Genome-wide identification of the NLR gene family in Haynaldia villosa by SMRT-RenSeq

Background Nucleotide-binding and leucine-rich repeat (NLR) genes have attracted wide attention due to their crucial role in protecting plants from pathogens. SMRT-RenSeq, combining PacBio sequencing after resistance gene enrichment sequencing (RenSeq), is a powerful method for selectively capturing and sequencing full-length NLRs. Haynaldia villosa, a wild grass species with a proven potential for wheat improvement, confers resistance to multiple diseases. So, genome-wide identification of the NLR gene family in Haynaldia villosa by SMRT-RenSeq can facilitate disease resistance genes exploration. Results In this study, SMRT-RenSeq was performed to identify the genome-wide NLR complement of H. villosa. In total, 1320 NLRs were annotated in 1169 contigs, including 772 complete NLRs. All the complete NLRs were phylogenetically analyzed and 11 main clades with special characteristics were derived. NLRs could be captured with high efficiency when aligned with cloned R genes, and cluster expansion in some specific gene loci was observed. The physical location of NLRs to individual chromosomes in H. villosa showed a perfect homoeologous relationship with group 1, 2, 3, 5 and 6 of other Triticeae species, however, NLRs physically located on 4VL were largely in silico predicted to be located on the homoeologous group 7. Fifteen types of integrated domains (IDs) were integrated in 52 NLRs, and Kelch and B3 NLR-IDs were found to have expanded in H. villosa, while DUF948, NAM-associated and PRT_C were detected as unique integrated domains implying the new emergence of NLR-IDs after H. villosa diverged from other species. Conclusion SMRT-RenSeq is a powerful tool to identify NLR genes from wild species using the baits of the evolutionary related species with reference sequences. The availability of the NLRs from H. villosa provide a valuable library for R gene mining and transfer of disease resistance into wheat. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08334-w.

To date, more than 300 resistance (R genes) defined by genetics have been cloned from a wide range of plant species [36]. The majority of these (> 80%) encode intracellular immune receptors of the nucleotide-binding and leucine-rich repeat (NLR) class of genes. NLRs have also been found to induce defense responses in animals [29,48]. Genomic reference quality assemblies now make it possible to characterize complete NLR repertoires in plants. Typically, several hundred NLRs are found in a plant genome. For example, 149 NLRs were identified in Arabidopsis [50], 459 in Vitis vinifera, 330 in Populus trichocarpa [81], 319 in Glycine max [35] and 327 in Manihot esculenta [46]. Genome-wide NLR complements have also been studied in five species of Brassicaceae [88], four species of Gossypium [78], seven species of Leguminosae [89], three species of Solanaceae [60], and in several species of grasses including Sorghum bicolor, Zea mays, Brachypodium distachion, cultivated and wild Oryza species, and several Triticeae species [4,15,41,61,69,80]. In hexaploid bread wheat, 3400 NLRs were identified, the largest number reported thus far in a plant species [69]. The number of NLRs appears to correlate positively with the total number of genes in the genome [3], but less so with genome size. For example, more than 1000 NLRs were identified in apple (740 Mb genome [27];), while only 151 were detected in maize (2.1 Gb genome [66];) and 54 in Carica papaya (370 Mb genome [58];). However, higher ploidy levels does tend to correlate with a larger number of NLRs, such as observed in wheat and apple [63,73].
With the recent rapid advances in bioinformatics and genomics, huge progress has been made in understanding Triticeae genomes, including more efficient and complete characterization of their NLR complements. The NLR contents have been identified in T. urartu, Ae. tauschii and T. aestivum by different researchers [22,69]. However, due to different draft genome versions, distinct annotation pipelines with various parameters and the high sequence similarity among NLRs, the number of identified NLRs can vary widely between different studies. In those species which lack a reference genome, it is even more challenging to perform a genome-wide NLR survey. Exon capture enrichment allows the selected sequencing of an exome [54], or a specific gene family [32]. The highly conserved domains shared by different NLRs provides perfect targets for enrichment. The R gene enrichment and sequencing (RenSeq) method provides a powerful and attractive tool for the identification of NLRs from plants without finished reference genome assemblies, in particular those plants with large genomes and higher ploidy levels, for example hexaploid wheat and octaploid strawberry [6]. RenSeq was first applied to identify NLRs from Solanum tuberosum, and it indicated that ~ 80% sequence identity between NLR genes and the corresponding oligonucleotide baits was sufficient for enrichment. This pioneering use of RenSeq increased the number of annotated NLRs in potato from 438 to 772, and facilitated the genetic mapping of NLRs associated with disease resistance to poorly or previously unannotated regions of the genome [34]. MutRenSeq, combining RenSeq with mutant development, was used to clone the stem rust resistance genes Sr22 and Sr45 from hexaploid wheat [68]. More recently, association genetics combined with RenSeq (AgRenSeq) on a wild population of diploid wheat (Aegilops tauschii), AgRenSeq, facilitated the rapid identification four stem rust resistance genes [2]. The assemblies generated by RenSeq with short-read Illumina sequencing technology are, however, typically fragmented and incomplete. For example, RenSeq targeting Sr22 and Sr33 resulted in two and three contigs, respectively, with missing gaps corresponding largely to the introns. RenSeq combined with long read sequencing, such as PacBio single-molecule real-time (SMRT) or Oxford Nanopore Technology, mitigates these limitations by generating more complete assemblies including NLRs with novel integrated domains [19,76]. SMRT-RenSeq facilitated the cloning of the Phytophothora infestans resistance gene Rpi-amr3i from the wild potato relative Solanum americanum [76], the Potato Virus Y resistance genes RySto from the wild potato relative Solanum stoloniferum [21], the powdery mildew resistance gene Pm21 from the wild wheat relative Haynaldia villosa [79], and a species-wide inventory of NLR genes and alleles in Arabidopsis thaliana [71].
Two sequential polyploidization events followed by domestication and intensive breeding have narrowed the genetic diversity in cultivated wheat [57,90]. This dearth of diversity can be offset by introducing natural variation from wild species through wide crosses. In disease resistance breeding, resistance has been introgressed into wheat from at least 52 species from 13 genera due to the remarkable plasticity of the wheat genome [77]. Haynaldia villosa (genome constitution VV, 2n = 14) is a wild diploid species of wheat, which has been introduced into hexaploid bread wheat by the development of individual chromosome arm translocation lines between H. villosa and wheat. We previously reported the introgression from H. villosa into wheat of the powdery mildew resistance genes Pm21, Pm55 and Pm62, the yellow virus resistance gene Wss1 and the cereal cyst nematode resistance gene CreV [14,16,[83][84][85]. Moreover, resistance to leaf rust, stripe rust, take-all, and sharp eye-spot resistances conferred by H. villosa chromatin in wheat have also been reported [26,43,52,59]. The broad-spectrum resistance gene Pm21 has been widely used in wheat breeding and more than 40 new varieties have been released and cultivated in regions of China where powdery mildew is prevalent. Pm55 and Wss1 have also been used in breeding programs and new wheat lines are currently under evaluation. Cloning of these resistance genes will accelerate their use by genetic engineering to reduce linkage drag. However, the lack of a reference genome and a high level of outcrossing of H. villosa makes resistance gene cloning in this species challenging.
Previously, we used SMRT-RenSeq to de novo assemble the NLRs of the inbred H. villosa accession 91C43. Then, NLR-Parser [67] was used to identify NLRs from the 1509 contigs, and 485 full-length NLRs were annotated [79]. In 2018, NLR-Annotator, the improved version for NLR prediction, was released (https:// github. com/ steue rnb/ NLR-Annot ator). The improved software differs from NLR-Parser in that it can distinguish the border between different NLRs located in long contigs [69]. Since many NLRs are tightly clustered in plant genomes [46], we hypothesized that NLR-Annotator would identify a larger number of NLRs than previously predicted by NLR-Parser. Here NLR-Annotator was used to identify NLRs from H. villosa, followed by determination of the NLR classes, assignment of NLRs to chromosomes, exploration of NLRs orthologous to cloned R genes, identification of NLR-IDs and comparison of NLRs among different Triticeae species. Our study provides a valuable resource to support R gene cloning in H. villosa and in H. villosa-wheat introgression lines.

NLR annotation in the SMRT-RenSeq assembly
Previously, 406 Mb of SMRT-RenSeq data consisting of 107,153 reads with an average length of 4.5 kb were generated and de novo assembled with Geneious v9.1.4 (Fig. S1). This assembly generated 1509 contigs in which 80% of the contigs ranged from 5 kb to 11 kb, with the largest one spanning 24.6 kb (Fig. S2). The data was analyzed following the pipeline described in Fig. S3. In the present study, the 1509 contigs were re-analysed. Firstly, contigs sharing more than 95% identity were removed leaving 1456 non-redundant contigs. Secondly, sequences with low-complexity and with interspersed repeats were masked. Thirdly, and importantly, the contigs were annotated by NLR-Annotator, which, unlike our previous analysis, allows detection of multiple NLRs on the same contig. In total, 1320 NLRs were annotated in 1169 contigs, including 776 complete NLRs, 289 complete (pseudogene) NLRs, 188 partial NLRs and 67 partial (pseudogene) NLRs.

Prediction and analysis of NLR domain composition
To obtain the corresponding protein sequences of the annotated full-length NLRs, the genomic sequences of the identified NLRs were used to search the protein database of barley, wheat and Ae. tauschii using BLASTx. Then, the predicted H. villosa NLR protein sequences were obtained using FGENESH+ based on the homologous proteins. The 776 complete NLR protein candidates were analyzed by Plant_rgene to search for coiled-coil (CC), NB-ARC and integrated domains, then re-analyzed by InterProScan to search for the conserved LRR domains deposited in the SUPERFAMILY database. The results indicated that 772 members carried an NB-ARC, so these 772 candidates were considered to be bona fide NLRs. Then the 772 NLR proteins were further divided into five subclasses based on sub-domain analysis; 618 NLRs were typed as CC-NB-LRR (CNL), 98 as NB-LRR (NL), three as CC-NB (CN), one as NB-ARC (N) and 52 as NLRs with integrated domains (NLR-ID) (Fig. 1).
The proteins were also downloaded from the newly released genomic database of several grass species, including T. aestivum, T. urartu, Ae. tauschii, H. vulgare, O. sativa and B. distachyon. Then, the NLRs in these species were identified using the same procedure as outlined above. The Triticeae B genome has the largest number of NLRs (834), followed by the V genome with 772 NLRs ( Table 1) distachyon. An NLR expansion in the A and D genome was also observed after polyploidization of wheat ( Table 1).
The domain compositions of all the NLRs were analyzed by InterProScan and Plant_rgene. Unusually for H. villosa, 80% of the NLRs were of the CNL type compared to ~ 50% in the other species. At first glance, this may suggest that different types of NLRs have traversed different evolutionary paths within the Triticeae. Alternatively, since the 'complete NLR' annotated by NLR-Annotator is defined as a sequence containing the P-loop, the start of the NB-ARC domain, as well as at least one LRR motif, the CN and N types NLRs were likely filtered out by our analysis.

Phylogenetic analysis of NLRs in H. villosa
All 772 NLRs of H. villosa were used to construct a phylogenetic tree, based on the sequences of the NB-ARC domain, to reveal the potential evolutionary relationships (Fig. 2, https:// itol. embl. de/ shared/ 20182 01031). The conserved motifs from each complete NLR were displayed in the phylogenetic tree to track the evolutionary characteristics of each clade; 11 main clades were thus derived and displayed in different colors. The structure and characteristics of each clade was analyzed according to the motif classification by Jupe [33]. Clade A is composed of NLs lacking CC domains. Clade B contains NLR-IDs carrying exclusively the DDE_Tnp_4 domain. Clade C members lack the NB-ARC 'motif 10' , and the CC 'motif 17' is followed by 'motif 15' but not by 'motif 16' as is usually the case. 'Motif 15' , also referred to as 'TIR-2' , is found in both monocots and dicots [33]. In this study the annotated NLRs containing this motif were still classified as being CNL but not TNL due to the CC domain being detected. All the NLRs in clade C are located on chromosome 3 V, indicating that this type of NLR has experienced active expansion but not migration. Clade D members contain only 'motif 16' in the CC domain but lack 'motif 17' . Clade E NB-ARC domains contain an additional 'motif 6' followed by 'motif 1' . Clade H members contain a longer linker between the CC domain and the NB-ARC domain, and the LRR domain is more irregular. Clade I contains three or four tandem repeats Based on sub-domain analysis, the 772 annotated NLRs were divided into five subclasses as Type I to Type V, among which 618 NLRs belong to Type I (CC-NB-LRR), 98 belong to Type II (NB-LRR), three belong to Type III (CC-NB), one belongs to Type IV (NB-ARC) and 52 belong to Type V (NLR-ID). Type V contains two subtypes with integrated domains located in N-terminal or C-terminal respectively, and the CC domain with the dotted line shows that it may not be present of 'motif 9' following the linker. Clade F lacks 'motif 10' in the NB-ARC domain. Clade G and J members tend to contain one of either three or five, respectively, integrated domains. Clade K is mainly composed of CNLs and a few NLs. In this clade, most members contain 'motif 14' between the CC and NB-ARC domains, a signature reported to be unique in monocots [33]. Another characteristic of clade K is that eight motifs associated with the NB-ARC domain are arranged in a uniform order. Compared to the CC and NB-ARC domains, a larger diversity was found in the LRR domain, such as motif numbers and arrangements, possibly reflecting the role of the LRRs in recognizing pathogen effectors.

NLR expression analysis
We sequenced a full-length transcriptome of H. villosa with PacBio SMRT chemistry. To detect expressed NLRs and to verify the predicted exon/intron boundaries, the 772 NLRs were searched against NLRs annotated in the transcriptome database. This revealed 91 NLRs with a transcript with more than > 95% identity and a query coverage > 90%. Sequence alignment between the transcript and the genomic DNA of these 91 NLRs indicated that the splicing pattern of 85 of the NLRs perfectly matched our predictions. The remaining 6 expressed NLRs, whose splicing patterns didn't match with the predictions, were used to test whether specific splicing patterns happened in H. villosa by comparing their splicing patterns with those of the corresponding orthologous NLRs. The comparisons indicated that there were no specific splicing patterns happened in 3 NLRs, including Hv_Contig_1470_nlr_1, Hv_Contig_696_nlr_1 and Hv_ Contig_1466_nlr_1, while the coding lengths were different between the predicted sequences and the expressed sequences. For Hv_Contig_249_nlr_2, its orthologous NLRs in wheat barley and Ae. tauschii contained A and B types of splicing patterns, and the previously predicted CDS was corresponding to the A type, while the expressed CDS was corresponding to the B type. So, there was no specific splicing pattern in Hv_Contig_249_nlr_2. However, the specific splicing patterns happened in Hv_ Contig_670_nlr_1 and Hv_Contig_61_nlr_1. Knowledge of NLR expression can provide additional powerful support for R gene cloning.

Assignment of NLRs to chromosomes
H. villosa displays good chromosome collinearity with barley, Ae. tauchii, Triticum urartu and wheat. Therefore the nucleotide sequences of the 772 complete NLRs were used as a query for BLASTn analysis against the genome databases of these species for in silico prediction of chromosomal location. This procedure assigned all 772 complete NLRs to homoelogous chromosomes; 139 NLRs to group 1, 85 to group 2, 164 to group 3, 15 to group 4, 60 to group 5, 101 to group 6, and 208 to group 7 (Table S3). However, due to the differentiation between the H. villosa genome and those of the species used in the analysis, we decided to independently confirm the assignment for a subset of NLRs. To this end, we took advantage of a previously developed full set of wheat-H. villosa translocation lines, each involving one of the 14 chromosome arms of H. villosa [86], to physically locate NLRs using PCR molecular markers. A subset of the in silico mapped NLRs were selected evenly from the seven chromosomes. The sequence of each selected NLR was aligned with the predicted wheat orthologues on the A, B, and D genome, then primers were designed based on insertions or deletions private to H. villosa. A total of 757 primer pairs were designed corresponding to 565 contigs, of which 105 primer pairs gave rise to polymorphisms between H. villosa and wheat. Thus, the polymorphism rate produced using InDel-markers based on NLRs was 14%, which is significantly lower than the 52% produced using IT (Intron-Target)-markers based on single copy genes [87]. The high sequence similarity between NLRs likely complicates the design of polymorphic markers. From the 105 polymorphic primer pairs, 61 complete and 26 partial (pseudogene) NLRs could be located to specific chromosome arms ( Fig. 3; Table 2). All the NLRs located in silico on homoeologous groups 1, 2, 3, 5 and 6 were physically located on chromosome 1 V, 2 V, 3 V, 5 V and 6 V, respectively, thus showing a perfect homoeologous relationship between groups 1, 2, 3, 5 and 6, respectively, of H. villosa and other Triticum species (Table 2). As to the distribution of NLRs on 4 V, the NLRs physically located on 4VS were located in silico on homoeologous groups 4, however, NLRs physically located on 4VL were largely in silico predicted to be located on the homoeologous group 7 (Table 2). It was also previously reported that four 4VL-specific markers of H. villosa detected homoeoloci on the group 7 chromosomes of wheat [86]. Therefore, our data, and those of Zhang et al. [86], suggest that 4VL did not translocate with 7 VS, unlike the reciprocal 4 L/7S translocation which happened in wheat and other Triticeae species.

Enrichment efficiency of NLR loci corresponding to cloned R genes
In the TSLMMHV1 bait design, baits corresponding to all the cloned barley Mla genes were added manually. To test the efficiency of enrichment, the orthologous H. villosa Mla genes were obtained from our H. villosa NLR assembly. In total, 18 NLRs in H. villosa were predicted to be orthologous to Mla genes because they showed the highest homology to NLRs at the barley Mla locus at 30.2 Mb on chromosome 1H (Table 3). When using the rye Sr50, the orthologous gene of barley Mla, as the query, the same set of homologous genes were obtained. It was reported that the number of Mla paralogues was five in wheat and barley but expanded to over 20 in rye [49]. We detected 19 paralogues in H. villosa indicating that the expansion of Mla genes also occurred in H. villosa. In barley, Mla paralogues also occur at a second locus at ~ 8.6 Mb on chromosome 1H; we obtained 15 Mla paralogues in H. villosa corresponding to this locus. The orthologous genes of Mla in other species confer resistance to different diseases, such as powdery mildew by TmMLA1 from Triticum monococcum [31], and stem rust by Sr50 from Secale cereale and Sr33 from Aegilops tauschii [49,56]. Moreover, barley Mla3, which confers resistance to barley powdery mildew [1], has recently been shown to also confer resistance to rice blast [10]. Our data suggest that the number of MLA paralogues    chromosomes of H. villosa, however, these R genes also lack orthologues in barley. Our results indicate that orthologues of cloned wheat R genes could be efficiently captured by NLR enrichment, and the data could be used for evolutionary an functional studies of NLR genes at specific loci.

Identification of NLRs with integrated domains
Atypical domains in NLRs have recently been found to play vital roles in the recognition of pathogens, and these domains can be found in any region of the protein. The additional atypical domains fused into NLRs were designated as integrated decoys (ID), and those proteins with IDs were then classified to be NLR-IDs [12]. Among the 772 determined NLRs of H. villosa, a total of 52 NLR-IDs were identified carrying domains other than NB-ARC, CC, and LRR. The identified 15 types of IDs included DDE_Tnp_4, Kelch repeats, Thioredoxin, Pkinase, zf-BED, B3 DNA binding, PP2C, WRKY and others (Fig. 4).
It was interesting to find that the DDE, Kelch and PP2C domains preferred to fuse with the LRR-terminal, the Zinc Finger-BED preferred to fuse with the CC-terminal, while the thioredoxin and Pkinase domains could fuse with both terminals (Fig. 4). As shown by phylogenetic analysis based on all the NLRs in Fig. 2, NLRs integrated with DDE_Tnp_4, the largest NLR-ID group, were clustered into one branch as Clade B. However, NLR-IDs containing different domains were clustered into 'Clade J' and 'Clade G' indicating that NLRs in these groups appear to have a predisposition to fuse with other proteins. In addition, the region involved in integration was different in that the NLRs in 'Clade J' tend to fuse decoys in the LRR-terminal, while the NLRs in Clade G tend to fuse decoys in the CC-terminal.
The phylogenetic analysis using 52 NLR-IDs of H. villosa was also conducted as shown in Fig. 5 (https:// itol. embl. de/ shared/ 20182 01031). Most of the NLR-IDs with the same IDs could be phylogenetically clustered, especially those NLR-IDs containing DDE, B3 and Kelch clustered into large groups, respectively. The tightly clustered NLR-IDs with similar domain compositions in the tree were usually in silico located on the same chromosomes, indicating that these members likely expanded by tandem duplication after the NLR fused with the ID. There are exceptions, for example, NLR-IDs with Pkinase were clustered into different groups indicating independent cases of fusion. The direct evidence is that the tightly grouped NLR-IDs, Hv-contig-705, Hv-contig-869 and Hv-contig-1233, contained a Pkinase integrated into the CC-terminal, while Hv-contig-316 and Hv-contig-317 contained a Pkinase integrated into the LRR-terminal (Figs. 4 and 5). Another example for independent fusion concerns the case of RVT_1 which is found integrated into Hvcontig-967, Hv-contig-127 and Hv-contig-663, which have different domain compositions and chromosome locations (Figs. 4 and 5).
The NLR-IDs were extracted from another six Triticeae species, then the number of NLR-IDs and the types of IDs were compared in detail. A total of 65 IDs were detected from the seven species (Table 4). The number of shared IDs and specific IDs in each species were as follows, 28 (Table 4). Among the 15 IDs found in H. villosa, 12 IDs were shared with at least one species. However, three IDs, including DUF948, NAM-associated and PRT_C, specifially existed in H. villosa implying the emergence of these after divergence of H. villosa. The phylogenetic tree was also constructed using all the NLR-IDs from these species. It was found that NLR-IDs fused with Kelch and B3 domains expanded dramatically in H. villosa, however, NLR-IDs containing Pkinase were fewer in number, whereas those containing Jacalin were altogether lacking in H. villosa (Fig. 6, https:// itol. embl. de/ shared/ 20182 01031).
We found that the homeologues of paired NLRs, such as RPS4/RRS1, Rpg5/RGA1, and RGA4/RGA5, usually displayed a pattern of head-to-head arrangement. NLR-IDs with physically tightly linked NLRs often form paired protein complexes. In this study, we found that nine NLR-IDs were located tandemly with another NLRs in the same contig, and in two contigs, namely Hv_Con-tig_48 and Hv_Contig_1157, NLR-IDs and its linked NLRs were arranged in a head-to-head pattern.

SMRT-RenSeq and NLR-annotator facilitates reference-free genome-wide mining of NLRs in a wild grass species
NLRs display high domain-conservation even across species. Previously, random isolation of NLRs from whole genomes was achieved using homology-based PCR in wide variety of species [8]. In recent years, great progress has been made in whole genome NLR gene discovery in species with reference quality genomes, but less so in the large number of non-sequenced species. RenSeq, however, has greatly promoted reference-free genome-wide identification of NLRs and accelerated the cloning of  disease resistance genes efficiently from non-model and non-crop species. The application of RenSeq in Solanum tuberosum, a wild potato, indicated that ~ 80% sequence identity is enough for capturing NLR genes using oligonucleotide baits [34]. Previously, we compared H. villosa and barley and found a high degree of identity between these two closely related species [11]. We therefore used a barley NLR bait library in this study to capture NLRs from H. villosa. The enrichment efficiency analysis using the sequences of cloned R genes indicated that the homologous genes in H. villosa were successfully enriched. Therefore, the bait library designed based on a species with ample genomic resources could be used to isolate NLRs from an evolutionary closely related species. Before annotation of the NLRs from the assembled contigs, the redundant contigs were removed using the cutoff value of 90 and 95%. Actually, the same 53 contigs were removed by both parameters. To avoid the situation that a recent duplication or residual heterozygosity perhaps could be ruled out as a source for some of the removed contigs, these 53 removed contigs were reanalyzed (Table S4). It was found that the 22 contigs contained no, partial or pseudogene NLRs, 16 [4,22,44,63]. In this study, using the latest version of the released genomic database, we identified 350 NLRs from B. distachyon, 347 from O. sativa, 397 from H. vulgare, 530 from T. urartu, 572 from A. tauschii and 772 from H. villosa by the same pipeline. H. villosa contained relatively more NLRs than what has been reported in most diploid genomes. The study in Solanum tuberosum indicated that more NLRs were revealed by RenSeq than those predicted by the then available annotation software and genome reference suggesting that RenSeq facilitates the recovery of NLRs from the poorly or previously unannotated regions of the genome [34]. Moreover, cDNA SMRT-RenSeq, with its longer reads, could help correct splicing errors generated by using short read sequencing technology. The large number of NLRs that we recorded in our study in H. villosa, might be accounted for by the NLR expansion that occurred at some loci.
Some of the in silico located NLRs were selected for physical location to specific chromosome arms, and good  Fig. 6 Phylogenetic analysis of the 315 NLR-IDs in seven Triticeae species. The phylogenetic tree of the 315 NLR-IDs from seven Triticeae species was constructed using MEGA7 by Neighbor-Joining method with 1000 bootstrap replicates, and the tree was visualized using iTOL (https:// itol. embl. de/ shared/ 20182 01031). NLR-IDs carrying Kelch and B3 domains showed a pronounced expansion in H. villosa, whereas NLR-IDs with Pkinase were less abundant, and those with Jacalin were altogether lacking congruency was found for chromosomes 1 V, 2 V, 3 V, 5 V and 6 V suggesting these NLRs could be used as candidates for mining for R genes genetically assigned to these chromosomes. However, some of the NLRs, which were in silico located on 7 VS were physically located on 4VL. Therefore, NLRs in silico located on 7 VS could be used as candidates for mining for R genes which map to 4VL. Chromosomal rearrangements are commonly found in wheat and its wild relatives [75], and our results will facilitate accurate R gene location and cloning in such chromosome regions.

NLRs with unique integrated domains are found in Haynaldia villosa
In host-pathogen interactions, a decoy is used to describe those molecules which mimic a component manipulated during infection [72]. The decoy strategy is used by both the pathogen to inhibit the host defense and by the host to inhibit the pathogen infection [55]. In the host, decoys are often integrated with NLRs, but decoys can also be physically independent of the NLRs [17]. For example, Pto in tomato mimics the RLKs that are targeted by the effector AvrPto injected into the host cell by the bacterial pathogen Pseudomonas syringae. The partner of Pto, Prf, an NLR receptor, senses the modification of Pto by AvrPto to trigger immune signaling [51]. Sometimes, decoys, referred to as integrated-decoys, are fused with the N or C terminal of NLRs. For example, RRS1, an integrated-decoy NLR from A. thaliana, carries a C-terminal WRKY domain which mimics the WRKY protein targeted by the PopP2 effector. Other examples include the NLRs RGA5 and Pik-1 in rice, which carry a heavy metal associated (HMA) domain targeted by AVR-Pia and AVR-Pik, respectively [13,53,82]. Intracellular detection of pathogen-derived molecules through integrated domains in NLRs is a typical resistance pathway among the nine mechanisms identified in hosts to date [36]. Integration of decoy domains in NLRs is frequent in plants. It was found that on average across 31 species analyzed, 3.5% of all NLR proteins are integrated with 90 diverse protein domains, with 2.5% in CNLs and 4.7% in TNLs [37]. However, Sarris [63] predicted that as many as 10% of plant NLRs contain highly variable domains. In the present study, 52 NLR-IDs (about 6.7%) were found among the 772 NLRs in H. villosa. While the NLR-ID ratio was smaller in H. villosa compared to T. urartu and Ae. tauschii, the total number of NLR-IDs in H. villosa was the largest among all the seven species studied (Table 4). Except for B. distachyon, unique IDs were detected in each species, indicating that novel fusions happened after species divergence. The most cases were observed in T. urartu harboring 19 distinct IDs, while three cases happened in H. villosa harboring three distinct IDs. We also found 'Clade J' and 'Clade G' of H. villosa appear to have a predisposition to fuse with other proteins, and a major integration clade whose members underwent repeated independent integration events was also described [4].
The decoys integrated in NLRs are usually the duplicated products of effector targets. Protein kinases, transcription factors and proteases, which have been reported to be effector targets previously, were found to be fused as decoys in NLRs with high frequency. Therefore, genome-wide mining of NLR-IDs can help identify putative effector targets. The integrated domains identified here in H. villosa include DDE_Tnp_4, kelch repeats, thioredoxin, protein kinase, zinc finger-BED, B3 DNA binding, integrase and PP2C. These protein families are therefore considered as likely effector targets.
Previous studies have found that NLR-IDs usually work in pairs and link proximally in genome. For example, RPS4 works in concert with RRS1 to confer resistance to P. syringae [53], RPP2A pairs with RPP2B to confer resistance to Hyaloperonospora arabidopsidis [65], Rpg5 pairs with RGA1 to confer resistance to P. tritici [74], while Pikh-1 with Pikh-2 [82] and Pi5-1 with Pi5-2 [39] to confer resistance to M. oryzae. Therefore, the ability to detect tightly-linked NLRs in genomes can provide important clues as to their modus operandi. In this study, we found nine NLRs-ID located physically proximal to another NLR in the same contig, among which two cases were arranged in a head-to-head orientation. However, NLR-IDs seem not to be physically paired NLRs at the DNA sequence level which was also predicted from a study in rice [37].

Genome-wide exploration and chromosomal location of NLRs is critical for cloning and introducing new resistance genes from H. villosa
Wild crop relatives represent an important reservoir of disease resistance genes, which in some cases can be introduced into cultivated crops by wide crosses. Genetically diverse accessions of H. villosa has been configured and shown to display variation in disease resistance [14,16,26,43,52,59,[83][84][85]. However, as is the case with most wild species, gene utilization is inefficient. In this study, the genome-wide identification of NLRs from H. villosa and mapping of NLRs to each chromosome arm of H. villosa will change the way of introducing new resistances into wheat. Firstly, the public availability of all the NLRs provides a precious resource for accelerating the cloning of resistance genes, which will help to introduce new resistances into wheat through gene transformation. Secondly, the information of NLRs sequences and their chromosome locations provides important data for different research groups to compare the NLRs sequences located on specific chromosome or even on specific chromosome region between different H. villosa accessions, which could help researchers identify new resistance germplasm and transferring new resistance genes or alleles of H.villosa into wheat. Furthermore, the availability of all the NLRs facilitates developing molecular markers specific to H.villosa based on the NLRs, so it could help transfer chromatin containing putative resistance genes of H.villosa into wheat by molecular marker assistant selection and help accelerate developing new resistance germplasms with small segments introgression.
Previously, the SMRT-RenSeq data of H. villosa accession 91C43 helped us to clone the broad-spectrum powdery mildew resistance gene Pm21 [79]. Following this, alleles of Pm21 were cloned by homology-based PCR from more than 100 accessions. The high genetic diversity between these alleles provides valuable information to understand the structure, function and evolution of Pm21.
NLRs are subject to intensive diversifying selection due to their genetic interaction with rapidly evolving pests and pathogens [23,64]. This process may lead to the production of large amounts of pseudogenes. In H. villosa, besides the 772 complete NLRs, we detected another 289 NLRs which were classified as pseudogenes. These were widely spread across the genome and may be the remnants of past evolutionary activity. In the hexaploid wheat cultivar Chinese Spring, there are 1540 complete NLRs and 2360 putative pseudogene NLRs [69]. Pseudogenes may retain useful evolutionary potential through the propensity of NLRs for intergenic recombination. For example, a functional chimeric allele of the leaf rust resistance gene Lr21 was recovered from a cross between two non-functional alleles [24]. Moreover, a locus containing a pseudogene in one accession may harbor a functional allele in another accession [47]. For example, the Pm2 allele in Chinese spring contains a premature stop codon, while in cultivar Ulka the open reading frame is retained giving rise to a functional resistance gene [62,69]. Therefore, the pseudogenes identified in H. villosa may provide valuable clues for mining of functional alleles in other accessions.

Conclusions
In this study, the genome-wide NLR complement of H. villosa was efficiently identified using SMRT-RenSeq, and a total of 772 complete NLRs were annotated. The information of the chromosome location of all the NLRs were provided, which is valuable for resistance gene mining from the specific chromosome. The physical location of NLRs from group 1, 2, 3, 5 and 6 showed a perfect homoeologous relationship with other Triticeae species except the NLRs on chromosome 4VL which were predicted to be located on the homoeologous group 7 in silico. Cluster expansion observed in some specific gene loci indicated that independent evolutionary cases occurred in H. villosa. We also identified 52 NLR-IDs with fifteen types of integrated domains (IDs), among which Kelch and B3-type NLR-IDs experienced expansion, and three type of IDs were unique in H. villosa. This study gave an example to successfully capture the genome-wide NLRs in wild species using the baits from another species in Triticeae. The availability of the NLRs from H. villosa provides a valuable library for mining and transferring of disease resistance into wheat.

NLR-enrichment library construction and physical mapping of NLR genes
Haynaldia villosa (genome constitution VV, 2n = 14), with long hairs on the keel of the glume and apex of the lemma, is an annual diploid species which belonging to Triticinae of Triticeae in Gramineae. It is a vigorous ruderal wild plant growing on the harsh, moisture-stressed soils in the northeastern part of the Mediterranean region and Caucasus area [20]. This species usually confers resistance to different diseases and tolerance to various abiotic stresses [20]. The H. villosa accession 91C43 was obtained from Cambridge University, UK and propagated by artificial bagging and self-pollination for 6-7 successive generations to obtain an inbred line. Tissue from the 91C43 inbred line was used for DNA extraction. This accession was also used as the donor to develop 14 wheat-H.villosa translocation lines through wide-crosses and chromosome engineering, each involving one of the 14 chromosome arms of H. villosa, respectively [86]. This set of translocation lines was used for chromosomal location of NLRs.

Library construction for NLR gene enrichment and SMRT sequencing
The barley NLR bait library TSLMMHV1 [9] was used to capture the NLR complement from H. villosa accession 91C43. The design of this NLR bait library has been previously described [9]. In brief, NLR-Parser [ 67], a tool for high-throughput identification of NLRs based on conserved motifs [33], was used to search eight barley tanscriptomes and six barley genomes [25] (IBGSC2012) to identify those sequences containing at least one CC and two NBS motifs, or two NBS and one LRR motifs [9]. Previously-reported barley disease resistance genes, including Mla, Mlo, Rpg1, etc., were manually added. Repetitive and redundent sequences were removed to yield the final set of 99,421,100-mer RNA baits.
Genomic DNA for SMRT library preparation was extracted from seedlings of H. villosa with the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany). SMRT library preparation and NLR capture followed the procedure described by Witek et al. [76]. The enriched library was sequenced on the PacBio RSII platform using four SMRT cells in the Genome Analysis Center (TGAC, Norwich Research Park, UK) with P4-C6 chemistry to minimize errors.

Assembly of the SMRT-RenSeq reads
The raw reads were screened by the SMRT Portal software with parameter settings > 3 full passes and > 90% accuracy to generate the inserted sequence ROI. The long ROI were analyzed and assembled using genetic R8 software (www. genei ous. com) with defaults of 1% mismatch, 1% gap (no more than 3 bp) and minimum read length overlap greater than 100 nt with at least 98% identity. Only contigs assembled from at least five sequences and having a minimum coverage of two were considered for further analysis. A total of 1509 contigs fulfilling these criteria were obtained.

Sequence annotation of the assembled SMRT-RenSeq contigs by NLR-annotator
To obtain non-redundant NLR-containing contigs, the raw assembled contigs were analysed by the global alignment program Cd-hit (version 4.8.1) [42] to construct a phylogenetic tree and remove the sequences with > 95% identity. The retained contigs were analysed with Repeat-Masker followed by screening for NLRs using NLR-Annotator (https:// github. com/ steue rnb/ NLR-Annot ator).

Extraction of protein sequences from the annotated NLRs
The predicted NLR loci were extended backwards by 3000 bp from the left border and forwards by 3000 bp from the right border, respectively. The extended sequences were cut out for BLASTx against the database of barley (ftp:// ftp. ensem blgen omes. org/ pub/ plants/ relea se-46/ fasta/ horde um_ vulga re), Aegilops tauchii (ftp:// ftp. ensem blgen omes. org/ pub/ plants/ relea se-46/ fasta/ aegil ops_ tausc hii), and common wheat (ftp:// ftp. ensem blgen omes. org/ pub/ plants/ relea se-46/ fasta/ triti cum_ aesti vum) to find the orthologous genes from the three species. In each species, the proteins with the highest identity and longest coverage were selected as the putative orthologous proteins. The protein sequence from the species with the highest identity and longest coverage compared with H. villosa was used as the reference to predict the coding sequence of the corresponding H. villosa NLR using FGENESH+ (http:// www. softb erry. com/).

Domain composition analysis of the annotated NLRs
The protein sequences of six species, including T. aestivum, Ae. tauschii, T. urartu, H. vulgare, O. sativa, and B. distachyon, were downloaded from Ensembl Plants (http:// plants. ensem bl. org/ info/ websi te/ ftp/ index. html) and the longest proteins corresponding to the longest transcripts of genes with alternative splicing were used for annotation analysis (Table S1). Plant_rgene (https:// github. com/ krasi leva/ plant_ rgenes) and InterProScan (interproscan-version 5.30-69.0) were used to analyze the NLRs annotated as being complete. Plant_rgene program is a pipeline for analyzing the domain composition of putative NLRs, however, sometimes it cannot accurately output LRR domains. The InterProScan software [30] is a complementary tool for identification of the LRR domains deposited in the SUPERFAMILY database. Therefore, all the proteins, including the 774 complete H. villosa NLRs and the proteomes from the six species listed above, were analyzed by the Plant_rgene software (evalue = 1e-3) to search for conserved domains (including CC, NB-ARC and integrated domains). Then the same set of proteins were analyzed again by InterProScan to search for the conserved LRR domains deposited in the SUPERFAMILY database. The conserved domains output from both programs were integrated. Only those proteins containing an NB-ARC domain were designated as NLRs. If any atypical domain was additionally predicted in the NLRs, these domains were considered to be the integrated-decoys (IDs), and those NLRs containing ID(s) were then designated as NLR-IDs. The diagrams of all the 52 NLR-IDs were displayed by IBS (Version 1.0.3) [45].

Phylogenetic tree construction
For construction of the phylogenetic tree of all the 772 complete NLRs, the conserved NB-ARC domain was extracted from each NLR and a phylogenetic tree was constructed using MEGA7 by Neighbor-Joining method with 1000 bootstrap replicates [38]. The tree was visualized using iTOL [40]. The conserved motif was searched using MAST (version 4.9.1) [5] based on the 'motif 1' to 'motif 20' defined by Jupe [33], and displayed in the phylogenetic tree. The phylogenetic tree of the 52 detected NLR-IDs in H. villosa and all the 315 NLR-IDs from seven Triticeae species were also constructed by the same method using the same parameters.

PacBio transcriptome sequencing of H. villosa and NLR expression analysis
PacBio sequencing of the full-length transcriptome extracted from ten mixed tissues of H. villosa accession 91C43 was performed by Novogene (Beijing). The mixed tissue sample included seedling leaves, seedling stems, seedling roots, immature spikes, seedling leaves inoculated with Bgt for 6 and 24 h, seedling leaves and roots treated with 200 mM NaCl for 3 days, and seedling leaves and roots treated with 16% PEG4000 for 3 days. The PacBio trasncriptome sequences were screened by NLR-Annotator. Then the annotated NLRs of SMRT RenSeq were used to search against the annotated transcribed NLRs. When an NLR from SMRT RenSeq could find a corresponding transcribed NLR with an identity higher than 95% and coverage longer than 90%, then the enriched NLR was considered to be expressed. The sequences of all the expressed NLRs were compared with their genomic counterparts one by one to analyze the accuracy of the protein prediction based on the genomic sequence.