Polymorphism between Popular Rice Varieties of Uttar Pradesh, Uttarakhand, Madhya Pradesh, Bihar, West Bengal and Jharkhand through Highly Variable HvSSR Marker

Polymorphism between Popular Rice Varieties of Uttar Pradesh, Uttarakhand, Madhya Pradesh, Bihar, West Bengal and Jharkhand through Highly Variable HvSSR Marker

Arvind Kumar Singh^1* , Neera Yadav² , Saurabh Verma³

¹ICAR-Krishi Vigyan Kendra Sant Kabir Nagar Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Ayodhya, Uttar Pradesh

²National Institute for Plant Biotechnology New Delhi, India

³KVK Sultanpur 2, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, 224229 Uttar Pradesh, India

Corresponding Author Email: arvind61063@gmail.com

DOI : http://dx.doi.org/10.53709/CHE.2020.v01i01.003

Abstract

Keywords

Download this article as:

INTRODUCTION

Genetic diversity serves a way for populations to adapt to changing environments. With more variation, it is more likely that some individuals in a population will possess variations of alleles that are suited for survival in the environment. Such individuals are more likely to survive to produce offspring(s) bearing that allele. Assessment of genetic diversity is an integral part of plant breeding as exploitation of the genetic diversity helps the plant breeder to develop new varieties. Rice is the staple food for more than half of the World’s population. Its rich genetic diversity in the form of thousands of landraces and progenitor species, besides its economic significance, has aroused unending interest among scientists for several decades. India is home to many varieties of rice, which have been obtained after centuries of breeding and selection by farmers based on various desirable characteristics such as yield, aroma, grain length, cooking quality, etc. Basmati rice also originates from India. The famous rice variety is valued for its slender grains, fragrance, and elongation upon cooking. In the present study, 42 different rice varieties were collected from other Indian states and then grouped according to their geographical location. Most of these rice varieties are local rice cultivars which are often preferred by the farmers for commercial cultivation based upon their choice of grain quality, yield, fragrance, market value, and also upon their cultivation practices. Most of these varieties have long been known by their local names, and hence there are no or very few records of the genetic nature or background of these varieties. It is believed that some of these varieties may have originally belonged to some other state/region and then taken to other states due to their superior performance. Keeping these things in mind, the current study was planned to study the genetic diversity among these varieties of rice collected from different parts of India. For this, twenty-four genome-wide simple sequence repeat (SSR) molecular markers located one each on the two arms of each of the twelve rice chromosomes was used. SSR markers are tandem repeats interspersed throughout the genome and can be amplified using primers that flank these regions. Genetic diversity is commonly measured by genetic distance or genetic similarity, implying that there are differences or similarities at the genetic level.

In rice, grain quality parameters are of utmost importance as most of the rice worldwide is consumed as whole grain or rice flakes. They are also the significant determinants of price both in national as well as international markets. Yield is the most noticeable characteristic to farmers while the crop is in the field. Still, when the crop product is harvested and milled rice reaches the market, quality becomes the key determinant of its value. In the present study, phenotypic analysis was also done to complement the genotypic data and establish a healthy relationship among the varieties. Grain quality evaluation comprised of grain length, cooking quality, alkali spreading value, and aroma. A dendrogram was finally obtained using cluster analysis depicting the genetic similarity among the rice varieties/groups.

MATERIALS AND METHODS

Rice Varieties

A total of 42 distinct rice varieties were collected from several Indian states. These varieties comprise lengthy grain basmati varieties, short-grain aromatic, short-grain non-aromatic, and some medium-grained rice varieties. The seeds were collected from several states, including Uttar Pradesh, Uttarakhand, Madhya Pradesh, Bihar, West Bengal, and Jharkhand. The seeds were freshly harvested from Kharif-2010. It may be noted that the seeds were collected from these locations from farmers, but initially these varieties may have belonged to some other place and may be genetically as well as phenotypically distinct from different types that may have originated from the exact place.

Genetic Diversity Analysis

Diversity analysis was done on selected 42 rice varieties using SSR markers. In all, 24 SSR markers covering the whole of the rice genome were selected and were amplified through PCR. 

Selection of primers

SSR markers are short DNA fragments that are interspersed throughout the genome and can be amplified using primers that flank these regions. Such markers are co-dominant and are considered one of the most favored markers for several molecular biology applications, including genetic diversity studies, due to their multi-allelic nature, high reproducibility, co-dominant inheritance, abundance, and extensive genome coverage. For the current study, 24 SSR markers were selected (Table 1), from different locations in the genome. Attempts were made to choose one primer each from the short and long arms of each chromosome. The two chosen markers had a minimum physical distance of approximately 7 Mb. The annealing temperature of all the 24 primers was found to be 55°C using gradient PCR.

DNA Isolation and Quantification

Seeds of all the 42 rice varieties were germinated in a BOD incubator at 37°C for 10-15 days. DNA was then isolated from young leaf tissue using the CTAB method of Murray and Thompson (1980). All the reagents were freshly prepared in the lab. Reagents for DNA isolation                     1M Tris-Cl (pH 8.0): 121g Tris-Cl base was dissolved in 800 ml double-distilled H₂O, and the pH was adjusted to 8.0 using concentrated HCl. The final volume of the solution was made up to 1000ml and then autoclaved. 0.5 M EDTA (pH 8.0):186g di-sodium salt of EDTA was dissolved in 800 ml double-distilled H₂O, and the pH was adjusted to 8.0 using NaOH pellets. The final volume was made up to 1000ml and then autoclaved. 4M NaCl: 234 g of NaCl was dissolved in 800 ml double distilled water. The final volume was adjusted to 1000ml and then autoclaved. 10% CTAB: 100 g of CTAB was dissolved in 1000ml of distilled water. DNA extraction buffer (100 ml working solution): 1M Tris-Cl (pH 8.0) – 10.0 ml (100mM) 0.5 M EDTA (pH 8.0)                          4.0 ml  (20mM). 4M NaCl – 35.0 ml, (1.4 M) 0% CTAB 20.0 ml (2%), b-Mercaptoethanol                               200 ml   (0.2%) Double distilled water 30.8 ml, 3M Sodium Acetate pH 5.2:  RNase A (10mg/ml) 10 mg RNase A was dissolved in 1 ml Autoclaved double-distilled H₂O, vortexed well and then kept in water bath at 100^oC for 10-15 minutes, cool down and store at -20 ^o C. Phenol Chloroform Isoamyl alcohol (PCI) mixture: Ready-to-use mixture of Phenol:Chloroform:Isoamyl alcohol (25:24:1) v/v was obtained from Amresco company. Chloroform: Isoamyl alcohol(24:1)v/v           Freshly prepared by mixing 96ml Chlorophorm and 4ml Isoamyl alcohol 10. Tris-EDTA buffer (TE): Tris (10mM): EDTA (1mM) buffer was prepared in 100 ml as follows,1mM Tris-Cl, pH 8.0                             1 ml 0.5M EDTA pH 8.0,  0.2 ml Autoclaved double-distilled H₂O 98.8 ml.

DNA Isolation and Quantification-Genomic DNA were isolated from all the 42 rice varieties using the standard CTAB method. 2-4 grams of freshly harvested leaf tissue from 15-day old seedlings were ground to a fine powder in liquid nitrogen using sterile pre-cooled mortar and pestle. The ground powder was transferred to a 50 ml polypropylene centrifuge tube containing 20ml of pre-warmed DNA extraction buffer. The suspension was incubated at 65^oC for one hour with intermittent mixing. The tubes were then cooled to room temperature, and an equal volume of chloroform: isoamyl alcohol (24:1) was added and mixed by gentle inversion for about 5 minutes. The contents were then centrifuged at 12000 rpm for 10 minutes at room temperature. The aqueous phase was transferred to a fresh centrifuge tube with comprehensive bore tips to avoid DNA shearing, and 0.6 volumes of chilled isopropanol were added. Contents were mixed by gentle inversion and incubated at –20⁰C for 30 minutes for DNA precipitation. The precipitated DNA so obtained was pooled out by centrifugation at 10,000 rpm for 5 minutes.  The DNA pellet was washed twice with 70% ethanol, dried at 37^oC for one hour, and dissolved in 500 ml TE buffer (pH 8.0). DNA was quantified by gel quantification method, in which the samples along with the known concentration of l genomic DNA as standard were loaded on 0.8% agarose gel prepared in 1X TBE buffer. The gel was then run at 80 volts for about 30 minutes. The intensity of individual samples was compared with a range of known amounts of l DNA (50,100 and 150 ng/µl. Accordingly, the samples were diluted to a working concentration of 10 ng /ml for further use.

DNA Purification

             The crude DNA extracted was purified by adding RNase (10mg/ml) to the sample at the rate of 5 ml/1g of leaf tissue used for extraction (approx.1ml/100ml of DNA solution in TE buffer) and was mixed gently by inversion. The mixture was incubated in a water bath for 1hour at 37^oC with intermittent mixing. An equal volume of Phenol Chloroform Isoamyl alcohol (PCI) solution was added to the mixture, mixed gently by inverting for about 5 minutes. The mixture was centrifuged at 10000 rpm for 10 minutes, and the aqueous phase was taken out in a new eppendorf tube. Chloroform: isoamyl alcohol (24:1) extraction was performed twice to remove all impurities. To the aqueous phase, 1/10^th volume of 3M sodium acetate (pH 5.2) was added, mixed gently, and then two volumes of chilled ethanol were added to the mixture. Contents were mixed gently and incubated at –20^oCfor 2 hr, centrifuged for 5 minutes at 10000rpm. The supernatant was discarded, and the pellet was washed twice with 70% ethanol. DNA Pellet was dried properly and dissolved in 100 ml TE buffer pH 8.0.

 Amplification by Polymerase Chain Reaction (PCR)

PCR reactions were set up in 10ml volume containing 50 ŋg of template DNA, 5 pmol (13 ng) each of forward and reverse primers, 0.2 mM dNTPs, 1X PCR buffer (10mM Tris, pH 8.0, 50 mM KCl and 50 mM ammonium sulfate), 1.5 mM MgCl₂ and 0.5 units of Taq DNA polymerase (all the reagents from Puregene). The volume was made up to 10ml by autoclaved Milli Q water. The cocktail was then subjected to PCR amplification in a thermal cycler (Biometra). The PCR cycling conditions were as follows: Initial denaturation at 94⁰C for 5 min, denaturation at 94⁰C for 1 min, annealing at 55⁰C for 1 min, Primer extension at 72⁰C for 30 seconds,  repeat step 2, 34 times. Final extension at 72⁰C for 10 min Step 7: Storage at 4⁰C until electrophoresis. 2.4  Agarose Gel Electrophoresis of the PCR products       4% MetaPhor agarose (Lonza) was used to resolve the PCR amplified DNA. The gel was prepared by dissolving metaphor agarose in 1X TBE buffer. 10 ŋg ethidium bromide was added per 100 ml of gel, and the cooling gel was poured into the gel cassette with wide combs and allowed to polymerize at room temperature. The gel and tray and combs, was shifted to the electrophoresis tank, where the combs were removed carefully. PCR samples were mixed with 1X loading dye and loaded in the preformed wells. Electrophoresis was carried out in 1X TBE buffer at 120 volts for about 3-3.5 hours. Gel photographs were taken by gel documentation system (Flourochem/ AlphaImager^TM image acquisition with CCD camera, San Leandro California).

Evaluation of SSR polymorphisms

Unambiguous and reproducible bands were scored according to their molecular weight and the concentration of the amplicon. The alleles were scored as absent (0) or present (1). The digit 9 was given for any missing alleles. A binary data input file was used for analysis through NTSYSpc software version 2.02. The binary data matrix was subjected to cluster analysis. Sequential agglomerative hierarchical non-overlapping (SAHN) clustering was performed on squared Euclidean distance matrix and similarity matrix using Jacquard’s coefficient utilizing the unweighted pair group method with arithmetic averages (UPGMA) method. Principle Component Analysis (PCA) was also done to determine the similarity between and among individual varieties/groups.

Grain quality parameters

For the present study, phenotypic characterization of the 42 rice varieties was done to complement the genetic data. Some essential quality traits of rice were considered, such as grain length and width, cooking quality, alkali spreading value, and aroma.

Grain length and width

Five healthy grains were taken to measure length and width through 3X magnification on a graph sheet. The average height and breadth, along with the L/B ratio, were calculated.

Grain cooking quality

Dehulling of rough paddy was done using dehuller (model no NF 271, 0.5 HP motor at 1400 rpm). Dehulled grains were then milled mechanically and were then used for the estimation of various quality parameters.

Grain Elongation

To determine the cooking quality, the average length and breadth were calculated before and after cooking. The size and width of five milled rice grains were recorded and then cooked for 5 minutes in normal water. Readings were again taken after cooling them at room temperature. Kernel elongation ratio was calculated by dividing observed length after cooking by size before cooking. 

2.3.2.2 Alkali spreading value

Gelatinization temperatureis the temperature at which the starch granules in rice absorb water and lose their crystalline nature, an irreversible change. Rice starch usually gelatinizes between 65°C and 85°C. Rice with a gelatinization temperature at the lower end of the range often cooks to a softer texture and retrogrades less than rice with a gelatinization temperature at the upper end of the spectrum. Gelatinization is often measured by the alkali spreading method on a scale of 0-7.

The method of Little et al. (1958) was used for conducting the alkali spreading test. 10 milled rice grains from each variety were added to a glass perti plate containing 10 ml of freshly prepared 1.7% KOH solution (17 g KOH pellets dissolved in 500 ml double distilled water and the volume made up to 1000 ml) and incubated at 30°C for 23 hours. Grains were carefully separated using forceps, and the spreading value of the grains was scored by visual assessment using the method of Jennings et al. (1979).

Following three blind checks were used to improve the efficiency of recorded observations: Taraori Basmati (ASV= 4), IR64 (ASV= 5), and Pusa Basmati 1 (ASV= 7).

Aroma

Aroma is one of the most essential traits of rice, especially with respect to its commercial value. Determination for the presence of aroma was made according to the method suggested by Sood and Siddique (1978). Ten milled grains were placed in Petri dishes, and 10 ml of freshly prepared 1.7% KOH solution was added. The plates were then incubated for ten minutes at room temperature. They were then opened one, and samples were smelled and rated for aroma. Since smell is a subjective trait, a panel of five members independently checked for aroma. The samples were categorized into four classes: nonaromatic, slightly aromatic, moderately aromatic, and highly aromatic and given scores of 0, 1, 2, and 3, respectively. Following four blind checks were included to increase the accuracy of the scores: Pusa 1342 as non-aromatic (score 0), Pusa Basmati 1 as slightly aromatic (score 1), Pusa Sugandha as aromatic (score 2), and Pusa 1121 as highly aromatic (score 3).

RESULTS AND DISCUSSION

Genetic diversity analysis along with the phenotypic characterization of all the 42 rice varieties was done. Allele diversity was done using 24 distinct rice microsatellite markers dispersed throughout the rice genome. The average product range of resultant amplicons was 80-580bp. A total of 66 alleles were observed.  

Selection of primers

24 SSR molecular markers were selected, covering as much of the genome as possible. The chosen primers and their physical location are summarized in table 1. 5 picomoles of each primer were used for amplification through PCR. Gradient PCR was done to identify the annealing temperature of the selected primers, which was found to be in a uniform range of 54-56°C (figure 1). Hence all the primers were annealed at 55°C.

Fig.1. Gradient PCR profile of primer RM273 using a temperature range of 50.0 to 65.0°C [M:100 bp DNA ladder; 1:50°C; 2:50.4°C; 3:51.3°C; 4:52.5°C; 5:54.2°C; 6:56.4°C; 7:58.9°C; 8:61.7°C; 9:62.7°C; 10:63.9°C; 11:64.7°C; 12:65.0°C]

 Table  1. Details of primers

Allelic diversity among the rice varieties A total of 66 different alleles were obtained with a total of 24 different rice microsatellite markers. Amplicons obtained by normal PCR were resolved on 4% agarose gel by horizontal gel electrophoresis (fig. 3). The results obtained based on analysis of 42 rice cultivars of rice using 24 random primers are tabulated in Table 2. Highly distinct and unambiguous bands were scored as present (1) or absent (0). Any missing allele was scored as 9. All of the SSR markers displayed polymorphism, with the number of alleles ranging between 2 and 4. The olymorphism Information Content (PIC) value was calculated for all the SSR markers using the formula PIC = 1-P_i², where Piis the allelic frequency of the i^th allele. The PIC values ranged from a minimum of 0.04 (RM6818) to a maximum of 0.66 (RM251). The product size range was 80-580bp, with RM6271 giving smallest product size of 80bp and RM24846 giving largest product size of 580bp.

Fig. 2: Diversity analysis of 42 rice varieties using RM432 molecular marker. [M:100 bp DNA ladder; 1-42: Farmers’ rice varieties. The PCR products were analyzed on 4% agarose gel.     

Statistical analysis of the scored raw data was done through NTSysPC version 2.02h software. Accordingly, a binary data matrix input file was prepared. Using this matrix, a similarity matrix was generated using Jacquard’s coefficient. This similarity matrix was finally clustered using Sequential agglomerative hierarchical non-overlapping (SAHN) clustering utilizing the unweighted pair group method with arithmetic averages (UPGMA) method, generating a dendrogram wherein all the 42 rice varieties were clustered into groups.

MTU, a local landrace from Andhra Pradesh, and Sapaluchai, a famous local cultivar from Madhya Pradesh, displayed the highest level of genetic similarity (79%), followed by Jeera Shankar and Suranit (77%).  A minimum similarity of 31 % was observed among the varieties and two major clusters (I and II) were formed, a small cluster containing Gonor-1 and BPT-52 and a large cluster containing the other 40 types. Cluster II was further subdivided into two unequal clusters at a similarity coefficient of around 0.34 (IIA and IIB). IIA contained three varieties (names) and IIB contained the remaining varieties. IIA formed the largest subgroup, which divides into two distinct subgroups (IIAi and IIBii) at a similarity coefficient of 0.37, one containing 9 varieties and the other containing 27 varieties (fig 4).

Table 2: Allelic distribution and PIC values of 24 rice microsatellite (SSR) markers across 42 farmers’ rice varieties

Fig. 4: Dendrogram depicting the genetic similarity among 42 rice varieties. The dendrogram was constructed using NTSysPC version 2,02 software.

Grain Quality Evaluation

            Grain quality holds a significant role for rice as they are the primary determinant of its price in the market. A general profile was generated for all the 42 rice varieties to characterize them into various groups according to their grain size (length and breadth), their aroma and alkali content, as well as their cooking qualities (like elongation ratio). Among all the varieties, the most extended grain length was recorded as 11.7 mm for SS20 variety, while the shortest distance was recorded for as 6.9 mm for Sulendas (fig. 5). Kakeria-2 showed the highest elongation ratio of 1.61 (fig. 6), while SS20 showed the lowest elongation ratio of 1.05. A few highly aromatic varieties were also identified, such as Komal, BPT-52, Tilakchandan, and Basmati-334, having a score of 3. A high level of phenotypic diversity was observed among the 42 rice varieties. The range of recorded length was 6.9-11.7mm. A diverse L/B ratio was also observed, ranging from 2.29 (Suranit) to 5.57 (SS20). Sensory analysis of aroma also yielded diverse results, covering the entire range of 0-3. The ASV score ranged from 1-7, with most of the varieties giving a score of 2 (fig. 7).

   Table 3. General grain characteristics of the 42 rice varieties used for diversity analysis

*These are average of five observations                

Table 4: Cooking quality of the 42 rice varieties used for diversity analysis

*These are average of 5 observations; **Non-availability of seed; #Elongation ratio= length after      cooking/length before cooking ## Alkali   Spreading Value

Fig. 5: Seeds of Kakeria-2 showed highest elongation ratio of 1.61

Fig.6:  The shortest and the longest grain in the present study were found to be Sulendas (left) and    SS20(right) respectively

Fig.7: Analysis of Alkali Spreading Value (ASV) of all the 42 rice varieties using the method of Jennings et al (year). Scores were given as follows: 1-No change at all; 2- swelling of grain; 3- cloudy appearance; 4-disintegration of grains starts; 5-disintegration nearly complete; 6- grains start dissolving; 7-grains completely dissolve.

ACKNOWLEDGEMENT

Organizations viz., Krishi Vigyan Kendra Santkabir Nagar Uttar Pradesh, Indian Council of Agricultural Research(ICAR) New Delhi, National Institute for Plant Biotechnology (NIPB) New Delhi, Krishi Vigyan Kendra Santkabir Nagar A.N.D.U.A.T Kumarganj, Ayodhya, UP under its have supported the researches on Rice Variety. Due acknowledgement is made for them as without their financial support the technologies for rice variety would have not been developed.

References

1. Babaei A. (2009): Genetic diversity analysis of Nemat rice mutant (Oryza sativa L) via RAPD marker.
2. Giovannoni, J.J., Wing, R.A., Ganal, M.W. and Tanksley, S. (1991). Isolation of molecular markers from specific chromosomal intervals using DNA pools from existing mapping populations. Nucleic Acids Res. 19:6553–6558.
3. IRGSP. Nature, 436 (2005) 793.
4. Jennings P.R., Coffman W.R. and Kauffman H.E. (1979): Grain Quality in rice improvement. Intl. Rice Res. Inst., Los Banos, Laguna, Philippines. Pp 101-120. 
5. Kibria K et al. (2009): Molecular marker based genetic diversity analysis in aromatic rice genotypes using SSR and RAPD markers. Int. J. Sustain. Crop Prod. 4(1):23-34.
6. Little R.R., Hilder G.B. and Dawson E.H. (1958): Differential effect of alkali on 25 varieties of milled white rice. Cereal Chem. 35: 111-126.
7. McCouch, S. R. et al (1997): Microsatellite marker development, mapping and application in rice genetics and breeding. Plant Mol.
   1. Biol., 35, 89–99.
8. Murray M.G. and Thompson W.F. (1980): Rapid isolation of high molecular weight plant DNA. Nucleic Acid Res. 8: 4321-4325.
9. Nagaraju J et al. (2001): Genetic analysis of traditional and evolved Basmati and non-Basmati rice varieties by using fluorescence-based ISSR-PCR and SSR markers.
10. Nei, M. (1972). Genetic distance between populations. Am. Nat. 106: 283-292
11. Sivaranjani A.K.P et al. (2010): Assessment of genetic diversity among basmati and non-basmati aromatic rice of India using SSR markers. Current Science, Vol. 99, No. 2.
12. Sood B.C. and Sidiq E.A. (1978): A rapid technique for scent determination in rice. Indian J. Genet, Plant Breed. 38: 268-271.
13. Stegink, S.J. and Vaughn, K.C. 1988. Norflurazon (SAN-9789) reduces abscisic acid levels in cotton seedlings: a glandless isoline is more sensitive than its glanded counterpart. Pestic Biochem Physiol, 31: 269-275.
14. Szabados, L. and Savouré, A. 2010. Proline: a multifunctional amino acid. Trends Plant Sci., 15(2):89-97.
15. Tian, X. and Darmency, H. 2006. Rapid bidirectional allele-specific PCR identification for atrazine resistance in higher plants. Pest Manag. Sci., 62:531–536.
16. Tong, L. 2013. Structure and function of biotin‐dependent carboxylases. Cell Mol. Life Sci., 70: 863‐891.
17. 17.Trebst, A. 2007. Inhibitors in the functional dissection of the photosynthetic electron transport system, Photosynth. Res., 92: 217–224.
18. 18.Triantaphylidès, C. and Havaux, M. 2009. Singlet oxygen in plants: production, Detoxification signal. Trends Plant Sci., 14: 219–228.