3. MATERIALS AND METHODS
3.1. Plant Material
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Total of twenty one introgression lines carrying African rice genes and its parent lines were used in this study (table 1)
3.2. Methods
3.2.1. DNA extraction
Genomic DNA was extracted from young leaves of the seedlings grown in fields of Huazhong Agricutural University, Wuhan, China. Method used was according to Cetyltrimethyl ammonium bromide (CTAB) method reported by Xu et al. (2011), but with some improvements.
Preparation of 1.5%CTAB buffer
CTAB 3.0g
1M Tris pH8.0 15.0ml
0.5M EDTA (ethylenediaminetetraacetic acid), pH8.0 6.0ml
NaCl 12.28g
dd H2O 200.0ml
PVP40Mw40, 000 1.0g
Tris-base buffer to make sure pH=8
Two gram of fresh or frozen leaf tissue was ground to a fine powder in liquid nitrogen with a conical hand tissue grinder by using mortar and pestle.
The powder was transferred to 1.5ml Eppendorf tube content 750?L of 1.5*CTAB and 25?L ? –mercaptoethanol.
Then incubated for 1hour in a 65?C water bath with an interval mixing by inverting the tubes every 15min.
Add 750?L of chloroform: Isoamyl alcohol (24:1) was done in a fume hood.
The samples were gentle mixed by shaking for 30min in shaker at room temperature and then spin in the microfuge at 10000rpm for 10min to precipitate the cell debris.
The upper aqueous phase (supernatant) was pipette and transferred into fresh eppendorf tubes.
The same volume from preceding mix was put to the liquid in fresh eppendorf and the mixed shaking for 30 min in the shaker then spin in microfuge at 10000 rpm for 10 min.
The supernatant was pipette and transferred to fresh eppendorf tubes, and then 2/3 the volume from iso-propyl alcohol 70% was added to each sample, then the samples was put in refrigerator 1 hour.
After that centrifuged for 3 min with the rotational speed of 7000 rpm in 220c.
After this step DNA collected on the tube sides.
The supernatant was carefully discarded from the tube, DNA pellets appeared as tiny white tear drop-shaped smears on the tube sides.
The pellets were washed twice with 75% ethanol, then air dried by inverting the tubes up side down over tissue paper.
To re-dissolve DNA pellets 100?L of ddH2O was added to the tubes and immediately stored at –200C
3.2.2. Quality and quantity check of DNA
DNA was checked for its purity and then quantified. The genomic DNA was run on 1.0% agarose gel stained with ethidium bromide and photographed under UV transilluminator using Image Lab TM software Version 4.0.1. The concentrations of the pure genomic DNA as assessed by agarose gel electrophoresis were estimated on spectrophotometer ND-2000. Based on the quantification data, all the genomic DNA samples were diluted to a final concentration of 100 ng•?L-1 with double-distilled water (ddH2O) and stored at -20o C for further use.
3.2.3. SSR analysis
3.2.3.1. PCR amplification and agarose gel electrophoresis
A total of 50 microsatellite primer pairs were used for analyzing and identification 21 genotypes and then 22 polymorphic primers were selected to provide genetic identity and assess the genetic relationships among genotypes.
PCR was performed in 20 ?L reactions by using Thermal cycler touchdown as described by Don, et al. (1991), with some modifications.
PCR mixture component
The Touchdown-PCR program:
950C
3min
950C
30sec
10X
650C
30sec; 10C decreased each cycle
720C
30sec
950C
30sec
22X
550C
30sec
720C
30sec
720C
10min
40C
For storge
PCR products were analyzed by electrophoresis using 2.0% agarose gel in 1x Tris Acetic cid EDTA (TAE), stained with ethidium bromide solution. then gel was visualized and photographed under UV light using Image Lab TM software Version 4.0.1. The SSR markers with high polymorphism were further used in SSR fingerprinting analysis
3.2.3.2. 6% denature polyacrylamide gel electrophoresis (PAGE)
polyacrylamide gel electrophoresis has been used an unique analytical tool for many studies related to the identification of cultivars, species and F1 hybrids
3.2.3.2.1. Materials
A. preparation of 6% PAGE for SSR analysis
Reagent
Amount
Urea
420g
40% Acrylamide solution
150ml
10*TBE
100ml
Bring the volume to 1000ml with ddH2O
B. preparation of 10*TBE (Tris-borate EDTA) buffer
Reagent
Amount
Tris-base
108g
Boric-acid
5 5g
Na2 EDTA.2H2O
7.44g
Dissolved in 800 ml double distilled water, filtered through 0.22 µm filter paper, made up to 1000 ml.
C. preparation of 40% Acrylamide
Reagent
Amount
Acrylamide
380g
Bis-Acrylamide
20g
Total volume (by adding distilled water) 1000 ml
d. Silver staining
Preparation of silver solution
2.5g AgNO3 (Silver nitrate)
2000 ml Distilled water
Then shaking well
Preparation of developer solution
28g NaOH (Sodium hydroxide)
10ml HCHO (Formaldehyde)
2000 ml Distilled water
3.2.3.2.2. Method
The large and short spacer glass plates, combs, and other pertinent materials were cleaned with water and completely dry.
The inside of both plates were cleaned with 95% ethanol to facilitate drying.
650?L of Repel-silane was applied to long plate and spread evenly using tissue paper.
800?L of glide-acrylamide (200?L qin he gui wan + 10 ml 75% alcohol then agitate) was applied to short plate and distributed evenly using tissue paper and leaved to dry for a short time.
Glass plates and sealers using clamps were assembled according to manufacturer’s instructions. 60ml of 6%PAGE, 40?L of TEMED (Tetra-methyl-ethylene-diamine), and 400?L of 10% ammonium persulfate (APS), previously stored at 40C were mixed into beaker.
The gel was leaved to polymerize for about 1hr.
After 1hr polymerization it was assembled in an electrophoresis unit.
After cleaning the wall with 1X TBE buffer, the gel was pre-run at constant 1500w for 30 min to clean the gel and pre heat the buffer to about 60-650. 20?Lof PCR amplified product was mixed with 5?L of the loading buffer and added 5?L in each well.
The electrophoresis unit was resumed and allowed to proceed at 1200w constant until loading buffer covered more than 3/4 of the distance.
Finally the unit was disassembled and the gel was subjected to silver staining.
Silver staining
Gel was soaked for 20-30 min in staining solution with gentle shaking followed by washing in water for 3 sec.
For developing color, gel was soaked in developer solution with gentle shaking until band appeared.
The gel was rinsed in water for several minutes.
Then the gel was air dried and visualized under normal light.
3.2.3.3. Data analysis
3.2.3.3.1. Analysis of polymorphism
Unambiguous polymorphic bands were scored visually for the presence or absence of corresponding bands among the tested accessions. Stutter and background bands were excluded. Those SSR markers displaying no polymorphisms, non-specific banding patterns or without PCR products were discarded. Molecular data were prepared by scoring the SSR markers amplification profile as present or absent for each marker to generate a binary matrix.
4. RESUTS
The study was designed to provide genetic identity for introgression lines carrying African rice genes by using molecular markers. Twenty one rice genotypes were used in this study (Table 1).
A total of twenty two pairs of SSR primers distributed in 9 rice chromosomes were selected to analyze the twenty one genotypes. The sequence and the details of selected primers showed in table 2
4.1. Analysis of polymorphism
SSR-PCR reaction system was optimized with 3% agarose gel electrophoresis and SSR markers were analysed with 6% denaturing polyacrylamide gel electrophoresis. Polymorphism was analysed with composited 22 pairs of SSR primers and optimized SSR-PCR system to determine the appropriate SSR markers applied in fingerprint mapping. Banding patterns generated by primer pairs RM310, RM213, RM202, and RM80 in 21 genotypes are shown in Fig 1.and Fig 3., while RM337 are shown in Fig 2. A total of 91 alleles were detected across 21 rice genotypes using 22 SSR markers. The maximum number of polymorphic alleles was 6, while the minimum number of polymorphic bands (2 alleles) was amplified with the markers RM 85, RM240. The average number of polymorphic alleles per marker was 4.1.
Molecular data were ready for recording the SSR markers amplification profile as current (1) or absent (0) for each marker to create a binary matrix. The binary matrix data were analyzed through the use of the Similarity for Qualitative Data (SIMQUAL) module to generate Dice similarity coefficients [Dice LR. 1945]. The similarity coefficients were used to construct dendrograms using the Unweighted Pair Group Methods with Arithmetic means (UPGMA).
4.2. Cluster analysis
The SSR markers were able to distinguish between different rice genotypes. The high degree of polymorphism of microsatellite markers allows rapid and efficient identification of rice genotypes. These markers classified the rice genotypes into eight clusters. (Fig4.)
According to the results of pylogenetic tree twenty one genotypes were divided into eight groups according to the standard genetic similarity o.73. The lowest diversity was found between ILA65 and ILA78 (similarity level 98%) that strengthen the supposition of close relationship between them. While the highest diversity was found between J23B and other genotypes used in this study at similarity level 35%. These obtained results could be due to the number of SSR markers used in the study or the bias of genetic similarity estimation conducted by the UPGMA-based method.
4.3. Principal component analysis
A principal component was performed using 22 SSR markers. The Values of the Eigen values and their contribution to variation are presented in Table 3. The score plot of 21 genotypes based on the first two principal components is presented in Fig 5.
4.4. Genetic similarity among Rice genotypes
The Dic similarity was computed according to the obtained data from the polymorphic primers. The similarity index value obtained for each pair wise comparison among the 21 genotypes and presented in Table 4. The similarity coefficients ranged from 23.08% to 97.8% among tested genotypes. ILA 65 and ILA78 were the most similar among all the genotypes with a coefficient of 0.9780. The least similar genotypes were J23B and ILA19, ILA123 with a coefficient of 0.2308.
4.5. The fingerprints for identification
The results showed the molecular identification of 21 Rice genotypes using 22 SSR polymorphic sequences. The thirteen Pair of these SSR primers were selected from the polymorphic primers which can amplify clear bands and have more alleles to identity nineteen introgression lines carrying African rice genes and three varieties. Table 5
The microsatellite assay generated cultivar-specific alleles in some of the genotypes screened; these used as DNA fingerprints for genotypes identification. This will be the assistance for the establishment and defense of proprietary rights and the determination of cultivar purity. The core SSR used to generate the fingerprint code of each used germplasm Table 6.
5. DISCUSSIONS
In our study, microsatellite markers were used for investigating genetic diversity of 21 rice genotypes under study (Table 1). To this end, 22 primer pairs of microsatellite were used which had relatively high polymorphism in available literatures (Table 2). According to the previous results primer pairs will be referred to as loci and DNA bands as alleles (Sefc et al., 2000). The number of alleles obtained by microsatellite markers varied from 2 to 6 with an average of 4.1 alleles per locus.
However, the average numbers of alleles detected in present study were significantly higher than this reported by JOSH et al. (2006) in non-Basmati aromatic rice genotypes of India which equals 2.6.
The disparity among reports might be due to genotype number, SSR loci distribution, concerned sets of germplasm and gel electrophoresis method adopted in various studies. Higher number of alleles was found when a large number of landraces from a wide range of geographical origins were included in the study (Brondani et al., 2006). The cluster analysis, using unweighted pair group method of arithmetic means (UPGMA) was constructed for measuring genetic diversity and relatedness among the genotypes (Fig. 3).
5.1 cluster analysis based on SSR markers
The similarity matrix was computed using SSR markers based on Dic’s coefficient following the UPGMA method using SHAN programme of NTSYS-pc.
The Dic’s similarity coefficient for the SSR data set varied from 0.2308 to 0.9780. According to the results of phylogenetic treei??Figure1) Twenty one introgression lines and cultivars were divided into eight groups according to the standard genetic similarity which is 0.73.
The first group ILA17, ILA13, STB, STA/F
The second group ILA11
The third group ILA145, ILA21
The fourth group ILA12, ILA166, ILA1
The fifth group ILA147, ILB19, ILA172, ILA65, ILA78, ILA30, ILA29, ILA60
The sixth group ILA19
The seventh group ILA123
The eighth group J23B
In group fifth we can distinguish two sub groups: sub group V-1 having ILA147, ILB19 and sub group V-2 having ILA172, ILA65, ILA78, ILA30, ILA29, and ILA60 but they were closely related groups.
And five groups according to the standard genetic similarity which is 0.67.
ILA17, ILA13, STB, STA/F
ILA11, ILA145, ILA21, ILA12, ILA166, ILA1
ILA147, ILB19, ILA172, ILA65, ILA78, ILA30, ILA29, ILA60
ILA19, ILA123
J23B
Rice genotypes clustered into eight well defined groups in accordance with their pedigree, probably due to the origin genetic of these materials, because most genotypes obtained from other genotypes and had similar pedigree. The studied Rice genotypes were showed existence of genetic diversity among 21 rice genotypes. The SSR markers played an important role in studying the germplasm diversity in rice (Yu et al., 2005). The results indicated that SSR analysis could be a better method to study the genetic diversity in rice. The highest genetic distance was found between ILA17 and J23B, where they held the first and last position of the dendrogram. On the other hand, the lowest genetic distance was found between ILA65 and ILA78 in the same group. This result consistent with the pedigree of these two ILs which shared a high proportion of ancestry (Table 1)
SSR markers efficiently separated the rice genotypes into groups consistent with their origin and pedigree. Gerdes and Tracy (1994) explained that pedigree relationship can be used as an indicator to test the effectiveness of markers in determining relationships among breeding lines. Our results showed that the SSR markers were able to detect the extent of genetic diversity among rice genotypes used in this study.
5.2 Principal component analysis
The principal component analysis study was also done using the subroutine EIGEN. The PCA results showed that the PC1 contributed 65.1389% followed by PC2 7.8560% and cumulative variance of first two PCA was 72.9948%. The results were close similarity of the results obtained based on unweighted pair group method with Arithmetic average (UPGMA)
5.3 similarity index
A similarity matrix according to the proportion of shared SSR fragments was used to establish the level of relatedness between the tested genotypes. Pair-wise estimates of similarity ranged from 0.2308 to 0.9780 and the average similarity among all genotypes was 0.6807 (table 3.). Two genotypes ILA65 and ILA78 were the closest related genotypes with the highest similarity index of 97.8%. This was followed by 94.51% similarity between two pairs of genotypes ILA166 and ILA1. The lowest similarity (23.08%) was observed between genotypes ILA19 and J23B, ILA123 and J23B. As expected, J23B had the greatest dissimilarity with all the other tested genotypes. The similarity coefficients of J23B with all the other genotypes ranged from 0.2308 to 0.4396. It could be concluded that Simple Sequence Repeat markers could identify the different rice genotypes, and some of rice genotypes under investigation have probably originated from closely related ancestors and possess high degree of genetic similarity.
5.4 DNA fingerprinting analysis
Finally the thirteen pairs of SSR primers were selected from the polymorphic primers as the core set of SSRs (Table4) which could detect varying numbers of polymorphic bands. Their amplified bands were clear, legible, easy to count, and distinguishable from one another. These markers were distributed among 8 rice chromosomes.
The microsatellites exhibited several bands that were shared among the check genotypes. Eight accessions (ILA123, ILA19, ILA21, ILA12, ILA145, ILA11, STA/F, STB) displayed unique bands in comparison with all other genotypes with different microsatellite markers.
SSR markers analysis will help the identification and differentiation of introgression lines. The information will enable construct a DNA fingerprinting database of tested rice genotypes (Table 6). Construct a unique DNA fingerprints of the tested genotypes can distinguish each of the tested materials and provide basic guidelines for its conservation.
CONCLUSION
Through the present study, a total of 18 introgression lines carrying African rice genes and three Varieties were identified with specific SSR primer. DNA-based SSR markers revealed high genetic diversity among the genotypes and were able to differentiate them successfully.
The similarity index values ranged from 0.2308 to 0.9780 Highest similarity (0.9780 ) observed between ILA65 and ILA78, whereas lowest similarity (0.2308) obtained between ILA19 and J23B, ILA123 and J23B. Thus, it can be inferred that more diversity was detected using SSR markers as it is evident from its similarity value.
Results showed the high polymorphism and abundance of SSR sequences in rice. Total of 13 primers were selected to generate fingerprint of 21 genotypes
Amany Kamel Elhabbak
