DOI: https://doi.org/10.21498/2518-1017.13.3.2017.110708

Rice (Oryza sativa L.) blast resistance genes bioinformatic analysis

К. В. Бондаренко, Г. І. Сліщук, Н. Е. Волкова

Abstract


Purpose. To investigate rice blast resistance genes polymorphism by using bioinformatic methods.

Methods. Global and local nucleotide alignment, phylogenetic analysis, HyPhy test.

Results. For Pib gene, numerous single nucleotide substitutions and deletions of 1–3 bp were established. The phylo­geny of this gene has been studied and homologues have been found both in various rice species and in other cereals. These sequences can encode proteins that «recognize» the phytopathogens effectors, and can also be associated with resistance to phytopathogens. The Pi4 gene is characterized by single nucleotide substitutions, insertions and deletions; the number of non-synonymous substitutions exceeds the number of sy­nonymous ones. The Pi54 gene variability is significantly lower than that of the Pi4 and Pib genes. The predominant types of polymorphism were single nucleotide substitutions and small-sized indels. It was found that non-synonymous substitutions in Pi54, Pi4 and Pib genes were in close proximity, sometimes forming clusters, while some coding regions were either superconservative or contained predominantly synonymous substitutions. On philodendrograms, cultivated rice samples were clustered with samples of ancestral wild-growing species.

Conclusions. Evolution of the rice blast resistance genes Pi4, Pib and Pi54 is characterized by diversification selection. Considering that tense coevolution and significant rate of adaptation and creation of new pathogen races are typical for a plant and a parasite, these genes are subjected to intensive selection aimed at increasing diversity for obtaining the resistance to new races of the pathogen.


Keywords


Genes Pib, Pi54, Pi4; sequence alignment; phylogenetic analysis; evolution; single nucleotide polymorphism; indels

References


Ashkani, S., Rafii, M., Shabanimofrad, M., Miah, G., Sahebi, M., Azizi, P., ... Nasehi, A. (2015). Molecular breeding strategy and challenges towards improvement of blast disease resistance in rice crop. Front. Plant Sci., 6, 1–14. doi: 10.3389/fpls.2015.00886

Dudchenko, V. V. (2015). Derzhavne zakonodavche rehuliuvannia rysovoho vyrobnytstva ta yoho naukove zabezpechennia v Ukraini [State legislative regulation of rice production and its scientific support inUkraine].Kherson: Ailant. [in Ukrainian]

Marone, D., Russo, M., Laidт, G., De Leonardis, A., & Mastrangelo, A. (2013). Plant nucleotide binding site–leucine-rich repeat (NBS-LRR) genes: Active guardians in host defense responses. Int. J. Mol. Sci., 14(4), 7302–7326. doi: 10.3390/ijms14047302.

Integrated Rice Science Database Oryzabase. Retrieved from https://shigen.nig.ac.jp/rice/oryzabase/

Chen, X., & Ronald, P. (2011). Innate immunity in rice. Trends Plant Sci., 16(8), 451–459. doi: 10.1016/j.tplants.2011.04.003

Divya, B., Biswas, A., Robin, S., Rabindran, R., & Joel, A. (2014). Gene interactions and genetics of blast resistance and yield attributes in rice (Oryza sativa L.). J. Genet., 93(2), 415–424. doi: 10.1007/s12041-014-0395-7

Halaiev, O., Halaieva, M., & Shpak, D. (2015). Detection of race­specific genes of resistance to piryculariosis Pi-ta and Pi-b in rice varieties (Oryza sativa L.). Zbirnyk naukovykh prats SHI–NTsNS [Collected scientific articles of PBGI–NCSCI], 25, 120–128. [in Ukrainian]

National Center for Biotechnology Information Database. Retrieved from http://www.ncbi.nlm.nih.gov/

Smith, S., & Waterman, M. (1981). Identification of common molecular subsequences. J. Mol. Biol., 147(1), 195–197.

Needleman, S., & Wunsch, C. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol., 48(3), 443–453.

Edgar, R. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res., 32(5), 1792–1797. doi: 10.1093/nar/gkh340

Sneath, P., & Sokal, R. (1973). Numerical taxonomy: The principles and practice of numerical classification.San Francisco: W. H. Freeman & Co.

Kosakovsky Pond, S. L., Frost, S. D. W., & Muse, S. V. (2005). HyPhy: hypothesis testing using phylogenies. Bioinformatics, 21(5), 676–679. doi: 10.1093/bioinformatics/bti079

Muse, S. V., & Gaut, B. S. (1994). A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates, with application to the chloroplast genome. Mol. Biol. Evol., 11(5), 715–724.

Felsenstein, J. (1981). Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol., 17(6), 368–376.

Kosakovsky Pond, S. L., & Frost, S. D. Not so different after all: A comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol., 22(5), 1208–1222. doi: 10.1093/molbev/msi105


GOST Style Citations


Ashkani S., Rafii M., Shabanimofrad M. et al. Molecular breeding strategy and challenges towards improvement of blast disease resistance in rice crop. Front. Plant Sci. 2015. Vol. 6. P. 1–14. doi: 10.3389/fpls.2015.00886

Дудченко В. В. Державне законодавче регулювання рисового виробництва та його наукове забезпечення в Україні. Херсон : Айлант, 2015. 60 с.

Marone D., Russo M., Laidт G. et al. Plant nucleotide binding site–leucine-rich repeat (NBS-LRR) genes: Active guardians in host defense responses. Int. J. Mol. Sci. 2013. Vol. 14, Iss. 4. P. 7302–7326. doi: 10.3390/ijms14047302.

Integrated Rice Science Database Oryzabase. URL: https://shigen.nig.ac.jp/rice/oryzabase/

Chen X., Ronald P. Innate immunity in rice. Trends Plant Sci. 2011. Vol. 16, Iss. 8. P. 451–459. doi: 10.1016/j.tplants.2011.04.003

Divya B., Biswas A., Robin S. et al. Gene interactions and genetics of blast resistance and yield attributes in rice (Oryza sativa L.). J. Genet. 2014. Vol. 93, Iss. 2. P. 415–424. doi: 10.1007/s12041-014-0395-7

Галаєв О. В., Галаєва М. В., Шпак Д. В. Виявлення расоспецифічних генів стійкості до пірикуляріозу Pi-ta та Pi-b у сортів рису (Oryza sativa L.). Збірник наук. праць СГІНЦНС. 2015. Вип. 25. С. 120–128.

National Center for Biotechnology Information Database. URL: http://www.ncbi.nlm.nih.gov/

Smith S., Waterman M. Identification of common molecular subsequences. J. Mol. Biol. 1981. Vol. 147, Iss. 1. P. 195–197.

Needleman S., Wunsch C. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 1970. Vol. 48, Iss. 3. Р. 443–453.

Edgar R. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004. Vol. 32, Iss. 5. P. 1792–1797. doi: 10.1093/nar/gkh340

Sneath P., Sokal R. Numerical taxonomy: The principles and practice of numerical classification.San Francisco: W. H. Freeman & Co, 1973. 573 р.

Kosakovsky Pond S. L., Frost S. D. W., Muse S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics. 2005. Vol. 21, Iss. 5. P. 676–679. doi: 10.1093/bioinformatics/bti079

Muse S. V., Gaut B. S. A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates, with application to the chloroplast genome. Mol. Biol. Evol. 1994. Vol. 11, Iss. 5. P. 715–724.

Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 1981. Vol. 17, Iss. 6. P. 368–376.

Kosakovsky Pond S. L., Frost S. D. Not so different after all: A comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol. 2005. Vol. 22, Iss. 5. P. 1208–1222. doi: 10.1093/molbev/msi105







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DOI: 10.21498/2518-1017

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