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Cited by 176Plant-Microbe Interactions and ImmunityPlant Pathogenic Bacteria StudiesPlant Pathogens and Fungal Diseases

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AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface

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Homma, Felix, Huang, Jie, van der Hoorn, Renier A. L.·2023·Nature Communications
DOI 10.1038/s41467-023-41721-9PMC PMC10533508

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Abstract

Adapted plant pathogens from various microbial kingdoms produce hundreds of unrelated small secreted proteins (SSPs) with elusive roles. Here, we used AlphaFold-Multimer (AFM) to screen 1879 SSPs of seven tomato pathogens for interacting with six defence-related hydrolases of tomato. This screen of 11,274 protein pairs identified 15 non-annotated SSPs that are predicted to obstruct the active site of chitinases and proteases with an intrinsic fold. Four SSPs were experimentally verified to be inhibitors of pathogenesis-related subtilase P69B, including extracellular protein-36 (Ecp36) and secreted-into-xylem-15 (Six15) of the fungal pathogens Cladosporium fulvum and Fusarium oxysporum , respectively. Together with a P69B inhibitor from the bacterial pathogen Xanthomonas perforans and Kazal-like inhibitors of the oomycete pathogen Phytophthora infestans , P69B emerges as an effector hub targeted by different microbial kingdoms, consistent with a diversification of P69B orthologs and paralogs. This study demonstrates the power of artificial intelligence to predict cross-kingdom interactions at the plant-pathogen interface.

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Research works printed but not linked: 85

  1. Doehlemann, G. & Hemetsberger, C. Apoplastic immunity and its suppression by filamentous plant pathogens. New Phytol. 198, 1001–1016 (2013). · DOI
  2. Darino, M., Kanyuka, K. & Hammond-Kosack, K. E. Apoplastic and vascular defences. Essays Biochem. 66, 595–605 (2022). · DOI
  3. van Loon, L. C., Rep, M. & Pieterse, C. M. Significance of inducible defense-related proteins in infected plants. Annu. Rev. Phytopathol 44, 135–162 (2006). · DOI
  4. Jordá, L., Coego, A., Conejero, V. & Vera, P. A genomic cluster containing four differentially regulated subtilisin-like processing protease genes is in tomato plants. J. Biol. Chem. 274, 2360–2365 (1999). · DOI
  5. Jordá, L., Conejero, V. & Vera, P. Characterization of P69E and P69F, two differentially regulated genes encoding new members of the subtilisin-like proteinase family from tomato plants. Plant Physiol. 122, 67–74 (2000). · DOI
  6. Tian, M., Huitema, E., Da Cunha, L., Torto-Alalibo, T. & Kamoun, S. A Kazal-like extracellular serine protease inhibitor from Phytophthora infestans targets the tomato pathogenesis-related protease P69B. J. Biol. Chem. 279, 26370–26377 (2004). · DOI
  7. 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 (2005). · DOI
  8. Tian, M. et al. A Phytophthora infestans cystatin-like protein targets a novel tomato papain-like apoplastic protease. Plant Physiol. 143, 364–377 (2007). · DOI
  9. Shabab, M. et al. Fungal effector protein AVR2 targets diversifying defense-related Cys proteases of tomato. Plant Cell 20, 1169–1183 (2008). · DOI
  10. van Esse, H. P. et al. The Cladosporium fulvum virulence protein Avr2 inhibits host proteases required for basal defense. Plant Cell 20, 1948–1963 (2008). · DOI
  11. Shindo, T. et al. Screen of non-annotated small secreted proteins of Pseudomonas syringae reveals a virulence factor that inhibits tomato immune proteases. PLoS Pathog. 12, e1005874 (2016). · DOI
  12. Mueller, A. N., Ziemann, S., Treitschke, S., Aßmann, D. & Doehlemann, G. Compatibility in the Ustilago maydis-maize interaction requires inhibition of host cysteine proteases by the fungal effector Pit2. PLoS Pathog. 9, e1003177 (2013). · DOI
  13. Clark, K. et al. An effector from the Huanglongbing-associated pathogen targets citrus proteases. Nat. Commun. 9, 1718 (2018). · DOI
  14. Ilyas, M. et al. Functional divergence of two secreted immune proteases of tomato. Curr. Biol. 25, 2300–2306 (2015). · DOI
  15. Buscaill, P. et al. Glycosidase and glycan polymorphism control hydrolytic release of immunogenic flagellin peptides. Science 364, eaav0748 (2019). · DOI
  16. Sueldo, D. J. et al. Activity-based proteomics uncovers suppressed hydrolases and a neo-functionalised antibacterial enzyme at the plant-pathogen interface. New Phytol. https://doi.org/10.1111/nph.18857 (2023). · DOI
  17. Bishop, J. G., Dean, A. M. & Mitchell-Olds, T. Rapid evolution in plant chitinases: molecular targets of selection in plant-pathogen coevolution. Proc. Natl. Acad. Sci. USA. 97, 5322–5327 (2000). · DOI
  18. Bishop, J. G. et al. Selection on Glycine beta-1,3-endoglucanase genes differentially inhibited by a Phytophthora glucanase inhibitor protein. Genetics 169, 1009–1019 (2005). · DOI
  19. Kourelis, J. et al. Evolution of a guarded decoy protease and its receptor in solanaceous plants. Nat. Commun. 11, 4393 (2020). · DOI
  20. Damasceno, C. M. et al. Structure of the glucanase inhibitor protein (GIP) family from Phytophthora species suggests coevolution with plant endo-beta-1,3-glucanases. Mol. Plant-Microbe Interact. 21, 820–830 (2008). · DOI
  21. Schuster, M. et al. Enhanced late blight resistance by engineering an EpiC2B-insensitive immune protease. bioRxiv, (2023). 2023.05.29.541874. · DOI
  22. Humphreys, I. R. et al. Computed structures of core eukaryotic protein complexes. Science 374, eabm4805 (2021). · DOI · Paper page
  23. Ibrahim, T. et al. AlphaFold2-multimer guided high-accuracy prediction of typical and atypical ATG8-binding motifs. PLoS Biol. 21, e3001962 (2023). · DOI
  24. Lu, S. M. et al. Predicting the reactivity of proteins from their sequence alone: Kazal family of protein inhibitors of serine proteinases. Proc. Natl. Acad. Sci. USA. 98, 1410–1415 (2000). · DOI
  25. Holm, L., Laiho, A., Törönen, P. & Salgado, M. DALI shines a light on remote homologs: one hundred discoveries. Protein Sci. 32, e4519 (2023). · DOI
  26. Maynes, J. T., Cherney, M. M., Qasim, M. A., Laskowski, M. Jr & James, M. N. Structure of the subtilisin Carlsberg-OMTKY3 complex reveals two different ovomucoid conformations. Acta Crystallogr. D Biol. Crystallogr. 61, 580–588 (2005). · DOI
  27. Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on TM-score. Nucleic Acids Res. 33, 2302–2309 (2005). · DOI · Paper page
  28. Chu, M. H., Liu, K. L., Wu, H. Y., Yeh, K. W. & Cheng, Y. S. Crystal structure of tarocystatin-papain complex: implications for the inhibition property of group-2 phytocystatins. Planta 234, 243–254 (2011). · DOI
  29. Houterman, P. M. et al. The mixed xylem sap proteome of Fusarium oxysporum-infected tomato plants. Mol. Plant Pathol. 8, 215–221 (2007). · DOI
  30. Dean, R. et al. The top 10 fungal pathogens in molecular plant pathology. Mol. Plant Pathol. 13, 414–430 (2012). · DOI
  31. Mansfield, J. et al. Top 10 plant pathogenic bacteria in molecular plant pathology. Mol. Plant Pathol. 13, 614–629 (2012). · DOI
  32. Kamoun, S. et al. The top 10 oomycete pathogens in molecular plant pathology. Mol. Plant Pathol. 16, 413–434 (2015). · DOI
  33. Buell, C. R. et al. The complete genome sequence of the Arabidopsis and tomato pathogen Pseudomonas syringae pv. tomato DC3000. Proc. Natl. Acad. Sci. USA 100, 10181–10186 (2003). · DOI
  34. Salanoubat, M. et al. Genome sequence of the plant pathogen Ralstonia solanacearum. Nature 415, 497–502 (2002). · DOI
  35. van Kan, J. A. et al. A gapless genome sequence of the fungus Botrytis cinerea. Mol. Plant Pathol. 18, 75–89 (2017). · DOI
  36. Ma, L. J., van der Does, H. C., Borkovich, K. A., Coleman, J. J. & Daboussi, M. J. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature 464, 367–373 (2010). · DOI
  37. Zaccaron, A. Z., Chen, L. H., Samaras, A. & Stergiopoulos, I. A chromosome-scale genome assembly of the tomato pathogen Cladosporium fulvum reveals a compartmentalized genome architecture and the presence of a dispensable chromosome. Microb. Genom. 8, 000819 (2022).
  38. Haas, B. J. et al. Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans. Nature 461, 393–398 (2009). · DOI
  39. Sperschneider, J., Dodds, P. N., Singh, K. B. & Taylor, J. M. ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytol. 217, 1764–1778 (2018). · DOI
  40. Almagro Armenteros, J. J. et al. Detecting sequence signals in targeting peptides using deep learning. Life Sci. Alliance 2, e201900429 (2019a). · DOI
  41. Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019). · DOI
  42. Joosten, M. H. A. J. & de Wit, P. J. G. M. Identification of several pathogenesis-related proteins in tomato leaves inoculated with Cladosporium fulvum (syn. Fulvia fulva) as 1,3-beta-glucanases and chitinases. Plant Physiol. 89, 945–951 (1989). · DOI
  43. Breitenbach, H. H. et al. Contrasting roles of the apoplastic aspartyl protease Apoplastic, enhanced disease Susceptibility1-Dependent1 and Legume Lectin-like Protein1 in Arabidopsis systemic acquired resistance. Plant Physiol. 165, 791–809 (2014). · DOI
  44. Xia, Y. et al. An extracellular aspartic protease functions in Arabidopsis disease resistance signaling. EMBO J. 23, 980–988 (2004). · DOI
  45. Ottmann, C. et al. Structural basis for Ca2+-independence and activation by homodimerization of tomato subtilase 3. Proc. Natl. Acad. Sci. USA 106, 17223–17228 (2009). · DOI
  46. Drenth, J., Kalk, K. H. & Swen, H. M. Binding of chloromethyl ketone substrate analogues to crystalline papain. Biochemistry 15, 3731–3738 (1976). · DOI
  47. Ohnuma, T. et al. Crystal structure and mode of action of a class V chitinase from Nicotiana tabacum. Plant Mol. Biol. 75, 291–304 (2011). · DOI
  48. Masuda, T., Zhao, G. & Mikami, B. Crystal structure of class III chitinase from pomegranate provides the insight into its metal storage capacity. Biosci. Biotechnol. Biochem. 79, 45–50 (2015). · DOI
  49. Fujinaga, M., Chernaia, M. M., Tarasova, N. I., Mosimann, S. C. & James, M. N. Crystal structure of human pepsin and its complex with pepstatin. Protein Sci. 4, 960–972 (1995). · DOI
  50. Richards, A. University of Oxford Advanced Research Computing. Zenodo 22558 (2015).
  51. Khokhani, D., Lowe-Power, T. M., Tran, T. M. & Allen, C. A single regulator mediates strategic switching between attachment/spread and growth/virulence in the plant pathogen Ralstonia solanacearum. mBio 8, e00895–17 (2017). · DOI
  52. Etalo, D. W. et al. System-wide hypersensitive response-associated transcriptome and metabolome reprogramming in tomato. Plant Physiol. 162, 1599–1617 (2013). · DOI
  53. Müller, N. et al. Investigations on VELVET regulatory mutants confirm the role of host tissue acidification and secretion of proteins in the pathogenesis of Botrytis cinerea. New Phytol. 219, 1062–1074 (2018). · DOI
  54. Zhao, M. et al. An integrated analysis of mRNA and sRNA transcriptional profiles in tomato root: insights on tomato wilt disease. PLoS One 13, e0206765 (2018). · DOI
  55. Paulus, J. K. et al. (2020) Extracellular proteolytic cascade in tomato activates immune protease Rcr3. Proc. Natl. Acad. Sci. USA 117, 17409–17417 (2020). · DOI
  56. Liu, Y., Patricelli, M. P. & Cravatt, B. F. Activity-based protein profiling: the serine hydrolases. Proc. Natl. Acad. Sci. USA 96, 14694–14699 (1999). · DOI
  57. Liao, Z. X. et al. Dual RNA-seq of Xanthomonas oryzae pv. oryzicola infecting rice reveals novel insights into bacterial-plant interaction. PLoS One 14, e0215039 (2019). · DOI
  58. Mesarich, C. H. et al. Specific hypersensitive response–associated recognition of new apoplastic effectors from Cladosporium fulvum in wild tomato. Mol. Plant-Microbe Interact. 31, 145–162 (2018). · DOI
  59. Rosengren, K. J., Daly, N. L., Scanlon, M. J. & Craik, D. J. (2001) Solution structure of BSTI: a new trypsin inhibitor from skin secretions of Bombina bombina. Biochemistry 40, 4601–4609 (2001). · DOI
  60. Simbaqueba, J., Rodríguez, E. A., Burbano-David, D., González, C. & Caro-Quintero, A. Putative novel effector genes revealed by the genomic analysis of the phytopathogenic fungus Fusarium oxysporum f. sp. physali (Foph) that infects Cape gooseberry plants. Front. Microbiol. 11, 593915 (2021). · DOI
  61. Liu, X. et al. Host-induced bacterial cell wall decomposition mediates pattern-triggered immunity in Arabidopsis. Elife 3, e01990 (2014). · DOI
  62. Wang, S. et al. Cleavage of a pathogen apoplastic protein by plant subtilases activates host immunity. New Phytol. 229, 3424–3439 (2021). · DOI
  63. Rooney, H. C. et al. Cladosporium Avr2 inhibits tomato Rcr3 protease required for Cf-2-dependent disease resistance. Science 308, 1783–1786 (2005). · DOI
  64. Lozano-Torres, J. L. et al. (2012) Dual disease resistance mediated by the immune receptor Cf-2 in tomato requires a common virulence target of a fungus and a nematode. Proc. Natl. Acad. Sci. USA 109, 10119–10124 (2012). · DOI
  65. Hemetsberger, C., Herrberger, C., Zechmann, B., Hillmer, M. & Doehlemann, G. The Ustilago maydis effector Pep1 suppresses plant immunity by inhibition of host peroxidase activity. PLoS Pathog. 8, e1002684 (2012). · DOI
  66. Gao, M., Nakajima An, D., Parks, J. M. & Skolnick, J. AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nat. Commun. 13, 1744 (2022). · DOI · Paper page
  67. Elfmann, C. & Stülke, J. PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks. Nucl. Acids Res. 51, W404–W410 (2023). · DOI
  68. Tomato Genome Consortium The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485, 635–641 (2012). · DOI
  69. Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109–1115 (2022). · DOI · Paper page
  70. Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, 1721–1729 (2016). · DOI · Paper page
  71. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). · DOI · Paper page
  72. 100 Tomato Genome Sequencing Consortium, Aflitos, S. et al. Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. 80, 136–148 (2014). · DOI
  73. Stam, R. et al. The de novo reference genome and transcriptome assemblies of the wild tomato species Solanum chilense highlights birth and death of NLR genes between tomato species. Genes Genomes Genet. 9, 3933–3941 (2019). · DOI
  74. Wang, X. et al. Genome of Solanum pimpinellifolium provides insights into structural variants during tomato breeding. Nat. Commun. 11, 5817 (2020). · DOI
  75. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 1303, 3997 (2013).
  76. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). · DOI · Paper page
  77. L,i, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011). · DOI
  78. McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010). · DOI · Paper page
  79. Martin, M. et al. WhatsHap: fast and accurate read-based phasing. bioRxiv https://doi.org/10.1101/085050 (2016). · DOI
  80. Slater, G. S. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 6, 31 (2005). · DOI
  81. Weber, E., Gruetzner, R., Werner, S., Engler, C. & Marillonnet, S. Assembly of designer TAL effectors by Golden Gate cloning. PLoS One 6, e19722 (2011). · DOI
  82. Engler, C. et al. A golden gate modular cloning toolbox for plants. ACS Synth. Biol. 3, 839–843 (2014). · DOI
  83. van der Hoorn, R. A. L., Rivas, S., Wulff, B. B., Jones, J. D. G. & Joosten, M. H. A. J. Rapid migration in gel filtration of the Cf-4 and Cf-9 resistance proteins is an intrinsic property of Cf proteins and not because of their association with high-molecular-weight proteins. Plant J. 35, 305–315 (2003). · DOI
  84. Novinec, M., Pavšič, M. & Lenarčič, B. A simple and efficient protocol for the production of recombinant cathepsin V and other cysteine cathepsins in soluble form in Escherichia coli. Protein Expr. Purif. 82, 1–5 (2012). · DOI
  85. Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004). · DOI

Metadata

Authors
Homma, F., Huang, J., & van der Hoorn, R. A. L.
Affiliations
University of Oxford (GB)
Corresponding author
van der Hoorn, Renier A. L.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-09-27
Volume
14
Issue
1
Pages
6040-6040
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-023-41721-9
PMC
PMC10533508
OpenAlex
W4387090137
Cited by
176
References
6
Citations by year
2026: 68 · 2025: 64 · 2024: 38 · 2023: 6
FWCI
27.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of Oxford
  • Guangxi University
  • Biotechnology and Biological Sciences Research Council (101019324, ERC-AdG-2019, DDT00060)
  • Clarendon Fund
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant-Microbe Interactions and Immunity
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Pathogenic Bacteria Studies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Pathogens and Fungal Diseases
Keywords
AlphaFold-Multimer, small secreted proteins, plant-pathogen interaction, tomato pathogens, chitinase inhibitors, protease inhibitors, Phytophthora infestans, Cladosporium fulvum, Fusarium oxysporum, Xanthomonas perforans, cross-kingdom interactions, protein-protein interaction prediction
MeSH terms
Artificial Intelligence, Peptide Hydrolases, Endopeptidases, Plant Diseases, Plant Proteins, Solanum lycopersicum

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