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Wheat stripe rust resistance locus YR63 is a hot spot for evolution of defence genes – a pangenome discovery

BMC Plant Biology volume 23, Article number: 590 (2023) Cite this article

Abstract

Background

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a threat to global wheat production. Deployment of widely effective resistance genes underpins management of this ongoing threat. This study focused on the mapping of stripe rust resistance gene YR63 from a Portuguese hexaploid wheat landrace AUS27955 of the Watkins Collection.

Results

YR63 exhibits resistance to a broad spectrum of Pst races from Australia, Africa, Asia, Europe, Middle East and South America. It was mapped to the short arm of chromosome 7B, between two single nucleotide polymorphic (SNP) markers sunCS_YR63 and sunCS_67, positioned at 0.8 and 3.7 Mb, respectively, in the Chinese Spring genome assembly v2.1. We characterised YR63 locus using an integrated approach engaging targeted genotyping-by-sequencing (tGBS), mutagenesis, resistance gene enrichment and sequencing (MutRenSeq), RNA sequencing (RNASeq) and comparative genomic analysis with tetraploid (Zavitan and Svevo) and hexaploid (Chinese Spring) wheat genome references and 10+ hexaploid wheat genomes. YR63 is positioned at a hot spot enriched with multiple nucleotide-binding and leucine rich repeat (NLR) and kinase domain encoding genes, known widely for defence against pests and diseases in plants and animals. Detection of YR63 within these gene clusters is not possible through short-read sequencing due to high homology between members. However, using the sequence of a NLR member we were successful in detecting a closely linked SNP marker for YR63 and validated on a panel of Australian bread wheat, durum and triticale cultivars.

Conclusions

This study highlights YR63 as a valuable source for resistance against Pst in Australia and elsewhere. The closely linked SNP marker will facilitate rapid introgression of YR63 into elite cultivars through marker-assisted selection. The bottleneck of this study reinforces the necessity for a long-read sequencing such as PacBio or Oxford Nanopore based techniques for accurate detection of the underlying resistance gene when it is part of a large gene cluster.

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Background

Rust diseases are one of the major threats to global wheat production. The emergence and spread of highly virulent strains of Puccinia striiformis f. sp. tritici (Pst) that causes wheat stripe rust has had a significant contribution towards its impact [1]. In Australia, Pst pathotypes detected so far belongs to four lineages, namely 104 E137 A- (belongs to global Pst lineage classification PstS0), 134 E16 A+ (PstS1), 198 E16 A+ J+ T+ 17+ (PstS13) and 239 E237 A- 17+ 33+ (PstS10). Interestingly, these pathotypes, each with its own unique virulence pattern, entered Australia through four independent incursion events [2]. These events made breeding for stripe rust resistance a highly challenging task as new resistance gene combinations are required to be effective against these distinct pathotypes. While genes belonging to adult plant resistance (APR) class are broadly effective, their expression only at the adult plant stages does not provide protection at the seedling and early juvenile plant growth stages. Hence, deployment of APR genes along with widely effective all-stage resistance (ASR) genes remains essential for protecting wheat crops against rust diseases [3].

Most of the cloned ASR genes encode nucleotide-binding-leucine-rich-repeat (NLR) proteins [4]. NLRs typically have three domains: an N-terminal coiled coil (CC) or Toll/Interleukin-1 receptor (TIR), a C-terminal leucine-rich repeat (LRR) and a central nucleotide-binding (NB) domain [5]. The recently developed mutagenesis, resistance gene enrichment and sequencing (MutRenSeq) approach is a powerful tool for ASR gene discovery and it relies on the assumption that most ASR genes encode NLR proteins. While there is a chance that the NLR bait library is not extensive enough to capture all NLRs, this technique unfortunately will not detect any non-NLR coding genes [6]. More recent research has revealed two more types of ASR genes; tandem kinases, and transmembrane proteins with ankyrin domains [4]. The ASR gene Yr15 and Sr60, encode a tandem kinase (ie. a protein containing two kinase domains), which confers strong and partial resistance to wheat stripe rust and stem rust, respectively [7, 8]. Similarly, the leaf rust ASR gene Lr14a was found to encode an ankyrin-transmembrane protein [9]. Hence it is important not to limit the search for new ASR genes to NLR class alone.

In such cases map-based approach paired with comparative genomics will be more appropriate. While the annotated International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.1 genome of Chinse Spring wheat is a valuable resource, there is a large degree of unexplored diversity in other wheat varieties [10]. Over the last few years, several hexaploid wheat cultivars have been sequenced and annotated by the “10+ Wheat Genomes Project” to develop a wheat pangenome [11]. In addition, reference genomes are also available for tetraploid wheat cultivars Svevo [12] and Zavitan (WEWseq v1.0) [13], and closely related diploid grasses.

The available common wheat landrace collections including the “Watkins Collection” representing over 32 wheat-producing nations have been a valuable resource to discover widely effective stripe rust resistance genes [14,15,16]. One of the stripe rust resistance genes identified in a Portuguese hexaploid wheat landrace AUS27955 [Australian gene bank (AGG) No: AGG27955WHEA1] from the Watkins Collection was located on the short arm of chromosome arm 7B and it was named YR63 (Bansal and Bariana, unpublished results). The gene exhibited resistance against all known Australian Pst pathotypes, except 239 E237 A- 17+ 33+ within the PstS10 lineage.

Here, we screened YR63 against globally important Pst isolates at the Global Rust Reference Center, Denmark to understand its broad-spectrum nature. We employed tGBS, MutRenSeq, RNA sequencing (RNASeq) and comparative genomic analysis to fine map and identify molecular markers closely linked with YR63.

Results

Effectiveness of YR63 against multiple Pst pathotypes

Against Australian Pst pathotypes, the YR63 donor accession AUS27955 produced infection type (IT) ‘0;’ against PstS1 (Figure 1A) and PstS13 (Figure 1B), while a susceptible response (IT ‘3+’) similar to the susceptible parent AUS27928S was observed against PstS10 (Figure 1C, Table 1). Among the AUS27955 x AUS27928S-derived recombinant inbred line (RIL) population, 93 lines produced IT ‘0;’ and 102 lines produced IT ‘3+’ against PstS1 pathotype, following an expected single-gene segregation ratio of 1:1 (χ2= 0.42, d.f. = 1, p-value = 0.5).

Fig. 1
figure 1

Infection of AUS27955 and AUS27928S against Pst pathotypes. Australian: PstS1 (A), PstS10 (B), PstS13 (C). Global: PstS2 (D), PstS7 (E), PstS8 (F), PstS9 (G), PstS10 (H), PstS11 (I), PstS13 (J), PstS14 (AUS27928S unavailable and replaced with Avocet ‘S’ (AvS)) (K) and PstS17 (L)

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Table 1 Resistance response of AUS27955 and AUS27928S against Australian and International Pst pathotypes
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Tests against 10 global Pst pathotypes representing different genetic groups and geographical regions such as Africa, Asia, Middle East and South America, AUS27955 exhibited IT 2C (against PstS2 & PstS11 and PstS17), 3C (PstS9) and 3+C (PstS7 and PstS14) (Figure 1, Table 1).

Targeted genotyping-by-sequencing (tGBS) analysis positions YR63 within 0.6 to 7.4 Mb interval of chromosome (Chr) 7B

In the tGBS analysis, a total of 4,442 markers, across the Chr 7 groups of A, B and D genomes were found polymorphic between AUS27955 and AUS27928S. Among them, 11 tGBS markers from the short arm of Chr 7B showed close association with YR63 and were targeted for SNP based KASP marker analysis. While the tGBS analysis predicted abundant scaffolds, there were only 4 KASP markers namely, sunKASP_401 (from scaffolds 62788), sunKASP_406 (scaffold 13660), sunKASP_409 and sunKASP_407 (scaffold 96545) showed clear polymorphism between the resistant and susceptible parents and were used for mapping YR63 on the RIL population (Table 2). Markers sunKASP_401 and sunKASP_406 positioned at 0.6 and 7.4 Mb interval of Chinese Spring genome assembly v2.1, flanked YR63 distally and proximally at genetic distances of 4.2 and 16.1 cM, respectively.

Table 2 KASP markers used to map YR63 on chromosome arm 7BS
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Marker enrichment via RNAseq

There were 57 SNPs identified between AUS27955 and AUS27928S from the RNAseq reads related to genes present in the 0.9 - 7.8 Mb interval of the Chinese Spring Chr 7B (IWGSC RefSeq v2.1). A total of 20 SNPs selected at random positions were converted into KASP markers. Only two markers, sunCS_67 and sunCS_36, at ~4 and ~5.8 Mb, respectively were found polymorphic and mapped proximal to YR63. Sixteen recombinants between the closest marker, sunCS_67 and YR63 were detected (Figure 2).

Fig. 2
figure 2

Genetic linkage map of YR63 locus in AUS27955 x AUS27928S F6 RIL population. Distances are shown in cM. n = 195

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MutRenSeq reveals a NLR gene as a possible candidate for YR63

Six loss-of-function mutants were identified for YR63 through Ethyl methanesulphonate (EMS) based mutagenesis. Subsequently in the MutRenSeq, the raw Illumina sequencing reads of the four mutants and the resistant accession AUS27955 had a quality score of Q30 over 90% for all base calls. Quality assessment via FastQC showed one over-represented sequence in the raw data from AUS27955. This sequence was added to the adapter sequences for trimming. Over 93% of the reads survived the trimming step and the trimmed reads were used for the de-novo assembly. The MutRenSeq pipeline revealed one NLR contig (Figure 3) that was mutated in three of the four mutants and was related to TraesCS7B03G0004700. It mapped near the telomere on Chr arm 7BS (Chr7B:900491-909163) of the IWGSC RefSeq v2.1 Chinese Spring reference [17]. Three of the four mutants contain SNPs within the NLR coding sequences (CDS), while mutant 3’s unique SNP is located 20 bp upstream of the start codon in the 5’ untranslated region (UTR). Analysis of the remaining mutants in Geneious Prime showed that all SNPs within the CDS altered the amino acid (‘aa’) sequence. In the case of mutant 1, a premature stop codon was introduced to result in a nonsense mutation within the LRR domain at the ‘aa’ position 1029. In mutant 2, a C>T SNP caused a serine>phenylalanine missense mutation in the NB-ARC domain at ‘aa’ position 451, and in mutant 4, a G>A SNP caused a glycine>arginine missense mutation in the LRR domain at ‘aa’ position 1107. There was a high degree of polymorphisms in mutant 3 compared to the wild-type and other mutants, which suggested several homoeologs of similar genes had collapsed into a single contig during assembly.

Fig. 3
figure 3

DNA sequence linked with YR63 resistance identified through MutRenSeq, for four mutants and wild-type (WT) AUS27955, visualised in Integrated Genomics Viewer (IGV) 2.8.7. Unique SNPs are circled in red

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KASP marker from the predicted NLR gene doesn’t co-segregate but linked closely with YR63

KASP markers were designed for YR63_NLRC based on SNPs between the resistant parent (AUS27955) and the IWGSC RefSeq v2.1 reference sequence. Of the markers designed from 8 different SNPs, sunCS_YR63 located at CDS position 2871 and at 0.9 Mb on Chr 7B of Chinese Spring reference IWGSC RefSeq v2.1 worked well to distinguish between the resistant and susceptible alleles and a heterozygous control (Table 2). Five recombinants were detected between sunCS_YR63 and YR63 among the RIL population.

YR63 homologous region in pan-genome is enriched with multiple NLR and kinase genes

As the marker generated from the MutRenSeq did not yield a co-segregating marker, we decided to investigate candidate genes from sources outside the Chinese Spring v2.1. To generate a list of candidate genes, the closest flanking markers sunCS_YR63 and sunCS_67 and the genes present in the YR63 locus from Chinese Spring v2.1, were used to BLAST against additional hexaploid wheat and durum reference genomes (Table 3). Unexpectedly, the flanking markers were mapped to Chr 5B instead of Chr 7B in ArinaLrFor and SY Mattis references and no conclusive region was determined, thus these genomes were removed from the analysis. The size of the YR63 locus ranged between 1.19 to 3.58 Mb across the pangenome and there were 31 to 141 genes within this interval.

Table 3 Pangenome summary for the YR63 locus
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Twenty-two genes were predicted to encode putative disease resistance proteins where 16 encoded NLRs and the remaining 6 genes encoded a kinase protein with three being annotated as LRR-receptor like protein kinases (Table 4). In the MutRenSeq analysis of the 16 NLR candidates, none of the genes showed polymorphism in all four loss-of-function mutants.

Table 4 Summary of NLR and kinase genes detected in the YR63 pangenome loci
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To determine the genetic relationship within the members of NLR and kinase genes at the YR63 locus, we obtained the gene sequences and aligned them using the Clustal-Omega multiple sequence alignment tool. This resulted in the formation of three distinct clusters for the NLR genes, of which group I held three of the four upregulated NLR genes (Figure 4A). The kinase genes formed two clusters, of which each group held a single upregulated kinase gene (Figure 4B).

Fig. 4
figure 4

Phylogenetic trees of orthologous and paralogous candidate NLR (A) and kinase (B) genes from the YR63 locus

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Marker validation on Australian hexaploid and tetraploid wheat and triticale varieties

The closely linked KASP marker, sunCS_YR63 of YR63 was screened against 123, 15 and 14 cultivars of hexaploid and tetraploid wheat and triticale, respectively. Interestingly, the marker was able to distinguish AUS27955 from all the tested varieties indicating its suitability for marker-assisted selection of YR63 carrying lines (Table 5; Supplementary figure S1).

Table 5 Validation data of closest flanking marker on Australian cereal cultivars
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Discussion

The persistent threat posed by Pst has triggered a global and extensive endeavour aimed at identifying and characterizing valuable resistance (R) genes in wheat. To ensure the ongoing protection of wheat production in Australia, where Pst incursions have been a recurring issue, it is imperative to test both existing and novel R genes against local and international Pst pathotypes. Landraces, that have adapted to specific geographical regions over time, present a valuable resource for discovering novel genes for diverse breeding traits [18].

In this study, we investigated the efficacy of YR63 against a range of Australian and International Pst pathotypes using the YR63 donor landrace accession AUS27955. Previous studies indicated that YR63 exhibited a strong resistance against Indian Pst pathotypes [19], alongside Yr47 and Yr57, all of which have been identified in the Watkins Wheat Landrace Collection [20, 21]. Indian wheat production heavily relied on the resistance provided by Yr9, Yr17 and Yr27, but the local Pst pathotypes such as 46S119, 110S119 and 238S119 evolved to acquire virulence against these genes [19, 22].

Our findings revealed that YR63 confers resistance against global pathotypes of Pst representing Africa, Asia, Middle East and South America. Furthermore, we also observed that YR63 provides resistance against PstS11, detected in Afghanistan in 2012, and PstS17, first observed in Egypt in 2018 [23, 24]. PstS11 has spread to several countries in the Middle East and Africa; including Turkey, Ethiopia, and Kenya, while PstS17 has been observed in Middle East, Turkey, Ethiopia and Baltic countries [25]. Critically, YR63 can also defend against PstS1 and PstS2, two of the most important Pst lineages, globally. In eastern Australia, PstS13 has been dominant in wheat production areas [2], but YR63 demonstrated high level of resistance against this pathotype under field conditions. Considering the broad spectrum of resistance exhibited by YR63 against currently prevalent global Pst pathotypes, this gene represents a valuable resource for international wheat breeding programs. The in-effectiveness of YR63 against PstS7, PstS9, PstS14 and 239 E237 A- 17+ 33+ (PstS10) indicates the necessity for the continuous search of novel ASR genes through mining of highly diverse germplasm such as the Watkins Collection.

In this study, we also confirmed the short arm of Chr 7B as the chromosomal location of YR63. This chromosome is known to carry stripe rust ASR genes Yr2, Yr6, and Yr67 and APR genes Yr39, Yr52, and Yr59 [26]. Considering the virulence profiles of Pst pathotypes used, it was concluded that AUS27955 does not carry Yr2, Yr6, Yr39, Yr52, or Yr59. YR63 differed from Yr67 for its in-effectiveness against the Pst pathotype 239 E237 A- 17+ 33+ [27]. Further, in the in-depth genome analysis of the locus, a cluster of 16 NLR genes and 6 kinase genes was detected in the homologous region (0.9 to 4.0 Mb of chromosome 7B) of YR63. It is worth noting that gene clustering has been observed in various organisms, including prokaryotes and eukaryotes. Although operons are commonly associated with prokaryotes [28], gene clusters in plants are typically attributed to homologous gene duplications or functionally linked non-homologous genes [29, 30]. In the case of wheat, NLR clusters have been observed in resistance genes including the stem rust gene Sr50 which contains an additional six homologous NLRs flanking the R gene [31].

Although the identification of gene clusters can assist in pinpointing favourable regions for gene selection, the presence of homologous elements hinders map-based cloning and inhibits sequencing techniques that offer comprehensive genomic insights. In the case of Lr1, cloning endeavours were impeded by the absence of specific markers tailored to the Chr 5D cluster [32]. Similarly, our efforts to narrow down this region using RNASeq and MutRenSeq failed to produce additional markers that reliably segregated with the YR63 phenotype.

This study utilised short-read next-generation sequencing (SR-NGS) with read lengths up to 150 bp. SR-NGS presents benefits such as diminished error rates, increased data yield, and cost-effectiveness when contrasted with long-read sequencing techniques; nevertheless, the 10 kb capacity of long-read sequencing holds the potential to enhance the resolution of the YR63 locus by mitigating the presence of multiple target sites within the YR63 locus [33]. The SR-NGS sequencing also offer the faster identification of SNP-based markers linked with the target trait.

This study successfully identified the marker sunCS_YR63 from the MutRenSeq dataset to effectively distinguish AUS27955 (YR63) from a comprehensive collection of 152 Australian bread and durum wheat and triticale cultivars. Availability of linked molecular markers is critical for pyramiding particularly for R genes with similar phenotype against diverse pathotypes of the target pathogen [34]. The YR63-linked marker sunCS_YR63 can be used for marker assisted selection of this gene in wheat breeding programs. However, due to the intricate and highly repetitive nature of the YR63 locus, long-read sequencing techniques as demonstrated in the cloning of Yr27 [35] may be more suitable for unravelling their complexities and distinguishing the candidate gene for the YR63 mediated resistance. This may also assist in future attempts to generate additional KASP markers within the YR63 locus.

Conclusion

In summary, our study demonstrates that the YR63 gene exhibits robust resistance against a wide range of Pst pathotypes from diverse global regions, making it a valuable asset for international wheat breeding programs. We located the YR63 gene on the short arm of chromosome 7B, alongside a cluster of NLR and kinase genes, which can aid in gene selection but present challenges for map-based cloning and sequencing. While our research employed short-read next-generation sequencing (SR-NGS) with its advantages in data quality and cost-efficiency, long-read sequencing techniques may offer a more comprehensive view of the complex and repetitive YR63 locus. The identification of the YR63-linked marker sunCS_YR63 provides a practical tool for marker-assisted selection in wheat breeding programs, especially when pyramiding resistance genes against diverse Pst pathotypes is required.

Methods

Plant materials

Landrace accession AUS27955, carrying YR63, is the resistant parent and positive control for all experiments, while AUS27928S, a selection from accession AUS27928 (AGG No: AGG27928WHEA1) lacking YR63 or any earlier known ASR genes for stripe rust was used as the susceptible parent. The mapping population consisted of 195 RILs generated from an initial crossing of AUS27955 with AUS27928S, single plant progeny was progressed forward. A mutant population was generated from the resistant parent, AUS27955. A kill-curve consisting of 0%, 0.2%, 0.4%, 0.6% and 0.8% EMS solution was applied to a small set of seeds (10-15) and grown in a glasshouse. The seed treatment that generated an approximate 50% reduction in germination and height of treated wheat was used. For the mutant population. ~2000 seeds were mutagenized with the chosen EMS solution following the procedure described by Mago et al. (2017) [36].

Rust inoculation and disease screening

Plant material was sown in 9 cm diameter plastic pots (12-16 plants per line), with a composite potting mixture of 80% composted pine bark and 20% sand. Aquasol® was applied to material at a rate of 20 g per 10 L of water. Both parent lines and ‘Morocco’ were sown as control lines for plant inoculation. The Australian Pst pathotypes were screened using inoculum at the Plant Breeding Institute, University of Sydney. Pst pathotype 134 E16 A+ 17+ 27+ (PstS1) which is avirulent on YR63 was used for screening the mapping population for gene segregation and marker-trait linkage analysis. The Pst pathotypes, 198 E16 A+ J+ T+ 17+ (PstS13) and 239 E237 A- 17+ 33+ (PstS10) were also used to test the parental accessions. Plants were inoculated at the two-leaf stage by spraying with urediniospores suspended in light mineral oil (Isopar L, approx. 5 mg spores per 10 mL oil). The plants were incubated in plastic-covered steel trays filled with water (a dew chamber) for 24 h at 9 °C before being moved to a greenhouse maintained at 17 °C. Stripe rust disease severity was scored at 12-14 days post-inoculation using the ; to 4 scale described by McIntosh et al. (1995) [37]. Parallelly, to check the broad-spectrum effectiveness of YR63, the two parental accessions were also screened against global Pst isolates representing PstS2, PstS7, PstS8, PstS9, PstS10, PstS11, PstS13, PstS14 and PstS17 at the Global Rust Reference Center (GRRC), Denmark (Table 1) using the procedures described in Hovmøller et al. (2017) [38]. A full list of the avirulence/virulence profiles of each tested Pst pathotype can be found in Supplementary table S1.

DNA extraction and marker analysis

DNA was extracted from the RIL mapping population using a Hamilton Microlab® NIMBUS automated liquid-handling robot and the procedure outlined in Kota et al. (2006) [39]. Approximately 2 cm of leaf tissue was collected from seedlings and ground in a Qiagen Tissue lyser II. The contents were then settled by centrifugation and DNA extraction buffer was added. The plates were incubated at 65 °C, cooled, and 6M ammonium acetate was added. The plates were centrifuged, and the supernatant was recovered into new deep-well microtiter plates containing isopropanol. The DNA was allowed to precipitate, then the plates were spun and washed in 70% ethanol. The pellets were allowed to fully dry before being resuspended in distilled water. The plates were centrifuged, and the supernatant was transferred to new microtiter plates for use in experiments.

Mapping through marker-trait linkage analysis

Genomic DNA from a subset of 115 RILs selected randomly from AUS27955 x AUS27928S cross were sent to Centre for AgriBioscience, Victoria, Australia for tGBS analysis. SNPs from the tGBS scaffold markers associated with YR63 resistance were converted into KASP markers to genotype the 195 individual lines of the RIL population. The automated pipeline Polymarker was used to assist in designing specific KASP markers identified. Markers were first screened on AUS27955 and AUS27928S before screening on the entire mapping population using the protocol described in Nsabiyera et al. (2016) [40]. Marker fluorescence was measured using a CFX96 Touch real-time PCR machine (Bio-Rad Laboratories Pty. Ltd., USA).

A Chi-squared (χ2) test was performed to confirm the inheritance of genes in the mapping population. Genetic distance was calculated using the Kosambi formula [41] available in the ‘onemap’ package [42] on RStudio 2022.02.2 [43] and was constructed using MapChart v2.32 [44].

RNASeq analysis

Three days after inoculation, leaf samples were collected from AUS27955 and AUS27928S and was immediately frozen in liquid nitrogen and stored at -80 °C for later use. Whole RNA was extracted using the Maxwell® RSC Plant RNA Kit (Promega) using the manufacturers protocol on the Maxwell® RSC instrument. RNA samples were sent to Novogene for paired end read sequencing. Quality of the raw RNA reads was assessed using the FastQC and trimmed using the Trimmomatic tool to remove highly repetitive sequences, adapter sequences or redundant sequences. A de novo assembly of the RNA sequences was performed using CLC Genomics software (v21), which produced a new set of transcripts representing the expressed genes in the leaf tissue. The trimmed RNA reads from both the resistant and susceptible plant lines were then aligned to this assembled transcriptome using a tool called Burrows-Wheeler Aligner v0.7.17 [45]. The module, SAMtools (v1.12) [46], was used to generate reads counts for individual transcripts, following which the read counts were normalised and calculated to reads per million.

MutRenSeq analysis

High quality DNA from the resistant accession AUS27955 and four loss-of-function mutants was sent to Arbor Biosciences (https://arborbiosci.com/) for enrichment and sequencing of DNA fragments related to NLRs. Targeted gene enrichment was based on the MYbaits protocol and bait library described in github.com/steuernb/MutantHunter. The sequence capture data supplied by Arbor Biosciences was processed as per the pipeline described by Steuernagelet al. (2016) [6]. First, the raw data was analysed for quality using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The reads were trimmed of adapters, repeat sequences, and low-quality regions using Trimmomatic [47] based upon the FastQC output. The wild-type sequences were assembled using the de-novo assembly tool in the CLC Genomics Workbench (https://digitalinsights.qiagen.com) using a minimum fragment length of 300, length fraction of 0.95, and a similarity fraction of 0.98. This served as a reference genome for the mutants. The trimmed reads (from both the mutants and wild-type) were mapped to the wild-type assembly using the Burrows-Wheeler Aligner [45]. Background noise was removed from each alignment using the program Noisefinder.pyc, then SNPs were called using SNPlogger.pyc. Another program, SNPtracker.pyc, was then used to generate a report summarising which contigs were polymorphic. Candidate contigs were shortlisted based on the presence of mutations in the maximum number of mutant lines screened. These custom programs (Noisefinder.pyc, SNPlogger.pyc and SNPtracker.pyc) were developed in-house, and are available on GitHub (https://github.com/TC-Hewitt/MuTrigo).

Using the wild-type assembly as a reference, SNPs were identified between AUS27955 and AUS27928S, then converted to KASP markers. The markers were used to screen the RIL population to determine whether candidates co-segregated with the YR63 phenotype.

Comparative genomic analysis of YR63 locus

To understand genomic architecture of YR63 locus, marker positions were first identified in Chinese Spring genome assembly v2.1. Matching positions were identified in publicly available genomes of ArinaLrFor, CDC Landmark, CDC Stanley, Jagger, Julius, LongReach Lancer, Mace, Norin 61, SY Mattis, PI190962 (spelt wheat), Zavitan and Svevo [11, 13]. The module BLAST+ (2.12.0) [48] was used to compare genes and sequences to identify homologous genes.

The NLR and kinase gene sequences from the YR63 locus were separately aligned using the Clustal-Omega platform using default settings (https://www.ebi.ac.uk/Tools/msa/clustalo/). The separate phylogenetic trees were constructed using the software FigTree (v1.4.4, https://github.com/rambaut/figtree/releases).

Availability of data and materials

The datasets of raw Illumina sequences generated in the current study were deposited to the National Center for Biotechnology Information (NCBI) and can be accessed in the Short Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) as accession number PRJNA988831. Seeds of plant materials used in this study are available from the corresponding author by request.

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Acknowledgements

‘Not applicable’.

Funding

The two joint first authors acknowledge the Australian Government Research Training Program scholarship from the University of Queensland and Sydney, respectively. Furthermore, thank Commonwealth Scientific and Industrial Research Organisation (CSIRO) Agriculture and Food business unit for the Postgraduate Top-Up Scholarship. Michael Norman additionally recognises the Sydney Institute of Agriculture, University of Sydney, for the Francis Henry Loxton Stipend and the Irvine Armstrong Watson Scholarships. Work carried out at CSIRO and the University of Sydney was also supported by Grains Research and Development Corporation, Australia.

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Author notes

  1. Amy Mackenzie and Michael Norman equal contribution.

Authors and Affiliations

  1. Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, 2601, Australia

    Amy Mackenzie, Michael Norman, Chunhong Chen, Lina Ma & Sambasivam Periyannan

  2. Centre for Crop Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, Queensland, 4072, Australia

    Amy Mackenzie, Lee Hickey & Sambasivam Periyannan

  3. School of Life and Environmental Sciences, Faculty of Science, The University of Sydney Plant Breeding Institute, 107 Cobbitty Road, Cobbitty, New South Wales, 2570, Australia

    Michael Norman, Mesfin Gessese, Harbans Bariana & Urmil Bansal

  4. Present address:, Wolaita sodo University, Sodo, Ethiopia

    Mesfin Gessese

  5. Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200, Slagelse, Denmark

    Chris Sørensen & Mogens Hovmøller

  6. Agriculture Victoria, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, 5 Ring Rd, Bundoora, Victoria, 3083, Australia

    Kerrie Forrest

  7. School of Science, Western Sydney University, Bourke Road, Richmond, New South Wales, 2753, Australia

    Harbans Bariana

  8. School of Agriculture and Environmental Science & Centre for Crop Health, University of Southern Queensland, Toowoomba, Queensland, 4350, Australia

    Sambasivam Periyannan

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  1. Amy Mackenzie
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Contributions

AM, MN, MG, CC, CS, MH, LM, KF, HB, UB and SP performed experiment and analysed data. LH, HB, UB, SP supervised AM and MN. HB, UB and SP planned the experiment. AM, MN, SP wrote the manuscript and all authors provided comments.

Corresponding authors

Correspondence to Urmil Bansal or Sambasivam Periyannan.

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Experiments conducted in accordance with the relevant institutional guidelines. Doesn’t involve any experiments with animals.

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The authors declare no competing interests.

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Supplementary Information

Additional file 1: Supplementary table S1.

Virulence/avirulence profiles of Pst pathotypes. Supplementary figure S1. Marker segregation of sunCS_YR63 on Australian Cereal cultivars. Blue indicates AUS27955. Orange indicates susceptible/other allele from the Australian cereal cultivars.

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Mackenzie, A., Norman, M., Gessese, M. et al. Wheat stripe rust resistance locus YR63 is a hot spot for evolution of defence genes – a pangenome discovery. BMC Plant Biol 23, 590 (2023). https://doi.org/10.1186/s12870-023-04576-2

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Keywords

BMC Plant Biology

ISSN: 1471-2229

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