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Combining bioinformatics and conventional PCR optimization strategy for one-time design of high-specificity primers for WRKY gene family using unigene database

  • Avinash Kumar
  • Simmi P. Sreedharan
  • Parvatam GiridharEmail author
Methods Paper
  • 22 Downloads

Abstract

Gene families, like the conserved transcription factor families, evolve through gene duplications and share moderate similarity between member genes. Lack of genomic data makes it difficult to design high-specificity primers to the target genes. Furthermore, many primers under-perform in highly sensitive assays like quantitative PCR due to issues of thermodynamic nature, thereby increasing the cost and time for analysis. A methodology involving intra-species and inter-generic bioinformatic sequence comparison combined with thermodynamic estimation of primer performance was used for one-time design of gene specific primers for different WRKYs, Mitogen Activated Protein-kinases and N-methyltransferases of Coffea canephora without the aid of genome sequence resources. Out of a total 37 primer sets including 31 pairs of primers for WRKY from 34 mined WRKY Unigenes/ESTs and six pairs for genes coding for MAP kinases and NBS-LRR proteins, 32 sets exhibited high specificity of amplification upon genome analysis as well as in the high-resolution melt analysis. Furthermore, PCR optimization strategies-both in silico and experimental-indicated a superior performance of the primer sets for different applications like quantitative PCR and rapid amplification of cDNA ends. Only one set of primer resulted in mis-priming upon confirmation by DNA sequencing of the cloned amplicons. The intra-species differences and inter-generic similarities ensure high specificity of primers in all cases studied. The procedure allowed design of primers for the use in different downstream applications with high performance, specificity, yield and ease-of-use.

Keywords

Homology Gene Specific Primer Transcriptome Polymerase Chain Reaction Gene Family Unigene 

Notes

Acknowledgements

AK and SPS are recipients of fellowship from Council of Scientific and Industrial Research, India. AK thanks Dr. Arun Chandrashekar (Retd. Scientist, CSIR-Central Food Technological Research Institute, Mysuru, India) for his valuable guidance, encouragement and support to the work. The project was majorly funded by research grants from Department of Biotechnology, Govt. India under the grant number BT/PR/6292/AGR/16/575/2005 and partly from Department of Science and Technology under grant number SERB/SR/SO/PS/20/2012.

Author contributions

AK conceived the study, carried out the bioinformatic analysis, designed all the primers used in the study, performed PCR optimization and RACE reactions. SPS validated the primers by quantitative RT-PCR, isolated full-length gene coding sequences and quality checked sequencing reactions. AK and SPS validated sequences from coffee genome resources and drafted the manuscript. PG supervised the project. All authors have read and approved until the final version of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

References

  1. 1.
    Dill V, Beer M, Hoffman B (2017) Simple, quick and cost-efficient: a universal RT-PCR and sequencing strategy for genomic characterization of foot-and-mouth disease viruses. J Virol Methods 246:58–64.  https://doi.org/10.1016/j.jvironet.2017.04.007 CrossRefGoogle Scholar
  2. 2.
    Bikandi J, San Millán R, Rementeria A, Garaizar J (2004) In silico analysis of complete bacterial genomes: PCR, AFLP-PCR, and endonuclease restriction. Bioinformatics 20:798–799.  https://doi.org/10.1093/bioinformatics/btg491 CrossRefGoogle Scholar
  3. 3.
    Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden T (2012) Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinform 13:134.  https://doi.org/10.1186/1471-2105-13-134 CrossRefGoogle Scholar
  4. 4.
    Srivastava GP, Hanumappa M, Kushwaha G, Nguyen H, Xu D (2011) Homolog-specific PCR primer design for profiling splice variants. Nucleic Acids Res 39:e69.  https://doi.org/10.1093/nar/gkr127 CrossRefGoogle Scholar
  5. 5.
    Chen SH, Lin CY, Cho CS, Lo CZ, Hsiung CA (2003) Primer design assistant (PDA): a web-based primer design tool. Nucleic Acids Res 31:3751–3754.  https://doi.org/10.1093/nar/gkg560 CrossRefGoogle Scholar
  6. 6.
    Hu Z, Zimmerman BG, Zhou H, Wang J, Henson BS, Yu W, Elashoff D, Krupp G, Wong DT (2008) Exon-level expression profiling: a comprehensive transcriptome analysis of oral fluids. Clin Chem 54:824–832.  https://doi.org/10.1373/clinchem.2007.096164 CrossRefGoogle Scholar
  7. 7.
    Linhart C, Shamir R (2005) The degenerate primer design problem: theory and applications. J Comput Biol 12:431–436.  https://doi.org/10.1089/cmb.2005.12.431 CrossRefGoogle Scholar
  8. 8.
    Wang Q, Wang M, Zhang X, Hao B, Kaushik SK, Pan Y (2011) WRKY gene family evolution in Arabidopsis thaliana. Genetica 139:973–983.  https://doi.org/10.1007/S10709-011-9599-4 CrossRefGoogle Scholar
  9. 9.
    Eulgem T, Rushton PJ, Robatzek S, Somssich IE (2000) The WRKY superfamily of plant transcription factors. Trends Plant Sci 5:199–206.  https://doi.org/10.1016/S1360-1385(00)01600-9 CrossRefGoogle Scholar
  10. 10.
    Mohanta TK, Park Y-H, Bae H (2016) Novel genomic and evolutionary insight of WRKY transcription factors in plant lineage. Sci Rep (UK) 6:37309.  https://doi.org/10.1038/srep37309 CrossRefGoogle Scholar
  11. 11.
    Borrone JW, Kuhn DN, Schnell RJ (2004) Isolation, characterization, and development of WRKY genes as useful genetic markers in Theobroma cacao. Theor Appl Genet 109:495–507.  https://doi.org/10.1007/s00122-004-1662-4 CrossRefGoogle Scholar
  12. 12.
    Xie T, Chen C, Li C, Liu J, Liu C, He Y (2018) Genome-wide investigation of WRKY gene family in pineapple: evolution and expression profiles during development and stress. BMC Genomics 19:490.  https://doi.org/10.1186/s12864-018-4880-x CrossRefGoogle Scholar
  13. 13.
    Wu J, Chen J, Wang L, Wang S (2017) Genome-wide investigation of WRKY transcription factors involved in terminal drought stress response in common bean. Front Plant Sci 8:380.  https://doi.org/10.3389/fpls.2017.00380 Google Scholar
  14. 14.
    Fernandez-Pozo N, Menda N, Edwards JD, Saha S, Tecle IY, Strickler SR, Bombarely A, Fisher-York T, Pujar A, Foerster H, Yan A, Mueller LA (2015) The Sol genomics network (SGN)-from genotype to phenotype to breeding. Nucleic Acids Res 43:D1036–D1041.  https://doi.org/10.1093/nar/gku1195 CrossRefGoogle Scholar
  15. 15.
    Kumar A, Naik GK, Giridhar P (2017) Dataset on exogenous application of salicylic acid and methyljasmonate and the accumulation of caffeine in young leaf tissues and catabolically inactive endosperms. Data Brief 13:22–27.  https://doi.org/10.1016/j.dib.2017.05.004 CrossRefGoogle Scholar
  16. 16.
    Kumar A, Giridhar P (2015) Salicylic acid and methyljasmonate restore the transcription of caffeine biosynthetic N-methyltransferases from a transcription inhibition noticed during late endosperm maturation in coffee. Plant Gene 4:38–44.  https://doi.org/10.1016/jplgene.2015.09.002 CrossRefGoogle Scholar
  17. 17.
    Kumar A, Simmi PS, Naik GK, Giridhar P (2015) RP-HPLC and transcript profile indicate increased leaf caffeine in Coffea canephora plants by light. J Biol Earth Sci 5:1–9Google Scholar
  18. 18.
    Kumar A, Naik GK, Simmi PS, Giridhar P (2015) Salinity and drought response alleviate caffeine content of young leaves of Coffea canephora var. Robusta cv. S274. J Appl Biol Biotechnol 3:50–60.  https://doi.org/10.7324/JABB.2015.3310 Google Scholar
  19. 19.
    Lin C, Mueller LA, McCarthy J, Crouzillat D, Pétiard V, Tanksley SD (2005) Coffee and tomato share common gene repertoires as revealed by deep sequencing of seed and cherry transcripts. Theor Appl Genet 112:114–130.  https://doi.org/10.1007/s00122-005-011-2 CrossRefGoogle Scholar
  20. 20.
    van Grup TP, McIntyre LM, Verhoeven KFJ (2013) Consistent errors in first strand cDNA due to random hexamer mispriming. PlosOne 8:e85583.  https://doi.org/10.1371/journal.pone.0085583 CrossRefGoogle Scholar
  21. 21.
    Zhang J, Byrne CD (1999) Differential priming of RNA templates during cDNA synthesis markedly affects both accuracy and reproducibility of quantitative competitive reverse-transcriptase PCR. Biochem J 337:231–241.  https://doi.org/10.1042/bj3370231 CrossRefGoogle Scholar
  22. 22.
    Svec D, Tichopad A, Novosadova V, Pfaffl MW, Kupista M (2015) How good is a PCR efficiency estimate: recommendations for precise and robust qPCR efficiency assessments. Biomol Detect Quantif 3:9–16.  https://doi.org/10.1016/j.bdq.2015.01.005 CrossRefGoogle Scholar
  23. 23.
    Ramiro D, Jalloul A, Petitot A-S, de Sá MFG, Maluf MP, Fernandez D (2010) Identification of coffee WRKY transcription factor genes and expression profiling in resistance response to pathogens. Tree Genet Genomes 6:767–781.  https://doi.org/10.1007/s11295-010-0290-1 CrossRefGoogle Scholar
  24. 24.
    Vieira LGE, Andrade AC, Colombo CA, de Araújo Moraes AH, Metha  et al (2006) Brazilian coffee genome project: an EST-based genomic resource. Braz J Plant Physiol 18:95–108.  https://doi.org/10.1590/S1677-04202006000100008 CrossRefGoogle Scholar
  25. 25.
    Giridhar P, Kumar A, Simmi PS, Ravishankar GA (2012) Differential expression of WRKY transcriptional factors in endosperm tissues during stress and ontogeny of fruits of Coffea canephora with respect to caffeine biosynthesis. In: Proceedings of 24th international conference on coffee science (ASIC), San José, Costa Rica, pp 522–526Google Scholar
  26. 26.
    Bustin S, Hugget J (2017) qPCR primer design revisited. Biomol Detect Quantif 14:19–28.  https://doi.org/10.1016/j.bdq.2017.11.001 CrossRefGoogle Scholar
  27. 27.
    Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622.  https://doi.org/10.1373/clinchem.2000.112797 CrossRefGoogle Scholar
  28. 28.
    Arunraj R, Samuel MA (2018) Integration of amplification in qPCR analysis allows precise and relative quantification of transcript abundance of genes from large gene families using RNA isolated from different tissues. Brief Funct Genomics 17:147–150.  https://doi.org/10.1093/bfgp/elx022 CrossRefGoogle Scholar
  29. 29.
    Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real time quantitative PCR and the 2–∆∆CT method. Methods 25:402–408.  https://doi.org/10.1006/meth.2001.1262 CrossRefGoogle Scholar
  30. 30.
    Kato M, Mizuno K (2004) Caffeine synthase and related methyltransferases in plants. Front Biosci 9:1833–1842.  https://doi.org/10.2741/1364 CrossRefGoogle Scholar
  31. 31.
    McCarthy AA, McCarthy JG (2007) The structure of two N-methyltransferases from the caffeine biosynthetic pathway. Plant Physiol 144:879–889.  https://doi.org/10.1104/pp.106.094854 CrossRefGoogle Scholar
  32. 32.
    Satyanarayana KV, Kumar V, Chandrashekar A, Ravishankar GA (2005) Isolation of promoter of N-methyltransferase gene associated with caffeine biosynthesis in C. canephora. J Biotechnol 119:20–25.  https://doi.org/10.1016/j.jbiotech.2005.06.008 CrossRefGoogle Scholar
  33. 33.
    Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R et al (2014) The coffee genome provides insights into the convergent evolution of caffeine biosynthesis. Science 345:1181–1184.  https://doi.org/10.1126/science.1255274 CrossRefGoogle Scholar
  34. 34.
    Jin JP, Tian F, Yang DC, Meng Y-Q, Kong L, Luo JC, Gao G (2017) PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res 45:D1040–D1045.  https://doi.org/10.1093/nar/gkw982 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Plant Cell Biotechnology DepartmentCSIR-Central Food Technological Research InstituteMysuruIndia

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