Вестник Томского государственного университета. Биология. 2020; 1: 101-118
Генетическое разнообразие и дифференциация ценопопуляций сосны обыкновенной (Pinus sylvestris L.), сформированных в болотных и суходольных экотопах
https://doi.org/10.17223/19988591/50/5Аннотация
На основе метода полимеразной цепной реакции с ISSR-праймерами проведены исследования генетической изменчивости и дифференциации трех болотных и четырех суходольных ценопопуляций сосны обыкновенной в Республике Марий Эл. С использованием шести ISSR-праймеров выявлено 215 ISSR-локусов, из которых 208 оказались полиморфными. Количество обнаруженных ISSR-локусов варьировало в разных ценопопуляциях от 162 до 194. Показатели генетического разнообразия исследованных ценопопуляций существенно варьировали (Р = 67,9–88,5%; Na = 1,679– 1,842; Ne = 1,279–1,331; He = 0,174–0,207) и не зависели от почвенно-гидрологических условий. Установлено, что суходольные и болотные ценопопуляции статистически значимо (p˂0,01) различались по частоте встречаемости ISSR-маркеров, полученных с пятью ISSR-праймерами из шести, что указывает на отличающуюся генетическую структуру. На генетическую изменчивость между группами суходольных и болотных ценопопуляций пришлось 9% всего генетического полиморфизма. Общий уровень генетической подразделённости ценопопуляций составил 17,0% (GST = 0,170), следовательно, основная часть генетической изменчивости находилась внутри изученных ценопопуляций (83%).
Список литературы
1. Седельникова Т.С., Пименов А.В., Ефремов С.П. Морфология пыльцы сосны обыкновенной на болотах и суходолах // Лесоведение. 2004. № 6. С. 58–75.
2. Пименов А.В., Седельникова Т.С. Качественная оценка формового разнообразия сосны обыкновенной в лесоболотных комплексах Западной Сибири // Хвойные бореальной зоны. 2012. Т. 30, № 1–2. С. 157–161.
3. Седельникова Т.С., Пименов А.В., Ефремов С.П., Муратова Е.Н. Особенности генеративной сферы сосны обыкновенной болотных и суходольных популяций // Лесоведение. 2007. № 4. С. 44–50.
4. Седельникова Т.С., Муратова Е.Н., Пименов А.В. Экологическая обусловленность дифференциации кариотипов болотных и суходольных популяций видов Pinaceae // Ботанический журнал. 2010. Т. 95, № 11. С. 1543–1520.
5. Ларионова А. Я., Экарт А. К. Генетическое разнообразие и дифференциация болотных популяций сосны // Хвойные бореальной зоны. 2010. Т. 27, № 1–2. С. 120–126.
6. Петрова И.В., Санников С.Н., Черепанова О.Е. Репродуктивная изоляция и генетическая дифференциация суходольных и болотных популяций Pinus sylvestris L. Западной Сибири и Русской равнины // Сибирский лесной журнал. 2017. № 4. С. 28–37.
7. Petrova I.V., Sannikov S.N., Cherepanova O.E., Sannikova N.S. Reproductive isolation and disruptive selection as factors of genetic divergence between Pinus sylvestris L. populations // Russian Journal of Ecology. 2013. № 4. РР. 296–302. doi: 10.1134/S1067413613040103
8. Doyle J.J., Doyle J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue // Phytochemical Bulletin. 1987. Vol. 19. РР. 11–15.
9. Hui-yu L., Jing J., Gui-feng L., Xu-jun M., Jing-xiang D., Shi-jie L. Genetic variation and division of Pinus sylvestris provenances by ISSR markers // Journal of Forest Research. 2005. Vol. 16 (3). РР. 216–218.
10. Yeh F.C., Yang R., Boyle T.J., Ye Z., Xiyan J.M. POPGENE 32, Microsoft Windowbased Freeware for Population Genetic Analysis, Version 1.32; Molecular Biology and
11. Biotechnology Centre, University of Alberta: Edmonton, Canada. 2000. URL: https://sites. ualberta.ca/~fyeh/popgene.pdf (accessed 15.04.2019).
12. Nei M. Genetic distance between populations // The American Naturalist. 1972. Vol. 106 (949). PP. 283–292.
13. Nei M. Molecular Population Genetics and Evolution. Amsterdam : North-Holland Publ. Co, 1975. 288 p.
14. Takezaki N., Nei M, Tamura K. POPTREEW: Web Version of POPTREE for Constructing Population Trees from Allele Frequency Data and Computing Some Other Quantities // Molecular Biology and Evolution. 2014. Vol. 31 (6). PP. 1622–1624. doi: 10.1093/molbev/msu093
15. Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update // Bioinformatics. 2012 Vol. 28 (19). PP. 2537–2539. doi: 10.1093/bioinformatics/bts460
16. Hammer O., Harper D.A.T., Ryan P.D. PAST: Paleontological Statistics software package for education and data analysis // Paleontologia Electronica. 2001. Vol. 4 (1). PP. 1–9.
17. Vidyakin A.I., Boronnikovab S.V., Nechayeva Y.S., Pryshnivskaya Y.V., Boboshina I.V. Genetic variation, population structure, and differentiation in scots pine (Pinus sylvestris L.) from the northeast of the Russian Plain as inferred from the molecular genetic analysis data // Russian Journal of Genetic. 2015. Vol. 51 (12). PP. 1213–1220. doi: 10.1134/S1022795415120133
18. Boyle. T., Liengsiri C., Piewluang C. Genetic structure of black spruce on two contrasting sites // Heredity. 1990. Vol. 65. РР. 393–399.
19. O’reilly G.J., Parker W.H., Cheliak W.M. Isozyme differentiation of upland and lowland Picea mariana stands in northern Ontario // Silvae Genetica. 1985. Vol. 34. РР. 214–220.
20. Mosca E., Eckert A.J., Di Pierro E.A., Rocchini D., La Porta N., Belletti P., Neal D.B. The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps // Molecular Ecology. 2012. Vol. 21 (22). РР. 5530–5545. doi: 10.1111/mec.12043
21. Oreshkova N.V., Sedel’nikova T.S., Pimenov A.V., Efremov S.P. Analysis of genetic structure and differentiation of the bog and dry land populations of Pinus sibirica Du Tour based on nuclear microsatellite loci // Russian Journal of Genetic. 2014. Vol. 50 (9). PP. 934–941. doi: 10.1134/S1022795414090105
22. Криворотова Т.Н., Шейкина О.В. Генетическая структура лесосеменных плантаций и насаждений сосны обыкновенной в Среднем Поволжье // Вестник Поволжского государственного технологического университета. Сер.: Лес. Экология. Природопользование. 2014. № 1 (21). С. 77–86.
23. Захарова К.В., Сейц К.С. Роль экологических факторов в формировании генетической структуры популяций P. abies (L.) Karst // Экологическая генетика. 2017. Т. 15, № 2. С. 11–20. doi: 10.17816/ecogen15211-20
Tomsk State University Journal of Biology. 2020; 1: 101-118
Genetic diversity and differentiation of Pinus sylvestris L. coenopopulations growing in bog land and upland ecotopes
https://doi.org/10.17223/19988591/50/5Abstract
Scots pine (Pinus sylvestris L.) forming coenopopulations in diverse soilhydrological conditions is of utmost interest in studying the environmental mechanisms of divergence among populations of tree species. The analysis of protein polymorphism educed the genetic differentiation between bog land and upland coenopopulations of Scots pine, emerging from phenological isolation. Thus, we find it essential to study further the mechanisms of developing the population structure of tree plants with the use of more meaningful methods of molecular genetic analysis that would allow identifying differences at the DNA level. The aim of the research is to study the indicators of genetic diversity and differentiation of coenopopulations of Scots pine growing in contrastive swamp and dry land conditions, using ISSR markers.
The polymerase chain reaction (PCR) method with ISSR primers was used to identify the level of genetic polymorphism and differentiation. DNA for analysis was isolated from the cambial layer of the trunk using the CTAB method. Four upland and three bog coenopopulations were studied in total (See Table 1). Within each coenopopulation, 30 trees were analyzed using (СA)6AGCT, (СA)6AG, (CA)6GT, (CA)6АC, (AG)8T and (AG)8GCT primers. We used the following reaction mixture (total volume of 10 μl) to amplify DNA with ISSR primers: 1 μl of PCR buffer; 0.2 μl 10 mM dNTPs; 0.1 μl of 100 μm primer; 1 μl of DNA sample; 0.1 μl of Taq polymerase (2 u / μl); 7.6 μl of water. PCR mode: 5 min. denaturation at 94 °C, 35 cycles: 0.5 min. denaturation at 94 ° С, 45 sec. annealing at 60 ° С, 45 sec elongation at 72 °С, final elongation for 7 min at 72 °C. The PCR results were visualized by electrophoresis in 1.5% agarose gel in TBE buffer. The results were recorded and data were processed using the GelDoc 2000 gel documentation system (Bio-Rad, USA) and the Quantity One® Version 4.6.3 software package. The date analysis was performed by the software tools POPGENE ver. 1.31., POPTREEW, GenAlEx and PAST 3.25.
We revealed that out of 215 ISSR-loci elicited in 240 trees out of eight coenopopulations 208 ISSR-loci were polymorphic. The number of elicited ISSR loci in different coenopopulations varied from 162 to 194 (See Table 2). The studied coenopopulations were characterized by different levels of genetic diversity. Indicators of genetic diversity ranged within the following: the proportion of polymorphic loci was 67.9-88.5; the observed number of alleles was 1.679-1.805; effective number of alleles 1.279-1.331; the expected heterozygosity was 0.174-0.207. However, no dependency of genetic diversity indicators on soil-hydrological conditions has been found.
Moreover, we considered that dry and bog coenopopulations differed significantly in the ISSR-markers frequency obtained with five of six ISSR-primers, which indicates a different genetic structure (See Table 3). Nei’s genetic distance between different coenopopulations varied from 0.100 to 0.317. The UPGMA dendrogram and analysis of principal coordinates (PCoA) show that the bog forests make up a separate cluster (See Fig. 1 and 2). This may be a sign that there are genetic processes inducing the divergence of dry and bog coenopopulations of Scots pine. The Mantel test shows that genetic distance between populations had a weak positive correlation with geographic distance (R=0.3085, P=0.1728). Analysis of molecular variance (AMOVA) shows that 9% of total variation was accounted for by differences between groups of upland and bog land coenopopulations (See Table 4). The percentage of genetic variation among coenopopulations within compared groups was 13%. The majority of the variation was found within populations (78%). The overall level of genetic subdivision of coenopopulations was 17% (GST=0.17), hence, the main part of genetic variation is inside the coenopopulations (83%) (See Table 5). Thus, we showed a significant impact on the genetic structure formation of P. sylvestris population in this research. The paper contains 5 Tables, 2 Figures and 22 References.
References
1. Sedel'nikova T.S., Pimenov A.V., Efremov S.P. Morfologiya pyl'tsy sosny obyknovennoi na bolotakh i sukhodolakh // Lesovedenie. 2004. № 6. S. 58–75.
2. Pimenov A.V., Sedel'nikova T.S. Kachestvennaya otsenka formovogo raznoobraziya sosny obyknovennoi v lesobolotnykh kompleksakh Zapadnoi Sibiri // Khvoinye boreal'noi zony. 2012. T. 30, № 1–2. S. 157–161.
3. Sedel'nikova T.S., Pimenov A.V., Efremov S.P., Muratova E.N. Osobennosti generativnoi sfery sosny obyknovennoi bolotnykh i sukhodol'nykh populyatsii // Lesovedenie. 2007. № 4. S. 44–50.
4. Sedel'nikova T.S., Muratova E.N., Pimenov A.V. Ekologicheskaya obuslovlennost' differentsiatsii kariotipov bolotnykh i sukhodol'nykh populyatsii vidov Pinaceae // Botanicheskii zhurnal. 2010. T. 95, № 11. S. 1543–1520.
5. Larionova A. Ya., Ekart A. K. Geneticheskoe raznoobrazie i differentsiatsiya bolotnykh populyatsii sosny // Khvoinye boreal'noi zony. 2010. T. 27, № 1–2. S. 120–126.
6. Petrova I.V., Sannikov S.N., Cherepanova O.E. Reproduktivnaya izolyatsiya i geneticheskaya differentsiatsiya sukhodol'nykh i bolotnykh populyatsii Pinus sylvestris L. Zapadnoi Sibiri i Russkoi ravniny // Sibirskii lesnoi zhurnal. 2017. № 4. S. 28–37.
7. Petrova I.V., Sannikov S.N., Cherepanova O.E., Sannikova N.S. Reproductive isolation and disruptive selection as factors of genetic divergence between Pinus sylvestris L. populations // Russian Journal of Ecology. 2013. № 4. RR. 296–302. doi: 10.1134/S1067413613040103
8. Doyle J.J., Doyle J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue // Phytochemical Bulletin. 1987. Vol. 19. RR. 11–15.
9. Hui-yu L., Jing J., Gui-feng L., Xu-jun M., Jing-xiang D., Shi-jie L. Genetic variation and division of Pinus sylvestris provenances by ISSR markers // Journal of Forest Research. 2005. Vol. 16 (3). RR. 216–218.
10. Yeh F.C., Yang R., Boyle T.J., Ye Z., Xiyan J.M. POPGENE 32, Microsoft Windowbased Freeware for Population Genetic Analysis, Version 1.32; Molecular Biology and
11. Biotechnology Centre, University of Alberta: Edmonton, Canada. 2000. URL: https://sites. ualberta.ca/~fyeh/popgene.pdf (accessed 15.04.2019).
12. Nei M. Genetic distance between populations // The American Naturalist. 1972. Vol. 106 (949). PP. 283–292.
13. Nei M. Molecular Population Genetics and Evolution. Amsterdam : North-Holland Publ. Co, 1975. 288 p.
14. Takezaki N., Nei M, Tamura K. POPTREEW: Web Version of POPTREE for Constructing Population Trees from Allele Frequency Data and Computing Some Other Quantities // Molecular Biology and Evolution. 2014. Vol. 31 (6). PP. 1622–1624. doi: 10.1093/molbev/msu093
15. Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update // Bioinformatics. 2012 Vol. 28 (19). PP. 2537–2539. doi: 10.1093/bioinformatics/bts460
16. Hammer O., Harper D.A.T., Ryan P.D. PAST: Paleontological Statistics software package for education and data analysis // Paleontologia Electronica. 2001. Vol. 4 (1). PP. 1–9.
17. Vidyakin A.I., Boronnikovab S.V., Nechayeva Y.S., Pryshnivskaya Y.V., Boboshina I.V. Genetic variation, population structure, and differentiation in scots pine (Pinus sylvestris L.) from the northeast of the Russian Plain as inferred from the molecular genetic analysis data // Russian Journal of Genetic. 2015. Vol. 51 (12). PP. 1213–1220. doi: 10.1134/S1022795415120133
18. Boyle. T., Liengsiri C., Piewluang C. Genetic structure of black spruce on two contrasting sites // Heredity. 1990. Vol. 65. RR. 393–399.
19. O’reilly G.J., Parker W.H., Cheliak W.M. Isozyme differentiation of upland and lowland Picea mariana stands in northern Ontario // Silvae Genetica. 1985. Vol. 34. RR. 214–220.
20. Mosca E., Eckert A.J., Di Pierro E.A., Rocchini D., La Porta N., Belletti P., Neal D.B. The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps // Molecular Ecology. 2012. Vol. 21 (22). RR. 5530–5545. doi: 10.1111/mec.12043
21. Oreshkova N.V., Sedel’nikova T.S., Pimenov A.V., Efremov S.P. Analysis of genetic structure and differentiation of the bog and dry land populations of Pinus sibirica Du Tour based on nuclear microsatellite loci // Russian Journal of Genetic. 2014. Vol. 50 (9). PP. 934–941. doi: 10.1134/S1022795414090105
22. Krivorotova T.N., Sheikina O.V. Geneticheskaya struktura lesosemennykh plantatsii i nasazhdenii sosny obyknovennoi v Srednem Povolzh'e // Vestnik Povolzhskogo gosudarstvennogo tekhnologicheskogo universiteta. Ser.: Les. Ekologiya. Prirodopol'zovanie. 2014. № 1 (21). S. 77–86.
23. Zakharova K.V., Seits K.S. Rol' ekologicheskikh faktorov v formirovanii geneticheskoi struktury populyatsii P. abies (L.) Karst // Ekologicheskaya genetika. 2017. T. 15, № 2. S. 11–20. doi: 10.17816/ecogen15211-20
