55中英文雙語外文文獻(xiàn)翻譯成品在酸棗中鑒定適當(dāng)?shù)膬?nèi)參基因用于rt-qpcr分析

1、<p>　　外文標(biāo)題：Identification of appropriate reference genes for RT-qPCR analysis in Ziziphus jujuba Mill</p><p>　　外文作者：Chunmei Zhang, Jian Huang, Xingang Li</p><p>　　文獻(xiàn)出處: 《Scientia Horticultu

2、rae》 , 2015 , 197 :166-169 </p><p>　　英文3007單詞， 16798字符，中文4692漢字。</p><p>　　此文檔是外文翻譯成品，無需調(diào)整復(fù)雜的格式哦！下載之后直接可用，方便快捷！只需二十多元。</p><p><b>　　原文：</b></p><p&g

3、t;　　Identification of appropriate reference genes for RT-qPCR analysis in Ziziphus jujuba Mill</p><p>　　Chunmei Zhang, Jian Huang, Xingang Li</p><p><b>　　Abstract:</b></p><

4、;p>　　Reverse transcription quantitative real-time PCR (RT-qPCR) is the most widely used method to evaluate the expression levels of mRNA, and the stability of reference genes is crucial for correct evaluation of RT-qP

5、CR data. In the present work, 11 candidate reference genes were investigated for their expression levels at six fruit developmental stages and among four tissues and ?ve genotypes of Ziziphus jujuba Mill. The geNorm, Nor

6、m?nder and Bestkeeper software programs were applied to estimat</p><p><b>　　Keywords</b></p><p>　　Ziziphus jujuba Mill, Reference genes, Gene expression, Quantitative real-time

7、PCR</p><p>　　Introduction</p><p>　　Chinese jujube, or jujube (Ziziphus jujuba Mill.), a native fruit tree from China, is one of the most important species in the fam- ily Rhamnaceae. With rich n

8、utritional and medicinal properties (Qu and Wang, 1993), the jujube cultivation area has reached 2 million hm2 and is also increasing around the world (Liu et al., 2013a). As an important economic fruit tree,

9、it is necessary to understand the molecular basis of its physiological patterns and economic traits for improved jujube b</p><p>　　Reverse transcription quantitative real-time PCR (RT-qPCR) is one of the mos

10、t important experimental methods to evaluate the expression levels of mRNA due to its sensitivity, speci?city and accuracy. However, the accuracy and reliability of RT-qPCR analysis are easily affected by several factors

11、, including the quality of RNA and the ef?ciency of the RT and PCR reactions (Nolan et al., 2006). In particular, selection of a reference gene is essential for normaliza- tion. Currently, many stable ref</p><

12、p>　　The ideal reference gene should be expressed stably and constantly. However, increasing evidence shows that many house- keeping genes have variable expression levels across tissues, genotypes and different experim

13、ental situations (Hu et al., 2009; Li et al., 2015). Therefore, to obtain accurate quanti?cation for RT-qPCR, selection of stable reference genes should be performed based on the experimental conditions.</p><

14、;p>　　In the present study, we investigated the expression of 11 house- keeping genes to select the most stable reference genes among different experimental conditions, including different fruit devel- opmental stages,

15、 tissues and genotypes.</p><p>　　Materials and methods</p><p>　　Plant materials</p><p>　　Young jujube fruits from ?ve different cultivars/genotypes (Muzao, Junzao, Goutouzao, Qiyuex

16、ian and Fengmiguan), Muzao fruits at six developmental stages (young fruit, enlarging fruit at two stages, white mature fruit, half red fruit and full red fruit) and the buds, leaves and ?owers of Muzao plants were harve

17、sted from the Experimental Station of Jujube, Northwest A&F University in Qingjian, Shaanxi, China. The collected fruit (?esh) was quickly cut into small pieces, immediately immersed in li</p><p>　　Fig.

18、1. Average expression stability values (M) of the 11 candidate reference genes estimated by the geNorm program. (A) Six fruit developmental stages, (B) four tissues, (C) ?ve genotypes and (D) all samples.</p><

19、p>　　RNA extraction and cDNA synthesis</p><p>　　Total RNA was extracted using the TaKaRa MiniBEST Plant RNA Extraction Kit (TaKaRa, Dalian) following the manufacturer’s instructions. The RNA was quanti?ed

20、using the NanoDrop 20000 (Thermo Scienti?c). cDNA was synthesized using the PrimeScript RT reagent kit (TaKaRa) according to the manufacturer’s instruc- tions and used for further analysis.</p><p>　　Selectio

21、n of reference genes and primer design</p><p>　　Eleven candidate reference genes, including ACTIN, his- tone (ZJH3), ubiquitin (UBQ and UBQ2), 60s ribosomal (RB1), glyceraldehyde-3-phosphate dehydrogenase (G

22、APDH), cyclophilin (CYP), elongation factor (EF1) and elongation factor 1-alpha (EF1?) from the NCBI database (http://www.ncbi.nlm.nih.gov/), and ACTIN7 and ACTIN9 from the genome sequencing of Z. jujuba cv. Dongzao

23、 (Liu et al., 2014) (Supplemental Table S1) are selected. The primers were designed using Primer3 software (Rozen and Ska</p><p>　　RT-qPCR ampli?cation and data analysis</p><p>　　Reverse transcr

24、iption quantitative real-time PCR was performed using the SYBR Premix Ex Taq Kit (TaKaRa) on a Bio-Rad IQ5 in a reaction volume of 25 µl. To con?rm the ampli?cation speci?ca- tion of each primer, the melting curve w

25、as analyzed at 60–95 ?C for 30 s after 40 cycles. The data were analyzed using the geNorm (Vandesompele et al., 2002), Norm?nder (Andersen et al., 2004) and Bestkeeper (Pfaf? et al., 2004) softwares. The geNorm algo- ri

26、thm provides a measure of gene expression stabilit</p><p>　　Normalization of GME</p><p>　　The GDP-mannose-3j,5j-epimerase (GME), important for ascor- bic acid biosynthesis, was used to assess th

27、e validity of the selected candidate reference genes by RT-qPCR. The GME sequence was obtained from the genome of Z. jujuba cv. Dongzao (Liu et al., 2014). Gene expression levels were quanti?ed for ?ve fruit devel- opm

28、ental stages (young fruit, enlarging fruit, white mature fruit, half red fruit and full red fruit), four tissues and ?ve genotypes using two or three of the most stab</p><p>　　Fig. 2. Relative GDP-

29、mannose-3j,5j-epimerase (GME) expression levels based on candidate reference genes. (A) Fruit samples at different developmental stages and (B) different tissues and (C) fruits of different genotypes. Vertical bars repre

30、sent standard deviations (n = 2).</p><p><b>　　Results</b></p><p>　　Primer speci?city and PCR ampli?cation ef?ciency</p><p>　　Agarose gel electrophoresis revealed that al

31、l of the primer pairs ampli?ed a single PCR product of the expected size (Supplemen- tal Fig. S1). At the same time, the speci?city of ampli?cation was con?rmed by the melting curves, showing a single peak for each

32、primer pair (Supplemental Fig. S2) after 40 cycles of ampli?cation. The E of the 11 reference genes varied from 96% to 107% for EF1 and GAPDH, and R2 ranged from 0.992 for UBQ to 1.000 for UBQ2/ZJH3 (Supplemental Table S

33、1).</p><p>　　Expression stability of candidate reference genes</p><p>　　geNorm analysis</p><p>　　The expression stability of 11 reference genes was estimated using the geNorm pro

34、gram. As shown in Fig. 1, the M values of all examined genes were below 1.5 (except for ZJH3 in different tis- sues). Among the various fruit samples, ACTIN and ACTIN9 had the lowest expression stability value (0.33), fo

35、llowed by UBQ (0.37). ZJH3 and GAPDH were less stable reference genes compared with the others (Fig. 1A). Among the different tissues evaluated, the two most stable genes were UBQ and UBQ2 (0.35)</p><p>　　N

36、orm?nder analysis</p><p>　　As shown in Supplemental Table S2, Norm?nder analysis revealed that UBQ (0.026), ACTIN7 (0.039) and ACTIN (0.041) were the most stable reference genes, while ZJH3 (0.179) was the l

37、east stable at the fruit developmental stages. For the different tissue samples, Norm?nder results suggested that UBQ (0.067), ACTIN (0.113) and GAPDH (0.137) were the most suitable reference genes, and ACTIN9 (0.712) wa

38、s the least stable. For the different geno- types, the most stable genes were UBQ2 (0.063), GAPDH (0</p><p>　　BestKeeper analysis</p><p>　　Because the BestKeeper program can assess the stability

39、 of up to 10 reference genes, we selected the 10 candidate genes determined by the geNorm and Norm?nder programs (ZJH3was excluded). As shown in Supplemental Table S3, at the fruit developmental stage samples, all SD val

40、ues for the reference genes tested were lower than 1.0 except for GAPDH, and the ACTIN, RB1 and UBQ genes had low SD values of 0.12, 0.22 and 0.26, respectively. For the four tissue samples, UBQ2 and UBQ were considered

41、the m</p><p>　　Overall rankings and selection of suitable reference genes</p><p>　　The results of each of the three programs identi?ed nearly the same set of genes as the most stable reference g

42、enes, with the main difference being the order in which these genes ranked, mainly due to the different algorithms used by the three software programs (Cruz et al., 2009; Zhu et al., 2012). The results derived fro

43、m the three programs together showed that at the fruit developmental stages, UBQ, ACTIN, CYP and ACTIN9 performed well; among the different tissues, UBQ2 and UBQ wer</p><p>　　Reference gene validation<

44、;/p><p>　　To validate the selected reference genes, the relative expres- sion level of one functional gene, GME, was evaluated in different fruit developmental stages, tissues and genotypes (Fig. 2). For e

45、ach experiment, two or three of the most stable and two unstable reference genes, based on the three programs, were selected for normalization. The expression of GME showed similar patterns with slight differences when

46、 ACTIN and UBQ were used as reference genes in the fruit developmental stages. </p><p>　　Discussion</p><p>　　According to our study, no one reference gene was consistently expressed across all

47、experimental samples evaluated. For example, GAPDH was not suitable as a reference gene for the six develop- mental stages assessed here, even though it was stably expressed among the different jujube genotypes. It was a

48、lso found to be sta- bly expressed among different tissues in eggplants (Zhou et al., 2014), but it was not a suitable reference gene for assessing differ- ent developmental stages in tomatoes (Ex</p><p>　　T

49、o validate the suitability of the 11 candidate reference genes, GME expression levels were assessed in four tissues, ?ve genotypes and ?ve fruit developmental stages. GME expression patterns were consistent when using th

50、e most stable reference genes for nor- malization. These results suggest that the selected reference genes were suitable for normalization.</p><p>　　In summary, 11 housekeeping genes for normalization of RT-

51、 qPCR analysis results in jujube were selected and assessed. UBQ, ACTIN, CYP and ACTIN9 at fruit developmental stages, UBQ2 and UBQ among different tissues, and UBQ2, GAPDH, ACTIN9 and EF1 among different genotypes, were

52、 determined to be stably expressed refer- ence genes for RT-qPCR normalization based on a combination of the geNorm, Norm?nder and Bestkeeper programs. Our results may enable more accurate gene expression analyses in Chi

53、ne</p><p>　　Acknowledgments</p><p>　　This work was partially supported by the National Science and Technology Support Program of China (Grant no. 2013BAD20B03) and the Public Welfare Project fro

54、m the State Forestry Administration of China (Grant no. 201304110).</p><p>　　Appendix A. Supplementary data</p><p>　　Supplementary data associated with this article can be found, in the online v

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78、One 7, e44405.</p><p>　　譯文： 在酸棗中鑒定適當(dāng)?shù)膬?nèi)參基因用于RT-qPCR分析</p><p>　　Chunmei Zhang, Jian Huang, Xingang Li</p><p><b>　　摘要:</b></p><p>　　反轉(zhuǎn)錄實時定量PCR（RT-qPCR）技術(shù)是使用最廣泛

79、的用于評估m(xù)RNA表達(dá)水平的方法，內(nèi)參基因的穩(wěn)定性對于正確評估RT-qPCR數(shù)據(jù)至關(guān)重要。 在本次的研究工作中，調(diào)查了11個候選內(nèi)參基因在6個果實發(fā)育階段以及酸棗中4個組織和5個基因型的表達(dá)水平。 運用geNorm，Norm Finder和Bestkeeper軟件程序去評估11個內(nèi)參基因的穩(wěn)定性。 研究結(jié)果表明對果實發(fā)育階段的UBQ，ACTIN，CYP和ACTIN9，不同組織的UBQ2和UBQ，不同基因型的UBQ2，GAPDH，ACTI

80、N9和EF1以及所有樣本的UBQ，ACTIN9，UBQ2和CYP的評估均為穩(wěn)定的表達(dá)。此外，使用相對GME表達(dá)水平來驗證內(nèi)參基因。這些發(fā)現(xiàn)有助于對酸棗基因的表達(dá)進(jìn)行更準(zhǔn)確的評估。</p><p><b>　　關(guān)鍵詞</b></p><p>　　酸棗, 內(nèi)參基因，基因表達(dá)，實時定量PCR</p><p><b>　　引言</b&g

81、t;</p><p>　　中國的棗樹或酸棗樹，是一種來自中國的本地果樹，是鼠李科植物中最重要的物種之一。它具有豐富的營養(yǎng)和藥用特性（曲和王，1993），棗樹的栽培面積達(dá)到200萬hm2，并且在世界范圍內(nèi)其種植面積也在增加（Liu等人，2013a）。 作為一種重要的經(jīng)濟林果樹，有必要了解其在生理型和經(jīng)濟性狀方面的分子基礎(chǔ)，以提高棗樹的育種和生長。 因此，這有助于闡明關(guān)鍵基因的表達(dá)模式，比如果實的發(fā)育和不同的基因型等

82、。然而，很少有研究關(guān)注重要基因的表達(dá)模式。</p><p>　　飯轉(zhuǎn)錄實時定量PCR（RT-qPCR）技術(shù)因其靈敏性、特異性和準(zhǔn)確性而成為評估m(xù)RNA表達(dá)水平的最重要的實驗方法之一。然而，RT-qPCR分析的準(zhǔn)確性和可靠性很容易受到多種因素的影響，包括RNA的質(zhì)量和RT和PCR反應(yīng)的效率（Nolan等人，2006）。特別是內(nèi)參基因的選擇對于正態(tài)化是必不可少的。目前，許多的研究報道表明果樹中有許多穩(wěn)定的內(nèi)參基因（L

83、iu et等人，2013b; Wu 等人，2014; Ye等人，2015; Zhong 等人，2011）。到目前為止，基于半定量RT-PCR分析，只有一項研究（Sun等人，2009）是針對酸棗果實枝條發(fā)育方面的內(nèi)參基因的選擇，對內(nèi)參基因尚未進(jìn)行選擇并進(jìn)行驗證用于RT-qPCR分析。</p><p>　　最合適的內(nèi)參基因應(yīng)該穩(wěn)定的且表達(dá)一致的。然而，越來越多的證據(jù)表明許多看家基因在組織、基因型和不同的實驗情況下具有

84、不同的表達(dá)水平（Hu等人，2009; Li等人，2015）。因此，為了獲得RT-qPCR的準(zhǔn)確定量，應(yīng)根據(jù)實驗條件選擇穩(wěn)定的內(nèi)參基因。</p><p>　　在本研究中，我們調(diào)查了11個看家基因的表達(dá)，以在不同的實驗條件下選擇最穩(wěn)定的內(nèi)參基因，包括不同的果實發(fā)育階段、組織和基因型。</p><p><b>　　研究材料和方法</b></p><p&g

85、t;<b>　　植物材料</b></p><p>　　幼棗果實來自五個不同栽培品種/基因型（分別是木棗、君棗、菜豆棗，七月仙和豐棉關(guān)），木棗果實有6個發(fā)育階段（幼果、兩期膨大果實、白色成熟果實、半紅色果實和全紅果實） 木棗植物的芽、葉和花是從陜西清建西北農(nóng)林科技大學(xué)棗試驗站采集的。 將收集的果實迅速切成小塊，立即浸入液氮中，然后儲存在零下80攝氏度的環(huán)境中。其他取樣的組織直接在液氮中冷凍，然

86、后儲存在零下80攝氏度的環(huán)境中。</p><p>　　圖1.通過geNorm軟件程序估計的11個候選參考基因的平均表達(dá)穩(wěn)定性值（M）。 （A）六個果實發(fā)育階段（B）四種組織，（C）五種基因型和（D）所有樣品。</p><p>　　RNA提取和cDNA合成</p><p>　　按照生產(chǎn)商的說明，使用TaKaRa MiniBEST植物RNA提取試劑盒（TaKaRa，大連

87、）提取總的RNA。使用NanoDrop 20000（熱科學(xué)）對RNA進(jìn)行定量。根據(jù)制造商的說明，使用PrimeScript RT試劑盒（TaKaRa）合成cDNA并將其用于進(jìn)一步分析。</p><p>　　內(nèi)參基因的選擇和引物設(shè)計</p><p>　　十一種候選內(nèi)參基因，包括ACTIN、his-tone（ZJH3）、泛素（UBQ和UBQ2），60s核糖體（RB1）、甘油醛-3-磷酸脫氫酶（

88、GAPDH）、親環(huán)蛋白（CYP）、延伸因子（EF1）和延伸因子1-alpha (EF1?)均來自NCBI數(shù)據(jù)庫（http://www.ncbi.nlm.nih.gov/），從酸棗cv冬棗的基因組測序中選擇ACTIN7和ACTIN9（劉等人，2014）（補充表格S1）。使用Primer3軟件（Rozen和Skaletsky，2000）設(shè)計引物。對引物特異性進(jìn)行了單個產(chǎn)品膨大的驗證，并使用2.0％的瓊脂糖凝膠電泳達(dá)到產(chǎn)品的大小。為計算膨大效

89、率（E = 10（-1 /斜率））和相關(guān)系數(shù)（R2）（Bustin等，2009），使用了5倍稀釋度的cDNA系列去測量標(biāo)準(zhǔn)曲線。</p><p>　　RT-qPCR擴增和數(shù)據(jù)分析</p><p>　　使用SYBR Premix Ex Taq試劑盒（TaKaRa）在Bio-Rad IQ5上以25μl的反應(yīng)體積進(jìn)行反轉(zhuǎn)錄定量實時PCR分析。為了確定每種引物的擴增規(guī)格，在40個循環(huán)后，在60-9

90、5攝氏度下分析30s的解鏈曲線。使用geNorm（Vandesompele等人，2002）、Norm finder（Andersen等人，2004）和Bestkeeper（Pfaf等人，2004）軟件分析數(shù)據(jù)。 geNorm算法提供了對基因表達(dá)穩(wěn)定性（M值）的測量。 NormFinder通過分析基于方差的模型來計算內(nèi)參基因的表達(dá)穩(wěn)定性值。最低穩(wěn)定性值表示內(nèi)參基因的最穩(wěn)定表達(dá)。 Bestkeeper通過使用原始Ct數(shù)據(jù)計算標(biāo)準(zhǔn)偏差（SD）

91、來確定表達(dá)最穩(wěn)定的基因（Pfaf fl 等人，2004）。 基因中SD 的值小于1.0的被認(rèn)為是可可接受的，并且SD值越低，參考基因表達(dá)越穩(wěn)定。</p><p><b>　　GME的正態(tài)化</b></p><p>　　GDP-甘露糖-3j，5'-差向異構(gòu)酶（GME），對抗壞血酸生物的合成被用于通過RT-qPCR評估選擇的候選參考基因的有效性極其重要。 GME序

92、列是通過酸棗cv冬棗的基因組來獲得（Liu 等人，2014）。 根據(jù)上述三種軟件程序分析的結(jié)果，使用兩個或三個最穩(wěn)定的內(nèi)參基因和兩個不穩(wěn)定的內(nèi)參基因?qū)?個果實發(fā)育階段（幼果、膨大果實、白色成熟果實、半紅色果實和全紅果實）、4個組織和5個基因型的基因表達(dá)水平進(jìn)行定量以用于正態(tài)化。 所用的GME引物是5j-GGCACCTACCATGAGATTG-3j（正向）和5j AGATGACCCATAAATTGACAG-3j（反向）。</p>

93、;<p>　　圖2.基于候選內(nèi)參基因的GDP-甘露糖-3j，5'-差向異構(gòu)酶（GME）的相對表達(dá)水平。 （A）不同發(fā)育階段的果實樣品和（B）不同基因型的不同組織和（C）果實。 垂直條代表標(biāo)準(zhǔn)偏差（n = 2）。</p><p><b>　　結(jié)果</b></p><p>　　引物特異性和PCR擴增效率</p><p>　　瓊

94、脂糖凝膠電泳顯示所有的引物對都能擴增出預(yù)期大小的單一PCR產(chǎn)物（補充圖S1）。 同時，擴增的特異性由熔解曲線進(jìn)行驗證，在40個擴增循環(huán)后，每個引物對顯示出一個單峰（補充圖S2）。 對于EF1和GAPDH，11個參考基因中的E從96％變化到107％，并且R2范圍從UBQ的0.992到UBQ2 / ZJH3的1.000（補充表S1）。</p><p>　　候選內(nèi)參基因的表達(dá)穩(wěn)定性</p><p&g

95、t;　　geNorm軟件分析</p><p>　　運用geNorm軟件程序去估計11個內(nèi)參基因的表達(dá)穩(wěn)定性。如圖1所示，所有檢測的基因的M值均低于1.5（不同組織中的ZJH3除外）。在各種水果樣品中，ACTIN和ACTIN9具有最低的表達(dá)穩(wěn)定性值（0.33），其次是UBQ（0.37）。與其他的相比，ZJH3和GAPDH是不太穩(wěn)定的內(nèi)參基因（圖1A）。在所評估的不同組織中，兩個最穩(wěn)定的基因是UBQ和UBQ2（0.3

96、5），其次是EF1（0.49）和GAPDH（0.70）;最不穩(wěn)定的基因是ZJH3（1.61）。在所評估的不同基因型中，UBQ（0.16），EF1（0.16）和ACTIN9（0.19）是最佳內(nèi)參基因，ZHJ3（0.80）排列最不穩(wěn)定。在所有檢測的樣品中，UBQ2和EF1（0.49）在表達(dá)中最穩(wěn)定，其次是ACTIN9（0.52），而ZJH3（1.31）是最不穩(wěn)定的。</p><p>　　Norm ?nder軟件分析&

97、lt;/p><p>　　正如補充表S2所示，Norm finder軟件分析表明，UBQ（0.026），ACTIN7（0.039）和ACTIN（0.041）是最穩(wěn)定的內(nèi)參基因，而ZJH3（0.179）在果實發(fā)育階段最不穩(wěn)定。對于不同的組織樣本，Norm finder分析結(jié)果表明UBQ（0.067），ACTIN（0.113）和GAPDH（0.137）是最適合的內(nèi)參基因，而ACTIN9（0.712）是最不穩(wěn)定的。對于不同的

98、基因型，最穩(wěn)定的基因是UBQ2（0.063），GAPDH（0.086）和ACTIN9（0.104），最不穩(wěn)定的基因是ZJH3（0.223）。所有實驗樣本一起分析表明UBQ（0.087）和ACTIN9（0.096）是最可靠的基因，而ZJH3（0.262）是最不穩(wěn)定的。</p><p>　　BestKeeper 軟件分析 </p><p>　　由于BestKeeper軟件程序可以評估多達(dá)10個

99、內(nèi)參基因的穩(wěn)定性，因此我們選擇了由geNorm和Norm finder程序（排除ZJH3）確定的10個候選基因。 如附表S3所示，在果實發(fā)育階段樣品中，除了GAPDH外，所測試的內(nèi)參基因的所有SD值都低于1.0，并且ACTIN，RB1和UBQ基因SD值較低，分別是0.12,0.22和0.26。 對于四種組織樣本，UBQ2和UBQ被認(rèn)為是最合適的內(nèi)參基因（SD = 0.76），其次是EF1a（SD = 0.91）和EF1（0.94）。所有