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
Jones DA, Bertazzoni S, Turo CJ et al (2018) Bioinformatic prediction of plant–pathogenicity effector proteins of fungi. Curr Opin Microbiol 46:43–49
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
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
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
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
Lo Presti L, Lanver D, Schweizer G et al (2015) Fungal effectors and plant susceptibility. Ann Rev Plant Biol 66:513–545
Dong S, Raffaele S, Kamoun S (2015) The two-speed genomes of filamentous pathogens: waltz with plants. Curr Opin Genet Dev 35:57–65
Raffaele S, Kamoun S (2012) Genome evolution in filamentous plant pathogens: why bigger can be better. Nat Rev Microbiol 10:417–430
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
Depotter JRL, Doehlemann G (2020) Target the core: durable plant resistance against filamentous plant pathogens through effector recognition. Pest Manag Sci 76:426–431
Sperschneider J, Gardiner DM, Dodds PN et al (2016) EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol 210:743–761
Sonah H, Deshmukh RK, Bélanger RR (2016) Computational prediction of effector proteins in fungi: opportunities and challenges. Front Plant Sci 7:1–14
Rice ES, Green RE (2019) New approaches for genome assembly and scaffolding. Annu Rev Anim Biosci 7:17–40
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
Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829
Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19(6):1117–1123
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
Simão FA, Waterhouse RM, Ioannidis P et al (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:3210–3212
Cissé OH, Stajich JE (2019) FGMP: assessing fungal genome completeness. BMC Bioinform 20:1–9
Holt C, Yandell M (2011) MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform 12:491
Stanke M, Waack S (2003) Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19(SUPPL 2):215–225
Korf I (2004) Gene finding in novel genomes. BMC Bioinform 5:1–9
Lomsadze A, Ter-Hovhannisyan V, Chernoff YO, Borodovsky M (2005) Gene identification in novel eukaryotic genomes by self-training algorithm. NAR 33:6494–6506
Peberdy JF (1994) Protein secretion in filamentous fungi – trying to understand a highly productive black box. Trends in Biotech 12:50–57
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
Armenteros JJA, Salvatore M, Emanuelsson O et al (2019) Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2:1–14
von Heijne G (1986) Mitochondrial targeting sequences may form amphiphilic helices. EMBO J 5:1335–1342
von Heijne G, Steppuhn J, Hermann RG (1989) Domain structure of mitochondrial and chloroplast targeting peptides. Eur J Biochem 180:535–545
von Heijne G (1990) The signal peptide J Membrane Biol 115:195–201
Robinson C, Mant A (1997) Targeting of proteins into and across the thylakoid membrane. Trends Plant Sci 2:431–437
Miura N, Ueda M (2018) Evaluation of unconventional protein secretion by Saccharomyces cerevisiae and other fungi. Cell 7(9):128
Liu T, Song T, Zhang X et al (2014) Unconventionally secreted effectors of two filamentous pathogens target plant salicylate biosynthesis. Nat Commun 5:4686
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
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
Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiol 5:1–13
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
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
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
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
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
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
Liu C, Talbot NJ, Chen XL (2021) Protein glycosylation during infection by plant pathogenic fungi. New Phytol 230:1329–1335
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
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
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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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