Predicating the Effector Proteins Secreted by Puccinia triticina Through Transcriptomic Analysis and Multiple Prediction Approaches

Wheat leaf rust caused by Puccinia triticina is one of the most common and serious diseases in wheat production. The constantly changing pathogens overcome the plant resistance to P. triticina. Plant pathogens secrete effector proteins that alter the structure of the host cell, interfere plant defenses, or modify the physiology of plant cells. Therefore, the identification of effector proteins is critical to reveal the pathogenic mechanism. We used SignalP v4.1, TargetP v1.1, TMHMM v2.0, and EffectorP v2.0 to screen the candidate effector proteins in P. triticina isolates – KHTT, JHKT, and THSN. As a result, a total of 635 candidate effector proteins were obtained. Structural analysis showed that effector proteins were small in size (50AA to 422AA) and of diverse sequences, and the conserved sequential elements or clear common elements were not involved, regardless of their secretion from the pathogen to the host. There were 427 candidate effector proteins that contain more than or equal to 4 cysteine residues, and 339 candidate effector proteins contained the known motifs. Sixteen families, 9 domains, and 53 other known functional types were found in 186 candidate effector proteins using the Pfam search. Three novel motifs were found by MEME. Heterogeneous expression system was performed to verify the functions of 30 candidate effectors by inhibiting the programmed cell death (PCD) induced by BAX (the mouse-apoptotic gene elicitor) on Nicotiana benthamiana. Hypersensitive response (HR) can be induced by the six effectors in the wheat leaf rust resistance near isogenic lines, and this would be shown by the method of transient expression through Agrobacterium tumefaciens infiltration. The quantitative reverse transcription PCR (qRT-PCR) analysis of 14 candidate effector proteins secreted after P. triticina inoculation showed that the tested effectors displayed different expression patterns in different stages, suggesting that they may be involved in the wheat–P. triticina interaction. The results showed that the prediction of P. triticina effector proteins based on transcriptomic analysis and multiple bioinformatics software is effective and more accurate, laying the foundation of revealing the pathogenic mechanism of Pt and controlling disease.