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Identification and evaluation of reference genes for quantitative real-time PCR analysis in Passiflora edulis under stem rot condition

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Abstract

Passion fruit (Passiflora edulis), an important tropical and subtropical fruit, has a high edible and medicinal value. Stem rot disease is one of the most important diseases of passion fruit. An effective way for control and prevention of this disease is to identify the genes associated with resistance to this disease. Quantitative real-time PCR (RT-qPCR) has mainly been widely applied to detect gene expression because of its simplicity, fastness, low cost and high sensitivity. One of the requirements for RT-qPCR is the availability of suitable reference genes for normalization of gene expression. However, currently, no Passiflora edulis reference genes have been identified andthus it has hindered the gene expression studies in this plant. The present study aimed to address this issue. We analyzed sixteen candidate reference genes, including nine common (GAPDH, UBQ, ACT1, ACT2, EF-1α-1, EF-1α-2, TUA, NADP, and GBP) and seven novel genes (C13615, C24590, C27182, C10445, C21209, C22199, and C22526), in different tissues (stem, leaf, flower and fruit) of two accessions under stem rot condition. We calculated the expression stability in twenty-four samples using the ΔCt, GeNorm, NormFinder, BestKeeper and RefFinder. The results showed that both C21209 and EF-1α-2 were sufficient to normalize gene expression under stem rot, whereas the commonly used reference genes, GAPDH and UBQ, were the least stable ones. The expression patterns of PeUFC under stem rot condition normalized by stable and unstable reference genes indicated the suitability of using the optimal reference genes. To our knowledge, this is the first systematic study of reference genes in Passiflora edulis, which identified a number of reliable reference genes suitable for gene expression studies in Passiflora edulis by RT-qPCR.

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References

  1. Munhoz CF, Costa ZP, Cauz-santos LA et al (2018) A gene-rich fraction analysis of the Passiflora edulis genome reveals highly conserved microsyntenic regions with two related Malpighiales species. Sci Rep 8:13024. https://doi.org/10.1038/s41598-018-31330-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wong ML, Medrano JF (2005) Real-time PCR for mRNA quantitation. Biotechniques 39:75–85. https://doi.org/10.2144/05391RV01

    Article  CAS  PubMed  Google Scholar 

  3. Kozera B, Rapacz M (2013) Reference genes in real-time PCR. J Appl Genet 54:391–406. https://doi.org/10.1007/s13353-013-0173-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rapacz M, Stępień A, Skorupa K (2012) Internal standards for quantitative RT-PCR studies of gene expression under drought treatment in barley (Hordeum vulgare L.): the effects of developmental stage and leaf age. Acta Physiol Plant 34:1723–1733. https://doi.org/10.1007/s11738-012-0967-1

    Article  CAS  Google Scholar 

  5. Monteiro F, Sebastiana M, Pais MS et al (2013) Reference gene selection and validation for the early responses to downy mildew infection in susceptible and resistant Vitis vinifera cultivars. PLoS ONE 8:e72998. https://doi.org/10.1371/journal.pone.0072998

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li QF, Sun SM, Yuan DY et al (2010) Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol Biol Rep 28:49–57. https://doi.org/10.1007/s11105-009-0124-1

    Article  CAS  Google Scholar 

  7. Zhao Y, Luo J, Xu S et al (2016) Selection of reference genes for gene expression normalization in Peucedanum praeruptorum Dunn under abiotic stresses, hormone treatments and different tissues. PLoS ONE 11:e0152356. https://doi.org/10.1371/journal.pone.0152356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Carvalho KD, Bespalhok FJ, Santos TD et al (2013) Nitrogen starvation, salt and heat stress in coffee (Coffea arabica L.): identification and validation of new genes for qPCR normalization. Mol Biotechnol 53:315–325. https://doi.org/10.1007/s12033-012-9529-4

    Article  CAS  PubMed  Google Scholar 

  9. Wan H, Zhao Z, Qian C et al (2010) Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem 399:257–261. https://doi.org/10.1016/j.ab.2009.12.008

    Article  CAS  PubMed  Google Scholar 

  10. Chen H, Yang ZQ, Hu Y et al (2016) Reference genes selection for quantitative gene expression studies in Pinus massoniana L. Trees 30:685–696. https://doi.org/10.1007/s00468-015-1311-3

    Article  CAS  Google Scholar 

  11. Liu D, Shi L, Han C et al (2012) Validation of reference genes for gene expression studies in virus-infected Nicotiana benthamiana using quantitative real-time PCR. PLoS ONE 7:e46451. https://doi.org/10.1371/journal.pone.0046451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xu M, Li A, Teng Y et al (2019) Exploring the adaptive mechanism of Passiflora edulis in karst areas via an integrative analysis of nutrient elements and transcriptional profiles. BMC Plant Biol 19:185. https://doi.org/10.1186/s12870-019-1797-8

    Article  PubMed  PubMed Central  Google Scholar 

  13. Liu S, Li AD, Chen CH et al (2017) De Novo transcriptome sequencing in Passiflora edulis sims to identify genes and signaling pathways involved in cold tolerance. Forests 8:435. https://doi.org/10.3390/f8110435

    Article  Google Scholar 

  14. Araya S, Martins AM, Junqueira N et al (2017) Microsatellite marker development by partial sequencing of the sour passion fruit genome (Passiflora edulis Sims). BMC Genomics 18:549. https://doi.org/10.1186/s12864-017-3881-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Costa ZD, Munhoz CF, Vieira M (2017) Report on the development of putative functional SSR and SNP markers in passion fruits. BMC Res Notes 10:445. https://doi.org/10.1186/s13104-017-2771-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Munhoz CF, Santos AA, Arenhart RA, Santini L et al (2015) Analysis of plant gene expression during passion fruit–xanthomonas axonopodis interaction implicates lipoxygenase 2 in host defence. Ann Appl Biol 167:135–155. https://doi.org/10.1111/aab.12215

    Article  CAS  Google Scholar 

  17. Xie F, Sun G, Stiller JW et al (2011) Genome-wide functional analysis of the cotton transcriptome by creating an integrated EST database. PLoS ONE 6:e26980. https://doi.org/10.1371/journal.pone.0026980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Vandesompele J, De PK, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:1–11. https://doi.org/10.1186/gb-2002-3-7-research0034

    Article  Google Scholar 

  19. Andersen CL, Jensen JL, Ørntoft TF (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 Res 64:5245–5250. https://doi.org/10.1158/0008-5472.CAN-04-0496

    Article  CAS  PubMed  Google Scholar 

  20. Pfaffl MW, Tichopad A, Prgomet C et al (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnol Lett 26:509–515. https://doi.org/10.1023/b:bile.0000019559.84305.47

    Article  CAS  PubMed  Google Scholar 

  21. Xie F, Xiao P, Chen D et al (2012) miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol 80:75–84. https://doi.org/10.1007/s11103-012-9885-2

    Article  CAS  Google Scholar 

  22. Grabherr MG, Haas BJ, Yassour M et al (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652. https://doi.org/10.1038/nbt.1883

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform 12:323. https://doi.org/10.1186/1471-2105-12-323

    Article  CAS  Google Scholar 

  24. Ye J, Zhong T, Zhang D et al (2019) The auxin-regulated protein ZmAuxRP1 coordinates the balance between root growth and stalk rot disease resistance in maize. Mol Plant 12:360–373. https://doi.org/10.1016/j.molp.2018.10.005

    Article  CAS  PubMed  Google Scholar 

  25. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25:402–408. https://doi.org/10.1006/meth.2001.1262

    Article  CAS  PubMed  Google Scholar 

  26. Untergasser A, Cutcutache I, Koressaar T et al (2012) Primer3–new capabilities and interfaces. Nucleic Acids Res 40:e115. https://doi.org/10.1093/nar/gks596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sun HF, Meng YP, Cui GM et al (2009) Selection of housekeeping genes for gene expression studies on the development of fruit bearing shoots in Chinese jujube (Ziziphus jujube Mill.). Mol Biol Rep 36:2183–2190. https://doi.org/10.1007/s11033-008-9433-y

    Article  CAS  PubMed  Google Scholar 

  28. Mughal BB, Leemans M, Spirhanzlova P et al (2018) Reference gene identification and validation for quantitative real-time PCR studies in developing Xenopus laevis. Sci Rep 8:496. https://doi.org/10.1038/s41598-017-18684-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Karuppaiya P, Yan XX, Liao W et al (2017) Identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas-A biodiesel plant. PLoS ONE 12:e0172460. https://doi.org/10.1371/journal.pone.0172460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Du W, Hu F, Yuan S et al (2019) Selection of reference genes for quantitative real-time PCR analysis of photosynthesis-related genes expression in Lilium regale. Physiol Mol Biol Plants 25:1497–1506. https://doi.org/10.1007/s12298-019-00707-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dos Santos CP, Da Cruz SK, Batista MC et al (2019) Identification and evaluation of reference genes for reliable normalization of real-time quantitative PCR data in acerola fruit, leaf, and flower. Mol Biol Rep 47:953–965. https://doi.org/10.1007/s11033-019-05187-7

    Article  CAS  PubMed  Google Scholar 

  32. Li Z, Lu H, He Z et al (2019) Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions. PLoS ONE 14:e0225926. https://doi.org/10.1371/journal.pone.0225926

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang X, Wu Z, Bao W et al (2019) Identification and evaluation of reference genes for quantitative real-time PCR analysis in Polygonum cuspidatum based on transcriptome data. BMC Plant Biol 19:498. https://doi.org/10.1186/s12870-019-2108-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hu X, Zhang L, Nan S et al (2018) Selection and validation of reference genes for quantitative real-time PCR in Artemisia sphaerocephala based on transcriptome sequence data. Gene 657:39–49. https://doi.org/10.1016/j.gene.2018.03.004

    Article  CAS  PubMed  Google Scholar 

  35. Gao D, Kong F, Sun P et al (2018) Transcriptome-wide identification of optimal reference genes for expression analysis of Pyropia yezoensis responses to abiotic stress. BMC Genomics 19:251. https://doi.org/10.1186/s12864-018-4643-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wang G, Tian C, Wang Y et al (2019) Selection of reliable reference genes for quantitative RT-PCR in garlic under salt stress. Peer J 7:e7319. https://doi.org/10.7717/peerj.7319

    Article  PubMed  PubMed Central  Google Scholar 

  37. Feng K, Liu JX, Xing GM et al (2019) Selection of appropriate reference genes for RT-qPCR analysis under abiotic stress and hormone treatment in celery. Peer J 7:e7925. https://doi.org/10.7717/peerj.7925

    Article  PubMed  PubMed Central  Google Scholar 

  38. Cheng T, Zhu F, Sheng J et al (2019) Selection of suitable reference genes for quantitive real-time PCR normalization in Miscanthus lutarioriparia. Mol Biol Rep 46:4545–4553. https://doi.org/10.1007/s11033-019-04910-8

    Article  CAS  PubMed  Google Scholar 

  39. Zhang K, Li M, Cao S et al (2019) Selection and validation of reference genes for target gene analysis with quantitative real-time PCR in the leaves and roots of Carex rigescens under abiotic stress. Ecotoxicol Environ Saf 168:127–137. https://doi.org/10.1016/j.ecoenv.2018.10.049

    Article  CAS  PubMed  Google Scholar 

  40. Phule AS, Barbadikar KM, Madhav MS et al (2018) Genes encoding membrane proteins showed stable expression in rice under aerobic condition: novel set of reference genes for expression studies. 3 Biotech 8:383. https://doi.org/10.1007/s13205-018-1406-9

    Article  PubMed  PubMed Central  Google Scholar 

  41. Borkowska P, Zielińska A, Paul-samojedny M et al (2020) Evaluation of reference genes for quantitative real-time PCR in Wharton's Jelly-derived mesenchymal stem cells after lentiviral transduction and differentiation. Mol Biol Rep 47:1107–1115. https://doi.org/10.1007/s11033-019-05207-6

    Article  CAS  PubMed  Google Scholar 

  42. Li C, Hu L, Wang X et al (2019) Selection of reliable reference genes for gene expression analysis in seeds at different developmental stages and across various tissues in Paeonia ostii. Mol Biol Rep 46:6003–6011. https://doi.org/10.1007/s11033-019-05036-7

    Article  CAS  PubMed  Google Scholar 

  43. Hou F, Li S, Wang J et al (2017) Identification and validation of reference genes for quantitative real-time PCR studies in long yellow daylily Hemerocallis citrina Borani. PLoS ONE 12:e0174933

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wang C, Yang Q, Wang W et al (2017) A transposon-directed epigenetic change in ZmCCT underlies quantitative resistance to Gibberella stalk rot in maize. New Phytol 215:1503–1515. https://doi.org/10.1111/nph.14688

    Article  CAS  PubMed  Google Scholar 

  45. Shen L, Zhong T, Wang L et al (2019) Characterization the role of a UFC homolog, AtAuxRP3, in the regulation of Arabidopsis seedling growth and stress response. J Plant Physiol 240:152990. https://doi.org/10.1016/j.jplph.2019.152990

    Article  CAS  PubMed  Google Scholar 

  46. Huot B, Yao J, Montgomery BL et al (2014) Growth-defense tradeoffs in plants: a balancing act to optimize fitness. Mol Plant 7:1267–1287. https://doi.org/10.1093/mp/ssu049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang W, Wang ZY (2014) At the intersection of plant growth and immunity. Cell Host Microbe 15:400–402. https://doi.org/10.1016/j.chom.2014.03.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by Guangxi Natural Science Foundation of China (2018GXNSFBA281024, 2019GXNSFAA245002), Guangxi’s Ministry of Science and Technology (AB18294007), Guangxi Academy of Agricultural Sciences (2018YT19, TS2016010). Thanks to Xu et al. for the transcriptome data.

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Authors and Affiliations

  1. Biotechnology Research Institute, Guangxi Academy of Agricultural Sciences, 174 East Daxue Road, Nanning, 530007, Guangxi, China

    Yanyan Wu, Qinglan Tian, Weihua Huang, Jieyun Liu & Haifei Mou

  2. Rice Research Institute, Guangxi Academy of Agricultural Sciences, 174 East Daxue Road, Nanning, 530007, Guangxi, China

    Xiuzhong Xia & Xinghai Yang

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  1. Yanyan Wu
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  2. Qinglan Tian
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  3. Weihua Huang
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Contributions

YW, HM and XY. contributed to study design, QT contributed to qPCR analysis, WH and JL. contributed to data analysis, XX. contributed to primer design, YW. wrote this manuscript, XY. revised the manuscript. All authors read and approve the paper.

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Correspondence to Xinghai Yang or Haifei Mou.

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Primer specificity and melting curve analysis of the sixteen candidate reference genes. (a-p): C13615, C24590, C27182, C10445, C21209, C22199, C22526, GAPDH, UBQ, Actin1, Actin2, EF-1α-1, EF-1α-2, TUA, NADP, and GBP. Below is the link to the electronic supplementary material.

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Wu, Y., Tian, Q., Huang, W. et al. Identification and evaluation of reference genes for quantitative real-time PCR analysis in Passiflora edulis under stem rot condition. Mol Biol Rep 47, 2951–2962 (2020). https://doi.org/10.1007/s11033-020-05385-8

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