PLoS Biology (Jan 2023)

Disentangling the complex gene interaction networks between rice and the blast fungus identifies a new pathogen effector

  • Yu Sugihara,
  • Yoshiko Abe,
  • Hiroki Takagi,
  • Akira Abe,
  • Motoki Shimizu,
  • Kazue Ito,
  • Eiko Kanzaki,
  • Kaori Oikawa,
  • Jiorgos Kourelis,
  • Thorsten Langner,
  • Joe Win,
  • Aleksandra Białas,
  • Daniel Lüdke,
  • Mauricio P. Contreras,
  • Izumi Chuma,
  • Hiromasa Saitoh,
  • Michie Kobayashi,
  • Shuan Zheng,
  • Yukio Tosa,
  • Mark J. Banfield,
  • Sophien Kamoun,
  • Ryohei Terauchi,
  • Koki Fujisaki

Journal volume & issue
Vol. 21, no. 1

Abstract

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Studies focused solely on single organisms can fail to identify the networks underlying host–pathogen gene-for-gene interactions. Here, we integrate genetic analyses of rice (Oryza sativa, host) and rice blast fungus (Magnaporthe oryzae, pathogen) and uncover a new pathogen recognition specificity of the rice nucleotide-binding domain and leucine-rich repeat protein (NLR) immune receptor Pik, which mediates resistance to M. oryzae expressing the avirulence effector gene AVR-Pik. Rice Piks-1, encoded by an allele of Pik-1, recognizes a previously unidentified effector encoded by the M. oryzae avirulence gene AVR-Mgk1, which is found on a mini-chromosome. AVR-Mgk1 has no sequence similarity to known AVR-Pik effectors and is prone to deletion from the mini-chromosome mediated by repeated Inago2 retrotransposon sequences. AVR-Mgk1 is detected by Piks-1 and by other Pik-1 alleles known to recognize AVR-Pik effectors; recognition is mediated by AVR-Mgk1 binding to the integrated heavy metal-associated (HMA) domain of Piks-1 and other Pik-1 alleles. Our findings highlight how complex gene-for-gene interaction networks can be disentangled by applying forward genetics approaches simultaneously to the host and pathogen. We demonstrate dynamic coevolution between an NLR integrated domain and multiple families of effector proteins. Studies focused solely on single organisms can fail to identify the networks underlying host–pathogen gene-for-gene interactions. Integrating genetic analyses of the pathogen rice blast fungus and its host plant helps to disentangle the complex interactions that determine the outcome of plant-pathogen interactions and reveals a previously overlooked pathogen effector.