Isolation and proteomic analysis of intracellular vesicles from the potato late blight pathogen Phytophthora infestans

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Isolation and proteomic analysis of intracellular vesicles from the potato late blight pathogen Phytophthora infestans

References

  1. Haverkort, A. J. et al. Societal costs of late blight in potato and prospects of durable resistance through cisgenic modification. Potato Res. 51, 47–57. https://doi.org/10.1007/s11540-008-9089-y (2008).

    Google Scholar 

  2. Berindean, I. V. et al. Modern breeding strategies and tools for durable late blight resistance in potato. Plants 13, 1711 (2024).

    Google Scholar 

  3. Rossman, A. Y. P. Why are Phytophthora and other oomycota not true fungi? Outlooks Pest Manage. 17, 217–219. https://doi.org/10.1564/17oct08 (2006).

    Google Scholar 

  4. Boevink, P. C., Birch, P. R. J., Turnbull, D. & Whisson, S. C. Devastating intimacy: the cell biology of plant–Phytophthora interactions. New Phytol. 228, 445–458. https://doi.org/10.1111/nph.16650 (2020).

    Google Scholar 

  5. Sabbadin, F. et al. Secreted pectin monooxygenases drive plant infection by pathogenic oomycetes. Science 373, 774–779. https://doi.org/10.1126/science.abj1342 (2021).

    Google Scholar 

  6. Pei, Y. et al. A receptor kinase senses sterol by coupling with elicitins in auxotrophic Phytophthora. Proc. Natl. Acad. Sci. 121, e2408186121. https://doi.org/10.1073/pnas.2408186121 (2024).

  7. Saraiva, M. et al. The molecular dialog between oomycete effectors and their plant and animal hosts. Fungal Biology Reviews. 43, 100289. https://doi.org/10.1016/j.fbr.2022.10.002 (2023).

    Google Scholar 

  8. Tian, M., Huitema, E., da Cunha, L., Torto-Alalibo, T. & Kamoun, S. A Kazal-like extracellular srine protease inhibitor from Phytophthora infestans targets the tomato pathogenesis-related protease P69B*. J. Biol. Chem. 279, 26370–26377. https://doi.org/10.1074/jbc.M400941200 (2004).

    Google Scholar 

  9. Tian, M., Benedetti, B. & Kamoun, S. A second Kazal-like protease inhibitor from Phytophthora infestans inhibits and interacts with the apoplastic pathogenesis-related protease P69B of tomato. Plant. Physiol. 138, 1785–1793. https://doi.org/10.1104/pp.105.061226 (2005).

    Google Scholar 

  10. Damasceno, C. M. B. et al. Structure of the glucanase inhibitor protein (GIP) family from Phytophthora species suggests coevolution with plant endo-β-1,3-glucanases. Mol. Plant Microbe Interact. 21, 820–830. https://doi.org/10.1094/mpmi-21-6-0820 (2008).

    Google Scholar 

  11. Haas, B. J. et al. Genome sequence and analysis of the Irish potato famine pathogen phytophthora infestans. Nature 461, 393–398. https://doi.org/10.1038/nature08358 (2009).

    Google Scholar 

  12. Wang, S., McLellan, H., Boevink, P. C. & Birch, P. R. J. RxLR effectors: master modulators, modifiers and manipulators. Mol. Plant Microbe Interact. 36, 754–763. https://doi.org/10.1094/mpmi-05-23-0054-cr (2023).

    Google Scholar 

  13. Théry, C. et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the international society for extracellular vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles. 7, 1535750. https://doi.org/10.1080/20013078.2018.1535750 (2018).

    Google Scholar 

  14. Thieron, H. et al. Practical advice for extracellular vesicle isolation in plant-microbe interactions: Concerns, considerations, and conclusions. J. Extracell. Vesicles. 13, e70022. https://doi.org/10.1002/jev2.70022 (2024).

    Google Scholar 

  15. Zhu, J. et al. Divergent sequences of tetraspanins enable plants to specifically recognize microbe-derived extracellular vesicles. Nat. Commun. 14, 4877. https://doi.org/10.1038/s41467-023-40623-0 (2023).

    Google Scholar 

  16. Breen, S. et al. Identification of marvelous protein markers for Phytophthora infestans extracellular vesicles. J. Extracell. Vesicles. 14, e70101. https://doi.org/10.1002/jev2.70101 (2025).

    Google Scholar 

  17. Linares, R., Tan, S., Gounou, C., Arraud, N. & Brisson, A. R. High-speed centrifugation induces aggregation of extracellular vesicles. J. Extracell. Vesicles. 4, 29509. https://doi.org/10.3402/jev.v4.29509 (2015).

    Google Scholar 

  18. Witwer, K. W. et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J. Extracell. Vesicles. 2, 20360. https://doi.org/10.3402/jev.v2i0.20360 (2013).

    Google Scholar 

  19. Duong, P., Chung, A., Bouchareychas, L. & Raffai, R. L. Cushioned-density gradient ultracentrifugation (C-DGUC) improves the isolation efficiency of extracellular vesicles. PLOS ONE. 14, e0215324. https://doi.org/10.1371/journal.pone.0215324 (2019).

    Google Scholar 

  20. Mateescu, B. et al. Obstacles and opportunities in the functional analysis of extracellular vesicle RNA – an ISEV position paper. J. Extracell. Vesicles. 6, 1286095. https://doi.org/10.1080/20013078.2017.1286095 (2017).

    Google Scholar 

  21. Palatinus, L. R., Schlemmer, T. & Koch, A. Technical advances in extracellular vesicle isolation from fungi and oomycetes: insights from plant-pathogenic species. Fungal Biology Reviews. 53, 100444. https://doi.org/10.1016/j.fbr.2025.100444 (2025).

    Google Scholar 

  22. Xu, L. et al. Proteolytic processing of both RXLR and EER motifs in oomycete effectors. New Phytol. 245, 1640–1654. https://doi.org/10.1111/nph.20130 (2025).

    Google Scholar 

  23. Kowal, J. et al. Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc. Natl. Acad. Sci. 113, E968–E977. https://doi.org/10.1073/pnas.1521230113 (2016).

    Google Scholar 

  24. Quillin, M. L. & Matthews, B. W. Accurate calculation of the density of proteins. Acta Crystallogr. Sect. D. 56, 791–794. https://doi.org/10.1107/S090744490000679X (2000).

    Google Scholar 

  25. Rutter, B. D. et al. The development of extracellular vesicle markers for the fungal phytopathogen Colletotrichum higginsianum. J. Extracell. Vesicles. 11, e12216. https://doi.org/10.1002/jev2.12216 (2022).

    Google Scholar 

  26. Pascucci, L. & Scattini, G. Imaging extracelluar vesicles by transmission electron microscopy: coping with technical hurdles and morphological interpretation. Biochim. Et Biophys. Acta (BBA) – Gen. Subj. 1865, 129648. https://doi.org/10.1016/j.bbagen.2020.129648 (2021).

    Google Scholar 

  27. Tian, Y. et al. Quality and efficiency assessment of six extracellular vesicle isolation methods by nano-flow cytometry. J. Extracell. Vesicles. 9, 1697028. https://doi.org/10.1080/20013078.2019.1697028 (2020).

    Google Scholar 

  28. Thumuluri, V., Almagro Armenteros, J. J., Johansen, Alexander, R., Nielsen, H. & Winther, O. DeepLoc 2.0: multi-label subcellular localization prediction using protein Language models. Nucleic Acids Res. 50, W228–W234. https://doi.org/10.1093/nar/gkac278 (2022).

    Google Scholar 

  29. Nickel, W. & Rabouille, C. Mechanisms of regulated unconventional protein secretion. Nat. Rev. Mol. Cell Biol. 10, 148–155. https://doi.org/10.1038/nrm2617 (2009).

    Google Scholar 

  30. Grieve, A. G. & Rabouille, C. Golgi bypass: skirting around the heart of classical secretion. Cold Spring Harb. Perspect. Biol. 3 https://doi.org/10.1101/cshperspect.a005298 (2011).

  31. Meijer, H. J. G. et al. Profiling the secretome and extracellular proteome of the potato late blight pathogen Phytophthora infestans. Mol. Cell. Proteom. 13, 2101–2113. https://doi.org/10.1074/mcp.M113.035873 (2014).

    Google Scholar 

  32. Zerial, M. & McBride, H. Rab proteins as membrane organizers. Nat. Rev. Mol. Cell Biol. 2, 107–117. https://doi.org/10.1038/35052055 (2001).

    Google Scholar 

  33. Stenmark, H. & Olkkonen, V. M. The Rab GTPase family. Genome Biol. 2, reviews3007.3001. https://doi.org/10.1186/gb-2001-2-5-reviews3007 (2001).

  34. Shikanai, M. et al. Rab21 regulates caveolin-1‐mediated endocytic trafficking to promote immature neurite pruning. EMBO Rep. 24, e54701. https://doi.org/10.15252/embr.202254701 (2023).

    Google Scholar 

  35. Conibear, E. & Stevens, T. H. Vps52p, Vps53p, and Vps54p form a novel multisubunit complex required for protein sorting at the yeast late golgi. Mol. Biol. Cell. 11, 305–323. https://doi.org/10.1091/mbc.11.1.305 (2000).

    Google Scholar 

  36. Conibear, E., Cleck, J. N. & Stevens, T. H. Vps51p mediates the association of the GARP (Vps52/53/54) complex with the late golgi t-SNARE Tlg1p. Mol. Biol. Cell. 14, 1610–1623. https://doi.org/10.1091/mbc.e02-10-0654 (2003).

    Google Scholar 

  37. Jahn, R., Cafiso, D. C. & Tamm, L. K. Mechanisms of SNARE proteins in membrane fusion. Nat. Rev. Mol. Cell Biol. 25, 101–118. https://doi.org/10.1038/s41580-023-00668-x (2024).

    Google Scholar 

  38. Welsh, L. R. J. & Whisson, S. C. Protoplast transformation of Phytophthora spp. Methods Mol. Biol. 2892, 35–47. https://doi.org/10.1007/978-1-0716-4330-3_3 (2025).

    Google Scholar 

  39. Ah-Fong, A. M. & Judelson, H. S. Vectors for fluorescent protein tagging in Phytophthora: tools for functional genomics and cell biology. Fungal Biology. 115, 882–890. https://doi.org/10.1016/j.funbio.2011.07.001 (2011).

    Google Scholar 

  40. Judelson, H. S., Tyler, B. M. & Michelmore, R. W. Transformation of the oomycete pathogen, Phytophthora infestans. Mol. Plant. Microbe Interact. 4, 602–607. https://doi.org/10.1094/mpmi-4-602 (1991).

    Google Scholar 

  41. Heard, W., Sklenar, J., Tome, D. F., Robatzek, S. & Jones, A. M. Identification of regulatory and cargo proteins of endosomal and secretory pathways in Arabidopsis thaliana by proteomic dissection. Mol. Cell. Proteomics. 14, 1796–1813. https://doi.org/10.1074/mcp.M115.050286 (2015).

    Google Scholar 

  42. Yeung, Y. G. & Stanley, E. R. Rapid detergent removal from peptide samples with ethyl acetate for mass spectrometry analysis. Curr. Protocols Protein Sci. 59, 16.12.11–16.12.15. https://doi.org/10.1002/0471140864.ps1612s59 (2010).

  43. Doellinger, J., Blumenscheit, C., Schneider, A. & Lasch, P. Isolation window optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling. Anal. Chem. 92, 12185–12192. https://doi.org/10.1021/acs.analchem.0c00994 (2020).

    Google Scholar 

  44. Tyanova, S. et al. The perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods. 13, 731–740. https://doi.org/10.1038/nmeth.3901 (2016).

    Google Scholar 

  45. Larsson, J. eulerr: area-proportional Euler and Venn diagrams with ellipses. R package version 7.0.2. https://CRAN.R-project.org/package=eulerr (2024).

  46. Warnes, G. R. et al. gplots: various R programming tools for plotting data. R package version 3.2.0. https://CRAN.R-project.org/package=gplots (2024).

  47. Wickham, H. ggplot2: elegant graphics for data analysis. https://ggplot2.tidyverse.org (2016).

  48. Zheng, J. et al. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res. 51, W115–W121. https://doi.org/10.1093/nar/gkad328 (2023).

    Google Scholar 

  49. Ebbert, D. chisq.posthoc.test [Chisq.posthoc.test is a R package that is designed to run a post hoc analysis for Pearson’s Chi-squared Test for Count Data]. https://github.com/ebbertd/chisq.posthoc.test (2019).

  50. Raffaele, S., Win, J., Cano, L. M. & Kamoun, S. Analyses of genome architecture and gene expression reveal novel candidate virulence factors in the secretome of Phytophthora infestans. BMC Genom. 11, 637. https://doi.org/10.1186/1471-2164-11-637 (2010).

    Google Scholar 

  51. Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629. https://doi.org/10.1093/bioinformatics/btz931 (2019).

    Google Scholar 

  52. Perez-Riverol, Y. et al. The PRIDE database at 20 years: 2025 update. Nucleic Acids Res. 53, D543–D553. https://doi.org/10.1093/nar/gkae1011 (2024).

    Google Scholar 

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