Avirulence depletion assay: combiningRgene-mediated selection with bulk sequencing for rapid avirulence gene identification in wheat powdery mildew

Avirulence depletion assay: combining R gene-mediated selection with bulk sequencing for rapid avirulence gene identification in wheat powdery mildew | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Avirulence depletion assay: combining R gene-mediated selection with bulk sequencing for rapid avirulence gene identification in wheat powdery mildew View ORCID Profile Lukas Kunz , View ORCID Profile Jigisha Jigisha , View ORCID Profile Fabrizio Menardo , View ORCID Profile Alexandros G. Sotiropoulos , Helen Zbinden , View ORCID Profile Shenghao Zou , View ORCID Profile Dingzhong Tang , View ORCID Profile Ralph Hückelhoven , View ORCID Profile Beat Keller , View ORCID Profile Marion C. Müller doi: https://doi.org/10.1101/2024.07.10.602895 Lukas Kunz 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lukas Kunz Jigisha Jigisha 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jigisha Jigisha Fabrizio Menardo 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fabrizio Menardo Alexandros G. Sotiropoulos 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland 3 Centre for Crop Health, University of Southern Queensland , Toowoomba, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexandros G. Sotiropoulos Helen Zbinden 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shenghao Zou 4 State Key Laboratory of Ecological Control of Fujian-Taiwan Crop Pests, Fujian Agriculture and Forestry University , Fuzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shenghao Zou Dingzhong Tang 4 State Key Laboratory of Ecological Control of Fujian-Taiwan Crop Pests, Fujian Agriculture and Forestry University , Fuzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dingzhong Tang Ralph Hückelhoven 2 Chair of Phytopathology, TUM School of Life Sciences, Technical University of Munich , Freising, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ralph Hückelhoven Beat Keller 1 Department of Plant and Microbial Biology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beat Keller Marion C. Müller 2 Chair of Phytopathology, TUM School of Life Sciences, Technical University of Munich , Freising, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marion C. Müller For correspondence: marion.mueller{at}tum.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Wheat production is threatened by multiple fungal pathogens, such as the wheat powdery mildew fungus ( Blumeria graminis f. sp. tritici , Bgt ). Wheat resistance breeding frequently relies on the use of resistance ( R ) genes that encode diverse immune receptors which detect specific avirulence ( AVR ) effectors and subsequently induce an immune response. While R gene cloning has accelerated recently, AVR identification in many pathogens including Bgt lags behind, preventing pathogen-informed deployment of resistance sources. Here we describe a new “avirulence depletion (AD) assay” for rapid identification of AVR genes in Bgt . This assay relies on the selection of a segregating, haploid F1 progeny population on a resistant host, followed by bulk sequencing, thereby allowing rapid avirulence candidate gene identification with high mapping resolution. In a proof-of- concept experiment we mapped the AVR component of the wheat immune receptor Pm3a to a 25kb genomic interval in Bgt harboring a single effector, the previously described AvrPm3 a2/f2 . Subsequently, we applied the AD assay to map the unknown AVR effector recognized by the Pm60 immune receptor. We show that AvrPm60 is encoded by three tandemly arrayed, nearly identical effector genes that trigger an immune response upon co- expression with Pm60 and its alleles Pm60a and Pm60b . We furthermore provide evidence that Pm60 outperforms Pm60a and Pm60b through more efficient recognition of AvrPm60 effectors, suggesting it should be prioritized for wheat breeding. Finally, we show that virulence towards Pm60 is caused by simultaneous deletion of all AvrPm60 gene paralogs and that isolates lacking AvrPm60 are especially prevalent in the US thereby limiting the potential of Pm60 in this region. The AD assay is a powerful new tool for rapid and inexpensive AVR identification in Bgt with the potential to contribute to pathogen-informed breeding decisions for the use of novel R genes and regionally tailored gene deployment. Introduction Global wheat production is threatened by numerous pathogenic organisms with fungal diseases alone resulting in 15-20% yield losses annually ( Figueroa et al ., 2018 ; Savary et al ., 2019 ). Sustainable wheat production therefore relies on extensive breeding efforts to identify new genetic resistance sources, including specific resistance ( R ) genes, and to introduce them into high yielding cultivars. Many R genes encode intracellular nucleotide-binding leucine-rich repeat (NLR) immune receptors that recognize the presence of pathogen effectors, so called avirulence (AVR) proteins, and subsequently induce an immune response. AVR recognition by NLRs often results in a hypersensitive response (HR) that includes localized cell death and thereby limits pathogen proliferation ( Dodds & Rathjen, 2010 ; Ngou et al ., 2022 ). Evasion of NLR recognition by mutation or loss of AVR genes is often observed in fast evolving plant pathogens and thereby significantly limits the durability of R genes deployed in agricultural settings ( Brown, 2015 ). Several recent studies have highlighted the need for AVR identification and analysis of AVR diversity within pathogen populations in order to predict R gene durability and effective deployment in agricultural settings ( Hafeez et al ., 2021 ; Müller et al ., 2022 ; Minter & Saunders, 2023 ). While R gene identification and cloning in wheat and other staple crops has significantly sped up in recent years due to technological advances and vastly improved genomic resources ( Running & Faris, 2024 ), identification of AVR genes lags behind for many pathogens and new, more efficient methods for AVR gene identification are urgently needed ( Minter & Saunders, 2023 ). Wheat powdery mildew ( Blumeria graminis f.sp. tritici, abbrev. Bgt ) is an obligate biotrophic ascomycete fungus that exhibits a high level of host specificity and exclusively infects wheat ( Kusch et al ., 2024 ). Due to its short asexual life cycle it can cause explosive epidemics among susceptible wheat monocultures and result in considerable yield losses ( Beest et al ., 2008 ). Until today, 69 R genes with over 100 functional alleles against Bgt have been genetically defined in wheat ( Pm1 to Pm69 ) with many more R gene candidates awaiting official definition ( McIntosh et al ., 2019 ; Hafeez et al ., 2021 ). However, only a fraction of all defined Pm genes have so far been molecularly isolated and cloned with many of them ( Pm1a , Pm2 , Pm3a-t , Pm5e , Pm8 , Pm17 , Pm21 , Pm41 , Pm60 and Pm69 ) encoding classic NLR proteins ( Yahiaoui et al ., 2004 ; Hurni et al ., 2013 ; Sánchez-Martín et al ., 2016 ; Singh et al ., 2018 ; Zou et al ., 2018 ; Hewitt et al ., 2020 ; Li et al ., 2020 ; Xie et al ., 2020 ; Li et al ., 2024 ). While some of the cloned Pm genes provide resistance against a narrow range of Bgt isolates or have been largely overcome by the pathogen population during agricultural deployment, other Pm genes such as Pm60 provide resistance against most Bgt isolates and therefore represent valuable R gene candidates for future breeding efforts ( Zou et al ., 2018 ; Zhao et al ., 2020 ). Pm60 has been identified and cloned from the wheat progenitor Triticum urartu and was found to encode an NLR ( Zou et al ., 2018 ). A recent study investigating the genetic diversity of the Pm60 locus in T. urartu has furthermore revealed two additional Pm60 alleles, Pm60a and Pm60b , that provide resistance against Bgt ( Zhao et al ., 2020 ). Interestingly, Pm60a is defined by a 240bp deletion, encompassing two entire LRR repeats. By contrast, Pm60b carries a 240bp insertion and therefore 2 additional LRR repeats when compared with Pm60 ( Zhao et al ., 2020 ). Based on the resistance spectrum found in Pm60 , Pm60a and Pm60b containing lines, it was hypothesized that the three alleles might recognize similar AVR effector targets albeit likely with differences in recognition strength ( Zhao et al ., 2020 ). However, the identity of the corresponding AVR effector AvrPm60 from Bgt is so far unknown, which hampered detailed investigations of Pm60 allele specificity and predictions of their exact potential for wheat breeding. In the last decade, multiple AVR genes have been identified and cloned from Bgt : AvrPm1a_1 , AvrPm1a_2 , AvrPm2 , AvrPm3 a2/f2 , AvrPm3 b2/c2 , AvrPm3 d3 , AvrPm8 , AvrPm17 were shown to be recognized by the NLRs Pm1a , Pm2 , Pm3a/Pm3f , Pm3b/Pm3c , Pm3d , Pm8 and Pm17, respectively ( Bourras et al ., 2015 ; Praz et al ., 2017 ; Bourras et al ., 2019 ; Hewitt et al ., 2020 ; Müller et al ., 2022 ; Kloppe et al ., 2023 ; Kunz et al ., 2023 ). Interestingly, all Bgt AVR effector genes known to date encode small, secreted effector proteins with a size of approximately 120 amino acids and are predicted to exhibit an RNAse-like structure ( Bauer et al ., 2021 ; Cao et al ., 2023 ). The identification and functional validation of AVR/R gene pairs combined with analyses of AVR diversity within the worldwide Bgt population has proven crucial to advance the understanding of gain-of-virulence mechanisms in Bgt and consequently resistance gene breakdown in wheat agriculture. For instance, two recent studies on the quick breakdown of the Pm8 and Pm17 resistance genes, introgressed from rye, found evidence for ancient genetic variation in the corresponding AVR genes within the Bgt population, including virulence alleles that evade R gene recognition. Importantly, these ancient AVR gene variants precede the introgression of Pm8 and Pm17 from rye into the wheat gene pool, explaining their rapid breakdown shortly after agricultural deployment ( Müller et al ., 2022 ; Kunz et al ., 2023 ). Such examples highlight the importance of parallel AVR and R gene identification in pathogen and host in order to predict durability of R genes and to allow prioritization of most promising gene candidates for wheat breeding or R gene stacking. AVR gene identification in Bgt has relied on a variety of experimental strategies using genetic mapping, GWAS, effector screens and, most recently, UV mutagenesis to induce and identify gain-of-virulence mutations ( Bourras et al ., 2015 ; Bourras et al ., 2019 ; Bernasconi et al ., 2024 ). Due to its haploid genome, the fast generation time and the experimentally controllable asexual (i.e. clonal) and sexual reproduction, genetic mapping approaches have proven particularly powerful in Bgt and remain the most used tool for AVR gene identification. However, genetic mapping using biparental crosses traditionally involves time-consuming isolation of 100+ individual F1 progeny from sexually formed chasmothecia, their subsequent asexual propagation, genotyping and phenotyping thereby resulting in work- and cost-intensive projects with timelines of 1-2 years. Hence, there is a need for technological advances to speed up AVR identification in this important wheat pathogen. In this study, we describe a new “ AVR depletion assay” (AD assay) for rapid and low-cost AVR gene identification in Bgt . The assay preserves the many advantages of genetic mapping approaches but circumvents the time- consuming isolation and propagation of individual F1 progenies by combining the generation of sexual recombinant F1 populations with R gene-mediated selection and bulk sequencing. In a proof-of-concept experiment, we show that the AD assay can identify the previously described AvrPm3 a2/f2 with high precision (i.e. identifying a single candidate effector). Furthermore, we apply the new method to identify and functionally validate the previously unknown AvrPm60 , recognized by the broadly acting Pm60 resistance gene. Results The avirulence depletion assay allows mapping of AvrPm3 a2/f2 with high resolution In this study, we aimed at developing an “avirulence depletion assay” (AD assay) as a new approach for the identification of avirulence factors in Bgt . The assay is based on principles of bi-parental mapping but without the need to establish, maintain, and genotype individuals of a mapping population ( Figure 1 ). As such, the approach relies on crossing two parental Bgt isolates displaying opposite virulence phenotypes on an R gene- containing wheat line and subsequent regeneration of a sexual F1 mapping population. In contrast to classical bi-parental mapping approaches (map-based cloning, QTL mapping), in the AD pipeline, conidiospores of a mixed F1 progeny population are directly used to infect a wheat line that carries the resistance of interest thereby creating a strong and directional selection pressure that depletes the F1 progeny population from individuals carrying the corresponding AVR factors. In parallel, the initial F1 progeny population is used to infect a susceptible wheat genotype subsequently serving as an unselected control. Following bulk harvesting and sequencing of the surviving F1 progenies, single nucleotide polymorphisms (SNPs) are used to identify regions with a deviation of the 1:1 parental genotype ratio expected for positions unaffected by any selection. Finally, the identified candidate regions are inspected using genomics and transcriptomics datasets to identify candidate AVR genes ( Figure 1 ). Download figure Open in new tab Figure 1: Schematic summary of the AVR depletion assay (AD assay) workflow. A bi-parental cross between Bgt isolates exhibiting opposite (a)virulence phenotypes results in a haploid F1 population which subsequently is selected on a resistant wheat line resulting in AVR depletion (selected bulk) or a susceptible control line (non- selected bulk). Bulk NGS sequencing and bioinformatic analyses are used to define genomic regions with AVR depletion and subsequently identify AVR candidate genes. Figure created with BioRender.com. In a first experiment, we aimed to establish a proof-of-concept dataset for the AD-assay by using the well- characterized Bgt avirulence gene AvrPm3 a2/f2 located on Chr-06. AvrPm3 a2/f2 encodes an RNAse-like effector recognized by the wheat NLR allele Pm3a ( Bourras et al ., 2015 ). To test if the AD-assay can re-discover AvrPm3 a2/f2 , we crossed the Swiss isolate CHVD_042201 ( AvrPm3a , MAT 1-2) with the Chinese isolate CHN_52_27 ( avrPm3a , MAT 1-1) that display opposite phenotypes on the Pm3a containing near-isogenic line ‘Asosan/8*CC’ ( Figure 2a ). From this cross, we generated a mixed population of an estimated 1500 F1 progenies on the susceptible wheat line ‘Kanzler’, which is devoid of R genes against Bgt . In the next step, we used conidiospores of this multiplied F1 progeny population to infect either ‘Asosan/8*CC’, thereby creating a Pm3a -mediated selection pressure ( Pm3a -selected bulk) or the susceptible wheat cultivar ‘Kanzler’ to create an unselected control bulk. Subsequently, we bulk-harvested conidiospores produced during the respective selection steps and subjected both bulks to DNA sequencing using Illumina paired-end reads to a coverage of approximately 120X. Download figure Open in new tab Figure 2: High-resolution mapping and functional validation of the previously described AvrPm3 a2/f2 in a proof- of-concept study using the newly developed AD assay. (a) Virulence phenotypes of the parental Bgt isolates CHN_52_27 and CHVD_042201 on the susceptible wheat line ‘Kanzler’ and the Pm3a -containing line ‘Asosan/8*CC’. (b) Statistical analysis of sequenced bulks selected on ‘Kanzler’ or ‘Asosan/8*CC’ to detect regions with deviations from an expected 1:1 parental genotype ratio. Individual datapoints represent –log10 transformed p-values of a G-test at individual SNPs marker positions. A single genomic region on Chr-06 exhibits deviations from an expected 1:1 parental genotype inheritance in the Pm3a -selected bulk. The mapped AvrPm3a target interval (≥95% reads from virulent parent) encompassing 25kb and the single effector gene CHVD042201- 04754 ( AvrPm3 a2/f2 ) is depicted in a zoom-in (bottom). (c) Protein sequence alignment of AvrPm3 a2/f2 -A found in the avirulent parental isolate CHVD_042201 and AvrPm3 a2/f2 -B in the virulent parental isolate CHN_52_27. Amino acid polymorphisms are highlighted in yellow. The signal peptide, the conserved Y/FxC motif, the C-terminal cysteine and the conserved position of a small intron, all serving as hallmarks of RNAse-like effectors are highlighted in grey or by a small arrowhead (intron position). (d, e) Agrobacterium -mediated expression of AvrPm3 a2/f2 -A, AvrPm3 a2/f2 -B and Pm3a in N. benthamiana . Co-infiltrations (top) were performed with a 4 (effector) : 1 (NLR) ratio. Leaves were imaged using a camera (d) or the Fusion FX imager system (e) at four days post inoculation. The assay was performed with n=6 leaves and repeated a total of three times with similar results (total n=18 leaves). (f) Quantification of HR intensity in the N. benthamiana expression assay depicted in (e). Datapoints from three independent experiments with n=6 leaves are color coded (total n=18). The AvrPm3 a2/f2 gene is located in an effector cluster consisting of 19 members of the E008 candidate effector family ( Bourras et al ., 2015 ). Due to its complexity, the locus was not fully resolved in the previously published Bgt reference genome assembly of strain CHE_96224, where it was found to contain several sequence gaps and collapsed regions for highly similar gene copies ( Müller et al ., 2019 ). Given the advances in long-read sequencing technologies, we saw the opportunity to generate an improved reference genome assembly that fully resolves the AvrPm3 a2/f2 locus and potentially other collapsed or incomplete regions in the published Bgt reference genome CHE_96224. To do so, we sequenced CHVD_042201, which served as an avirulent parent in our AD assay, with PacBio HiFi to a coverage of 100X and assembled the genome using the hifiasm assembler (see Supplementary Note 1 for details). This strategy resulted in a near-complete telomere-to-telomere assembly with fully resolved centromeres and only two remaining sequence gaps in the highly expanded and repetitive rRNA encoding region on Chr-09 and a previously identified array of tandem repeats on Chr-04. Importantly, the new genome assembly also fully resolved the AvrPm3 a2/f2 locus and multiple other previously collapsed regions and thus represents a significant improvement in terms of genome resolution and continuity compared to previous Bgt assemblies (Supplementary Table 1, Supplementary Note 1). To analyse the bulked DNA sequencing data generated as part of the AD assay pipeline, we mapped the Illumina reads from the Pm3a -selected bulk, the non-selected control bulk, and the parental isolates CHVD_042201 and CHN_52_27 on the new genome assembly of CHVD_042201 and identified 198’027 high-quality SNPs between the two parental isolates serving as genetic markers in the subsequent analysis. In the sequenced bulks, we expected regions unaffected by any selection to show a 1:1 ratio of parental genotypes at any given marker. In contrast, in regions under selection, we expected ratios to significantly deviate from a 1:1 ratio, with the genotype of the avirulent parent CHVD_042201 being underrepresented (i.e. depleted). As expected, we did not identify any region with a strong deviation from a 1:1 ratio for the control bulk selected on the susceptible cultivar ‘Kanzler’ that is devoid of any R genes against Bgt ( Figure 2b ). In contrast, in the Pm3a -selected bulk, we found a single region located on Chr-06 that showed strong deviation from a 1:1 ratio towards the genotype from the virulent CHN_52_27 parental isolate ( Figure 2b ). The identified genomic segment overlapped with the previously identified E008 candidate effector cluster containing the AvrPm3 a2/f2 gene. Importantly, the region with the strongest depletion signal (i.e., ≥95% of reads from virulent parent) was constricted to 25kb and encompassed a single gene CHVD042201-04754 ( Figure 2b ). The identified gene is identical to the AvrPm3 a2/f2 variant AvrPm3 a2/f2-A -A , which was previously shown to be recognized by Pm3a ( Bourras et al ., 2015 ; McNally et al ., 2018 ). Based on resequencing data, we determined that the CHVD042201-04754 homolog in the Pm3a- virulent isolate CHN_52_27 encodes for a protein variant with three amino acid changes compared to CHVD_042201 (H36Q, G84E, E121D) ( Figure 2c ). This haplovariant was previously identified in isolates from China and Israel and was termed AvrPm3 a2/f2 -B and designated as a putative AvrPm3 a2/f2 gain-of-virulence allele ( McNally et al ., 2018 ). To confirm this finding, we expressed the AvrPm3 a2/f2 variants from the two parental isolates (without signal peptide) together with Pm3a in Nicotiana benthamiana using Agrobacterium -mediated transient transformation. Consistent with the results from the AD-assay, CHVD042201-04754 ( AvrPm3 a2/f2 -A ) elicited a Pm3a -dependent hypersensitive response (HR), whereas the AvrPm3 a2/f2 -B from the virulent parent CHN_52_27 did not, thereby confirming AvrPm3 a2/f2 -B to be a virulence allele ( Figure 2d-f ). Based on these findings, we concluded that the avirulence allele depletion observed in the Pm3a -selected bulk is a direct consequence of differential recognition of AvrPm3 a2/f2 variants found in the two parental isolates. In conclusion, our proof-of-concept datasets showed that the AD-assay is a powerful new tool to identify avirulence factors in Blumeria down to single gene resolution. For this proof-of-concept study of the AD assay, we relied on the high-quality genome assembly of CHVD_042201, which represented the avirulent parental isolate in our genetic cross. Even though the availability of a complete genome sequence of the avirulent parent is likely ideal to identify candidates genes, we wanted to test whether the AD assay succeeds in AVR identification also with an alternative reference genome. We therefore tested our AD pipeline using the previously published reference genome assembly of isolate CHE_96224 (Bgt_genome_v3_16) (Müller et al., 2019). Similar to the above-described analysis based on CHVD_042201, we found no deviations from the expected 1:1 parental genotype ratio in the unselected F1 bulks but a single region with strong avirulence depletion signal on Chr-06 in the Pm3a -selected bulk (Supplementary Figure S1, Supplementary Table 2, Supplementary Note 2). The identified genomic region in the CHE_96224 overlapped with the identified AvrPm3 a2/f2 locus in CHVD_042201, thereby confirming that the AD approach succeeds in identifying AvrPm3 a2/f2 also with an alternative reference genome assembly (see Supplementary Note 2 for details). The AvrPm60 effector is encoded by three tandem duplicated genes in the Pm60 -avirulent isolate CHVD_042201 Next, we aimed to use the AD assay to identify the so far unknown AvrPm60 , the avirulence factor corresponding to the NLR Pm60 ( Zou et al., 2018 ) . The parental Bgt isolates CHVD_042201 and CHN_52_27 displayed opposite virulence phenotypes on ‘Kn199 Pm60’ ( Figure 3a ), a transgenic line expressing the Pm60 gene in the susceptible background ‘Kn199’ ( Zou et al ., 2018 ). We therefore used the ‘Kn199 Pm60’ transgenic line to apply a Pm60 - mediated selection to the CHVD_042201 x CHN_52_27 F1 progeny population described above. Again, the AD- assay identified a single genomic region in which the Pm60 -selected bulk showed a strong deviation from the 1:1 parental genotype ratio, with a depletion of the avirulent CHVD_042201 parental genotype. Strikingly, the AvrPm60 candidate locus partially overlapped with the above-described AvrPm3 a2/f2 locus on Chr-06 ( Figure 3b ). A parallel analysis of the Pm60 -selected bulk based on the alternative CHE_96224 reference genome assembly identified a single genomic region with strong depletion of the avirulent genotype on Chr-06 which overlapped with the identified region in CHVD_042201, again highlighting the independence of the AD assay from individual reference genomes (Supplementary Figure S1, Supplementary Table 2, Supplementary Note 2). Download figure Open in new tab Figure 3: Mapping and functional validation of AvrPm60_1 and AvrPm60_2 using the AD assay. (a) Virulence phenotype of the parental Bgt isolates CHN_52_27 and CHVD_042201 on the transgenic wheat line ‘Kn199 Pm60’. (b) Statistical analysis of sequenced bulks selected on ‘Kn199 Pm60 ’ to detect regions with deviations from an expected 1:1 parental genotype ratio. Individual datapoints represent –log10 transformed p-values of a G-test at individual SNPs marker positions. A single genomic region on Chr-06 exhibits deviations from an expected 1:1 parental genotype inheritance in the Pm60 -selected bulk. (c) Schematic zoom-in of the mapped AvrPm60 target interval (≥95% reads from virulent parent) encompassing 667 kb and 16 candidate effector genes. Polymorphic candidate effectors are individually labelled, and non-synonymous SNPs indicated by a red line. The genomic region encompassing CHVD042201-04743, CHVD042201-04745 and CHVD042201-04747, which was found to be deleted in the virulent parental isolate CHN_52_27, is highlighted in yellow. (d) Protein sequence alignment of the identical effectors CHVD042201-04743/CHVD042201-04745 and CHVD042201-04747 (E103G). The E103G polymorphism is highlighted in yellow. The signal peptide, the conserved Y/FxC motif, the C- terminal cysteine and the conserved position of a small intron, all serving as hallmarks of RNAse-like effectors are highlighted in grey or by a small arrowhead (intron position). (e) Co-expression of polymorphic effector candidates found within the mapped AvrPm60 locus together with Pm60 using Agrobacterium- mediated expression in N. benthamiana . Co-expression of GUS + Pm60 and AvrPm3 a2/f2 -A + Pm3a were used as negative and positive controls, respectively. Co-infiltrations were performed with a 4 (effector) : 1 (NLR) ratio and imaged with a Fusion FX imager system 4 days post inoculation. HR intensity was quantified from three independent experiments with n=6 leaves (color-coded datapoints, total n=18). (f, g) Agrobacterium -mediated expression of AvrPm60_1, AvrPm60_2 and Pm60 in in N. benthamiana . Co-infiltrations (top) were performed with a 4 (effector) : 1 (NLR) ratio. Leaves were imaged using a camera (f) or the Fusion FX imager system (g) at four days post inoculation. The assay was performed with n=6 leaves and repeated a total of three times with similar results (total n=18 leaves). (h) Quantification of HR intensity in the N. benthamiana expression assay depicted in (g). Datapoints from three independent experiments with n=6 leaves are color coded (total n=18). * indicates statistical difference according to a Wilcoxon signed rank test (p<0.05). (i) Predicted three dimensional structures of AvrPm60_1 (yellow) and AvrPm3 a2/f2 (grey) according to Alphafold 3 structural modelling. Both effector proteins are predicted to exhibit an RNAse-like structure. The region with the strongest signal of avirulence allele depletion (i.e. ≥95% of the reads originated from the virulent parent CHN_52_27) in the Pm60 -selected bulk encompassed a region of 667kb in CHVD_042201 containing 16 annotated, high-quality genes, all belonging to the E008 candidate effector family ( Figure 3c ). We inspected all genes in the locus with re-sequencing data from CHVD_042201 and CHN_52_27 and found that only seven of the 16 candidate effectors, including the above-described AvrPm3 a2/f2 gene (CHVD042201-04754) exhibited polymorphisms between the parental isolates and therefore represented good AvrPm60 candidate genes ( Figure 3c ). Among the seven polymorphic genes, only four carried SNPs resulting in amino acid polymorphisms between the virulent and avirulent parents. For the remaining three genes ( CHVD042201-04743, CHVD042201-04745, and CHVD042201-04747 ), we did not detect any alignment of sequencing reads from CHN_52_27, indicating that these three genes are deleted in the virulent parent ( Figure 3c ). Interestingly, CHVD042201-04743, CHVD042201-04745 , and CHVD042201-04747 constitute tandem duplicates of the same effector gene in the avirulent isolate CHVD_042201. The resulting effector proteins CHVD042201-04743 and CHVD042201-04745 are identical, whereas CHVD042201-04747 differs by a single amino acid change (E103G) ( Figure 3d ). To functionally validate AvrPm60 , we co-expressed the seven polymorphic candidate genes found within the mapped locus together with Pm60 using transient overexpression in N. benthamiana . Both the CHVD042201-04743/CHVD042201-04745 and CHVD042201-04747 effectors triggered a Pm60 -dependent HR response, whereas none of the other candidates or a GUS negative control resulted in cell death ( Figure 3e-h ). Hence, we concluded that three genes CHVD042201-04743, CHVD042201-04745 , and CHVD042201-04747 represent AvrPm60 by encoding two nearly identical effector proteins that we designated as AvrPm60_1 (CHVD042201-04743/CHVD042201-04745) and AvrPm60_2 (CHVD042201-04747). Interestingly, the AvrPm60_2 variant carrying the amino acid polymorphism E103G elicited a slightly stronger HR response compared to AvrPm60_1 ( Figure 3e-h ). AvrPm60_1 and AvrPm60_2 belong to the RNAse-like effector superfamily The identified AvrPm60 effectors are part of the large effector family E008 with over 40 members, including the AvrPm3 a2/f2 effector ( Müller et al ., 2019 ). Like other member of the E008 family, the two AvrPm60 are small proteins of 121 amino acids in size, with the first 23 amino acids constituting a predicted signal peptide ( Figure 3d ). Similar to all previously identified AVRs in Bgt , the AvrPm60 proteins contain a Y/FxC motif and a conserved C-terminal cysteine, two features that were defined as hallmarks of RNAse-like effectors which comprise more than half of all effector proteins found in the Blumeria genus ( Cao et al ., 2023 ; Seong & Krasileva, 2023 ). Indeed, structural modelling using Alphafold 3 ( Abramson et al ., 2024 ) predicted that the AvrPm60 proteins exhibit an RNAse-like structure and are structurally similar to the E008 family member AvrPm3 a2/f2 ( Figure 3i ). Multiple previously identified RNAse-like AVRs in Bgt were found to exhibit very high expression levels during early phases of infection ( Bourras et al ., 2015 ; Praz et al ., 2017 ; Bourras et al ., 2019 ; Kunz et al ., 2023 ). Similarly, analysis of RNA sequencing data from five Bgt isolates at two days post inoculation (2dpi) showed that the AvrPm60 genes are consistently among the top 5% of the highest expressed genes in each isolate (Supplementary Figure S2). In summary, the AvrPm60 effectors are bona-fide members of the RNAse-like effector class in Blumeria . Gain-of-virulence through deletion of AvrPm60 genes is rare within the worldwide Bgt population but widespread in the US The Pm60 -virulent isolate CHN_52_27 used in the AD assay evades Pm60 -mediated resistance due to a large- scale deletion encompassing all three AvrPm60 copies found in CHVD_042201 ( Figure 3c ). We therefore aimed to investigate the frequency and distribution of this striking gain-of-virulence mechanism within the global Bgt population. To estimate the number of AvrPm60 gene copies, we used a previously described in-silico approach using publicly available resequencing data from 382 Bgt isolates ( Menardo et al ., 2016 ; Praz et al ., 2017 ; Müller et al ., 2019 ; Müller et al ., 2022 ; Sotiropoulos et al ., 2022 ; Kloppe et al ., 2023 ). This approach uses normalized read coverage as a proxy for the number of gene copies in each isolate. As a control we used the GAPDH gene which was previously shown to be present as a single copy gene, and AvrPm3 a2/f2 found to occur in 1-4 copies in Bgt isolates ( Müller et al ., 2019 ). As expected, the coverage analysis indicated that GAPDH occurs as a single copy gene in all 382 analysed isolates, whereas the analysis found signs of copy number variations for both the AvrPm3 a2/f2 and the AvrPm60 gene in this worldwide Bgt panel ( Figure 4a , Supplementary Figure S3). Consistent with the literature, the coverage analysis indicates that all isolates carry at least one copy of the AvrPm3 a2/f2 , although higher-order duplications with two or more copies are readily observed (Supplementary Figure S3), ( McNally et al ., 2018 ; Müller et al ., 2019 ; Müller et al ., 2021 ). In contrast, we estimated that the majority of Bgt isolates contain two AvrPm60 copies ( Figure 4a ). However, we detected sizeable additional copy number variation in the Bgt diversity panel with some isolates containing a single AvrPm60 gene and isolates with three or more AvrPm60 copies. Consistent with the broad functionality of Pm60 described in the literature ( Zou et al ., 2018 ), only a minority of isolates (13/382), including the Chinese isolate CHN_52_27, were devoid of any AvrPm60 copies as indicated by the absence of any sequencing coverage in our analysis ( Figure 4a , Table 1 ). Interestingly, among the 13 isolates lacking AvrPm60 only one additional isolate originated from China (2 out of 63 Chinese isolates), while the remaining isolates all originated from the US, where 19% of investigated isolates carried the AvrPm60 deletion ( Table 1 ). To experimentally confirm the deletion of AvrPm60 gene copies in specific isolates from China and the US, we designed AvrPm60 -specific PCR primers based on conserved flanking sequences of all three AvrPm60 genes in the reference isolate CHVD_042201. Using these primers we successfully amplified AvrPm60 from genomic DNA of CHVD_042201 and six additional Bgt isolates with diverse geographic origin (Switzerland, UK, China, Japan, Argentina, US) for which our coverage analysis estimated between one and four AvrPm60 copies in the genome. In contrast, PCR amplification failed from CHN_52_27 and three isolates from the US with a predicted complete AvrPm60 deletion, thereby confirming the results of the coverage analysis (Supplementary Figure S4). We then subjected the same 11 Bgt isolates to virulence phenotyping on the transgenic wheat line ‘Kn199 + Pm60’ and the susceptible control ‘Kn199’. Importantly, all isolates with at least one AvrPm60 gene copy in the genome exhibited an avirulent phenotype on the Pm60 transgenic line similar to CHVD_042201, whereas all tested isolates with AvrPm60 gene deletions exhibited full virulence on Pm60 wheat, comparable to the virulent CHN_52_27 isolate ( Figure 4b ). Hence, we conclude that deletion of AvrPm60 genes represents a gain-of-virulence mechanism that allows Bgt to overcome Pm60 - mediated resistance and that such AvrPm60 gene deletions are particularly prevalent in the US, likely limiting Pm60 efficacy in this geographic area. Download figure Open in new tab Figure 4: Functional characterization of AvrPm60 copy number variation and Pm60 allelic variants. (a) Copy-number estimation of AvrPm60 gene copies in a worldwide diversity panel of 382 publicly available sequenced Bgt isolates. Genomic coverage of Illumina reads aligning to AvrPm60 genes copies were normalized to the coverage of all genes in the genome. (b) Virulence phenotypes of 11 Bgt isolates with varying copy numbers of AvrPm60 genes on the transgenic wheat line ‘Kn199 Pm60’ and ‘Kn199’, serving as a susceptible control. The estimated number of AvrPm60 gene copies according to sequencing coverage analysis are indicated next to the isolate name with normalized sequencing coverage indicated in brackets. The isolates CHVD_042201 and CHN_52_27 used for initial AvrPm60 identification are shown as comparison. (c) Schematic representation of the three tested Pm60 alleles originating from Triticum urartu . Yellow boxes indicate a pair of LRR repeats that show copy number variations between the tested Pm60 alleles. (b,c) Agrobacterium -mediated expression of AvrPm60_1 (c), AvrPm60_2 (b) with Pm60, Pm60a and Pm60b in N. benthamiana . Co-infiltrations were performed with a 4 (effector) : 1 (NLR) ratio. Leaves were imaged at 4dpi using the Fusion FX imager system. The assay was performed with n=6 leaves and repeated a total of three times with similar results (total n=18 leaves). Boxplots represent quantification of HR intensity in the N. benthamiana expression assay. Datapoints from the three independent experiments are color coded. Different letters next to the boxplot represent statistical differences according to a pairwise Wilcoxon rank sum exact test (p<0.05). View this table: View inline View popup Download powerpoint Table 1: Estimated copy number of AvrPm60 genes in Bgt isolates originating from different geographic regions Pm60 and its allelic variants Pm60a and Pm60b recognize AvrPm60_1 and AvrPm60_2 with varying efficacy The Pm60 resistance gene was originally isolated from T. urartu , the progenitor of the A genome of hexaploid wheat. Allele mining in T. urartu identified two additional functional alleles of Pm60 , termed Pm60a and Pm60b , that provide resistance against Bgt ( Zou et al ., 2018 ; Zou et al ., 2022 ). Pm60a and Pm60b differ from Pm60 through the deletion or tandem duplication of two LRR repeats, respectively ( Figure 4c ) ( Zou et al ., 2018 ). In the literature, the Bgt recognition spectra of Pm60a , Pm60b and Pm60 are described as largely overlapping thus prompting us to hypothesize that the three Pm60 alleles recognize the same avirulence component in Bgt . To test this hypothesis, we co-expressed the three Pm60 alleles with the AvrPm60_1 and AvrPm60_2 in N. benthamiana. Both AvrPm60 effector variants induced a hypersensitive response upon co-expression with Pm60a, Pm60b and Pm60 but not with a uidA (GUS) negative control ( Figure 4d,e ) thus showing that the three Pm60 alleles recognize the same avirulence components in Bgt . Although both AvrPm60 variants are recognized by all three Pm60 alleles, we observed significant differences in the strength of the hypersensitive response depending on the specific Avr / R combination. For the stronger AvrPm60_2 variant, the Pm60a allele showed a marked reduction in the strength of HR when compared to Pm60 . In contrast, recognition of the weaker AvrPm60_1 variant resulted in reduced HR output for both Pm60a and Pm60b alleles when compared to Pm60 ( Figure 4d,e ). In summary, our findings indicate that the three Pm60 alleles, originating from T. urartu , recognize the same avirulence effectors AvrPm60_1 and AvrPm60_2, albeit with differences in the strength of the elicited HR. This indicates that the copy number variation in the LRR repeats does not influence the recognition specificity of the Pm60 alleles, but it might play a role in modulating the amplitude of the HR upon effector recognition. Discussion Breeding genetically resistant cultivars is a cornerstone of sustainable agricultural production, with global efforts focusing on the identification of new resistance sources and their efficient deployment against fast evolving pathogenic organisms ( Huang et al ., 2023 ). In wheat, more than 460 resistance gene loci against various pathogens are genetically defined but only a small fraction of the underlying R genes have been cloned to date ( McIntosh et al ., 2019 ; Hafeez et al ., 2021 ). Owing to technological advances and significantly improved genomic resources, the speed of R gene cloning in wheat has increased tremendously in recent years and it is expected that the majority of defined R genes will be molecularly identified within the next decade ( Sánchez-Martín & Keller, 2021 ; Wulff & Krattinger, 2022 ). However, the question remains how these resistance sources can be deployed in modern agricultural systems to durably withstand continually evolving pathogen populations. In a widely acclaimed perspective paper, Hafeez and colleagues in 2021 called for a concerted, international effort to generate a wheat R gene atlas to tackle these problems ( Hafeez et al ., 2021 ). In the outlined vision, R gene identification in wheat must be accompanied by AVR identification and subsequent analysis of AVR diversity in corresponding pathogen populations in order to guide future breeding decisions and allowing for efficient, regionally tailored, R gene deployment. The authors argued that for the success of such an endeavor it is crucial to improve our current limited knowledge about AVR factors in wheat pathogens and in particular develop methods to speed up AVR gene identification in order to keep up with the improved pace of R gene cloning in the host ( Hafeez et al ., 2021 ). In contrast to other wheat pathogens where few or no AVR factors have been identified to date, the field of AVR gene identification in Bgt is relatively advanced. Using various methods including classic genetic mapping, GWAS, mutagenesis and effector screens, a total of 8 AVR effectors have been cloned and molecularly characterized to date ( Bourras et al ., 2015 ; Praz et al ., 2017 ; Bourras et al ., 2019 ; Hewitt et al ., 2020 ; Müller et al ., 2022 ; Kloppe et al ., 2023 ; Kunz et al ., 2023 ; Bernasconi et al ., 2024 ). Due to the haploid genome and experimentally accessible sexual reproduction of Bgt , classic genetic mapping has proven to be a powerful tool. For example, genetic mapping approaches can outperform GWAS in resolving genetically complex traits with multiple components or when components occur at low frequency in the Bgt population. Furthermore, in contrast to effector screening approaches, genetic mapping does not solely rely on HR as the primary immune outcome. However, the requirement to isolate, phenotype and genotype individual F1 progenies represents a major obstacle in genetic mapping experiments, leading to work- and cost-intensive projects in the range of 1-2 years. The newly developed AD assay described here leverages the many strengths of genetic mapping in Bgt while eliminating the work-intensive characterization of individual F1 progenies. It thereby shortens project timelines to approximately 4 months and reduces associated sequencing efforts to a selected and an unselected F1 pool resulting in much lower costs. Avoiding the most work-intensive steps in mapping projects furthermore allows AVR identification to be parallelized by either selecting the same progeny population on multiple R gene containing lines, as done in this study on Pm3a and Pm60 , or even by simultaneously generating multiple progeny populations arising from different genetic crosses that segregate for different AVR factors. Our proof-of-concept experiment aiming at the mapping of AvrPm3 a2/f2 identified a narrowly defined genomic region of 25kb harboring a single candidate effector gene (i.e. AvrPm3 a2/f2 ), exemplifying the mapping power and resolution of the AD assay ( Figure 2b ). In the case of AvrPm60 the mapped interval was however significantly bigger and encompassed a region of 667 kb with 16 effectors in total (7 polymorphic), indicating that mapping resolution varies depending on the mapped factor ( Figure 3c ). We hypothesize that these differences arise in part from the fact that AvrPm60 is represented by three genes spread over 130 kb of sequence. The mapping resolution is likely also influenced by the rate of recombination within the mapped AVR locus, which was shown to differ throughout the Bgt genome ( Müller et al ., 2019 ) and the strength of selection exerted by the resistant wheat line used to generate the selected F1 bulks. Interestingly, we observed near complete selection for the virulence allele (i.e. ≥95% of sequenced reads) in Pm3a - and Pm60 -selected pools after a single asexual reproduction cycle likely due to the strong resistance effect exerted by both R genes. We are however optimistic that the AD assay could also be used to map AVR factors corresponding to R genes with weaker resistance effects, such as Pm8 which was shown to result in partial resistance against isolates carrying AvrPm8 under endogenous Pm8 expression levels and only resulted in complete resistance in transgenic overexpression lines ( Kunz et al ., 2023 ). In such cases, the deviation from a 1:1 inheritance ratio in selected progeny pools might be less drastic, resulting in larger mapped intervals and consequentially a higher number of candidate genes. The situation could however be alleviated by extending the number of asexual cycles under selection or, if available, by the use of transgenic overexpression lines with stronger or complete resistance. Several recent studies have shown that the genetic control of avirulence phenotypes in Bgt can be complex and can involve multiple genetic loci ( Bourras et al ., 2015 ; Hewitt et al ., 2020 ; Müller et al ., 2022 ; Kloppe et al ., 2023 ). In the case of a rye introgression carrying Pm17 and a genetically linked unknown second resistance gene, a QTL mapping approach using ∼120 F1 progenies successfully mapped both corresponding AVR loci in Bgt ( Müller et al ., 2022 ). However, in another study the complex avirulence landscape for several Pm3 alleles was only partially resolved in a biparental mapping population of ∼140 F1 progenies ( Bourras et al ., 2015 ). It will be interesting to see whether the newly developed AD assay, due to the high number of processed progenies (∼1500 in this study), could improve mapping resolution in these complex cases that include multiple AVR loci. We hypothesize that the resolution achieved in the AD assay could be further improved by extending the F1 progeny population and in particular by sequencing bulks at significantly higher coverages (∼120X in this study), thereby capturing a more detailed picture of the recombination landscape within the F1 progeny population. In conclusion, we argue that the AD assay is a powerful new tool for AVR identification in Bgt that will significantly speed up AVR cloning in this important wheat pathogen in the future. Furthermore, we hypothesize that the AD assay could be adapted to map avirulence components in other pathosystems, where the sexual reproduction cycle of the pathogen is experimentally accessible and genetic resources of the host include clearly defined R genes that allow for efficient selection of progeny pools. Our knowledge about AVR effectors in cereal powdery mildews has improved significantly in recent years. With a total of 8 known and functionally validated AVRs in the wheat infecting Bgt and another 6 AVRs from barley powdery mildew ( Blumeria hordei ), common principles of AVR effectors in this group of cereal pathogens can be defined ( Bourras et al ., 2015 ; Lu et al ., 2016 ; Praz et al ., 2017 ; Bourras et al ., 2019 ; Saur et al ., 2019 ; Hewitt et al ., 2020 ; Bauer et al ., 2021 ; Müller et al ., 2022 ; Kloppe et al ., 2023 ; Kunz et al ., 2023 ). Interestingly, all 14 AVRs belong to an effector superfamily of structurally similar proteins exhibiting an RNAse-like fold, but lacking RNAse activity ( Bauer et al ., 2021 ; Cao et al ., 2023 ). Apart from the presence of a signal peptide, these proteins share several characteristics such as a N-terminal Y/FxC motif and a conserved C-terminal cysteine involved in disulfide bridge formation and hence stability of the RNAse fold ( Pennington et al ., 2019 ; Cao et al ., 2023 ). Furthermore, RNAse-like effector genes share a single small intron at a conserved position indicating they evolved through diversification from the same ancestral gene, although they often share little amino acid identity ( Pedersen et al ., 2012 ; Cao et al ., 2023 ). While RNAse-like effectors can also be found in other phytopathogenic fungi, this group of effectors appears to be strongly expanded within the Blumeria genus where it comprises more than half of the total effector complement ( Müller et al ., 2019 ; Cao et al ., 2023 ; Seong & Krasileva, 2023 ). In this study, we found yet another three members of this effector superfamily to encode AvrPm60_1 and AvrPm60_2, thereby further highlighting the importance of the RNAse-like effectors in cereal powdery mildews. Interestingly, the previously described AvrPm3 a2/f2 and the newly identified AvrPm60 effectors are part of the same effector family E008, whereas their corresponding NLRs belong to phylogenetically distinct NLR clades ( Avni et al ., 2022 ). The question remains, however, why RNAse-like effectors in general, and the E008 family specifically, are especially prone to be recognized by NLRs. It has been hypothesized that the high expression levels observed during early stages of infection for many RNAse-like AVR s, including AvrPm3 a2/f2 and the newly identified AvrPm60 genes (Supplementary Figure S2), makes this class of effectors predestined to be recognized by the hosts immune system ( Bourras et al ., 2018 ; Müller et al ., 2019 ). Another characteristic observed with AVRs in Bgt is extensive copy number variation of AVR genes within the global pathogen population. For example, AvrPm3 d3 was found to occur in up to six copies in some isolates, while other AVRs, such as AvrPm3 a2/f2 or AvrPm17, were found to occur in one to four copies (Supplementary Figure S3) ( Bourras et al ., 2019 ; Müller et al ., 2019 ; Müller et al ., 2022 ). Interestingly, we found three neighboring, nearly identical AvrPm60 genes in the Pm60- avirulent isolate CHVD_042201, with the majority of isolates within the global collection containing two or three copies ( Figure 4a ). We hypothesize that gain of virulence mutations affecting a single AvrPm60 gene copy might therefore not suffice to overcome Pm60 resistance. This is consistent with the observation that complete deletion of the AvrPm60 locus, as observed in the Pm60 -virulent isolates CHN_52_27, USA_2, USA_3 and USA_5, is a potent gain-of-virulence mechanism ( Figure 4a,b ). Interestingly, AvrPm60 deletion is rare among Chinese Bgt isolates with only two out of a 63 analyzed isolates exhibiting complete absence of AvrPm60 genes ( Figure 4a , Table 1 ). This observation is in line with earlier findings showing that Pm60 provides broad resistance against the Chinese Bgt population ( Zou et al ., 2018 , Zou et al ., 2022 ). By contrast, deletion of AvrPm60 genes is relatively common among Bgt isolates from the US ( Figure 4a,b , Table 1 ). This finding is intriguing, given that we are not aware of any documented use of Pm60 in agricultural production in the US. Nevertheless, we hypothesize that the frequent deletion of AvrPm60 in this Bgt subpopulation could be the result of previous use of Pm60 or Pm60-like resistance genes in this region, knowingly or unknowingly. Alternatively, it could be the consequence of the use of yet another resistance gene that recognizes the same effector proteins. Irrespective of underlying reasons for the frequent absence of AvrPm60s in the US Bgt population, we conclude that the resistance provided by Pm60 is locally ineffective and therefore will likely provide only partial protection against Bgt infection in this geographic region. A study by Zou and colleagues (2022) showed that the resistance provided by Pm60a and Pm60b resembles the one mediated by Pm60 and concluded that the three alleles might have overlapping, albeit not identical, recognition spectra. In their study the authors showed that in particular Pm60a only provides resistance against a subset of isolates recognized by Pm60 and Pm60b , which might indicate weaker or partially divergent recognition activity of Pm60a as compared to the other two alleles found in T. urartu ( Zou et al ., 2018 ; Zou et al ., 2022 ). This agrees with the findings in this study where we could show that AvrPm60_1 and AvrPm60_2 trigger HR immune responses in the presence of the Pm60, Pm60a and Pm60b NLRs albeit at varying levels ( Figure 4d,e ). For AvrPm60_2 we observed strong HR responses upon co-expression with Pm60 and Pm60b but a weaker response with Pm60a ( Figure 4d ), corroborating the initial observations of Zou and colleagues ( Zou et al ., 2018 ; Zou et al ., 2022 ). Furthermore, co-expression of Pm60a and Pm60b with AvrPm60_1 resulted in a significantly weaker HR response as compared to Pm60 ( Figure 4e ). Our findings indicate that the deletion of two LRR repeats in Pm60a results in a lower sensitivity towards AvrPm60_1 and AvrPm60_2 whereas the duplication of the same two LRR repeats only influences AvrPm60_1 recognition. In conclusion, these observations indicate that Pm60 outperforms its allelic variants Pm60a and Pm60b in their ability to recognize AvrPm60_2 and in particular AvrPm60_1 effectors and should therefore be prioritized for wheat breeding. Interestingly several allele pairs of the Pm3 allelic series were shown to recognize the same AVR protein with differing recognition strength. The alleles Pm3a (strong) and Pm3f (weak) were shown to recognize AvrPm3 a2/f2 , Pm3b (strong) and Pm3c (weak) recognize AvrPm3 b2/c2 and finally, Pm3s (strong) and Pm3m (weak) a so far unknown AvrPm3 m/s ( Stirnweis et al ., 2014 ; Bourras et al ., 2015 ; Bourras et al ., 2019 ). In the case of Pm3 diversity the underlying amino acid polymorphisms defining strong and weak alleles were identified and reside within the NB-ARC domain of the NLR ( Stirnweis et al ., 2014 ). In contrast, the weaker Pm60a and Pm60b alleles are defined by the lack or duplication of two LRR repeats, respectively, compared to the stronger Pm60 allele. Thus, different categories of polymorphisms within NLR proteins can influence their strength and ability to recognize AVR proteins, highlighting the importance of studying NLR diversity in order to define the most potent variants for wheat protection. Multiple studies identified additional Pm60 diversity in wild emmer wheat (WEW, Triticum dicoccoides ) and defined 11 haplotypes that differed from Pm60 found in T. urartu predominantly by single amino acid polymorphisms affecting the CC, the NB-ARC domain as well as the LRR repeats ( Li et al ., 2021 ; Wu et al ., 2021 ; Wu et al ., 2022 ). While resistance activity of some Pm60 alleles in WEW was verified, the ability of the remaining Pm60 diversity to recognize Bgt remains unknown. The identification of AvrPm60_1 and AvrPm60_2 therefore provides the opportunity to study, and potentially validate additional Pm60 alleles from WEW as well as investigate the consequences of polymorphisms found within Pm60 on its ability to recognize the two known AvrPm60’s and, potentially, additional effector proteins. Indeed, the identification and cloning of Pm60 and AvrPm60 genes in combination with investigations into their natural diversity opens new avenues of research and provides the opportunity to study the consequences of individual polymorphisms. These investigations will be crucial to estimate the value of Pm60 for breeding and deployment and could, as exemplified by the high frequency of AvrPm60 deletion observed in the US Bgt population, inform and guide regional deployment of resistance alleles. These considerations exemplify how the R / AVR atlas envisioned by Hafeez and colleagues could improve wheat resistance breeding in the future and how new technologies for rapid and cost-efficient AVR identification, such as the AD assay described in this study, can pave the way for pathogen-informed resistance gene deployment. Methods Bgt isolates, sexual crosses and selection of F1 populations The Bgt isolate CHVD_042201 (mating type MAT 1-2) was collected from a powdery mildew infected wheat field in Begnins, canton of Vaud, Switzerland in spring 2022 and subsequently single spore isolated twice in order to ensure a genetically uniform culture. The Bgt isolate CHN_52_27 (MAT 1-1) has previously been described in ( Zeng et al ., 2014 ; Praz et al ., 2017 ). All other Bgt isolates used in this study have previously been described in ( Sotiropoulos et al ., 2022 ). Bgt isolates were maintained clonally on the susceptible wheat cultivar ‘Kanzler’. For conidiospore production, infected leaf segments were placed on food grade agar plates (0.5%, PanReac AppliChem) supplied with 4.23mM benzimidazole and incubated at 20°C as described previously ( Parlange et al ., 2011 ). The sexual cross between CHVD_042201 and CHN_52_27 was performed as previously described ( Parlange et al ., 2015 ) by co-infecting the susceptible wheat cultivar ‘Kanzler’. Leaf segments harboring chasmothecia were harvested and dried at room temperature for several weeks. For ascospore ejection, dried chasmothecia were exposed to high humidity using Whatman filter paper soaked in sterilized water for up to 10 days and arising F1 progeny collected and grown on the susceptible wheat cultivar ‘Kanzler’. Resulting F1 conidiospores were subsequently subjected to bulk selection on wheat cultivars ‘Kanzler’ (no selection), ‘Asosan/8*CC’ ( Pm3a selection) or ‘Kn199 + Pm60’ transgenic plants ( Pm60 selection). Selected F1 conidiospore bulks were harvested after 10 days and fungal DNA extracted as described below. Plant material, virulence scoring The Pm3a containing line ‘Asosan/8*CC’ has been previously described in ( Bourras et al ., 2015 ). The ‘Kn199 + Pm60’ transgenic line, expressing Pm60 under endogenous promoter and terminator sequences in the susceptible ‘Kn199’ background, has been previously described in ( Zou et al ., 2018 ). To determine virulence phenotypes, leaf segments of the cultivars ‘Kanzler’, ‘Asosan/8*CC’ or ‘Kn199+ Pm60 ’ were placed on agar plates as described above, infected with the indicated Bgt isolates and disease phenotypes imaged at 8-9 days post infection. Virulence scoring was performed on at least six biological replicates for each tested interaction. Representative images were chosen for the depiction of virulence phenotypes throughout the manuscript. DNA sequencing and genome assembly Fungal DNA was extracted from conidiospores using a previously described CTAB/phenol-chloroform extraction procedure ( Bourras et al ., 2015 ). For the CHVD_042201 genome assembly, 5µg of high molecular weight DNA was used for library preparation and PacBio HiFi sequencing was performed on the PacBio Sequel Ile platform using a 30h movie at the Functional Genomics Center Zurich (FCGZ). Resulting PacBio HiFi raw data is available at the sequence read archive (SRA, accession: PRJNA1131794). PacBio HiFi reads were assembled using HiCanu ( Nurk et al ., 2020 ), HiFlye ( Kolmogorov et al ., 2019 ) and hifiasm ( Cheng et al ., 2021 ) as described in Supplementary Note 1. HiCanu assembly was performed with the options genomeSize=141m -pacbio-hifi. HiFlye assembly was performed with the options -g 141m –pacbio-hifi. Hifiasm assembly was performed with the options -t16 -l0 -f0 --hg-size 141m. Subsampling of PacBio HiFi reads was performed with seqtk sample command ( https://github.com/lh3/seqtk ). Whole genome alignments of assemblies against Bgt_genome_v3_16 was performed using the mummer suite (v4.0.0) ( Marcais et al ., 2018 ) using the command nucmer. Subsequent plots were rendered with the mummerplot command using the following specification: --filter --color –png. Subsequently, plots were produced with gnuplot. Blast searches of PacBio HiFi reads against the mitochondrial sequence of CHE_96224 (Genebank: MT880591.1) were performed using the BLAST+ suite (v2.12.0) ( Camacho et al ., 2009 ) with the following specifications: - qcov_hsp_perc 50 and all reads that aligned to the mitochondrial genome were retained. Subsequently, these reads were used to perform an assembly using hifiasm with the -l0 option. The final assembly was polished with Illumina reads from isolate CHVD_042201 that were mapped against the assembly with the method described in ( Kunz et al ., 2023 ). Subsequent polishing was performed with Pilon (v1.24) ( Walker et al ., 2014 ) with the following specifications: --fix bases –changes. The polished genome assembly of isolate CHVD_042201 is available as Bgt_CHVD_042201_genome_v1 on Zenodo ( https://zenodo.org/records/11233413 ) Annotation Annotation of the Bgt_CHVD_042201_genome_v1 was performed using the MAKER2 software (v2.31.11) ( Cantarel et al ., 2008 ), available from the European Galaxy server ( https://usegalaxy.eu/ ). Repeat masking of the genome was achieved using the TREP database (trep-db_nr_Rel-19.fasta and trep-db_proteins_Rel-19.fasta) available at https://trep-db.uzh.ch/ . We used the prot2genome option of MAKER2 to create a homology-based draft annotation of Bgt_CHVD042201_genome_v1 in two rounds. The first round was performed using the predicted proteome of CHE_96224 (v4_23, https://zenodo.org/records/7018501 ), and the second round using the proteome of Bh strain DH14 ( https://github.com/lambros-f/blumeria_2017/tree/master/annotation_genome_dh14 ) . Genes predicted in the second round were only included if they did not overlap with any genes predicted in the first round. The annotation of of Bgt_CHVD042201_genome_v1 is available on Zenodo: ( https://zenodo.org/records/11233413 ) Avirulence depletion assay DNA from the Bgt isolates CHVD_042201 and CHN_52_27 was sequenced at the Functional Genomics Center Zurich (FGCZ). Sequencing libraries were generated using the Illumina Trueseq Nano protocol and sequencing was performed on the Illumina Novaseq 6000 platform. DNA from unselected, Pm3a- or Pm60- selected F1 bulks was sequenced to a coverage of ∼120X with our commercial partner Novogene UK on a NovaSeq X Plus platform. Mapping of Illumina reads was performed as described previously ( Kunz et al ., 2023 ). For the analysis of the bulk- sequencing data, we established a pipeline specifically tailored towards the haploid genomic structure of Bgt . All steps of the pipeline were executed using a custom Python script, available on Github: https://github.com/MarionCMueller/AD-assay . In detail, the pipeline first identified high-quality single nucleotide polymorphisms (SNPs) in the two parental isolates, CHVD_042201 and CHN_52_27 as follows: Illumina mapping files were simultaneously used to perform SNP calling with FreeBayes (v1.3.6) ( Garrison & Marth, 2012 ), using the following options: --haplotype-length 0, --min-alternate-count 20, --min-alternate-fraction 0, --pooled-continuous, and --limit-coverage 400. The resulting polymorphic sites were further filtered to retain only those SNP positions where both parents had at least 10 reads and exhibited opposite genotypes. Genotypes were only accepted if 95% of the reads supported the genotype. Subsequently, the pipeline identified SNPs in the alignment files (BAM files) of the unselected and selected bulk only at the polymorphic positions between the parental isolates using FreeBayes with the options: --haplotype-length 0, --min-alternate-count 1, --min-alternate-fraction 0, --pooled-continuous, and --report- monomorphic. The subsequent statistical analysis was conducted in R using the functions provided in the BSA_Blumeria_functions.R object available on GitHub at https://github.com/MarionCMueller/AD-assay . To ensure the removal of sites exhibiting copy number variation in one of the parental isolates, the read coverage at each marker position was analysed. Positions with sequencing coverage either above or below twice the standard deviation of the coverage of all sites were excluded. Next, the G.test() function of the R package RVAideMemoire was utilized to assess the deviation of the parental SNP ratio from an expected 1:1 distribution for unselected marker positions. Finally, resulting p-values were averaged over 10 SNPs using the runner() command. Scripts used for analysis are available as an R Markdown object from Github ( https://github.com/MarionCMueller/AvrPm60 ). Cloning For expression of fungal effectors in N. benthamiana , the predicted signal peptide (SignalP4.0 ( Petersen et al ., 2011 ) was removed and replaced by a start codon. The remaining coding sequences of all effector candidates were codon-optimized for expression in N. benthamiana based on the codon-optimization tool of Integrated DNA technologies ( https://eu.idtdna.com ). Optimized sequences were gene synthesized with gateway compatible flanking attL sites with our commercial partners (BioCat GmbH https://www.biocat.com ; Thermo Fisher Scientific https://www.thermofisher.com ). The resulting gateway compatible entry clones were subsequentially mobilized into the binary expression vector pIPKb004 (Himmelbach et al ., 2007) using Invitrogen LR clonase II according to the manufacturer. For expression of Pm3a in N. benthamiana we made use of a pIPKb004-Pm3a-HA construct that has been previously described ( Bourras et al ., 2015 ; Bourras et al ., 2019 ). For the expression of Pm60 , Pm60a and Pm60b in N. benthamiana we first amplified the Pm60 coding sequence from pEarlyGate-Pm60 described in ( Zou et al ., 2018 ) using KAPA HiFi Polymerase (KAPA Biosystems) with the primers listed in Supplementary Table 3 and cloned the resulting PCR amplicon into the gateway compatible entry vector pDONR221 using BP clonase (Invitrogen), resulting in pDONR221-Pm60. The polymorphic regions defining the Pm60a and Pm60b alleles were gene synthesized with our commercial partner Thermo Fisher Scientific ( https://www.thermofisher.com ) and introduced into the Pm60 coding sequence using PCR amplification with KAPA HiFi Polymerase (KAPA Biosystems) and the primers listed in Supplementary Table 3 applying the In-Fusion cloning method (Takara Bio) according to the manufacturer, resulting in pDONR221-Pm60a and pDONR221-Pm60b. The entry clones of Pm60 , Pm60a and Pm60b were mobilized into pIPKb004 as described above. The sequences of all DNA fragments produced by gene-synthesis can be found in Supplementary Table 4. All constructs in the binary expression vector pIPKb004 were transformed into A. tumefaciens strain GV3101 using freeze-thaw transformation ( Weigel & Glazebrook, 2006 ). Co-expression of AVR candidates and R genes for HR quantification in N. benthamiana Agrobacterium -mediated transient expression of effector candidates and resistance genes in N. benthamiana was achieved with the detailed protocol described in ( Bourras et al ., 2019 ). For co-expression of AVR candidates and corresponding R genes, Agrobacteria OD1.2 were mixed in a 4:1 ratio (AVR:R) prior to infiltration. HR imaging and quantification was performed 4-5 days after Agrobacterium infiltration using a Fusion FX imaging system (Vilber Lourmat https://www.vilber.com/ ) and the Fiji software ( Schindelin et al ., 2012 ) as described previously in ( Bourras et al ., 2019 ). Gene expression analysis To quantify gene expression, we used previously published dataset of Bgt isolates CHE_96226, CHE_94202, GBR_JIW2, ISR_7 and CHN_17_40 ( Praz et al ., 2018 ; Müller et al ., 2022 ; Kunz et al ., 2023 ). Accession numbers of the RNAseq libraries are listed in Supplementary Table 5. RNAseq reads were pseudoaligned to the CHVD_042201 CDS using the salmon software v1.4.0 ( Patro et al ., 2017 ). First, CHVD_042201 CDS (Bgt_CHVD042201_CDS_v1_1.fasta) was indexed using the command salmon index. Then, single or paired end reads were quantified with the command salmon quant -l A. Subsequently, raw read counts were converted to RPKM values using the edgeR package 3.40.2 ( Robinson et al ., 2010 ) using the rpkm() command. Plots were generated with ggplot2 v3.4.3 using a custom R script in RStudio v2023.03.0+386 ( Wickham, 2009 ; RStudio- Team, 2018). An R Markdown script detailing all code used in this analysis to conduct read count, quantification and plot generation is available on Github ( https://github.com/MarionCMueller/AvrPm60 ). Copy number variation Analysis of copy number variation was performed based on previously published Bgt diversity data available described in ( Sotiropoulos et al ., 2022 ; Kloppe et al ., 2023 ). Sequencing reads were aligned to the Bgt reference genome Bgt_genome_v3_16 using the method described in ( Sotiropoulos et al ., 2022 ). Subsequently read coverage for each gene was extracted and normalised to the average coverage of all genes in the genome with a previously published script available on Github ( https://gist.github.com/caldetas/24576da33d1ff91057ecabb1c5a3b6af ). Genes exhibiting a normalized coverage below 0.1 were considered deleted. A table with normalized coverage values for all isolates is available here: ( https://github.com/MarionCMueller/AvrPm60 ). Data was visualized and further analysed with a custom R script available on Github ( https://github.com/MarionCMueller/AvrPm60 ). AlphaFold 3 Modelling For AlphaFold 3 modelling of AVRPM3A2/f2 and AVRPM60, the signal peptides were removed based on predictions from SignalP 5.0 ( https://services.healthtech.dtu.dk/services/SignalP-5.0/ ). The structures were then predicted using the AlphaFold 3 server ( https://alphafoldserver.com/ ) ( Abramson et al ., 2024 ). Visualization of the structures was performed using ChimeraX software ( Meng et al ., 2023 ). Author contributions L.K., M.C.M, and B.K. designed the research. L.K. and M.C.M wrote the manuscript. B.K., R.H. and F. M. edited the manuscript. L.K. and H.Z. performed the experiments. M.C.M., L.K., A.G.S. performed bioinformatic analyses. L.K. and M.C.M. analysed data. J.J. and F.M. provided powdery mildew isolates. S.Z. and D.T. provided transgenic wheat lines. All authors read and approved the final manuscript. Data availability PacBio HiFi reads for isolate CHVD_042201 are available from the sequence read archive (SRA, accession: PRJNA1131794 ). Genome assembly and annotation of isolate CHVD_042201 are available from Zenodo: https://zenodo.org/records/11233413 . 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