|
|
|
|
|
| Identification of suitable reference genes in leaves and roots of rapeseed (Brassica napus L.) under different nutrient deficiencies |
| HAN Pei-pei*, QIN Lu*, LI Yin-shui, LIAO Xiang-sheng, XU Zi-xian, HU Xiao-jia, XIE Li-hua, YU Chang-bing, WU Yan-feng, LIAO Xing |
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetics Improvement of Oil Crops, Ministry of Agriculture, Wuhan 430062, P.R.China |
|
|
|
|
Abstract Nutrient deficiency stresses often occur simultaneously in soil. Thus, it’s necessary to investigate the mechanisms underlying plant responses to multiple stresses through identification of some key stress-responsive genes. Quantitative real-time PCR (qRT-PCR) is essential for detecting the expression of the interested genes, of which the selection of suitable reference genes is a crucial step before qRT-PCR. To date, reliable reference genes to normalize qRT-PCR data under different nutrient deficiencies have not been reported in plants. In this study, expression of ten candidate reference genes was detected in leaves and roots of rapeseed (Brassica napus L.) after implementing different nutrient deficiencies for 14 days. These candidate genes, included two traditionally used reference genes and eight genes selected from an RNA-Seq dataset. Two software packages (GeNorm, NormFinder) were employed to evaluate candidate gene stability. Results showed that VHA-E1 was the highest-ranked gene in leaves of nutrient-deficient rapeseed, while VHA-G1 and UBC21 were most stable in nutrient-deficient roots. When rapeseed leaves and roots were combined, UBC21, HTB1, VHA-G1 and ACT7 were most stable among all samples. To evaluate the stabilities of the highest-ranked genes, the relative expression of two target genes, BnTrx1;1 and BnPht1;3 were further determined. The results showed that the relative expression of BnTrx1;1 depended on reference gene selection, suggesting that it’s necessary to evaluate the stability of reference gene prior to qRT-PCR. This study provides suitable reference genes for gene expression analysis of rapeseed responses to different nutrient deficiencies, which is essential for elucidation of mechanisms underlying rapeseed responses to multiple nutrient deficiency stresses.
|
|
Received: 26 April 2016
Accepted:
|
| Fund: This work was supported by the grants from the Agricultural Science and Technology Innovation Program, Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2013-OCRI) and the Excellent Young Scientist Fund of Chinese Academy of Agricultural Sciences (1610172015004), and an open project funded by State Key Laboratory for the Conservation and Utilization of Subtropical Agro-bioresources, China (SKLCUSA-b201403). |
|
Corresponding Authors:
LIAO Xing, Tel/Fax: +86-27-86819709, E-mail: liaox@oilcrops.cn
|
Cite this article:
HAN Pei-pei, QIN Lu, LI Yin-shui, LIAO Xiang-sheng, XU Zi-xian, HU Xiao-jia, XIE Li-hua, YU Chang-bing, WU Yan-feng, LIAO Xing.
2017.
Identification of suitable reference genes in leaves and roots of rapeseed (Brassica napus L.) under different nutrient deficiencies. Journal of Integrative Agriculture, 16(04): 809-819.
|
| Andersen C L, Jensen J L, Ørntoft T F. 2004. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64, 5245–5250.Artico S, Nardeli S M, Brilhante O, Grossi-De-Sa M F, Alves-Ferreira M. 2010. Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data. BMC Plant Biology, 10, 1–12.Ashraf M, McNeilly T. 2004. Salinity tolerance in Brassica oilseeds. Critical Reviews in Plant Sciences, 23, 157–174.Avice J C, Etienne P. 2014. Leaf senescence and nitrogen remobilization efficiency in oilseed rape (Brassica napus L.). Journal of Experimental Botany, 65, 3813–3824.Bergmüller E, Gehrig P M, Gruissem W. 2007. Characterization of post-translational modifications of histone H2B-variants isolated from Arabidopsis thaliana. Journal of Proteome Research, 6, 3655–3668.Bouain N, Kisko M, Rouached A, Dauzat M, Lacombe B, Belgaroui N, Ghnaya T, Davidian J C, Berthomieu, Abdelly C, Rouached H. 2014. Phosphate/zinc interaction analysis in two lettuce varieties reveals contrasting effects on biomass, photosynthesis, and dynamics of Pi transport. BioMed Research International, 2014, 548254.Chalhoub B, Denoeud F, Liu S Y, Parkin I A P, Tang H B, Wang X Y, Chiquet J, Belcram H, Tong C B, Samans B, Corréa M, Da Silva C, Just J, Falentin C, Koh C S, Le Clainche I, Bernard M, Bento P, Noel B, Labadie K, et al. 2014. Early allopolyploid evolution in the post-neolithic Brassica napus oilseed genome. Science, 345, 950–953.Czechowski T, Stitt M, Altmann T, Udvardi M K, Scheible W R. 2005. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiology, 139, 5–17.Ferdous J, Li Y, Reid N, Langridge P, Shi B J, Tricker P J. 2015. Identification of reference genes for quantitative expression qnalysis of microRNAs and mRNAs in barley under various stress conditions. PLOS ONE, 10, e0118503-e0118503.Ferradás Y, Rey L, Martínez Ó, Rey M, González M V. 2016. Identification and validation of reference genes for accurate normalization of real-time quantitative PCR data in kiwifruit. Plant Physiology and Biochemistry, 102, 27–36.Ginzinger D G. 2002. Gene quantification using real-time quantitative PCR: An emerging technology hits the mainstream. Experimental Hematology, 30, 503–512.Hao X Y, Horvath D P, Chao W S, Yang Y J, Wang X C, Xiao B. 2014. Identification and evaluation of reliable reference genes for quantitative real-time PCR analysis in tea plant (Camellia sinensis (L.) O. Kuntze). International Journal of Molecular Sciences, 15, 22155–22172.Jain M, Nijhawan A, Tyagi A K, Khurana J P. 2006. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochemical and Biophysical Research Communications, 345, 646–651.Jensen C R, Mogensen V O, Mortensen G, Andersen M N, Schjoerring J K, Thage J H, Koribidis J. 1996. Leaf photosynthesis and drought adaptation in field-grown oilseed rape (Brassica napus L.). Functional Plant Biology, 23, 631–644.Livak K J, Schmittgen T D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods, 25, 402–408.Mourato M, Moreira I, Leitão I, Pinto F, Sales J, Martins L. 2015. Effect of heavy metals in plants of the genus Brassica. International Journal of Molecular Sciences, 16, 17975–17998.Pollier J, Bossche R V, Rischer H, Goossens A. 2014. Selection and validation of reference genes for transcript normalization in gene expression studies in Catharanthus roseus. Plant Physiology and Biochemistry, 83, 20–25.Qi J N, Yu S C, Zhang F L, Shen X Q, Zhao X Y, Yu Y J, Zhang D S. 2010. Reference gene selection for real-time quantitative polymerase chain reaction of mRNA transcript levels in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Plant Molecular Biology Reporter, 28, 597–604.Ren F, Chen L, Guo Q Q, Zhong H, Wu Y, Chang L L, Li X B. 2011. Identification and expression analysis of genes induced by phosphate starvation in leaves and roots of Brassica napus. Plant Growth Regulation, 65, 65–81.Roberta Fogliatto M, Luisa Abruzzi D O, Voorhuijzen M M, Martijn S, Hutten R C B, Dijk J P, Van Esther K, Jeverson F. 2015. Selection of reference genes for transcriptional analysis of edible tubers of potato (Solanum tuberosum L.). PLOS ONE, 10, e0120854. Saenchai C, Bouain N, Kisko M, Prom-u-thai C, Doumas P, Rouached H. 2016. The involvement of OsPHO1;1 in the regulation of iron transport through integration of phosphate and zinc deficiency signalling. Frontiers in Plant Science, 7, 396.Shi T X, Li R Y, Zhao Z K, Ding G D, Long Y, Meng J L, Xu F S, Shi L. 2013. QTL for yield traits and their association with functional genes in response to phosphorus deficiency in Brassica napus. PLOS ONE, 8, 1970–1970.Vandesompele J, Preter K D, Pattyn F, Poppe B, Roy N V, Paepe A D, Speleman F. 2002. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3, 1-11.Wang Z, Chen Y, Fang H D, Shi H F, Chen K P, Zhang Z Y, Tan X L. 2014. Selection of reference genes for quantitative reverse-transcription polymerase chain reaction normalization in Brassica napus under various stress conditions. Molecular and General Genetics, 289, 1023–1035.Zhang H W, Yu H, Ye X S, Xu F S. 2010. Analysis of the contribution of acid phosphatase to P efficiency in Brassica napus under low phosphorus conditions. Science China (Life Sciences), 53, 709–717.Zhuang H H, Fu Y P, He W, Wang L W, Wei Y H. 2015. Selection of appropriate reference genes for quantitative real-time PCR in Oxytropis ochrocephala Bunge using transcriptome datasets under abiotic stress treatments. Frontiers in Plant Science, 6, 475. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
