Comparative secretome analysis of Indian wheat leaf rust pathogen Puccinia triticina


Abstract views: 160 / PDF downloads: 79

Authors

  • RAJDEEP JASWAL Senior Research Fellow, National Agri-Food Biotechnology Institute, Mohali, Punjab
  • HIMANSHU DUBEY Senior Research Fellow, National Agri-Food Biotechnology Institute, Mohali, Punjab
  • KANTI KIRAN Research Associate, ICAR-National Research Centre on Plant Biotechnology, New Delhi 110 012
  • PANKAJ KUMAR SINGH Senior Research Fellow, National Agri-Food Biotechnology Institute, Mohali, Punjab
  • HUKAM C RAWAL Research Associate, ICAR-National Research Centre on Plant Biotechnology, New Delhi 110 012
  • SUBHASH C BHARDWAJ Principal Scientist (HOD), National Agri-Food Biotechnology Institute, Mohali, Punjab
  • PRAMOD PRASAD Scientist, ICAR-Indian Institute of Wheat and Barley Research, Regional Station, Shimla
  • NAVEEN GUPTA Assistant Professor, Department of Microbiology, Panjab University, Chandigarh, Punjab.
  • T R SHARMA Executive Director , National Agri-Food Biotechnology Institute (NABI), Mohali, Punjab

https://doi.org/10.56093/ijas.v89i10.94629

Keywords:

Effector proteins, Puccinia, Rust pathogen, Secretome, Wheat

Abstract

The secretome of two races, 77-5 and 106 of wheat leaf rust pathogen Puccinia triticina with known virulence and avirulent trait, respectively were analyzed in this study. The secretome analysis revealed 546 putative secretory proteins (PSPs) present in the race 77-5, and 481 PSPs in race 106. Race-specific PSPs analysis also showed that race 77-5 had higher number of PSPs (19.72%) in comparison to race 106. Various other gene families like pathogenicity and virulence factor were also expanded in higher number in the race 77-5 and few of them having multiple domains which are known for pathogenicity, were exclusively present in this virulent race. The candidate secretory effector proteins (CSEP) analysis also showed that the virulent race contained 8.98% higher CSEP proteins compared to the avirulent race 106. The results suggest that these genes are playing important roles in their respective race-specific manner. Surprisingly the carbohydrate metabolism-related enzymes were found 5.26% higher in the avirulent race in comparison to the virulent one, and few of them have shown race specificity. The in-silico expression analysis of the selected candidate's genes also revealed their role in the pathogenesis process. The three dimensional (3D) structure predictions were performed for few of the candidate genes that were highly expressed. In this comparative secretome analysis, our findings provide a baseline for the characterization of effectors and avirulence factors in different races of P. triticina.

Downloads

Download data is not yet available.

References

Bhardwaj S C. 2011. Resistance genes and adult plant rust resistance of released wheat varieties of India. Regional Station, Directorate of Wheat Research.

Bhardwaj S C. 2013. Puccinia–Triticum interaction: an update. Journal of Indian Phytopathology 66: 14–9.

Bruce S A, Saville B J and Emery R N. 2011. Ustilago maydis produces cytokinins and abscisic acid for potential regulation of tumor formation in maize. Journal of Plant Growth Regulation 30(1): 51–63.

Bruce, Stacey A, Barry J Saville and R J Neil Emery. 2011. "Ustilago maydis produces cytokinins and abscisic acid for potential regulation of tumor formation in maize." Journal of Plant Growth Regulation 30: 151–63. DOI: https://doi.org/10.1007/s00344-010-9166-8

Chanclud E, Kisiala A, Emery N R, Chalvon V, Ducasse A, Romiti- Michel C and Morel J B. 2016. Cytokinin production by the rice blast fungus is a pivotal requirement for full virulence. PLoS Pathogens 12(2): e1005457. DOI: https://doi.org/10.1371/journal.ppat.1005457

Cortazar A R, Aransay A M, Alfaro M, Oguiza J A and Lavín J L. 2014. SECRETOOL: integrated secretome analysis tool for fungi. Amino Acids 46(2): 471–3. DOI: https://doi.org/10.1007/s00726-013-1649-z

Crafts C B and Miller C O. 1974. Detection and identification of cytokinins produced by mycorrhizal fungi. Plant Physiology 54(4): 586–8. DOI: https://doi.org/10.1104/pp.54.4.586

Draz I S, Abou-Elseoud M S, Kamara A E M, Alaa-Eldein O A E and El-Bebany A F. 2015. Screening of wheat genotypes for leaf rust resistance along with grain yield. Annals of Agricultural Sciences 60(1): 29–39. DOI: https://doi.org/10.1016/j.aoas.2015.01.001

German S, Barcellos A, Chaves M, Kohli M, Campos P and de Viedma L. 2007. The situation of common wheat rusts in the Southern Cone of America and perspectives for control. Australian Journal of Agricultural Research 58(6): 620–30. DOI: https://doi.org/10.1071/AR06149

Herrera-Foessel S A, Lagudah E S, Huerta-Espino J, Hayden M J, Bariana H S, Singh D and Singh R P. 2011. New slow-rusting leaf rust and stripe rust resistance genes Lr67 and Yr46 in wheat are pleiotropic or closely linked. Theoretical and Applied Genetics 122(1): 239–49. DOI: https://doi.org/10.1007/s00122-010-1439-x

Hinsch J, Galuszka P and Tudzynski P. 2016. Functional characterization of the first filamentous fungal tRNA is open tenyltransferase and its role in the virulence of Claviceps purpurea. New Phytologist 211(3): 980–92. DOI: https://doi.org/10.1111/nph.13960

Jiang C J, Shimono M, Sugano S, Kojima M, Liu X, Inoue H and Takatsuji H. (2013). Cytokinins act synergistically with salicylic acid to activate defense gene expression in rice. Molecular Plant-Microbe Interactions 26(3): 287–296. DOI: https://doi.org/10.1094/MPMI-06-12-0152-R

Kazan K and Lyons R. 2014. Intervention of phytohormone pathways by pathogen effectors. Plant Cell 26(6): 2285–2309. DOI: https://doi.org/10.1105/tpc.114.125419

Kiran K, Rawal H C, Dubey H, Jaswal R, Devanna B N, Gupta D K and Balasubramanian P. 2016. Draft genome of the wheat rust pathogen (Puccinia triticina) unravels genome-wide structural variations during evolution. Genome Biology and Evolution 8(9): 2702–21. DOI: https://doi.org/10.1093/gbe/evw197

Li B, Yu J P J, Brunzelle J S, Moll G N, Van Der Donk W A and Nair S K. 2006. Structure and mechanism of the lantibioticcyclase involved in nisin bio synthesis. Repka L M, Chekan J R, Nair S. Science 311(5766): 1464–7. DOI: https://doi.org/10.1126/science.1121422

K Van and Der Donk W A. 2017. Mechanistic understanding of lanthipeptide biosynthetic enzymes. Chemical Reviews 117(8): 5457–20. DOI: https://doi.org/10.1021/acs.chemrev.6b00591

Ma Z, Song T, Zhu L, Ye W, Wang Y, Shao Y and Tyler B M. 2015. A Phytophthora sojae glycoside hydrolase 12 protein is a major virulence factor during soybean infection and is recognized as a PAMP. The Plant cell tpc-15. DOI: https://doi.org/10.1105/tpc.15.00390

Marchler-Bauer A and Bryant S H. 2004.CD-Search: protein domain annotations on the fly. Nucleic Acids Research 32(suppl_2), W327-W331. DOI: https://doi.org/10.1093/nar/gkh454

Miller C O. 1967. Zeatin and zeatin riboside from a mycorrhizal fungus. Science 157(3792): 1055–7. DOI: https://doi.org/10.1126/science.157.3792.1055

Murphy A M, Pryce-Jones E, Johnstone K and Ashby A M. 1997. Comparison of cytokinin production in vitro by Pyrenopeziza brassicae with other plant pathogens. Physiological and Molecular Plant Pathology 50(1): 53–65. DOI: https://doi.org/10.1006/pmpp.1996.0070

Nayar S K, Prashar M, Kumar J, Bhardwaj S C and Verma L R. 1996. Distribution pattern of Puccinia recondita tritici pathotypes in India during 1990-94. Indian Journal of Agricultural Sciences 66(10): 621–30.

Pliego C, Nowara D, Bonciani G, Gheorghe D M, Xu R, Surana P and Schweizer P. 2013. Host-induced gene silencing in barley powdery mildew reveals a class of ribonuclease-like effectors. Molecular Plant-Microbe Interactions 26(6): 633–42. DOI: https://doi.org/10.1094/MPMI-01-13-0005-R

Praz C R, Bourras S, Zeng F, Sanchez Martin J, Menardo F, Xue M and McNally K E. 2017. AvrPm2 encodes an RNase like avirulence effector which is conserved in the two different specialized forms of wheat and rye powdery mildew fungus. New Phytologist 213(3): 1301–14. DOI: https://doi.org/10.1111/nph.14372

Rawlings N D and Barrett A J. 1995. Evolutionary families of metallopeptidases. Methods in Enzymology 248: 183–228. DOI: https://doi.org/10.1016/0076-6879(95)48015-3

Seals D F and Courtneidge S A. 2003. The ADAMs family of metalloproteases: multidomain proteins with multiple functions. Genes and Development 17(1): 7–30. DOI: https://doi.org/10.1101/gad.1039703

Sperschneider J, Dodds P N, Singh K B and Taylor J M. 2018. ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytologist 217(4): 1764–78. DOI: https://doi.org/10.1111/nph.14946

Sperschneider J, Gardiner D M, Dodds P N, Tini F, Covarelli L, Singh K B and Taylor J M. 2016. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytologist 210(2): 743–61. DOI: https://doi.org/10.1111/nph.13794

Stergiopoulos I and de Wit, P. J. (2009). Fungal effector proteins. Annual Review of Phytopathology 47: 233–63. DOI: https://doi.org/10.1146/annurev.phyto.112408.132637

Thind T S. 2005. Diseases of field crops and their management. Daya Books.

Wood J D, Nucifora F C, Duan K, Zhang C, Wang J, Kim Y and Ross C A. 2000. Atrophin-1, the dentato-rubral and pallido-luysian atrophy gene product, interacts with ETO/MTG8 in the nuclear matrix and represses transcription. Journal of Cell Biology 150(5): 939–48. DOI: https://doi.org/10.1083/jcb.150.5.939

Yang J and Zhang Y. 2015. Protein structure and function prediction using I-TASSER. Current Protocols in Bioinformatics 52(1): 5–8. DOI: https://doi.org/10.1002/0471250953.bi0508s52

Yu C S, Chen Y C, Lu C H and Hwang J K. 2006. Prediction of protein subcellular localization. Proteins: Structure, Function, and Bioinformatics 64(3): 643–51. DOI: https://doi.org/10.1002/prot.21018

Downloads

Submitted

2019-10-22

Published

2019-10-22

Issue

Section

Articles

How to Cite

JASWAL, R., DUBEY, H., KIRAN, K., SINGH, P. K., RAWAL, H. C., BHARDWAJ, S. C., PRASAD, P., GUPTA, N., & SHARMA, T. R. (2019). Comparative secretome analysis of Indian wheat leaf rust pathogen Puccinia triticina. The Indian Journal of Agricultural Sciences, 89(10), 1688–1692. https://doi.org/10.56093/ijas.v89i10.94629
Citation