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A Bioinformatic Guide to Identify Protein Effectors from Phytopathogens

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Plant-Pathogen Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2659))

Abstract

Phytopathogenic fungi are a diverse and widespread group that has a significant detrimental impact on crops with an estimated annual average loss of 15% worldwide. Understanding the interaction between host plants and pathogenic fungi is critical to delineate underlying mechanisms of plant defense to mitigate agricultural losses. Fungal pathogens utilize suites of secreted molecules, called effectors, to modulate plant metabolism and immune response to overcome host defenses and promote colonization. Effectors come in many flavors including proteinaceous products, small RNAs, and metabolites such as mycotoxins. This review will focus on methods for identifying protein effectors from fungi. Excellent reviews have been published to identify secondary metabolites and small RNAs from fungi and therefore will not be part of this review.

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References

  1. Jones DA, Bertazzoni S, Turo CJ et al (2018) Bioinformatic prediction of plant–pathogenicity effector proteins of fungi. Curr Opin Microbiol 46:43–49

    Article  CAS  PubMed  Google Scholar 

  2. Sperschneider J, Dodds PN et al (2018) Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0. Mol Plant Pathol 19:2094–2110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wang C, Wang P, Han S et al (2020) FunEffector-Pred: identification of fungi effector by activate learning and genetic algorithm sampling of imbalanced data. IEEE. Access 8(Ml):57674–57683

    Article  Google Scholar 

  4. Lu S, Edwards MC (2016) Genome-wide analysis of small secreted cysteine-rich proteins identifies candidant effector proteins potentially involved in Fusarium graminearum-wheat interactions. Gen and Res. https://doi.org/10.1094/PHYTO-09-15-0215-R

  5. Alouane T, Rimbert H, Bormann J et al (2021) Comparative genomics of eight Fusarium graminearum strains with contrasting aggressiveness reveals an expanded open pangenome and extended effector content signatures. Int J Mol Sci 22:6257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lo Presti L, Lanver D, Schweizer G et al (2015) Fungal effectors and plant susceptibility. Ann Rev Plant Biol 66:513–545

    Article  CAS  Google Scholar 

  7. Dong S, Raffaele S, Kamoun S (2015) The two-speed genomes of filamentous pathogens: waltz with plants. Curr Opin Genet Dev 35:57–65

    Article  CAS  PubMed  Google Scholar 

  8. Raffaele S, Kamoun S (2012) Genome evolution in filamentous plant pathogens: why bigger can be better. Nat Rev Microbiol 10:417–430

    Article  CAS  PubMed  Google Scholar 

  9. Catanzariti AM, Dodds PN, Lawrence GJ et al (2006) Haustorially expressed secreted proteins from flax rust are highly enriched for avirulence elicitors. Plant Cell 18:243–256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Depotter JRL, Doehlemann G (2020) Target the core: durable plant resistance against filamentous plant pathogens through effector recognition. Pest Manag Sci 76:426–431

    Article  CAS  PubMed  Google Scholar 

  11. Sperschneider J, Gardiner DM, Dodds PN et al (2016) EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol 210:743–761

    Article  CAS  PubMed  Google Scholar 

  12. Sonah H, Deshmukh RK, Bélanger RR (2016) Computational prediction of effector proteins in fungi: opportunities and challenges. Front Plant Sci 7:1–14

    Article  Google Scholar 

  13. Rice ES, Green RE (2019) New approaches for genome assembly and scaffolding. Annu Rev Anim Biosci 7:17–40

    Article  CAS  PubMed  Google Scholar 

  14. Jung H, Ventura T, Sook Chung J et al (2020) Twelve quick steps for genome assembly and annotation in the classroom. PLoS Comp Biol 16:1–25

    Article  Google Scholar 

  15. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19(6):1117–1123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bankevich A, Nurk S, Antipov D et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comp Biol 19:455–477

    Article  CAS  Google Scholar 

  18. Simão FA, Waterhouse RM, Ioannidis P et al (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:3210–3212

    Article  PubMed  Google Scholar 

  19. Cissé OH, Stajich JE (2019) FGMP: assessing fungal genome completeness. BMC Bioinform 20:1–9

    Article  Google Scholar 

  20. Holt C, Yandell M (2011) MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform 12:491

    Article  Google Scholar 

  21. Stanke M, Waack S (2003) Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19(SUPPL 2):215–225

    Article  Google Scholar 

  22. Korf I (2004) Gene finding in novel genomes. BMC Bioinform 5:1–9

    Article  Google Scholar 

  23. Lomsadze A, Ter-Hovhannisyan V, Chernoff YO, Borodovsky M (2005) Gene identification in novel eukaryotic genomes by self-training algorithm. NAR 33:6494–6506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Peberdy JF (1994) Protein secretion in filamentous fungi – trying to understand a highly productive black box. Trends in Biotech 12:50–57

    Article  CAS  Google Scholar 

  25. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK et al (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotech 37:420–423

    Article  CAS  Google Scholar 

  26. Armenteros JJA, Salvatore M, Emanuelsson O et al (2019) Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2:1–14

    Google Scholar 

  27. von Heijne G (1986) Mitochondrial targeting sequences may form amphiphilic helices. EMBO J 5:1335–1342

    Article  Google Scholar 

  28. von Heijne G, Steppuhn J, Hermann RG (1989) Domain structure of mitochondrial and chloroplast targeting peptides. Eur J Biochem 180:535–545

    Article  Google Scholar 

  29. von Heijne G (1990) The signal peptide J Membrane Biol 115:195–201

    Article  Google Scholar 

  30. Robinson C, Mant A (1997) Targeting of proteins into and across the thylakoid membrane. Trends Plant Sci 2:431–437

    Article  Google Scholar 

  31. Miura N, Ueda M (2018) Evaluation of unconventional protein secretion by Saccharomyces cerevisiae and other fungi. Cell 7(9):128

    Article  CAS  Google Scholar 

  32. Liu T, Song T, Zhang X et al (2014) Unconventionally secreted effectors of two filamentous pathogens target plant salicylate biosynthesis. Nat Commun 5:4686

    Article  CAS  PubMed  Google Scholar 

  33. Sperschneider J, Dodds PN, Singh KB et al (2017) ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytol 217:1764–1778

    Article  PubMed  Google Scholar 

  34. Bendtsen JD, Jensen LJ, Blom N et al (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17:349–356

    Article  CAS  PubMed  Google Scholar 

  35. Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiol 5:1–13

    Article  Google Scholar 

  36. Lonsdale A, Davis MJ, Doblin MS, Bacic A (2016) Better than nothing? Limitations of the prediction tool secretomeP in the search for leaderless secretory proteins (LSPs) in plants. Front Plant Sci 7:1–13

    Article  Google Scholar 

  37. Sperschneider J, Williams AH, Hane JK et al (2015) Evaluation of secretion prediction highlights differing approaches needed for oomycete and fungal effectors. Front Plant Sci 6:1–14

    Article  Google Scholar 

  38. Sonnhammer ELL, Krogh A (1998) A hidden Markov model for predicting transmembrane helices in protein sequence. Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, 8. papers://4b986d00-906f-493f-a74b-71e29d82b719/Paper/p6291

    Google Scholar 

  39. Ludwig N, Reissmann S, Schipper K et al (2021) A cell surface-exposed protein complex with an essential virulence function in Ustilago maydis. Nat Microbiol 6:22–730

    Article  Google Scholar 

  40. Weigele BA, Orchard RC, Jimenez A et al (2017) A systematic exploration of the interactions between bacterial effector proteins and host cell membranes. Nat Commun 8:532

    Article  PubMed  PubMed Central  Google Scholar 

  41. Fernando U, Chatur S, Joshi M et al (2019) Redox signalling from NADPH oxidase targets metabolic enzymes and developmental proteins in Fusarium graminearum. Mol Plant Pathol 20:92–106

    Article  CAS  PubMed  Google Scholar 

  42. Liu C, Talbot NJ, Chen XL (2021) Protein glycosylation during infection by plant pathogenic fungi. New Phytol 230:1329–1335

    Article  CAS  PubMed  Google Scholar 

  43. Sperschneider J, Dodds P (2021) EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol Plant-Microbe Interact. https://doi.org/10.1094/MPMI-08-21-0201-R

  44. Jones DAB, Rozano L, Debler JW et al (2021) An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens. Sci Rep 11:19731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Rajagopal Subramaniam .

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© 2023 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food

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Blackman, C., Subramaniam, R. (2023). A Bioinformatic Guide to Identify Protein Effectors from Phytopathogens. In: Foroud, N.A., Neilson, J.A.D. (eds) Plant-Pathogen Interactions. Methods in Molecular Biology, vol 2659. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3159-1_8

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  • DOI: https://doi.org/10.1007/978-1-0716-3159-1_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3158-4

  • Online ISBN: 978-1-0716-3159-1

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