QTL-seq reveals a major root-knot nematode resistance locus on chromosome 11 in rice (Oryza sativa L.)
The root-knot nematode Meloidogyne graminicola is a serious pest in rice affecting production in many rice growing areas. Natural host resistance is an attractive control strategy because the speed of the parasite’s life cycle and the broad host range it attacks make other control measures challenging. Although resistance has been found in the domesticated African rice Oryza glaberrima and the wild rice species O. longistaminata, the introgression of resistance genes to Asian rice O. sativa is challenging. Resistance due to a major gene in O. sativa would greatly aid breeding. Recently two accessions resistant to M. graminicola have been identified in a screen of 332 diverse O. sativa cultivars. In this study, these two resistant cultivars, LD 24 (an indica from Sri Lanka) and Khao Pahk Maw (an aus from Thailand), were crossed with a moderately susceptible cultivar, Vialone Nano (a temperate japonica from Italy). Approximately 175 F2 progeny of both populations were screened for susceptibility to M. graminicola infection. Between 20 and 23 individuals with highest and lowest galls per plants were pooled to make susceptible and resistant bulks which were sequenced to conduct bulked segregant analysis using the QTL-seq method. This revealed a nematode resistance locus from 23 Mbp to the bottom of rice chromosome 11 in both crosses suggesting a rare introgression of the same locus is responsible for resistance in both cultivars. While this information can be used in marker-assisted breeding, analysis of available SNP data revealed candidate loci and genes worthy of further investigation for gene identification.
KeywordsM. graminicola O. sativa QTL-seq Bulk segregant analysis Nematode resistance genes
Rice (Oryza sativa) is an essential food crop hosting various pests and diseases including plant-parasitic nematodes which pose a threat to production. With over 41,000 species of plant-parasitic nematodes described (Decraemer and Hunt 2006), they pose a great threat to world agriculture. It has been estimated that plant nematodes alone can cause damage of around USD80 billion per year world-wide (Nicol et al. 2011).
Amongst plant parasitic nematodes, root-knot nematodes (RKN) are obligate parasites which are distributed all over the world with 98 different species infecting almost every plant species (Moens et al. 2009). The Meloidogyne genus was listed first in the top 10 most important plant pathogenic nematodes in a survey of 1100 members of the Nematology Society (Jones et al. 2013).
Within the genus Meloidogyne, the rice root-knot nematode (M. graminicola) (Golden and Birchfield 1965) is considered a serious threat to rice production (Plowright and Bridge 1990). The second stage juveniles (J2s) are the only infective stage of these nematodes and they invade rice roots near the root tip (Bridge et al. 2005). After migration into the stele, the J2s establish a feeding site consisting of giant cells in the vascular tissue. The infection causes the development of hook-like galls inside which the nematodes complete their life cycle (Mantelin et al. 2017).
M. graminicola damages upland, lowland, deep-water and irrigated rice (Bridge et al. 2005; Win et al. 2011) and yield losses of up to 80% have been reported (Padhgham et al. 2004; Soriano et al. 2000). Once inside the roots, they can multiply even under flooded conditions because the J2s hatch from an egg mass that is retained within the root in contrast to other RKN. The J2s might not be able to infect new roots under flooded conditions, but they can move to penetrate other plants as soon as the fields are drained. As water is getting scarce everywhere, water-saving rice production is being encouraged. This will make soil conditions more favourable for high M. graminicola reproduction (De Waele and Elsen 2007). To quote these authors “observations increasingly indicate that the large-scale introduction of these [water saving] techniques is favouring the development of high populations of M. graminicola, drastically increasing its economic significance”.
M. graminicola is widely distributed in many rice growing areas in South and Southeast Asia (Jain et al. 2012). Although M. graminicola is considered a serious pest in the tropics (Jones et al. 2013), it has recently been detected in rice fields in Italy (Fanelli et al. 2017). This is the first report of this pest in temperate rice production. This observation is important for two reasons. First, it fits with the prediction that major tropical pests will move north with global warming (Bebber et al. 2013) meaning breeders will have to incorporate new breeding targets. Second, when screening a global diversity panel Dimkpa et al. (2016) found temperate rice cultivars on average more susceptible to M. graminicola, presumably as resistance has not previously been selected for (deliberately or otherwise).
With the advent of plant molecular genetics, many nematode resistance genes or quantitative trait loci (QTLs) for resistance to plant nematodes have been mapped to chromosomal locations and some genes have been cloned. Mi-1.2 is one of the best characterised root-knot nematode resistance genes which was found in a wild relative of tomato and confers resistance to several Meloidogyne species (Veremis and Roberts 2000). Similarly, Hs1pro−1, from wild beet against Heterodera schachtii (Cai et al. 1997), and Gpa-2, from potato against Globodera pallida (van der Vossen et al. 2000), are some of the identified natural resistance genes that can be used for developing nematode resistant cultivars. With the RKN infecting almost all the cultivars of O. sativa rice, it has been assumed that there is only a limited opportunity for breeding for nematode resistance using O. sativa. Natural resistance to M. graminicola has been reported in Oryza longistaminata and Oryza glaberrima (Soriano et al. 1999). However, O. glaberrima, the domesticated rice originating from West Africa, is low yielding and of minor economic importance compared to Asian rice O. sativa (Linares 2002). Introgression of O. glaberrima into O. sativa has lead, for example, to the new rice for Africa (NERICA) cultivars (Jones et al. 1997) but introgression of M. graminicola resistance from O. glaberrima to O. sativa has not been successful (Cabasan et al. 2017). Therefore natural resistance in O. sativa cultivars is potentially very important. In Asian rice, QTLs for partial resistance to M. graminicola have been reported on chromosomes 1, 2, 6, 7, 9, and 11 using the Bala × Azucena mapping population (Shrestha et al. 2007). Mapping of a M. graminicola resistance on chromosome 10 in Asian rice (cv. Abhishek) using bulk segregant analysis was reported by Mhatre et al. (2017). More recently, Phan et al. (2018) reported a Hypersensitivity-Like Reaction (HR) to M. graminicola infection in the Asian rice cultivar Zhonghua 11 suggesting this resistance to M. graminicola to be qualitative rather than quantitative involving (a) major gene(s). Galeng-Lawilao et al. (2018) have reported main effect QTLs for field resistance in Asian rice on chromosomes 4, 7 and 9 plus two epistatic interactions (between loci on chromosome 3 and 11, and between 4 and 8).
Screening of 332 cultivars of a global rice panel, Rice Diversity Panel 1 (RDP1) identified two Asian rice cultivars, LD 24 and Khao Pahk Maw (KPM) to be resistant to M. graminicola (Dimkpa et al. 2016). In that study, data on 44,000 SNP markers was used to suggest three loci where a resistance locus might reside (around 42 MbP on chromosome 1, 1 Mbp on chromosome 3 and 26 Mbp on chromosome 11) assuming that resistance in both cultivars was the result of the introgression of rare alleles of the same major resistance gene.
Bulk segregant analysis (BSA) has proved to be an effective way to locate genes or QTLs from populations with two extreme phenotypic traits, which is most applicable to segregation of major genes (Michelmore et al. 1991; Trick et al. 2012; Venuprasad et al. 2009). In the past, BSA has been an important tool for rapidly identifying markers in a genomic region associated with a trait of interest (Giovannoni et al. 1991). QTL-seq is a novel and rapid way for performing bulk segregant analysis using next-generation sequencing data which was first reported by Takagi et al. (2013) who used it to identify QTLs for partial resistance to rice blast. In principle, this calculates a SNP index as the relative frequency of the parental alleles for each SNP in both the resistant and susceptible pools, then calculates a delta-SNP index as the difference between the proportions from the two bulks. Regions with a delta SNP-index that pass a confidence interval threshold, as calculated by statistical simulation, should contain a QTL. This method has recently been used to detect major QTLs in several crops (Illa-Berenguer et al. 2015; Nowak et al. 2015; Sagawa et al. 2016; Shu et al. 2018).
Two cultivars (LD 24 and Khao Pahk Maw) identified as resistant to M. graminicola (Dimkpa et al. 2016) were crossed with a susceptible cultivar (Vialone Nano). The main objective of this current study was to use the QTL-seq method to test the hypothesis that these two resistant cultivars which are themselves genetically quite different, harbour the same allele for resistance, and identify loci and candidate genes for conferring resistance to M. graminicola with the long-term goal of improving nematode resistance in cultivated rice, O. sativa.
Plant materials and screening for nematode gall formation
Two recombinant F2 populations were used to screen for M. graminicola resistance. The first population (LD 24 × VN) was the progeny from the nematode resistant LD 24, which is an indica from Sri Lanka, crossed with the susceptible Italian temperate japonica cultivar, Vialone Nano (VN). For the other population (VN × KPM) Vialone Nano was crossed with the resistant aus rice cultivar Khao Pahk Maw (KPM) from Thailand. LD 24 and KPM are part of the Rice Diversity Panel 1 (Zhao et al. 2010) and seeds were originally obtained from the National Rice Research Centre, USA, and bulked in Aberdeen, UK. Seeds of VN were obtained from Giampiero Vale of the Consiglio per la Ricerca e la Sperimentazione in Agricoltura (CRA), Vercelli, Italy.
The screenings were carried out at Ghent University, Belgium using 178 individual F2 plants of LD 24 × VN and 174 individual F2 plants of VN × KPM. Seeds were first pre-germinated in petri dishes at 30 °C for 4 days in dark. Each germinated seedling was planted into a specially made polyvinylchloride tube containing sand and absorbent polymer (Reverstat et al. 1999). Then the seedlings were grown in a rice culture room under controlled environmental conditions (26/24 °C day/night temperature, 70% relative humidity, 12/12 h light/dark cycle). Each plant was fertilized with 10 ml of Hoagland’s solution 2 times per week. The root-knot nematode, M. graminicola was provided by Prof. Dirk De Waele (University of Leuven, Belgium) and was originally isolated from rice in Philippines. They were multiplied and maintained using a susceptible rice genotype Nipponbare or the grass host Echinocloa crusgalli. The second stage juveniles (J2) of M. graminicola were extracted from 2 to 3 months old infected roots using the modified Baermann method and 200 J2s per plant was added to 2-week old seedlings. Two weeks after inoculation, the plants were individually washed and stained with acid fuchsin (Byrd et al. 1983) to count the number of galls per plant.
For the LD 24 × VN cross, six separate batches of screening were conducted assessing 26–34 F2 plants with check cultivars of the parents and Nipponbare. For the VN × KPM cross, five separate batches of screens were conducted assessing 33–37 F2 plants with checks of the parents and Nipponbare. The results are shown as histograms for each screening run in Online Resources 1 and 2.
The resistant pool for the first population (LD 24 × VN) was made from 23 individual plants with no or few galls (0–2 galls) and the susceptible pool was made from 23 individuals with higher gall numbers per plant (10–34 galls). Similarly, the resistant pool of the second population (VN × KPM) was made from 20 plants with low numbers of galls per plant (0–4 galls) and the susceptible bulk contained 20 individual plants with a high number of galls per plant (21–47 galls). The CTAB method (Murray and Thompson 1980) was used to extract DNA from the bulks and from 10 or 11 individual plants of each of the parents LD 24, KPM and VN.
Quantity of genome sequence obtained for each sample
Base pair reads (bp)
LD 24 × VN resistant (R) pool
LD 24 × VN susceptible (S) pool
VN × KPM resistant (R) pool
VN × KPM susceptible (S) pool
LD 24 parent pool
KPM parent pool
VN parent pool
Quality assessment of read data was performed for all samples using FASTQC (version 0.11.5; Andrews 2010) and MultiQC (version 1.1; Ewels et al. 2016) using default parameters. Raw reads from each of the samples were filtered to remove poor quality sequences and trimmed to remove contaminating adapter sequences as well as any unwanted bias from their ends using Trim Galore! (Version 0.4.0; Krueger 2012). A Phred score of 30 was used as the overall quality threshold for the tool.
Complementary SNP calling
All samples were subjected to complementary independent SNP calling by having, firstly, their corresponding read datasets aligned to the ENSEMBL’s release 32 Oryza_sativa.IRGSP-1.0 reference sequence of cultivar Nipponbare using BWA-MEM algorithm (version 0.7.12-r1039; Li and Durbin 2010). Alignments obtained were, respectively, sorted and had duplicates marked with Samtools (version 0.1.19-44428 cd; Li et al. 2009) and Picard (version 1.104; http://broadinstitute.github.io/picard). All tools were configured with default parameters. Subsequently, FreeBayes (version v0.9.14; Garrison and Marth 2012) was used to perform the SNP calling task over each alignment file using parameters –m 20 –q 20 –n 4 –J –j –min-repeat-entropy 1 –no-partial-observations –F 0.1 –C 2. Ploidy parameter –p was adjusted according to the number of individuals in each sample.
Two instances of the QTL-seq pipeline (version 1.4.4; Takagi et al. 2013) were employed to analyse the SNP profiles of the respective S- and R-bulks of both populations. In the first one, the S-bulk from the cross LD 24 × VN was set up as bulk “A” while the R-bulk was set up as the “B” one. Similarly, in the second instance, the S-bulk from the cross VN × KPM was set up as bulk “A” while the R-bulk was set up as bulk “B”. Respectively, the genotypes of both parents LD 24 and KPM were used to develop the reference sequences. In both scenarios, the ENSEMBL’s release 32 Oryza_sativa.IRGSP-1.0 FASTA file was used as the public genome sequence. SNP-index and Δ (SNP-index) were calculated to identify the region of interest and plotted on chromosome maps (Takagi et al. 2013). Each Δ (SNP-index) was obtained by subtracting the respective SNP-index value of the R-bulk from the SNP-index value of the S-bulk. Due to the previous quality control step of the reads, the “Qualify read” stage of the QTL-seq pipeline was configured with Phred score values of 20.
SNP analysis on the bottom of chromosome 11
QTL-seq was used to identify the genomic region involved in resistance to the rice root-knot nematode (M. graminicola) in O. sativa cultivars LD24 and KPM. Bulked segregant analysis (BSA) is a mapping technique used to identify DNA markers linked to a particular locus. In the current study, paired bulked DNA samples were developed from two populations segregating for nematode susceptibility in terms of number of galls per plant. Resistant and susceptible bulks were generated by pooling DNA from plants with low gall numbers and plants with high gall numbers respectively.
The success of bulk segregant analysis depends on the heritability of the QTL in question. This is maximised if the QTL has a large effect or even more so if there is a single major gene responsible. It is also maximised by using a large population and having low error in the estimate of phenotype. Here we use a large population (approximately 175) but phenotyping was performed on an F2 which does not allow replication. The trait used here tends to have quite high variation between replicates (e.g. coefficient of variance tended to range from 50 to 100% in screen of RDP1 as reported in Dimkpa et al. (2016)). This means that the approach used here was only likely to work if great care was taken in phenotyping and if the variation was explained by a major QTL or major gene. The fact that the approach worked, revealing loci on the bottom of chromosome 11 in both crosses, validates the decision to progress rapidly without generating F3 material that would have allowed replicated phenotyping.
Both QTL-seq experiments revealed segregation from 23 Mb to the bottom of chromosome 11. This locus does not seem to be close to previously detected QTLs for nematode resistance on chromosome 11 from Shrestha et al. (2007) or Galeng-Lawilao et al. (2018). Within this region there are 859 annotated genes (according to the Rice Genome Annotation Project) including 88 (annotated as) transposons and 167 retrotransposons, 231 “expressed protein” and 29 hypothetical genes. Within this list there are 30 NBS containing disease resistance genes, eight other “disease resistance protein” genes, three “stripe rust resistance protein” genes, two “rust resistance protein” genes, two RGH genes (also resistance genes) and MLA10, a mildew resistance gene. This region on chromosome 11 corresponds to one identified by Dimkpa et al. (2016) as a potential location for a nematode resistance gene. Dimkpa et al. (2016) used 44 k SNP data to reveal 16 SNPs that were common between KPM and LD 24, not shared with the cultivar Seratoes Hari (a susceptible cultivar closely related to the resistant LD 24) and which are rare in the Rice Diversity Panel 1. Three of these are at 26.3 Mbp on chromosome 11, within the region containing the QTL detected here. Importantly, the authors acknowledged that the approach assumed that the resistance locus was the same between the two resistant cultivars. This assumption seems to be valid since both crosses reveal the same locus with QTL-seq. A similar approach to that of Dimkpa et al. (2016) was used to try to narrow down the likely position of the resistance genes using the expanded SNP data set provided by sequencing reported here. A total of 8440 SNPs in the region from 23 to 29 Mbs on chromosome 11 were common between LD 24 and KPM and both resistant bulks, but different to VN and the susceptible bulks. Since these are spread over the region, and not clustered (Fig. 4a) it does not help to predict the locus more accurately. The RDP1 has been assessed with a 700 K array (McCouch et al. 2016) meaning it is possible to integrate that data with the SNPs detected by QTL-seq. Only 159 SNPs occur in both the 8440 revealed by QTL-seq and the 700 K SNP database and are different to Seratoes Hari. These predominantly fall into four clusters at 25.1, 26.4, 27.8, and 28.0 Mbp (Fig. 4b). Rather than the global SNP analysis that identified Seratoes Hari reported by Dimkpa et al. (2016), a slightly different approach was used to exploit the 700 K SNP data available for the Rice Diversity Panel. This allowed the identification of three cultivars susceptible to M. graminicola but none the less very similar to KPM only in this region (23–29 Mbp of chromosome 11 only). There were 1770 SNPs which differ between these cultivars and KPM from this part of chromosome 11. Only 151 of these SNPs also differentiate KPM, LD 24 and the resistant bulks from VN and the susceptible bulks and these are clustered around 26.9 Mbp (Fig. 4c).
There are excellent candidate genes in the other clusters reported here. Notably, there is an NBS-LRR resistance gene (LOC_Os11g44580) at 26.95 Mbp while between 27.8 and 27.9 Mbp there are five NBS-LRR genes (LOC_Os11g45930, 45970, 45980, 46080 and 46100 two rust stripe resistance proteins (LOC_Os11g46130 and 46140) and an MLA10 gene (LOC_Os11g46070). The RGH1A, rust stripe resistance and the MLA gene are all similar at the sequence level, with the MLA genes having been associated with nematode resistance (Wei et al. 2002). Importantly, from examination of sequence reads on IGV the genes listed above from LOC_Os11g46070 to 46140 all appear to be missing in LD 24 and KPM (see Online Resource 5 for IGV screen dump of 5 genes in this region). If this observation truly reflects the absence of these genes in LD 24 and KPM they cannot be candidate genes for resistance. It is possible that the genes missing here in LD 24 and KPM are disease susceptibility genes. This class of gene was introduced by Vogel (2002) and described genes required for susceptibility, and their molecular mechanism reviewed by Van Shie and Takken (2014). It must be noted, however, that both the alignment of QTL-seq reads and the listing of genes (above) is based on Nipponbare and its annotation. The strong possibility exists that the resistance locus discovered here represents sequence variation that is not present in Nipponbare rendering the alignment to Nipponbare problematic. If that is the case, de-novo assembly of KPM and LD 24 in this region should reveal the true nature of the genome relevant to the resistance locus, and that would require higher sequence depth and a diversity of sequencing methodologies to give some long reads. This may offer a method to identify the responsible gene(s) more rapidly than fine mapping and map-based cloning. In advance of that, this 6 Mbp region of chromosome 11 can be used for marker assisted selection of resistance to M. graminicola.
This is the first report of bulk segregant analysis using QTL-seq to identify a nematode resistance locus in rice. Although the two resistant cultivars used in this study (LD 24 and KPM) are genetically different, the same locus on chromosome 11 was found to be responsible for M. graminicola resistance in both cultivars. Through the analysis of SNP data, we were able to identify some candidate genes that might confer resistance to M. graminicola in O. sativa. This locus can be used for marker-assisted breeding but further sequencing in the resistant parents and functional analysis of these candidate genes should facilitate gene identification for better biological understanding and improved resistance breeding.
This project was funded by FAACE-JPI NET project “GreenRice” (Sustainable and environmental friendly rice cultivation systems in Europe); BBSRC award BB/M018415/1. The authors would like to acknowledge the support of the Maxwell computer cluster funded by the University of Aberdeen for sequencing work and the financial support of GOA 01GB3013 from Ghent University.
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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