Plant materials

A set of 272 diverse genotypes were used in the present study. The set included 270 wild rice accessions (O. nivara/O. rufipogon/O. sativa f. spontanea) collected from different agro-climatic zones of India; 156 from Mid Gangetic Plains (MGP), 14 from Upper Gangetic Plains (UGP), 12 from Lower Gangetic Plains (LGP), 20 from West Himalayan Regions (WHR), 9 from East Himalayan Regions (EHR), 22 from West Coastal Plains (WCP), 3 from East Coastal Plains (ECP), 5 from East Plateau Hills (EPH), 23 from Gujarat Plains and Hills (GPH), and 6 from Island Regency (IR). As reference genotypes, O. sativa elite cultivars ‘Pusa 1121’ (indica) and ‘Taipei 309’ (japonica) were used. Details of the rice accessions used in the current study are provided in S1 Table and the online database at http://nksingh.nationalprof.in:8080/iwrdb/index.jsp.

Nematode culture

A pure culture of an Indian isolate of M. graminicola Golden & Birchfield was maintained on rice (O. sativa cv. Pusa 1121) in a greenhouse. Egg masses were collected from galled roots via sterilized forceps and were kept for hatching on a double-layered tissue paper supported on a mould sieve of wire gauze in a Petri dish containing distilled water. Freshly hatched second stage juveniles (J2s) were used for subsequent experiments.

Rice accessions screening assay

Seeds of different rice accessions were surface-sterilized via 70% ethanol prior to soaking overnight in distilled water. Seeds were germinated in Petri dish containing wet filter paper in a growth chamber at 28 ± 2°C and 4–5 days old seedlings were used for infection bioassays in Pluronic gel medium (PF-127, Sigma-Aldrich) as described previously [26, 27]. Briefly, 60 ml of 23% PF-127 was poured into 150 × 20 mm Petri dish containing 7–9 uniformly distributed seedlings of identical genotype at <15°C and approximately 30 RRKN J2s were inoculated at the root tip of each seedling by a pipette tip. The gel was set at room temperature and Petri plates were incubated at 28°C and 14: 10 h light: dark photoperiod in a growth chamber (light level– 300 μmol m^-2 s^-1). At 16 dpi, plantlets were extracted from PF-127 by placing the dishes in an ice bath as the gel liquefies below 15°C. Roots were stained with acid fuchsin [28], galls were dissected under the microscope, and the number and stage of penetrated nematodes were recorded. Photomicrographs were obtained in a Zeiss Axiocam MRm microscope. Nematode multiplication factor (MF) ratio was calculated [(number of egg masses × number of eggs per egg mass) ÷ nematode inoculum level] to determine the reproductive potential of RRKN in different rice accessions. For each accession three plates were included and the experiment was repeated at least twice.

Host response of 40 rice accessions to RRKN infection in soil and PF-127

After initial screening of 272 accessions in PF-127 medium, 40 accessions were selected (based on lower counts (< 2) of gall and MF value) for further screening based on root galling, total endoparasite counts and MF. Plug trays with 50 wells [45 × 45 × 35 mm (length × width × height)] were filled with autoclaved soil (collected from IARI rice field) and two seedlings of identical genotype were sown per well. Seedlings were thinned to one healthy seedling in each well at 10 days. Five days later, plantlets were transferred to 18 well [80 × 80 × 70 mm (length × width × height)] trays containing 200 g soil in each well and each plantlet was inoculated with 200 J2 of M. graminicola. The experiment was carried out in a growth chamber at 28°C, 60% humidity and 14: 10 h light: dark photoperiod (light level– 300 μmol m^-2 s^-1). Plants were regularly watered to field capacity and fertilized with Yoshida’s nutrient solution [23]. In order to avoid the secondary galling by RRKN, the experiment was terminated at 16 dpi as M. graminicola completes its life cycle in 19 days at 24–30°C in well-drained soil [3, 5, 6]. In PF-127 medium, M. graminicola completes its life cycle in 15 days [26, 27]. Plants were carefully harvested, roots were washed in water, galls and other parameters were observed under the microscope. For each accession 12 plants were screened and the experiment was repeated at least twice.

Further, for stringent screening of 40 accessions, a higher nematode inoculum (~2000 J2s) was applied in PF-127 in Petri plates (150 × 20 mm) containing 50–55 seedlings of identical genotypes. Dishes were incubated, plantlets were harvested at 16 dpi and nematode infection potential was assessed as described above.

Genome-wide association analysis

The set of 272 rice accessions were genotyped using in-house developed “OsSNPnks” 50K genic Affymetrix chip [29, 30] containing a total of 50,051 high-quality SNPs. Alleles for specific SNPs were detected through Axiom Analysis Suite version 2.0 (https://media.affymetrix.com/). SNP density plot was created using R package CM-plot. GWAS was performed with the rice accessions based on five important diverse nematode phenotypic traits such as number of galls, endoparasites, egg masses, eggs per egg mass and MF. A mixed linear model (MLM) was used to infer the association between each SNP and the phenotype, where markers and population structure (Q matrix) effects were fixed and the genotypic effect (K matrix) was random. The MLM was run in TASSEL v.5.2 (http://www.maizegenetics.net), where the additive genetic and residual variance components of the random factors were re-estimated for each SNP. TASSEL 5.2 computed the log likelihoods of the null and alternative models, and the effect of a SNP with its standard error. A p-value of < 0.0001 (for traits gall, egg mass and MF) with minor allele frequency (MAF) value of >5% was used as the threshold for significance analysis of marker-trait associations. A p-value of < 0.001 was used as the threshold for trait eggs per egg mass. To evaluate the genetic diversity across these diverse rice accessions, a phylogenetic tree was constructed by using the haplotype-based neighbour-joining algorithm, and the tree was visualized with FigTree v.1.4.0 [31].

STRUCTURE v.2.3.4 was employed to determine the genetic subpopulations among 272 accessions using a model-based Bayesian clustering method. Ten independent runs were performed setting the hypothetical number of expected populations (K) range from 1–10. The data were processed with burn-in period and replication values of 50,000 each. The number of distinct subpopulations signify the optimal K value which was determined by estimating natural logarithm of the probability of fit (LnP(D)) obtained in the STRUCTURE output, whilst ΔK indicates the rate of change in LnP(D) between successive K values [32]. The Evanno plot was generated in Structure Harvester v.6.0 [33] to determine the peak ΔK values.

Additionally, pairwise linkage disequilibrium (LD) between selective SNP markers was calculated using squared correlation coefficient (r²) between two markers. Graphical LD decay was imputed by GAPIT R package. The broad-sense heritability (h²) of phenotypic traits was estimated as h² = V_G/(V_G+V_E), where V_G is genotypic variance and V_E is residual variance. An additive ANOVA model was constructed to determine the phenotypic variation captured by the each set of SNPs.

Candidate gene prediction

Sequences flanking the SNP markers by 200 kb were selected via rice Pseudomolecule version 7 in the database Rice Genome Annotation Project (RGAP; http://rice.plantbiology.msu.edu/). In silico functional annotation of candidate genes was performed (BLASTp, BLAST2GO, gene ontology, Interpro and PFAM hits) to analyze the putative function of genes associated to RRKN or pathogen resistance with reference to previously published literature. Transposon or retrotransposon-related hits were excluded from the analysis.

RT-qPCR analysis

Expression analysis of the candidate genes was performed on the uninfected and infected root samples of Pusa 1121 at 7 dpi (J2s inoculated in PF-127 medium as described above). Total RNA was extracted from the snap frozen (in liquid nitrogen) whole root systems using NucleoSpin total RNA kit (Macherey-Nagel, Germany) by following the manufacturer’s protocol. RNA quantity and quality were assessed in Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific) and 2% (w/v) high resolution MetaPhor agarose gel (S1 Fig). First-strand cDNA was synthesized from the total RNA (1 μg) using a SuperScript VILO cDNA synthesis kit (Invitrogen) by following the manufacturer’s instructions. Samples were analyzed by RT-qPCR using SYBR Green Supermix kit (Eurogentec) in a Realplex² thermal cycler (Eppendorf). To quantify the expression level of candidate genes, specific gene primers were used (S2 Table). cDNA matching actin gene of O. sativa was amplified as a reference for constitutive expression using primers listed in S2 Table. Reaction mixture (10 μl) for each sample consisted of 5 μl SYBR Green PCR Master mix (Eurogentec), 750 nM of each primer and 1.5 ng cDNA. RT-qPCR cycling conditions and melt curve program were followed as previously described [26, 27]. At least three biological and three technical replicates were used for each sample. Quantification cycle (Cq) values were imported from Realplex² software (Eppendorf). The relative quantities of each candidate gene transcript in the samples were calculated via 2^-ΔCq [ΔCq = (Cq for candidate gene)–(Cq for actin)] method [34]. The stability of the expression of actin in different experimental conditions (leaf, root, stem and seed tissue of resistant and susceptible genotypes in uninfected and nematode infected condition) in terms of reaction efficiency, R² and Cq values are demonstrated in S2 Fig.

Statistical analysis

Data of the bioassay experiments were normalized using generalized linear model and subjected to two-way analysis of variance (ANOVA) in SAS software (version 14.1). Relative gall numbers, egg masses, MF etc. in different accessions were compared via post-hoc Tukey’s honest significant difference test (HSD) at P < 0.01. For qRT-PCR data, significant differential expression of candidate genes between uninfected and infected plants was compared by post-hoc Tukey’s HSD test (P < 0.01).

Host response of 272 rice accessions to RRKN infection in Pluronic gel medium

Screening of 272 rice accessions in Petri dishes (containing Pluronic gel/PF-127 medium) revealed variability in response to RRKN infection at 16 days post inoculation (dpi). Fig 1A and 1B show the frequency distribution of the gall numbers and nematode multiplication factor (MF), respectively, while all values (including numbers of total endoparasites, egg masses and eggs per egg mass) are detailed in S3 Table. According to Fig 1A, 49 of the accessions showed highly resistant response (0–2 mean number of galls), 8 exhibited highly susceptible response (7–12 galls), while 87 accessions showed moderately resistant response (4–5 galls). The mean number of galls was recorded to be 7–8 in reference genotypes Taipei 309 and Pusa 1121. A similar trend was observed in terms of MF; a moderate MF value of 12–20 was calculated in maximum of 89 accessions (Fig 1B). The results of a repeat run with 50 randomly selected accessions revealed a strong correlation with the initial gall (R² = 0.780, P < 0.001) and MF (R² = 0.720, P < 0.001) data, indicating the high reproducibility of PF-127-based assay.

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Fig 1. Host response of 272 rice accessions to M. graminicola infection at 16 dpi (inoculum level– 30 J2s per plant) in PF-127 medium.

Frequency distribution of gall numbers (A) and nematode multiplication factor (B) in 272 genotypes. Box plot of relative gall numbers (C) and multiplication factor (D) across the different taxonomic groups of wild rice, namely O. nivara, O. rufipogon and O. spontanea. Box plots with the same letters are not significantly different (P > 0.01, Tukey’s HSD test; n > 30).

https://doi.org/10.1371/journal.pone.0239085.g001

When all the genotypes were categorized into different taxonomic groups of wild rice, accessions from O. nivara (2.9 mean numbers of galls) showed the least number of galls as compared to the accessions that are classified as O. rufipogon (4.1) and O. spontanea (5.2). A significant difference (P < 0.01, n > 30) in gall numbers between different species (Fig 1C) was found, which explains the genetic variation between wild rice species in our study. When MF values were taken into account a significant difference (P < 0.01, n > 30) was observed between O. spontanea and O. nivara/O. rufipogon but not between O. nivara and O. rufipogon (Fig 1D). When accessions were stratified into different agro-climatic zones (independent of species), no significant difference was observed. This may be because each geographic location contained all the three species of wild rice (S1 Table). Although accessions from Mid-Gangetic Plains (MGP; majority of them were O. nivara) showed the least number of galls compared to accessions from other geographic locations (data not shown).

NKSWR 30, an O. nivara accession from MGP, showed no gall in any of the twelve replicates (Fig 2A and 2B; S3 Table) whereas NKSWR 11, NKSWR 13, NKSWR 15, NKSWR 18, NKSWR 19, NKSWR 23, NKSWR 25, NKSWR 43, NKSWR 48, NKSWR 108, NKSWR 123, NKSWR 124, NKSWR 128, NKSWR 141, NKSWR 144, NKSWR 156, NKSWR 160 (O. nivara, MGP), NKSWR 259 (O. nivara, Gujarat Plains and Hills (GPH)), NKSWR 101 (O. spontanea, MGP) and IC 336687 (O. rufipogon, East Plateau Hills (EPH)) had only one gall in some of the replicates and zero galls in the remaining replicates (S3 Table).

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Fig 2. RRKN infected plantlets of selective accessions in Petri dishes containing PF-127 medium at 16 dpi (inoculum level– 30 J2s per plant).

(A) The susceptible check Pusa 1121 had 7.88 ± 0.44 galls in each plantlet. A wild accession NKSWR 49 exhibited 7.25 ± 0.88 mean number of galls. By contrast, NKSWR 30 and NKSWR 43 showed zero and 0.38 ± 0.18 galls, respectively. (B) A magnified view shows typical hook-shaped galls (indicated as white arrows) in Pusa 1121 and no gall in NKSWR 30. Scale bar = 1 cm.

https://doi.org/10.1371/journal.pone.0239085.g002

Host response of 40 rice accessions to RRKN infection in soil

Based on extremely low counts (< 2) of gall numbers and MF in PF-127 assay, 40 wild rice accessions were further screened for M. graminicola resistance in soil. Absolute numbers of galls, endoparasites, egg masses, eggs per egg mass and the resultant MF ratio at 16 dpi are detailed in S4 Table. As an example, the results for some accessions that show low (NKSWR 30, NKSWR 259, NKSWR 43, NKSWR 123, NKSWR 19), medium (NKSWR 101, NKSWR 156, NKSWR 128, NKSWR 15, NKSWR 23) and high (NKSWR 113, NKSWR 13, NKSWR 35, NKSWR 5, NKSWR 39) gall numbers and MF are presented in Fig 3A and 3B. However, compared to the reference genotype Pusa 1121 (gall number– 41.5 ± 3.44; MF– 61.14 ± 5.95) RRKN infection in the highest susceptible wild rice genotype NKSWR 39 (gall number– 12.5 ± 1.75; MF– 8.33 ± 2.25) was remarkably lower (P < 0.01; Fig 3A and 3B). In accordance with the PF-127 assay, genotypes NKSWR 30 (gall number– 0.25 ± 0.05; MF– 0.02 ± 0.01) and NKSWR 259 (gall number– 0.25 ± 0.1; MF– 0.05 ± 0.03) showed highly resistant response indicating no J2s had penetrated the roots in the majority of the replicates. Moreover, with reference to Taipei 309 and Pusa 1121, comparatively lower number of egg masses, eggs per egg mass and the resultant MF ratio in all the 40 genotypes (S4 Table) indicate the retarded nematode development and reproduction in selected wild rice accessions. A similar trend was observed when a highly stringent assay was conducted with 40 accessions in PF-127 medium (data not shown).

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Fig 3. Host response of selective rice accessions to M. graminicola infection at 16 dpi (inoculum level– 200 J2s per plant) in soil.

Accessions were grouped into five each for low, medium and high incidence of gall numbers (A) and nematode multiplication factor (B). Data of reference genotype Pusa 1121 is shown. Each bar represents the mean ± standard error (n = 12). Genotypes having different letters indicate significant difference at P < 0.01, Tukey’s HSD test. Plots of mean and standard error of gall numbers (C) and MF values (D) in all the 40 accessions in soil versus the screening of identical genotypes in PF-127 medium at 16 dpi.

https://doi.org/10.1371/journal.pone.0239085.g003

The number of galls and MF ratio documented in all the 40 accessions in soil assay had correlated very strongly with the identical accessions in PF-127 assay on same parameters (R² = 0.906 (gall number), R² = 0.772 (MF), P < 0.001; Fig 3C and 3D).

Population structure and genetic diversity analysis of wild rice accessions

For determining the population structure of 270 wild rice accessions and one each of japonica (Taipei 309) and indica (Pusa 1121) cultivars, genotyping was performed using genome-wide 50K SNPs with no missing data. The Bayesian model-based analysis of population structure of 272 accessions showed the optimum population structure at K = 3 (Fig 4A), suggesting these accessions were stratified into three major genetic sub-populations. Population I (indicated in red color) consisted 38.60% of accessions (105) in which 84 accessions were pure and 21 were admixed. Population II (indicated in green color) accounted for 22.04% of accessions (60) of which 17 were admixed. Population III (indicated in blue color) accounted for 39.33% of accessions (107) of which 19 were admixed (Fig 4B; S3 Fig). The mean Fst values for population I, II, and III were 0.4691, 0.6877 and 0.8366, respectively, exemplifying a considerable proportion of admixture types in our wild rice accessions possibly because of natural intercrossing among these sub-populations. Based on our earlier studies with subsets of these wild rice accessions and their co-clustering with known Aus and Indica rice cultivars ([24]; 418 wild rice accessions were genotyped via 48-plex Illumina GoldenGate assay), the populations I and II are designated as ‘Pro-Aus’ and ‘Pro-Indica’ populations, respectively. Pro-Aus and Pro-Indica populations were distributed throughout India, whereas population III was primarily concentrated in MGP and thus designated as ‘Mid-Gangetic’ population (Fig 4B; S1 Table). Notably, Pro-Indica populations contained the highest percentage (28.33%) of admixture types compared to Pro-Aus (20%) and Mid-Gangetic (17.75%) populations. The results of principal component and kinship matrix analysis were consistent with the finding that our association panel consisted of three different groups of population (S4 Fig).

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Fig 4. Population structure and genetic diversity of 272 rice genotypes based on genome-wide 50K SNP chip.

(A) Estimation of the number of populations (K) in wild rice accessions using ΔK values. Three subgroups inferred from STRUCTURE analysis. (B) Distribution of rice accessions into three different populations. The colored region grouped the accessions in corresponding populations as red (Pro-Aus), green (Pro-Indica) and blue (Mid-Gangetic). X- and Y-axis indicate rice accessions and % membership to a genetic group, respectively. (C) SNP haplotype-based neighbor-joining phylogenetic tree of 272 accessions shows wide genetic diversity among wild rice accessions. Five different clusters (highlighted as blue, red, green, magenta and sky blue) contained nine distinct sub-clusters. Reference genotypes (Taipei 309 and Pusa 1121) are indicated by arrows.

https://doi.org/10.1371/journal.pone.0239085.g004

Diversity analysis revealed 272 rice accessions were grouped into five major clusters (Fig 4C). Cluster I was split into two sub-clusters, in which majority of Mid-Gangetic populations were nested into sub-cluster Ia and sub-cluster Ib was of mixed type. Majority of Pro-Aus populations were grouped into cluster II which was further subdivided into three sub-clusters each containing a mixture of Pro-Indica and Mid-Gangetic populations. Cluster III and IV was of admixed type containing accessions from diverse agro-climatic zones such as MGP, Upper Gangetic Plains (UGP), Lower Gangetic Plains (LGP), GPH, West Himalayan Regions (WHR), East Himalayan Regions (HER), East Coastal Plains (ECP) and Island Regency (IR). Cluster V nested O. nivara accessions from MGP (Fig 4C). Our results suggested that population structure analysis may not always conform with diversity analysis as in former case inherent genetic structure are inferred based on their ancestral lineage while in latter genotypes can be grouped into additional clusters according to their geographical origin.

Genome-wide association analysis (GWAS)

The filtered SNPs detected via Axiom Analysis Suite (version 2.0) were used for GWAS after incorporating the phenotypic data. These SNPs provide a whole genome-wide coverage along the 12 chromosomes of rice (Fig 5). A total of 17 significant SNPs associated with M. graminicola resistance (based on four different parameters including gall number, egg mass, eggs per egg mass and MF) to rice were identified by GWAS using mixed linear model (MLM), which controlled for population structure and familial relatedness (Fig 6A and 6B; Table 1). The 17 association SNPs explained 5.33–12.61% of the total phenotypic variation (Table 1). We identified two SNPs to be associated with gall number, appearing to represent two QTLs on chromosome 2 and 6 in rice (threshold for significance–log₁₀(P) > 4; Fig 6; Table 1). Two SNPs each were associated with egg mass (on chromosome 2 and 4) and eggs per egg mass (in chromosome 1 and 11). No significant SNPs for total endoparasites were observed (only one SNP was detected at–log₁₀(P) > 2.75). The greatest number of SNPs (9) was detected (in chromosome 1, 2, 3, 4, 6, 10 and 11) to be associated with the most important trait, i.e. nematode multiplication factor (–log₁₀(P) > 4; Fig 6; Table 1). Overall, two QTLs on chromosome 1 and 4 were found to be associated with all the traits (Table 1). Percent heritability explained by corresponding SNPs for phenotypic traits such as gall number, egg mass, eggs per egg mass and MF were 78, 71, 65 and 89%, respectively (Table 2). The relationship between 40 resistant accessions and genotypes of significant SNPs are described in S5 Fig. Notably, polymorphism occurred in four different allelic combinations (TC, AG, CG and AT) across the genotypes. Accessions NKSWR101 (O. spontanea), NKSWR259, NKSWR30 (O. nivara), and IC336687 (O. rufipogon) which earlier conferred greatest degree of resistance to M. graminicola also harbored greatest number of SNPs.

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Fig 5. SNP density plot across the 12 chromosomes of rice representing number of SNPs within 1 Mb window size.

The horizontal axis represents the chromosome length in Mb. Different colors correspond to SNP density.

https://doi.org/10.1371/journal.pone.0239085.g005

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Fig 6. Genome-wide association analyses for RRKN resistance in wild rice.

(A) Manhattan plots of P-values from the mixed linear model (MLM) for different traits such as numbers of galls, egg masses, eggs per egg mass and MF value. X-axis indicates the SNP location along the 12 chromosomes, Y-axis is the–log₁₀ (P-value). The dashed line represents the threshold defined by 1000 permutations of the association analysis. (B) Quantile-Quantile plots for different phenotypic traits with mixed model results. Dotted red line is the one-to-one line. X-axis represents expected–log₁₀ (p) and Y-axis is observed–log₁₀ (p) of each SNPs.

https://doi.org/10.1371/journal.pone.0239085.g006

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Table 1. Significant SNP markers associated with QTLs conferring resistance to M. graminicola in 272 rice accessions.

https://doi.org/10.1371/journal.pone.0239085.t001

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Table 2. Percent heritability of different phenotypic traits with corresponding number of significantly associated SNPs.

R² reflects the amount of heritable variation explained by an additive model of the SNPs (ANOVA).

https://doi.org/10.1371/journal.pone.0239085.t002

In accordance with Dimkpa et al. [23], herein, QTLs were considered to be within 200 kb of a significant SNP. This 200 kb window falls within the estimated window of LD decay (~50–500 kb) in rice [35–38]. More specifically, in wild rice LD decays at approximately 1–200 kb (O. nivara maintains LD over larger distance due to their high level of self-pollination; [39]). Notably, O. nivara constituted 66.67% of total wild rice accessions in our study (S1 Table). Our genotyping data of 272 accessions revealed that the average LD decayed at 218 Kb, with whole panel r² estimate of 0.2 for the 50K chip and the highest r² estimate was 0.4. The overall LD decay was slower (Fig 7). Based on the detailed annotations in Rice Genome Annotation Project and NCBI, we identified candidate genes either flanking or within the 200 kb region on each side of the 17 associated SNPs (Table 1). A number of transcription factors (TFs) including leucine zipper (bZIP), SCARECROW, MYB, MADS-box, WRKY, auxin response factor (ARF), GRAS etc. (regulate plant defense gene expression during pathogen stress) were located on different chromosomes (Table 1). Several resistance proteins such as leucine-rich repeat (LRR), NBS-LRR, Cf2/Cf5, RGA3, Lr34 analog ABC transporter, zinc finger motifs, WD repeat, F-box (analogous to WD and LRR) were located in different QTLs (Table 1). Additionally, a number of protein kinases, Exo70 exocyst complex and kelch motifs were identified which are not listed in Table 1. Gibberellin, pathogenesis-related (PR) proteins, HEAT repeats and 14-3-3 proteins which are required for plant immunity to pathogen stress were also identified. Aquaporin, nodulin and auxin regulatory proteins were identified which are known to regulate the giant cell initiation and maintenance during nematode infection (Table 1).

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Fig 7. Linkage disequilibrium (LD) measured r² plotted versus the physical map (bp) of 50K SNP markers in a panel of 272 genotypes.

https://doi.org/10.1371/journal.pone.0239085.g007

Interestingly, a number of NBS-LRR proteins were located in QTL 11.2. Similarly, QTLs 1.3 and 4.2 were rich in WRKY TFs and zinc fingers, respectively. However, many candidate genes were common between QTL 2.2 and 2.3 (r² = 0.75), 4.2 and 4.3 (r² = 0.7), 4.3 and 4.4 (r² = 0.55), as they were within 200-kb LD (Table 1). SNPs were located within the candidate genes in QTL 1.3 (ubiquitin-conjugating receptor kinase), 2.1 (ADP-ribosylation factor), 2.3 (DEAD/DEAH-box helicase), 4.2 (zinc finger) and 6.2 (GTP-binding Rac protein).

RT-qPCR-based expression analysis of candidate genes

To further test the role of selective candidate genes in resistance of rice to M. graminicola infection, RT-qPCR was performed in infected and uninfected root tissues of Pusa 1121 seedlings. Expression of the fourteen genes was quantified in whole roots collected at the time of nematode inoculation and at 7 dpi in infected and uninfected plants. This enabled us to investigate the developmental regulation of the genes in young rice seedlings, as well as their regulation upon nematode infection. Except LOC_Os01g18440 (MADS-box), Os02g22140 (ADP-ribosylating protein), Os03g48450 (SCARECROW TF), Os04g50070 (zinc finger) and Os11g34460 (F-box), all the candidate genes were significantly (P < 0.01) upregulated in mock-inoculated plants (7 dpi) compared to 0 dpi control plants (Fig 8). The temporal expression of Os01g11200 (MYB TF), ADP-ribosylating protein, SCARECROW TF, zinc finger, Os06g47260 (Rac protein), Os11g05640 (bZIP TF), Os11g10720 (Cf2/Cf5), Os11g10760 (NBS-LRR) and Os11g34450 (14-3-3) was significantly (P < 0.05) elevated in RRKN-infected roots at 7 days after inoculation (Fig 8), suggesting these genes may have a role in eliciting the defense response against M. graminicola. In contrast, expression of MADS-box, Os02g40450 (DEAD-box), Os04g50660 (WD repeat) and F-box protein was unaltered (P > 0.01) in 7 dpi infected plants compared to 7 dpi uninfected plants. Intriguingly, steady-state mRNA levels of Os10g33940 (ARF18) was significantly (P < 0.01) attenuated in 7 dpi infected plants than 7 dpi uninfected ones (Fig 8), indicating the role of this gene in rice to RRKN susceptibility.

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Fig 8. RT-qPCR-based differential expression patterns of 14 candidate genes in infected and uninfected root tissues of Pusa 1121 plants at 7 days post inoculation (dpi) with M. graminicola.

Pusa 1121 was used as the reference cultivar. Data reflect gene expression levels in whole roots collected at the time of nematode inoculation (0 dpi control), in whole roots at 7 days after mock inoculation (7 dpi control) and at 7 days after nematode inoculation (7 dpi infected). Data are expressed as the normalized fold change in expression relative to the internal reference gene of O. sativa, i.e. actin. Bars represent mean expression levels ± standard error from three biological and three technical replicates each containing a pool of ten plants. Different lower-case letters (within each gene) indicate statistical difference determined by ANOVA with post-hoc Tukey’s HSD test, P < 0.01.

https://doi.org/10.1371/journal.pone.0239085.g008

Broad and novel genetic resources for M. graminicola resistance in rice

The wild relatives of rice harbor rich and novel genetic resources which can be used to improve pest and disease resistance in cultivated rice [40, 41]. For example, resistance to grassy stunt virus, bacterial leaf blight, neck blast and brown plant hopper was successfully introgressed into the cultivated rice from their wild relatives [42–44]. A number of beneficial traits including tolerance to biotic and abiotic stresses have been lost in cultivated rice which possesses a narrow genetic base because of domestication and breeding bottlenecks [24, 25]. According to an estimate, modern rice varieties have retained only 20% of the genetic diversity present in their wild relatives [45]. India has unprecedented diversity of wild rice germplasm and landraces that are spread over fifteen diverse agro-climatic zones [24, 25]. Therefore, these untapped genetic resources were taken as the prime candidates for the association panel in our study in order to unravel the conserved loci that govern M. graminicola resistance in rice. Indeed, a large proportion (14.8%; 40 of 270 accessions) of the wild accessions was found to be highly resistant to M. graminicola infection in the present study.

Herein, we screened 272 diverse rice accessions collected from ten agro-climatic zones for M. graminicola resistance by measuring the relative numbers of galls, endoparasites, egg mass, eggs per egg mass and MF ratio in PF-127 medium. A significant variation in the susceptibility level of rice accessions to RRKN infection was documented. A repeat experiment with fifty randomly selected accessions from this diversity panel showed strong correlation with the initial screening results. This suggests that PF-127-based screening is a robust and reproducible method for dissecting the rice-RRKN interaction, in agreement with the previous reports from our laboratory [26, 27]. In addition, 40 highly resistant accessions were further evaluated for M. graminicola resistance in large plug trays containing soil as the medium. Taken together the data of both soil- and PF-127-based screening, O. nivara accessions NKSWR 30 and NKSWR 259 showed almost immune response to M. graminicola with zero galls and zero MF in majority of the replicates at 16 dpi. Other O. nivara accessions such as NKSWR 43, NKSWR 123, NKSWR 19, NKSWR 108, NKSWR 25, NKSWR 18 and an O. rufipogon accession IC 336687 also supported extremely low population level of M. graminicola. The negligible susceptibility of these accessions cannot be attributed to poor root growth because root weight of these accessions was comparable with that of susceptible accessions (data not shown). The differential susceptibility of 332 O. sativa accessions to M. graminicola was earlier reported via soil-based screening in which two accessions LD 24 (indica) and Khao Pahk Maw (aus) were found to be almost immune to RRKN infection [23, 46].

Among 40 highly resistant wild accessions, 34 belonged to O. nivara type (33 of them collected from middle Gangetic plains agro-climatic zone) whereas 3 each belonged to O. rufipogon and O. sativa f. spontanea types. According to PF-127-based screening, a clear difference in resistance level in 272 accessions was also evident when genotypes were categorized into different taxonomic groups, i.e. nivara-, rufipogon- and spontanea-type. These differences can be explained by the possibility of different selection pressures among the geographic regions. When compared within different agro-climatic zones, accessions from MGP showed least nematode infection. The majority of resistant accessions originally from middle Gangetic plains (occupies the eastern part of Uttar Pradesh and Bihar) presumably have experienced higher selection pressure from RRKN infection. A similar stratification of cyst nematode (Heterodera glycines) resistance level across the different geographic location of soybean crop was reported when 235 wild accessions were included for association mapping analysis [22].

The genetic variation among Indian wild rice accessions were studied via model-based population structure analysis using genome-wide unlinked SNP markers (unlinked markers provide high reproducibility and success rate of population structure analysis; [47]) which grouped the accessions into three distinct subpopulations, namely Pro-Aus, Pro-Indica and Mid-Gangetic populations. Additionally, genetic diversity and cluster analysis based on SNP markers revealed the current wild accessions were highly diverse indicating each populations of an agro-climatic zone constituted mixture of genetically diverse individuals. According to the Fst values, Pro-Indica and Mid-Gangetic populations contained the greatest and least proportion of admixture types, respectively. Thus, accessions collected from middle Gangetic plains agro-climatic zone represented a conserved wild rice subpopulation whereas relatively greater level of gene flow and outcrossing was speculated among the geographically adjacent than the distant populations. Notably, overlapping geographic distribution pattern of wild rice subpopulations was reported by several studies [39, 48, 49]. Due to their open inflorescence the sympatric species, O. nivara and O. rufipogon can outcross with each other and also with cultivated O. sativa [50, 51]. Nevertheless, the greater genetic diversity of present wild rice accessions is in line with the hypothesis that wild rice are the source of useful new genes for future varietal improvement program [29, 40].

Phenotyping of the association panel has offered important information about the degree and distribution of RRKN susceptibility in Indian wild rice species including O. nivara, O. rufipogon and O. sativa f. spontanea, and in two cultivated species, i.e. O. sativa indica and japonica. This provided new insights into the evolution of resistance/susceptibility of wild and cultivated rice species to M. graminicola parasitism. The higher susceptibility of indica and especially japonica rice is not surprising maybe because no selection is in function to introduce resistance in japonica as RRKN is not a pest of temperate rice cultivation [23]. Intriguingly, a majority of O. nivara accessions from middle Gangetic plains (a suspected genetic diversity hotspot; [24]) has shown the least susceptibility to RRKN infection. This may be because of their eco-geographical isolation in accordance with the rationale that gene flow is inversely related to geographic distance of natural populations [52].

Genetic loci determining the parasitic success of M. graminicola in rice

By analyzing the marker-trait associations in 272 rice accessions via GWAS, we identified 17 significant SNPs that governed the RRKN resistance-related traits such as numbers of galls, egg mass, eggs per egg mass and MF ratio. By applying the–log₁₀(P) score of 4 as a threshold for significance, we identified two QTLs at chromosome 1 and 4 of rice were associated with all the traits. A similar stringency level or a less stringent threshold (–log₁₀(P) > 3) was adopted while reporting significant SNPs associated with nematode resistance/susceptibility in rice [23], wheat [21], soybean [22] and Arabidopsis [53]. In particular, four SNPs in chromosome 4, three SNPs each in chromosome 1, 2, 11, two SNPs in chromosome 6, and one SNP each in chromosome 3 and 10 were found to be associated with different traits. The trait such as total endoparasite counts in rice accessions was not taken into consideration as only one SNP was detected for this trait at–log₁₀(P) > 2.75. Lowering the threshold for significance in GWAS study may increase the false discovery rate which in turn reveals more common alleles with smaller effect size in test populations [20].

A number of transcription factors (bZIP, SCARECROW, MYB, MADS-box, WRKY, ARF, GRAS etc.) that regulate plant defense responses to nematode and various biotic stress [54–61], were located on different chromosomes within 200-kb LD of the suspected QTLs in this study. The 200 kb window was adopted because of the fact that LD decay occurs in rice at 50–500 kb [35–38]. Although in wild rice the decay is much higher at 1–200 kb, O. nivara maintains LD over larger distance due to their high level of self-pollination [39]. Notably, O. nivara constituted 66.67% of total wild rice accessions in our study. Other important candidate genes located in different QTLs include resistance genes such as NBS-LRR, Cf2/Cf5, RGA3, Lr34 analog ABC transporter, zinc finger, WD repeat, leucine-rich repeat, HEAT repeat etc. All these genes putatively function to elicit plant innate immunity during pathogen invasion [62–65]. A number of candidate genes putatively involved in plant defense response to biotic stress were located within the SNPs in QTL 1.3 (ubiquitin-conjugating receptor kinase; [66, 67]), 2.1 (ADP-ribosylation factor; [68]), 2.3 (DEAD/DEAH-box; [69]), 4.2 (zinc finger; [64]) and 6.2 (GTP-binding Rac protein; [70, 71]) in our study.

We assume that chromosomes 1 (QTL 1.3 harbored a number of WRKY TF), 2, 4 (QTL 4.2 contained several zinc finger motifs), 6 and 11 (QTL 11.2 displayed several NBS-LRR resistance genes) might play a decisive role in contributing resistance to M. graminicola in rice because a number of significant SNP loci were identified in each of these chromosomes. A previous association mapping analysis have identified chromosomes 4 and 11 to harbor candidate genes such as lectin and homolog of stripe rust resistance protein [23]. Using RIL-based bi-parental mapping study, QTLs for RRKN resistance were identified on almost all the 12 chromosomes of rice to date [7, 9, 13, 14]. It is evident that RRKN resistance in rice is a complex trait that involves a number of genes which regulate a cascade of plant defense responses. Therefore, unravelling the conserved and novel RRKN-resistance loci from the non-domesticated wild rice population could improve our understanding of molecular mechanisms underlying the rice-RRKN interaction.

Candidate genes involved in M. graminicola resistance in rice

Unlike a report of homologous stripe rust and powdery mildew resistance genes in chromosome 11 of O. sativa [23], no information is available about the canonical nucleotide-binding leucine-rich repeat (NB-LRR)-type genes that impart resistance against M. graminicola in rice. Herein, we report a number of NBS-LRR proteins (LOC_Os11g10550, 10570, 10610, 10620 and 10760) which was mapped on chromosome 11 of rice within 200-kb LD of QTL 11.2. Incidentally, Hsa-1^Og gene that conferred resistance against the cyst nematode, H. sacchari, was mapped on chromosome 11 of O. glaberrima [72]. The nucleotide-binding state of NBS-LRR proteins regulates the activity of plant resistance (R) proteins that are involved in activation of plant innate immunity upon pathogen recognition [73]. The R gene in tomato contains Cf2/Cf5 locus (encode LRR) that confer resistance in tomato to M. incognita [62]. Upon pathogen invasion in host plants, the activated membrane-bound NBS-LRR immune receptors translocate to the cell nucleus and interact with specific transcription factors (although not exclusive, comprise members of ERF, bHLH, bZIP, MYB, NAC and WRKY families) which modulate the plant immunity [57]. In our qRT-PCR analysis, compared to uninfected plants the expression of NBS-LRR and Cf2/Cf5 protein was positively regulated in M. graminicola-infected Pusa 1121 at 7 dpi. In coherence, other defense response regulatory genes such as MYB TF, SCARECROW, zinc fingers, bZIP TF and 14-3-3 were upregulated in nematode-infected plants than the uninfected ones.

Overexpression of a rice ADP-ribosylation factor induced pathogen resistance in tobacco by regulating the transcript accumulation of pathogenesis-related (PR) genes and salicylic acid (SA) [68]. OsRac1 (GTP binding Rac protein) was shown to be a component of disease resistance pathway acting downstream of R gene when OsRac1 transformed japonica rice was infected with the blast fungus, Magnaporthe oryzae [70]. Increased resistance to tobacco mosaic virus was documented when tobacco was transformed with OsRac1 [71, 74]. In our qRT-PCR study, both ADP-ribosylating and Rac protein was substantially upregulated in M. graminicola infected plants than the uninfected ones at 7 dpi. Together, our data suggest changes in expression level of these candidate genes can be a contributory factor to confer resistance in rice to M. graminicola. However, expression of ARF18 TF (auxin response factor) was downregulated in nematode-infected plants compared to uninfected plants at 7 dpi in our qRT-PCR study. Auxin-regulated proteins are known to coordinate the balance between plant root growth and disease resistance by promoting the auxin biosynthesis and suppressing the benzoxazinoid-based defense compound formation [75]. The direct role of auxin influx (AUX1, LAX3) and efflux (PIN3) proteins in giant cell formation were unraveled during Arabidopsis-M. incognita compatible interaction [76].

In conclusion, forty accessions displaying a high degree of resistance to M. graminicola were identified. This data uncovers the potential of O. nivara, O. rufipogon and O. sativa f. spontanea as novel resources for RRKN management. GWAS was successfully applied to dissect the genetic architecture of resistance to RRKN in Indian wild rice populations. The present study provides an example of exploring the untapped genetic resources and novel genes which enrich the repository of candidate genes utilizable for future marker-assisted rice breeding program for RRKN resistance.