Quantitative real-time PCR based evaluation and validation of reference genes in Gossypium arboreum
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Keywords:
Cotton, Gene Expression, Gossypium arboreum, Reference gene, qPCRAbstract
Estimation of gene expression levels plays a crucial role in understanding the function of the target gene(s). Intersample variance in gene expression can be more precisely measured if transcripts levels are accurately normalized. Normalization is pre-requisite step prior to the determination of candidate gene expression by qPCR. In this study conducted at ICAR-Central Institute for Cotton Research, Nagpur during 2015–16, six candidate reference genes, viz. actin4 (ACT4), actin7(ACT7), RNA Helicase (RNAH), Serine/threonine-protein phosphatase PP2A-1(PP2A1), ubiquitin7 (UBQ7) and α tubulin (αTUB) were systematically analysed for their expression patterns in different tissues pertaining to three development stages of cotton namely seedling, early reproductive and fiber development. The study has identified actin-4/actin-7/ubiquitin-7 as the most ideal reference genes for fiber development stages whereas actin-4/ ubiquitin-7 and actin-7/RNA helicases for seedling and early reproductive development stages, respectively. Validation of identified reference genes for relative expression analysis of Gacobl9, a COBRA-like protein, demonstrated their usefulness in qPCR analysis in Gossypium arboreum.
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Abd El-Moghny A M, Santosh H B, Raghavendra K P, Sheeba, J A, Singh S B and Kranthi K R. 2017. Microsatellite marker based genetic diversity analysis among cotton (Gossypium hirsutum) accessions differing for their response to drought stress. Journal of Plant Biochemistry and Biotechnology 26: 366-70. DOI: https://doi.org/10.1007/s13562-016-0395-1
Andersen C L, Jensen J L and Orntoft 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: 5245–50. DOI: https://doi.org/10.1158/0008-5472.CAN-04-0496
Artico S, Nardeli S M., Brilhante O, Grossi-de-sa F M and 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: 49. DOI: https://doi.org/10.1186/1471-2229-10-49
Chen Y, Hu B, Tan Z, Liu J, Yang Z, Li Z and Huang. 2015. Selection of reference genes for quantitative real-time PCR normalization in creeping bentgrass involved in four abiotic stresses. Plant Cell Reports 34: 1825–34. DOI: https://doi.org/10.1007/s00299-015-1830-9
Czechowski T, Stitt M, Altmann T and Udvardi M K. 2005. Genome-wide identification and testing of superior reference genes for transcript normalization. Genome Analysis 139: 5–17. DOI: https://doi.org/10.1104/pp.105.063743
Ermei C, Shengqing S, Jianfeng L, Tielong C, Liang X, Xiuyan Y, Wenjuan, Y, Qian L and Zeping J. 2012. Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (cupressaceae) using real-time PCR. PLoS One 7: e33278. DOI: https://doi.org/10.1371/journal.pone.0033278
Fausto A K S, Silva T F, Romanel E and Vaslin M F S. 2017. microRNAs as reference genes for quantitative PCR in cotton. PLoS ONE 12: e0174722. DOI: https://doi.org/10.1371/journal.pone.0174722
Hu R, Fan C, Li H, Zhang Q and Fu Y. 2009. Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Molecular Biology 10: 93. DOI: https://doi.org/10.1186/1471-2199-10-93
Huggett J, Dheda K, Bustin S and Zumla A. 2005. Real-time RT-PCR normalisation; strategies and considerations. Genes & Immunity 6: 279–284. DOI: https://doi.org/10.1038/sj.gene.6364190
Jain M, Nijhawan A, Tyagi A K and 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: 646–51. DOI: https://doi.org/10.1016/j.bbrc.2006.04.140
Jian B, Liu B, Bi Y, Hou W, Wu C and Han T. 2008. Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Molecular Biology 9: 59. DOI: https://doi.org/10.1186/1471-2199-9-59
Kim H J and Triplett B. 2001. Cotton fiber growth in planta and in vitro models for plant cell elongation and cell wall biogenesis. Plant Physiology 127: 1361–6. DOI: https://doi.org/10.1104/pp.010724
Li F, Fan G, Wang K et al. 2014. Genome sequence of the cultivated cotton Gossypium arboreum. Nature Genetics 46: 567–72. DOI: https://doi.org/10.1038/ng.2987
Lin Y, Zhang C, Lan H, Lan H, Gao S, Liu H, Liu J, Cao M, Pan G, Rong T and Zhang S. 2014. Validation of potential reference genes for qPCR in maize across abiotic stresses, hormone treatments, and tissue types. PLoS One 9: e95445. DOI: https://doi.org/10.1371/journal.pone.0095445
Marum L, Miguel A, Ricardo C P and Miguel C. 2012. Reference gene selection for quantitative real-time PCR normalization in quercus suber. PLoS One 7: e35113. DOI: https://doi.org/10.1371/journal.pone.0035113
Nicot N, Hausman J F, Hoffmann L and Evers D. 2005. Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. Journal of Experimental Botany 56: 2907–2914. DOI: https://doi.org/10.1093/jxb/eri285
Niu E, Shang X, Cheng C, Bao J, Zeng Y, Cai C, Du X and Guo W. 2015. Comprehensive analysis of the COBRA-like (COBL) gene family in Gossypium identifies two COBLs potentially associated with fiber quality. PLoS One 10: e0145725. DOI: https://doi.org/10.1371/journal.pone.0145725
Paterson A H, Wende J F, Gundlach H, Guo H, Jenkins J, Jin D, Llewellyn D et al. 2012. Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres. Nature 492: 423–7. DOI: https://doi.org/10.1038/nature11798
Pfaffl M W, Tichopad A, Prgomet C and Neuvians T P. 2004. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-- Excel-based tool using pair-wise correlations. Biotechnology Letters 26: 509–15. DOI: https://doi.org/10.1023/B:BILE.0000019559.84305.47
Raghavendra K P, Phanindra M L V and Kumar P A. 2014. Internal control gene for gene expression studies using real time quantitive RT-PCR during cotton boll development. Cotton Research Journal 6: 32-6.
Reid K E, Olsson N, Schlosser J, Peng F and Lund S T. 2006. An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biology 6: 27. DOI: https://doi.org/10.1186/1471-2229-6-27
Roudier F, Fernandez A G, Fujita M, Himmelspach R, Borner G H, Schindelman G, Song S, Baskin T I, Dupree P, Wasteneys G O and Benfey, P.N. 2005. COBRA, an Arabidopsis extracellular glycosyl-phosphatidyl inositol-anchored protein, specifically controls highly anisotropic expansion through its involvement in cellulose microfibril orientation. Plant Cell 17: 1749–63. DOI: https://doi.org/10.1105/tpc.105.031732
Tu L, Zhang X, Liu D, Jin S, Cao J, Zhu L, Deng F, Tan J and Zhang C. 2007. Suitable internal control genes for qRT-PCR normalization in cotton fiber development and somatic embryogenesis. Chinese Science Bulletin 52: 3110–7. DOI: https://doi.org/10.1007/s11434-007-0461-0
Vandesompele J, Preter K D, Pattyn F, Poppe B, Roy N V, Paepe A D, Roy N V, Paepe, A D and Speleman F. 2002. Accurate normalization of realtime quantitative RT–PCR data by geometric averaging of multiple internal control genes. Genome Biology 3: 0034. DOI: https://doi.org/10.1186/gb-2002-3-7-research0034
Wan D, Wan Y, Yang Q, Zou B, Ren W, Ding Y, Wang Z, Wang R, Wang K and Hou X. 2017. Selection of reference genes for qRT-PCR analysis of gene expression in Stipa grandis during environmental stresses. PLoS One 12: 0169465. DOI: https://doi.org/10.1371/journal.pone.0169465
Wang K, Wang Z, Li F et al. 2012. The draft genome of a diploid cotton Gossypium raimondii. Nature Genetics 44: 1098–1103. DOI: https://doi.org/10.1038/ng.2371
Wang M Wang Q and Zhang B. 2013. Evaluation and selection of reliable reference genes for gene expression under abiotic stress in cotton (Gossypium hirsutum L.). Gene 530: 44–50. DOI: https://doi.org/10.1016/j.gene.2013.07.084
Yu-xiang W, Jin-hong C, Qiu-ling H and Shui-jin Z. 2013. Parental origin and genomic evolution of tetraploid Gossypium species by molecular marker and GISH analyses. Caryologia 66: 368–74. DOI: https://doi.org/10.1080/00087114.2013.857830
Zhang C, Fu J, Wang Y, Bao Z and Zhao H. 2015. Identification of suitable reference genes for gene expression normalization in the quantitative real-time PCR analysis of sweet osmanthus (Osmanthus fragrans Lour.). PLoS One 10: 0136355. DOI: https://doi.org/10.1371/journal.pone.0136355
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