Nucleotide-binding domain leucine-rich repeat–containing (NLR) proteins constitute an ancient class of intracellular immune receptors that confer innate immunity in plants and animals (Dodds and Rathjen, 2010; Jones et al., 2016). In plants, NLRs function by sensing pathogen-derived virulence molecules, known as effectors, and subsequently activating an immune response (Jacob et al., 2013; Kourelis and van der Hoorn, 2018). The majority of functionally validated NLRs in plants display broadly conserved domain architectures, typically consisting of the NB-ARC (nucleotide-binding adaptor shared by APAF-1, certain R gene products and CED-4) domain, the LRR (leucin-rich repeat) region, and either a TIR (Toll/interleukin 1 receptor), CC (coiled-coil), or CC_R (RPW8-type CC) domain at the N-terminus (Kourelis and Kamoun, 2020; Shao et al., 2016). However, coevolution with pathogen effectors has led to a remarkable diversification of NLR repertoires, which form one of the most diverse protein families in plants (Lee and Chae, 2020; Prigozhin and Krasileva, 2020). An emerging paradigm in plant immunity is that some NLRs acquired novel recognition specificities through fusions of noncanonical integrated domains (IDs) that mediate perception of effectors (Cesari et al., 2014a; Wu et al., 2015). Although NLR-IDs have been described across various plant families (Gao et al., 2018; Kroj et al., 2016; Sarris et al., 2016; Van de Weyer et al., 2019), little is known about their emergence and subsequent evolution. In addition, our knowledge about how NLRs adapt to rapidly evolving pathogen effectors remains sparse.

Given that many IDs exhibit homology to molecules required for immune responses, they are generally thought to have derived from effector operative targets, which then act as baits for effector recognition within NLRs (Cesari et al., 2014a; Wu et al., 2015). IDs can perceive effectors by direct binding, by serving as substrates for their enzymatic activities, or by detecting effector-induced perturbations (Bao et al., 2017; Cesari et al., 2014a; Fujisaki et al., 2017; Heidrich et al., 2013; Sarris et al., 2016; Wu et al., 2015). The RGA5 (also known as Pia-2) and Pik-1 receptors are well-characterised examples of NLR-IDs. RGA5 and Pik-1 detect three unrelated effectors from the rice blast fungus, Magnaporthe oryzae, AVR-Pia/AVR1-CO39 and AVR-Pik, respectively, via their integrated heavy metal-associated (HMA) domains (De la Concepcion et al., 2018; Guo et al., 2018). HMAs are commonly found in a family of HMA plant proteins (HPPs) or HMA isoprenylated plant proteins (HIPPs) known to contribute to abiotic and biotic stress responses (de Abreu-Neto et al., 2013; Fukuoka et al., 2009; Li et al., 2020a; Radakovic et al., 2018; Zschiesche et al., 2015). Recently, the AVR-Pik effectors have been shown to bind and stabilise rice HMA proteins to co-opt their function in immunity (Maidment et al., 2020; Oikawa et al., 2020), providing direct evidence that integrated HMAs indeed mimic host targets of effectors.

NLR-triggered immunity is usually accompanied by the hypersensitive response (HR), a type of localised cell death associated with disease resistance. Notably, several NLR-IDs appear to have lost the ability to autonomously trigger a defence response (Cesari et al., 2014b; Zdrzałek et al., 2020). As a consequence, they often function in pairs, where the NLR-ID serves as a sensor for pathogen effectors and its partner acts as a helper that mediates activation of an immune response (Adachi et al., 2019; Bonardi et al., 2011; Feehan et al., 2020). There are now many examples of such NLR pairs, including RRS1/RPS4 from Arabidopsis thaliana (Saucet et al., 2015) as well as Pik-1/Pik-2 (Ashikawa et al., 2008), Pii-2/Pii-1 (Fujisaki et al., 2017), and RGA5/RGA4 (the Pia locus) (Cesari et al., 2014b; Okuyama et al., 2011) from rice. Many NLR pairs are encoded by two adjacent genes in a head-to-head orientation (Bailey et al., 2018; Van de Weyer et al., 2019). This genetic linkage likely provides an evolutionary advantage by facilitating co-segregation, coevolution, and transcriptional coregulation of functionally linked genes (Baggs et al., 2017; Griebel et al., 2014). Genetic linkage may also reduce the genetic load caused by autoimmunity (Wu et al., 2018), which is a common phenomenon observed across NLRs (Alcázar et al., 2009; Bomblies et al., 2007; Chae et al., 2016; Deng et al., 2019; Yamamoto et al., 2010).

Rice Pik-1 and Pik-2 proteins form a CC-type NLR pair. Two Pik haplotypes, N- and K-type, are present in the genetic pool of wild and cultivated rice (Zhai et al., 2011). While the function of the N-type haplotypes remains obscure, K-type Pik NLRs confer resistance to the rice blast fungus. In the K-type pair, Pik-1 acts as a sensor that binds the AVR-Pik effector via the Pik-1-integrated HMA domain, whereas Pik-2 is required for activation of immune response upon effector recognition (Maqbool et al., 2015; Zdrzałek et al., 2020). This NLR pair was initially cloned from Tsuyuake rice (Ashikawa et al., 2008) and has since been shown to occur in allelic variants, which include Pikp, Pikm, Piks, Pikh, and Pik* (Costanzo and Jia, 2010; Jia et al., 2009; Wang et al., 2009; Yuan et al., 2011; Zhai et al., 2011). Remarkably, the integrated HMA domain is the most sequence-diverse region among Pik-1 variants, consistent with the view that the receptor is under selection imposed by AVR-Pik (Białas et al., 2018; Costanzo and Jia, 2010; De la Concepcion et al., 2021; Zhai et al., 2014). Conversely, AVR-Pik alleles carry only five amino acid replacements, all of which map to regions located at the HMA-binding interface, indicating the adaptive nature of those polymorphisms (Longya et al., 2019). While the most ancient of the AVR-Pik allelic variants, AVR-PikD, is recognised by a wide range of Pik-1 proteins, the most recent variants, AVR-PikC and AVR-PikF, evade recognition by all known Pik-1 variants (Kanzaki et al., 2012; Longya et al., 2019). These recognition specificities are thought to reflect the ongoing arms race between rice and the rice blast fungus (Białas et al., 2018; Kanzaki et al., 2012; Li et al., 2019) and have been linked to the effector–HMA binding affinity (De la Concepcion et al., 2021; De la Concepcion et al., 2018; Maqbool et al., 2015). Despite the wealth of knowledge about mechanisms governing effector recognition by the Pik-1-integrated HMA domain, we know little about its evolutionary history.

Evolutionary molecular biology can inform mechanistic understanding of protein function. After decades of parallel research, molecular evolution and mechanistic research are starting to be used in conjunction to unravel the molecular basis of protein function within an evolutionary framework (Delaux et al., 2019). One approach to investigate the biochemical drivers of adaptation is to reconstruct the evolutionary trajectories of proteins of interest (Dean and Thornton, 2007; Harms and Thornton, 2013; Thornton, 2004). Using phylogenetic techniques and algorithms for ancestral sequence reconstruction (ASR), it is now possible to statistically infer ancestral sequences, which can then be synthesised, expressed, and experimentally studied in the context of modern sequences (Ashkenazy et al., 2012; Cohen and Pupko, 2011; Pupko et al., 2000). In the field of plant–microbe interactions, experimental analyses of resurrected ancestral effector sequences have helped unravel biochemical bases of effector specialisation and adaptive evolution following a host jump (Dong et al., 2014; Tanaka et al., 2019; Zess et al., 2019). To date, ancestral reconstruction has not been used to study the evolution of NLR immune receptors.

Despite remarkable advances in the field of NLR biology, there is still a significant gap in our understanding of how these receptors have adapted to fast-evolving pathogens. In this work, we used the rice Pik-1/Pik-2 system, coupled with ASR, to test hypotheses about adaptive evolution of NLRs and their IDs and to bridge the gap between mechanistic and evolutionary research. We leveraged the rich genetic diversity of the Pik genes in grasses and discovered that they likely derived from a single ancestral gene pair that emerged before the radiation of the major grass lineages. In addition, we show that the HMA integration predates speciation of Oryzinae dated at ~15 million years ago (MYA) (Jacquemin et al., 2011; Stein et al., 2018). Functional characterisation of a resurrected ancestral HMA (ancHMA), dating back to early Oryza evolution, revealed that different allelic variants of Pik-1, Pikp-1 and Pikm-1, convergently evolved from the weakly binding ancestral state towards high-affinity binding and recognition of the AVR-PikD effector through different biochemical paths. We conclude that for most of its evolutionary history Pik-HMA did not sense AVR-PikD and that recognition of this effector is a recent adaptation. This work provides new insights into our understanding of the dynamic nature of NLR adaptive evolution.

The molecular evolution events associated with the transition of NLR IDs from pathogen effector targets to baits remain elusive. Here, we investigated the evolution of these unconventional domains of NLR receptors using rice Pik as a model system. First, we performed extensive phylogenetic analyses to determine that the integration of the HMA domain emerged over 15 MYA, predating the radiation of Oryzinae (Figure 1D). Using sequence reconstruction and resurrection of an ancestral integrated HMA domain that dates back to early divergence of Oryza spp., we showed that the capacity of Pik-1 to sense and respond to AVR-PikD evolved relatively recently through distinct evolutionary and biochemical paths in two alleles of Pik-1, Pikp-1 and Pikm-1. This combination of evolutionary and biochemical approaches allowed us to develop a model of the adaptive evolution of the Pik proteins towards high-affinity AVR-PikD binding (Figure 9).

The molecular bases of functional transitions in NLR evolution remain poorly understood, especially over extended timescales. Here, we showed that adaptive evolution of Pikp-1 and Pikm-1 from weak to high-affinity binding to the AVR-PikD effector involves two distinct regions within the HMA domain. Overall, these interfaces seem to function in a synergistic yet interchangeable manner, such that weak interaction at one interface can be compensated by strong interaction at a different one (De la Concepcion et al., 2021; De la Concepcion et al., 2018). We propose that this modularity between different regions of the HMA increases the HMA’s capacity for rapid adaptive evolution as it can follow alternative mutational paths to produce similar phenotypic outcomes and counteract rapidly evolving pathogen effectors. Indeed, HMA domains can also detect another M. oryzae effector AVR-Pia through an alternative interface (Guo et al., 2018; Varden et al., 2019), further illustrating the capacity of the HMA domain to bait pathogen effectors through different interfaces. This may have contributed to the recurrent emergence of HMAs as NLR IDs. Previous studies have revealed that HMAs have independently integrated into NLR immune receptors from at least four flowering plant families (Kroj et al., 2016; Sarris et al., 2016).

The HMA domain of Pik-1 exhibits signatures of positive selection in contrast to the NB-ARC domain (Figure 2), likely reflecting coevolution with pathogen effectors versus overall purifying selection. This further suggests that HMA domains are malleable platforms that can accommodate accelerated mutational rates (Białas et al., 2018; Costanzo and Jia, 2010). Similar observations have previously been made in a number of plant NLRs, whose individual domains display patterns of asymmetrical evolution and distinct rates of selection, suggesting that NLRs evolve in a modular fashion (Kuang et al., 2004; Maekawa et al., 2019; Prigozhin and Krasileva, 2020; Read et al., 2020; Seeholzer et al., 2010). Moreover, having a domain responsible for effector recognition may release other domains from the pressure of diversification and reduce the risk of compromising or mis-regulating NLR activity (Cesari, 2018). In addition, coupling with a helper NLR such as Pik-2 likely provides yet another mechanism of functional compartmentalisation, further enhancing the evolvability of the sensor by freeing it from the constraint of executing the hypersensitive cell death (Adachi et al., 2019; Cesari, 2018; Wu et al., 2018).

We showed that the evolutionarily derived AV-VE in Pikp-1 (Figure 5) and ANK-VKE polymorphisms in Pikm-1 (Figure 8) enabled high-affinity binding to AVR-PikD. Although the high sequence divergence and elevated mutation rates among HMA sequences precluded rigorous dating of the emergence of these key adaptations, the low level of total nucleotide polymorphisms among closely related Pik alleles—in particular, the very few synonymous substitutions among Pikp- and Pikm-related alleles—points to a very recent emergence of the adaptive polymorphisms. Given that the rice-infecting lineage of M. oryzae is estimated to have arisen about 7000–9000 years ago (Couch et al., 2005; Latorre et al., 2020), our findings are consistent with the view that Pik-1 alleles evolved during rice domestication as previously suggested (Kanzaki et al., 2012; Zhai et al., 2011). In addition, AVR-Pik is widespread in rice-infecting isolates but absent in other blast fungus lineages (Bentham et al., 2021; Langner et al., 2021; Latorre et al., 2020; Yoshida et al., 2016). Therefore, it is tempting to speculate that the rice agroecosystem has created the ecological context that led to Pik neofunctionalisation towards recognition of the new pathogen threat imposed by the blast fungus. Different rice populations may have independently encountered fungal pathogens carrying AVR-Pik, leading to intense natural selection and independent emergence of the Pikp and Pikm adaptations.

We concluded that the Pik-1-integrated HMA domain did not function in sensing AVR-PikD for most of its over 15-million-year-long evolutionary history, inviting the question about the role of the ancestral integrated HMA. It is likely that over millions of years, prior to rice domestication, the Pik-1 HMA domain had recognised effectors other than AVR-Pik. These could be other members of AVR-Pik–like (APikL) effector family (Bentham et al., 2021) or their ancestors, the structurally related MAX-effectors—an ancient effector family present across blast lineages and other fungal pathogens (de Guillen et al., 2015; Petit-Houdenot et al., 2020)—or effectors from other plant pathogen taxa. Indeed, the HMA domain is known to bind effectors from diverse pathogens including bacteria and oomycetes, in addition to fungi (González-Fuente et al., 2020). Karasov et al., 2014 proposed that NLRs caught in pairwise arms races (one NLR recognising one effector) are likely to be short-lived, whereas NLRs entangled in diffuse evolution (functioning against multiple effectors and/or multiple pathogens) are more likely to persist over longer timescales. Our model paints a more complex picture of the macroevolutionary dynamics of NLR-IDs. These receptors have the capacity to switch from one effector to another, while also engaging in short-term arms race dynamics, as seems to be the case of Pik-1 vs. AVR-Pik (Białas et al., 2018; Kanzaki et al., 2012). It is remarkable that the Pik-1 gene and its paired Pik-2 gene have been maintained in grass populations for tens of millions of years, even after the integration of the HMA domain. This points to a successful evolutionary strategy for generating long-lived disease resistance traits, with HMA promiscuity towards pathogen effectors at the centre of this model.

We discovered that the Pikp-1:ancHMA fusions trigger spontaneous hypersensitive cell death when co-expressed with Pikp-2 and mapped the region responsible for the autoactivity to two HMA parallel loops, β1–α1 and α2–β4 (Figure 6—figure supplement 1, Figure 6—figure supplement 3). Although the precise mechanism underpinning this autoactivity remains to be elucidated, we propose that coevolution of the HMA with the canonical domains of Pik-1 and/or Pik-2 drives this molecular incompatibility. Mismatching domains from different evolutionary timepoints may disrupt fine-tuned biochemical interactions between HMA and other domains. Indeed, intra- and intermolecular incompatibilities of NLRs are known causes of autoimmunity in plants (Harris et al., 2013; Li et al., 2020b; Lukasik-Shreepaathy et al., 2012; Qi et al., 2012; Rairdan and Moffett, 2006; Tran et al., 2017; Wang et al., 2015). We further noted that some Pik-1 orthologues, namely LpPik-1 and N-type Pik-1 genes, carry large deletions within their HMAs, which may have emerged to eliminate autoimmunity (Figure 2—figure supplement 1). This is consistent with the view that the risk of autoactivity acts as a strong evolutionary constraint narrowing NLR mutational pathways (Chae et al., 2014).

We uncovered a rich genetic diversity of Pik genes beyond Oryza species (Mizuno et al., 2020; Stein et al., 2018; Zhai et al., 2011; Figure 1). This enabled us to date the emergence of the Pik pair to before the split of two major grass lineages: the BOP and PACMAD clades, which corresponds to 100–50 MYA (Hodkinson, 2018). Furthermore, we estimated that Pik-1 acquired the HMA domain prior to the emergence of Oryzinae but after the split from Panicoideae, between 15 and 100 MYA (Hodkinson, 2018; Jacquemin et al., 2011; Stein et al., 2018). Remarkably, the vast majority of Pik-2 and Pik-1 orthologues across the Poaceae exist as genetically linked pairs in a head-to-head orientation. This applies to Pik-1 orthologues with and without the HMA domain, indicating that Pik-1 and Pik-2 pairing occurred prior to HMA integration. Tight genetic linkage of paired NLRs, such as Pik-1/Pik-2 (Ashikawa et al., 2008), RGA5/RGA4 (Cesari et al., 2013; Okuyama et al., 2011), RRS1/RPS4 (Saucet et al., 2015), or RPP2A/RPP2B (Sinapidou et al., 2004), is thought to facilitate coregulation and coevolution, thereby ensuring proper cooperation between these NLRs and reducing the genetic load caused by autoimmunity (Baggs et al., 2017; Griebel et al., 2014; Wu et al., 2018). However, Pik-1 and Pik-2 paralogues also occur adjacent to the paired genes—a phenomenon previously observed in wild and cultivated rice (Mizuno et al., 2020)—raising the possibility that these Pik genes may form an NLR receptor network beyond the Pik-1/Pik-2 pair (Wu et al., 2018). In the future, it would be interesting to investigate the functions of paired Pik-1/Pik-2 and their paralogues and determine whether functional pairing and genetic linkage with Pik-2 predisposed Pik-1 for the HMA integration.

In summary, our study illustrates the value of ASR—a method that has rarely been used in the field of plant–microbe interactions (Dong et al., 2014; Tanaka et al., 2019; Zess et al., 2019)—in transcending phylogenetic inference to yield a more elaborate evolutionary model. ASR combined with biochemical and biophysical studies enabled us to determine the directionality of evolution and therefore develop an experimentally validated model of NLR adaptation. The Pik-1/Pik-2 receptor pair emerged as an excellent system to not only provide a framework for drawing links between NLR structure and function but also to place this knowledge in an evolutionary context. This adds to our understanding of selection forces, historical contingency, and functional constraints shaping NLR activities. This approach illustrates how mechanistic research structured by a robust evolutionary framework can enhance our understanding of plant–microbe systems.

Diffraction data have been deposited in PDB under the accession code 7BNT. Sequencing data have been deposited in GenBank under accession codes: MW553204–MW553215 and MW568030–MW568049. All relevant data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figure 1—source data 1, Figure 2—source data 1, Figure 5—source data 1, Figure 6—source data 1, Figure 6—figure supplement 1—source data, Figure 6—Figure supplement 3—source data 1, Figure 8—source data 1.

1. 1. International Brachypodium Initiative

   (2018) Ensembl Plants

   ID GCA_000005505.4. Genome sequencing and analysis of the model grass Brachypodium distachyon.

   https://plants.ensembl.org/Brachypodium_distachyon/Info/Annotation/
2. 1. Mascher M
   2. Gundlach H
   3. Himmelbach A
   4. Beier S
   5. Twardziok SO
   6. Wicker T
   7. RadchukV
   8. Dockter C
   9. Hedley PE
   10. Russell J
   11. Bayer M
   12. Ramsay L
   13. Liu H
   14. Haberer G
   15. ZhangXQ
   16. Zhang Q
   17. Barrero RA
   18. Li L
   19. Taudien S
   20. Groth M
   21. Felder M
   22. Hastie A
   23. ŠimkováH
   24. Staňková H
   25. Vrána J
   26. Chan S
   27. Muñoz-Amatriaín M
   28. Ounit R
   29. Wanamaker S
   30. BolserD
   31. Colmsee C
   32. Schmutzer T
   33. Aliyeva-Schnorr L
   34. Grasso S
   35. Tanskanen J
   36. Chailyan A
   37. Sampath D
   38. Heavens D
   39. Clissold L
   40. Cao S
   41. Chapman B
   42. Dai F
   43. Han Y
   44. Li H
   45. Li X
   46. Lin C
   47. McCooke JK
   48. Tan C
   49. Wang P
   50. Wang S
   51. Yin S
   52. Zhou G
   53. Poland JA
   54. Bellgard MI
   55. Borisjuk L
   56. Houben A
   57. Doležel J
   58. Ayling S
   59. Lonardi S
   60. Kersey P
   61. Langridge P
   62. Muehlbauer GJ
   63. Clark MD
   64. Caccamo M
   65. Schulman AH
   66. Mayer KFX
   67. Platzer M
   68. Close TJ
   69. Scholz U
   70. Hansson M
   71. Zhang G
   72. Braumann I
   73. Spannagl M
   74. Li C
   75. Waugh R
   76. Stein N

   (2019) Ensembl Plants

   ID GCA_901482405.1. A chromosome conformation capture ordered sequence of the barley genome.

   https://plants.ensembl.org/Hordeum_vulgare/Info/Annotation/
3. 1. Chen J
   2. Huang Q
   3. Gao D
   4. Wang J
   5. Lang Y
   6. Liu T
   7. Li B
   8. Bai Z
   9. Luis G
   10. Liang C
   11. Chen C
   12. Zhang W
   13. Sun S
   14. Liao Y
   15. Zhang X
   16. Yang L
   17. Song C
   18. Wang M
   19. ShiJ
   20. Liu G
   21. Liu J
   22. Zhou H
   23. Zhou W
   24. Yu Q
   25. An N
   26. Chen Y
   27. Cai Q
   28. Wang B
   29. Liu B
   30. MinJ
   31. Huang Y
   32. Wu H
   33. Li Z
   34. Zhang Y
   35. Yin Y
   36. Song W
   37. Jiang J
   38. Jackson SA
   39. Wing RA

   (2012) Ensembl Plants

   ID GCA_000231095.2. Whole-genome sequencing of Oryza brachyantha reveals mechanisms underlying Oryza genome evolution.

   https://plants.ensembl.org/Oryza_brachyantha/Info/Annotation/
4. 1. Kawahara Y
   2. de la Bastide M
   3. Hamilton JP
   4. Kanamori H
   5. McCombie WR
   6. Ouyang S
   7. Schwartz DC
   8. Tanaka T
   9. Wu J
   10. Zhou S
   11. Childs KL
   12. Davidson RM
   13. Lin H
   14. Quesada-Ocampo L
   15. Vaillancourt B
   16. Sakai H
   17. Lee SS
   18. Kim J
   19. Numa H
   20. Itoh T
   21. Buell CR
   22. Matsumoto T

   (2015) Ensembl Plants

   ID GCA_001433935.1. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data.

   https://plants.ensembl.org/Oryza_sativa/Info/Annotation
5. 1. Bennetzen JL
   2. Schmutz J
   3. Wang H
   4. Percifield R
   5. Hawkins J
   6. Pontaroli AC
   7. Estep M
   8. Feng L
   9. Vaughn JN
   10. Grimwood J
   11. Jenkins J
   12. Barry K
   13. Lindquist E
   14. Hellsten U
   15. Deshpande S
   16. Wang X
   17. Wu X
   18. Mitros T
   19. Triplett J
   20. Yang X
   21. Ye CY
   22. Mauro-Herrera M
   23. Wang L
   24. Li P
   25. Sharma M
   26. Sharma R
   27. Ronald PC
   28. Panaud O
   29. Kellogg EA
   30. Brutnell TP
   31. Doust AN
   32. Tuskan GA
   33. Rokhsar D
   34. Devos KM

   (2015) Phytozome v12.1

   ID AGNK01000000.1. Reference genome sequence of the model plant Setaria.

   https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Sitalica
6. 1. Saski C
   2. Lee SB
   3. Fjellheim S
   4. Guda C
   5. Jansen RK
   6. Luo H
   7. Tomkins J
   8. Rognli OA
   9. Daniell H
   10. Clarke J

   (2017) Ensembl Plants

   ID GCA_000003195.3. Complete chloroplast genome sequences of Hordeum vulgare, Sorghum bicolor and Agrostis stolonifera, and comparative analyses with other grass genomes.

   https://plants.ensembl.org/Sorghum_bicolor/Info/Annotation/
7. 1. International Wheat Genome Sequencing Consortium (IWGSC)

   (2018) Ensembl Plants

   ID GCA_900519105.1. Bread wheat variety Chinese Spring for generating IWGSC RefSeq v1.0 assembly.

   https://plants.ensembl.org/Triticum_aestivum/Info/Annotation/
8. 1. Jiao Y
   2. Peluso P
   3. Shi J
   4. Liang T
   5. Stitzer MC
   6. Wang B
   7. Campbell MS
   8. Stein JC
   9. Wei X
   10. Chin CS
   11. Guill K
   12. Regulski M
   13. Kumari S
   14. Olson A
   15. Gent J
   16. Schneider KL
   17. Wolfgruber TK
   18. May MR
   19. Springer NM
   20. Antoniou E
   21. McCombie WR
   22. Presting GG
   23. McMullen M
   24. Ross-Ibarra J
   25. Dawe RK
   26. Hastie A
   27. Rank DR
   28. Ware D

   (2017) Ensembl Plants

   ID GCA_000005005.6. Improved maizereference genome with single-molecule technologies.

   https://plants.ensembl.org/Zea_mays/Info/Annotation

1. 1. Adachi H
   2. Derevnina L
   3. Kamoun S

   (2019) NLR singletons, pairs, and networks: evolution, assembly, and regulation of the intracellular immunoreceptor circuitry of plants

   Current Opinion in Plant Biology 50:121–131.

   https://doi.org/10.1016/j.pbi.2019.04.007

   • PubMed
   • Google Scholar
2. 1. Alcázar R
   2. García AV
   3. Parker JE
   4. Reymond M

   (2009) Incremental steps toward incompatibility revealed by Arabidopsis epistatic interactions modulating salicylic acid pathway activation

   PNAS 106:334–339.

   https://doi.org/10.1073/pnas.0811734106

   • PubMed
   • Google Scholar
3. 1. Altschul SF
   2. Gish W
   3. Miller W
   4. Myers EW
   5. Lipman DJ

   (1990) Basic local alignment search tool

   Journal of Molecular Biology 215:403–410.

   https://doi.org/10.1016/S0022-2836(05)80360-2

   • PubMed
   • Google Scholar
4. 1. Ashikawa I
   2. Hayashi N
   3. Yamane H
   4. Kanamori H
   5. Wu J
   6. Matsumoto T
   7. Ono K
   8. Yano M

   (2008) Two adjacent nucleotide-binding site-leucine-rich repeat class genes are required to confer Pikm-specific rice blast resistance

   Genetics 180:2267–2276.

   https://doi.org/10.1534/genetics.108.095034

   • PubMed
   • Google Scholar
5. 1. Ashkenazy H
   2. Penn O
   3. Doron-Faigenboim A
   4. Cohen O
   5. Cannarozzi G
   6. Zomer O
   7. Pupko T

   (2012) FastML: a web server for probabilistic reconstruction of ancestral sequences

   Nucleic Acids Research 40:W580–W584.

   https://doi.org/10.1093/nar/gks498

   • Google Scholar
6. 1. Baggs E
   2. Dagdas G
   3. Krasileva KV

   (2017) NLR diversity, helpers and integrated domains: making sense of the NLR IDentity

   Current Opinion in Plant Biology 38:59–67.

   https://doi.org/10.1016/j.pbi.2017.04.012

   • PubMed
   • Google Scholar
7. 1. Bailey PC
   2. Schudoma C
   3. Jackson W
   4. Baggs E
   5. Dagdas G
   6. Haerty W
   7. Moscou M
   8. Krasileva KV

   (2018) Dominant integration locus drives continuous diversification of plant immune receptors with exogenous domain fusions

   Genome Biology 19:23.

   https://doi.org/10.1186/s13059-018-1392-6

   • PubMed
   • Google Scholar
8. 1. Bao J
   2. Chen M
   3. Zhong Z
   4. Tang W
   5. Lin L
   6. Zhang X
   7. Jiang H
   8. Zhang D
   9. Miao C
   10. Tang H
   11. Zhang J
   12. Lu G
   13. Ming R
   14. Norvienyeku J
   15. Wang B
   16. Wang Z

   (2017) PacBio sequencing reveals transposable elements as a key contributor to genomic plasticity and virulence variation in Magnaporthe oryzae

   Molecular Plant 10:1465–1468.

   https://doi.org/10.1016/j.molp.2017.08.008

   • PubMed
   • Google Scholar
9. Preprint

   1. Bentham AR
   2. Petit-Houdenot Y
   3. Win J

   (2021) A single amino acid polymorphism in a conserved effector of the multihost blast fungus pathogen expands host-target binding spectrum

   bioRxiv.

   https://doi.org/10.1101/2021.03.15.435478

   • Google Scholar
10. 1. Berrow NS
    2. Alderton D
    3. Sainsbury S
    4. Nettleship J
    5. Assenberg R
    6. Rahman N
    7. Stuart DI
    8. Owens RJ

    (2007) A versatile ligation-independent cloning method suitable for high-throughput expression screening applications

    Nucleic Acids Research 35:e45.

    https://doi.org/10.1093/nar/gkm047

    • PubMed
    • Google Scholar
11. 1. Białas A
    2. Zess EK
    3. De la Concepcion JC
    4. Franceschetti M
    5. Pennington HG
    6. Yoshida K
    7. Upson JL
    8. Chanclud E
    9. Wu CH
    10. Langner T
    11. Maqbool A
    12. Varden FA
    13. Derevnina L
    14. Belhaj K
    15. Fujisaki K
    16. Saitoh H
    17. Terauchi R
    18. Banfield MJ
    19. Kamoun S

    (2018) Lessons in effector and NLR biology of Plant-Microbe systems

    Molecular Plant-Microbe Interactions 31:34–45.

    https://doi.org/10.1094/MPMI-08-17-0196-FI

    • PubMed
    • Google Scholar
12. Software

    1. Białas A
    2. Langner T
    3. Banfield MJ
    4. Kamoun S

    (2021) Mutations That Abolish Pikm-1:anchma Autoactivity Are Inadequate for Testing the Impact of AVR-PikD Binding on Pikm-Mediated Immunity, version 1

    Zenodo.

    http://doi.org/10.5281/zenodo.4770746
13. 1. Bomblies K
    2. Lempe J
    3. Epple P
    4. Warthmann N
    5. Lanz C
    6. Dangl JL
    7. Weigel D

    (2007) Autoimmune response as a mechanism for a Dobzhansky-Muller-type incompatibility syndrome in plants

    PLOS Biology 5:e236.

    https://doi.org/10.1371/journal.pbio.0050236

    • PubMed
    • Google Scholar
14. 1. Bonardi V
    2. Tang S
    3. Stallmann A
    4. Roberts M
    5. Cherkis K
    6. Dangl JL

    (2011) Expanded functions for a family of plant intracellular immune receptors beyond specific recognition of pathogen effectors

    PNAS 108:16463–16468.

    https://doi.org/10.1073/pnas.1113726108

    • Google Scholar
15. 1. Bos JI
    2. Kanneganti TD
    3. Young C
    4. Cakir C
    5. Huitema E
    6. Win J
    7. Armstrong MR
    8. Birch PR
    9. Kamoun S

    (2006) The C-terminal half of Phytophthora infestans RXLR effector AVR3a is sufficient to trigger R3a-mediated hypersensitivity and suppress INF1-induced cell death in Nicotiana benthamiana

    The Plant Journal 48:165–176.

    https://doi.org/10.1111/j.1365-313X.2006.02866.x

    • PubMed
    • Google Scholar
16. 1. Cesari S
    2. Thilliez G
    3. Ribot C
    4. Chalvon V
    5. Michel C
    6. Jauneau A
    7. Rivas S
    8. Alaux L
    9. Kanzaki H
    10. Okuyama Y
    11. Morel JB
    12. Fournier E
    13. Tharreau D
    14. Terauchi R
    15. Kroj T

    (2013) The rice resistance protein pair RGA4/RGA5 recognizes the Magnaporthe oryzae effectors AVR-Pia and AVR1-CO39 by direct binding

    The Plant Cell 25:1463–1481.

    https://doi.org/10.1105/tpc.112.107201

    • PubMed
    • Google Scholar
17. 1. Cesari S
    2. Bernoux M
    3. Moncuquet P
    4. Kroj T
    5. Dodds PN

    (2014a) A novel conserved mechanism for plant NLR protein pairs: the "integrated decoy" hypothesis

    Frontiers in Plant Science 5:606.

    https://doi.org/10.3389/fpls.2014.00606

    • PubMed
    • Google Scholar
18. 1. Cesari S
    2. Kanzaki H
    3. Fujiwara T
    4. Bernoux M
    5. Chalvon V
    6. Kawano Y
    7. Shimamoto K
    8. Dodds P
    9. Terauchi R
    10. Kroj T

    (2014b) The NB-LRR proteins RGA4 and RGA5 interact functionally and physically to confer disease resistance

    The EMBO Journal 33:1941–1959.

    https://doi.org/10.15252/embj.201487923

    • PubMed
    • Google Scholar
19. 1. Cesari S

    (2018) Multiple strategies for pathogen perception by plant immune receptors

    New Phytologist 219:17–24.

    https://doi.org/10.1111/nph.14877

    • PubMed
    • Google Scholar
20. 1. Chae E
    2. Bomblies K
    3. Kim ST
    4. Karelina D
    5. Zaidem M
    6. Ossowski S
    7. Martín-Pizarro C
    8. Laitinen RA
    9. Rowan BA
    10. Tenenboim H
    11. Lechner S
    12. Demar M
    13. Habring-Müller A
    14. Lanz C
    15. Rätsch G
    16. Weigel D

    (2014) Species-wide genetic incompatibility analysis identifies immune genes as hot spots of deleterious epistasis

    Cell 159:1341–1351.

    https://doi.org/10.1016/j.cell.2014.10.049

    • PubMed
    • Google Scholar
21. 1. Chae E
    2. Tran DT
    3. Weigel D

    (2016) Cooperation and conflict in the plant immune system

    PLOS Pathogens 12:e1005452.

    https://doi.org/10.1371/journal.ppat.1005452

    • PubMed
    • Google Scholar
22. 1. Chen VB
    2. Arendall WB
    3. Headd JJ
    4. Keedy DA
    5. Immormino RM
    6. Kapral GJ
    7. Murray LW
    8. Richardson JS
    9. Richardson DC

    (2010) MolProbity : all-atom structure validation for macromolecular crystallography

    Acta Crystallographica Section D Biological Crystallography 66:12–21.

    https://doi.org/10.1107/S0907444909042073

    • Google Scholar
23. 1. Chen J
    2. Huang Q
    3. Gao D
    4. Wang J
    5. Lang Y
    6. Liu T
    7. Li B
    8. Bai Z
    9. Luis G
    10. Liang C
    11. Chen C
    12. Zhang W
    13. Sun S
    14. Liao Y
    15. Zhang X
    16. Yang L
    17. Song C
    18. Wang M
    19. Shi J
    20. Liu G
    21. Liu J
    22. Zhou H
    23. Zhou W
    24. Yu Q
    25. An N
    26. Chen Y
    27. Cai Q
    28. Wang B
    29. Liu B
    30. Min J
    31. Huang Y
    32. Wu H
    33. Li Z
    34. Zhang Y
    35. Yin Y
    36. Song W
    37. Jiang J
    38. Jackson SA
    39. Wing RA
    40. Wang J
    41. Chen M

    (2013) Whole-genome sequencing of Oryza brachyantha reveals mechanisms underlying Oryza genome evolution

    Nature Communications 4:1595.

    https://doi.org/10.1038/ncomms2596

    • PubMed
    • Google Scholar
24. 1. Cohen O
    2. Pupko T

    (2011) Inference of gain and loss events from phyletic patterns using stochastic mapping and maximum parsimony--a simulation study

    Genome Biology and Evolution 3:1265–1275.

    https://doi.org/10.1093/gbe/evr101

    • PubMed
    • Google Scholar
25. 1. Costanzo S
    2. Jia Y

    (2010) Sequence variation at the rice blast resistance gene Pi-km locus: implications for the development of allele specific markers

    Plant Science 178:523–530.

    https://doi.org/10.1016/j.plantsci.2010.02.014

    • Google Scholar
26. 1. Couch BC
    2. Fudal I
    3. Lebrun MH
    4. Tharreau D
    5. Valent B
    6. van Kim P
    7. Nottéghem JL
    8. Kohn LM

    (2005) Origins of host-specific populations of the blast pathogen Magnaporthe oryzae in crop domestication with subsequent expansion of pandemic clones on rice and weeds of rice

    Genetics 170:613–630.

    https://doi.org/10.1534/genetics.105.041780

    • PubMed
    • Google Scholar
27. 1. de Abreu-Neto JB
    2. Turchetto-Zolet AC
    3. de Oliveira LF
    4. Zanettini MH
    5. Margis-Pinheiro M

    (2013) Heavy metal-associated isoprenylated plant protein (HIPP): characterization of a family of proteins exclusive to plants

    FEBS Journal 280:1604–1616.

    https://doi.org/10.1111/febs.12159

    • PubMed
    • Google Scholar
28. 1. de Guillen K
    2. Ortiz-Vallejo D
    3. Gracy J
    4. Fournier E
    5. Kroj T
    6. Padilla A

    (2015) Structure analysis uncovers a highly diverse but structurally conserved effector family in phytopathogenic fungi

    PLOS Pathogens 11:e1005228.

    https://doi.org/10.1371/journal.ppat.1005228

    • PubMed
    • Google Scholar
29. 1. De la Concepcion JC
    2. Franceschetti M
    3. Maqbool A
    4. Saitoh H
    5. Terauchi R
    6. Kamoun S
    7. Banfield MJ

    (2018) Publisher correction: polymorphic residues in rice NLRs expand binding and response to effectors of the blast pathogen

    Nature Plants 4:734.

    https://doi.org/10.1038/s41477-018-0248-0

    • PubMed
    • Google Scholar
30. 1. De la Concepcion JC
    2. Franceschetti M
    3. MacLean D
    4. Terauchi R
    5. Kamoun S
    6. Banfield MJ

    (2019) Protein engineering expands the effector recognition profile of a rice NLR immune receptor

    eLife 8:e47713.

    https://doi.org/10.7554/eLife.47713

    • PubMed
    • Google Scholar
31. 1. De la Concepcion JC
    2. Maidment JHR
    3. Longya A
    4. Xiao G
    5. Franceschetti M
    6. Banfield MJ

    (2021) The allelic rice immune receptor pikh confers extended resistance to strains of the blast fungus through a single polymorphism in the effector binding interface

    PLOS Pathogens 17:e1009368.

    https://doi.org/10.1371/journal.ppat.1009368

    • PubMed
    • Google Scholar
32. 1. Dean AM
    2. Thornton JW

    (2007) Mechanistic approaches to the study of evolution: the functional synthesis

    Nature Reviews Genetics 8:675–688.

    https://doi.org/10.1038/nrg2160

    • PubMed
    • Google Scholar
33. 1. Delaux PM
    2. Hetherington AJ
    3. Coudert Y
    4. Delwiche C
    5. Dunand C
    6. Gould S
    7. Kenrick P
    8. Li FW
    9. Philippe H
    10. Rensing SA
    11. Rich M
    12. Strullu-Derrien C
    13. de Vries J

    (2019) Reconstructing trait evolution in plant evo-devo studies

    Current Biology 29:R1110–R1118.

    https://doi.org/10.1016/j.cub.2019.09.044

    • PubMed
    • Google Scholar
34. 1. Deng J
    2. Fang L
    3. Zhu X
    4. Zhou B
    5. Zhang T

    (2019) A CC-NBS-LRR gene induces hybrid lethality in cotton

    Journal of Experimental Botany 70:5145–5156.

    https://doi.org/10.1093/jxb/erz312

    • PubMed
    • Google Scholar
35. 1. Dodds PN
    2. Rathjen JP

    (2010) Plant immunity: towards an integrated view of plant-pathogen interactions

    Nature Reviews Genetics 11:539–548.

    https://doi.org/10.1038/nrg2812

    • PubMed
    • Google Scholar
36. 1. Dong S
    2. Stam R
    3. Cano LM
    4. Song J
    5. Sklenar J
    6. Yoshida K
    7. Bozkurt TO
    8. Oliva R
    9. Liu Z
    10. Tian M
    11. Win J
    12. Banfield MJ
    13. Jones AM
    14. van der Hoorn RA
    15. Kamoun S

    (2014) Effector specialization in a lineage of the irish potato famine pathogen

    Science 343:552–555.

    https://doi.org/10.1126/science.1246300

    • PubMed
    • Google Scholar
37. 1. Eddy SR

    (1998) Profile hidden markov models

    Bioinformatics 14:755–763.

    https://doi.org/10.1093/bioinformatics/14.9.755

    • PubMed
    • Google Scholar
38. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • Google Scholar
39. 1. Emsley P
    2. Lohkamp B
    3. Scott WG
    4. Cowtan K

    (2010) Features and development of coot

    Acta Crystallogr Sect D Biol Crystallogr 66:486–501.

    https://doi.org/10.1107/S0907444910007493

    • Google Scholar
40. 1. Evans PR
    2. Murshudov GN

    (2013) How good are my data and what is the resolution?

    Acta Crystallographica Section D Biological Crystallography 69:1204–1214.

    https://doi.org/10.1107/S0907444913000061

    • Google Scholar
41. 1. Feehan JM
    2. Castel B
    3. Bentham AR
    4. Jones JD

    (2020) Plant NLRs get by with a little help from their friends

    Current Opinion in Plant Biology 56:99–108.

    https://doi.org/10.1016/j.pbi.2020.04.006

    • PubMed
    • Google Scholar
42. 1. Felsenstein J

    (1985)

    Confidence limits on phylogenies: an approach using the bootstrap

    Evolution 39:783.

    • Google Scholar
43. Preprint

    1. Fujisaki K
    2. Abe Y
    3. Kanzaki E
    4. Ito K
    5. Utsushi H
    6. Saitoh H
    7. Białas A
    8. Banfield MJ
    9. Kamoun S
    10. Terauchi R

    (2017) An unconventional NOI/RIN4 domain of a rice NLR protein binds host EXO70 protein to confer fungal immunity

    bioRxiv.

    https://doi.org/10.1101/239400

    • Google Scholar
44. 1. Fukuoka S
    2. Saka N
    3. Koga H
    4. Ono K
    5. Shimizu T
    6. Ebana K
    7. Hayashi N
    8. Takahashi A
    9. Hirochika H
    10. Okuno K
    11. Yano M

    (2009) Loss of function of a proline-containing protein confers durable disease resistance in rice

    Science 325:998–1001.

    https://doi.org/10.1126/science.1175550

    • PubMed
    • Google Scholar
45. 1. Gao Y
    2. Wang W
    3. Zhang T
    4. Gong Z
    5. Zhao H
    6. Han GZ

    (2018) Out of Water: the origin and early diversification of plant R-Genes

    Plant Physiology 177:82–89.

    https://doi.org/10.1104/pp.18.00185

    • PubMed
    • Google Scholar
46. 1. Ginestet C

    (2011) ggplot2: elegant graphics for data analysis

    Journal of the Royal Statistical Society: Series A 174:245–246.

    https://doi.org/10.1111/j.1467-985X.2010.00676_9.x

    • Google Scholar
47. 1. González-Fuente M
    2. Carrère S
    3. Monachello D
    4. Marsella BG
    5. Cazalé AC
    6. Zischek C
    7. Mitra RM
    8. Rezé N
    9. Cottret L
    10. Mukhtar MS
    11. Lurin C
    12. Noël LD
    13. Peeters N

    (2020) EffectorK, a comprehensive resource to mine for Ralstonia, xanthomonas, and other published effector interactors in the Arabidopsis proteome

    Molecular Plant Pathology 21:1257–1270.

    https://doi.org/10.1111/mpp.12965

    • PubMed
    • Google Scholar
48. 1. Griebel T
    2. Maekawa T
    3. Parker JE

    (2014) NOD-like receptor cooperativity in effector-triggered immunity

    Trends in Immunology 35:562–570.

    https://doi.org/10.1016/j.it.2014.09.005

    • PubMed
    • Google Scholar
49. 1. Guo L
    2. Cesari S
    3. de Guillen K
    4. Chalvon V
    5. Mammri L
    6. Ma M
    7. Meusnier I
    8. Bonnot F
    9. Padilla A
    10. Peng YL
    11. Liu J
    12. Kroj T

    (2018) Specific recognition of two MAX effectors by integrated HMA domains in plant immune receptors involves distinct binding surfaces

    PNAS 115:11637–11642.

    https://doi.org/10.1073/pnas.1810705115

    • PubMed
    • Google Scholar
50. 1. Harms MJ
    2. Thornton JW

    (2013) Evolutionary biochemistry: revealing the historical and physical causes of protein properties

    Nature Reviews Genetics 14:559–571.

    https://doi.org/10.1038/nrg3540

    • PubMed
    • Google Scholar
51. 1. Harris CJ
    2. Slootweg EJ
    3. Goverse A
    4. Baulcombe DC

    (2013) Stepwise artificial evolution of a plant disease resistance gene

    PNAS 110:21189–21194.

    https://doi.org/10.1073/pnas.1311134110

    • PubMed
    • Google Scholar
52. 1. Heidrich K
    2. Tsuda K
    3. Blanvillain-Baufumé S
    4. Wirthmueller L
    5. Bautor J
    6. Parker JE

    (2013) Arabidopsis TNL-WRKY domain receptor RRS1 contributes to temperature-conditioned RPS4 auto-immunity

    Frontiers in Plant Science 4:403.

    https://doi.org/10.3389/fpls.2013.00403

    • PubMed
    • Google Scholar
53. Book

    1. Hodkinson TR

    (2018) Evolution and Taxonomy of the Grasses (Poaceae): A Model Family for the Study of Species‐Rich Groups Annual Plant Reviews Online

    Wiley.

    https://doi.org/10.1002/9781119312994.apr0622

    • Google Scholar
54. 1. Jacob F
    2. Vernaldi S
    3. Maekawa T

    (2013) Evolution and conservation of plant NLR functions

    Frontiers in Immunology 4:297.

    https://doi.org/10.3389/fimmu.2013.00297

    • PubMed
    • Google Scholar
55. 1. Jacquemin J
    2. Chaparro C
    3. Laudie M
    4. Berger A
    5. Gavory F
    6. Goicoechea JL
    7. Wing RA
    8. Cooke R

    (2011) Long-Range and targeted ectopic recombination between the two homeologous chromosomes 11 and 12 in Oryza species

    Molecular Biology and Evolution 28:3139–3150.

    https://doi.org/10.1093/molbev/msr144

    • Google Scholar
56. 1. Jia Y
    2. Lee FN
    3. McClung A

    (2009) Determination of resistance spectra of the Pi-ta and Pi-k genes to U.S. races of Magnaporthe oryzae causing rice blast in a recombinant inbred line population

    Plant Disease 93:639–644.

    https://doi.org/10.1094/PDIS-93-6-0639

    • PubMed
    • Google Scholar
57. 1. Jones DT
    2. Taylor WR
    3. Thornton JM

    (1992) The rapid generation of mutation data matrices from protein sequences

    Bioinformatics 8:275–282.

    https://doi.org/10.1093/bioinformatics/8.3.275

    • Google Scholar
58. 1. Jones JD
    2. Vance RE
    3. Dangl JL

    (2016) Intracellular innate immune surveillance devices in plants and animals

    Science 354:aaf6395.

    https://doi.org/10.1126/science.aaf6395

    • PubMed
    • Google Scholar
59. 1. Kanzaki H
    2. Yoshida K
    3. Saitoh H
    4. Fujisaki K
    5. Hirabuchi A
    6. Alaux L
    7. Fournier E
    8. Tharreau D
    9. Terauchi R

    (2012) Arms race co-evolution of Magnaporthe oryzae AVR-Pik and rice pik genes driven by their physical interactions

    The Plant Journal : For Cell and Molecular Biology 72:894–907.

    https://doi.org/10.1111/j.1365-313X.2012.05110.x

    • PubMed
    • Google Scholar
60. 1. Karasov TL
    2. Kniskern JM
    3. Gao L
    4. DeYoung BJ
    5. Ding J
    6. Dubiella U
    7. Lastra RO
    8. Nallu S
    9. Roux F
    10. Innes RW
    11. Barrett LG
    12. Hudson RR
    13. Bergelson J

    (2014) The long-term maintenance of a resistance polymorphism through diffuse interactions

    Nature 512:436–440.

    https://doi.org/10.1038/nature13439

    • PubMed
    • Google Scholar
61. Preprint

    1. Kourelis J
    2. Kamoun S

    (2020) RefPlantNLR: a comprehensive collection of experimentally validated plant NLRs

    bioRxiv.

    https://doi.org/10.1101/2020.07.08.193961

    • Google Scholar
62. 1. Kourelis J
    2. van der Hoorn RAL

    (2018) Defended to the nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function

    The Plant Cell 30:285–299.

    https://doi.org/10.1105/tpc.17.00579

    • Google Scholar
63. 1. Kroj T
    2. Chanclud E
    3. Michel-Romiti C
    4. Grand X
    5. Morel JB

    (2016) Integration of decoy domains derived from protein targets of pathogen effectors into plant immune receptors is widespread

    New Phytologist 210:618–626.

    https://doi.org/10.1111/nph.13869

    • PubMed
    • Google Scholar
64. 1. Kuang H
    2. Woo S-S
    3. Meyers BC
    4. Nevo E
    5. Michelmore RW

    (2004) Multiple genetic processes result in heterogeneous rates of evolution within the major cluster disease resistance genes in lettuce[W]

    The Plant Cell 16:2870–2894.

    https://doi.org/10.1105/tpc.104.025502

    • Google Scholar
65. 1. Kumar S
    2. Stecher G
    3. Suleski M
    4. Hedges SB

    (2017) TimeTree: a resource for timelines, timetrees, and divergence times

    Molecular Biology and Evolution 34:1812–1819.

    https://doi.org/10.1093/molbev/msx116

    • Google Scholar
66. 1. Kumar S
    2. Stecher G
    3. Li M
    4. Knyaz C
    5. Tamura K

    (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms

    Molecular Biology and Evolution 35:1547–1549.

    https://doi.org/10.1093/molbev/msy096

    • PubMed
    • Google Scholar
67. 1. Kurata N
    2. Yamazaki Y

    (2006) Oryzabase an integrated biological and genome information database for rice

    Plant Physiology 140:12–17.

    https://doi.org/10.1104/pp.105.063008

    • PubMed
    • Google Scholar
68. 1. Langner T
    2. Harant A
    3. Gomez-Luciano LB
    4. Shrestha RK
    5. Malmgren A
    6. Latorre SM
    7. Burbano HA
    8. Win J
    9. Kamoun S

    (2021) Genomic rearrangements generate hypervariable mini-chromosomes in host-specific isolates of the blast fungus

    PLOS Genetics 17:e1009386.

    https://doi.org/10.1371/journal.pgen.1009386

    • PubMed
    • Google Scholar
69. 1. Latorre SM
    2. Reyes-Avila CS
    3. Malmgren A
    4. Win J
    5. Kamoun S
    6. Burbano HA

    (2020) Differential loss of effector genes in three recently expanded pandemic clonal lineages of the rice blast fungus

    BMC Biology 18:88.

    https://doi.org/10.1186/s12915-020-00818-z

    • PubMed
    • Google Scholar
70. 1. Lee RRQ
    2. Chae E

    (2020) Variation patterns of NLR clusters in Arabidopsis thaliana genomes

    Plant Communications 1:100089.

    https://doi.org/10.1016/j.xplc.2020.100089

    • PubMed
    • Google Scholar
71. 1. Letunic I
    2. Bork P

    (2007) Interactive tree of life (iTOL): an online tool for phylogenetic tree display and annotation

    Bioinformatics 23:127–128.

    https://doi.org/10.1093/bioinformatics/btl529

    • PubMed
    • Google Scholar
72. 1. Li J
    2. Wang Q
    3. Li C
    4. Bi Y
    5. Fu X
    6. Wang R

    (2019) Novel haplotypes and networks of AVR-Pik alleles in Magnaporthe oryzae

    BMC Plant Biology 19:204.

    https://doi.org/10.1186/s12870-019-1817-8

    • PubMed
    • Google Scholar
73. 1. Li J
    2. Zhang M
    3. Sun J
    4. Mao X
    5. Wang J
    6. Liu H
    7. Zheng H
    8. Li X
    9. Zhao H
    10. Zou D

    (2020a) Heavy metal Stress-Associated proteins in rice and Arabidopsis: Genome-Wide Identification, Phylogenetics, Duplication, and Expression Profiles Analysis

    Frontiers in Genetics 11:477.

    https://doi.org/10.3389/fgene.2020.00477

    • PubMed
    • Google Scholar
74. 1. Li L
    2. Habring A
    3. Wang K
    4. Weigel D

    (2020b) Atypical resistance protein RPW8/HR triggers oligomerization of the NLR immune receptor RPP7 and autoimmunity

    Cell Host & Microbe 27:405–417.

    https://doi.org/10.1016/j.chom.2020.01.012

    • Google Scholar
75. 1. Longya A
    2. Chaipanya C
    3. Franceschetti M
    4. Maidment JHR
    5. Banfield MJ
    6. Jantasuriyarat C

    (2019) Gene duplication and mutation in the emergence of a novel aggressive allele of the AVR-Pik Effector in the Rice Blast Fungus

    Molecular Plant-Microbe Interactions 32:740–749.

    https://doi.org/10.1094/MPMI-09-18-0245-R

    • PubMed
    • Google Scholar
76. 1. Lukasik-Shreepaathy E
    2. Slootweg E
    3. Richter H
    4. Goverse A
    5. Cornelissen BJ
    6. Takken FL

    (2012) Dual regulatory roles of the extended N terminus for activation of the tomato MI-1.2 resistance protein

    Molecular Plant-Microbe Interactions 25:1045–1057.

    https://doi.org/10.1094/MPMI-11-11-0302

    • PubMed
    • Google Scholar
77. Software

    1. MacLean D

    (2019) Besthr

    Besthr.

    https://besthr.co.uk/
78. 1. Maekawa T
    2. Kracher B
    3. Saur IML
    4. Yoshikawa-Maekawa M
    5. Kellner R
    6. Pankin A
    7. von Korff M
    8. Schulze-Lefert P

    (2019) Subfamily-Specific specialization of RGH1/MLA immune receptors in wild barley

    Molecular Plant-Microbe Interactions 32:107–119.

    https://doi.org/10.1094/MPMI-07-18-0186-FI

    • PubMed
    • Google Scholar
79. Preprint

    1. Maidment JHR
    2. Franceschetti M
    3. Maqbool A
    4. Saitoh H

    (2020) Multiple variants of the blast fungus effector AVR-Pik bind the HMA domain of the rice protein OsHIPP19 with high affinity

    bioRxiv.

    https://doi.org/10.1101/2020.12.01.403451

    • Google Scholar
80. 1. Maqbool A
    2. Saitoh H
    3. Franceschetti M
    4. Stevenson CEM
    5. Uemura A
    6. Kanzaki H
    7. Kamoun S
    8. Terauchi R
    9. Banfield MJ

    (2015) Structural basis of pathogen recognition by an integrated HMA domain in a plant NLR immune receptor

    eLife 4:e08709.

    https://doi.org/10.7554/eLife.08709

    • Google Scholar
81. 1. McNicholas S
    2. Potterton E
    3. Wilson KS
    4. Noble ME

    (2011) Presenting your structures: the CCP4mg molecular-graphics software

    Acta Crystallogr Sect D Biol Crystallogr 40:386–394.

    https://doi.org/10.1107/S0907444911007281

    • Google Scholar
82. 1. Mizuno H
    2. Katagiri S
    3. Kanamori H
    4. Mukai Y
    5. Sasaki T
    6. Matsumoto T
    7. Wu J

    (2020) Evolutionary dynamics and impacts of chromosome regions carrying R-gene clusters in rice

    Scientific Reports 10:1–2.

    https://doi.org/10.1038/s41598-020-57729-w

    • Google Scholar
83. Software

    1. Moscou M

    (2019) QKphylogeny, version 36ad9a4

    Github.

    https://github.com/matthewmoscou/QKphylogeny
84. 1. Murshudov GN
    2. Skubák P
    3. Lebedev AA
    4. Pannu NS
    5. Steiner RA
    6. Nicholls RA
    7. Winn MD
    8. Long F
    9. Vagin AA

    (2011) REFMAC 5 for the refinement of macromolecular crystal structures

    Acta Crystallographica Section D Biological Crystallography 67:355–367.

    https://doi.org/10.1107/S0907444911001314

    • Google Scholar
85. 1. Myszka DG

    (1999) Improving biosensor analysis

    Journal of Molecular Recognition 12:279–284.

    https://doi.org/10.1002/(SICI)1099-1352(199909/10)12:5<279::AID-JMR473>3.0.CO;2-3

    • PubMed
    • Google Scholar
86. 1. Nei M
    2. Gojobori T

    (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions

    Molecular Biology and Evolution 3:418–426.

    https://doi.org/10.1093/oxfordjournals.molbev.a040410

    • PubMed
    • Google Scholar
87. Preprint

    1. Oikawa K
    2. Fujisaki K
    3. Shimizu M
    4. Takeda T
    5. Saitoh H
    6. Hirabuchi A
    7. Hiraka Y
    8. Białas A
    9. Langner T
    10. Kellner R
    11. Bozkurt T
    12. Cesari S
    13. Kroj T
    14. Maidment J
    15. Banfield M
    16. Kamoun S
    17. Terauchi R

    (2020) The blast pathogen effector AVR-Pik binds and stabilizes rice heavy metal-associated (HMA) proteins to co-opt their function in immunity

    bioRxiv.

    https://doi.org/10.1101/2020.12.01.406389

    • Google Scholar
88. 1. Okuyama Y
    2. Kanzaki H
    3. Abe A
    4. Yoshida K
    5. Tamiru M
    6. Saitoh H
    7. Fujibe T
    8. Matsumura H
    9. Shenton M
    10. Galam DC
    11. Undan J
    12. Ito A
    13. Sone T
    14. Terauchi R

    (2011) A multifaceted genomics approach allows the isolation of the rice Pia-blast resistance gene consisting of two adjacent NBS-LRR protein genes

    The Plant Journal 66:467–479.

    https://doi.org/10.1111/j.1365-313X.2011.04502.x

    • Google Scholar
89. 1. Petit-Houdenot Y
    2. Langner T
    3. Harant A
    4. Win J
    5. Kamoun S

    (2020) A clone resource of Magnaporthe oryzae Effectors That Share Sequence and Structural Similarities Across Host-Specific Lineages

    Molecular Plant-Microbe Interactions 33:1032–1035.

    https://doi.org/10.1094/MPMI-03-20-0052-A

    • PubMed
    • Google Scholar
90. 1. Potterton L
    2. Agirre J
    3. Ballard C
    4. Cowtan K
    5. Dodson E
    6. Evans PR
    7. Jenkins HT
    8. Keegan R
    9. Krissinel E
    10. Stevenson K
    11. Lebedev A
    12. McNicholas SJ
    13. Nicholls RA
    14. Noble M
    15. Pannu NS
    16. Roth C
    17. Sheldrick G
    18. Skubak P
    19. Turkenburg J
    20. Uski V
    21. von Delft F
    22. Waterman D
    23. Wilson K
    24. Winn M
    25. Wojdyr M

    (2018) CCP 4 i 2: the new graphical user interface to the CCP 4 program suite

    Acta Crystallogr Sect D Struct Biol 74:68–84.

    https://doi.org/10.1107/S2059798317016035

    • Google Scholar
91. Preprint

    1. Prigozhin DM
    2. Krasileva KV

    (2020) Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites

    bioRxiv.

    https://doi.org/10.1101/2020.07.10.190785

    • Google Scholar
92. 1. Pupko T
    2. Pe'er I
    3. Shamir R
    4. Graur D

    (2000) A fast algorithm for joint reconstruction of ancestral amino acid sequences

    Molecular Biology and Evolution 17:890–896.

    https://doi.org/10.1093/oxfordjournals.molbev.a026369

    • PubMed
    • Google Scholar
93. 1. Qi D
    2. DeYoung BJ
    3. Innes RW

    (2012) Structure-function analysis of the coiled-coil and leucine-rich repeat domains of the RPS5 disease resistance protein

    Plant Physiology 158:1819–1832.

    https://doi.org/10.1104/pp.112.194035

    • PubMed
    • Google Scholar
94. 1. Radakovic ZS
    2. Anjam MS
    3. Escobar E
    4. Chopra D
    5. Cabrera J
    6. Silva AC
    7. Escobar C
    8. Sobczak M
    9. Grundler FMW
    10. Siddique S

    (2018) Arabidopsis HIPP27 is a host susceptibility gene for the beet cyst nematode Heterodera schachtii

    Molecular Plant Pathology 19:1917–1928.

    https://doi.org/10.1111/mpp.12668

    • Google Scholar
95. 1. Rairdan GJ
    2. Moffett P

    (2006) Distinct domains in the ARC region of the potato resistance protein rx mediate LRR binding and inhibition of activation

    The Plant Cell 18:2082–2093.

    https://doi.org/10.1105/tpc.106.042747

    • PubMed
    • Google Scholar
96. 1. Randall RN
    2. Radford CE
    3. Roof KA
    4. Natarajan DK
    5. Gaucher EA

    (2016) An experimental phylogeny to benchmark ancestral sequence reconstruction

    Nature Communications 7:1–6.

    https://doi.org/10.1038/ncomms12847

    • Google Scholar
97. 1. Read AC
    2. Moscou MJ
    3. Zimin AV
    4. Pertea G
    5. Meyer RS
    6. Purugganan MD
    7. Leach JE
    8. Triplett LR
    9. Salzberg SL
    10. Bogdanove AJ

    (2020) Genome assembly and characterization of a complex zfBED-NLR gene-containing disease resistance locus in Carolina gold select rice with nanopore sequencing

    PLOS Genetics 16:e1008571.

    https://doi.org/10.1371/journal.pgen.1008571

    • PubMed
    • Google Scholar
98. 1. Saitou N
    2. Nei M

    (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees

    Molecular Biology and Evolution 4:406–425.

    https://doi.org/10.1093/oxfordjournals.molbev.a040454

    • PubMed
    • Google Scholar
99. 1. Sarris PF
    2. Cevik V
    3. Dagdas G
    4. Jones JD
    5. Krasileva KV

    (2016) Comparative analysis of plant immune receptor architectures uncovers host proteins likely targeted by pathogens

    BMC Biology 14:8.

    https://doi.org/10.1186/s12915-016-0228-7

    • PubMed
    • Google Scholar
100. 1. Saucet SB
     2. Ma Y
     3. Sarris PF
     4. Furzer OJ
     5. Sohn KH
     6. Jones JDG

     (2015) Two linked pairs of Arabidopsis TNL resistance genes independently confer recognition of bacterial effector AvrRps4

     Nature Communications 6:1–12.

     https://doi.org/10.1038/ncomms7338

     • Google Scholar
101. 1. Seeholzer S
     2. Tsuchimatsu T
     3. Jordan T
     4. Bieri S
     5. Pajonk S
     6. Yang W
     7. Jahoor A
     8. Shimizu KK
     9. Keller B
     10. Schulze-Lefert P

     (2010) Diversity at the mla powdery mildew resistance locus from cultivated barley reveals sites of positive selection

     Molecular Plant-Microbe Interactions 23:497–509.

     https://doi.org/10.1094/MPMI-23-4-0497

     • PubMed
     • Google Scholar
102. 1. Segretin ME
     2. Pais M
     3. Franceschetti M
     4. Chaparro-Garcia A
     5. Bos JI
     6. Banfield MJ
     7. Kamoun S

     (2014) Single amino acid mutations in the potato immune receptor R3a expand response to Phytophthora effectors

     Molecular Plant-Microbe Interactions 27:624–637.

     https://doi.org/10.1094/MPMI-02-14-0040-R

     • PubMed
     • Google Scholar
103. 1. Shao ZQ
     2. Xue JY
     3. Wu P
     4. Zhang YM
     5. Wu Y
     6. Hang YY
     7. Wang B
     8. Chen JQ

     (2016) Large-Scale analyses of angiosperm Nucleotide-Binding Site-Leucine-Rich repeat genes reveal three anciently diverged classes with distinct evolutionary patterns

     Plant Physiology 170:2095–2109.

     https://doi.org/10.1104/pp.15.01487

     • PubMed
     • Google Scholar
104. 1. Sinapidou E
     2. Williams K
     3. Nott L
     4. Bahkt S
     5. Tör M
     6. Crute I
     7. Bittner-Eddy P
     8. Beynon J

     (2004) Two TIR:nb:lrr genes are required to specify resistance to Peronospora parasitica isolate Cala2 in Arabidopsis

     The Plant Journal : For Cell and Molecular Biology 38:898–909.

     https://doi.org/10.1111/j.1365-313X.2004.02099.x

     • PubMed
     • Google Scholar
105. 1. Stamatakis A

     (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

     Bioinformatics 30:1312–1313.

     https://doi.org/10.1093/bioinformatics/btu033

     • PubMed
     • Google Scholar
106. 1. Stein JC
     2. Yu Y
     3. Copetti D
     4. Zwickl DJ
     5. Zhang L
     6. Zhang C
     7. Chougule K
     8. Gao D
     9. Iwata A
     10. Goicoechea JL
     11. Wei S
     12. Wang J
     13. Liao Y
     14. Wang M
     15. Jacquemin J
     16. Becker C
     17. Kudrna D
     18. Zhang J
     19. Londono CEM
     20. Song X
     21. Lee S
     22. Sanchez P
     23. Zuccolo A
     24. Ammiraju JSS
     25. Talag J
     26. Danowitz A
     27. Rivera LF
     28. Gschwend AR
     29. Noutsos C
     30. Wu CC
     31. Kao SM
     32. Zeng JW
     33. Wei FJ
     34. Zhao Q
     35. Feng Q
     36. El Baidouri M
     37. Carpentier MC
     38. Lasserre E
     39. Cooke R
     40. Rosa Farias DD
     41. da Maia LC
     42. Dos Santos RS
     43. Nyberg KG
     44. McNally KL
     45. Mauleon R
     46. Alexandrov N
     47. Schmutz J
     48. Flowers D
     49. Fan C
     50. Weigel D
     51. Jena KK
     52. Wicker T
     53. Chen M
     54. Han B
     55. Henry R
     56. Hsing YC
     57. Kurata N
     58. de Oliveira AC
     59. Panaud O
     60. Jackson SA
     61. Machado CA
     62. Sanderson MJ
     63. Long M
     64. Ware D
     65. Wing RA

     (2018) Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza

     Nature Genetics 50:285–296.

     https://doi.org/10.1038/s41588-018-0040-0

     • PubMed
     • Google Scholar
107. 1. Steuernagel B
     2. Jupe F
     3. Witek K
     4. Jones JD
     5. Wulff BB

     (2015) NLR-parser: rapid annotation of plant NLR complements

     Bioinformatics 31:1665–1667.

     https://doi.org/10.1093/bioinformatics/btv005

     • PubMed
     • Google Scholar
108. 1. Tanaka S
     2. Schweizer G
     3. Rössel N
     4. Fukada F
     5. Thines M
     6. Kahmann R

     (2019) Neofunctionalization of the secreted Tin2 effector in the fungal pathogen Ustilago maydis

     Nature Microbiology 4:251–257.

     https://doi.org/10.1038/s41564-018-0304-6

     • PubMed
     • Google Scholar
109. 1. Tavaré S

     (1986)

     Some probabilistic and statistical problems in the analysis of DNA sequences

     Biology 17:86.

     • Google Scholar
110. 1. Thornton JW

     (2004) Resurrecting ancient genes: experimental analysis of extinct molecules

     Nature Reviews Genetics 5:366–375.

     https://doi.org/10.1038/nrg1324

     • PubMed
     • Google Scholar
111. 1. Tran DTN
     2. Chung EH
     3. Habring-Müller A
     4. Demar M
     5. Schwab R
     6. Dangl JL
     7. Weigel D
     8. Chae E

     (2017) Activation of a plant NLR complex through heteromeric association with an autoimmune risk variant of another NLR

     Current Biology 27:1148–1160.

     https://doi.org/10.1016/j.cub.2017.03.018

     • PubMed
     • Google Scholar
112. 1. Van de Weyer AL
     2. Monteiro F
     3. Furzer OJ
     4. Nishimura MT
     5. Cevik V
     6. Witek K
     7. Jones JDG
     8. Dangl JL
     9. Weigel D
     10. Bemm F

     (2019) A Species-Wide inventory of NLR genes and alleles in Arabidopsis thaliana

     Cell 178:1260–1272.

     https://doi.org/10.1016/j.cell.2019.07.038

     • PubMed
     • Google Scholar
113. 1. Varden FA
     2. Saitoh H
     3. Yoshino K
     4. Franceschetti M
     5. Kamoun S
     6. Terauchi R
     7. Banfield MJ

     (2019) Cross-reactivity of a rice NLR immune receptor to distinct effectors from the rice blast pathogen Magnaporthe oryzae provides partial disease resistance

     Journal of Biological Chemistry 294:13006–13016.

     https://doi.org/10.1074/jbc.RA119.007730

     • PubMed
     • Google Scholar
114. 1. Wang L
     2. Xu X
     3. Lin F
     4. Pan Q

     (2009) Characterization of rice blast resistance genes in the pik cluster and fine mapping of the Pik-p locus

     Phytopathology 99:900–905.

     https://doi.org/10.1094/PHYTO-99-8-0900

     • PubMed
     • Google Scholar
115. 1. Wang GF
     2. Ji J
     3. El-Kasmi F
     4. Dangl JL
     5. Johal G
     6. Balint-Kurti PJ

     (2015) Molecular and functional analyses of a maize autoactive NB-LRR protein identify precise structural requirements for activity

     PLOS Pathogens 11:e1004674.

     https://doi.org/10.1371/journal.ppat.1004674

     • PubMed
     • Google Scholar
116. 1. Waterhouse A
     2. Bertoni M
     3. Bienert S
     4. Studer G
     5. Tauriello G
     6. Gumienny R
     7. Heer FT
     8. de Beer TAP
     9. Rempfer C
     10. Bordoli L
     11. Lepore R
     12. Schwede T

     (2018) SWISS-MODEL: homology modelling of protein structures and complexes

     Nucleic Acids Research 46:W296–W303.

     https://doi.org/10.1093/nar/gky427

     • PubMed
     • Google Scholar
117. 1. Weber E
     2. Engler C
     3. Gruetzner R
     4. Werner S
     5. Marillonnet S

     (2011) A modular cloning system for standardized assembly of multigene constructs

     PLOS ONE 6:e16765.

     https://doi.org/10.1371/journal.pone.0016765

     • PubMed
     • Google Scholar
118. Book

     1. Win J
     2. Kamoun S
     3. Jones AME

     (2011) Purification of Effector–Target Protein Complexes via Transient Expression in Nicotiana Benthamiana, Plant Immunity, Methods in Molecular Biology

     Totowa, NJ: Humana Press.

     https://doi.org/10.1007/978-1-61737-998-7_15

     • Google Scholar
119. 1. Win J
     2. Kamoun S

     (2003)

     pCB301-p19: a binary plasmid vector to enhance transient expression of transgenes by agroinfiltration

     The Plant Journal : For Cell and Molecular Biology 33:949–956.

     • Google Scholar
120. 1. Winter G

     (2010) xia2 : an expert system for macromolecular crystallography data reduction

     Journal of Applied Crystallography 43:186–190.

     https://doi.org/10.1107/S0021889809045701

     • Google Scholar
121. 1. Wu CH
     2. Krasileva KV
     3. Banfield MJ
     4. Terauchi R
     5. Kamoun S

     (2015) The “sensor domains” of plant NLR proteins: more than decoys?

     Frontiers in Plant Science 6:134.

     https://doi.org/10.3389/fpls.2015.00134

     • PubMed
     • Google Scholar
122. 1. Wu CH
     2. Derevnina L
     3. Kamoun S

     (2018) Receptor networks underpin plant immunity

     Science 360:1300–1301.

     https://doi.org/10.1126/science.aat2623

     • PubMed
     • Google Scholar
123. 1. Yamamoto E
     2. Takashi T
     3. Morinaka Y
     4. Lin S
     5. Wu J
     6. Matsumoto T
     7. Kitano H
     8. Matsuoka M
     9. Ashikari M

     (2010) Gain of deleterious function causes an autoimmune response and Bateson-Dobzhansky-Muller incompatibility in rice

     Molecular Genetics and Genomics 283:305–315.

     https://doi.org/10.1007/s00438-010-0514-y

     • PubMed
     • Google Scholar
124. 1. Yang Z

     (1997) PAML: a program package for phylogenetic analysis by maximum likelihood

     Bioinformatics 13:555–556.

     https://doi.org/10.1093/bioinformatics/13.5.555

     • Google Scholar
125. 1. Yang Z

     (2000) Maximum likelihood estimation on large phylogenies and analysis of adaptive evolution in human influenza virus A

     Journal of Molecular Evolution 51:423–432.

     https://doi.org/10.1007/s002390010105

     • PubMed
     • Google Scholar
126. 1. Yang Z
     2. Swanson WJ
     3. Vacquier VD

     (2000) Maximum-likelihood analysis of molecular adaptation in abalone sperm lysin reveals variable selective pressures among lineages and sites

     Molecular Biology and Evolution 17:1446–1455.

     https://doi.org/10.1093/oxfordjournals.molbev.a026245

     • PubMed
     • Google Scholar
127. 1. Yang Z
     2. Wong WS
     3. Nielsen R

     (2005) Bayes empirical bayes inference of amino acid sites under positive selection

     Molecular Biology and Evolution 22:1107–1118.

     https://doi.org/10.1093/molbev/msi097

     • PubMed
     • Google Scholar
128. 1. Yang Z
     2. Nielsen R

     (1998) Synonymous and nonsynonymous rate variation in nuclear genes of mammals

     Journal of Molecular Evolution 46:409–418.

     https://doi.org/10.1007/PL00006320

     • PubMed
     • Google Scholar
129. 1. Yang Z
     2. Nielsen R

     (2000) Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models

     Molecular Biology and Evolution 17:32–43.

     https://doi.org/10.1093/oxfordjournals.molbev.a026236

     • PubMed
     • Google Scholar
130. 1. Yoshida K
     2. Saunders DG
     3. Mitsuoka C
     4. Natsume S
     5. Kosugi S
     6. Saitoh H
     7. Inoue Y
     8. Chuma I
     9. Tosa Y
     10. Cano LM
     11. Kamoun S
     12. Terauchi R

     (2016) Host specialization of the blast fungus Magnaporthe oryzae is associated with dynamic gain and loss of genes linked to transposable elements

     BMC Genomics 17:370.

     https://doi.org/10.1186/s12864-016-2690-6

     • PubMed
     • Google Scholar
131. 1. Yuan B
     2. Zhai C
     3. Wang W
     4. Zeng X
     5. Xu X
     6. Hu H
     7. Lin F
     8. Wang L
     9. Pan Q

     (2011) The Pik-p resistance to Magnaporthe oryzae in rice is mediated by a pair of closely linked CC-NBS-LRR genes

     Theoretical and Applied Genetics 122:1017–1028.

     https://doi.org/10.1007/s00122-010-1506-3

     • PubMed
     • Google Scholar
132. 1. Zdrzałek R
     2. Kamoun S
     3. Terauchi R
     4. Saitoh H
     5. Banfield MJ

     (2020) The rice NLR pair Pikp-1/Pikp-2 initiates cell death through receptor cooperation rather than negative regulation

     PLOS ONE 15:e0238616.

     https://doi.org/10.1371/journal.pone.0238616

     • PubMed
     • Google Scholar
133. 1. Zess EK
     2. Białas A
     3. Kamoun S

     (2019) Old fungus, new trick

     Nature Microbiology 4:210–211.

     https://doi.org/10.1038/s41564-018-0351-z

     • PubMed
     • Google Scholar
134. 1. Zhai C
     2. Lin F
     3. Dong Z
     4. He X
     5. Yuan B
     6. Zeng X
     7. Wang L
     8. Pan Q

     (2011) The isolation and characterization of pik, a rice blast resistance gene which emerged after rice domestication

     New Phytologist 189:321–334.

     https://doi.org/10.1111/j.1469-8137.2010.03462.x

     • PubMed
     • Google Scholar
135. 1. Zhai C
     2. Zhang Y
     3. Yao N
     4. Lin F
     5. Liu Z
     6. Dong Z
     7. Wang L
     8. Pan Q

     (2014) Function and interaction of the coupled genes responsible for Pik-h encoded rice blast resistance

     PLOS ONE 9:e98067.

     https://doi.org/10.1371/journal.pone.0098067

     • PubMed
     • Google Scholar
136. 1. Zschiesche W
     2. Barth O
     3. Daniel K
     4. Böhme S
     5. Rausche J
     6. Humbeck K

     (2015) The zinc-binding nuclear protein HIPP3 acts as an upstream regulator of the salicylate-dependent plant immunity pathway and of flowering time in Arabidopsis thaliana

     New Phytologist 207:1084–1096.

     https://doi.org/10.1111/nph.13419

     • PubMed
     • Google Scholar

Author details

1. Aleksandra Białas

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1135-2189

2. Thorsten Langner

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Conceptualization, Supervision, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3401-8888

3. Adeline Harant

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Mauricio P Contreras

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Investigation, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6002-0730

5. Clare EM Stevenson

   Department of Biological Chemistry, John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

6. David M Lawson

   Department of Biological Chemistry, John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7637-4303

7. Jan Sklenar

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

8. Ronny Kellner

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4618-0110

9. Matthew J Moscou

   The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Resources, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2098-6818

10. Ryohei Terauchi

    1. Division of Genomics and Breeding, Iwate Biotechnology Research Centre, Iwate, Japan
    2. Laboratory of Crop Evolution, Graduate School of Agriculture, Kyoto University, Kyoto, Japan

    Contribution

    Resources, Funding acquisition, Methodology

    Competing interests

    No competing interests declared

11. Mark J Banfield

    Department of Biological Chemistry, John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Contribution

    Resources, Supervision, Funding acquisition, Writing - original draft

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8921-3835

12. Sophien Kamoun

    The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Writing - original draft, Project administration

    For correspondence

    sophien.kamoun@tsl.ac.uk

    Competing interests

    receives funding from industry on NLR biology.

    "This ORCID iD identifies the author of this article:" 0000-0002-0290-0315

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