Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining

Abstract

Background

Apparent Amylose Content (AAC), regulated by the Waxy gene, represents the key determinant of rice cooking properties. In occidental countries high AAC rice represents the most requested market class but the availability of molecular markers allowing specific selection of high AAC varieties is limited.

Results

In this study, the effectiveness of available molecular markers in predicting AAC was evaluated in a collection of 127 rice accessions (125 japonica ssp. and 2 indica ssp.) characterized by AAC values from glutinous to 26%. The analyses highlighted the presence of several different allelic patterns identifiable by a few molecular markers, and two of them, i.e., the SNPs at intron1 and exon 6, were able to explain a maximum of 79.5% of AAC variation. However, the available molecular markers haplotypes did not provide tools for predicting accessions with AAC higher than 24.5%. To identify additional polymorphisms, the re-sequencing of the Waxy gene and 1kbp of the putative upstream regulatory region was performed in 21 genotypes representing all the AAC classes identified. Several previously un-characterized SNPs were identified and four of them were used to develop dCAPS markers.

Conclusions

The addition of the SNPs newly identified slightly increased the AAC explained variation and allowed the identification of a haplotype almost unequivocally associated to AAC higher than 24.5%. Haplotypes at the waxy locus were also associated to grain length and length/width (L/W) ratio. In particular, the SNP at the first intron, which identifies the Wx^a and Wx^b alleles, was associated with differences in the width of the grain, the L/W ratio and the length of the kernel, most likely as a result of human selection.

Background

Rice (Oryza sativa L.) is the most important staple food for over half of the world population. Rice quality is primarily influenced by starch which is composed of two polysaccharides: amylose and amylopectin. The percentage of amylose on total starch, measured as Apparent Amylose Content (AAC), is the key determinant of rice cooking properties. High amylose varieties, like risotto varieties, cook dry with firm and separate grain; while low amylose cultivars (cvs.) are tender, glossy and cohesive after cooking. Suggested classification of amylose content identified classes as waxy (0–5%), very low (5–12%), low (12–20%), intermediate (20–25%), and high (25–33%), even considering that commercially rice is classified by amylose content as either low (less than 20% amylose), medium (21–25%) and high (26–33%) (Juliano 1992; Suwannaporn et al. 2007).

Over the past several decades, various methods have been reported for the determination of amylose content, including iodine binding, near infrared spectroscopy, size-exclusion chromatography and most recently, asymmetric field flow fractionation (Juliano 1971; Wesley et al. 2003; Ward et al. 2006; Chiaramonte et al. 2012). However, none of these methods is cost-effective in terms of high throughput screening (Caffagni et al. 2013) and the iodine binding represents the only one which has been validated for routine use (Fitzgerald et al. 2009). The utilization of marker-assisted selection (MAS) could overcome the shortcomings of amylose content measurements and, considering its lower cost and higher throughput, can be applied as a selection tool in the early phases of the breeding, while AAC direct analysis would require seeds setting. In addition, DNA markers for AAC also allow homozygous and heterozygous plants to be readily distinguished and often provides a more definitive way of classifying Granule-Bound Starch Synthase (GBSS) alleles compared to AAC assays since it avoids complications such as modifier genes, cytoplasmic factors, as well as environmental effects such as differences in temperature during grain development (McKenzie and Rutger 1983; Asaoka et al. 1985; Kumar and Khush 1987).

Amylose synthesis in developing seeds is primarily regulated by the Waxy (Wx) gene which encodes the Granule-Bound Starch Synthase I (GBSSI) enzyme and the level of grain amylose is directly associated to the amount of GBSSI in the endosperm (Mikami et al. 2008). The Waxy gene is located on chromosome 6 and consists of 13 exons and 12 introns. Two wild type alleles, Wx^a, primarily found in indica subspecies, and Wx^b, mainly found in japonica subspecies, have been found to predominate at the waxy locus for high and low AAC respectively (Dobo et al. 2010). The difference between the two alleles is related to the presence in Wx^b of a G to T Single Nucleotide Polymorphism (SNP) at the 5′ splice site of the first 1,124 bp long intron, localized 1,164 bp upstream the start codon (haplotype AGT T instead of AGG T). Most of the waxy and low AAC cvs. screened so far carry this polymorphism which results, for Wx^b, in the reduction of pre-mRNA splicing efficiency and promotion of alternative splicing at cryptic sites in exon 1, leading to a decreased production of functional enzymes and causing the glutinous and low amylose phenotypes (Wang et al. 1995; Ayres et al. 1997; Bligh et al. 1998; Cai et al. 1998; Isshiki et al. 1998; Larking and Park 1999). As the SNP implies the generation of a restriction site, a Cleaved Amplified Polymorphic Sequence (CAPS) marker was developed and largely used to discriminate between high (G allele) and low (T allele) amylose varieties. Another diagnostic molecular marker widely utilized is the RM190 CT_n microsatellite, located in the 5′ Untranslated Region (UTR) of Waxy exon 1 (Bligh et al. 1995). Seven variants of this marker are known enabling the discrimination between low, intermediate and high AAC genotypes (Ayres et al. 1997; Shu et al. 1999; Bergman et al. 2001; Tan and Zhang 2001). However, failures in detecting specific correlation between the number of RM190 repeats and an AAC group were frequently observed, suggesting that this microsatellite is simply a closely linked marker rather than the causal mutation for AAC variation (Ayres et al. 1997; Dobo et al., 2010).

The ever-increasing demand of rice varieties with higher eating quality drives to the identification of superior and novel alleles to be used in breeding programs. The allele mining approach, based on the sequencing of different alleles of a single gene in different genotypes within a specie, has been largely applied to mine the sequence diversification at the level of the key genes for quality with the aim of studying the available diversity and/or developing allele-specific molecular markers for Marker-Assisted Selection (MAS). One example in rice is the identification of 24 SNPs and one InDel mutation obtained through the comparison of the Starch Synthase II (SSII) gene sequence in 30 rice genotypes. The polymorphisms were then utilized for marker-assisted backcross breeding programs (Bao et al. 2006). Moreover, Takanokai and co-workers (2009) compared the sequence of the GS3 gene, responsible of grain size, in 54 rice cvs. and identified 86 SNPs and 20 InDels. The allele mining for the rice fragrant gene badh2 also allowed the development of diagnostic molecular markers (Shi et al. 2008).

Allele mining experiments applied to the Waxy gene led to the identification of five different allelic variants. Mikami et al. (2008) studied the allelic diversification at the Wx locus in Asian rice associating different alleles to grain amylose content alterations. They identified the Wx^op allele in indica varieties from India, Nepal, Indonesia and China showing an opaque and chalky endosperm with a very low amylose content. This allele is characterized by an A to G SNP in exon 4 at position +715 from the ATG causing an Asp to Gly aminoacid change. The allele Wxⁱⁿ was frequent in accessions belonging to an aromatic group and to tropical japonica which exhibited an intermediate AAC and is determined by a non-conservative mutation in exon 6 at position +1,083. The mutation alters a Tyr to Ser in the active site of the enzyme reducing its specific activity (Dobo et al. 2010). The last allele analyzed by Mikami and co-workers (2008), the wx allele, is present only in waxy varieties and is characterized by a 23 bp duplication in the second exon, 100 bp downstream the ATG, causing a premature stop codon which inactivates the Waxy gene. A minor allele, represented by Wx^mq, was identified in the low AAC rice cv. Milky Queen and was characterized by two base changes within the coding region: G to A in exon 4 and T to C in exon 5. Each of these mutations generated missense aminoacid substitutions (Sato et al. 2002). One additional low AAC-associate allele, Wx^hp, was identified by Liu et al. (2009) in a few low AAC Yunnan landraces. An A to G SNP occurring in exon 2 at position +497 causes an Asp to Gly substitution resulting in a reduction of the activity of GBSSI.

Following the discovery of this variability at the Wx locus, additional AAC class-specific molecular markers were developed. The combination of the Wxⁱⁿ SNP in exon 6 and a SNP in exon 10, which consists of a C/T non-conservative SNP, with the RM190 microsatellite in exon 1 enabled the discrimination between intermediate and high AAC in a small number of genotypes (Larkin and Park 2003). More recently, Dobo and co-workers (2010) performed the same analysis increasing the number of genotypes and identified three allelic groups based on the combination of the three SNPs explaining 89.2% of the variation in AAC among 85 US varieties and 93.8% of the variation in 279 European accessions.

To date, the attention has been focalised in the identification of Wx genetic variants explaining amylose reduction in the endosperm of intermediate, low and waxy genotypes and the molecular markers used so far can discriminate only waxy, low, intermediate and high AAC. There are no markers explaining the different percentages of seed amylose within the high amylose class and further elucidations are needed to improve rice quality particularly in occidental countries where dry and firm rice is largely preferred.

In this study a collection of 127 rice accessions (125 japonica ssp. and 2 indica ssp.) was analyzed for AAC leading to the identification of the AAC classes low, intermediate and high. Effectiveness of available markers diagnostic for Apparent Amylose Content was verified and a low predictability within the high AAC class was observed. To discover new molecular markers associated to the different AACs, twenty-one genotypes representing each AAC class were selected and subjected to the re-sequencing of the waxy locus. New SNPs were identified in four high AAC accessions and used to develop new SNP-based molecular markers. Moreover, un-expected associations between grain shape characters and polymorphisms associated to the waxy locus were identified and analyzed.

Results

AAC assessment in a rice germplasm collection

A collection of 125 temperate japonica and two indica rice accessions originated from different rice cultivation areas was evaluated for AAC and four classes were identified, ranging from waxy to high AAC (Table 1). No accessions of the very low AAC class (5–12%) were detected; high frequency was instead observed for low and intermediate amylose classes (Figure 1) while a low frequency of the high amylose class was highlighted. Accessions showing AAC higher than 20% (60 in total) originated from very different countries (20 from USA, 11 from Italy, 4 from Portugal, 3 from Spain, 2 from France and the remaining from other countries), indicating that the trait conferring a relatively high amylose content was selected in different rice cultivation areas.

Table 1 Germplasm collection of 127 rice accessions

Figure 1

Frequency distribution of amylose content classes in the analyzed germplasm collection: Waxy (0-5%), low amylose (5-20%), intermediate (20-25%) and high amylose (>25%).

Molecular markers analyses

The germplasm collection of 127 rice accessions (Table 1) was chosen to evaluate the effectiveness of known molecular markers in predicting Apparent Amylose Content. Ten different alleles for the RM190 CTn microsatellite were identified (Table 2) but a clear relationship with the different AAC groups was observed only for some alleles (Table 1). Repeats CT₉, CT₁₀ and CT₁₄ were present only in genotypes with 23–24.85% AAC, while CT₁₁ and CT₂₀ also identified accessions with AAC higher than 25%. Among this AAC classes, two varieties showed unique CT alleles: CT₁₃ for Ota (22.55% AAC) and CT₂₁ for Bomba (22.84% AAC). The most frequent allele, CT₁₈ identified in 67 accessions, represents a wide range of AAC (14.92-21.56%). Similar results were observed for CT₁₇ and CT₁₉ which were associated to heterogeneous AAC intervals ranging from 15.55% to 23.27%. Considering non-glutinous genotypes, the RM190 microsatellite explained the 74.9% of the AAC variation in our collection (Table 3).

Table 2 Alleles identified for the RM190 microsatellite in the germplasm collection

Table 3 Percentage of variation for Apparent Amylose Content (AAC) explained by the tested GBSSI molecular markers in the germplasm collection

For the SNP in the splicing site of the leader intron (intron 1), results confirming previous investigations were observed (Wang et al. 1995; Ayres et al. 1997; Bligh et al. 1998; Cai et al. 1998; Isshiki et al. 1998; Larking and Park 2003; Dobo et al. 2010). With only two exceptions represented by Rotundus and Antoni, all the accessions with AAC lower than 22% showed the T allele, while all the genotypes with AAC higher than 22% had the G allele (Table 1). This SNP explained a higher level of AAC variation (77.5%) with respect to the RM190 (Table 3) and the combination of the two polymorphisms did not significantly increase the level of explained variability (77.7%).

The presence of SNPs characterizing the different known Wx alleles was assessed in our collection through sequencing of exons 2, 4, 5 and 6. The wx allele was identified in the waxy cv. Calmochi 101, in which the 23 bp duplication (sequence motif: ACGGGTTCCAGGGCCTCAAGCCC) in exon 2 responsible for Waxy gene inactivation was present; literature data in fact provide AAC values for this cv. ranging from 0.8 to 1% (Park et al. 2007; Li et al. 2008). Allelic variation in the Wxⁱⁿ allele, characterized by a non-conservative A/C mutation in exon 6, was observed with the A allele being detected, with few exceptions, in accessions with AAC ranging from waxy to 22% and higher than 24%, and the C allele generally present in genotypes with an AAC from 22 to 24% (Table 1). No polymorphisms were identified in exons 4 and 5, representative of the Wx^mq allele and, unlike in previous observations (Dobo et al. 2010), we did not observe mutations in exon 10 thus excluding the presence of SNPs in this exon.

Combining the results obtained for SNPs in intron 1 and exon 6, three allelic patterns were identified: GA, TA and GC (Table 2), the first letter indicating the G/T polymorphism in intron 1 while the second the A/C SNP in exon 6. Similarly to the behaviour observed for intron 1, most of the accessions from waxy to 22% AAC (with two exceptions) carried the TA haplotype (Table 1), GC pattern was present in accessions with 22-24% AAC (with two exceptions) and GA in accessions with AAC >24% (with three exceptions). Statistical analyses showed that the A/C SNP in exon 6 alone explained only the 30.9% of the variation in AAC, but altogether the two SNPs explained the 79.5%. Adding the RM190 microsatellite to the analysis, we did not find a significantly higher explanation of the AAC variation: 79.6% vs. 79.5% (Table 3).

The allelic variation at the RM190 locus together with intron 1/exon 6 SNPs data allowed the identification of allelic patterns associated to different AAC classes. In particular, even considering that no association of CT₁₇ with a specific AAC group could be identified, all the accessions with haplotype CT₁₇, G in intron 1 and C in exon 6 shared an AAC level ranging from 22 to 23%, with the exception of Giada (23.93%). Similarly, the CT₁₄ allele associated with the CG and AG allelic patterns shared similar AAC (ranging from 23.2 to 23.8%). Among the CT₂₀ group, the most frequent allelic pattern for high AAC class was CG, which identifies an AAC range from 22 to 24%, while in combination with AG frequently identified accessions with more than 25% AAC. CT₉, CT₁₀ and CT₁₁ associated to the allelic pattern AG were typical of genotypes with more than 24% of AAC.

Despite the fact that the level of variation in AAC explained by our results is in agreement to the one recently evaluated in an Italian rice collection (Caffagni et al. 2013), it is consistently lower than previously observed. As examples, Ayres et al. (1997) using the combination of RM190 and the G/T in intron1 explained the 85.9% of the variation in AAC; Dobo and co-workers (2010) could explain the 93.8% of variation in AAC with RM190, the SNP in intron 1, the SNP in exon 6 and the SNP in exon 10 which was not present in our collection. Owing these results, it was realized that additional molecular markers could be needed to increase predictability of the different AAC classes within our germplasm panel.

Allele mining of the GBSSI gene

To mine the genetic variation at the level of the GBSSI locus within our germplasm collection, the gene as well as 1kbp of the upstream putative regulatory region were sequenced in twenty-one genotypes representing all the AAC classes identified: Calmochi 101 for the waxy type; Prever, Yrl 196, Delta, Lomellino and Campino for 14-16% AAC; Yrm 6–2, Timich 108, Augusto, Loto and Sant’Andrea for 16-19% AAC; Upla 91, Antoni, Gigante Vercelli, A201 and Gladio for 21-25% AAC; and Fragrance, L 202, Zhen Shang 47, Arroyogrande and Alinano C for >25% AAC (Table 1). The twenty-one GBSSI sequences were compared by multiple alignments considering the Nipponbare sequence as the reference. Sequence comparisons showed an overall high level of similarity (Figure 2), indicating that the coding sequence was conserved in most of the genotypes with some exceptions and led to the identification of 32 SNPs (Table 4). The waxy cv. Calmochi 101 carried the wx allele with the 23 bp duplication in exon 2, as described before. Antoni and Gigante Vercelli accessions showed the non-conservative A/C SNP in exon 6, previously identified and causing a Serine/Tyrosine substitution in the GBSSI protein (Larking and Park, 2003). Additionally, these genotypes contained two common SNPs: one in the putative regulatory region (position −1,514) and one in the first intron (position −399). Gigante Vercelli also showed a SNP at −2,174 bp upstream the ATG (Figure 2; Table 4). Zhen Shang 47 and Alinano C carried a C/T SNP at position +1,801 in exon 9 which results in the substitution of Pro415 to Ser in the GBSS protein. Also for Zhen Shang 47 and Alinano C, SNP mutations were identified in the non-coding sequences: 2 in the promoter region, 11 in intron 1, 1 in intron 6, 9 in intron 10 and 2 in intron 12 (Figure 2; Table 4). Most of the SNPs identified in the present work are not classified in the group of SNPs computationally characterized by Kharabian (2010) and present in OryzaSNP (http://oryzasnp.plantbiology.msu.edu/cgi-bin/gbrowse/osa_snp_tigr/) and dbSNP (http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp&TAbCmd=Limits) databases. Additionally to these SNPs polymorphisms, a 4 bp tandem repeat that was identified in rice cvs. showing high level of Wx transcripts (Cai et al. 1998) was highlighted at position +272 in the rice accessions Alinano C and Zhen Shang 47. In these two accessions, also a 1 bp deletion was present in intron 1 at position −862 and a 3 bp deletion in intron 10 at 2,241 bp from the start codon (data not shown).

Figure 2

Schematic representation of sequence alignment for the GBSSI alleles. Nipponbare Wx sequence was used as reference. The position of the ATG is indicated with 0; negative numbers are referred to bp positions in the putative regulatory regions while positive numbers indicate bp positions in the coding region. Colour shade represents the different AAC classes from white corresponding to waxy to dark grey corresponding to high AAC. SNPs are symbolized by black bars, insertions by blue triangles and deletions by red crosses. Thick, intermediate and tight boxes indicate exons, 5′ and 3′ UTR and the promoter region, respectively. Lines between boxes represent introns.

Table 4 SNPs identified from re-sequencing of the Waxy gene and the putative regulatory region

Generation of new molecular markers from allele mining

With the aim of identifying more informative markers allowing a better discrimination between accessions with AAC higher than 25% from those with lower levels, one and three SNPs were selected from Antoni/Gigante Vercelli and Alinano C/Zhen Shang 47, respectively, for molecular markers development. dCAPS molecular markers were obtained from the SNPs identified at positions −1,514 (promoter region) (Antoni and Gigante Vercelli), +1,801 (exon 9), +2,282 (intron 10) and +2,806 (intron 12) (Alinano C and Zhen Shang 47) and used to screen the 127 accessions (Figure 3; Table 1). Antoni and Gigante Vercelli haplotype (T) at position −1,514 was identified in 17 additional accessions belonging to the group with AAC 20.65 – 24.85%. Among them, 11 carried the C allele for the SNP in exon 6 as Antoni and Gigante Vercelli. Considering both the SNPs, four allelic patterns associated to different levels of AAC were identified (Table 5). The AG allele (the first base referred to the SNP in exon 6, while the second to position −1,514) was present in 89 accessions and associated to a mean AAC value of 18.99% (without considering the waxy variety Calmochi 101); the GC allele was found in 18 accessions with a mean AAC value of 23.50%; the AT allele, carried by 6 accessions, corresponded to an AAC average of 24.24%; the last allele, CT, was identified in 13 genotypes with a mean AAC of 22.75%.

Figure 3

dCAPS molecular markers developed from SNPs found in Antoni, Gigante Vercelli, Alinano C and Zhen Shang 47 Waxy alleles. Lomellino and Prever were used as control. Primers and restriction enzymes for each molecular marker are listed in Additional file 1: Table S1.

Table 5 Apparent Amylose Content (AAC) values observed in accessions with different allelic patterns for the newly identified SNP at position −1,514 in Antoni and Gigante Vercelli associated to the exon 6 SNP

Considering the RM190 alleles associated to the four haplotypes identified for exon 6 and the SNP at −1,514, it was observed that the combination of CT₂₀ with A (exon 6) and G (SNP −1,514) was always associated to an AAC higher than 24.5% (Table 1), thus providing a previously un-indentified tool for selecting rice accessions with high AAC. Allelic variation observed for the SNPs at positions +1,801, +2,282 and +2,806 can finally provide useful diagnostic tools for selecting accessions with AAC higher than 24% when specific donors like Merlè, CNA 4081, Orione, Zhen Shang 47 and Alinano C are used (Table 1); for these accessions, in fact, a unique CGT allelic pattern was observed for the three SNPs. The SNP at position −1,514 slightly increased the AAC explained variation to 80.1% when considering the SNPs only, and to 80.3% when the RM190 was included (Table 3). However, when only the models having all the variables significant at P ≤ 0.05 were considered, the model with the highest ability to explain AAC variation (79.5%) was the one with SNPs at intron1 and exon 6 (Table 3), a result which is fully in agreement with previous work (Caffagni et al. 2013).

Relationships between grain shape parameters and Waxy haplotypes

Correlation analyses between grain shape parameters for the 126 non-glutinous rice accessions (Table 1) are indicated in Figure 4 A, B and showed a close agreement with those reported by Tran et al. (2012). The most obvious correlations were between the L/W ratio and either grain length (positive) or grain width (negative). A negative correlation exists between the length and the width of the grain while regarding AAC, grain length and the L/W ratio were positively correlated with it, whereas grain width was negatively correlated. An analysis of the histogram distribution plots of the variables indicated that the distributions of AAC and grain width clearly showed two modes (Figure 4 A).

Figure 4

Correlations between grain shape parameters and AAC. A) Scatterplot matrix of grain (dehulled caryopses, i.e., brown rice) characters. Histogram distribution plots for the single variables are shown on the diagonal cells. Below them the bivariate distributions are shown for every pair of traits. Confidence ellipses (ELL) mark confidence limit for each distribution (P = 0.95, for a normal bivariate); B) Pearson’s correlation coefficients for grain (dehulled caryopses, i.e., brown rice) characters. All the correlations are significant with P ≤ 0.001 (probabilities were adjusted by Bonferroni’s correction for multiple tests; n = 126).

In rice, traits related to grain size, shape and cooking properties have a large impact on market appreciation and play a pivotal role in the adoption of new varieties (Webb 1991; Juliano 2003). It is therefore interesting to note that, as shown in Table 6, we found some surprising associations between the haplotypes at the waxy locus and the shape of the rice caryopsis. In particular, the SNP at the first intron (which identifies Wx^a and Wx^b genotypes) was associated with differences in the width of the grain, the length to width ratio (L/W), and the length of the kernel. The SNP at the sixth intron showed no association with these traits by itself, but it slightly increased the overall explained variance once the SNP at the first intron was considered. This suggests that the latter SNP was the actual responsible of the association, whereas the SNP at the intron 6 has only an ancillary effect. Even the CT_n showed a significant association, but its explanatory capability of the variance of grain biometric parameters was lower than that of the SNP at the first intron, thus that, given it also has no direct effect on the functionality of GBSSI, its association is most probably indirect and most likely ascribable to the phylogenetic association of some CT_n haplotypes with the two versions of the SNP at the first intron.

Table 6 Percentage of variation for seed biometric indexes explained by the tested GBSSI molecular markers in the germplasm collection

The positive correlation between AAC and L/W ratio, and the negative one with the width of the grain were further analysed (Figure 5). In fact, by plotting L/W ratio versus AAC for the overall genotype set used in this work, the positive correlation (r = 0.517, P < 0.001) is immediately apparent (Figure 5A). When the genotype set is grouped according to the haplotype at the SNP at intron 1 (G/T), that is, the Wx^a/Wx^b allelic version is superimposed onto the correlation plot, it becomes evident that: (a)- this SNP offers a sharp distinction between genotypes with AAC ≤ 21% (Wx^b) and AAC ≥ 21% (Wx^a); (b)- in our set of genotypes, there seems to be a break for AAC around 21.5%, which, therefore, appears to be a more precise threshold value for discriminating between Wx^a/Wx^b allelic versions, even though a few genotypes spill over, and small differences in AAC can actually occur because of the method of assay (Fitzgerald et al. 2009); (c)- the frequencies of genotypes having an intermediate Apparent Amylose Content (haplotype Wx^b) are skewed towards low L/W ratios, and, vice-versa, the frequencies of high amylose genotypes (haplotype Wx^a ) are skewed towards high L/W ratios. It is indeed the presence of these skewed distributions that generates the positive correlation between L/W ratio and AAC, and, then, the association between L/W ratio and the SNP at the first intron.

Figure 5

Relationships between AAC and L/W ratio correlations. Scatterplots of: L/W ratio versus AAC (A), and grain width versus AAC (B). Datapoints for dehulled caryopses of 126 non glutinous cultivars, grouped according to the haplotype at the SNP of intron 1 (47 accessions with G allele; 79 accessions with T allele). The regression line for the whole dataset and its determination coefficient are shown in each plot.

Furthermore, the inverse correlation between AAC and grain width is slightly stronger than that between AAC and L/W (Figure 4; Tran et al. 2012). Correspondingly, grain width has a slightly higher explanatory ability on AAC than L/W ratio (Figure 5B). This is due to the stronger skewing of the grain width distribution within the Wx^a haplotype, which, in turn, is linked to the up-mentioned presence of two clearly distinct modes in grain width (Figure 4A): one, at 2.2 mm, typifies Wx^a genotypes, while the other, around 3 mm, characterizes Wx^b genotypes (Figure 5B). In the case of width, Wx^a genotypes are more sharply crowded close to their mode than what occurs for the L/W ratio, that is, their frequencies are more skewed towards thinner grains. This makes the existence of two different modes, one for each Wx haplotype, immediately evident even in the overall population (Figure 4A), and also augments the slope of the regression line (Figure 5B), thus that the effect of the Wx haplotype upon grain width appears to be greater than that on L/W ratio.

Interestingly, AAC also positively correlates with cycle length (r = 0.378, P < 0.001; data for 124 non-glutinous rice accessions for which maturity data were available; Table 1), whereas no significant correlation was observed between cycle length (days to maturity) and grain shape parameters (data not shown). The relationship would appear to be owed to a preponderance of long-cycle accessions in the high-amylose Wx^a group (data not shown). It is however not a necessary correlation, since some genotypes with high AAC and short cycle were present as well (Gladio and Sandora; Table 1). For the same reason, this should not be an environmentally-caused relationship, although the amylose content can be affected by the temperature during grain ripening (Gomez 1979).

Discussion

Allelic analyses of the Waxy gene

Different alleles of the Waxy gene in rice are defined by a relatively small number of molecular markers. The RM190 CT_n itself could explain more than 80% of Apparent Amylose Content variation in several different rice collections (Shu et al. 1999; Bergman et al. 2001; Tan and Zhang 2001; Dobo et al. 2010). According to other works, for our germplasm collection RM190 could explain a high percentage of variation in AAC (74.5%), considering non-glutinous accessions. Dobo and co-workers (2010) found that the CT₂₀ allele was associated to high or intermediate AAC for European or US cvs., respectively; similarly, in our analyses, this allele was always associated to AAC higher than 22% but regardless to the origin of the genotype. As previously observed (Bergman et al. 2001; Tan and Zhang 2001; Dobo et al. 2010), accessions with a number of CT repeats ranging from 17 to 19 showed an AAC variable from 15 to 23%. This behaviour is in agreement to the fact that the RM190 microsatellite is only a molecular marker associated to the Waxy gene without a functional role in determining the level of amylose in rice seeds (Chen et al. 2008; Dobo et al. 2010).

A functional role was instead characterized for the G/T SNP in the donor splicing site of the leader intron. In literature the G haplotype, which characterises the Wx^a allele, is associated to high AAC, while the T allele (Wx^b) to low and intermediate AAC (Shu et al. 1999; Bergman et al. 2001; Tan and Zhang 2001; Dobo et al. 2010). For our non-glutinous germplasm, the G haplotype was always associated to AAC higher than 22%. Considering the high level of AAC variation explained (77.5%) and the observation that only a very small increase in explained AAC variation resulted from adding the RM190 data, we concluded that our results are in accordance with the SNP in intron 1 as being the main determinant affecting amylose amount in endosperm.

Several researches have been focused in identifying different Wx alleles associated to the wide variability in AAC. Larkin and Park (2003) found two SNPs, an A/C in exon 6 and a C/T in exon 10, causing non-conservative amino acid substitutions at the protein level which could change the specific activity of the GBSSI protein. Together with the SNP in intron 1, these polymorphisms explained 93.8% of ACC variation in European and US germplasm collections (Dobo et al. 2010). Chen et al. (2008) screened 171 rice accessions originating from 43 countries for the above mentioned GBSSI SNPs and found that the majority of cvs. with an AAC ranging from 21 to 22% carried the haplotype C at exon 6. Results with our collection highlighted no variations for exon 10 and an association of the SNP in exon 6 to AAC values from 21 to 24% for the C allele, while the A allele was present in accessions with AAC ranging from waxy to 21% and from 24 to 26%. The three haplotypes defined by the combination of the two SNPs in intron 1 and exon 6 were able to explain the 79.5% of AAC variation in non-glutinous accessions. Even in this case, when the RM190 was added to statistical analyses it did not show a significant effect on the percentage of explained AAC variation, confirming that this molecular marker does not have a direct effect on the functionality of the enzyme (Dobo et al., 2010). These results underline that the two SNPs (intron 1 and exon 6) represent the more suitable tools to be used in breeding programs for our germplasm collection and that a priori generalization of the efficiency for molecular markers in predicting AAC values could represent a risky procedure.

A higher level of AAC variation was found to be explained in previous work with respect to the ones observed here, even when comparisons were based on the same molecular markers. Considering that our collection is represented by genotypes originated from a wide range of countries, it is possible that the genetic diversity to be explained was greater than in other studies where the germplasm originated from more restricted areas: US (Ayres et al. 1997; Bergman et al. 2001), Europe and US (Dobo et al. 2010), China (Tan and Zhang 2001) and Korea (Shu et al. 1999).

Identification of new molecular markers associated to apparent Amylose content

To date, most of the studies on the topic have been based on the characterization of molecular markers associated to waxy and low AAC (Isshiki et al. 1998; Larkin and Park 2003; Wanchana et al. 2003; Mikami et al. 2008; Liu et al. 2009; Tran et al. 2012). However the preference for rice with high AAC in occidental countries underlines the need of developing new markers facilitating the identification of genotypes with high AAC. A sub-sample of 21 genotypes belonging to our collection and representing four AAC classes was selected for the GBSSI gene re-sequencing. A very high level of sequence identity was observed and most of the identified polymorphisms were detected in non-coding regions. Thus, the GBSSI gene has been likely conserved during evolution as its key role in controlling the accumulation of amylose in caryopsis.

As previously reported, the amount of amylose in endosperm is principally related to the post-transcriptional regulation of the Waxy gene which is influenced by the intron 1 SNP (Wang et al. 1995; Bligh et al. 1998; Cai et al. 1998). However, our sequencing comparisons revealed that genotypes belonging to different AAC classes showed the same GBSSI haplotypes and alleles suggesting that other genes involved in starch synthesis can affect the amylose content in endosperm. These additional genes could be represented by the Du gene, encoding for a Starch Synthase enzyme (Zeng et al. 2007), the Shr gene encoding for a Sucrose Synthase (Kawagoe et al. 2005), or other genes encoding for enzymes involved in starch metabolism such as ADP glucose pyrophosphorylase (Venu et al. 2011) and α-glucan phosphorylase (Satoh et al. 2008).

Antoni and Gigante Vercelli accessions carried the SNP in exon 6 typical of the Wxⁱⁿ allele, the SNP in intron 1 (position −399) and one in the promoter region (position −1,514). Moreover, two accessions with high AAC, Zhen Shang 47 and Alinano C, showed the highest sequence variation with respect to Nipponbare that included a non-conservative SNP in the coding region of exon 9, causing a Pro to Ser substitution in the GBSSI protein. As this mutation implicates the substitution of an apolar amino acid to a polar one it should be possible that it increases the activity of the GBSSI enzyme, even if its effect should be small as the SNP does not affect the AAC variation significantly (Tab. 3).

New dCAPS markers were developed exploiting the SNPs in intron 1 at position −1,514 (identified in Antoni and Gigante Vercelli), exon 9, intron 10 and intron 12 (identified in Zhen Shang 47 and Alinano C). After screening of the 127 accessions with these molecular markers it was observed that the combination of the CT₂₀ RM190 alleles in combination with the A haplotype for exon 6 and the G haplotype for SNP −1,514 was always associated to an AAC higher than 24.5% thus providing an efficient tool for selecting high AAC rice accessions.

Associations between molecular markers and grain shape parameters

The results presented in this work confirm general observations (Webb 1991; Juliano 2003) that high AAC is associated with a slender grain, whereas rices having an intermediate amylose content most often have bold grains. We clearly highlighted that the frequencies of genotypes having a low AAC (< 21.5%) are skewed towards low L/W ratios while the frequencies of intermediate-high amylose genotypes (>21.5% AAC) are skewed towards high L/W ratios. These skewings can be imputed to selection for consumer preferences: for cooking and processing, rice are conventionally classified as short-, medium- and long-grain types, and an AAC ≥ 20% is preferred for long-grain rice, whereas an AAC < 20% is commonly favoured for short- and medium-grain types (Webb 1991). The width of the grain is as much, or even more, linked to the cooking application of rice than the L/W ratio: long, thin-grain rice is typically used in applications requiring distinct shape and texture, and high amylose ensures that cooked grains are firm and remain separate; on the other hand, medium- and short-grain rice are ideal for puddings, desserts, and similar applications, and low amylose content allows to obtain cooked grains that are soft, moist and sticky. Grain width and L/W ratio are more distinctive of the cooking application than grain length because long-grain rice with bold kernel are typically used to prepare ‘risotto’, and many of them are traditional varieties with intermediate AAC. In fact, the EU classification (see Ferrero and Nguyen, 2004 for a synoptic table of USA and EU grades) further distinguishes long-grain rice (length > 6.0 mm) into long A (2 < L/W < 3) and long B types (L/W ≥ 3). The former wide-grain type is used for ‘risotto’ and often has intermediate AAC, while the latter thin-grain type includes high amylose rice for oriental and side dishes, prepared entrees, rice salad and garnitures. Thus, grain width is more related to cooking applications than grain length, and then it has a better association with AAC. Further work is crucial to characterize the genetic basis of the two different modes observed for grain width in Wx^a and Wx^b haplotypes, and many candidate genes are available (Huang et al. 2013).

It can be worthy to note that the positive correlation between AAC and cycle length may occur since the high-amylose Wx^a haplotype, primarily found in the indica subspecies (Hirano and Sano 1991; Dobo et al. 2010), was most probably introduced into the japonica subspecies, which till to some decades ago lacked cultivars with high amylose (Juliano 1979), from indica genotypes, which frequently have longer cycles. It can be supposed that breeding for high AAC japonica cultivars has then been commonly done by crossing within a restrict group of late-maturing materials of indica derivation, thus that the long cycle would be an instance of genetic drift (or draft). On the other hand, the fact that no significant correlation was observed between cycle length and grain shape parameters suggests that breeding for both early and late cultivars within each of the grain-type varietal groups has broken up any hitchhiking effect between these traits. The strong association between the SNP at the first intron (which identifies Wx^a and Wx^b genotypes) and the width of the grain, the length to width ratio (L/W), and the length of the kernel is owed to the skewing of genotype frequencies towards a bold grain in Wx^a rice and towards a slender grain in Wx^b ones (Figure 5), and hence it is most likely ascribable to human selection. Simultaneous selection for two traits can increase the frequency of alleles that affect both traits favourably and leave the frequency of alleles that affect one trait favourably and one trait unfavourably at lower levels (Bennett and Swiger 1980), which is precisely what is observed in Figure 5. Selection for optimal character combinations, generating genetic correlation between suites of linked traits, is also known as correlational selection (Sinervo and Svensson 2002). Together with selection for non-shattering grains, reduced seed dormancy, whitish kernels and aroma (McCouch et al. 2012; Sang and Ge 2013), correlational selection for grain shape and AAC has marked the evolution of the rice crop according to human preferences for grain characteristics.

Conclusions

The available molecular markers utilized for evaluation of AAC classes did not provide tools for predicting accessions with AAC higher than 24.5%. New SNPs were identified through re-sequencing of the Waxy gene and 1kbp of the upstream region. The related dCAPS markers increased the AAC explained variation and allowed the identification of a haplotype almost unequivocally associated to AAC higher than 24.5%, which represent the AAC class preferred in the occidental countries markets. The SNP at the first intron, which identifies the Wx^a and Wx^b alleles, was associated with differences in the width of the grain, the L/W ratio and the length of the kernel, most likely as a result of human selection.

Methods

Plant material and phenotyping for AAC and biometric parameters

An Italian Oryza sativa L. collection of 127 accessions was used in the present study and included 40 national rice varieties belonging to the japonica ssp. and 87 foreign accessions (6 from Spain, 6 from France, 29 from the US, 10 from Portugal, 7 from Argentina and 29 from other countries worldwide), of which 125 were japonica and 2 indica. The Italian rice varieties were selected because of their past and present relevance for breeding programs, and their contribution to rice production in the last decades. The 87 foreign accessions were selected as being relevant to Italian rice breeding, or considered reference varieties at the international level.

Each rice accession was grown in triplicate field trials for two years. Maturity time of each field trials was recorded when at least 50% of the plants was ready for harvesting. Data was represented by the mean value of three replicates and indicates the days from sowing to panicles maturation. Harvested rice seeds were used for amylose quantification and biometric parameters evaluations. According to the protocol of Williams et al. (1958), with modifications by Inatsu (1988), the AAC of milled grain was measured with a FOSS FIAstar 5000 auto-analyzer which is based on a flow injection of a solution of NaOH 0.09% to the sample, the addition of an iodine solution and the spectrometric determination of the absorbance of the formed color at 720 nm. The calibration was performed measuring the absorbance of standard rice samples carrying 15.40%, 23.10% and 27.7% of AAC, respectively, using a white reference. These reference samples were supplied and certified by FOSS as having their amylose content determined against amylose/amylopectin standards. The SoFIA software (FOSS) was used to build up the calibration curve and to obtain the percentage of amylose in our samples. Each reference analysis was repeated twice for each sample. Biometric parameters of rice seeds (decorticated grain length and decorticated grain width) were evaluated trough optical scanner-produced high resolution images analyzed with the WinSEEDLE 2011a software (Regent Instruments Inc.).

Molecular markers analyses

To obtain genomic DNA from the rice accessions, seeds were germinated in petri dishes at 30°C and one-week old seedlings were transplanted and grown in a greenhouse until three leaf stages; leaves were therefore collected, frozen in liquid nitrogen and store at −80°C. Genomic DNA was extracted on plates using the Wizard® Magnetic 96 DNA Plant System (Promega) according to manufacturer’s instructions. The CTAB DNA extraction method (Doyle and Doyle 1987) was instead applied for the 21 genotypes selected for GBSSI gene re-sequencing.

The RM190 CT repeat was assayed using the M13-tailed forward primer RM-190 F (CACGACGTTGTAAAACGACCTTTGTCTATCTCAAGACAC) and the reverse primer RM-190R (TTGCAGATGTTCTTCCTGATG) (Ayres et al. 1997; Chen et al. 2008). PCR reactions were performed in 10 μl containing 15 ng of genomic DNA, 0.1 μM of RM-190 F, 1 μM of RM-190R and FAM-labelled M13 (CACGACGTTGTAAAACGAC), 0.2 mM dNTPs and 1 U GoTaq DNA Polymerase (Promega). DNA was amplified using a touchdown program as follows: denaturation at 94°C per 3 min, 20 cycles at 94°C per 45 sec, from 61°C to 51.5°C per 45 sec, reducing the annealing temperature of 0.5°C for each cycle, and 72°C per 45 sec, 24 cycles at 94°C per 45 sec, 51°C per 45 sec and 72°C per 45 sec and a final extension of 72°C per 10 min. Labelled PCR products were run in a 3130 Genetic Analyzer (Applied Biosystems). ROX size standard (Applied Biosystems) was used.

PCRs for dCAPS analyses of the G/T polymorphism in intron 1 were carried out in 20 μl using GoTaq DNA Polymerase (Promega) supplemented with 5% DMSO and with 20 ng of genomic DNA, 1 μM of GBSS-W2F, 1 μM of GBSS-W2R (Ayres et al. 1997; Chen et al. 2008). Amplification conditions were: 94°C per 4 min followed by 40 cycles of 94°C per 40 sec, 60°C per 50 sec and 72°C per 1 min per kb and a final extension of 72°C per 10 min. After amplification 5 μl of PCR products were digested with 1 U of AccI restriction enzyme (New England BioLabs) in a total volume of 10 μl at 37°C over night. The samples were run on 2% agarose gel. Each digested sample was compared alongside with its not-digested cognate control.

The presence of Wx^op, Wxⁱⁿ, Wx^mq, Wx^hp and wx alleles and the variability for the SNP in exon 10 identified by Larking and Park (2003) were assessed in our collection through the sequencing of exons 4, 6, 5, 2 and 10 using the same procedure for the allele mining experiment described below.

To detect the SNPs at position −1,514, +1,801, +2,282 and +2,806 by dCAPS analysis, mismatched forward primers were designed by dCAPS Finder 2.0 software (http://helix.wustl.edu/dcaps/dcaps.html). Primers and restriction enzymes for dCAPS assay are listed in Additional file 1: Table S1. For PCR amplification, the same protocol used for detecting the G/T polymorphism in the leader intron was used.

Allele mining

For the allele mining of the GBSSI alleles in the 21 selected genotypes, six overlapping regions, ranging from 800 bp to 2,500 bp, were PCR amplified from genomic DNA with the same protocol described above for the G/T polymorphism. Primers designed on the Nipponbare genomic sequence (Genebank AC NC_008399; Additional file 2: Table S2) were utilized for genomic DNA amplifications using the combinations indicated in Additional file 2: Table S2. The amplified regions covered the entire gene plus 1 kbp of the upstream putative regulatory region. After gel purification by the Wizard® SV Gel and PCR Clean-Up System (Promega), PCR products were directly sequenced. Sequencing reactions were accomplished by the use of ABI BigDye Terminator version 3.1 (Applied Biosystem) in forward and reverse directions with 5 μl of each PCR amplification product and the same primers used for PCR amplifications or internal primers. All primers were designed using the Primer3 0.4.0 software (http://frodo.wi.mit.edu/) and blasted against the rice genomic sequence on the Gramene website (http://www.gramene.org) to ensure the specificity for the GBSSI gene.

Computational analyses

The RM190 microsatellite was analyzed using the GeneMapper software (Applied Biosystem). Sequence assembly was assessed with the ContigExpress tool of Vector NTI Software (Invitrogen) using the Nipponbare genomic sequence as reference. Sequence comparison was carried out by MultAlin software (http://multalin.toulouse.inra.fr/multalin/). All data were analysed with the Systat 12 software (SPSS Inc., Chicago, IL, USA). The relationships between numerical variables (AAC and grain biometrical characters) were evaluated by Pearson correlation coefficients and regression analysis. The associations between numerical variables (AAC) and categorical variables (marker haplotypes) were analysed according to the General Linear Model (GLM) procedure.