Association of saponin concentration, molecular markers, and biochemical factors with enhancing resistance to alfalfa seedling damping-off

Abstract

Fifteen alfalfa populations were tested for resistance to the seedling damping-off disease sourced by Rhizoctonia solani, Fusarium solani, and Macrophomina phaseolina. In a laboratory experiment, saponin treatment significantly diminished the mycelial growth of the causal fungi of alfalfa damping-off disease. Roots of the fifteen alfalfa populations varied in saponin and lignin content. Selection for the considerably resistant plants leads to the best growth performance, desirable yield, and high nutritive values such as crude protein (CP), crude fier (CF), nitrogen free extract (NFE), ash, and ether extract (EE) contents. For the PCR reaction, 10 SSR pairs of the JESPR series primers and the cDNA-SCoT technique with seven primers were used. SSR and SCoT revealed some unique markers that could be linked to resistance to damping-off disease in alfalfa that appeared in the considerably resistant alfalfa population (the promised pop.). SSR and SCoT markers can be an excellent molecular method for judging genetic diversity and germplasm classification in tetraploid alfalfa. We recommend breeding for saponin concentration in the alfalfa plant may affect resistance to some diseases like root rot and damping-off because saponin might improve plant growth, yield, and nutritional values.

Keywords: Medicago sativa L; Root rot diseases; SCoT Molecular genetic markers; SSR; Saponin and lignin content.

Fig. 1

The effect of 3 tested fungi on alfalfa seedling Damping-off % ±SE (Standard error), (a) Fusarium solani, (b) Macrophomina phaseolina, and (c) Rhizoctonia solani.

Fig. 2

The saponin content of fifteen alfalfa root dry matter samples ± SE (Standard error).

Fig. 3

The lignin content of fifteen alfalfa root dry matter samples ± SE (Standard error).

Fig. 4

Simple sequence repeat variation of fifteen Alfalfa plants using 10 SSR pair primers. The letter ‘M’ denotes the molecular marker, Whereas 1 = Wadi-1, 2 = Wadi-2, 3 = Wadi-3, 4 = Wadi-4, 5 = Wahat-1, 6 = Wahat-2, 7 = Wahat-3, 8 = Siwa-1, 9 = Siwa-2, 10 = Siwa-3, 11 = Promised pop., 12 = Nubaria, 13 = Rammah, 14 = Cuf-101 and 15 = Sea-reiver.

Fig. 5

Cluster tree illustrating the relationship among 15 alfalfa populations based on the analysis of 10 polymorphic SSR markers polymorphism, constructed utilizing the Euclidean similarity matrices computed as Dice coefficients and unweighted pair group method with arithmetic mean (UPGMA) algorithm in the SPSS software. Whereas, 1 = Wadi-1, 2 = Wadi-2, 3 = Wadi-3, 4 = Wadi-4, 5 = Wahat-1, 6 = Wahat-2, 7 = Wahat-3, 8 = Siwa-1, 9 = Siwa-2, 10 = Siwa-3, 11 = Promised pop., 12 = Nubaria, 13 = Rammah, 14 = Cuf-101 and 15 = Sea-reiver.

Fig. 6

DNA polymorphism of 15 alfalfa populations amplified using 7 SCoT primers. The letter ‘M’ denotes the molecular marker, Whereas, 1 = Wadi-1, 2 = Wadi-2, 3 = Wadi-3, 4 = Wadi-4, 5 = Wahat-1, 6 = Wahat-2, 7 = Wahat-3, 8 = Siwa-1, 9 = Siwa-2, 10 = Siwa-3, 11 = Promised pop., 12 = Nubaria, 13 = Rammah, 14 = Cuf-101 and 15 = Sea-reiver.

Fig. 7

Cluster tree illustrating the relationship among 15 alfalfa populations based on the analysis of 7 polymorphic SCoT markers polymorphism, constructed using the Euclidean similarity matrices computed as Dice coefficients and unweighted pair group method with arithmetic mean (UPGMA) algorithm in the SPSS software. Whereas, 1 = Wadi-1, 2 = Wadi- 2, 3 = Wadi-3, 4 = Wadi-4, 5 = Wahat-1, 6 = Wahat-2, 7 = Wahat-3, 8 = Siwa-1, 9 = Siwa-2, 10 = Siwa-3, 11 = Promised pop., 12 = Nubaria, 13 = Rammah, 14 = Cuf-101 and 15 = Sea-reiver.