Rice blast caused by Magnaporthe oryzae is one of the most important diseases of rice. Pi54, a rice gene that imparts resistance to M. oryzae isolates prevalent in India, was already cloned but its avirulent counterpart in the pathogen was not known. After decoding the whole genome of an avirulent isolate of M. oryzae, we predicted 11440 protein coding genes and then identified four candidate effector proteins which are exclusively expressed in the infectious structure, appresoria. In silico protein modeling followed by interaction analysis between Pi54 protein model and selected four candidate effector proteins models revealed that Mo-01947_9 protein model encoded by a gene located at chromosome 4 of M. oryzae, interacted best at the Leucine Rich Repeat domain of Pi54 protein model. Yeast-two-hybrid analysis showed that Mo-01947_9 protein physically interacts with Pi54 protein. Nicotiana benthamiana leaf infiltration assay confirmed induction of hypersensitive response in the presence of Pi54 gene in a heterologous system. Genetic complementation test also proved that Mo-01947_9 protein induces avirulence response in the pathogen in presence of Pi54 gene. Here, we report identification and cloning of a new fungal effector gene which interacts with blast resistance gene Pi54 in rice.
Rice blast, caused by the ascomycete fungus Magnaporthe oryzae is a destructive disease of rice and responsible for causing extensive damage to the crop. Pi54, a dominant blast resistance gene cloned from rice line Tetep, imparts a broad spectrum resistance against various M. oryzae isolates. Many of its alleles have been explored from wild Oryza species and landraces whose sequences are available in the public domain. Its cognate effector gene AvrPi54 has also been cloned from M. oryzae. Complying with the Flor’s gene-for-gene system, Pi54 protein interacts with AvrPi54 protein following fungal invasion leading to the resistance responses in rice cell that prevents the disease development. In the present study Pi54 alleles from 72 rice lines were used to understand the interaction of Pi54 (R) proteins with AvrPi54 (Avr) protein. The physiochemical properties of these proteins varied due to the nucleotide level polymorphism. The ab initio tertiary structures of these R- and Avr- proteins were generated and subjected to the in silico interaction. In this interaction, the residues in the LRR region of R- proteins were shown to interact with the Avr protein. These R proteins were found to have variable strengths of binding due to the differential spatial arrangements of their amino acid residues. Additionally, molecular dynamic simulations were performed for the protein pairs that showed stronger interaction than Pi54tetep (original Pi54 from Tetep) protein. We found these proteins were forming h-bond during simulation which indicated an effective binding. The root mean square deviation values and potential energy values were stable during simulation which validated the docking results. From the interaction studies and the molecular dynamics simulations, we concluded that the AvrPi54 protein interacts directly with the resistant Pi54 proteins through the LRR region of Pi54 proteins. Some of the Pi54 proteins from the landraces namely Casebatta, Tadukan, Varun dhan, Govind, Acharmita, HPR-2083, Budda, Jatto, MTU-4870, Dobeja-1, CN-1789, Indira sona, Kulanji pille and Motebangarkaddi cultivars show stronger binding with the AvrPi54 protein, thus these alleles can be effectively used for the rice blast resistance breeding program in future.
Gene regulatory network (GRN) construction involves various steps of complex computational steps. This step-by-step procedure requires prior knowledge of programming languages such as R. Development of a web tool may reduce this complexity in the analysis steps which can be easy accessible for the user. In this study, a web tool for constructing consensus GRN by combining the outcomes obtained from four methods, namely, correlation, principal component regression, partial least square, and ridge regression, has been developed. We have designed the web tool with an interactive and user-friendly web page using the php programming language. We have used R script for the analysis steps which run in the background of the user interface. Users can upload gene expression data for constructing consensus GRN. The output obtained from analysis will be available in downloadable form in the result window of the web tool.
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