Nucleotide Diversity Analysis of Three Major Bacterial Blight Resistance Genes in Rice

Waikhom Bimolata; Anirudh Kumar; Sai Kiran Reddy M; Raman Meenakshi Sundaram; Gouri Sankar Laha; Insaf Ahmed Qureshi; Irfan Ahmad Ghazi

doi:10.1371/journal.pone.0120186

Abstract

Nucleotide sequence polymorphisms among R gene alleles influence the process of co-evolutionary interaction between host and pathogen by shaping the response of host plants towards invading pathogens. Here, we present the DNA sequence polymorphisms and diversities present among natural alleles of three rice bacterial blight resistance genes, Xa21, Xa26 and xa5. The diversity was examined across different wild relatives and cultivars of Oryza species. Functional significance of selected alleles was evaluated through semi-quantitative reverse transcription polymerase chain reaction and real time PCR. The greatest nucleotide diversity and singleton variable sites (SVS) were present in Xa26 (π = 0.01958; SVS = 182) followed by xa5 and Xa21 alleles. The highest frequency of single nucleotide polymorphisms were observed in Xa21 alleles and least in xa5. Transition bias was observed in all the genes and ‘G’ to ‘A’ transitions were more favored than other form of transitions. Neutrality tests failed to show the presence of selection at these loci, though negative Tajima’s D values indicate the presence of a rare form of polymorphisms. At the interspecies level, O. nivara exhibited more diversity than O. sativa. We have also identified two nearly identical resistant alleles of xa5 and two sequentially identical alleles of Xa21. The alleles of xa5 showed basal levels of expression while Xa21 alleles were functionally not expressed.

Citation: Bimolata W, Kumar A, M SKR, Sundaram RM, Laha GS, Qureshi IA, et al. (2015) Nucleotide Diversity Analysis of Three Major Bacterial Blight Resistance Genes in Rice. PLoS ONE 10(3): e0120186. https://doi.org/10.1371/journal.pone.0120186

Academic Editor: Jauhar Ali, International Rice Research Institute, PHILIPPINES

Received: August 21, 2014; Accepted: January 23, 2015; Published: March 25, 2015

Copyright: © 2015 Bimolata et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research work is supported by grants from Department of Science & Technology, New Delhi, India (grant no. SR/SO/PS-21/09). WB is also grateful to CSIR for the fellowship (award no. 09/414/ (0832)/2008- EMR-I). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Plants are constantly exposed to various biotic stresses like insect pests and pathogens and have evolved several mechanisms to guard themselves these stresses. As pathogens challenge the fitness of the plant, they are regarded as natural selective agents shaping evolutionary dynamics of host-pathogen co-evolution [1, 2]. In crops like rice, infections by pathogens influence yield, morphology, grain quality and texture [3]. Bacterial blight (BB) is a major disease of rice caused by Xanthomonas oryzae pv. oryzae. The annual yield loss by this disease is recorded up to 20–50% [4–6]. At the phenotypic level, symptomatic variations in natural rice populations are generally observed after pathogen infection. This variation may be correlated to the genetic diversity in host plants. Bacterial blight is prevalent and widespread in most of the rice cultivating countries of South and South East Asia including India. For combating the disease, the most effective and economical measure is exploitation of host plant resistance. To date, more than 38 R genes for BB resistance have been reported [7, 8]. Diversity analysis of these genes in natural population will facilitate identification of allelic variations which can be exploited in resistance breeding programs [9]. Natural diversity is an important asset for functional genomics studies and crop improvement. Abundant genetic resources available in nature give us an opportunity to explore and discover new forms of resistance genes.

Study of the evolutionary dynamics of disease resistance genes began twenty years ago, using Arabidopsis and tomato as model plants. Recently, various reports on molecular evolution and genetic diversity of blast resistance genes for blast disease caused by Magnaporthe grisae are available; however similar studies for bacterial blight resistance genes are limited [10–13]. Among different factors, pathogens play an important role as a selective agent in the evolution of R genes. Host and pathogen co-evolve, resulting in continuance of allelic disparity of R genes through balanced selection [14]. Sequence polymorphism in naturally occurring alleles may alter the gene expression profile and functional characteristics of proteins leading to phenotypic variations of a trait [15, 16]. Recent studies conclude that nucleotide changes in the non-coding and regulatory sites of R genes also contribute to resistance or susceptibility phenotypes of a disease in addition to nucleotide variations in the coding region [17, 18].

In this study, we present the analysis of natural polymorphism with respect to three major bacterial blight resistance genes: Xa21, Xa26 and xa5, at the sequence level in a set of rice germplasm. Xa21 is a broad spectrum bacterial blight resistance gene, originally derived from a wild rice O. longistaminata and introgressed into cultivated rice [19]. Song et al. [20] reported the cloning of this gene which encodes a LRR threonine rich receptor like kinase domain containing protein. Xa26 is a dominant R gene, which provides resistance against Chinese, Japanese and Korean Xoo strains. Both Xa21 and Xa26 genes had been mapped on the long arm of Chromosome 11 of rice. The presence of Xa26 in rice was first reported in Chinese O. sativa cultivar Minghui 63. Plants with this gene exhibited broad resistance both at seedling and adult stages. Xa26 also encodes a LRR receptor kinase-type protein [21]. Both Xa26 and Xa3 have been shown to be the same gene [22] and is an important gene in the breeding of japonica cultivars with BB resistance in China [23]. xa5 is a recessive gene, which confers race specific blight resistance against Philippines Xoo race 1 (PXO86). The xa5 gene is a mutant form of rice transcription factor OsTFIIAγ5 (Xa5) where there is a substitution variant of a single amino acid: V39E [24]. xa5 codes for 106 amino acids and both the dominant and recessive genes are constitutively expressed in different tissues of the plant [25]. The predicted 3-D structure of xa5 protein after its superimposition with Xa5 and TFIIA shows that due to the substitution of V39E, a minor variation occurs in the third helix domain of xa5 protein [25].

Previously, we reported the nucleotide diversity analysis of Xa27 [13]. The present study was designed to analyze the molecular evolution and nucleotide sequence diversity existing among naturally occurring alleles of rice bacterial blight resistance genes Xa21, Xa26 and xa5. The objectives of this study were to (i) determine the DNA polymorphism level of Xa21, Xa26 and xa5 in different rice germplasms; (ii) to analyze the evolutionary relationship among the alleles of each gene; (iii) to study the type of selection acting at these loci.

Materials and Methods

Plant material, screening and DNA isolation

In our previous study, we reported screening of wild and cultivated rice accessions for resistance against set of five Indian Xoo isolates DX011, DX133, DX020, DX015 and DX127 [13]. Based on this, we isolated alleles of Xa26, Xa21 and xa5 genes from rice accessions mentioned in Table 1. Plants were inoculated following Kauffman et al. [26]. Rice plants at booting stage were inoculated with suspension of Xoo culture at the concentration of 0.1–0.2 OD (1x10⁸–1x 10⁹ CFU/ml) by leaf clipping method. Three plants (five leaves per plant) were used to inoculate for each isolate. Sterile water was used in place of bacterial suspension for the control plants. Disease spectrum was evaluated after 15 days of inoculation by measuring the lesion lengths and calculating the relative lesion length (RLL), that is the ratios of lesion length to leaf length (RLL). Disease scoring was performed by following Standard Evaluation System for Rice (SES scale) [27]. Genomic DNA was isolated from fresh tender leaves of rice plants following CTAB method [28]. The required chemicals were procured from Sigma Aldrich (USA).

Download:

Table 1. List of rice accessions used for isolation and nucleotide diversity analysis of alleles.

https://doi.org/10.1371/journal.pone.0120186.t001

Isolation and sequencing of alleles

Gene specific primers were designed for Xa21 (U37133.1) [20], Xa26 (DQ426646.1, cultivar Zhachanglong) [22], and xa5 (Os05g0107700). The entire coding and non-coding region of each gene was covered using overlapping primers (S1 Table). For Xa21, 18 alleles were isolated from O. nivara (M209A, 81832, 81852, 100963, 102166, 106133, ON132, ON15 and ON34), O. sativa (IR20, TN1, PB1, PAU933, MohemPhou, MustiLaiphou and R4), O. longistaminata (OL1 and OLD); 14 alleles of Xa26, were amplified from O. nivara (ON 38–1, M 209 A, 81832, 81852, 106133, 210223 and ON132), O. sativa (TN1, PB1, MohemPhou, MustiLaiphou and R47), O. alta (IC384116), O. officinalis (IC203740); and 15 alleles of xa5 were amplified from (M209A, 81832, 81852, 100963, 106133, ON132 and ON15), O. sativa (TN1, PB1, MohemPhou, MustiLaiphou, Kalamekri and AC32753), O. longistaminata (OL1 and OLD). PCR reactions were carried out using a standard protocol under the following conditions: The total reaction volume was 30μL and the reaction mixture consisted of 50 ng genomic DNA, 0.9 μM of each primer, 0.2 mM of each dNTP, 5 x PCR buffer (10 mM Tris-HCl, pH 8.0, 1.5 mM MgCl₂), and 0.6 unit (2U/ μL) of High Fidelity Phusion polymerase (Finnzymes, Thermo Scientific USA). Hi-Fi polymerase was used as it has proof reading ability. The PCR program was set for the initial denaturation at 98°C for 3 min, 35 cycles of amplification with DNA denaturation at 98°C for 15 s, and annealing was set at different temperatures for different primers for 30 s. Elongation was kept at 72°C with a different time period specific for the product size. There was a slight variation in the PCR amplification pattern from one primer to another based on the annealing temperature. The amplified product was then separated and resolved on 1% agarose gel and purified using a gel extraction kit (Sigma Aldrich, USA). The purified PCR products were then cloned into pTZ57R/T vector (Thermo Scientific, USA) and sequenced commercially by SciGenome Labs (Cochin, India). Two clones of each allele were sequenced to confirm the accuracy of the sequence. The sequences obtained in this study were submitted to the NCBI GenBank database. The GenBank accession numbers of the alleles are KF689653- KF689667 and KJ890413-KJ890443.

Allelic diversity and sequence data analysis

The raw sequence data obtained were edited and aligned with the help of the “align two sequences” option of NCBI BLAST (http://blast.ncbi.nlm.nih.gov) and the overlapping nucleotide sequences were removed. The sequenced products of different primer combinations were then arranged into a complete sequence. The coding regions and the UTR regions were determined from the reference sequences (sequences used for designing primers). The alleles obtained for Xa21, Xa26 and xa5, respectively, were compared with that of the gene sequence from IRBB21 (possessing Xa21), IRBB3 (possessing Xa3/Xa26), IRBB5 (possessing xa5) and IR24 (possessing susceptible alleles at the three loci, i.e. xa21, xa3/xa26 and Xa5). Allelic and nucleotide diversity were then analyzed as follows: the percentage sequence identity between allele and reference sequences were determined by pairwise alignment using NCBI BLAST [29] (http://blast.ncbi.nlm.nih.gov). The sequence variation among alleles was compared using summary statistics ‘π’ (average pairwise difference or average number of nucleotide diversity per site) [30] and ‘θ’ (population mutation parameter or number of segregating sites) [31]. Both inter and intra-specific polymorphisms among alleles of the three genes were analyzed using DnaSP program version 5.1 (http://www.ub.edu/dnasp) [32]. Sequences were first aligned using Clustal Omega (www.clustal.org/omega/‎) [33]. The output aligned file was saved in Fasta format and used as an input file for analysis in DnaSP. Total number of polymorphic sites with single nucleotide polymorphism (SNPs), insertions and deletions (InDel), and synonymous and non-synonymous substitutions were estimated [34]. The analysis was performed with both the coding and non-coding regions. All the diversity analysis was performed as previously described [13]. Briefly, Tajima’s D [35] and Fu & Li’s D [36] were analyzed to determine nature of selection/departure from neutrality. Linkage disequilibrium was estimated based on the parameter of R² between all SNPs using DnaSP v5.10. The significance of LD was measured statistically using Fisher’s exact test and Chi-square test. Decay in LD was determined by plotting graph of R² versus pairwise distance (bp).

Constructing phylogenetic trees

The phylogenetic trees depicting the genetic relatedness among alleles were constructed for each gene using MEGA 4 [37] (www.megasoftware.net/mega4/mega.html‎). The multiple aligned sequences were used as an input file for constructing each tree. An unrooted linear Neighbor joining (NJ) tree was plotted with an option of 10,000 bootstrap values. Missing gaps were excluded from the analysis. Gene tree using Maximum Parsimony, Minimum Evolution and UPGMA method were also drawn to verify consistency.

Allelic expression analysis

Based on the sequence and genotypic screening analysis through marker study conducted with pTA248 marker [38] for Xa21 and xa5 functional marker [39], four alleles each of the genes Xa21 and xa5 were selected for expression analysis. Selected alleles included IRBB21 (Xa21) and IRBB5 (xa5) as resistant controls and IR24 as a susceptible control for both genes. The Xa21 test alleles were from two accessions of O. longistaminata and the xa5 test alleles were from Kalamekri and AC32753 (O. sativa). Expression analysis of Xa26 alleles were not performed as Xa26 gene being reported as constitutively expressed gene [40]. The rice plants were grown in glass house with adequate water and nutrients. The day and night temperature of the glass house was maintained at 28°C. Plants at booting stage were then inoculated with Xoo strain DX011 (which was incompatible with rice genotypes possessing Xa21 and xa5) in triplicates by leaf clipping method [26]. The control plants were treated in a similar manner with sterile water instead of Xoo inoculums. Leaf samples for RNA isolation were collected at different time intervals between 0 and 24 h. Total RNA was extracted with 100 mg leaf samples using an RNA isolation kit (Genetix, India) according to the manufacturer’s instructions, from triplicate samples. Extracted RNA was treated with DNase to remove genomic DNA impurities. Equal amounts of RNA (1–2 μg) were taken from each sample and cDNA was synthesized using Superscript III cDNA synthesis kit (Invitrogen, USA) following manufacturer’s instructions. For the Xa21 alleles, semi quantitative RT-PCR was performed. A 30 μL reaction mixture consist of 100 ng cDNA, 10X PCR buffer (10 mM Tris-HCl, pH 8.0, 1.5 mM MgCl₂), 4 pmole of each primer, 0.2mM of each dNTPs and 0.6 unit (2U/μL) of Taq polymerase (Invitrogen, USA). Initial denaturation was set for 4 min at 95°C with 26 cycles of amplification with 45 s DNA denaturation at 94°C, 30 s annealing at 60°C and extension at 72°C for 1 min. Primers for xa5 and Xa21 were designed from the exonic junction (S1 Table). Actin primer (Forward 5' GAGTATGATGAGTCGGGTCCAG 3’ and Reverse 5’ ACACCAACAATCCCAAACAGAG 3’) was used as reference gene for both semi-quantitative and quantitative RT-PCR (qRT-PCR) [41]. For qRT-PCR, a master mix containing 2X Sybr Select Master Mix (Invitrogen, USA), 150–400 nM forward and reverse primers, and 100 ng of cDNA was used as a template. The PCR program was set for UDG activation at 50°C for 2 min, AmpliTaq DNA Polymerase UP activation at 95°C for 2 min, and denaturation at 95°C for 15 s. Primer annealing was performed at the melting temperature of the primer for 15 s and extension at 72°C for 1 min. The cycle was repeated for 40 times from denaturation step. The real time data were analyzed by relative quantification methods and was calculated using the following formula:

Fold changes in expression was expressed as [42]_. Statistical significance of the data was analyzed using two way ANOVA and Bonferroni post test.

Results

Nucleotide Polymorphism analysis of Xa21, Xa26 and xa5 loci

Sequence analysis of Xa21 alleles.

The total ORF of Xa21 comprising of 3,921 nucleotides including one intron of 843 base pairs was sequenced from 18 accessions. Allelic sequences ranged between 3,828 to 3,921 nucleotides and the length of the total aligned sequences was 3,997 base pairs. The percentage identity of the nucleotide sequences of the alleles ranged between 95–99% in comparison to IRBB21. There were a total of 196 mutations, 191 segregating sites, 113 parsimony informative sites, 64 InDel events and 78 singleton variable sites (rare kind of polymorphism, which is present once in the sample) within the entire sequenced region. The overall average number of nucleotide difference (K) was found to be 41.977 (Table 2). There were a total of 136 SNPs in exonic regions and 40 SNPs in the intron. The frequency of SNP was one SNP per 22.7 bp for the entire sequence, one SNP per 23 bp for the exonic region and one SNP per 21.5 bp for the intron. The SNP frequency was higher in O. sativa with one SNP per 51.9 bp and one SNP per 55.25 bp in O. nivara. The average frequency of InDel polymorphism was one InDel site for every 62.4 bp. Maximum InDel was observed in the first exon and least in the last exon. The frequency of InDel was highest in the intron followed by exon 1 and least in exon 2. Nucleotide substitution was observed throughout Xa21 but the highest nucleotide diversity was seen within the first 500 bp of the coding region. The nucleotide diversity π of the total sequence was 0.01112 and the intronic region had higher nucleotide diversity (π = 0.01328; Ө_w = 0.01500) than the coding region (π = 0.01055; Ө_w = 0.01433). Within the coding region, exon 1 (π = 0.01083) had substantially higher diversity than exon 2 (π = 0.00874). O. nivara exhibited slightly higher sequence variation (π = 0.00782; Ө_w = 0.00846) compared to O. sativa (π = 0.00688; Ө_w = 0.00782). Nucleotide diversity was higher than InDel diversity (0.00238). All alleles, except OLD and OL1, showed an equal level of polymorphism (S1 Fig.). Frequency of ‘A’ to ‘G’ transition was the most frequent polymorphism and overall transition bias of the alleles was 1.221.

Download:

Table 2. Summary statistics of nucleotide diversity analysis of Xa21, Xa26 and xa5 genes.

https://doi.org/10.1371/journal.pone.0120186.t002

Divergence at the synonymous site (π_syn = 0.01439) was higher than the non-synonymous site (π_non = 0.00876). Most of the polymorphic sites in the coding region resulted in silent substitution, both conservative and non-conservative amino acid changes along with internal stop codons in all 18 alleles. We observed that the value of K_s was greater than K_a leading to the ratio of K_a/K_s < 1, indicating the alleles were under the process of purifying selection with respect to IRBB21 (Table 3). Tajima’s test showed no significant difference between π and Ө, thus, showing consistency with the neutral theory [43]. The Tajima’s D value was estimated to be -1.05476 (P > 0.10,). Similarly, separate Tajima’s D test for coding, non-coding and intraspecies also showed negative and non-significant departure from neutrality. Linkage Disequilibrium was significant for both O. nivara (R² > 0.02) and O. sativa (R² > 0.03) (0.001< P < 0.01). LD plots of R² values as a function of pairwise distance between polymorphic sites revealed slight decay of the analyzed loci within 3,500 bp in O. sativa and 4,000 bp in O. nivara (S2 Fig.). Fisher’s exact test and Chi-square test provided the number of significant pairwise comparisons. While determining the recombination sites of this locus, a minimum of seven Rm events were detected considering only the coding region.

Download:

Table 3. Summary statistics of different types of mutations present in Xa21, Xa26 and xa5 alleles.

https://doi.org/10.1371/journal.pone.0120186.t003

Xa21 gene tree and molecular evolution.

Nucleotide sequences were used to deduce neighbor joining and parsimony trees. Two major clades, I and II, were present which were supported by high bootstrap values (Fig. 1). Clade I comprised two minor clades. IRBB21 and two alleles from O. longistaminata were clustered separately in clade II, showing that these alleles were highly distinct from the remaining alleles of different species (Fig. 1). These three alleles emerged together in NJ, ME, MP and also UPGMA. Between clade I and II, there were 33 fixed differences across the entire sequence. Clade II showed slightly higher within clade diversity (π = 0.00946) than those of clade I (π = 0.00772). There were 55 singletons and 0 parsimony sites in clade II and 61 singletons and 60 parsimony informative sites among alleles in clade I. Highest divergence among the alleles was observed between IRBB21 related and remaining alleles. The intra specific divergence was quite low compared to inter specific divergence; however the divergence between alleles of O. sativa and O. nivara individuals were minimal (S3 Fig.).

Download:

Fig 1. A Neighbor-Joining plot showing the evolutionary relationship between Xa21 alleles.

The tree was constructed using 10,000 bootstrap values. The scale of the tree with branch lengths is indicated at the bottom. There are two major clades, clade I and II. The IRBB21 related alleles were clustered together in clade II. Each allele is indicated with the accession name followed by species name. Phylogenetic analyses were conducted in MEGA4 (Tamura et al. 2007).

https://doi.org/10.1371/journal.pone.0120186.g001

Allelic expression analysis of Xa21.

In order to confirm the functional significance of alleles identical to resistant Xa21 (IRBB21), we checked the expression of Xa21 alleles in two accessions of O. longistaminata (OL1 and OLD). However, these two tested alleles were not expressed or detected before or after infection. We could see the up regulation of this gene only in IRBB21 after infection as shown by Semi-QRT-PCR (Fig. 2).

Download:

Fig 2. Semi-quantitative PCR analysis of Xa21 alleles in control and treated samples of IR24, IRBB21, OL1 and OLD.

(a) Expression of Xa21 gene in IRBB21 control and infected with Xoo. Xa21 expression is absent in remaining samples. (b) Expression pattern of reference gene rice actin in the samples. (c) Phenotypic response of the genotypes after Xoo infection. Leaves with lesion length more than 5 cm are susceptible. IR24 and IRBB21 was used as susceptible and resistance controls, respectively. Control (C), Treated/Infected (T).

https://doi.org/10.1371/journal.pone.0120186.g002

Sequence analysis of Xa26 alleles

Alleles of Xa26 were isolated from 14 rice accessions of O. sativa and O. nivara. Complete sequence analysis was performed with the alleles from IR24, IRBB3, Minghui 63 and Zhachanglong (O. sativa). Sequence polymorphism was detected across 3879 bp of sequence covering 3315 bp in exonic regions, 106 bp in intronic regions and 458 bp in the 3’UTR. The total alignment length was 3987 bp. The percentage sequence similarity among alleles ranged between 95–99%. IRBB3 showed least sequence similarity with IC203740 and IR24 of 97% and 96%, respectively.

Nucleotide sequence polymorphisms were analyzed across 18 accessions of different species on the Xa26 locus. There were a total of 304 polymorphic (segregating sites) and 312 mutations with an estimated average nucleotide divergence (K) of 63.072. Among them, 182 sites belonged to singleton variable and 122 parsimony informative sites. There were 16 haplotypes among 18 alleles and the haplotype diversity, Hd = 0.98 ± 0.028. The overall nucleotide diversity was π = 0.01958 and the Watterson estimator ‘Ө_w’ = 0.02744 (Table 2). The highest nucleotide diversity was observed in the 3’ UTR followed by coding region (π = 0.01684). As many as 61 InDel events and 315 nucleotide substitutions were present. Frequency of SNPs was more than the InDels being one SNP per 24.91 bp and one InDel per 65.36 bp. The average InDel length was longer in the coding region (8.2271) as compared to the entire gene (2.15) and its InDel diversity (π_i = 0.0012) was least different from that of coding region (π_i = 0.00091). The longest InDel was found in two O. nivara accessions (ON132 and 210223). Among alleles, the highest number of polymorphisms was found between IR24 and IRBB3. Many of the SNPs and InDels occurred in the exons, which resulted in stop codons and premature termination of coding frame. Pattern of nucleotide substitution was estimated by the maximum composite likelihood method where the overall transition bias ‘R’ = 1.421, the highest among the four genes. The ‘G’ to ‘A’ transition was the most favored transition and the transition and transversion rate was almost the same in all alleles when compared individually with IRBB3.

Similarly, as observed for Xa21 alleles, the divergence at silent site (π_syn = 0.02299) was higher than the non-synonymous site (π_a = 0.01507). When individual alleles were compared with IRBB3/Minghui 63, the total number of synonymous substitutions were more than the non-synonymous changes resulting in Ka/Ks < 0, which illustrates that the alleles are undergoing purifying selection with reference to Xa26 of IRBB3 (Table 3). For the neutrality test, the Ө value was greater than π giving negative Tajima’s D (-1.297, p > 0.10) (Table 2). The difference was statistically not significant, which illustrates an absence of significant selection of this gene in our study population. Out of the 14 alleles isolated, only two alleles (M209A and 106133) showed an intact ORF giving a full length protein, whereas internal stop codons were found in the sequences of the remaining alleles. Amino acid substitutions were found both in LRR and kinase domain.

As in the case of Xa21 alleles, interspecific nucleotide diversity was slightly higher in O. nivara (π = 0.02216) than O. sativa (π = 0.02139). However intra specifically, O. sativa (254) had more mutations than O. nivara (191). Both species showed negative Tajima’s D with non-significant departure from neutrality. The linkage disequilibrium was found to be significant for both O. sativa (R²≈ 0.54) and O. nivara (R² = 0.7). Decay in LD was also observed in both the species when R² was plotted against pairwise distance between polymorphic sites (S4 Fig.).

Molecular evolution tree of Xa26.

A dendrogram depicting the relationship and divergence of 18 Xa26 alleles was plotted using Maximum Parsimony and NJ. The plot formed two major clades supported by strong bootstrap values. Clade I had resistant alleles from IRBB3, Minghui 63 and Zhachanglong while two susceptible alleles from O. sativa (PB1 and IR24) were clustered in clade II. The cluster representing the remaining alleles from O. nivara, O. sativa, and O. officinalis were not distinguished at the species level (Fig. 3). Intermixing of genetic components was observed, which may be due to outcrossing.

Download:

Fig 3. Neighbor joining gene tree depicting the relationship of all studied individuals at Xa26 locus.

The tree was inferred from 10,000 replicates bootstrap values to represent the evolutionary history of the alleles. The highest bootstrap value of each branch is indicated correspondingly. The scale of branch length is indicated at the bottom. Each allele is indicated with the accession name followed by species name. Phylogenetic analyses were conducted in MEGA4 (Tamura et al. 2007).

https://doi.org/10.1371/journal.pone.0120186.g003

Sequence analysis of xa5 alleles

xa5 alleles were isolated from 15 accessions, out of which alleles from two O. sativa accessions were similar to recessive xa5 and remaining alleles were similar to dominant Xa5. Comparative analysis of the alleles with respect to IRBB5 showed an average sequence identity in the range of 97–99%. The total alignment length was 6395 sites and size of the alleles ranged between 6140 and 6264 bp. The comparative sequence analysis showed 310 polymorphic sites (out of 316 total mutations), 105 singleton variable sites and 205 parsimony informative sites. There were a total of 16 haplotypes (Hd = 0.993 ± 0.023) and 87 InDel events. The frequency of SNPs and InDels were higher in the intronic and the non-coding region compared to the exonic region. The mean frequency of InDel was present at every 73.5 bp and that of SNP was one SNP per 27.56 bp. As observed in previous studies [13], transition/transversion bias was present with R = 1.066, where ‘G’ to ‘A’ and ‘C’ to ‘T’ transitions were most prevalent. In the exonic region, we observed significant SNPs consisting of both synonymous and non-synonymous substitutions. Apart from the type of substitution reported by Iyer et al. [24] we found 9 additional SNPs in the coding region. Among these SNPs, 7 resulted in amino acid substitution (S5 Fig.) and included an ‘S’ to ‘A’ substitution in AC32753 (O. sativa) and a ‘Q’ to ‘L’ in PB1 (O. sativa) which were non conservative amino acid substitutions. The frequency of SNP was higher in O. nivara accessions (one SNP per 63.31 bp) than O. sativa (one SNP per 70.27 bp). We also observed 39 fixed differences between O. longistaminata and remaining accessions.

The overall diversity analysis showed a mean nucleotide diversity of π = 0.01324, Ө_w = 0.01539 and an average nucleotide difference of K = 78.904. Maximum diversity was observed in the second intron. Intraspecific polymorphisms were found to be higher in O. nivara (π = 0.01106, Ө_w = 0.01029) than O. sativa (π = 0.00733, Ө_w = 0.00644) (Table 2). Ka/Ks values for each allele were determined in comparison to xa5 (IRBB5) sequence. Ka/Ks were greater than one for PB1, TN1 (O. sativa), 81832 and 81852 (O. nivara). The selection was neutral for AC32753 and Kalamekri while, remaining alleles were undergoing purifying selection with respect to IRBB5. We observed a similar trend in all the genes where Tajima’s test showed no significant difference between π and Ө, which is consistent with the neutral theory [35]. Though, some individual alleles showed Ka/Ks > 1, the average Tajima’s D was estimated to be -0.67191 (P > 0.10). Similarly, separate Tajima’s D test for intraspecies variation was negative and did not depart from neutrality.

Linkage Disequilibrium was significant for both O. nivara (R² > 0.75, Panel A in S6 Fig.) and O. sativa (R² > 0.55, Panel B in S6 Fig.) (0.001< P < 0.01). The LD plot of R² values as a function of pairwise distance between polymorphic sites revealed slight decay of the analyzed loci within 4000 bp in O. nivara. The pairwise comparison was significant as determined by Fisher’s exact test and Chi-square test. While determining the recombination sites at this locus, zero Rm events were detected considering only the coding region.

xa5 gene tree and molecular evolution.

An unrooted phylogenetic tree (NJ plot) depicting genetic relatedness among the alleles was plotted using Mega 4. The entire sequence comprising 5’UTR, coding, non-coding and 3’UTR were considered in plotting the tree. Two major clades, supported with high bootstrap values, were present in the plot. Resistant alleles were found to be closer to alleles from O. nivara accessions than the susceptible alleles from O. sativa accessions. IRBB5 was found clustered in clade I along with other O. nivara accessions (Fig. 4). A similar pattern of clade formation was also observed with ME, MP and UPGMA tree. Within clades, diversity was found to be higher in clade II (π = 0.01288) than clade I (π = 0.01040). There were 66 singleton variable sites, 109 parsimony informative sites in clade I and 76 SVS and 114 PIS in clade II. In disparity index analysis, the highest composite distance was found between 81852 (O. nivara) and OLD (O. longistaminata) (S7 Fig.).

Download:

Fig 4. A neighbor joining gene tree of all studied individuals at xa5 locus.

The tree was constructed with 10,000 bootstrap values. The highest bootstrap value is indicated on each branch. The scale of branch length is indicated at the bottom. Each allele is indicated with the accession name followed by species name. The alleles are clustered separately at the species level, while the xa5 alleles (resistant and recessive form) IRBB5, AC32753 and Kalamekri show more relatedness to O. nivara.

https://doi.org/10.1371/journal.pone.0120186.g004

Allelic expression analysis of xa5.

Expression analysis of xa5 alleles identified from accessions AC32753 and Kalamekri were tested using IRBB5 and IR24 as resistant and susceptible, respectively. We were interested to see if any of the SNPs in the alleles other than the previous report had any influence in its expression or not. Only basal level of expression of these alleles were observed in all the tested samples (Fig. 5) which suggests the level of expression of this gene is not related to its resistance towards the pathogen.

Download:

Fig 5. This graph depicts the expression pattern of xa5 alleles in control and treated samples of IR24, IRBB5, AC32753 and Kalamekri.

** P value significant with P < 0.01; * P value < 0.05 significant confirmed through two way ANOVA Bonferroni post test.

https://doi.org/10.1371/journal.pone.0120186.g005

Discussion

Functional variations of genes which lead to different phenotypes are being influenced by polymorphisms such as SNPs and InDels of single nucleotide or large DNA fragments. As a natural phenomenon, different allelic forms of R genes are maintained in a population so as to protect plants against evolving pathogens [2, 14]. The high-end, low-cost next generation sequencing technologies in combination with vast genetic resources available in rice, gives us an opportunity to discover potential resistance gene and trace their evolutionary pattern. In this study, we have analyzed the sequence polymorphisms of three major bacterial blight resistance genes xa5, Xa21 and Xa26 in order to identify novel alleles. This may help to uncover genetic variants of these genes in wild and cultivated species.

Among the resistance genes of bacterial blight disease, Xa21 is one of the most commercially exploited and studied gene. It has been reported to confer broad spectrum resistance to diverse Xoo strains across the world, yet there are reports on the susceptibility of Xa21 to pathotypes of Indian and South Asian origin [44–48], which may be due to mutation and evolution of these isolates. Discovery of candidate disease resistance alleles and analysis of novel variant alleles of Xa21 will help in combating new and more virulent strains of pathogens. Xa26 is another receptor kinase gene, which is structurally similar to Xa21. Phenotypically, even though IRBB3 (which has Xa3/Xa26) was susceptible to the five virulent isolates used in our study, we were interested in understanding its molecular diversity statistics, hence this gene was considered for analysis. xa5, on the other hand is a recessive resistance gene. Finding any sequence variation which influences the activity of this gene other than the previously reported forms of mutations would be interesting.

Nucleotide diversities of Xa21, Xa26 and xa5 genes were found to be comparatively less as compared to reported R genes of Arabidopsis and other crop plants [49–51]. At the species level, we found a slightly higher diversity in O. nivara than the cultivated O. sativa. We also observed a decrease in diversity among O. sativa, which may be due to the domestication bottleneck [52–54]. Under natural conditions, introns evolve more rapidly than exons [55], likewise sequence polymorphisms were higher in the intron and non-coding region than coding region, which is often seen in most of the genes studied. Haldane [51] stated that high polymorphism is expected at the locus involved in pathogen recognition; likewise, elevated levels of nucleotide and amino acid polymorphisms were observed at Xa21 and Xa26 loci, which also have an LRR domain. LRR are known to be involved in pathogen recognition. Numerous substitutions and InDels resulted in amino acid polymorphisms resulting in non-functional genes, including two alleles of Xa21 from O. longistaminata that were due to a premature termination codon, which were confirmed by expression analysis.

Universally, transition bias is prevalent across different kingdom, genera and species and has been observed to be a common phenomenon during the course of evolution. The alleles studied herein showed more transitions from ‘G’ to ‘A’ and ‘C’ to ‘T’ than vice versa. A similar feature has been observed in prokaryotes [56], nematodes [57], Drosophila, mammals [58] and plants [59]. High rates of ‘C’ to ‘T’ transitions may occur due to methylation of cytosine, which increases the probability of this form of substitution [60]. It was difficult to determine the ancestral root of the alleles from the phylogenetic tree as the trees were unrooted. Among Xa21 alleles, OL1 and OLD showed maximum homology with IRBB21. This finding defined their genetic relatedness and similar origin, as Xa21 in IRBB21 was also derived from O. longistaminata. The presence of two divergent groups and distinctness of IRBB21 related alleles from remaining alleles were evident from the divergence table and two separate major clades supported with high bootstrap values in the phylogram (Fig. 1). For xa5 and Xa26 genes, a clear differentiation among alleles at the species or phenotypic level was difficult to determine from the gene tree. For a comprehensive study to elucidate evolutionary relationship of the alleles, it is vital to increase the number of natural populations of O. sativa and O. nivara.

Tajima’s D statistics signify the nature of selection at a locus. Natural selection was indicated in the case of rice blast resistance genes in the O. rufipogon population and R genes in other plants with significant Tajima’s D value. However, the resistance genes analyzed in this study failed to show a significant Tajima’s D, which suggest the absence of natural selection at all the three loci. However, the possibility of selection at these loci cannot be precluded as the samples under study were small in size and randomly selected from a natural population [13]. A large number of polymorphisms among the alleles leading to amino acid substitution and premature termination of protein translation were also observed. In addition, the negative Tajima’s D value, though not significant, shows an excess of rare variants and indicates the likelihood of selection as reported in the case of RPP13 [51] and Pto [2]. Alternatively, relaxed selection pressure or the genes under consideration might be evolving neutrally resulting in the gene being depleted from the population through deletion, frame-shift or nonsense mutation [51].

LD was measured by plotting R² as a function of pairwise distance between the SNPs. The LD value was found to be the same as that of other plants reported. A locus specific selection leads to an increase in the LD. Higher R² was observed in O. sativa than O. nivara and the extent of LD was higher in O. nivara in the case of Xa21 alleles. In contrast, a much higher value of R² and rapid decay in LD was observed in O. nivara than O. sativa for Xa26 and xa5 alleles. This decay in LD may be because of high recombination rate, which mainly happens in the cross pollinated plants. Recombination and cross pollination influences decay of LD.

Though, OL1 and OLD showed the presence of an identical allele of Xa21, functionally, these alleles failed to show any expression after pathogen infection. Phylogenetically and based on sequence analysis, these two alleles were nearest to IRBB21 (resistant allele), however, they were non-functional due to nonsense mutations in the coding region. These two alleles were not expressed, even after infection with a pathogen while IRBB21 was observed to be expressed (Fig. 2). Since, OL1 and OLD were found to be resistant to BB, resistance in these two accessions might be contributed by loci other than Xa21. Further studies with segregating population will confirm this. Looking for Xa21 alleles in more O. longistaminata accessions and other wild species may help in finding the desired form of the allele. As in the case of xa5 alleles, IRBB5, susceptible IR24, and the remaining tested alleles AC32753 and Kalamekri showed almost equal level of expression both under infected and non-infected state. This indicates the level of expression of the alleles might not influence its function, however, their ability or non-ability to bind DNA polymerase and to manipulate the induction of susceptibility genes may determine their role in resistance [61]. The functional significance of amino acid substitutions in AC32753, 81832 (O. nivara) and 81852 (O. nivara) may be elucidated however, structural amino acid substitutions in these accessions occurred in the loop region. Hence, functional changes in these alleles with respect to resistance/susceptibility due to these mutations are least expected. Phenotypically, AC32753 and Kalamekri were resistant to the disease and these two alleles may be employed for gene pyramiding into elite cultivars, after genetically establishing that resistance in these two accessions is controlled by novel alleles of xa5 through analysis of segregating populations.

In summary, we have analyzed the genetic diversities of three major BB resistance genes in rice. This analysis is the first of its kind for BB related genes after Xa27 and may also help in molecular evolutionary studies and mining useful alleles for crop improvement. The major contribution of these variations among alleles on BB management has yet to be analyzed and their potential impact ascertained. Including more individuals from natural populations in the analysis after phenotypic screening with pathogens possessing specific Avr genes will help in determining the co-evolutionary relationship between host resistance and pathogen. A comparative study on susceptible and resistance accessions which define their distinct reaction towards pathogens may help in developing effective strategies for managing the infectious plant disease. Some of the accessions identified in this study which showed high resistance to BB will be used as donor lines for BB resistance genes to BB susceptible elite cultivars. The alleles identified in this study will also be transferred to elite susceptible varieties and further confirmed their functional significance.

Supporting Information

S1 Table. List of primers used for isolation of alleles and expression analysis.

https://doi.org/10.1371/journal.pone.0120186.s001

(DOCX)

S1 Fig. Graph depicting different types of polymorphisms found at Xa21 locus among different accessions.

Blue, red and green colors indicate InDels, SNPs and total number of polymorphic sites, respectively. Different accessions are shown in X-axis and number of polymorphisms on Y- axis.

https://doi.org/10.1371/journal.pone.0120186.s002

(TIF)

S2 Fig. Pattern of Linkage disequilibrium among Xa21 alleles (A) O. nivara (B) O. sativa.

Decay of LD ‘R²’ as a function of distance between pairs of polymorphic sites in Xa21 alleles. The Black line depicts the expected decline of LD against distance based on the equation given by HILL and WEIR (1988).

https://doi.org/10.1371/journal.pone.0120186.s003

(TIF)

S3 Fig. Estimates of base composition bias difference between sequences of Xa21 alleles.

The difference in base composition bias per site is shown in each column. Left column indicate the alleles and the numbering columns indicate the divergence rate corresponding to the numbered rows.

https://doi.org/10.1371/journal.pone.0120186.s004

(TIFF)

S4 Fig. Pattern of Linkage disequilibrium among Xa26 alleles (A) O. nivara (B) O. sativa.

Decay of LD ‘R²’ as a function of distance between pairs of polymorphic sites in Xa26 alleles. The Black line depicts the expected decline of LD against distance based on the equation given by HILL and WEIR (1988).

https://doi.org/10.1371/journal.pone.0120186.s005

(TIF)

S5 Fig. (A) Multiple alignment of coding region of xa5 alleles.

Substitutions in the CDS are represented with different colors. (B) Output of multiple alignment for the predicted amino acid sequences of xa5 alleles. The rice genotypes are indicated in left column. The numbers on the top of the sequences indicate the position of amino acids. Different colors in amino acid shows non synonymous changes. Alignment was performed using Multalin program.

https://doi.org/10.1371/journal.pone.0120186.s006

(TIF)

S6 Fig. Pattern of Linkage disequilibrium among xa5 alleles (A) O. nivara (B) O. sativa.

Decay of LD ‘R²’ on X-axis as a function of distance between pairs of polymorphic sites in xa5 alleles and nucleotide distance on Y-axis. The Black line depicts the expected decline of LD against distance based on the equation given by HILL and WEIR (1988).

https://doi.org/10.1371/journal.pone.0120186.s007

(TIF)

S7 Fig. Estimates of base composition bias difference between sequences of xa5 alleles.

The difference in base composition bias per site is shown in each column. Left column indicate the alleles and the numbering columns indicate the divergence rate corresponding to the numbered rows.

https://doi.org/10.1371/journal.pone.0120186.s008

(TIF)

Acknowledgments

We would like to thank DRR, Hyderabad (TS), Rajendra Agricultural College, Pusa (Bihar), CRRI, Cuttack (Odisha), NBPGR, New Delhi for providing rice germplasms. We also thank DRR, Hyderabad (TS) for providing different Xoo isolates. We also acknowledge Prof. A. R. Podile for his critical suggestions and support. We also thank the facilities given by DBT-CREBB, DST-FIST level II support, UGC-SAP-CAS to Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India. Author WB also thank CSIR for the JRF and SRF fellowship.

Author Contributions

Conceived and designed the experiments: WB RMS IAQ IAG. Performed the experiments: WB AK SKRM. Analyzed the data: WB IAQ IAG. Contributed reagents/materials/analysis tools: GSL IAQ IAG. Wrote the paper: WB IAG.

References

1. Kover PX, Schaal BA. Genetic variation for disease resistance and tolerance among A. thaliana accessions. Proc Natl Acad Sci USA. 2002;99: 11270–11274. pmid:12172004
- View Article
- PubMed/NCBI
- Google Scholar
2. Rose LE, Michelmore RW, Langley CH. Natural variation in the Pto disease resistance gene within species of wild tomato (Lycopersicon). II. Population genetics of Pto. Genet. 2007;175: 1307–1319. pmid:17179076
- View Article
- PubMed/NCBI
- Google Scholar
3. Kumar A, Guha A, Bimolata W, Reddy A, Gouri S, Sundaram RM et al. Leaf gas exchange physiology in rice genotypes infected with bacterial blight: An attempt to link photosynthesis with disease severity and rice yield. Austral Jr Crop Sci. 2013;7: 32–39.
- View Article
- Google Scholar
4. Adhikari TB, Mew TW, Teng PS. Progress of bacterial blight on rice cultivars carrying different Xa-genes for resistance in the field. Plant Dis. 1994;78: 73–77.
- View Article
- Google Scholar
5. Reddy APK. Bacterial blight: crop loss assessment and disease management. In Proceeding of the International Workshop on Bacterial Blight of Rice. International Rice Research Institute, Manila, the Philippines. 1989. pp. 79–88.
6. Mew TW Current status and future prospects of research on bacterial blight of rice. Annu Rev Phytopathol. 1987;25: 359–382.
- View Article
- Google Scholar
7. Chen S, Huang ZH, Zeng LX, Yang JY, Liu QG, Zhu X. High resolution mapping and gene prediction of Xanthomonas oryzae pv. oryzae resistance gene Xa7. Mol Breed. 2008;22: 433–441.
- View Article
- Google Scholar
8. Kumar PN, Sujatha K, Laha GS, Rao KS, Mishra B, Viraktamath BC et al. Identification and fine-mapping of Xa33, a novel gene for resistance to Xanthomonas oryzae pv. oryzae. Phytopathol. 2012;102: 222–228.
- View Article
- Google Scholar
9. Iyer-Pascuzzi AS, Sweeney MT, Sarla N, McCouch SR.Use of naturally occurring alleles for crop improvement. In: Upadhyaya NM, editors. Rice functional genomics- Challenges, progress and prospects: Springer; 2007. pp. 113–143.
10. Huang CL, Hwang SY, Chiang YC, Lin TP. Molecular Evolution of the Pi-ta Gene Resistant to Rice Blast in Wild Rice (Oryza rufipogon). Genet. 2008;179: 1527–1538. pmid:18622033
- View Article
- PubMed/NCBI
- Google Scholar
11. Lee S, Jia Y, Jia M, Gealy DR, Olsen KM, Caicedo AL. Molecular Evolution of the Rice Blast Resistance Gene Pi-ta in Invasive Weedy Rice in the USA. PLoS ONE. 2011;6(10): e26260. pmid:22043312
- View Article
- PubMed/NCBI
- Google Scholar
12. Yoshida K, Miyashita NT. DNA polymorphism in the blast resistance gene Pita of wild rice Oryza rufipogon and its related species. Genes Genet Syst. 2009;84: 121–126. pmid:19556706
- View Article
- PubMed/NCBI
- Google Scholar
13. Bimolata W, Kumar A, Sundaram RM, Laha GS, Qureshi IA, Reddy GA, et al.)Analysis of nucleotide diversity among alleles of the major bacterial blight resistance gene Xa27 in cultivars of rice (Oryza sativa) and its wild relatives. Planta. 2013;238: 293–305. pmid:23652799
- View Article
- PubMed/NCBI
- Google Scholar
14. May RM, Anderson RM. Parasite-host coevolution. Futuyma DJ, Slatkin M, editors. Coevolution: Sinauer, Sunderland, Mass, USA; 1983. pp. 186–206.
15. Buckler ES, Thornsberry JM. Plant molecular diversity and applications to genomics. Curr Opin Plant Biol. 2002;5: 107–111. pmid:11856604
- View Article
- PubMed/NCBI
- Google Scholar
16. Doebley J, Lukens L. Transcriptional regulators and the evolution of plant form. Plant Cell. 1998;10: 1075–1082. pmid:9668128
- View Article
- PubMed/NCBI
- Google Scholar
17. Gu K, Yang B, Tian D, Wu L, Wang D, Sreekala C, et al. R-gene expression induced by a type-III effector triggers disease resistance in rice. Nature. 2005;435: 1122–1125. pmid:15973413
- View Article
- PubMed/NCBI
- Google Scholar
18. Romer P, Recht S, Lahaye T. A single plant resistance gene promoter engineered to recognize multiple TAL effectors from disparate pathogens. Proc Natl Acad Sci USA. 2009;106: 20526–20531. pmid:19910532
- View Article
- PubMed/NCBI
- Google Scholar
19. Khush GS, Bacalangco E, Ogawa T. A new gene for resistance to bacterial blight from O. longistaminata. Rice Genet Newsl. 1990;7: 121–122.
- View Article
- Google Scholar
20. Song W, Wang G, Chen L, Kim HS, Pi LY, Holsten T, et al. () A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science. 1995;270: 1804–1806. pmid:8525370
- View Article
- PubMed/NCBI
- Google Scholar
21. Sun X, Cao Y, Yang Z, Xu C, Li X, Wang S et al. Xa26, a gene conferring resistance to Xanthomonas oryzae pv. oryzae in rice, encodes an LRR receptor kinase-like protein. Plant Jr. 2004;37: 517–527.
- View Article
- Google Scholar
22. Xiang Y, Cao Y, Xu C, Li X, Wang S. Xa3, conferring resistance for rice bacterial blight and encoding a receptor kinase-like protein, is the same as Xa26. Theor Appl Genet. 2006;113: 1347–1355. pmid:16932879
- View Article
- PubMed/NCBI
- Google Scholar
23. Xu S, Cao Y, Li X, Wang S. Expressional and biochemical characterization of rice disease resistance gene Xa3/Xa26 family. Journal of Integr Plant Biol. 2007;49: 852–862.
- View Article
- Google Scholar
24. Iyer AS, McCouch SR. The rice bacterial blight resistance gene xa5 encodes a novel form of disease resistance. Mol Plant Microbe Interact. 2004;17: 1348–1354. pmid:15597740
- View Article
- PubMed/NCBI
- Google Scholar
25. Jiang GH, Xia ZH, Zhou YL, Wan J, Li DY, Chen RS et al. Testifying the rice bacterial blight resistance gene xa5 by genetic complementation and further analyzing xa5 (Xa5) in comparison with its homolog TFIIAgamma1. Mol Genet Genomics. 2006;275: 354–366. pmid:16614777
- View Article
- PubMed/NCBI
- Google Scholar
26. Kauffman HE, Reddy APK, Hsieh SPY, Merca SD An improved technique for evaluating resistance of rice varieties to Xanthomonas oryzae. Plant Dis Rep. 1973;57: 537–541.
- View Article
- Google Scholar
27. IRRI. Standard evaluation system for rice (SES). International Rice Research Institute, Manila. 2002.
28. Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1989;12: 13–15.
- View Article
- Google Scholar
29. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Jr Mol Biol. 1990;215: 403–410. pmid:2231712
- View Article
- PubMed/NCBI
- Google Scholar
30. Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA. 1979;76: 5296–5273.
- View Article
- Google Scholar
31. Watterson GA. On the number of segregating sites in genetical models without recombination. Theor Pop Biol. 1975;7: 256–276.
- View Article
- Google Scholar
32. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinform. 2009;25: 1451–1452.
- View Article
- Google Scholar
33. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011 Oct 11;7: 539. pmid:21988835
- View Article
- PubMed/NCBI
- Google Scholar
34. Nei M, Gojobori T. Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol. 1986;3: 418–426. pmid:3444411
- View Article
- PubMed/NCBI
- Google Scholar
35. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genet. 1989;123: 585–595.
- View Article
- Google Scholar
36. Fu YX, Li WH. Statistical tests of neutrality of mutations. Genet. 1993;133: 693–709.
- View Article
- Google Scholar
37. Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24: 1596–1599. pmid:17488738
- View Article
- PubMed/NCBI
- Google Scholar
38. Ronald PC, Albano B, Tabien R, Abenes L, Wu K, McCouch S, Tanksley SD. Genetic and physical analysis of the rice bacterial blight disease resistance locus, Xa21. Mol Gen Genet. 1992;236: 113–120. pmid:1362973
- View Article
- PubMed/NCBI
- Google Scholar
39. Iyer-Pascuzzi AS, McCouch SR. Functional markers for xa5-mediated resistance in rice. Mol Breed. 2007;19: 291–296.
- View Article
- Google Scholar
40. Sun X, Cao Y, Yang Z, Xu C, Li X, Wang Set al. Xa26, a gene conferring resistance to Xanthomonas oryzae pv. oryzae in rice, encodes an LRR receptor kinase-like protein. The Plant Jr. 2004;37: 517–527.
- View Article
- Google Scholar
41. Mitsuhara I, Iwai T, Seo S, Yuki Y, Hiroyuki K, Sakino H et al. Characteristic expression of twelve rice PR1 family genes in response to pathogen infection, wounding, and defense-related signal compounds. Mol Genet Genomics. 2008;279: 415–427. pmid:18247056
- View Article
- PubMed/NCBI
- Google Scholar
42. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2 [-Delta Delta C(T)] Method. Methods. 2001;25: 402–408. pmid:11846609
- View Article
- PubMed/NCBI
- Google Scholar
43. Haseneyer G, Ravel C, Dardevet M, Balfourier F, Sourdille P, Charmet G et al. High level of conservation between genes coding for the GAMYB transcription factor in barley (Hordeum vulgare L.) and bread wheat (Triticum aestivum L.) collections. Theor Appl Genet. 2008;117: 321–331. pmid:18488187
- View Article
- PubMed/NCBI
- Google Scholar
44. DRR.Directorate of Rice Research Progress Report, 2001, Vol. 2. Entomology and Pathology, All India Coordinated Rice Improvement Programme (ICAR), Directorate of Rice Research, Rajendranagar, Hyderabad-500 030, A.P., India. 2002.
45. Lavanya B, Priyadarisini VB, Leenakmari S, Gnanamanickam SS, Levy M. Lineage-exclusion resistance breeding: pyramiding of blast and bacterial blight resistance genes for rice disease management in India. In: Page 21 Proc. 7^th Natl. Rice Biotechnology Network Meeting, Oct 25–29, 1998, UAS-NCBS, Bangalore, India.
46. Goel RK, Kaur L, Saini RG. Effectiveness of different Xa genes against Xanthomonas oryzae pv. oryzae population causing bacterial blight of rice in Punjab (India). Rice Genet Newslet. 1998;15: 131–133.
- View Article
- Google Scholar
47. Bustamam M, Yunus M, Hifni HR, Baroidan M, Ardales EY, Nelson RJ. Population structure of the rice bacterial pathogen, Xanthomonas oryzae pv. oryzae in Java and Bali, Indonesia. In: Pages 107, Proc. Of the 3^rd Asia-Pacific Conf. on Agricultural Biotechnology: Issues and Choices. 1996 Nov 10–15, Prachuapkhirikhan, Thailand.
48. Ochiai H, Horino O, Miyajima K, Kaku H. Genetic diversity of Xanthomonas oryzae pv. oryzae strains from Sri Lanka. Phytopathol. 2000;90: 415–421.
- View Article
- Google Scholar
49. Mauricio R, Stahl EA, Korves T, Tian D, Kreitman M, Bergelson J. Natural selection for polymorphism in the disease resistance gene Rps2 of Arabidopsis thaliana. Genet. 2003;163: 735–746.
- View Article
- Google Scholar
50. Stahl EA, Dwyer G, Mauricio R, Kreitman M, Bergelson J. Dynamics of disease resistance polymorphism at the Rpm1 locus of Arabidopsis. Nature. 1999;400: 667–671. pmid:10458161
- View Article
- PubMed/NCBI
- Google Scholar
51. Rose LE, Bittner Eddy PD, Langley CH, Holub EB, Michelmore RW, Beynon JL. The maintenance of extreme amino acid diversity at the disease resistance gene, RPP13, in Arabidopsis thaliana. Genet. 2004;166: 1517–1527. pmid:15082565
- View Article
- PubMed/NCBI
- Google Scholar
52. Zhu Q, Zheng X, Luo J, Gaut BS, Ge S. Multilocus analysis of nucleotide variation of Oryza sativa and its wild relatives: Severe bottleneck during domestication of rice. Mol Biol Evol. 2007;24: 875–882. pmid:17218640
- View Article
- PubMed/NCBI
- Google Scholar
53. Eyre Walker A, Gaut RL, Hilton H, Feldman DL, Gaut BS. Investigation of the bottleneck leading to the domestication of maize. Proc Natl Acad Sci USA. 1998;95: 4441–4446. pmid:9539756
- View Article
- PubMed/NCBI
- Google Scholar
54. Buckler ES, Thornsberry JM, Kresovich S. Molecular Diversity, Structure and Domestication of Grasses. Genet Res. 2001;77: 213–218. pmid:11486504
- View Article
- PubMed/NCBI
- Google Scholar
55. Small RL, Wendel JF. Copy number lability and evolutionary dynamics of the Adh gene family in diploid and tetraploid cotton (Gossypium). Genet 2000;155: 1913–1926. pmid:10924485
- View Article
- PubMed/NCBI
- Google Scholar
56. Haldane JBS. Disease and evolution. Ricerca Science Supplement. 1949;19: 3–10.
- View Article
- Google Scholar
57. Kowalczuk M, Mackiewicz P, Mackiewicz D, Nowicka A, Dudkiewicz M, Dudek MR et al. DNA asymmetry and the replicational mutational pressure. J Appl Genet. 2001;42: 553–577. pmid:14564030
- View Article
- PubMed/NCBI
- Google Scholar
58. Blouin MS, Yowell CA, Courtney CH, Dame JB. Substitution bias, rapid saturation, and the use of mtDNA for nematode systematic. Mol Biol Evol. 1998;15: 1719–1727. pmid:9866206
- View Article
- PubMed/NCBI
- Google Scholar
59. Petrov DA, Hartl DL. Patterns of nucleotide substitution in Drosophila and mammalian genomes. Proc Natl Acad Sci USA. 1999;96: 1475–1479. pmid:9990048
- View Article
- PubMed/NCBI
- Google Scholar
60. Olmstead RG, Reeves PA, Yen AC. Patterns of sequence evolution and implications for parsimony analysis of chloroplast DNA. In: Soltis DE, Soltis PS, Doyle JJ, editors. Molecular systematics of plants II: Kluwer Academic, London; 1998. pp. 164–187.
61. Gu K, Tian D, Qiu C, Yin Z. Transcription activator-like type III effector AvrXa27 depends on OsTFIIAg5 for the activation of Xa27 transcription in rice that triggers disease resistance to Xanthomonas oryzae pv. oryzae. Mol Plant Pathol. 2009;10: 829–835. pmid:19849788
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Kover PX, Schaal BA. Genetic variation for disease resistance and tolerance among A. thaliana accessions. Proc Natl Acad Sci USA. 2002;99: 11270–11274. pmid:12172004
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Rose LE, Michelmore RW, Langley CH. Natural variation in the Pto disease resistance gene within species of wild tomato (Lycopersicon). II. Population genetics of Pto. Genet. 2007;175: 1307–1319. pmid:17179076
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Kumar A, Guha A, Bimolata W, Reddy A, Gouri S, Sundaram RM et al. Leaf gas exchange physiology in rice genotypes infected with bacterial blight: An attempt to link photosynthesis with disease severity and rice yield. Austral Jr Crop Sci. 2013;7: 32–39.
View Article
Google Scholar

[10] View Article

[11] Google Scholar

[ref4] 4. Adhikari TB, Mew TW, Teng PS. Progress of bacterial blight on rice cultivars carrying different Xa-genes for resistance in the field. Plant Dis. 1994;78: 73–77.
View Article
Google Scholar

[13] View Article

[14] Google Scholar

[ref5] 5. Reddy APK. Bacterial blight: crop loss assessment and disease management. In Proceeding of the International Workshop on Bacterial Blight of Rice. International Rice Research Institute, Manila, the Philippines. 1989. pp. 79–88.

[ref6] 6. Mew TW Current status and future prospects of research on bacterial blight of rice. Annu Rev Phytopathol. 1987;25: 359–382.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref7] 7. Chen S, Huang ZH, Zeng LX, Yang JY, Liu QG, Zhu X. High resolution mapping and gene prediction of Xanthomonas oryzae pv. oryzae resistance gene Xa7. Mol Breed. 2008;22: 433–441.
View Article
Google Scholar

[20] View Article

[21] Google Scholar

[ref8] 8. Kumar PN, Sujatha K, Laha GS, Rao KS, Mishra B, Viraktamath BC et al. Identification and fine-mapping of Xa33, a novel gene for resistance to Xanthomonas oryzae pv. oryzae. Phytopathol. 2012;102: 222–228.
View Article
Google Scholar

[23] View Article

[24] Google Scholar

[ref9] 9. Iyer-Pascuzzi AS, Sweeney MT, Sarla N, McCouch SR.Use of naturally occurring alleles for crop improvement. In: Upadhyaya NM, editors. Rice functional genomics- Challenges, progress and prospects: Springer; 2007. pp. 113–143.

[ref10] 10. Huang CL, Hwang SY, Chiang YC, Lin TP. Molecular Evolution of the Pi-ta Gene Resistant to Rice Blast in Wild Rice (Oryza rufipogon). Genet. 2008;179: 1527–1538. pmid:18622033
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref11] 11. Lee S, Jia Y, Jia M, Gealy DR, Olsen KM, Caicedo AL. Molecular Evolution of the Rice Blast Resistance Gene Pi-ta in Invasive Weedy Rice in the USA. PLoS ONE. 2011;6(10): e26260. pmid:22043312
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref12] 12. Yoshida K, Miyashita NT. DNA polymorphism in the blast resistance gene Pita of wild rice Oryza rufipogon and its related species. Genes Genet Syst. 2009;84: 121–126. pmid:19556706
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref13] 13. Bimolata W, Kumar A, Sundaram RM, Laha GS, Qureshi IA, Reddy GA, et al.)Analysis of nucleotide diversity among alleles of the major bacterial blight resistance gene Xa27 in cultivars of rice (Oryza sativa) and its wild relatives. Planta. 2013;238: 293–305. pmid:23652799
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref14] 14. May RM, Anderson RM. Parasite-host coevolution. Futuyma DJ, Slatkin M, editors. Coevolution: Sinauer, Sunderland, Mass, USA; 1983. pp. 186–206.

[ref15] 15. Buckler ES, Thornsberry JM. Plant molecular diversity and applications to genomics. Curr Opin Plant Biol. 2002;5: 107–111. pmid:11856604
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref16] 16. Doebley J, Lukens L. Transcriptional regulators and the evolution of plant form. Plant Cell. 1998;10: 1075–1082. pmid:9668128
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref17] 17. Gu K, Yang B, Tian D, Wu L, Wang D, Sreekala C, et al. R-gene expression induced by a type-III effector triggers disease resistance in rice. Nature. 2005;435: 1122–1125. pmid:15973413
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref18] 18. Romer P, Recht S, Lahaye T. A single plant resistance gene promoter engineered to recognize multiple TAL effectors from disparate pathogens. Proc Natl Acad Sci USA. 2009;106: 20526–20531. pmid:19910532
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref19] 19. Khush GS, Bacalangco E, Ogawa T. A new gene for resistance to bacterial blight from O. longistaminata. Rice Genet Newsl. 1990;7: 121–122.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref20] 20. Song W, Wang G, Chen L, Kim HS, Pi LY, Holsten T, et al. () A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science. 1995;270: 1804–1806. pmid:8525370
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref21] 21. Sun X, Cao Y, Yang Z, Xu C, Li X, Wang S et al. Xa26, a gene conferring resistance to Xanthomonas oryzae pv. oryzae in rice, encodes an LRR receptor kinase-like protein. Plant Jr. 2004;37: 517–527.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref22] 22. Xiang Y, Cao Y, Xu C, Li X, Wang S. Xa3, conferring resistance for rice bacterial blight and encoding a receptor kinase-like protein, is the same as Xa26. Theor Appl Genet. 2006;113: 1347–1355. pmid:16932879
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref23] 23. Xu S, Cao Y, Li X, Wang S. Expressional and biochemical characterization of rice disease resistance gene Xa3/Xa26 family. Journal of Integr Plant Biol. 2007;49: 852–862.
View Article
Google Scholar

[74] View Article

[75] Google Scholar

[ref24] 24. Iyer AS, McCouch SR. The rice bacterial blight resistance gene xa5 encodes a novel form of disease resistance. Mol Plant Microbe Interact. 2004;17: 1348–1354. pmid:15597740
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref25] 25. Jiang GH, Xia ZH, Zhou YL, Wan J, Li DY, Chen RS et al. Testifying the rice bacterial blight resistance gene xa5 by genetic complementation and further analyzing xa5 (Xa5) in comparison with its homolog TFIIAgamma1. Mol Genet Genomics. 2006;275: 354–366. pmid:16614777
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref26] 26. Kauffman HE, Reddy APK, Hsieh SPY, Merca SD An improved technique for evaluating resistance of rice varieties to Xanthomonas oryzae. Plant Dis Rep. 1973;57: 537–541.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref27] 27. IRRI. Standard evaluation system for rice (SES). International Rice Research Institute, Manila. 2002.

[ref28] 28. Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1989;12: 13–15.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref29] 29. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Jr Mol Biol. 1990;215: 403–410. pmid:2231712
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref30] 30. Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA. 1979;76: 5296–5273.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref31] 31. Watterson GA. On the number of segregating sites in genetical models without recombination. Theor Pop Biol. 1975;7: 256–276.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref32] 32. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinform. 2009;25: 1451–1452.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref33] 33. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011 Oct 11;7: 539. pmid:21988835
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref34] 34. Nei M, Gojobori T. Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol. 1986;3: 418–426. pmid:3444411
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref35] 35. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genet. 1989;123: 585–595.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref36] 36. Fu YX, Li WH. Statistical tests of neutrality of mutations. Genet. 1993;133: 693–709.
View Article
Google Scholar

[116] View Article

[117] Google Scholar

[ref37] 37. Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24: 1596–1599. pmid:17488738
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref38] 38. Ronald PC, Albano B, Tabien R, Abenes L, Wu K, McCouch S, Tanksley SD. Genetic and physical analysis of the rice bacterial blight disease resistance locus, Xa21. Mol Gen Genet. 1992;236: 113–120. pmid:1362973
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref39] 39. Iyer-Pascuzzi AS, McCouch SR. Functional markers for xa5-mediated resistance in rice. Mol Breed. 2007;19: 291–296.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref40] 40. Sun X, Cao Y, Yang Z, Xu C, Li X, Wang Set al. Xa26, a gene conferring resistance to Xanthomonas oryzae pv. oryzae in rice, encodes an LRR receptor kinase-like protein. The Plant Jr. 2004;37: 517–527.
View Article
Google Scholar

[130] View Article

[131] Google Scholar

[ref41] 41. Mitsuhara I, Iwai T, Seo S, Yuki Y, Hiroyuki K, Sakino H et al. Characteristic expression of twelve rice PR1 family genes in response to pathogen infection, wounding, and defense-related signal compounds. Mol Genet Genomics. 2008;279: 415–427. pmid:18247056
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref42] 42. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2 [-Delta Delta C(T)] Method. Methods. 2001;25: 402–408. pmid:11846609
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref43] 43. Haseneyer G, Ravel C, Dardevet M, Balfourier F, Sourdille P, Charmet G et al. High level of conservation between genes coding for the GAMYB transcription factor in barley (Hordeum vulgare L.) and bread wheat (Triticum aestivum L.) collections. Theor Appl Genet. 2008;117: 321–331. pmid:18488187
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref44] 44. DRR.Directorate of Rice Research Progress Report, 2001, Vol. 2. Entomology and Pathology, All India Coordinated Rice Improvement Programme (ICAR), Directorate of Rice Research, Rajendranagar, Hyderabad-500 030, A.P., India. 2002.

[ref45] 45. Lavanya B, Priyadarisini VB, Leenakmari S, Gnanamanickam SS, Levy M. Lineage-exclusion resistance breeding: pyramiding of blast and bacterial blight resistance genes for rice disease management in India. In: Page 21 Proc. 7^th Natl. Rice Biotechnology Network Meeting, Oct 25–29, 1998, UAS-NCBS, Bangalore, India.

[ref46] 46. Goel RK, Kaur L, Saini RG. Effectiveness of different Xa genes against Xanthomonas oryzae pv. oryzae population causing bacterial blight of rice in Punjab (India). Rice Genet Newslet. 1998;15: 131–133.
View Article
Google Scholar

[147] View Article

[148] Google Scholar

[ref47] 47. Bustamam M, Yunus M, Hifni HR, Baroidan M, Ardales EY, Nelson RJ. Population structure of the rice bacterial pathogen, Xanthomonas oryzae pv. oryzae in Java and Bali, Indonesia. In: Pages 107, Proc. Of the 3^rd Asia-Pacific Conf. on Agricultural Biotechnology: Issues and Choices. 1996 Nov 10–15, Prachuapkhirikhan, Thailand.

[ref48] 48. Ochiai H, Horino O, Miyajima K, Kaku H. Genetic diversity of Xanthomonas oryzae pv. oryzae strains from Sri Lanka. Phytopathol. 2000;90: 415–421.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref49] 49. Mauricio R, Stahl EA, Korves T, Tian D, Kreitman M, Bergelson J. Natural selection for polymorphism in the disease resistance gene Rps2 of Arabidopsis thaliana. Genet. 2003;163: 735–746.
View Article
Google Scholar

[154] View Article

[155] Google Scholar

[ref50] 50. Stahl EA, Dwyer G, Mauricio R, Kreitman M, Bergelson J. Dynamics of disease resistance polymorphism at the Rpm1 locus of Arabidopsis. Nature. 1999;400: 667–671. pmid:10458161
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref51] 51. Rose LE, Bittner Eddy PD, Langley CH, Holub EB, Michelmore RW, Beynon JL. The maintenance of extreme amino acid diversity at the disease resistance gene, RPP13, in Arabidopsis thaliana. Genet. 2004;166: 1517–1527. pmid:15082565
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref52] 52. Zhu Q, Zheng X, Luo J, Gaut BS, Ge S. Multilocus analysis of nucleotide variation of Oryza sativa and its wild relatives: Severe bottleneck during domestication of rice. Mol Biol Evol. 2007;24: 875–882. pmid:17218640
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref53] 53. Eyre Walker A, Gaut RL, Hilton H, Feldman DL, Gaut BS. Investigation of the bottleneck leading to the domestication of maize. Proc Natl Acad Sci USA. 1998;95: 4441–4446. pmid:9539756
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref54] 54. Buckler ES, Thornsberry JM, Kresovich S. Molecular Diversity, Structure and Domestication of Grasses. Genet Res. 2001;77: 213–218. pmid:11486504
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref55] 55. Small RL, Wendel JF. Copy number lability and evolutionary dynamics of the Adh gene family in diploid and tetraploid cotton (Gossypium). Genet 2000;155: 1913–1926. pmid:10924485
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref56] 56. Haldane JBS. Disease and evolution. Ricerca Science Supplement. 1949;19: 3–10.
View Article
Google Scholar

[181] View Article

[182] Google Scholar

[ref57] 57. Kowalczuk M, Mackiewicz P, Mackiewicz D, Nowicka A, Dudkiewicz M, Dudek MR et al. DNA asymmetry and the replicational mutational pressure. J Appl Genet. 2001;42: 553–577. pmid:14564030
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref58] 58. Blouin MS, Yowell CA, Courtney CH, Dame JB. Substitution bias, rapid saturation, and the use of mtDNA for nematode systematic. Mol Biol Evol. 1998;15: 1719–1727. pmid:9866206
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref59] 59. Petrov DA, Hartl DL. Patterns of nucleotide substitution in Drosophila and mammalian genomes. Proc Natl Acad Sci USA. 1999;96: 1475–1479. pmid:9990048
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref60] 60. Olmstead RG, Reeves PA, Yen AC. Patterns of sequence evolution and implications for parsimony analysis of chloroplast DNA. In: Soltis DE, Soltis PS, Doyle JJ, editors. Molecular systematics of plants II: Kluwer Academic, London; 1998. pp. 164–187.

[ref61] 61. Gu K, Tian D, Qiu C, Yin Z. Transcription activator-like type III effector AvrXa27 depends on OsTFIIAg5 for the activation of Xa27 transcription in rice that triggers disease resistance to Xanthomonas oryzae pv. oryzae. Mol Plant Pathol. 2009;10: 829–835. pmid:19849788
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Plant material, screening and DNA isolation

Isolation and sequencing of alleles

Allelic diversity and sequence data analysis

Constructing phylogenetic trees

Allelic expression analysis

Results

Nucleotide Polymorphism analysis of Xa21, Xa26 and xa5 loci

Sequence analysis of Xa21 alleles.

Xa21 gene tree and molecular evolution.

Allelic expression analysis of Xa21.

Sequence analysis of Xa26 alleles

Molecular evolution tree of Xa26.

Sequence analysis of xa5 alleles

xa5 gene tree and molecular evolution.

Allelic expression analysis of xa5.

Discussion

Supporting Information

S1 Table. List of primers used for isolation of alleles and expression analysis.

S1 Fig. Graph depicting different types of polymorphisms found at Xa21 locus among different accessions.

S2 Fig. Pattern of Linkage disequilibrium among Xa21 alleles (A) O. nivara (B) O. sativa.

S3 Fig. Estimates of base composition bias difference between sequences of Xa21 alleles.

S4 Fig. Pattern of Linkage disequilibrium among Xa26 alleles (A) O. nivara (B) O. sativa.

S5 Fig. (A) Multiple alignment of coding region of xa5 alleles.

S6 Fig. Pattern of Linkage disequilibrium among xa5 alleles (A) O. nivara (B) O. sativa.

S7 Fig. Estimates of base composition bias difference between sequences of xa5 alleles.

Acknowledgments

Author Contributions

References