DNA FINGERPRINTING AND GENETIC DIVERSITIES IN SOME BANGLADESHI AUS RICE (Oryza sativa L.) GENOTYPES

The allelic diversity and relationship among 120 Aus rice landraces were determined through DNA fingerprinting using microsatellite (SSR) markers. A total of 85 SSR markers were used to characterize and discriminate all tested Aus rice genotypes, 45 of which were polymorphic for different chromosome numbers. The number of alleles per locus varied from 6 alleles (RM484 and RM541) to 30 alleles (RM519) with an average of 13 alleles per locus. The polymorphic information content (PIC) values varied ranged from 0.5211 (RM536) to 0.9369 (RM519) with an average 0.8217. The highest PIC value (0.9369) was obtained for RM519 followed by RM286 (0.9357). The genetic distance-based results seen in the unrooted neighbor-joining tree clustering revealed nine genetic groups. Being grouped into distant clusters and with highest genetic distance, eleven genotypes viz., Atithi dhan, Kadar chap, Pankiraj, Japanese-7, Jamri saity, Logi jota, Joba, Lada moni, Manik Mondal-2, Boilum and Brmulka-2 could be selected as potential parents for crop improvement for their distinctive characters. Panchash and Parija had closest distance in the SSR based CS-Chord distance (0.000) might have same genetic background. The highest genetic dissimilarity (1.000) was found among the nineteen Aus genotypes combinations followed by the second highest (0.9778) among 94 Aus rice combinations. Whereas lowest genetic dissimilarity was found between Kala and Kalo Hizli (0.1778) followed by Holat and Holae (0.2667). This information will be useful in the selection of diverse parents, background selection during backcross breeding programs and assist in broadening germplasm-based rice breeding programs in the near future.


INTRODUCTION
Rice (Oryza sativa) is one of the most important food crops and a primary source of food for more than half of the world's population (Khush, 2005).According to the United Nations (UN) estimates, the current world population 6.1 billion is expected to reach 8.0 billion by 2025.Most of this increase (93%) will take place in the developing world.Global rice production must reach 800 million tones of paddy rice to meet projected demand in 2025 (Peng et al., 1999) which is about 200 million tones more than rice production in 2006.This additional rice must come mainly from irrigated land in Asia, because improving rice yield in most rainfed regions is constrained by drought, flooding and poor soil quality (Cassman, 1999).Bangladesh is already under pressure both from huge and increasing demands for food, and from problems of agricultural land and water resources depletion.Bangladesh needs to increase the rice yield in order to meet the growing demand for food emanating from population growth.
For the study of genetic diversity, the plant scientists have used generally morphological, physiological as well as chemical features of plant.The number of scoreable morphological characters is varying as compared to the biological active genes.Moreover in most cases, plant genomes have large amount of repetitive DNA which are not expressed and do not contribute to the physiological or morphological appearance of plants.In the case of very closely related plant varieties, there are very few morphological differences, which as a matter of fact do not represent the true genetic differences at DNA level.So, there is always a need to study polymorphism at DNA level, which can be an indicative of genetic diversity.Several types of molecular markers viz., RFLP, RAPD, AFLP, microsatellites and SNP have been developed.PCR-based markers such as microsatellites are co-dominant, hyper variable, abundant and well distributed throughout the rice genome (Temnykh et al., 2001).Microsatellites have shown great promise in genetic diversity, genome mapping, gene tagging and marker-assisted selection (MAS) because they are technically simple, time saving, highly informative and require small amount of DNA.Abundance of microsatellite markers is now available through the published high-density linkage map (McCouch et al., 2002;IRGSP 2005) or public database.A study was conducted on 234 rice landraces in Plant breeding division, Cornell University and they identified five distinct groups corresponding to indica, aus, aromatic, temperate japonica and tropical japonica rice (Amanda et al., 2004).They also have very high diversity with 98% of loci polymorphic in Aus groups.Despite of their drought tolerance and early maturity, the group has received less attention compared to indica and japonica group.
There are four distinct ecotypes of rice-Boro, Aus, Transplanted aman and Deep water aman in Bangladesh.Bangladesh has a good source of indigenous rice cultivars.About 4000 T. Aman, 2500 Boro and 1500 Aus landraces are present in BRRI rice germplasm gene bank.Only a few decades ago large numbers of farmers were growing local cultivars as their main crop.Those cultivars have good adaptation but are poor yielder.Actually cultivation of these landraces was gradually replaced by high yielding varieties during last twenty years.These landraces adapted in different parts of this country, some of which have very nice quality, fineness, aroma, taste and high protein contents (Dutta et al., 1998).After establishment of BRRI, DNA fingerprinting has been done only for a small number of local germplasm.Indigenous crop landraces were characterized at both molecular and phenotypic level by many countries.Such types of characterization have been done for keeping the crop identity and searching for new genes for further crop improvement.But information on the genetic diversity of local landraces particularly for Aus rice is very scanty.Precise information on the extent of genetic diversity among population is crucial in any crop improvement program, as selection of plants based on genetic diversity has become successful in several crops (Ananda and Rawat, 1984;De et al., 1988).That's why, the present investigation has been undertaken in order to find out the genetic diversities among Aus genotypes at the molecular level.

Plant materials and genomic DNA isolation
One hundred and twenty genotypes, including twelve BRRI released Aus genotypes were used in this study (Table 1).Genomic DNA was isolated from young leaves from 21 days old plants with minor modification of CTAB method.The concentration of extracted DNA was estimated by DNA confirmation test by (1.5%) agarose gel electrophoresis with lambda DNA (50ng/μl).

SSR primers analysis
A total of 45 primers were selected (Table 2) to detect polymorphic DNA alleles for discriminating the tested Aus rice genotypes.Each PCR was carried out in a 10 µl reaction volume containing 1 µl of MgCl 2 free 10X PCR buffer with (NH 4 ) 2 SO 4 , 1.2 µl of 25 mM MgCl 2, 0.2 µl of 10 mM dNTPs, 0.2 µl of 5 U/µl Taq DNA polymerase, 0.5 µl of 10 µM forward and reverse primers and 3 µl (10ng) of DNA using a 96 well thermal cycler.The mixture was overlaid with one drop (3 µl) of mineral oil to prevent evaporation.The temperature profile used for PCR amplification comprised 94° C for 5 minutes (initial denaturation) followed by 35 cycles of 94° C for 1 minute (denaturation), 55° C for 1 minute (annealing), 72° C for 2 minutes (extension) with a final extension for 7 minutes at 72° C at the end of 35 cycles.The annealing temperatures were adjusted based on the specific requirements of each primer combination.The PCR products were mixed with gel loading dye (bromophenol blue, xylene cyanol and sucrose) and electrophoresed in 8% polyacrylamide gel using vertical poly acrylamide gels for high throughput manual genotyping.Three-Four µl of amplification products were resolved by running gel in 1X TBE buffer for 1.5 hrs to 2.5 hrs depending upon the allele size at around 90 volts and 500 mA electricity.The gels were stained in 1 µg/ml ethidium bromide and were documented using UVPRO (Uvipro Platinum, EU) gel documentation unit.

Data analysis
Size for each amplified allele was measured in base pair using Alpha-EaseFC 5.0 software.The summary statistics including the number of alleles per locus, major allele frequency, gene diversity, Polymorphism Information Content (PIC) values were determined using Power Marker version 3.25 (Liu and Muse, 2005).The allele frequency data from Power Marker was used to export in binary format (allele presence=1 and allele absence=o) for analysis with NTSYS-pc version 2.1 (Rohlf, 2002).A similarity matrix was calculated with the Simqual subprogram using the Dice coefficient, followed by cluster analysis with the SAHN subprogram using the UPGMA clustering method as implemented in NTSYS-pc.

Overall microsatellite diversity
One hundred and twenty Aus genotypes were assessed for genetic variability using 45 polymorphic Microsatellite DNA markers.A total of 228 alleles were detected at the loci of 45 microsatellite markers across the Aus rice genotypes.The highest amplicon size was produced by RM171 (334 bp) and the lowest by RM1 (70 bp).The number of alleles per locus ranged from 6 alleles (RM484 and RM541) to 30 alleles (RM519), with an average of 13 alleles across the 45 loci (Table 3).The frequency of the most common allele at each locus ranged from 10% (RM519) to 62% (RM536).On average, 25% of the 120 Aus rice genotypes shared a common major allele at any given locus.The polymorphic information content (PIC) values were ranged 0.5211 (RM536) to 0.9369 (RM519) with an average 0.8217.The highest PIC value (0.9369) was obtained for RM519 followed by RM286 (0.9357), RM153 (0.9332), RM144 (0.9218) and RM169 (0.9211), respectively.PIC value revealed that RM519 and RM286 were considered as the best marker for 120 Aus genotypes.The gel pictures of figure 1 showed amplified fragment using primer designed for the SSR marker RM519 for all 120 genotypes.

Genetic distance-based analysis
An unrooted neighbor-joining tree showing the genetic relationships among 120 Aus rice genotypes of Bangladesh was constructed based on the alleles detected by 45 microsatellite markers.The genetic distance-based results seen in the unrooted neighbor-joining tree revealed nine groups in the 120 genotypes (Figure 2).Aus genotypes of BRRI released modern varieties were clustered in the same group in the cluster IX.All Aus landraces were distributed into different cluster but Panchash (sl no.92, acc.no.4039) were not found in any cluster, it may be duplicate with Parija (sl no.93, acc.no.4566).Cluster number VIII contain highest number of genotypes ( 23) and cluster no IV contain only one genotypes, it was Jamri saity.Furthermore, the two Aus landraces viz., Madhu Mala (73) and Khushni (52) were clustered in the same group (cluster II).Three Aus landraces (Hati Bajor, Haji Sail, and IR19746-28-2-2) were clustered distinctly in the same group (cluster VII).Cluster III and Cluster V contains same number of genotypes (21) on the other hand cluster I and cluster VI contain 17 and 19 number of genotypes, respectively.Genetic dissimilarity coefficient was recognized between every two genotypes based on DNA profile.The highest genetic dissimilarity (1.000) was found among the nineteen Aus genotypes combinations (Table 4.) Followed by the second highest (0.9778) ninety four Aus rice combinations (Table 5).Whereas lowest (0.1778) genetic dissimilarity was found Kala and Kalo Hizli.

DISCUSSIONS
In crop improvement breeding program these genetically diverse genotypes could be chosen as parents for crossing program to create genetic variability and produce transgressive segregants.It was also recognized that two Aus landraces (Panchash and Parija) were sorted out as exactly same genotypes in this analysis (zero dissimilarity) might possess same genetic background.Hence, microsatellite marker based molecular fingerprinting could serve as a potential basis in the identification of genetically distant accessions as well as in duplicate sorting of the morphologically close accessions.In contrast, DNA-based molecular markers have proven to be powerful tools in the assessment of genetic variation and in the elucidation of genetic relationships within and among species, characterized by abundance and untouched by environmental influence (Powell et al. 1996).Ravi et al. (2003) also generated unique SSR profiles in rice by using a few primers that covered all 12 chromosomes.In the present investigation, SSR marker loci generated by 45 primers were used to assess the genetic diversity among 120 Aus rice genotypes.The SSR primers generated 228 alleles with the number of alleles per locus varying from 6 to 30.Similar number of microsatellite markers was previously used as subset for genetic diversity analysis of O. sativa (Garris et al.;2005 Chakrabarthia andNaravaneni, 2006).The average number of alleles per locus was 13.0, indicating a greater magnitude of diversity among the plant materials included in this investigation.This value is comparable to 4 alleles (RM484) to 31 alleles (RM474), with an average of 13.0 alleles across the 30 loci (Thomson et al., 2007).The polymorphic information content (PIC) values were ranged 0.5211 (RM536) to 0.9369 (RM519) with an average 0.8217.The PIC values observed, are comparable to three previous estimates of microsatellite analysis in rice viz., o.67-0.88(Gohain et al., 2006), 0.20-.90 with an average 0.560 (Jain et al., 2003).Many studies have also reported significant differences in allelic diversity among various microsatellite loci (Ravi et al., 2003).The alleles revealed by markers showed a high degree of polymorphism.The mean PIC value observed in this study was higher than the PIC value of 0.578 recorded by Ravi et al. (2003) in an earlier study among rice cultivars, landraces and wild relatives.The findings indicated that the genotypes used in the present study were more diversed due to differences in origin, ecotype and speciation.Panaud et al. (1996) studied using SSR markers in rice, described similarly high genetic similarity among landraces of common geographic origin and low similarity among landraces of diverse geographic origins.
The efficient use of SSR markers to discriminate between Oryza species with various genomes was also demonstrated by Cai and Morishima (2002).The multi allelic nature of SSR markers has the unambiguous advantage of discriminating between the genotypes more precisely.The Unrooted neighbor-joining tree cluster analysis of the SSR-based genetic similarity matrix resulted in the classification of Aus genotypes into separate clusters.Moreover, varietal profiling based on SSR markers will be more reliable as compared to profiling based on other markers, since SSR markers detect finer levels of variations among closely related lines.Cluster I was obtained as largest constellation and included 23 genotypes.

CONCLUSION
The allelic diversity revealed by 45 SSR primers was sufficient enough to distinguish among the tested Aus rice genotypes.The allelic variation was lower within the genotypes group than the other genotypes, indicating the possibility to exploit distant relatives to broaden the genetic base of rice.

Table 1 .
List of one hundred and twenty Aus rice genotypes * = Modern BRRI released Aus Variety; # = Sl No. 7-94 local Aus landraces

Table 2 .
Selected primers, their sequence and chromosome number

Table 3 .
Data on the number of alleles, allele size range, highest frequency allele and polymorphism information content (PIC) * Motif of the SSR and number of repeats as previously published (http://www.gramene.org)

Table 4 .
100% dissimilarity of the nineteen Aus genotypes

Table 5 .
98% dissimilarity found in 98 combinations of Aus genotypes