Skip to main content
Log in

Characterization of β-Tubulin Genes in Prunus persica and Prunus dulcis for Fingerprinting of their Interspecific Hybrids

  • Published:
Cytology and Genetics Aims and scope Submit manuscript

Abstract

Peach is one of the most important fruit crops, and its cultivation occupies the third largest area among all fruit crops grown in the temperate climate zone. Cultivation of this crop under less favorable climate conditions would require the creation of new resistant genotypes via intra- or interspecific hybridization, including crossing with almond. Efficient breeding of hybrids and their long-term selection will require a rapid and accurate method of DNA barcoding that would be able to distinguish closely related genotypes or to detect interspecific hybrids. One such approach is TBP-analysis, which is based on the evaluation of intron length polymorphism of β-tubulin genes. However, the correct interpretation of the results of such analysis should be based on data on the diversity of the β-tubulin gene panel in the genomes of the analyzed species. Thus, here we report on the successful whole-genome identification and on comprehensive analysis of the phylogeny and synteny of the β-tubulin genes of P. persica and P. dulcis, and the possibility is demonstrated to use such data of the genomic search for interpretation of data of TBP genotyping of intra- and interspecific hybrids of peach and almond species. In general, 11 β-tubulin genes were identified within the P. persica genome and 10 genes in the P. dulcis genome accounting for pseudogenes. Additionally, phylogenetic and synteny analyzes of the identified genes made it possible to identify the orthologues in the genomes of A. thaliana and A. lyrata as well as to designate identified β-tubulins to specific isotypes. Genotyping via the TBP-method allowed obtaining distinct molecular profiles for 11 investigated accessions, among which eight were intra- or interspecific hybrids. Based on the obtained results of genotyping, a cluster analysis was carried out, the results of which correlated well with the breeding history of the analyzed genotypes, which additionally confirmed the effectiveness and accuracy of the genotyping approach used.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

REFERENCES

  1. Alioto, T., Alexiou, K.G., Bardil, A., et al., Transposons played a major role in the diversification between the closely related almond and peach genomes: results from the almond genome sequence, Plant J., 2020, vol. 101, pp. 455–472. https://doi.org/10.1111/tpj.14538

    Article  CAS  PubMed  Google Scholar 

  2. Altschul, S.F., Basic local alignment search tool, J. Mol. Biol., 1990, vol. 215, no. 3, pp. 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  PubMed  Google Scholar 

  3. Altschul, S.F., A protein alignment scoring system sensitive at all evolutionary distances, J. Mol. Evol., 1993, vol. 36, no. 3, pp. 290–300. https://doi.org/10.1007/BF00160485

    Article  CAS  PubMed  Google Scholar 

  4. Aranzana, M.J., Garcia-Mas, J., Carbo, J., and Arús, P., Development and variation analysis of microsatellite markers in peach, Plant Breed, 2002, vol. 121, no. 1, pp. 87–92. https://doi.org/10.1046/j.1439-0523.2002.00656.x

    Article  CAS  Google Scholar 

  5. Bardini, M., Lee, D., Donini, P., et al., Tubulin based polymorphism (TBP): a new tool, based on functionally relevant sequences, to assess genetic diversity in plant species, Genome, 2004, vol. 47, no. 2, pp. 281–291.https://doi.org/10.1139/g03-132

    Article  CAS  PubMed  Google Scholar 

  6. Benbouza, H., Jean-Marie, J., and Jean-Pierre, B., Otimization of a reliable, fast, cheap and sensitive silver staining method to detect SSR markers in polyacrylamide gels, Biotechnol. Agron. Soc. Environ., 2006, vol. 10, pp. 77–81.

    CAS  Google Scholar 

  7. Blume, R.Y., Rabokon, A.N., Postovitova, A.S., et al., Evaluating diversity and breeding perspectives of Ukrainian spring camelina genotypes, Cytol. Genet., 2020, vol. 54, no. 5, pp. 420–436. https://doi.org/10.3103/S0095452720050084

    Article  Google Scholar 

  8. Blume, Ya.B., A journey through plant cytoskeleton: hot spots in signalling and functioning, Cell Biol. Int., 2019, vol. 43, no. 9, pp. 978–982. https://doi.org/10.1002/cbin.11210

    Article  PubMed  Google Scholar 

  9. Braglia, L., Gavazzi, F., Morello, L., et al., On the applicability of the Tubulin-Based Polymorphism (TBP) genotyping method: a comprehensive guide illustrated through the application on diferent genetic resources in the legume family, Plant Methods, 2020, vol. 16, p. 86. https://doi.org/10.1186/s13007-020-00627-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Braglia, L., Lauria, M., Appenroth, K.J., et al., Duckweed species genotyping and interspecific hybrid discovery by tubulin-based polymorphism fingerprinting, Front. Plant Sci., 2021, vol. 12, p. 625670.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Breviario, D., Baird, W.V., Sangoi, S., et al., High polymorphism and resolution in targeted fingerprinting with combined β-tubulin introns, Mol. Breed., 2007, vol. 20, no. 3, pp. 249–259. https://doi.org/10.1007/s11032-007-9087-9

    Article  CAS  Google Scholar 

  12. Breviario, D., Giani, S., and Morello, L., Multiple tubulins: evolutionary aspects and biological implications, Plant J., 2013, vol. 75, no. 2, pp. 202–218. https://doi.org/10.1111/tpj.12243

    Article  CAS  PubMed  Google Scholar 

  13. Chen, C., Chen, H., Zhang, Y., et al., TBtools: An integrative toolkit developed for interactive analyses of big biological data, Mol. Plant, 2020, vol. 13, no. 8, pp. 1194–1202. https://doi.org/10.1016/j.molp.2020.06.009

    Article  CAS  PubMed  Google Scholar 

  14. Chen, B., Zhao, J., Fu, G., et al., Identification and expression analysis of Tubulin gene family in upland cotton, J. Cotton Res., 2021, vol. 4, p. 20. https://doi.org/10.1186/s42397-021-00097-1

    Article  CAS  Google Scholar 

  15. Dar, R.A., Rai, A.N., and Shiekh, I.A., Stigmina carpophyla detected on Prunus americana and Prunus persica in India, Australas. Plant Dis. Notes, 2017, vol. 12, p. 19. https://doi.org/10.1007/s13314-017-0245-6

    Article  Google Scholar 

  16. Didur, O., Kulbachko, Y., Ovchynnykova, Y., et al., Zoogenic mechanisms of ecological rehabilitation of urban soils of the park zone of megapolis: Earthworms and soil buffer capacity, Environ. Res. Eng. Manage., 2019, vol. 75, no. 1, pp. 24–33. https://doi.org/10.5755/j01.erem.75.1.21121

    Article  Google Scholar 

  17. Edgar, R.C., MUSCLE: multiple sequence alignment with high accuracy and high throughput, Nucleic Acid Res., 2004, vol. 32, no. 5, pp. 1792–1797. https://doi.org/10.1093/nar/gkh340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Findeisen, P., Muhlhausen, S., Dempewolf, S., et al., Six subgroups and extensive recent duplications characterize the evolution of the eukaryotic tubulin protein family, Genome Biol. Evol., 2014, vol. 6, no. 9, pp. 2274–2288. https://doi.org/10.1093/gbe/evu187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gasanov, E., Jędrychowska, J., Kuźnicki, J., and Korzh, V., Evolutionary context can clarify gene names: Teleosts as a case study, BioEssays, 2021, vol. 43, no. 6, p. e2000258. https://doi.org/10.1002/bies.202000258

    Article  PubMed  Google Scholar 

  20. Gavazzi, F., Pigna, G., Braglia, L., et al., Evolutionary characterization and transcript profiling of beta-tubulin genes in flax (Linum usitatissimum L.) during plant development, BMC Plant Biol., 2017, vol. 17, p. 237. https://doi.org/10.1186/s12870-017-1186-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gertz, E.M., Yu, Y.K., Agarwala, R., et al., Composition-based statistics and translated nucleotide searches: Improving the TBLASTN module of BLAST, BMC Biol., 2006, vol. 4, p. 41. https://doi.org/10.1186/1741-7007-4-41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Guadalupi, C., Braglia, L., Gavazzi, F., et al., A combinatorial Q-Locus and tubulin-based polymorphism (TBP) approach helps in discriminating Triticum species, Genes, 2022, vol. 13, no. 4, p. 633. https://doi.org/10.3390/genes13040633

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Henikoff, S. and Henikoff, J.G., Amino acid substitution matrices from protein blocks, Proc. Natl. Acad. Sci. U. S. A., 1992, vol. 89, no. 22, pp. 10915–10919. https://doi.org/10.1073/pnas.89.22.10915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hu, B., GSDS 2.0: an upgraded gene feature visualization server, Bioinformatics, 2015, vol. 31, no. 8, pp. 1296–1297. https://doi.org/10.1093/bioinformatics/btu817

    Article  PubMed  Google Scholar 

  25. Jung, S. and Main, D., Genomics and bioinformatics resources for translational science in Rosaceae, Plant Biotechnol. Rep., 2014, vol. 8, no. 2, pp. 49–64. https://doi.org/10.1007/s11816-013-0282-3

    Article  PubMed  Google Scholar 

  26. Khokhryakova, A., Classification and characteristic of soils in urban areas (on the example of Odessa city), EUREKA: Life Sci., 2020, vol. 5, pp. 3–15. https://doi.org/10.21303/2504-5695.2020.001404

    Article  Google Scholar 

  27. Khromykh, N.O., Lykholat, Y.V., Kovalenko, I.M., et al., Variability of the antioxidant properties of Berberis fruits depending on the plant species and conditions of habitat, Regul. Mechanisms Biosyst., 2018a, vol. 9, no. 1, pp. 56–61. https://doi.org/10.15421/021807

    Article  Google Scholar 

  28. Khromykh, N., Lykholat, Y., Shupranova, L., et al., Interspecific differences of antioxidant ability of introduced Chaenomeles species with respect to adaptation to the steppe zone conditions, Biosyst. Diversity, 2018b, vol. 26, no. 2, pp. 132–138. https://doi.org/10.15421/011821

    Article  Google Scholar 

  29. Khromykh, N.O., Lykholat, Y.V., Anishchenko, A.A., et al., Cuticular wax composition of mature leaves of species and hybrids of the genus Prunus differing in resistance to clasterosporium disease, Biosyst. Diversity, 2020, vol. 28, no. 4, pp. 370–375. https://doi.org/10.15421/012047

    Article  Google Scholar 

  30. Kumar, S., Stecher, G., Li, M., et al., MEGA X: molecular evolutionary genetics analysis across computing platforms, Mol. Biol. Evol., 2018, vol. 35, pp. 1547–1549. https://doi.org/10.1093/molbev/msy096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li, S., Cao, P., Wang, C., et al., Genome-wide analysis of tubulin gene family in cassava and expression of family member FtsZ2-1 during various stress, Plants, 2021, vol. 10, no. 4, p. 668. https://doi.org/10.3390/plants10040668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lykholat, Y.V., Khromykh, N.O., Didur, O.O., et al., Features of the fruit epicuticular waxes of Prunus persica cultivars and hybrids concerning pathogens susceptibility, Ukr. J. Ecol., 2021, vol. 11, no. 1, pp. 261–266. https://doi.org/10.15421/2021_238

    Article  Google Scholar 

  33. Morimoto, T., Inaoka, M., Banno, K., Itai, A., Genetic mapping of a locus controlling the intergeneric hybridization barrier between apple and pear, Tree Genet. Genomes, 2020, vol. 16, p. 5. https://doi.org/10.1007/s11295-019-1397-7

    Article  Google Scholar 

  34. Nei, M., Genetic distance between populations. Am. Nat., 1972, vol. 106, pp. 283–292. https://doi.org/10.1086/282771

    Article  Google Scholar 

  35. Nei, M. and Li, W.H., Mathematical model for studying genetic variation in terms of restriction endonucleases, Proc. Natl. Acad. Sci. U. S. A., 1979, vol. 76, no. 10, pp. 5269–5273. https://doi.org/10.1073/pnas.76.10.5269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Oakley, R.V., Wang, Y.S., Ramakrishna, W., et al., Differential expansion and expression of α- and β- gene families in Populus, Plant Physiol., 2007, vol. 145, no. 3, pp. 961–973. https://doi.org/10.1104/pp.107.107086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pavlíček, A., Hrdá, Š., and Flegr, J., FreeTree – Freeware program for construction of phylogenetic trees on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD analysis of the genus Frenkelia, Folia Biol., 1999, vol. 45, pp. 97–99.

    Google Scholar 

  38. Pirko, N.N., Demkovych, A.Y., Kalafat, L.O., et al., Intron length polymorphism of β-tubulin genes in different representatives of Pinaceae Lindl. family, Revista Botanică, 2016, vol. VIII, no. 2/13, pp. 5–9.

  39. Pydiura, N., Pirko, Y., Galinousky, D., et al., Genome-wide identification, phylogenetic classification, and exon-intron structure characterization of the tubulin and actin genes in flax (Linum usitatissimum), Cell Biol. Int., 2019, vol. 43, no. 9, pp. 1010–1019. https://doi.org/10.1002/cbin.11001

    Article  CAS  PubMed  Google Scholar 

  40. Rabokon, A.N., Pirko, Y.V., Demkovych, A.Y., et al., Comparative analysis of the efficiency of intron-length polymorphism of β-tubulin genes and microsatellite loci for flax varieties genotyping, Cytol. Genet., 2018, vol. 52, no. 1, pp. 1–10. https://doi.org/10.3103/S0095452718010115

    Article  Google Scholar 

  41. Radchuk, V.V., The transcriptome of the tubulin gene family in plants, in The Plant Cytoskeleton: a Key Tool for Agro-Biotechnology, Blume, Y.B., Baird, W.V., Yemets, A.I., Breviario, D., Eds., New York: Springer-Verlag, 2008, pp. 219–241. https://doi.org/10.1007/978-1-4020-8843-8_11

  42. Rao, G., Zeng, Y., He, C., and Zhang, J., Characterization and putative post-translational regulation of α- and β-tubulin gene families in Salix arbutifolia, Sci. Rep., 2016, vol. 6, p. 19258. https://doi.org/10.1038/srep19258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sambrook, J. and Russel, D.W., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Laboratory, 2001, vol. 2.

    Google Scholar 

  44. Spokevicius, A.V., Southerton, S.G., MacMillan, C.P., et al., β-Tubulin affects cellulose microfibril orientation in plant secondary fibre cell walls, Plant J., 2007, vol. 51, no. 4, pp. 717–726. https://doi.org/10.1111/j.1365-313x.2007.03176.x

    Article  CAS  PubMed  Google Scholar 

  45. Stöcker, M., Jordi, G.M., Arús, P., et al., A highly conserved α-tubulin sequence from Prunus amygdalus, Plant Mol. Biol., 1993, vol. 22, pp. 913–916. https://doi.org/10.1007/BF00027377

    Article  PubMed  Google Scholar 

  46. Swarbreck, D., Wilks, C., Lamesch, P., et al., The Arabidopsis Information Resource (TAIR): gene structure and function annotation), Nucleic Acid Res., 2008, vol. 36, no. 1, pp. 1009–1014. https://doi.org/10.1093/nar/gkm965

    Article  CAS  Google Scholar 

  47. Thurow, L.B., Raseira, M.C.B., Bonow, S., et al., Population genetic analysis of brazilian peach breeding germplasm, Rev. Bras. Frutic., 2017, vol. 39, no. 5, p. 166. https://doi.org/10.1590/0100-29452017166

    Article  Google Scholar 

  48. Verde, I., Jenkins, J., Dondini, L., et al., The Peach v2.0 release: high-resolution linkage mapping and deep resequencing improve chromosome-scale assembly and contiguity, BMC Genomics, 2017, vol. 18, no. 1, p. 225. https://doi.org/10.1186/s12864-017-3606-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wang, Y., Tang, H., Debarry, J.D., et al., MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity, Nucleic Acids Res., 2012, vol. 40, no. 7, p. e49. https://doi.org/10.1093/nar/gkr1293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yemets, A., Radchuk, V., Bayer, O., et al., Development of transformation vectors based upon a modified plant α-tubulin gene as the selectable marker, Cell Biol. Int., 2008, vol. 32, no. 5, pp. 566–570. https://doi.org/10.1016/j.cellbi.2007.11.012

    Article  CAS  PubMed  Google Scholar 

  51. Zacchino, S.A., Butassi, E., Liberto, M.D., et al., Plant phenolics and terpenoids as adjuvants of antibacterial and antifungal drugs, Phytomedicine, 2017, vol. 37, pp. 27–48. https://doi.org/10.1016/j.phymed.2017.10.018

    Article  CAS  PubMed  Google Scholar 

  52. Zhang, J., Li, Y., Shi, G., et al., Characterization of α-tubulin gene distinctively presented in a cytoplasmic male sterile and its maintainer line of non-heading Chinese cabbage, J. Sci. Food Agric., 2009, vol. 89, no. 2, pp. 274–280. https://doi.org/10.1002/jsfa.3438

    Article  CAS  Google Scholar 

Download references

Funding

The work was carried out within the topics “Biologically active substances of rare fruit plants as the effective means of increasing the quality of products and the value of raw materials for functional nutrition” (state registration number 0121U109772, 2021-2022) and “Bioinformatics and molecular cellular studies of the structure and functions of the plant cytoskeleton” (state registration number 0120U100937, 2020-2024).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Y. V. Lykholat, A. M. Rabokon or R. Ya. Blume.

Ethics declarations

The authors declare that they have no conflicts of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

Additional information

Translated by V. Mittova

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lykholat, Y.V., Rabokon, A.M., Blume, R.Y. et al. Characterization of β-Tubulin Genes in Prunus persica and Prunus dulcis for Fingerprinting of their Interspecific Hybrids. Cytol. Genet. 56, 481–493 (2022). https://doi.org/10.3103/S009545272206007X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S009545272206007X

Keywords:

Navigation