Metabolic biomarker-based phenotyping unveils quantitative effects of plant resistance and pathogen aggressiveness in the grapevine (Vitis spp.) - downy mildew (Plasmopara viticola) pathosystem

Metabolic biomarker-based phenotyping unveils quantitative effects of plant resistance and pathogen aggressiveness in the grapevine (Vitis spp.) - downy mildew (Plasmopara viticola) pathosystem | 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 Metabolic biomarker-based phenotyping unveils quantitative effects of plant resistance and pathogen aggressiveness in the grapevine (Vitis spp.) - downy mildew (Plasmopara viticola) pathosystem View ORCID Profile Tyrone Possamai , View ORCID Profile Raymonde Baltenweck , View ORCID Profile Sabine Wiedemann-Merdinoglu , Marie-Céline Lacombe , Marie-Annick Dorne , Matéo Bareyre , View ORCID Profile Erik Griem , View ORCID Profile René Fuchs , View ORCID Profile Jochen Bogs , Éric Duchêne , View ORCID Profile Pere Mestre , View ORCID Profile Didier Merdinoglu , View ORCID Profile Philippe Hugueney doi: https://doi.org/10.1101/2025.09.25.678557 Tyrone Possamai 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tyrone Possamai For correspondence: tyrone.possamai{at}inrae.fr raymonde.baltenweck{at}inrae.fr Raymonde Baltenweck 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raymonde Baltenweck For correspondence: tyrone.possamai{at}inrae.fr raymonde.baltenweck{at}inrae.fr Sabine Wiedemann-Merdinoglu 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sabine Wiedemann-Merdinoglu Marie-Céline Lacombe 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marie-Annick Dorne 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matéo Bareyre 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erik Griem 2 State Education and Research Center of Viticulture, Horticulture and Rural Development , Neustadt an der Weinstraße, Germany 3 Heidelberg Institute of Plant Sciences, University of Heidelberg , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erik Griem René Fuchs 4 Department of Biology, State Institute of Viticulture and Enology , Freiburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for René Fuchs Jochen Bogs 2 State Education and Research Center of Viticulture, Horticulture and Rural Development , Neustadt an der Weinstraße, Germany 5 Technische Hochschule Bingen, Bingen am Rhein , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jochen Bogs Éric Duchêne 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pere Mestre 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pere Mestre Didier Merdinoglu 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Didier Merdinoglu Philippe Hugueney 1 INRAE, Université de Strasbourg , UMR SVQV, Colmar, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philippe Hugueney Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Grapevine resistance to downy mildew has been primarily associated with major “Resistance to Plasmopara viticola ” ( Rpv ) loci, which are extensively used in breeding programs. Resistant varieties represent an effective solution to mitigate the environmental impact of fungicide application in viticulture, but P. viticola strains able to overcome major Rpv have become a main threat to their cultivation. Pyramiding resistance loci in the same variety enhances plant resistance, but interactions involving stacked and defeated Rpv and different P. viticola strains are poorly documented. Investigation of these interactions may uncover new information for the development of efficient breeding strategies, the optimal exploitation of Rpv, and the building of durable resistance. In the present study, a grapevine offspring carrying single and pyramided Rpv1 , Rpv3.1 and Rpv10 was phenotyped in laboratory conditions for the resistance to P. viticola using a naive strain and a strain virulent towards Rpv10 . By using a high-resolution phenotyping strategy based on P. viticola metabolic biomarkers, we demonstrated that the efficacy of Rpv combinations and aggressiveness of P. viticola strains can be quantified in the early phase of infection and were often related to sporulation outcome. Furthermore, we described how a limited residual effect of a defeated Rpv may become significant in pyramiding. In conclusion, in addition to providing the keys to streamlining resistance utilization in grapevine, our research presents a distinctive case study that provides valuables information for breeding new resistant varieties, thanks to an innovative “omic”-based phenotyping approach, which may be adapted to other plant pathosystems. Introduction Grapevine ( Vitis spp.) is a fruit crop cultivated worldwide for table grape, wine and raisin production [ 1 ]. The oomycete Plasmopara viticola is the causal agent of grape downy mildew. Its control is usually based on the recurrent use of agrochemicals, which have adverse human health, socio-economic and environmental impacts [ 2 ]. P. viticola has shown the capacity to develop resistance to a wide range of fungicides and represents a significant threat for viticulture [ 3 ]. The breeding of new cultivars resistant to pathogens is an effective solution to reduce the use of agrochemicals, control plant diseases and enhance agriculture sustainability [ 4 , 5 ]. Over the past two decades, more than 30 grapevine resistance loci to P. viticola ( Rpv ) [ 6 ] have been identified and some major loci, like Rpv1 , Rpv3 , Rpv10 and Rpv12 , have been successfully introgressed into cultivated grapevines and represent common genetic resources implemented in breeding [ 5 , 7 ]. Major Rpv -mediated resistance has been associated to nucleotide-binding leucine-rich repeat (NLR) genes [ 8 , 9 ], a main type of resistance genes involved in effector-triggered immunity (ETI) [ 10 ]. According to the gene-for-gene concept, the efficacy of major genes can be affected by the specific combination of virulence genes found in different pathogen strains [ 10 ]. The pathogen P. viticola has demonstrated a high evolutionary potential, with strains isolated from resistant hosts exhibiting a greater aggressiveness, characterized by a short latency period and high levels of spore production [ 11 ]. Furthermore, virulent strains that completely breakdown one or more Rpv have been identified and now pose a significant threat to the cultivation of new resistant varieties [ 12 , 13 ]. Combining several resistance genes (pyramiding) in the same variety is an established strategy to enhance the efficacy, stability and durability of plant resistances [ 14 , 15 ], and a correlation between the resistance level and the number of resistance genes has been observed in grapevine [ 16 ]. The phenotyping of grapevine-downy mildew interaction is usually based on the visual evaluation of P. viticola sporulation, which emerges on the abaxial surface of the leaf, and of grapevine cells necrosis [ 7 , 12 ], which are indicative of plant’s recognition of the pathogen and deployment of an active plant response [ 17 ]. However, visible symptoms are often assessed by human experts using categorical scales, whose limited resolution and potential subjectivity complicate the interpretation and comparison of results. [ 7 ]. Image analysis represents a quantitative and objective phenotyping method, but it can be affected by interferences from water droplets, leaf morphology and hairiness as well as poorly resolved pathogen structures [ 7 ]. Finally, phenotyping methods based on visible symptoms are only applicable in advanced stages of infection. Recently, P. viticola -specific metabolites have been identified as biomarkers for the monitoring and quantification of the pathogen in grapevine leaves [ 18 ]. Metabolic biomarkers represent a new tool to overcome some issues in grapevine resistance and P. viticola interactions phenotyping, towards a better quantification of plant resistance and pathogen aggressiveness. In this study, the potential of single and pyramided combinations of Rpv1 , Rpv3.1 and Rpv10 to enhance grapevine resistance against P. viticola was investigated. Plants carrying all possible combinations of these Rpvs were challenged with a naive pathogen strain, and a strain virulent on Rpv10 , termed avrRpv10- . The outcomes of the interactions between the Rpv combinations and P. viticola strains were characterized by integrating the visual assessment with the high-resolution quantification of P. viticola -specific metabolic biomarkers. This comprehensive approach yields new information on the effectiveness of single and pyramided Rpv and provides a better understanding of P. viticola strains’ adaptation to the host, with a view toward the construction of effective and durable resistances in grapevine breeding programs. Moreover, this work validates the use of metabolomics-based phenotyping for the precise characterization of quantitative aspects of plant genetic resistance and pathogen aggressiveness. Results Visual assessment of P. viticola infection A total of eight Rpv combinations based on Rpv1 , Rpv3.1 and Rpv10 were tested in laboratory conditions (3 genotypes/ Rpv combination), under parallel inoculations with a naive P. viticola strain and a strain virulent towards Rpv10 (16 Rpv - P. viticola interactions). The inoculated leaf discs (3 discs/genotype/strain) were visually assessed between 3 and 6 days-post inoculation (dpi) for the incidence and severity of P. viticola sporulation by using the variable OIV452-1 [ 19 , 20 ], and for the incidence of necrosis (overview of the study and experimental design in Supplementary File S1). The progression of the P. viticola infection and the outcomes at 6 dpi were found to depend on the Rpv combination and strain under consideration ( Fig. 1 ; Table 1 ; Supplementary File S2) in two experiment replicates (Supplementary File S3-4a). A different aggressiveness of the P. viticola naive and avrRpv10- strains was observed on all the studied Rpv combinations ( Fig. 2 ; Supplementary File 4b). Thus, the Rpv - P. viticola strain interactions for the two strains were separately analysed. Download figure Open in new tab Figure 1. Grapevine leaf discs infected with P. vitico/a. a) OIV452-1 mean scores (points) and confidence limits (dashed area; 95%) describing the severity of P vitico/a infection on leaf discs (1 = sporulation densely covering the whole disc; 9 = absence of sporulation), collected for the different Rpv combinations under infection with the two P vitico/a strains. Data for 3 genotypes/Rpv combina­ tion and 3 discs /genotype/strain/dpi of two experiments are displayed. b) Representative discs at 6 dpi for the non-resistant (Rpv-) and single Rpv combinations inoculated with the two P vitico/a strains evidencing different rates of pathogen sporulation and grapevine necrotic response depen­ ding on the Rpv-P viticola interaction. OIV452-1 mean scores for single experiments are available in Supplementary File S3, and images of the discs at 3-4-5-6 dpi in Supplementary File S2. Download figure Open in new tab Figure 2. Visual assessment of the infection in the Rpv combination- P. viticola strains interactions. OIV452-1 scores at 6 dpi for the different Rpv combinations challenged with the two P. viticola strains (1 = sporulation densely covering the whole disc; 9 = absence of sporulation). Data for two experiments, 3 genotypes/ Rpv combination and 3 discs /genotype/strain are displayed. Comparisons refer to t-test between P. viticola strains infection on the same Rpv combination (p value < 0.05 = *, < 0.01 = ** and < 0.001 = ***). Detailed data for the statistical analysis are available in Supplementary File S4b. View this table: View inline View popup Download powerpoint Table 1. Visual outcomes of the Rpv combination- P. viticola strain interactions. Mean values for the OIV452-1, P. viticola sporulation incidence, P. viticola sporulation latency period and plant necroses incidence at 6 dpi for the 16 Rpv combination - P. viticola strain interactions studied (3 genotypes/ Rpv combination and 3 leaf discs/genotype/strain for two experiments). Detailed data for the experiments are available in Supplementary File S4a. The infection with the P. viticola naive strain yielded visible necrosis from 3 dpi, with a high frequency on the interactions involving at least one effective Rpv (on average the 88% of discs), and a low frequency on the susceptible combination Rpv- (34% of discs). Pathogen sporulation started at 4 dpi and increased from 4 to 6 dpi, varying in severity and incidence according to the Rpv combination ( Fig. 1 ; Table 1 ). The OIV452-1 at 6 dpi described susceptibility to P. viticola for Rpv- , which was always characterized by a severe pathogen sporulation (scores around 3), and partial resistance for single Rpv1 , Rpv3.1 and Rpv10 with lower severity of sporulation (scores between 6 and 8; Fig. 2 ; Table 1 ). Accordingly, ANOVA and pairwise comparisons (PWC) between Rpv , identified a significant effect of Rpv1 , Rpv3.1 and Rpv10 on the overall offspring resistance and showed an averaged resistance level Rpv10≥Rpv3.1>Rpv1 ( Table 2 ; Supplementary File S4c-4d). The Rpv3.1Rpv10, Rpv1Rpv3.1 and Rpv1Rpv3.1Rpv10 pyramiding combinations enhanced and stabilised the resistance with respect Rpv1 and Rpv3.1 single loci, resulting in less frequent, delayed (5-6 dpi) and reduced pathogen sporulation (scores typically around 7-9; Fig. 1 ; Table. 1; Supplementary File S4d). With respect to Rpv10 , pyramiding gains were more limited and not significant, in particular for Rpv1Rpv10 combination, which showed intermediate level of infection between Rpv10 and Rpv1 (Table. 1; Supplementary File S4d). View this table: View inline View popup Download powerpoint Table 2. ANOVA results for visual assessment of Rpv combination -P. viticola strains interactions. Results from strain-independent ANOVA conducted with the OIV452-1 scores at 6 dpi, and including Rpv1 , Rpv3.1 and Rpv10 presence (two levels with complete cross interactions), and the Experiment, as factors. Data for 3 genotypes/ Rpv combination and 3 discs /genotype/strain of two experiments were considered. Detailed data for the statistical analysis are available in Supplementary File S4c. Rpv- and Rpv10 genotypes when challenged with the virulent strain avrRpv10-, exhibited both susceptible phenotypes with high sporulation rates and severity (OIV452-1 scores of 1 and 3) and reduced incidence of necrosis (about 11% of the discs; Fig. 1 ; Table 1 ). Additionally, the virulent strain displayed a general higher aggressiveness compared to the naive strain, displaying a fast, more frequent and pronounced sporulation on Rpv- , single Rpv1 and Rpv3.1 , and pyramided Rpv (scores between 4 and 6 for all resistance interactions; Fig. 1 - 2 ; Table 1 ). ANOVA of OIV452-1 scores at 6 dpi identified as significant the effect of both Rpv1 and Rpv3.1 on the offspring resistance, while Rpv10 presence was not identified as a significant factor in the analysis ( Table 2 ; Supplementary File S4c). PWC between Rpv combinations confirmed similar severity of the infection at 6 dpi on Rpv1 and Rpv3.1 single loci, and, due to Rpv10 breakdown, the severity of the infection on Rpv1Rpv10 and Rpv3.1Rpv10 combinations did not differ from single loci (Supplementary File 4d). Only the pyramiding of Rpv1Rpv3.1 and Rpv1Rpv3.1Rpv10 significantly increased the resistance level with respect other single and pyramided Rpv combinations (details of comparisons in Supplementary File 4d). This last result also suggested a residual effectiveness of Rpv10 in controlling the P. viticola avrRpv10- strain. The ANOVA analysis revealed a significant effect of the experiment on both strains ( Table 1 ). According to data distribution a lower rate of infection occurred in the second experiment (Supplementary File 4b). However, the infection progression and relative level of infection between Rpv combinations and P. viticola strains were consistent in the two replicates (Supplementary File 3; 4a). Thus, the experiments were both validated and considered equally representative of the studied conditions, and the first experiment was chosen to perform in-depth metabolomics analysis. Assessment of P. viticola metabolic biomarkers The metabolomic analysis focused on 12 P. viticola -specific biomarkers characterized previously [ 18 ] (Supplementary File S5a), including: eicosapentaenoic acid (EPA) and three of its derivatives (eicosapentaenoyl-glycerol – EPG, dieicosapentaenoyl-glycerol – DEPG and trieicosapentaenoylglycerol – TEPG), arachidonic acid (AA) and three of its derivates (arachidonoyl-glycerol – AG, diarachidonoyl-glycerol – DAG and triarachidonoyl-glycerol – TAG), and four ceramides (Cer(d16:1/16:0), Cer(d16:1/18:0), Cer(d16:1/20:0) and Cer(d16:1/22:0)). The compounds were quantified by an optimized analysis protocol based on liquid chromatography-based mass spectrometry (LC-MS) and targeted metabolomics (details in Material and methods section). All the 16 Rpv loci- P. viticola strain interactions were studied at 6-12 hours post inoculation (hpi) and at 1-2-3 dpi to investigate the early phase of infection, and at 6 dpi to precisely quantify the infection (3 infected discs/genotype/strain/time). All the P. viticola biomarkers were found in visually high-infected discs at 6 dpi, but only EPA, AA, AG, Cer(d16:1/18:0), Cer(d16:1/20:0) and Cer(d16:1/22:0) were consistently identified in the different studied interactions and in the early phase of the infection (at least in the 70% of the samples; Supplementary File S5b). Finally, all the better-detected biomarkers except AG resulted highly correlated with each other (R 2 = 0.87 - 0.98) and with the OIV452-1 scores (R 2 = 0.82 to 0.91; Supplementary File S5c-5d) and were retained as informative features for the quantitative evaluation of the Rpv - P. viticola interactions ( Table 3 ). In particular, Cer(d16:1/22:0) showed the earliest and largest signal variation in the different studied conditions ( Fig. 3a ; Supplementary File S6) Download figure Open in new tab Figure 3. Assessment of the early phase of the infection in the Rpv combination-P. viticola strain interactions. a) The heatmap shows the mean peak area (arbitrary unit -AU) for the six most informative P vitico/a-specific metabolic biomarkers (right labels) recorded in the discs of the different Rpv combinations mock-inoculated or infected with the two P viticola strains (naive and avrRpv10-) at 1-2-3 dpi. Data for one experiment, 3 genotypes/Rpv combination and 3 discs/genotype/strain/dpi are displayed. b) Representative microscopic images after the staining of P vitico/a mycelium by aniline-blue for the non-resistant (Rpv-) and single Rpv combinations inoculated with the two P vitico­ /a strains at 3 dpi: the white arrows indicate P vitico/a mycelium, the white dashed-square the area with dense and overlapping layers of mycelium, and red arrows sites where P vitico/a infection is associated to plant necrotic response (scale bar 100 µm). View this table: View inline View popup Download powerpoint Table 3. P. viticola metabolic biomarkers followed by targeted analysis. Information on the P. viticola metabolic biomarkers consistently identified throughout the experiment and informative for the quantification of P. viticola infection/biomass: compound name and acronym, ion formula, mass (m/z) and retention time (RT) utilized for the quantification. The complete list of P. viticola biomarkers investigated is available in Supplementary File S5a. Rpv loci-P. viticola strain interactions in the early phase of the infection Metabolomics did not show significant variations in the accumulation of P. viticola -specific compounds before 1 dpi (Supplementary File S6). According to pathogen biomarkers abundances fold-changes (fc), the P. viticola infection increased between 2-50 folds from 1 to 3 dpi, depending on the Rpv - P. viticola strain interaction considered. The P. viticola avrRpv10- strain showed higher biomarkers accumulations from 2 dpi on, comparisons at 3 dpi clearly demonstrating an enhanced aggressiveness in the early phase of the infection compared to the naive strain on all Rpv combinations (e.g., 4-64 fc for the quantified ceramides; Fig. 3 - 4a ; Supplementary File S7a-8a). Download figure Open in new tab Figure 4. Metabolomics-based quantification of infection in the in the Rpv combination- P. viticola strain interactions. P. viticola metabolic biomarker Cer(d16:1/22:0) abundances at 3 (a) and 6 (b) dpi for the different Rpv combinations challenged with the two P. viticola strains. Abundances are displayed as log10 of the peak area arbitrary unit (AU). Data for one experiment, 3 genotypes/ Rpv combination and 3 discs/genotype/strain/dpi are displayed. Comparisons refer to t-test between P. viticola strains infection on the same Rpv combination (p value < 0.05 = *, < 0.01 = ** and < 0.001 = ***). Detailed data for the statistical analysis are available in Supplementary File S8a. Microscopy observations of P. viticola aniline blue-stained mycelium supported the results inferred from the analysis metabolic biomarkers, confirming significant differences in the pathogen colonisation rate at 3 dpi on the three single Rpv loci studied. For the P. viticola naive strain, an important colonisation with ramified pathogen mycelium (white arrows in Fig. 3b ) was observed on Rpv- , limited hyphae growth was associated to plant necrosis (red arrows in Fig. 3b ) on Rpv1 and Rpv3.1 , and stopped infections occurred on Rpv10 ( Fig. 3b ). For the P. viticola avrRpv10- strain, dense and overlapping layers of mycelium was observed on both Rpv- and Rpv10 , and important colonisations with ramified mycelium occurred on Rpv1 and Rpv3.1 despite a wide presence of plant necrosis ( Fig. 3b ). Limitation of P. viticola infection at 3 dpi (e.g., 8-32 fc for ceramides accumulation compared to Rpv- ) were associated with Rpv- mediated resistance ( Fig. 3 - 4a ; Supplementary File S7b). For the naive strain, the ANOVA of Cer(d16:1/22:0) data identified the presence of Rpv3.1 and Rpv10 as significant on the offspring resistance, while Rpv1 contribution was not identified ( Table 4 ; Supplementary File S8b), probably due to the limited differences between Rpv combinations, as described by the data distribution and the PWC ( Fig. 4a ; Supplementary File S8c). For the avrRpv10- strain, the globally higher rate of infection allowed to better differentiate the resistance mediated by the different Rpv combinations ( Fig. 4a ). The ANOVA of Cer(d16:1/22:0) data identified both the Rpv1 and Rpv3.1 effects on the offspring resistance ( Table 4 ; Supplementary File S8b), and PWC evidenced a lower rate of infection also between Rpv1Rpv3.1 and Rpv1Rpv3.1Rpv10 compared to the other resistance combinations ( Fig. 4a ; Supplementary File S8c). Intriguingly, Rpv10 as a single locus induced a reduced pathogen biomass at 2-3 dpi compared to Rpv- . Similarly, Rpv1Rpv10 or Rpv3.1Rpv10 combinations exhibited a reduced pathogen biomass compared to Rpv1 or Rpv3.1 single loci, although none of these differences were statistically significant (Supplementary File 8b-8c). View this table: View inline View popup Download powerpoint Table 4. ANOVA results for the metabolomics-based quantification of infection in the Rpv combination -P. viticola strain interactions. Results from strain-independent ANOVA conducted with P. viticola biomarker Cer(d16:1/22:0) abundances (log10 of the peak area arbitrary unit AU) at 3 and 6 dpi, and including Rpv1 , Rpv3.1 and Rpv10 presence (two levels with complete cross interactions) as factors. Data for one experiment, 3 genotypes/ Rpv combination and 3 discs/genotype/strain/dpi were considered. Detailed data for the statistical analysis are available in Supplementary File S8b. Rpv loci-P. viticola strain interactions in the later phase of infection P. viticola infection for the naive and avrRpv10- strains increased consistently from 3 to 6 dpi on all the studied Rpv combinations, except for the naive strain on the most effective pyramided combinations Rpv3.1Rpv10 , Rpv1Rpv3.1 and Rpv1Rpv3.1Rpv10 ( Fig. 4 ). The amplitude of some differences in P. viticola biomarker abundances between Rpv combinations or P. viticola strains at 3 and 6 dpi changed. For example, the differences between P. viticola strains on Rpv- and Rpv1, and between Rpv- and Rpv10 for the avrRpv10- strain, were more important at 3 dpi than at 6 dpi ( Fig. 4 ), while the difference between Rpv1 and Rpv3.1 for the naive strain were higher at 6 dpi than at 3 dpi. This suggests that some specificities of Rpv - P. viticola interactions could be better visible in the early or in the late phase of infection. Although the majority of outcomes at 3 and 6 dpi remained strongly in agreement and defined a general relationship between the early pathogen colonization and the later sporulation ( Fig. 4 ; Supplementary File S6). Biomarker data at 6 dpi confirmed OIV452-1 observations, but they provided a more objective and quantitative perspective on Rpv -mediated resistance and P. viticola aggressiveness. Compared to Rpv- , P. viticola ceramides showed a significant infection reduction of 6-9 fc on Rpv1 for both strains, of 47 and 6 fc on Rpv3.1 for the naive and avrRpv10- strains, respectively, and of 100 fc on Rpv10 for the naive strain ( Fig. 4b ; Supplementary File S7b-S8c). On the other hand, for the P. viticola avrRpv10- strain, the biomarkers highlighted a greater infection of 2-3 fc - on Rpv- and Rpv1 , and of 16 fc on Rpv3.1 , suggesting a partial virulence or an adaptation of the avrRpv10- strain towards Rpv3.1 ( Fig. 4b ; Supplementary File S7a-S8a). The Rpv -pyramiding effects were also precisely quantified, as evidenced for Rpv1Rpv3.1 combination, which, in comparison with single loci, reduced the pathogen biomass of 10-50 fc for the naive strain, and of 2-2.5 fc for the avrRpv10- strain ( Fig. 4b ; Supplementary File S7b-S8c). Interestingly, the ANOVA of Cer(d16:1/22:0) at 6 dpi identified a significant resistance effect for Rpv10 against the avrRpv10- strain ( Fig. 5a ; Table 4 ; Supplementary File S8b), suggesting a general residual resistance effect of the locus. In particular, the Rpv10 residual effect significantly reduced the infection of 2 folds when pyramided in the Rpv1Rpv3.1Rpv10 combination ( Fig. 5b ; Supplementary File S8c). Download figure Open in new tab Figure 5. Rpv10 effect against the virulent P. viticola strain avrRpv10- . ANOVA (a) and PWC (b) for P. viticola biomarker Cer(d16:1/22:0) abundances detected at 6 dpi in the different Rpv combinations infected with the P. viticola strain avrRpv10- ( Rpv combinations considered in the comparisons are indicated by black dots-lines on the x-axis). Abundances are displayed as log10 of the peak area arbitrary unit (AU) in the box plots, while the black dots and error bars indicate the estimated marginal means and standard errors for each comparison group (Tukey’s tests p value < 0.05 = *, < 0.01 = ** and < 0.001 = ***). Data for one experiment, 3 genotypes/ Rpv combination and 3 discs/genotype/strain/dpi are displayed. Detailed data for the statistical analysis are available in Supplementary File S8b-8c. Discussion Several Rpv loci and virulent P. viticola strains have been described [ 7 , 12 , 13 ]. However, former studies usually relied on a limited number of unrelated grape varieties and visual descriptors for the characterization of Rpv -mediated resistance and P. viticola aggressiveness [ 11 , 12 , 21 ], with potential influences of varietal genetic background [ 21 , 22 ] and phenotyping technical limitations. In our study, we address these issues by examining all the possible combinations of single and pyramided Rpv in a full-sib offspring, using P. viticola metabolic biomarkers to provide an objective and accurate quantification of the infection. This approach highlights the early and late effects of Rpv- mediated resistance, as well as the impact P. viticola aggressiveness on infection dynamics, revealing positive interactions among pyramided loci, even for the a defeated Rpv, and different level of adaptations of the virulent P. viticola strains towards the host genetic resistance. Single Rpv showed specific interactions when inoculated with different P. viticola strains Rpv loci have been associated with different resistance levels, ranging from total to strong, intermediate, or weak partial resistances [ 7 ]. In our study, the resistance conferred by Rpv1 , Rpv3.1 and Rpv10 was partial and changed in response to the two P. viticola strains tested. We observed that the efficacy of Rpv followed the ranking Rpv10 > Rpv3.1 > Rpv1 with the naive P. viticola strain. A different efficacy was observed with the avrRpv10- strain, for which Rpv 1 = Rpv3.1 and Rpv10=Rpv- , suggesting specific interaction of the strains with the studied Rpv . On Rpv - we observed for the P. viticola avrRpv10- strain, compared to the naive strain, a greater infection and mycelium growth at 3 dpi, and a greater sporulation at 6 dpi, which suggested an enhanced basal aggressiveness of the avrRpv10- strain. The existence of variations in pathogen aggressiveness is a key factor in pathogen adaptation [ 11 , 23 ], and higher P. viticola basal aggressiveness has been described in isolates adapted to cope with resistant varieties [ 11 , 23 ]. Thus, an enhanced aggressiveness can be considered as a typical characteristic of P. viticola strains with specific adaptations towards Rpv loci, and it could be essential for deploying certain virulence mechanisms. Rpv10 provided a strong partial resistance towards the P. viticola naive strain. However, large resistance variability was associated to Rpv10 when challenged with a single P. viticola strain [ 24 ], revealing possible impacts of the varietal genetic background [ 25 ] and the environment [ 24 , 26 ] on the Rpv10 effectiveness. In our study, the stability of resistance phenotypes probably resulted from the structure of the grapevine population and the controlled laboratory conditions. The interaction between Rpv10 and the avrRpv10- strain resulted in susceptible phenotypes, characterised by high sporulation and low frequency of necrosis, in agreement with previous studies on Rpv breakdown [ 12 , 27 ]. The described P. viticola avrRpv10- strain represents an additional pathotype among those identified in Europe, as the strains virulent towards Rpv10 were usually described as virulent towards Rpv3.1 as well [ 12 , 21 , 27 ]. However, Rpv10 breakdown has been associated to a single genomic admixture event localized in Central Europe [ 27 ]. Thus, the virulence of avrRpv10- strain should be link to a dominant locus and to the suppressor activity of specific pathogen effectors [ 27 ]. On the other hand, the P. viticola avrRpv10- strain showed only a partial adaption on Rpv3.1 compared to other strains overcoming Rpv10, posing questions on the original pathotype of the newly identified P. viticola population and the specific adaptation of the avrRpv10- strain. Different level of partial resistance were described for Rpv3.1 , and were associated to weak, intermediate or strong sporulation of P. viticola [ 7 , 12 , 21 ]. In our study, Rpv3.1 was effective in controlling the P. viticola naive strain, while the avrRpv10- strain showed an enhanced sporulation on Rpv3.1 -carrying combinations. Different levels of P. viticola adaptation to Rpv3.1 were associated with specific changes in the assortment of a set of pathogen effectors [ 28 ]. The P. viticola virulence on Rpv3.1 is a recessive character determined by the loss of a couple of effectors [ 28 ]. The variability in the Rpv3.1- mediated resistance recorded in our study can be probably depend from different patterns characterizing the effector set of the two P. viticola strains used here. Our findings indicated a similar effectiveness of Rpv1 in controlling the P. viticola naive strain and the avrRpv10- strain, in agreement with other screenings of P. viticola strains [ 12 , 21 ]. Pathogens usually quickly adapt to qualitative plant resistances, which exert strong selection pressures on pathogen populations [ 29 ]. The collected evidences indicate that in grapevine the widely used and strong Rpv loci, such as Rpv3.1 , have been rapidly defeated [ 12 , 21 , 28 ]. On the other hand, the weaker and less utilized locus Rpv1 has retained its effectiveness for longer [ 12 , 21 ], although climatic conditions highly favourable to P. viticola have recently determined localized Rpv1 breakdown [ 30 ]. Toward reasoned pyramiding to improve grapevine resistance Plant genetic resistances are limited resources and their breakdown is an irreversible gain for pathogens, that compromise resistance breeding in the long-term [ 24 , 31 ]. Therefore, Rpvs should be used in pyramiding in viticulture, as this strategy is the most appropriate for the deployment of grapevine genetic diversity [ 24 , 31 ], in order to increase the resistance efficacy, stability and durability [ 14 , 32 , 33 ]. Our study confirmed the efficacy of pyramiding, and showed that the stacking of two functional Rpvs was already highly effective against the naive P. viticola strain, severely delaying and reducing the pathogen infection. Pyramiding was less effective when Rpv1 , Rpv3.1 and Rpv10 were challenged with the avrRpv10- strain, adapted to cope with resistant varieties thanks to a higher basal aggressiveness and Rpv -specific adaptations. However, we still observed the highest resistance levels in the Rpv combination carrying three loci, identifying a significant residual benefit from the defeated Rpv10 . This results is in agreement with the observations made for the pyramiding of the defeated Rpv10 and Rpv12 in another grapevine population [ 24 ], and the outcomes linked to defeated resistance genes in other plant-fungal pathosystems [ 34 , 35 ]. The cumulation of defeated loci has been demonstrated as a strategy to gain moderate-to-high levels of resistance [ 36 ], but in grapevine the stacking of defeated Rpvs has instead yielded negative effects on the final level of resistance in some instances [ 24 ]. Nevertheless, residual quantitative resistance effects should be monitored over time and across different sites [ 35 ]. Indeed, P. viticola showed both common and distinct genetic mutations in its convergent adaptation to Rpv, which differentially affected the pathogen virulence [ 37 ]. The number of resistance genes is not the only factor affecting pyramiding outcomes [ 38 ]. Among the multiple pyramiding combinations tested, the Rpv1Rpv10 combination showed neutral (towards Rpv1 ) or negative (towards Rpv10 ) effects on resistance with the P. viticola naive strain. Undesired effects in loci pyramiding have been described in other in crops [ 38 , 39 ], and were identified in grapevine for the combination Rpv3.1Rpv10 [ 21 ] in some varieties, and for Rpv1Rpv10 when challenged with an avrRpv10- strain [ 24 ]. However, in our study the Rpv3.1Rpv10 combination was beneficial in enhancing the resistance towards the naive strain, and Rpv10 did not compromise Rpv1 resistance towards the avrRpv10- strain. Despite the unpredictability of negative Rpv interactions, in-depth studies are needed to confirm and explain the causes of these interactions, as they pose a threat to the resistance efficacy and durability [ 24 ]. Finally, resistance breeding should include both the pyramiding of compatible NLR genes [ 24 ] and alternative sources of quantitative resistance [ 14 , 32 , 33 ], such as constitutive (e.g., structural barriers and secondary metabolites) and other induced defences (e.g., pathogenesis-related proteins and cell wall thickening) [ 32 , 33 ]. Quantitative resistance is still underutilized in grapevine breeding, but it could be highly beneficial, as it could have additive resistance effects, be effective against multiple strains, and provide protection against the breakdown of major genes [ 33 ]. However, the quantitative nature of such resistance, which is often based on multiple genes with limited effects, makes it difficult to study and introduce in selection programs [ 32 , 33 ]. From ephemeral to effective plant resistance to pathogens The early stages of P. viticola infection have typically been described using microscopy and the attempts to quantify the early resistance effects have resulted in laborious and low resolution protocols [ 13 , 40 , 41 ], as the detection and classification of P. viticola spores growth stages [ 40 , 41 ]. Recent advancements in the early assessment of P. viticola infection have involved the application of real-time PCR [ 42 , 43 ] and metabolomics [ 18 ]. In our study, considerable variations in P. viticola biomarkers abundances were observed between Rpv combinations and strains from 1 dpi. The observations indicated that the effects of resistance loci and pathogen aggressiveness manifested at the onset of the infection. However, some resistance effects were ephemeral or became less evident in later stages of infection, as the limited effect of single Rpv10 towards the avrRpv10- strain. This information can be of great help to understand the mechanisms of plant-pathogen interactions. For example, the detection of early effects of Rpv10 on the P. viticola avrRpv10- strain may corroborate the hypothesis that, as a single locus, Rpv10 activates early resistance mechanisms, but these are subsequently suppressed by the pathogen [ 27 ], which is then able to recover from the initial delay with exponential growth. In contrast, the defeated Rpv10 has proved to be more effective in pyramiding, but the data collected did not permit to determine whether Rpv10 interacts directly with the other Rpv , for an early and/or stronger resistance response, or if the initial Rpv10- dependent delay in pathogen progression allows a more effective deployment of resistance mechanisms mediated by other pyramided Rpvs . This finding also suggests that ephemeral resistances during the early stages of infection can provide valuables effects when combined with other resistance factors. Consequently, we propose for future research on plant resistance to encompass evaluations in both the early and late phases of infection, as early interactions in pyramiding may provide significant benefits for efficient pathogen control. This aspect is of particular interest in characterization of alternative sources of quantitative resistance, which often depends on several loci with effects of variable amplitude [ 32 , 33 ]. Metabolic biomarkers as an effective strategy to characterize plant-pathogen interactions The assessment of P. viticola infection on leaf discs has traditionally been conducted through visual variables, taking into account the severity of the disease, the density of the pathogen structures, and the count of sporangia as the primary criteria [ 7 , 12 ]. In our study, we employed LC-MS to quantify P. viticola metabolic biomarkers and subsequently inferred the rate of infection. In comparison to visual assessment, biomarkers were objective, had a continuous quantification and enhanced precision, sensitivity and dynamics, which permitted to detect variations in infection rate at any level of the visual scoring, including samples that did not display visual symptoms or reached the highest level of the assessment scale. Therefore, metabolomics provided high-resolution results and allowed to better uncover, infer, and communicate the biological considerations on the pathosystem. Based on the current literature, our utilization of pathogen-specific metabolic biomarkers represents a novel research approach, given that metabolomics approaches have often been focused on the plant metabolome to characterize changes in metabolic pathways linked to the resistance response [ 44 ], as evidenced by the studies on grapevine [ 45 , 46 ], and that pathogen biomarkers have been rather used as detection tools [ 47 ]. The development of common/standard metabolomics quantification protocols for pathogen-related compounds could represent an opportunity in the quantitative study of plant-pathogen interactions, as some compounds are shared between species or higher taxonomic levels. For instance, EPA, AA [ 48 ] and pathogen-specific ceramides [ 49 ] have been also associated to Phytophthora infestans , the oomycete causing the late blight of potato ( Solanum tuberosum ). Metabolomics also offers the possibility to discriminate pathogen species and strains [ 50 ] and investigate fungal metabolisms [ 51 ]. Metabolomics is therefore a concrete transversal resource that can be used to both characterize and quantify different aspects of the plants-pathogen interaction, as well as to elucidate the genetic basis and mechanisms of plant resistance or pathogen aggressiveness. Materials and Methods Plant material Grapevines carrying Rpv1 , Rpv3.1 and Rpv10 in eight different combinations were selected from a study full-sib population produced and conserved at INRAE - UMR 1131 SVQV (Colmar, France) [ 52 ]. Since 2017, the progenies have been grown as grafted plants in 4 l pots in greenhouse, receiving natural light and a nutritive solution [ 20 ]. During the vegetative season, shoots were periodically pruned and pests and diseases were managed through the application of sprays every two weeks. For the bioassays, the fifth and sixth leaves from the apex of two actively growing shoots (typically the 1 st -2 nd not shiny leaves) were collected and used to produce 2 cm leaf discs [ 20 ]. Disease evaluation The plants were phenotyped in laboratory conditions on leaf discs as described in Macia et al., [ 20 ]. Two P. viticola strains were parallelly spray inoculated (50,000 sporangia/ml) on different plates: the naive, or avirulent, strain was isolated from the susceptible grape cultivar ‘Chardonnay’ [ 13 , 20 ]; and a Rpv10 -breaking ( avrRpv10- ), or virulent, strain was isolated from the resistant Rpv10 -carrying cultivar ‘Muscaris’ [ 24 ]. Discs were visually evaluated by the OIV-452-1 descriptor [ 19 ] as described in Macia et al., [ 20 ], from 3 to 6 dpi, using a five-classes scales: 1 = sporulation densely covering the whole disc; 3 = sporulation present in most of the disc area in large patches; 5 = sporulation present in delimited intercostal patches; 7 = sparse sporulation; and 9 = absence of sporulation. For each Rpv combination - P. viticola strain interaction, the incidence of discs showing necrotic response to the infection was collected, while the data for the OIV452-1 were used to infer P. viticola sporulation incidence and the sporulation latency period. A total of 3 genotypes/ Rpv and 3 discs/strain/genotype were produced and assessed in two experiments. P. viticola -specific biomarkers were extensively quantified through LC-MS at 6 and 12 hpi, at 1, 2 and 3 dpi, and at 6 dpi using the visually assessed leaf discs. A total of 3 discs/ genotype/strain/time, as well as 2 mock (water)-inoculated discs/ genotype/ time were produced, sampled and stored in 2 ml tubes at -20°C until the start of metabolomics analysis. A randomization scheme for 72 discs was replicated for each sampling condition (strain/time). Metabolomics-based assessment at 3 dpi was corroborated by investigating P. viticola development at by aniline-blue staining as described in Trouvelot et al. [ 53 ]. Observations were carried out using a Zeiss Axio Imager M2 (Zeiss, Oberkochen, Germany) in epifluorescence microscopy under UV (λexc 340 nm, λem380 nm, stop filter LP 430 nm). Two consecutive experiments were carried out in summer 2023 and both were visually assessed. Metabolomics analysis and microscopy observations were completed for one bioassay. Metabolomics analysis The quantification of P. viticola biomarkers was based on ultra-high performance liquid chromatography (LC) coupled with mass spectrometry (MS), as previously described by Negrel et al. [ 18 ], with some modifications. Metabolites were extracted from the freeze-dried and ground leaf discs (lyophilised for 24 h and processed twice at 30 Hz for 30 s with a stainless-steel bead of 3.0 mm in a Tissue-Lyser II instrument - Qiagen, Hilden, Germany) with methanol (MeOH) using 30 μL per mg of dry weight. The suspension of leaf powder and MeOH was sonicated for 15 min in an ultrasound bath and subjected to centrifugation at 13,300 rcf for 10 minutes, from the resulting solution, 50 μL were transferred to vials. LC separation was performed using a Vanquish Flex binary UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA) on a Nucleodur C18 HTec column (30 × 2mm, 1.8μm particle size; Macherey- Nagel, Düren, Germany) maintained at 40°C and a mobile phase consisted of methanol/water (7/3) with formic acid (0.1%, v/v) at a flow rate of 0.60 mL/min. The gradient elution programme was as follows: 0 to 0.7 min, 100% A; 0.7 to 2.5 min, 0% A; 2.5 to 3.5 min, 0% A isocratic; 3.5 to 4.5 min, 100% A. he injected volume was 1 µl. The LC system was coupled to an Exploris 120 Q-orbitrap (Thermo Fisher Scientific, Waltham, MA, USA) equipped with an atmospheric pressure chemical ionization (APCI) source operating in positive mode. Parameters were set at 300°C for ion transfer capillary temperature and the corona discharge current was set at 4 μA. Nebulization with nitrogen sheath gas and auxiliary gas were maintained at 30 and 10 arbitrary units, respectively, and the nebulizer temperature was maintained at 400°C. The spectra were acquired within the m/z mass range of 90–1,200 atomic mass units (u.), using a resolution of 60,000 at m/z 200 amu. The system was calibrated internally using EASY-IC calibrating source allowing single mass calibration for full mass range, giving a mass accuracy lower than 1 ppm. The targeted metabolomics analysis of 12 P. viticola biomarkers (Supplementary File 5a), including derivatives of eicosapentaenoic (EPA) and arachidonic acids (AA) and ceramides (Cer), was completed using Xcalibur 4.5 software (Thermo Fisher Scientific, Waltham, MA, USA). The integration of each peak was manually checked before validation. Not found-missing data for targeted metabolomics analysis were imputed with the methodology described by Wei et al. [ 54 ], and data were log10 transformed to better satisfy the assumptions of the statistical analysis. Statistical analysis Statistical analysis were carried out using R software [ 55 ]. P. viticola metabolic biomarker quantification for single leaf discs was tested for Pearson correlation with the OIV452-1 scores excluding “not found” peaks and areas data below 10,000 AU (Supplementary File 5c-5d). The OIV452-1 scores and the P. viticola biomarker Cer(d16:1/22:0) data were used in independent t-tests (with Welch’s approximation) to compare P. viticola strains infection on the different Rpv combinations (Supplementary File 4b-8a). The investigation on the Rpv contribution to the resistance to P. viticola was carried out with strain-independent ANOVA for the OIV452-1 scores and the P. viticola biomarker Cer(d16:1/22:0), considering Rpv1 , Rpv3.1 and Rpv10 (two levels with complete cross interactions), and the Experiment for visual data, as factors (Supplementary File 4c-8b). Homoscedasticity and normality of the residuals were visually checked. Estimated marginal means and standard errors of the Rpv combinations for the response variables were calculated by using the ‘emmeans’ package [ 56 ] and used to compare the Rpv combinations with Tukey’s tests (p-value with the Benjamini-Hochberg correction; Supplementary File 4d; 8c). All data analysed in the manuscript are available in the Supplementary File 9. Funding This work was supported by the ‘FUNDUR’ project in the frame of the Agence Nationale de la Recherche (ANR) - Deutsche Forschungsgemeinschaft (DFG) French-German collaboration (ANR-22-CE92-0005; DFG project n° 504993256) and by the European Fund for Regional Development in the frame of the ‘Wivitis - Interreg Upper Rhine’ project. Authors’ contributions PH, DM, SW and JB conceived the research; PH, DM, JB and RF acquired the financial support; RB, SW and PH supervised the research work; ED, PM and DM conceived the generation of the offspring; RF isolated the P. viticola strains virulent on Rpv10 ( avrRpv10- ); TP, SW, MCL, MAD and MB performed the phenotyping; TP and RB performed the metabolomic analyses; TP, MB and EG were involved in data analysis; TP produced the statistical analysis and the visualizations; TP, SW, RB, PH, ED, PM, JB, MB and EG were involved in data and results interpretation; TP drafted the manuscript with inputs from all authors; all the authors reviewed, edited and approved the final version of the manuscript. Data availability statement All the data are included in the article and its supplementary files. Conflict of interests The authors declare that they have no competing interests. Supplementary Files Supplementary File S1. Overview of the study and its experimental and technical design. Summary of the information on the plant material, the pathogen source, the phenotyping strategy and the studied variables of the research. A total of 3 genotypes for each possible combination of the studied resistance loci to Plasmopara viticola ( Rpv ) Rpv1 , Rpv3.1 and Rpv10 were challenged with a naive P. viticola strain, or avirulent on all the studied Rpv , and a P. viticola strain virulent toward Rpv10 ( avrRpv10- ). A total of 3 discs/genotype were visually assessed from 3 to 6 days post inoculation (dpi) recording the incidence and severity of P. viticola sporulation, according to the OIV452-1 descriptor, and the incidence of plant necrosis. A total of 3 leaf discs/genotype/strain/time and 2 mock (water)-inoculated discs/genotype/time were collected at 6-12 hours post inoculation (hpi) and at 1-2-3-6 dpi for metabolomics-based phenotyping (discs collected at 6 dpi were those visually assessed). For metabolomics, a methanol (MeOH)-based extraction protocol and high-resolution liquid chromatography-based mass spectrometry (LC-MS) with an ‘Atmospheric pressure chemical ionization’ (APCI) source were adopted for the analysis, and a targeted quantification was performed for 12 P. viticola -specific metabolic biomarkers. Supplementary File S2. Progression of Plasmopara viticola infection on grapevine leaf discs. Representative disc between 3 and 6 day post inoculation (dpi) for non-resistant ( Rpv- ) and single Rpv combinations ( Rpv1 , Rpv3.1 and Rpv10 ) inoculated with the two P. viticola strains of the study (naive and avrRpv10- ). The discs evidence a different severity and incidence of P. viticola sporulation and plant necrosis depending on the Rpv - P. viticola interaction considered. Supplementary File S3. OIV452-1 scores progression on Plasmopara viticola -infected grapevine leaf discs. OIV452-1 mean scores (points) and confidence limits (dashed area; 95%) at 3-4-5-6 days post inoculation (dpi) for each Rpv combination - P. viticola strain (naive and avrRpv10- ) interaction studied (1 = sporulation covering the whole disc area; 3 = sporulation covering the most of the disc area in large patches; 5 = sporulation present in delimited patches; 7 = sparse sporulation; and 9 = no sporulation). A total of 3 genotypes/ Rpv combination and 3 discs/genotype/dpi were assessed in two experiments (Exp; different colors) by stereomicroscope observations. Supplementary File S4a. Data for the variables characterizing the outcomes of the Rpv combination - Plasmopara viticola strain interactions. Mean value for the OIV452-1 descriptor, P. viticola sporulation incidence (Spo_inc; i.e., OIV452-1 ≠ 9) and plant necrosis incidence (Nec_inc) at 6 days post inoculation (dpi), and sporulation latency period (dpi), for the different Rpv combination - P. viticola strain interactions studied and for the two experiments carried out (Exp1 and Exp2). Supplementary File S4b. T-test comparison of Plasmopara viticola strains based on OIV452-1 scores. T-test (with Welch approximation) to compare P. viticola strains (naive and avrRpv10- ) severity of infection were carried out by using the OIV452-1 scores collected at 6 days post inoculation (dpi) on the different Rpv combinations in two experiments (3 genotypes/ Rpv combination and 3 leaf discs/genotype/strain/experiment). The plots present the OIV452-1 data distribution (box-plot), the mean and standard error of the mean (grey squares and error bars) for the Rpv combinations under infection with the two P. viticola strains (naive = red; avrRpv10- = blue). Supplementary File S4c. ANOVA results for the OIV452-1 scores. Detailed results from ANOVA conducted with the OIV452-1 scores at 6 days post inoculation (dpi), and including Rpv1 , Rpv3.1 and Rpv10 presence (two levels with complete cross interactions), and the Experiment, as factors (degree of freedom-df, f value and p values). Independent ANOVA were conducted for the two Plasmopara viticola strains (naive and avrRpv10- ) and data for 3 genotypes/ Rpv combination and 3 discs/genotype/strain/dpi of two experiments were used. The plots present the OIV452-1 data distribution (box-plot) and the estimated marginal means and standard errors (black dots and error bars) for the factors investigated in the complete ANOVA analysis for the two P. viticola strains (naïve = red; avrRpv10- = blue). Supplementary File S4d. Pairwise comparisons of Rpv combinations based on OIV452-1 scores. Pairwise comparisons (PWC) between Rpv combinations carried out after Plasmopara viticola strain (naive and avrRpv10- )-independent ANOVA analysis of OIV452-1 scores collected at 6 days post inoculation (dpi) by using the estimated marginal means and standard errors (Tukey’ test with p values with Benjamini-Hochberg correction). The plots present the OIV452-1 data distribution (box-plot) and the estimated marginal means and standard errors (black dots and error bars) for the Rpv combinations under infection with the two P. viticola strains (naive = red; avrRpv10- = blue). Supplementary File S5a. Complete list of Plasmopara viticola -specific metabolic biomarkers ions quantified by targeted metabolomics analysis. Supplementary File S5b. Plasmopara viticola metabolic biomarkers detection. Percentage (%) of “not found” peaks, and consequent missing area values, for the main different studied conditions: the eight different Rpv combinations infected with the two P. viticola strains at 3 and 6 days post inoculation (dpi). A total of 3 genotypes/ Rpv combination, 3 leaf discs/genotype/strain/time and 2 mock (water)-inoculated discs/genotype/time for one experiment were analyzed. Supplementary File S5c. Correlations between the OIV452-1 scores and Plasmopara viticola metabolic biomarkers abundances. a) Correlation coefficients (Pearson correlation) between the visual variable OIV452-1 and P. viticola -specific metabolic biomarkers for seven retained biomarkers (peak area data were log10-transformed) at 6 days post inoculation (dpi) in 72 samples. b) P. viticola strain (naive = red; avrRpv10- = blue)-specific correlations between the OIV452-1 scores the metabolic biomarkers abundances. Supplementary File S5d. Correlations between Plasmopara viticola metabolic biomarkers . Correlation plots, coefficients and significances (Pearson correlation, p-value < 0.05 for *; < 0.01 for ** and < 0.001 for ***) between the six most informative P. viticola -specific metabolic biomarkers (peak area data log10-transformed) at 6 days post inoculation (72 samples). Supplementary File 6. Plasmopara viticola metabolic biomarkers abundances progression in grapevine leaf discs. The heatmap shows the mean peak area (log10 of the arbitrary unit) for the six most informative P. viticola -specific metabolic biomarkers (Eicosapentaenoic acid - EPA, Eicosapentaenoyl-glycerol - EPG, Arachidonic acid - AA and the three ceramides Cer(d16:1/18:0), Cer(d16:1/18:0) and Cer(d16:1/18:0); right labels) quantified in the discs of the different Rpv combination (bottom labels) - P. viticola strain (left labels) interactions studied at 6-12 hours post inoculation (hpi; top labels) and at 0-1-2-3-6 days post inoculation (dpi; top labels). A total of 9 discs/ Rpv combination/strain/time for the inoculated samples, and 6 discs/ Rpv combination/time for the mock (water)-inoculated samples were analyzed by high-resolution liquid chromatography-based mass spectrometry (LC-MS). Supplementary File S7a. Plasmopara viticola metabolic biomarkers abundances ratios comparing P. viticola strains. Mean peak area data per Rpv combination per P. viticola strain (naive and avrRpv10- ) at 3 and 6 days post inoculation (dpi) for the six P. viticola metabolic biomarkers analyzed and log2 ratios between the mean metabolite peak areas for the same Rpv combination (3 genotypes/ Rpv and 3 discs/genotype/strain/time; table and heatmap). The ratios highlight the different aggressiveness of the P. viticola strains on the studied Rpv combinations. Supplementary File S7b. Plasmopara viticola metabolic biomarkers abundances ratios comparing the infection on the Rpv combinations. Mean peak area data per Rpv combination per P. viticola strain (naive and avrRpv10- ) at 3 and 6 days post inoculation (dpi) for the six P. viticola metabolic biomarkers analyzed and log2 ratios between the mean areas for different Rpv combinations (3 genotypes/ Rpv and 3 discs/genotype/strain/time; table and heatmap). The ratios highlight the different efficacy of Rpv combinations in limiting the infection of the P. viticola strains. Supplementary File S8a. T-test comparison of Plasmopara viticola strains based on the abundances of P. viticola metabolic biomarkers. T-tests (with Welch approximation) to compare P. viticola strains (naive and avrRpv10- ) severity of infection were carried by using the peak area data (log10 of the arbitrary unit-AU) for the most informative P. viticola metabolic biomarker Cer(d16:1/22:0) collected at 3 and 6 days post inoculation (dpi) on the different Rpv combinations (3 genotypes/ Rpv combination and 3 leaf discs/genotype/strain). The plots present the Cer(d16:1/22:0) data distribution (box-plot), the mean and standard error of the mean (grey squares and error bars) for the Rpv combinations under infection with the two P. viticola strains (naive = red; avrRpv10- = blue). Supplementary File S8b. ANOVA results for the Plasmopara viticola metabolic biomarkers. Detailed results from ANOVA conducted with the most informative P. viticola -metabolic biomarker Cer(d16:1/22:0) abundances (log10 of the arbitrary unit-AU) at 3-6 days post inoculation (dpi), and including Rpv1 , Rpv3.1 and Rpv10 presence (two levels with complete cross interactions), and the Experiment, as factors (degree of freedom-df, f value and p values). Independent ANOVA were conducted for the two P. viticola strains (naive and avrRpv10- ) and data for 3 genotypes/ Rpv combination and 3 discs /genotype/strain/dpi of one experiment were used. The plots present the Cer(d16:1/22:0) abundances distribution (box-plot) and the estimated marginal means and standard errors (black dots and error bars) for the factors investigated in the complete ANOVA analysis for the two P. viticola strains (naive = red; avrRpv10- = blue). Supplementary File S8c. Pairwise comparisons between Rpv combinations for the Plasmopara viticola Cer(d16:1/22:0) biomarker. Pairwise comparisons (PWC) between Rpv combinations carried out after P. viticola strain (naive and avrRpv10- )-independent ANOVA analysis of Cer(d16:1/22:0) peak area data (log10 of the arbitrary unit; log10 AU) collected at 3 and 6 days post inoculation (dpi) by using estimated marginal means and standard errors (Tukey’ test with p-values with Benjamini-Hochberg correction). The plots present the Cer(d16:1/22:0) data distribution (box-plot) and the estimated marginal means and standard errors (black dots and error bars) for the Rpv combinations under infection with the two P. viticola strains (naive = red; avrRpv10- = blue). Supplementary File S9a. Data for visually-assessed variables. Scores for single leaf disc (DiscID) for the variables OIV452-1 (1=extend and dense sporulation/high susceptibility; 9=absence of sporulation/total resistance) and necrosis incidence (0=absence; 1=presence) collected at different days post inoculation (dpi). Supplementary File S9b. Data for Plasmopara viticola metabolic biomarkers quantification. Peak area data, or abundances, (arbitrary unit-AU) with missing values (not found peaks) attributed as described in Wei et al. (2018) for the P. viticola metabolic biomarkers analyzed: eicosapentaenoic acid (EPA), eicosapentaenoyl-glycerol (EPG=, dieicosapentaenoyl-glycerol (DEPG), trieicosapentaenoyl-glycerol (TEPG), arachidonic acid (AA), arachidonoyl-glycerol (AG), diarachidonoyl-glycerol (DAG), triarachidonoyl-glycerol (TAG), and ceramides Cer(d16:1/16:0), Cer(d16:1/18:0), Cer(d16:1/20:0) and Cer(d16:1/22:0). Sample data correspond to single leaf discs collected at different hours or days post the inoculation (hpi and dpi) with a P. viticola strain (naive or avrRpv10- ) or water (mock). Supplementary File S9c. Data to study the correlation between the OIV452-1 scores and the abundances of Plasmopara viticola metabolic biomarkers. Data for the OIV452-1 descriptor and the most informative P. viticola biomarkers for the leaf discs/samples collected at 6 dpi and used to investigate the correlation between variables used to assessed the severity of P. viticola infection. Acknowledgements The authors thank Claire Villeroy for the help in processing the samples for metabolomic analysis (INRAE UMR1131 SVQV, Colmar, France); the staff of Unité Expérimentale Agronomique et Viticole (UE0871, INRAE-Centre Grand Est-Colmar, France) for maintenance of the plant material; and the VEGOIA phenotyping platform (INRAE-Centre Grand Est-Colmar, France), part of the Strasbourg University ‘Cortecs’ network ( https://cortecs.unistra.fr ). Funder Information Declared Agence Nationale de la Recherche (ANR) , ANR-22-CE92-0005 Deutsche Forschungsgemeinschaft (DFG) , 504993256 European Fund for Regional Development , Wivitis - Interreg Upper Rhine References 1. ↵ OIV . Annual assessment of the world vine and wine sector in 2023 . 2023 . 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