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.

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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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