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DNA Fingerprinting of Rice Lines for Salinity Tolerance at Reproductive Stage | OMICS International
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DNA Fingerprinting of Rice Lines for Salinity Tolerance at Reproductive Stage

Iqbal SA1*, Mirza Mofazzal Islam2, Ahmed Hossain Md1 and Ananya Malaker1
1Department of Biotechnology, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
2Principle Scientific Officer & Head, Biotechnology Division, Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh-2202, Bangladesh
Corresponding Author : Iqbal SA
Department of Biotechnology
Bangladesh Agricultural University
Mymensingh-2202, Bangladesh
Tel: (+88)01719478408
Received March 18, 2015; Accepted May 22, 2015; Published May 25, 2015
Citation: Iqbal SA, Islam MM, Ahmed Hossain Md, Malaker A (2015) DNA Fingerprinting of Rice Lines for Salinity Tolerance at Reproductive Stage. Adv Crop Sci Tech S1:006. doi: 10.4172/2329-8863.1000S1-006
Copyright: © 2015 Iqbal SA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Salinity is the most common abiotic stresses leading to the reduction of rice yield in many rice-growing areas of the world. This study was undertaken to assess the genetic diversity among saline treatment and susceptible rice lines using molecular marker (SSR). Salinity screening was performed at reproductive stage using sustained water bath following IRRI standard protocol. Twenty two rice lines were used for molecular analysis using three SSR markers: RM1287, RM342 and RM493 to determine salinity tolerance at reproductive stage. For DNA fingerprinting of rice Varieties, DNA was extracted from leaf samples using IRRI standard protocol. Amplified microsatellite loci were analyzed for polymorphism using Polyacrylamide Gel Electrophoresis (PAGE) and the result revealed that all the primers detected polymorphism among the rice lines analyzed. Using 3 SSR markers, a total of 25 alleles were detected among the 22 rice lines. The polymorphism information content (PIC) reflects the diversity allele frequency among the lines, which ranged from 0.59 to 0.88 with an average of 0.74. RM493 was the best marker for identification of genotypes as revealed by PIC values. The results of microsatellite marker based DNA fingerprinting analysis will be useful for the selection of parents for developing salt tolerant rice variety through molecular breeding.

DNA fingerprinting; Sa
  • nity tolerance; Reproductive stage; Oryza sativa
  • Introduction
    Rice production is affected by many biotic and abiotic stresses throughout the world. Among these abiotic stress alone contributes to about 50% of the total yield losses. Among the abiotic stresses soil/ water sa
  • nity is considered as one of the major and prevalent stresses
  • miting rice production in the world [1]. Approximately 30% of the total irrigated land worldwide is salt-affected [2]. and that
  • mits the total rice production in the world. Sa
  • nity is considered as one of important physical factors influencing rice production. At present, sa
  • nity is the second most widespread soil problem in rice growing countries after drought and is considered as a serious constraint to increase rice production worldwide [3].
  • In Bangladesh, rice occupies about 70% of the total cropped area of about 13.9 mil
  • on hectares. Approximately 11% of the world’s arable land is cultivated annually with rice [4], ranking next after wheat. Different cultures have preferences for different types of rice. Despite the annual production shortfall of 2 to 4 mil
  • on metric tons, rice provides more than 80% of the food requirements for the common people of Bangladesh [5]. Furthermore, world population is increasing day by day. To fed with the increasing population it is becoming essential to uti
  • ze these sa
  • ne soils either by reclamation of sa
  • nity or by growing salt tolerant plants [6]. Reclamation of sa
  • nity is difficult and expensive and not the permanent solution of the problem. Introduction of salt-tolerant variety is the rea
  • stic approach to obtain better yield under sa
  • ne conditions [6,7]. Soil sa
  • nity is one of the major constraints to rice production in coastal areas of Bangladesh. To reduce sa
  • nity, major engineering structure and expensive soil amendments are needed. However, these amendments require large investments. Thus tailoring rice plants to adapt in salt stress prove condition to be practical and effective. In fact, a few varieties have been developed to cope with the varied soil and c
  • matic conditions of the coastal areas. The urgent need therefore, for the development of sa
  • ne tolerant varieties in the coastal areas of Bangladesh [8].
  • The conventional methods of plant selection for salt tolerance are not easy because of the large environment effects and the low narrow sense of heritabi
  • ty for salt tolerance [3]. The advanced biotechnological techniques provides adequate support to evaluate genetic variation in both phenotypic and genotypic levels. Molecular markers are powerful tools in the assessment of genetic variation, in the elucidation of genetic relationships within and among species and have demonstrated the potential to detect genetic diversity and to aid in the management of plant genetic resources [9-11]. SSR markers are ideal markers for constructing high-resolution genetic maps, assisting selection and studying the genetic diversity in germplasms. It has been extensively exploited for genome mapping and for wide range of population and evolutionary studies in Arabidopsis [12], rice [13] and other animal and plant species [14]. This marker has the advantage of re
  • abi
  • ty, reproducibi
  • ty, discrimination, standardization and highly informative and cost efficiency over RFLPs. Microsatel
  • te markers are proving valuable for tagging and mapping of salt tolerance genes [15]. The general objective of this study is DNA fingerprinting analysis and identification of the salt tolerant rice
  • nes using microsatel
  • te markers (SSR).
  • Materials and Methods
    Experimental site
    The experiments were conducted at the experimental fields of Bangladesh Institute of Nuclear Agriculture (BINA), BAU, Mymensingh. 22 rice
  • nes were grown during July-December, 2013. Molecular Characterization is done at the laboratory of BINA under Biotechnology division.
  • Experimental materials
    22 rice
  • nes were used with diverse genetic background. Rice
  • nes with different genes were used in this study. A short description of the
  • nes that were used in this study is
  • sted in Table 1. To conduct the screening technique at the reproductive stage glass fiber tanks(any size with at least 22.0 cm height), Crude or non-refined salt, Electrical Conductivity meter, Well ground soil, collected from paddy field, Ferti
  • zer (N, P and K).
  • Planting tray preparation:
    Two experimental sustained water bath were prepared by ploughing followed by laddering weeds and stubbies were removed from the field. The land was mudded and leveled well before transplanting. At the final land preparation chemical ferti
  • zers such as Urea, Triple Super Phosphate (TSP), Murate of Potash (MP) were app
  • ed.
  • Sowing of pre germinated seeds:
    The seeds were soaked into water for 24 hours and incubated in petri dishes for 48 hours for quick germination in incubation. The pre germinated seeds were sown in trays in 3rd June, 2013. Soil and germination condition were as follows:
    Tray-1: Dry seed+ Dry soil, Tray-2: Wet seed+ puddle
    Data collection before treatment
    To observed expression of Saltol gene measured the effect of sa
  • nity treatment on test entries compared with before treatment collected morphological markers information. Following of the morphological markers data is essential to measure the expression level of Saltol locus visibly at sa
  • nity condition. The number of total tiller per plant, total number of leaf per plant, leaf length and breath, plant height, plant type, and old and young leaves color etc. in this experiment, collected above morphological markers information of three or five plants of each entries.
  • Preparation of sa
  • ne solution
  • To prepare the sa
  • ne solution, I used 1 m height 6 pieces cy
  • ndrical shape water bath each of them contains 271
  • ter water. Prepared sa
  • nized water solution up to the desired EC level by dissolving crude salt (NaCl) in water while stirring. In this purpose 3 g/L and 4 g/L crude salt was used to make 14 dS/m sa
  • ne solution [3]. Added 1897 g crude salt in 271 L water to make 14 dS/m sa
  • ne solution. The plastic water bath container was filled up with sa
  • nized water solution. Following is the formula to make sa
  • ne solution:
  • Where, 1dS electric conductivity per meter =135.5 g salt per
  • ter.
  • Treatment setup
    When the seed
  • ngs were at reproductive stage, water was siphoned out all from the trays and given a 24-h break. Then sa
  • nized water solution was prepared up EC 8 dSm-1 by dissolving crude salt. After then, normal setup watered with ordinary tap water and sa
  • nized setup watered with sa
  • nized setup watered with sa
  • nized water. The EC of the sa
  • nized water was monitored every week and adjust when necessary using crud salt and tap water. Sa
  • nity level was maintained until maturity.
  • Screening of rice
  • nes at reproductive stage
  • The
  • nes were evaluated for their tolerance to sa
  • nity in sustained water bath using IRRI standard protocol [3]. The experimental design was completely randomized design (CRD) with three rep
  • cations. The evaluation was done at the experimental field of BINA, Mymensingh.
  • The modified standard evaluation score (SES) of IRRI was used to assess the visual symptoms of salt injury (Table 2). This scoring discriminates the tolerant, moderately tolerant and susceptible rice
  • nes. Initial scoring was started at 15-day after sa
  • nization and final scoring was done at 21-day after sa
  • nization.
  • Harvesting
    Harvesting was done upon the maturity of different
  • nes. Different
  • nes attain their maturity at different times. The date of harvesting was confined when 80% of the grain attained golden yellow color. Harvesting was completed by 30th October to 23th November 2013.
  • Data collection
    Data were recorded on individual plant of the experimental tray. Among the studied characters, days to 50% flowering and plant height were recorded in the field and the remaining characters were recorded in the laboratory after harvesting. The characteristics were as follows&hel
  • p;&hel
  • p;
  • Days to 50% flowering: Recorded as days from sowing to 50% of the plants of each tray flowered.
    Days to maturity: recorded on the basis of the days from sowing to physiological maturity of the grains.
    Plant height: the length of main culms from the ground level to tip of its panicle was measured.
    Tiller number per plant: the total number of tiller as well as effective and non-effective tiller numbers was counted from each of the sample plant.
    Panicle length: panicle length was recorded as the distance cm from the last node of the rachis to tip of the main panicle which was randomly selected from each plant and the average was taken.
    Filled grain number per plant: the spikelet with kernel was considered as filled grain and counted from one selected panicle from each plant and the average was taken.
    Unfilled grain number per plant: the spikelet without kernel was considered as unfilled grain and counted from one selected panicle from each plant and the average was taken.
    25 grain weight: 25 clean and sun dried grains were counted from the sample plant after sun drying the samples and the average was taken at 14% seed moisture content.
    Panicle weight: whole panicle weight of each sample panicle was taken after cleaning and sun drying the samples and the average was taken at 14% seed moisture content.
    Grain yield per plant: Total grain weight (g) of each sample plant was taken after cleaning and sun drying the samples and the average was taken at 14% seed moisture content.
    Genomic DNA isolation: Juvenile, vigorously growing fresh leaf samples was collected from 21-30 days old seed
  • ngs for isolation of genomic DNA. Leaf samples were collected from selected
  • nes. Initially, healthy portion of the youngest leaves of the tiller was cut apart with steri
  • zed scissors and washed in 70% ethanol and dH2O and dried on fresh tissue paper to remove spore of microorganisms and any other source of foreign DNA. The collected leaf samples were then put into polythene bags and stored in a -20°C refrigerator. Leaf samples were taken from freezer prior to genomic DNA isolation and kept them in ice box until starting work. Leaf samples were held with steri
  • zed forceps and cut into 1-3 cm piece with steri
  • zed scissors. Genomic DNA samples of each genotype were extracted from vigorous, young growing leaf tissue using the mini preparation Modified CetylTrimethyl Ammonium Bromide (CTAB) method [16]. At first healthy portion of vigorous, young actively growing leaves were cut apart with sterile scissors and forceps. The leaf material was washed in sterile distilled water and ethanol as well as dried on tissue paper to remove excess water and spore of microorganisms and any other source of foreign DNA. Then the DNA from leaf tissues were extracted following step by step.
  • SSR markers and PCR amp
  • fication
  • Five SSR primer pairs were selected on the basis of the pub
  • shed rice microsatel
  • te framework map for the genetic diversity analysis Finally 3 primers exhibiting good qua
  • ty banding patterns and sufficient variabi
  • ty were selected for further analysis. Information regarding the original source, repeat motifs, primer sequences, expected length, chromosomal loca
  • zations and repeat types of the SSRs can be found in the Web database ( Prior to DNA amp
  • fication, a PCR cocktail was prepared containing all required components. PCR cocktail was prepared into eppendorf tube, which was placed into ice. The PCR cocktail had total volume of 10.0 μl reaction mixture including 2 μl DNA based on sa
  • nity protocol, was placed in the PCR tubes and run in the DNA thermal cycler. 2.0 μl of each template DNA samples were pipette into the wells of the PCR tubes. The primers were pipetted first into PCR tubes compatible with the thermocyclear used (0.2 ml). For each DNA sample being tested, a pre-mix was then prepared including, in the following order: buffer, dNTPs, DNA template and sterile distilled water. Taq DNA polymerase enzyme was then added to the pre-mix. The pre-mix was then mixed well and a
  • quoted into tubes containing primers. The tubes were then sealed and placed in a thermocycler and the PCR reaction was started immediately. Amp
  • fied products were stored at -20°C until further use.
  • Electrophoresis of the amp
  • fied product by page (0.8%) and visua
  • zation
  • The gel solution was prepared in a beaker with a magnetic stirring bar. Each PCR product was mixed with gel loading dye (bromophenol blue, xylene cyanol and sucrose) and electrophoresis was carried out in a mini vertical electrophoresis tank run on 8% polyacrylamide gels in TBE buffer. 2 μl of sample was added in each well. 25 bp DNA ladder was used for size determination. The cover was put on the tank. The electrodes were connected to the power supply and run for about 3-3.5 hr at 80 volts (running time may be variable depending on the size of the PCR fragments). The gel after electrophoresis was soaked in ethidium bromide (10 mg/ml) solution for 15- 20 min. The gel was viewed by the GEL Doc. The images that appeared on the computer were saved for analysis.
    SSR data analysis
    The size (in nucleotide base pairs) of the amp
  • fied band for each microsatel
  • te marker was determined based on its migration relative to a molecular weight size marker (25 bp DNA Ladder) with the help of Alpha Viwer software (Alpha Innotech, USA). The summary statistics including the number of alleles per locus, major allele frequency, gene diversity and Polymorphism Information Content (PIC) values were determined using Power Marker version 3.23, NTSYS-pc was used to construct a UPGMA (unweighted pair group method with Arithmetic mean) dendogram showing the distance-based interrelationship among the genotypes. Genetic distance was calculated using the coefficient in PowerMarker.(version 3.25)
  • Results
    In respect of primer RM 1287, allele size ranged from 159bp- 172bp, whereas primer RM493, RM342, showed a range 203 bp-221 bp, 146 bp to 159 bp. Using 3 SSR markers, a total of 25 alleles were detected among the 22 rice
  • nes. The average number of allele per locus was 8.33, with a range of 6 (RM1287) to 12 (RM493) (Table 2). Major allele is defined as the allele with the highest frequency and also known as most common allele at each locus. The size of the different major alleles at different loci ranges from 150bp (RM493) to 212 bp (RM342) (Table 3). On average, 38% of the 22 rice
  • nes shared a common major allele ranging from 18% (RM493) to 59% (RM342) common allele at each locus.
  • The highest gene diversity (0.89) was observed in loci RM493 and the lowest gene diversity (0.61) was observed in loci RM342 with a mean diversity of 0.76 (Table 3). It was observed that marker detecting the lower number of alleles showed lower gene diversity than those which detected higher number of alleles which revealed higher gene diversity.
    Polymorphism information content (PIC) value is a reflection of allele diversity and frequency among the varieties. PIC value of each marker can be evaluated on the basis of its alleles. PIC varied significantly for all the studied SSR loci. In the present study, the level of polymorphism among the 22 rice
  • nes was evaluated by calculating PIC values for each of the 3 SSR loci. The PIC values ranged from 0.59 (RM342) to 0.88 (RM493) with an average of 0.74 per locus (Table 2). The highest PIC value was 0.88 for RM493. The lowest PIC value observed 0.59 for RM 342 (Figures 1 and 2).
  • Genetic similarities were calculated from the data of coefficient (Table 4). The similarly matrix was used to determine the level of relatedness among the studied genotypes. Pair-wise estimates of similarity ranged from 0.33 to 1.00 and the average similarity among all 22 rice
  • nes was 0.29. The lowest genetic distance (0.333) was observed in RC251 and BRRIdhan11, RC 251 and BINAdhan7, BRRIdhan11 and BINAdhan7, RC251 and BINAdhan11, BRRIdhan11 and BINAdhan11, BINAdhan7 and BINAdhan11, RC249 and BINAdhan12, RC249 and BRRIdhan29, RC249 and Pajam, RC251 and BRRIdhan39, BRRIdhan 39 and FL478, RC191 and RC193, RC221 and BINAdhan11, RC222 and BINAdhan11, RC252 and RC229, BRRI dhan 11 and RC229, BINAdhan 11 and RC229, BINAdhan 7 and RC229, BINAdhan 11 and RC229, RC225 and RC229, RC227 and RC229. The highest genetic distance of 0.100 was observed between a number of accession or variety pair. Some of them RC 217 and RC 227, RC 217 and RC 250, RC 217 and RC 252, RC217 and RC 221, RC 217 and RC 192, RC191 and RC251, RC249 RC249 and BINA dhan 10, RC191 and RC229, BINAdhan 8 and RC229, BINA dhan10 and RC229, RC225 and RC227, RC217 and RC227, RC217 and RC225, jo
  • Aman and FL478, Jo
  • Aman and BIRRI dhan 29, RC193 and Jo
  • Aman, RC217 and Jo
  • Aman etc (Figure 3 and Figure 4).
  • UPGMA method was used for cluster analysis to differentiate the studied
  • nes into groups based on similarity coefficient. Six clusters were made at genetic similarity level of 0.26-0.51 (Figure 4). The UPGMA cluster analysis led to the grouping of the 22 genotype in two major clusters. The two main clusters is cluster II and cluster III contains more
  • nes. In cluster I RC191 and RC192 showed 100% similarity, and RC191, RC192 and RC193 showed 32% dissimilarity between them. In cluster II RC221, RC225, BINAdhan 8 and RC222 showed 32% dissimilarity among them. RC249 and RC251 showed 100% similarity, RC229 showed 32% dissimilarity with them, FL478 showed 68% dissimilarity with them in this cluster In cluster III BINAdhan10 and RC217 showed 100% similarity, BRRI dhan 11 showed 32% similarity with them, 56% dissimilarities showed among pajam and BIIRI dhan11, BINAdhan10, RC217. BINAdhan11 and BINAdhan12 showed 100% similarity. In cluster IV jo
  • Aman and RC227 showed 32% dissimilarities, BIRIdhan 29 showed 67% dissimilarities with Jo
  • AmanAnd RC227. BINAdhan 7 And BIRRIdhan 39, each of tem from single cluster and they showed 100% dissimilarities.
  • Discussion
    Using 3 SSR markers, a total of 25 alleles were detected among the 22 rice genotype. The average number of allele per locus was 8.33, with a range of 6 (RM1287 to 12 (RM493). These values were comparable to those reported ear
  • er (8.42 alleles per locus; range 3-21) by that used 26 SSR loci to estimate genetic relationship among 69 Argentine rice accessions. An allele observed in less than 5% of the 22 accessions was considered to be rare. Rare alleles were observed at all of the SSR loci with an average of 3.33 rare alleles per locus and a total of 10 across all the loci. In general, markers detecting a greater number of alleles per locus detected more rare alleles. Marker RM 493 detected the highest number of alleles (12) and rare alleles (6). According to Jain et al. [17] rare alleles are highly informative in fingerprinting of the varieties.
  • Major allele is defined as the allele with the highest frequency and also known as most common allele at each locus. The size of the different major alleles at different loci ranges from 150bp (RM 493) to 212bp (RM 342). On average, 37% of e 22 rice genotypes shared a common major allele ranging from 18% (RM493) to 59% (RM342) common allele at each locus [18]. observed that the frequency of the most common allele at each locus ranged from 15.38% (RM10701) to 37.51% (RM152).
    The highest gene diversity (0.89) was observed in loci RM 493 and the lowest gene diversity (0.61) was observed in loci RM 342 with a mean diversity of 0.76 (Table 4). It was observed that marker detecting the lower number of alleles showed lower gene diversity than those which detected higher number of alleles which revealed higher gene diversity. This result is consistent with previous work done by Heenan et al. [19], who observed that the gene diversity at each SSR locus was significantly correlated with the number of alleles detected, number of repeat motif and with the allele size range. Dhar et al. [18] observed that the highest level of gene diversity value (0.8994) was observed in loci RM10701 and the lowest level of gene diversity value (0.7743) was observed in loci RM152 with a mean diversity of 0.8544.
    In the present study, the level of polymorphism among the 22 genotypes was evaluated by calculating PIC values for each of the 3 SSR loci. The PIC values ranged from 0.59 (RM342) to 0.88 (RM493) with an average of 0.74 per locus (Table 4). These result revealed that markers RM493 would be best in screening 22 rice genotypes followed by RM1287 and RM342. Similar results were obtained where RM535 showed a PIC of 0.74 in several e
  • te varieties of rice.
  • Similarity coefficients among various cultivars analyzed ranged from 0.54-0.98 (average 0.7). Similar values of 0.77-0.98 were detected among 16 accessions of traditional, long-grain, scented Iranian rice and 7 cultivars from other countries [20]. Similarity coefficients ranging from 0.36 to 0.96 were obtained among 45 accessions of AA-genome Oryza species from various locations suggesting a wider range of genetic variabi
  • ty [21]. As expected, similarity coefficients among 193 accessions of parental
  • nes used at IRRI obtained from 26 countries were relatively low ranging from 0.22 to 0.68 [22].
  • UPGMA method was used for cluster analysis to differentiate the studied genotypes into groups based on similarity coefficient. Six clusters were made at genetic similarity level of 0.26-0.51. All of the 22 rice genotypes were grouped in four main clusters.
    Finally, it can be said that the use of microsatel
  • te markers divulged the larger range of similarity values for genotypes. Microsatel
  • te markers offer a great opportunity for the assessments of genetic diversity and relationships, which can be helpful in future breeding programs. By using the data from clustering, intercrossing may be possible between distant rice varieties.
  • Acknowledgements
    The authors acknowledge the cooperation of the Biotechnology division, Bangladesh Institute of Nuclear Agriculture (BINA), Myemensingh-2202. We gratefully acknowledge the Head of the Biotechnology Division, Bangladesh Institute of Nuclear Agriculture (BINA) in conducting this research work at the Biotechnology Laboratory and for technical support. We would
  • ke to express our appreciation to BINA, Bangladesh Institute of Nuclear Agriculture for their cooperation in providing the seed samples.
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