Transcriptomic Analysis of Pathogenicity Genes in Sclerotinia sclerotiorum Affecting Brassica napus

Document Type : Research Article

Authors

1 Department of Plant Production and Genetics, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

2 Department of Plant Protection, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran Mollasani, Iran.

Abstract

Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum is one of the most destructive diseases of rapeseed (Brassica napus L.) globally. This study identifies key genes involved in the pathogenicity of S. sclerotiorum during rapeseed infection. It examines these genes' codon usage bias (CUB) to find the optimal codons effective in gene expression and the factors effective in the formation of CUB. Protein-protein interaction network was drawn and several hub genes were determined and ranked according to the CytoHubba tool among which the genes encoding nitrate reductase, beta-glucosidase, glucanase, PA14 domain-containing protein, carbohydrate-binding module family 1, and acyl-coenzyme A oxidase were found to be associated with the SSR induction. Gene ontology (GO) analysis showed that the genes related to the metabolism of organic substances, catalytic activity, and cellular anatomical entity had the highest count. Also, KEGG pathways analysis revealed 16 biological pathways modulated by S. sclerotiorum among which the genes associated with the metabolic pathways exhibited the highest count. CUB indices including CAI (codon adaptation index), ENC (effective number of codons), GC, GC3S (GC content in the third open position of the codon), and RSCU (relative synonymous codon usage) were determined and the results showed a significant positive correlation between GC and GC3S. Also, the possible effects of mutation pressure and natural selection in shaping CUB were determined. The mean ENC range from 42.4-56.65 indicating less orientation in codon usage. The range CAI was found to be 0.74-0.89 indicating the importance of genes in adapting to environmental stresses. The maximum RSCU was 1.76 for the CCA codon, which encodes the amino acid (aa) proline with a high preference for that codon compared to other synonymous codons of that aa. These results demonstrated that S. sclerotiorum modulates some genes involved in disease induction (e.g., cell wall-degrading enzymes, biosynthesis of secondary metabolites & metabolic pathways) to infect its host plant.

Keywords

Main Subjects

References

Alahakoon, A. A. Y. (2019). Engineering disease resistance and frost tolerance in rapeseed (Brassica napus L.) using ACYL-COENZYME A-BINDING PROTEINS. PhD thesis, The University of Melbourne, Australia. Retrieved from https://rest.neptune-prod.its.unimelb.edu.au.
Bardin, S. D., & Huang, H. C. (2001). Research on biology and control of Sclerotinia diseases in Canada. Canadian Journal of Plant Pathology, 23(1), 88-98. https://doi.org/10.1080/07060660109506914
Bastien, M., Sonah, H., & Belzile, F. (2014). Genome wide association mapping of Sclerotinia sclerotiorum resistance in soybean with a genotyping‐by‐sequencing approach. The Plant Genome, 7(1), 1-13. https://doi.org/10.3835/plantgenome2013.10.0030
Calvo, A. M., & Cary, J. W. (2015). Association of fungal secondary metabolism and sclerotial biology. Frontiers in Microbiology, 6, 62. https://doi.org/10.3389/fmicb.2015.00062
Chahed, H., Ezzine, A., Mlouka, A. B., Hardouin, J., Jouenne, T., & Marzouki, M. N. (2014). Biochemical characterization, molecular cloning, and structural modeling of an interesting β-1, 4-glucanase from Sclerotinia sclerotiorum. Molecular Biotechnology, 56, 340-350. https://doi.org/10.1007/s12033-013-9714-0
Chen, H., Sun, S., Norenburg, J. L., & Sundberg, P. (2014). Mutation and selection cause codon usage and bias in mitochondrial genomes of ribbon worms (Nemertea). PloS One, 9(1), e85631. https://doi.org/10.1371/journal.pone.0085631
Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2014). CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 8, 1-7. https://doi.org/10.1186/1752-0509-8-S4-S11
Chittem, K., Yajima, W. R., Goswami, R. S., & del Río Mendoza, L. E. (2020). Transcriptome analysis of the plant pathogen Sclerotinia sclerotiorum interaction with resistant and susceptible canola (Brassica napus) lines. PLoS One, 15(3), e0229844. https://doi.org/10.1371/journal.pone.0229844
Duke, K. A., Becker, M. G., Girard, I. J., Millar, J. L., Dilantha Fernando, W. G., Belmonte, M. F., & de Kievit, T. R. (2017). The biocontrol agent Pseudomonas chlororaphis PA23 primes Brassica napus defenses through distinct gene networks. BMC Genomics, 18, 1-16. https://doi.org/10.1186/s12864-017-3848-6
Fuglsang, A. (2008). Impact of bias discrepancy and amino acid usage on estimates of the effective number of codons used in a gene, and a test for selection on codon usage. Gene, 410, 82-88. https://doi.org/10.1016/j.gene.2007.12.001
Guillén, D., Sánchez, S., & Rodríguez-Sanoja, R. (2010). Carbohydrate-binding domains: multiplicity of biological roles. Applied Microbiology and Biotechnology, 85, 1241-1249. https://doi.org/10.1007/s00253-009-2331-y
Colagar, A. H., Saadati, M., Zarea, M., & Talei, S. A (2010). Genetic variation of the Iranian Sclerotinia sclerotiorum isolates by standardizing DNA polymorphic fragments. Biotechnology (-Pakistan), 9(1), 67-72. https://doi/full/10.5555/20103179789
Khangura, R., Beard, C., & Hills, A. (2015). Managing sclerotinia stem rot in rapeseed. Department of Agriculture and Food, Australian Government, Australia. https://library.dpird.wa.gov.au/
Kubicek, C. P., Starr, T. L., & Glass, N. L. (2014). Plant cell wall–degrading enzymes and their secretion in plant-pathogenic fungi. Annual Review of Phytopathology, 52(1), 427-451. https://doi.org/10.1146/annurev-phyto-102313-045831
Larroque, M., Barriot, R., Bottin, A., Barre, A., Rougé, P., Dumas, B., & Gaulin, E. (2012). The unique architecture and function of cellulose-interacting proteins in oomycetes revealed by genomic and structural analyses. BMC Genomics, 13, 1-15. https://doi.org/10.1186/1471-2164-13-605
Li, M., Li, D., Tang, Y., Wu, F., & Wang, J. (2017). CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. International Journal of Molecular Sciences, 18(9): 1880. https://doi.org/10.3390/ijms18091880
Li, Y., Song, S., Chen, B., Zhang, Y., Sun, T., Ma, X., ... & Zhang, X. (2024). Deleting an xylosidase-encoding gene VdxyL3 increases growth and pathogenicity of Verticillium dahlia. Frontiers in Microbiology, 15, 1428780. https://doi.org/10.3389/fmicb.2024.1428780
Liu, B., Ma, Z., Gai, X., Sun, Y., Wang, Y., He, S., & Gao, Z. (2018). Analysis of putative sclerotia maturation-related gene expression in Rhizoctonia solani AG1-IA. Archives of Biological Sciences, 70(4), 647-653. http://orcid.org/0000-0002-8342-8613
Nath Choudhury, M., Uddin, A., Chakraborty, S. (2017). Codon usage bias and its influencing factors for Y-linked genes in human. Computational Biology and Chemistry, 69, 77-86. https://doi.org/10.1016/j.compbiolchem.2017.05.005
Pazhamala, L. T., Kudapa, H., Weckwerth, W., Millar, A. H., & Varshney, R. K. (2021). Systems biology for crop improvement. The Plant Genome, 14(2), e20098. https://doi.org/10.1002/tpg2.20098
Peng, Q., Xie, Q., Chen, F., Zhou, X., Zhang, W., Zhang, J., ... & Chen, S. (2017). Transcriptome analysis of Sclerotinia sclerotiorum at different infection stages on Brassica napus. Current Microbiology, 74, 1237-1245. https://doi.org/10.1007/s00284-017-1309-8
Pieterse, C. M., Zamioudis, C., Berendsen, R. L., Weller, D. M., Van Wees, S. C., & Bakker, P. A. (2014). Induced systemic resistance by beneficial microbes. Annual Review of Phytopathology, 52(1), 347-375. https://doi.org/10.1146/annurev-phyto-082712-102340
Riou, C., Freyssinet, G., & Fevre, M. (1991). Production of cell wall-degrading enzymes by the phytopathogenic fungus Sclerotinia sclerotiorum. Applied and Environmental Microbiology, 57(5), 1478-1484. https://doi.org/10.1128/aem.57.5.1478-1484.1991
Saharan, G. S. & Mehta, N.  (2008). Sclerotinia diseases of crop plants: biology, ecology and disease management. Springer Science & Business Media, Germany. https://doi.org/10.1007/978-1-4020-8408-9
Seifbarghi, S., Borhan, M. H., Wei, Y., Coutu, C., Robinson, S. J., & Hegedus, D. D. (2017). Changes in the Sclerotinia sclerotiorum transcriptome during infection of Brassica napus. BMC Genomics, 18, 1-37. https://doi.org/10.1186/s12864-017-3642-5
Sharp, P. M., & Li, W. H. (1986). An evolutionary perspective on synonymous codon usage in unicellular organisms. Journal of Molecular Evolution, 24(12), 28-38. https://doi.org/10.1007/BF02099948
Tanwar, B., & Goyal, A. (2021). Oilseeds: health attributes and food applications. Springer. https://doi.org/10.1007/978-981-15-4194-0
Tisch, D., & Schmoll, M. (2010). Light regulation of metabolic pathways in fungi. Applied Microbiology and Biotechnology, 85, 1259-1277. https://doi.org/10.1007/s00253-009-2320-1
Tyagi, S., Kabade, P. G., Gnanapragasam, N., Singh, U. M., Gurjar, A. K. S., Rai, A., ... & Singh, V. K. (2023). Codon usage provide insights into the adaptation of rice genes under stress condition. International Journal of Molecular Sciences, 24(2), 1098. https://doi.org/10.3390/ijms24021098
Unkles, S. E., Wang, R., Wang, Y., Glass, A. D., Crawford, N. M., & Kinghorn, J. R. (2004). Nitrate reductase activity is required for nitrate uptake into fungal but not plant cells. Journal of Biological Chemistry, 279(27), 28182-28186. https://doi.10.1074/jbc.M403974200
Waksman, G. (1989). Molecular cloning of a beta-glucosidase-encoding gene from Sclerotinia sclerotiorum by expression in Escherichia coli. Current Genetics, 15, 295-297. https://doi.org/10.1007/BF00447047
Wang, H., Liu, S., Zhang, B., & Wei, W. (2016). Analysis of synonymous codon usage bias of Zika virus and its adaption to the hosts. PloS One, 11(11), e0166260. https://doi.org/10.1371/journal.pone.0166260
Wei, J., Yao, C., Zhu, Z., Gao, Z., Yang, G., & Pan, Y. (2023). Nitrate reductase is required for sclerotial development and virulence of Sclerotinia sclerotiorum. Frontiers in Plant Science, 14, 1096831. https://doi.org/10.3389/fpls.2023.1096831
Wong, A. L., & Willetts, H. J. (1975). A taxonomic study of Sclerotinia sclerotiorum and related species: mycelial interactions. Microbiology, 88(2), 339-344. https://doi.org/10.1099/00221287-88-2-339
Wright, F. (1990). The “effective number of codons” used in a gene. Gene, 87, 23-29. https://doi.org/10.1016/0378-1119(90)90491-9
Wu, J., Zhao, Q., Yang, Q., Liu, H., Li, Q., Yi, X., ... & Zhou, Y. (2016). Comparative transcriptomic analysis uncovers the complex genetic network for resistance to Sclerotinia sclerotiorum in Brassica napus. Scientific Reports, 6(1), 19007. https://doi.org/10.1038/srep19007
Xu, B., Gong, X., Chen, S., Hu, M., Zhang, J., & Peng, Q. (2021a). Transcriptome analysis reveals the complex molecular mechanisms of Brassica napus-Sclerotinia sclerotiorum interactions. Frontiers in Plant Science, 12, 716935. https://doi.org/10.3389/fpls.2021.716935
Xu, L., Li, G., Jiang, D., & Chen, W. (2018). Sclerotinia sclerotiorum: an evaluation of virulence theories. Annual Review of Phytopathology, 56(1), 311-338. https://doi.org/10.1146/annurev-phyto-080417-050052
Xu, Y., Liu, K., Han, Y., Xing, Y., Zhang, Y., Yang, Q., & Zhou, M. (2021b). Codon usage bias regulates gene expression and protein conformation in yeast expression system P. pastoris. Microbial Cell Factories, 20(1), 91. https://doi.org/10.1186/s12934-021-01580-9
Zomorodian, A., Kavoosi, Z., & Momenzadeh, L. (2011). Determination of EMC isotherms and appropriate mathematical models for rapeseed. Food and Bioproducts Processing, 89(4), 407-413. https://doi.org/10.1016/j.fbp.2010.10.006
 
Journal of Genetic Resources
Volume 10, Issue 2 - Serial Number 2
September 2024
Pages 112-123

Files

Share

How to cite

Statistics