In the present investigation of genetic diversity related to salinity tolerance in rice at the early seedling stage, ten salt stress responsive candidate genes were employed to screen a collection of eighteen rice varieties with two tolerant (Pokkali, CSR-36) and two sensitive (IR-29, IR-64) as checks. The microsatellite repeat motifs found in the six salt stress responsive candidate genes (OsHKT1;5, SNAC1, CDMK, CCC, SHMT1, SHMT2) revealed a comparatively higher alleles number per locus, ranged from 3 (OsHKT1;5) to 12 (SHMT1). The remaining four salt stress responsive candidate genes (OsHKT1;1, OsHKT1;3, OsHKT2;3, OsHKT2;4) produced a significantly smaller number of alleles because they lacked microsatellite. The polymorphism percentage was found to be lowest in the cases of OsHKT1;5 and CDMK (00.00%) and highest in the case of SHMT1 (75.00%). Amplification profiles dependent similarity indices, together with hierarchical classification and spatial distribution patterns, clearly distinguished 18 varieties according to their tolerance to salt stress. The results of the hierarchical classification of the varieties were fully supported by principal coordinate analysis. Microsatellite including six salt stress responsive candidate gene (OsHKT1;5, SNAC1, CDMK, CCC, SHMT1, SHMT2) as well as microsatellite lacking four salt stress responsive candidate gene (OsHKT1;1, OsHKT1;3, OsHKT2;3, OsHKT2;4) specific markers based genetic profiling facilitated clear discrimination between salt stress sensitive and tolerant varieties, validating their utility for differentiation. The identified salt tolerant varieties, along with validated markers, can be influential in developing salt tolerant rice varieties through targeted breeding programs.
Research Article
Analysis of genetic diversity and validation of salt stress responsive candidate genes at early seedling stage of rice varieties for salt tolerance
https://doi.org/10.21203/rs.3.rs-4117922/v1
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Breeding program
Candidate gene
Rice
Salinity tolerance
Rice is a considerable balanced food and backbone for the nutrition of rural communities and their food security. In contrast to maize and wheat, it has the highest net use of protein ratio (Kumar et al. 2015). According to Lu et al. (1980) and Kumari et al. (2018), in comparison to other cereals, it produces the most calories and carbohydrates per hectare. Rice can be used for a wide range of purposes, from religious ceremonies to as food in cereals, snacks, brewed drinks, flour, and rice bran oil. Due to its diploid status, comparatively small genome size, abundant genetic polymorphism, well conserved genetically diverse material in large amount, and being accessible of compatible wild species; it has been viewed as a model plant species for genetic and genomic studies in addition to its economic significance (Priyadarshini et al. 2018).
Salt, drought, water logging, as well as heat are potent environmental abiotic stresses that restrict the yield of many commercial crops. Globally, these stresses lead to substantial economic losses due to circumventions (Kumari et al. 2016b). On the contrary, salinity is a considerable abiotic stress for affecting crops (Kumari et al. 2018). It is a state of soil where there is an abundance of soluble salts. When ECe (electrical conductivity) is 4 dS/m or higher about 40 mM NaCl and produces an osmotic pressure around 0.2 MPa, then subsequently the soil is presumed as saline (Munns and Tester, 2008; Kranto et al. 2016). Rock weathering, extensive saline irrigation, seawater intrusion onto freshwater areas, inadequate water management, elevated evaporation, and using chemical fertilizers on a regular basis are the salt stress contributing factors (Kumari et al. 2016a; Kumari et al. 2018). In many countries where rice is grown, salinity is the biggest problem (Senadhira 1987). In South as well as Southeast Asia, 54 million hectares of around 400 million hectares worldwide are impacted by salinity (Akbar & Ponnamperuma 1982). According to Mandal et al. (2010), the impacted area by salt in India is roughly 6.7 million hectares, of which 1.2 million hectares are coastal saline. According to estimates (Yamaguchi and Blumwald 2005; Kumari et al. 2016a), salinity impacts 20% of the world's irrigated land. Furthermore, it has been estimated that rising salinization in farming areas will cause a 30% decrease in arable land during the next 25 years and a 50% decrease by the year 2050 (Wang et al. 2003; Kumari et al. 2016a). Over-salting soil causes issues such ion imbalance, mineral shortage, osmotic stress, ion toxicity as well as oxidative stress by interfering with a number of biochemical as well as physiological processes (Munns and Tester 2008; Kumari et al. 2016b). The great majority of crops are impeded in their growth and development by these conditions, which ultimately connect with various cellular components like pigments, lipids, protein, and DNA in plants (Zhu 2002; Kumari et al. 2016a). Numerous biochemical and molecular defense systems have evolved by plants to shield them from the damaging salt stress effects. The creation of suitable organic solutes, ion homeostasis, and the stimulation of antioxidant enzymes are the three primary biochemical mechanisms. A workable solution to this issue would be the creation of salt-tolerant plants which can withstand elevated levels of saline in the soil (Yamaguchi and Blumwald 2005).
In rice crop to increase salt tolerance, it is critical to detect adequate variance and develop dependable screening methods that can distinguish salt-tolerant cultivars (Kranto et al. 2016). Hence, the need for developing salt tolerant rice requires proper molecular studies. Thus, molecular technique offers great potential to boost crops through genetic evaluation of salinity tolerance of rice varieties. An important method for characterizing salt-tolerant varieties is the application of molecular markers. Even though salt tolerance in rice is a complicated and quantitative trait (Yeo and Flowers 1986; Ganie et al. 2016), molecular markers have been a great tool for QTLs mapping associated with abiotic tolerance especially salt tolerance in rice (Lang et al. 2001; Singh et al. 2007). On chromosomal number one, a substantial salinity-responsive QTL known as Saltol is found through extensive research. The markers linked to this QTL are then identified and widely employed in the quest for the Saltol QTL as genetic resources in the rice (Gregorio et al. 1997; Islam et al. 2012). According to reports (Gregorio et al. 1997; Mohammadi-Nejad et al. 2008; Ganie et al. 2016), Saltol QTL plays a major role in early seedling salinity stress tolerance by maintaining the Na+/K + equilibrium. Furthermore, distinct gene families control the complicated trait of salt tolerance, which has various tolerance mechanisms. The primary gene families linked to salt tolerance include transcription factors, metal ion transporters as well as genes involved in intermediate metabolic pathways for the management of oxidative damage. The cation chloride co-transporters and the HKT gene family are significant transporter genes that regulate the ionic balance of the cell. Thus, the production of improved varieties of rice can benefit from the use of the tolerance alleles found in landraces and wild cousins. Discovering novel tolerant alleles and allelic variations that can withstand the field's current and likely future salt levels is critically important. In light of this, we focused on evaluating the genetic diversity of rice varieties and validating candidate genes that respond to salt stress during the early seedling stage of rice varieties for salt tolerance. Our efforts are expected to have a significant influence on the creation of new, highly productive rice varieties that are salt tolerant.
Experimental materials
The primary experimental materials of the present study consisted of eighteen rice varieties (listed in Table 1) along with two tolerant (Pokkali, CSR-36) and two sensitive (IR-29, IR-64) as checks (Kumari et al. 2018) with different genetic backgrounds. These varieties were screened to determine their salinity tolerance level in the early seedling stage. The experiment was carried out at Molecular Biology Laboratory, Deptt. of Agril. Biotech. & Mol. Biology, Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar.
|
Sl. No. |
Rice varieties |
Source |
|---|---|---|
|
i. |
Pokkali as check |
UAS, Dharwad |
|
ii. |
CSR-36 as check |
CSSRI, Karnal |
|
iii. |
IR-29 as check |
IRRI, Philippines |
|
iv. |
IR-64 as check |
IRRI, Philippines |
|
v. |
Ratnagiri-4 |
Landrace, Ratnagiri |
|
vi. |
Rajendra Dhan-102 |
RAU, Pusa |
|
vii. |
Mandakini |
OUAT, Bhubaneswar |
|
viii. |
Sahbhagi Dhan |
CRURRS, Hazaribag |
|
ix. |
Pusa Sugandh-2 |
IARI, New Delhi |
|
x. |
Daya |
OUAT, Bhubaneswar |
|
xi. |
Sarsa |
Landrace, Bihar |
|
xii. |
Saraswathi |
RRS, Chinsurah |
|
xiii. |
Golaka |
Landrace, Bihar |
|
xiv. |
Shatabdi |
CRRI, Cuttack |
|
xv. |
Vaisak |
Landrace, Bihar |
|
xvi. |
MTU-7029 |
ANGRAU, Hydrabad |
|
xvii. |
Jyotrirmayee |
Titabar, Assam |
|
xviii. |
Badami |
OUAT, Bhubaneswar |
Genotypic assessment
Using 10 salt stress responsive candidate genes, eighteen varieties of rice namely, Pokali, CSR-36, IR-29, IR-64, Mandakini, Pusa Sugandh-2, Saraswathi, Ratnagiri-4, Rajendra Dhan-102, Sahbhagi Dhan, Badami, Sarsa, Jyotrirmayee, MTU-7029, Golaka, Daya, Vaisak, and Shatabdi were assessed for their salinity tolerance.
Primer design
The nucleotide sequences of candidate genes responsive to salt stress were obtained from the NCBI database (http://www.ncbi.nlm.nih.gov). Subsequently, PCR primers were designed using Primar3 software (www.genome.wi.mit.edu) to amplify the entire genes, including the promoter, coding, and non-coding regions. Table 2 presented details about the 10 salt stress-responsive candidate genes.
|
Sl. No. |
Gene ID (Locus) |
Gene |
Putative function |
GC% |
Primer sequence (5’-3’) |
Annealing temp. (0C) |
|---|---|---|---|---|---|---|
|
1. |
LOC_Os04g51820 |
OsHKT1;1 |
Na+ transporter |
55.00 |
F: GGGAGACTGGCTACAAACA |
56 |
|
45.00 |
R: AATGAAGCATCGGAAGGA |
|||||
|
2. |
LOC_Os02g07830 |
OsHKT1;3 |
Na+ transporter |
50.00 |
F: GGATCACTCACCTTGCATT |
56 |
|
45.00 |
R: TGATCCCAATGCCTTTTCTC |
|||||
|
3. |
LOC_Os01g20160 |
OsHKT1;5 |
Na+ transporter |
50.00 |
F: TCTTCATCGTCGTCATCTGC |
58 |
|
50.00 |
R: ACTTCTTGAGCCTGCCGTA |
|||||
|
4. |
LOC_Os01g34850 |
OsHKT2;3 |
Na+ transporter |
45.00 |
F: TTTGTGGTGCTGGCATACAT |
56 |
|
50.00 |
R: CAGGAGGCCATTGTTTGAG |
|||||
|
5. |
LOC_Os06g48800 |
OsHKT2;4 |
Na+ transporter |
45.00 |
F: CTTGGTTTTGTTGCCTTGGT |
56 |
|
50.00 |
R: CCAAGAAAGGAAAGGAAC |
|||||
|
6. |
LOC_Os03g60080 |
SNAC1 |
NAC domain containing protein 67, putative |
50.00 |
F: GGGATCGCAAGTATCCTAA |
56 |
|
55.00 |
R: GCATCTTCTCCCACTCGTTC |
|||||
|
7. |
LOC_Os07g06740 |
CDMK |
Calmodulin dependent protein kinases |
60.00 |
F: AGGACTCGCCACTCAAGAC |
56 |
|
45.00 |
R: CAGGGATCAGATGAAAAA |
|||||
|
8. |
LOC_Os01g19860 |
CCC |
Expressed protein |
66.67 |
F: GTACCGCACGGTGGAGTC |
60 |
|
45.00 |
R: TTGAATCGCCTTGAGATCC |
|||||
|
9. |
LOC_Os01g65410 |
SHMT1 |
Serine hydroxylmethyl transferase, mitochondrial precursor, putative |
45.00 |
F:AAGCTTTGGCATCAGCCTTA |
58 |
|
50.00 |
R: TGCGATCACCTCAAAGTCA |
|||||
|
10. |
LOC_Os03g52840 |
SHMT2 |
Serine hydroxylmethyl transferase, mitochondrial precursor, putative |
45.00 |
F: GAAAGCACTGGCTTGATTG |
58 |
|
55.00 |
R: CCACGGAGTGACTTGTGAG |
Genomic DNA isolation
Total genomic DNA was extracted from 15-day-old seedlings of young rice leaves, employing a modified version of Doyle and Doyle's (1990) CTAB protocol. The isolated DNA samples were preserved in TE buffer at -20°C. Quality of the extracted DNA was assessed using agarose gel electrophoresis (0.8%) and absorbance was measured at 260 and 280 nm using a Varian Cary 50 Spectrophotometer. The quantity of the extracted DNA in the samples was determined using a biospectrometer (Eppendorf).
Genomic DNA amplification
To specifically amplify genomic DNA responsive to salt stress, a set of ten potential genes was utilized. For the polymerase chain reaction (PCR) based amplification, a 15 µl reaction mixture was prepared, consisting of 5X PCR buffer, 1 mM deoxyribonucleotide triphosphates (dNTPs), 10 mM magnesium chloride, 5 µM each of forward and reverse primers, 1 unit Taq DNA polymerase (Fermentas), and 20 ng template DNA. A Thermocycler (Eppendorf) was programmed for an initial cycle of 4 minutes at 94°C to facilitate the first step of strand separation during PCR amplification. Subsequently, 35 cycles were performed, including one minute of denaturation at 94°C, one minute of primer annealing at a temperature depending on the primer pairs employed (56°C − 60°C), and two minutes of primer extension at 72°C. After the final extension cycle, comprising 10 minutes at 72°C, the amplification products were stored at 4°C until separation and resolution by electrophoresis.
Electrophoresis was conducted using 2% Top vision Agarose gels (Fermentas), and the results were separated and resolved. The gels were stained with an ethidium bromide (EtBr) solution, and a 50 bp DNA ladder (Gene ruler, Fermentas) served as a size marker for determining the molecular size of the amplified products. Electrophoresis was carried out in 0.5X TBE buffer for 90 minutes at 100V, and the gels were documented using a gel documentation system (Alpha Innotech) post-electrophoresis. Rf values for each band were calculated by assuming the well's location as the initial position (Rf = 0) and the migrating dye's position as the initial position (Rf = 1) in a frame of reference.
Polymorphism Analysis and amplified product scoring
Eighteen different rice varieties were found to differ not only in the presence or lack of bands, but also in the position of 10 genomic regions based on putative genes that respond to salt stress. By calculating the PP (Polymorphism Percent) = [un/tn] x 100, here, un as well as tn represent the unique alleles number and total number of alleles detected among the varieties, respectively, then the efficiency of primer pairs in relation to their ability to yield unique and genotype specific allele(s) was evaluated (Kumar et al., 2018). The Polymorphic information content i.e. PIC (Anderson et al., 1993) of the primer pairs was applied for calculating the allelic diversity of the markers. The formula is 
here, k is the total alleles number determined for a marker, Pij is the frequency of the jth allele for the ith marker, and summation is applied over k alleles. In order to perform pairwise comparisons, similarity coefficients (Dice, 1945) were calculated using the percentages of shared bands generated by the primers. These were expressed as similarity coefficient (SC) = 2a/(2a + b + c), here a, b, as well as c stand for the bands number that were shared between the Jth and Kth genotypes, the bands number that were present in the Jth genotype but absent in the Kth genotype, and the bands number that were absent in the Jth genotype but present in, respectively.
The data of similarity coefficients were applied to conduct a sequential agglomerative hierarchical nested (SAHN) cluster assessment, and the dendrogram based on these similarity indices was produced using an unweighted pair-group technique with UPGMA (an arithmetic mean). NTSYS-pc software was used to assist the analysis (Rohlf 1997). Selecting the clusters at the right phenon level allowed researchers to investigate the trend of molecular differentiation and divergence. The genetic profiles of rice varieties were subjected to principal coordinate analysis of the salt stress sensitive candidate genes, and the outcomes of cluster analysis were compared with the two-dimensional geographical distribution pattern.
Genetic diversity assessment of salt stress responsive candidate genes at early seedling stage of rice varieties for salt tolerance
The rice varieties utilized during molecular profiling were characterized using a panel of 10 primers specific to salt stress responsive candidate genes. The amplification patterns of candidate genes loci obtained with different primer pairs (Fig. 1) yielded salt stress responsive candidate genes specific molecular profiles of 18 rice varieties.
Allelic diversity and allelic frequency among salt stress responsive candidate genes
The polymorphism in respect of 10 salt stress responsive candidate genes based genomic regions was recorded among 18 rice varieties on the basis of variation in bands position, additionally presence or absence of bands. It was found that the four primer pairs pertaining to the potential genes that respond to salt stress lacking microsatellite revealed genetic polymorphism concerning the targeted genomic regions in the form of absence or presence of amplified products (Table 3). Furthermore, polymorphisms exhibited as variations in amplified product length were produced by the remaining six primer pairs that were specific to the candidate genes having microsatellite. These polymorphisms were inferred based on the distinct positions that amplicons occupied when being resolved using agarose gel electrophoresis.
The 6 salt stress responsive candidate genes that had microsatellite repeat motifs, namely, OsHKT1;5, SNAC1, CDMK, CCC, SHMT1and SHMT2, recorded relatively greater number of alleles. Remaining four salt stress responsive candidate genes, namely, OsHKT1;1, OsHKT1;3, OsHKT2;3 and OsHKT2;4, which did not contain microsatellite, yielded comparatively lesser number of alleles (Table 3). Among microsatellite containing six salt stress responsive candidate genes, alleles number per locus ranged from 3 (OsHKT1;5) to 12 (SHMT1). While classifying the total number of identified alleles amongst the eighteen rice varieties into the groups of unique alleles as well as shared alleles (Table 4), it was recognized that 26 unique as well as 24 shared allelic variants were produced as amplified products using microsatellite containing six salt stress responsive candidate genes specific primers.
| Sl. No. | Locus | Name of gene | Repeat motif | No. of allele* |
|---|---|---|---|---|
| 1. | LOC_Os04g51820 | OsHKT1;1 | X | 01 |
| 2. | LOC_Os02g07830 | OsHKT1;3 | X | 01 |
| 3. | LOC_ Os01g20160 | OsHKT1;5 | (ACC)4, (CGT)4, (GAC)4, (CTC)4, (GCA)6, (CATG)3 | 03 |
| 4. | LOC_Os01g34850 | OsHKT2;3 | X | 01 |
| 5. | LOC_Os06g48800 | OsHKT2;4 | X | 01 |
| 6. | LOC_ Os03g60080 | SNAC1 | (GCC)6, (TGTA)3 | 07 |
| 7. | LOC_ Os07g06740 | CDMK | (CT)9, (CT)11, (CGC)4, (GCC)4, (ATA)4 | 06 |
| 8. | LOC_ Os01g19860 | CCC | (GAAT)3 | 11 |
| 9. | LOC_ Os01g65410 | SHMT1 | (TGT)4, (GGT)7, (GGA)5, (CGGAGG)3 | 12 |
| 10. | LOC_ Os03g52840 | SHMT2 | (CT)11 | 11 |
| Gene ID (Locus) | Gene | Range of amplicon size in bp | difference of amplicon size in bp | Allele numbers | Unique allele numbers | Shared allele numbers | PP | PIC |
|---|---|---|---|---|---|---|---|---|
| LOC_Os01g20160 | OsHKT1;5 | 906–937 | 31 | 3 | 0 | 3 | 00.00 | 0.515 |
| LOC_Os03g60080 | SNAC1 | 1600–1933 | 333 | 7 | 4 | 3 | 57.00 | 0.774 |
| LOC_Os07g06740 | CDMK | 437–481 | 44 | 6 | 0 | 6 | 00.00 | 0.771 |
| LOC_Os01g19860 | CCC | 781–968 | 187 | 11 | 7 | 4 | 63.00 | 0.876 |
| LOC_Os01g65410 | SHMT1 | 494–655 | 161 | 12 | 9 | 3 | 75.00 | 0.882 |
| LOC_Os03g52840 | SHMT2 | 558–658 | 100 | 11 | 6 | 5 | 54.54 | 0.882 |
Amplicon size range and size difference
The overall amplified products size produced by using microsatellite containing six salt stress responsive candidate genes specific primers (Table 4) ranged from 437 bps (CDMK) to 1933 bps (SNACK1). The difference in molecular size between the smallest and the biggest allele for a given candidate gene locus varied from 31 bps (OsHKT1;5) to 333 bps (SNACK1).
Polymorphism per cent and information content
It was observed that the polymorphism percentage, expressed as the percentage of unique allele, was highest in the case of SHMT1 (75.00%) and lowest in the cases of OsHKT1;5, CDMK, and OsHKT1;5. Using salt stress responsive six candidate gene specific primer pairs (OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 and SHMT2), the degree of polymorphism among the rice varieties in this study was further evaluated by comparing the PIC (polymorphism information content) for each candidate gene based primer pairs (Table 4). Relevant values that showed allele frequency and diversity across different rice varieties ranged from 0.882 (SHMT1 and SHMT2) to 0.515 (OsHKT1;5).
Similarity coefficients between rice varieties
Similarity coefficients of eighteen rice varieties were calculated based on the basis of absence and presence of the amplified products produced by using ten salt stress sensitive candidate genes specific primers. The highest magnitude of similarity coefficient was discovered for pairwise pairings of rice varieties between CSR-36 and Pokkali (0.736) and IR-64 and IR-29 (0.736). The similarity coefficients between Sahbhagi Dhan and Mandakini (0.631), Sahbhagi Dhan and Pusa Sugandh-2 (0.600), and Shatabdi and Daya (0.600) were noticeably higher after that. It was intriguing to note that 33 pairwise combinations of 18 different rice varieties had similarity coefficients whose magnitudes were equal to zero (Table 5). Consequently, there was no commonality found in the molecular profiles of the salt stress sensitive candidate genes among the 33 rice varieties that were combined.
| Pokkali | CSR-36 | IR-29 | IR-64 | Mandakini | Pusa Sugandh-2 | Saraswathi | Ratnagiri-4 | R.Dhan-102 | Sahbhagi | Badami | Sarsa | Jyotrirmayee | MTU-7029 | Golaka | Daya | Vaisak | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CSR-36 | 0.736 | ||||||||||||||||
| IR-29 | 0.000 | 0.000 | |||||||||||||||
| IR-64 | 0.000 | 0.000 | 0.736 | ||||||||||||||
| Mandakini | 0.333 | 0.315 | 0.000 | 0.000 | |||||||||||||
| P.Sugandh-2 | 0.315 | 0.400 | 0.100 | 0.000 | 0.0526 | ||||||||||||
| Saraswathi | 0.315 | 0.400 | 0.100 | 0.000 | 0.421 | 0.300 | |||||||||||
| Ratnagiri-4 | 0.352 | 0.333 | 0.111 | 0.117 | 0.470 | 0.444 | 0.333 | ||||||||||
| R.Dhan-102 | 0.333 | 0.315 | 0.000 | 0.222 | 0.444 | 0.421 | 0.315 | 0.588 | |||||||||
| Sahbhagi | 0.421 | 0.400 | 0.000 | 0.000 | 0.631 | 0.600 | 0.500 | 0.555 | 0.526 | ||||||||
| Badami | 0.210 | 0.300 | 0.000 | 0.000 | 0.315 | 0.300 | 0.200 | 0.222 | 0.210 | 0.400 | |||||||
| Sarsa | 0.105 | 0.200 | 0.000 | 0.000 | 0.210 | 0.100 | 0.300 | 0.111 | 0.105 | 0.200 | 0.300 | ||||||
| Jyotrirmayee | 0.210 | 0.200 | 0.100 | 0.000 | 0.210 | 0.200 | 0.200 | 0.111 | 0.105 | 0.200 | 0.200 | 0.100 | |||||
| MTU-7029 | 0.105 | 0.000 | 0.100 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.100 | 0.100 | 0.400 | ||||
| Golaka | 0.100 | 0.095 | 0.285 | 0.300 | 0.100 | 0.095 | 0.000 | 0.105 | 0.200 | 0.095 | 0.000 | 0.000 | 0.000 | 0.190 | |||
| Daya | 0.105 | 0.100 | 0.300 | 0.315 | 0.105 | 0.200 | 0.000 | 0.111 | 0.210 | 0.100 | 0.000 | 0.000 | 0.200 | 0.100 | 0.381 | ||
| Vaisak | 0.315 | 0.400 | 0.100 | 0.000 | 0.315 | 0.300 | 0.400 | 0.333 | 0.315 | 0.400 | 0.400 | 0.200 | 0.500 | 0.200 | 0.000 | 0.200 | |
| Shatabdi | 0.105 | 0.200 | 0.300 | 0.315 | 0.210 | 0.200 | 0.100 | 0.222 | 0.315 | 0.200 | 0.100 | 0.100 | 0.200 | 0.200 | 0.476 | 0.600 | 0.200 |
Clustering of rice varieties based on salt stress responsive candidate genes
Based on the dendrogram, the rice varieties were categorized broadly into three groups (Fig. 2), which were then further broken down into clusters, sub-clusters, and sub-sub clusters with increasing phenon levels. The first multi-genotypic group had eleven rice varieties, namely, Pokkali, CSR-36, Mandakini, Pusa Sugandh-2, Sahbhagi, Vaisak, Saraswathi, Ratnagiri-4, Rajendra Dhan-102, Badami and Sarsa, whereas the second di-genotypic group had two rice varieties namely, IR-29 and IR-64. The third multi-genotypic group had five rice varieties namely, Jyotrirmayee, MTU-7029, Golaka, Daya and Shatabdi. Varietal groups created by this approach were not strictly in accordance with the salt stress response reaction of the varieties and susceptible rice varieties were not observed to be perfectly discriminated from tolerant rice varieties due to intermixing of susceptible and tolerant varieties in the same varietal group.
Clustering of rice varieties by utilization of binary data matrix based on amplification patterns generated by a panel of microsatellite containing six candidate genes specific primer pairs resulted in perfectly unambiguous discrimination of susceptible and tolerant entries into different varietal groups. Analysis of genetic inter-relationship by utilization of binary data matrix based on amplification patterns generated by a panel of microsatellite lacking four candidate genes specific primer pairs also resulted in perfectly unambiguous discrimination of susceptible and tolerant entries into different varietal groups. In both the cases, susceptible rice varieties were observed to be perfectly discriminated from salt tolerant varieties of rice without any intermixing of tolerant and susceptible rice varieties (Figs. 3 & 5) in the same varietal group. The findings from principal coordinate analysis fully justified the conclusions drawn from the hierarchical categorization based on similarity coefficients (Figs. 4 & 6). and two dimensional spatial distribution patterns of the entries basically yielded three varietal groups with perfect discrimination of susceptible and tolerant varieties into distinct groups without any type of intermixing of varieties.
Validation of salt stress responsive candidate genes
Genetic profiling using microsatellite-based candidate gene namely OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 as well as SHMT2 specific markers enabled clear differentiation of salt stress-responsive and tolerant varieties and hence, validating their usefulness for differentiating and separating salt stress-tolerant and sensitive varieties (Figs. 3 & 4). Similarly, to distinguish and differentiate between salt stress-tolerant and -sensitive types, a panel of four microsatellite missing salt stress associated candidate genes (OsHKT1;1, OsHKT1;3, OsHKT2;3, OsHKT2;4) specific primers was validated (Figs. 5 & 6).
According to Ma et al. (2007), rice is a staple food and a source of income for over three billion people globally. After drought, salinity is the most common soil issue in nations that cultivate rice, and it is seen as a major barrier to raising rice production globally (Gregorio et al. 1997). Furthermore, the necessity for allelic diversity of stress tolerance genes is further highlighted by the changing global climate and growing concerns about food security. These genes may be employed in upcoming breeding programs. Additionally, an essential source of tolerance to salt stress may be provided by the allelic variation (Singh et al. 2016). Ten salt stress sensitive candidate genes were used to reveal 18 varieties of rice for salt tolerance in order to get allelic diversity. Six salt stress related candidate genes namely OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 as well as SHMT2 with microsatellite repeat motifs recorded a comparatively higher number of alleles in the current investigation. Table 3 displays the significantly smaller number of alleles obtained from the other four salt stress responsive candidate genes, which are OsHKT1;1, OsHKT1;3, OsHKT2;3 as well as OsHKT2;4. These genes do not contain microsatellite. As the total number of alleles found in the eighteen rice varieties was categorized into unique and shared alleles (Table 4), it became apparent that 26 unique and 24 shared allelic variants were produced as amplified products through the use of microsatellite containing six primer pairs specific to salt stress related candidate genes. These six salt stress responsive candidate genes produced allelic variants in a higher number as a result of amplification of the repeats surrounding them, which varied the length of the amplified products based on microsatellite repeats (Kumari et al. 2019). The polymorphism per cent (PP) indicated in the form of percentage of unique allele, was found to be highest in the case of SHMT1 (75.00%) and lowest in the cases of OsHKT1;5, CDMK, and OsHKT1;5. Among the 18 varieties of rice, the PIC values showing allele diversity and frequency ranged from 0.515 (OsHKT1;5) to 0.882 (SHMT1 and SHMT2). Allelic variations were found by Singh et al. (2016) among the candidate genes (OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 as well as SHMT2) linked to salt tolerance. Allele variations that disclose primers based on candidate genes may be utilized to develop markers and may help increase salt tolerance in specific varietals. Mishra et al. (2016) achieved similar results. Furthermore, we found that, among pairwise pairings of 18 rice varieties, the magnitude of similarity coefficient between CSR-36 and Pokkali (0.736) and IR-64 and IR-29 (0.736) was the highest. The similarity coefficients between Sahbhagi Dhan and Mandakini (0.631), Sahbhagi Dhan and Pusa Sugandh-2 (0.600), and Shatabdi and Daya (0.600) were noticeably higher after that. Additionally, we noticed that for 33 pairwise combinations of 18 different rice varieties, the magnitude of the similarity coefficients was equal to zero (Table 5). As a result, there was no commonality found in the molecular profiles of the salt stress sensitive candidate genes based on the entries included in these 33 pairwise combinations. Overall, the results showed that among the 18 rice varieties used in the current experiment, there was a great deal of genetic variability among them according to salt stress responsive candidate genes (Singh et al. 2016; Krupa et al. 2017; Kumari et al. 2019). On a dendrogram-based broad categorization system, rice cultivars were essentially categorized into three groups (Fig. 2). The resulting varietal groups were not strictly aligned with the varieties' responses to salt stress, and because susceptible and tolerant rice varieties coexisted within the same varietal group, it was not possible to distinguish between susceptible and tolerant rice varieties. Additionally, using a binary data matrix to cluster entries based on amplification patterns produced by a panel of microsatellite microsatellite containing six candidate gene specific primer pairs, it was possible to clearly distinguish between susceptible and tolerant varieties into various varietal groups. A completely clear distinction between susceptible and tolerant varieties into distinct varietal groups was also obtained from the analysis of genetic interrelationships using a binary data matrix based on amplification patterns produced by a panel of microsatellite lacking four candidate gene specific primer pairs. Both times, it was found that susceptible and tolerant rice varieties could be distinguished from one another precisely, with no tolerant and susceptible rice varieties intermixing within the same varietal group (Figs. 3 & 5). The results of principal coordinate analysis (Figs. 4 & 6) thus fully supported the inferences drawn from the similarity coefficients (SC) based on hierarchical classification, and the two-dimensional spatial distribution patterns of the varieties essentially produced three varietal groups with perfect discrimination of susceptible and tolerant varieties into distinct groups without any type of intermixing of varieties (Kumar et al. 2018; Kumari et al. 2019). To differentiate and discriminate between salt stress-tolerant and -sensitive varieties, microsatellite containing candidate gene such as OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 as well as SHMT2) and microsatellite lacking salt stress-responsive candidate gene such as OsHKT1;1, OsHKT1;3, OsHKT2;3 as well as OsHKT2;4) specific markers were validated. Therefore, the evaluation of genetic diversity using these salt stress responsive candidate genes can be used as useful and trustworthy instruments to distinguish between rice varieties that are susceptible to salt and those that are not, as well as to clearly identify which of these varieties of rice are susceptible to salt.
Genetic polymorphism among the rice varieties was revealed through the use of a panel of ten salt stress responsive candidate genes specific markers. The results showed that the presence or absence of bands and the position of bands with four (OsHKT1;1, OsHKT1;3, OsHKT2;3 as well as OsHKT2;4) and six (OsHKT1;5, SNAC1, CDMK, CCC, SHMT1 as well as SHMT2) markers, respectively. Microsatellites are absent from the first four candidate gene groups, but they are present in the later six candidate gene groups. The use of microsatellite containing candidate genes specific markers based genetic profiling enabled clear separation between salt stress tolerant and sensitive varieties, confirming the usefulness of these markers for this purpose. To distinguish between salt stress tolerant and sensitive varieties, a panel of four microsatellite missing potential gene specific primers responsive to salt stress was also validated. To create salt tolerant rice varieties, a breeding program for rice can make use of the verified markers and discovered salt-tolerant rice varieties. The validated markers and identified salt tolerant rice varieties can be employed in rice breeding programme to create salt tolerant rice varieties. The same strategy may be used with other crops.
CCC Cation chloride co-transporter
CDMK Calcium/Calmodulin dependent protein kinase
CTAB Cetyl trimethylammonium bromide
HKT High-affinity potassium transporter
PCA Principal coordinate analysis
PIC Polymorphism information content
PP Polymorphism per cent
SHMT Serine hydroxymethyltransferase
QTL Quantitative trait loci
Acknowledgement
The department of Plant Breeding & Genetics at Dr. Rajendra Prasad Central Agricultural University, Pusa, India, is sincerely acknowledged by the authors for their cooperation for giving the seeds of rice varieties.
Authors’ contribution
RK,VKS, PK, HK: Conceptualization of research; RK, VKS, PK, HK: Designing of the experiments; RK, PK, VKS, HK: Contribution of experimental materials; RK, PK: Execution of field/lab experiments and data collection; RK, PK, VKS: Analysis of data and interpretation; RK, PK: Preparation of manuscript; RK, PK: Writing-Original draft preparation; DRS, VKS, HK: Supervision; VSB, VKS, HK: Writing-Review & Editing; All authors have read and given their approval to the published version of the manuscript.
Conflicts of Interest: All authors have no conflicts of interest.
Data Availability- No data associated in the manuscript
Ethics approval Not applicable
Funding- The authors report that they did not receive any grants or funds for this research work.
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No competing interests reported.