Abstract
The resistance gene Pi-ta has been effectively used to control rice blast disease, but some populations of cultivated and wild rice have evolved resistance. Insights into the evolutionary processes that led to this resistance during crop domestication may be inferred from the population history of domesticated and wild rice strains. In this study, we applied a recently developed statistical method, time-dependent Poisson random field model, to examine the evolution of the Pi-ta gene in cultivated and weedy rice. Our study suggests that the Pi-ta gene may have more recently introgressed into cultivated rice, indica and japonica, and U.S. weedy rice from the wild species, O. rufipogon. In addition, the Pi-ta gene is under positive selection in japonica, tropical japonica, U.S. cultivars and U.S. weedy rice. We also found that sequences of two domains of the Pi-ta gene, the nucleotide binding site and leucine-rich repeat domain, are highly conserved among all rice accessions examined. Our results provide a valuable analytical tool for understanding the evolution of disease resistance genes in crop plants.
Blast disease, caused by the filamentous fungus Magnaporthe oryzae, is one of the most damaging diseases of rice. Genetic analysis has identified more than 100 major and minor resistance (R) genes (Ashikawa et al. 2008). Thus far, 22 of these R genes have been cloned, and most of the genes encode putative receptor proteins with nucleotide binding sites (NBS) and leucine-rich repeat (LRR) domains (Lei et al. 2013). The presence of NBS and LRR domains in R genes suggests that they are involved in signal recognition, protein−protein interaction, and ligand binding for initiating plant disease responses (DeYoung and Innes 2006). One of the best-studied R genes for blast disease (Bryan et al. 2000) is Pi-ta, which encodes a putative protein with NBS and degenerate LRR domains. Pi-ta has been used effectively in preventing infection by strains of M. oryzae in rice (Bryan et al. 2000; Orbach et al. 2000). Baseline information on the structure, function, natural variation of the Pi-ta allele, and signal recognition of Pi-ta has accumulated from a concerted effort to understand the factors governing rice disease (Jia et al. 2000, 2003; Wang et al. 2008). All the resulting knowledge to understand the molecular evolution of the Pi-ta gene has been used effectively for developing disease-resistant varieties against rice blast (Jia 2003; Jia et al. 2002, 2004a).
The resistance Pi-ta allele, isolated from landrace varieties Tetep (Vietnam) and Taducan (Philippine), was introgressed into a diverse range of rice varieties to prevent rice blast (Wang et al. 2007). However, strains carrying the resistance Pi-ta contain a large linkage block of 5.3 Mb that is difficult to break, even after five generations of backcrossing under selection for blast resistance (Jia 2009; Jia et al. 2009b). Consequently, it has been a real challenge to clone additional components involved in the Pi-ta−mediated resistance pathway (Jia and Martin 2008). Sequence analysis of genomic regions surrounding the Pi-ta gene in 159 geographically diverse accessions of O. sativa, O. rufipogon, and five other closely related AA genome Oryza species, O. nivara, O. meridionalis, O. glaberrima, O. barthii, and O. glumaepatula, demonstrated that a single amino acid change determines resistance specificity (Bryan et al. 2000; Orbach et al. 2000; Lee et al. 2009)
The R genes encoding NBS-LRR proteins are the most diversified gene families in dicot and monocot plant species, and the genes have been under strong selection (Huang et al. 2008; Thakur et al. 2013; Lee et al. 2009, 2011). There are about 150 NBS-LRR genes in Arabidopsis and about 400 in rice alone (Mchale et al. 2006). When its pathogen successfully defeats the targeted R gene, plants are subsequently compelled to evolve a much stronger defense mechanism to survive. It has been known that the pathogens containing high levels of polymorphic effectors trigger the rapid evolution of the alleles with newly arisen mutations of their corresponding R genes (Dodds et al. 2006; Jia et al. 2009b). For example, R genes against rice blast are tremendously diverse and evolve rapidly, counteracting the Avr-genes in the blast pathogens (Dai et al. 2010b; Huang et al. 2014). Recent studies have made progress in determining the evolution of the rice blast R gene Pi-ta and Pi2/9 locus, and their results have been effectively adopted (Dai et al. 2010a; Huang et al. 2008; Lee et al. 2009, 2011; Zhou et al. 2007).
Asian cultivated rice, Oryza sativa L., is divided into several genetically distinct groups, including indica, aus, aromatic, temperate, and tropical japonica (Garris et al. 2005). Weedy strains of rice are predicted to possess advantages in natural ecosystems and have been predicted to be either undomesticated or imported (Londo et al. 2006; Londo and Schaal 2007). It would be of great interest to determine whether any footprint of the blast R genes can be identified among these cultivated rice groups and weedy rice.
In this study, we modeled the evolutionary history of the Pi-ta gene by estimating population genetic parameters for 10 rice strains. The objectives of the present study were to i) estimate divergence time of the Pi-ta gene in 10 rice groups from their wild-type ancestor O. rufipogon; ii) estimate synonymous and nonsynonymous substitution rates of the Pi-ta gene across the 10 rice groups; and iii) calculate the magnitude and direction of selection on newly arisen nonsynonymous substitution in the Pi-ta gene.
Materials and Methods
Data analysis
To compare Oryza sativa with its wild relative O. rufipogon, a total of 10 artificial groups were assembled. The dataset used in this study contains the full-length coding region (2787 bp) of the Pi-ta gene from 10 groups within O. sativa [all cultivated sativa (57 accessions), aromatic (3), aus (7), indica (22), japonica (32), temperate japonica (4), tropical japonica (13), U.S. cultivars (16), weedy rice black hull (BHA) (24) and all weedy rice (58)] and the wild progenitor species O. rufigopon. The sequences were obtained from previous studies (Lee et al. 2009, 2011). Ten comparison groups were formed by pairing the wild rice species O. rufigopon with the 10 rice groups, respectively. Information for the samples is shown in the Supporting Information, File S1, and McDonald-Kreitman tables (McConald and Kreitman 1991) for the 10 rice groups were generated using the software DnaSP 4.9 (Rozas et al. 2003). As shown in Figure 1, the Pi-ta gene was divided into four functional regions, NB-ARC (APAF-1, R proteins, and CED-4), non-NBS, LRR, and non-LRR, to examine their selective effects individually.
Description of the Pi-ta gene structure and domains, non-nucleotide binding site (NBS), NB-ARC, non-leucine-rich repeat (LRR), and LRR. Nucleotides of intron are not included for the region of NB-ARC. All domains contain only coding region sequences of Pi-ta.
Model
We used a Markov chain Monte Carlo simulation method to estimate divergence times of the 10 rice pairs, selection coefficients of nonsynonymous substitutions, and two types of substitution rates by applying a time-dependent Poisson random field (PRF) model to the available data (Amei and Sawyer 2010, 2012). Due to the fact that the model is suitable for multiple genes, we treated the four functional regions of a single Pi-ta gene as four separate genes under the assumption that nucleotide sites on a gene evolve independently. The traditional McDonald-Kreitman table is a 2 × 2 contingency table consisting of numbers of fixed differences and polymorphisms at synonymous and non-synonymous nucleotide sites. In the time-dependent PRF model, the table has been extended to a 2 × 3 table by classifying polymorphic sites into the following two types: sites that are polymorphic in only one sample and sites that are polymorphic in both samples. Suppose that two closely related species have the same haploid effective population size 
; they diverged 
generations ago; the synonymous substitution rate of sites in a coding gene in the two lineages is 
per site per 
generations (
for nonsynonymous substitution rate); and the selection coefficient of nonsynonymous substitution is γ per gene per
generations. Selective effects of synonymous substitution are assumed to be neutral, i.e., 
. Under certain assumptions, such as equal fitness for nonsynonymous substitution on the same locus, constant population sizes, random mating, no migration between species, and genic selection, the six counts in the extended McDonald-Kreitman table are independent Poisson random variables whose means are functions of the genetic parameters 
, 
, 
and γ (Amei and Sawyer 2010, 2012).
To estimate the parameters of interest using a Bayesian framework, we assume that selection coefficients for the four functional regions, 
, 
, are random draws from a normal distribution with mean 
and variance 
, and an inverse-gamma-normal distribution was assigned as a joint prior distribution of 
and 
. Gamma distributions with given parameters are chosen as prior distributions for the two types of substitution rates, 
and 
, and a uniform prior distribution for the divergence time 
. The posterior distributions of the genetic parameters, given the observed numbers of fixed differences and polymorphisms at synonymous and nonsynonymous sites, are obtained by Markov chain Monte Carlo simulations (see detailed expression of the likelihood function in Amei and Sawyer 2012).
In our model, the 
represents an aggregated synonymous substitution rate at a specific locus per 
generations (Amei and Sawyer 2012), and we divide the 
by the total number of protein coding synonymous sites at that locus to obtain a rate of substitution per synonymous site per 
generations, denoted by 
. To estimate the haploid effective population size 
(
for diploid population size) for each of the 10 rice groups, we used the average of the 
over the four functional regions as a final estimate of synonymous substitution rate and compared it with a neutral substitution rate of 
per site per generation (Caicedo et al. 2007).
Results
Divergence time
The PRF model applied in this study estimates species divergence time in units of 
generations ago. Assuming 1 year as one generation for rice species, the time of differentiation in the unit of 1 year ago is given by multiplying the model estimated 
by 
. For the 10 rice groups, the model estimated divergence times along with their 95% credible intervals as well as the corresponding converted speciation times are listed in Table S1, and a bar graph of the speciation times in ascending order is displayed in Figure 2. Across the 10 rice groups, temperate japonica is the oldest group that diverged approximately 110,000 years ago from its wild-type ancestor O. rufipogon, and the youngest is the group of Oryza sativa with an estimated divergence time of approximately 35,000 years ago (Figure 2).
Bar graph of divergence times of 10 rice groups, O. sativa, from youngest to oldest: all cultivated sativa, all weedy rice, japonica, weedy rice BHA, US cultivars, tropical japonica, indica, aromatic, aus, temperate japonica. The unit is years ago.
Selective effects
Under the assumption that synonymous substitutions are neutral, the PRF model estimates selection coefficients γ of nonsynonymous substitutions for the four functional regions and the 10 rice groups. The sign of theγ indicates the direction of the selection in the sense that the substitution is beneficial if 
and detrimental if 
. The absolute value of the γ measures the selection intensity of amino acid nonsynonymous substitution per functional region per 
generations. Based on our estimates, the selection coefficients of nonsynonymous substitutions at the four functional regions, NB-ARC, non-NBS, LRR, and non-LRR, are quite close to each other, and this result is consistent across the 10 rice groups. The medians and corresponding 95% credible interval estimates of the selection coefficients are given in Table S2 and plotted in Figure 3 with species sorted with the medians. Despite this, it is possible that some of these effects can be attributed to pooling groups that have been selected in different directions. Overall, there are four rice groups, temperate japonica, aus, aromatic, and indica, that have negative selection coefficients, and the Pi-ta gene is positively selected in the remaining six groups (Table S2). As far as the strength of the selection, the absolute value of the γ ranges from 1 to 4.31 for the four negatively selected groups and only 0.17 to 1.25 for the six positively selected groups (Table S2).
Selection coefficients (γ) for the four functional domains, non-nucleotide binding site (NBS), NB-ARC, non-leucine-rich repeat (LRR), and LRR with the 10 rice groups sorted by the values of the estimates (medians). Error bars represent 95% credible interval estimates of the γ.
Substitution rates
As we mentioned previously, the substitution parameter θ (
for synonymous sites and 
for non-synonymous sites) in the PRF model is an aggregate substitution rate, and we divide the θ by the total number of protein coding sites to obtain a site-specific rate. Numerical estimates of the synonymous and nonsynonymous substitution rates per site per
generations of the four functional regions for the 10 rice groups are given in Tables 1 and 2. For both synonymous and nonsynonymous sites, substitution rates for noncritical domains are faster than those of critical domains (Figures 4 and 5). Specifically, the average synonymous substitution rates over the 10 groups are 
in the non-NBS domain, 
in the non-LRR domain, 
in NB-ARC, and 
in LRR. The average nonsynonymous substitution rates over the 10 rice groups are 
in the non-NBS domain, 
in the non-LRR domain, 
in LRR, and 
in NB-ARC. Our results showed that the nonsynonymous substitution rates are higher than the synonymous substitution rates.
, per site per Ne generations of Pi-ta
, per site per Ne generations of Pi-ta
Synonymous substitution rates of the four regions across the 10 rice groups.
Nonsynonymous substitution rates of the four regions across the 10 rice groups.
Variation of Pi-ta haplotypes and phylogenetic relationships
A different number of the Pi-ta haplotype was determined in cultivated, weedy, and wild species rice (O. rufipogon), as shown in Table 3. The total number of haplotypes was 77 nucleotide and 48 in amino acid sequence of the Pi-ta coding region. The greatest number of haplotype was found in O. rufipogon (32 in nucleotide and 22 in amino acid), whereas 12 (nucleotide) and 7 (amino acid) were found in cultivated rice. The haplotype numbers were low in temperate japonica and aromatic. This may be due to the small number of samples used in this study (Table 3). As shown in Figure 6, we found a total of six major Pi-ta haplotype groups. All resistant rice accessions containing the resistant Pi-ta allele were found to belong to Group I, while all accessions possessing the susceptible pi-ta allele were in Group VI. Phylogenetic analyses revealed that the other Pi-ta haplotypes from cultivated (aus, aromatic, indica, and japonica) and weedy rice were present in Groups II, III, and IV. The accessions of O. rufipogon were found to be present in all Pi-ta haplotype groups. There was no cultivated or weedy rice found in Group V; only O. rufipogon was found. This finding suggests that the Pi-ta haplotype in this group may be a newly derived haplotype from O. rufipogon or present only in O. rufipogon (Figure 6).
A phylogenetic tree describing the evolutionary relationship of the Pi-ta haplotypes among rice populations. The tree was constructed by the neighbor-joining method using MEGA 6 (http://www.megasoftware.net/). The phylogeny was tested by 500 bootstrap replications. The bootstrap values are shown on each branch of the tree, and the O. rufipogon accessions were marked in green.
Discussion
Pi-ta is one of the best understood R genes for gene-for-gene resistance in plants (Bryan et al. 2000; Jia et al. 2003, 2009b). Several studies have been conducted in cultivated rice and its relative wild rice species to understand the molecular evolution of the Pi-ta gene (Caicedo et al. 2007 ; Huang et al. 2008; Lee et al. 2009, 2011; Molina et al. 2011; Thakur et al. 2013). Huang et al. (2008) and Thakur et al. (2013) analyzed the patterns of the Pi-ta gene evolution in cultivated and wild rice species by using statistical tests of neutrality such as Tajima’s D, Fu and Li’s D, Fay and Wu’s H, and the McDonald-Kreitman test. Based on their results, the null hypothesis of the neutral model was rejected by the values of Tajima’s D and Fu and Li’s D, but the Fay and Wu’s H and MK tests failed to provide evidence of selection. Using similar sets of statistics, Lee et al. (2009 and 2011) showed that the Pi-ta gene is under positive selection in cultivated rice and directional selection was observed in wild rice. The test statistics used in the existing studies are powerful in detecting selection when the underlying assumptions of the tests are met. However, they do not provide information on the magnitude and strength of the selection that they identified. On the other hand, the model applied in the current study, time-dependent PF model, is based on a quantitative theory for the amount of selection between two recently diverged species (Sawyer and Hartl 1992). Under certain assumptions, the model compares intraspecific polymorphism with interspecific divergence to generate quantitative estimates of genetic parameters such as selection coefficient, species divergence time and substitution rate. A recent application of the time-dependent PRF model to a nuclear and mitochondrial DNA dataset of 22 sister pairs of birds found evidence that selective effects in dry habitat species were stronger than those of humid habitat species (Amei and Smith 2014).
The rice group temperate japonica is shown to be the oldest group diverged from its wild-type ancestor O. rufipogon whereas the youngest is the group of cultivated sativa (Figure 2). Our estimates of species divergence times, in the unit of years ago, are much earlier than those in the existing studies. It has been reported that all forms of Asian rice, both indica and japonica, were domesticated around 8200 to 13,000 years ago although the precise date of the first domestication is still unknown (Caicedo et al. 2007; Molina et al. 2011). The differences are due to estimates of the haploid effective population size 
for various species. In PRF models, the speciation time 
is scaled in terms of the
. For example, we estimated that indica has diverged 0.27
years ago, and this is in agreement with Caicedo et al.’s estimate of 0.21
years ago. In their study, the time back to the beginning of domestication of 12,000 years was used as a standard and compared with their model estimated speciation time to estimate population size while we first estimated population size
by using a neutral substitution rate of 
per bp. However, one limitation of the PRF models is that they do not incorporate demographic features such as bottleneck, population subdivision and migration due to the model assumption of constant population size for both the ancestral species and its descendant.
As shown in Figure 3 (also Table S2), Pi-ta is under negative selection in indica while it shows positive selection in japonica, tropical japonica, and weedy rice. It has been reported that Pi-ta originated from landrace accessions and was used for many Asian and US cultivated rice varieties over the course of decades. In the US rice industry, Pi-ta has been used for many years to control rice blast, and most major US cultivars grown in Arkansas contain Pi-ta (Jia 2003, 2009; Jia et al. 2004b, 2009a). Thus, many accessions of Asian and US cultivated japonica rice used in this study possess the same Pi-ta allele that is resistant to common blast pathogen strains in the United States. This finding indicates that Pi-ta has been artificially selected during rice domestication in Asia and the United States, respectively. In addition, Pi-ta may have been evolved recently in O. sativa. As shown in Figure 6, the resistant Pi-ta haplotype present in O. rufipogon, which is the putative progenitor of cultivated rice, was found in many modern cultivated rice strains used in this study. Another interesting finding is that Pi-ta in US weedy rice is also under positive selection. The origin of the weedy species of rice in the United States is still not clear. It has been noted that weedy rice is genetically similar to wild species of rice, especially O. rufipogon (Gealy et al. 2003; Londo and Schaal 2007; Olsen et al. 2007). We found that US weedy rice has positive selection values, indicating that positive selection presents in US weedy rice. This result supports the previous report on the event of natural hybridization between cultivated and weedy rice (Chen et al. 2004; Gealy et al. 2003).
It has been known that LRR of Pi-ta is important to the function of blast disease resistance by recognizing the AVR-Pita effector. We also found that the amino acid replacement substitution rates are consistently low at LRR in all rice accessions. This result indicates that a strong positive selection has predominantly acted on the LRR regions of the Pi-ta gene, in agreement with previous studies (Huang et al. 2008; Lee et al. 2009; Thakur et al. 2013). Interestingly, in addition to the functional constraint of LRR, we found the lowest level of nonsynonymous substitution rates at the NBS-ARC domain of the Pi-ta gene in all rice accessions examined. It seems that the N-terminal coding region of the Pi-ta gene has played a critical role in the evolution of the Pi-ta function, similar to the role found in LRR. In our previous study (Lee et al. 2009), the resistance Pi-ta allele always contained a 3364-bp insertion near the approximate Pi-ta promoter region. It would be interesting to determine whether there is a correlation between the insert fragment and the evolutionary relationship of NBS-ARC at Pi-ta. In contrast to these two regions, we found that the 5′ regulatory sequence of the Pi-ta gene has the greatest level of nonsynonymous substitution rate among all rice accessions. The 5′ region of the gene has been known as a critical region for the meiotic recombination (Wu and Lichten 1994). Thus, the highly diverse region may suppress the recombination between the paralogs at the Pi-ta locus. The extreme divergence of the 5′ regulatory region was also identified in another rice blast R gene, Pi2/9 locus (Zhou et al. 2007).
In conclusion, we demonstrated that the time-dependent PRF model presented in this study provides an analytical tool for understanding the evolution of disease R genes in crop plants. The model can be applied to multilocus datasets to quantitatively estimate selective effect, species divergence time and substitution intensity to yield a comprehensive view of genetic patterns across species. PRF models assume that nucleotide sites evolve independently, and, hence, they are more suitable to studies of genetic regions with high recombination rates. Simulation studies using multi-locus data have shown that in estimation of selective effects, methods based on PRF are relatively robust against the violation of this assumption (Abel 2009; Boyko et al. 2008; Bustamante et al. 2001; Zhu and Bustamante 2005). One limitation of the PRF model due to its restrictive assumption of constant population size for ancestral and daughter populations makes the model fail to capture any demographic changes that have happened in the history of the species. A more realistic PRF model with changing population size within species is under development, and it would be interesting to apply the new model to rice or other plant R genes to make statistical inferences about their evolution. Resulting knowledge will be essential for developing more effective disease management strategies.
Acknowledgments
We thank Jeffery Shen for effective communication and logistical assistance to host Y. Jia’s seminar at University of Nevada, Las Vegas, and Ken Olsen of Washington University in St. Louis for critical review. The US Department of Agriculture is an equal opportunity provider and employer.
Footnotes
Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.114.014969/-/DC1
Communicating editor: J. D. Faris
- Received October 12, 2014.
- Accepted October 16, 2014.
- Copyright © 2014 Amei et al.
This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
