Genome Analysis of Mungbean [Vigna radiata (L.) Wilczek] using Simple Sequence Repeats (SSR) Mapping Data

For the developing world especially the vegetarian population of India, mungbean [Vigna radiata (L.) Wilczek] is considered as a magical diet to fight against malnutrition. Mungbean is abundant in carbohydrate, protein and micronutrients especially iron content which makes it an economical source of getting nutrition. It is also known as green gram, moong bean or golden gram. It is a notable leguminous crop which falls in sub-genous Ceratotropis (2n=2x=22) (Smartt, 1990).  It is a warm season crop of short life cycle (approx 60 days) it is well known rotational crop (Singh et al., 2018). It requires minimal inputs thus can perform well in the rainfed as well as in the irrigated land. Other benefits which mungbean offers is it has the ability to fix atmospheric nitrogen thus improves soil conditions and help in increase in the productivity of the successive crop (Kim et al., 2015). At a more disaggregated country level, India is the world’s largest producer, accounting for 34% of area and 24% of production (Joshi and Rao, 2016).
 
The protein content in mungbean is quiet high (20%) in comparison to the staple crops alongwith it is a credible source of folate and dietary fibre.  Its high digestibility and low flatulence further make mungbean a favourable food for the masses (Lavanya, 2008). Microelement deficiencies especially of iron, zinc, selenium etc are major human growth limiting factors worldwide. These deficiencies play a major role in malnutrition among the global population. One of the significant factors for this deficient condition is   non-availability of these trace elements in soil. And if these micronutrients are present in soil then there is lack of these nutrients in edible plant parts like in leaves, seeds or fruits. Keeping all these benefits of SSRs in mind the existing research was planned to evaluate the genetic diversity for micronutrients especially iron and zinc in mungbean genotypes. With the identification of trait linked to molecular marker it will be easy for scheming and executing the subsequent breeding experiments. These identified makers can prove helpful in association and linkage studies. This in turn can be advantageous in expansion of biofortification programme.
 

Plant material
 
Fifty-one green gram genotypes were used in this study (Appendix I) which the varieties released from CCS Haryana Agricultural University (HAU), Hisar, Punjab Agricultural University (PAU), Ludhiana, Indian Institute of Pulse Research (IIPR), Kanpur and some advanced breeding lines.


 
Estimation of Fe and Zn contents
 
Finely powdered healthy seeds were used in chemical analysis for Fe and Zn content. Seeds were surface cleaned and oven dried before grinding. Ground seed sample (1 g) was mixed with 25 mL di-acid mixture (HNO₃:HClO₄, 5:1 v/v) and kept overnight. Heat digestion was done till clear white precipitates settled down at the bottom. Crystals were dissolved by diluting in double distilled water. Contents were filtered through Whatman filter paper and filtrate was made 50 mL with double distilled water. This acid digested sample was used for determination of Fe and Zn by Atomic Absorption Spectrophotometer 2380 (Perkin Elmer, USA). The absorbance as recorded at 248 nm and 213 nm for Fe and Zn content, respectively. Data were evaluated against standard curves prepared from standard iron solution of 1000ppm which is diluted to 0 to 100 ppm and zinc solution of 100ppm which is diluted to 0 to 50 ppm.
 
DNA extraction and PCR assay
 
Young seedlings leaves were used for isolation of genomic DNA using 2% CTAB (cetyltrimethyl ammonium bromide) extraction method (Saghai et al., 1984). All DNA samples were given RNase treatment and were further purified. Quality and quantity of DNA was checked by agarose gel electrophoresis (0.8% agarose gel) and UV spectrophotometer. The DNA was normalized to the final concentration of 40ng/μl by comparing with lambda DNA (50 ng/μl). Fifty primers were screened to identify primers that were reproducible and generated the most polymorphic pattern. PCR reactions were carried out in Thermo Cycler (Eppendorf) in 20 µL reaction mixture containing 1U Taq DNA polymerase (MBI Fermentas), 1X- PCR buffer, 0.7 µM primer, 5% DMSO and 200 µM of dNTPs mix. PCR cycles consisted of initial denaturation at 94°C for 3 min, 38 cycles of denaturation at 94°C for 1 min, variable annealing temperature for 1 min, extension at 72 °C for 1 min and a final extension at 72 °C for 5 min. The amplification product (10 µL) was electrophoresed on 1.5% agarose gel in 1Ã- TBE buffer and stained with ethidium bromide. Bands were visualized under UV transilluminator and photographed using BioRad Gel Documentation system. The isolated DNA was used for PCR amplification using fifty SSR primers (Table 1, Appendix II). PCR reaction was carried out in 10 µl reaction mixture containing 25 ng genomic DNA, 1.5 units of Taq DNA polymerase, 0.2 mM of dNTPs, 1.5 mM MgCl₂ and 0.2 µM of each primer. The optimized PCR programme was as follows: an initial step of 3 minutes at 9 5°C, followed by 38 cycles of 30 seconds at 94°C, variable annealing temperature for 40 seconds, 30 second at 72°C and a final extension step of 7 minutes at 72°C. PCR products were separated on 2.5% agarose gel electrophoresis or 6% polyacrylamide gel electrophoresis for better resolution.




 
Data analysis
 
Clearly resolved bands were scored for presence (1) or absence (0). Molecular weights of the bands were estimated by using 100 bp DNA ladder (MBI Fermentas) as standards. The data set of cultivars and reproducible bands were used to calculate pair-wise similarity coefficient using Jaccard coefficient. The similarity matrix thus generated was subjected to cluster analysis by UPGMA and a dendrogram was generated to study the relatedness of the cultivars. All numerical taxonomic analysis was computed by NTSYS software.
 

Chemical analysis
The micronutrient in different selected mungbean genotypes content i.e. iron ranges from 36.90 to 107.1 mg/ kg and zinc from 14.2 to 53.8 mg/kg (Fig 1, Table 2). The study reported A12 (Ganga-8), A10 (Basanti), A50 (ML-776), A6 (Pusa-1501), A3 (Pusa-1431), A19 (Pusa-1542), A43 (IPM-3022), A45 (IPM-4103), A5 (MH-565), A18 (IPM-99018) and A25 (SMH-99-1A) showing more than 100 mg/kg Fe content. A5 (MH-565) had the highest Fe content (107.1 mg/kg), while A34 (IPM-3072) had the lowest Fe content (36.9 mg/kg). Zinc content also varied in selected mungbean i.e. 23.15 to 40.46 mg/kg. In genotypes ML-776 (A50), MH-565 (A5), Pusa-1501 (A6), SML-668 (A7), MH-421 (A2) and Basanti (A10) Zn content was reported more than 35mg/kg. In present study reported that genotype ML-776 (A50) had the highest Zn content (53.8 mg/kg) and M169 (A15) genotype had the lowest Zn content (14.2 mg/kg). Both micronutrient Fe and Zn content found higher in A6 (Pusa-1501), A50 (ML-776) and A10 (Basanti) genotypes.





Genome diversity in fifty-one mungbean genotypes were identified using SSR markers (Fig 2). Sixteen primers were found polymorphic out of fifty primers which generated 35 bands (Table 2). The number of band varied between 2 (MBSSRG15) and 4 (MBSSRG12), with an average of bands 2.19 per primers. In PCR amplified product size of the band ranged from 180-35 bp. By using MBSSRG14 primer a representative SSR profile was obtained (Fig 3).




 
SSR polymorphism among genotypes
 
Among the fifty one genotypes polymorphism range was 75-100% and average polymorphism was observed 97.14%. The Jaccard TMS similarity coefficient was reported to be in range from 0.45 to 0.90, in SSR profile analysis, showing the wide range of genetic diversity at molecular level. Highest value of similarity (0.94) was reported in genotypes IPM-02-03-3, IPM-05-03-6, IPM2K-08, 1PM-3072, Asha and SMH-99-1, whereas minimum similarity values of 0.11 was reported in genotypes i.e. MH421 and NDM2-215-1.
 
Cluster analysis
 
In fifty one genotypes cluster analysis showed the genetic relatedness ranged from 0.45 to 0.90, i.e., 60-89 percent. At an arbitrary cut-off 47% similarity level on a dendrogram, the mungbean genotypes were grouped into two main groups, cluster I and cluster II (Fig 4). Cluster I and cluster II contains the seventeen and thirty four accessions respectively. At similarity coefficient value of 0.51 these two clusters are sub-clustered as IA, IB, IIA and IIB. The sub-cluster IA and IB consisted of 10 and 7 genotypes respectively. Sub-group I1A and IIB contain 9 and 25 genotypes respectively. These subgroups are further subdivided into sub-groups. The genotypes A31, A32, A35, A36, A23 and A27 in IA, IIA sub-cluster are genetically related. More than 94% genetic similarity is found.
 


Principal component analysis (PCA)
 
Total variation of 78.8% showed in SSR data could be interpreted by three main component based on first, second and third eigen vectors, which explain for 51.03, 19.64 and 8.13% variations, respectively. The clustering of 51 genotypes are presented in 3-D scaling (Fig 5).


 
Majority of clustering showed the same pattern as presented in dendrogram with small variations. A2 and A29 genotypes were distantly placed in both the analyses. Polymorphic information content value range from 0.43 (MBSSRG10) to 0.70 (MBSSRG12). Reasonable diversity of mungbean genotypes may be exploited in breeding program by selecting parents for development of micronutrient improved variety.
 
A deficiency of micronutrient i.e. iron, zinc, iodine, folate and lack of vitamins A in staple food crops leads to malnutrition in human population. It is a major challenge to the agriculture scientist (Jawal-deh et al., 2019). Deficiency of micronutrients is effected 2 billion peoples all over the world, especially in developing countries (Stein, 2010; Cakmak et al., 2010). Metals deficiencies like iron and zinc are mainly affecting the population of developing countries because they are depending for their daily diet mainly on cereal crops (Kenzhebayeva et al., 2019). Micronutrient enrichment of the staple food crops by genetic manipulation is the most promising strategy to combat the malnutrition problem (Tiwari et al., 2010). Micronutrient enrichment of the crops can be done by several approaches i.e. conventional or molecular breeding (Welch and Graham, 2004), genetic engineering (Pederson et al., 2007) and agronomic biofortification (Cakmak, 2008). Due to low, non-recurrent expenditure and higher public acceptability breedind for micronutrient enrichment has been considered as best approach (Nestel et al., 2006; Monasterio et al., 2007). Genome diversity and phylogenetic relationship in plants can be identified by using different markers. Extensive observations of mature plants are required in traditional methods which are base on morphological traits (Wrigley et al., 1987). A vast amount of information and a number of databases are generated by using molecular markers in genome analysis which is used in crop breeding (Joshi et al., 1999).
 
Among fifty four accessions of mungbean Lavanya et al., (2008) reported the extent of genetic diversity by using random amplified polymorphic DNA (RAPD) profile. They reported seven primer generated 174 amplification product out of 40 primers. The average number of band was observed 24.85 bands per primer. Selvi et al., (2006) was also found a RAPD marker associated with mungbean yellow mosaiv virus (MYMV) resistance and suggested that may be useful in selection of MYMV resistant mungbean genotypes. Genetic diversity (83% polymorphism) among mungbean cultivars, wild accessions and landraces by using RAPD and ISSR marker were reported by Chattopadhyay et al., (2005). Microsatellite markers were used by Seehalak et al., (2009) to study the polymorphism by using 78 primers within 22 Thai accessions of mungbean and eight polymorphic loci detected 2 to 3 alleles per locus with an average of 2.25.

In our report the different genotypes selected using SSR markers could be potential source of germplasm for mungbean improvement. Our study will work as milestones in identification of micronutrient enrich diverse genotype, which is used to generate a micronutrient (Fe and Zn) improved mungbean variety in breeding programmes.
 
In our study different high iron genotypes i.e. Ganga-8, Basanti, ML-776, Pusa-1431, Pusa-1542, IPM-4103, MH-1565, IPM-99018 and SMH-99-1A were found to be quite different based on similarity coefficient and cluster analysis and they can be used for micronutrient improvement in breeding programmes. Based on similarity coefficient and cluster analysis, high zinc content containing genotypes such as ML-776, MH-1565, pusa-1501, SML-668, MH-421 and Basanti were also found to be quite distinct as these fall in different sub-groups. In present study ML-776 (high Fe and Zn) and Satya (low Fe and Zn) genotypes showed reasonable diversity which are from different sub group that may be exploited or selecting parents for breeding programmes. In previous study genotype ML-776 also reported as high iron and zinc and Satya as low iron and zinc variety based on RAPD and SRAP analysis (Aneja et al., 2013).
 
 

Critical analysis of the results revealed that V. radiata (L.) genotypes; Ganga-8, Basanti, ML-776, Pusa-1431, Pusa-1542, IPM-4103, MH-1565, IPM-99018 and SMH-99-1A having high iron content and ML-776, MH-1565, Pusa-1501, SML-668, MH-421 and Basanti having high zinc content can be effectively utilized in crop improvement programmes for biofortification (iron and zinc) in mungbean.