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14Figure 2. Statistical analysis of morpho-biological traits of F3 generation hybrids of the NAM-population.
There was almost no change in grain weight per grain. The highest score was 2.1 g for the F3 [D-4 x M] combination, while the lowest score was 1.7 g for the F3 [D-3 x M] and F3 [D-7 x M] combination.
CONCLUSION
Based on this conducted experiment of segregation analyses of agronomical traits and diseases resistance performance in hybrid plants, we may conclude that D-4 (Yr 15/6 Avocet S), D-3 (Yr 10/6 Avocet S), and D-12 (Jupateco "S") have a high potential producing yield in the fields where fungal diseases are prevalent.
References
1. Chen W., Wellings C., Chen X., Kang Z., Liu T. "Wheat stripe (yellow) rust caused by Puccinia stri-iformis f. sp. Tritici" Mol Plant Pathol. 2014 Jun; 15 (5): 433-46. doi: 10.1111 / mpp. 12116
2. McIntosh R.A., Wellings C.R., Park R.F. Wheat rusts: an atlas of resistance genes. CSIRO Publications (1995), Victoria
3. McNeal F.H., Berg M.A., Brown P.L., McGuire C.F. Productivity and quality response of five spring wheat genotypes, Triticum aestivum L., to nitrogen fertilizer. Agron. J. (1971) 63: 908-910
4. Boukhatem N., Baret P.V., Mingeot D., Jacquemin J.M. Quantitative trait loci for resistance against Yellow rust in two wheat-derived recombinant
inbred line populations. Springer-Verlag. Theor Appl Genet (2002) 104: 111-118.
5. Pooja, Vikram S., Meenakshi R., Bunty Sh. Molecular Characterization of Diverse Genotypes of Indian Bread Wheat (Triticum aestivum L. Em. Thell) by Using SSRs Markers for Leaf and Stripe Rust Resistance. Annual Research & Review in Biology 12 (2): 1-8, 2017; Article no. ARRB.32651 ISSN: 2347-565X, NLM ID: 101632869 SCIENCEDOMAIN international www.sciencedomain.org
6. Reddy M.P., Sarla N., Siddiq E.A. Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica, (2002), 128 (1): 9-17.
7. Singh R.P., Nelson J.C., Sorrells M.E. Mapping Yr28 and other genes for resistance to stripe rust in wheat. Crop Sci. (2000). 40, 1148-1155.William M., Singh R.P., Huerta-Espino J., Islas S.O., Hoisington D. Molecular Marker Mapping of Leaf Rust Resistance Gene Lr46 and Its Association with Stripe Rust Resistance Gene Yr29 in Wheat. Phytopathology (2003), 93 (2): 153-159.
8. ZahraP., Ali D., Ali N., Bahram H. Identification of Wheat Stripe Rust resistance genes in Iranian wheat cultivars using molecular markers. Annual Research & Review in Biology 4 (17): 2766-2778, 2014, DOI: 10.9734 / ARRB / 2014/9821 # sthash.k6L6e3ZL.dpuf.
ANALYSIS OF SOME PHYSIOLOGICAL TRAITS OF PARENTAL SAMPLES OF COTTON NAM
POPULATION UNDER DROUGHT STRESS
Kholmuradova M., Boykobilov U., Normamatov I., Norbekov J., Makamov A., Darmanov M., Imamhodjaeva A., Nabiev S., Kushanov F., Buriev Z.
Center of Genomics and Bioinformatics AS RUz
Abstract
This paper highlights the results of a study of the drought tolerance of parental genotypes of the NAM population of cotton. The result of ANOVA statistical analysis of the studied physiological traits showed that parental samples of the population sharply differed from each other. These differences in the parental genotypes indicate wide genetic segregation in their population.
Keywords: cotton, nested associative mapping population, relative water content (RWC), water retention capacity (WRC), and transpiration rate (TR), resistance.
Currently, agriculture accounts for 70 percent of the world's water use, of which three percent is used on cotton production. In Uzbekistan and other Asian countries, cotton is produced by 100 percent irrigation. The problem of water scarcity remains a pressing issue in most cotton-growing areas. One of the important factors for the normal growth and development of cotton is irrigating on time with sufficient water. Water is a necessary source throughout the life of cotton for all life processes, from seed germination to ripening. According to the data, an adequate water supply of tissues, especially leaves, is a favorable environment for the passage of basic physiological processes. Almost 98-99% of the total water consumed by the root evaporates through the leaves while 1-2% uses for the synthesis of organic matter and the formation of various organs of cotton [1].
Water deficit conditions adversely affect the course of these physiological processes, leading to a decrease in the total amount of water in the leaves of cotton plants and a violation of cell membrane stability [2]. It has found that low absorption of CO2 due to the closure of the leaf axils leads to a decrease in its concentration in the chloroplasts and many changes such as shrinkage of the leaf surface. Such changes in metabolic processes have a direct and indirect effect on the productivity and efficiency of plants [3]. Many scientific articles have shown that paying attention to the physiological properties of plants in the selection of drought-resistant lines has yielded great results [4]. Due to the low transpiration rate [5] and high water retention properties in plants with drought-resistant genes, several physiological adaptation traits are observed in such plants, such as optimality of relative water content, cell membrane integrity [6], and leaf surface normality [7].
The study of the molecular basis of drought tolerance traits in biparental populations of cotton has led to identifying several DNA markers associated with certain morpho-physiological traits that are corresponding with drought tolerance. However, among the many methods developed for the identification of DNA markers associated with economically important traits, the nested association mapping (NAM) method is distinguished by its high potential [8] while bi-parental mapping populations have low resolution due to a few recombination events occur during the development of the population [9]. Identification of quantitative traits loci or genes associated with important traits in mapping populations developed by multi-parental genotypes like NAM methods are effective in crops. The development of such populations in cotton also allows high-resolution mapping of genetic loci/genes that control valuable economic traits.
Identification of the markers linked to fiber quality and agronomic traits carried out in some combinations of the cotton NAM population developed by Turaev
and his colleagues. [10]. This population was developed by hybridization of 19 different cotton germplasm accessions as donor genotypes and a local cotton cultivar Namangan-77 obtained as a common female parent, where consist 100 recombinant inbred lines (RILs) in each combination and a totally of 1,900 individual RILs. In such a large population, it is difficult to conduct physiological and biochemical studies related to drought. To do this, it is necessary to study the morphological, physiological, biochemical characteristics associated with drought stress in parental genotypes of the population under optimal and water deficit regimes, and to select combinations involving strongly differentiated lines with common parent Namangan-77 cotton variety.
Therefore, we aimed to conduct a study on some physiological features in parental genotypes of the NAM population in order to select the most contrasting combination under drought stress.
Totally, 20 founders including 19 cotton germplasm accessions and a local elite cotton cultivar Namangan-77 were selected as the subjects of the study (Figures 1, 2, 3, 4). Namangan-77 cotton variety has been grown by farmers in the Fergana Valley for more than 30 years due to its high cottonseed yield and high lint percentage.
The study was conducted in the experimental field of Specialized Seed Production Facility of the Center in optimal irrigation (1*2x1) and deficit irrigation (0x1x0) regimes. All other agrotechnical works were carried out in the same way in both studies.
Relative water content (RWC), water retention capacity (WRC), and transpiration rate (TR) were determined using the third leaves of the main stem according to Tretyakov [11], Kushnirenko [12], and Ivanov [13] methods, respectively.
The statistical analysis of obtained data was performed in the General Linear Model (GLM) of the statistical program ANOVA (Analysis Of Variances).
According to the results of the study, it was observed that the amount of water evaporated per hour in 1 g of leaf mass varied in the initial samples. The water loss value of leaves in plants grown in a water deficit regime was less than in plants grown in an optimal water regime (Fig. 1, B).
Specifically, cotton line L-141 (473,1 mg) evaporated the most volume of water in optimal conditions, while line Hapicala 19 (195,4 mg) lost the least water (Fig. 1, A). In the water deficit condition, C-9006 (364,7 mg) evaporated more water than other plants, while cotton lines KK-1086 (166,2 mg), S-417 (168,3 mg), L-45 (217,4 mg), and KK-1795 (226,1 mg) with low transpiration intensity probably have adapted to this stress.
In addition, the transpiration intensity in the lines Catamarca 811 and L-45 was close to each other on both optimal and deficit irrigation regimes 347,2 -347,3 mg and 209,2 - 217,4 mg respectively.
• i
• 2
O 3
• 4
O 5
O 6
O 7
• S
O 14
• 15
• 16 O 17
O 18 O 19 O 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Optimal
Fig. 1. GLM analysis of the transpiration rate of the parental forms (mg / h), A - samples; B - environments. 1-Namangan -77, 2-KK1796, 3-KK-1795, 4-L-1000, 5-C-9006, 6-KK-1086,7-Catamarca 811, 8-C-9008, 9-L-N1, 10-L-141, 11-Hapicala19, 12-0-030,13-C-4769, 14-L-45, 15-Zangi ota, 16-Saenr Pena 85, 17-C-2025, 18-KK-
602, 19-SAD-35-11, 20-C-417.
Relative water content that is one of the features of physiological traits of drought tolerance was relatively higher in optimal irrigation regime (Fig. 2, B). In particular, relative water content was high in Catamarca 811, SAD-35-11, and C-9008 samples 2,264 g, 2,058 g, and 1,986 g, while the lowest ones observed in Hapi-cala19, L30, and KK1796 samples 1,185 g, 1,191 g, and 1,242 g, respectively (Fig. 2, A).
The total amount of water was lower in the plants grown in the deficit irrigation regime than the plants
A
grown in the optimal one. However, some of these samples retained normal amounts of water despite the effects of drought stress. For instance, Namangan-77, L-1000, KK-1086, L-45, and Zangi-ota samples were adapted to the environmental effects with the following relative water content of 1,403 g, 1,443 g, 1,359 g, 1,465 g, and 1,404 g, respectively. Hapicala19 (1,436 g), 0-030 (1,475 g), L-141 (1,465 g), and L-45 (1,817 g) lines grown in water deficit irrigation regimes had an average of 0,223 g more water content than plants grown in the optimal condition.
B
WW
12 3
S 6 7 8 9 lo 11 12 13 14 15 16 17 IS 19 20
1
i AIA 1 1
l.OOO - W T \l
Fig. 2. GLM analysis of the relative water content in the leaves of the parent forms (g), A - samples; B -environments. 1-Namangan -77, 2-KK1796, 3-KK-1795, 4-L-1000, 5-C-9006, 6-KK-1086,7-Catamarca 811, 8-C-9008, 9-L-N1, 10-L-141, 11-Hapicala19, 12-0-030,13-C-4769, 14-L-45, 15-Zangi ota, 16-Saenr Pena 85, 17-
C-2025, 18-KK-602, 19-SAD-35-11, 20-C-417.
Plants growing under drought stress begin to show signs of adaptation to this environment. They try to retain water in the body as much as possible. Therefore, plants adapt to several physiological changes. In particular, adaptation to stress factors is observed by providing a normal relative water content through a high ability of water retention capacity (WRC) in the plant leaves.
The WRC of the leaves is the percentage of the water consumed by the leaves for evaporation within two hours in relation to its original content. The higher the WRC value, the lower the percentage of water loss and vice versa. Drought tolerant plants have good water retention properties and can retain water in leaves for a
long time. In such plants, the water retention property is high and therefore the amount of water lost is small.
Resulted in measuring of the WRC in the leaves of cotton lines which harvested 2 h before analysis from the lines S-9006, Saenr Pena 85, KK-602, and SAD-35-11 were grown in an optimal environment, the water loss consist 26,6 %, 27,5 %, 27,5 % and 26,1 %, respectively while the smallest number WRC observed in the L-141 line, which lost 33,4 % of water in 2 hours.
The lowest water loss were observed in KK-1795 (19,3 %), KK-1086 (20,0 %), S-4769 (18,8 %) and L-45 (18,5%) cotton lines grown in the optimal irrigation regime (Fig. 3).
A
B
40,00 -,
30,00 -
20,00 -
10,00
Drought
Optimal
17 18 19 20
• Drought • Optimal
Fig. 3. GLM analysis of the water retention capacity in the parent forms (2 hours (%)), A - samples; B -environments. 1-Namangan -77, 2-KK1796, 3-KK-1795, 4-L-1000, 5-C-9006, 6-KK-1086,7-Catamarca 811, 8-C-9008, 9-L-N1, 10-L-141, 11-Hapicala19, 12-0-030,13-C-4769, 14-L-45, 15-Zangi ota, 16-Saenr Pena 85, 17-
C-2025, 18-KK-602, 19-SAD-35-11, 20-C-417.
Cotton lines were grown in the water deficit irrigation regime also showed different results on this feature. In particular, the leaves of S-9006, Catamarca-811, Seaner Pena 85, and SAD-35-11 cotton varieties lost 22,5 %, 23,2 %, 22,5 %, and 21,3 % of water in 2 hours, respectively, and these results indicating the water retention capacity of these lines were low. In addition, cotton genotypes such as KK-1795, KK-1086, L-141, S-2025, and S-417 lost less water 17,1 %, 11,7 %, 16,8 %, 16,8 %, and 12,3 %, respectively, and it shows that possessing high water retention properties in these lines.
Also, the WRC was measured in excised leaf samples 4 hours after collecting. Accordingly, cotton lines S-9006 (37,8 %), L-141 (45,2 %), Seaner-Pena 85 (37,8 %), and KK-602 (37,8 %) have grown in the
A
optimal regime were identified low water retention properties with more evaporated water within four hours. Also, cotton genotypes C-9006 (31,7 %), Catamarca-811 (32,2 %), Seaner Pena 85 (31,6 %) grown in deficit watering regime demonstrated the same results as optimal regime (Fig. 4).
Samples that retained much water retention capacity in the leaves for 4 hours were compiled from the following sequences with small percentages of water loss showing relative resistance. In this case, low amount of water were lost in both irrigation regimes such as in optimal irrigation regime cotton lines KK-1795 (26,9 %), KK-1086 (28,5 %), Catamarca-811 (29,9 %), S-4769 (28,5 %) and L-45 (28,0 %) and in the deficit irrigation regime L-1000 (24,5 %), KK-1086 (17,3 %), L-N1 (33,4 %) and S-417 (20,4 %).
B
\A
10 11 12 13 14 15 16 17 18 19 20
Drought
Optimal
Drought ▲ Optimal
Fig. 4. GLM analysis of the water retention capacity in parental genotypes (4 hours (%)), A - samples; B -environments. 1 -Namangan -77, 2-KK1796, 3-KK-1795, 4-L-1000, 5-C-9006, 6-KK-1086,7-Catamarca 811, 8-C-9008, 9-L-N1, 10-L-141, 11-Hapicala19, 12-0-030,13-C-4769, 14-L-45, 15-Zangi ota, 16-Saenr Pena 85, 17-C-
2025, 18-KK-602, 19-SAD-35-11, 20-C-417.
Based on the results of the study, it can be concluded that the parental genotypes of the NAM population on the resistance level were different. This indicates widespread genetic segregation of the population based on these indicators, which, in turn, allows a deeper study of the regions of the genome responsible for environmental stresses, such as drought, and provides the high-precision molecular mapping of the genes that control this trait in the future.
References
1. P.F. Pace, H.T. Cralle, S.H. El-Halawany, J.Y. Cothren, S.A. Senseman. Drought-induced changes in shoot and root growth of young cotton plants. Journal Cotton Science, (1999), 3: 183-187.
2. Wang C.Y, Isoda A, Li M.S, Wang D.L. Growth and eco-physiological performance of cotton under water stress conditions. Agricultural Sciences in China, (2007); 6(8): 949-955.
3. Garg A.K., Kim J.K., Owens T.G., Ranwala A.P., Choi Y.D., Kochian L.V., Wu R.J., Trehalose accumulation in rice plants confers high tolerance levels to different abiotic stresses. Proc. Natl. Acad. Sci. USA, (2002), 99:15898-15903.
4. Ludlow M.M. A critical evaluation of traits for improving crop yields in water-limited environments. Advances in Agronomy, (1990), 43: 107-153
5. Izanloo A., A.G. Condon, P. Langridge, M. Tester, T. Different mechanisms of adaptation to cyclic water stress in two South Australian bread wheat cultivars. Journal Experimental Botany, (2008) 59 (12): 3327-3346.
6. Bajjii M, Kinet JM, Lutts S. The use of the electrolyte leakage method for assessing cell membrane stability as a water stress tolerance test in durum wheat.
Plant Growth Reg, (2001): 1-10.
7. Cabuslay G.S., Osamu I., Alejar A.A. Physiological evaluation of responses of rice (Oryza sativa L.) to water deficit. Plant Sci, (2002) 163: 815827.
8. Abdurakhmonov I.Y., Kohel R.J., Yu J.Z., Pepper A.E., Abdullaev A.A., Kushanov F.N., Salakhutdinov I.B., Buriev Z.T., Saha S., Scheffler B.E., Jenkins J.N., Abdukarimov A. Molecular Diversity and Association Mapping of Fiber Quality Traits in Exotic G. hirsutum L. Germplasm. Genomics, (2008), Vol. 92, No.6:478-487.
9. Y. Xu, et al., Genetic mapping of quantitative trait loci in crops, The Crop Journal (2016), http://dx.doi.org/ 10.1016/j.cj.2016.06.003
10. Тураева О.С., Туланов А.А., Дарманов М.М., Макамов А.Х., Хусенов Н.Н., Норбеков Ж.К., Кушанов Ф.Н., Адылова А.Т., Абдурахмонов И.Ю. //Молекулярное корторование локусов прочности волокнв рекомбинантных инбредных линий ГАК популяции хлопчатника. // "УзМУ хабарлари". 2017. №3/1. 147-153.
11. Третьяков Н.Н., Карнаухова Т.В., Паничкин Л.А. Практикум по физиологии растений. М.: Агропромиздат, 1990. -271 с.
12. Кушниренко М.Д., Гончарова Э.А., Бондарь Е.М. Методы изучения водного обмена и засухоустойчивости плодовых растений. Кишинев, 1970. - 79 с.
13. Иванов А.А., Силина А.А., Цельникер Ю.Л. О методе быстрого взвешивания для определения транспирации в естественных условиях. Ботанический журнал., 1950. Т.35, №2:171-185 с.
