The health status of a plant depends on the immune system it inherits from its parents. Plants have many receptor proteins that can recognize distinct molecules from insects and microbes, and trigger an immune response. Inheriting the right set of receptors allows plants to detect certain threats and to cope with diseases and pests.

Soybeans, chickpeas and other closely-related crop plants belong to a family of plants known as the legumes. Previous studies have found that, unlike other plants, some legumes are able to respond to oral secretions from caterpillars. These plants have a receptor known as INR that binds to a molecule called inceptin in the secretions. However, it remained unclear how or when INR evolved.

To address this gap, Snoeck et al. tested immune responses to inceptin in the leaves of 22 species of legume. The experiments revealed that only members of a subgroup of legumes called the Phaseoloids were able to recognize the molecule.

Analyzing the genomes of several legume species revealed that the gene encoding INR first emerged around 28 million years ago. Among the descendants of the legumes that first evolved this receptor, only the crop plant soybean and a few other species were unable to respond to inceptin. The genomic data indicated that these species had in fact lost the gene encoding INR over evolutionary time.

Snoeck et al. then combined data from genes encoding modern-day receptors to reconstruct the sequence of building blocks that make up the 28-million-year-old version of INR. This ancestral receptor was able to respond to inceptin in the caterpillar secretion, whereas an older version of the protein, which had a slightly different set of building blocks, could not. This suggests that INR evolved the ability to respond to inceptin as a result of small mutations in the gene encoding a more ancient receptor.

The work of Snoeck et al. reveals how the Phaseoloids evolved to respond to caterpillars, and how this ability has been lost in soybeans and other members of the subgroup. In the future, these findings may aid plant breeding or genetic engineering approaches for enhancing soybeans and other crops resistance to caterpillar pests.

Plant and animal innate immunity is mediated by germline-encoded pattern recognition receptors (PRRs; Ronald and Beutler, 2010). PRRs serve as specific sensors of various pathogen-, herbivore-, and damage-associated molecular patterns (PAMPs, HAMPs, and DAMPs; Snoeck et al., 2022; Albert et al., 2020; Gust et al., 2017). In plants, PRRs in the large receptor kinase (RK) family comprise an extracellular domain, a single-pass transmembrane motif, and an intracellular kinase domain (DeFalco and Zipfel, 2021). Receptor-like proteins (RLPs) share structural similarities with RKs but lack a kinase domain (Wang et al., 2008; Fritz-Laylin et al., 2005). Extracellular domains of RK/RLPs include LysM, lectin, and malectin domains, but the most common ectodomain is a series of leucine-rich repeats (LRRs) which mediate PAMP binding and co-receptor association (Albert et al., 2020; Shiu and Bleecker, 2003; Shiu and Bleecker, 2001; Fischer et al., 2016; Hohmann et al., 2017; Restrepo-Montoya et al., 2020). In plants, LRR-RK/RLPs form a large gene family; for example, the Arabidopsis thaliana genome contains about 223 LRR-RKs and 57 LRR-RLPs (Wang et al., 2008; Fritz-Laylin et al., 2005; Shiu and Bleecker, 2001; Lehti-Shiu et al., 2009), and the number of annotated LRR-RK/RLPs per genome varies across plant species (Restrepo-Montoya et al., 2020; Ngou et al., 2022). Moreover, comparative genomic analyses of LRR-RK/RLPs involved in biotic interactions have revealed strong diversifying selection, lineage-specific expanded gene clusters, and immune receptor repertoire variation both within and between species (Fritz-Laylin et al., 2005; Jamieson et al., 2018; Tang et al., 2010; Pruitt et al., 2021; Steinbrenner, 2020), making the gene family a model for the evolution of innate immune systems.

Evidence is accumulating for highly specialized roles of plant LRR-RLPs as immune sensors (Jamieson et al., 2018; Steinbrenner, 2020). Various LRR-RLPs from Arabidopsis, tomato, wild tobacco, rapeseed, and cowpea have been shown to detect molecular patterns from fungi, bacteria, parasitic weeds, and herbivores (Albert et al., 2020; Jamieson et al., 2018). For example, Cf-9, Cf-4, and Cf-2 interact with respective molecular patterns of Cladosporium fulvum, Avr9, Avr4, and Avr2 (via host protein Rcr3), and are restricted to the Solanum genus (Jones et al., 1994; Thomas et al., 1997; Kruijt et al., 2007; Dixon et al., 1996; Luderer et al., 2002; Krüger et al., 1979; Rooney et al., 2005). Arabidopsis RLP42 recognizes fungal endopolygalacturonases (PGs) eptitope pg9(At) derived from Botrytis cinerea (Zhang et al., 2014; Zhang et al., 2021). Similarly, RLP23 is an Arabidopsis-specific LRR-RLP, which recognizes nlp20 peptide from the necrosis and ethylene-inducing peptide1 (NEP1)-like proteins (NLPs) found in bacterial/fungal/oomycete species (Jamieson et al., 2018; Albert et al., 2015). RXEG1 and RE02 were identified in wild tobacco and are respectively triggered by the fungal elicitors XEG1 (Phytophthora sojae) and E02 (Valsa mali) (Ma et al., 2015; Nie et al., 2021). LepR3 (AvrLm1) and RLM2 (AvrLm2) convey both race specific resistance to the fungal pathogen Leptosphaeria maculans in rapeseed (Larkan et al., 2015; Larkan et al., 2022). ReMAX is restricted to the Brassicaceae and triggered by the PAMP eMax originating from Xanthomonas (Jehle et al., 2013). Cuscuta Receptor1 (CuRe1) is specific to Solanum lycopersicum and senses the peptide Crip21, which originates from parasitic plants of the genus Cuscuta (Hegenauer et al., 2020; Hegenauer et al., 2016). Finally, the inceptin receptor (INR) appears to be specific to the legume tribe Phaseoleae and recognizes inceptin (In11), a HAMP found in the oral secretions of multiple caterpillars (Steinbrenner et al., 2020; Schmelz et al., 2006; Schmelz et al., 2012; Schmelz et al., 2007). Notably, all the above examples of LRR-RLPs are family-specific, restricted to the Solanaceae (Cf-2, Cf-4, Cf-9, RXEG1, RE02, and CuRe1), Brassicaceae (RLP23, RLP42, LepR3, RLM2, and ReMAX), or Leguminosae (tribe Phaseoleae; INR; Steinbrenner, 2020; Zhang et al., 2021). However, despite clear signatures of lineage-specific functions, specific evolutionary steps leading to novel LRR-RLP functions across multiple species have not been described.

Mechanistic understanding of LRR-RLP sensing functions is also currently limited. Structural data exists for only one PAMP or HAMP sensing LRR-RLP, and genetic experiments have been limited to receptors from model species (Zhang et al., 2021; Albert et al., 2019; Kourelis et al., 2020; Hohmann and Hothorn, 2019; Sun et al., 2022). Ancestral sequence reconstruction (ASR) is a powerful approach for studying the evolution of protein function and specificity and has been applied to a limited number of receptor functions (Delaux et al., 2019; Białas et al., 2021; Scossa and Fernie, 2021; Carroll et al., 2011; Chantreau et al., 2019). However, ASR has not yet been applied to rapidly evolving LRR-RLPs functions, often encoded by complex multi-copy loci (Steinbrenner, 2020). Importantly, LRR-RLP recognition functions can be assessed through heterologous expression in a model plant, wild tobacco (Nicotiana benthamiana; Zhang et al., 2021; Steinbrenner et al., 2020; Albert et al., 2019; Goodin et al., 2008).

In this study, we use the legume-specific LRR-RLP INR as a model to perform dense species phenotyping, comparative genomics, and functional validation to associate gain and loss of HAMP response with evolution of the contiguous INR receptor locus. By leveraging both existing high-quality assemblies and long read, de novo assemblies of key legume species, we were able to analyze the ~53 million year (my) evolution of the INR locus. We show that In11 response is restricted to species in the ~28 my-old clade of the Phaseoloid legumes, which includes the agriculturally important subtribes Phaseolinae, Glycininae, and Cajaninae. Dynamic evolution of the syntenic INR receptor locus predates the divergence of Phaseoloids, but a single clade of INR homologs is active in recognition of In11. Finally, we used chimeric and ancestrally reconstructed LRR-RLPs to pinpoint key domains and amino acids (AA) involved in In11 recognition. Patterns of INR variation provide a framework for the evolution of novel functions within PRR gene families.

Specificity of PRR recognition functions underlies innate immunity in plants and animals (Ronald and Beutler, 2010). Here, we described the evolution of a lineage-specific recognition function of the legume-specific LRR-RLP INR. We identified specific steps in INR evolution: an ancestral gene insertion event, receptor gene diversification, and the evolution of specific INR recognition capability. Finally, comparisons between In11-responsive and unresponsive homologs, especially through chimeric receptors, ASR, and single AA substitutions, pinpoint key AA residues which mediate peptide recognition and response. Our work illuminates themes in the evolution of lineage-specific immune sensing functions, which will inform the broad use of PRRs as resistance traits in agriculture (Kourelis et al., 2020; Schultink and Steinbrenner, 2021; Lacombe et al., 2010; Pfeilmeier et al., 2019).

Evolutionary analysis of diverse plant immune responses to a single PAMP or HAMP is rarely performed beyond a single model species, although this can be a powerful approach to understand the emergence of specific immune receptor functions (Snoeck et al., 2022). We measured In11 responses across 22 legume species, indicating that In11 response is restricted to the Phaseoloids (Figure 1). However, within the Phaseoloids, several tested species/accessions were not able to respond to In11, suggesting multiple independent losses of INR. These phenotypic observations allowed the investigation of the evolution of a specific LRR-RLP involved in plant immunity over a long (~32 my), detailed timescale.

We complemented broad analysis of In11 response with comparative genomics of key legume species within the NPAAA papilionoid clade, which diverged ~53 mya (Egan et al., 2016; Li et al., 2013; Lavin et al., 2005; Stefanović et al., 2009). Analysis of the INR receptor locus across 20 existing and newly sequenced legume genomes revealed gene insertion and diversification at the INR locus in Millettioid species, which diverged prior to the Phaseoloids and consequently the emergence of In11 recognition. The INR locus shows high variability and diversification among the larger group of Phaseoloid and non-Phaseoloid legumes of the NPAAA papilionoids, with zero to seven LRR-RLP encoding-genes per species. Legume species within the Hologalegina (L. angustifolius, C. arietinum, M. truncatula, and T. pratense) do not contain an LRR-RLP encoding gene at the locus, whereas all species investigated herein the Millettioids except for P. erosus contain at least one LRR-RLP (Figure 2). This observation is consistent with a gene insertion event at the INR locus in the ancestor of extant Millettioids ~32 mya, which likely led to extant copy number variation in early-branching Millettioid species. Processes generating receptor diversity in high copy number variation loci will be interesting to explore for model PRRs including INR (Krasileva, 2019).

Analysis of INR provides an example of a PRR with conserved recognition function across a relatively long evolutionary history (since the emergence of the Phaseoloids ~ 28 mya), compared to other extensive studied LRR-RLPs which are generally studied within a single genus or species (Albert et al., 2019; Kourelis et al., 2020). Each of 364 tested varieties of V. unguiculata are able to respond to In11 and our analysis now extends this conservation across legume species (Steinbrenner et al., 2020). A phylogenetic analysis revealed potential INR-homologs in 10 additional species as they clustered together with V. unguiculata INR. Orthologous LRR-RLPs of the INR clade from five legume genera were tested and able to induce an In11-induced ethylene and ROS burst upon heterologous expression in N. benthamiana (Figure 3). This includes INR homologs from Cajanus cajan and M. pruriens of the Phaseoloid clade. Certain Phaseoloids and earlier diverged non-Phaseoloids also contain INR-like homologs at the contiguous INR locus; thus, INR most likely arose from an existing LRR-RLP, ~28 mya. The role of INR-like homologs is not clear, but they may detect a related, In11-like ligand. This evolutionary pattern is reminiscent of the evolution of LRR-RLP Cf-2 which evolved <6 mya within the genus Solanum, potentially by intergenic recombination (Kourelis et al., 2020). In summary, an ancestral gene insertion event is the likely source of the extant gene and copy number variation at the INR locus, which preceded the evolution of a specific peptide recognition function. The high level of AA conservation of the recognized ligand In11 (Schmelz et al., 2006) may drive overall stability of the INR locus since the divergence of the Phaseoloids ~28 mya.

The abundance of closely related legume genomes provided by this study and others reveals the dynamic nature of INR receptor loci (Chen et al., 2000; Shi et al., 2014). Interestingly, we observed at least two independent cases of INR loss within the Phaseoloids. Two reference genomes as well as 26 de novo assemblies of the closely related Glycininae, G. soja and G. max, do not encode INR (Liu et al., 2020). Nevertheless, INR loss does not seem to predate the divergence of the Glycininae as Teramnus labialis is able to respond to In11 (Figure 1), although genome data are lacking for this species. Besides reciprocal gene loss of INR, gene tree-species tree reconciliation also suggests the involvement of five tandem duplications of an INR-like gene predating radiation of the genus Glycine (Figure 2—figure supplement 2). This is consistent with whole-genome observations of preferential gene loss in tandem clusters (Shi et al., 2014). Similarly, within the separate Desmodieae lineage, our analysis also predicts that a H. podocarpum INR-like gene underwent specific tandem duplication events after the loss of INR, as its genome contains seven INR-like homologs which cluster together in a phylogenetic analysis. In contrast, P. vulgaris and M. pruriens lost INR-like, and P. erosus seems to have lost both INR and INR-like as it does not contain any complete coding sequence of an LRR-RLP at the INR locus. Repetitive elements and transposable elements within the INR locus may have disrupted ancestral INR and INR-like genes in these lineages and can be found between LRR-RLP genes and gene fragments (Figure 2—figure supplement 2). Loss of INR (and INR-like) may reflect the propensity of tandemly duplicated loci to lose functions through gene conversion and/or reciprocal gene loss (Shi et al., 2014).

Our detailed analysis of the emergence of INR provides a roadmap for understanding evolution of recognition specificity for other lineage-specific PRRs (Steinbrenner, 2020; Zhang et al., 2021). For analogous RLP-mediated responses within the Brassicaceae, extensive phenotyping was earlier performed for several PAMPs – eMax, nlp20, SCFE1, IF1, and pg13 – across Arabidopsis accessions and related species (Zhang et al., 2021; Albert et al., 2015; Jehle et al., 2013; Shi et al., 2014; Wei et al., 2020). However, analysis of the respective PRR loci across Brassicaceae has not yet been conducted and may reveal processes leading to their dynamic evolution. A recent pangenomic analysis of Arabidopsis indicates that while many RLPs occur in complex or copy-number variable loci (Steinbrenner, 2020; Zhang et al., 2021), three PAMP sensing LRR-RLPs (RLP 23, RLP30 and RLP32) are conserved across A. thaliana varieties (Pruitt et al., 2021). Further cross-species analysis may reveal a similar trajectory to INR via ancestral duplications preceding fixation in the Arabidopsis lineage. In summary, additional case studies for specific receptors are needed to reveal broader patterns in PRR evolution.

Our functional analysis of INR and INR-like genes in a heterologous model (N. benthamiana) also provides insight into LRR-RLP function. Chimeric receptors formed by combining INR and an INR-like paralog revealed LRR-RLP subdomains required for In11 response. Across all chimeric receptors, those encoding the LRR (C1) and intervening motif (C2) domains of VuINR responded to In11 treatment with both an ethylene and ROS burst, suggestive of crucial elements for elicitor interaction in the C1-C2 subdomain. Notably, a chimeric receptor with mixed C1 and C2, 219600-C1, is the sole construct that responded to the control treatment, suggestive of a critical role for C1-C2 compatibility. In addition, a chimeric receptor with mixed C2 and C3 (219600-C2) had delayed In11-induced ROS burst relative to all other In11-responsive constructs tested here (Figure 4). These findings are consistent with previous truncation and chimeric protein analyses for Arabidopsis RLP23 and RLP42. Truncations of the RLP23 ectodomain abolished function, suggesting the necessity of the entire ectodomain for elicitor binding or proper assembly of the receptor (Albert et al., 2019). Additionally, chimeric receptors implicated the importance of RLP42 its 12 N-terminal LRRs (C1) and LRR21-LRR24 which includes the intervening motif (C2) for recognition of fungal PG (Zhang et al., 2021). Furthermore, RXEG1, the only PAMP or HAMP sensing LRR-RLP with structural information interacts primarily with its ligand XEG1 through two loopout regions, one in the N-terminal C1 domain and another comprising the C2 domain (Sun et al., 2022). Both the C1 domain and the C2 intervening motif are therefore critical for LRR-RLPs in multiple plant families, but their specific role in PAMP/HAMP recognition needs further investigation.

Additionally, INR functional analyses are consistent with a vestigial role for the cytoplasmic tail of PRRs in the LRR-RLP family. Strikingly, all identified INR homologs encode a cytoplasmic tail of only 10 AA, shorter relative to all identified INR-like homologs here. Nevertheless, swapping the VuINR (Vigun07g219600) cytoplasmic tail to the extended version of VuINR-like 1 (Vigun07g219700), 219600 F, did not affect In11-response (Figure 4). Previously, the complete deletion of the intracellular 17-amino-acid tail of RLP23 reduced but did not abolish receptor function (Albert et al., 2019). Additionally, a previously described chimeric swap replacing the RLP42 terminal LRR, transmembrane helix, and cytoplasmic tail with the respective subdomains of a non-responsive paralog was still responsive to the pg9(At) elicitor (Zhang et al., 2021). Intriguingly, quantitative differences were earlier reported as the C-terminally truncated RLP23 had a reduced nlp20 response, consistent with auxiliary rather than essential function for the RLP23 cytoplasmic tail (Albert et al., 2019).

In addition to the use of chimeric receptors, reconstruction of the ancestral INR LRR domain revealed further functional insights contributing to INR function. Our analysis suggests that the common ancestral LRR ectodomain of extant INRs conferred In11 response (N4), whereas the common ancestor of both INR and INR-like (N3) did not confer In11 response (Figure 5). N3 and N4 differ in In11 response although they only vary by 16 AA, and 5 of the 16 show strongly conserved (100% AA similarity) across all members of the extant INR clade (Supplementary file 8). Two of five single AA substitutions in N4 to the corresponding N3 AA, H404N and R406K, abolished In11-induced responses (Figure 6). Thus, these AA substitutions and change in sidechain charge (N to H) were critical for the evolution of the novel recognition functions of INR. The role of key LRR-RLP residues in ligand-specific responses is consistent with previous single AA substitutions to the functional RLP42 receptor, where several were sufficient to abolish pg9(At)-induced co-receptor association and defense responses (Zhang et al., 2021). Hence, based on studies of LRR-RLP function, evidence is accumulating for the critical roles of specific residues for several LRR-RLPs in both C1 and C2 domains.

A similar ASR approach was previously employed to understand an effector recognition domain, the 98-AA heavy metal-associated (HMA) domain of the plant intracellular NOD-like receptor Pik-1 (Białas et al., 2021). Specific AA changes introduced into an ancestrally reconstructed backbone were sufficient to confer effector binding and immune functions. For Pik-1 HMA, structural data provided additional insight into the mechanism of effector binding in ancestral and extant proteins. Our use of an ASR approach for a relatively long LRR domain (720 AA) now demonstrates the power of dense comparative genomic analyses to also identify key residues in extant PRRs without defined binding sites. In the absence of structural data for ligand-binding LRR-RLPs, an ASR approach may be useful to identify sets of co-varying residues critical for binding and signaling functions of PRRs.

Sequencing data and genome assemblies have been deposited on NCBI under the following bioprojects: PRJNA817236, PRJNA817235, PRJNA817241, PRJNA817237 and PRJNA817234. Figure 1 - Suppl. data 2 contains the numerical data used to generate this figure.

1. 1. Snoeck S

   (2022) NCBI BioProject

   ID PRJNA817236. Hylodesmum podocarpum, de novo genome assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA817236
2. 1. Snoeck S

   (2022) NCBI BioProject

   ID PRJNA817235. Canavalia ensiformis, de novo genome assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA817235
3. 1. Snoeck S

   (2022) NCBI BioProject

   ID PRJNA817241. Macroptilium lathyroides Raw sequence reads.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA817241
4. 1. Snoeck S

   (2022) NCBI BioProject

   ID PRJNA817237. Cyamopsis tetragonoloba, de novo genome assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA817237
5. 1. Snoeck S

   (2022) NCBI BioProject

   ID PRJNA817234. Pachyrhizus erosus, de novo genome assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA817234

1. 1. Hane JK

   (2016) NCBI BioProject

   ID 299755. Lupinus angustifolius Genome sequencing and assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/299755
2. 1. Varshney RK

   (2013) NCBI BioProject

   ID PRJNA175619. Cicer arietinum Genome sequencing and assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA175619
3. 1. Tang H

   (2014) NCBI BioProject

   ID PRJNA10791. Medicago truncatula strain:A17 Genome sequencing and assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA10791
4. 1. De Vega JJ

   (2015) NCBI BioProject

   ID PRJEB9186. Assembly of red clover (Trifolium pratense L.) genome.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB9186
5. 1. Los Alamos National Laboratory

   (2018) NCBI BioProject

   ID 482671. Abrus precatorius isolate:BTA Genome sequencing and assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/482671
6. 1. African Centre of excellence in Phytomedicine Resaerch

   (2018) NCBI BioProject

   ID PRJNA414658. Mucuna pruriens isolate:JCA_2017 Genome sequencing and assembly.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA414658
7. 1. Varshney RK

   (2011) NCBI BioProject

   ID 72815. Cajanus cajan Genome Sequencing.

   https://www.ncbi.nlm.nih.gov/bioproject/72815
8. 1. Xie M

   (2019) NCBI BioProject

   ID 486704. Genome sequencing and de novo assembly of Glycine soja W05.

   https://www.ncbi.nlm.nih.gov/bioproject/486704
9. 1. Chang S

   (2013) NCBI BioProject

   ID PRJNA19861. Soybean WGS sequencing project.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA19861
10. 1. University of California, Riverside

    (2019) NCBI BioProject

    ID 381312. Vigna unguiculata isolate:L. Walp | cultivar:IT97K-499-35 Genome sequencing and assembly.

    https://www.ncbi.nlm.nih.gov/bioproject/381312
11. 1. Yang K

    (2015) NCBI BioProject

    ID 261643. Vigna angularis cultivar:Jingnong 6 Genome sequencing and assembly.

    https://www.ncbi.nlm.nih.gov/bioproject/261643
12. 1. Kang YJ

    (2014) NCBI BioProject

    ID 243847. Vigna radiata var. radiata cultivar:VC1973A Genome sequencing and assembly.

    https://www.ncbi.nlm.nih.gov/bioproject/243847
13. 1. Chang Y

    (2019) NCBI BioProject

    ID PRJNA474418. Genome sequencing of four African orphan crops.

    https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA474418
14. 1. Andes UDL
    2. Colombia UND

    (2020) NCBI BioProject

    ID PRJNA596114. Phaseolus lunatus genomic resources.

    https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA596114
15. 1. Michigan StateUniversity

    (2020) NCBI BioProject

    ID PRJNA607288. Genome and transcriptome sequencing of Phaseolus acutifolius and Phaseolus vulgaris.

    https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA607288
16. 1. JGI-PGF JGI

    (2013) NCBI BioProject

    ID PRJNA41439. Phaseolus vulgaris cultivar:G19833 Genome sequencing and assembly.

    https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA41439

1. 1. Albert I
   2. Böhm H
   3. Albert M
   4. Feiler CE
   5. Imkampe J
   6. Wallmeroth N
   7. Brancato C
   8. Raaymakers TM
   9. Oome S
   10. Zhang H
   11. Krol E
   12. Grefen C
   13. Gust AA
   14. Chai J
   15. Hedrich R
   16. Van den Ackerveken G
   17. Nürnberger T

   (2015) An RLP23-SOBIR1-BAK1 complex mediates NLP-triggered immunity

   Nature Plants 1:15140.

   https://doi.org/10.1038/nplants.2015.140

   • PubMed
   • Google Scholar
2. Website

   1. Albert I
   2. Zhang L
   3. Bemm H
   4. Nürnberger T

   (2019) Structure-function analysis of immune receptor AtRLP23 with its ligand nlp20 and coreceptors AtSOBIR1 and AtBAK1

   Accessed December 22, 2019.

   https://apsjournals.apsnet.org/doi/abs/10.1094/MPMI-09-18-0263-R
3. 1. Albert I
   2. Hua C
   3. Nürnberger T
   4. Pruitt RN
   5. Zhang L

   (2020) Surface sensor systems in plant immunity

   Plant Physiology 182:1582–1596.

   https://doi.org/10.1104/pp.19.01299

   • PubMed
   • Google Scholar
4. 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

   • PubMed
   • Google Scholar
5. 1. Białas A
   2. Langner T
   3. Harant A
   4. Contreras MP
   5. Stevenson CE
   6. Lawson DM
   7. Sklenar J
   8. Kellner R
   9. Moscou MJ
   10. Terauchi R
   11. Banfield MJ
   12. Kamoun S

   (2021) Two NLR immune receptors acquired high-affinity binding to a fungal effector through convergent evolution of their integrated domain

   eLife 10:e66961.

   https://doi.org/10.7554/eLife.66961

   • PubMed
   • Google Scholar
6. 1. Cardoso D
   2. São-Mateus WMB
   3. da Cruz DT
   4. Zartman CE
   5. Komura DL
   6. Kite G
   7. Prenner G
   8. Wieringa JJ
   9. Clark A
   10. Lewis G
   11. Pennington RT
   12. de Queiroz LP

   (2015) Filling in the gaps of the papilionoid legume phylogeny: the enigmatic amazonian genus Petaladenium is a new branch of the early-diverging Amburaneae clade

   Molecular Phylogenetics and Evolution 84:112–124.

   https://doi.org/10.1016/j.ympev.2014.12.015

   • PubMed
   • Google Scholar
7. 1. Carroll SM
   2. Ortlund EA
   3. Thornton JW

   (2011) Mechanisms for the evolution of a derived function in the ancestral glucocorticoid receptor

   PLOS Genetics 7:e1002117.

   https://doi.org/10.1371/journal.pgen.1002117

   • PubMed
   • Google Scholar
8. 1. Chang Y
   2. Liu H
   3. Liu M
   4. Liao X
   5. Sahu SK
   6. Fu Y
   7. Song B
   8. Cheng S
   9. Kariba R
   10. Muthemba S
   11. Hendre PS
   12. Mayes S
   13. Ho WK
   14. Yssel AEJ
   15. Kendabie P
   16. Wang S
   17. Li L
   18. Muchugi A
   19. Jamnadass R
   20. Lu H
   21. Peng S
   22. Van Deynze A
   23. Simons A
   24. Yana-Shapiro H
   25. Van de Peer Y
   26. Xu X
   27. Yang H
   28. Wang J
   29. Liu X

   (2019) The draft genomes of five agriculturally important African orphan crops

   GigaScience 8:1–16.

   https://doi.org/10.1093/gigascience/giy152

   • PubMed
   • Google Scholar
9. 1. Chantreau M
   2. Poux C
   3. Lensink MF
   4. Brysbaert G
   5. Vekemans X
   6. Castric V

   (2019) Asymmetrical diversification of the receptor-ligand interaction controlling self-incompatibility in Arabidopsis

   eLife 8:e50253.

   https://doi.org/10.7554/eLife.50253

   • PubMed
   • Google Scholar
10. 1. Chen K
    2. Durand D
    3. Farach-Colton M

    (2000) NOTUNG: A program for dating gene duplications and optimizing gene family trees

    Journal of Computational Biology 7:429–447.

    https://doi.org/10.1089/106652700750050871

    • PubMed
    • Google Scholar
11. 1. DeFalco TA
    2. Zipfel C

    (2021) Molecular mechanisms of early plant pattern-triggered immune signaling

    Molecular Cell 81:4346.

    https://doi.org/10.1016/j.molcel.2021.09.028

    • PubMed
    • Google Scholar
12. 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
13. 1. Dixon MS
    2. Jones DA
    3. Keddie JS
    4. Thomas CM
    5. Harrison K
    6. Jones JDG

    (1996) The tomato Cf-2 disease resistance locus comprises two functional genes encoding leucine-rich repeat proteins

    Cell 84:451–459.

    https://doi.org/10.1016/S0092-8674(00)81290-8

    • PubMed
    • Google Scholar
14. 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

    • PubMed
    • Google Scholar
15. 1. Egan AN
    2. Vatanparast M
    3. Cagle W

    (2016) Parsing polyphyletic Pueraria: delimiting distinct evolutionary lineages through phylogeny

    Molecular Phylogenetics and Evolution 104:44–59.

    https://doi.org/10.1016/j.ympev.2016.08.001

    • PubMed
    • Google Scholar
16. 1. Engler C
    2. Youles M
    3. Gruetzner R
    4. Ehnert T-M
    5. Werner S
    6. Jones JDG
    7. Patron NJ
    8. Marillonnet S

    (2014) A golden gate modular cloning toolbox for plants

    ACS Synthetic Biology 3:839–843.

    https://doi.org/10.1021/sb4001504

    • PubMed
    • Google Scholar
17. 1. Felix G
    2. Duran JD
    3. Volko S
    4. Boller T

    (1999) Plants have a sensitive perception system for the most conserved domain of bacterial flagellin

    The Plant Journal 18:265–276.

    https://doi.org/10.1046/j.1365-313x.1999.00265.x

    • PubMed
    • Google Scholar
18. 1. Fischer I
    2. Diévart A
    3. Droc G
    4. Dufayard JF
    5. Chantret N

    (2016) Evolutionary dynamics of the leucine-rich repeat receptor-like kinase (LRR-RLK) subfamily in angiosperms

    Plant Physiology 170:1595–1610.

    https://doi.org/10.1104/pp.15.01470

    • PubMed
    • Google Scholar
19. 1. Fritz-Laylin LK
    2. Krishnamurthy N
    3. Tör M
    4. Sjölander KV
    5. Jones JDG

    (2005) Phylogenomic analysis of the receptor-like proteins of rice and Arabidopsis

    Plant Physiology 138:611–623.

    https://doi.org/10.1104/pp.104.054452

    • PubMed
    • Google Scholar
20. 1. Goodin MM
    2. Zaitlin D
    3. Naidu RA
    4. Lommel SA

    (2008) Nicotiana benthamiana: its history and future as a model for plant-pathogen interactions

    Molecular Plant-Microbe Interactions 21:1015–1026.

    https://doi.org/10.1094/MPMI-21-8-1015

    • PubMed
    • Google Scholar
21. 1. Gust AA
    2. Pruitt R
    3. Nürnberger T

    (2017) Sensing danger: key to activating plant immunity

    Trends in Plant Science 22:779–791.

    https://doi.org/10.1016/j.tplants.2017.07.005

    • PubMed
    • Google Scholar
22. 1. Hegenauer V
    2. Fürst U
    3. Kaiser B
    4. Smoker M
    5. Zipfel C
    6. Felix G
    7. Stahl M
    8. Albert M

    (2016) Detection of the plant parasite Cuscuta reflexa by a tomato cell surface receptor

    Science 353:478–481.

    https://doi.org/10.1126/science.aaf3919

    • PubMed
    • Google Scholar
23. 1. Hegenauer V
    2. Slaby P
    3. Körner M
    4. Bruckmüller JA
    5. Burggraf R
    6. Albert I
    7. Kaiser B
    8. Löffelhardt B
    9. Droste-Borel I
    10. Sklenar J
    11. Menke FLH
    12. Maček B
    13. Ranjan A
    14. Sinha N
    15. Nürnberger T
    16. Felix G
    17. Krause K
    18. Stahl M
    19. Albert M

    (2020) The tomato receptor cure1 senses a cell wall protein to identify Cuscuta as a pathogen

    Nature Communications 11:1–7.

    https://doi.org/10.1038/s41467-020-19147-4

    • PubMed
    • Google Scholar
24. 1. Hohmann U
    2. Lau K
    3. Hothorn M

    (2017) The structural basis of ligand perception and signal activation by receptor kinases

    Annual Review of Plant Biology 68:109–137.

    https://doi.org/10.1146/annurev-arplant-042916-040957

    • PubMed
    • Google Scholar
25. 1. Hohmann U
    2. Hothorn M

    (2019) Crystal structure of the leucine-rich repeat ectodomain of the plant immune receptor kinase SOBIR1

    Acta Crystallographica. Section D, Structural Biology 75:488–497.

    https://doi.org/10.1107/S2059798319005291

    • PubMed
    • Google Scholar
26. 1. Jamieson PA
    2. Shan L
    3. He P

    (2018) Plant cell surface molecular Cypher: receptor-like proteins and their roles in immunity and development

    Plant Science 274:242–251.

    https://doi.org/10.1016/j.plantsci.2018.05.030

    • PubMed
    • Google Scholar
27. 1. Jehle AK
    2. Lipschis M
    3. Albert M
    4. Fallahzadeh-Mamaghani V
    5. Fürst U
    6. Mueller K
    7. Felix G

    (2013) The receptor-like protein remax of Arabidopsis detects the microbe-associated molecular pattern eMax from Xanthomonas

    The Plant Cell 25:2330–2340.

    https://doi.org/10.1105/tpc.113.110833

    • PubMed
    • Google Scholar
28. 1. Jones DA
    2. Thomas CM
    3. Hammond-Kosack KE
    4. Balint-Kurti PJ
    5. Jones JDG

    (1994) Isolation of the tomato Cf-9 gene for resistance to Cladosporium fulvum by transposon tagging

    Science 266:789–793.

    https://doi.org/10.1126/science.7973631

    • PubMed
    • Google Scholar
29. 1. Jumper J
    2. Evans R
    3. Pritzel A
    4. Green T
    5. Figurnov M
    6. Ronneberger O
    7. Tunyasuvunakool K
    8. Bates R
    9. Žídek A
    10. Potapenko A
    11. Bridgland A
    12. Meyer C
    13. Kohl SAA
    14. Ballard AJ
    15. Cowie A
    16. Romera-Paredes B
    17. Nikolov S
    18. Jain R
    19. Adler J
    20. Back T
    21. Petersen S
    22. Reiman D
    23. Clancy E
    24. Zielinski M
    25. Steinegger M
    26. Pacholska M
    27. Berghammer T
    28. Bodenstein S
    29. Silver D
    30. Vinyals O
    31. Senior AW
    32. Kavukcuoglu K
    33. Kohli P
    34. Hassabis D

    (2021) Highly accurate protein structure prediction with alphafold

    Nature 596:583–589.

    https://doi.org/10.1038/s41586-021-03819-2

    • PubMed
    • Google Scholar
30. 1. Kang YJ
    2. Kim SK
    3. Kim MY
    4. Lestari P
    5. Kim KH
    6. Ha BK
    7. Jun TH
    8. Hwang WJ
    9. Lee T
    10. Lee J
    11. Shim S
    12. Yoon MY
    13. Jang YE
    14. Han KS
    15. Taeprayoon P
    16. Yoon N
    17. Somta P
    18. Tanya P
    19. Kim KS
    20. Gwag JG
    21. Moon JK
    22. Lee YH
    23. Park BS
    24. Bombarely A
    25. Doyle JJ
    26. Jackson SA
    27. Schafleitner R
    28. Srinives P
    29. Varshney RK
    30. Lee SH

    (2014) Genome sequence of mungbean and insights into evolution within Vigna species

    Nature Communications 5:1–9.

    https://doi.org/10.1038/ncomms6443

    • PubMed
    • Google Scholar
31. 1. Kang YJ
    2. Satyawan D
    3. Shim S
    4. Lee T
    5. Lee J
    6. Hwang WJ
    7. Kim SK
    8. Lestari P
    9. Laosatit K
    10. Kim KH
    11. Ha TJ
    12. Chitikineni A
    13. Kim MY
    14. Ko JM
    15. Gwag JG
    16. Moon JK
    17. Lee YH
    18. Park BS
    19. Varshney RK
    20. Lee SH

    (2015) Draft genome sequence of adzuki bean, Vigna angularis

    Scientific Reports 5:1–8.

    https://doi.org/10.1038/srep08069

    • PubMed
    • Google Scholar
32. 1. Katoh K
    2. Misawa K
    3. Kuma K
    4. Miyata T

    (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

    Nucleic Acids Research 30:3059–3066.

    https://doi.org/10.1093/nar/gkf436

    • PubMed
    • Google Scholar
33. 1. Kohany O
    2. Gentles AJ
    3. Hankus L
    4. Jurka J

    (2006) Annotation, submission and screening of repetitive elements in repbase: repbasesubmitter and censor

    BMC Bioinformatics 7:474.

    https://doi.org/10.1186/1471-2105-7-474

    • PubMed
    • Google Scholar
34. 1. Kourelis J
    2. Malik S
    3. Mattinson O
    4. Krauter S
    5. Kahlon PS
    6. Paulus JK
    7. van der Hoorn RAL

    (2020) Evolution of a guarded decoy protease and its receptor in solanaceous plants

    Nature Communications 11:4393.

    https://doi.org/10.1038/s41467-020-18069-5

    • PubMed
    • Google Scholar
35. 1. Krasileva KV

    (2019) The role of transposable elements and DNA damage repair mechanisms in gene duplications and gene fusions in plant genomes

    Current Opinion in Plant Biology 48:18–25.

    https://doi.org/10.1016/j.pbi.2019.01.004

    • PubMed
    • Google Scholar
36. 1. Krüger J
    2. Thomas CM
    3. Golstein C
    4. Dixon MS
    5. Smoker M
    6. Tang S

    (1979) A tomato cysteine protease required for Cf-2-dependent disease resistance and suppression of autonecrosis

    Science 296:744–777.

    https://doi.org/10.1126/science.1069288

    • Google Scholar
37. 1. Kruijt M
    2. Kip DJ
    3. Joosten M
    4. Brandwagt BF
    5. De Wit P

    (2007) The Cf-4 and Cf-9 resistance genes against Cladosporium fulvum are conserved in wild tomato species

    APS 18:1011–1021.

    https://doi.org/10.1094/MPMI-18-1011

    • Google Scholar
38. 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
39. 1. Lacombe S
    2. Rougon-Cardoso A
    3. Sherwood E
    4. Peeters N
    5. Dahlbeck D
    6. van Esse HP
    7. Smoker M
    8. Rallapalli G
    9. Thomma BPHJ
    10. Staskawicz B
    11. Jones JDG
    12. Zipfel C

    (2010) Interfamily transfer of a plant pattern-recognition receptor confers broad-spectrum bacterial resistance

    Nature Biotechnology 28:365–369.

    https://doi.org/10.1038/nbt.1613

    • PubMed
    • Google Scholar
40. 1. Larkan NJ
    2. Ma L
    3. Borhan MH

    (2015) The Brassica napus receptor-like protein RLM2 is encoded by a second allele of the lepr3/RLM2 blackleg resistance locus

    Plant Biotechnology Journal 13:983–992.

    https://doi.org/10.1111/pbi.12341

    • PubMed
    • Google Scholar
41. Website

    1. Larkan NJ
    2. Lydiate DJ
    3. Parkin IAP
    4. Nelson MN
    5. Epp DJ
    6. Cowling WA

    (2022) The Brassica napus blackleg resistance gene LepR3 encodes a receptor-like protein triggered by the Leptosphaeria maculans effector AVRLM1

    Accessed August 29, 2022.

    https://onlinelibrary.wiley.com/doi/full/10.1111/nph.12043
42. 1. Lavin M
    2. Herendeen PS
    3. Wojciechowski MF

    (2005) Evolutionary rates analysis of Leguminosae implicates a rapid diversification of lineages during the tertiary

    Systematic Biology 54:575–594.

    https://doi.org/10.1080/10635150590947131

    • PubMed
    • Google Scholar
43. 1. Lehti-Shiu MD
    2. Zou C
    3. Hanada K
    4. Shiu SH

    (2009) Evolutionary history and stress regulation of plant receptor-like kinase/pelle genes

    Plant Physiology 150:12–26.

    https://doi.org/10.1104/pp.108.134353

    • PubMed
    • Google Scholar
44. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with burrows-wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
45. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/map format and samtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
46. 1. Li H
    2. Wang W
    3. Lin L
    4. Zhu X
    5. Li J
    6. Zhu X
    7. Chen Z

    (2013) Diversification of the Phaseoloid legumes: effects of climate change, range expansion and habit shift

    Frontiers in Plant Science 4:386.

    https://doi.org/10.3389/fpls.2013.00386

    • PubMed
    • Google Scholar
47. 1. Liu Y
    2. Du H
    3. Li P
    4. Shen Y
    5. Peng H
    6. Liu S
    7. Zhou G-A
    8. Zhang H
    9. Liu Z
    10. Shi M
    11. Huang X
    12. Li Y
    13. Zhang M
    14. Wang Z
    15. Zhu B
    16. Han B
    17. Liang C
    18. Tian Z

    (2020) Pan-Genome of wild and cultivated soybeans

    Cell 182:162–176.

    https://doi.org/10.1016/j.cell.2020.05.023

    • PubMed
    • Google Scholar
48. 1. Lonardi S
    2. Muñoz-Amatriaín M
    3. Liang Q
    4. Shu S
    5. Wanamaker SI
    6. Lo S
    7. Tanskanen J
    8. Schulman AH
    9. Zhu T
    10. Luo MC
    11. Alhakami H
    12. Ounit R
    13. Hasan AM
    14. Verdier J
    15. Roberts PA
    16. Santos JRP
    17. Ndeve A
    18. Doležel J
    19. Vrána J
    20. Hokin SA
    21. Farmer AD
    22. Cannon SB
    23. Close TJ

    (2019) The genome of cowpea (Vigna unguiculata [ L. ] walp.)

    The Plant Journal 98:767–782.

    https://doi.org/10.1111/tpj.14349

    • PubMed
    • Google Scholar
49. 1. Luderer R
    2. Takken FLW
    3. Wit P
    4. Joosten M

    (2002) Cladosporium fulvum overcomes Cf-2-mediated resistance by producing truncated AVR2 elicitor proteins

    Molecular Microbiology 45:875–884.

    https://doi.org/10.1046/j.1365-2958.2002.03060.x

    • PubMed
    • Google Scholar
50. 1. Lutz KA
    2. Wang W
    3. Zdepski A
    4. Michael TP

    (2011) Isolation and analysis of high quality nuclear DNA with reduced organellar DNA for plant genome sequencing and resequencing

    BMC Biotechnology 11:54.

    https://doi.org/10.1186/1472-6750-11-54

    • PubMed
    • Google Scholar
51. 1. Ma Z
    2. Song T
    3. Zhu L
    4. Ye W
    5. Wang Y
    6. Shao Y
    7. Dong S
    8. Zhang Z
    9. Dou D
    10. Zheng X
    11. Tyler BM
    12. Wang Y

    (2015) A Phytophthora sojae glycoside hydrolase 12 protein is a major virulence factor during soybean infection and is recognized as A PAMP

    The Plant Cell 27:2057–2072.

    https://doi.org/10.1105/tpc.15.00390

    • PubMed
    • Google Scholar
52. Conference

    1. Miller MA
    2. Pfeiffer W
    3. Schwartz T

    (2010) IEEE

    Gateway Computing Environments Workshop (GCE. pp. 1–8.

    https://doi.org/10.1109/GCE.2010.5676129

    • Google Scholar
53. 1. Mirdita M
    2. von den Driesch L
    3. Galiez C
    4. Martin MJ
    5. Söding J
    6. Steinegger M

    (2017) Uniclust databases of clustered and deeply annotated protein sequences and alignments

    Nucleic Acids Research 45:D170–D176.

    https://doi.org/10.1093/nar/gkw1081

    • PubMed
    • Google Scholar
54. 1. Mirdita M
    2. Steinegger M
    3. Söding J

    (2019) MMseqs2 desktop and local web server app for fast, interactive sequence searches

    Bioinformatics 35:2856–2858.

    https://doi.org/10.1093/bioinformatics/bty1057

    • PubMed
    • Google Scholar
55. 1. Mirdita M
    2. Schütze K
    3. Moriwaki Y
    4. Heo L
    5. Ovchinnikov S
    6. Steinegger M

    (2022) ColabFold: making protein folding accessible to all

    Nature Methods 19:679–682.

    https://doi.org/10.1038/s41592-022-01488-1

    • PubMed
    • Google Scholar
56. 1. Mitchell AL
    2. Almeida A
    3. Beracochea M
    4. Boland M
    5. Burgin J
    6. Cochrane G
    7. Crusoe MR
    8. Kale V
    9. Potter SC
    10. Richardson LJ
    11. Sakharova E
    12. Scheremetjew M
    13. Korobeynikov A
    14. Shlemov A
    15. Kunyavskaya O
    16. Lapidus A
    17. Finn RD

    (2020) MGnify: the microbiome analysis resource in 2020

    Nucleic Acids Research 48:D570–D578.

    https://doi.org/10.1093/nar/gkz1035

    • PubMed
    • Google Scholar
57. 1. Ngou BPM
    2. Heal R
    3. Wyler M
    4. Schmid MW
    5. Jones JDG

    (2022) Concerted expansion and contraction of immune receptor gene repertoires in plant genomes

    Nature Plants 8:1146–1152.

    https://doi.org/10.1038/s41477-022-01260-5

    • PubMed
    • Google Scholar
58. 1. Nie J
    2. Zhou W
    3. Liu J
    4. Tan N
    5. Zhou JM
    6. Huang L

    (2021) A receptor-like protein from Nicotiana benthamiana mediates vme02 PAMP-triggered immunity

    The New Phytologist 229:2260–2272.

    https://doi.org/10.1111/nph.16995

    • PubMed
    • Google Scholar
59. 1. Offord V
    2. Coffey TJ
    3. Werling D

    (2010) LRRfinder: a web application for the identification of leucine-rich repeats and an integrative Toll-like receptor database

    Developmental and Comparative Immunology 34:1035–1041.

    https://doi.org/10.1016/j.dci.2010.05.004

    • PubMed
    • Google Scholar
60. 1. Pfeilmeier S
    2. George J
    3. Morel A
    4. Roy S
    5. Smoker M
    6. Stransfeld L
    7. Downie JA
    8. Peeters N
    9. Malone JG
    10. Zipfel C

    (2019) Expression of the Arabidopsis thaliana immune receptor EFR in Medicago truncatula reduces infection by a root pathogenic bacterium, but not nitrogen-fixing rhizobial symbiosis

    Plant Biotechnology Journal 17:569–579.

    https://doi.org/10.1111/pbi.12999

    • PubMed
    • Google Scholar
61. 1. Pruitt RN
    2. Locci F
    3. Wanke F
    4. Zhang L
    5. Saile SC
    6. Joe A
    7. Karelina D
    8. Hua C
    9. Fröhlich K
    10. Wan W-L
    11. Hu M
    12. Rao S
    13. Stolze SC
    14. Harzen A
    15. Gust AA
    16. Harter K
    17. Joosten MHAJ
    18. Thomma BPHJ
    19. Zhou J-M
    20. Dangl JL
    21. Weigel D
    22. Nakagami H
    23. Oecking C
    24. Kasmi FE
    25. Parker JE
    26. Nürnberger T

    (2021) The EDS1-PAD4-ADR1 node mediates Arabidopsis pattern-triggered immunity

    Nature 598:495–499.

    https://doi.org/10.1038/s41586-021-03829-0

    • PubMed
    • Google Scholar
62. Software

    1. PyMOL

    (2020)

    The pymol molecular graphics system, version 2.5.2

    Schrödinger, LLC.
63. 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

    • PubMed
    • Google Scholar
64. Software

    1. R Development Core Team

    (2015) R: a language and environment for statistical computing, version 1.0.1

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
65. 1. Restrepo-Montoya D
    2. Brueggeman R
    3. McClean PE
    4. Osorno JM

    (2020) Computational identification of receptor-like kinases “RLK” and receptor-like proteins “RLP” in legumes

    BMC Genomics 21:1–17.

    https://doi.org/10.1186/s12864-020-06844-z

    • PubMed
    • Google Scholar
66. 1. Ronald PC
    2. Beutler B

    (2010) Plant and animal sensors of conserved microbial signatures

    Science 330:1061–1064.

    https://doi.org/10.1126/science.1189468

    • PubMed
    • Google Scholar
67. 1. Rooney HCE
    2. Van’t Klooster JW
    3. van der Hoorn RAL
    4. Joosten M
    5. Jones JDG
    6. De Wit P

    (2005) Cladosporium avr2 inhibits tomato rcr3 protease required for Cf-2-dependent disease resistance

    Science 308:1783–1786.

    https://doi.org/10.1126/science.1111404

    • PubMed
    • Google Scholar
68. 1. Schmelz EA
    2. Alborn HT
    3. Banchio E
    4. Tumlinson JH

    (2003) Quantitative relationships between induced jasmonic acid levels and volatile emission in Zea mays during Spodoptera exigua herbivory

    Planta 216:665–673.

    https://doi.org/10.1007/s00425-002-0898-y

    • PubMed
    • Google Scholar
69. 1. Schmelz EA
    2. Carroll MJ
    3. LeClere S
    4. Phipps SM
    5. Meredith J
    6. Chourey PS
    7. Alborn HT
    8. Teal PEA

    (2006) Fragments of ATP synthase mediate plant perception of insect attack

    PNAS 103:8894–8899.

    https://doi.org/10.1073/pnas.0602328103

    • PubMed
    • Google Scholar
70. 1. Schmelz E.A
    2. LeClere S
    3. Carroll MJ
    4. Alborn HT
    5. Teal PEA

    (2007) Cowpea chloroplastic ATP synthase is the source of multiple plant defense elicitors during insect herbivory

    Plant Physiology 144:793–805.

    https://doi.org/10.1104/pp.107.097154

    • PubMed
    • Google Scholar
71. 1. Schmelz E.A
    2. Huffaker A
    3. Carroll MJ
    4. Alborn HT
    5. Ali JG
    6. Teal PEA

    (2012) An amino acid substitution inhibits specialist herbivore production of an antagonist effector and recovers insect-induced plant defenses

    Plant Physiology 160:1468–1478.

    https://doi.org/10.1104/pp.112.201061

    • PubMed
    • Google Scholar
72. 1. Schmutz J
    2. Cannon SB
    3. Schlueter J
    4. Ma J
    5. Mitros T
    6. Nelson W
    7. Hyten DL
    8. Song Q
    9. Thelen JJ
    10. Cheng J
    11. Xu D
    12. Hellsten U
    13. May GD
    14. Yu Y
    15. Sakurai T
    16. Umezawa T
    17. Bhattacharyya MK
    18. Sandhu D
    19. Valliyodan B
    20. Lindquist E
    21. Peto M
    22. Grant D
    23. Shu S
    24. Goodstein D
    25. Barry K
    26. Futrell-Griggs M
    27. Abernathy B
    28. Du J
    29. Tian Z
    30. Zhu L
    31. Gill N
    32. Joshi T
    33. Libault M
    34. Sethuraman A
    35. Zhang X-C
    36. Shinozaki K
    37. Nguyen HT
    38. Wing RA
    39. Cregan P
    40. Specht J
    41. Grimwood J
    42. Rokhsar D
    43. Stacey G
    44. Shoemaker RC
    45. Jackson SA

    (2010) Genome sequence of the palaeopolyploid soybean

    Nature 463:178–183.

    https://doi.org/10.1038/nature08670

    • PubMed
    • Google Scholar
73. 1. Schmutz J
    2. McClean PE
    3. Mamidi S
    4. Wu GA
    5. Cannon SB
    6. Grimwood J
    7. Jenkins J
    8. Shu S
    9. Song Q
    10. Chavarro C
    11. Torres-Torres M
    12. Geffroy V
    13. Moghaddam SM
    14. Gao D
    15. Abernathy B
    16. Barry K
    17. Blair M
    18. Brick MA
    19. Chovatia M
    20. Gepts P
    21. Goodstein DM
    22. Gonzales M
    23. Hellsten U
    24. Hyten DL
    25. Jia G
    26. Kelly JD
    27. Kudrna D
    28. Lee R
    29. Richard MMS
    30. Miklas PN
    31. Osorno JM
    32. Rodrigues J
    33. Thareau V
    34. Urrea CA
    35. Wang M
    36. Yu Y
    37. Zhang M
    38. Wing RA
    39. Cregan PB
    40. Rokhsar DS
    41. Jackson SA

    (2014) A reference genome for common bean and genome-wide analysis of dual domestications

    Nature Genetics 46:707–713.

    https://doi.org/10.1038/ng.3008

    • PubMed
    • Google Scholar
74. 1. Schultink A
    2. Steinbrenner AD

    (2021) A playbook for developing disease-resistant crops through immune receptor identification and transfer

    Current Opinion in Plant Biology 62:102089.

    https://doi.org/10.1016/j.pbi.2021.102089

    • PubMed
    • Google Scholar
75. 1. Scossa F
    2. Fernie AR

    (2021) Ancestral sequence reconstruction-an underused approach to understand the evolution of gene function in plants?

    Computational and Structural Biotechnology Journal 19:1579–1594.

    https://doi.org/10.1016/j.csbj.2021.03.008

    • PubMed
    • Google Scholar
76. 1. Shi T
    2. Huang H
    3. Sanderson MJ
    4. Tax FE

    (2014) Evolutionary dynamics of leucine-rich repeat receptor-like kinases and related genes in plants: a phylogenomic approach

    Journal of Integrative Plant Biology 56:648–662.

    https://doi.org/10.1111/jipb.12188

    • Google Scholar
77. 1. Shiu SH
    2. Bleecker AB

    (2001) Receptor-like kinases from Arabidopsis form a monophyletic gene family related to animal receptor kinases

    PNAS 98:10763–10768.

    https://doi.org/10.1073/pnas.181141598

    • Google Scholar
78. 1. Shiu SH
    2. Bleecker AB

    (2003) Expansion of the receptor-like kinase/pelle gene family and receptor-like proteins in Arabidopsis

    Plant Physiology 132:530–543.

    https://doi.org/10.1104/pp.103.021964

    • PubMed
    • Google Scholar
79. 1. Snoeck S
    2. Guayazán-Palacios N
    3. Steinbrenner AD

    (2022) Molecular tug-of-war: plant immune recognition of herbivory

    The Plant Cell 34:1497–1513.

    https://doi.org/10.1093/plcell/koac009

    • PubMed
    • Google Scholar
80. 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
81. 1. Stefanović S
    2. Pfeil BE
    3. Palmer JD
    4. Doyle JJ

    (2009) Relationships among phaseoloid legumes based on sequences from eight chloroplast regions

    Systematic Botany 34:115–128.

    https://doi.org/10.1600/036364409787602221

    • Google Scholar
82. 1. Steinbrenner AD

    (2020) The evolving landscape of cell surface pattern recognition across plant immune networks

    Current Opinion in Plant Biology 56:135–146.

    https://doi.org/10.1016/j.pbi.2020.05.001

    • PubMed
    • Google Scholar
83. 1. Steinbrenner AD
    2. Muñoz-Amatriaín M
    3. Chaparro AF
    4. Aguilar-Venegas JM
    5. Lo S
    6. Okuda S
    7. Glauser G
    8. Dongiovanni J
    9. Shi D
    10. Hall M
    11. Crubaugh D
    12. Holton N
    13. Zipfel C
    14. Abagyan R
    15. Turlings TCJ
    16. Close TJ
    17. Huffaker A
    18. Schmelz EA

    (2020) A receptor-like protein mediates plant immune responses to herbivore-associated molecular patterns

    PNAS 117:31510–31518.

    https://doi.org/10.1073/pnas.2018415117

    • PubMed
    • Google Scholar
84. 1. Sun Y
    2. Wang Y
    3. Zhang X
    4. Chen Z
    5. Xia Y
    6. Wang L
    7. Sun Y
    8. Zhang M
    9. Xiao Y
    10. Han Z
    11. Wang Y
    12. Chai J

    (2022) Plant receptor-like protein activation by a microbial glycoside hydrolase

    Nature 610:335–342.

    https://doi.org/10.1038/s41586-022-05214-x

    • PubMed
    • Google Scholar
85. 1. Tang P
    2. Zhang Y
    3. Sun X
    4. Tian D
    5. Yang S
    6. Ding J

    (2010) Disease resistance signature of the leucine-rich repeat receptor-like kinase genes in four plant species

    Plant Science 179:399–406.

    https://doi.org/10.1016/j.plantsci.2010.06.017

    • Google Scholar
86. 1. Thomas CM
    2. Jones DA
    3. Parniske M
    4. Harrison K
    5. Balint-Kurti PJ
    6. Hatzixanthis K
    7. Jones JD

    (1997) Characterization of the tomato Cf-4 gene for resistance to Cladosporium fulvum identifies sequences that determine recognitional specificity in Cf-4 and Cf-9

    The Plant Cell 9:2209–2224.

    https://doi.org/10.1105/tpc.9.12.2209

    • PubMed
    • Google Scholar
87. 1. Varshney RK
    2. Chen W
    3. Li Y
    4. Bharti AK
    5. Saxena RK
    6. Schlueter JA
    7. Donoghue MTA
    8. Azam S
    9. Fan G
    10. Whaley AM
    11. Farmer AD
    12. Sheridan J
    13. Iwata A
    14. Tuteja R
    15. Penmetsa RV
    16. Wu W
    17. Upadhyaya HD
    18. Yang SP
    19. Shah T
    20. Saxena KB
    21. Michael T
    22. McCombie WR
    23. Yang B
    24. Zhang G
    25. Yang H
    26. Wang J
    27. Spillane C
    28. Cook DR
    29. May GD
    30. Xu X
    31. Jackson SA

    (2011) Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers

    Nature Biotechnology 30:83–89.

    https://doi.org/10.1038/nbt.2022

    • PubMed
    • Google Scholar
88. 1. Wang G
    2. Ellendorff U
    3. Kemp B
    4. Mansfield JW
    5. Forsyth A
    6. Mitchell K
    7. Bastas K
    8. Liu C-M
    9. Woods-Tör A
    10. Zipfel C
    11. de Wit PJGM
    12. Jones JDG
    13. Tör M
    14. Thomma BPHJ

    (2008) A genome-wide functional investigation into the roles of receptor-like proteins in Arabidopsis

    Plant Physiology 147:503–517.

    https://doi.org/10.1104/pp.108.119487

    • PubMed
    • Google Scholar
89. 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
90. 1. Wei Y
    2. Balaceanu A
    3. Rufian JS
    4. Segonzac C
    5. Zhao A
    6. Morcillo RJL
    7. Macho AP

    (2020) An immune receptor complex evolved in soybean to perceive a polymorphic bacterial flagellin

    Nature Communications 11:3763.

    https://doi.org/10.1038/s41467-020-17573-y

    • PubMed
    • Google Scholar
91. 1. Wickham H

    (2009) Ggplot2 elegant graphics for data analysis

    Media 35:211.

    https://doi.org/10.1007/978-0-387-98141-3

    • Google Scholar
92. Software

    1. Wickham H
    2. Francois R

    (2015) A grammar of data manipulation, version 0.4.3

    Dplyr.

    http://CRAN.R-project.org/package=dplyr
93. 1. Xie M
    2. Chung CY-L
    3. Li M-W
    4. Wong F-L
    5. Wang X
    6. Liu A
    7. Wang Z
    8. Leung AK-Y
    9. Wong T-H
    10. Tong S-W
    11. Xiao Z
    12. Fan K
    13. Ng M-S
    14. Qi X
    15. Yang L
    16. Deng T
    17. He L
    18. Chen L
    19. Fu A
    20. Ding Q
    21. He J
    22. Chung G
    23. Isobe S
    24. Tanabata T
    25. Valliyodan B
    26. Nguyen HT
    27. Cannon SB
    28. Foyer CH
    29. Chan T-F
    30. Lam H-M

    (2019) A reference-grade wild soybean genome

    Nature Communications 10:1–12.

    https://doi.org/10.1038/s41467-019-09142-9

    • PubMed
    • Google Scholar
94. 1. Zhang L
    2. Kars I
    3. Essenstam B
    4. Liebrand TWH
    5. Wagemakers L
    6. Elberse J
    7. Tagkalaki P
    8. Tjoitang D
    9. van den Ackerveken G
    10. van Kan JAL

    (2014) Fungal endopolygalacturonases are recognized as microbe-associated molecular patterns by the Arabidopsis receptor-like protein responsiveness to Botrytis POLYGALACTURONASES1

    Plant Physiology 164:352–364.

    https://doi.org/10.1104/pp.113.230698

    • PubMed
    • Google Scholar
95. 1. Zhang L
    2. Hua C
    3. Pruitt RN
    4. Qin S
    5. Wang L
    6. Albert I
    7. Albert M
    8. van Kan JAL
    9. Nürnberger T

    (2021) Distinct immune sensor systems for fungal endopolygalacturonases in closely related Brassicaceae

    Nature Plants 7:1254–1263.

    https://doi.org/10.1038/s41477-021-00982-2

    • PubMed
    • Google Scholar
96. 1. Zhao Y
    2. Zhang R
    3. Jiang K-W
    4. Qi J
    5. Hu Y
    6. Guo J
    7. Zhu R
    8. Zhang T
    9. Egan AN
    10. Yi T-S
    11. Huang C-H
    12. Ma H

    (2021) Nuclear phylotranscriptomics and phylogenomics support numerous polyploidization events and hypotheses for the evolution of rhizobial nitrogen-fixing symbiosis in Fabaceae

    Molecular Plant 14:748–773.

    https://doi.org/10.1016/j.molp.2021.02.006

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Evolutionary gain and loss of a plant pattern-recognition receptor for HAMP recognition" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor (me!) and Detlef Weigel as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Daniil M Prigozhin (Reviewer #1).

We have discussed your manuscript. Overall, we are all very positive about your work. The paper is nicely constructed and was fun to read. However, both reviewers would like to see a bit more analysis/interpretation of the predicted structure and I had one technical concern. Please see below for detail. I have drafted the following to help you prepare a revised submission. Please feel free to reach out should you require any clarification.

Essential revisions:

1) Please include additional analysis/interpretation based on structural predictions (see detailed comments from both reviewers, below). In addition, this may allow additional interpretation of the data including the auto activity of 219600-C1.

2) Both reviewers made several suggestions for ways to improve clarity and correct minor typos.

3) I had one small technical question – I notice that the ROS responses are cyclic for some receptors and a single burst for others. Any thoughts on this? Also, in a few cases, you see a response with ethylene but not ROS or the other way around. My guess is that these are false positives. Do you agree? For example Glyma_10G228000. Perhaps a sentence or two in the discussion is warranted.

Reviewer #1 (Recommendations for the authors):

1. Independent losses of INR are important to the overall understanding of the story. Following up on the likely genomic events that lead to several independent losses is advisable.

2. Evolution of protein receptor recognition is best interpreted in the context of protein structure. It is up to the authors whether this makes main figures or not, but including a predicted protein model with N3 to N4 and N3 to N14 changes highlighted should at least make a supplemental figure. Alphafold/Rosettafold results are well accepted as a best guess estimates absent an experimental structure.

3. For the ROS time courses, unifying the Y-axis across figures would improve sample-to-sample comparison. Same for the X-axis of the ethylene subfigures of Figure 3 to Figure 5.

4. The use of "autoactivity" (in line 374) with regards to the mutant 219600-c1 is surprising, and the nature of the differences between 219600-F, C3, C2, and C1 is somewhat underexplored. Would the 219600-c1 receptor signal in the absence of scratch wounding etc? Is there a phenotype in the transient assay after protein expression but before any other manipulations are performed? I would agree with a "responded to control treatment only" characterization absent more details.

(I believe C2 is likely critical for relaying binding at C1 down to the co-receptor, so a mismatch between C1 and C2 is bound to be "strange". The differences between +/- peptides could be due to the pH of the peptide solution, its reduction potential, or some other general property. I also found the pulse shape difference between -C2 and -F absolutely fascinating, yet not mentioned in the paper.)

Reviewer #2 (Recommendations for the authors):

Line 44 – include Benthamiana RLPs – several have been identified here e.g. RXEG1 and RE02.

Whilst LRR-RLP structures have not currently been solved, based upon what we know from other LRR domains predictions of structure should be quite accurate. It would be nice to highlight the residues that you identify as being important for the recognition of a structural prediction. This could help the reader.

Figure 1 – it could be interesting to show the correlation between In11-induced ethylene and flg22-induced ethylene (Sup Figure 1 a). Is there a strong correlation?

158 – 'contained a orthologs'.

Figure 4 b – why does mock give a response in 219600? Would you expect a burst if it is constitutive autoactivity as suggested (374)? Does this RLP induce cell death? Is the burst in the mock reproducible? Also, the burst seems weaker following treatment in the rep shown. If it was autoactive I think it would not matter whether it is mock or treatment.

For expression western blots it would have been nice to include a -ve control of an untransformed leaf – however, this is not strictly required as some of the receptors have slightly different sizes.

Whilst perception seems to have been lost in G. max/G. soja it would be interesting to know how widely INR is present in the pangenome of a responsive species. In Arabidopsis, it seems that many RLPs have significant presence/absence polymorphism. It would be interesting to know if this is the case with INR, however, I understand that the availability of genomic/germplasm resources may be limiting. E.g. what proportion of cowpea accessions are In11 responsive?

Whilst many of the receptors are not responsive to In11, they could also be functional receptors for an unknown ligand. I think this could be made more apparent in the text. One option could be polymorphisms within In11, is it known if these exist? E.g. 327-328.

Does INR1-like form a constitutive interaction with SOBIR1? I would expect it can be given the activity of the C2 chimera, however, it could be nice to demonstrate this via Co-IP.

It would be nice to test each of the 5 substitutions between N3 and N4 for a loss of In11 perception in Benth, however, I do not think this is necessary.

316 – 'unusually long evolutionary history' – please provide more context what are you comparing with? It would be nice to add context to what is currently known.

Personally, I think the discussion could be made more concise.

326 – I would not agree that it represents an 'alternative' mechanism – I don't think there is currently a paradigm for how these receptors evolve. I think this work represents one of the few attempts to characterise the origin of the receptor in detail.

349 – gives the impression that EFR/XPS1/CORE is RLPs – rephrase. I would also argue that for some of the receptors there has been some extensive phenotyping than others – e.g. for RLP42.

356 – I would argue that this paper suggests that RLPs have dynamic evolutionary patterns – similar to NLRs, unlike RKs (in general). They highlight two examples that are conserved, but I would not say that these examples are representative.

I would prefer that P-values are also shown on the graphs (potentially in addition to the notation of significance).

I am not qualified to comment on the quality of the sequencing – but I believe it represents a valuable resource of the community and is sufficient to support the claims in the manuscript.

364 – I would consider TMM a ligand binding LRR-RLP (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458759/) although I appreciate it is significantly

I would recommend citing this paper for the lack of RLP structure https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503760/.

Essential revisions:

1) Please include additional analysis/interpretation based on structural predictions (see detailed comments from both reviewers, below). In addition, this may allow additional interpretation of the data including the auto activity of 219600-C1.

We have generated an AlphaFold-based structural prediction of INR. In addition, we have included clarified interpretations of the 219600-C1 construct, which showed ROS response to water-only treatment. Please see specific responses to reviewers for additional details.

2) Both reviewers made several suggestions for ways to improve clarity and correct minor typos.

We appreciate the suggestions and corrections of both reviewers and incorporated them in the manuscript.

3) I had one small technical question – I notice that the ROS responses are cyclic for some receptors and a single burst for others. Any thoughts on this? Also, in a few cases, you see a response with ethylene but not ROS or the other way around. My guess is that these are false positives. Do you agree? For example Glyma_10G228000. Perhaps a sentence or two in the discussion is warranted.

We are also interested in cyclic ROS responses but unable at this stage to account for differences. We tested if cyclic ROS was a function of the C-terminal GFP tag. Although more subtle, cyclic ROS responses were still observed when two receptor homologs did not have a C-terminal GFP tag. Both constructs also gave equivalent ethylene burst to the original GFP tagged constructs, so it is not clear that cyclic ROS translates into different downstream response. We have added this data on the effect of C-terminal tag in a new Figure 3—figure supplement 2. Our overall model is that each ortholog or tagged variant interfaces slightly differently with N. benthamiana signaling machinery to activate RBOHD. We added the following sentences to address the observation in the results:

“Intriguingly, cyclic ROS responses were observed for three out of five In-11 responding receptors (Vigun07g219600, Phvul.007G077500 and Mlathy INR). Corresponding constructs without a C-terminal GFP tag reduced cyclic characteristics of the ROS response but resulted in equivalent ethylene bursts (Figure 3—figure supplement 2).”

Regarding ROS versus ethylene correspondence -- ROS responses were previously shown with scaled y-axes, which inflated the apparent size of certain responses. This is now adjusted to only use one of 3 scales throughout all figures. In11-induced ROS and ethylene responses were complementary for all constructs. Nevertheless, the results for Glyma_10G228000 are indeed remarkable as our control treatment resulted in a low and late ROS burst relative to any of the other ROS bursts observed in this study for other constructs. Similarly, the control treatment resulted in a higher ethylene production for this construct.

Reviewer #1 (Recommendations for the authors):

1. Independent losses of INR are important to the overall understanding of the story. Following up on the likely genomic events that lead to several independent losses is advisable.

We agree that the INR loss events, potentially mediated via TE activity, are intriguing and of interest for further investigation. We therefore used the CENSOR tool to annotate repetitive elements in INR loci using the Repbase database with Arabidopsis as a sequence source.

In the INR locus of jicama (Pachyrhizus erosus), multiple fragments of an LRR-RLP receptor can be found by BLAST analysis as indicated by Figure 2b. Transposon annotation with CENSOR revealed a hit with a partial transposable element of the DNA/Mariner class (117 bp) in between two LRR-RLP fragments, possibly consistent with an ancient full transposition interrupting an LRR-RLP and/or INR function (Figure 2—figure supplement 1, supplementary table 5). We added the following sentence to the results:

“A Mariner-like transposase element exists between the LRR-RLP gene fragments (Figure 2—figure supplement 1, supplementary table 5).”

Beyond the example in jicama, we found various TE hits in all INR loci of the Millettioids. This finding aligns well with structural variation and variable number of LRR-RLPs found at the corresponding INR loci (Figure 2 and Suppl. Figure 2). We adapted the result section as follows:

“The organization of the INR locus is highly diverse among legumes, with zero to seven LRR-RLP encoding-genes per species in addition to extensive repetitive sequence and transposable element content (Figure 2a, Figure 2-supplement figure 1, supplementary table 5).”

And the discussion as follows:

“In contrast, P. vulgaris and M. pruriens lost INR-like, and P. erosus seems to have lost both INR and INR-like as it does not contain any complete coding sequence of an LRR-RLP at the INR locus. Repetitive elements and transposable elements within the INR locus may have disrupted ancestral INR and INR-like genes in these lineages, and can be found between LRR-RLP genes and gene fragments (Figure 2-supplement figure 2). Loss of INR (and INR-like) may reflect the propensity of tandemly duplicated loci to lose functions through gene conversion, and/or reciprocal gene loss (62).”

While intriguing, we believe we cannot draw firm conclusions about the role of these elements with respect to the loss of INR in specific species. For example, high-quality genomes of close Glycininae outgroups would be needed to identify specific losses of Glycine max and tight correlation with TE variation. Nevertheless, we agree that this could be an interesting and achievable project in the near future upon release of additional legume genomes.

We are also hesitant to draw stronger conclusions due to the TE source used for our CENSOR analysis. Genome specific pipelines for TE annotation would improve the analysis as the use of a TE database from a distantly related species (Arabidopsis) decreases annotation quality (Platt 2016, https://doi.org/10.1093/gbe/evw009). Manual curation of automated predictions is also advisable as discussed by (Goubert et.al. 2022, https://doi.org/10.1186/s13100-021-00259-7). Fine-scale tracking of TE behavior within the Glycininae would allow genome-wide analysis of both NLR and RLP evolution.

2. Evolution of protein receptor recognition is best interpreted in the context of protein structure. It is up to the authors whether this makes main figures or not, but including a predicted protein model with N3 to N4 and N3 to N14 changes highlighted should at least make a supplemental figure. Alphafold/Rosettafold results are well accepted as a best guess estimates absent an experimental structure.

We thank the reviewer for this suggestion. We now include a new main figure (Figure 6) which includes a structural prediction of INR (Vigun07g219600). In this structure we highlight 5 AA of interest based on analysis of N4 vs N3, the respective ancestors of the INR clade and the combined INR+INR-like clade. We also added additional functional data which shows that H404 and R406 are essential for function in the N4 ancestrally reconstructed construct. For more details, please see the adapted manuscript (305-336, 479-490 and 705-723).

3. For the ROS time courses, unifying the Y-axis across figures would improve sample-to-sample comparison. Same for the X-axis of the ethylene subfigures of Figure 3 to Figure 5.

We thank the reviewer for this suggestion. We agree that a consistent use of x- and y-axes limits improves the figure readability. We now use three y-axes for ROS with small changes in upper limit for readability (0-2000, 0-4000, or 0-6000 RLU) rather than previous mixed axes with scales as fine as 0-400 RLU. We changed the main Figures 3-5 and the newly added figures (Figure 6 and Figure 3—figure supplement 2) to the manuscript accordingly for both ROS and ethylene data.

4. The use of "autoactivity" (in line 374) with regards to the mutant 219600-c1 is surprising, and the nature of the differences between 219600-F, C3, C2, and C1 is somewhat underexplored. Would the 219600-c1 receptor signal in the absence of scratch wounding etc? Is there a phenotype in the transient assay after protein expression but before any other manipulations are performed? I would agree with a "responded to control treatment only" characterization absent more details.

We agree with the reviewer and changed the text accordingly (new text underlined):

“(Line 244) Intriguingly, the chimeric receptor 219600−C1, containing C1 of VuINR and C2 of VuINR-like, responded to the control treatment.”

(Line 445) “Notably, a chimeric receptor with mixed C1 and C2, 219600-C1, is the sole construct that responded to the control treatment, suggestive of a critical role for C1-C2 compatibility.”

Luminescence for the ROS assays is measured in leaf disk punches. Hence, we are unable to measure ROS without creating leaf damage which potentially has an influence on the observed ROS response to control treatment. We attempted to further explore the heightened responsiveness of 219600-C1 using two additional approaches. First, we expressed chimeric receptor 219600-C1 and parent receptors without any subsequent manipulations to check for a potential phenotype by eye, such as cell death or chlorosis. No phenotype was observed 7 days after infiltration. We show this lack of response in a new panel, Figure 4—figure supplement 2a. Secondly, we hypothesized that heightened ROS responses to control treatment might manifest as cell death responses to PAMPs or HAMPs. We therefore repeated the above-described set-up except that 24h post infiltrations, we infiltrated the following treatments: H₂O, 1 µM In11 and 1 µM flg22. Similarly, no phenotypes were observed for 7 days after construct infiltration (Figure 4—figure supplement 2b). We added the following sentences to the results:

“Intriguingly, the chimeric receptor 219600−C1, containing C1 of VuINR and C2 of VuINR-like, responded to the control treatment. Nonetheless, no visible phenotype was observed seven days post infiltration of 219600-C1 in N. benthamiana with or without subsequent infiltration of flg22, In11 or H₂O one day post the construct infiltration (Figure 4—figure supplement 2a-2b).”

(I believe C2 is likely critical for relaying binding at C1 down to the co-receptor, so a mismatch between C1 and C2 is bound to be "strange". The differences between +/- peptides could be due to the pH of the peptide solution, its reduction potential, or some other general property. I also found the pulse shape difference between -C2 and -F absolutely fascinating, yet not mentioned in the paper.)

We agree that multiple factors could result in what we observed and thank reviewer 1 for suggesting other hypotheses. We changed the text accordingly:

“Notably, a chimeric receptor with mixed C1 and C2, 219600-C1, is the sole construct that responded to the control treatment, suggestive of a critical role for C1-C2 compatibility.”

The interface between C1 and C2 is also informed by structural modeling. The AlphaFold prediction of INR (new Figure 6) suggests an interaction between C2 and the N-terminal part of the C1 LRR. Additionally, one of the five conserved AA (Y91) identified by our ASR analysis is predicted to play a part in this interaction. Y91F did not completely abolish ROS-burst although it was relatively low. We now note this C1-C2 predicted interaction in the Results section (lines 314-317).

Finally, we discussed in our response to the editor above that the cyclic ROS response seems to be somewhat dependent on the C-terminal tag and specific homologue used (new Figure 3—figure supplement 2). We therefore do not want to overinterpret the single peak of 219600-C2. However, we have added the following sentence acknowledging cyclic responses and the role of C-terminal tags.

“Intriguingly, cyclic ROS responses were observed for three out of five In-11 responding receptors (Vigun07g219600, Phvul.007G077500 and Mlathy INR). Corresponding constructs without a C-terminal GFP tag resulted in equivalent ethylene bursts, but ROS bursts only had relatively subtle cyclic characteristics (Figure 3—figure supplement 2).”

Reviewer #2 (Recommendations for the authors):

Line 44 – include Benthamiana RLPs – several have been identified here e.g. RXEG1 and RE02.

We included a few extra examples of LRR-RLPs with identified molecular patterns for both Nicotiana benthamiana and Brassica napus. The introduction changed as follows (new text underlined):

“Evidence is accumulating for highly specialized roles of plant LRR-RLPs as immune sensors (15,18). Various LRR-RLPs from Arabidopsis, tomato, wild tobacco, rapeseed and cowpea have been shown to detect molecular patterns from fungi, bacteria, parasitic weeds and herbivores (3,15). For example, Cf-9, Cf-4 and Cf-2 interact with respective molecular patterns of Cladosporium fulvum, Avr9, Avr4 and Avr2 (via host protein Rcr3), and are restricted to the Solanum genus (19–25). Arabidopsis RLP42 recognizes fungal endopolygalacturonases (PG) eptitope pg9(At) derived from Botrytis cinerea (26,27). Similarly, RLP23 is an Arabidopsis-specific LRR-RLP, which recognizes nlp20 peptide from the NECROSIS AND ETHYLENE-INDUCING PEPTIDE1 (NEP1)-LIKE PROTEINS (NLPs) found in bacterial/fungal/oomycete species (15,28). RXEG1 and RE02 were identified in wild tobacco and are respectively triggered by the fungal elicitors XEG1 (Phytophthora sojae) and E02 (Valsa mali) (29,30). LepR3 (AvrLm1) and RLM2 (AvrLm2) convey both race specific resistance to the fungal pathogen Leptosphaeria maculans in rapeseed (31,32). ReMAX is restricted to the Brassicaceae and triggered by the MAMP eMax originating from Xanthomonas (33). Cuscuta Receptor1 (CuRe1) is specific to Solanum lycopersicum and senses the peptide Crip21 which originates from parasitic plants of the genus Cuscuta (34,35). Finally, the inceptin receptor (INR) appears to be specific to the legume tribe Phaseoleae and recognizes inceptin (In11), a HAMP found in the oral secretions of multiple caterpillars (36–39). Notably, all the above examples of LRR-RLPs are family-specific, restricted to the Solanaceae (Cf-2, Cf-4, Cf-9, RXEG1, RE02 and CuRe1), Brassicaceae (RLP23, RLP42, LepR3, RLM2 and ReMAX) or Leguminosae (tribe Phaseoleae) (INR) (18,27). However, despite clear signatures of lineage-specific functions, specific evolutionary steps leading to novel LRR-RLP functions across multiple species have not been described.”

Whilst LRR-RLP structures have not currently been solved, based upon what we know from other LRR domains predictions of structure should be quite accurate. It would be nice to highlight the residues that you identify as being important for the recognition of a structural prediction. This could help the reader.

We thank the reviewer for the suggestion which was also given by reviewer 1. We agree and our response to reviewer 1 is reproduced here:

We now include a new main figure (Figure 6) which includes a structural prediction of INR (Vigun07g219600). In this structure we highlight 5 AA of interest from analysis of N4 vs N3, the respective ancestors of the INR clade and the combined INR+INR-like clade. We also added additional functional data which shows that H404 and R406 are essential for function in the N4 ancestrally reconstructed construct. For more details, please see the adapted manuscript (305-336, 479-490 and 705-723).

Figure 1 – it could be interesting to show the correlation between In11-induced ethylene and flg22-induced ethylene (Sup Figure 1 a). Is there a strong correlation?

We thank the reviewer for this interesting idea. Of 12 total In11-responding species, 4 species (V. radiata, P. vulgaris, M. pruriens and L. purpureus) do not respond to flg22 with an ethylene burst (Figure 1 and Suppl. Figure 1). Hence, ethylene responses to both elicitors are uncorrelated for those four species. Nevertheless, all four of them do respond to flg22 with a ROS burst.

In contrast, 8/12 species respond to both flg22 and In11 with an ethylene burst (D. unicatum, C. cajan, T. labialis, M. lathyroides, R. minima, C. angustifolia, M. artropurpureum and V. unguiculata). However, a strong correlation between the ethylene ratio of response against both elicitors is absent for these species as can be seen in Author response image 1.

Author response image 1

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158 – 'contained a orthologs'.

Corrected.

Figure 4 b – why does mock give a response in 219600? Would you expect a burst if it is constitutive autoactivity as suggested (374)? Does this RLP induce cell death? Is the burst in the mock reproducible? Also, the burst seems weaker following treatment in the rep shown. If it was autoactive I think it would not matter whether it is mock or treatment.

We do agree with reviewer 2 and agree that autoactivity is not the correct way to describe the behavior of this specific construct (219600-C1). We changed the text accordingly as suggested by reviewer 1 in both the result and the Discussion section:

“Intriguingly, the chimeric receptor 219600−C1, containing C1 of VuINR and C2 of VuINR-like, responded to the control treatment.”

The ROS burst following the mock treatment was consistent over multiple biological replicates measured on different days (i.e. different plant batches) and was not observed for any other constructs tested on the same leaves.

Reviewer 1 raised similar questions regarding 219600-C1 and we reproduce our response here:

Luminescence for the ROS assays is measured in leaf disk punches. Hence, we are unable to measure ROS without creating leaf damage which potentially has an influence on the observed ROS response to control treatment. We attempted to further explore the heightened responsiveness of 219600-C1 using two additional approaches. First, we expressed chimeric receptor 219600-C1 and parent receptors without any subsequent manipulations to check for a potential phenotype by eye, such as cell death or chlorosis (Figure 4—figure supplement 2a). No phenotype was observed 7 days after infiltration. Secondly, we hypothesized that heightened ROS responses to control treatment might manifest as cell death responses to PAMPs or HAMPs. We therefore repeated the above-described set-up except that 24h post infiltrations, we infiltrated the following treatments: H₂O, 1 µM In11 and 1 µM flg22. Similarly, no phenotypes were observed for 7 days after construct infiltration (Figure 4—figure supplement 2b). We added the following sentences to the results:

“Intriguingly, the chimeric receptor 219600−C1, containing C1 of VuINR and C2 of VuINR-like, responded to the control treatment. Nonetheless, no visible phenotype was observed seven days post infiltration of 219600-C1 in N. benthamiana with or without subsequent infiltration of flg22, In11 or H₂O one day post the construct infiltration (Figure 4—figure supplement 2a-2b).”

For expression western blots it would have been nice to include a -ve control of an untransformed leaf – however, this is not strictly required as some of the receptors have slightly different sizes.

Our revised manuscript includes an additional western blot for mutated ancestral sequence reconstructed (ASR) receptors. This time, we included an infiltration media only control (Figure 6, figure supplement 1).

Whilst perception seems to have been lost in G. max/G. soja it would be interesting to know how widely INR is present in the pangenome of a responsive species. In Arabidopsis, it seems that many RLPs have significant presence/absence polymorphism. It would be interesting to know if this is the case with INR, however, I understand that the availability of genomic/germplasm resources may be limiting. E.g. what proportion of cowpea accessions are In11 responsive?

We agree that the degree of conservation of INR is a very interesting aspect of its evolution. In a previous study, all 364 tested Vigna unguiculata accessions were In11 responsive (https://doi.org/10.1073/pnas.2018415117 – Table S2). Additional Phaseolus vulgaris and Vigna unguiculata reference genomes are now available on Phytozome, and all encode INR. We added the following sentence to the manuscript:

“Each of 364 tested varieties of V. unguiculata are able to respond to In11 and our analysis now extends this conservation across legume species (36).”

The recently published soybean pangenome (https://doi.org/10.1016/j.cell.2020.05.023) provided 26 de novo assemblies for Glycine max and G. soja. None have INR. We note this observation in the results and the discussion:

“To confirm that INR was lost in Glycine and not just in reference assemblies, we performed BLASTP searches using Vigun07g219700 AA sequence against 26 G. soja and G. max de novo assemblies from a recent pangenome analysis (56).”

“Two reference genomes as well as 26 de novo assemblies of the closely related Glycininae, G. soya and G. max, do not encode INR (56).”

Whilst many of the receptors are not responsive to In11, they could also be functional receptors for an unknown ligand. I think this could be made more apparent in the text. One option could be polymorphisms within In11, is it known if these exist? E.g. 327-328.

We agree with reviewer 2 and assume that they are functional receptors as they seem to be similarly conserved in comparison the In11-responsive INR-clade. However, so far, we can only hypothesize about the ligand it interacts with. Several polymorphisms do exist for In11 as shown in Author response image 2, which is based on the γ-subunit ATP synthase sequence from 15 legume species. However, we believe they most likely do not influence the interaction with INR based on the below arguments:

Author response image 2

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First, Schmelz 2006 (Figure 3B, https://www.pnas.org/doi/full/10.1073/pnas.0602328103#s-1) showed that V. unguiculata also responds quantitatively similar to the In11 variant from the monocot Zea mays (ICDVNGVCVDA), the exact same In11 variant which we identified in multiple Millettioids as shown in the attached alignment (L. purpureus, G. max, G. soja, P. erosus, H. podocarpum and M. pruriens).Second, Schmelz 2007 (Figure 8., https://doi.org/10.1104/pp.107.097154) investigated the effect of alanine substitutions on the elicitor potential of In11. Only mutations of positions 3, 8, and 10 of the mature In11 peptide (D314A, C319A and D321A) lost elicitation activity, and these are different from the polymorphic sites 4, 7, and 9 across legumes.

We have added a sentence to add our thinking about the function of INR-like:

“Certain Phaseoloids and earlier diverged non-Phaseoloids also contain INR-like homologues at the contiguous INR locus; thus, INR most likely arose from an existing LRR-RLP, ~28 mya ago. The role of INR-like homologues is not clear, but they may detect a related, In11-like ligand.”

Does INR1-like form a constitutive interaction with SOBIR1? I would expect it can be given the activity of the C2 chimera, however, it could be nice to demonstrate this via Co-IP.

We thank the reviewer for this idea and interest in INR1-like signaling mechanisms. As described in Steinbrenner 2020, Vigna unguiculata INR associates with both AtSOBIR1 and VuSOBIR1 (Vigun09g096400). However, no SOBIR1 association data is available for Vigna unguiculata INR-like1. We agree with reviewer 2 that this would be a valuable experiment and believe that Co-IP could potentially give us valuable insights not only for INR-like 1 but also for multiple chimeric and even ancestrally reconstructed constructs. However, we believe these experiments would be best performed in a full study of SOBIR1 association, BAK1/SERK association, and ligand binding – especially if a ligand for INR-like1 can be identified.

It would be nice to test each of the 5 substitutions between N3 and N4 for a loss of In11 perception in Benth, however, I do not think this is necessary.

We thank the reviewer for the interest in these sites, which we agree are interesting with AlphaFold structural context. We tested these mutations and now place data alongside the INR structural model. We added an extra main figure (Figure 6) which provides ROS and ethylene data of N4 constructs with the five single AA switches.

316 – 'unusually long evolutionary history' – please provide more context what are you comparing with? It would be nice to add context to what is currently known.

We have rephrased this sentence as follows. We specifically cite RLP42 and Cf-2 as examples where a broader comparative approach has been taken, but where the receptors seem to be present only in a single genus.

“Analysis of INR provides an example of a PRR with conserved recognition function across relatively long evolutionary history (since the emergence of the Phaseoloids ~28 mya), compared to other extensive studied LRR-RLPs which are generally studied within a single genus or species (40,41).”

Personally, I think the discussion could be made more concise.

We have simplified where possible (413-431, 433-440, 472-477).

326 – I would not agree that it represents an 'alternative' mechanism – I don't think there is currently a paradigm for how these receptors evolve. I think this work represents one of the few attempts to characterise the origin of the receptor in detail.

We thank the reviewer for this comment, and agree that additional case studies for receptors are needed to reveal broader patterns in PRR evolution. We do not aim to draw a sharp contrast with Cf-2 but still would like to highlight its evolution within the Solanum genus. We changed the cited text fragment accordingly:

“This evolutionary pattern is reminiscent of the evolution of LRR-RLP Cf-2 which evolved <6 mya within a genus Solanum, potentially by intergenic recombination (41). In summary, an ancestral gene insertion event is the likely source of extant gene and copy number variation at the INR locus, which preceded the evolution of a specific peptide recognition function.”

349 – gives the impression that EFR/XPS1/CORE is RLPs – rephrase. I would also argue that for some of the receptors there has been some extensive phenotyping than others – e.g. for RLP42.

We thank the reviewer for the helpful feedback on the paragraph regarding receptor evolution. We have re-written the paragraph to focus on examples where phenotyping has been performed and comparative genomic analysis could be helpful to define evolutionary patterns. We removed mention of RK for simplicity.

“Our detailed analysis of the emergence of INR provides a roadmap for understanding evolution of recognition specificity for other lineage-specific PRRs. For analogous RLP-mediated responses within the Brassicaceae, extensive phenotyping was earlier performed for several PAMPs -- eMax, nlp20, SCFE1, IF1 and pg13 – across Arabidopsis accessions and related species (27,28,33,63,64). However, analysis of the respective PRR loci across Brassicaceae has not yet been conducted, and may reveal processes leading to their dynamic evolution.”

356 – I would argue that this paper suggests that RLPs have dynamic evolutionary patterns – similar to NLRs, unlike RKs (in general). They highlight two examples that are conserved, but I would not say that these examples are representative.

The comments about complex vs conserved RLPs in the Arabidopsis pangenome are very interesting. We agree that Pruitt 2021 data (https://doi.org/10.1038/s41586-021-03829-0) did indeed show that many RLPs show rapid evolving patterns, just like NLRs. We have rewritten the paragraph to highlight the broader finding, while still pointing out these specific exceptions.

“A recent pangenomic analysis of Arabidopsis indicates that while many RLPs occur in complex or copy-number variable loci (18,27), three PAMP sensing LRR-RLPs (RLP 23, RLP30 and RLP32) are conserved across A. thaliana varieties (17). Further cross-species analysis may reveal a similar trajectory to INR, via ancestral duplications preceding fixation in the Arabidopsis lineage. In summary, additional case studies for specific receptors are needed to reveal broader patterns in PRR evolution.”

I would prefer that P-values are also shown on the graphs (potentially in addition to the notation of significance).

We opted to add the p-values and other statistical information in an extra supplementary for aesthetic reasons (supplementary table 7).

I am not qualified to comment on the quality of the sequencing – but I believe it represents a valuable resource of the community and is sufficient to support the claims in the manuscript.

We thank reviewer 2 for the appreciation of the assemblies provided in the manuscript.

364 – I would consider TMM a ligand binding LRR-RLP (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458759/) although I appreciate it is significantly

We agree with the reviewer and changed the sentence accordingly:

“Despite multiple intense attempts, and in contrast to LRR-RKs (11,69), structural information for PAMP or HAMP sensing LRR-RLPs is not available, including INR (27,40,42).”

I would recommend citing this paper for the lack of RLP structure https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503760/.

We added the reference as suggested by reviewer 2 in both the introduction and the discussion.

Author details

1. Simon Snoeck

   Department of Biology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5288-0308

2. Bradley W Abramson

   The Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Anthony GK Garcia

   Department of Biology, University of Washington, Seattle, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4650-1348

4. Ashley N Egan

   Department of Biology, Utah Valley University, Orem, United States

   Contribution

   Supervision, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7803-4444

5. Todd P Michael

   The Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Resources, Supervision

   Competing interests

   No competing interests declared

6. Adam D Steinbrenner

   Department of Biology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   For correspondence

   astein10@uw.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7493-678X

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