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Exploring fern pathosystems and immune receptors to bridge gaps in plant immunity

BMC Biology volume 23, Article number: 301 (2025) Cite this article

Abstract

Land plants include angiosperms, gymnosperms, bryophytes, lycophytes, and ferns, each of which may deploy distinct strategies to resist pathogens. Here, we investigate fern-pathogen interactions by characterizing novel pathosystems and analyzing the diversity of fern immune receptors. A collection of fern species was inoculated with a diverse set of filamentous microbes, and disease symptoms were assessed. We further leveraged published genome mining tools to analyse the diversity of receptor-like kinases, receptor-like proteins (RLKs/RLPs) and nucleotide-binding and leucine-rich repeats (NLRs), along with key immune signalling components, in ferns. Our results reveal that ferns exhibit a range of responses to pathogens, including putative non-host resistance and more specific resistance mechanisms. Among ten ferns tested, Pteris vittata displays the broadest spectrum of pathogen compatibility. Genome mining indicates that ferns encode a diverse repertoire of putative immune receptors, antimicrobial peptides, and mediators of systemic acquired resistance. Ferns possess numerous RLKs/RLPs, resembling those required for cell-surface immunity in angiosperms. They also encode diverse NLRs, including sub-families lost in flowering plants. These findings provide insights into disease resistance evolution and open promising perspectives for crop protection strategies.

Background

Ferns are an ancient plant lineage, estimated to be 400 million years old (Devonian) based on fossils and molecular data [1, 2]. But in fact, Polypodiales, which include about 80% of extant fern species, co-diversified with angiosperms around 100 million years ago, during the Cretaceous [3]. This suggests that ferns have experienced selective pressures similar to those of flowering plants. Yet their genomic evolution and environmental adaptations remain largely understudied. While angiosperm genomes have been extensively characterized, only a few fern genomes are publicly available. These include the aquatic ferns Azolla filiculoides [4], Salvinia cucullata [4], Marsilea vestita [5], and Ceratopteris richardii [6]; the terrestrial ferns Adiantum capillus-veneris [7] and Adiantum nelumboides [8]; and the tree-fern Alsophila spinulosa [9]. Fern genomes stand out for their large size, high chromosome numbers, long introns, and abundance of repetitive sequences.

Historically, ferns have been primarily studied through morphological traits [10, 11]. While Linnaeus originally described 11 fern genera, recent phylogenies classify the ca. 10,000 known species into 200–350 genera and 11 orders [1, 11]. Among these, Polypodiales represent 80% of all fern species, Cyatheales includes most tree ferns, Salviniales are aquatic, and Equisetales form the sister lineage to all other ferns. Ferns form a monophyletic group with spermatophytes (seed plants) with which they share vascular tissues and branched veins in the leaf, called frond in ferns. They remain distinct due to their macroscopic gametophytic phase and lack of seed reproduction [12].

Ferns can be infected by microbes, although they are overall less affected by pathogens than angiosperms [13]. In the USA alone, at least 746 filamentous pathogens have been reported on ferns, including ascomycetes such as Colletotrichum [14] and Fusarium species [15], basidiomycetes such as Puccinia and Hyalopsora species, and oomycetes such as Pythium and Achlya species [16]. These genera contain major pathogens of angiosperm crops [17, 18], suggesting that ferns could serve as a valuable genetic reservoir for disease resistance in crops.

Understanding how ferns defend themselves against pathogens could reveal both conserved and novel plant immunity mechanisms. Several models developed in angiosperms help explain the concept of incompatibility, i.e., when a pathogen cannot grow on its resistant host. One well-known mechanism is non-host resistance, a strong and durable form of resistance. It is defined as the immunity exhibited by an entire plant species against all strains or isolates of a given pathogen species [19]. This type of resistance relies on multiple layers, including physical barriers, pre-formed antimicrobial compounds, and inducible molecular defences [19]. Another model is the gene-for-gene concept, in which a single molecular factor from the pathogen is recognized by a single molecular factor in the plant, triggering immunity [20]. In angiosperms, the plant recognition factor is often a cell-surface or an intracellular immune receptor [21]. Cell-surface immune receptors are generally receptor-like kinases (RLKs) or receptor-like proteins (RLPs), recognize conserved pathogen-associated molecular patterns (PAMPs) and activate PAMP-triggered immunity (PTI) [22]. Intracellular immune receptors are generally nucleotide-binding and leucine-rich repeat (NLR), detect pathogen effectors and activate effector-triggered immunity (ETI) [23]. Three major classes of NLRs are the following: toll, interleukin1, and some R-genes product NLRs (TIR-NLRs); coiled-coil NLRs (CC-NLRs); and resistance to powdery mildew 8 NLRs (RPW8-NLRs). Bryophytes additionally contain lineage-specific kinase NLRs (Kin-NLRs) and α/β-hydrolase NLRs (Hyd-NLRs) [24,25,26]. PTI and ETI cooperatively enhance plant resistance to pathogens [27, 28].

Fern genomes encode RLKs, including families that are well described in angiosperms such as LRR-RLKs and LysM-RLKs [29, 30]. They also encode NLRs, including TIR-NLRs and RPW8-NLRs [31]. Pioneering work on NLR biology in non-flowering plants has shown that some fern NLRs retain immune-related functions [31]. Particularly, the N-terminal domain of some fern NLRs is sufficient to trigger a hypersensitive response in Nicotiana benthamiana, a typical proxy for testing NLR activity [31].

In this study, we investigated fern pathosystems by testing cross-compatibility between various ferns and filamentous microbes, including major crop pathogens. We also explored the diversity of immune receptors in ferns to assess their potential as a genetic reservoir for disease resistance. Our results showed that some crop pathogens, such as Sclerotinia sclerotiorum and Fusarium proliferatum, can infect multiple fern species. Among them, the fern Pteris vittata appears to be a particularly suitable model for studying fern immunity, as it displays various levels of resistance to a broad range of tested filamentous pathogens. Surprisingly, we found that gametophytes of Pteris vittata exhibit distinct resistance levels to certain pathogens compared to sporophytes. Additionally, we identified a diverse repertoire of immune receptors in ferns, including well-characterized TIR-NLRs, CC-NLRs, and RPW8-NLRs but not the bryophyte-specific Kin-NLRs and Hyd-NLRs. Furthermore, we detected also non-canonical NLRs and NLR sub-families lost in angiosperms. These results highlight the potential of ferns as a resource to explore plant immunity and improve disease resistance in crops.

Results

Ferns are hosts for various pathogens

We assessed the compatibility between diverse ferns and filamentous microbes (Additional file 1: Table S1 and Additional file 2: Fig.S1) [32,33,34,35,36,37,38,39,40]. Disease symptoms were quantified as the area of brown tissue, 8 days after inoculation using agar plugs containing filamentous microbes (Fig. 1). Several microbes did not induce visible symptoms in any of the ferns tested. These include microbes that are not pathogenic on their natural host: Hypoxylon sp., Biscogniauxia mediterranea, Coniochatea sp. [36]. They also include host-specific pathogens, such as Aphanomyces euteiches and Colletotrichum magnum, suggesting non-adapted isolates or non-host resistance in ferns [19]. Colletotrichum trifolii race FR was also not compatible with any fern tested, but race 2 was able to grow on Polystichum setiferum and Pteris vittata (Fig. 1B). This suggests that the lack of symptoms observed for race FR is more likely due to a specific resistance mechanism in these species, rather than non-host resistance. In contrast, Sclerotinia sclerotiorum and Fusarium proliferatum induced significant disease symptoms in multiple fern species and exhibited the broadest host range among the pathogens tested. Among the ferns, Pteris vittata was susceptible to nearly all tested filamentous pathogens, whereas Nephrolepis exaltata, Polystichum setiferum, and the three Equisetum species tested displayed broad resistance. Overall, these findings indicate that some generalist filamentous pathogens, including those known to infect angiosperms, can also infect ferns. Notably, P. vittata appears to be broadly compatible with many filamentous pathogens.

Fig. 1
figure 1

Some filamentous microbes cause disease symptoms in ferns (sporophyte). A Representative disease symptoms caused by Sclerotinia sclerotiorum on diverse ferns. Detached fronds were inoculated with plugs of PDA containing actively growing fungus. Photos were taken at 0 dpi (top panel) and 8 dpi (bottom panel). Fern species names are indicated in the top panel. Bar = 3 cm. B Quantification of disease symptoms caused by filamentous microbes on ferns. Fronds (or stem for Equisetum spp.) were collected and placed on a moist paper towel as in panel “A.” Tissues were inoculated with PDA plugs containing actively growing microbes. Disease symptoms were measured at 8 dpi, as the area (in cm2) of brown tissue extending from the inoculation sit, using ImageJ. Due to limited plant material, the number of replicates (n) ranges from 2 to 15 (average ~ 5) across independent inoculations. *, **, or *** indicate p-value < 0.05, < 0.01, or < 0.001 respectively, from a t-test for normally distributed data or a Wilcoxon test for non-normally distributed data (normality was assessed using a Shapiro test), for values with a mean > 0.05 cm2, as smaller values are too small to reliably describe symptoms. Figure generated with the R package “tidyplots”

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Gametophytes and sporophytes respond differently to some pathogens

The fern life cycle consists of two morphologically and functionally distinct phases: the haploid gametophyte, which produces gametes via mitosis, and the diploid sporophyte, which develops after fertilization and represents the dominant life stage in most species [41]. Because sporophytes and gametophytes differ in structure and physiology, particularly in the presence of vascular tissues, cuticle development, and overall size, we tested whether they could respond differently to the same pathogens (Fig. 2).

Fig. 2
figure 2

Sporophytes and gametophytes of Pteris vittata respond differently to some filamentous microbes. A, B Quantification of disease symptoms on sporophytes (A) or gametophytes (B) of Pteris vittata. Disease symptoms were measured as the area (cm2) of brown tissue extending from the inoculation site at 8 dpi. Letters indicate significantly different groups, analysed independently for each plot A and B, based on an ANOVA followed by a Duncan test on data normalized by quantiles, with significance threshold p-value < 0.05. Plots were generated with the R package “tidyplots.” A Detached fronds of sporophytes were inoculated with plugs containing a given microbe. Due to limited plant material, the number of replicates (n) ranges from 5 to 15 (average ~ 13), across independent inoculations. B Gametophytes of Pteris vittata were grown under sterile conditions and inoculated with plugs containing a given microbe. Due to limited plant material, the number of replicates (n) ranges from 17 to 36 (average ~ 27), across independent inoculations. C Table summarising significant symptoms for each pathogen, as presented in A and B. “Symptoms” indicates a significant difference with the non-inoculated plants. D Representative images of symptoms in Pteris vittata sporophytes (bar = 1 cm) and gametophytes (bar = 2 mm). Photos were taken at 8 dpi

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Pathogen inoculations revealed distinct susceptibility patterns in P. vittata sporophytes (Fig. 2A and D). Two tested Fusarium species were highly aggressive, inducing strong symptoms that initially localized along the veins before spreading to surrounding frond tissues. Similarly, Xylaria cubensis, a natural pathogen of Marchantia polymorpha [36], generated large necrotic and dead tissue areas, suggesting an active necrotrophic behavior. A second group of pathogens, including C. nymphaeae, C. graminicola, and C. tabacum, triggered maceration symptoms but in a less frequent or less extensive manner. Finally, the remaining filamentous microbes exhibited rare, weak or no visible pathogenic effects, failing to induce significant symptoms across different assays.

Gametophytes exhibited lower symptom levels compared to sporophytes, suggesting a slower progression of fungal colonization (Fig. 2B and D). Moreover, gametophytes are not infected by the same range of filamentous pathogens as sporophytes (Fig. 2 A–C) F. oxysporum, F. proliferatum, C. nymphaeae, and C. tabacum, caused significant symptoms in both sporophytes and gametophytes. In contrast, C. graminicola and X. cubensis caused severe symptoms in sporophytes but were unable to establish infection in gametophytes, which generally remain green. Conversely, some pathogens that failed to induce symptoms in sporophytes caused significant browning in gametophytes, such as C. gloeosporioides, C. orbiculare, and C. magnum.

These findings suggest that the pathogen virulence varies between gametophytes and sporophytes in P. vittata, highlighting a generation-dependent component of susceptibility.

Pathogens can successfully colonize fern tissues

Among the different pathogens tested, Colletotrichum nymphaeae, Fusarium proliferatum, and Fusarium oxysporum were selected for further microscopic analyses due to their virulence on both gametophytes and sporophytes in Pteris vittata (Fig. 2). We investigated their infection processes in P. vittata using scanning electron microscopy (SEM), confocal laser scanning microscopy, and bright-field/fluorescence microscopy (Fig. 3).

Fig. 3
figure 3

Pathogenic fungi can colonize superficial and internal tissues of Pteris vittata. A Scanning electron microscopy (SEM) images showing fungal germination and development on the epidermis of Pteris vittata gametophytes and fronds. Left panels: False-colored images (Affinity Designer v2.5.5), showing germinated spores and appressorium-like structures formed by C. nymphaeae at 2 dpi. Scale bar = 10 µm. Right panels: Later infection stages show extensive hyphal colonization of superficial tissues in P. vittata sporophytes or gametophytes at 6 dpi. Host cells in contact with mycelium appear swollen and macerated. Pen. = penetration structure (appressorium-like). Scale bar = 50 µm. B Macroscopic phenotype of P. vittata inoculated with spores of C. nymphaeae, or F. oxysporum. Dashed line indicates the area used for microscopy images in D. Scale bar = 1 cm. C Microscopy images of 100 µm cross-section P. vittata fronds with F. oxysporum showing extensive maceration and tissue disorganization on the right side of the frond, with cell wall degradation, at 4 dpi following plug inoculation. Left panel: bright field. Right panel: WGA-Alexa Fluor staining (green) highlights massive hyphal colonization in destructured tissues and intercellular fungal progression in the left-side tissue. Autofluorescence of lignin is visible in the central vasculature. Scale bar = 100 µm. D Microscopy images of a 100 µm cross-section of P. vittata frond inoculated with C. nymphaeae, showing a symptomatic area on the right (brown epidermis) and asymptomatic area on the left (white/transparent epidermis). Top left panel: bright field. Top right panel: calcofluor staining (cyan) shows plant cell wall degradation in the symptomatic area. Middle right panel: WGA-Alexa Fluor staining (green) shows hyphae in both symptomatic and non-symptomatic areas, with some background signal from the plant. Bottom right panel: autofluorescence (red) shows intact chloroplasts in both symptomatic and asymptomatic areas. Scale bar = 100 µm

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SEM images revealed that both F. proliferatum and C. nymphaeae germinated on the surface of P. vittata sporophyte and gametophyte tissues (Fig. 3A, left panels). C. nymphaeae produced appressorium-like structures, suggesting a penetration strategy similar to that on waterlilies and strawberries [42, 43]. No penetration structures were observed for F. proliferatum, suggesting direct penetration through cell wall digestion. At later infection stage (6 dpi), hyphae formed dense networks across the surface of both life stages. Host cells in direct contact with the mycelium appeared swollen and macerated, consistent with tissue degradation observed in necrotrophic infections (Fig. 3A, right panels). Later, fronds inoculated with droplets of conidia (13 dpi), displayed brown macerated tissues localised beneath the droplet surface. The infection appears to spread through secondary veins that darken progressively as the disease develops (Fig. 3B).

Bright-field microscopy performed in symptomatic tissues confirmed severe tissue degradation in P. vittata fronds caused by F. oxysporum (Fig. 3C), with extensive cell wall degradation and disorganized tissues, as well as massive colonization by fungal hyphae, visualized by WGA-AF fluorescence, likely indicating a switch to a necrotrophic fungal stage. C. nymphaeae also caused visible symptoms in P. vittata, characterized by browning of the epidermis (Fig. 3D, bright field panel) and of the veins, consistent with the macroscopic symptoms observed (Fig. 3B). At the microscopic level, the hyphal density appeared similar in both asymptomatic and symptomatic areas (Fig. 3D, green panel), indicating different stages of infection. Chloroplasts are visibly intact in both symptomatic and asymptomatic areas (Fig. 3D, red panel), indicating that both regions represent biotrophic phases of the fungus. However, calcofluor staining revealed a much stronger cell wall degradation in the symptomatic area (Fig. 3D, cyan panel).

Together, these findings confirm that P. vittata is a viable host for C. nymphaeae, F. proliferatum, and F. oxysporum, with successful pathogen attachment, penetration, and intercellular colonization. Many of the observed infection characteristics resemble those of plant filamentous pathogens with a hemibiotrophic lifestyle in angiosperms [44].

Fern genomes reveal a rich diversity of putative immune receptors

Receptor-like kinases (RLKs), receptor-like proteins (RLPs), and nucleotide-binding leucine-rich repeat receptors (NLRs) are major classes of immune receptors in angiosperms [23, 45]. To determine the diversity of these receptors in ferns, we analyzed available fern genomes, along with selected reference species representing the diversity of land plants, including bryophytes (Marchantia paleacea [46], Marchantia polymorpha [47], Physcomitrium patens [48], Takakia lepidozioides [49]); lycophytes (Diphasiastrum complanatum [50], Isoetes sinensis [51], Selaginella moellendorffii [52]), ferns (Alsophila spinulosa [9], Ceratopteris richardii [6], Adiantum capillus-veneris [7], Adiantum nelumboides [8], Azolla filiculoides [4], Salvinia cucullata [4], Marsilea vestita [5]); monocots (Dendrobium catenatum [53], Oryza sativa [54], Setaria italica [55]); and dicots (Camellia sinensis [56], Arabidopsis thaliana [57], Medicago truncatula [58], Quercus suber [59]). We identified 11,390 RLKs (including 3,286 from ferns), 1,843 RLPs (including 371 from ferns) (Additional file1: Table S2) and 3,775 NLRs (including 396 from ferns) (Additional file1: TableS3).

Fern genomes encode RLKs/RLPs

Within the RLK/RLP families, we identified well-known receptor classes including leucine-rich repeat (LRR), lectin, malectin, LRR-Malectin (LRR-Mal.), wall-associated kinase (WAK), thaumatin, cysteine-rich repeat (CRR), regulator of chromosome condensation 1 (RCC1), and lysin motif (LysM) (Additional file1: Table S2) [29, 60]. Their distribution in ferns is comparable to that of other land plants, with LRR- and Lectin-RLKs being the most abundant among RLKs, and LRR-RLPs being the most abundant among RLPs (Fig. 4). All categories are present in all fern genomes investigated.

Fig. 4
figure 4

Diversity of cell-surface receptors in ferns. Distribution of receptor-like kinases (RLKs, A) and receptor-like proteins (RLPs, B) in ferns and other plant lineages. RLKs and RLPs were identified from plant genomes using RGAugury (https://doi.org/10.1186/s12864-016-3197-x) and classified into receptor families based on HMM scan predictions. Plots were generated with the R package “tidyplots.” Cartoons representing ferns, bryophytes, lycophytes, monocots, and dicots are from PhyloPic

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In addition, some RLKs/RLPs could not be assigned to any of these categories. We investigated whether they could represent fern-specific classes. The predicted domains were mostly identical between ferns and non-ferns unclassified RLKs/RLPs (Additional file 2 : Fig. S2). We identified Romo1 (IPR018450), Vps39_1 (IPR019452), Photo_RC (PF00124), and DUF642 (PF04862) unique to ferns. However, we also identified some of these domains in tandem with kinases in angiosperms and/or gymnosperms such as A0A3P6FCZ3 (Vps39_1-kinase from Brassica oleracea), A0A438D9U7 (Photo_RC-kinase from Vitis vinifera), and A0AA38CBH3 (DUF642-kinase from Taxus chinensis), in the InterPro database. This indicates that these classes of proteins are not specific to ferns.

Fern genomes encode well-described NLRs

We classified NLRs using a phylogeny of their NB-ARC (Fig. 5A). Using protein domain annotations, we identified clusters of NLRs associated with known NLR families, including TIR-NLRs, CC-NLRs, and RPW8-NLRs (Additional file1: Table S3). TIR-NLRs form three distinct clusters, with “Cluster I” being the most widespread across land plants, including angiosperms, bryophytes, and ferns. “Cluster II” is specific to ferns, while “Cluster III” includes both ferns and bryophytes and is unexpectedly related to certain CC-NLRs from tea (Camellia sinensis), suggesting potential functional diversification. RPW8-NLRs are present across all land plant lineages. CC-NLRs segregate into three main groups: “EDVID” and “G10,” both specific to angiosperms, and “Cbl-N,” which is found only in non-angiosperms, including ferns, lycophytes, and bryophytes. These findings suggest that CC-NLRs underwent lineage-specific expansions, with “EDVID” and “G10” evolving predominantly in spermatophytes, while “Cbl-N” represents the ancestral state retained in non-angiosperms.

Fig. 5
figure 5

Diversity of NLRs in ferns and other land plants. A Phylogeny of NB-ARC domain from NLRs across representative land plants, including ferns, mosses, liverworts, lycophytes, monocots, and dicots (species detailed in Table S1). Some branches were manually colored based on annotation. The indicated clades are supported by bootstrap values > 70/100. The outer ring indicates the plant clades from which the NLR was identified. Branch lengths are not to scale. Sequences and raw tree files are available (https://doi.org/10.5281/zenodo.17067744). B Structure of a representative CC-like NLR, Gene02698 from the tree fern Alsophila spinulosa, as predicted by AlphaFold. Dark blue indicates a very high confidence (> 90%), pale blue indicates a good confidence (70–90%), yellow indicates a low confidence (50–70%), and orange indicates a very low confidence (< 50%). C Plot of the predicted template modelling (pTM) scores of diverse N-terminal domains from each clade of NLRs from the phylogeny in “A.” Regions from the first amino acid to the start of the NB-ARC domain were predicted with AlphaFold for 10 representative NLRs of each clade. The structures and NLR names are presented in Figure S3

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Fern genomes encode CC-like NLRs

We identified three additional clusters of fern NB-ARC-containing proteins: TIR-NB-TPRs, CC-NB-ARMs, and one unclassified lineage (Fig. 5A). TIR-NB-TPRs form a conserved class of NLRs, potentially regulating immunity, containing a TIR domain but signalling independently of TIR-NLRs [61]. CC-NB-ARMs are also conserved in plant genomes [62, 63]. The rice CC-NB-ARM “RLS1” could be a regulator of immunity [64].

The unidentified NLR branch (in grey in Fig. 5A) is closely related to Hyd-, Kin-, and TIR-NLR clusters I and II. This cluster is particularly rich in fern NLRs (Fig. 5A), prompting further investigation. In a previous study, NLRs from this cluster were referred to as “OG5” and “OG11” based on sequence, and “AF-6 (CC-like)” based on predicted structure [31]. We used AlphaFold [65] to investigate further the structure of these CC-like NLRs (Fig. 5C,D and Additional file2 : Fig.S3). Gene02698, a representative CC-like NLR from Alsophila spinulosa, has typical LRR, NB-ARC, and CC dmains, with a peculiar, disordered strand at the N-terminus of the CC (Fig. 5C). The LRR and NB-ARC are predicted with high confidence, while the N-terminal domain is predicted with a low confidence (Fig. 5C). In fact, the N-terminal domains of many CC-like NLRs are predicted with very low confidence, and sometimes with an N-terminal disordered region, unlike other CC domains (Fig. 5D and Additional file2: Figure S3). The actual function of CC-like NLRs requires further investigation. Notably, two CC-like domains (from Mp1g20580 and Mp4g08790 of Marchantia polymorpha) failed to induce a hypersensitive response in Nicotiana benthamiana, a typical readout of NLR activity [31].

Known immune-related genes are conserved in fern transcriptomes

A recent study that interrogated 26 fern genomes or transcriptomes [2] found that many immune genes are conserved in ferns (Fig. 6 and Additional file1 : Table S4). This includes BAK1 and BIK1 required for signalling of many LRR-RKs [22]. Two well-characterized CC-NLRs were not detected in ferns (Fig. 6). However, our phylogenetic analyses confirmed the presence in ferns of CC-NLRs (CNLs), but only from the “Cbl-N” type, which was lost in angiosperms (Fig. 5). Despite their sequence divergence, “Cbl-N” CC-NLRs likely function similarly to “EDVID” CC-NLRs found in angiosperms [31]. In addition, RIN4, a protein targeted by multiple effectors and guarded by several CC-NLRs in angiosperms [66], is well conserved in ferns (Fig. 6).

Fig. 6
figure 6

Prevalence of known immune-related genes across 26 ferns transcriptomes. Data extracted from (Ali et al., 2025, Table S9). Genes annotated as involved in response to pathogen or virus were included. The plot was made with the R package ggplot2, based on data show in Additional file 6: Table S4. Gene presence is expressed as a percentage of ferns transcriptome containing an ortholog, out of 26 transcriptomes analyzed. PTI: PAMP-triggered immunity, DAMP: Danger-associated molecular pattern, ETI: effector-triggered immunity, CNL: CC-NLR, TNL: TIR-NLR, SAR: systemic acquired resistance, AMP: antimicrobial peptide

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Proteins acting downstream of TIR-NLRs, such as EPs (EDS1, PAD4) and the RPW8-NLRs NRG1 and ADR1, are present in some ferns but absent in many species (Fig. 6). However, ferns encode other types of RWP8-NLRs (Fig. 5) [67].

In Arabidopsis, pathogen detection triggers the synthesis of salicylic acid (SA) that activates both local and systemic immune responses, known as systemic acquired resistance (SAR) [68]. SARD1 and CBP60g that regulate the expression of genes for SA biosynthesis such as ICS1 and EDS5 [69] are strongly conserved in ferns (Fig. 6). However, genes required for SA biosynthesis were not analyzed by [2]. NPR1, an SA receptor required for SAR in Arabidopsis [70], is mostly absent from ferns (Fig. 6) [50].

Additionally, antimicrobial peptides (AMP), including chitinases and defensins, are widely present in ferns (Fig. 6). This supports the idea that AMPs constitute a conserved defence strategy across eukaryotes [71,72,73].

Discussion

Ferns are susceptible to a broad range of pathogens

The literature on fern diseases is sparser than that for angiosperms [13]. Yet, several pathogens have been reported on ferns, primarily filamentous fungi [16, 74,75,76], but also bacteria [77,78,79] and viruses [80]. Some of the reported filamentous pathogens belong to the same species as crop pathogens, such as Colletotrichum gloeosporioides [14] and Botrytis cinerea [16], both causing diseases on many crops including soybean, wheat, and tomato [81, 82].

Here, we expand this category by identifying Fusarium oxysporum, Fusarium proliferatum, Sclerotinia sclerotiorum, Colletotrichum nymphaeae, and a specific race of Colletotrichum trifolii as pathogens capable of colonizing ferns (Fig. 1). This raises the question of whether these interactions reflect true compatibility or opportunistic infections facilitated by stress conditions in experimental settings. Microscopy observations revealed several conserved features of fungal pathogenicity (Fig. 3). Pathogens such as C. nymphaeae and F. oxysporum retained classical infection strategies, including the formation of appressorium-like structures, intercellular hyphal development, vein colonization, and an alternation between biotrophic and necrotrophic phases. These observations align with previous findings on the evolutionary dynamics of plant-parasite interactions, which suggest that many core mechanisms of pathogen virulence remain conserved despite host diversification [83].

All three Equisetum species tested were largely resistant to all filamentous pathogens tested (Fig. 1B). Because leaves are reduced to scale-like microphylls in horsetails, we inoculated stems or branches instead. Although horsetail stems and branches contain stomata and have taken over the photosynthetic function of the leaf [84], they remain structurally different from true leaves, which could hinder pathogen infection in our experiment. For example, they have a distinct cuticle wax layer, known to contribute to resilience against abiotic stress [85], which could also play a role in disease resistance. Horsetail can also produce antimicrobial compounds [86], which could further explain their resistance to filamentous pathogens in our experiments (Fig. 1B).

The inability of Aphanomyces euteiches to infect any of the tested fern species suggests non-host resistance, a widely observed phenomenon in plant-pathogen interactions where an entire plant lineage remains incompatible with a given pathogen [19]. Conversely, the differential susceptibility observed between P. vittata and the two tested races of C. trifolii strongly suggests host-race specific immunity, which may be linked to the recognition of an effector present in race FR, but not in race 2.

The ferns pathosystems reported here provide valuable insights into the evolution of plant–pathogen interactions, offering new perspectives on resistance mechanisms in land plants. Together, they also highlight the need to expand such studies to include obligate biotrophs, especially rust fungi (Pucciniales), which are the most frequently reported pathogens on ferns [13].

Pteris vittata could be a model to study fern-pathogen interactions

Pteris vittata exhibits the broadest range of compatibility with filamentous pathogens, including those that typically infect angiosperms or bryophytes (Figs. 1 and 2). This fern is particularly interesting as it can accumulate arsenic [32, 87], likely through the arsenate reductase PvACR2 [88], and it can establish arbuscular mycorrhizal symbiosis with the fungus Glomus intraradices [89], making it a valuable model to investigate the conservation of the common symbiosis signalling pathway (CSP) in ferns [90]. In addition to its role in soil arsenic decontamination and symbiosis, we found that Pteris vittata is partially compatible with multiple pathogens, including F. oxysporum, F. proliferatum, X. cubensis, C. nymphaeae, and C. trifolii race 2. These findings strengthen the potential of P. vittata as a model to study fern-microbe interactions and the evolution of molecular plant–microbe interactions.

Gametophyte and sporophytes are differentially resistant to pathogens

The life cycle of ferns consists of two independent and macroscopic phases: the gametophyte and the sporophyte [11]. Fern gametophytes, called prothalli, develop from spore germination. They are generally heart-shaped, one cell layer thick, and only a few millimetres in size. They generate gametes, whose fertilization gives rise to a sporophyte, the dominant form of most ferns. The sporophyte forms roots, a stem (called rhizome, even when above ground) and leaves (called fronds) which bear spore-producing sporangia. Spore germination results in a new gametophyte. These structural and physiological differences suggest that sporophytes might be more resilient to stress. Surprisingly, our results revealed that P. vittata gametophytes can exhibit greater resistance to some fungi such as Colletotrichum graminicola and Xylaria cubensis, challenging the assumption that gametophytes are inherently more susceptible to pathogens (Fig. 2).

Despite their seemingly fragile structure, gametophytes demonstrate remarkable resilience to environmental stresses. For instance, fern gametophytes exhibit a high tolerance to desiccation [91], a trait that is rare in sporophytes and likely a homoplasy [92, 93]. Fern gametophytes also show adaptation to light stress [94], likely through the accumulation of reactive oxygen species-scavenging molecules, such as β-carotene or tocopherols [95, 96]. Additionally, some fern gametophytes are more resistant to freezing than their sporophytes, enabling their survival under snow during winter [97]. These differences suggest that gametophytes and sporophytes deploy distinct physiological or biochemical defence mechanisms. In angiosperms, developmental stage-dependent resistance to pathogens has been also reported, with mature plants generally more resistant than seedlings [98, 99]. In the case of P. vittata, gametophytes appear more resistant than sporophytes to C. graminicola and X. cubensis (Fig. 2). One possible explanation is the absence of vasculature in gametophytes, which may limit systemic pathogen spread. Another possibility is the accumulation of antimicrobial compounds specific to gametophytes. Indeed, ferns are known to produce a wide range of specialised metabolites in response to pests [100,101,102,103]. Further investigation, particularly at the molecular level, is needed to determine whether gametophyte-specific immune strategies exist and how they compare to those of sporophytes.

Putative immune receptors are conserved within tracheophytes

Tracheophytes have been exposed to pathogen infections for at least 407 million years [104] and likely since terrestrialization [26, 105, 106]. Our findings indicate that ferns can resist angiosperm pathogens, raising the question of whether these defence mechanisms are ancestral traits shared with seed plants or represent lineage-specific innovations. To investigate this question, we examined the conservation of well-characterized immune-related genes in ferns (Fig. 6).

CC-NLR-mediated immunity is ancestral to land plants, but angiosperms evolved distinct “EDVID” and “G10” classes of CC-NLRs, while other lineages including ferns retain the “Cbl-N” ancestral class (Fig. 5A). Ferns and others non-flowering plants contain NLRs with a CC-like domain, predicted with low confidence and often with a disordered N-terminal tail, or belonging to the “Cbl-N” type (Figs. 5 and Additional file 2: Fig. S3). CC-like and Cbl-N represent the ancestral state, lost in angiosperms, which in contrast have evolved distinct “EDVID” and “G10” classes of CC-NLRs.

Ferns encode numerous TIR-NLRs (Fig. 5A). Based on studies in angiosperms, TIR-NLRs require EDS1, PAD4, SAG101 (coreceptors of the TIR-NLR enzymatic product), and ADR1 and NRG1 (RPW8-NLRs recruited downstream of EDS1, PAD4 and SAG101) [107]. These proteins are absent from many fern transcriptomes (Fig. 6) [2]. A simple explanation is that these ferns evolved receptors different from EDS1, PAD4, and SAG101, specifically for fern TIR-NLR enzymatic product. Even if most ferns lack ADR1 or NRG1 (Fig. 6), they contain other types of RPW8-NLRs (Fig. 5A) [67], which could be recruited downstream of these unknown receptors. Although rare, some ferns contain close homologues of EDS1, PAD4 and SAG101 (Fig. 6) [2], indicating that this pathway is ancestral to tracheophytes, but has been lost in many ferns.

Many ferns lack the salicylic acid (SA) receptor NPR1. In Marchantia polymorpha, NPR1 is conserved, can complement Arabidopsis npr1 mutants, and can bind SA, suggesting that this function is ancestral to land plants [108]. The absence of NPR1 in many ferns raises questions about the role of SA in SARD1/CBP60g-mediated immunity and SAR in ferns, and whether alternative mechanisms exist. Pipecolic acid is another molecule required for SAR, and its biosynthesis requires SARD4 [109]. Unlike NPR1, SARD4 is conserved in ferns, suggesting that pipecolic acid may play a predominant role in fern SAR.

Overall, many components of PTI and ETI appear to be ancestral to tracheophytes. However, some pathways, such as the EP-mediated regulation of TIR-NLR signalling, are reduced in ferns, suggesting the presence of yet-to-be-identified compensatory mechanisms.

Ferns can be a reservoir of R-genes

The plant immune system relies on RLK/RLP cell-surface receptors for PTI and NLR cytoplasmic receptors for ETI [21, 27, 110]. Many disease resistance strategies in crops involve the introgression of such immune receptors [111]. Since we found that ferns can resist pathogens (Figs. 1 and 2), we propose that they could serve as natural reservoirs of immune receptors, to be transferred into crops.

We found that PTI components are largely conserved across land plants (Figs. 4 and 6). This is corroborated by functional studies showing conservation of central PTI regulators such as LysM receptors [112, 113], SERK receptors [114], CPK28 [115], or RBOH [116]. This conservation indicates that RLKs/RLPs could be prioritized as candidate determinants for resistance to pathogens in ferns.

In addition, NLRs have evolved differently across plant lineages (Fig. 5). In ferns, we found three major classes of NLRs: CC-, TIR-, and RPW8-NLRs (Fig. 5). Fern CC-NLRs belong to the “Cbl-N” cluster (Fig. 5A), which is found in bryophytes, lycophytes, and ferns, but was lost in angiosperms, suggesting that “Cbl-N” represents the ancestral state of CC-NLRs. Instead, angiosperms evolved “G10” and “EDVID” CC-NLRs. “G10” and “EDVID” expanded (Fig. 5) to become overrepresented in asterids and monocots [25]. Their success in asterids could be explained by the robustness of the “NRC” networks, formed between many CC-NLRs “EDVID” [105, 117,118,119]. TIR-NLRs and RPW8-NLRs are found in all lineages (Additional file1:Table S3) [67]. They have been shown to function in the same immune signalling pathway, along with the protein EDS1 [107, 120, 121]. This broad conservation suggests that it dates back to the earliest land plants. These data suggest that fern NLRs could be functional in angiosperm crops, despite millions of years of independent evolution. However, no fern NLR has yet been demonstrated to confer resistance to a pathogen.

Conclusions

We identified novel pathosystems in ferns, some of which are likely controlled by specific resistance mechanisms. We also found that fern genomes encode a remarkable diversity of putative immune receptors, including some absent in angiosperms. These findings suggest that ferns could serve as a reservoir of immune receptors for crop protection. Further characterization of the CC-like NLRs, which have been lost in angiosperms, could provide valuable insights into plant immune signalling and the evolution of disease resistance.

Methods

Fern and microbe material used in this study

A collection of fern species and filamentous microbial isolates (Additional file1:Table S1) was assembled for a systematic evaluation of cross-compatibility. Some fern specimens were obtained from external laboratories, others were collected from the wild, and some were purchased from commercial sources. Most ferns used here belong to the Polypodiales order, the largest order of ferns. Additionally, three species from the Equisetales order, the sister lineage to all other ferns, were included (Additional file2:Fig.S1). Only Pteris vittata could be successfully maintained and propagated under our laboratory conditions, while commercial ferns did not survive long-term. However, we still have access to the ferns harvested from the wild, due to their known geographic sampling area (Castanet-Tolosan, FR).

For microbial isolates, some were isolated from the bryophyte Marchantia polymorpha [36] while others belong to our institutional collection (Additional file1:Table S1 and Additional file2:Fig.S1). These include broad-spectrum plant pathogens such as the ascomycete fungi Sclerotinia sclerotiorum [122]; Colletotrichum spp. (many of them were kindly provided by Dr Richard O’Connell, BIOGER, INRAE, Versailles-Saclay, France) [17]; or Fusarium spp. [18, 35], as well as host-specific pathogens like the oomycete Aphanomyces euteiches (specialist of Fabaceae) [123], and fungi not commonly considered pathogenic, such as Biscogniauxia mediterranea, Coniochaeta sp., and Hypoxylon sp. [36].

Plant growth conditions

Ferns at the sporophytic stage were grown in pots containing compost and pozzolan (3:1) in a greenhouse supplemented with LED lightning, maintaining a 16-h light/8-h dark photoperiod. Temperature was set at 22 °C and relative humidity at 70%. Gametophytes were grown from surface-sterilized spores. Spores were sterilized as follows: 2 h imbibition in water, 10 min in 1% bleach, with 0.01% Tween-20, followed by three washes with sterile water. Spores were then sown on LS ½ medium [2.36 g/l (Caisson, LSP03), 0.8 g/l agar] in Petri dishes. Cultures were sealed with parafilm and maintained in a Snijder growth cabinet (type EB1) under 15-h light/9-h dark photoperiod at 25 °C.

Microbe culture and inoculation

Filamentous microbes were propagated on Potato Dextrose Agar (PDA, 39 g/l) at 22 °C in the dark. For long-term storage, spores were kept in 30% glycerol at − 80 °C. For mid-term storage, mycelium was stored in the dark at 10 °C on PDA Petri dishes. For inoculation, plugs of ~5 mm × 5 mm containing actively growing mycelium were excised from 10-day-old cultures. For sporophytes, detached fronds (or stems for Equisetum species) were placed on a moist paper towel. Gametophytes were inoculated directly on their growth medium, approximatively 2 months after sowing, prior to sporophyte formation. PDA plugs with mycelium were placed in direct contact with the frond or gametophyte surface, and the Petri dishes were sealed with parafilm to maintain high humidity. All collected ferns were inoculated in several trials with multiple pathogens. Disease symptoms were assessed at 8 days post-inoculation (dpi) by measuring the area of brown tissue surrounding the inoculation site, using ImageJ software.

Microscopy

For scanning electron microscopy (SEM), detached fronds or gametophytes were inoculated with F. proliferatum, F. oxysporum, or C. nymphaeae, using 10 µl of a solution adjusted to 105 spores/ml. Samples were collected at different infection stages for microscopic observations: spore germination (2 dpi) and mycelium colonization (6–17 dpi). 1 × 1 cm sections of fronds were fixed in a solution of 0.05 M sodium cacodylate buffer (pH 7.2) containing 2.5% glutaraldehyde, dehydrated through an ethanol series, and critical-point dried using liquid CO₂. Samples were mounted on the observation plate using conductive silver paint and sputter-coated with platinum. Images were acquired using a Quanta 250 FEG FEI scanning electron microscope at 5 kV, with a spot size of 3 and a working distance of 10 mm. For florescence microscopy with Colletotrichum nymphaeae, detached fronds were harvested at 13 dpi after inoculation using 10 µl of a solution adjusted to 105 spores/ml. Cross Sects. (100 µm thick) were obtained using a Leica VT1000S vibratome and incubated for 15 min in 1X phosphate buffer saline (PBS) with 0.01% calcofluor and 5 mg/l of Wheat Germ Agglutinin-AlexaFluor-488 (WGA-AF488). For fluorescence microscopy with Fusarium oxysporum, detached fronds were harvested at 4 dpi after inoculation using agar plugs containing actively growing fungus. Cross Sects. (100 µm thick) were obtained using a Leica VT1000S vibratome and incubated for 15 min in 1X phosphate buffer saline (PBS) with 1 mg/l of Wheat Germ Agglutinin-AlexaFluor-488 (WGA-AF488). Cross sections were imaged using a Nikon Eclipse TI inverted Wide-field microscope with a color CMOS camera DS Ri2 and driven by Nikon NIS software. We used 10x (N.A 0.3) or X20 (N.A 0.45) objectives. Fluorescence of WGA-AF488, to stain the fungal cell wall, was observed through a bandpass GFP B filter set (ex, 475/50 nm; dich, 505 nm; em, 535/50 nm). Autofluorescence of chloroplasts was observed with a longpass B2-A filter set (ex, 470/50 nm; dich, 505 nm; em, 520 nm). Fluorescence of calcofluor, to stain cellulose of the plant cell wall, was observed by a bandpass DAPI filter set (ex, 377/50 nm; dich, 409 nm; em, 447/60 nm).

NLR and RLK/RLP mining

Immune receptors were predicted across available fern genomes and representative species from other land plant lineages (Additional file1:Table S1). To identify putative cell-surface immune receptors, we extracted RLKs and RLPs using RGAugury v2.2 [124] which were further classified based on domain composition (Additional file1: Table S2). For intracellular immune receptors, NLRs (Additionalfile1: Table S3) were identified using both RGAugury and NLRtracker v0fe62b3 [63, 124], followed by functional domain annotation with HMMER v3.2.1 to confirm the presence of NB-ARC domains. We assigned each NLR to a category based on NLRtracker classification and/or identification of typical domains by HMM (e.g., TIR, NB, and LRR for TIR-NLR). NLRs that could not be categorised using this method were labelled as “UNDETERMINED.” A subset of 3,775 NLRs (from 3637 proteins) was retained for phylogenetic analysis. Protein sequences were aligned using MUSCLE v5.1.0 and trimmed with trimAl v1.4.1 to remove poorly aligned positions. Maximum-likelihood phylogenies were reconstructed using IQ-TREE v2.2.2.6, with the best-fitting evolutionary model selected via ModelFinder and branch support assessed with 10,000 replicates of both sh-aLRT and ultrafast bootstrap. We used the NB-ARC domain only, as it is the most conserved domain of NLRs, while the LRR domain is under diversifying selection [106]. The resulting phylogeny was visualized and annotated with iTOL v7. NLRs were classified into functional groups based on domain composition. For cell-surface receptors, RLK/RLPs were identified using RGAugury, followed by functional domain annotation with HMMER v3.2.1 to assess the nature of the ectodomain. They were classified based on both RGAugury annotation and presence of typical ectodomain identified by HMM (e.g., RLK and LRR for LRR-RLK). Proteins that could not be categorised using this method were classified as “other.” Additional methodological details are provided in Additional file3:Supplemental Methods, and https://doi.org/10.5281/zenodo.17067744) [63, 124,125,126,127,128,129].

Data availability

All novel data and datasets presented in this study are available as main or supporting information.

Abbreviations

AF:

AlexaFluor

AMP:

Antimicrobial peptide

ANOVA:

Analysis of variance

ARM:

Armadillo repeat motif

CC:

Coiled-coil

CNL:

CC-type nucleotide-binding leucine-rich repeat (NLR)

CRR:

Cysteine-rich receptor

CSP:

Common symbiosis pathway

dpi:

Days post-inoculation

DAPI:

4′,6-Diamidino-2-phenylindole

DAMP:

Damage-associated molecular pattern

DUF:

Domain of unknown function

EDVID:

Conserved motif in CC-NLRs (specific to asterids/monocots)

EP:

EDS1/PAD4/SAG101 complex

ETI:

Effector-triggered immunity

GFP:

Green fluorescent protein

HMM:

Hidden Markov Model

HMMER:

Hidden Markov Model-based sequence alignment tool

ICS1:

Isochorismate synthase 1

iTOL:

Interactive Tree of Life

LRR:

Leucine-rich repeat

LS ½:

Half-strength Linsmaier and Skoog medium

MB:

Megabase pairs

NLR:

Nucleotide-binding domain and leucine-rich repeat receptor

NB-ARC:

Nucleotide-binding domain shared among APAF-1, R proteins and CED-4

NRC:

NLR required for cell death

ORF:

Open reading frame

PAD4:

Phytoalexin deficient 4

PBS:

Phosphate buffered saline

PDA:

Potato dextrose agar

pTM:

Predicted template modelling score

PTI:

PAMP-triggered immunity

PAMP:

Pathogen-associated molecular pattern

RGA:

Resistance gene analogs

RLK:

Receptor-like kinase

RLP:

Receptor-like protein

RPW8:

Resistance to powdery mildew 8

SAR:

Systemic acquired resistance

SA:

Salicylic acid

SEM:

Scanning electron microscopy

Sh-aLRT:

Shimodaira–Hasegawa approximate likelihood ratio test

SARD1:

SAR deficient 1

SNP:

Single nucleotide polymorphism

TIR:

Toll/interleukin-1 receptor domain

TNL:

TIR-type NLR

TPR:

Tetratricopeptide repeat

WAK:

Wall-associated kinase

WGA-AF:

Wheat germ agglutinin conjugated with AlexaFluor

X:

Variable (used in NLR nomenclature)

References

  1. Nitta JH, Schuettpelz E, Ramírez-Barahona S, Iwasaki W. An open and continuously updated fern tree of life. Front Plant Sci. 2022;13:909768.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ali Z, Tan QW, Lim PK, Chen H, Pfeifer L, Julca I, et al. Comparative transcriptomics in ferns reveals key innovations and divergent evolution of secondary cell wall. Nat Plants. 2025;11:1025–48.

    Article  Google Scholar 

  3. Schneider H, Schuetipelz E, Pryer KM, Cranfill R, Magallón S, Lupia R. Ferns diversified in the shadow of angiosperms. Nature. 2004;428:553–7.

    Article  CAS  PubMed  Google Scholar 

  4. Li FW, Brouwer P, Carretero-Paulet L, Cheng S, De Vries J, Delaux P-M, et al. Fern genomes elucidate land plant evolution and cyanobacterial symbioses. Nat Plants. 2018;4:460–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rahmatpour N, Kuo LY, Kang J, Herman E, Lei L, Li M, et al. Analyses of Marsilea vestita genome and transcriptomes do not support widespread intron retention during spermatogenesis. New Phytol. 2023;237:1490–4.

    Article  PubMed  Google Scholar 

  6. Marchant DB, Chen G, Cai S, Chen F, Schafran P, Jenkins J, et al. Dynamic genome evolution in a model fern. Nat Plants. 2022;8:1038–51.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Fang Y, Qin X, Liao Q, Du R, Luo X, Zhou Q, et al. The genome of homosporous maidenhair fern sheds light on the euphyllophyte evolution and defences. Nat Plants. 2022;8:1024–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhong Y, Liu Y, Wu W, Chen J, Sun C, Liu H, et al. Genomic insights into genetic diploidization in the homosporous fern Adiantum nelumboides. Genome Biol Evol. 2022;14:evac127.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Huang X, Wang W, Gong T, Wickell D, Kuo LY, Zhang X, et al. The flying spider-monkey tree fern genome provides insights into fern evolution and arborescence. Nat Plants. 2022;8:500–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tryon RM. A sketch of the history of fern classification. Ann Mo Bot Gard. 1952;39:255–62.

    Article  Google Scholar 

  11. Christenhusz MJM, Chase MW. Trends and concepts in fern classification. Ann Bot. 2014;113:571–94.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Donoghue PCJ, Harrison CJ, Paps J, Schneider H. The evolutionary emergence of land plants. Curr Biol. 2021;31:R1281–98.

    Article  CAS  PubMed  Google Scholar 

  13. Antonovics J. Pathogenic fungi in ferns and angiosperms: a comparative study. Am Fern J. 2020;110:79–94.

    Article  Google Scholar 

  14. Jones KA, Rayamajhi MB, Pratt PD, Van TK. First report of the pathogenicity of Colletotrichum gloeosporioides on invasive ferns, Lygodium microphyllum and L. japonicum, in Florida. Plant Dis. 2003;87:101–101.

    Article  CAS  PubMed  Google Scholar 

  15. Mali AM, Patil VB, Pise NM, Ade AB. First report of leaf spot caused by Fusarium sp. NFCCI 2882 on Angiopteris evecta: a king fern from Western Ghats, India. Plant Dis. 2016;100:646.

    Article  Google Scholar 

  16. Antonovics J. Fern family clade age and fungal pathogen diversity. Am Fern J. 2023;113:137–69.

    Article  Google Scholar 

  17. Cannon PF, Damm U, Johnston PR, Weir BS. Colletotrichum: current status and future directions. Stud Mycol. 2012;73:181–213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Okungbowa FI, Shittu H. Fusarium wilt: an overview. Environmental Research Journal. 2012;6:83–102.

    Google Scholar 

  19. Panstruga R, Moscou MJ. What is the molecular basis of nonhost resistance? Mol Plant Microbe Interact. 2020;33:1253–64.

    Article  CAS  PubMed  Google Scholar 

  20. Dodds PN. From gene-for-gene to resistosomes: Flor’s enduring legacy. Mol Plant Microbe Interact. 2023;36:461–7.

    Article  CAS  PubMed  Google Scholar 

  21. Jones JDG, Dangl JL. The plant immune system. Nature. 2006;444:323–9.

    Article  CAS  PubMed  Google Scholar 

  22. Bender KW, Zipfel C. Paradigms of receptor kinase signaling in plants. Biochemical Journal. 2023;480:835–54.

    Article  CAS  PubMed  Google Scholar 

  23. Contreras MP, Lüdke D, Pai H, Toghani A, Kamoun S. NLR receptors in plant immunity: making sense of the alphabet soup. EMBO Rep. 2023;24:e57495.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu Y, Zhang YM, Tang Y, Chen JQ, Shao ZQ. The evolution of plant NLR immune receptors and downstream signal components. Curr Opin Plant Biol. 2023;73:102363.

    Article  CAS  PubMed  Google Scholar 

  25. Shao Z-Q, Xue J-Y, Wu P, Zhang Y-M, Wu Y, Hang Y-Y, et al. Large-scale analyses of angiosperm nucleotide-binding site-leucine-rich repeat genes reveal three anciently diverged classes with distinct evolutionary patterns. Plant Physiol. 2016;170:2095–109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Castel B, El Mahboubi K, Jacquet C, Delaux P-M. Immunobiodiversity: conserved and specific immunity across land plants and beyond. Mol Plant. 2024;17:92–111.

    Article  CAS  PubMed  Google Scholar 

  27. Jones JDG, Staskawicz BJ, Dangl JL. The plant immune system: from discovery to deployment. Cell. 2024;187:2095–116.

    Article  CAS  PubMed  Google Scholar 

  28. Ngou BPM, Ding P, Jones JDG. Thirty years of resistance: zig-zag through the plant immune system. Plant Cell. 2022;34:1447–78.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dievart A, Gottin C, Peacuterin C, Ranwez V, Chantret N. Origin and diversity of plant receptor-like kinases. Annu Rev Plant Biol. 2020;71:131–56.

    Article  CAS  PubMed  Google Scholar 

  30. Furumizu C, Sawa S. Insight into early diversification of leucine-rich repeat receptor-like kinases provided by the sequenced moss and hornwort genomes. Plant Mol Biol. 2021;107:337–53.

    Article  CAS  PubMed  Google Scholar 

  31. Chia K-S, Kourelis J, Teulet A, Vickers M, Sakai T, Walker JF, et al. The N-terminal domains of NLR immune receptors exhibit structural and functional similarities across divergent plant lineages. Plant Cell. 2024;36:2491–511.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gumaelius L, Lahner B, Salt DE, Banks JA. Arsenic hyperaccumulation in gametophytes of Pteris vittata. a new model system for analysis of arsenic hyperaccumulation. Plant Physiol. 2004;136:3198–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Plackett ARG, Rabbinowitsch EH, Langdale JA. Protocol: genetic transformation of the fern Ceratopteris richardii through microparticle bombardment. Plant Methods. 2015;11:37.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Guyon K, Balagué C, Roby D, Raffaele S. Secretome analysis reveals effector candidates associated with broad host range necrotrophy in the fungal plant pathogen Sclerotinia sclerotiorum. BMC Genomics. 2014. https://doi.org/10.1186/1471-2164-15-336.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Redkar A, Gimenez Ibanez S, Sabale M, Zechmann B, Solano R, Di Pietro A. Marchantia polymorpha model reveals conserved infection mechanisms in the vascular wilt fungal pathogen Fusarium oxysporum. New Phytol. 2021;234:227–41.

    Article  PubMed  Google Scholar 

  36. Nelson JM, Hauser DA, Hinson R, Shaw AJ. A novel experimental system using the liverwort Marchantia polymorpha and its fungal endophytes reveals diverse and context-dependent effects. New Phytol. 2018;218:1217–32.

    Article  CAS  PubMed  Google Scholar 

  37. Crouch J, O’Connell R, Gan P, Buiate E, Torres MF, Beirn L, et al. The genomics of Colletotrichum. In: Genomics of plant-associated fungi: monocot pathogens. Springer, Berlin, Heidelberg; 2014. p. 69–102.

  38. Ameline-Torregrosa C, Cazaux M, Danesh D, Chardon F, Cannon SB, Esquerré-Tugayé MT, et al. Genetic dissection of resistance to anthracnose and powdery mildew in Medicago truncatula. Mol Plant Microbe Interact. 2008;21:61–9.

    Article  CAS  PubMed  Google Scholar 

  39. Jaulneau V, Cazaux M, Hoi JWS, Fournier S, Esquerré-Tugayé MT, Jacquet C, et al. Host and nonhost resistance in Medicago-Colletotrichum interactions. Mol Plant Microbe Interact. 2010;23:1107–17.

    Article  CAS  PubMed  Google Scholar 

  40. Moussart A, Onfroy C, Lesne A, Esquibet M, Grenier E, Tivoli B. Host status and reaction of Medicago truncatula accessions to infection by three major pathogens of pea (Pisum sativum) and alfalfa (Medicago sativa). Eur J Plant Pathol. 2007;117:57–69.

    Article  Google Scholar 

  41. Watkins JE, Mack MK, Mulkey SS. Gametophyte ecology and demography of epiphytic and terrestrial tropical ferns. Am J Bot. 2007;94:701–8.

    Article  PubMed  Google Scholar 

  42. Rose C, Damm U. Diversity of Colletotrichum species on strawberry (Fragaria × ananassa) in Germany. Phytopathol Mediterr. 2024;63:155–78.

    Article  CAS  Google Scholar 

  43. Johnson DA, Carris LM, Rogers JD. Morphological and molecular characterization of Colletotrichum nymphaeae and C. nupharicola sp. nov. on water-lilies (Nymphaea and Nuphar). Mycol Res. 1997;101:641–9.

    Article  CAS  Google Scholar 

  44. Perfect SE, Green JR. Infection structures of biotrophic and hemibiotrophic fungal plant pathogens. Mol Plant Pathol. 2001;2:101–8.

    Article  CAS  PubMed  Google Scholar 

  45. Barragan AC, Weigel D. Plant NLR diversity : the known unknowns of Pan-NLRomes a brief history of plant NLRs. Plant Cell. 2020;33:814–31.

    Article  Google Scholar 

  46. Rich MK, Vigneron N, Libourel C, Keller J, Xue L, Hajheidari M, et al. Lipid exchanges drove the evolution of mutualism during plant terrestrialization. Science. 2021;372:864–8.

    Article  CAS  PubMed  Google Scholar 

  47. Montgomery SA, Tanizawa Y, Galik B, Wang N, Ito T, Mochizuki T, et al. Chromatin organization in early land plants reveals an ancestral association between H3K27me3, transposons, and constitutive heterochromatin. Curr Biol. 2020;30:573-588.e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lang D, Ullrich KK, Murat F, Fuchs J, Jenkins J, Haas FB, et al. The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution. Plant J. 2018;93:515–33.

    Article  CAS  PubMed  Google Scholar 

  49. Hu R, Li X, Hu Y, Zhang R, Lv Q, Zhang M, et al. Adaptive evolution of the enigmatic Takakia now facing climate change in Tibet. Cell. 2023;186:3558-3576.e17.

    Article  CAS  PubMed  Google Scholar 

  50. Li C, Wickell D, Kuo L-Y, Chen X, Nie B, Liao X, et al. Extraordinary preservation of gene collinearity over three hundred million years revealed in homosporous lycophytes. Proc Natl Acad Sci. 2024;121:e2312607121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cui J, Zhu Y, Du H, Liu Z, Shen S, Wang T, et al. Chromosome-level reference genome of tetraploid Isoetes sinensis provides insights into evolution and adaption of lycophytes. Gigascience. 2022;12:giad079.

    Article  PubMed  Google Scholar 

  52. Banks JA, Nishiyama T, Hasebe M, Bowman JL, Gribskov M, DePamphilis C, et al. The selaginella genome identifies genetic changes associated with the evolution of vascular plants. Science. 2011;332:960–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang GQ, Liu KW, Li Z, Lohaus R, Hsiao YY, Niu SC, et al. The apostasia genome and the evolution of orchids. Nature. 2017;549:379–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, et al. The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Res. 2007;35(suppl_1):D883-7.

    Article  CAS  PubMed  Google Scholar 

  55. Bennetzen JL, Schmutz J, Wang H, Percifield R, Hawkins J, Pontaroli AC, et al. Reference genome sequence of the model plant Setaria. Nat Biotechnol. 2012;30:555–61.

    Article  CAS  PubMed  Google Scholar 

  56. Wei C, Yang H, Wang S, Zhao J, Liu C, Gao L, et al. Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality. Proc Natl Acad Sci U S A. 2018;115:E4151–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, et al. The arabidopsis information resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012;40:D1202–10.

    Article  CAS  PubMed  Google Scholar 

  58. Pecrix Y, Staton SE, Sallet E, Lelandais-Brière C, Moreau S, Carrère S, et al. Whole-genome landscape of Medicago truncatula symbiotic genes. Nat Plants. 2018;4:1017–25.

    Article  CAS  PubMed  Google Scholar 

  59. Arias-Baldrich C, Silva MC, Bergeretti F, Chaves I, Miguel C, Saibo NJM, et al. CorkOakDB—the cork oak genome database portal. Database. 2020;2020:baaa114.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Lehti-Shiu MD, Zou C, Shiu S-H. Origin, diversity, expansion history, and functional evolution of the plant receptor-like kinase/Pelle family. In: Receptor-like kinases in plants. 2012; https://doi.org/10.1007/978-3-642-23044-8_1 .

  61. Johanndrees O, Baggs EL, Uhlmann C, Locci F, Läßle HL, Melkonian K, et al. Variation in plant toll/interleukin-1 receptor domain protein dependence on ENHANCED DISEASE SUSCEPTIBILITY 1. Plant Physiol. 2023;191:626–42.

    Article  CAS  PubMed  Google Scholar 

  62. Urbach JM, Ausubel FM. The NBS-LRR architectures of plant R-proteins and metazoan NLRs evolved in independent events. Proc Natl Acad Sci. 2017;114:1063–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kourelis J, Sakai T, Adachi H, Kamoun S. Refplantnlr is a comprehensive collection of experimentally validated plant disease resistance proteins from the NLR family. PLoS Biol. 2021;19:e3001124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wang Y, Teng Z, Li H, Wang W, Xu F, Sun K, et al. An activated form of NB-ARC protein RLS1 functions with cysteine-rich receptor-like protein RMC to trigger cell death in rice. Plant Commun. 2023;4:100459.

    Article  CAS  PubMed  Google Scholar 

  65. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kim H, Prokchorchik M, Sohn KH. Investigation of RIN4 natural variants reveals a motif crucial for function and provides an opportunity to broaden NLR regulation specificity. Plant J. 2022;110:58–70.

    Article  CAS  PubMed  Google Scholar 

  67. Qin H, Cheng J, Han G-Z, Gong Z. Phylogenomic insights into the diversity and evolution of RPW8-NLRs and their partners in plants. Plant J. 2024;120:1032–46.

    Article  CAS  PubMed  Google Scholar 

  68. Ding P, Ding Y. Stories of salicylic acid: a plant defense hormone. Trends Plant Sci. 2020;25:549–65.

    Article  CAS  PubMed  Google Scholar 

  69. Wang L, Tsuda K, Truman W, Sato M, Nguyen LV, Katagiri F, et al. CBP60g and SARD1 play partially redundant critical roles in salicylic acid signaling. Plant J. 2011;67:1029–41.

    Article  CAS  PubMed  Google Scholar 

  70. Dong X. NPR1, all things considered. Curr Opin Plant Biol. 2004;7:547–52.

    Article  CAS  PubMed  Google Scholar 

  71. Zasloff M. Antimicrobial peptides of multicellular organisms. Nature. 2002;415:389–95.

    Article  CAS  PubMed  Google Scholar 

  72. Kulaeva O, Kliukova M, Afonin A, Sulima A, Zhukov V, Tikhonovich I. The role of plant antimicrobial peptides (AMPs) in response to biotic and abiotic environmental factors. Biol Commun. 2020;65:187–99.

    Article  Google Scholar 

  73. Zhu S. Evidence for myxobacterial origin of eukaryotic defensins. Immunogenetics. 2007;59:949–54.

    Article  CAS  PubMed  Google Scholar 

  74. Kirschner R, Lee PH, Huang Y-M. Diversity of fungi on Taiwanese fern plants: review and new discoveries. Taiwania. 2019;64:163–75.

    Google Scholar 

  75. Toome M, Ohm RA, Riley RW, James TY, Lazarus KL, Henrissat B, et al. Genome sequencing provides insight into the reproductive biology, nutritional mode and ploidy of the fern pathogen Mixia osmundae. New Phytol. 2014;202:554–64.

    Article  CAS  PubMed  Google Scholar 

  76. Stevenson JA. Ferns and fungi. Am Fern J. 1945;35:97–104.

    Article  Google Scholar 

  77. Kloepper JW, McInroy JA, Liu K, Hu CH. Symptoms of fern distortion syndrome resulting from inoculation with opportunistic endophytic fluorescent Pseudomonas spp. PLoS ONE. 2013;8:e58531.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Grenz K, Chia KS, Turley EK, Tyszka AS, Atkinson RE, Reeves J, et al. A necrotizing toxin enables Pseudomonas syringae infection across evolutionarily divergent plants. Cell Host Microbe. 2025;33:20-29.e5.

    Article  CAS  PubMed  Google Scholar 

  79. Chase AR, Miller JW, Jones JB. Leaf spot and blight of Asplenium nidus caused by Pseudomonas gladioli. Plant Dis. 1984;68:344–7.

    Article  Google Scholar 

  80. Sastry KS, Mandal B, Hammond J, Scott SW, Briddon RW. Encyclopedia of plant viruses and viroids. 2019.

  81. Sharma M, Kulshrestha S. Colletotrichum gloeosporioides: an anthracnose causing pathogen of fruits and vegetables. Biosciences Biotechnologiy Research Asia. 2015;12:1233–46.

    Article  Google Scholar 

  82. Caseys C, Shi G, Soltis N, Gwinner R, Corwin J, Atwell S, et al. Quantitative interactions: the disease outcome of Botrytis cinerea across the plant kingdom. G3: Genes, Genomes, Genetics. 2021;11:jkab1175.

    Article  Google Scholar 

  83. Brown JKM, Tellier A. Plant-parasite coevolution: bridging the gap between genetics and ecology. Annu Rev Phytopathol. 2011;49:345–67.

    Article  CAS  PubMed  Google Scholar 

  84. Knowlton A. Equisetum. Curr Biol. 2012;22:R388–90.

    Article  CAS  PubMed  Google Scholar 

  85. Brune T, Haas K. Equisetum species show uniform epicuticular wax structures but diverse composition patterns. AoB Plants. 2011;11:plr009.

    Google Scholar 

  86. Yeganegi M, Tabatabaei Yazdi F, Mortazavi SA, Asili J, Alizadeh Behbahani B, Beigbabaei A. Equisetum telmateia extracts: chemical compositions, antioxidant activity and antimicrobial effect on the growth of some pathogenic strain causing poisoning and infection. Microb Pathog. 2018;116:62–7.

    Article  CAS  PubMed  Google Scholar 

  87. Ma LQ, Komar KM, Tu C, Zhang W, Cai Y, Kennelley ED. A fern that hyperaccumulates arsenic. Nature. 2001;409:579.

    Article  CAS  PubMed  Google Scholar 

  88. Ellis DR, Gumaelius L, Indriolo E, Pickering IJ, Banks JA, Salt DE. A novel arsenate reductase from the arsenic hyperaccumulating fern Pteris vittata. Plant Physiol. 2006;141:1544–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Martinez AE, Chiocchio V, Em LT, Rodriguez MA, Godeas AM. Mycorrhizal association in gametophytes and sporophytes of the fern Pteris vittata (Pteridaceae) with Glomus intraradices. Rev Biol Trop. 2012;60:857–65.

    Article  PubMed  Google Scholar 

  90. Radhakrishnan GV, Keller J, Rich MK, Vernié T, Mbadinga Mbadinga DL, Vigneron N, et al. An ancestral signalling pathway is conserved in intracellular symbioses-forming plant lineages. Nat Plants. 2020;6:280–9.

    Article  PubMed  Google Scholar 

  91. Watkins JE, Mack MC, Sinclair TR, Mulkey SS. Ecological and evolutionary consequences of desiccation tolerance in tropical fern gametophytes. New Phytol. 2007;176:708–17.

    Article  PubMed  Google Scholar 

  92. Proctor MCF, Tuba Z. Poikilohydry and homoihydry: antithesis or spectrum of possibilities? New Phytol. 2002;156:327–49.

    Article  PubMed  Google Scholar 

  93. Farrar DR, Dassler C, Watkins JE, Skelton C. Gametophyte ecology. In: Ranker T, Haufler C, editors. Biology and evolution of ferns and lycophytes. Cambridge University Press; 2008. p. 222–56.

    Chapter  Google Scholar 

  94. Krieg CP, Chambers SM. The ecology and physiology of fern gametophytes: a methodological synthesis. Appl Plant Sci. 2022;10:e11464.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Tausz M, Hietz P, Briones O. The significance of carotenoids and tocopherols in photoprotection of seven epiphytic fern species of a Mexican cloud forest. Aust J Plant Physiol. 2001;28:775–83.

    CAS  Google Scholar 

  96. Fernández-Marín B, Alfaro SJA, Becerril JM, García-Plazaola JI. Do fern gametophytes have the capacity for irradiance acclimation? Biol Plant. 2012;56:351–6.

    Article  Google Scholar 

  97. Sato T, Sakai A. Freezing resistance of gametophytes of the temperate fern, Polystichum retroso-paleaceum. Can J Bot. 1980;58:1144–8.

    Article  Google Scholar 

  98. DeMell A, Alvarado V, Scholthof HB. Molecular perspectives on age-related resistance of plants to (viral) pathogens. New Phytol. 2023;240:80–91.

    Article  CAS  PubMed  Google Scholar 

  99. Develey-Rivière MP, Galiana E. Resistance to pathogens and host developmental stage: a multifaceted relationship within the plant kingdom. New Phytol. 2007;175:405–16.

    Article  PubMed  Google Scholar 

  100. Mitoi EM, Aldea F, Helepciuc FE, Ciocan AG, Frum A, Popescu DI, et al. The production of useful phenol compounds with antioxidant potential in gametophytes and sporophytes from in vitro cultures in four ornamental ferns species. Horticulturae. 2024;10:799.

    Article  Google Scholar 

  101. Guha P, Mukhopadhyay R, Gupta K. Antifungal activity of the crude extracts and extracted phenols from gametophytes and sporophytes of two species of Adiantum. Taiwania. 2005;50:272–83.

    Google Scholar 

  102. San Francisco M, Cooper-Driver G. Anti-microbial activity of phenolic acids in Pteridium aquilinum. Am Fern J. 1984;74:87–96.

    Article  Google Scholar 

  103. Page CN. Ecological strategies in fern evolution: a neopteridological overview. Rev Palaeobot Palynol. 2002. https://doi.org/10.1016/S0034-6667(01)00127-0.

    Article  Google Scholar 

  104. Strullu-Derrien C, Goral T, Spencer ART, Kenrick P, Catherine Aime M, Gaya E, et al. A fungal plant pathogen discovered in the Devonian Rhynie chert. Nat Commun. 2023;14:7932.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Upson JL, Zess EK, Białas A, Wu C-H, Kamoun S. The coming of age of EvoMPMI: evolutionary molecular plant–microbe interactions across multiple timescales. Curr Opin Plant Biol. 2018;44:108–16.

    Article  PubMed  Google Scholar 

  106. Delaux P-M, Schornack S. Plant evolution driven by interactions with symbiotic and pathogenic microbes. Science. 2021;371:eaba6605.

    Article  CAS  PubMed  Google Scholar 

  107. Locci F, Wang J, Parker JE. TIR-domain enzymatic activities at the heart of plant immunity. Curr Opin Plant Biol. 2023;74:102373.

    Article  CAS  PubMed  Google Scholar 

  108. Jeon HW, Iwakawa H, Naramoto S, Herrfurth C, Gutsche N, Schlüter T, et al. Contrasting and conserved roles of NPR pathways in diverged land plant lineages. New Phytol. 2024;243:2295–310.

    Article  CAS  PubMed  Google Scholar 

  109. Ding P, Rekhter D, Ding Y, Feussner K, Busta L, Haroth S, et al. Characterization of a pipecolic acid biosynthesis pathway required for systemic acquired resistance. Plant Cell. 2016;28:2603–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Ngou BPM, Jones JDG, Ding P. Plant immune networks. Trends Plant Sci. 2022;27:255–73.

    Article  CAS  PubMed  Google Scholar 

  111. van Esse HP, Reuber TL, van der Does D. Genetic modification to improve disease resistance in crops. New Phytol. 2019;225:70–86.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Yotsui I, Matsui H, Miyauchi S, Iwakawa H, Melkonian K, Schlüter T, et al. LysM-mediated signaling in Marchantia polymorpha highlights the conservation of pattern-triggered immunity in land plants. Curr Biol. 2023;33:3732-3746.e8.

    Article  CAS  PubMed  Google Scholar 

  113. Teyssier E, Grat S, Landry D, Ouradou M, Rich MK, Fort S, et al. A plant lysin motif receptor-like kinase plays an ancestral function in mycorrhiza. Proc Natl Acad Sci. 2025;122:e2426063122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Yan Y, Mellüh J, Mecchia MA, Jeon H-W, Melkonian K, Holzberger C, et al. Conserved role of the SERK–BIR module in development and immunity across land plants. Current Biology. 2025;35:2202–2211.E7.

  115. Dou R, Mahboubi K El, Tanney CAS, Chu J, Bredow M, Gallo MCR, et al. Conservation of an immune homeostasis module in land plants. bioRxiv. 2024; https://doi.org/10.1101/2024.10.01.616128.

  116. Chu J, Monte I, DeFalco TA, Köster P, Derbyshire P, Menke FLH, et al. Conservation of the PBL-RBOH immune module in land plants. Curr Biol. 2023;33:1130-1137.e5.

    Article  CAS  PubMed  Google Scholar 

  117. Adachi H, Kamoun S. NLR receptor networks in plants. Essays Biochem. 2022;66:541–9.

    Article  CAS  PubMed  Google Scholar 

  118. Goh FJ, Huang CY, Derevnina L, Wu CH. NRC immune receptor networks show diversified hierarchical genetic architecture across plant lineages. Plant Cell. 2024;36:3399–418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Wu C-H, Derevnina L, Kamoun S. Receptor networks underpin plant immunity. Science. 2018;360:1300–1.

    Article  CAS  PubMed  Google Scholar 

  120. Feehan JM, Castel B, Bentham AR, Jones JDG. Plant NLRs get by with a little help from their friends. Curr Opin Plant Biol. 2020;56:99–108.

    Article  CAS  PubMed  Google Scholar 

  121. Castel B, Ngou PM, Cevik V, Kim DS, Yang Y, Ding P, et al. Diverse NLR immune receptors activate defence via the RPW8-NLR NRG1. New Phytol. 2019;222:966–80.

    Article  CAS  PubMed  Google Scholar 

  122. Purdy LH. Sclerotinia sclerotiorum: history, diseases and symptomatology, host range, geographic distribution, and impact. Phytopathology. 1979;69:875–80.

    Article  Google Scholar 

  123. Gaulin E, Jacquet C, Bottin A, Dumas B. Root rot disease of legumes caused by Aphanomyces euteiches. Mol Plant Pathol. 2007;8:539–48.

    Article  PubMed  Google Scholar 

  124. Li P, Quan X, Jia G, Xiao J, Cloutier S, You FM. RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants. BMC Genomics. 2016;17:852.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, Von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Kalyaanamoorthy S, Minh BQ, Wong TKF, Von Haeseler A, Jermiin LS. Modelfinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.

  129. Minh BQ, Nguyen MAT, Von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to the Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.5572369328961167E12) for providing computing resources.

Funding

This study was supported by the Fédération de Recherche Agrobiosciences, Interactions et Biodiversité, the Laboratoires d’Excellence (LABEX) TULIP (ANR-10-LABX-41). This work was supported by the Agence Nationale de la Recherche (ANR LEVEL-UP ANR-21-CE20-0010-01) to C.J and by a funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 101001675—ORIGINS) to P.-M.D.

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Authors and Affiliations

  1. Laboratoire de Recherches en Sciences Végétales, CNRS, Université Toulouse III Paul Sabatier, Institut National Polytechnique Toulouse, Castanet-Tolosan, 31320, France

    Baptiste Castel, Madeleine Baker, Jean Keller, Maxime Bonhomme, Pierre-Marc Delaux & Christophe Jacquet

  2. Fédération de Recherche 3450, Plateforme Imagerie, Pôle de Biotechnologie Végétale, Castanet-Tolosan , 31320, France

    Yves Martinez

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  1. Baptiste Castel
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  2. Madeleine Baker
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  3. Jean Keller
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  4. Yves Martinez
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  5. Maxime Bonhomme
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  6. Pierre-Marc Delaux
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  7. Christophe Jacquet
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Contributions

B.C., C.J. and P.M.D. designed the research; B.C., MB1 and YM conducted the experiments; B.C., J.K. and MB2 analysed the data; B.C. and C.J. wrote the manuscript with input from all authors. All authors read and approved the final manuscript.

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Correspondence to Christophe Jacquet.

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Supplementary Information

12915_2025_2413_MOESM1_ESM.xlsx

Additional file 1: Supplemental tables: Table S1: List of microbes, fern and control plant species genomes used in this study; Table S2: List of RLKs used in this study; Table S3: List of NLRs used in this study; Table S4: Presence/absence of known immune genes.

12915_2025_2413_MOESM2_ESM.docx

Additional file 2: Supplemental figures: Fig. S1: Phylogeny of ferns and filamentous microbes tested in this study; Fig. S2: Wordclouds of unclassified fern RLK/RLP; Fig. S3: Predicted structures of NLR N-terminal domains.

Additional file 3: Supplemental Method: Detailed Methods for NLR and RLK/RLP Mining.

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Castel, B., Baker, M., Keller, J. et al. Exploring fern pathosystems and immune receptors to bridge gaps in plant immunity. BMC Biol 23, 301 (2025). https://doi.org/10.1186/s12915-025-02413-6

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