence system of <italic>H</italic>. <italic>annuus</italic> (Fernandez et al., <xref rid="B18" ref-type="bibr">2011</xref>), and among different tissues and PEG-treated samples of <italic>P</italic>. <italic>orientalis</italic> (Chang et al., <xref rid="B6" ref-type="bibr">2012</xref>). In this study, similarly, all of the algorithms ranked <italic>TUB</italic>β in the first position, indicating that <italic>TUB</italic>β was the optimal choice as an internal control gene for different tissues investigated in jute (<italic>C</italic>. <italic>capsularis</italic>). <italic>UBI</italic>, another stable reference gene, showed relatively small variation in tissues subset, which was also the most stable reference gene in different tissues of <italic>C</italic>. <italic>sativus</italic> (Wan et al., <xref rid="B52" ref-type="bibr">2010</xref>). In different tissues subset, in addition to expression of <italic>TUB</italic>β and <italic>UBI</italic>, the expression of <italic>EF1</italic>α and <italic>RAN</italic> was also stable and therefore they were considered as the suitable reference genes (Figure <xref ref-type="fig" rid="F2">2</xref>; Table <xref ref-type="table" rid="T4">4</xref>).</p><p>Interestingly, the commonly used reference genes <italic>TUB</italic>α, 18S <italic>rRNA</italic>, and <italic>ACT</italic> performed poorly and were not suitable for most of the experimental conditions. Several studies have shown similar results. For example, <italic>TUB</italic>α was found to be unstable as reference gene in the developmental stage in tomato (Expósito-Rodríguez et al., <xref rid="B15" ref-type="bibr">2008</xref>) and in different flax tissue (Huis et al., <xref rid="B22" ref-type="bibr">2010</xref>). 18S <italic>rRNA</italic> was considered as the least reliable reference gene in viral-infected <italic>N. benthamiana</italic> (Liu et al., <xref rid="B30" ref-type="bibr">2012</xref>) and under different conditions in <italic>C</italic>. <italic>sativus</italic> (Wan et al., <xref rid="B52" ref-type="bibr">2010</xref>). <italic>ACT</italic> was proved unsuitable for normalization in different flax tissues (Huis et al., <xref rid="B22" ref-type="bibr">2010</xref>), during abiotic and biotic stress in <italic>S</italic>. <italic>tuberosum</italic> (Nicot et al., <xref rid="B37" ref-type="bibr">2005</xref>), and in viral-infected <italic>N. benthamiana</italic> (Liu et al., <xref rid="B30" ref-type="bibr">2012</xref>). Taken together, these findings indicate that large numbers of experimental data on gene expression should be acquired to investigate the transcript stability of commonly used reference genes under different experimental conditions.</p><p>To further validate the feasibility of the reference genes screened in this study, we analyzed the transcription profiles of the WRKY domain gene <italic>CcWRKY15</italic>, a homolog <italic>AtWRKY15</italic> of <italic>A</italic>. <italic>thaliana</italic>. <italic>AtWRKY15</italic> has been shown to be a key regulator of plant growth and salt/osmotic stress responses in <italic>A</italic>. <italic>thaliana</italic> (Vanderauwera et al., <xref rid="B50" ref-type="bibr">2012</xref>). In this study, the expression of <italic>CcWRKY15</italic> was normalized using the most stable reference genes in each subset both singly and combined as well as a least stable gene as an internal control (Figure <xref ref-type="fig" rid="F4">4</xref>). Our results showed that expression of <italic>CcWRKY15</italic> was negatively induced by NaCl- and PEG-treated stress (Figures <xref ref-type="fig" rid="F4">4A,B</xref>) and was significantly increased after 24 h of inoculation treatment (Figure <xref ref-type="fig" rid="F4">4C</xref>) (<italic>p</italic> < 0.01). By comparing the expression pattern of <italic>CcWRKY15</italic> with that reported in <italic>A</italic>. <italic>thaliana</italic> (Vanderauwera et al., <xref rid="B50" ref-type="bibr">2012</xref>), a supported result was found. Therefore, the results obtained from this study are credible. Moreover, we compared the results from the jute species <italic>C</italic>. <italic>olitorius</italic> selected by Ferdou et al. (<xref rid="B17" ref-type="bibr">2015</xref>) under the same conditions, the results showed great differences between them. These results underscore the fact that inappropriate utilization of reference genes without validation may generate bias in the analysis and lead to misinterpretation of qRT-PCR data.</p></sec><sec sec-type="conclusions" id="s5"><title>Conclusion</title><p>We present here a systematic attempt to validate a set of candidate reference genes for the normalization of gene expression using qRT-PCR in jute (<italic>C</italic>. <italic>capsularis</italic>) under abiotic (salt and drought) and biotic (<italic>Colletotrichum siamense</italic>) stress conditions as well as across different tissue types. The expression stability of the 11 candidates was analyzed by the three applications (geNorm, NormFinder, and BestKeeper), and their results were furtherly integrated into a comprehensive ranking based on the geometric mean. For gene expression study under biotic stress and NaCl stress, we recommend <italic>ACT7</italic> and <italic>RAN</italic> to normalize the qRT-PCR data. For gene expression study under PEG stress, <italic>UBC</italic>, and <italic>DnaJ</italic> are the two most suitable reference genes in <italic>C</italic>. <italic>capsularis</italic>. For the study of gene expression in the different tissues, <italic>TUB</italic>β, <italic>UBI, EF1</italic>α, and <italic>RAN</italic> are recommended as the best reference genes for normalization. In addition, the two least stable reference genes 18S <italic>rRNA</italic> and <italic>ACT</italic> should be carefully used for normalization. Furthermore, the feasibility of the reference genes screened was further confirmed by comparing the expression pattern between <italic>CcWRKY15</italic> and <italic>AtWRKT15</italic>, and the selected reference genes can perform significantly better in gene expression normalization. In particular, the reference genes selected in current study will facilitate the future work on gene expression studies in <italic>C</italic>. <italic>capsularis</italic>.</p><sec><title>Conflict of interest statement</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></sec></body><back><ack><p>This work was funded by the 948 project of the Agricultural Department of China (2013–Z70), the National Bast Fiber Germplasm Resources Project of China (K47NI201A), the National Bast Fiber Research System of China (CARS–19–E06), and the National Natural Science Foundation of China (31471549).</p></ack><sec sec-type="supplementary-material" id="s6"><title>Supplementary material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="http://journal.frontiersin.org/article/10.3389/fpls.2015.00848">http://journal.frontiersin.org/article/10.3389/fpls.2015.00848</ext-link></p><supplementary-material content-type="local-data" id="SM1"><media xlink:href="Image1.PDF"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="SM2"><media xlink:href="Image2.PDF"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id="B1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Alam</surname><given-names>M. 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