Evaluation of genetic diversity among Saudi Arabian and Egyptian cultivars of alfalfa (Medicago sativa L.) using ISSR and SCoT markers

Abstract

Medicago sativa (Alfalfa) is valued for its high nutrient content as livestock feed for the production of dairy and meat products globally. The genetic improvement of alfalfa is limited because it is an allogamous autotetraploid. In this study, Inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) markers were used to assess the extent of genetic divergence between sixteen cultivars: twelve from Saudi Arabia and four from Egypt. The genetic diversity was assessed using eleven ISSR and eight SCoT primers. The ISSR primers produce 163 amplified bands with an average of 14.82 bands per primer and 60% polymorphism. The SCoT primers reveal 150 bands, with an average of 18.75 bands per primer and 77% polymorphism.  The dendrogram clustered the Saudi and Egyptian cultivars into two distinct populations. The information revealed here will help in the breeding program of alfalfa, which will produce more drought tolerant and high-yielding varieties.

KEYWORDS:

• Genetic diversity
• alfalfa
• ISSR
• SCoT Markers
• genetic improvement

1. Introduction

Alfalfa (Medicago sativa L.) is an important forage crop belonging to the family Fabaceae. It is popular as Lucerne and has grown in every region of the world. It covers more than 35 million hectares of cultivated area in different kinds of climatic and geographical conditions ranging from semi-arid to humid regions [1]. Alfalfa is grown primarily for hay, pasturage and silage. It is valued for its high protein content, minerals and vitamins. It is nutritious and palatable as livestock feed for the production of dairy and meat products [2]. Alfalfa has excellent qualities that make it ideal for sustainable agriculture [3]. It is highly adaptable to grow in different environmental conditions, stabilize soils and produce high biomass. It can fix atmospheric nitrogen through symbiosis with rhizobia and does not depend upon external nitrogen fertilizer for high protein levels in leaves [4,5]. However, the genetic improvement of alfalfa is limited because it is an allogamous autotetraploid (2n = 4x = 32) [6] and highly prone to inbreeding depression [7] and the artificial composition of cultivars [8]. There is a high variability among the commercial cultivars, as it is created by a frequent phenotypic selection after a few generations of panmictic crosses [9,10].

The Kingdom of Saudi Arabia (KSA) and Egypt have deserts and depend upon import for their major agricultural requirements. To reduce water consumption, since 2016 the KSA has started to phase out domestic green forage production. The total forage production of KSA is about 4 million metric tons per year at present and Alfalfa Market is projected to grow at a compound annual growth rate of 5.2% during 2021–2026. The country is hence dependent upon imports to meet its green fodder demand for its dairy and livestock industries [11,12]. However, in Egypt, there is a break in the output and supply of green forages, particularly during the warmest season of the year. During this time, the availability of fodder is inadequate as vegetable crops occupy the land. Alfalfa is suitable to resolve this issue, as it is high yielding, can grow on reclaimed lands with high nutritive quality forages and have great longevity [13]. As per Central Agency for Public Mobilization and Statistics (CAPMAS) [14] around 90,000 acres of alfalfa are grown annually and the demand for the crop is continuously increasing. The data suggest an 18% increase in cultivation area in the growing season of 2014–2017 but only a 7% increase in crop production.

The livestock and dairy industries can perform better on the availability of the superior quality forage, which is associated with leafiness percentage, leaf/stem ratio and nutritive value of the stem [15]. Moreover, the quality of forage is also based on its digestibility, palatability and animal performance [16]. The ability of livestock to digest and metabolize the nutrient components of their feed is called digestibility [17]. Therefore, it is very important that high-quality forage has superior nutrient levels, a high-energy intake by the animal and improved protein content [16]. It must be low in cellulose or lignin, a non-digestible part of fodder [18,19]. As a result, both yield and quality are the main factors targeted for enhancement in forage crops. Plant materials having rich genetic differences and analysis of morpho-agronomic traits can enhance the agronomic and quality traits in alfalfa. In one of the reports, over the years and season the forage yield (fresh and dry), quality characters (protein content) and genetic variability of twenty-five genotypes of Egyptian varieties were evaluated [20]. The available information on the genetic diversity and relatedness between alfalfa cultivars and landraces is limited, but essential for breeding new genotypes of alfalfa with improved crop quality. The cultivars of KSA and Egypt are the source of natural variation and are valuable genetic resources in the region. It can be used for the development of a superior line of grazing legumes,especially for a drier region where green fodder availability is absent.

The genetic variability of populations of M. sativa has been identified using allozyme markers [21], histological traits [22] and morphological and yield characteristics [23]. Among different markers, DNA-based markers are independent of environmental effects, have numerous forms, and can take many different forms [24,25]. There are reports that suggest the importance of the evaluation and selection of varieties for quantitative and yield parameters in any breeding programme. It helps in the introduction of varieties in a given local environment [26]. Furthermore, multivariate statistical methods and numerical classifications are employed, as cluster analysis, which intends to determine the level of genetic variation among tested genotypes based on their performance and their contributing physiognomies [27]. Genetic markers develop a highly accurate, environmentally separate approach to assess the genetic variability that can be used for cultivar identification, seed purity analysis, breeding programme, germplasm management and gene discovery [28–30]. Numerous studies were conducted on alfalfa using DNA and the foundation genetic markers such as Restriction Fragment Length Polymorphism (RFLP) [31], Random Amplified Polymorphic DNA (RAPD) [32], Amplified Fragment Length Polymorphism (AFLP) [33] and Simple Sequence Repeat (SSR) [10] and Sequence Related Amplified Polymorphisms (SRAP) [34]. These reports suggest that alfalfa has abundant variability among its populations. In recent years, new alternatives and successful marker methods have emerged. These markers involve the short preserved area surrounding the ATG translation start codon in plant genomes [35]. The Start Codon Targeted (SCoT) markers, due to high annealing temperatures and a great length of the primer, would be more efficient than other random markers. Furthermore, the design of SCoT marker assays does not require wide information about the genome sequence [36]. The SCoT marker has been successfully used to assess genetic diversity or identify varieties and map quantitative trait loci in different species, generating high polymorphism and repeatability of the results [35,37–41].

In the present study, ISSR and SCoT markers are employed to determine the genetic diversity among different cultivars of alfalfa from Saudi Arabia and Egypt. The efficiency of ISSR primers of di- and tri-nucleotide repeats has been tested in producing polymorphic DNA bands in the alfalfa genome. The data on the genetic diversity among the cultivars are useful for the authentication of alfalfa genotypes in Saudi Arabia and Egypt. It is also important for the management and protection of landraces in the gene bank. The preserved and genuine cultivars are vital sources for the selection of superior and genetically different parents to improve genetic variation in succeeding breeding programmes.

2. Material & methods

2.1. Plant material and DNA extraction

The study was conducted by collecting 16 alfalfa cultivars (12 cultivars from Saudi Arabia and four from Egypt) (Table 1). The fresh leaves were used for DNA isolation using DNeasy Plant Mini Kit (Qiagen, Germany) as per the manufacturer’s instructions. The DNA concentration in the samples was determined using a NanoDrop 2000 Spectrophotometer (Thermo Scientific, Germany).

Table 1. The list of 16 alfalfa varieties seed collection sources used in the current study for the ISSR and SCoT Marker Analysis.

S.N.	Local name	Source	Collection place
1	Local clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Buraidah, Saudi Arabia
2	Arab clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Unaizah, Saudi Arabia
3	Hijazi clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Dilam, Saudi Arabia
4	Qatify balady clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Qateef, Saudi Arabia
5	Local clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Hareeq, Saudi Arabia
6	Al-Hasawi clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Ahsa, Saudi Arabia
7	Clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Hail, Saudi Arabia
8	Balady clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Qateef, Saudi Arabia
9	Clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Buraidah, Saudi Arabia
10	Clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Unaizah, Saudi Arabia
11	Hijazi clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Taif, Saudi Arabia
12	Clover	Plant Gene bank in the Ministry of Environment Water and Agriculture	Al-Ahsa, Saudi Arabia
13	Helaly	Agriculture Research Center (ARC)	Giza, Egypt
14	Sarw–1	Agriculture Research Center (ARC)	Giza, Egypt
15	Sakha–4	Agriculture Research Center (ARC)	Giza, Egypt
16	Giza–6	Agriculture Research Center (ARC)	Giza, Egypt

2.2. ISSR and SCoT markers for the analysis of genetic diversity

The genetic variety was investigated between sixteen alfalfa cultivars using eleven ISSR and eight SCoT primers (Table 2) by using PCR amplification parameters as per (Table 3). The PCR amplification products were isolated on a 1.5% agarose gel stained with ethidium bromide (EtBr). The PCR products were visualized and photographed under UV light using a gel documentation system, BIO-RAD 2000 (BIO-RAD, USA).

Table 2. The PCR Primers information used for the ISSR and SCoT Marker Analysis.

Name	Sequence 5’–3’	References	Name	Sequence 5’–3’	References
SCoT–2	CAACAATGGCTACCACCC	[42]	SCoT–3	CAACAATGGCTACCACCG	[42]
SCoT–4	CAACAATGGCTACCACCT	[42]	SCoT–5	CAACAATGGCTACCACGA	[42]
SCoT–16	ACCATGGCTACCACCGAC	[42]	SCoT–21	CCATGGCTACCACCGGCC	[42]
SCoT–22	AACCATGGCTACCACCAC	[42]	SCoT–24	CCATGGCTACCACCGCAG	[42]
ISSR–1	AGAGAGAGAGAGAGAGYC	[43]	ISSR–2	AGAGAGAGAGAGAGAGYG	[44]
ISSR–3	ACACACACACACACACYT	[45]	ISSR–4	ACACACACACACACACYG	[45]
ISSR–5	GTGTGTGTGTGTGTGTYG	[45]	ISSR–6	CGCGATAGATAGATAGATA	[45]
ISSR–7	GACGATAGATAGATAGATA	[45]	ISSR–9	GATAGATAGATAGATAGC	[45]
ISSR–10	GACAGACAGACAGACAAT	[45]	ISSR–11	ACACACACACACACACYA	[43]
ISSR–12	ACACACACACACACACYC	[43]

Table 3. The PCR Reaction Parameters of ISSR and SCoT.

	Temperature		Time
Steps	ISSR	SCoT	ISSR	SCoT	Cycles
Initial denaturation	94 °C	94 °C	3 min	3 min	1
Denaturation	94 °C	94 °C	40sec	40sec	40
Annealing	45°C	50°C	50sec	50sec
Extension	72 °C	72 °C	1 min	1 min
Final extension	72 °C	72 °C	7 min	7 min	1

2.3. Molecular data analysis

The analysis for ISSR and SCoT markers was done by recording clear and apparent bands as present (1) and absent (0). The last dataset involved polymorphic and monomorphic bands, and then, a duple data matrix was developed. The cube degree of similarities among cultivars was determined to utilize the unweighted pair-group method with arithmetic average (UPGMA). Thereafter, a similarity matrix was performed to structure a phylogenetic tree (dendrogram) and major coordinate analysis as stated by the Euclidean similarity index using the PAST software Version 1.91 [46]. The people structure of the alfalfa cultivars was predestined according to the Hardy-Weinberg Equilibrium using the software STRUCTURE V2.3.4 [47]. The various forms of associate content (PIC) were specified utilizing the Power Marker software [48]. The NCBI BLAST tool was used to identify gene sequences from PCR bands [49] The ClustalW [42] sequence alignment tool was used to infer the evolutionary theory of DNA sequences. The iTOL was also used to study the phylogenetic analysis and for building a phylogenetic tree [50].

2.4. DNA sequencing and data analysis

Five specific fragments were selected from the SCoT PCR amplifications for DNA sequencing. The DNA sequencing reactions were completed using the Big Dye terminator cycle sequencing preparation reaction kits. The reaction was managed in a total volume of 20 µL, containing 8 µL of terminator preparation reaction mix, 100–500 ng of PCR product and 2 pmol of SCoT primer. A Fluorometric Scanning Technique was used for data collection and sequence analysis software was employed to assemble the DNA sequences [51]. The application of the BLAST programme aided to determine the homology search between the obtained nucleotide sequences and the nucleotide sequences in the Genbank.

3. Results and discussion

The ISSR are a class of molecular markers constructed on inter-tandem repeats of small fragments of DNA sequences. These regions are present within the microsatellite repeats and provide a high possibility to define intra-genomic and inter-genomic variety compared to other random primers. As they disclose dissimilarity within unique regions of the genome at many loci simultaneously. They display the precision of sequence-tagged-site markers but do not require sequence data for primer synthesis and act as random marker [52,53]. In ISSR analysis, primers can be arranged on any of the SSR motifs (di-, tri-, tetra- or penta-nucleotides) present at microsatellite loci, dispensing a large variety of probable amplification products. It can be fixed to genomic sequences making either side of the targeted simple sequence repeats [52]. The ISSR method is beneficial for the analysis of alfalfa varieties [24]. In the current study, the analysis of ISSR polymorphism divided the examined 16 alfalfa cultivars using eleven primers (Figure 1) as given in Table 4. The study reveals 163 amplified bands, generated with a mean value of 14.82 bands/primer. The ISSR-1 produced highest bands (20), whereas the ISSR-6 produced least bands (10). 65 monomorphic bands were generated with a mean value of 5.9 bands/primer and the maximum number of bands (12) was produced by ISSR-7 and the minimum bands (3) were obtained by ISSR-4, ISSR-5 and ISSR-6. 98 polymorphic bands were formed with a mean value of 8.91 bands/primer. The highest value of bands (15) was formed in ISSR-1 and the lowest was recorded in ISSR-7. The percentage of polymorphism was recorded as highest (79%) in ISSR-4 and lowest in ISSR-7 (25%) with a mean value of 60%.

Figure 1. ISSR profiles and PCR patterns of the sixteen alfalfa cultivars using the ISSR Primers; (A) ISSR–1, (B) ISSR–3, (C) ISSR–5, M: 1 kbp DNA ladder (Fermentas, Germany). Lanes 1–16 Represent the alfalfa cultivars.

Table 4. The analysis of the 16 alfalfa cultivars using ISSR Marker.

Primers name	TB	MB	PB	%P	F	PIC
ISSR–1	20	5	15	75	0.59	0.85
ISSR–2	18	7	11	61	0.62	0.8
ISSR–3	19	5	14	74	0.56	0.89
ISSR–4	14	3	11	79	0.57	0.73
ISSR–5	12	3	9	75	0.51	0.9
ISSR–6	10	3	7	70	0.55	0.85
ISSR–7	16	12	4	25	0.83	0.4
ISSR–9	15	8	7	47	0.66	0.64
ISSR–10	13	5	8	62	0.65	0.8
ISSR–11	14	7	7	50	0.68	0.73
ISSR–12	12	7	5	42	0.63	0.75
Total	163	65	98	–	–	–
Means	14.8	5.9	8.9	60	0.62	0.76

TB: Total number of Bands, MB: Monomorphic Bands, PB: Polymorphic Bands, %P: Percentage of Polymorphism, F: Frequency and PIC: Polymorphism Information Content.

Touil et al. [24] reported the genetic diversity of the Mediterranean populations of cultivated alfalfa using ISSR markers. The Rogers and Tanimoto index evaluates the genetic uniformity between these 29 populations using 19 primers. The genetic variation was analyzed by one statistical procedure: hierarchical classification. The total number of bands ranged between the various populations from 9 to 16 and the percentage of total polymorphism was recorded at about 60% similar to our results. The genetic differences between the 20 alfalfa populations were identified by a diversity study of the selected populations using 5 ISSR primers. These primers produced 51 different amplification products with 33 polymorphic bands. Polymorphisms ranged from 20 to 95.56%, with an average of 57.78%, indicating varying degrees of polymorphism [54]. In another report, eight ISSR primers were used to assess genetic diversity between 17 alfalfa genotypes. The PCR band for the 44–40 polymorphisms averaged 5.5, with an average per cent polymorphism of 90.18% [55]. Furthermore, genetic diversity, using 10 primers from each ISSR and SCoT, was applied to wild and cultivated bamboo panels, producing 115 and 138 bands with 100% polymorphism of the polymorphism band [56].

Data analysis of molecular data using PIC is of great value in true hybrid selection, DNA fingerprinting, identification of cultivar-specific markers and analysis of genetic diversity [57]. The PIC values represent the genetic diversity based on the used marker system, values in the range 0–0.2 indicate low genetic variation and values in the range 0.5–1 indicate high-observed variability [55]. In our study, the frequencies were 0.51 and–0.83 for ISSR-5 and ISSR-7, respectively. PIC values range from approximately 0.40 (ISSR-7) to 0.90 (ISSR-5), with an average of 0.76 (Table 4). The PIC values for ISSR primers in 17 genotypes of perennial alfalfa ranged from 0 to 1 [55] and were in sync with our results. In addition, due to the high polymorphism of the sample, 8 ISSR markers selected from a set of 20 primers were used to determine the genetic diversity of 8 different alfalfa cultivars. The PIC value for ISSR markers ranges from 0 to 0.5 [58]. The ISSR and SCoT systems, on the other hand, were used to assess the genetic diversity of Iran’s Aegilops triuncialis accessions, with mean PIC values of 0.3 and 0.26, respectively, to detect polymorphisms under tested accessions. It shows the effectiveness of the two markers in doing so [43].

3.1. Genetic similarity based on the cluster analysis using ISSR markers

The genetic similarity based on UPGMA cluster analysis was examined for 16 alfalfa cultivars employing ISSR markers and a similarity matrix was calculated using the dice factor (Figure 2 and Table 6). The genetic similarity was in the range of 0.68–0.98, a fairly higher genetic similarity between the examined alfalfa cultivars. Varieties 3 and 4 depict the highest (0.98) similarity index, followed by varieties 4 and 5 and between 4 and 6 (0.97), respectively. Besides that, there are low (0.68) genetic similarities between varieties 9, 16, 10 and 14, 10 and 15, 11 (Table 5). Earlier, ISSR and SCoT markers assessed the genetic diversity of Clerodendrum species, with genetic similarities ranging from 0.98 to –0.41 [44]. Almost similar results were recorded in Avena sativa L. varieties that show a similarity index in the range of 0.814–0.949 with an ISSR average of 0.885 and SCoT value of 0.891. The combined ISSR and SCoT results showed similarity index values in the range of 0.817–0.946, with an average of 0.887 [45]. The results display the ability of these markers to detect genetic similarities and differences between the members of the same species.

Figure 2. Dendrogram for the sixteen alfalfa cultivars constructed from ISSRs data using UPGMA and similarity matrix computed according to Dice coefficient.

Table 5. Similarity matrix among the 16 alfalfa cultivars according to the coefficient of Dice as shown by ISSR markers.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	1.00
2	0.93	1.00
3	0.95	0.92	1.00
4	0.94	0.89	0.98	1.00
5	0.94	0.90	0.94	0.97	1.00
6	0.94	0.88	0.95	0.97	0.96	1.00
7	0.94	0.92	0.94	0.95	0.95	0.96	1.00
8	0.92	0.87	0.91	0.92	0.92	0.90	0.92	1.00
9	0.93	0.94	0.93	0.92	0.93	0.93	0.96	0.90	1.00
10	0.95	0.93	0.94	0.92	0.93	0.93	0.94	0.89	0.94	1.00
11	0.93	0.94	0.94	0.91	0.90	0.91	0.93	0.87	0.94	0.95	1.00
12	0.86	0.84	0.83	0.84	0.85	0.86	0.86	0.87	0.88	0.86	0.83	1.00
13	0.74	0.72	0.78	0.79	0.77	0.76	0.75	0.74	0.73	0.73	0.72	0.70	1.00
14	0.70	0.69	0.72	0.72	0.71	0.71	0.71	0.71	0.96	0.68	0.96	0.96	0.93	1.00
15	0.69	0.69	0.74	0.74	0.71	0.72	0.71	0.70	0.96	0.68	0.68	0.96	0.93	0.96	1.00
16	0.70	0.70	0.70	0.70	0.69	0.70	0.70	0.69	0.68	0.67	0.68	0.70	0.92	0.96	0.94	1.00

In the dendrogram, there are two main clusters. In the first cluster, four Egyptian alfalfa cultivars (13, 14, 15 and 16) are present. The second cluster is split into two sub-clusters, in which the first sub-cluster consists of only one variety of alfalfa (12). The second sub-cluster is further breakdown into three sub-clusters, here only one alfalfa cultivar (8) is present in the first sub-cluster and the second sub-cluster consists of five alfalfa cultivars (7, 9, 10, 11 and 2). In the third sub-cluster, also five alfalfa cultivars (3, 4, 5, 6 and 1) are present (Figure 2).

The dice similarity matrix (Figure 3) and ISSR markers were used to calculate the principle coordinate analysis (PCoA) and depict the evolutionary associations between 16 alfalfa cultivars. The study revealed that the first axis of PCoA accounted for about 31.1% of total genetic dispersal, and the second axis showed a variation of 18.3%. The connections obtained from multivariate analysis of PCoA confirmed the investigation of different bunching.

Figure 3. Principal coordinate analysis (PCoA) based on the calculation of the first three coordinates was performed according to the analysis of ISSR markers of the sixteen alfalfa cultivars.

3.2. Genetic similarity based on a cluster analysis utilizing SCoT markers

The use of the SCoT markers is direct as a planning marker-assisted breeding programme compared to RAPDs, ISSRs and SSRs [59]. Besides that, it is simple, inexpensive and highly polymorphic which provides large genomic data using prevalent primers in plants. In this study eight SCoT primers were employed to investigate the genetic polymorphism among sixteen alfalfa cultivars. These primers give the amplification profile, and good reproducible patterns which were screened for polymorphism (Figure 4 and Table 6). In short, 150 amplified bands were generated using eight primers with a mean value of 18.75 bands per primer. The primers SCoT–16 produced ≥12 bands and SCoT–5 produced ≤(24) bands. 34 amplified monomorphic bands were found with a mean value of 4.25 bands/ primer. The primer SCoT–24 revealed highest monomorphic bands (9), while in primers SCoT–5 and SCoT–16, lowest monomorphic band (1) was recorded. In polymorphic bands, 116 bands were amplified in 16 cultivars with a mean value of 14.5 bands/primer. The primer SCoT–24 reported least bands (7), whereas SCoT-5 revealed highest bands (23).

Figure 4. SCoT profiles and the PCR patterns of the sixteen alfalfa cultivars using the eight SCoT primers; A: SCoT–2, B: SCoT–4, C: SCoT–22, M: 1 kb DNA ladder. Lanes 1–16 cultivar alfalfa.

Table 6. The analysis of the 16 alfalfa cultivars using SCoT Markers.

Name	TB	MB	PB	% P	F	PIC
SCoT-2	21	4	17	81	0.60	0.93
SCoT-3	17	3	14	82	0.54	0.83
SCoT-4	19	5	14	74	0.56	0.93
SCoT-5	24	1	23	96	0.33	0.93
SCoT-16	12	1	11	92	0.55	0.62
SCoT-21	19	5	14	74	0.52	0.92
SCoT-22	22	6	16	73	0.63	0.85
SCoT-24	16	9	7	44	0.74	0.80
Total	150	34	116	–	–	–
	18.75	4.25	14.50	77	0.56	0.85

TB: Total number of Bands, MB: Monomorphic Bands, PB: Polymorphic Bands, %P: Percentage of Polymorphism, F: Frequency and PIC: Polymorphism Information Content.

The genetic diversity of 20 mango varieties was assessed using 19 SCoT markers [60]. These primers produced 117 loci across 20 varieties, among which 96 (79.57%) were polymorphic. In another study, 39 SCoT primers investigated the diversity among different varieties of Egyptian olive; these primers produced 642 PCR bands with a mean value of 16.46 bands/ primer. The primer SCoT–02 produced 7 bands, whereas SCoT–31 reported 30 bands. These findings are in concurrence as reported by [61] and confirm that SCoT markers exhibit a high degree of polymorphism. It settles that the SCoT marker analysis is desirable for studies of functional variation in genes and relationships among different plant genotypes.

In the current study, the highest percentage of polymorphism (96%) is depicted by SCoT-5 and the lowest percentage of polymorphism (44%) is revealed in SCoT-24. The value of frequencies is 0.33 and 0.74 for SCoT–5 and SCoT–24, respectively. The efficacy of SCoT markers in selecting the studied cultivars was assessed by the PIC values of primers. The PIC values varied from 0.80 (SCoT–24) to 0.93 (SCoT–2, SCoT–5 and SCoT–16), with a mean value of 0.85 (Table 6). The PIC values produced by 39 SCoT primers in olive genotypes were calculated for different types of markers. The PIC values were different from 0.1036 (SCoT–02) to 0.2846 (SCoT–31), with a mean value of 0.2038 [10]. The evaluation of genetic variation in maize using SCoT markers revealed that PIC values varied from 0.652 (SCoT 8) to 0.816 (SCoT 23) with a mean value of 0.738 [62].

3.3. Genetic structure and cluster analysis using SCoT markers

The genetic resemblance was deciphered, based on UPGMA cluster analysis of sixteen alfalfa cultivars using SCoT markers. The resemblance matrix was calculated by the Dice coefficient (Figure 5). The evaluated genetic resemblance from 0.51 to 0.93 was of significant levels of genetic similarity between the studied alfalfa genotypes (Table 7). The highest degree of genetic similarities (0.93) was among cultivars 14 and 15, followed by (0.92) cultivars 3 and 5. However, the lowest degree of genetic similarities (0.51) was detected between cultivars (3 and 15), (5 and 15), (9 and 15), and (11 and 15) as shown in Table 7. Based on this data, a dendrogram was constructed consisting of two major clusters; the first cluster group contained four Egyptian alfalfa cultivars (13, 14, 15 and 16) and the second cluster was divided into two sub-clusters. The first sub-cluster retained eight alfalfa cultivars (11, 2, 7, 3, 5, 10, 12 and 1) and the second sub-cluster included four alfalfa cultivars (4, 6, 8 and 9) (Figure 5).

Figure 5. Dendrogram for the sixteen alfalfa cultivars constructed from SCoT data using UPGMA and similarity matrix computed according to Dice coefficient.

Table 7. Similarity matrix among the 16 alfalfa cultivars according to the coefficient of Dice as shown by SCoT markers.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	1
2	0.89	1
3	0.86	0.89	1
4	0.82	0.86	0.87	1
5	0.87	0.88	0.92	0.88	1
6	0.81	0.82	0.87	0.9	0.87	1
7	0.85	0.91	0.9	0.89	0.88	0.88	1
8	0.77	0.82	0.81	0.86	0.8	0.86	0.81	1
9	0.84	0.87	0.87	0.87	0.85	0.83	0.87	0.87	1
10	0.86	0.88	0.89	0.85	0.89	0.84	0.86	0.82	0.87	1
11	0.81	0.84	0.86	0.82	0.82	0.83	0.85	0.79	0.83	0.85	1
12	0.86	0.89	0.88	0.82	0.88	0.88	0.86	0.79	0.82	0.88	0.87	1
13	0.57	0.55	0.57	0.55	0.56	0.57	0.59	0.56	0.57	0.57	0.59	0.57	1
14	0.55	0.55	0.57	0.58	0.55	0.58	0.58	0.57	0.58	0.58	0.56	0.57	0.84	1
15	0.53	0.53	0.51	0.54	0.51	0.55	0.53	0.53	0.51	0.53	0.51	0.54	0.82	0.93	1
16	0.58	0.55	0.56	0.56	0.55	0.57	0.57	0.55	0.57	0.57	0.55	0.56	0.84	0.9	0.87	1

Furthermore, the genetic similarities between sixteen alfalfa cultivars using SCoT markers were represented by the Principal Coordinate Analysis (PCoA), obtained by the Dice similarity matrix (Table 7 and Figure 6). The multivariate analysis of PCoA complements the results of the group examination, as bunch investigation revealed greater resolution for closely related populations. The first axis of PCoA shows about 44.3% of total genetic variance, while the second axis displays approximately 18.3% of the variation. The links obtained from PCoA coincided with and affirmed the investigation of different bunching (Figure 6). To assess Crepidium acuminatum genetic diversity, a PCoA analysis was performed using SCoT, ISSR and combined data. Axis 1 and 2 represented 46.6% and 18.7% variations for ISSR markers, respectively, whereas axis 1 and 2 represented 34.2% and 22.7% variations for SCoT markers, respectively [63].

Figure 6. Principal coordinate analysis (PCoA) based on the calculation of the first three coordinates was performed according to the analysis of SCoT markers of the sixteen alfalfa cultivars.

Furthermore, the population structure of sixteen alfalfa cultivars was determined according to the Hardy-Weinberg Equilibrium utilizing the software structure V2.3.4., based on the maximum likelihood value inferred from the delta K (ΔK) value. The optimum group value was five (Figure 7). The values of ΔK (Figure 7(A)) also revealed that the population structure was composed of three different populations, where Egyptian and Saudi cultivars were clustered into two different populations (Figure 7(B)). The population structure analysis of hexaploid wheat and two Aegilops species, using CBDP and SCoT markers, revealed that the maximum ΔK for both data sets was 3, with accessions falling into three subpopulations [64]. The genetic structure of the sponge gourd (Luffa cylindrica) was revealed by data from ISSR and SCoT markers. In this case, the highest value of K was 2, indicating the existence of two sub-populations and revealing the admixture type of population [65]. Recently, ISSR and SCoT markers were used for the evaluation of hereditary differences of 29 wild plants in Al Jubail of Saudi Arabia [40].

Figure 7. Population structure analysis of the 16 alfalfa cultivars, (A) the delta inferring number of population K, (B) population alpha and (C) the assignment of different alfalfa cultivars to different populations.

3.4. Sequence analysis of PCR bands

The use of molecular markers in plant genome analysis based on band sequences is very promising. It reveals new and related genes, especially with limited information about the plant genomes. These molecular markers are immune to environmental effects and guarantee that a feature can be selected irrespective of the geographical location and climate conditions of the plants. Moreover, when new varieties are formed, they can be checked and followed with their unique genetic fingerprint. On a pilot basis, with a hypothesis to find new and related genes, in this study, five SCoT-PCR bands were selected. The NCBI BLAST tool and the multiple sequence alignment were used to identify the biological significance of these sequences. A phylogenetic tree was constructed using band sequences retrieved from PCR analysis and the most similar gene sequences obtained from NCBI databases (Figure 8). The sequences S24–1, S22–2 and S2–2 were very similar to some ribosomal genes and retrieved from the SCoT-PCR bands. The sequence S2–2 was similar to ATP synthase CF1 alpha subunit (atpA) in Medicago sativa, which has been reported to increase the stress tolerance in cyanobacteria and for use in phylogenetic analysis for the identification of Weissella sp. isolated from regions of South Africa [66]. The SCoT band of S22–1 was similar to NADH dehydrogenase subunit K (ndhC), which is a member of the NDH complex (Figure 8). This complex involves a transfer of electrons from NAD (P)H to plastoquinone, shielding the plant cell from photo-oxidative stress and maintaining optimal conditions for cyclic photophosphorylation [67]. SCoT band of S24–2 was highly similar to photosystem II protein j (psbJ) (Figure 8), which is one of the components of the Photosystem II core complex (PSII) and is responsible for the regulation of electron transfer during photosynthesis [68]. The results display the efficiency of using SCoT-PCR bands in sixteen varieties of alfalfa and would be beneficial for advanced studies in conservation genetics, population genetics and cultivar improvement.

Figure 8. The phylogenetic tree constructed using the sequence alignment of SCoT PCR bands.

4. Conclusion

The ISSR and SCoT marker analyses show a genetic contrast among 16 cultivars of alfalfa growing in Saudi Arabia and Egypt. Based on the values of ΔK, the sixteen cultivars were divided into three populations. However, ISSR and SCoT marker analyses divided these cultivars into two major genetically diverse groups, 1 and 2 and distinguished certain cultivars in sub-clusters in the region. The information revealed in this work shows the proficiency of ISSR and SCoT markers for the assessment of genetic connections between populations. The ISSR and SCoT markers add useful information for estimating the genetic diversity among alfalfa cultivars in Saudi Arabia and Egypt for the future sustainable breeding of new cultivars of alfalfa. The authors believe that the results obtained in this research will help in the breeding of new varieties of alfalfa that will be more tolerant to abiotic stresses, especially drought-tolerant and help in reducing the consumption of water. It will produce more biomass with a higher forage quality than current cultivars.

Acknowledgements

The authors extend their thanks and appreciation to the University of Tabuk for providing the infrastructural support. Authors also acknowledge the personnel at Plant Gene Bank in the Ministry of Environment Water & Agriculture in Saudi Arabia and the Agriculture Research Center, Giza, Egypt for their work on sample collection and their kind cooperation.

Disclosure statement

No potential conflict of interest was reported by the author(s).