Comparative secretome analysis unveils species-specific virulence factors in <i>Elsinoe perseae</i>, the causative agent of the scab disease of avocado (<i>Persea americana</i>)

Biju Vadakkemukadiyil Chellappan; Biju Vadakkemukadiyil Chellappan

doi:10.3934/microbiol.2024039

AIMS Microbiology

2024, Volume 10, Issue 4: 894-916. doi: 10.3934/microbiol.2024039

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Research article

Comparative secretome analysis unveils species-specific virulence factors in Elsinoe perseae, the causative agent of the scab disease of avocado (Persea americana)

Biju Vadakkemukadiyil Chellappan ^,

Department of Biological Sciences, College of Science, King Faisal University, P.O. Box 420, Al-Ahsa 31982, Saudi Arabia

Received: 11 July 2024 Revised: 17 October 2024 Accepted: 21 October 2024 Published: 28 October 2024

The scab disease, caused by Elsinoe perseae, poses a significant risk to avocado (Persea americana) production in countries with warm and humid climates. Although the genome has been published, the precise virulence factors accountable for the pathogenicity of E. perseae have not yet been determined. The current study employed an in silico approach to identify and functionally characterize the secretory proteins of E. perseae. A total of 654 potential secretory proteins were identified, of which 190 were classified as carbohydrate-active enzymes (CAZymes), 49 as proteases, and 155 as potential effectors. A comparison to six other closely related species identified 40 species-specific putative effectors in E. perseae, indicating their specific involvement in the pathogenicity of E. perseae on avocado. The data presented in this study might be valuable for further research focused on understanding the molecular mechanisms that contribute to the pathogenicity of E. perseae on avocado.
- CAZymes,
- cell wall–degrading enzymes,
- effector,
- Elsinoe perseae,
- proteases,
- secretome
Citation: Biju Vadakkemukadiyil Chellappan. Comparative secretome analysis unveils species-specific virulence factors in Elsinoe perseae, the causative agent of the scab disease of avocado (Persea americana)[J]. AIMS Microbiology, 2024, 10(4): 894-916. doi: 10.3934/microbiol.2024039

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Abstract

The scab disease, caused by Elsinoe perseae, poses a significant risk to avocado (Persea americana) production in countries with warm and humid climates. Although the genome has been published, the precise virulence factors accountable for the pathogenicity of E. perseae have not yet been determined. The current study employed an in silico approach to identify and functionally characterize the secretory proteins of E. perseae. A total of 654 potential secretory proteins were identified, of which 190 were classified as carbohydrate-active enzymes (CAZymes), 49 as proteases, and 155 as potential effectors. A comparison to six other closely related species identified 40 species-specific putative effectors in E. perseae, indicating their specific involvement in the pathogenicity of E. perseae on avocado. The data presented in this study might be valuable for further research focused on understanding the molecular mechanisms that contribute to the pathogenicity of E. perseae on avocado.

Conflict of Interest

The authors declare no conflict of interest.

Disclosure statement

The author reports there are no competing interests to declare.

References

[1]	Dean R, Van Kan JAL, Pretorius ZA, et al. (2012) The Top 10 fungal pathogens in molecular plant pathology. Mol Plant Pathol 13: 414-430. https://doi.org/10.1111/j.1364-3703.2011.00783.x
[2]	Biju VC, Fokkens L, Houterman PM, et al. (2017) Multiple evolutionary trajectories have led to the emergence of races in Fusarium oxysporum f. sp. lycopersici. Appl Environ Microbiol 83. https://doi.org/10.1128/AEM.02548-16
[3]	Singh RP, Hodson DP, Jin Y, et al. (2015) Emergence and spread of new races of wheat stem rust fungus: continued threat to food security and prospects of genetic control. Phytopathology 105: 872-884. https://doi.org/10.1094/PHYTO-01-15-0030-FI
[4]	Pontes JG de M, Fernandes LS, dos Santos R Vander, et al. (2020) Virulence factors in the phytopathogen–host interactions: an overview. J Agric Food Chem 68: 7555-7570. https://doi.org/10.1021/acs.jafc.0c02389
[5]	Jaswal R, Kiran K, Rajarammohan S, et al. (2020) Effector biology of biotrophic plant fungal pathogens: current advances and future prospects. Microbiol Res 241: 126567. https://doi.org/10.1016/j.micres.2020.126567
[6]	Stergiopoulos I, de Wit PJGM (2009) Fungal effector proteins. Annu Rev Phytopathol 47: 233-263. https://doi.org/10.1146/annurev.phyto.112408.132637
[7]	Pradhan A, Ghosh S, Sahoo D, et al. (2021) Fungal effectors, the double edge sword of phytopathogens. Curr Genet 67: 27-40. https://doi.org/10.1007/s00294-020-01118-3
[8]	Jeffress S, Arun-Chinnappa K, Stodart B, et al. (2020) Genome mining of the citrus pathogen Elsinoë fawcettii; prediction and prioritisation of candidate effectors, cell wall degrading enzymes and secondary metabolite gene clusters. PLoS One 15: e0227396. https://doi.org/10.1371/journal.pone.0227396
[9]	Tan KLS, Mohamad SB (2024) Fungal pathogen in digital age: review on current state and trend of comparative genomics studies of pathogenic fungi. Adv Microbiol 63: 23-31. https://doi.org/10.2478/am-2024-0003
[10]	Azeez SO, Adeboye SE (2024) Advances in understanding plant-pathogen interactions: insights from tomato as a model system. VirusDis . https://doi.org/10.1007/s13337-024-00889-4
[11]	Soanes DM, Richards TA, Talbot NJ (2007) Insights from sequencing fungal and oomycete genomes: what can we learn about plant disease and the evolution of pathogenicity?. Plant Cell 19: 3318-3326. https://doi.org/10.1105/tpc.107.056663
[12]	Wang Y, Wu J, Yan J, et al. (2022) Comparative genome analysis of plant ascomycete fungal pathogens with different lifestyles reveals distinctive virulence strategies. BMC Genomics 23: 34. https://doi.org/10.1186/s12864-021-08165-1
[13]	Li J, Ai M, Hou J, et al. (2024) Plant–pathogen interaction with root rot of Panax notoginseng as a model: Insight into pathogen pathogenesis, plant defence response and biological control. Mol Plant Pathol 25. https://doi.org/10.1111/mpp.13427
[14]	Pernezny K, Marlatt RB Diseases of Avocado in Florida, EDIS Publication PP21 (2007).
[15]	Belizaire CM, Gañán-Betancur L, Gazis R (2023) Avocado scab caused by Elsinoe perseae: A diagnostic guide. Plant Health Prog . https://doi.org/10.1094/PHP-10-23-0084-DG
[16]	Jenkins AE (1934) Sphaceloma Perseae, the cause of avocado scab. J Agric Res 49: 859-869.
[17]	Jenkins AE (1934) A species of Sphaceloma on Avocado. Phytopathology 24.
[18]	Everett KR, Rees-George J, Pushparajah IPS, et al. (2011) Molecular identification of sphaceloma perseae (avocado scab) and its absence in New Zealand. J Phytopathol 159: 106-113. https://doi.org/10.1111/j.1439-0434.2010.01739.x
[19]	Belizaire CM, Gañán-Betancur L, Gazis R (2024) Avocado scab caused by Elsinoe perseae: a diagnostic guide. Plant Health Prog 25: 218-225. https://doi.org/10.1094/PHP-10-23-0084-DG
[20]	Everett KR, Stevens PS, Cutting JGM (1999) Postharvest fruit rots of avocado are reduced by benomyl applications during flowering. Proceedings of the New Zealand Plant Protection Conference 52: 153-156. https://doi.org/10.30843/nzpp.1999.52.11598
[21]	Everett KR, Owen SG, Cutting JGM (2005) Testing efficacy of fungicides against postharvest pathogens of avocado (Persea americana cv Hass). N Z Plant Prot 58: 89-95. https://doi.org/10.30843/nzpp.2005.58.4260
[22]	Sumida CH, Fantin LH, Braga K, et al. (2020) Control of root rot (Phytophthora cinnamomi) in avocado (Persea Americana) with bioagents. Summa Phytopathol 46: 205-211. https://doi.org/10.1590/0100-5405/192195
[23]	Guarnizo N, Álvarez A, Oliveros D, et al. (2022) Elicitor activity of curdlan and its potential application in protection of hass avocado plants against Phytophthora cinnamomi rands. Horticulturae 8: 646. https://doi.org/10.3390/horticulturae8070646
[24]	Gañán-Betancur L, Gazis R (2023) Genome sequence resource of the avocado scab pathogen Elsinoe perseae. Microbiol Resour Announc 12. https://doi.org/10.1128/mra.00190-23
[25]	Humann JL, Lee T, Ficklin S, et al. (2019) Structural and functional annotation of eukaryotic genomes with GenSAS. Methods Mol Biol : 29-51. https://doi.org/10.1007/978-1-4939-9173-0_3
[26]	Li Z, Wang Y, Fan Y, et al. (2021) Transcriptome analysis of the grape- Elsinoë ampelina pathosystem reveals novel effectors and a robust defense response. Mol Plant-Microbe Interact 34: 110-121. https://doi.org/10.1094/MPMI-08-20-0227-R
[27]	Yun HK, Louime C, Lu J (2007) First report of anthracnose caused by Elsinoë ampelina on muscadine grapes (Vitis rotundifolia) in Northern Florida. Plant Dis 91: 905-905. https://doi.org/10.1094/PDIS-91-7-0905B
[28]	Stanke M, Keller O, Gunduz I, et al. (2006) AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res 34: W435-W439. https://doi.org/10.1093/nar/gkl200
[29]	Borodovsky M, Lomsadze A (2011) Eukaryotic Gene Prediction Using GeneMark.hmm-E and GeneMark-ES. Curr Protoc Bioinformatics 35. https://doi.org/10.1002/0471250953.bi0406s35
[30]	Haas BJ, Salzberg SL, Zhu W, et al. (2008) Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol 9. https://doi.org/10.1186/gb-2008-9-1-r7
[31]	Li Z, Fan Y, Chang P, et al. (2020) Genome sequence resource for Elsinoë ampelina , the causal organism of grapevine anthracnose. Mol Plant-Microbe Interact 33: 576-579. https://doi.org/10.1094/MPMI-12-19-0337-A
[32]	Shanmugam G, Jeon J, Hyun J-W (2020) Draft genome sequences of Elsinoë fawcettii and Elsinoë australis causing scab diseases on citrus. Mol Plant-Microbe Interact 33: 135-137. https://doi.org/10.1094/MPMI-06-19-0169-A
[33]	Su J, Liu J, Hu Y, et al. (2022) High-quality genome sequence resource of Elsinoë arachidis strain LY-HS-1, causing scab disease of peanut. Plant Dis 106: 1506-1509. https://doi.org/10.1094/PDIS-11-21-2549-A
[34]	Zhang X, Zou H, Yang Y, et al. (2022) Genome resource for Elsinoë batatas, the causal agent of stem and foliage scab disease of sweet potato. Phytopathology 112: 973-975. https://doi.org/10.1094/PHYTO-08-21-0344-A
[35]	Wingfield BD, Berger DK, Coetzee MPA, et al. (2022) IMA genome-F17. IMA Fungus 13: 19. https://doi.org/10.1186/s43008-022-00104-3
[36]	Chellappan BV, El-Ganainy SM, Alrajeh HS, et al. (2023) In silico characterization of the secretome of the fungal pathogen thielaviopsis punctulata, the causal agent of date palm black scorch disease. J Fungi 9: 303. https://doi.org/10.3390/jof9030303
[37]	Teufel F, Almagro Armenteros JJ, Johansen AR, et al. (2022) SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40: 1023-1025. https://doi.org/10.1038/s41587-021-01156-3
[38]	Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338: 1027-1036. https://doi.org/10.1016/j.jmb.2004.03.016
[39]	Hallgren J, Tsirigos KD, Pedersen MD, et al. (2022) DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. bioRxiv . https://doi.org/10.1101/2022.04.08.487609
[40]	de Castro E, Sigrist CJ a, Gattiker A, et al. (2006) ScanProsite: Detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Nucleic Acids Res 34: 362-365. https://doi.org/10.1093/nar/gkl124
[41]	Almagro Armenteros JJ, Salvatore M, Emanuelsson O, et al. (2019) Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2: e201900429. https://doi.org/10.26508/lsa.201900429
[42]	Horton P, Park K-J, Obayashi T, et al. (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35: W585-W587. https://doi.org/10.1093/nar/gkm259
[43]	Gíslason MH, Nielsen H, Almagro Armenteros JJ, et al. (2021) Prediction of GPI-anchored proteins with pointer neural networks. Curr Res Biotechnol 3: 6-13. https://doi.org/10.1016/j.crbiot.2021.01.001
[44]	Finn RD, Bateman A, Clements J, et al. (2014) Pfam: The protein families database. Nucleic Acids Res 42. https://doi.org/10.1093/nar/gkt1223
[45]	Zdobnov EM, Apweiler R (2001) InterProScan-An integration platform for the signature-recognition methods in InterPro. Bioinformatics 17: 847-848. https://doi.org/10.1093/bioinformatics/17.9.847
[46]	Cantarel BL, Coutinho PM, Rancurel C, et al. (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res 37: D233-D238. https://doi.org/10.1093/nar/gkn663
[47]	Yin Y, Mao X, Yang J, et al. (2012) dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 40: W445-W451. https://doi.org/10.1093/nar/gks479
[48]	Sperschneider J, Gardiner DM, Dodds PN, et al. (2016) EffectorP: Predicting fungal effector proteins from secretomes using machine learning. New Phytologist . https://doi.org/10.1111/nph.13794
[49]	Winnenburg R (2006) PHI-base: a new database for pathogen host interactions. Nucleic Acids Res 34: D459-D464. https://doi.org/10.1093/nar/gkj047
[50]	Rawlings ND, Barrett AJ, Thomas PD, et al. (2018) The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res 46: D624-D632. https://doi.org/10.1093/nar/gkx1134
[51]	Sun J, Lu F, Luo Y, et al. (2023) OrthoVenn3: an integrated platform for exploring and visualizing orthologous data across genomes. Nucleic Acids Res 51: W397-W403. https://doi.org/10.1093/nar/gkad313
[52]	Morales-García JL, López-Cornejo CI, Pedraza-Santos ME, et al. (2023) Morpho-molecular identification of the causal agent of avocado scab in Michoacán. Mexican J Phytopathol 41. https://doi.org/10.18781/R.MEX.FIT.2302-4
[53]	Gavande PV, Goyal A, Fontes CMGA (2023) Carbohydrates and Carbohydrate-Active enZymes (CAZyme): An overview. Glycoside Hydrolases.Elsevier 1-23. https://doi.org/10.1016/B978-0-323-91805-3.00012-5
[54]	Benini S (2020) Carbohydrate-active enzymes: structure, activity, and reaction products. Int J Mol Sci 21: 2727. https://doi.org/10.3390/ijms21082727
[55]	Defilippi BG, Ejsmentewicz T, Covarrubias MP, et al. (2018) Changes in cell wall pectins and their relation to postharvest mesocarp softening of “Hass” avocados (Persea americana Mill.). Plant Physiol Biochem 128: 142-151. https://doi.org/10.1016/j.plaphy.2018.05.018
[56]	Platt-Aloia KA, Thomson WW, Young RE (1980) Ultrastructural changes in the walls of ripening avocados: transmission, scanning, and freeze fracture microscopy. Botanical Gazette 141: 366-373. https://doi.org/10.1086/337169
[57]	Kubicek CP, Starr TL, Glass NL (2014) Plant cell wall–degrading enzymes and their secretion in plant-pathogenic fungi. Annu Rev Phytopathol 52: 427-451. https://doi.org/10.1146/annurev-phyto-102313-045831
[58]	Lairson LL, Henrissat B, Davies GJ, et al. (2008) Glycosyltransferases: structures, functions, and mechanisms. Annu Rev Biochem 77: 521-555. https://doi.org/10.1146/annurev.biochem.76.061005.092322
[59]	Horn SJ, Vaaje-Kolstad G, Westereng B, et al. (2012) Novel enzymes for the degradation of cellulose. Biotechnol Biofuels 5: 45. https://doi.org/10.1186/1754-6834-5-45
[60]	Phillips CM, Beeson WT, Cate JH, et al. (2011) Cellobiose dehydrogenase and a copper-dependent polysaccharide monooxygenase potentiate cellulose degradation by Neurospora crassa. ACS Chem Biol 6: 1399-1406. https://doi.org/10.1021/cb200351y
[61]	Villares A, Moreau C, Bennati-Granier C, et al. (2017) Lytic polysaccharide monooxygenases disrupt the cellulose fibers structure. Sci Rep 7: 40262. https://doi.org/10.1038/srep40262
[62]	Scheller HV, Ulvskov P (2010) Hemicelluloses. Annu Rev Plant Biol 61: 263-289. https://doi.org/10.1146/annurev-arplant-042809-112315
[63]	van den Brink J, de Vries RP (2011) Fungal enzyme sets for plant polysaccharide degradation. Appl Microbiol Biotechnol 91: 1477-1492. https://doi.org/10.1007/s00253-011-3473-2
[64]	Kubicek CP, Starr TL, Glass NL (2014) Plant cell wall–degrading enzymes and their secretion in plant-pathogenic fungi. Annu Rev Phytopathol 52: 427-451. https://doi.org/10.1146/annurev-phyto-102313-045831
[65]	Wilkens C, Andersen S, Dumon C, et al. (2017) GH62 arabinofuranosidases: Structure, function and applications. Biotechnol Adv 35: 792-804. https://doi.org/10.1016/j.biotechadv.2017.06.005
[66]	Chauhan PS, Gupta N (2017) Insight into microbial mannosidases: a review. Crit Rev Biotechnol 37: 190-201. https://doi.org/10.3109/07388551.2015.1128878
[67]	Garron M-L, Cygler M (2014) Uronic polysaccharide degrading enzymes. Curr Opin Struct Biol 28: 87-95. https://doi.org/10.1016/j.sbi.2014.07.012
[68]	Husain Q (2010) β Galactosidases and their potential applications: a review. Crit Rev Biotechnol 30: 41-62. https://doi.org/10.3109/07388550903330497
[69]	Prade RA (1996) Xylanases: from Biology to BioTechnology. Biotechnol Genet Eng Rev 13: 101-132. https://doi.org/10.1080/02648725.1996.10647925
[70]	Flutto L (2003) PECTIN\|Properties and determination. Encyclopedia of Food Sciences and Nutrition.Elsevier 4440-4449. https://doi.org/10.1016/B0-12-227055-X/00901-9
[71]	Willats WG, McCartney L, Mackie W, et al. (2001) Pectin: cell biology and prospects for functional analysis. Plant Mol Biol 47: 9-27. https://doi.org/10.1023/A:1010662911148
[72]	Li J, Peng C, Mao A, et al. (2024) An overview of microbial enzymatic approaches for pectin degradation. Int J Biol Macromol 254: 127804. https://doi.org/10.1016/j.ijbiomac.2023.127804
[73]	Arya GC, Cohen H (2022) The multifaceted roles of fungal cutinases during infection. J Fungi 8: 199. https://doi.org/10.3390/jof8020199
[74]	Jashni MK, Mehrabi R, Collemare J, et al. (2015) The battle in the apoplast: further insights into the roles of proteases and their inhibitors in plant–pathogen interactions. Front Plant Sci 6. https://doi.org/10.3389/fpls.2015.00584
[75]	Todd JNA, Carreón-Anguiano KG, Islas-Flores I, et al. (2022) Fungal effectoromics: a world in constant evolution. Int J Mol Sci 23: 13433. https://doi.org/10.3390/ijms232113433
[76]	Liu W, Liu J, Ning Y, et al. (2013) Recent progress in understanding pamp- and effector-triggered immunity against the rice blast fungus magnaporthe oryzae. Mol Plant 6: 605-620. https://doi.org/10.1093/mp/sst015
[77]	Moreno-Sánchez I, Pejenaute-Ochoa MD, Navarrete B, et al. (2021) Ustilago maydis secreted endo-xylanases are involved in fungal filamentation and proliferation on and inside plants. J Fungi 7: 1081. https://doi.org/10.3390/jof7121081
[78]	Chen S, Songkumarn P, Venu RC, et al. (2013) Identification and characterization of in planta–expressed secreted effector proteins from Magnaporthe oryzae that induce cell death in rice. Mol Plant-Microbe Interact 26: 191-202. https://doi.org/10.1094/MPMI-05-12-0117-R
[79]	Polonio Á, Fernández-Ortuño D, de Vicente A, et al. (2021) A haustorial-expressed lytic polysaccharide monooxygenase from the cucurbit powdery mildew pathogen Podosphaera xanthii contributes to the suppression of chitin-triggered immunity. Mol Plant Pathol 22: 580-601. https://doi.org/10.1111/mpp.13045
[80]	Kombrink A, Thomma BPHJ (2013) LysM effectors: secreted proteins supporting fungal life. PLoS Pathog 9: e1003769. https://doi.org/10.1371/journal.ppat.1003769
[81]	Miya A, Albert P, Shinya T, et al. (2007) CERK1, a LysM receptor kinase, is essential for chitin elicitor signaling in Arabidopsis. Proceedings of the National Academy of Sciences 104: 19613-19618. https://doi.org/10.1073/pnas.0705147104
[82]	Pazzagli L, Seidl-Seiboth V, Barsottini M, et al. (2014) Cerato-platanins: Elicitors and effectors. Plant Sci 228: 79-87. https://doi.org/10.1016/j.plantsci.2014.02.009
[83]	Wösten HAB (2001) Hydrophobins: multipurpose proteins. Annu Rev Microbiol 55: 625-646. https://doi.org/10.1146/annurev.micro.55.1.625

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