Identification of the Causal Agent of Downy Mildew of Plasmopara viticola Grapes by Quantitative PCR

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

A new method is proposed for the early diagnosis of the causal agent of grapes downy mildew, Plasmopara viticola, based on the method of quantitative real-time PCR (qPCR RT) using SYBR Green I fluorescence. Six pairs primers were developed for the diagnosis of P. viticola, among the designed primers, PvITS1_2-real-s/a demonstrated the highest effectiveness for early detection of grapevine downy mildew with a strong positive correlation with the metagenomic data of P. viticola distribution in Far Eastern grape species and varieties, where a linear dependence was found (R2 = 0.86). Thus, qPCR RT of PvITS1_2 can be used for early detection and monitoring of asymptomatic P. viticola infections. The developed method can be used as a basis for predicting epidemics of downy mildew of grapes and for its control in vineyards.

Full Text

Restricted Access

About the authors

N. N. Nityagovsky

Federal Scientific Center of the Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Laboratory of Biotechnology

Email: aleynova@biosoil.ru
Russian Federation, Vladivostok, 690022

A. A. Dneprovskaya

Federal Scientific Center of the Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Laboratory of Biotechnology; Far Eastern Federal University, Institute of the World Ocean

Email: aleynova@biosoil.ru
Russian Federation, Vladivostok, 690022; Vladivostok, 690922

A. A. Ananev

Federal Scientific Center of the Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Laboratory of Biotechnology

Email: aleynova@biosoil.ru
Russian Federation, Vladivostok, 690022

K. V. Kiselev

Federal Scientific Center of the Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Laboratory of Biotechnology

Email: aleynova@biosoil.ru
Russian Federation, Vladivostok, 690022

О. А. Aleynova

Federal Scientific Center of the Biodiversity, Far Eastern Branch of the Russian Academy of Sciences, Laboratory of Biotechnology

Author for correspondence.
Email: aleynova@biosoil.ru
Russian Federation, Vladivostok, 690022

References

  1. Koledenkova K., Esmaeel Q., Jacquard C., Nowak J., Clément C., Ait Barka E. // Frontiers in Microbiology. 2022. V. 13. P. 889472. https://doi.org/10.3389/fmicb.2022.889472
  2. Toffolatti S.L., Russo G., Campia P., Bianco P.A., Borsa P., Coatti M., Torriani S.F., Sierotzki H. // Pest Management Science. 2018. V. 74. № 12. P. 2822–2834.
  3. Toffolatti S.L., Serrati L., Sierotzki H., Gisi U., Vercesi A. // Pest Management Science. 2007. V. 63. № 2. P. 194–201.
  4. Burruano S. // Mycologist. 2000. V. 14. № 4. P. 179–182.
  5. Díez-Navajas A.M., Greif C., Poutaraud A., Merdinoglu D. // Micron. 2007. V. 38. № 6. P. 680–683.
  6. Vercesi A., Sirtori C., Vavassori A., Setti E., Liberati D. // Med. Biol. Eng. Comput. 2000. V. 38. № 1. P. 109–112.
  7. Vercesi A., Toffolatti S.L., Zocchi G., Guglielmann R., Ironi L. // Eur J Plant Pathol. 2010. V. 128. № 1. P. 113–126.
  8. Hong C.-F., Scherm H. // Journal of Phytopathology. 2020. V. 168. № 5. P. 297–302.
  9. Negrel L., Halter D., Wiedemann-Merdinoglu S., Rustenholz C., Merdinoglu D., Hugueney P., Baltenweck R. // Frontiers in Plant Science. 2018. V. 9. P. 360. https://doi.org/10.3389/fpls.2018.00360
  10. Si Ammour M., Bove F., Toffolatti S.L., Rossi V. // Frontiers in Plant Science. 2020. V. 11. P. 1202. https://doi.org/10.3389/fpls.2020.01202
  11. Valsesia G., Gobbin D., Patocchi A., Vecchione A., Pertot I., Gessler C. // Phytopathology. 2005. V. 95. № 6. P. 672–678.
  12. Yang L., Chu B., Jie D., Yuan K., Sun Q., Jiang C., Ma Z. // Phytopathology Research. 2023. V. 5. № 1. P. 19. https://doi.org/10.1186/s42483-023-00178-w
  13. Kong X., Qin W., Huang X., Kong F., Schoen C.D., Feng J. et al. // Sci Rep. Nature Publishing Group, 2016. V. 6. № 1. P. 28935. https://doi.org/10.1038/srep28935
  14. Kiselev K.V., Nityagovsky N.N., Aleynova O.A. // Appl. Biochem. Microbiol. 2023. V. 59. № 3. P. 361–367.
  15. Nityagovsky N.N., Ananev A.A., Suprun A.R., Ogneva Z.V., Dneprovskaya A.A., Tyunin A.P. et al. // Horticulturae. 2024. V. 10. № 4. P. 326. https://doi.org/10.3390/horticulturae10040326
  16. Ye J., Coulouris G., Zaretskaya I., Cutcutache I., Rozen S., Madden T.L. // BMC Bioinformatics. 2012. V. 13. № 1. P. 134. https://doi.org/10.1186/1471-2105-13-134
  17. Robideau G.P., De COCK A.W. a. M., Coffey M.D., Voglmayr H., Brouwer H., Bala K. et al. // Molecular Ecology Resources. 2011. V. 11. № 6. P. 1002–1011.
  18. Choi Y.-J., Beakes G., Glockling S., Kruse J., Nam B., Nigrelli L. et al. // Molecular Ecology Resources. 2015. V. 15. № 6. P. 1275–1288.
  19. Kiselev K.V., Aleynova O.A., Grigorchuk V.P., Dubrovina A.S. // Planta. 2017. V. 245. № 1. P. 151–159.
  20. R Core Team // R Foundation for Statistical Computing. 2021. https://www.r-project.org/
  21. Kassambara A. // ggpubr: “ggplot2” Based Publication Ready Plots. R package. 2023. https://rpkgs.datanovia.com/ggpubr/
  22. Lou D., Meurer M., Ovchinnikova S., Burk R., Denzler A., Herbst K. et al. // EMBO reports. 2023. V. 24. № 5. P. e57162. https://doi.org/10.15252/embr.202357162
  23. Mouafo-Tchinda R.A., Beaulieu C., Fall M.L., Carisse O. // Canadian Journal of Plant Pathology. 2021. V. 43. № 1. P. 73–87.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Quantitative determination of amplification of PvITS1_1, PvITS1_2 and PvCox1_1 sequence sites in grape DNA samples performed by PB PCR (a); relative abundance of P. viticola in NGS samples (b). The origin of all samples is indicated in Table 1. Nc is the PB PCR reaction without grape DNA. NM — it was not measured. The data is presented as an average value ± SE (combined data from samples of leaves and stems of the same plant). The average values for each digit followed by the same letter did not differ when using one-way analysis of variance (ANOVA) followed by the Tukey multiple comparison test.

Download (367KB)
Indexing metadata
3. Fig. 2. Determination of the Pearson correlation coefficient of the relationship between estimates of the relative level of amplification according to RV PCR data (rel. units) and the relative representation of P. viticola ITS1 amplicons in NGS samples: 1, 2. 3 are linear regression lines for PvITS1_1, PvITS1_2 and PvCox1_1 amplicons, respectively.

Download (246KB)
Indexing metadata

Copyright (c) 2025 Russian Academy of Sciences