Evaluation of suitable reference genes in Brassica juncea and its wild relative Camelina sativa for qRT-PCR analysis under various stress conditions
Autoři:
Shikha Dixit aff001; Vinod Kumar Jangid aff001; Anita Grover aff001
Působiště autorů:
Plant-Pathogen Interaction Laboratory, National Institute for Plant Biotechnology, Pusa Campus, New Delhi, India
aff001
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222530
Souhrn
Quantitative real-time PCR (qRT-PCR) is an efficient method to estimate the gene expression levels but the accuracy of its result largely depends on the stability of the reference gene. Many studies have reported considerable variation in the expression of reference genes (RGs) in different tissue and conditions. Therefore, screening for appropriate RGs with stable expression is crucial for functional analysis of the target gene. Two closely related crucifers Brassica juncea (cultivated) and Camelina sativa (wild) respond differently towards various abiotic and biotic stress where C. sativa exhibits higher tolerance to various stress. Comparative gene expression analysis of the target genes between two such species is the key approach to understand the mechanism of a plant’s response to stress. However, using an unsuitable RG can lead to misinterpretation of expression levels of the target gene in such studies. In this investigation, the stability of seven candidate RGs including traditional housekeeping genes (HKGs) and novel candidate RGs were identified across diverse sample sets of B. juncea and C. sativa representing- hormone treated, wounded, Alternaria brassicae inoculated and combination treated samples (exogenous hormone treatment followed by A. brassicae inoculation). In this investigation, we identified stable RGs in both the species and the most suitable RGs to perform an unbiased comparative gene expression analysis between B. juncea and C. sativa. Results revealed that TIPS41 and PP2A were identified as the overall best performing RGs in both the species. However, the most suitable RG for each sample subset representing different condition must be individually selected. In Hormone treated and wounded samples TIPS41 expressed stably in both the species and in A. brassicae inoculated and combination treatment performance of PP2A was the best. In this study, for the first time, we have identified and validated stable reference gene in C. sativa for accurate normalization of gene expression data.
Klíčová slova:
Biology and life sciences – Genetics – Gene expression – Organisms – Eukaryota – Plants – Research and analysis methods – Animal studies – Experimental organism systems – Model organisms – Plant and algal models – Molecular biology – Database and informatics methods – Bioinformatics – Sequence analysis – Sequence alignment – Molecular biology techniques – Computer and information sciences – Artificial gene amplification and extension – Polymerase chain reaction – Electrophoretic techniques – Gel electrophoresis – Agarose gel electrophoresis – Gene amplification – Brassica – Computer software – Arabidopsis thaliana
Zdroje
1. Gachon C, Mingam A, Charrier B. Real-time PCR: what relevance to plant studies? Journal of Expperimental Botony. 2004; 55(402):1445–54.
2. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR–a perspective. Journal of Molecular Endocrinology. 2005;34(3):597–601. doi: 10.1677/jme.1.01755 15956331
3. Derveaux S, Vandesompele J, Hellemans J. How to do successful gene expression analysis using real-time PCR. Methods. 2010; 50(4):227–30. doi: 10.1016/j.ymeth.2009.11.001 19969088
4. Zhu J, Zhang L, Li W, Han S, Yang W, Qi L. Reference Gene Selection for Quantitative Real-time PCR Normalization in Caragana intermedia under Different Abiotic Stress Conditions. PLoS One. 2013;8(1):1–10.
5. Pfaffl MW. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acid Research. 2001;29(9):16–21.
6. Kundu A, Patel A, Pal A. Defining reference genes for qPCR normalization to study biotic and abiotic stress responses in Vigna mungo. Plant Cell Reports. 2013;32(10):1647–58. doi: 10.1007/s00299-013-1478-2 23868569
7. Wang Z, Chen Y, Fang H, Shi H, Chen K, Zhang Z, et al. Selection of reference genes for quantitative reverse-transcription polymerase chain reaction normalization in Brassica napus under various stress conditions. Molecular Genetics and Genomics. 2014; 289(5):1023–35. doi: 10.1007/s00438-014-0853-1 24770781
8. Wang C, Cui H, Huang T, Liu T, Hou X. Identification and Validation of Reference Genes for RT-qPCR Analysis in Non-Heading Chinese Cabbage Flowers. Frontiers in Plant Science. 2016;7:1–12.
9. Duan M, Wang J, Zhang X, Yang H, Wang H, Qiu Y, et al. Identification of Optimal Reference Genes for Expression Analysis in Radish (Raphanus sativus L.) and Its Relatives Based on Expression Stability. Frontiers in Plant Sciences. 2017; 8:1605.
10. Brunner AM, Yakovlev IA, Strauss SH. Validating internal controls for quantitative plant gene expression studies. BMC Plant Biology. 2004;4:1–7.
11. Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochemical and Biophysical Research Communications. 2006;345(2):646–51. doi: 10.1016/j.bbrc.2006.04.140 16690022
12. Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefebvre JF, Louvet R, et al. The lack of a systematic validation of reference genes: A serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnology Journal. 2008;6(6):609–18. doi: 10.1111/j.1467-7652.2008.00346.x 18433420
13. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible W. Genome-Wide Identification and Testing of Superior Reference Genes for Transcript Normalization in Arabidopsis thaliana. Plant Physilogy. 2005;139(1):5–17.
14. Chandna R, Augustine R, Bisht NC. Evaluation of Candidate Reference Genes for Gene Expression Normalization in Brassica juncea Using Real Time Quantitative RT-PCR. PLoS One. 2012;7(5): e36918. doi: 10.1371/journal.pone.0036918 22606308
15. Yang H, Liu J, Huang S, Guo T, Deng L, Hua W. Selection and evaluation of novel reference genes for quantitative reverse transcription PCR (qRT-PCR) based on genome and transcriptome data in Brassica napus L. Gene. 2014;538 (1):113–22. doi: 10.1016/j.gene.2013.12.057 24406618
16. Schmidt GW, Delaney SK. Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress. Molecular Genetics and Genomics. 2010 Mar 23;283(3):233–41. doi: 10.1007/s00438-010-0511-1 20098998
17. Kim BR, Nam HY, Kim SU, Kim S Il, Chang YJ. Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnology Letters. 2003;25(21):1869–72. 14677714
18. Nicot N, Hoffmann L, Lippmann CDRP. Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. 2005;56(421):2907–14.
19. Zhu X, Li X, Chen W, Chen J, Lu W, Chen L, et al. Evaluation of New Reference Genes in Papaya for Accurate Transcript Normalization under Different Experimental Conditions. 2012;7(8): e44405.
20. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002;3(7): research0034.1–0034.11.
21. Andersen CL, Jensen JL, Ørntoft TF. 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 Normalization of Real-Time Quantitative Reverse. Cancer Research. 2004; 64: 5245–50 doi: 10.1158/0008-5472.CAN-04-0496 15289330
22. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of most stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper©—Excel spreadsheet tool using a Repeated Pair-wise Correlation and Regression Analysis. Biotechnology Letters. 2004; 26: 509–515. 15127793
23. Zubr J. Qualitative variation of Camelina sativa seed from different locations. Industrial Crops and Products. 2003;17(3):161–69.
24. Sharma G, Kumar VD, Haque A, Bhat SR, Prakash S, Chopra VL. Brassica coenospecies: A rich reservoir for genetic resistance to leaf spot caused by Alternaria brassicae. Euphytica. 2002;125(3):411–17.
25. Heydarian Z, Gruber M, Glick BR, Hegedus DD. Gene Expression Patterns in Roots of Camelina sativa With Enhanced Salinity Tolerance Arising From Inoculation of Soil With Plant Growth Promoting Bacteria Producing Deaminase or Expression the Corresponding acdS. Gene. 2018;9:1–15.
26. Li H, Barbetti MJ, Sivasithamparam K. Hazard from reliance on cruciferous hosts as sources of major gene-based resistance for managing blackleg (Leptosphaeria maculans) disease. Field Crop Research. 2005;91(2–3):185–98.
27. Bari R, Jones JDG. Role of plant hormones in plant defence responses. Plant Molecular Biology. 2009;69(4):473–88. doi: 10.1007/s11103-008-9435-0 19083153
28. Pieterse MJ, Van Der Does D, Zamioudis C, Leon-reyes A, Van Wees SCM. Hormonal Modulation of Plant Immunity. Annual Review of Cell and Developmental Biology. 2012; 28: 489–521. doi: 10.1146/annurev-cellbio-092910-154055 22559264
29. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3-new capabilities and interfaces. Nucleic Acids Research. 2012;40(15):1–12.
30. Mazumder M, Das S, Saha U, Chatterjee M, Bannerjee K, Basu D. Salicylic acid-mediated establishment of the compatibility between Alternaria brassicicola and Brassica juncea is mitigated by abscisic acid in Sinapis alba. Plant Physiol Biochem. 2013;70:43–51. doi: 10.1016/j.plaphy.2013.04.025 23770593
31. Lay F, Anderson M. Defensins—Components of the Innate Immune System in Plants. Current Protein and Peptide Science. 2005;6(1):85–101. 15638771
32. Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1(3):1559–82. doi: 10.1038/nprot.2006.236 17406449
33. Liu J, Li P, Lu L, Xie L, Chen X, Zhang B. Selection and evaluation of potential reference genes for gene expression analysis in avena fatua linn. Plant Prot Sci. 2019;55(1):61–71.
34. Zheng T, Chen Z, Ju Y, Zhang H, Cai M, Pan H, et al. Reference gene selection for qRT-PCR analysis of flower development in Lagerstroemia indica and L. speciosa. PLoS One. 2018;13(3):1–14.
35. Chen X, Truksa M, Shah S, Weselake RJ. A survey of quantitative real-time polymerase chain reaction internal reference genes for expression studies in Brassica napus. Analytical Biochemistry. 2010;405(1):138–40. doi: 10.1016/j.ab.2010.05.032 20522329
36. Spoel SH, Johnson JS, Dong X. Regulation of tradeoffs between plant defenses against pathogens with different lifestyles. PNAS. 2007; 104(47): 18842–47. doi: 10.1073/pnas.0708139104 17998535
37. Pieterse MJ. Cross Talk in Defense Signaling. Plant Physisology. 2008;146:839–44.
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Je Fuchsova endotelová dystrofie rohovky neurodegenerativní onemocnění?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania