SciELO - Scientific Electronic Library Online

 
vol.41 número especialEfeito inibitório de extratos radiculares de plantas herbáceas na atividade de Phytophthora cinnamomiElucidando a ligação entre subtilases e a sinalização lipídica na resistência da videira ao míldio índice de autoresíndice de assuntosPesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Revista de Ciências Agrárias

versão impressa ISSN 0871-018X

Rev. de Ciências Agrárias vol.41 no.spe Lisboa dez. 2018

https://doi.org/10.19084/RCA.17066 

ARTIGO

Uncovering markers for downy mildew resistance in grapevine through mass spectrometry-based metabolomics

A descoberta de biomarcadores de resistência ao míldio através de metabolómica baseada em espectrometria de massa

Marisa Maia1,2,3, António E. N. Ferreira1,2, Ana P. Marques1,2, Joana Figueiredo1,2,3, Ana Ponces Freire2, Carlos Cordeiro1,2, Andreia Figueiredo3,† and Marta Sousa Silva1,2,†,*

1 Laboratório de FTICR e Espectrometria de Massa Estrutural, Faculdade de Ciências da Universidade de Lisboa, Portugal

2 Centro de Química e Bioquímica, Faculdade de Ciências da Universidade de Lisboa, Portugal

3 Biosystems & Integrative Sciences Institute (BioISI), Faculdade e Ciências da Universidade de Lisboa, Portugal

(* E-mail: mfsilva@fc.ul.pt)


ABSTRACT

Downy mildew is a major threat to the wine industry and the development of hybrids between Vitis vinifera and wild resistant Vitis species is a promising strategy to cope with this disease. The discovery of metabolic markers associated with grapevine resistance is paramount. In this work we aim at speeding up the assignment of innate resistance to downy mildew in grapevine cultivars by applying high-resolution mass-spectrometry- based untargeted metabolomics as a profiling and resistance marker discovery method. We compared the susceptible cultivar ‘Trincadeira’ with the more resistant one ‘Regent’. Both cultivars could be easily discriminated based on their metabolic profile and we found several peak features exclusive of each variety.

Keywords: Grapevine, metabolomics, mildew, resistance, biomarkers


RESUMO

O míldio é uma ameaça importante à indústria vitivinícola e o desenvolvimento de híbridos entre Vitis vinifera e Vitis spp. selvagens, resistentes à doença, é uma estratégia promissora no combate a esta doença. A descoberta de marcadores metabólicos é essencial. Neste trabalho pretendemos acelerar o processo de seleção das características de resistência ao míldio utilizando metabolómica baseada em espectrometria de massa de elevada resolução para a descoberta de marcadores de resistência. Comparámos a cultivar suscetível ‘Trincadeira’ com a mais resistente ‘Regent’. Ambas foram facilmente discriminadas com base no seu perfil metabólico, tendo sido encontrados vários picos exclusivos de cada cultivar.

Palavras-chave: Videira, metabolómica, míldio, resistência, biomarcadores


Introduction

Vitis vinifera is one of the most important and cultivated fruit plant in the world, occupying a global area of 7.5 mha and with a global grape production of 7.8 mt in 2016 (OIV Statistical Report on World Vitiviniculture, 2017). Most of the produced grape is targeted for wine production, a highly strategic industry for the economy of several countries, Portugal included. With 14 wine producing regions and 190 kha of vineyards, Portugal is the eleventh world wine producer and the fifth in Europe, accounting for ~700 million euros per year of exports (OIV Statistical Report on World Vitiviniculture, 2017).

One major threat to the wine industry is downy mildew, caused by the biotrophic oomycete Plasmopara viticola (Berk. et Curt.) Berl. et de Toni, infecting all domesticated V. vinifera cultivars frequently used for wine production. This pathogen affects leaves, shoots, bunches and fruits, causing quality and yield reduction with significant production and financial losses (Gessler et al., 2011).

Currently used strategies to cope with downy mildew include the intensive use of fungicides, applied soon after the first leaves appear. However, the general recommendations of the European agricultural policy encourage the reduction of pesticides towards environmental sustainability and consumer health. A promising approach is the creation of new cultivars through breeding programs, combining the high degree of resistance against downy mildew from the wild Vitis spp., with the good berry quality of V. vinifera. One successful example already used in wine production is the interspecific hybrid ‘Regent’, developed from V. vinifera ‘Diana’ (a Silvaner x Müller-Thurgau cross) and from the interspecific hybrid ‘Chambourcin’, possessing a higher resistance to downy mildew and other relevant fungal diseases (Gessler et al., 2011).

The complete process of a new cultivar’s breeding, from plant crossing to market release, can take about 25 to 30 years. Since grapevine is a perennial crop, the selection process concerning pathogen resistance is only possible 2 to 3 years after plant crossing, after which the more resistant seedlings are kept (Eibach and Töpfer, 2015). Considering the high number of newly developed seedlings, shortening this time would represent a considerable financial benefit to the producers. Hence, any new or advanced selection methods of these new cultivars can lead to a more efficient breeding process.

The discovery of mildew resistance-associated biomarkers in grapevine will allow a quick and accurate identification of the seedlings that inherited the resistant trait soon after germination. Disease-resistance genetic markers have been searched for the past 14 years and quantitative trait loci (QTLs) for resistance to downy mildew were identified (Gessler et al., 2011). Among these, the Resistance to Plasmopara viticola (RPV) loci were identified in several Vitis species. These loci can be passed into the offspring in cross-breeding programs between V. vinifera and other Vitis species, but their presence in the cross-bred cultivars does not guarantee resistance to downy mildew (Peressotti et al., 2010). Hence, the resistance of the wild Vitis species to P. viticola most probably goes beyond the presence of these DNA markers in their genome. Metabolic biomarkers have proven their value to predict phenotypical traits before they are observed (Wolfender et al., 2013). In this area, metabolomics is a powerful tool, for its ability to simultaneously characterize and quantify multiple metabolites (Shepherd et al., 2011). Recently, biomarkers associated with the defence response to downy mildew were identified in a resistant cultivar after leaf inoculation with P. viticola (Chitarrini et al., 2017). While most of these studies in grapevine are focused in the metabolic profile of host-pathogen interactions, little is known about the constitutive differences of resistant and susceptible cultivars that could undoubtedly discriminate both groups.

One of the first studies on the discrimination of downy mildew resistant and susceptible grapevine varieties followed a metabolic profiling approach using nuclear magnetic resonance (NMR) (Figueiredo et al., 2008). About 13 metabolites were identified and 7 were differentially accumulated in ‘Regent’ (more resistant to mildew) and ‘Trincadeira’ (susceptible). To substantially increase metabolome coverage, in the present work we analysed the same cultivars by Fourier Transform Ion Cyclotron Resonance mass spectrometry (FT-ICR-MS). Due to its ultra-high-resolution and ultra-high-mass accuracy, this technology is one of the best tools to fingerprint complex samples, allowing very high metabolome coverage (Maia et al., 2016). With this untargeted metabolomics analysis, we increased the number of features detected, significantly increased the ones exclusively present in ‘Trincadeira’ or ‘Regent’ and identified several metabolites that discriminate them.

Materials and Methods

Plant Material

Vitis vinifera accessions ‘Trincadeira’ and ‘Regent’ were collected at the Portuguese Ampelographic Grapevine Collection (CAN, international code PRT051), INIAV-Dois Portos. For each accession, the third to fifth leaves (from the shoot apex) were harvested from 5 fully developed plants and combined in 1 biological replicate, being collected 3 biological replicates for analysis.

Metabolite extraction and FT-ICR-MS analysis

Metabolite extraction was performed as previously reported (Maia et al., 2016). Only the methanol fraction was analysed by direct infusion on the 7T-FT-ICR mass spectrometer (Brüker Daltonics), in positive electrospray ionization (ESI) mode. The internal standard leucine enkephalin (YGGFL, Sigma Aldrich Portugal) was added to all samples at a final concentration of 0.5 µg/mL, being considered a mass of [M+H]+ = 556.276575 Da for analysis by ESI+. Spectra were recorded between 100 and 1000 m/z. Spectra analysis and alignment were performed as previously reported (Maia et al., 2016).

Multivariate analysis

Multivariate analysis considering peak intensities (Principal Component Analysis (PCA) and Partial Least Squares - Discriminant Analysis (PLS-DA)) was performed using the program MetaboAnalyst (http://www.metaboanalyst.ca/, Xia et al., 2015). Missing value imputation was done by substitution by half of the minimum positive value found within the data. Intensity data were normalized to the internal standard (leucine enkephalin), generalized log transformed, and Pareto scaled prior to multivariate methods.

Metabolite identification

The search for candidate compounds among the peaks that clearly discriminate the ‘Trincadeira’ and ‘Regent’ cultivars was performed in the MassTRIX database (http://masstrix3.helmholtz-muenchen.de/, Suhre & Schmitt-Kopplin, 2008). Mass lists were searched in positive ionization mode, considering the adducts M+H, M+K and M+Na and 2 ppm as maximum m/z deviation from theoretical mass. Vitis vinifera was the selected organism and the search was performed in the combined database of KEGG (Kyoto Encyclopedia of Genes and Genomes) / HMDB (Human Metabolome Data Base) / LIPID MAPS without isotopes.

Results and Discussion

Metabolomics has been widely used to discriminate samples based on the natural variance in metabolite content (Plumb et al., 2006; Gougeon et al., 2009; Rhourrhi-Frih et al., 2012; Becker et al., 2013). In this approach, metabolite identification is not a requirement for sample discrimination (Plumb et al., 2006; Becker et al., 2013). We performed an untargeted metabolomics analysis by FT-ICR-MS to compare the metabolome between two V. vinifera cultivars presenting different degrees of resistance against downy mildew, ‘Trincadeira’ and ‘Regent’. A total of 1912 peaks were detected in V. vinifera ‘Regent’ and 1615 in ‘Trincadeira’. Of these, 665 were exclusive to ‘Regent’ and 368 to ‘Trincadeira’. When compared to NMR analysis of the same cultivars (Figueiredo et al., 2008), the metabolic profiling by FT-ICR-MS significantly increased metabolome coverage and the number of cultivar-specific features observed. PCA showed a clear separation between the two cultivars for the first two components (Figure 1A). This separation was confirmed by sample hierarchical clustering using the Euclidean distance for the most significantly different peaks (Figure 1B). Seeking the features most responsible for these differences, we obtained the top 15 of the most discriminatory peaks of a PLS-DA classification model using Variable Importance in Projection (VIP) scores (Figure 1C). Features with a higher VIP score (normally above 1) are regarded as significant in a given model. The top 15 most discriminatory peaks between ‘Regent’ and ‘Trincadeira’ present VIP scores between 2 and 3, being therefore highly important in this model.

 

 

The search for candidate compounds among the peaks that clearly discriminate the two cultivars was performed in the MassTRIX database. For V. vinifera ‘Regent’, one of these compounds was identified as caffeic acid 3-glucoside. This result corroborated the previous finding using NMR (Figueiredo et al., 2008), which reported the presence of higher amounts of caffeic acid in ‘Regent’ when compared to ‘Trincadeira’. Two other discriminatory compounds for ‘Regent’ were oleic acid (18:1) and its methyl ester form. In Arabidopsis thaliana, oleic acid induces the activation of defence responses mediated by jasmonic acid (JA) and represses the salicylic acid (SA) signalling pathway (reviewed in Lim et al., 2017). Indeed, these signalling pathways are highly relevant for grapevine resistance, with the activation of the JA signalling pathway and the interaction between JA and SA being tailored in the defence response against P. viticola (Guerreiro et al., 2016). Another compound only identified in ‘Regent’, also associated with lipid signalling, was palmitoleic acid (16:1). This fatty acid also plays a likely role in plant defence against fungal pathogens (reviewed in Lim et al., 2017).

CONCLUSIONS

Grapevine breeding approaches offer forward-looking perspectives for an environmental friendly and sustainable viticulture. The discovery of biomarkers will allow a quick and accurate identification of the plantlets that inherited the resistant characteristic soon after germination. Using an untargeted metabolomics approach, based on ultra-high resolution and high-mass accuracy mass spectrometry, we compared two V. vinifera cultivars with different degrees of resistance towards the downy mildew. The Portuguese ‘Trincadeira’ is susceptible to this disease, whereas the interspecific hybrid ‘Regent’ contains a high degree of resistance. We were able to clearly discriminate both cultivars (without pathogen infection) and identify the most discriminatory compounds. Additionally, we identified features that are exclusive to one or the other cultivar. With the present work, we were able to show the potential of the metabolomics based on ultra-high resolution and ultra-high mass accuracy (FT-ICR). Our work will contribute, not only to grapevine variety discrimination, but also a deeper identification of compounds that participate in the grapevine resistance mechanisms. Moreover, this approach may also contribute for the development of efficient biomarker assays, based on resistance-associated metabolites, to help future breeding programs and introgression line analysis.

 

References

Becker, L.; Poutaraud, A.; Hamm, G.; Muller, J.-F.; Merdinoglu, D.; Carré, V. & Chaimbault, P. (2013) - Metabolic study of grapevine leaves infected by downy mildew using negative ion electrospray–Fourier transform ion cyclotron resonance mass spectrometry. Analytica Chimica Acta, vol. 795, p. 44-51. https://doi.org/10.1016/j.aca.2013.07.068        [ Links ]

Chitarrini,G; Soini, E.; Riccadonna, S.; Franceschi, P.; Zulini, L.; Masuero, D.; Vecchione, A.; Stefanini, M.; Di Gaspero, G.; Mattivi, F. & Vrhovsek, U. (2017) - Identification of Biomarkers for Defense Response to Plasmopara viticola in a Resistant Grape Variety. Frontiers in Plant Science, vol. 8, art. 1524. https://doi.org/10.3389/fpls.2017.01524        [ Links ]

Eibach, R. & Töpfer, R. (2015) - Traditional grapevine breeding techniques. In: Reynolds, A.G. (Ed.) - Grapevine Breeding Programs for the Wine Industry. Woodhead Publishing, Oxford, p. 3-22.         [ Links ]

Figueiredo, A.; Fortes, A.M.; Ferreira, S.; Sebastiana, M.; Choi, Y.H.; Sousa, L.; Acioli-Santos, B.; Pessoa, F.; Verpoorte, R. & Pais, M.S. (2008) - Transcriptional and metabolic profiling of grape (Vitis vinifera L.) leaves unravel possible innate resistance against pathogenic fungi. Journal of Experimental Botany, vol. 59, n. 12, p. 3371-3381. https://doi.org/10.1093/jxb/ern187        [ Links ]

Gessler, C.; Pertot, I. & Perazzolli, M. (2011) - Plasmopara viticola: a review of knowledge on downy mildew of grapevine and effective disease management. Phytopathologia Mediterranea vol. 50, n. 1, p. 3-44. http://dx.doi.org/10.14601/Phytopathol_Mediterr-9360        [ Links ]

Gougeon, R.D.; Lucio, M.; Frommberger, M.; Peyron, D.; Chassagne, D.; Alexandre, H.; Feuillat, F.; Voilley, A.; Cayot, P.; Gebefügi, I.; Hertkorn, N. & Schmitt-Kopplin, P. (2009) - The chemodiversity of wines can reveal a metabologeography expression of cooperage oak wood. Proceedings of the National Academy of Sciences, vol. 106, n. 23, p. 9174-9179. https://doi.org/10.1073/pnas.0901100106        [ Links ]

Guerreiro, A.; Figueiredo, J.; Sousa Silva, M. & Figueiredo, A. (2016) - Linking Jasmonic Acid to Grapevine Resistance against the Biotrophic Oomycete Plasmopara viticola. Frontiers in Plant Science, vol. 7, art. 565. https://doi.org/10.3389/fpls.2016.00565        [ Links ]

Lim, G.-H.; Singhal, R.; Kachroo, A. & Kachroo, P. (2017) - Fatty Acid- and Lipid-Mediated Signaling in Plant Defense. Annual Review of Phytopathology, vol. 55, p. 505-536. https://doi.org/10.1146/annurev-phyto-080516-035406        [ Links ]

Maia, M.; Monteiro, F.; Sebastiana, M.; Marques, A.P.; Ferreira, A.E.N.; Ponces Freire, A.; Cordeiro, C.; Figueiredo, A. & Sousa Silva, M. (2016) - Metabolite extraction for high-throughput FTICR-MS-based metabolomics in grapevine. EuPA Open Proteomics, vol. 12, p. 4-9. https://doi.org/10.1016/j.euprot.2016.03.002        [ Links ]

Peressotti, E.; Wiedemann-Merdinoglu, S.; Delmotte, F.; Bellin, D.; Gaspero, G.D.; Testolin, R.; Merdinoglu, D. & Mestre, P. (2010) - Breakdown of resistance to grapevine downy mildew upon limited deployment of a resistant variety. BMC Plant Biology, vol. 10, art. 147. https://doi.org/10.1186/1471-2229-10-147        [ Links ]

Plumb, R.S.; Johnson, K.A.; Rainville, P.; Shockcor, J.P.; Williams, R.; Granger, J.H. & Wilson, I.D. (2006) - The detection of phenotypic differences in the metabolic plasma profile of three strains of Zucker rats at 20 weeks of age using ultra‐performance liquid chromatography/orthogonal acceleration time‐of‐flight mass spectrometry. Rapid Communications in Mass Spectrometry, vol. 20, p. 2800-2816. https://doi.org/10.1002/rcm.2655        [ Links ]

Rhourrhi-Frih, B.; West, C.; Pasquier, L.; André, P.; Chaimbault, P. & Lafosse, M. (2012) - Classification of natural resins by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry using chemometric analysis. Journal of Chromatography A, vol. 1256, p. 177-190. https://doi.org/10.1016/j.chroma.2012.07.050        [ Links ]

Shepherd, L.V.T.; Fraser, P. & Stewart, D. (2011) - Metabolomics: a second-generation platform for crop and food analysis. Bioanalysis, vol. 3, n. 10, p. 1143-1159. https://doi.org/10.4155/BIO.11.61        [ Links ]

Suhre, K. & Schmitt-Kopplin, P. (2008) - MassTRIX: Mass TRanslator Into Pathways. Nucleic Acids Research, vol. 36, sup. 2, p. W481-W484. https://doi.org/10.1093/nar/gkn194        [ Links ]

Wolfender. J.L.; Rudaz, S.; Choi, Y.H. & Kim, H.K. (2013) - Plant Metabolomics: From Holistic Data to Relevant Biomarkers. Current Medicinal Chemistry, vol. 20, n. 8, p. 1056-1090. https://doi.org/10.2174/0929867311320080009        [ Links ]

Xia, J.; Sinelnikov, I.; Han, B. & Wishart, D.S. (2015) - MetaboAnalyst 3.0 - making metabolomics more meaningful. Nucleic Acids Research, vol. 43, n. W1, p. W251-W257. https://doi.org/10.1093/nar/gkv380        [ Links ]

 

Acknowledgments

Work supported by projects EXPL/BBB-BIO/0439/2013, UID/MULTI/00612/2013, PEst-OE/QUI/UI0612/2013, PEst-OE/BIA/UI4046/2014, FCT Investigator IF/00819/2015 and grant SFRH/BD/116900/2016 from Fundação para a Ciência e Tecnologia (Portugal). We acknowledge the support from the Portuguese Mass Spectrometry Network (LISBOA-01-0145-FEDER-022125) and the Project EU_FT-ICR_MS funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement nr. 731077.

 

Received/recebido: 2017.12.21

Accepted/aceite: 2018.06.26

 

† These authors are co-senior authors in this publication.

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons