Integrative approach to identify robust Pattern Recognition Receptors in Eucalyptus grandis: novel candidates for disease resistance

Integrative approach to identify robust Pattern Recognition Receptors in Eucalyptus grandis: novel candidates for disease resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative approach to identify robust Pattern Recognition Receptors in Eucalyptus grandis: novel candidates for disease resistance Yesica Elisa Gonzalez, Natalia Cristina Aguirre, Carla Valeria Filippi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8407889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Pattern-recognition receptors (PRRs) initiate plant pattern-triggered immunity (PTI), encompassing receptor-like kinases (RLKs) and receptor-like proteins (RLPs). Eucalyptus grandis , an economically important species worldwide, is a long-lived organism that faces multiple disease pressures. The deployment of PRR-based breeding tools offers a route to broad, non-race-specific resistance that can remain effective across outbreaks. The objective of this work was to identify and characterise the PRR repertoire of E. grandis using public multi-omics data. Two state-of-the-art predictors were compared with a multi-species curation and a meta-analysis was conducted compiling transcriptomic data under biotic stress. A set of 730 PRR candidates were consistently identified across three sources (~ 1.6% of protein-coding genes), of which 283 were differentially expressed (PRR-DEGs). A PFAM domain-based scheme was applied to standardise classification across tools. RLKs (TM-K-LRR) predominated over RLPs (TM-LRR), while many carried malectin or malectin-like ectodomains. PRR genes were unevenly distributed across the genome: chromosome 6 had the highest count and the densest clustering. Gene-family expansion appeared mainly driven by duplication, with extensive tandem arrays supported by segmental, proximal and dispersed events. Gene Ontology and cis-elements annotations in PRR/PRR-DEGs showed significant enrichment in terms related to cell cycle development, hormonal regulation and stress response. We proposed a catalogue of 16 PRR which resulted in DEGs against at least three pathogens, suggesting their broad spectrum and robustness. Most of them presented orthologues with cross-taxon evidence of defence roles. This study delineates novel multi-pathogen candidate PRR genes, providing valuable information to assist Eucalyptus breeding programs. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The Eucalyptus genus, which belongs to the Myrtaceae family, is native to Australia, New Guinea, Timor, Indonesia and the Philippines, and comprises over 700 species and subspecies (Ladiges et al., 2003 ; Thornhill et al., 2019 ). Due to traits such as rapid growth, short rotation cycles, year-round harvesting, and high adaptability and productivity (Gullón et al., 2020 ), Eucalyptus has become widely cultivated across tropical, subtropical, and temperate regions worldwide, covering over 20 million hectares. It plays a key role in the global hardwood forestry industry, with applications in timber, pulp and paper and biofuels. Among the cultivated species, Eucalyptus grandis stands out for its fast growth and early yield, making it particularly valuable in commercial plantations (Naidoo et al., 2014 ). However, despite its economic importance, E. grandis is highly susceptible to several biotic and abiotic stresses that compromise growth and wood quality. Biotic threats include myrtle rust caused by Austropuccinia psidii (syn. Puccinia psidii ); the stem canker pathogen Chrysoporthe austroafricana ; root rot pathogen Phytophthora cinnamomi ; leaf blight caused by Calonectria spp. ; and pests such as Leptocybe invasa (Naidoo et al., 2014 ). These threats have intensified in recent decades due to climate change, which promotes disease emergence by extending infection windows, increasing pathogen virulence, and expanding host ranges (Younessi-Hamzekhanlu & Gailing, 2022 ). Current management strategies include the use of tolerant Eucalyptus genotypes and integrated pest management, such as biological control for L. invasa (Naidoo et al., 2014 ). However, these measures often fall short, underscoring the need for improved resistance strategies through breeding and biotechnological approaches (Wingfield et al., 2013 ). In this context, understanding the molecular mechanisms of plant immunity is essential for developing more resilient varieties. A major breakthrough for Eucalyptus research was the sequencing of the E. grandis genome, with > 94% of the genome anchored to chromosomes, which has enabled omics-based studies and large-scale analysis of gene families involved in defence responses (Myburg et al., 2014 ; Zwart et al., 2017 ). This genomic resource has established E. grandis as a genomic model for studying the defence mechanisms of woody perennials, benefiting not only this species but also other members of the Myrtaceae family (Christie et al., 2015 ). In plants, the first line of inducible immune defence, known as pattern-triggered immunity (PTI), is initiated when pattern recognition receptors (PRRs) located on the plasma membrane detect pathogen-, microbe-, or damage-associated molecular patterns (PAMPs, MAMPs, or DAMPs) (Jones & Dangl, 2006 ; Yuan et al., 2021 ). Two main classes of PRRs are recognised: receptor-like kinases (RLKs) and receptor-like proteins (RLPs), which play a pivotal role in sensing pathogens and maintaining cellular homeostasis (Silva et al., 2022 ). RLKs are characterised by a typical tripartite structure: an extracellular domain (ectodomain) at the amino-terminal (N-terminal) region, a transmembrane domain (TM), and an intracellular serine/threonine kinase domain at the carboxy-terminal (C-terminal) region. The ectodomain, responsible for ligand perception, can include leucine-rich repeats (LRR), the lysine motif (LysM), the lectin domain (Lec), and/or an epidermal growth factor-like domain (EGF) (Liu et al., 2023 ). This ectodomain interacts with PAMPs/MAMPs or DAMPs, while the kinase domain activates intracellular signalling through phosphorylation (Silva et al., 2022 ). RLKs are grouped into families based on ectodomain structure, with LRR-RLKs being the most abundant, followed by lectin-type RLKs (Minkoff et al., 2017 ). RLPs, in contrast, share similar ectodomains with RLKs but lack the intracellular kinase domain. Their signalling relies on forming complexes with RLKs or with receptor-like cytoplasmic kinases (RLCKs), which transduce the signal into the cell (Liu et al., 2023 ). The functional characterisation of RLPs has proven more challenging, as their variable domains and lack of conserved kinase motifs hinder identification through standard homology-based methods (Silva et al., 2022 ). Given the importance of PRRs in the early stages of immune response, their identification is a crucial step in the genetic improvement of disease-resistant plants. Although experimental approaches can confirm PRR function, they are time-consuming and resource-intensive. In this regard, bioinformatics tools for in silico prediction and classification of PRRs have become increasingly valuable to accelerate the discovery and annotation process (Del Hierro et al., 2021 ). This study addresses the identification and characterisation of PRR genes involved in PTI in E. grandis by integrating computational predictions with functional analysis. Two state-of-the-art prediction tools: RRGPredictor (Santana Silva & Micheli, 2020 ) and DRAGO3 (Calle García et al., 2022 ) were employed, and their outputs were compared with a recent large-scale PRR dataset derived from 350 plant species (Ngou et al., 2022 ). In addition, we conducted a multi-layered characterisation that included differential expression meta-analysis, updated functional annotation, phylogenetic and evolutionary analysis, and promoter region examination to identify cis-regulatory elements (CREs). This integrative approach aims to shed light on the diversity, structure, and potential regulatory mechanisms of PRRs in E. grandis , contributing to a better understanding of defence responses in woody species. 2. Materials and Methods 2.1 Genomic, proteomic, and transcriptomic resources This study used publicly available omics data for E. grandis, retrieved from the Phytozome database 1 (Goodstein et al., 2012). The resources included genome, transcriptome, and proteome data corresponding to version 2.0 of the E. grandis reference genome (Myburg et al., 2014; Bartholomé et al., 2015), which incorporates structural and functional annotations. 2.2 Identification of PRR genes To identify PRR genes involved in plant defence, we implemented three complementary bioinformatics strategies: 1) DRAGO3 (Disease Resistance Analysis and Gene Orthology version 3; Calle García et al., 2022) was used to scan the E. grandis v2.0 proteome; 2) RRGPredictor (Santana Silva & Micheli, 2020) was applied after analysing the proteome with InterProScan5 (Jones et al., 2014); and 3) filtering the E. grandis only PRR gene set reported by Ngou et al. (2022). As DRAGO3 and RRGPredictor permit the annotation of resistance genes in general, we filtered only PRRs by retaining proteins with transmembrane (TM) domains predicted by TMHMM 2.0 (Krogh et al., 2001) and without nucleotide binding site (NBS) domains, which are specific of R genes (Christie et al., 2015). Redundancy among predicted sequences was reduced using CD-HIT v4.7 (Li & Godzik, 2006), with a 90% sequence identity threshold, retaining only the longest protein. Genes consistently predicted across the three strategies were defined as putative PRRs. To harmonise classification across tools, we established a unified domain-based caracterisation using PFAM domains identified via InterProScan5, with e-values <0.001 and an alignment coverage greater than 50%. Functionally related domains, such as various leucine-rich repeats (LRRs) and kinase-related motifs, were grouped under "LRR" and "K", respectively. 2.3 Meta-Analysis of RNAseq data In order to assess the expression dynamics of candidate PRRs, a meta-analysis of RNA-seq datasets derived from Eucalyptus species challenged with pathogens was conducted, encompassing studies published between 2014 ( E. grandis genome release) and March 2023. Only studies with publicly available raw RNA-seq data were considered, including responses to C. austroafricana (Mangwanda et al., 2015), L. invasa (Oates et al., 2015), A. psidii (Santos et al., 2020), and Ralstonia solanacearum (Xiaohui et al., 2022). Datasets were obtained from the National Center for Biotechnology Information repository (NCBI: PRJNA280236 2 ; PRJNA305347 3 and PRJNA588626 4 ) and the Genome Sequence Archive (PRJCA006666 5 ). Reads were quality-filtered using Trimmomatic v0.36 (Bolger et al., 2014), removing adapters and discarding sequences shorter than 80 bp or with average Phred scores below 30. Transcript-level quantification was performed with Salmon v0.12.0 (Patro et al., 2017) in quasi-mapping mode, enabling GC bias correction. The mapping index was constructed using the E. grandis reference transcriptome (v2.0). Gene-level abundance estimates were summarised using Tximport v1.26.1 (Soneson et al., 2015). Differential gene expression analysis was conducted using DESeq2 v1.38.3 (Love et al., 2014), employing its default pipeline based on a negative binomial regression model. Exploratory analyses included principal component analysis (PCA) and volcano plots, both of which were visualized using ggplot2 v3.4.2 (Wickham H., 2016). 2.4 Phylogenetic Analysis of PRRs Putative PRR protein sequences were aligned using ClustalOmega (Sievers & Higgins, 2021). Phylogenetic trees were constructed using the Neighbor-Joining (NJ) method based on pairwise sequence identity matrices. The R packages "msa v1.30.1" (Bodenhofer et al., 2015), "seqinr v4.2-30" (Charif & Lobry, 2007), and "ape v5.7-1" (Paradis & Schliep, 2019) were used for alignment, distance calculation and tree construction, respectively. PRRs were classified into DEGs and non-DEGs based on the RNA-seq meta-analysis results. 2.5 New functional annotation and Orthology Putative PRR proteins were functionally annotated using PANNZER (Protein ANNotation with Z-scoRE, Törönen & Holm, 2022), and results were compared with the E. grandis v2.0 genome annotation. Gene Ontology (GO) enrichment analysis was performed using TopGO v2.50.0 (Alexa & Rahnenfuhrer, 2024), applying the 'weight01' algorithm to explore overrepresented Biological Process and Molecular Function terms among PRR annotated by both methodologies. Orthologs in Arabidopsis thaliana were identified using PLAZA 5.0 Dictos 6 , enabling comparative insights across species (Van Bel et al., 2022). 2.6 Gene duplication analysis Gene duplication events were identified with McScanX (Wang et al., 2012), using the amino acid sequences of PRRs. Duplicated gene pairs and their chromosomal locations were visualized using TBtools v.2.119 (Chen et al., 2023). Non-synonymous (Ka) and synonymous (Ks) substitution rates were estimated for duplicated gene pairs based on coding sequence (CDS) alignments, applying the Nei-Gojobori method (Nei & Gojobori, 1986). 2.7 Analysis of Cis-Regulatory Elements Sequences 1,500 bp upstream of the start codon (ATG) of putative PRR genes were retrieved to identify cis-regulatory elements (CREs) in the promoter regions, using PlantCare 7 (Lescot et al., 2002). CREs associated with stress responses were visualised using ggplot2 and their enrichment in DEGs versus non-DEG PRRs was evaluated via Fisher's exact test. 3. Results 3.1 Identification of genes involved in defense mechanisms and PRR The analysis of the E. grandis proteome (46,280 sequences) using DRAGO3 identified 4,976 proteins potentially involved in defence mechanisms. Filtering for proteins containing at least one transmembrane domain (TM) reduced this number to 4,186 proteins. Exclusion of proteins with NBS domains resulted in 3,258 putative PRR proteins. A redundancy reduction step using CD-HIT yielded 2,831 unique putative PRR proteins, corresponding to 2,793 genes. Using a similar workflow, RRGPredictor identified 3,151 putative resistance proteins without NBS motifs. After applying the TM domain filter and CD-HIT redundancy reduction, a total of 1,271 putative PRR proteins were retained, corresponding to 1,261 genes. Additionally, 810 PRR genes and proteins were compiled from the study by Ngou et al. (2022), which defined 19 PRR categories. These included 300 genes as RLK-LRR-XII, 224 as RLP-LRR, 65 as RLK-LRR-XI, 51 as RLK-LRR-VIII, 38 as RLK-LRR-III, 25 each as RLK-LysM and RLK-LRR-I, and smaller numbers in other RLK-LRR subfamilies (Figure 1.A). Altogether, a total of 2,835 PRR genes were identified by at least one of the three approaches: 2,793 by DRAGO3 (2,800 proteins), 1,261 by RRGPredictor (1,261 proteins), and 810 by Ngou et al. (2022) (810 proteins). DRAGO3 classified these gene products into 19 categories: 1,016 as KIN, 588 as RLK, 581 as RLP, 341 as LECRK, 77 as CK, 42 as TRAN, 39 as T, 35 as LEC, 29 as LYK, 20 as CLK, 7 as LYP, 7 as CL, 5 as LYS, 4 as TL, 4 as C, 2 as CLECRK, and 1 each as CT, CLYSK, and UNDEFINED (Figure 1.A). RRGPredictor assigned these genes to six categories: 802 as UNKNOWN, 269 as RLK, 146 as RLK-GNK2, 30 as MLO, 8 as T, and 6 as RLP (Figure 1.A). 3.1.1 Recategorization of PRR proteins consistently identified by three strategies A comparison among the three strategies revealed 730 genes identified consistently across all datasets (Supplementary Information 1). These were considered high-confidence PRRs. DRAGO3 classified them into RLK, RLP, CLK, CL, and UNDEFINED; RRGPredictor into UNKNOWN and RLK; and Ngou et al. (2022), into various RLK-LRR and RLP-LRR subgroups. The observed disparity in categorization criteria among the three strategies prompted the development of a unified classification system for PRR proteins and genes. To achieve this, we analysed the presence and absence of protein domains based on PFAM domain annotations from InterProScan5. Only domains passing the filtering criteria (e-value < 0.001 and alignment coverage greater than 50%) were considered. According to the PFAM database (Mistry et al., 2021), the following domains in different combinations were present in the 730 PRRs: TM domains, K endodomains (PFAM: “PF00069” and “PF07714”) and among the ectodomains LRR (PFAM: “PF08263”, “PF12799”, and “PF13855”), Malectin (PFAM: “PF11721”), Malectin-like (PFAM: “PF12819”). Consequently, the 730 putative PRR proteins were grouped into 11 new categories (Figure 1.B). 3.2 Meta-Analysis of RNAseq A meta-analysis of four published transcriptomic studies identified 12,157 differentially expressed genes (DEGs). Exploratory analyses, including PCA and volcano plots, are shown in Supplementary Information 2, 3, 4, 5. Cross-referencing these DEGs with the 730 putative PRRs revealed 283 genes with evidence of pathogen-responsive expression, hence defined as PRR-DEGs. Pathogen-specific expression patterns were observed. A total of 70, 147, 106 and 75 PRR-DEGs were detected for A. psidii (rust), R. solanacearum (bacterial wilt), C. austroafricana (canker) and L. invasa (gall wasp), respectively. Besides, 28, 90, 47 and 25 were unique for each pathogen, respectively. On the other hand, among the PRR-DEGs, approximately 41% were consistently downregulated and 34% consistently upregulated. The remaining 25% showed variable directionality depending on the pathogen, treatment, or genetic background. Additionally, 18 genes responded to three or all pathogens, suggesting potential broad-spectrum (Figure 2). 3.3 Phylogenetic analysis of PRR and PRR-DEG To explore relationships between domain composition and functional roles, Neighbor-Joining phylogenetic trees were generated for both the full set of 730 PRRs (Supplementary Information 6) and the subset of 283 PRR-DEGs. Proteins were colour-coded by the new 11 categories, and by their differential expression (or not) against specific pathogens, respectively. An integrated overview tree summarises these results (Figure 3). Initial analysis revealed clear clustering patterns between RLKs and RLPs. Among the grouped sequences, the TM-LRR category accounted for approximately 25% of the total, notable for its well-defined clustering. Similarly, the various categories within RLKs also showed distinctly defined clusters. Regarding the pathogen response of PRR-DEGs, it was observed that about 30% of the RLPs were DEGs and only exhibited evidence of response to one or two pathogens. Conversely, approximately 40% of the RLK-related categories were DEGs, showing responses from one to four pathogens. Furthermore, RLKs with malectin domains predominantly responded to more than one pathogen. 3.3.1 Chromosome distribution and clustering of PRR and PRR-DEG An analysis of gene distribution across chromosomes identified 663 putative PRR genes on the eleven chromosomes and 67 at the scaffold level. Chromosomes 6 and 4 harboured the highest and the lowest number of PRR genes, with 130 and 29 respectively. This finding indicates no direct correlation between the number of PRR genes and chromosome size, as chromosomes 3 and 10 are the longest and the shortest, respectively (Figure 4). Additionally, the proportion of PRR genes per chromosome ranged from 1% on chromosomes 2 and 8 to 3.2% on chromosome 6 (Table 1). Moreover, PRR genes were classified as within a "Cluster", as genes located less than 200,000 base pairs apart from each other (Holub, 2001), or "Singleton". This analysis revealed 462 genes in clusters and 201 as singletons across the eleven chromosomes. Notably, the majority of clusters were found on chromosome 6, with the minority on chromosome 2. Among the 283 PRR-DEGs, 265 were identified on the eleven chromosomes and 18 at the scaffold level (Figure 4). Additionally, chromosomes 10 and 11 exhibited a >50% higher proportion of PRR-DEGs relative to the overall PRR distribution (Table 1). Tab. 1 Summary of the pattern recognition receptors (PRR) genes identified on the eleven chromosomes and scaffolds. Chromosome: chromosome number; Size in bp: size of the chromosome in base pairs; No. of total genes: number of total genes per chromosome; No. of PRR: number of putative PRR genes identified by the three strategies; % PRR: percentage of PRR is represented based on the total number of coding genes on the chromosome;. No. of differentially expressed gene (DEG): the number of PRR-DEG genes per chromosome; No. of Clustered genes: the number of PRR genes that are clustered together (distance < 200 kpb); No. of cluster: the number of clusters per chromosome; No. of Singleton: the number of genes that are not clustered together; No. of Singleton: the number of genes that are not clustered together. Chromosome Size in bp N° of total genes N° of PRR % PRR N° of DEG N° of Clustered genes N° of Cluster N° of Singleton 1 45,527,262 2,698 57 2.1 27 46 9 11 2 60,273,285 3,287 33 1 16 12 5 21 3 85,001,648 3,513 103 3 30 78 12 25 4 41,674,560 2,231 29 1.3 1 18 4 11 5 77,196,103 3,306 63 1.9 21 48 14 15 6 58,190,708 4,054 130 3.2 49 109 23 21 7 55,515,396 2,854 67 2.3 24 44 9 23 8 73,422,429 4,087 41 1 19 23 8 18 9 39,799,183 2,398 45 1.9 15 27 10 18 10 38,249,343 2,663 43 1.6 24 29 9 14 11 45,397,251 3,030 52 1.7 39 28 8 24 Scaffold - 2,228 67 - 18 - - - Total 640,439,468 36,349 730 2 283 462 111 201 3.4 New functional annotation and GO enrichment Using the described methodology, GO enrichment graphs for Biological Processes and Molecular Function were generated utilising both the original annotation ( E. grandis v2.0 reference genome) and the new annotation (with PANNZER tool) of PRR genes. It was generally observed that the new annotation revealed a greater number of significant GO enriched terms that were related to various biotic stresses. For "Biological Processes", the original annotation of PRRs only yielded the significantly enriched term "protein phosphorylation". Although the phosphorylation process corresponds to the kinase domains of the RLKs, no other GO terms related to the plant Inducible Immunity System were assigned. In contrast, the new annotation identified not only the terms "phosphorylation" (go id: 0016310) and "protein phosphorylation" (go id: 0006468) but also significant terms specific to responses to various biotic stresses, such as "defence response" (go id: 0006952), "defence/response to other organism" (go id: 0098542), and terms specifically related to fungal response like "defence/response to fungus" (go id: 0050832). Additionally, terms related to PTI and PRR responses, such as "transmembrane receptor protein serine/threonine kinase" (go id: 0007178), were identified. (Figure 5.A) When all putative PRRs and PRR-DEGs were compared based on the new annotation, terms such as “defence response”, “response to other organisms” and “response to fungus” remained significant and abundant among the PRR-DEGs. Notably, the term “positive regulation of defence response” also emerged as significant (Figure 5.B). With regard to the orthologs identified in A. thaliana , their functional annotation was found to be consistent with defence responses to a range of stresses. Regarding “Molecular Function”, although the most significantly enriched and abundant terms corresponded to general molecular functions, other significant terms were also observed that could be specifically associated with functions involved in PTI. These included receptor-related functions such as “signaling receptor activity” (go id: 0038023), “receptor serine/threonine kinase binding” (go id: 0033612), “protein serine/threonine kinase activity” (go id: 0004674), and “transmembrane signaling receptor activity” (go id: 0004888) (Supplementary Information 7). 3.5 Analysis of Cis-Regulatory Elements The analysis of promoter regions located 1500 bp upstream of the coding region was conducted to examine cis-regulatory elements (CREs) in PRR genes. Elements related to cell cycle development, hormonal regulation, and stress response were identified (Figure 6.A). Among these, some genes exhibited cis-elements associated with hormonal responses, such as to methyl jasmonate (MeJA), a derivative of jasmonic acid, abscisic acid (ABA), and other phytohormones like gibberellin, auxin, and salicylic acid (SA). Other stress-related elements included regulatory elements responsive to light, drought, low temperatures, anaerobic conditions, defence, and pathogen response. Furthermore, the enrichment analysis of CREs revealed that, of a total of 26 CREs significantly enriched (p-value < 0.05) in PRR-DEGs compared to those without DEG evidence to date, the most significantly enriched were elements responsive to auxin and drought (Supplementary Information 8). To delve into potential transcriptional regulatory mechanisms of PRRs, an additional analysis was conducted focused on PRR-DEGs showing responses to 3 and 4 pathogens. The results showed enriched cis-elements linked to stress responses and hormonal regulation. Each gene exhibited a unique configuration of these elements, with a predominance of those responsive to light and MeJA (Figure 6.B). However, less frequent but notable elements were also identified, such as those associated with pathogen defence, drought and low temperature stress responses, as well as elements associated with anaerobic and anoxic stress responses. 3.6 Gene duplication Using the MCScanX software, PRR classification based on duplication mechanisms revealed 662 genes categorised into four groups: 1) 122 segmental duplicates or related to whole-genome duplication (WGD), where paralogs are found in collinear blocks within the same species, relating to more ancestral duplications; 2) 128 dispersed duplicates, characterised by a lack of proximity between paralogs on chromosomes or conservation of synteny; 3) 318 tandem duplicates are noted for their location side by side and relation to more recent duplication events in genome evolution; 4) 94 proximal duplicates, where paralogs are close together but separated by fewer than 20 genes. Based on the segmental duplicates identified by MCScanX (74 pairs), the nonsynonymous to synonymous substitution ratio (Ka/Ks) was calculated. For 47 gene pairs, the Ka/Ks ratio was less than 1 (ranging from 0.051 to 0.597), indicating that these PRRs have undergone purifying (negative) selection. In addition, 27 of these pairs showed a synonymous substitution ratio (pS) greater than 0.75, suggesting that the sequence divergence was sufficient to estimate a reliable synonymous substitution rate (kS). Additionally, an analysis of duplicate pairs was performed, focusing on genes with evidence as DEGs and those without. Among these gene pairs, 24 lacked evidence of response to biotic stress. In 14 pairs, both genes were DEGs, while in 36 pairs, at least one gene was identified as DEG (Figure 7). 3.7 Catalogue of PRR genes of potential interest in Eucalyptus breeding programs Based on our results, we propose a catalogue of 16 PRR genes that were differentially expressed in at least three diseases, identified in multiple publications, and upregulated in response to at least one pathogen (Table 2). Among these, four genes responded to all four pathogen types analyzed—bacteria, two distinct fungal pathogens, and the gall wasp. Additionally, nine PRR-DEGs, while not responsive to all pathogens included in the meta-analysis, were induced by a range of organisms, including fungi, insects, and bacteria. The remaining three genes responded to two types of fungi as well as bacteria, highlighting the potential broad-spectrum defense capability of these PRRs against diverse biotic stresses. Regarding the chromosomal distribution of the 16 PRR-DEGs included in the catalogue, two were annotated in scaffolds, while the remaining 14 were distributed across seven of the eleven chromosomes (2, 3, 6, 8, 9, 10, and 11). Notably, twelve potentially broad-spectrum PRR-DEGs were grouped into nine clusters located on chromosomes 3, 6, 8, 10, and 11. Four of these clusters consisted exclusively of PRRs belonging to the same category. On chromosome 6, one cluster comprised RLKs with malectin domains; similarly, chromosome 8 contained another RLK cluster. In addition, two clusters located on chromosome 11 were composed of RLK categories. Consistent with these findings, all candidate genes had orthologues in Arabidopsis thaliana , which themselves have been reported to respond to multiple biotic stresses. Moreover, these orthologous groups are conserved and stress-responsive across a variety of plant species, including forest trees such as pine ( Pinus pinaster ) and olive ( Olea europaea ), as well as citrus species such as lemon ( Citrus limon ) and grapefruit ( Citrus maxima ) (Table 2). Tab. 2 Catalogue of PRR genes of potential interest in Eucalyptus breeding programs . The table summarises 16 PRR genes of interest, including: gene identifier (Gene_ID_V2), chromosome (CHR), genomic position (Start, End), cluster assignment within 200 kbp (N° Cluster (<200 kbp)), pathogens for which the gene shows differential expression (Pathogen), condition of differential expression (Condition DEG), unified PRR category (Categories), orthologues in A. thaliana ( A. thaliana Orthologues), functional annotation in E. grandis v2.0 (Functional Annotation ( E. grandis )), updated functional annotation (New annotation), and supporting literature (Evidence). Gene_ID_V2 CHR Start End N° Cluster (<200 kpb) Pathogen Condition DEG Categories Annotation in E. grandis v 2.0 New annotation A. thaliana Orthologous Functional Annotation Functional Annotation ( E. grandis ) A.thaliana Orthologous Gene Name Functional Annotation Evidence Eucgr.B03087 02 51188519 51192057 Singleton C. austroafricana L. invasa R. solanacearum DOWN (R,S) DOWN (R) DOWN/UP TM_K_LRR AT3G25560 NSP-interacting kinase 2 LRR receptor-like serine/threonine-protein kinase At2g23950 family AT3G25560 NSP-interacting kinase 2 Fontes et al. (2004); Sakamoto et al. (2012); Santos et al. (2009) Eucgr.C00984 03 15768031 15771767 16 C. austroafricana A. psidii R. solanacearum UP (R,S) UP (S) UP TM_K_LRR AT4G29990 Protein kinase family protein with leucine-rich repeat domain Receptor-like protein kinase HSL1 AT5G25930 kinase family with leucine-rich repeat domain-containing protein Chalupowicz et al. (2023); Navarro et al. (2004); Mendy et al. (2017); Artico et al. (2014); Little et al. (2007); Lu et al. (2023) Eucgr.F00314 06 5586470 5590029 47 L. invasa A. psidii R. solanacearum DOWN (R) UP (S) DOWN TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Probable leucine-rich repeat receptor kinase At1g35710 AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.F00316 06 5561785 5571145 47 L. invasa A. psidii R. solanacearum DOWN (S) UP (S) DOWN TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein LRR receptor-like serine/threonine-protein kinase AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.F01307 06 17466429 17483294 52 C. austroafricana L. invasa R. solanacearum DOWN (R) DOWN (S) DOWN/UP TM_K_LRR_Malec AT3G14840 Leucine-rich repeat transmembrane protein kinase LRR receptor-like kinase AT3G14840 Leucine-rich repeat transmembrane protein kinase Hussan et al. (2020); Marchese et al. (2023); Little et al. (2007) Eucgr.F04434 06 56878674 56890048 67 C. austroafricana L. invasa A. psidii R. solanacearum UP (R) DOWN (R,S) UP (S) UP TM_K_Malec AT1G56130 Leucine-rich repeat transmembrane protein kinase LRR receptor-like serine/threonine-protein kinase AT1G56130 Leucine-rich repeat transmembrane protein kinase Binagwa et al. (2021); Xu et al. (2020); Zhang et al. (2023) Eucgr.H00707 08 9621912 9625470 77 C. austroafricana A. psidii R. solanacearum DOWN (R,S) UP (S) DOWN TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Probable leucine-rich repeat receptor-like protein kinase At1g35710 AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.H00710 08 9652678 9656274 77 C. austroafricana L. invasa A. psidii R. solanacearum DOWN (R,S) DOWN (R) UP (S) DOWN TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein LRR receptor-like serine/threonine-protein kinase AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.H00949 06 7852826 7856009 49 C. austroafricana A. psidii R. solanacearum DOWN (R) UP (S) DOWN TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein LRR receptor-like serine/threonine-protein kinase AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.I00656 09 13607921 13610024 Singleton C. austroafricana L. invasa A. psidii DOWN (R) DOWN (R) UP (S) TM_K_LRR AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Protein kinase domain-containing protein AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.J02909 10 34748881 34755956 102 L. invasa A. psidii R. solanacearum DOWN (R) UP (S) DOWN TM_K_LRR_MalecLike AT2G19190 FLG22-induced receptor-like kinase 1 Leucine-rich repeat receptor-like serine/threonine-protein kinase At2g19230 AT2G19190 FLG22-induced receptor-like kinase 1 Yang et al. (2024); He et al. (2006) Eucgr.K02381 11 31739538 31744299 109 L. invasa A. psidii R. solanacearum DOWN (R,S) UP (S) DOWN TM_K_LRR AT1G35710 Protein kinase family protein with leucine-rich repeat domain Probable leucine-rich repeat receptor-like protein kinase At1g35710 AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.K02386 11 31822271 31826333 109 C. austroafricana L. invasa A. psidii R. solanacearum DOWN (S) DOWN (R) UP (S) DOWN TM_K_LRR AT1G35710 Protein kinase family protein with leucine-rich repeat domain Probable leucine-rich repeat receptor-like protein kinase At1g35710 AT4G08850 Leucine-rich repeat receptor-like protein kinase family protein Chalupowicz et al. (2023) Eucgr.K02775 11 35174545 35176836 110 C. austroafricana L. invasa A. psidii R. solanacearum DOWN (S) DOWN (R) UP (S) DOWN TM_K_LRR AT2G31880 Leucine-rich repeat protein kinase family protein Leucine-rich repeat receptor-like serine/threonine/tyrosine-protein kinase SOBIR1 AT2G31880 Leucine-rich repeat protein kinase family protein Bahar et al. (2016); Navarro et al. (2004); Li et al. (2004); Takahashi et al. (2018); Takahashi et al. (2016); Whitham et al. (2003) Eucgr.L00469 scaff_24 88611 91807 - C. austroafricana L. invasa R. solanacearum DOWN (R) DOWN (R) DOWN/UP TM_K_LRR AT3G47570 Leucine-rich repeat protein kinase family protein Receptor kinase-like protein Xa21 AT3G47110 Leucine-rich repeat protein kinase family protein Dyda et al. (2022); Modesto et al. (2021) Eucgr.L01229 scaff_140 58951 61469 - L. invasa A. psidii R. solanacearum DOWN (R) UP (S) UP TM_K_LRR AT3G47570 Leucine-rich repeat protein kinase family protein Receptor kinase-like protein XA21-like protein AT3G47090 Leucine-rich repeat protein kinase family protein Cantila et al. (2020); Little et al. (2007) 4. Discussion Pattern-recognition receptors (PRRs) form the first cellular barrier against pathogens. Breeding strategies that leverage PRRs can deliver broad, non race-specific defence, in contrast to many R genes. Although several tools exist to annotate disease-resistance genes and proteins, the limited conservation of PRR domains and the use of different algorithms cause each method to recover a different gene set. Here, two state-of-the-art PRR predictors (DRAGO3 and RRGPredictor) were compared with a broad, multi-species curation from Ngou et al. ( 2022 ) in Eucalyptus grandis. DRAGO3 predicted 2,793 PRR-encoding genes (~ 6.1% of the proteome), RRGPredictor predicted 1,261 (~ 2.8%), and the Eucalyptus subset in Ngou et al. comprised 810 (~ 1.8%). This spread reflects divergent domain requirements and decision rules across strategies. After harmonising gene identifiers, categories and metadata, 730 high-confidence PRR gene candidates were identified (i.e., consistently by all three approaches), representing ~ 1.62% of protein-coding genes. Relative to Populus trichocarpa (~ 1% PRRs; Ngou et al., 2022 ), the higher proportion in E. grandis is consistent with high duplication rates reported for Eucalyptus and may reflect lineage-specific expansion (Myburg et al., 2014 ; Borthakur et al., 2022 ). PRR classification also proved challenging because each tool presented different categories, sometimes using the same label with different composition. For example, between the PRRs assigned by DRAGO3, the broad “KIN” class showed limited overlap with PRRs predicted by RRGPredictor or by Ngou et al. ( 2022 ). Inspection of motif composition indicated that many members lacked ectodomains and therefore fall outside PRRs sensu stricto (Ngou et al., 2022 ; Restrepo-Montoya et al., 2020 ). The 'UNKNOWN' labels used for almost all PRRs by RRGPredictor likewise underscored the limits in fine-grained categorisation. To reduce tool-specific bias, classification was reframed based on PFAM domain combinations. This approach allowed categories to be tracked across methods while avoiding circularity. Eleven domain-based categories (plus a small bin without PFAM annotation) were defined. TM-K-LRR (RLK) sequences were the most abundant, whereas TM-LRR (RLP) sequences were less represented, likely reflecting detection difficulty rather than true biological scarcity (Silva et al., 2022 ). Many categories contained malectin or malectin-like ectodomains. Phylogenetic patterns were concordant with domain architecture: RLKs formed large clades, RLPs clustered separately, and malectin versus malectin-like ectodomains resolved into related but distinct lineages. Within the set of 730 robust PRRs, genes were unevenly distributed across the genome. Chromosome 6 showed the highest number and proportion of PRRs and the densest clustering, suggesting an immune-enriched region. Prior work in E. grandis has also highlighted chromosome 6 as stress-responsive, as it harbours the largest set of VQ (valine–glutamine) genes, regulators implicated in SA- and JA-mediated defence signalling. This convergence of VQ factors and dense PRR clustering suggests coordinated regulation of immune responses on this chromosome (Yan et al., 2019 ; Jing & Lin, 2015 ; Glazebrook, 2005 ). Clusters enriched for RLKs with malectin/malectin-like ectodomains were also observed on chromosomes 6, 8, 10, and 11, a pattern consistent with local duplication and coordinated evolution of cell-wall–sensing modules; similar proximity has been described in Arabidopsis (Yang et al., 2021 ). Duplication emerged as a major driver of PRR content: extensive tandem duplication (318 genes in 127 tandems) co-occurred with segmental, proximal, and dispersed events. Ka/Ks estimates for segmental pairs indicated predominantly purifying selection, with a minority of more divergent pairs consistent with sub- or neofunctionalisation. A great number (40.5%) of intergenic-distance clusters (< 200 kb) showed total identity with MCScanX-defined tandems (135 genes), connecting local genome structure to PRR expansion. By contrast, chromosome 5 lacked duplicated PRRs, in agreement with earlier reports of ETI-related NBS-LRR enrichment on that chromosome (Christie et al., 2015 ), suggesting that PTI and ETI components occupy partially distinct genomic regions. An initial functional view based on the E. grandis v2.0 GO annotation in Phytozome suggested limited defence enrichment and risked under-interpretation. Re-annotation with PANNZER improved functional resolution and recovered defence-related Biological Process and Molecular Function terms, with strong signals for phosphorylation/kinase activity, signalling-receptor activity, and ATP binding, features expected for PRR pathways (Naidoo et al., 2014 rönen & Holm, 2022; Bolger et al., 2018 ). Given that genome annotations evolve as tools and biological knowledge improve, periodic re-annotation is warranted (Bolger et al., 2018 ). A meta-analysis of four RNA-seq studies spanning E. grandis, E. urophylla, and E. grandis × E. camaldulensis under rust, canker, bacterial wilt, and gall-wasp challenge identified 283 of the 730 catalogue genes as differentially expressed, indicating broad engagement of the PRR repertoire during biotic stress. It is worth noting that additional proteomics and transcriptomics studies on other fungi, including Calonectria pseudoreteaudii (leaf blight) and C. austroafricana , were identified in the literature but could not be incorporated, as raw sequencing data were not publicly available; consequently, these works were excluded from the meta-analysis despite their relevance (Chen et al., 2015 ; Zwart et al., 2017 ). From the analysed data, approximately 19% of duplicated PRRs were differentially expressed under biotic stress, linking duplication history to functional deployment. More than half of malectin/malectin-like RLKs were differentially expressed, consistent with an important contribution at the interface of cell-wall status, growth, and immunity (Shiu & Bleecker, 2003 ; Zhang et al., 2022 ; Ngou et al., 2024 ). While LRR-RLKs are expected to recognise bacterial epitopes (e.g., flagellin, EF-Tu), malectin/malectin-like RLKs have been linked to sensing DAMPs and chitin-like cues (Baez et al., 2022 ; Gandhi et al., 2024 ). Responses were largely pathogen-specific and often depended on species or clones, and mixed directionality (up- and down-regulation) across pathosystems suggested context-dependent tuning of PRR signalling. Several PRR-DEGs genes present in the E. grandis reference were only induced in response to a single pathogen. In contrast, 93 PRR-DEGs were shared by at least two pathogens, with 16 responding to three or more. At least one instance of up-regulation was observed per gene, features consistent with broad-response candidates. Promoter analyses supported these assignments: cis-elements associated with MeJA, SA, ethylene, auxin, light, and abiotic stress were common to all PRRs, and the 16 multi-pathogen PRR-DEGs were enriched for MeJA- and light-responsive motifs, with frequent auxin motifs. These regulatory patterns align with hormone crosstalk and growth–defence trade-offs during PTI, including modulation of cell-wall properties (Kaur & Pati, 2016 ; Baez et al., 2022 ; Sajjad et al., 2023 ; Wilson et al., 2023 ). These 16 PRR-DEGs include orthologues with cross-taxon evidence of defence roles. For example, the Arabidopsis gene AT1G35710 (Eucgr.K02381/K02386) was detected here under fungi ( C. austroafricana , A. psidii ), insects ( L. invasa ), and bacteria ( R. solanacearum ). It also has been reported as up-regulated in pine resistance to nematodes and upon exposure to Xanthomonas outer-membrane vesicles in Arabidopsis , leading to reduced pathogen growth (Modesto et al., 2021 ; Chalupowicz et al., 2023 ). AT3G14840 (Eucgr.F01307) was differentially expressed here in canker, gall-wasp, and wilt challenges, in line with prior reports as a resistance-gene analogue in olive leaf spot, as an interactor of Xanthomonas LPS in Arabidopsis , and as induced by pierid oviposition (Marchese et al., 2023 ; Hussan et al., 2020 ; Little et al., 2007 ). AT3G47570 (Eucgr.L00469/L01229) responded here to canker, rust, gall-wasp and wilt, consistent with up-regulation during citrus greening responses and with high non-synonymous polymorphism among candidate wilt-resistance genes in pepper (Gao et al., 2023 ; Kang et al., 2016 ). Beyond expression, the 16 candidates showed a non-random genomic arrangement: 12 were located within nine clusters distributed across chromosomes 3, 6, 8, 10, and 11, while two occurred as singletons on chromosomes 2 and 9. Several clusters consisted of receptors belonging to the same category, including groups of RLKs with malectin or malectin-like ectodomains on chromosomes 6 and 11. It is consistent with local duplication followed by retention of functionally related modules and mirroring patterns described for wall-sensing RLKs in Arabidopsis (Yang et al., 2021 ). The present work uses publicly available data and resources in order to generate new knowledge with potential breeding impact, contributing to giving significance to the large amount of sequencing data available and working towards open science. Their recurrence across systems, together with the multi-pathogen responses observed here and their predicted hormone response, identify the 16 PRR-DEGs as strong candidates for subsequent validation and improvement efforts, including marker development, association mapping in breeding populations, and functional tests such as ligand identification, transient expression, or CRISPR-based perturbations in Eucalyptus or proxy systems (Modesto et al., 2021 ; Chalupowicz et al., 2023 ; Li et al., 2019 ; Marchese et al., 2023 ; Gao et al., 2023 ; Kang et al., 2016 ). Declarations Funding This work was supported by Universidad Nacional de Moreno (PICyDT UNM VII 2021). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Data collection and analysis were performed by YEG and CVF. All authors participated in the interpretation of the results. 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Footnotes https://phytozome-next.jgi.doe.gov/ https://www.ncbi.nlm.nih.gov/bioproject/PRJNA280236 https://www.ncbi.nlm.nih.gov/bioproject/305347 https://www.ncbi.nlm.nih.gov/sra/PRJNA588626 https://ngdc.cncb.ac.cn/gsa/browse/CRA005055 https://bioinformatics.psb.ugent.be/plaza/versions/plaza_v5_dicots/ http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ Additional Declarations No competing interests reported. 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Categories according to Ngou (green) RLK-LRR -(I a XVI), RLK-LysM,, RLP-LRR, RLP-LysM. According to RRGPredictor (light blue) MLO, RLK, RLK-GNK2, RLP, T, UNKNOWN.\u003c/p\u003e\n\u003cp\u003eIt can be observed that the predominant category in DRAGO3 was KIN, for Ngou et al. (2022) the predominant category was RLK-LRR-XII, while for RRGPredictor it was UNKNOWN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e \u003cem\u003eFrequency of putative PRR genes classified under the proposed new categories.\u003c/em\u003e TM: transmembrane, K: kinase, LRR: Leucine-Rich Repeat, Malec: malectin, MalecLike: malectin like.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/817667f5b98ec2af089c584c.png"},{"id":99316323,"identity":"82efa8b6-a7b0-4430-a062-379049023d64","added_by":"auto","created_at":"2025-12-31 16:28:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":221048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e283 PRR-DEGs differentiated by pathogen. Venn diagram reporting the number of PRR-DEG genes identified by the three strategies grouped by their response to different pathogens.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/475e04435737262406fbbb92.png"},{"id":99314937,"identity":"f6595a09-edc3-4ba6-9555-4b913d232860","added_by":"auto","created_at":"2025-12-31 16:24:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1009441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePhylogenetic tree for the 283 proteins encoded by PRR-DEGs.\u003c/em\u003eThe branches of the tree are coloured in response or not to pathogens. In grey correspond the proteins identified by the three strategies that were not DEGs, in green the proteins encoded by PRR-DEGs in response to one pathogen, in yellow the proteins encoded by PRR-DEGs in response to two pathogens, in violet the proteins encoded by PRR-DEGs in response to three pathogens and in fuchsia the proteins encoded by PRR-DEGs in response to four pathogens. Clustering based on the categories for the encoded proteins is indicated above the tree.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/85e7580fbfa031489c1e6172.png"},{"id":99314798,"identity":"a36067c9-8e5a-4155-9061-2f29ffbcb857","added_by":"auto","created_at":"2025-12-31 16:23:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocalisation and chromosomal distribution of PRR and PRR-DEG in E. grandis.\u003c/em\u003e In grey PRR no DEG (to date); in green PRR-DEG in response to one pathogen, in orange PRR-DEG in response to two pathogens, in violet PRR-DEG in response to three pathogens and in fuchsia PRR-DEG in response to 4 pathogens.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/016f128c93a34bb52b518617.png"},{"id":99315204,"identity":"da564f37-0f57-49ef-84bb-1134b9ed51e4","added_by":"auto","created_at":"2025-12-31 16:26:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e \u003cem\u003eNew annotation GO (Biological Processes) enrichment for the 730 PRR genes identified by the three strategies.\u003c/em\u003e The p-value scale corresponds from 1E-30 to 0.02793.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e \u003cem\u003eNew annotation GO Enrichment (Biological Processes) for the 283 PRR-DEGs.\u003c/em\u003e The p-value scale corresponds from 1E-30 to 0.0597.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/740966d036eea8ffe8899018.png"},{"id":99131233,"identity":"dd4fec03-545b-4fdf-bdd8-a99ce697d84f","added_by":"auto","created_at":"2025-12-29 04:37:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":382509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e \u003cem\u003ePie chart of motifs identified in E. grandis, based on their biological functions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003e\u003cem\u003ePredicted cis-acting elements in the promoter regions (1500 bp upstream) of 18 PRR-DEGs that showed evidence of biotic stresses for 3 and 4 pathogens.\u003c/em\u003eArrows pointing to the right indicate that the element is located on the positive strand, while arrows pointing to the left indicate its presence on the negative strand.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/4b1096451f76035c408726be.png"},{"id":99131214,"identity":"9efe5d6b-742d-42cc-8f26-e09b7e5ae9a5","added_by":"auto","created_at":"2025-12-29 04:37:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":511889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eChromosomal location and colinearity of PRR genes in E. grandis.\u003c/em\u003e Blue grey lines indicate duplicated gene pairs that were not DEGs, yellow lines indicate gene pairs where at least one of them is DEG and in fuchsia duplicated pairs where both were DEGs. Eg1 - 11 refers to the chromosome number of E. grandis.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/b8e9a69c23b1c9d7586f50aa.png"},{"id":99323683,"identity":"5e88502e-e75c-41fb-85ba-d1ceb1e4c766","added_by":"auto","created_at":"2025-12-31 16:45:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3410912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/4ce708bc-425f-4a43-adc1-1fd64dee9946.pdf"},{"id":99131218,"identity":"93bc9f94-b8d8-4b37-90cc-f818e2194ea3","added_by":"auto","created_at":"2025-12-29 04:37:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4060574,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryGonzalezetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/be82abc12b8aa6fd745caa55.docx"},{"id":99131215,"identity":"20d65712-fcb9-4019-bae6-7f140e2c3629","added_by":"auto","created_at":"2025-12-29 04:37:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":87233,"visible":true,"origin":"","legend":"","description":"","filename":"SI1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8407889/v1/0df058e818cd4ef66b0c7ea1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative approach to identify robust Pattern Recognition Receptors in Eucalyptus grandis: novel candidates for disease resistance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe \u003cem\u003eEucalyptus\u003c/em\u003e genus, which belongs to the Myrtaceae family, is native to Australia, New Guinea, Timor, Indonesia and the Philippines, and comprises over 700 species and subspecies (Ladiges et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Thornhill et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to traits such as rapid growth, short rotation cycles, year-round harvesting, and high adaptability and productivity (Gull\u0026oacute;n et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eEucalyptus\u003c/em\u003e has become widely cultivated across tropical, subtropical, and temperate regions worldwide, covering over 20\u0026nbsp;million hectares. It plays a key role in the global hardwood forestry industry, with applications in timber, pulp and paper and biofuels.\u003c/p\u003e \u003cp\u003eAmong the cultivated species, \u003cem\u003eEucalyptus grandis\u003c/em\u003e stands out for its fast growth and early yield, making it particularly valuable in commercial plantations (Naidoo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, despite its economic importance, \u003cem\u003eE. grandis\u003c/em\u003e is highly susceptible to several biotic and abiotic stresses that compromise growth and wood quality. Biotic threats include myrtle rust caused by \u003cem\u003eAustropuccinia psidii\u003c/em\u003e (syn. \u003cem\u003ePuccinia psidii\u003c/em\u003e); the stem canker pathogen \u003cem\u003eChrysoporthe austroafricana\u003c/em\u003e; root rot pathogen \u003cem\u003ePhytophthora cinnamomi\u003c/em\u003e; leaf blight caused by \u003cem\u003eCalonectria spp.\u003c/em\u003e; and pests such as \u003cem\u003eLeptocybe invasa\u003c/em\u003e (Naidoo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These threats have intensified in recent decades due to climate change, which promotes disease emergence by extending infection windows, increasing pathogen virulence, and expanding host ranges (Younessi-Hamzekhanlu \u0026amp; Gailing, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent management strategies include the use of tolerant \u003cem\u003eEucalyptus\u003c/em\u003e genotypes and integrated pest management, such as biological control for \u003cem\u003eL. invasa\u003c/em\u003e (Naidoo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, these measures often fall short, underscoring the need for improved resistance strategies through breeding and biotechnological approaches (Wingfield et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this context, understanding the molecular mechanisms of plant immunity is essential for developing more resilient varieties.\u003c/p\u003e \u003cp\u003eA major breakthrough for \u003cem\u003eEucalyptus\u003c/em\u003e research was the sequencing of the \u003cem\u003eE. grandis\u003c/em\u003e genome, with \u0026gt;\u0026thinsp;94% of the genome anchored to chromosomes, which has enabled omics-based studies and large-scale analysis of gene families involved in defence responses (Myburg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zwart et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This genomic resource has established \u003cem\u003eE. grandis\u003c/em\u003e as a genomic model for studying the defence mechanisms of woody perennials, benefiting not only this species but also other members of the Myrtaceae family (Christie et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn plants, the first line of inducible immune defence, known as pattern-triggered immunity (PTI), is initiated when pattern recognition receptors (PRRs) located on the plasma membrane detect pathogen-, microbe-, or damage-associated molecular patterns (PAMPs, MAMPs, or DAMPs) (Jones \u0026amp; Dangl, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Two main classes of PRRs are recognised: receptor-like kinases (RLKs) and receptor-like proteins (RLPs), which play a pivotal role in sensing pathogens and maintaining cellular homeostasis (Silva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRLKs are characterised by a typical tripartite structure: an extracellular domain (ectodomain) at the amino-terminal (N-terminal) region, a transmembrane domain (TM), and an intracellular serine/threonine kinase domain at the carboxy-terminal (C-terminal) region. The ectodomain, responsible for ligand perception, can include leucine-rich repeats (LRR), the lysine motif (LysM), the lectin domain (Lec), and/or an epidermal growth factor-like domain (EGF) (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This ectodomain interacts with PAMPs/MAMPs or DAMPs, while the kinase domain activates intracellular signalling through phosphorylation (Silva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). RLKs are grouped into families based on ectodomain structure, with LRR-RLKs being the most abundant, followed by lectin-type RLKs (Minkoff et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRLPs, in contrast, share similar ectodomains with RLKs but lack the intracellular kinase domain. Their signalling relies on forming complexes with RLKs or with receptor-like cytoplasmic kinases (RLCKs), which transduce the signal into the cell (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional characterisation of RLPs has proven more challenging, as their variable domains and lack of conserved kinase motifs hinder identification through standard homology-based methods (Silva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the importance of PRRs in the early stages of immune response, their identification is a crucial step in the genetic improvement of disease-resistant plants. Although experimental approaches can confirm PRR function, they are time-consuming and resource-intensive. In this regard, bioinformatics tools for \u003cem\u003ein silico\u003c/em\u003e prediction and classification of PRRs have become increasingly valuable to accelerate the discovery and annotation process (Del Hierro et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses the identification and characterisation of PRR genes involved in PTI in \u003cem\u003eE. grandis\u003c/em\u003e by integrating computational predictions with functional analysis. Two state-of-the-art prediction tools: RRGPredictor (Santana Silva \u0026amp; Micheli, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and DRAGO3 (Calle Garc\u0026iacute;a et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were employed, and their outputs were compared with a recent large-scale PRR dataset derived from 350 plant species (Ngou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, we conducted a multi-layered characterisation that included differential expression meta-analysis, updated functional annotation, phylogenetic and evolutionary analysis, and promoter region examination to identify cis-regulatory elements (CREs). This integrative approach aims to shed light on the diversity, structure, and potential regulatory mechanisms of PRRs in \u003cem\u003eE. grandis\u003c/em\u003e, contributing to a better understanding of defence responses in woody species.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch2\u003e2.1 Genomic, proteomic, and transcriptomic resources\u003c/h2\u003e\n\u003cp\u003eThis study used publicly available omics data for \u003cem\u003eE. grandis,\u0026nbsp;\u003c/em\u003eretrieved from the Phytozome database\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e (Goodstein et al., 2012). The resources included genome, transcriptome, and proteome \u0026nbsp;data corresponding to version 2.0 \u0026nbsp;of the \u003cem\u003eE. grandis\u003c/em\u003e reference genome (Myburg et al., 2014; Bartholom\u0026eacute; et al., 2015), which incorporates \u0026nbsp;structural and functional annotations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2 Identification of PRR genes\u003c/h2\u003e\n\u003cp\u003eTo identify PRR genes involved in plant defence, we implemented three complementary bioinformatics strategies: 1) DRAGO3 (Disease Resistance Analysis and Gene Orthology version 3; Calle Garc\u0026iacute;a et al., 2022) was used to scan the \u003cem\u003eE. grandis\u003c/em\u003e v2.0 proteome; 2) RRGPredictor (Santana Silva \u0026amp; Micheli, 2020) was applied after analysing the proteome with InterProScan5 (Jones et al., 2014); and 3) filtering the\u003cem\u003e\u0026nbsp;E. grandis\u003c/em\u003e only PRR gene set reported by Ngou et al. (2022).\u003c/p\u003e\n\u003cp\u003eAs DRAGO3 and RRGPredictor permit the annotation of resistance genes in general, we filtered only PRRs by retaining proteins with transmembrane (TM) domains predicted by TMHMM 2.0 (Krogh et al., 2001) and without nucleotide binding site (NBS) domains, which are specific of R genes (Christie et al., 2015). Redundancy among predicted sequences was reduced using CD-HIT v4.7 (Li \u0026amp; Godzik, 2006), with a 90% sequence identity threshold, retaining only the longest protein.\u003c/p\u003e\n\u003cp\u003eGenes consistently \u0026nbsp;predicted across the three strategies were defined as putative PRRs. To harmonise classification across tools, we established a unified domain-based caracterisation using PFAM domains identified via InterProScan5, with e-values \u0026lt;0.001 and an alignment coverage greater than 50%. Functionally related \u0026nbsp; domains, such as various leucine-rich repeats (LRRs) and kinase-related motifs, were grouped under \u0026quot;LRR\u0026quot; and \u0026quot;K\u0026quot;, respectively.\u003c/p\u003e\n\u003ch2\u003e2.3 Meta-Analysis of RNAseq data\u003c/h2\u003e\n\u003cp\u003eIn order to assess the expression dynamics of candidate PRRs, a meta-analysis of RNA-seq datasets derived from \u003cem\u003eEucalyptus\u003c/em\u003e species challenged with pathogens was conducted, encompassing studies published \u0026nbsp;between 2014 (\u003cem\u003eE. grandis\u0026nbsp;\u003c/em\u003egenome release) and March 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly studies with publicly available raw RNA-seq data were considered, including responses to \u003cem\u003eC. austroafricana\u003c/em\u003e (Mangwanda et al., 2015), \u003cem\u003eL. invasa\u003c/em\u003e (Oates et al., 2015), \u003cem\u003eA. psidii\u003c/em\u003e (Santos et al., 2020), and \u003cem\u003eRalstonia solanacearum\u003c/em\u003e (Xiaohui et al., 2022). Datasets were obtained from the National Center for Biotechnology Information repository (NCBI: PRJNA280236\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e; PRJNA305347\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e and PRJNA588626\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e) and the Genome Sequence Archive (PRJCA006666\u003ca href=\"#_ftn5\" name=\"_ftnref5\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e5\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReads were quality-filtered using Trimmomatic v0.36 (Bolger et al., 2014), removing adapters and discarding sequences shorter than 80 bp or with average Phred scores below 30.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscript-level quantification was performed with Salmon v0.12.0 (Patro et al., 2017) in quasi-mapping mode, enabling GC bias correction. The mapping index was constructed using the \u003cem\u003eE. grandis\u003c/em\u003e reference transcriptome (v2.0). Gene-level abundance estimates were summarised using Tximport v1.26.1 (Soneson et al., 2015). Differential gene expression analysis was conducted using DESeq2 v1.38.3 (Love et al., 2014), employing its default pipeline based on \u0026nbsp;a \u0026nbsp; negative binomial regression model.\u003c/p\u003e\n\u003cp\u003eExploratory analyses included principal component analysis (PCA) and volcano plots, both of which were visualized using ggplot2 v3.4.2 (Wickham H., 2016).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.4 Phylogenetic Analysis of PRRs\u003c/h2\u003e\n\u003cp\u003ePutative PRR protein sequences were aligned using ClustalOmega (Sievers \u0026amp; Higgins, 2021). Phylogenetic trees were constructed using the Neighbor-Joining (NJ) method based on pairwise sequence identity matrices. The R packages \u0026quot;msa v1.30.1\u0026quot; (Bodenhofer et al., 2015), \u0026quot;seqinr v4.2-30\u0026quot; (Charif \u0026amp; Lobry, 2007), and \u0026quot;ape v5.7-1\u0026quot; (Paradis \u0026amp; Schliep, 2019) were used for alignment, distance calculation and tree construction, respectively. PRRs were classified into DEGs and non-DEGs based on the RNA-seq meta-analysis results.\u003c/p\u003e\n\u003ch2\u003e2.5 New functional annotation and Orthology\u003c/h2\u003e\n\u003cp\u003ePutative PRR proteins were functionally annotated using PANNZER (Protein ANNotation with Z-scoRE, T\u0026ouml;r\u0026ouml;nen \u0026amp; Holm, 2022), and results were compared with the \u003cem\u003eE. grandis\u003c/em\u003e v2.0 genome annotation. Gene Ontology (GO) enrichment analysis was performed using TopGO v2.50.0 (Alexa \u0026amp; Rahnenfuhrer, 2024), applying the \u0026nbsp;\u0026apos;weight01\u0026apos; algorithm to explore overrepresented Biological Process and Molecular Function terms among PRR annotated by both methodologies.\u003c/p\u003e\n\u003cp\u003eOrthologs in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e were identified using PLAZA 5.0 Dictos\u003ca href=\"#_ftn6\" name=\"_ftnref6\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e, enabling comparative insights across species (Van Bel et al., 2022).\u003c/p\u003e\n\u003ch2\u003e2.6 Gene duplication analysis\u003c/h2\u003e\n\u003cp\u003eGene duplication events were identified with McScanX (Wang et al., 2012), using the amino acid sequences of PRRs. Duplicated gene pairs and their chromosomal locations were visualized using TBtools v.2.119 (Chen et al., 2023).\u003c/p\u003e\n\u003cp\u003eNon-synonymous (Ka) and synonymous (Ks) substitution rates were estimated for duplicated \u0026nbsp;gene pairs based on coding sequence (CDS) alignments, applying the Nei-Gojobori method (Nei \u0026amp; Gojobori, 1986).\u003c/p\u003e\n\u003ch2\u003e2.7 Analysis of Cis-Regulatory Elements\u003c/h2\u003e\n\u003cp\u003eSequences 1,500 bp upstream of the start codon (ATG) of putative PRR genes were retrieved to identify cis-regulatory elements (CREs) in the promoter regions, using PlantCare\u003ca href=\"#_ftn7\" name=\"_ftnref7\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e7\u003c/sup\u003e (Lescot et al., 2002). CREs associated with stress responses were visualised using ggplot2 and their enrichment in DEGs versus non-DEG PRRs was evaluated via Fisher\u0026apos;s exact test.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Identification of genes involved in defense mechanisms and PRR\u003c/h2\u003e\n\u003cp\u003eThe analysis of the \u003cem\u003eE. grandis\u003c/em\u003e proteome (46,280 sequences) using DRAGO3 identified 4,976 proteins potentially involved in defence mechanisms. Filtering for proteins containing at least one transmembrane domain (TM) reduced this number to 4,186 proteins. Exclusion of proteins with NBS domains resulted in 3,258 putative PRR proteins. A redundancy reduction step using CD-HIT yielded 2,831 unique putative PRR proteins, corresponding to 2,793 genes. Using a similar workflow, RRGPredictor identified 3,151 putative resistance proteins without NBS motifs. After applying the TM domain filter and CD-HIT redundancy reduction, a total of 1,271 putative PRR proteins were retained, corresponding to 1,261 genes. Additionally, 810 PRR genes and proteins were compiled from the study by Ngou et al. (2022), which defined 19 PRR categories. These included 300 genes as RLK-LRR-XII, 224 as RLP-LRR, 65 as RLK-LRR-XI, 51 as RLK-LRR-VIII, 38 as RLK-LRR-III, 25 each as RLK-LysM and RLK-LRR-I, and smaller numbers in other RLK-LRR subfamilies (Figure 1.A).\u003c/p\u003e\n\u003cp\u003eAltogether, a total of 2,835 PRR genes were identified by at least one of the three approaches: 2,793 by DRAGO3 (2,800 proteins), 1,261 by RRGPredictor (1,261 proteins), and 810 by Ngou et al. (2022) (810 proteins). DRAGO3 classified these gene products into 19 categories: 1,016 as KIN, 588 as RLK, 581 as RLP, 341 as LECRK, 77 as CK, 42 as TRAN, 39 as T, 35 as LEC, 29 as LYK, 20 as CLK, 7 as LYP, 7 as CL, 5 as LYS, 4 as TL, 4 as C, 2 as CLECRK, and 1 each as CT, CLYSK, and UNDEFINED (Figure 1.A). RRGPredictor assigned these genes to six categories: 802 as UNKNOWN, 269 as RLK, 146 as RLK-GNK2, 30 as MLO, 8 as T, and 6 as RLP (Figure 1.A).\u003c/p\u003e\n\u003ch3\u003e3.1.1 Recategorization of PRR proteins consistently identified by three strategies\u003c/h3\u003e\n\u003cp\u003eA comparison among the three strategies revealed 730 genes identified consistently across all datasets (Supplementary Information 1). These were considered high-confidence PRRs. DRAGO3 classified them into RLK, RLP, CLK, CL, and UNDEFINED; RRGPredictor into UNKNOWN and RLK; and Ngou et al. (2022), into various RLK-LRR and RLP-LRR subgroups. The observed disparity in categorization criteria among the three strategies prompted the development of a unified classification system for PRR proteins and genes. To achieve this, we analysed the presence and absence of protein domains based on PFAM domain annotations from InterProScan5. Only domains passing the filtering criteria (e-value \u0026lt; 0.001 and alignment coverage greater than 50%) were considered. According to the PFAM database (Mistry et al., 2021), the following domains in different combinations were present in the 730 PRRs: TM domains, K endodomains (PFAM: \u0026ldquo;PF00069\u0026rdquo; and \u0026ldquo;PF07714\u0026rdquo;) and among the ectodomains LRR (PFAM: \u0026ldquo;PF08263\u0026rdquo;, \u0026ldquo;PF12799\u0026rdquo;, and \u0026ldquo;PF13855\u0026rdquo;), Malectin (PFAM: \u0026ldquo;PF11721\u0026rdquo;), Malectin-like (PFAM: \u0026ldquo;PF12819\u0026rdquo;). Consequently, the 730 putative PRR proteins were grouped into 11 new categories (Figure 1.B).\u003c/p\u003e\n\u003ch2\u003e3.2 Meta-Analysis of RNAseq\u003c/h2\u003e\n\u003cp\u003eA meta-analysis of four published transcriptomic studies identified 12,157 differentially expressed genes (DEGs). Exploratory analyses, including PCA and volcano plots, are shown in Supplementary Information 2, 3, 4, 5. Cross-referencing these DEGs with the 730 putative PRRs revealed 283 genes with evidence of pathogen-responsive expression, hence defined \u0026nbsp;as PRR-DEGs.\u003c/p\u003e\n\u003cp\u003ePathogen-specific expression patterns \u0026nbsp;were observed. A total of 70, 147, 106 and 75 PRR-DEGs were detected for \u003cem\u003eA. psidii\u003c/em\u003e (rust), \u003cem\u003eR. solanacearum\u003c/em\u003e (bacterial wilt),\u003cem\u003e\u0026nbsp;C. austroafricana\u003c/em\u003e (canker) and \u003cem\u003eL. invasa\u003c/em\u003e (gall wasp), respectively. Besides, 28, 90, 47 and 25 were unique for each pathogen, respectively. On the other hand, among the PRR-DEGs, approximately 41% were consistently downregulated and 34% consistently upregulated. The remaining 25% showed variable directionality depending on the pathogen, treatment, or genetic background. Additionally, 18 genes responded to three or all pathogens, suggesting potential broad-spectrum (Figure 2).\u003c/p\u003e\n\u003ch2\u003e3.3 Phylogenetic analysis of PRR and PRR-DEG\u003c/h2\u003e\n\u003cp\u003eTo explore relationships between domain composition and functional roles, Neighbor-Joining phylogenetic trees were generated for both the full set of 730 PRRs (Supplementary Information 6) and the subset of 283 PRR-DEGs. Proteins were colour-coded by the new 11 categories, and by their differential expression (or not) against specific pathogens, respectively. An integrated overview tree summarises these results (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitial analysis revealed clear clustering patterns between RLKs and RLPs. Among the grouped sequences, the TM-LRR category accounted for approximately 25% of the total, notable for its well-defined clustering. Similarly, the various categories within RLKs also showed distinctly defined clusters.\u003c/p\u003e\n\u003cp\u003eRegarding the pathogen response of PRR-DEGs, it was observed that about 30% of the RLPs were DEGs and only exhibited evidence of response to one or two pathogens. Conversely, approximately 40% of the RLK-related categories were DEGs, showing responses from one to four pathogens. Furthermore, RLKs with malectin domains predominantly responded to more than one pathogen.\u003c/p\u003e\n\u003ch3\u003e3.3.1 Chromosome distribution and clustering of PRR and PRR-DEG\u003c/h3\u003e\n\u003cp\u003eAn analysis of gene distribution across chromosomes identified 663 putative PRR genes on the eleven chromosomes and 67 at the scaffold level. Chromosomes 6 and 4 harboured the highest and the lowest number of PRR genes, with 130 and 29 respectively. This finding indicates no direct correlation between the number of PRR genes and chromosome size, as chromosomes 3 and 10 are the longest and the shortest, respectively (Figure 4). Additionally, the proportion of PRR genes per chromosome ranged from 1% on chromosomes 2 and 8 to 3.2% on chromosome 6 (Table 1).\u003c/p\u003e\n\u003cp\u003eMoreover, PRR genes were classified as within a \u0026quot;Cluster\u0026quot;, as genes located less than 200,000 base pairs apart from each other (Holub, 2001), or \u0026quot;Singleton\u0026quot;. This analysis revealed 462 genes in clusters and 201 as singletons across the eleven chromosomes. Notably, the majority of clusters were found on chromosome 6, with the minority on chromosome 2.\u003c/p\u003e\n\u003cp\u003eAmong the 283 PRR-DEGs, 265 were identified on the eleven chromosomes and 18 at the scaffold level (Figure 4).\u003c/p\u003e\n\u003cp\u003eAdditionally, chromosomes 10 and 11 exhibited a \u0026gt;50% higher proportion of PRR-DEGs relative to the overall PRR distribution (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 1\u003c/strong\u003e \u003cem\u003eSummary of the pattern recognition receptors (PRR) genes identified on the eleven chromosomes and scaffolds.\u003c/em\u003e Chromosome: chromosome number; Size in bp: size of the chromosome in base pairs; No. of total genes: number of total genes per chromosome; No. of PRR: number of putative PRR genes identified by the three strategies; % PRR: percentage of PRR is represented based on the total number of coding genes on the chromosome;. No. of differentially expressed gene (DEG): the number of PRR-DEG genes per chromosome; No. of Clustered genes: the number of PRR genes that are clustered together (distance \u0026lt; 200 kpb); No. of cluster: the number of clusters per chromosome; No. of Singleton: the number of genes that are not clustered together; No. of Singleton: the number of genes that are not clustered together.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize in bp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of total genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of PRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% PRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of DEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of Clustered genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of Cluster\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u0026deg; of Singleton\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e45,527,262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e60,273,285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e3,287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e85,001,648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e3,513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e41,674,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e77,196,103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e3,306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e58,190,708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e4,054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e55,515,396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e73,422,429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e4,087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e39,799,183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e38,249,343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e45,397,251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e3,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScaffold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e2,228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.1892%;\"\u003e\n \u003cp\u003e640,439,468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e36,349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.78378%;\"\u003e\n \u003cp\u003e730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.9527%;\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.473%;\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.96622%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.4 New functional annotation and GO enrichment\u003c/h2\u003e\n\u003cp\u003eUsing the described methodology, GO enrichment graphs for Biological Processes and Molecular Function were generated utilising both the original annotation (\u003cem\u003eE. grandis v2.0\u003c/em\u003e reference genome) and the new annotation (with PANNZER tool) of PRR genes. It was generally observed that the new annotation revealed a greater number of significant GO enriched terms that were related to various biotic stresses.\u003c/p\u003e\n\u003cp\u003eFor \u0026quot;Biological Processes\u0026quot;, the original annotation of PRRs only yielded the significantly enriched term \u0026quot;protein phosphorylation\u0026quot;. Although the phosphorylation process corresponds to the kinase domains of the RLKs, no other GO terms related to the plant Inducible Immunity System were assigned. In contrast, the new annotation identified not only the terms \u0026quot;phosphorylation\u0026quot; (go id: 0016310) and \u0026quot;protein phosphorylation\u0026quot; (go id: 0006468) but also significant terms specific to responses to various biotic stresses, such as \u0026quot;defence response\u0026quot; (go id: 0006952), \u0026quot;defence/response to other organism\u0026quot; (go id: 0098542), and terms specifically related to fungal response like \u0026quot;defence/response to fungus\u0026quot; (go id: 0050832). Additionally, terms related to PTI and PRR responses, such as \u0026quot;transmembrane receptor protein serine/threonine kinase\u0026quot; (go id: 0007178), were identified. (Figure 5.A)\u003c/p\u003e\n\u003cp\u003eWhen all putative PRRs and PRR-DEGs were compared based on the new annotation, terms such as \u0026ldquo;defence response\u0026rdquo;, \u0026ldquo;response to other organisms\u0026rdquo; and \u0026ldquo;response to fungus\u0026rdquo; remained significant and abundant among the PRR-DEGs. Notably, the term \u0026ldquo;positive regulation of defence response\u0026rdquo; also emerged as significant (Figure 5.B).\u003c/p\u003e\n\u003cp\u003eWith regard to the orthologs identified in \u003cem\u003eA. thaliana\u003c/em\u003e, their functional annotation was found to be consistent with defence responses to a range of stresses.\u003c/p\u003e\n\u003cp\u003eRegarding \u0026ldquo;Molecular Function\u0026rdquo;, although the most significantly enriched and abundant terms corresponded to general molecular functions, other significant terms were also observed that could be specifically associated with functions involved in PTI. These included receptor-related functions such as \u0026ldquo;signaling receptor activity\u0026rdquo; (go id: 0038023), \u0026ldquo;receptor serine/threonine kinase binding\u0026rdquo; (go id: 0033612), \u0026ldquo;protein serine/threonine kinase activity\u0026rdquo; (go id: 0004674), and \u0026ldquo;transmembrane signaling receptor activity\u0026rdquo; (go id: 0004888) (Supplementary Information 7).\u003c/p\u003e\n\u003ch2\u003e3.5 Analysis of Cis-Regulatory Elements\u003c/h2\u003e\n\u003cp\u003eThe analysis of promoter regions located 1500 bp upstream of the coding region was conducted to examine cis-regulatory elements (CREs) in PRR genes. Elements related to cell cycle development, hormonal regulation, and stress response were identified (Figure 6.A). Among these, some genes exhibited cis-elements associated with hormonal responses, such as to methyl jasmonate (MeJA), a derivative of jasmonic acid, abscisic acid (ABA), and other phytohormones like gibberellin, auxin, and salicylic acid (SA). Other stress-related elements included regulatory elements responsive to light, drought, low temperatures, anaerobic conditions, defence, and pathogen response.\u003c/p\u003e\n\u003cp\u003eFurthermore, the enrichment analysis of CREs revealed that, of a total of 26 CREs significantly enriched (p-value \u0026lt; 0.05) in PRR-DEGs compared to those without DEG evidence to date, the most significantly enriched were elements responsive to auxin and drought (Supplementary Information 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo delve into potential transcriptional regulatory mechanisms of PRRs, an additional \u0026nbsp;analysis was conducted focused on PRR-DEGs showing responses to 3 and 4 pathogens. The results showed enriched cis-elements linked to stress responses and hormonal regulation. Each gene exhibited a unique configuration of these elements, with a predominance of those responsive to light and MeJA (Figure 6.B). However, less frequent but notable elements were also identified, such as those associated with pathogen defence, drought and low temperature stress responses, as well as elements associated with anaerobic and anoxic stress responses.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.6 Gene duplication\u003c/h2\u003e\n\u003cp\u003eUsing the MCScanX software, PRR classification based on duplication mechanisms revealed 662 genes categorised into four groups: 1) 122 segmental duplicates or related to whole-genome duplication (WGD), where paralogs are found in collinear blocks within the same species, relating to more ancestral duplications; 2) 128 dispersed duplicates, characterised by a lack of proximity between paralogs on chromosomes or conservation of synteny; 3) 318 tandem duplicates are noted for their location side by side and relation to more recent duplication events in genome evolution; 4) 94 proximal duplicates, where paralogs are close together but separated by fewer than 20 genes.\u003c/p\u003e\n\u003cp\u003eBased on the segmental duplicates identified by MCScanX (74 pairs), the nonsynonymous to synonymous substitution ratio (Ka/Ks) was calculated. For 47 gene pairs, the Ka/Ks ratio was less than 1 (ranging from 0.051 to 0.597), indicating that these PRRs have undergone purifying (negative) selection. In addition, 27 of these pairs showed a synonymous substitution ratio (pS) greater than 0.75, suggesting that the sequence divergence was sufficient to estimate a reliable synonymous substitution rate (kS).\u003c/p\u003e\n\u003cp\u003eAdditionally, an analysis of duplicate pairs was performed, focusing on genes with evidence as DEGs and those without. Among these gene pairs, 24 lacked evidence of response to biotic stress. In 14 pairs, both genes were DEGs, while in 36 pairs, at least one gene was identified as DEG (Figure 7).\u003c/p\u003e\n\u003ch2\u003e3.7 Catalogue of PRR genes of potential interest in \u003cem\u003eEucalyptus\u003c/em\u003e breeding programs\u003c/h2\u003e\n\u003cp\u003eBased on our results, we propose a catalogue of 16 PRR genes that were differentially expressed in at least three diseases, identified in multiple publications, and upregulated in response to at least one pathogen (Table 2). Among these, four genes responded to all four pathogen types analyzed\u0026mdash;bacteria, two distinct fungal pathogens, and the gall wasp. Additionally, nine PRR-DEGs, while not responsive to all pathogens included in the meta-analysis, were induced by a range of organisms, including fungi, insects, and bacteria. The remaining three genes responded to two types of fungi as well as bacteria, highlighting the potential broad-spectrum defense capability of these PRRs against diverse biotic stresses.\u003c/p\u003e\n\u003cp\u003eRegarding the chromosomal distribution of the 16 PRR-DEGs included in the catalogue, two were annotated in scaffolds, while the remaining 14 were distributed across seven of the eleven chromosomes (2, 3, 6, 8, 9, 10, and 11). Notably, twelve potentially broad-spectrum PRR-DEGs were grouped into nine clusters located on chromosomes 3, 6, 8, 10, and 11. Four of these clusters consisted exclusively of PRRs belonging to the same category. On chromosome 6, one cluster comprised RLKs with malectin domains; similarly, chromosome 8 contained another RLK cluster. In addition, two clusters located on chromosome 11 were composed of RLK categories.\u003c/p\u003e\n\u003cp\u003eConsistent with these findings, all candidate genes had orthologues in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, which themselves have been reported to respond to multiple biotic stresses. Moreover, these orthologous groups are conserved and stress-responsive across a variety of plant species, including forest trees such as pine (\u003cem\u003ePinus pinaster\u003c/em\u003e) and olive (\u003cem\u003eOlea europaea\u003c/em\u003e), as well as citrus species such as lemon (\u003cem\u003eCitrus limon\u003c/em\u003e) and grapefruit (\u003cem\u003eCitrus maxima\u003c/em\u003e) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 2\u003c/strong\u003e \u003cem\u003eCatalogue of PRR genes of potential interest in Eucalyptus breeding programs\u003c/em\u003e. The table summarises 16 PRR genes of interest, including: gene identifier (Gene_ID_V2), chromosome (CHR), genomic position (Start, End), cluster assignment within 200 kbp (N\u0026deg; Cluster (\u0026lt;200 kbp)), pathogens for which the gene shows differential expression (Pathogen), condition of differential expression (Condition DEG), unified PRR category (Categories), orthologues in \u003cem\u003eA. thaliana\u003c/em\u003e (\u003cem\u003eA. thaliana\u003c/em\u003e Orthologues), functional annotation in \u003cem\u003eE. grandis\u003c/em\u003e v2.0 (Functional Annotation (\u003cem\u003eE. grandis\u003c/em\u003e)), updated functional annotation (New annotation), and supporting literature (Evidence).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"1016\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGene_ID_V2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 28px;\"\u003e\n \u003cp\u003eCHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 55px;\"\u003e\n \u003cp\u003eStart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003eEnd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003eN\u0026deg; Cluster (\u0026lt;200 kpb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 97px;\"\u003e\n \u003cp\u003ePathogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCondition DEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 40px;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAnnotation in \u003cem\u003eE. grandis\u003c/em\u003e v 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 379px;\"\u003e\n \u003cp\u003eNew annotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. thaliana\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eOrthologous\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Annotation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Annotation (\u003cem\u003eE. grandis\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA.thaliana\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Orthologous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Annotation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.B03087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e51188519\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e51192057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSingleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R,S)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eDOWN/UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G25560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNSP-interacting kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase At2g23950 family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G25560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNSP-interacting kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eFontes et al. (2004); Sakamoto et al. (2012); Santos et al. (2009)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.C00984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e15768031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e15771767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eUP (R,S)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G29990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProtein kinase family protein with leucine-rich repeat domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eReceptor-like protein kinase HSL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT5G25930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ekinase family with leucine-rich repeat domain-containing protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023); Navarro et al. (2004); Mendy et al. (2017); Artico et al. (2014); Little et al. (2007); Lu et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.F00314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5586470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5590029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eProbable leucine-rich repeat receptor kinase At1g35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.F00316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5561785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5571145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (S)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.F01307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e17466429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e17483294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eDOWN (S)\u003c/p\u003e\n \u003cp\u003eDOWN/UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR_Malec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G14840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat transmembrane protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G14840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat transmembrane protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eHussan et al. (2020); Marchese et al. (2023); Little et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.F04434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e56878674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e56890048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eUP (R)\u003c/p\u003e\n \u003cp\u003eDOWN (R,S)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_Malec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT1G56130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat transmembrane protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT1G56130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat transmembrane protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eBinagwa et al. (2021); Xu et al. (2020); Zhang et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.H00707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9621912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9625470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R,S)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eProbable leucine-rich repeat receptor-like protein kinase At1g35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.H00710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9652678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9656274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R,S)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.H00949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e7852826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7856009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.I00656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e13607921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e13610024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSingleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eProtein kinase domain-containing protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.J02909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e34748881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e34755956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR_MalecLike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT2G19190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFLG22-induced receptor-like kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like serine/threonine-protein kinase At2g19230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT2G19190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFLG22-induced receptor-like kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eYang et al. (2024); He et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.K02381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e31739538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e31744299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R,S)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT1G35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProtein kinase family protein with leucine-rich repeat domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eProbable leucine-rich repeat receptor-like protein kinase At1g35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.K02386\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e31822271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e31826333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (S)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT1G35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProtein kinase family protein with leucine-rich repeat domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eProbable leucine-rich repeat receptor-like protein kinase At1g35710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT4G08850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eChalupowicz et al. (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.K02775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e35174545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e35176836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (S)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT2G31880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLeucine-rich repeat receptor-like serine/threonine/tyrosine-protein kinase SOBIR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT2G31880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eBahar et al. (2016); Navarro et al. (2004); Li et al. (2004); Takahashi et al. (2018); Takahashi et al. (2016); Whitham et al. (2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.L00469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003escaff_24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e88611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e91807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eC. austroafricana\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eDOWN/UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G47570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eReceptor kinase-like protein Xa21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G47110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDyda et al. (2022); Modesto et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEucgr.L01229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003escaff_140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e58951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e61469\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eL. invasa\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eA. psidii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eR. solanacearum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDOWN (R)\u003c/p\u003e\n \u003cp\u003eUP (S)\u003c/p\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eTM_K_LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G47570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eReceptor kinase-like protein XA21-like protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAT3G47090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLeucine-rich repeat protein kinase family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCantila et al. (2020); Little et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePattern-recognition receptors (PRRs) form the first cellular barrier against pathogens. Breeding strategies that leverage PRRs can deliver broad, non race-specific defence, in contrast to many R genes. Although several tools exist to annotate disease-resistance genes and proteins, the limited conservation of PRR domains and the use of different algorithms cause each method to recover a different gene set. Here, two state-of-the-art PRR predictors (DRAGO3 and RRGPredictor) were compared with a broad, multi-species curation from Ngou et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in \u003cem\u003eEucalyptus grandis.\u003c/em\u003e DRAGO3 predicted 2,793 PRR-encoding genes (~\u0026thinsp;6.1% of the proteome), RRGPredictor predicted 1,261 (~\u0026thinsp;2.8%), and the Eucalyptus subset in Ngou et al. comprised 810 (~\u0026thinsp;1.8%). This spread reflects divergent domain requirements and decision rules across strategies. After harmonising gene identifiers, categories and metadata, 730 high-confidence PRR gene candidates were identified (i.e., consistently by all three approaches), representing\u0026thinsp;~\u0026thinsp;1.62% of protein-coding genes. Relative to \u003cem\u003ePopulus trichocarpa\u003c/em\u003e (~\u0026thinsp;1% PRRs; Ngou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the higher proportion in \u003cem\u003eE. grandis\u003c/em\u003e is consistent with high duplication rates reported for Eucalyptus and may reflect lineage-specific expansion (Myburg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Borthakur et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePRR classification also proved challenging because each tool presented different categories, sometimes using the same label with different composition. For example, between the PRRs assigned by DRAGO3, the broad \u0026ldquo;KIN\u0026rdquo; class showed limited overlap with PRRs predicted by RRGPredictor or by Ngou et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Inspection of motif composition indicated that many members lacked ectodomains and therefore fall outside PRRs \u003cem\u003esensu stricto\u003c/em\u003e (Ngou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Restrepo-Montoya et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The 'UNKNOWN' labels used for almost all PRRs by RRGPredictor likewise underscored the limits in fine-grained categorisation. To reduce tool-specific bias, classification was reframed based on PFAM domain combinations. This approach allowed categories to be tracked across methods while avoiding circularity. Eleven domain-based categories (plus a small bin without PFAM annotation) were defined. TM-K-LRR (RLK) sequences were the most abundant, whereas TM-LRR (RLP) sequences were less represented, likely reflecting detection difficulty rather than true biological scarcity (Silva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Many categories contained malectin or malectin-like ectodomains. Phylogenetic patterns were concordant with domain architecture: RLKs formed large clades, RLPs clustered separately, and malectin versus malectin-like ectodomains resolved into related but distinct lineages.\u003c/p\u003e \u003cp\u003eWithin the set of 730 robust PRRs, genes were unevenly distributed across the genome. Chromosome 6 showed the highest number and proportion of PRRs and the densest clustering, suggesting an immune-enriched region. Prior work in \u003cem\u003eE. grandis\u003c/em\u003e has also highlighted chromosome 6 as stress-responsive, as it harbours the largest set of VQ (valine\u0026ndash;glutamine) genes, regulators implicated in SA- and JA-mediated defence signalling. This convergence of VQ factors and dense PRR clustering suggests coordinated regulation of immune responses on this chromosome (Yan et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jing \u0026amp; Lin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Glazebrook, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Clusters enriched for RLKs with malectin/malectin-like ectodomains were also observed on chromosomes 6, 8, 10, and 11, a pattern consistent with local duplication and coordinated evolution of cell-wall\u0026ndash;sensing modules; similar proximity has been described in Arabidopsis (Yang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Duplication emerged as a major driver of PRR content: extensive tandem duplication (318 genes in 127 tandems) co-occurred with segmental, proximal, and dispersed events. Ka/Ks estimates for segmental pairs indicated predominantly purifying selection, with a minority of more divergent pairs consistent with sub- or neofunctionalisation. A great number (40.5%) of intergenic-distance clusters (\u0026lt;\u0026thinsp;200 kb) showed total identity with MCScanX-defined tandems (135 genes), connecting local genome structure to PRR expansion. By contrast, chromosome 5 lacked duplicated PRRs, in agreement with earlier reports of ETI-related NBS-LRR enrichment on that chromosome (Christie et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), suggesting that PTI and ETI components occupy partially distinct genomic regions.\u003c/p\u003e \u003cp\u003eAn initial functional view based on the \u003cem\u003eE. grandis\u003c/em\u003e v2.0 GO annotation in Phytozome suggested limited defence enrichment and risked under-interpretation. Re-annotation with PANNZER improved functional resolution and recovered defence-related Biological Process and Molecular Function terms, with strong signals for phosphorylation/kinase activity, signalling-receptor activity, and ATP binding, features expected for PRR pathways (Naidoo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003er\u0026ouml;nen \u0026amp; Holm, 2022; Bolger et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that genome annotations evolve as tools and biological knowledge improve, periodic re-annotation is warranted (Bolger et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA meta-analysis of four RNA-seq studies spanning \u003cem\u003eE. grandis, E. urophylla, and E. grandis \u0026times; E. camaldulensis\u003c/em\u003e under rust, canker, bacterial wilt, and gall-wasp challenge identified 283 of the 730 catalogue genes as differentially expressed, indicating broad engagement of the PRR repertoire during biotic stress. It is worth noting that additional proteomics and transcriptomics studies on other fungi, including \u003cem\u003eCalonectria pseudoreteaudii\u003c/em\u003e (leaf blight) and \u003cem\u003eC. austroafricana\u003c/em\u003e, were identified in the literature but could not be incorporated, as raw sequencing data were not publicly available; consequently, these works were excluded from the meta-analysis despite their relevance (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zwart et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). From the analysed data, approximately 19% of duplicated PRRs were differentially expressed under biotic stress, linking duplication history to functional deployment. More than half of malectin/malectin-like RLKs were differentially expressed, consistent with an important contribution at the interface of cell-wall status, growth, and immunity (Shiu \u0026amp; Bleecker, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ngou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While LRR-RLKs are expected to recognise bacterial epitopes (e.g., flagellin, EF-Tu), malectin/malectin-like RLKs have been linked to sensing DAMPs and chitin-like cues (Baez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gandhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Responses were largely pathogen-specific and often depended on species or clones, and mixed directionality (up- and down-regulation) across pathosystems suggested context-dependent tuning of PRR signalling. Several PRR-DEGs genes present in the \u003cem\u003eE. grandis\u003c/em\u003e reference were only induced in response to a single pathogen. In contrast, 93 PRR-DEGs were shared by at least two pathogens, with 16 responding to three or more. At least one instance of up-regulation was observed per gene, features consistent with broad-response candidates. Promoter analyses supported these assignments: cis-elements associated with MeJA, SA, ethylene, auxin, light, and abiotic stress were common to all PRRs, and the 16 multi-pathogen PRR-DEGs were enriched for MeJA- and light-responsive motifs, with frequent auxin motifs. These regulatory patterns align with hormone crosstalk and growth\u0026ndash;defence trade-offs during PTI, including modulation of cell-wall properties (Kaur \u0026amp; Pati, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Baez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sajjad et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese 16 PRR-DEGs include orthologues with cross-taxon evidence of defence roles. For example, the Arabidopsis gene AT1G35710 (Eucgr.K02381/K02386) was detected here under fungi (\u003cem\u003eC. austroafricana\u003c/em\u003e, \u003cem\u003eA. psidii\u003c/em\u003e), insects (\u003cem\u003eL. invasa\u003c/em\u003e), and bacteria (\u003cem\u003eR. solanacearum\u003c/em\u003e). It also has been reported as up-regulated in pine resistance to nematodes and upon exposure to \u003cem\u003eXanthomonas\u003c/em\u003e outer-membrane vesicles in \u003cem\u003eArabidopsis\u003c/em\u003e, leading to reduced pathogen growth (Modesto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chalupowicz et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AT3G14840 (Eucgr.F01307) was differentially expressed here in canker, gall-wasp, and wilt challenges, in line with prior reports as a resistance-gene analogue in olive leaf spot, as an interactor of \u003cem\u003eXanthomonas\u003c/em\u003e LPS in \u003cem\u003eArabidopsis\u003c/em\u003e, and as induced by pierid oviposition (Marchese et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hussan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Little et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). AT3G47570 (Eucgr.L00469/L01229) responded here to canker, rust, gall-wasp and wilt, consistent with up-regulation during citrus greening responses and with high non-synonymous polymorphism among candidate wilt-resistance genes in pepper (Gao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Beyond expression, the 16 candidates showed a non-random genomic arrangement: 12 were located within nine clusters distributed across chromosomes 3, 6, 8, 10, and 11, while two occurred as singletons on chromosomes 2 and 9. Several clusters consisted of receptors belonging to the same category, including groups of RLKs with malectin or malectin-like ectodomains on chromosomes 6 and 11. It is consistent with local duplication followed by retention of functionally related modules and mirroring patterns described for wall-sensing RLKs in \u003cem\u003eArabidopsis\u003c/em\u003e (Yang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present work uses publicly available data and resources in order to generate new knowledge with potential breeding impact, contributing to giving significance to the large amount of sequencing data available and working towards open science. Their recurrence across systems, together with the multi-pathogen responses observed here and their predicted hormone response, identify the 16 PRR-DEGs as strong candidates for subsequent validation and improvement efforts, including marker development, association mapping in breeding populations, and functional tests such as ligand identification, transient expression, or CRISPR-based perturbations in \u003cem\u003eEucalyptus\u003c/em\u003e or proxy systems (Modesto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chalupowicz et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marchese et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Universidad Nacional de Moreno (PICyDT UNM VII 2021).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Data collection and analysis were performed by YEG and CVF. All authors participated in the interpretation of the results. The first draft of the manuscript was written by YEG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eNo data were generated for this study; the data analysed were publicly available and duly referenced in the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe would like to express our gratitude to Pamela Villalba, Juan Gabriel Rivas, and Pablo Aguilera for their valuable contributions to this work.\u003c/p\u003e\n\u003cp\u003eCVF is a member of the National Research System (SNI, Uruguay), and Programa de Desarrollo de Ciencias B\u0026aacute;sicas (PEDECIBA, Uruguay).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexa A, Rahnenfuhrer J (2024). topGO: Enrichment Analysis for Gene Ontology. doi:10.18129/B9.bioc.topGO, R package version 2.59.0, https://bioconductor.org/packages/topGO.\u003c/li\u003e\n\u003cli\u003eArtico, S., Ribeiro-Alves, M., Oliveira-Neto, O. B., de Macedo, L. L. 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Evidence for salicylic acid signalling and histological changes in the defence response of Eucalyptus grandis to Chrysoporthe austroafricana. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 45402.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA280236\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/bioproject/PRJNA280236\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/305347\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/bioproject/305347\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA588626\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra/PRJNA588626\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa/browse/CRA005055\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/gsa/browse/CRA005055\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/plaza/versions/plaza_v5_dicots/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.psb.ugent.be/plaza/versions/plaza_v5_dicots/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/plantcare/html/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/plantcare/html/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plan","sideBox":"Learn more about [Plant Molecular Biology](https://www.springer.com/journal/11103)","snPcode":"11103","submissionUrl":"https://submission.nature.com/new-submission/11103/3","title":"Plant Molecular Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8407889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8407889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePattern-recognition receptors (PRRs) initiate plant pattern-triggered immunity (PTI), encompassing receptor-like kinases (RLKs) and receptor-like proteins (RLPs). \u003cem\u003eEucalyptus grandis\u003c/em\u003e, an economically important species worldwide, is a long-lived organism that faces multiple disease pressures. The deployment of PRR-based breeding tools offers a route to broad, non-race-specific resistance that can remain effective across outbreaks. The objective of this work was to identify and characterise the PRR repertoire of \u003cem\u003eE. grandis\u003c/em\u003e using public multi-omics data. Two state-of-the-art predictors were compared with a multi-species curation and a meta-analysis was conducted compiling transcriptomic data under biotic stress. A set of 730 PRR candidates were consistently identified across three sources (~\u0026thinsp;1.6% of protein-coding genes), of which 283 were differentially expressed (PRR-DEGs). A PFAM domain-based scheme was applied to standardise classification across tools. RLKs (TM-K-LRR) predominated over RLPs (TM-LRR), while many carried malectin or malectin-like ectodomains. PRR genes were unevenly distributed across the genome: chromosome 6 had the highest count and the densest clustering. Gene-family expansion appeared mainly driven by duplication, with extensive tandem arrays supported by segmental, proximal and dispersed events. Gene Ontology and cis-elements annotations in PRR/PRR-DEGs showed significant enrichment in terms related to cell cycle development, hormonal regulation and stress response. We proposed a catalogue of 16 PRR which resulted in DEGs against at least three pathogens, suggesting their broad spectrum and robustness. Most of them presented orthologues with cross-taxon evidence of defence roles. This study delineates novel multi-pathogen candidate PRR genes, providing valuable information to assist \u003cem\u003eEucalyptus\u003c/em\u003e breeding programs.\u003c/p\u003e","manuscriptTitle":"Integrative approach to identify robust Pattern Recognition Receptors in Eucalyptus grandis: novel candidates for disease resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 04:37:22","doi":"10.21203/rs.3.rs-8407889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-23T04:40:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T10:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185260667320205114128585486625439148996","date":"2026-01-06T22:23:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T22:19:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-20T07:00:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-20T06:58:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Molecular Biology","date":"2025-12-19T19:47:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plan","sideBox":"Learn more about [Plant Molecular Biology](https://www.springer.com/journal/11103)","snPcode":"11103","submissionUrl":"https://submission.nature.com/new-submission/11103/3","title":"Plant Molecular Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a82733fe-1112-45af-ad53-aada668309e6","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T02:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 04:37:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8407889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8407889","identity":"rs-8407889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}