Morphological and Molecular Characterization of Novel Salt-tolerant Rice Germplasms from the Philippines and Bangladesh

Wilson F. Aala Jr, Glenn B. Gregorio



Morphological and Molecular Characterization of Novel Salt-tolerant Rice Germplasms from the Philippines and Bangladesh

Wilson F. Aala Jr, Glenn B. Gregorio

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To screen for new sources of salinity tolerance, 688 traditional rice varieties from the Philippines and Bangladesh were obtained, and their tolerance to hypersaline conditions at the seedling stage was examined. A total of 29 Philippine lines and 15 Bangladeshi lines were scored as salt-tolerant. Morphological assessment (plant height, biomass and Na-K ratio) revealed that among the 44 salt-tolerant accessions, Casibon, Kalagnon and Ikogan had significantly higher relative shoot length difference, relative shoot growth reduction and shoot Na-K ratio than the tolerant check FL478. Additionally, AC and Akundo exhibited significantly higher Na-K ratios than the other genotypes. The genetic diversity of the 44 genotypes was assessed using 34 simple sequence repeat markers. A total of 133 alleles were detected across all loci. Cluster analysis showed that AC, Akundo and Kuplod were clustered along with FL478, indicating a strong genetic relatedness between these genotypes. IR29 (susceptible check) was singly separated. The haplotype analysis revealed that none of the 44 genotypes had a similar allele combination as FL478. These accessions are of interest since each genotype might be different from the classical salinity-tolerant Pokkali.

rice;simple sequence repeat; Na-K ratio; salinity tolerance; traditional landrace

Rice feeds about half of the world’s population, and constitutes 30% to 80% of the daily caloric requirements in Asia (Lafitte et al, 2004). Rice is generally grown on coastal areas flanked by sea water, and as thus salinity is one of the major abiotic stresses affecting its growth and yield (Mohamed et al, 2007). In Bangladesh, salinity affects 30% of its total agricultural lands, with over 1.02 × 106hm2of coastal land constantly under the direct effects of salinity (Mazid et al, 2016). The Philippines, which also harbours coastal agricultural areas, have reported a total of 0.4 × 106hm2of land being affected by salinity, with half categorized as severe salinity (PhilRice, 2011).

Among the cereals, rice ranks as the most saline-sensitive, losing 10% of its yield at 3 dSm-1of salinity, and moderate levels of salinity (7 dSm-1) could potentially reduce its yield by 50% (Umali, 1993). Excess soil salinity induces osmotic stress, ion toxicity and nutrient imbalance (Flowers and Flowers, 2005), leading to a reduction in shoot length, fresh weight and dry weight (Khan et al, 1997; Roy et al, 2002; Janaguiraman et al, 2003). It is most widely accepted that salt injury is due to an imbalance in the shoot Na-K ratio, which reduces tillering capacity and grain yield (Munns and Tester, 2008; Horie et al, 2012).

Rice is most sensitive to salt stress at the early seedling stage (Gregorio et al, 1997), and hence seedlingstage screening of genotypes for salt tolerance remains valuable due to its rapidity and scalability, which is difficult for the vegetative and reproductive stages (Bhowmik et al, 2009). However, salinity tolerance is heavily affected by the environment, and narrow-sense heritability estimates from such experiments tend to be low (Gregorio and Senadhira, 1993). The efficacy of mass phenetic screening could be increased if aided by suitable genetic techniques (Schmidt et al, 2014). Molecular markers such as simple sequence repeat (SSR), which can tag major genes more commonly known as quantitative trait loci (QTL), holds much value in marker-assisted selection (MAS) (Rines et al, 2006;Ganie et al, 2014). A major QTL on chromosome 1, termed as, has been identified to be tightly associated with salinity tolerance (Mohammadi-Nejad et al, 2009; Islam et al, 2011), and SSR markers linked to this QTL have also been reported in Bonilla et al (2002). Use of such markers to delineate test genotypes and aid in selection have also been reported previously (Moreno et al, 2003; Xu and Crouch, 2008).

Genetic and phenetic screening relies on the wide array of alleles present in the gene pool representing a multitude of possible traits for salt tolerance (Reddy et al, 2017). Previous studies involving genetic and physiological screening have yielded highly salt-tolerant lines like Pokkali and FL478 (Thomson et al, 2010), although novel sources of salt-tolerant genotypes that could be used as the parents in breeding programs are few (Lisa et al, 2004). In this regard, landraces have been shown to be good sources of new alleles (Hoisington et al, 1999;Guevarra et al, 2001), which remains greatly uncharacterized and unused. The purpose of this study was to identify and characterize new sources of salinity tolerance from traditional landraces in the Philippines and Bangladesh by utilizing morphological and molecular screening techniques (SSR analysis).

Materials and Methods

Plant materials

A total of 688 rice accessions, 505 Philippine traditional landraces [including FL478 (tolerant check) and IR29 (susceptible check)] and 183 Bangladeshilandraces, were collected from the International Rice Research Institute Gene Bank. Seed dormancy was broken by incubation at 50ºC for 5 d. The seeds were then sterilized using a 0.2% bleach solution for 5 min, and allowed to pre-germinate in a dark room for 3 d.

Initial seedling stage screening for salt-tolerant accessions

Two pre-germinated seeds were sown in each hole of a styrofoam seedling float wherein four holes were allotted per accession. Each float was placed on top of an 8L plastic tray containing tap water and placed inside the IRRI Phytotron (temperatures of 29 ºC day /21 ºC night, minimum relative humidity of 70%). After 5 d, the water in each tray was replaced with a half-strength Simple Nutrient Addition Program A and B (SNAP) culture solution (0.5% SNAP A, 0.5% SNAP B, 99% H2O, 200 mg FeSO4) salinized to EC=12dSm-1using analytic sodium chloride, and pH was maintained at 5.0. After 14 d, the degree of salt stress injury was evaluated using the modified standard evaluating score (SES) (Table 1). After scoring, the salinity level was increased to EC of 18dSm-1, and scoring was carried out again.

Table 1. Modified standard evaluation score for assessing the visual symptoms of salt injury at the seedling stage.

Replicated screening of selected accessions

The replicated screening process was essentially similar with the initial screening. Three salinized and normal replicates were prepared using the randomized complete block design. The tolerant genotypes from the initial phenotyping, as well as IR29 and FL478, were seeded in seedling floats and placed in trays containing Peter’s culture solution (1 g solid Peter’s fertilizer, 200 mg FeSO4, 1 L H2O, pH=5). The pre-germinated seeds were allowed to grow in the normal solution (culture solution only) for one week with regular checking of pH for acclimatization purposes. Salinization to an EC value of 12dSm-1was done at 7 d after seeding. Scoring was carried out 14 d after salinization, and salinization to an EC of 18dSm-1was done thereafter. Scoring was carried out after two weeks or prior to sample acquisition (Table 2).

Physio-morphometric screening

Fresh samples were obtained after scoring of the samples were carried out. The plants were cut at the shoot-root interface, and measured bya standard meter stick with millimeter gradations. For the biomass analysis, shoot fresh and dry weights were obtained. The fresh weight (three plants per accession) was measured using an analytical balance. The samples were then placed inside paper envelopes with proper labels and dried inside an oven for 3 d with an ambient temperature of 65ºC. The dry weight of each sample was then measured using an analytical balance.

Table 2. Physio-morphometric data for the 44 salt-tolerant accessions under salinized conditions (EC = 18 dSm-1).

CN, Control number; ACCNO, Accession number at IRRI genebank; BD, Bangladesh; PH, the Phillippines; SES, Standard evaluation score; SL, Shoot length; SFW, Shoot fresh weight; SDW, Shoot dry weight; RSLD, Relative shoot length difference; RSWC, Relative shoot water content; RSDR, Relative shoot dry weight reduction.

Means in the same column with the same lowercase letters are not significantly different at the 0.05 level.

Na-K ratio wasmeasured after the sensitive check IR29 registered a score of 7. The whole shoot was washed thrice with nanopure water,and dried in an oven for 3 d with an ambient temperature of 65ºC. The samples were then finely cut, and 10 mg of each sample was placed in individual vials with proper labels.Sample processing for Na-K ratio determination was based on Yoshida et al (1976) with modifications. For each vial, 10 mL of 100 mmol/L acetic acid was dispensed, and the vials were then placed in a hot water bath set at 90ºC for 2 h. The samples were then allowed to cool down for 12 h. Prior to reading, filtering of the shoot particles, if any, was carried out so as not to impede the flow of the sample into the feed tube. Samples were diluted (10× dilution) by adding 9mL of deionized water to 1mL of the sample solution. Once ready, a feed tube was placed inside the vials and a flame photometer was used to read the sodium and potassium content of the samples.

Analyses of physio-morphometric data

Mean shoot length (SL), shoot fresh weight (SFW), shoot dry weight (SDW), SES score, relative shoot length difference (RSLD), relative shoot water content (RSWC), relative shoot dry weight reduction (RSDR), and Na-K ratio were analyzed using the analysis of variance (ANOVA) in R software.

RSLD (%) = (SLN– SLS) / SLN× 100

RSWC (%) = (SFWS– SDWS) / SFWN× 100

RSDR (%) =(SDWN– SDWS) / SDWN× 100

Where N indicates normal treatment and S indicates salinized treatment. The two-tailed-test in R software was utilized to compare means between the normal and salinized conditions. Duncan’s multi-range test in R software was used to separate the means of accessions in variables that showed statistical significance in the ANOVA. Linear correlation analysis (Pearson) was done for SES score and Na-K ratio.

Molecular screening

Total DNA was extracted from 21-day-old leaf samples as described in Delaporta et al (1983). DNA quality and quantity were assessed using 1% agarose gel electrophoresis using 50, 100 and 150 ng/µL Lambda DNA as reference.

Genetic relationship for the 44 selected traditional varieties was determined using 38 previously reported SSR markers which included 16markers (RM315, RM490, RM1287, RM10694, AP3206f, RM3412b, RM10748, RM493, RM10793, RM562, RM7075, RM10745, RM10764, RM10772, RM140 and RM495) on chromosome 1and 2 SSR markers each for chromosomes 2 to 12. Information about all the markers were collected from the Gramene database (http://www.gramene.org).

Amplification of the targeted loci were done in a 20 µL reaction volume containing 300 ng template DNA, 0.05 mmol/L dNTP mix, 0.25 µmol/L forward and reverse primers, 1× PCR buffer, 0.45 mmol/L MgCl2, 4UDNA polymerase, sterile nanopure water to volume and an overlay of sterile mineral oil. The amplification profile consisted of an initial denaturation at 94ºC for 5 min; followed by 35 cycles of 94ºC denaturation for 1 min, 55ºC to 65ºC annealing for 45 s, 72ºC extension for 1 min; and a final extension at 72ºC for 5 min in a G-Storm Thermal Cycler (Life Science Research Products).

Amplification products were resolved in 6% or 8% native polyacrylamide gel and documented using the Alpha Imager system (Fisher Scientific) (Supplmental Fig. 1). Band scores were summarized and exported to the PowerMarker v.3.0 software (Liu and Muse, 2005) as a tab-delimited text file. From this, the allele number, expected heterozygosity (genetic diversity), and polymorphism information content (PIC) were obtained. A frequency-based distance algorithm was employed to determine the relative similarity of the accessions to each other, and an unweighted pair-group mean analysis using the similarity index as described in Nei et al (1983) was employed to construct a dendrogram using PYLIP v. 3.965 (Felsentein, 1989).

Results

Physio-morphometric screening

Among the 688 traditional accessions, 44 were found to be moderately to highly salt-tolerantwith EC of 18 dSm-1. ANOVA showed that there were statistical significances within replicates, and between genotypes (<0.05) for SES score (Table 3). ANOVA for RSLD showed statistical significance across genotypesat the 0.01 level whereasRSDR showed statistical significance between genotypes at the 0.05 level. ANOVA for shoot length, RSWC, SDW, SFW and Na-K ratio indicated statistical significances across genotypes and for the combined treatment (<0.01) (Tables 3 and 4). Two-tailed-test between the normal and salinized treatments showed statistical significance for all the test traits (<0.05). Duncan’s multi-range test revealed that none of the test accessions had a mean SES score that was statistically similar to IR29 upon salinization, showing that the initial screening was effective in identifying putative salt tolerant cultivars. Betalga, Mararnao, Ikogan and Anangka had statistically lower mean RSLD than FL478 (Table 2). Thirty accessions and FL478 had mean RSWC which were statistically different from IR29. The mean RSDR of Rao, Ikogan, Casibon and Kalagnonwas statistically lower than that of FL478.The mean Na-K ratio of all the test accessions, excluding IR4493-5-5-3, were statistically lower than that of IR29 under saline conditions. Only AC had a mean Na-K ratio that was significantly lower than FL478.

Table 3. Mean square values of each physiomorphometric data.

SES, Standard evaluation score; RSWC, Relative shoot water content; RSDR, Relative shoot dry weight reduction; SDW, Shoot dry weight; SFW, Shoot fresh weight; SL, Shoot length; RSLD, Relative shoot length difference.

* and ** indicate significant differences at the 0.05 and 0.01 levels, respectively.

The frequency distribution of the 44 accessions and the two checks showed a bell-shaped curve for RSLD and RSDR which reflected the quantitative nature of these traits (Fig.1-A and B). Using a simple linear correlation analysis, the mean Na-K ratio was found to have a strong positive relationship with the mean SES score (=0.72). Computing for the2value revealed that Na-K ratio accounted for 51.4% of the variation within the 46 samples with respect to the SES scores (Fig.1-C).

Fig. 1. General distributions of selected accessions for relative shoot length difference (A), relative shoot dry weight reduction (B) and scatter plot of Na-K ratio to standard evaluation score (SES) (C).

Table 4. Mean square values using the combined treatment as an error term in each of the physio-morphometric data.

RSWC, Relative shoot water content; SDW, Shoot dry weight; SFW, Shoot fresh weight; SL, Shoot length.

** indicates significant difference at the 0.01 level. Combined treatment = Normal vs. Saline.

Microsattelite marker-assisted screening

For each marker, the genotype number, major allele frequency, allele number, gene diversity, observed heterozygosity and PIC were obtained for each locus (Table 5). Across the 34 loci considered, a total of 133 alleles were detected ranging from 2 to 10 alleles per locus. Heterozygous genotypes were observed using RM490, RM7075, RM13197, RM152, RM413, RM10764, RM105, RM171 and RM495. The mean gene diversity was observed to be 0.4910, while the mean PIC value was found to be 0.4579. PIC values ranged from 0.0416 to 0.8555 wherein a total of 18 markers showed PIC values higher than 0.5000.

Utilizing the polymorphisms detected across the 34 loci, a dendogram was constructed and bootstrap trees were generated to form the consensus tree as shown in Fig.2. FL478 was found to form a sub-clade along with AC, Kuplod and Akundo (boxed). IR29 formed a separate clade from the rest of the 45 accessions. Most Bangladeshi lines co-clustered together, and a similar observation could be made for the Philippine lines. A genotype analysis was also carried out (Fig.3) which showed that none of the 44 test genotypes had allele combination similar to FL478 which was used as the reference genotype. AC exhibited the most number of common alleles (21/34), while IR5494 had the lowest number of common alleles (11/34).

Fig. 2. Consensus tree for 46 rice accessions using 34 simple sequence repeat markers.

Boxed accessions clustered tightly along with FL478.

Table 5. Summary statistics for the 34 markers used to screen the selected rice accessions.

MAF, Major allele frequency; GN, Genotype number; AN, Allele number; GD, Genetic diversity; H, Observed heterozygozity; PIC, Polymorphism information content.

Fig. 3. Haplotype analysis for the 46 rice accessions using FL478 as the reference genotype.

Black boxes represent similarity to FL478 allele.

Discussion

Mining for genetic and phenotypic variation for salinity tolerance is key to generating better hybrids, and traditional varieties which are mostly uncharacterized holds much potential since each cultivar might possess unique adaptations which are different from Pokkali or FL478, the most commonly used salt-tolerant parents by rice breeders (Mishra et al, 2003; Mohammadi-Nejad et al, 2009; Hoang et al, 2016). In this study, a total of 688 Philippine and Bangladeshi rice accessionswere screened for salt stress response at seedling stage. Based on the SES scores at 18 dSm-1, 44 accessions were identified as salt tolerant. The salinity intensity of 18dSm-1was chosen since rice salt tolerance allows 50% emergence at such levels (Wahhab, 1961), and coastal areas in Bangladesh and the Philippines experience a typical EC of 18dSm-1(Iqbal and Shoajib, 1993). The pairwise mean analysis of the SES scores in the salinized treatment showed that none of the landraces have a mean SES score that was statistically similar tothat of the susceptible check IR29. This shows the discriminatory ability of the standardized evaluation score in evaluating salinity tolerance at seedling stage. Indeed, the efficiency of a standardized score in screening salt-tolerant cultivars had also been shown previously (Lisa et al, 2004;Sexcion et al, 2009).

Key physiomorphological indicators of salt stress (shoot length, fresh weight, dry weight and Na-K ratio)are correlated with salinity tolerance at seedling stage in rice (Bhowmik et al, 2009). Treatment with NaCl resulted in a decline in the mean shoot length of the genotypes when compared with the control, which is in agreement with previous reports (Javed and Khan, 1975; Saxena and Pandey, 1981; Zeng and Shannon, 2000). However, since plant height is highly cultivar-specific, RSLD was also computed. Pairwise comparison of mean RSLD across genotypes revealed that Betalga, Maranao, Ikogan and Anangka were statistically lower than FL478, wherein Ikogan had a mean RSLD of 0.00. These four landraces might have better Na+sequestering mechanism than FL478 since RSLD could be attributed to leaf senescence. Leaf senescence in the setting of salinization is mainly due to an excess of ions, mostly Na+and Cl-, in the leaf tissue (Munns and Tester, 2008). For most cases, senescence starts at the leaf tips of older leaves and progresses to younger leaves. Closing of stomates due to increased influx of Na+could also increase leaf temperature, leading to reduced shoot length growth (Rajendran et al, 2009; Sirault et al, 2009).

Shoot fresh weight and dry weight were significantly reduced upon treatment with NaCl. In both parameters, Akundo ranked the highest for SFW (1.708) and SDW (0.413). This was followed by FL478 with SFW of 1.303 and SDW of 0.340 (Table 1). For SFW, AKundo registered a significantly higher value than FL478, whereas their SDW values were not significantly different. IR5494 ranked the lowest (right after IR29) for both SFW and SDW. In wheat, biomass reduction is an initial response to extreme salt stress due to osmotic stress (James et al, 2002). This osmotic imbalance hinders the normal uptake of water and nutrients, leading to reduced tissue development. To detect the difference in performance of the test accessions under normal and salinized conditions for shoot dry weight, RSDR was calculated. The multicomponent test for RSDR showed that FL478, IR29 and 40 of the test accessions had non-significantly different means. This observation appears to contradict the notion that tolerant accessions should have lower biomass reduction than susceptible cultivars. However, cultivars which are sensitive to salt stress (glyophytes) induce early plant vigour in an attempt to avoid salinity-induced damage by rapid growth (Kumar et al, 2013; Reddy et al, 2017). Fast growing rice seedlings were shown to sequester less Na+than slow growers (Yeo et al, 1990). A similar finding is also reported by Asfaw (2011) in sorghum wherein seedling shoot dry weight and fresh weight show no significant difference across genotypes. Lee et al (2003) also reported the same occurrence in Pokkali (tolerant) and IR29, wherein their mean RSWRare non-significantly different under saline conditions at the seedling stage,and IR29 shows significantly lower relative reduction values than Pokkali, whichis attributed to its dwarf stature relative to the tall-type Pokkali. Further scrutiny of the RSDR data showed that Kalagnon, Casibon, Ikogan and Roa have significantly lower mean RSDR than the other accessions. Kalagnon and Casibon showed non-significant difference inshoot lengh comparedto FL478, while Roa and Ikogan hadsignificantly higher values than FL478, which might indicate that RSDR could not be easily correlated with plant height.

Increased influx of Na+in the roots perturbs the osmotic potential of plant cells and induces physiological drought, which dampens the ability of the plant to acquire water (Verslues et al, 2006). Thus, RSWC was calculated to assess the osmotic stress-induced damage in the test accessions as a function of the relative water content of the plants. Here, Casibon, Ampipit and Binutiti registered significantly superior values when compared to FL478. The ability to tolerate the increased extracellular levels of Na+could be due to: synthesis of osmoprotectants (tertiary amines, dimethylsulfoniopropionate and sulfonium compounds) which stabilizes extracelluar and vacuolar Na+(Turkana and Demiral, 2009); oxidative stability from the upregulation of enzymatic antioxidants (superoxide dismutase, catalase and glutathione reductase) (Demiral and Turkan, 2005); or maintaining ion homeostasis by mobilizing Na+into vacuoles where damage could be contained which is one of the mechanisms observed in Pokkali (Kader and Lindberg, 2005; Ghosh et al, 2011).

In terms of ion homeostasis, the ratio of Na+and K+in the shoot has been previously established to have a strong inverse correlation with salinity tolerance (Moradi et al, 2007), and exhibit a strong positive linear relationship with SES, which is depicted in Fig.1-c. In this study, the mean Na-K ratio of almost all the test accessions showed non-significant difference with that of FL478. In a previous study, Akundo also accumulates less Na+in its leaves and is thereby protected from osmotic damage (Rahman et al, 2016). Additionally, Chakol, which also registers a shoot Na-K ratio that is significantly lower than FL478, is previously identified to be a popular traditional variety with an expected yield of 3–4 t/hm2by the Bangladesh Rice Research Institute (BRRI) (Hamid and Islam, 1984). Landraces with good yield traits are highly sought due to their ability to be crossed with established lines (Reddy et al, 2017). Notably, AC was the only test accession that obtained a mean Na-K ratio (0.33) that was lower than FL478 (1.75). In a recent study using Na-K ratio to assess salinity tolerance in Bangladeshi landraces, Komol Bhog (0.750), Sona Toly (0.379), Nakraji (0.850), Dud Sail (3.47) and Tal Mugur (1.64) are salt tolerant (Tahjib-Ul-Arif et al, 2018). Among the 44 genotypes screened in this study, three Bangladeshi landraces [Moisdol (0.65), Kuplod (0.76) and Akundo (0.39)] and three Philippine landraces [AC (0.33), Anangka (0.50) and Betalga (0.43)] showed similar values. Dud Sail with SES of 3.0 obtained a relatively high Na-K ratio (3.47), similar to Asha, Sada Rupa, Binutiti, Reppeng, IR5494, IR4493-5-5-3, Anangka, Dukab and Binangahon. Differing tolerance mechanisms aside from ion homeostasis (Na-K ratio) might have contributed to this observation although additional studies on measuring such mechanisms have to be carried out.

Clearly apparent from the physio-morphometric data is the degree of variation relative to the selected indicators of salt stress. Such varied responses are favourable in developing salt-tolerant cultivars since conventional breeding generally employs lines with extreme characters for the target trait as parentals. Such was the case for the salt-tolerant FL478, which was derived from a cross of Pokkali and IR29 (Walia et al, 2005). One way to complement such efforts is by utilizing molecular markers linked with the salinity tolerance major QTL(Mohammadi-Nejad et al, 2008) for genotypic selection. Here, 16 riceSSR markers on chromosome 1, along with the other markers mentioned above, were utilized to determine the allelic diversity of the test population.

Based on the constructed consensus tree (Fig. 2), Casibon, Kalagnon, Roa and Ikogan, which showed superior RSDR values against FL478, did not cluster close to each other although they did fall under clade II to which FL478 also belongs, hinting at divergent mechanisms for biomass accumulation under salt stress. Four loci, namely RM10772 (chromosome 1), RM178 (chromosome 1), RM171 (chromosome 7), and RM455 (chromosome 10), were common among these three accessions (Fig. 3). RM10772 has already been mapped to theQTL on rice chromosome 1 (Lapitan et al, 2007;Ganie et al, 2014). Similarly, Akundo, Kuplod and AC cluster together under clade II. RM105, RM10764 and RM413 were found to be common loci among them. Lal Moni, Chakol, Chorua Kartiksail and Aguni Kartiksail clustered close to each other forming clade I. These accessions also obtained significantly lower Na-K ratio values compared to FL478. Clade I being clustered away from FL478 indicates that another mechanism could be complimenting the good Na-K transport system of these accessions. In a recent genotyping study on Bangladeshi landraces using SSR markers, AP3206fwas reported to discriminate cultivars in terms of overall salt tolerance and obtain a high PIC value of 0.7 (Rashid et al, 2018).However, in the present study, AP3206f showed a monomorphic banding pattern excluding IR29 which was heterozygous in this locus. This might be attributed to the replicated screening that was carried out in this experiment, generating a homogeneous population of putatively tolerant group.

Apparent from all these is the sheer complexity of merging physio-morphometric data with the molecular data which shows just how intricate salt tolerance is, both at the phenotypic and genetic level. Additionally, even with the vast number of SSR markers available to screen for salt tolerant varieties, there is yet to be a definitive locus that could clearly and irrefutably distinguish tolerant from sensitive genotypes. It is therefore a crucial task to characterize as many rice genotypes as possible to fully grasp the full extent of this trait.

Conclusions

Due to the complexity of salt stress in plants, novel sources of tolerance must be explored to continuously improve critical crops, including rice. From this study, a total of 44 genotypes of varying degrees of tolerance were screened using physio-morphometric markers of salt stress and microsatellite markers in theQTL. Among these landraces, Casibon, Kalagnon, Ikogan, AC, Akundo, Maranao, Betalga, Anangka, Roa, Ampipit and Binutiti outperformed most accessions and performed better than FL478 (tolerant check at IRRI) in terms of biomass and Na-K ratio. Chakol, which had good performance and yield parameters under salt stress based on a previous selection, could be a candidate for breeding programs. Cluster analysis grouped the accessions sharing the most number of FL478 alleles under cluster II, although it was unable to reflect the performance of accessions in terms of RSLD, RSWC and RSDR. Screening for salt-tolerance in these accessions at the maturity stage could greatly contribute to the gene pool of rice relative to salt stress tolerance.

Acknowledgements

We humbly thank the International Rice Research Institute for the germplasm samples. We also thank Ms. Meddy Arceta, Ms. Aiza Vispo, Mr. Junrey Amas for their help during the screening phase, and Ms. Kirsten Gonzalvo for her statistical expertise.

SUPPlemental DATA

The following material is available in the online version of this article at http://www.sciencedirect.com/science/ journal/16726308; http://www.ricescience.org.

Supplemental Fig. 1.Representative gel documentation and banding pattern of AP3206f, RM3421b and RM10793 used to detect the genetic polymorphisms in the 44 test accessions.

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http://dx.doi.org/10.1016/j.rsci.2018.09.001

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28 September 2018

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