Polymorphism of Russian Populations of Rhopalosiphum padi L. Based on DNA Markers

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

Using the next-generation sequencing (NGS) technology, the nucleotide polymorphism in a fragment of the ND4 gene encoding NADH dehydrogenase subunit 4 was studied in 14 samples from three populations of the bird cherry-oat aphid (Rhopalosiphum padi L.) and the range of nucleotide polymorphism was determined. The insects were collected in 2021 and 2022 in the North-West of Russia (in the vicinity of St. Petersburg) and in the northern Caucasus (Krasnodar Territory and Dagestan). Mitochondrial DNA haplotypes were identified, which have 97.95–99.80% sequence identity with the reference GenBank accession number KT447631.1. The level of intraspecific polymorphism of a 438 bp ND4 gene fragment in Rh. padi varied from 0.2 to 4.3%. In the two-year experiments, 33 polymorphic sites (17 transitions and 16 transversions) were found in the ND4 sequences, which made it possible to identify 30 mitochondrial DNA haplotypes. The Rh. padi populations collected simultaneously on different host plants or at different times on bird cherry (spring) and cereals (summer) differed in the proportion of the main haplotype, as well as in the composition of unique minor haplotypes. Analysis of the ratio of mitochondrial DNA haplotypes suggests the important role of the host plant genotype in the formation of the structure of Rh. padi populations.

全文:

受限制的访问

作者简介

E. Radchenko

Vavilov All-Russian Institute of Plant Genetic Resources

编辑信件的主要联系方式.
Email: eugene_radchenko@rambler.ru
俄罗斯联邦, St. Petersburg, 190000

I. Anisimova

Vavilov All-Russian Institute of Plant Genetic Resources

Email: eugene_radchenko@rambler.ru
俄罗斯联邦, St. Petersburg, 190000

N. Alpatieva

Vavilov All-Russian Institute of Plant Genetic Resources

Email: eugene_radchenko@rambler.ru
俄罗斯联邦, St. Petersburg, 190000

参考

  1. Wiktelius S. Long range migration of aphids into Sweden // Int. J. Biometeorol. 1984. V. 28. № 3. P. 185–200. https://doi.org/10.1007/BF02187959
  2. Kieckhefer R.W., Lytle W.F. Summer and autumn flight of cereal aphids in South Dakota // J. Econ. Entomol. 1976. V. 69. № 3. P. 421–422. https://doi.org/10.1093/jee/69.3.421
  3. Song N., Zhang H., Li H., Cai W. All 37 mitochondrial genes of aphid Aphis craccivora obtained from transcriptome sequencing: Implications for the evolution of aphids // PLoS One. 2016. V. 11. № 6. https://doi.org/10.1371/journal.pone.0157857
  4. Wongsa K., Duangphakdee O., Rattanawannee A. Genetic structure of the Aphis craccivora (Hemiptera: Aphididae) from Thailand inferred from mitochondrial COI gene sequence // J. Insect Sci. 2017. V. 17. № 4. Article 84. https://doi.org/10.1093/jisesa/iex058
  5. Guo J., Li J., Massart S. et al. Analysis of the genetic diversity of two Rhopalosiphum species from China and Europe based on nuclear and mitochondrial genes // Insects. 2023. V. 14. Article 57. https://doi.org/10.3390/insects14010057
  6. Aikhionbare F.O., Mayo Z.B. Mitochondrial DNA sequences of greenbug (Homoptera: Aphididae) biotypes // Biomol. Eng. 2000. V. 16. № 6. P. 199–205. https://doi.org/10.1016/S1389-0344(99)00054-4
  7. Radchenko E.E., Alpatieva N.V., Chumakov M.A., Abdullaev R.A. Variability of the North Caucasian populations of greenbug for the virulence to host plants and by the molecular markers // Russ. J. Genet. 2019. V. 55. № 11. P. 1417–1425. https://doi.org/10.1134/S1022795419110127
  8. Radchenko E.E., Berim M.N., Zubov A.A. Genetic control of different types of the aphid resistance in cereal crops // Entomol. Rev. 2007. V. 87. № 3. P. 253–262. https://doi.org/10.1134/S0013873807030025
  9. Алпатьева Н.В., Антонова О.Ю., Радченко Е.Е. и др. ПЦР-диагностика вредных организмов гуара: методические указания / Под ред. Потокиной Е.К. СПб.: ВИР, 2019. https://doi.org/10.30901/978-5-907145-44-3
  10. Bolger A.M., Lohse M., Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data // Bioinformatics. 2014. V. 30. № 15. P. 2114–2120. https://doi.org/10.1093/bioinformatics/btu170
  11. Caporaso J.G., Kuczynski J., Stombaugh J. et al. QIIME allows analysis of high throughput community sequencing data // Nat. Methods. 2010. V. 7. № 5. P. 335–336. https://doi.org/10.1038/nmeth.f.303
  12. Callahan B.J., McMurdie P.J., Rosen M.J. et al. DADA2: High-resolution sample inference from Illumina amplicon data // Nat. Methods. 2016. V. 13. № 7. P. 581–583. https://doi.org/10.1038/nmeth.3869
  13. Kumar S., Stecher G., Tamura K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets // Mol. Biol. Evol. 2016. V. 33. № 7. P. 1870–1874. https://doi.org/10.1093/molbev/msw054
  14. Tamura K., Nei M., Kumar S. Prospects for inferring very large phylogenies by using the neighbor-joining method // Proc. Natl. / Acad. Sci. USA. 2004. V. 101. № 30. P. 11030–11035. https://doi.org/10.1073/pnas.0404206101
  15. Giraud T., Enjalbert J., Fournier E. et al. Population genetics of fungal diseases of plants // Parasite. 2008. V. 15. № 3. P. 449–454. https://doi.org/10.1051/parasite/2008153449
  16. Peakall R., Smouse P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research – an update // Bioinformatics. 2012. V. 28. №19. P. 2537–2539. https://doi.org/10.1093/bioinformatics/bts460
  17. Sneath P.H.A., Sokal R.R. Numerical Taxonomy: The Principles and Practice of Numerical Classification. San Francisco: Freeman, 1973. 573 p.
  18. Kinyanjui G., Khamis F.M., Mohamed S. et al. Identification of aphid (Hemiptera: Aphididae) species of economic importance in Kenya using DNA barcodes and PCR-RFLP-based approach // Bull. Entomol. Res. 2016. V. 106. № 1. P. 63–72. https://doi.org/10.1017/S0007485315000796
  19. Wang K., Liu G.M., Song C.M. et al. Mitochondrial genetic diversity in the bird cherry-oat aphid Rhopalosiphum padi (L.) in China // J. Agr. Sci. Tech. 2018. V. 20. № 1. P. 95–107.

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Diversity of the 438 bp fragment of the mitochondrial ND4 gene in Rh. padi samples. Analysis of 34 nucleotide sequences was performed in the MEGA7 program [13] using the UPGMA algorithm (unweighted pairwise grouping with averaging) [17].

下载 (201KB)
索引源数据
3. Fig. 2. Alignment of selected nucleotide sequences found in Rh. padi samples. Unique substitutions found in the insect sample collected in 2021 on wheat are highlighted.

下载 (588KB)
索引源数据
4. Fig. 3. Frequency of mtDNA haplotypes in Rh. padi samples collected in St. Petersburg on bird cherry, wheat and barley in 2021.

下载 (115KB)
索引源数据
5. Fig. 4. Occurrence of mtDNA haplotypes in Rh. padi samples collected in St. Petersburg on bird cherry, barley, oats and two wheat varieties in 2022.

下载 (158KB)
索引源数据
6. Fig. 5. Multivariate diagram of genetic similarity by mtDNA haplotypes between Rh. padi samples (1–14) according to Fst fixation index. 1 – Dagestan, wild sorghum, 04.2021; 2 – St. Petersburg, bird cherry, 05.2021; 3 – St. Petersburg, wheat collection, 06.2021; 4 – St. Petersburg, barley collection, 06.2021; 5 – Krasnodar Krai, barley collection, 07.2021; 6 – Krasnodar Krai, sorghum (variety SLV-2), 07.2021; 7 – Krasnodar Krai, sorghum (Efremovskoye white), 07.2021; 8 – Dagestan, wheat (Bezostaya 1), 05.2022; 9 – St. Petersburg, wheat (Leningradka), 07.2022; 10 – St. Petersburg, barley (Belogorsky), 08.2022; 11 – St. Petersburg, wheat (Leningradka), 08.2022; 12 – St. Petersburg, wheat (Delfi 400), 08.2022; 13 – St. Petersburg, oats (Borrus), 08.2022; 14 – St. Petersburg, bird cherry, 05.2022.

下载 (70KB)
索引源数据
7. Fig. 6. Multivariate diagram of genetic similarity by mtDNA haplotypes between Rh. padi populations according to the Fₛₜ fixation index.

下载 (68KB)
索引源数据

版权所有 © Russian Academy of Sciences, 2024