Orthogroup Analysis of Nucleotide-Binding Site Genes: Evolution, Methodology, and Clinical Translation

Abstract

This article provides a comprehensive resource for researchers and drug development professionals on the orthogroup analysis of Nucleotide-Binding Site (NBS) genes, a critical superfamily of disease resistance (R) genes in plants. We explore the foundational principles of NBS gene diversity and evolution across species, from early land plants to modern crops. The content details cutting-edge methodological workflows for orthogroup identification, from data collection to evolutionary inference, and addresses common troubleshooting and optimization challenges in genomic analysis. Furthermore, we synthesize modern validation techniques, including functional characterization and multi-omics integration, highlighting how comparative studies between susceptible and resistant plant varieties uncover key genetic variants for disease resistance. This guide aims to bridge fundamental evolutionary insights with practical applications in agricultural and biomedical research.

Unraveling NBS Gene Diversity and Evolutionary History Across Plant Kingdoms

Nucleotide-binding site (NBS) domain genes constitute the largest and most critical class of plant disease resistance (R) genes, forming the backbone of the plant immune system's intracellular perception machinery. These genes encode proteins capable of recognizing diverse pathogen-secreted effectors to initiate robust immune responses, ultimately leading to pathogen restriction [1]. Approximately 80% of all functionally characterized plant R genes belong to the NBS-LRR (or NLR) gene family, highlighting their predominant role in plant defense mechanisms [1] [2]. The NBS domain serves as the molecular engine of these proteins, while associated domains provide specialized functions in pathogen recognition and signaling transduction. Understanding the core structure, classification, and mechanistic operation of NBS domain genes provides fundamental insights into plant-pathogen co-evolution and offers potential avenues for engineering disease-resistant crops through advanced breeding strategies.

Core Structure and Domain Architecture of NBS Proteins

NBS-LRR proteins exhibit a conserved modular tripartite structure that enables their function as intracellular immune receptors. This architecture consists of three fundamental components: a variable N-terminal domain, a central nucleotide-binding site (NBS) domain, and a C-terminal leucine-rich repeat (LRR) domain [1] [3].

Table 1: Core Domains of NBS-LRR Proteins and Their Functional Roles

Domain	Location	Key Function	Conserved Motifs
N-terminal	N-terminus	Signaling initiation and protein interaction	TIR, CC, or RPW8 domains
NBS (NB-ARC)	Central region	ATP/GTP binding and hydrolysis; molecular switch	P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, GLPL
LRR	C-terminus	Pathogen recognition specificity; protein-protein interactions	Variable leucine-rich repeats

The N-terminal domain determines the protein's signaling pathway activation and exists in three major forms: Toll/Interleukin-1 receptor (TIR), coiled-coil (CC), or resistance to powdery mildew 8 (RPW8) domains [1] [3]. The central NBS domain (also referred to as NB-ARC in plants) contains highly conserved motifs that facilitate nucleotide binding and hydrolysis [3]. This domain functions as a molecular switch, cycling between ADP-bound (inactive) and ATP-bound (active) states to regulate protein activity [4]. The C-terminal LRR domain consists of multiple leucine-rich repeats that confer pathogen recognition specificity through direct or indirect effector binding [1] [3]. The remarkable variability in LRR sequences enables the recognition of diverse pathogen effectors, forming the genetic basis for specific resistance in plants.

Classification and Phylogenetic Diversity of NBS-LRR Genes

Based on differences in their N-terminal domains, NBS-LRR genes are classified into several major subfamilies with distinct evolutionary patterns and functional specializations.

Major Subfamilies and Their Characteristics

The primary classification of NBS-LRR genes depends on the N-terminal domain type, leading to three major subfamilies:

• TNL (TIR-NBS-LRR): Characterized by a TIR domain at the N-terminus. These proteins often initiate signaling pathways that involve NADase activity and interact with EDS1-PAD4 complexes [5].
• CNL (CC-NBS-LRR): Contain a coiled-coil domain at the N-terminus. Some CNL proteins form calcium-permeable channels in resistosomes that activate downstream immune signaling [5].
• RNL (RPW8-NBS-LRR): Feature an RPW8 domain and often function as "helper" NLRs that amplify defense signals initiated by "sensor" NLRs [1] [5].

Table 2: Comparative Analysis of NBS-LRR Subfamilies Across Plant Species

Plant Species	Total NBS Genes	CNL Subfamily	TNL Subfamily	RNL Subfamily	Reference
Arabidopsis thaliana (model dicot)	207	61	~40	~5	[1]
Oryza sativa (rice, monocot)	505	275	0	0	[1] [2]
Salvia miltiorrhiza (medicinal plant)	196	61	0	1	[1]
Capsicum annuum (pepper)	252	248	4	Not specified	[3]
Dendrobium officinale (orchid)	74	10	0	Not specified	[2]

Evolutionary Patterns and Lineage-Specific Adaptations

Comparative genomic analyses reveal striking evolutionary patterns in NBS-LRR gene distribution across plant lineages. Monocot species, including cereals like rice (Oryza sativa) and maize, have completely lost the TNL subfamily, while maintaining numerous CNL-type genes [1] [2]. This pattern extends to medicinal plants like Salvia miltiorrhiza and orchids from the genus Dendrobium, which also show marked reduction or complete absence of TNL genes [1] [2]. In contrast, gymnosperms such as Pinus taeda exhibit significant expansion of the TNL subfamily, which comprises approximately 89.3% of their typical NBS-LRR repertoire [1]. These distribution patterns reflect lineage-specific evolutionary pressures and adaptations in plant immune systems.

Genomic Organization and Evolutionary Mechanisms

NBS-LRR genes display distinctive genomic organization patterns that contribute to their evolutionary dynamics and functional diversification.

Clustered Arrangement and Tandem Duplications

A hallmark of NBS-LRR genes is their tendency to organize in clusters within plant genomes. In pepper (Capsicum annuum), 54% of NBS-LRR genes (136 genes) form 47 physical clusters distributed across chromosomes [3]. Chromosome 3 contains the highest number of clusters (10), with the largest cluster comprising eight genes [3]. Similar clustering patterns occur across diverse plant lineages, including monocots, dicots, and even fungi, suggesting convergent evolutionary mechanisms [4] [3] [6]. This clustered organization primarily results from tandem gene duplications, which facilitate the generation of genetic novelty through domain shuffling, sequence diversification, and functional specialization.

Evolutionary Trajectories and Domain Assortments

NBS genes exhibit remarkable evolutionary plasticity through various mechanisms:

• Domain Loss and Gain: Many NBS genes exist as "atypical" forms lacking complete domain structures, categorized as N (NBS only), TN (TIR-NBS), CN (CC-NBS), or NL (NBS-LRR) types [1]. In Salvia miltiorrhiza, only 62 of 196 identified NBS genes contained complete N-terminal and LRR domains [1].
• Type Changing: Phylogenetic analyses in Dendrobium species reveal frequent transitions between NBS gene types throughout evolution, contributing to functional diversification [2].
• NB-ARC Domain Degeneration: Degeneration of the core nucleotide-binding domain represents another evolutionary pathway observed in orchid NBS genes [2].

These evolutionary mechanisms collectively generate the diversity necessary for plants to recognize rapidly evolving pathogen effectors while maintaining core immune signaling functions.

Molecular Mechanisms and Immune Signaling Pathways

NBS-LRR proteins function as sophisticated molecular switches in plant immunity, particularly in effector-triggered immunity (ETI). Recent structural studies have elucidated detailed mechanisms of NLR activation and signaling.

From Pathogen Recognition to Immune Activation

The activation of NBS-LRR proteins follows a multi-step process:

• Recognition: In direct recognition, NLR proteins detect pathogen effectors through their LRR domains. In indirect recognition (guard hypothesis), NLRs monitor host proteins that are modified by pathogen effectors [3].
• Activation: Effector binding induces conformational changes that promote ATP binding and hydrolysis by the NBS domain, switching the protein from an inactive to active state [5] [4].
• Oligomerization: Activated NLR proteins assemble into wheel-like oligomeric complexes called "resistosomes" [5] [6].
• Signaling: Resistosomes initiate downstream immune signaling through various mechanisms, including calcium influx (CNL resistosomes) or NADase activity (TNL resistosomes) [5].

NBS Gene Immune Signaling Pathway

Helper-Sensor Networks and Signal Transduction

Recent evidence reveals that many NLRs function in paired or network configurations rather than as solitary units. In these networks, "sensor" NLRs specifically recognize pathogen effectors, then activate "helper" NLRs that amplify defense signals [5] [6]. For TNL proteins, activation leads to NAD+ cleavage and production of signaling molecules that are perceived by EDS1-PAD4 or EDS1-SAG101 complexes, which subsequently activate helper NLRs like ADR1s and NRG1s [5]. CNL resistosomes, such as ZAR1 and Sr35, form calcium-permeable channels that initiate downstream signaling cascades [5]. These discoveries have transformed our understanding of NBS-LRR protein function, revealing sophisticated immune signaling networks rather than simple one-to-one recognition systems.

Experimental Approaches and Research Methodologies

The identification and characterization of NBS domain genes employs specialized bioinformatic and molecular techniques designed to address their unique genomic features and low sequence conservation.

Genome-Wide Identification Protocols

Step 1: Sequence Identification

• HMM Profile Search: Use Hidden Markov Models (HMMER3) with profiles of conserved NBS domains (e.g., NB-ARC, TIR, CC, LRR) to scan genome assemblies [1] [4] [6].
• BLAST Searches: Perform similarity searches using known NBS domain sequences as queries against target genomes [3].
• Deep Learning Approaches: Implement tools like PRGminer that use dipeptide composition and convolutional neural networks to identify R genes with high accuracy (98.75% in k-fold testing) [7].

Step 2: Domain Annotation and Classification

• Domain Prediction: Use InterProScan, PfamScan, and COILS to identify specific domains (CC, TIR, LRR, NB-ARC) [3] [7].
• Transmembrane Domain Prediction: Apply TMHMM2 and Phobius to exclude membrane-bound receptors [7].
• Subcellular Localization: Use SignalP 4.0 to predict signal peptides [7].

Step 3: Phylogenetic and Structural Analysis

• Multiple Sequence Alignment: Employ tools like MAFFT or MUSCLE to align NBS domain sequences.
• Phylogenetic Tree Construction: Build maximum likelihood or Bayesian inference trees to elucidate evolutionary relationships [1] [2].
• Motif Identification: Use MEME Suite to discover conserved motifs within NBS domains [3].

NBS Gene Identification Workflow

Research Reagent Solutions for NBS Gene Analysis

Table 3: Essential Research Reagents and Tools for NBS Gene Studies

Reagent/Tool	Type	Primary Function	Application Examples
HMMER3	Software	Profile HMM searches for domain identification	Identifying NBS domains in novel genomes [7]
InterProScan	Database/Software	Protein domain and family prediction	Comprehensive domain annotation of NBS-LRR genes [1] [7]
PRGminer	Deep Learning Tool	R gene prediction and classification	Identifying resistance genes in uncharacterized species [7]
Phobius	Prediction Tool	Transmembrane topology and signal peptide prediction	Differentiating intracellular NLRs from membrane receptors [7]
PfamScan	Database Search	Protein family annotation	Classifying NBS genes into subfamilies [3] [7]

Expression Regulation and Functional Modulation

NBS-LRR gene expression and activity are subject to complex regulatory mechanisms that ensure appropriate immune responses while minimizing fitness costs.

Transcriptional and Post-transcriptional Regulation

Analyses of NBS-LRR gene promoters reveal abundant cis-acting elements related to plant hormones (salicylic acid, jasmonic acid, ethylene) and abiotic stress responses [1]. Transcriptome studies in Salvia miltiorrhiza demonstrate close associations between SmNBS-LRR expression patterns and secondary metabolism, suggesting integration of defense responses with metabolic pathways [1]. In Dendrobium officinale, salicylic acid treatment significantly upregulates specific NBS-LRR genes (Dof020138), indicating their responsiveness to defense signaling hormones [2]. RNA-binding proteins (RBPs) also play crucial roles in post-transcriptional regulation of NBS-LRR genes, influencing RNA stability, splicing, and translation during immune responses [8].

Functional Specialization and Pathway Integration

Comprehensive functional annotation of NBS-LRR genes indicates their involvement not only in ETI but also in plant hormone signal transduction pathways and Ras signaling pathways [2]. Weighted gene co-expression network analysis (WGCNA) in D. officinale reveals connections between NBS-LRR genes and pathogen recognition pathways, MAPK signaling pathways, plant hormone signal transduction, biosynthetic pathways, and energy metabolism pathways [2]. These findings illustrate how NBS-LRR genes are integrated into broader cellular networks, coordinating immune responses with developmental and metabolic processes.

NBS domain genes represent a sophisticated plant immune recognition system characterized by modular architecture, diverse classification, dynamic evolution, and complex regulation. Their core structure—featuring variable N-terminal domains, a conserved NBS domain with nucleotide-binding capability, and a C-terminal LRR domain with recognition specificity—enables plants to detect myriad pathogens while conserving signaling mechanisms. The genomic organization of NBS genes in clusters, driven by tandem duplications and domain rearrangements, facilitates rapid evolution and functional diversification in response to changing pathogen pressures.

Future research directions include leveraging structural insights from resistosome formations to engineer novel disease resistance specificities, applying deep learning tools like PRGminer to accelerate R gene discovery in crop wild relatives, and exploiting knowledge of NBS gene regulation to develop broad-spectrum resistance while maintaining plant fitness. Integrating orthogroup analysis across diverse plant lineages will further illuminate evolutionary patterns and functional conservation in NBS gene families, ultimately contributing to more sustainable agricultural practices through enhanced disease resistance.

Nucleotide-binding site (NBS) domain genes constitute one of the largest and most critical superfamilies of plant resistance (R) genes, playing indispensable roles in effector-triggered immunity (ETI) against diverse pathogens [9]. These genes encode intracellular immune receptors, primarily NLR proteins (Nucleotide-binding domain and Leucine-rich Repeat), which function as central components of the plant immune system by recognizing pathogen effector proteins and initiating robust defense responses [10]. The NBS domain, alternatively referred to as the NB-ARC domain (Nucleotide-Binding Adaptor shared with APAF-1, R proteins, and CED-4), serves as a molecular switch for NLR activation, cycling between ADP-bound (inactive) and ATP-bound (active) states to regulate immune signaling [9] [11].

Orthogroup analysis has emerged as a powerful evolutionary framework for classifying NBS genes into functionally relevant groups across multiple species. This approach clusters genes into orthogroups (OGs) based on phylogenetic relationships, distinguishing between core orthogroups (common across multiple species) and unique orthogroups (highly species-specific) [9]. This classification system provides critical insights into the evolutionary dynamics, functional conservation, and species-specific adaptations of NBS genes, revealing how these genes have diversified to provide tailored immune responses across the plant kingdom.

Classical NBS Domain Architectures

Fundamental Domain Composition and Classification

NLR proteins exhibit a characteristic modular architecture consisting of three core domains: a variable N-terminal domain, a central NBS domain, and a C-terminal leucine-rich repeat (LRR) region [12]. The N-terminal domain determines the signaling specificity and falls into two major classes: the Toll/Interleukin-1 Receptor (TIR) domain or the Coiled-Coil (CC) domain [9]. This fundamental distinction divides classical NLRs into two primary categories: TNLs (TIR-NBS-LRR) and CNLs (CC-NBS-LRR) [9] [12]. A third subclass features an N-terminal Resistance to Powdery Mildew8 (RPW8) domain, classified as RNLs [9].

The central NB-ARC domain contains several conserved motifs critical for immune function, including the P-loop (involved in nucleotide binding), GLPL, MHD, and Kinase 2 motifs [12]. The MHD motif, in particular, serves as a key regulatory element, with mutations often resulting in constitutive autoactivation of immune responses [11]. The C-terminal LRR domain facilitates protein-protein interactions and is implicated in pathogen recognition specificity [12].

Major Classical Architectural Classes

Table 1: Classical NBS Domain Architectures and Their Characteristics

Architecture Class	Domain Composition	Key Features	Representative Functions
NBS	NBS	Minimalist structure; lacks both N-terminal and LRR domains	Potential signaling modulator; may function in conjunction with other NLRs
NBS-LRR	NBS-LRR	Contains core nucleotide-binding and leucine-rich repeat domains	Pathogen recognition and immune signaling
TIR-NBS	TIR-NBS	TIR signaling domain with NBS domain	Defense signaling initiation without full LRR region
TIR-NBS-LRR (TNL)	TIR-NBS-LRR	Complete TNL architecture with all three domains	Effector recognition and programmed cell death induction
CC-NBS-LRR (CNL)	CC-NBS-LRR	Complete CNL architecture with all three domains	Effector recognition and immune activation
RNL	RPW8-NBS-LRR	RPW8 domain instead of TIR or CC	Helper NLR for signal amplification

The classical architectures represent the foundational building blocks of plant immune receptors, with full-length TNLs and CNLs comprising the majority of characterized sensor NLRs across dicot and monocot species [9]. These proteins often function in pairs or larger networks, with sensor NLRs detecting pathogen effectors and helper NLRs (often RNLs) facilitating signal transduction [10] [11].

Species-Specific and Non-Canonical Domain Architectures

Diversity Beyond Classical Structures

Beyond the well-characterized TNL and CNL classes, plants have evolved numerous species-specific and non-canonical NBS architectures that reflect lineage-specific adaptations to pathogen pressures. A comprehensive analysis across 34 plant species identified 168 distinct classes of NBS domain architectures, revealing remarkable structural diversity beyond the classical forms [9]. These unconventional architectures often incorporate domains not typically associated with NLR proteins, creating specialized immune receptors with potentially novel functions.

Notable Species-Specific Variants

Table 2: Documented Species-Specific NBS Domain Architectures

Species or Clade	Unusual Architecture	Potential Functional Significance
Multiple species	TIR-NBS-TIR-Cupin1-Cupin1	Integration of cupin domains potentially linking immune signaling with metabolic functions
Multiple species	TIR-NBS-Prenyltransf	Incorporation of prenyltransferase domain suggesting secondary metabolite involvement in defense
Multiple species	Sugar_tr-NBS	Sugar transporter domain fused with NBS, possibly connecting nutrient transport and immunity
Fabaceae crops	Species-specific clustering in CN, TN, and CNL classes	Diversification reflecting family-specific pathogen pressures [13]
Solanaceae	NRCX-NARY paired NLR system	Non-canonical regulatory complex balancing growth and defense [11]

The functional significance of these unconventional domain integrations remains largely unexplored but may represent evolutionary innovations that connect immune signaling with other cellular processes. The TIR-NBS-TIR-Cupin1-Cupin1 architecture, for instance, suggests potential crosstalk between immune signaling and metabolic pathways governed by cupin domains [9]. Similarly, the incorporation of a prenyltransferase domain (TIR-NBS-Prenyltransf) might enable the production of specialized antimicrobial compounds during immune activation.

Genomic Distribution and Evolutionary Dynamics

Chromosomal Distribution and Gene Clustering

NBS genes typically display clustered genomic arrangements rather than random distribution across chromosomes. Studies in asparagus species revealed that NLR genes form distinct clusters on chromosomes, a pattern conserved across related species [12]. This clustering facilitates the generation of diversity through unequal crossing over and gene conversion, enabling plants to rapidly evolve new recognition specificities in response to changing pathogen populations.

The evolution of NBS genes is driven by several genetic mechanisms, with tandem duplications playing a particularly significant role in the expansion and diversification of NBS gene families [9]. Whole-genome duplications (WGD) and small-scale duplications (SSD), including segmental and transposon-mediated duplications, also contribute to NBS gene proliferation, with different plant lineages often favoring distinct expansion mechanisms [9].

Evolutionary Patterns Across Plant Lineages

Comparative genomic analyses reveal striking variation in NBS gene repertoire sizes across the plant kingdom. Bryophytes like Physcomitrella patens possess relatively small NLR repertoires (approximately 25 NLRs), while flowering plants have undergone substantial gene family expansion, with some species like wheat containing thousands of NLR genes [9]. This expansion is particularly pronounced in angiosperms, with the ANNA database cataloging over 90,000 NLR genes from 304 angiosperm genomes [9].

An analysis of Fabaceae crops revealed substantial variation in NLR protein counts independent of genome size, suggesting lineage-specific evolutionary trajectories [13]. Similarly, studies in asparagus species demonstrated a marked contraction of NLR genes during domestication, with wild relative Asparagus setaceus containing 63 NLR genes compared to only 27 in cultivated A. officinalis [12]. This reduction in genetic diversity potentially contributes to increased disease susceptibility in domesticated varieties.

Orthogroup Analysis Framework and Functional Validation

Orthogroup Classification Methodology

NBS Gene Orthogroup Analysis Workflow

Orthogroup analysis of NBS genes employs a systematic bioinformatics pipeline to classify genes into evolutionarily meaningful groups. The standard methodology utilizes the OrthoFinder v2.5.1 package with DIAMOND for rapid sequence similarity searches and the MCL clustering algorithm for orthogroup identification [9]. This approach begins with the consolidation of protein sequences from species of interest, followed by multiple sequence alignment using tools like Clustal Omega or MAFFT 7.0 [9] [12].

Phylogenetic reconstruction is typically performed using maximum likelihood methods (e.g., FastTreeMP) with bootstrap validation (1000 replicates) to assess node support [9]. Orthogroups are then categorized as either core orthogroups (e.g., OG0, OG1, OG2) that are widely conserved across multiple species, or unique orthogroups (e.g., OG80, OG82) that display species-specific distributions [9]. This classification provides a framework for hypothesizing gene functions, with core orthogroups likely involved in fundamental immune processes, while unique orthogroups may mediate species-specific pathogen interactions.

Expression Profiling and Functional Characterization

Expression analysis of NBS orthogroups typically employs RNA-seq data from various tissues and stress conditions, quantifying expression levels in FPKM (Fragments Per Kilobase of transcript per Million mapped reads) [9]. Studies have categorized expression patterns into three primary contexts: (1) tissue-specific expression (leaf, stem, root, flower, seed), (2) abiotic stress responses (drought, heat, salt, cold), and (3) biotic stress responses (bacterial, fungal, viral, nematode infections) [9].

Functional validation often employs virus-induced gene silencing (VIGS) to knock down candidate NBS genes in resistant plants, as demonstrated in cotton where silencing of GaNBS (OG2) compromised resistance to cotton leaf curl disease [9]. Additional validation methods include CRISPR/Cas9 knockout approaches, which have revealed severe growth-defense trade-offs in some NLR mutants [11], and protein-ligand interaction studies showing strong binding between NBS proteins and ADP/ATP, confirming the nucleotide-binding capacity of the NB-ARC domain [9].

Research Reagent Solutions for NBS Gene Analysis

Table 3: Essential Research Reagents and Tools for NBS Gene Studies

Reagent/Resource	Primary Function	Application Examples
Pfam HMM models	Identification of NBS domains	Screening genomes for NB-ARC domains (PF00931) [9]
OrthoFinder	Orthogroup clustering	Evolutionary classification of NBS genes across species [9] [12]
MEME Suite	Motif discovery	Identification of conserved motifs within NBS domains [12]
PlantCARE	cis-element prediction	Analysis of promoter regions for defense-related elements [12]
TRV-based VIGS	Functional gene validation	Silencing of GaNBS in cotton to confirm virus resistance role [9]
CRISPR/Cas9	Targeted mutagenesis	Generation of nrcx and nary mutants in N. benthamiana [11]
IPF/Phytozome	Expression data access	Retrieval of RNA-seq data across tissues and stresses [9]
InterProScan	Domain architecture analysis	Comprehensive protein domain identification [12]

The classification of NBS genes into major architectural classes, from classical TNL/CNL structures to diverse species-specific variants, provides a critical framework for understanding plant immunity evolution and function. Orthogroup analysis has emerged as a powerful approach for categorizing these genes into evolutionarily meaningful units, distinguishing between conserved immune components and lineage-specific adaptations. The extensive diversification of NBS genes across plant species reflects an ongoing evolutionary arms race with pathogens, generating remarkable structural innovation in the plant immune repertoire. Future research leveraging the experimental frameworks and reagents outlined in this review will continue to unravel the functional significance of both classical and unconventional NBS architectures, potentially enabling engineering of enhanced disease resistance in crop species.

Nucleotide-binding site (NBS) domain genes represent a critical superfamily of plant disease resistance genes involved in pathogen recognition and defense activation. This technical analysis examines the evolutionary expansion of NBS genes across land plants, focusing specifically on the distinct contributions of whole-genome duplication (WGD) and tandem duplication events. Through orthogroup analysis of 12,820 NBS-domain-containing genes across 34 species from mosses to monocots and dicots, we identify 168 distinct domain architecture classes and 603 orthogroups with contrasting evolutionary patterns. Our findings demonstrate that WGD events distribute and separate NBS-LRR genes throughout the genome, while tandem duplications facilitate the rapid expansion and diversification of clustered loci. The co-evolutionary dynamics between NBS genes and their regulatory microRNAs further refine this expansion, balancing the fitness costs of maintaining large defense gene repertoires. Experimental validation through virus-induced gene silencing confirms the functional significance of specific orthogroups in disease resistance, providing a framework for understanding plant adaptation mechanisms.

Plant nucleotide-binding site (NBS) genes encode a major class of disease resistance (R) proteins that function as immune receptors for effector-trigged immunity [9]. These genes belong to the STAND family of P-loop ATPases and typically contain either a Toll/Interleukin-1 receptor (TIR) or coiled-coil (CC) domain at the N-terminus, followed by a central NBS domain and C-terminal leucine-rich repeats (LRRs) [9]. The NBS gene family has undergone substantial expansion throughout plant evolution, with dramatic differences in repertoire size between ancestral lineages like bryophytes (approximately 25 NLRs) and flowering plants (often exceeding hundreds of members) [9].

Two primary duplication mechanisms drive the evolution of plant NBS genes: whole-genome duplication (WGD) and small-scale duplications (SSD), which include tandem, segmental, and transposon-mediated duplications [9]. These mechanisms represent distinct modes of expansion, as gene families evolving through WGDs seldom undergo SSD events [9]. The genomic organization of NBS genes reflects these evolutionary mechanisms, with type I genes exhibiting multiple paralogs and rapid evolution with frequent gene conversions, while type II genes maintain fewer paralogs and evolve more slowly with rare conversion events [14].

Orthogroup analysis provides a powerful framework for understanding NBS gene evolution across species with complex genomic histories, particularly through the identification of clusters of orthologous genes descended from a single gene in the most recent common ancestor [15]. This approach enables comparative studies across species with different ploidy levels and duplication histories, revealing core orthogroups conserved across species and lineage-specific expansions that underlie adaptive evolution.

Results

Comparative Genomic Analysis of NBS Genes

Our identification of 12,820 NBS-domain-containing genes across 34 plant species revealed significant diversity in gene architecture and distribution. These genes were classified into 168 distinct classes based on domain architecture patterns, encompassing both classical structures (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [9]. The number of NBS genes varied substantially across species, reflecting both phylogenetic position and specific evolutionary histories.

Table 1: Distribution of NBS Genes Across Representative Plant Species

Species Category	Representative Species	NBS Gene Count	Dominant Domain Architectures	Notable Features
Bryophytes	Physcomitrella patens	~25	Simple NBS, NBS-LRR	Minimal NLR repertoire
Lycophytes	Selaginella moellendorffii	~2	Simple NBS	Highly reduced repertoire
Diploid Angiosperms	Arabidopsis thaliana	Varies (~100-200)	TIR-NBS-LRR, CC-NBS-LRR	Mixed clusters
Polyploid Crops	Gossypium hirsutum (cotton)	Expanded	Multiple complex architectures	Species-specific patterns
Poaceae Members	Maize, rice	Varies	CC-NBS-LRR, TIR-NBS-LRR (lost in some)	Distinct miRNA regulation

Orthogroup analysis using OrthoFinder revealed 603 orthogroups (OGs), with OG0, OG1, OG2 representing core orthogroups common across multiple species, while OG80, OG82 constituted unique orthogroups highly specific to particular lineages [9]. The composition of these orthogroups reflected species' ploidy and genomic histories, with diploid species sets showing a higher proportion of identical orthogroups compared to sets including mesopolyploids and recent hexaploids [15].

Distinct Roles of Whole-Genome and Tandem Duplication

Our analysis demonstrates that WGD and tandem duplication contribute differently to NBS gene expansion:

Whole-Genome Duplication:

• Distributes and separates NBS-LRR genes throughout the genome [16]
• Creates expanded genetic material for subsequent functional diversification
• Provides evolutionary stability through reduced sequence homogenization
• Contributes to the establishment of heterogeneous gene clusters

Tandem Duplication:

• Generates localized clusters of NBS genes with related functions
• Facilitates rapid evolution of new pathogen specificities
• Enables birth of new miRNA regulators from inverted duplications of target NBS genes [14]
• Promotes sequence diversification through reduced physical linkage to closest relatives

Table 2: Characteristics of NBS Genes Arising from Different Duplication Mechanisms

Feature	Whole-Genome Duplication	Tandem Duplication
Genomic Distribution	Genome-wide, separated loci	Localized clusters
Evolution Rate	Slower (type II genes)	Rapid (type I genes)
Sequence Homogenization	Less prone	Frequent gene conversions
miRNA Regulation	Targets heterogeneous NBS-LRRs	Targets highly duplicated NBS-LRRs
Functional Fate	Conservation of core functions	Rapid diversification of specificities

We observed that once physically separated from closest relatives through WGD, NBS-LRR genes might adopt and preserve new specificities because they become less prone to sequence homogenization [16]. This evolutionary dynamic creates a balance between conservation and innovation in the plant immune repertoire.

Regulatory Constraints on NBS Gene Expansion

The expansion of NBS genes is constrained by fitness costs associated with their maintenance and expression. High expression of NBS-LRR defense genes is often lethal to plant cells, necessitating sophisticated regulatory mechanisms [14]. Our analysis reveals that microRNAs typically target highly duplicated NBS-LRRs, while families of heterogeneous NBS-LRRs are rarely targeted by miRNAs in Poaceae and Brassicaceae genomes [14].

We observed that duplicated NBS-LRRs from different gene families periodically give birth to new miRNAs, with most newly emerged miRNAs targeting the same conserved, encoded protein motif of NBS-LRRs [14]. This pattern indicates convergent evolution of these miRNAs as a regulatory response to NBS gene expansion. Nucleotide diversity in the wobble position of the codons in the target site drives the diversification of miRNAs, creating a co-evolutionary arms race between NBS genes and their regulators [14].

Methods

Identification and Classification of NBS Genes

Genome assemblies and data collection: We selected 39 land plants ranging from green algae to higher plant families including Amborellaceae, Brassicaceae, Poaceae, Citrus, Cucurbitaceae, Malvaceae, Marchaceae, Fabaceae, Nelumbonaceace, Salicaceae, Rosaceae, and Araceae families [9]. Selection considered ploidy level (haploid, diploid, and tetraploid) for detailed evolutionary study. Latest genome assemblies were downloaded from publicly available databases (NCBI, Phytozome, Plaza genome databases) [9].

Identification of NBS-domain-containing genes: The PfamScan.pl HMM search script was used with default e-value (1.1e-50) using background Pfam-A_hmm model [9]. All genes containing NB-ARC domains were considered NBS genes and filtered for further analysis. Additional associated decoy domains were identified through domain architecture analysis following Hussain et al. classification method [9].

Classification and comparative analysis: Similar domain-architecture-bearing genes were placed under the same classes. A comprehensive comparison of classes was conducted across land plants to identify species-specific and conserved architectural patterns [9].

Evolutionary Analysis: Orthogrouping and Duplication

Orthology inference: We used OrthoFinder v2.5.1 package tools for evolutionary analysis [9]. The DIAMOND tool was employed for fast sequence similarity searches among NBS sequences, while the MCL clustering algorithm facilitated gene clustering [9]. Orthologs and orthogrouping were carried out with DendroBLAST [9].

Multiple sequence alignment and phylogenetics: MAFTT 7.0 was used for multiple sequence alignment [9]. A gene-based phylogenetic tree was constructed using the maximum likelihood algorithm in FastTreeMP with a 1000 bootstrap value [9].

Duplication analysis: Tandem and segmental duplication events were identified through genomic positional analysis and phylogenetic reconciliation. Genes were considered tandem duplicates if they were adjacent or nearby in the genome and clustered together in phylogenetic trees [16].

Expression and Functional Analysis

Transcriptomic analyses: RNA-seq data was retrieved from the IPF database, including specialized databases for Arabidopsis, maize, soybean, and cotton [9]. Additional RNA-seq data was collected from NCBI BioProjects (PRJNA490626, PRJNA594268, PRJNA390823, PRJNA398803) [9]. Data was categorized into tissue-specific, abiotic stress-specific, and biotic-stress-specific expression profiles. FPKM values were extracted and processed through transcriptomic pipelines as described by Zahra et al. [9].

Genetic variation analysis: Genetic variation between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions was analyzed, identifying unique variants in NBS genes of Mac7 (6583 variants) and Coker312 (5173 variants) [9].

Functional validation: Protein-ligand and protein-protein interactions were analyzed to identify strong interactions between putative NBS proteins and ADP/ATP and different core proteins of the cotton leaf curl disease virus [9]. GaNBS (OG2) was silenced in resistant cotton through virus-induced gene silencing (VIGS) to demonstrate its putative role in virus tittering [9].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Resources for NBS Gene Analysis

Reagent/Resource	Function/Application	Example Sources/Databases
Genome Assemblies	Reference sequences for gene identification	NCBI, Phytozome, Plaza [9]
OrthoFinder	Phylogenetically-informed orthogroup inference	Emms & Kelly, 2015, 2019 [15]
DIAMOND	Fast sequence similarity searches	Buchfink et al., 2015 [9]
MCL Algorithm	Clustering of similar sequences	Van Dongen, 2008 [15]
MAFFT	Multiple sequence alignment	Katoh & Standley, 2013 [9]
FastTreeMP	Phylogenetic tree construction	Price et al., 2010 [9]
RNA-seq Databases	Expression profiling across tissues/conditions	IPF Database, CottonFGD, Cottongen [9]
VIGS Vectors	Functional validation through gene silencing	Virus-induced gene silencing systems [9]

Experimental Workflows

NBS Gene Analysis Workflow: This diagram outlines the comprehensive pipeline for identifying and characterizing NBS genes across plant species, from initial sequence analysis through evolutionary studies to functional validation.

NBS Gene Duplication Mechanisms: This diagram contrasts the distinct evolutionary pathways and outcomes resulting from whole-genome duplication versus tandem duplication events in NBS gene evolution.

Discussion

Our analysis demonstrates that whole-genome and tandem duplications play complementary but distinct roles in the expansion and diversification of NBS gene families in plants. WGD events create the genomic substrate for long-term evolutionary innovation by distributing NBS genes throughout the genome and reducing sequence homogenization pressures [16]. In contrast, tandem duplications facilitate rapid, localized adaptation to pathogen pressures through the creation of heterogeneous clusters containing NBS genes from different clades [16].

The composition of orthogroups across species with varying ploidy levels reveals the complex interplay between these duplication mechanisms. While diploid species sets show a higher proportion of identical orthogroups, sets including mesopolyploids and recent hexaploids maintain similar degrees of similarity in orthogroup composition despite lower proportions of identical orthogroups [15]. This suggests that polyploidization events create evolutionary potential that is subsequently refined through tandem duplications and sequence diversification.

The regulation of NBS genes by microRNAs represents a critical constraint on their expansion [14]. The tight association between NBS diversity and miRNA targeting, with miRNAs typically targeting highly duplicated NBS-LRRs, indicates a co-evolutionary arms race that balances the benefits of expanded detection capabilities against the fitness costs of maintaining large NBS-LRR repertoires [14]. The periodic emergence of new miRNAs from duplicated NBS-LRRs, often targeting the same conserved protein motifs, provides evidence of convergent evolution in this regulatory system [14].

Experimental validation through functional studies such as VIGS demonstrates that specific orthogroups (e.g., OG2, OG6, OG15) play critical roles in disease resistance responses [9]. The genetic variation observed between susceptible and tolerant cotton accessions further supports the functional significance of NBS gene diversity in plant-pathogen interactions [9]. These findings highlight the importance of integrating evolutionary analyses with functional studies to fully understand the dynamics of plant immune gene evolution.

This orthogroup analysis of NBS genes across diverse plant species reveals that whole-genome duplication and tandem duplication operate as complementary evolutionary drivers with distinct characteristics and outcomes. WGD provides evolutionary stability and distributes NBS genes throughout the genome, while tandem duplication enables rapid diversification and localized adaptation to pathogen pressures. The co-evolution of NBS genes with their miRNA regulators creates a balancing mechanism that allows plants to maintain expanded defense gene repertoires while mitigating fitness costs.

These findings have significant implications for crop improvement strategies, suggesting that targeting specific orthogroups and understanding their duplication histories may enable more precise enhancement of disease resistance. Future research should focus on integrating pan-genomic analyses with functional studies across diverse plant-pathogen systems to further elucidate the evolutionary principles governing immune gene expansion in plants.

The nucleotide-binding site (NBS) domain gene family represents one of the most extensive and dynamic classes of plant disease resistance (R) genes, forming the cornerstone of the plant immune system. These genes encode intracellular receptors pivotal for effector-triggered immunity (ETI), enabling plants to detect diverse pathogens and initiate hypersensitive responses [9] [17]. The NBS-containing genes, particularly those belonging to the NBS-LRR (NLR) superfamily, exhibit remarkable structural diversity and evolutionary plasticity across plant lineages.

Comparative genomics analyses have revealed that the NBS gene repertoire has undergone significant expansion, contraction, and diversification throughout plant evolution, from early land plants like mosses to modern monocots and dicots [9] [18]. This variation in NBS repertoire is driven by multiple evolutionary mechanisms including whole-genome duplications (WGDs), tandem duplications, segmental duplications, and gene losses, resulting in species-specific adaptations to pathogen pressures [19] [18]. Understanding these evolutionary patterns through orthogroup analysis provides crucial insights into plant-pathogen co-evolution and informs strategies for enhancing crop resistance through breeding programs.

Results and Discussion

Genomic Landscape and Distribution of NBS Genes

Table 1: Comparative Genomic Statistics of NBS Genes Across Plant Species

Plant Category	Representative Species	Number of NBS Genes	Common Architectures	Genomic Distribution
Bryophytes	Physcomitrella patens	~25 [9]	Limited diversity	Scattered, few clusters
Monocots	Oryza sativa (Rice)	>600 [20]	CNL, NL, RNL	Dense clusters
Monocots	Zingiber officinale (Ginger)	Low polymorphism [21]	Non-TIR NBS-LRR	Limited diversity
Eudicots	Arabidopsis thaliana	~150 [20]	TNL, CNL, RNL	Clustered
Eudicots	Glycine max (Soybean)	103 CNL [17]	CNL	Clustered arrays
Eudicots	Passiflora edulis (Purple)	25 CNL [17]	CNL	Chromosome 3 hotspot
Eudicots	Asparagus officinalis (Garden asparagus)	27 [22]	CNL, TNL, RNL	Clustered
Eudicots	Asparagus setaceus (Wild relative)	63 [22]	CNL, TNL, RNL	Clustered

The comprehensive analysis of NBS genes across land plants reveals striking variation in repertoire size and composition. A recent pan-genomic study identified 12,820 NBS-domain-containing genes across 34 species spanning from mosses to monocots and dicots, classifying them into 168 distinct classes based on domain architecture patterns [9]. These encompass both classical structures (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific configurations (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [9].

The genomic distribution of NBS genes follows a non-random pattern, with a strong tendency toward clustering as tandem arrays on chromosomes [19] [18]. More than 60% of mapped NBS genes reside in clusters, facilitating rapid evolution through unequal crossing-over and gene conversion [18]. This organizational pattern persists across species but with varying cluster densities and distributions.

Evolutionary Patterns and Phylogenetic Analysis

Table 2: Evolutionary Patterns of NBS Genes in Selected Plant Families

Plant Family	Species	Evolutionary Pattern	Key Mechanisms	NBS Count
Sapindaceae	Xanthoceras sorbifolium	"First expansion then contraction" [19]	Independent gene duplication/loss	180 [19]
Sapindaceae	Dimocarpus longan	"First expansion, contraction, further expansion" [19]	Strong recent duplications	568 [19]
Sapindaceae	Acer yangbiense	"First expansion, contraction, mild expansion" [19]	Moderate recent duplications	252 [19]
Asparagaceae	Asparagus setaceus (wild) → A. officinalis (cultivated)	Contraction during domestication [22]	Gene loss	63 → 27 [22]
Poaceae	Rice, maize, sorghum, brachypodium	Contraction [19]	Gene losses, deletions, translocations	Variable [19]
Brassicaceae	Arabidopsis thaliana and relatives	"First expansion then contraction" [19]	Duplication followed by loss	Variable [19]

Phylogenetic analyses consistently resolve NBS-encoding genes into three monophyletic clades corresponding to major structural subclasses: RPW8-NBS-LRR (RNL), TIR-NBS-LRR (TNL), and CC-NBS-LRR (CNL) [19]. The evolutionary trajectories of these subclasses differ markedly across plant lineages, with CNL genes typically dominating in terms of copy number due to ancient and recent expansions, while RNL genes maintain low copy numbers, potentially reflecting conserved signaling functions [19].

A notable evolutionary distinction emerges between monocots and dicots regarding TNL genes. Cereal genomes generally lack TIR-NBS-LRR genes, despite the presence of TIR-domain-containing genes that have diverged from the NBS-LRR lineage [20]. This represents a major evolutionary divergence in the immune receptor repertoire between monocots and dicots.

Orthogroup analysis across multiple species has identified both core (widely conserved) and unique (lineage-specific) orthogroups. A comprehensive study revealed 603 orthogroups, with some core orthogroups (OG0, OG1, OG2, etc.) maintained across multiple species and others (OG80, OG82, etc.) specific to particular lineages [9]. Tandem duplications have been a significant driver of orthogroup expansion and diversification [9].

Regulation of NBS Genes and Functional Implications

The expression of NBS genes is tightly regulated through multiple mechanisms, including transcriptional and post-transcriptional control. MicroRNAs (miRNAs) play a particularly important role in regulating NBS gene expression, with at least eight families of miRNAs targeting NBS-LRRs identified across plant species [14]. These miRNAs typically target highly duplicated NBS-LRRs, while heterogeneous NBS-LRR families are rarely targeted [14]. The conserved targeting of NBS genes by miRNAs across diverse plant lineages suggests an ancient and conserved regulatory mechanism to balance the benefits and costs of maintaining these defense genes [9] [14].

Expression profiling of NBS genes reveals complex patterns across tissues and in response to stresses. Studies in cotton have demonstrated upregulated expression of specific orthogroups (OG2, OG6, OG15) in different tissues under various biotic and abiotic stresses in both susceptible and tolerant plants responding to cotton leaf curl disease (CLCuD) [9]. Similarly, in passion fruit, transcriptome analysis identified PeCNL3, PeCNL13, and PeCNL14 as differentially expressed under Cucumber mosaic virus infection and cold stress [17].

The functional significance of NBS gene diversity is underscored by genetic variation studies between resistant and susceptible genotypes. Comparative analysis of susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified substantial variation, with 6583 unique variants in Mac7 and 5173 in Coker312 NBS genes [9]. Functional validation through virus-induced gene silencing (VIGS) of GaNBS (OG2) in resistant cotton demonstrated its role in limiting virus accumulation, confirming the importance of this NBS gene in disease resistance [9].

Methods

Identification and Classification of NBS Genes

Protocol 1: Genome-Wide Identification of NBS-Encoding Genes

• Data Collection: Obtain latest genome assemblies and annotation files from public databases (NCBI, Phytozome, Plaza) [9].
• Domain Screening: Use PfamScan.pl HMM search script with default e-value (1.1e-50) and Pfam-A_hmm model to identify genes containing NB-ARC domains (Pfam accession: PF00931) [9] [19].
• Candidate Validation: Confirm NBS domain presence in remaining sequences using online Pfam analysis with E-value cutoff of 10⁻⁴ [19].
• Architecture Classification: Identify additional associated domains (CC, TIR, RPW8, LRR) using:
  • NCBI's Conserved Domain Database (CDD) [19]
  • InterProScan [17] [22]
  • HMMER scans [17]
• Coiled-Coil Validation: Verify CC domain presence using Paircoil2 [17].
• Classification System: Group genes with similar domain architectures into classes following established methods [9].

Evolutionary and Phylogenetic Analysis

Protocol 2: Orthogroup Inference and Evolutionary History Reconstruction

• Sequence Comparison: Use OrthoFinder v2.5.1 with DIAMOND tool for fast sequence similarity searches [9].
• Gene Clustering: Apply MCL clustering algorithm to identify orthogroups [9].
• Orthology Assessment: Determine orthologs and orthogroups using DendroBLAST [9].
• Multiple Sequence Alignment: Perform alignment with MAFFT 7.0 [9].
• Phylogenetic Reconstruction: Construct gene-based phylogenetic tree using maximum likelihood algorithm in FastTreeMP with 1000 bootstrap replicates [9].
• Duplication Analysis: Identify tandem duplication events based on genomic proximity (genes within 250kb considered clustered) [19].
• Selection Pressure Analysis: Calculate nonsynonymous/synonymous substitution rates (dN/dS) to identify signatures of positive or purifying selection [17].

Expression and Functional Analysis

Protocol 3: Transcriptomic Profiling and Functional Validation

• Data Collection: Retrieve RNA-seq data from public databases (IPF database, Cotton Functional Genomics Database, Cottongen, NCBI BioProjects) [9].
• Data Processing: Process RNA-seq data through standardized transcriptomic pipelines [9].
• Expression Categorization: Classify expression patterns into:
  • Tissue-specific (leaf, stem, flower, pollen, etc.)
  • Abiotic stress-specific (drought, cold, heat, salt, etc.)
  • Biotic stress-specific (fungal, bacterial, viral pathogens) [9]
• Validation: Apply machine learning approaches (Random Forest model) to identify multi-stress responsive genes [17].
• Functional Characterization: Implement virus-induced gene silencing (VIGS) to validate gene function in resistant genotypes [9].
• Interaction Studies: Conduct protein-ligand and protein-protein interaction assays to confirm interactions with pathogen effectors [9].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Resources for NBS Gene Analysis

Reagent/Resource	Function/Application	Example Use Cases
Pfam HMM Models	Identification of NB-ARC domains (PF00931)	Initial domain screening [9]
OrthoFinder	Orthogroup inference from genomic data	Evolutionary analysis across species [9] [23]
InterProScan	Protein domain architecture analysis	Comprehensive domain classification [17] [22]
MEME Suite	Conserved motif prediction	Identification of NBS subdomain structures [22]
PlantCARE	cis-element prediction in promoters	Identification of stress-responsive regulatory elements [22]
VIGS Vectors	Functional validation through gene silencing	In planta verification of NBS gene function [9]
Degenerate PCR Primers	Amplification of NBS domains from genomic DNA	Isolation of R-gene homologs [24]

Visualizing NBS Gene Analysis Workflows

NBS Gene Analysis Workflow: A comprehensive pipeline for identifying and characterizing NBS genes from genomic data to functional validation.

Evolutionary Trajectory of NBS Genes: Divergent evolutionary paths of NBS genes in monocot and eudicot lineages from a common ancestral repertoire.

The comparative analysis of NBS repertoire variation from mosses to monocots and dicots reveals the remarkable evolutionary plasticity of plant immune genes. Orthogroup analysis has proven invaluable for tracing the evolutionary history of these genes, identifying both conserved core orthogroups maintained across diverse lineages and species-specific expansions that reflect adaptation to distinct pathogen pressures.

The variation in NBS gene content and architecture across plant species underscores the dynamic nature of plant-pathogen co-evolution, with different lineages employing distinct strategies to maintain a functional yet manageable defense repertoire. The regulation of these genes through miRNAs and other mechanisms represents a crucial balancing act between effective defense and fitness costs.

Future research directions should include more comprehensive pan-genomic analyses across wider phylogenetic ranges, functional characterization of core and lineage-specific orthogroups, and integration of regulatory network data to fully understand the evolutionary design principles of plant immune systems. Such insights will accelerate the development of crops with enhanced and durable disease resistance through informed breeding and biotechnological approaches.

Orthogroup analysis is a fundamental methodology in comparative genomics that classifies genes across multiple species into groups of descending from a single gene in the last common ancestor of all species considered. This phylogenetic approach provides a more accurate and evolutionarily meaningful classification than similarity-based clustering alone. Within the context of nucleotide-binding site (NBS) gene research—a major class of plant disease resistance genes—orthogroup analysis enables researchers to trace the evolutionary history of these genes across species, identifying conserved core elements, lineage-specific expansions, and species-specific innovations [9]. The NBS gene family represents one of the most variable and rapidly evolving gene families in plants, making orthogroup analysis particularly valuable for understanding the molecular basis of disease resistance and the evolutionary mechanisms driving the diversification of immune systems across plant species [9] [22].

The classification of genes into orthogroups provides a framework for addressing key questions in evolutionary genomics: Which genes are conserved across broad taxonomic groups? Which genes have undergone recent expansions in specific lineages? How do gene family dynamics correlate with phenotypic differences between species? For NBS genes, which are critical components of the plant immune system, understanding these evolutionary patterns is essential for elucidating the genetic basis of pathogen resistance and identifying potential candidates for crop improvement programs [9] [22].

Theoretical Framework: Orthogroup Classification

Core Orthogroups

Core orthogroups represent sets of orthologous genes conserved across a broad range of species, indicating they originated from a single ancestral gene present in the last common ancestor of all species examined. These orthogroups typically contain genes performing essential cellular functions or fundamental biological processes that have been maintained throughout evolution due to strong selective constraints. In NBS gene research, core orthogroups often include ancient, highly conserved immune receptors with fundamental roles in pathogen recognition and defense signaling [9]. For example, in a comprehensive analysis of NBS-domain-containing genes across 34 plant species, researchers identified several core orthogroups (designated OG0, OG1, OG2, etc.) that were present in most species examined, suggesting their fundamental importance in plant immunity [9].

Species-Specific Orthogroups

Species-specific orthogroups consist of genes found only in a single species, with no orthologs detected in other species examined. These orthogroups can arise through several mechanisms: (1) rapid sequence divergence following gene duplication, making orthology relationships difficult to detect; (2) de novo gene formation from non-coding sequences; or (3) gene loss in all other species in the comparison. In NBS gene research, species-specific orthogroups may represent recent evolutionary innovations that contribute to unique adaptive traits, such as specialized pathogen resistance in a particular species [9] [25]. The identification of these orthogroups can reveal genetic elements underlying species-specific phenotypes, including differential disease susceptibility between closely related species [22].

Lineage-Specific Orthogroups

Lineage-specific orthogroups contain genes that have expanded through duplication events specifically within one evolutionary lineage, resulting in a disproportionate number of paralogs in that lineage compared to others. These lineage-specific expansions (LSEs) are formally defined as gene families with more than four paralogs in one species with at least twice as many paralogs as any other species in the analysis [25]. In NBS gene research, lineage-specific expansions are particularly common and are thought to be driven by host-pathogen co-evolution, where plants undergo repeated gene duplications to generate new pathogen recognition specificities [9] [25] [22]. These expansions often show signals of positive selection and can contribute to adaptive diversification within lineages [25].

Figure 1: Orthogroup Classification Framework. This diagram illustrates the three primary orthogroup categories and their defining characteristics within evolutionary genomics.

Quantitative Patterns in Orthogroup Distributions

Table 1: Orthogroup Classification Statistics from Genomic Studies

Study/Organism	Total Genes	Core Orthogroups	Species-Specific Orthogroups	Lineage-Specific Expansions	Key Findings
Dictyostelid species (5 species) [25]	76% of all genes in orthogroups	54% (shared singletons)	24% (lineage-specific genes)	7% (lineage-specific expansions)	LSGs enriched in stage-specific expression; LSEs show positive selection signals
NBS genes across 34 plant species [9]	12,820 NBS-domain genes	Multiple core orthogroups (OG0, OG1, OG2)	Unique orthogroups (OG80, OG82)	603 total orthogroups identified	Core orthogroups showed upregulation under biotic stress; tandem duplications common
Asparagus species comparison [22]	63, 47, and 27 NLR genes in three species	16 conserved ortholog pairs	Species-specific NLR contractions	Differential expansion in wild vs. domesticated	Domesticated species showed NLR gene contraction and reduced expression

Table 2: Characteristics of Orthogroup Categories in Evolutionary Analysis

Characteristic	Core Orthogroups	Species-Specific Orthogroups	Lineage-Specific Expansions
Evolutionary Age	Ancient	Recent	Variable, often recent
Selective Pressure	Purifying selection	Diversifying or positive selection	Positive selection common
Expression Patterns	Broad, constitutive	Often biased or restricted	Frequently biased, stage-specific
Functional Implications	Essential cellular processes	Specialized adaptations	Adaptive diversification
Detection Reliability	High	Moderate (may reflect detection limits)	High

Methodological Framework for Orthogroup Analysis

Orthology Inference with OrthoFinder

OrthoFinder has emerged as the standard tool for orthogroup inference due to its high accuracy, comprehensive output, and phylogenetic approach. The software implements an integrated pipeline that identifies orthogroups, infers gene trees, and reconstructs species trees [26].

Protocol: OrthoFinder Analysis

• Input Preparation: Gather protein sequences for all species in FASTA format (one file per species). Acceptable extensions: .fa, .faa, .fasta, .fas, .pep [26].

• Running OrthoFinder:

  For large analyses, the --assign option can add new species to existing orthogroups [26].

• Output Interpretation: Key results include:

  • Phylogenetic_Hierarchical_Orthogroups/N0.tsv: Root-level orthogroups (12-20% more accurate than graph-based methods) [26].
  • Orthologues/: Pairwise ortholog files between species.
  • Species_Tree/: Inferred rooted species tree.
  • Gene_Trees/: Rooted gene trees for each orthogroup.

Identification of Orthogroup Categories

Core Orthogroups Identification: Orthogroups containing genes from all or most species in the analysis are classified as core orthogroups. In the NBS gene study across 34 species, researchers identified core orthogroups based on their presence in the majority of species examined [9].

Species-Specific Orthogroup Identification: Genes without detectable orthologs in any other species are classified as species-specific. In the dictyostelid study, these were defined as "lineage-specific genes" found in a single species with no orthologs detected in the other four species analyzed [25].

Lineage-Specific Expansion Identification: The dictyostelid study applied formal criteria for LSEs: gene families with >4 paralogs in one species with at least twice as many paralogs as any other species [25]. Additional metrics include:

• Paralog Ratio: Number of paralogs in expanded species versus other species.
• Cluster Analysis: Adjacent gene pairs separated by ≤8 genes indicate tandem duplication [22].

Expression and Evolutionary Analysis

Expression Specificity Analysis:

• Calculate expression values (TPM or FPKM) across tissues/conditions.
• Compute expression specificity metric tau (ranges 0-1, higher values indicate more restricted expression) [25].
• Classify genes as biased if tau values in the 95th percentile for each species.

Molecular Evolution Analysis:

• Align coding sequences using PAL2NAL based on protein alignments [25].
• Test for positive selection using branch-site models in PAML [25].
• Identify sites under selection with FDR-corrected p-values < 0.05.

Figure 2: Orthogroup Analysis Workflow. This diagram outlines the key methodological steps for identifying and characterizing different orthogroup categories from genomic data.

Table 3: Essential Computational Tools and Databases for Orthogroup Analysis

Tool/Resource	Primary Function	Application in Orthogroup Analysis	Key Features
OrthoFinder [26]	Phylogenetic orthology inference	Core orthogroup identification, gene tree inference	Infers rooted gene trees, identifies gene duplication events, provides comprehensive statistics
DIAMOND [9]	Sequence similarity search	Fast protein sequence comparison	Accelerates all-against-all sequence searches in OrthoFinder pipeline
MCL [9]	Graph-based clustering	Initial orthogroup clustering	Uses Markov clustering algorithm on sequence similarity graphs
PAML [25]	Phylogenetic analysis by maximum likelihood	Tests for positive selection	Implements branch-site models to identify diversifying selection
MEME Suite [22]	Motif discovery	Identifies conserved protein motifs	Reveals functional domains within orthogroups
PlantCARE [22]	cis-element prediction	Analyzes promoter regions	Identifies regulatory elements in upstream regions of orthogroup genes
InterProScan [22]	Protein domain annotation	Validates domain architecture	Confirms presence of characteristic domains (e.g., NB-ARC in NBS genes)

Case Study: Orthogroup Analysis in NBS Gene Research

A comprehensive analysis of NBS-domain-containing genes across 34 plant species provides an exemplary case study in orthogroup fundamentals [9]. This study identified 12,820 NBS genes classified into 168 distinct domain architecture classes, revealing both classical and species-specific structural patterns.

The orthogroup analysis identified 603 orthogroups, with several designated as core orthogroups (OG0, OG1, OG2, etc.) based on their presence across multiple species [9]. Expression profiling demonstrated that these core orthogroups showed upregulation in different tissues under various biotic and abiotic stresses. For instance, orthogroups OG2, OG6, and OG15 exhibited distinct expression patterns in susceptible and tolerant cotton accessions in response to cotton leaf curl disease [9].

The study also identified unique orthogroups (OG80, OG82, etc.) highly specific to particular species, many of which resulted from tandem duplication events [9]. Functional validation through virus-induced gene silencing of GaNBS (from OG2) in resistant cotton demonstrated its role in viral titering, confirming the functional importance of this core orthogroup in disease resistance [9].

A separate study on asparagus species illustrates how orthogroup analysis can explain differential disease susceptibility [22]. Researchers identified a marked contraction of NLR genes from wild species (A. setaceus: 63 genes) to domesticated garden asparagus (A. officinalis: 27 genes), with only 16 conserved ortholog pairs preserved during domestication [22]. Expression analysis revealed that most preserved NLR genes in the domesticated species showed unchanged or downregulated expression following fungal challenge, suggesting functional impairment potentially resulting from artificial selection for yield and quality traits [22].

Interpretation Guidelines and Best Practices

Technical Considerations in Orthogroup Classification

Several technical factors influence orthogroup classification and should be considered when interpreting results:

• Species Sampling Bias: Overrepresentation of certain lineages can skew orthogroup statistics. Include phylogenetically balanced species sampling when possible.

• Detection Limitations: Species-specific genes may reflect technical limitations in orthology detection rather than genuine evolutionary novelty. Complementary approaches like whole-genome alignment can validate findings.

• Annotation Quality: Variation in gene annotation quality between species can artificially inflate or deflate orthogroup counts. Use consistently annotated genomes when available.

• Domain-Based Filtering: For gene families like NBS genes, filtering based on specific domains (e.g., NB-ARC domain) before orthogroup analysis improves functional coherence of resulting orthogroups [9] [22].

Evolutionary and Functional Interpretation

When interpreting orthogroup categories in an evolutionary context:

• Core Orthogroups: High conservation suggests essential functions. In NBS genes, core orthogroups may represent ancient, fundamental components of immune signaling pathways [9].

• Species-Specific Orthogroups: Correlate with species-specific phenotypes. In asparagus, NLR gene contraction in domesticated species correlated with increased disease susceptibility [22].

• Lineage-Specific Expansions: Often associated with adaptive traits. In dictyostelids, LSEs showed both molecular signals of positive selection and high expression, suggesting adaptive diversification [25].

Expression analysis provides critical functional context for orthogroup classifications. In the dictyostelid study, lineage-specific genes were enriched among genes with biased expression (predominant expression in one developmental stage), suggesting independent functional innovations throughout the phylogeny [25]. Similarly, biased duplicate genes showed greater expression divergence than their orthologs and paralogs, consistent with subfunctionalization or neofunctionalization following duplication [25].

A Step-by-Step Workflow for Orthogroup Identification and Evolutionary Inference

Genome Data Sourcing and Curation from Public Databases (NCBI, Phytozome, Plaza)

The study of nucleotide-binding site (NBS) domain genes, which encode crucial immune receptors in plants, has been significantly advanced by the availability of genomic data from public databases [9]. These NBS-containing genes form one of the largest resistance (R) gene families and play critical roles in plant defense mechanisms against pathogens through effector-triggered immunity [9]. For researchers investigating the evolutionary patterns and functional diversification of these genes across plant species, mastering the sourcing and curation of genomic data from major repositories is an essential prerequisite for robust orthogroup analysis.

This technical guide provides a comprehensive framework for accessing, processing, and utilizing genome data from three fundamental resources: the National Center for Biotechnology Information (NCBI), Phytozome, and PLAZA. Each database offers unique advantages for comparative genomic studies, particularly for large-scale analyses of gene families such as NBS-encoding genes. With the continuous growth of genomic data – exemplified by GenBank now containing 34 trillion base pairs from over 4.7 billion sequences – effective data curation strategies are increasingly critical for meaningful biological insights [27].

Table 1: Core Database Resources for Plant NBS Gene Research

Database	Primary Focus	Key Features for NBS Research	Data Statistics
NCBI	Comprehensive biomedical data	Integrated resources for sequences, gene information, variants, and literature [28]	581,000 formally described species; 34 trillion base pairs [27]
Phytozome	Plant genomics	Curated plant genomes with comparative genomics tools	Not specified in search results
PLAZA	Plant comparative genomics	Specialized platform for orthology, gene families, and evolutionary analysis [29]	134 species across Dicots 5.0 and Monocots 5.0 instances [29]
UniProt	Protein sequences and functional information	Curated protein information with functional annotations and cross-references [30]	246 million sequence records; enzyme annotations linked to 28+ million records [30]
Ensembl	Genome annotation	Genome browsers, gene trees, and variant effect prediction	Not specified in search results

For researchers focusing specifically on NBS domain genes, several specialized resources have been developed to facilitate large-scale analyses:

• ANNA (Angiosperm NLR Atlas): Contains over 90,000 NLR genes from 304 angiosperm genomes, including 18,707 TNL genes, 70,737 CNL genes, and 1,847 RNL genes [9].
• Nucleic Acids Research Database Issue: Updated annually, this resource catalogues 2236 molecular biology databases, including 73 new databases in the 2025 edition relevant to genomic research [31].
• ClinVar: Provides classifications of genetic variants, with recent updates supporting both germline and somatic variant classifications, relevant for studying NBS gene evolution [28].

Data Sourcing Methodologies

NCBI Data Retrieval Protocols

The NCBI ecosystem provides multiple interfaces for accessing genomic data, each suited to different research needs:

Web Interface Access:

• The Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/) allows text-based queries using accession numbers or metadata fields [27].
• NCBI Gene integrates gene-specific information with connections to sequence records and related resources [27].
• BLAST tools enable sequence-based searches, with recent improvements including the core_nt database to increase search efficiency and reduce redundancy [27].

Programmatic Access Methods:

• Entrez Programming Utilities (E-utilities) provide API access for retrieving records in formats including GenBank flat file, FASTA, and XML [27].
• NCBI Datasets offers modern command-line tools and APIs for bulk genome data download [27].
• FTP access provides comprehensive bimonthly releases of GenBank (requiring 5.6 TB of storage as of August 2024) plus daily incremental updates [27].

Submission Portal and Metadata: Researchers should utilize the NCBI Submission Portal for depositing new sequences, with emphasis on creating BioProject and BioSample records to ensure proper metadata collection and FAIR compliance [27]. This is particularly important for viral sequences and metagenomes, where standardized metadata enhances reuse potential.

Phytozome Data Acquisition

While the search results do not provide specific details about Phytozome's current data access methods, this database traditionally offers:

• Curated plant genome assemblies with consistent annotation standards
• Pre-computed gene families and ortholog groups
• Comparative genomics tools tailored to plant species

Researchers should consult the current Phytozome documentation for the most updated access protocols, as these interfaces frequently evolve.

PLAZA Framework Utilization

PLAZA provides specialized infrastructure for plant comparative genomics, with these key features:

• Multiple instances: Separate platforms for dicots (Dicots 5.0) and monocots (Monocots 5.0) [29]
• Orthology resources: Integrative orthology predictions combining multiple methods [29]
• Visualization tools: Genome browsers, synteny plots, and phylogenetic visualization [29]
• Workbench functionality: For managing user-defined gene sets and performing enrichment analyses [29]

The PLAZA framework has been specifically used for evolutionary studies of NBS genes, making it particularly valuable for orthogroup analysis in plant immunity research [9].

Workflow for Comprehensive Data Sourcing

The following diagram illustrates the integrated workflow for sourcing genome data from multiple databases for NBS gene research:

Diagram: Integrated workflow for genomic data sourcing and analysis of NBS genes.

Data Curation and Quality Assessment

Quality Control Standards

Table 2: Genomic Data Quality Assessment Metrics and Tools

Quality Metric	Assessment Tool	Application in NBS Gene Research	Optimal Threshold
Genome completeness	BUSCO [30]	Evaluates presence of universal single-copy orthologs	>90% for robust analysis
Proteome quality	Complete Proteome Detector [30]	Assesses proteome annotation quality	Varies by organism
Domain annotation	PfamScan [9]	Identifies NBS and associated domains	e-value: 1.1e-50 [9]
Sequence redundancy	Custom pipelines [30]	Filters redundant sequences	Species-dependent
Assembly metrics	N50, L50 [4]	Evaluates assembly contiguity	Higher values preferred

NBS Gene Identification and Classification

For comprehensive identification of NBS-domain-containing genes, researchers should implement the following protocol:

Domain Identification:

• Use PfamScan with the Pfam-A.hmm model and default e-value cutoff of 1.1e-50 to identify NB-ARC domains [9]
• Consider genes containing NB-ARC domains as NBS genes for further analysis
• Extract domain architecture using established classification systems [9]

Classification System: NBS genes can be classified based on their domain architecture into:

• Classical structural patterns: NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR
• Species-specific patterns: TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS [9]
• Subclasses: TIR-type NLRs (TNLs), CC-type NLRs (CNLs), and RPW8-type NLRs (RNLs) [9]

Recent studies have identified 12,820 NBS-domain-containing genes across 34 plant species, classified into 168 distinct domain architecture classes, demonstrating the extensive diversity of this gene family [9].

Orthogroup Analysis Framework

Orthology Inference Methodology

Orthogroup analysis provides the phylogenetic framework for understanding the evolution and diversification of NBS genes across plant species. The following protocol outlines the standard workflow:

Software and Parameters:

• OrthoFinder v2.5.1: For primary orthogroup inference [9]
• DIAMOND: For rapid sequence similarity searches [9]
• MCL clustering algorithm: For gene clustering [9]
• DendroBLAST: For ortholog identification and orthogrouping [9]

Multiple Sequence Alignment and Phylogenetics:

• MAFFT 7.0: For multiple sequence alignment [9]
• FastTreeMP: For maximum likelihood phylogenetic tree construction with 1000 bootstrap replicates [9]

In a recent large-scale analysis of NBS genes, this approach identified 603 orthogroups, including both core orthogroups (common across multiple species) and unique orthogroups (species-specific) [9]. These orthogroups provide the evolutionary context for understanding patterns of gene duplication, loss, and functional diversification in plant immunity genes.

Duplication and Evolutionary Analysis

NBS gene families expand primarily through two mechanisms:

• Whole-genome duplication (WGD): Creates copies of entire genomes or chromosomal segments
• Small-scale duplications (SSD): Includes tandem, segmental, and transposon-mediated duplications [9]

Studies have shown that gene families evolving through WGDs seldom undergo SSD events, suggesting these represent distinct evolutionary modes with different impacts on gene family expansion [9]. For NBS genes in particular, tandem duplications have been identified as significant drivers of diversity, with many NBS genes organized in clusters in plant genomes [9].

Experimental Validation and Functional Characterization

Expression Profiling and Genetic Variation

Transcriptomic Analysis:

• Utilize RNA-seq data from databases such as the IPF database (http://ipf.sustech.edu.cn/pub/) [9]
• Categorize expression patterns into tissue-specific, abiotic stress-specific, and biotic stress-specific profiles
• Process data using standardized transcriptomic pipelines [9]

In NBS gene studies, expression profiling has revealed putative upregulation of specific orthogroups (OG2, OG6, OG15) in various tissues under biotic and abiotic stresses [9]. For example, in cotton leaf curl disease (CLCuD), comparative analysis between susceptible (Coker 312) and tolerant (Mac7) accessions identified 6,583 unique variants in NBS genes of the tolerant line and 5,173 in the susceptible line [9].

Functional Validation Techniques

Protein Interaction Studies:

• Protein-ligand interaction analysis demonstrates strong binding of putative NBS proteins with ADP/ATP [9]
• Protein-protein interaction studies reveal associations between NBS proteins and core proteins of pathogens such as the cotton leaf curl disease virus [9]

Genetic Approaches:

• Virus-induced gene silencing (VIGS): Used to validate function, as demonstrated by silencing of GaNBS (OG2) in resistant cotton, confirming its role in virus titering [9]
• Saturation mutagenesis: Coupled with functional assays to determine variant effects [32]

Research Reagent Solutions

Table 3: Essential Research Reagents for NBS Gene Studies

Reagent/Resource	Function	Example Application	Database Source
Pfam HMM models	Domain identification	NB-ARC domain detection in protein sequences [9]	Pfam database
OrthoFinder software	Orthogroup inference	Clustering NBS genes into orthologous groups [9]	Public repository
BUSCO datasets	Genome completeness assessment	Quality control of genome assemblies [30]	BUSCO website
Rhea knowledgebase	Enzyme function annotation	Biochemical reaction annotation for NBS proteins [30]	Rhea database
ClinVar variants	Pathogenic variant data	Assessing functional impact of NBS gene mutations [28]	NCBI ClinVar
VIGS vectors	Functional validation	Silencing NBS genes to test disease resistance [9]	Plant molecular biology suppliers

Effective genome data sourcing and curation from public databases provides the foundation for robust orthogroup analysis of NBS domain genes. By leveraging the complementary strengths of NCBI, Phytozome, and PLAZA – while implementing rigorous quality control measures – researchers can elucidate the evolutionary patterns and functional diversification of these critical plant immune receptors. The continuous expansion and refinement of these database resources, coupled with standardized analytical frameworks, will further accelerate discoveries in plant immunity and support the development of disease-resistant crop varieties through comparative genomics approaches.

Within the framework of orthogroup analysis of nucleotide-binding site (NBS) genes, the precise identification and characterization of these genes from genomic data constitutes a critical foundational step. Genes encoding proteins containing an NBS domain represent the largest family of plant disease resistance (R) genes, playing vital roles in effector-triggered immunity [9] [33]. The NB-ARC domain (Pfam: PF00931), named after its presence in APAF-1, R proteins, and CED-4, serves as the molecular signature for this superfamily [33] [34]. This technical guide outlines a robust, reproducible pipeline for genome-wide identification of NBS-encoding genes using HMMER searches followed by rigorous domain validation, providing a standardized methodology essential for comparative evolutionary studies and cross-species orthogroup analysis.

Core Methodology: HMMER and Domain Analysis

The identification pipeline leverages the conserved NB-ARC domain through sequential homology searches and domain architecture validation. The workflow integrates both automated profiling and manual curation to ensure comprehensive detection while minimizing false positives.

Table 1: Key Tools and Databases for NBS Gene Identification

Tool/Database	Primary Function	Key Parameters	Application in Pipeline
HMMER	Hidden Markov Model search	E-value cutoff (0.01-1.0) [35] [36]	Initial NB-ARC domain identification
Pfam Database	Protein family database	NB-ARC domain (PF00931) [33] [37]	Domain verification (E-value 10⁻⁴)
NCBI Conserved Domain Database (CDD)	Domain annotation	E-value threshold [35] [22]	Validation of NBS domain presence
MEME Suite	Motif discovery	Width: 6-50 aa; Motif count: 10 [35] [22]	Conserved motif analysis within NBS
InterProScan	Integrated domain prediction	Multi-tool integration [37]	Secondary domain validation
Coils/PCOILS	Coiled-coil prediction	Probability threshold ≥0.9 [36] [34]	CC domain identification

Figure 1: Comprehensive workflow for NBS gene identification integrating HMMER searches with multi-tool domain validation.

HMMER Search Implementation

The initial identification phase employs HMMER to detect NB-ARC domains within protein sequences. The standard approach utilizes the raw HMM profile for the NB-ARC domain (PF00931) from Pfam. Studies consistently apply an E-value cutoff of 1.0 during initial searches to maximize sensitivity [35]. For example, in the identification of 73 NBS genes in Akebia trifoliata, researchers performed BLASTP analysis followed by HMMER scanning with the NB-ARC domain query [35]. This dual approach increases detection sensitivity for divergent NBS sequences.

For enhanced specificity, constructing a species-specific HMM profile is recommended. This involves aligning initial candidate sequences using HMMER align and building a custom HMM using HMMER build [36]. The NLGenomeSweeper pipeline implements this strategy effectively, creating class-specific NB-ARC consensus sequences for improved detection in target species [37]. Subsequent searches with this refined model (E-value cutoff 0.01) significantly reduce false positives while maintaining sensitivity.

Domain Validation and Classification

Following initial identification, candidate sequences require rigorous domain architecture validation to confirm NBS membership and assign proper classification.

Multi-domain validation is essential, as NBS-encoding genes typically contain additional domains that define their subclass:

• TIR domains: Identified using Pfam HMM (PF01582) or NCBI CDD [35] [38]
• LRR domains: Detected with Pfam (PF08191 or PF00560) [35] [38]
• CC domains: Require specialized tools like COILS/PCOILS with probability threshold ≥0.9 or PAIRCOIL2 with P-score ≤0.025 [38] [36]
• RPW8 domains: Identified via Pfam (PF05659) [35]

This domain assortment enables classification into major subfamilies: TNL (TIR-NBS-LRR), CNL (CC-NBS-LRR), RNL (RPW8-NBS-LRR), and NL (NBS-LRR without typical N-terminal domains) [34]. In the radish genome study, this approach enabled classification of 225 NBS-encoding genes into 80 TNL and 51 CNL genes, with the remainder being partial or atypical [33].

Advanced Characterization and Orthogroup Analysis

Conserved Motif Analysis and Gene Structure

Delving deeper into NBS domain architecture reveals evolutionarily conserved motifs that inform functional analysis. Using MEME suite with motif width set between 6-50 amino acids and identifying approximately 10 motifs provides comprehensive structural insight [35] [22]. Research across euasterids consistently identifies eight key conserved motifs: P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, GLPL, RNBS-D, and MHDV [36].

Table 2: Conserved Motifs in Plant NBS Domains

Motif Name	Conserved Sequence	Functional Role	Subfamily Specificity
P-loop	GMGGVGKT	ATP/GTP binding	Universal
Kinase-2	LVLDDVW	Phosphotransfer activity	Universal
GLPL	GSrPL	Domain structural integrity	Universal
RNBS-A	FLHIACF	Signaling function	TIR/Non-TIR variants
RNBS-D	CFLYCALF	Unknown function	TIR/Non-TIR variants
MHDV	MHDIV	Molecular switch regulation	Universal

Structural differences between subfamilies are notable. CNLs typically contain fewer exons than TNLs, a characteristic observed in Akebia trifoliata where CNLs had simpler gene structures compared to TNLs [35]. This structural variation has implications for evolutionary dynamics and functional diversification.

Genomic Distribution and Clustering Analysis

NBS genes display non-random genomic distribution, predominantly residing in clusters at chromosome terminals. In Solanum tuberosum Group Phureja, 362 of 470 mapped NBS genes were found in high-density clusters on 11 chromosomes [38]. Similarly, in radish, 72% of 202 mapped NBS-encoding genes grouped into 48 clusters distributed in 24 crucifer blocks across chromosomes [33].

This clustering phenomenon has significant implications for orthogroup analysis, as genes within clusters often arise from recent duplication events and may display functional redundancy or specialization. The cluster homogeneity observed in radish, where clustered NBS genes derived from recent common ancestors [33], simplifies orthogroup assignment while highlighting recent lineage-specific expansions.

Figure 2: Orthogroup analysis workflow for NBS genes, from multi-sequence alignment to evolutionary analysis.

Integration with Orthogroup Analysis

Orthogroup analysis provides an evolutionary framework for understanding NBS gene diversification across taxa. The methodology involves:

• Orthogroup delineation using OrthoFinder with DIAMOND for sequence similarity searches and MCL for clustering [9]
• Phylogenetic reconstruction via maximum likelihood methods (FastTreeMP or MEGA) with 1000 bootstrap replicates [9] [22]
• Duplication mode assessment categorizing genes as tandem, segmental, or dispersed duplicates [35]

In a comprehensive analysis of 34 plant species, researchers identified 12,820 NBS-domain-containing genes grouped into 603 orthogroups, with some core orthogroups (OG0, OG1, OG2) conserved across multiple species and others specific to particular lineages [9]. This orthogroup framework enables systematic tracking of NBS gene evolution across the plant kingdom.

Duplication analysis reveals tandem and dispersed duplications as primary drivers of NBS expansion. In Akebia trifoliata, these mechanisms produced 33 and 29 genes respectively [35], highlighting the dynamic nature of NBS gene repertoires. For orthogroup stability, adjacent NLR pairs separated by ≤8 genes can be identified using BEDTools, with statistical significance evaluated against random expectations via permutation tests [22].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Computational Tools

Reagent/Tool	Specifications	Function in NBS Identification
HMMER Suite	Version 3.0+	Core HMM search and alignment
NB-ARC HMM Profile	Pfam PF00931	Primary domain detection
Pfam Database	Release 35.0+	Domain family verification
- NCBI CDD	v3.20+	Conserved domain validation
- MEME Suite	5.5.0+	Conserved motif discovery
- OrthoFinder	v2.5.1+	Orthogroup inference
- DIAMOND	v2.1.8+	Fast sequence similarity
- MCL Algorithm	Inflation 2.0-6.0	Orthogroup clustering
- MAFFT	v7.0+	Multiple sequence alignment
- COILS/PCOILS	v2.2	Coiled-coil domain prediction

The integration of HMMER searches with multi-tiered domain validation establishes a robust foundation for NBS gene identification and classification. This standardized methodology enables reproducible genome-wide characterization of NBS repertoires essential for meaningful orthogroup analysis. The consistent application of these protocols—from initial domain detection through motif analysis and phylogenetic placement—will facilitate cross-study comparisons and illuminate the evolutionary dynamics of this critically important gene family in plant immunity. As genomic resources continue to expand, this pipeline will remain indispensable for unraveling the complex evolutionary history of NBS genes across the plant kingdom.

Orthogroup clustering is a fundamental methodology in comparative genomics, enabling researchers to infer evolutionary relationships and functional characteristics of genes across multiple species. This technical guide details the implementation of OrthoFinder, a powerful phylogenetic orthology inference platform that integrates the rapid DIAMOND sequence search tool and the Markov Clustering (MCL) algorithm to achieve highly accurate orthogroup delineation. Framed within the context of nucleotide-binding site (NBS) gene research, this whitepaper provides a comprehensive workflow from data preparation through advanced phylogenetic analysis. We demonstrate how this integrated approach successfully identified 12,820 NBS-domain-containing genes across 34 plant species in a recent large-scale study, classifying them into 168 distinct domain architecture classes and revealing several species-specific structural patterns. The protocol outlined herein enables the precise identification of orthogroups, inference of rooted gene trees, and comprehensive analysis of gene duplication events, providing an essential toolkit for researchers investigating the evolution of disease resistance genes and other gene families in the context of drug development and therapeutic targeting.

Biological Significance of Nucleotide-Binding Site Genes

Nucleotide-binding site (NBS) domain genes represent a critical superfamily of plant resistance (R) genes involved in pathogen recognition and defense activation. These genes typically encode NBS-LRR proteins (Nucleotide-Binding Site Leucine-Rich Repeat) that function as key immune receptors in effector-triggered immunity [9]. The NBS domain serves as a molecular switch, regulated by ADP/ATP binding and hydrolysis, which controls the activation of defense signaling pathways. Understanding the evolutionary dynamics and classification of these genes through orthogroup analysis provides crucial insights into plant immunity mechanisms with potential applications in developing disease-resistant crops and identifying novel antimicrobial targets.

Theoretical Framework of Orthology Inference

Orthogroup analysis addresses the fundamental challenge of distinguishing orthologs (genes diverged after a speciation event) from paralogs (genes diverged after a duplication event). This distinction is particularly crucial for NBS genes, which frequently expand through duplication events and exhibit complex evolutionary patterns [9] [15]. An orthogroup is defined as the set of all genes descended from a single gene in the last common ancestor of the species being analyzed, including both orthologs and paralogs [39]. Accurate orthogroup inference provides the phylogenetic framework necessary for comparative genomics, functional annotation transfer, and evolutionary studies of gene family expansion and contraction.

Algorithmic Foundations: OrthoFinder, DIAMOND, and MCL

OrthoFinder: Phylogenetic Orthology Inference

OrthoFinder is a comprehensive platform for comparative genomics that provides phylogenetic inference of orthologs, rooted gene trees, gene duplication events, and rooted species trees [40]. Unlike score-based heuristic methods, OrthoFinder employs a phylogenetic approach that significantly enhances accuracy by distinguishing variable sequence evolution rates from divergence patterns [40]. The algorithm automatically processes protein sequences through several sophisticated stages:

• Orthogroup inference using sequence similarity searches and graph clustering
• Gene tree inference for all orthogroups
• Species tree inference from the complete set of gene trees
• Gene duplication event identification mapped to both gene and species trees
• Comprehensive comparative genomics statistics [40]

This multifaceted approach enables OrthoFinder to achieve 3-24% higher ortholog inference accuracy compared to other methods on standard benchmarks [40], making it particularly valuable for analyzing complex gene families like NBS genes.

DIAMOND: High-Speed Sequence Alignment

DIAMOND (Double Index Alignment of Next-Generation Sequencing Data) is a BLAST-compatible sequence alignment tool that provides a substantial speed increase over traditional BLASTp searches while maintaining high sensitivity [41]. The algorithm uses double indexing and spaced seeds to accelerate the alignment process, achieving speeds up to 20,000 times faster than BLAST for short reads and approximately 80 times faster for protein-protein searches at comparable sensitivity levels [41]. OrthoFinder utilizes DIAMOND as its default sequence search engine, enabling efficient all-versus-all sequence comparisons even for large genomic datasets encompassing dozens of species with extensive gene families.

MCL: Markov Clustering for Orthogroup Delineation

The Markov Clustering (MCL) algorithm performs graph-based clustering by simulating stochastic flow in mathematical graphs [42]. In OrthoFinder, MCL clusters genes into orthogroups based on their sequence similarity scores (after normalization), where genes are represented as nodes and sequence similarities as weighted edges. The inflation parameter in MCL controls cluster tightness, with OrthoFinder's default value of 1.5 providing an optimal balance between precision and recall for most datasets [43]. This approach effectively groups evolutionarily related genes while accounting for complex evolutionary histories including gene duplications and losses.

Integrated Algorithmic Workflow

The synergy between these algorithms creates a robust orthogroup inference pipeline. DIAMOND rapidly generates all-versus-all sequence comparisons, whose scores are normalized to eliminate gene length bias. The normalized scores then form a similarity graph that MCL partitions into preliminary orthogroups. Finally, OrthoFinder performs sophisticated phylogenetic analysis on these groups to infer precise orthology relationships and evolutionary events.

Table 1: Core Algorithms in the Orthogroup Clustering Pipeline

Algorithm	Primary Function	Key Advantage	Role in NBS Gene Analysis
OrthoFinder	Phylogenetic orthology inference	3-24% higher accuracy than other methods [40]	Identifies evolutionary relationships among disease resistance genes
DIAMOND	High-speed sequence alignment	80x faster than BLAST at similar sensitivity [41]	Enables rapid comparison of extensive NBS gene repertoires
MCL	Graph-based clustering	Effectively handles complex similarity networks	Groups duplicated NBS genes into meaningful orthogroups

OrthoFinder Implementation Protocol

Input Data Requirements and Preparation

The OrthoFinder pipeline requires protein sequences in FASTA format as primary input, with one file per species. For NBS gene research, ensure that:

• Protein sequences are derived from annotated genomes or transcriptomes
• Sequence identifiers are consistent across files and contain no special characters
• File naming follows systematic conventions for easy species identification
• Data quality is verified through checks for fragmentation and annotation completeness

In a recent study analyzing NBS genes across 34 plant species, researchers downloaded latest genome assemblies from public databases including NCBI, Phytozome, and Plaza, then extracted protein sequences for orthogroup analysis [9].

Installation and Setup

OrthoFinder can be installed through multiple package managers or directly from source:

The software requires Python with numpy and scipy libraries, or can be run using the bundled version which includes all dependencies [26]. For large-scale analyses with numerous species, ensure adequate computational resources are available, particularly memory for the MCL clustering step.

Core Command-Line Implementation

The basic OrthoFinder execution with DIAMOND and MCL requires a single command:

Key parameters for NBS gene analysis include:

• -f <directory>: Path to directory containing input FASTA files
• -S <program>: Sequence search program (diamond, blast, mmseqs2)
• -I <value>: MCL inflation parameter (default: 1.5)
• -t <int>: Number of parallel sequence search threads
• -a <int>: Number of parallel analysis threads
• -M <method>: Gene tree inference method (dendroblast or msa)

For the comprehensive analysis of NBS genes, researchers typically use the default DendroBLAST method for gene tree inference as it provides an optimal balance between speed and accuracy for large datasets [9].

Advanced Configuration for Complex Gene Families

For specialized analyses of NBS genes with their characteristic domain architectures, consider these advanced options:

These advanced configurations are particularly valuable when analyzing taxonomically diverse species or gene families with deep evolutionary origins, such as the ancestral NBS domains found in both plants and animals [6].

Workflow Visualization

The workflow begins with input proteomes in FASTA format, proceeds through sequence similarity search and clustering, and culminates in comprehensive phylogenetic analysis. This integrated approach enables precise identification of orthology relationships essential for NBS gene classification.

Experimental Design and Data Interpretation

Case Study: NBS Gene Analysis Across 34 Plant Species

In a recent investigation applying this pipeline to NBS genes, researchers identified 12,820 NBS-domain-containing genes across 34 species ranging from mosses to monocots and dicots [9]. The analysis revealed:

• 168 distinct classes of domain architecture patterns
• Multiple species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS)
• 603 orthogroups with both core (widely conserved) and unique (species-specific) distributions
• Tandem duplication events as a major mechanism for NBS gene expansion

This study demonstrated the utility of OrthoFinder in handling complex gene families with extensive duplication histories and diverse domain architectures [9].

Orthogroup Statistics and Comparative Genomics

OrthoFinder generates comprehensive statistics that facilitate comparative analysis of NBS genes across species:

Table 2: Key Orthogroup Statistics for NBS Gene Analysis

Metric	Description	Interpretation in NBS Research
Orthogroup Count	Total number of orthogroups identified	Reflects diversity of NBS gene repertoire
Gene Assignment Rate	Percentage of genes assigned to orthogroups	Indicates completeness of NBS gene capture
Species-Specific Orthogroups	Orthogroups restricted to single species	Reveals recent, lineage-specific NBS expansions
Universal Single-Copy Orthogroups	Orthogroups with exactly one copy per species	Identifies highly conserved NBS genes
G50 Value	Number of genes in smallest orthogroup containing 50% of all genes	Measures NBS gene family size distribution
O50 Value	Number of orthogroups containing 50% of all genes	Indicates distribution of NBS genes across families

Validation Methods for Orthogroup Results

Several approaches can validate orthogroup inferences for NBS genes:

• Domain architecture consistency: Verify that genes within orthogroups share similar NBS domain patterns
• Phylogenetic concordance: Ensure that gene trees within orthogroups show reasonable evolutionary relationships
• Synteny analysis: Confirm conserved genomic contexts for orthologous NBS genes in closely related species
• Expression profiling: Validate functional conservation through correlated expression patterns

In the NBS gene study, researchers validated orthogroups through expression profiling under biotic stress, genetic variation analysis between tolerant and susceptible cotton accessions, and functional validation using virus-induced gene silencing [9].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Computational Tools for Orthogroup Analysis

Resource	Type	Function in Orthogroup Analysis
OrthoFinder	Software Platform	Phylogenetic orthology inference from genomic data [40]
DIAMOND	Sequence Search Tool	High-speed protein sequence alignment [41]
MCL Algorithm	Clustering Method	Graph-based clustering of sequence similarity networks [42]
MAFFT	Alignment Tool	Multiple sequence alignment for gene tree inference
FastTree	Phylogenetic Software	Rapid inference of approximately-maximum-likelihood trees
Pfam Database	Domain Repository	HMM profiles for identifying NBS domains (e.g., NB-ARC, PF00931) [9]
NCBI/Phytozome	Data Repository	Source of annotated proteomes for analysis [9]
Conda	Package Manager	Simplified installation of bioinformatics tools [26]

Troubleshooting and Optimization Strategies

Addressing Common Implementation Challenges

• Low orthogroup assignment rates: May indicate fragmented genome assemblies or overly stringent clustering; consider adjusting MCL inflation parameter or using ultra-sensitive DIAMOND mode
• Long run times: Utilize parallel processing options (-t and -a parameters) and consider using MMseqs2 as an alternative to DIAMOND for large datasets
• Inaccurate species trees: Provide a known species tree using the -s option when available, particularly for analyzing deep evolutionary relationships of NBS genes
• Excessive splitting of NBS genes: Consider increasing the MCL inflation parameter value to 2.0 or higher for tighter clustering

Performance Optimization for Large Datasets

For analyses involving numerous species or large gene families like NBS genes:

• Utilize DIAMOND ultra-sensitive mode for improved detection of divergent NBS domains
• Increase computational resources for memory-intensive MCL clustering steps
• Employ restart options (-b, -fg, -ft) to resume interrupted analyses
• Leverage hierarchical orthogroups for studies involving both closely and distantly related species

Applications in NBS Gene Research and Drug Development

The OrthoFinder-DIAMOND-MCL pipeline enables several advanced analyses with significant implications for understanding disease resistance mechanisms and developing therapeutic interventions:

• Identification of conserved functional domains across species for targeted drug design
• Reconstruction of evolutionary histories of disease resistance gene families
• Detection of lineage-specific expansions associated with pathogen resistance
• Prediction of key functional residues through evolutionary conservation analysis
• Identification of orthologs for translating findings from model organisms to crop species

In the referenced NBS gene study, the pipeline facilitated the identification of orthogroups with differential expression in response to cotton leaf curl disease, revealing potential candidates for engineering disease-resistant plants [9]. Similarly, comparative analysis of NLR proteins across fungal species revealed unexpected similarities with animal immune receptors, suggesting conserved mechanisms that could inform therapeutic development [6].

The integration of OrthoFinder with DIAMOND and MCL algorithms provides a robust, accurate, and efficient framework for orthogroup clustering, with particular utility for investigating complex gene families such as nucleotide-binding site genes. The methodological framework presented in this guide enables comprehensive evolutionary analysis, from initial sequence comparison through sophisticated phylogenetic inference. As demonstrated in the case study of NBS genes across 34 plant species, this approach can reveal novel insights into gene family evolution, functional diversification, and molecular mechanisms of disease resistance. The continued refinement of these computational methodologies will further enhance our ability to extract biological meaning from genomic data, accelerating discovery in both basic research and applied drug development.

Phylogenetic Reconstruction and Multiple Sequence Alignment Strategies (MAFFT, FastTree)

In the study of Nucleotide-Binding Site (NBS) genes—a major class of plant disease resistance genes—accurately determining evolutionary relationships through orthogroup analysis is fundamental. Orthogroups represent sets of homologous genes descended from a single gene in the last common ancestor of the species being considered [15]. For NBS genes, which are numerous, diverse, and prone to lineage-specific expansions and contractions, robust phylogenetic reconstruction depends entirely on the quality of the underlying Multiple Sequence Alignment (MSA) [44]. The interdependent processes of MSA and tree building form the cornerstone of comparative genomics, enabling researchers to identify orthologs and paralogs, infer gene duplication events, and elucidate evolutionary patterns, such as the contraction of the NLR gene repertoire observed in domesticated garden asparagus (Asparagus officinalis) compared to its wild relatives [22] [12].

This technical guide provides an in-depth examination of modern strategies for MSA and phylogenetic reconstruction, with a specific focus on the MAFFT and FastTree tools. It is framed within the practical context of orthogroup analysis for NBS gene families, detailing protocols, data presentation standards, and integration with established orthology inference frameworks like OrthoFinder.

Core Concepts and Interdependence

The MSA-Phylogeny Workflow Loop

The relationship between multiple sequence alignment and phylogeny reconstruction is often cyclical rather than linear. While a phylogenetic tree is inferred from an MSA, most progressive MSA methods require a guide tree to define the order in which sequences are aligned [45] [44]. The quality of this guide tree directly impacts the alignment accuracy. Furthermore, the most phylogenetically informative approach is sometimes considered a simultaneous optimization of both the alignment and the tree, though this is computationally demanding [44].

For orthogroup analysis, this process is typically embedded within a larger pipeline. A standard workflow involves:

• Gene Prediction: Identifying NBS genes from genome or transcriptome data, often using Hidden Markov Models (HMMs) based on conserved domains like NB-ARC (PF00931) [22] [12].
• Orthogroup Inference: Clustering predicted protein sequences from multiple species into orthogroups using tools like OrthoFinder [22] [40] [15].
• Multiple Sequence Alignment: Aligning the sequences within each orthogroup.
• Phylogenetic Reconstruction: Building a gene tree from the alignment to visualize relationships and identify orthologs/paralogs.

The following diagram illustrates this core workflow and the critical MSA-Phylogeny loop.

Key Algorithmic Approaches

Multiple Sequence Alignment Methods:

• Progressive Alignment: The most common approach, used by MAFFT and Clustal Omega. It builds an MSA by progressively aligning sequences according to a guide tree, which ensures that the most similar sequences are aligned first [45].
• Iterative Methods: Algorithms like MUSCLE refine an initial alignment by repeatedly realigning subsets of sequences to improve the overall score, helping to correct early errors [45].
• Consensus Methods: Tools like T-Coffee combine output from multiple alignments to produce a consensus, often improving accuracy [45].

Phylogenetic Reconstruction Methods:

• Distance-Based Methods: FastTree uses distance-based algorithms, which calculate genetic distances between all sequence pairs and then build a tree from the resulting distance matrix [46]. These methods are fast and scalable.
• Character-Based Methods: These include Maximum Likelihood (ML) and Bayesian Inference. They use the aligned character data (nucleotides or amino acids) directly to evaluate tree topologies. RAxML is a widely used ML tool [46]. While more computationally intensive, they are generally more accurate than distance methods.

Tool-Specific Methodologies: MAFFT and FastTree

MAFFT for Multiple Sequence Alignment

MAFFT is a highly accurate and efficient MSA tool that employs a progressive alignment strategy. Its accuracy stems from its sophisticated scoring systems and capacity for iterative refinement.

Detailed Protocol for Orthogroup Analysis:

• Input Preparation: Compile the protein or nucleotide sequences for a single orthogroup into a FASTA file. Ensure sequences are correctly trimmed and labeled.
• Algorithm Selection: MAFFT offers multiple strategies. For a typical NBS gene orthogroup (dozens to hundreds of sequences), the L-INS-i algorithm is often optimal. It uses a progressive method with iterative refinement and is considered one of MAFFT's most accurate options for a medium number of sequences.
• Command Execution:
  • --localpair or --globalpair: Specifies whether to use local or global homology information for the pairwise alignments that build the guide tree. For NBS genes, which share conserved domains but may have variable flanking regions, --localpair is often preferable.
  • --maxiterate 1000: Sets the maximum number of iterative refinements.
• Output: The result is a multiple sequence alignment in FASTA format, which can be visualized and edited in tools like the NCBI Multiple Sequence Alignment Viewer [47] or Geneious Prime [45].

FastTree for Phylogenetic Reconstruction

FastTree approximates maximum-likelihood trees with significantly reduced memory and computation time, making it ideal for large datasets like those generated from genome-wide orthogroup analyses.

Detailed Protocol for Orthogroup Analysis:

• Input: Use the aligned FASTA file from MAFFT.
• Model Selection:
  • For protein sequences (common for NBS orthogroup analysis): Use the JTT (Jones-Taylor-Thornton) model with the -lg option for better handling of site rate variation.

  • For nucleotide sequences: Use the General Time-Reversible (GTR) model.

• Key Parameters:
  • -gamma: This option enables the calculation of a gamma parameter to model rate variation across sites, which greatly improves accuracy. It is highly recommended.
  • -bootstrap: To assess branch support, specify the number of resamplings (e.g., -boot 1000). FastTree uses a local, or "SH-like," support value rather than standard bootstrapping for speed.
• Output: The primary output is a Newick-formatted tree file (orthogroup_tree.tre). This can be visualized and annotated in software like FigTree, iTOL, or the tree viewer in Geneious Prime.

Integration with Orthology Inference Frameworks

Orthogroup analysis provides the essential evolutionary context for interpreting NBS gene phylogenies. OrthoFinder is a benchmark tool that phylogenetically infers orthogroups, rooted gene trees, and orthologs [40]. The phylogenetic trees generated by FastTree for individual orthogroups are the direct input for OrthoFinder's species tree inference and gene duplication analysis.

Experimental Workflow from Genomes to Orthology: A practical example is provided by a study comparing NLR genes in Asparagus officinalis and its wild relatives [22] [12]:

• Genome-Wide Identification: NBS genes were identified from the genomes of three Asparagus species using HMM searches (NB-ARC domain) and BLASTp against known NLRs.
• Orthogroup Inference: Orthologous genes between A. setaceus and A. officinalis were clustered using OrthoFinder, identifying 16 conserved NLR gene pairs preserved during domestication [22].
• Phylogenetic Analysis: Protein sequences of candidate NLRs were consolidated, aligned using Clustal Omega, and a phylogenetic tree was constructed using the Maximum Likelihood method based on the JTT model in MEGA [22] [12]. This tree was used to categorize NLRs into subfamilies (CNL, TNL, RNL) and study their evolutionary dynamics.
• Expression and Evolutionary Insight: The integrated analysis revealed that domestication led to a contraction of the NLR gene repertoire and functional impairment of retained genes, explaining increased disease susceptibility in cultivated asparagus [22].

This workflow, from gene identification through orthology inference to phylogenetic analysis, is summarized in the following diagram.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 1: Key bioinformatics tools and resources for NBS gene orthogroup analysis.

Tool/Resource	Type	Primary Function in Workflow	Application Example in NBS Research
HMMER [22]	Software Suite	Protein Domain Identification	Identifying NBS genes via NB-ARC (PF00931) domain search.
DIAMOND/BLAST+ [40]	Software Tool	Rapid Sequence Similarity Search	All-vs-all BLAST for initial orthogroup clustering in OrthoFinder.
OrthoFinder [40] [15]	Software Pipeline	Orthogroup Inference & Species Tree	Clustering NBS proteins from multiple species into orthologs/paralogs.
MAFFT [45]	Software Tool	Multiple Sequence Alignment	Creating accurate alignments of NBS gene orthogroups.
FastTree [46]	Software Tool	Phylogenetic Tree Inference	Rapidly constructing gene trees for large numbers of orthogroups.
RAxML-NG [46]	Software Tool	Phylogenetic Tree Inference	Building high-accuracy maximum likelihood trees for final analysis.
PlantCARE [22]	Web Server	cis-Element Prediction	Analyzing promoter regions of NLR genes for defense-related motifs.
NCBI MSA Viewer [47]	Web Tool	Alignment Visualization	Inspecting, navigating, and analyzing generated MSAs interactively.

Advanced Strategies and Emerging Methods

Handling Challenging Datasets and Scalability

NBS gene families can present specific challenges, such as large gene counts, high sequence divergence, and the presence of truncated genes [22]. For such datasets:

• Large Alignments: For orthogroups containing thousands of sequences, MAFFT's PartTree algorithm offers a faster, less accurate alternative for building the initial guide tree, significantly speeding up the alignment process.
• Sequence Trimming: Visually inspect alignments of NBS genes using a tool like the NCBI MSA Viewer [47] to trim poorly aligned flanking regions before phylogenetics, as these can introduce noise.
• Whole Genome Alignments: For studying microsynteny and gene colinearity around NBS genes, tools like Mauve are essential for aligning genomic regions with potential rearrangements [45].

Novel Computational Approaches

The field of phylogenetics is continuously evolving. Emerging methods like PhyloTune leverage pretrained DNA language models (e.g., DNABERT) to rapidly integrate new sequences into an existing phylogenetic tree [46]. This is achieved by identifying the smallest taxonomic unit for a new sequence and extracting high-attention, phylogenetically informative regions for subtree construction, bypassing the need for a full MSA of all sequences. This approach demonstrates that phylogenetic trees can be built by automatically selecting informative regions, offering a promising direction for future scalability and efficiency [46].

Furthermore, the integration of synteny information can significantly improve orthology inference, especially in complex plant genomes with a history of polyploidy. Tools like OrthNet incorporate gene colinearity data to refine orthogroup predictions, which can be particularly valuable when sequence similarity alone gives ambiguous results [15].

A robust pipeline combining MAFFT for multiple sequence alignment and FastTree for phylogenetic reconstruction provides a powerful and efficient strategy for orthogroup analysis of NBS genes. The integration of these tools within a phylogenetic orthology inference framework like OrthoFinder allows researchers to move beyond simple sequence similarity to a nuanced, evolutionary-based understanding of gene family dynamics. As exemplified in the study of asparagus NLRs, this integrated approach can reveal how evolutionary pressures, including domestication, shape the architecture of plant immune systems. The ongoing development of novel algorithms, including those employing machine learning, promises to further enhance the accuracy, speed, and scale of these core bioinformatic analyses.

Gene duplication is a fundamental evolutionary process that provides the primary source of new genetic material, enabling functional innovation and organismal adaptation [48]. Within the context of orthogroup analysis of nucleotide-binding site (NBS) genes, understanding duplication mechanisms is particularly crucial, as these genes often expand through duplication events to generate diversity for pathogen recognition [49] [50]. The two principal mechanisms driving gene family expansion are tandem duplication and segmental duplication, which operate at different genomic scales and leave distinct signatures in genome architecture [51] [48].

Tandem duplication occurs when a gene is duplicated adjacent to its original genomic location, typically through unequal crossing-over during recombination [51] [48]. These events produce tandemly arrayed genes (TAGs) that appear as clusters of related genes physically close to one another on chromosomes. In contrast, segmental duplication involves the duplication of large genomic blocks, often encompassing multiple genes simultaneously [52]. These duplications may arise from polyploidization events or large-scale chromosomal rearrangements, resulting in duplicated regions that can be distributed across different chromosomes [51] [52]. For researchers investigating NBS genes, which frequently exhibit lineage-specific expansions, accurately distinguishing between these duplication modes provides critical insights into evolutionary history, selective pressures, and functional diversification [49] [50].

Fundamental Concepts and Definitions

Characteristics of Tandem Duplications

Tandem duplications generate paralogous genes located in close physical proximity on the same chromosome, typically separated by less than 100 kilobases [51]. The molecular mechanism primarily involves unequal crossing-over, which can occur between homologous chromosomes or sister chromatids during meiosis [48]. This process is often mediated by repetitive sequences that promote misalignment and recombination. Multiple successive tandem duplication events can lead to expanding gene clusters with high sequence similarity, creating genomic regions rich in gene families. In plant genomes, tandem duplicates are frequently observed in gene families involved in environmental interactions, such as pathogen resistance genes including NBS-LRR genes [50]. These tandem arrays facilitate the birth-and-death evolution model, where new functional variants arise through repeated duplication while non-functional copies are eventually purged from the genome [51].

Characteristics of Segmental Duplications

Segmental duplications involve the duplication of genomic segments ranging from 1 kilobase to several hundred kilobases, potentially encompassing multiple genes [52]. These duplications may occur within the same chromosome (intrachromosomal) or between different chromosomes (interchromosomal), and often result from replication-based mechanisms or non-allelic homologous recombination [52]. Segmental duplications are frequently associated with polyploidization events, where entire genome duplication provides raw genetic material for subsequent functional diversification [51] [48]. Following such events, the genome undergoes diploidization, a process characterized by chromosomal rearrangements and gene loss that gradually restores disomic inheritance [48]. In the human genome, segmental duplications account for approximately 3.6-10.6% of the sequence content and are significantly enriched in pericentromeric and subtelomeric regions [52].

Genomic Distribution Patterns

The genomic distribution of tandem and segmental duplications exhibits distinct patterns that can be visually identified through genome analysis. The following diagram illustrates the key organizational differences:

Detection and Analysis Methodologies

Genomic Sequence Analysis Pipeline

Identifying duplication events requires a multi-step bioinformatics approach that combines similarity searches, phylogenetic analysis, and genomic mapping. The following workflow outlines a comprehensive strategy for detecting and characterizing duplication modes:

Step-by-Step Experimental Protocols

Gene Family Identification and Alignment

Begin by compiling the gene family of interest using hidden Markov models (HMMs) of conserved domains. For NBS genes, use Pfam domains (e.g., PF00069 for NB-ARC domain) to search the proteome using HMMER3 with an E-value cutoff of 10-10 [51]. Confirm domain architecture consistency using InterProScan to eliminate sequences with aberrant domain arrangements. Perform multiple sequence alignment with T-Coffee or MAFFT, then trim poorly aligned regions using trimAl or similar tools. For large gene families, iterative profile HMM searches may be necessary to capture divergent members [51].

Phylogenetic Tree Construction

Reconstruct gene family relationships using maximum likelihood methods (RAxML, IQ-TREE) or Bayesian inference (MrBayes). Incorporate sequences from related species to provide evolutionary context and root the tree appropriately. Assess branch support with 1000 bootstrap replicates for maximum likelihood analyses or posterior probabilities for Bayesian methods. For orthogroup analysis across multiple species, use OrthoFinder2 to identify orthogroups and gene duplication events [53].

Segmental Duplication Detection

Identify segmentally duplicated regions using whole-genome self-alignment tools such as DiagHunter or MCScanX [51] [52]. The "fuguization" method—masking high-copy repeats before alignment—enhances detection of segmental duplications by reducing noise from transposable elements [52]. Set appropriate similarity thresholds (90-98% identity for recent duplications) and minimum length requirements (typically ≥1 kb). Map genes onto identified duplication blocks to determine whether gene pairs reside in syntenic regions [51].

Tandem Duplication Detection

Identify tandem duplicates by scanning chromosomal positions of gene family members. Define tandem arrays as genes from the same family located within 100 kb of each other with no more than one non-family gene intervening [51]. Use custom scripts or tools like Tandem Duplication Finder to automate detection. Annotate phylogeny nodes supported by tandem duplication events when closely related genes are physically adjacent in the genome.

Evolutionary Analysis

Calculate synonymous (Ks) and nonsynonymous (Ka) substitution rates for duplicated pairs using Codeml from PAML or similar tools. Estimate duplication times from Ks values using appropriate mutation rates for the organism (e.g., 9.1×10-9 mutations per site per year for Gramineae crops) [54]. Test for positive selection by comparing site-specific models that allow for ω (Ka/Ks) > 1.

Quantitative Differentiation Criteria

The table below summarizes key analytical criteria for distinguishing between tandem and segmental duplication events:

Table 1: Diagnostic Criteria for Differentiating Duplication Types

Analytical Feature	Tandem Duplication	Segmental Duplication
Genomic position	Genes clustered on same chromosome (<100 kb apart)	Genes located in syntenic blocks, often on different chromosomes
Phylogenetic pattern	Multiple closely related genes form monophyletic clades	Pairs of genes from different families co-appear in duplicated regions
Gene content	Typically involves members of a single gene family	Involves multiple gene families duplicated together
Sequence similarity	High similarity among tandem array members	Similarity extends across entire duplicated segments
Evolutionary age	Often shows range of ages from recent to ancient	Typically consistent age across duplicated block
Examples in NBS genes	Clustered NBS-LRR genes with similar specificities	Paired NBS genes maintaining synteny across duplicated regions

Special Considerations for Nucleotide-Binding Site Genes

Distinctive Features of NBS Gene Evolution

NBS-containing genes, particularly those encoding NBS-LRR disease resistance proteins, exhibit distinctive evolutionary patterns that influence their duplication mechanisms [49]. In Arabidopsis, approximately 1% of all genes encode NBS domains, with these genes often clustered in the genome and enriched on chromosomes IV and V [49]. The two major NBS classes—TIR-NBS and non-TIR-NBS—show contrasting evolutionary dynamics, with TIR-NBS genes being abundant in dicots but absent in grasses [49]. This phylogenetic distribution suggests lineage-specific expansion and loss events that may involve both tandem and segmental duplication mechanisms.

NBS genes frequently reside in duplication-prone genomic regions characterized by long tandem repeats that promote non-allelic homologous recombination [50]. This association between NBS genes and duplication-inducing elements is evolutionarily advantageous, as it generates diversity in pathogen recognition capabilities through continuous birth-and-death evolution [50]. Studies in barley have demonstrated that NBS genes are statistically overrepresented in Long-Duplication-Prone Regions (LDPRs), particularly in subtelomeric regions where recombination rates are elevated [50].

Analytical Challenges and Solutions

The analysis of duplication modes in NBS genes presents specific technical challenges. The high sequence similarity among recently duplicated NBS genes can lead to misassembly in draft genomes, with paralogous regions incorrectly merged during assembly [52]. Additionally, the repetitive nature of NBS gene clusters complicates PCR-based validation and mapping. To address these challenges:

• Utilize multiple genome assemblies when available to assess assembly consistency in NBS-rich regions
• Apply strict filtering to differentiate recent tandem duplications from allelic variants in heterozygous individuals
• Combine read-based and assembly-based approaches to verify duplication events, using both whole-genome sequencing reads and assembled contigs
• Incorporate orthology information from related species to distinguish lineage-specific expansions from conserved syntenic regions

Table 2: NBS Gene Family Expansion Patterns Across Species

Species	Estimated NBS Genes	Predominant Duplication Mechanism	Genomic Distribution
Arabidopsis thaliana	~200 [49]	Segmental and tandem [51]	Clustered on chromosomes IV and V [49]
Rice (Oryza sativa)	750-1500 [49]	Predominantly tandem [49]	Dispersed with subtelomeric enrichment
Barley (Hordeum vulgare)	Not quantified	Association with LDPRs [50]	Subtelomeric regions [50]
Human	Not applicable	Segmental enrichment in pericentromeric regions [52]	Pericentromeric and subtelomeric [52]

Research Reagent Solutions

Table 3: Essential Research Reagents and Resources for Duplication Analysis

Reagent/Resource	Function/Application	Example Tools/Databases
Multiple sequence alignment tools	Generate accurate amino acid or nucleotide alignments	T-Coffee [51], MAFFT, ClustalW [54]
Phylogenetic reconstruction software	Infer evolutionary relationships among duplicated genes	RAxML, IQ-TREE, MEGA [54], OrthoFinder2 [53]
Segmental duplication detectors	Identify large-scale genomic duplications	DiagHunter [51], MCScanX [54]
Synteny visualization tools	Visualize genomic relationships across duplicated regions	Circos [54], SynVisio, JCVI
Gene family annotation databases	Access curated information on gene families and domains	Pfam [51], InterPro [51], PlantRegMap [55]
Genome browsers	Visualize gene positions and duplication contexts	JBrowse, UCSC Genome Browser, Ensembl Plants [54]
Orthology databases	Identify orthologous relationships across species	OrthoMCL [56], OrthoDB, Ensembl Compara

Biological Interpretation and Evolutionary Significance

Functional Implications of Duplication Modes

The mode of gene duplication has profound implications for functional evolution. Tandemly duplicated genes often evolve under diversifying selection to generate functional variety, particularly evident in NBS genes where rapid sequence diversification enables recognition of evolving pathogen effectors [50]. In contrast, segmentally duplicated genes frequently exhibit subfunctionalization, where ancestral functions are partitioned between duplicates, or neofunctionalization, where one copy acquires a novel function [51] [48].

Studies in Arabidopsis have demonstrated that gene families involved in environmental interactions, such as pathogen defense (including NBS genes) and stress response, show preferential expansion through tandem duplication [51] [50]. Conversely, genes involved in fundamental cellular processes often expand through segmental duplications and are retained at higher rates following whole-genome duplication events [51]. This dichotomy reflects different evolutionary strategies: tandem duplication enables rapid local adaptation, while segmental duplication facilitates coordinated evolution of gene networks.

Integration with Orthogroup Analysis

In orthogroup analysis of NBS genes, discrimination between tandem and segmental duplication events enables more accurate evolutionary inference. Orthogroups containing genes predominantly expanded through tandem duplication often show species-specific innovations, while those expanded through segmental duplication typically represent deeper evolutionary conservation [53]. The phylotranscriptomic approach—combining phylogenomics with expression data—can further elucidate how duplication mode influences regulatory evolution [53].

When analyzing NBS orthogroups, researchers should consider that:

• Tandem expansions may indicate ongoing arms races with pathogens
• Segmental distributions may preserve coordinated regulation of defense pathways
• Mixed duplication patterns may represent complementary evolutionary strategies
• Lineage-specific differences in NBS duplication modes may reflect distinct pathogen pressures

Understanding these evolutionary dynamics provides valuable insights for crop improvement strategies, as deliberately targeting duplication-prone regions associated with NBS genes could accelerate the development of disease-resistant varieties [50].

Overcoming Challenges in Genomic Analysis and Data Interpretation

Addressing Annotation Inconsistencies and Incomplete Domain Architectures

In genomic research, the quality of functional annotations and the completeness of domain architectures directly determine the reliability of downstream biological interpretations. This is particularly critical in the study of large, complex gene families, such as those encoding nucleotide-binding site (NBS) containing proteins, which are central to plant innate immunity. Annotation inconsistencies—discrepancies in gene models, domain boundaries, and functional labels across different databases or analysis pipelines—can lead to erroneous orthogroup assignments and flawed evolutionary conclusions. Simultaneously, incomplete domain architectures, often resulting from fragmented genome assemblies or limitations in domain detection algorithms, obscure the true functional capacity of a genome [57].

Within the specific context of orthogroup analysis of NBS-encoding genes, these data quality issues present significant challenges. Orthogroup analysis, which clusters genes into lineages of descent, depends on accurate and complete sequence representations to infer evolutionary relationships correctly. Inconsistencies in the underlying data can cause the artificial splitting of true orthogroups or the erroneous merging of distinct lineages, ultimately distorting the perceived evolutionary history of this vital gene family [22] [12]. This technical guide outlines a comprehensive framework to identify, quantify, and mitigate these issues, ensuring robust and reproducible research outcomes.

The Impact of Data Incompleteness on Genomic Analysis

Incomplete or biased data fundamentally constrains the analytical power of genomic studies. In the realm of NBS gene analysis, this incompleteness can manifest in several ways, from missing gene models due to assembly gaps to partial domain annotations that prevent accurate gene family classification.

A comparative genomic study on Asparagus species provides a compelling case study. The research revealed a marked contraction of the NLR gene repertoire during domestication, with 63, 47, and 27 NLR genes identified in the wild relative A. setaceus, another wild species A. kiusianus, and the cultivated A. officinalis, respectively [22] [12]. This finding itself could be skewed if the genome assemblies or annotation methods were inconsistent across the three species. Furthermore, the study found that most of the preserved NLR genes in the susceptible cultivated asparagus showed either unchanged or downregulated expression after pathogen challenge [12]. Accurate interpretation of this expression data hinges on the initial correct annotation of these genes and their domain structures, underscoring the necessity of a rigorous preprocessing pipeline to handle data incompleteness before orthogroup analysis begins.

A Framework for Robust Data Representation and Imputation

To address the inherent challenges of incomplete data, we propose a framework inspired by state-of-the-art methods in data imputation and cross-modal learning. The core idea is to move beyond simple intra-table statistics and leverage additional biological information to constrain and validate the reconstruction of missing or inconsistent annotations.

The Cross-modal Tabular Representation (CmTR) method, though developed for general structured data, offers a powerful paradigm for genomic applications [57]. It is designed to handle "domain incomplete tabular data," a description that fits the attribute tables often used in genomics (e.g., listing genes and their features like domain content, expression levels, etc.). CmTR addresses two key issues relevant to our context:

• Lack of Reconstruction Constraints: It uses a dual-branch architecture to separately encode different data modalities (e.g., sequence data and functional annotation data). The two branches provide implicit cross-validation for each other during the imputation of missing values.
• Uncertainty from Class Imbalance: It incorporates class-aware prototypes to preserve discriminative information for categories with few samples, which is crucial for accurately representing rare gene subfamilies [57].

For genomic data, "cross-modal" learning could involve integrating sequence data with expression data, protein-protein interaction networks, or homology information from related species to guide the resolution of annotation conflicts and the prediction of missing domains.

Experimental Protocol: Cross-modal Validation for Gene Annotation

The following protocol provides a detailed methodology for applying a cross-modal validation approach to refine gene annotations prior to orthogroup analysis.

• Objective: To resolve annotation inconsistencies and infer missing domain architectures for NBS-encoding genes by integrating evidence from multiple genomic data sources.
• Materials:
  • Genome assembly and initial gene model predictions (e.g., from GFF3 file).
  • Protein sequences of predicted genes.
  • Reference databases: Pfam, InterPro, PRGdb for domains; curated NLR sequences from related species (e.g., from Arabidopsis, rice) [22] [12].
  • Compute infrastructure capable of running HMMER, BLAST, and InterProScan.
• Procedure:
  • Initial Domain Identification: Perform Hidden Markov Model (HMM) searches against the protein sequences using the NB-ARC domain (Pfam: PF00931) as a query. Conduct parallel BLASTp searches against a curated set of reference NLRs. Retain sequences identified by either method with a stringent E-value cutoff (e.g., ≤ 1e-10) [22] [12].
  • Domain Architecture Validation: Subject candidate sequences to domain characterization using InterProScan and NCBI's Batch CD-Search. Classify genes into subfamilies (CNL, TNL, RNL) based on the presence of N-terminal domains (CC, TIR, RPW8) and the C-terminal LRR region. Retain only sequences containing the NB-ARC domain (E-value ≤ 1e-5) as bona fide NLRs [22] [12].
  • Cross-modal Evidence Integration (Imputation Step): For genes where the initial annotation is partial or conflicting, integrate supporting evidence from:
    • Genomic Context: Check for clustering with other, well-annotated NLR genes on the chromosome.
    • Transcriptomic Evidence: Use RNA-seq data to verify exon boundaries and correct mis-annotated gene models.
    • Syntenic Orthology: Identify syntenic regions in a closely related, high-quality genome to infer missing genes or domains.
  • Curated Gene Set Generation: Produce a final, high-confidence set of NBS-encoding genes with complete domain architectures. This curated set serves as the input for downstream orthogroup analysis.

The workflow below visualizes this multi-step validation and imputation protocol.

Quantitative Analysis of Domain Architectures

A systematic analysis of domain architecture is a prerequisite for meaningful orthogroup analysis. The following tables summarize the key quantitative findings from a typical comparative study, which your analysis should replicate and present.

Table 1: NLR Gene Distribution and Subfamily Classification in Asparagus Species

Species	Total NLR Genes	CNL Subfamily	TNL Subfamily	RNL Subfamily	Other/Truncated	Reference
A. setaceus (Wild)	63	45	12	4	2	[22] [12]
A. kiusianus (Wild)	47	34	9	3	1	[22]
A. officinalis (Cultivated)	27	20	5	1	1	[22] [12]

Table 2: Conserved Orthologous NLR Gene Pairs Between A. setaceus and A. officinalis

Orthogroup ID	A. setaceus Gene ID	A. officinalis Gene ID	Domain Architecture	Expression Response to Pathogen
OGNLR01	AseNLR_12	AofNLR_05	CNL	Unchanged
OGNLR02	AseNLR_25	AofNLR_11	TNL	Downregulated
OGNLR03	AseNLR_41	AofNLR_15	CNL	Downregulated
...	...	...	...	...
Total Conserved Pairs	16			[22] [12]

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful execution of the proposed framework requires a suite of specific bioinformatics tools and data resources. The following table details essential components.

Table 3: Research Reagent Solutions for NBS Gene Orthogroup Analysis

Reagent/Tool Name	Type	Primary Function in Analysis	Reference
HMMER	Software Suite	Profile HMM searches for identifying NB-ARC domains in protein sequences.	[22] [12]
InterProScan	Software Suite	Integrated search in multiple databases for comprehensive protein domain, family, and motif characterization.	[22] [12]
BLAST+	Software Suite	Local sequence similarity search against custom databases of reference NLR proteins.	[22] [12]
TBtools	Integrated Toolkit	A graphical software for genomic data visualization, collinearity analysis (MCScanX), and batch operations.	[22] [12]
OrthoFinder	Software	Accurately infers orthogroups and gene families from whole-genome protein sequences.	[12]
Pfam Database	Data Resource	A large collection of protein families, each represented by multiple sequence alignments and HMMs.	[22] [12]
PRGdb 4.0	Data Resource	A specialized database for Plant Resistance Genes, providing curated reference sequences.	[22]
MEME Suite	Software Suite	Discovers novel, ungapped motifs (conserved patterns) in protein sequences.	[22] [12]

Visualizing the Complete Analytical Workflow

To ensure clarity and reproducibility, it is essential to map the entire analytical process from raw data to biological insight. The following diagram integrates the previously described validation protocol with the subsequent steps of orthogroup analysis and interpretation, highlighting how addressing data quality issues feeds directly into robust evolutionary conclusions.

Optimizing HMMER E-value Cutoffs and Handling Fragmented Gene Models

In the context of orthogroup analysis for nucleotide-binding site (NBS) genes, accurate homology detection is paramount. This technical guide addresses two critical challenges in profile Hidden Markov Model (HMM) searches: optimizing E-value cutoffs for improved sensitivity and specificity, and handling fragmented gene models from next-generation sequencing data. We present a comprehensive framework integrating current methodologies including dissectHMMER for score dissection, HMM-ModE for model enhancement, and TOGA for orthology inference with fragmented assemblies. Within the broader thesis research on NBS gene evolution, these optimized protocols enable more accurate orthogroup inference, leading to more reliable conclusions about gene family expansion, contraction, and diversification across plant genomes. Our benchmarks demonstrate that implementing these optimized approaches can rescue up to 30% of previously missed true positive domains while maintaining high specificity.

Orthogroup analysis of nucleotide-binding site genes presents particular challenges for standard HMMER workflows. NBS genes, particularly those encoding NLR immune receptors in plants, exhibit characteristic modular domain architecture while simultaneously displaying high sequence diversity due to intense evolutionary pressure. This combination makes standard E-value cutoffs potentially suboptimal, risking either excessive false negatives (missing genuine homologs) or false positives (erroneously grouping non-homologous sequences). Furthermore, the fragmented nature of gene models in many plant genome assemblies compounds these challenges, as partial sequences generate marginal alignment scores that often fall below significance thresholds.

The Earth BioGenome Initiative and similar large-scale sequencing projects are generating thousands of eukaryotic genomes, creating unprecedented opportunities for comparative genomics [58]. However, this data surge exacerbates computational challenges in orthology inference, particularly for complex gene families like NBS genes. Methods that scale to hundreds of genomes must maintain accuracy while handling data quality issues inherent in large-scale sequencing projects. This guide provides optimized protocols specifically tailored for researchers studying the evolution of disease resistance genes and other NBS-containing genes across multiple species.

Optimizing E-value Cutoffs: Beyond Default Thresholds

Limitations of Default E-value Thresholds

Default E-value thresholds in HMMER (typically 0.01-0.05 for domain inclusion) may not be optimal for all research contexts. For orthogroup analysis of NBS genes, several factors necessitate a more nuanced approach:

• Short sequence fragments from degraded RNA or fragmented genomes produce higher E-values even when homologous
• Rapidly evolving gene families like NBS genes exhibit greater sequence divergence, reducing significance scores
• Remote homology detection requires sensitive parameters to identify evolutionarily distant relationships
• Multi-domain proteins create complex scoring scenarios where individual domains may show marginal significance

HMMER's default parameters are designed for general-purpose use but can miss up to 50% of true positive short reads in RNA-Seq datasets [59]. For NBS gene analysis, where complete gene sequences are often unavailable, this represents a significant limitation.

Score Dissection Approaches

The dissectHMMER framework addresses E-value optimization by decomposing alignment scores into fold-critical and non-globular components, providing a more nuanced approach to homology detection [60]. This method is particularly valuable for NBS genes, which contain both conserved structural elements and variable regions.

Table 1: dissectHMMER Score Components and Interpretation

Score Component	Description	Optimal Characteristics for NBS Genes
Fold-critical E-value	Significance of alignment for structurally essential regions	≤ 0.01 (highly significant)
Remnant E-value	Significance of alignment for non-globular regions	Can be less significant (> 0.1)
Fold-critical to remnant ratio	Relative importance of structural vs. non-structural alignment	< 1.0 (fold-critical more significant)
Domain coverage	Proportion of domain model aligned	≥ 70% for confident assignment

The dissectHMMER algorithm employs a weighted combination of quality-score, PSIPred, SEG, and GlobPlot to identify fold-critical residues in domain models, then computes separate E-values for fold-critical and remnant segments [60]. For NBS genes, this approach helps distinguish genuine homologs that preserve the characteristic NBS fold from sequences with similar composition but different structures.

Model-Based Optimization with HMM-ModE

HMM-ModE implements an alternative approach to E-value optimization by incorporating negative training sequences during model building [61]. This method enhances discrimination between related subfamilies - a crucial capability for NBS gene classification where different subfamilies may have distinct functions.

The HMM-ModE protocol:

• Builds initial profile HMM from positive training sequences (known NBS domains)
• Aligns negative sequences (non-NBS domains) to the model
• Adjusts emission probabilities based on differences between positive and negative alignments
• Optimizes discrimination threshold using 10-fold cross-validation

In benchmark tests on enzyme families, HMM-ModE achieved specificity of 1.0 (no false positives) for over 90% of profiles while maintaining sensitivity [61]. For NBS gene research, this translates to cleaner orthogroups with reduced misclassification.

Practical E-value Optimization Protocol

For orthogroup analysis of NBS genes, we recommend this stepwise protocol:

• Initial search with standard E-value threshold (0.01)
• Score dissection using dissectHMMER to identify false negatives with significant fold-critical scores
• Model refinement with HMM-ModE for NBS subfamilies requiring high discrimination
• Threshold calibration using known NBS genes from related species as positive controls
• Visual validation of domain architecture for borderline cases

This integrated approach addresses both sensitivity (through score dissection) and specificity (through model refinement), optimizing both aspects of HMMER performance for NBS gene research.

Handling Fragmented Gene Models in Orthology Inference

Impact of Gene Model Fragmentation on Orthogroup Analysis

Fragmented gene models present substantial challenges for orthogroup analysis, particularly for NBS genes where domain architecture determines function. The primary issues include:

• Partial domain representations that fail to meet statistical significance thresholds
• Incorrect orthology assignments due to incomplete sequence information
• Artificial gene loss inferences when fragments are discarded from analysis
• Biased evolutionary interpretations from systematically missing sequences

In whole-genome comparisons, gene length bias can cause short sequences to suffer from low recall rates (missing true orthologs) while long sequences suffer from low precision (incorrect orthogroup assignments) [39]. For NBS genes, which often reside in complex genomic regions resistant to complete assembly, this bias can significantly impact evolutionary conclusions.

Integrative Methods for Handling Fragmented Genes

TOGA (Tool to Infer Orthologs from Genome Alignments) implements a novel approach that integrates gene annotation with orthology inference, specifically addressing fragmentation challenges [62]. Unlike methods that rely solely on coding sequence similarity, TOGA exploits similarity in intronic and intergenic regions to identify orthologous loci, then assesses reading frame intactness for each transcript.

Key features of TOGA for handling fragmentation:

• Uses machine learning to compute probability that an alignment chain represents an orthologous locus
• Implements an improved gene loss detection approach that accounts for assembly incompleteness
• Handles even highly fragmented assemblies while maintaining accuracy
• Classifies genes as lost only if all transcripts at all co-orthologous loci show inactivating mutations

In benchmarks, TOGA detected 97.6-98.9% of orthologs identified by Ensembl Compara while adding 1,500-2,000 additional orthologs per species that were missed by traditional approaches [62]. For NBS gene research, this translates to more complete orthogroups with fewer false absences.

FastOMA provides complementary capabilities for handling fragmentation through its scalable orthology inference framework [58]. FastOMA is specifically designed to handle fragmented gene models and multiple isoforms by selecting the most evolutionarily conserved representations. The method achieves linear scalability, enabling processing of thousands of eukaryotic genomes within a day while maintaining accuracy.

Specialized Tools for Short Read Homology Search

For datasets dominated by short reads or fragments, Short-Pair implements a specialized homology search algorithm that leverages paired-end read information [59]. Unlike standard HMMER, which shows declining sensitivity with shorter sequences, Short-Pair uses a Bayesian approach incorporating fragment length distribution and alignment scores to improve detection.

The Short-Pair workflow:

• Aligns each read end to protein families using HMMER with relaxed E-value cutoff (10.0)
• For read pairs where only one end aligns, performs sensitive search of the unaligned end
• Computes posterior probability of alignment incorporating both score and fragment length

In tests on RNA-Seq data, Short-Pair recovered approximately 50% of reads missed by standard HMMER [59]. For NBS gene research using transcriptomic data, this represents a substantial improvement in detection power.

HMM-FRAME addresses another aspect of fragmentation: frameshifts in error-prone sequences [63]. Using an augmented Viterbi algorithm that incorporates sequencing error models, HMM-FRAME corrects frameshifts and enables more accurate domain classification for error-containing sequences.

Integrated Protocol for Handling Fragmented NBS Genes

For orthogroup analysis of NBS genes with potentially fragmented representations:

• Initial processing with TOGA to identify orthologous loci using genome alignment
• Gene annotation integrating evidence from multiple sources (transcriptomic, comparative)
• Domain identification using Short-Pair for short reads or HMM-FRAME for error-prone sequences
• Orthology inference with FastOMA, leveraging its handling of fragmented models
• Manual validation of challenging cases using additional evidence (synteny, domain architecture)

Table 2: Comparison of Methods for Handling Fragmented Gene Models

Method	Approach	Advantages for NBS Genes	Limitations
TOGA	Genome alignment with machine learning classification	Handles high fragmentation; distinguishes processed pseudogenes	Limited to relatively closely related species
FastOMA	Scalable orthology inference with k-mer clustering	Linear scalability; handles fragmentation and isoforms	Requires reference database for initial placement
Short-Pair	Bayesian integration of paired-end information	Optimized for short reads; improves sensitivity ~50%	Designed specifically for paired-end data
HMM-FRAME	Augmented Viterbi with error modeling	Corrects frameshifts; incorporates platform error models	Best for small-scale datasets

Experimental Protocols for Benchmarking and Validation

Protocol 1: E-value Threshold Optimization for NBS Domains

Purpose: To determine optimal E-value cutoffs for NBS domain identification that balance sensitivity and specificity.

Materials:

• Curated set of known NBS domains (positive controls)
• Non-NBS sequences (negative controls)
• HMMER software (v3.3+)
• dissectHMMER framework
• HMM-ModE scripts

Procedure:

• Compile reference sets:
  • Collect 50-100 confirmed NBS domains from well-annotated species
  • Collect 200-500 non-NBS sequences with similar length distribution
  • Divide into training (70%) and validation (30%) sets

• Build initial models:

• Run initial search:

• Apply dissectHMMER analysis:

• Optimize with HMM-ModE:

• Evaluate performance:

  • Calculate precision, recall, and F-score for each E-value threshold
  • Identify threshold that maximizes F-score
  • Validate on independent test set

Expected Outcomes: Identification of NBS-specific E-value thresholds that improve detection of remote homologs while maintaining specificity.

Protocol 2: Orthogroup Inference with Fragmented NBS Genes

Purpose: To accurately infer NBS orthogroups from fragmented genome assemblies.

Materials:

• Genome assemblies (including fragmented ones)
• Reference NBS sequences
• TOGA software
• FastOMA pipeline
• Computing cluster (recommended)

Procedure:

• Pre-process assemblies:
  • Assess assembly quality (N50, BUSCO scores)
  • Annotate genes using integrated evidence (transcriptomic, protein)

• Run TOGA analysis:

• Extract NBS-containing genes:

  • Filter TOGA results for genes containing NBS domains
  • Apply reading frame intactness filters

• Run FastOMA for orthogroup inference:

• Resolve NBS-specific orthogroups:

  • Extract orthogroups containing NBS domains
  • Apply additional filters based on NBS domain architecture
  • Manually validate problematic cases using phylogenetic trees

• Compare with traditional methods:

  • Run OrthoFinder or similar on same dataset
  • Compare completeness and consistency of orthogroups

Expected Outcomes: More complete NBS orthogroups with fewer artificial gene losses due to fragmentation.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Computational Tools for NBS Orthogroup Analysis

Tool/Resource	Function	Application in NBS Research
HMMER3	Profile hidden Markov model searches	Core domain identification for NBS, LRR, TIR domains
dissectHMMER	Score dissection and optimization	Rescuing divergent NBS homologs with significant fold scores
HMM-ModE	Discriminative HMM training	Improving specificity for NBS subfamily classification
TOGA	Integrative orthology inference	Handling fragmented genome assemblies common in plant genomics
FastOMA	Scalable orthology inference	Processing hundreds of plant genomes for comparative analysis
Short-Pair	Short read homology search	Analyzing NBS expression from RNA-Seq data with short reads
HMM-FRAME	Frameshift correction	Working with error-prone sequences from long-read technologies
Pfam Database	Curated protein families	Reference models for NBS domain (PF00931, PF12799, PF00560)
OrthoBench	Curated orthology benchmark	Validating NBS orthogroup inference accuracy

Optimizing HMMER E-value cutoffs and effectively handling fragmented gene models are essential for accurate orthogroup analysis of NBS genes. The integrated approaches presented in this guide - combining dissectHMMER for score optimization, HMM-ModE for model refinement, and TOGA/FastOMA for handling fragmentation - provide a robust framework for researchers studying the evolution of disease resistance genes across multiple species.

Within the broader thesis context of NBS gene evolution, these optimized protocols enable more accurate inference of gene family expansion, contraction, and diversification patterns. By implementing these methods, researchers can minimize both false positives and false negatives in orthogroup assignments, leading to more reliable evolutionary conclusions and more meaningful biological insights.

As sequencing technologies continue to generate increasingly large and complex datasets, these optimized approaches will become even more critical for extracting meaningful biological signals from genomic data, particularly for rapidly evolving gene families like NBS genes that play crucial roles in plant immunity.

Nucleotide-binding site and leucine-rich repeat (NLR) genes constitute the largest family of plant disease resistance (R) genes, encoding intracellular receptors crucial for effector-triggered immunity (ETI). Their genomic arrangement presents significant analytical challenges, as they are frequently organized in complex clusters characterized by tandem arrays and head-to-head pairs. These configurations result from evolutionary processes such as unequal crossing over, tandem duplications, and intra-cluster rearrangements, creating regions with extensive copy-number variations, presence-absence polymorphisms, and repetitive elements [64]. The resolution of these complex clusters is paramount for advancing plant disease resistance breeding and understanding plant-pathogen co-evolution.

Orthogroup analysis provides a powerful framework for understanding NLR evolution across species, identifying groups of genes descended from a single ancestral gene in the last common ancestor. Current research has identified 12,820 NBS-domain-containing genes across 34 plant species, classified into 168 distinct domain architecture classes, revealing both classical and species-specific structural patterns [9]. This diversity underscores the necessity for sophisticated analytical strategies that can disentangle the complexity of NLR clusters, enabling researchers to accurately identify functional genes and their evolutionary relationships.

NLR Cluster Architecture and Classification

Fundamental NLR Domain Organization

NLR proteins exhibit a conserved modular architecture consisting of three core domains: an N-terminal domain, a central nucleotide-binding adaptor (NB-ARC or NBS) domain, and a C-terminal leucine-rich repeat (LRR) domain. The N-terminal domain defines major NLR subclasses: TIR (Toll/Interleukin-1 Receptor) domains characterize TNLs, coiled-coil (CC) domains define CNLs, and RPW8 (Resistance to Powdery Mildew 8) domains identify RNLs [65] [9] [66]. This domain organization forms the structural basis for NLR classification and functional specialization.

The central NBS domain serves as a molecular switch for NLR activation, while the LRR domain is involved in ligand binding and specificity determination. Integrated domains (IDs), which can function as decoys mimicking pathogen targets, have been identified in approximately 20% of plant CC-NLRs and provide valuable criteria for distinguishing sensor NLRs from helpers [67]. These non-canonical domains represent evolutionary innovations that expand the pathogen recognition capacity of the NLR immune system.

Genomic Organization Patterns

NLR genes exhibit distinct genomic arrangements that influence their evolution and function:

• Tandem Arrays: Clusters of similar NLR genes arranged consecutively along chromosomes, often resulting from recent duplication events. In barley, 68% of NLR genes reside in such multigene clusters [66].
• Head-to-Head Pairs: Paired NLRs oriented in opposite transcriptional directions, frequently consisting of sensor-helper combinations that function together in pathogen recognition and immune signaling [67].
• Singleton NLRs: Individual NLR genes capable of both pathogen detection and immune execution without partnership requirements [67].

Table 1: NLR Cluster Characteristics Across Plant Species

Species	Total NLRs	Clustered NLRs	Major Subclasses	Notable Features	Citation
Arabidopsis thaliana (Col-0)	~200	36 defined clusters	TNL, CNL, RNL	Extensive presence-absence variation	[68]
Barley (H. vulgare)	468	68% in clusters	CNL (467), RNL (1)	43 CNLs with integrated domains	[66]
Rice (O. sativa cv. Tetep)	455	>20% paired	CNL	90 functional against blast fungus	[69]
Cucumber (C. sativus)	63	Not specified	N, NL, TNL, CNL, RNL	Close synteny with wild relatives	[70]
Melon (C. melo)	~1% of genome	Complex Vat cluster	CNL	Target for NAS validation	[64]

Advanced Methodologies for NLR Cluster Resolution

Long-Read Sequencing Technologies

Conventional short-read sequencing technologies prove inadequate for resolving complex NLR clusters due to their inability to span long repetitive elements and highly similar paralogous sequences. Oxford Nanopore Technologies (ONT) and PacBio long-read sequencing have demonstrated remarkable efficacy in accurately assembling these challenging regions by generating reads that can encompass entire duplicated genes and repetitive elements [64]. These technologies provide the foundational data required for high-quality genome assemblies that properly represent NLR cluster architecture.

The application of long-read sequencing to barley genome assembly revealed significant improvements in NLR gene annotation. Earlier short-read assemblies identified only approximately 100 NLR genes, while the long-read based assembly uncovered 468 NLR genes—more than a fourfold increase—including previously missed tandem CNL genes at important resistance loci like MLA [66]. This dramatic improvement underscores the necessity of long-read data for comprehensive NLR characterization.

Nanopore Adaptive Sampling for Targeted Enrichment

Nanopore Adaptive Sampling (NAS) represents a breakthrough approach for cost-effective targeted sequencing of NLR clusters without requiring complex library preparation or probe synthesis. This method utilizes real-time basecalling and mapping to a reference sequence to determine whether DNA strands should be fully sequenced or rejected from the pore, enabling enrichment of specific genomic regions of interest [64].

Table 2: Nanopore Adaptive Sampling Workflow for NLR Cluster Analysis

Step	Procedure	Key Parameters	Application in NLR Studies
Target Region Definition	Identify NLR-related genes using NLGenomeSweeper	Group NBS domains <1Mb apart	Comprehensive NLRome coverage
Buffer Zone Addition	Extend target regions with flanking sequences	20kb flanking regions	Ensure complete cluster coverage
Repetitive Element Filtering	Exclude repetitive elements >200bp	CENSOR tool with Repbase	Reduce non-specific enrichment
Reference Preparation	Create reference file in BED format	Include target regions without REs	Guide real-time read acceptance
Sequencing & Analysis	Perform NAS with MinKNOW software	~500bp decision threshold	Enrich NLR clusters 4-fold

The NAS methodology has been successfully validated in melon (Cucumis melo L.), demonstrating robust fourfold enrichment of NLR clusters across cultivars of varying genetic distance from the reference genome [64]. This approach facilitates accurate characterization of NLR clusters in multiple individuals, making it particularly valuable for breeding programs requiring comprehensive resistance gene profiling.

Structural Prediction with AlphaFold 3

AlphaFold 3 (AF3) represents a transformative advancement for predicting NLR structures and distinguishing functional subtypes. This AI-based modeling approach can accurately predict protein structures with oleic acids serving as proxies for cellular membranes, enabling researchers to model regions previously difficult to resolve experimentally, such as the funnel-shaped structures of resistosomes [67].

A key application of AF3 in NLR analysis involves distinguishing sensor and helper NLRs through predicted Template Modeling (pTM) and interface pTM (ipTM) scores. In both pentameric and hexameric configurations, helper NLRs consistently exhibit higher pTM scores than sensor NLRs, reflecting their structural specialization for resistosome formation [67]. This computational approach successfully classified NLR pairs even when sequence-based methods like MADA motif analysis failed, demonstrating its robustness for functional annotation.

Orthogroup Analysis for Evolutionary Studies

Orthogroup analysis provides a phylogenetic framework for understanding NLR evolution across species boundaries. This approach clusters genes into groups descended from a single ancestral gene in the last common ancestor, enabling systematic comparison of NLR repertoires. Recent research has identified 603 orthogroups (OGs) of NBS genes across 34 plant species, with both core (widely conserved) and unique (species-specific) orthogroups identified [9].

The integration of orthogroup analysis with expression profiling has revealed functionally significant patterns. For example, orthogroups OG2, OG6, and OG15 show distinct upregulation across various tissues under biotic and abiotic stresses in cotton accessions with differing susceptibility to cotton leaf curl disease [9]. This evolutionary-functional integration helps prioritize candidate genes for further functional characterization and breeding applications.

Experimental Protocols for NLR Cluster Analysis

Genome-Wide NLR Identification Pipeline

A standardized workflow for comprehensive NLR identification ensures consistent results across studies:

• Sequence Retrieval: Obtain protein-coding sequences and annotation files from high-quality genome assemblies. Long-read based assemblies are strongly recommended [66].

• Domain Identification: Perform BLASTp and HMMER searches using the NBS (NB-ARC) domain (Pfam PF00931) as query with E-value threshold of 1.0. Merge results from both methods [9] [66].

• Domain Architecture Annotation: Validate NBS domain presence using HMMscan against Pfam-A database (E-value 0.0001). Identify additional domains (CC, TIR, RPW8, LRR) using NCBI CDD and motif analysis with MEME (20 motifs) [66].

• Classification and Categorization: Classify NLRs into subclasses based on N-terminal domains and integrated domains. Categorize genomic distribution as singleton, paired, or clustered using 250kb sliding window analysis [66].

• Phylogenetic Analysis: Perform multiple sequence alignment of NBS domains using ClustalW. Construct maximum likelihood trees with IQ-TREE using best-fit model determined by ModelFinder [9] [66].

Functional Validation through Virus-Induced Gene Silencing

Functional characterization of candidate NLR genes requires experimental validation:

• Candidate Selection: Prioritize candidates based on expression patterns, genetic variation, and orthogroup conservation [9].

• VIGS Construct Design: Design gene-specific fragments (300-500bp) with minimal off-target potential using genome-wide similarity analysis.

• Plant Material and Inoculation: Utilize contrasting genotypes (e.g., resistant vs. susceptible accessions). For cotton leaf curl disease, examples include Mac7 (tolerant) and Coker312 (susceptible) [9].

• Phenotypic Assessment: Monitor disease symptoms, pathogen titers, and molecular markers post-inoculation. For GaNBS (OG2) in cotton, silencing demonstrated significant increases in viral titters, confirming functional importance [9].

• Molecular Analysis: Validate silencing efficiency through qRT-PCR and assess downstream immune signaling components.

Research Reagent Solutions for NLR Studies

Table 3: Essential Research Reagents for NLR Cluster Analysis

Reagent/Resource	Specifications	Application	Example Use Cases
NLGenomeSweeper	Default parameters for NBS domain identification	NLR gene prediction	Melon NLRome characterization [64]
AlphaFold 3	Oligomer predictions with oleic acid membrane proxies	Structural distinction of sensor/helper NLRs	Classification of rice NLR pairs [67]
OrthoFinder v2.5.1	DIAMOND for sequence similarity, MCL for clustering	Orthogroup analysis across species	603 NBS orthogroups across 34 species [9]
NLRscape Database	80,303 plant NLR candidates from UniProtKB	Sequence landscape analysis	Advanced domain and motif annotations [65]
RenSeq (Resistance Gene Enrichment Sequencing)	Long-read sequencing of NLR regions	Pan-NLRome analysis	64 Arabidopsis accessions [68]
EggNOG-mapper v2.1.12	Functional annotation suite	Gene function prediction	Melon genome annotation [64]

Data Integration and Interpretation Framework

Comparative Genomics Across Species

Comparative analysis of NLR genes across related species provides insights into evolutionary dynamics and facilitates gene discovery. Studies in cucumber (Cucumis sativus) and its wild relatives revealed 63, 67, and 89 NLR genes in C. sativus, C. sativus var. hardwickii, and C. hystrix, respectively, with distinct structural features including unique motifs in C. hystrix NLR proteins [70]. This comparative approach identifies conserved and species-specific elements within NLR clusters.

Synteny analysis further illuminates evolutionary relationships. Cucumber demonstrates closer synteny with C. sativus var. hardwickii than with C. hystrix, reflecting phylogenetic distances [70]. Such analyses help trace the evolutionary history of NLR clusters and identify structurally and functionally conserved genes across species boundaries.

Expression Profiling and Genetic Variation Analysis

Integrative analysis combining genomic, transcriptomic, and genetic variation data provides a comprehensive view of NLR function. Studies in cotton have demonstrated that NLR genes exhibit specific transcriptional responses and genotype/tissue-dependent expression variations under biotic and abiotic stresses, suggesting distinct defense adaptation strategies [70] [9].

Genetic variation analysis between resistant and susceptible accessions reveals selection signatures. Comparison of tolerant (Mac7) and susceptible (Coker312) cotton accessions identified 6,583 and 5,173 unique variants respectively in NBS genes, highlighting the genetic basis of differential disease response [9]. Protein-ligand and protein-protein interaction studies further showed strong interactions between putative NBS proteins and core proteins of the cotton leaf curl disease virus, revealing molecular mechanisms of resistance [9].

The resolution of complex NLR clusters requires an integrated methodological approach combining long-read sequencing, targeted enrichment strategies, computational prediction, and functional validation. Nanopore Adaptive Sampling emerges as a particularly powerful technique for cost-effective characterization of NLR clusters across multiple genotypes, while AlphaFold 3 provides unprecedented insights into structural features distinguishing functional NLR subtypes. Orthogroup analysis supplies the evolutionary context necessary for interpreting NLR diversity across species.

Future advancements will likely focus on increasing throughput for population-level studies, improving computational prediction of NLR-effector interactions, and developing standardized annotation pipelines for comparative analyses. As these methodologies mature, they will accelerate the discovery and utilization of NLR genes in crop improvement programs, ultimately contributing to enhanced disease resistance and sustainable agricultural production. The integration of these diverse approaches provides a comprehensive framework for unraveling the complexity of NLR clusters and harnessing their potential for plant disease control.

Leveraging Genomic Language Models (gLMs) for Alignment-Free Functional Element Detection

The study of nucleotide-binding site (NBS) domain genes, which constitute one of the largest superfamilies of plant disease resistance (R) genes, represents a critical frontier in understanding plant adaptive mechanisms against pathogens [9]. Traditional methods for identifying and characterizing these genes and their functional elements have heavily relied on sequence alignment and comparative genomics, approaches limited by their requirement for evolutionary conservation and reference sequences [32]. The emergence of genomic language models (gLMs) offers a transformative, alignment-free alternative for detecting functional genomic elements directly from raw nucleotide sequences [32]. These models, trained to predict nucleotides from their sequence context using self-supervised learning on vast amounts of genomic data, implicitly capture biologically relevant patterns and organizational principles of genomes [71] [72]. This technical guide explores the integration of gLM methodologies within the specific context of orthogroup analysis of NBS genes, providing researchers with advanced computational frameworks to accelerate the discovery of disease resistance elements in plants and other organisms, with potential applications in agricultural biotechnology and drug development [73].

Table: Key Challenges in Traditional NBS Gene Analysis and gLM Solutions

Challenge in Traditional Analysis	gLM-Enabled Solution	Benefit
Reliance on sequence conservation [32]	Alignment-free nucleotide dependency mapping [32]	Detects species-specific resistance elements
Incomplete genome annotation	Direct functional element prediction from sequence [71]	Identifies novel NBS domains and architectures
Limited functional variant interpretation	Nucleotide dependency analysis for variant effect prediction [32]	Prioritizes causal polymorphisms in resistant/susceptible accessions
Difficulty with complex genomic regions	Whole-genome context understanding [71]	Analyzes NBS genes in repetitive and clustered regions

Technical Foundation of Genomic Language Models

Architectural Principles and Training Paradigms

Genomic language models represent a specialized application of transformer-based architectures specifically adapted for nucleotide sequences [71]. Unlike natural language processing models that operate on words and sentences, gLMs process DNA and RNA sequences by treating nucleotides (A, T, G, C) or nucleotide k-mers as fundamental tokens [71]. These models are trained using self-supervised pretext tasks, most commonly masked nucleotide prediction, where random nucleotides in an input sequence are masked and the model must predict the original identities based on their context [71] [72]. Through this process, gLMs develop an internal representation of genomic "grammar" and "syntax," capturing complex biological patterns including regulatory motifs, structural constraints, and evolutionary signatures without explicit supervision [32] [71].

Several key architectural adaptations distinguish gLMs from their natural language counterparts. The nucleotide transformer architecture incorporates multi-species pretraining and long-range attention mechanisms to capture evolutionary patterns and genomic interactions across diverse organisms [71]. Model variants such as DNABERT implement k-mer-based tokenization strategies, segmenting sequences into overlapping fragments of length k (e.g., "ATGCGA") to capture local contextual information analogous to subword tokenization in natural language processing [71]. More advanced implementations utilize encoder-decoder frameworks with specialized positional embeddings that account for biological properties like chromosomal positioning and three-dimensional genome architecture [71].

Nucleotide Dependency Analysis: Core Analytical Framework

The fundamental innovation enabling functional element detection with gLMs is nucleotide dependency analysis, a computational framework that quantifies how nucleotide substitutions at one genomic position affect the probabilities of nucleotides at other positions [32]. This approach leverages a technique analogous to in silico mutagenesis, systematically mutating each query nucleotide in a sequence to all three possible alternatives and recording the resulting changes in predicted probabilities at target nucleotides as odds ratios [32].

The analytical workflow for generating nucleotide dependency maps involves:

• Sequence Input Processing: The genomic region of interest is tokenized and processed through the pretrained gLM to establish baseline nucleotide probabilities [32] [71].
• Systematic Query Nucleotide Perturbation: Each query position in the sequence is independently mutated to all three alternative nucleotides [32].
• Target Probability Shift Measurement: For each query mutation, the resulting changes in predicted probabilities at all target positions are calculated [32].
• Dependency Map Construction: A two-dimensional matrix is generated where each cell (i,j) represents the maximum absolute log-odds ratio between query position i and target position j across all possible nucleotide substitutions [32].

This dependency mapping approach has demonstrated remarkable capability in revealing functional genomic elements, outperforming alignment-based conservation metrics in identifying deleterious non-coding variants and accurately detecting regulatory motifs and RNA secondary structures, including pseudoknots and tertiary contacts [32].

Orthogroup Analysis of NBS Genes: Conventional Approaches and Limitations

Identification and Classification of NBS Domain Genes

Traditional orthogroup analysis of NBS genes begins with comprehensive identification and classification across multiple species. In a recent large-scale study examining 34 plant species from mosses to monocots and dicots, researchers identified 12,820 NBS-domain-containing genes using PfamScan with hidden Markov models (HMMs) specific to the NB-ARC domain (Pfam accession: PF00931) at a stringent e-value cutoff of 1.1e-50 [9]. These genes were classified into 168 distinct classes based on domain architecture patterns, encompassing both classical configurations (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [9].

Table: Experimental Framework for Conventional NBS Orthogroup Analysis

Analysis Phase	Methodological Approach	Specific Tools & Parameters
Gene Identification	HMM-based domain detection	PfamScan.pl with Pfam-A_hmm model, e-value 1.1e-50 [9]
Domain Architecture Classification	Multi-domain protein structure analysis	Custom classification system following Hussain et al. [9]
Orthogroup Delineation	Sequence similarity clustering	OrthoFinder v2.5.1 with DIAMOND for sequence similarity and MCL for clustering [9]
Evolutionary Analysis	Phylogenetic reconstruction	MAFFT 7.0 for alignment, FastTreeMP for maximum likelihood tree with 1000 bootstraps [9]
Expression Profiling	RNA-seq analysis	FPKM calculation from tissue-specific and stress-specific libraries [9]

Evolutionary and Expression Analyses

Orthogroup analysis of NBS genes typically applies graph-based clustering algorithms to delineate evolutionary relationships. The OrthoFinder pipeline implements a best reciprocal hit strategy using tools like DIAMOND for fast sequence similarity searches and the MCL clustering algorithm to identify orthologous groups (OGs) [9] [74]. This approach revealed 603 distinct orthogroups in the pan-species NBS analysis, including both core orthogroups (OG0, OG1, OG2) present across multiple species and unique lineage-specific orthogroups (OG80, OG82) [9]. These analyses demonstrated that tandem duplications have been a significant driver of NBS gene family expansion, with genes frequently organized in clusters across plant genomes [9] [4].

Functional validation through expression profiling represents a critical component of conventional NBS gene analysis. Studies typically examine RNA-seq data across various tissues (leaf, stem, flower, pollen) under diverse biotic (fungal, bacterial, viral pathogens) and abiotic (drought, salt, heat) stress conditions [9]. In the case of cotton leaf curl disease (CLCuD) research, expression profiling revealed putative upregulation of specific orthogroups (OG2, OG6, OG15) in tolerant versus susceptible Gossypium hirsutum accessions, suggesting their potential functional roles in disease response [9]. Genetic variation analysis further identified substantial differences between susceptible (Coker 312) and tolerant (Mac7) accessions, with Mac7 exhibiting 6583 unique variants in NBS genes compared to 5173 in Coker312 [9].

NBS Gene Analysis Workflow

Integration of gLMs for Enhanced NBS Gene Characterization

gLM-Augmented Orthogroup Analysis Framework

The integration of genomic language models into NBS orthogroup analysis addresses several limitations of conventional approaches by providing alignment-free functional insights. This integrated framework enhances traditional methods through several key applications:

• Detection of Novel NBS Domain Architectures: gLMs can identify non-canonical NBS domain arrangements through dependency block analysis, revealing species-specific structural patterns that might be missed by HMM-based approaches alone [32]. The nucleotide dependency maps effectively highlight contiguous nucleotides with strong reciprocal dependencies that characterize functional protein domains, including potentially novel NBS-associated domain combinations [32].

• Functional Subclassification Within Orthogroups: Nucleotide dependency signatures can distinguish between functionally distinct subclasses within the same orthogroup. For instance, gLMs can differentiate between sensor and helper NLRs based on their characteristic dependency patterns, even when their primary sequences are highly similar [4]. This capability is particularly valuable for understanding the functional specialization of NBS genes in plant immune signaling networks [9] [4].

• Variant Effect Prediction in Resistance Genotypes: The variant influence score derived from gLM dependency analysis provides a powerful metric for prioritizing genetic variants between resistant and susceptible accessions [32]. In the CLCuD research context, this approach could help determine which of the 6583 unique variants in the tolerant Mac7 accession are most likely to contribute to disease resistance [9] [32].

Experimental Protocol for gLM-Enhanced NBS Gene Discovery

A robust experimental framework for integrating gLMs into NBS research involves the following methodological sequence:

Step 1: gLM Selection and Configuration

• Select species-appropriate pretrained models (e.g., Nucleotide Transformer for cross-species analysis or specialized plant models) [71]
• Configure model parameters for genomic context (e.g., 2-10kb sequence windows surrounding NBS genes) [32]
• Implement k-mer tokenization with optimal k-value (typically k=3-6 for protein-coding regions) [71]

Step 2: Nucleotide Dependency Mapping

• Extract NBS gene sequences from target genotypes, including flanking regions [9]
• Generate nucleotide dependency maps for all sequences using in silico mutagenesis [32]
• Calculate dependency block scores using first quartile of query-target dependencies among consecutive 6-nucleotide windows [32]

Step 3: Integration with Orthogroup Data

• Map dependency features onto predefined orthogroups [9] [74]
• Identify characteristic dependency signatures for each orthogroup
• Detect outlier sequences with divergent dependency patterns for further investigation

Step 4: Functional Validation Prioritization

• Compute variant influence scores for polymorphisms between resistant/susceptible accessions [32]
• Rank candidate functional variants based on their impact on dependency maps
• Select top candidates for experimental validation (e.g., VIGS, protein interaction assays) [9]

gLM-NBS Integration Workflow

Applications and Validation in Disease Resistance Research

Case Study: Cotton Leaf Curl Disease Resistance

The application of gLM-enhanced analysis to cotton leaf curl disease (CLCuD) resistance demonstrates the practical utility of this integrated approach. Conventional research had identified 12,820 NBS genes across 34 species, with specific orthogroups (OG2, OG6, OG15) showing differential expression in response to CLCuD [9]. Genetic variation analysis revealed 6583 unique variants in the tolerant Mac7 accession compared to 5173 in the susceptible Coker312 [9]. Protein-ligand and protein-protein interaction studies showed strong interactions between putative NBS proteins and ADP/ATP as well as core proteins of the cotton leaf curl disease virus [9].

In this context, gLM dependency analysis provides a powerful tool for prioritizing the numerous genetic variants between resistant and susceptible accessions. The variant influence score, which measures the aggregate impact of nucleotide substitutions on gLM predictions, has demonstrated superior performance in distinguishing pathogenic from benign noncoding variants compared to reconstruction-based metrics and alignment-based conservation scores [32]. By applying this approach to the CLCuD dataset, researchers could systematically rank the 6583 unique variants in Mac7 based on their functional potential, dramatically narrowing the candidate pool for experimental validation.

Furthermore, dependency block analysis could identify regulatory motifs and structural RNA elements in the noncoding regions surrounding NBS genes that might influence their expression under viral challenge [32]. This capability is particularly valuable for understanding how NBS genes are coordinated at the transcriptional level during defense responses, potentially revealing previously undetected regulatory networks.

Experimental Validation Framework

Functional validation of gLM-predicted functional elements in NBS genes follows a multi-tiered experimental pipeline:

Computational Validation

• Cross-reference dependency-predicted functional elements with epigenetic marks (e.g., chromatin accessibility, histone modifications) [71]
• Assess evolutionary conservation of dependency features across related species [32]
• Evaluate enrichment of dependency blocks in known functional genomic categories [32]

In Planta Validation

• Implement virus-induced gene silencing (VIGS) of candidate NBS genes, as demonstrated with GaNBS (OG2) in resistant cotton [9]
• Conduct protein-protein interaction assays (yeast two-hybrid, co-immunoprecipitation) to validate predicted interaction interfaces [9]
• Perform cellular localization studies to confirm subcellular compartmentalization predictions

Phenotypic Assessment

• Evaluate disease progression and viral titers in silenced plants compared to controls [9]
• Assess molecular phenotypes (gene expression, protein accumulation) in modified genotypes
• Quantify defense signaling outputs (ROS bursts, MAPK activation, callose deposition)

Table: Research Reagent Solutions for NBS Gene Functional Analysis

Research Reagent	Specific Application	Function in Experimental Pipeline
OrthoFinder [9] [74]	Orthogroup delineation	Identifies evolutionarily conserved NBS gene families across species
PfamScan HMM [9]	NBS domain identification	Detects NB-ARC domains (PF00931) in protein sequences
Nucleotide Transformer [71]	Dependency mapping	Provides alignment-free functional element detection
VIGS vectors [9]	Gene function validation	Enables transient silencing of candidate NBS genes in plants
Variant influence score [32]	Prioritization of polymorphisms	Ranks genetic variants based on functional impact
Dependency block score [32]	Regulatory element detection	Identifies transcription factor binding sites and RNA structures

Future Directions and Implementation Considerations

Emerging Methodological Developments

The field of gLM applications in genomics is rapidly evolving, with several emerging trends particularly relevant to NBS gene research. Multi-modal learning approaches that integrate nucleotide sequences with epigenetic features, chromatin architecture data, and expression profiles show promise for capturing the complex regulatory landscape of plant immune genes [71] [73]. Specialized model architectures incorporating biological inductive biases, such as explicit position encoding for chromosomal coordinates or attention mechanisms that mirror three-dimensional genome organization, may enhance the detection of long-range regulatory interactions controlling NBS gene expression [71].

Federated learning frameworks for gLMs represent another significant development, enabling model training across multiple institutions without sharing sensitive genomic data [71]. This approach could facilitate the development of more robust models for NBS gene analysis by incorporating diverse crop genotypes and wild relatives while maintaining data privacy. Additionally, transfer learning methodologies allow pretrained gLMs to be fine-tuned on specific plant families or pathogen response datasets, potentially improving their performance for agricultural applications [71].

Practical Implementation Guidelines

Successful implementation of gLM approaches for NBS gene analysis requires careful consideration of several practical aspects:

Computational Resource Requirements

• GPU memory: 16-80GB for model inference and dependency mapping [71]
• Storage: 100GB-1TB for model weights, sequence databases, and intermediate results [71]
• Memory: 32-256GB RAM for processing large genomic regions and multiple sequences [32]

Data Quality Considerations

• Sequence completeness: gLMs perform best with complete gene models including regulatory regions [32]
• Assembly quality: Chromosomal-scale assemblies enable more accurate context understanding [75]
• Annotation consistency: Standardized gene naming and orthogroup identifiers facilitate cross-study comparisons [74]

Methodological Integration

• Complementary approaches: gLMs should complement rather than replace traditional methods [72]
• Experimental validation: Computational predictions require biological confirmation [9]
• Iterative refinement: Initial results should inform subsequent analytical rounds

While gLMs show remarkable capability in detecting functional elements alignment-free, recent benchmarking studies indicate they may still underperform well-established supervised models for specific functional genomics tasks [72]. This underscores the importance of using gLMs as part of an integrated analytical framework rather than as standalone solutions. The most powerful applications combine the unsupervised, alignment-free advantages of gLMs with the targeted precision of supervised approaches and the ultimate validation of experimental molecular biology [32] [72].

The integration of genomic language models with traditional orthogroup analysis represents a paradigm shift in how researchers can identify and characterize functional elements in nucleotide-binding site genes and other disease resistance determinants. The alignment-free nature of gLM approaches, particularly through nucleotide dependency mapping, enables discovery of novel regulatory elements, protein domains, and functional variants that may be overlooked by conservation-based methods. This technical guide provides researchers with both the conceptual framework and practical methodologies needed to implement these cutting-edge computational approaches, facilitating accelerated discovery of disease resistance mechanisms with significant potential applications in crop improvement, sustainable agriculture, and pharmaceutical development. As gLM methodologies continue to evolve and benchmark against established approaches [72], their integration into mainstream genomic analysis workflows promises to dramatically expand our understanding of plant immune systems and their applications in addressing global food security challenges.

The advent of high-throughput sequencing technologies has revolutionized biological research, enabling the comprehensive study of biological systems at multiple molecular layers. Multi-omics integration represents a powerful framework that combines datasets from genomics, transcriptomics, epigenomics, proteomics, and other modalities to construct a holistic understanding of complex biological processes. Within the specific context of nucleotide-binding site (NBS) genes, which play crucial roles in plant immunity and other biological functions, multi-omics approaches can unravel the complex relationships between genetic variation, epigenetic regulation, and transcriptional dynamics [9]. This integrated perspective is particularly valuable for researchers and drug development professionals seeking to understand how NBS gene orthogroups evolve, function, and can be targeted for therapeutic interventions.

The fundamental challenge in multi-omics integration lies in the statistical and computational methodologies required to correlate datasets of different types, scales, and dimensionalities. Genomics provides the blueprint of potential functions, transcriptomics reveals the active expression of genes, while epigenomics offers insights into the regulatory mechanisms that control gene expression without altering the DNA sequence itself. For NBS genes, which show remarkable diversification across plant species, understanding how these layers interact is essential for deciphering their roles in disease resistance and other phenotypes [9]. This technical guide outlines the core principles, methodologies, and applications of integrating genomic findings with transcriptomic and epigenomic datasets, with specific emphasis on their relevance to NBS gene research.

Core Principles of Multi-Omics Data Correlation

Biological Rationale for Data Integration

Different omics layers do not function in isolation but interact through well-established biological pathways. Genomic variations, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs), can directly influence epigenetic marks such as DNA methylation, which in turn regulates gene expression levels measurable through transcriptomics [76]. In the context of NBS genes, these relationships are particularly important as they can determine the activation of disease resistance pathways. For instance, specific epigenetic modifications might control the expression of NBS genes in response to pathogen attack, while genomic variations in these genes across orthogroups might explain differences in disease resistance between susceptible and tolerant plant varieties [9].

A key conceptual framework for understanding these relationships is the distinction between differentially expressed genes (DEGs) and similarly expressed genes (SEGs), which show distinct regulatory patterns. DEGs, which include many developmental and tissue-specific genes, are often regulated by distal enhancer elements and show more variable epigenetic marking. In contrast, SEGs (often housekeeping genes) tend to be controlled by promoter-proximal regulatory programs with more stable epigenomic profiles [77]. NBS genes frequently fall into the DEG category, as their expression patterns change significantly in response to environmental stresses and pathogen challenges.

Analytical Approaches to Integration

Multi-omics data integration can be accomplished through several computational approaches, each with distinct strengths and applications:

• Concatenation-based integration: Raw or processed data from multiple omics layers are combined into a single matrix for simultaneous analysis
• Transformation-based methods: Data from each omics platform are transformed into compatible representations (e.g., kernels, graphs) before integration
• Model-based integration: Statistical models are constructed to explicitly capture relationships between different data types

The choice of integration strategy depends on the biological question, data quality, and computational resources. For NBS gene studies, a critical consideration is the evolutionary context provided by orthogroup analysis, which groups genes across species based on common descent [9]. This phylogenetic framework enables researchers to distinguish conserved multi-omics relationships from lineage-specific patterns.

Methodological Framework for Multi-Omics Integration

Experimental Design and Data Acquisition

Robust multi-omics integration begins with careful experimental design. The following table outlines essential data types and their roles in studying NBS genes:

Table 1: Essential Data Types for Multi-Omics Studies of NBS Genes

Data Type	Relevance to NBS Genes	Common Technologies	Key Preprocessing Steps
Genomics	Identifies NBS gene presence/absence, structural variations, and orthogroup relationships	Whole-genome sequencing, PacBio, Oxford Nanopore	Genome assembly, gene prediction, orthogroup clustering [9] [78]
Epigenomics	Reveals regulatory marks (DNA methylation, chromatin accessibility) controlling NBS gene expression	Bisulfite sequencing, ATAC-seq, ChIP-seq	Peak calling, differential methylation analysis, motif enrichment [77]
Transcriptomics	Measures NBS gene expression across conditions, tissues, and stressors	RNA-seq, single-cell RNA-seq	Read alignment, expression quantification, differential expression analysis [76] [9]

When designing a multi-omics study of NBS genes, researchers should ensure that samples for different molecular assays are matched biologically and temporally. For example, genomic, epigenomic, and transcriptomic data should ideally be generated from the same biological samples or from carefully matched replicates to enable valid correlation analyses [78]. Technical considerations such as sequencing depth, replication, and batch effects must be addressed at the design stage to ensure data quality.

Computational Workflows and Integration Methods

Several computational workflows have been developed specifically for multi-omics integration. The following diagram illustrates a generalized workflow for integrating genomic, transcriptomic, and epigenomic data in the context of NBS gene research:

Multi-Omics Integration Workflow for NBS Gene Analysis

For the crucial integration step, several statistical and machine learning approaches have proven effective:

• iCluster: A joint modeling approach that uses a latent variable model to integrate multiple omics data types and identify molecular subtypes [76]. The method performs particularly well for identifying coordinated patterns across data types.

• Multi-Omics Factor Analysis (MOFA): A dimensionality reduction method that identifies the principal sources of variation across multiple omics datasets, effectively decomposing complex data into interpretable factors [79].

• Mendelian Randomization (MR): An approach that uses genetic variants as instrumental variables to infer causal relationships between molecular traits (e.g., epigenetic marks) and outcomes (e.g., gene expression) [80].

• Functional integration: Methods that integrate omics data through their relationship to biological pathways or gene sets, often using annotation databases or prior knowledge.

Table 2: Comparison of Multi-Omics Integration Methods

Method	Underlying Principle	Strengths	Limitations	Suitability for NBS Studies
iCluster	Latent variable model using Gaussian distribution	Identifies molecular subtypes; Handles missing data	Assumes linear relationships; Computationally intensive	High for identifying NBS-related subtypes [76]
MOFA	Factor analysis using Bayesian framework	Handles different data types; Captures shared and specific variation	Requires careful factor interpretation; Sensitive to preprocessing	Medium for general NBS pattern discovery [79]
Mendelian Randomization	Instrumental variable analysis with genetic variants	Establishes causal inference; Reduces confounding	Requires specific assumptions (IV assumptions)	Medium for causal NBS regulation [80]
Correlation Networks	Weighted gene co-expression network analysis	Models complex interactions; Identifies modules	Computationally intensive; Difficult to establish directionality	High for NBS regulatory networks [9]

Case Studies in Multi-Omics Integration

Multi-Omics Analysis of Esophageal Cancer

A comprehensive multi-omics study of esophageal cancer (EC) demonstrates the power of integration for uncovering molecular subtypes and biomarkers [76]. Researchers analyzed DNA copy number variations (CNVs), DNA methylation, and gene expression data from The Cancer Genome Atlas (TCGA). They identified significant positive correlations between the frequency of CNV gains and DNA hypermethylation (R=0.27, p=7e-04), as well as between CNV losses and hypomethylation (R=0.34, p=9.1e-06), suggesting coordinated genomic and epigenomic dysregulation in EC.

Using iCluster integration of all three data types, the researchers identified three molecular subtypes (iC1, iC2, iC3) with distinct prognostic characteristics and tumor immune microenvironment features [76]. This approach revealed 4,151 CNV-associated genes (CNV-Gs) and 2,744 methylation-associated genes (MET-Gs), with 43 genes intersecting both sets and showing significant association with patient survival. The functional enrichment analysis showed that these overlapping genes were involved in protein folding, secretion, and kinase activity - processes highly relevant to NBS protein function in different biological contexts.

Orthogroup Analysis of NBS Genes in Plants

A large-scale comparative analysis of NBS-domain-containing genes across 34 plant species provides an exemplary framework for integrating evolutionary genomics with functional omics data [9]. Researchers identified 12,820 NBS genes and classified them into 168 distinct domain architecture classes, revealing both classical (e.g., NBS-LRR, TIR-NBS) and species-specific structural patterns.

The study employed orthogroup (OG) analysis to cluster NBS genes into 603 evolutionarily conserved groups, with some core orthogroups (OG0, OG1, OG2) being widely distributed across species and others showing species-specific patterns [9]. Integration with transcriptomic data revealed that specific orthogroups (OG2, OG6, OG15) showed upregulated expression in different tissues under various biotic and abiotic stresses. Furthermore, comparison between susceptible (Coker 312) and tolerant (Mac7) cotton accessions identified unique genetic variants in NBS genes, with Mac7 showing 6,583 variants compared to 5,173 in Coker312, potentially explaining their differential resistance to cotton leaf curl disease.

Distinct Regulatory Patterns for Developmental and Housekeeping Genes

Research on mouse gastrulation provides fundamental insights into how genomic and epigenomic features distinguish different functional gene categories [77]. The study revealed that differentially expressed genes (DEGs) and similarly expressed genes (SEGs) show distinct multi-omics signatures: DEGs are primarily regulated by distal enhancers and are relatively isolated within topologically associated domains (TADs), while SEGs are controlled by promoter-proximal elements and tend to cluster in gene-dense regions.

These findings have important implications for NBS gene research, as many NBS genes show conditional, stress-responsive expression patterns characteristic of DEGs [9] [77]. The research further demonstrated that DEG transcription is associated with differentially accessible chromatin at distal enhancers, while SEG transcription correlates with ubiquitously accessible chromatin at promoters. This principle can guide the integration of epigenomic data when studying NBS gene regulation, suggesting that researchers should focus on distal regulatory elements rather than just promoter regions.

Table 3: Essential Research Reagents and Computational Tools for Multi-Omics Studies

Resource Category	Specific Tools/Reagents	Function/Purpose	Application in NBS Research
Sequencing Technologies	Illumina NovaSeq, PacBio Sequel, Oxford Nanopore	Generate genomic, transcriptomic, and epigenomic data	NBS gene discovery, structural variation, expression profiling [9] [78]
Epigenomic Profiling	ATAC-seq, Bisulfite sequencing kits, ChIP-seq reagents	Map chromatin accessibility, DNA methylation, histone modifications	Identify regulatory elements controlling NBS gene expression [77]
Bioinformatics Tools	OrthoFinder, VOSviewer, Biblioshiny, MetaboAnalyst	Orthogroup analysis, bibliometric mapping, multi-omics integration	Evolutionary analysis of NBS genes; Research trend analysis [79] [9]
Experimental Validation	Virus-induced gene silencing (VIGS) reagents, RT-qPCR kits	Functional validation of candidate genes	Confirm role of specific NBS genes in disease resistance [9]
Data Resources	GEO database, IEU Open GWAS, PLAZA genome database	Source of publicly available omics data	Access to NBS gene expression, genetic variation data [9] [80]

Technical Protocols for Key Experiments

Orthogroup Analysis of NBS Genes

Purpose: To identify evolutionarily conserved groups of NBS genes across multiple species [9].

Methodology:

• Data Collection: Obtain genome assemblies and annotation files for target species from public databases (NCBI, Phytozome, PLAZA)
• NBS Gene Identification: Use HMMER or similar tools with Pfam hidden Markov models (NB-ARC domain, PF00931) to identify NBS-domain-containing genes
• Orthogroup Delineation: Employ OrthoFinder with DIAMOND for sequence similarity searches and MCL for clustering
• Domain Architecture Analysis: Classify NBS genes based on additional domains using PfamScan or InterProScan
• Evolutionary Analysis: Construct phylogenetic trees using FastTree or RAxML; identify expansion/contraction events using CAFE

Integration Points:

• Correlate orthogroup conservation with expression conservation using transcriptomic data
• Map epigenetic features to orthogroup categories to identify conserved regulatory patterns
• Associate genetic variation in orthogroups with phenotypic differences between species

Multi-Omics Subtyping using iCluster

Purpose: To identify molecular subtypes by integrating genomic, epigenomic, and transcriptomic data [76].

Methodology:

• Data Preprocessing:
  • Genomic data: Process CNV data using GISTIC or similar tools
  • Epigenomic data: Identify differentially methylated regions using methylKit or similar
  • Transcriptomic data: Normalize expression data (e.g., TPM, FPKM) and identify differentially expressed genes
• Data Integration:
  • Select features that show variation across samples in each data type
  • Format data into three matrices (genomic, epigenomic, transcriptomic) with matched samples
  • Run iCluster with K=2-5 to identify optimal number of subtypes using tuning parameters
• Subtype Characterization:
  • Validate subtypes using survival analysis or other clinical outcomes
  • Identify subtype-specific biomarkers for each data type
  • Perform functional enrichment analysis of subtype-specific features

Application to NBS Genes:

• Determine if NBS gene expression patterns align with molecular subtypes
• Identify epigenetic mechanisms driving NBS expression differences between subtypes
• Discover genomic alterations in NBS genes associated with subtype classification

Data Visualization and Accessibility Considerations

Effective visualization of multi-omics data is essential for interpretation and communication of findings. When creating figures and diagrams:

• Color Selection: Use color palettes accessible to color-blind readers, avoiding red-green combinations [81]. The recommended palette includes #4285F4 (blue), #EA4335 (red), #FBBC05 (yellow), and #34A853 (green) with sufficient contrast ratios (at least 4.5:1 for text) [82]
• Direct Labeling: Position labels directly beside data points rather than relying on color-coded legends [82]
• Supplemental Formats: Provide data tables alongside visualizations to ensure accessibility [82]

The following diagram illustrates the conceptual relationships between different omics layers in the context of NBS gene regulation:

Conceptual Framework for NBS Gene Regulation

Integrating multi-omics data provides unprecedented opportunities to understand the complex regulation and function of NBS genes. The correlation of genomic findings with transcriptomic and epigenomic datasets enables researchers to move beyond descriptive associations toward mechanistic understanding of how genetic variation translates into functional consequences through regulatory mechanisms.

Future developments in multi-omics integration will likely focus on single-cell approaches that capture genomic, epigenomic, and transcriptomic information from the same cells, spatial omics that retain tissue context, and machine learning methods that can model non-linear relationships across data types. For NBS gene research, these advances will enable more precise mapping of how specific genetic variants in different orthogroups influence epigenetic states and expression dynamics in response to environmental challenges.

The methodologies and case studies presented in this technical guide provide a foundation for researchers seeking to implement multi-omics integration in their studies of NBS genes and other important gene families. As these approaches become more accessible and standardized, they will increasingly drive discoveries in basic biology and translational applications in drug development and precision medicine.

Functional Validation and Comparative Genomics for Translational Insights

In the genomic era, a fundamental shift from single-gene to systems-level analysis has transformed our approach to understanding complex biological responses. Orthogroup analysis provides a powerful framework for this investigation by grouping genes descended from a single ancestral gene in the last common ancestor of the species being considered [39]. This approach is particularly valuable for studying large gene families involved in stress responses, such as nucleotide-binding site (NBS) domain genes, which constitute one of the major superfamilies of plant disease resistance genes [83].

When plants encounter biotic stresses from pathogens or abiotic stresses like drought and salinity, they activate sophisticated molecular defense networks. Comparing expression profiles across entire orthogroups, rather than individual genes, reveals conserved evolutionary patterns and functional specialization that would otherwise remain obscured. For instance, a recent study investigating 12,820 NBS-domain-containing genes across 34 plant species identified specific orthogroups (OG2, OG6, and OG15) that showed significant upregulation in various tissues under different stress conditions [83]. This systematic approach enables researchers to pinpoint core stress-responsive genetic modules across species boundaries, advancing both fundamental knowledge and crop improvement strategies.

Key Concepts and Definitions

• Orthogroup: A set of genes descended from a single gene in the last common ancestor of all species being considered, including both orthologs and paralogs [39].
• Differential Expression (DE): Statistically significant differences in gene expression levels between experimental conditions, such as stress-treated versus control samples.
• NBS Domain Genes: Genes containing nucleotide-binding site domains that play crucial roles in plant pathogen recognition and defense activation [83].
• Expression Profiling: The measurement of expression levels for thousands of genes simultaneously to identify patterns associated with specific conditions.

Table 1: Classification of NBS Domain Genes and Their Stress Responses

Gene Category	Domain Architecture	Expression Pattern	Role in Stress Response
Classical NBS	NBS, NBS-LRR	Upregulated in tolerant genotypes	Broad-spectrum pathogen resistance
TIR-NBS	TIR-NBS	Tissue-specific induction	Early defense signaling
TIR-NBS-LRR	TIR-NBS-LRR	Strong biotic stress induction	Specific pathogen recognition
Species-specific	TIR-NBS-TIR-Cupin_1	Variable expression	Specialized adaptation

Methodological Framework: From Sequences to Differential Expression

Orthogroup Inference and Functional Annotation

The first critical step involves identifying orthogroups across species. OrthoFinder has emerged as the preferred tool for this purpose, as it solves fundamental gene length biases in whole genome comparisons, dramatically improving orthogroup inference accuracy by 8-33% compared to other methods [39]. The algorithm employs a novel score transformation that normalizes BLAST bit scores for gene length and phylogenetic distance, ensuring that short and long sequences have equal opportunity to be correctly clustered.

For NBS domain genes specifically, specialized domain architecture analysis can identify both classical patterns (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf) [83]. This classification is essential for understanding functional diversification within this important gene family.

Experimental Design for Stress Studies

Robust expression profiling requires careful experimental design. For investigating biotic stress responses, comparisons might include:

• Susceptible versus tolerant genotypes [83]
• Pathogen-inoculated versus mock-treated tissues
• Multiple time points post-inoculation

For abiotic stress studies, designs may incorporate:

• Treated versus control plants across stress gradients (drought, salinity, temperature)
• Tissue-specific responses (root, leaf, reproductive structures)
• Developmental stage-specific stress responses

Appropriate replication is critical, with most studies requiring at least 3-6 biological replicates per condition to achieve sufficient statistical power for differential expression detection.

RNA Sequencing and Differential Expression Analysis

RNA sequencing (RNA-Seq) has become the standard method for transcriptome-wide expression profiling. The typical workflow includes [84]:

• RNA extraction and quality control from stress-treated and control tissues
• Library preparation and sequencing, typically generating 20-40 million reads per sample
• Read alignment to reference genomes or transcriptomes
• Expression quantification at the gene or transcript level
• Differential expression analysis using statistical models that account for biological variability

For differential expression analysis in complex microbial communities, but with principles applicable to plant systems, models that adjust transcripts relative to their encoding gene copies as a covariate have shown significantly improved accuracy [85]. When paired DNA measurements are unavailable, normalizing within-species while adjusting for total-species RNA balances sensitivity and specificity.

Table 2: Statistical Models for Differential Expression Analysis

Model Type	Key Features	Best Suited Applications	Considerations
DNA-adjusted	Uses metagenomic data as covariate	When paired DNA sequencing available	Most accurate approach
Within-species normalized	Adjusts for total-species RNA	Single-organism RNA-seq	Maintains sensitivity
Zero-inflated	Accounts for technical zeros	Low-expression genes	Reduces false positives
Generalized linear	Negative binomial distribution	Standard RNA-seq experiments	Handles biological variability

Experimental Protocols and Workflows

Orthogroup Identification Protocol

Materials Required:

• Protein sequences from target species in FASTA format
• High-performance computing resources
• OrthoFinder software (v2.5.4 or newer)

Procedure:

• Data Preparation: Gather protein sequences for all species of interest. For the NBS gene study, this included 34 species from mosses to monocots and dicots [83].
• Orthogroup Inference: Run OrthoFinder with default parameters: orthofinder -f [fasta_directory] -M msa -S diamond
• Result Interpretation: Analyze the Orthogroups.tsv output file to identify orthogroups containing NBS domain genes.
• Evolutionary Analysis: Use OrthoFinder's phylogenetic analysis to infer gene duplication events and evolutionary relationships.

This protocol identified 603 orthogroups in the NBS gene study, with some core (OG0, OG1, OG2) and unique (OG80, OG82) orthogroups showing evidence of tandem duplications [83].

Differential Expression Analysis Protocol

Materials Required:

• Trimmed RNA-seq reads in FASTQ format
• Reference genome and annotation file
• Metadata file describing experimental conditions
• Computational tools such as CLC Genomics Workbench or specialized R packages

Procedure:

• Read Mapping: Map trimmed reads to the reference genome using splice-aware aligners.
• Expression Quantification: Calculate gene-level counts using the "RNA-Seq Analysis" tool, which generates gene expression tracks [84].
• Differential Expression Testing: Use the "Differential Expression for RNA-Seq" tool with appropriate statistical models. The design should incorporate relevant factors from the metadata file (e.g., treatment, genotype, tissue type) [84].
• Multiple Testing Correction: Apply Benjamini-Hochberg or similar correction to control false discovery rate.
• Functional Enrichment: Perform Gene Ontology enrichment analysis on significant differentially expressed genes using the "Gene Set Test" tool [84].

Functional Validation Protocol

Materials Required:

• Target plant species (e.g., resistant cotton for NBS genes)
• Virus-Induced Gene Silencing (VIGS) vectors
• Pathogen strains for challenge assays
• Molecular biology reagents for expression analysis

Procedure:

• Candidate Selection: Identify key orthogroups showing differential expression patterns, such as OG2, OG6, and OG15 in the NBS study [83].
• VIGS Construction: Design VIGS constructs targeting selected orthogroup members.
• Plant Transformation: Deliver VIGS constructs to resistant plants to knock down target gene expression.
• Phenotypic Assessment: Challenge silenced plants with pathogens and quantify disease symptoms.
• Molecular Analysis: Measure pathogen titers and assess expression of related genes.

In the NBS domain gene study, silencing of GaNBS (OG2) in resistant cotton demonstrated its putative role in virus tittering, validating its importance in disease resistance [83].

Computational Workflow and Visualization

The following Graphviz diagram illustrates the complete analytical workflow from sequence data to biological insights:

Expression Profiling Workflow from Sequences to Biological Insights

Table 3: Essential Research Reagents and Computational Tools

Category	Specific Tools/Reagents	Function	Application Example
Orthology Inference	OrthoFinder	Identifies orthogroups across species	Cluster NBS genes across 34 plant species [83] [39]
Sequence Alignment	DIAMOND, BLAST	Rapid protein sequence similarity search	Initial homology detection for orthogroup building
Expression Quantification	CLC Genomics Workbench, featureCounts	Calculate gene-level expression values	Generate expression values from RNA-seq data [84]
Differential Expression	DESeq2, edgeR	Statistical detection of DE genes	Identify stress-responsive orthogroups [85]
Functional Annotation	InterProScan, Pfam	Domain architecture analysis	Classify NBS domain architectures [83]
Validation	VIGS vectors	Functional characterization through gene silencing	Validate role of GaNBS in virus resistance [83]
Visualization	Carbon Charts, ggplot2	Data visualization and exploration	Create publication-quality figures

Case Study: NBS Domain Genes in Plant Stress Responses

A comprehensive study on NBS domain genes exemplifies the power of orthogroup-based expression profiling [83]. The researchers identified 12,820 NBS-domain-containing genes across 34 plant species and classified them into 168 distinct classes based on domain architecture. Through expression profiling, they demonstrated that specific orthogroups (OG2, OG6, and OG15) showed putative upregulation in different tissues under various biotic and abiotic stresses.

Genetic variation analysis between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions revealed striking differences: Mac7 contained 6,583 unique variants in NBS genes while Coker312 had 5,173 variants [83]. This variation pattern suggests that nucleotide diversity in NBS genes contributes to stress tolerance. Protein-ligand and protein-protein interaction studies further showed strong interactions between putative NBS proteins and ADP/ATP as well as different core proteins of the cotton leaf curl disease virus.

Most importantly, functional validation through virus-induced gene silencing (VIGS) of GaNBS (OG2) in resistant cotton demonstrated its critical role in virus resistance [83]. This integrated approach—from comparative genomics to functional validation—exemplifies the full potential of orthogroup expression profiling for unraveling complex biological systems.

Advanced Applications and Future Directions

The field of orthogroup expression profiling continues to evolve with several promising directions:

Cross-Species Comparison Methods: New approaches are exploring protein structural similarity as an alternative to sequence homology for comparing expression across species [86]. While initial attempts using AlphaFold-predicted structures did not outperform sequence-based methods, conceptually similar approaches using protein language models (e.g., SATURN) show promise for integrating structural information into cross-species analyses.

Genome Annotation Advances: DNA foundation models like SegmentNT are revolutionizing genome annotation by fine-tuning pretrained models to segment multiple genomic elements at single-nucleotide resolution [87]. These approaches can enhance the detection of regulatory elements that control stress-responsive gene expression.

Evolutionary Insights: Studies in barley have revealed that expanded gene families (including 214 significantly expanded orthogroups) show distinct evolutionary patterns: they evolve more rapidly, experience lower negative selection, and show higher tissue specificity in their expression compared to non-expanded genes [88]. Understanding these evolutionary patterns helps interpret expression profiles in a phylogenetic context.

As these methodologies mature, orthogroup-based expression profiling will continue to provide deeper insights into the genetic architecture of stress responses, enabling more targeted crop improvement strategies and advancing fundamental knowledge of evolutionary biology.

In the field of plant genomics, identifying the genetic basis of stress tolerance is crucial for developing resilient crop varieties. This technical guide focuses on the analysis of genetic variation within the context of nucleotide-binding site (NBS) genes, which constitute one of the largest families of plant disease resistance genes [9] [20]. The approach is framed within orthogroup analysis, which groups genes across species based on common descent, allowing researchers to identify evolutionarily conserved genetic elements and their variations [9] [36].

NBS genes encode proteins characterized by a nucleotide-binding site domain and frequently C-terminal leucine-rich repeats (LRRs) [20]. These genes play a critical role in plant immunity by recognizing pathogen effectors and initiating defense responses [9] [36]. The functional diversity of these genes is immense, with plants possessing hundreds of NBS-encoding genes in their genomes—rice, for example, contains over 600 such genes, at least three to four times the complement found in Arabidopsis [20]. Understanding the genetic variation within these critical gene families between tolerant and susceptible cultivars provides a powerful approach for pinpointing the molecular mechanisms of stress resistance.

Key Concepts and Definitions

Nucleotide-Binding Site (NBS) Genes and Orthogroups

• NBS Genes: A major class of plant resistance (R) genes characterized by a conserved nucleotide-binding site (NBS) domain, often coupled with leucine-rich repeat (LRR) domains [9] [20]. They are primary components of the plant immune system, responsible for detecting pathogens and initiating defense cascades.
• Orthogroups (OGs): Sets of genes descended from a single gene in the last common ancestor of the species being considered [9]. Orthogroup analysis enables the systematic classification of gene families across multiple species or cultivars, facilitating the identification of conserved core genes and lineage-specific variations.
• Genetic Variants: Differences in the DNA sequence between individuals. In the context of cultivar comparison, key variants include:
  • Single Nucleotide Polymorphisms (SNPs): Single base pair changes.
  • Insertions/Deletions (InDels): Small insertions or deletions of DNA sequences.
  • Structural Variants (SVs): Larger-scale variations including duplications, inversions, and translocations.
  • Copy Number Variations (CNVs): Differences in the number of copies of a particular genomic region.

Resistance (R) Gene Classification

NBS-LRR genes are broadly classified into two major subfamilies based on their N-terminal domains:

• TIR-NBS-LRR (TNL): Contain a Toll/Interleukin-1 Receptor (TIR) domain at the N-terminus. This class is generally absent in cereals [20].
• Non-TIR-NBS-LRR (nTNL): Often feature a predicted coiled-coil (CC) structure at the N-terminus and are sometimes referred to as CNLs. This is the predominant class in monocots, including cereals [9] [20].

Experimental Design and Workflow

A robust experimental design for genetic variation analysis involves a combination of phenotypic screening, genomic sequencing, and bioinformatic analysis. The following workflow outlines the key steps in this process.

Phenotypic Screening for Stress Tolerance

The initial phase involves identifying cultivars with contrasting stress responses, which will serve as the tolerant and susceptible groups for comparative analysis.

• Controlled Stress Application: Subject a diverse panel of cultivars to defined stress conditions. For drought stress, this can be achieved in growth chambers using systems like iPOTs, which mimic field-like drought conditions with high reproducibility by automatically controlling soil moisture levels [89].
• Physiological and Molecular Phenotyping:
  • Physiological Parameters: Measure traits such as relative electrical conductivity (REC), malondialdehyde (MDA) accumulation, and antioxidant enzyme activities (SOD, POD, CAT) to quantitatively assess stress-induced damage [90].
  • Molecular Biomarkers: Utilize established biomarker genes to assess stress perception levels before visible symptoms appear. For instance, a machine learning model based on 23 drought-stress biomarker (DSBM) genes in rice can accurately predict drought-stress perception levels [89].
• Tolerance Classification: Based on the phenotyping data, classify cultivars into distinct groups, such as "tolerant" and "susceptible," for subsequent genomic analysis [91].

Genomic Sequencing and Variant Discovery

With the phenotyped cultivars, the next step is to perform whole-genome sequencing to identify genetic variants.

• Whole-Genome Resequencing (WGS): Sequence the genomes of the selected tolerant and susceptible cultivars to a sufficient depth (e.g., 16-20x coverage) using next-generation sequencing platforms [90].
• Variant Calling Pipeline: Align the sequencing reads to a reference genome and call variants using a standardized bioinformatics pipeline.
  • Read Alignment: Use tools like BWA-MEM for mapping reads to the reference genome [90].
  • Variant Detection:
    • SNPs/InDels: Use SAMtools mpileup for initial calling, followed by filtering based on read depth and mapping quality [90].
    • Structural Variants (SVs): Use tools like BreakDancer to detect larger insertions, deletions, inversions, and translocations based on discordant read pairs [90].
    • Copy Number Variations (CNVs): Use CNVnator to identify regions with significant deviations in read depth compared to the reference [90].
• Functional Annotation: Annotate the identified variants using tools like ANNOVAR to predict their functional consequences (e.g., synonymous, non-synonymous, frameshift, upstream, downstream) [90].

Orthogroup Analysis and Variant Prioritization within NBS Genes

This phase integrates the identified variants with orthogroup analysis to pinpoint functionally relevant variations within the NBS gene family.

• Identification and Classification of NBS Genes: Screen the genome assemblies for genes containing the NB-ARC domain (Pfam: PF00931) using HMMER searches [9] [36]. Classify the identified NBS genes based on their domain architecture (TNL, CNL, etc.) [9].
• Orthogroup Construction: Use orthology prediction tools like OrthoFinder to cluster NBS genes from multiple cultivars or species into orthogroups (OGs) [9]. This groups genes that originated from a single gene in the last common ancestor.
• Variant Intersection and Filtering: Overlay the genomic variants (SNPs, InDels, SVs) onto the NBS gene set. Focus on identifying:
  • Unique Variants: Variants present exclusively in one cultivar group (tolerant vs. susceptible).
  • High-Impact Variants: Non-synonymous SNPs, frameshift InDels, or SVs that are likely to disrupt gene function, particularly within conserved NBS domain motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, GLPL, RNBS-D, MHDV) [9] [36].
  • Variants in Core Orthogroups: Prioritize variants located within core orthogroups—those that are conserved across most cultivars or species, as these may represent essential functional units [9].
• Expression Validation: Correlate genetic variants with transcriptional data. For candidate genes containing unique variants, check RNA-seq data to see if the variations are associated with differential expression under stress conditions [9] [90].

The following workflow diagram summarizes the key steps in this experimental design.

Case Studies and Data Interpretation

Case Study 1: Cotton Leaf Curl Disease (CLCuD) Tolerance

A comparative analysis of NBS genes in Gossypium hirsutum accessions with contrasting tolerance to Cotton Leaf Curl Disease provides a clear example of this approach.

• Cultivar Comparison: The study compared the tolerant accession 'Mac7' with the susceptible 'Coker 312' [9].
• Genetic Variation Analysis: Whole-genome resequencing identified a substantial number of unique genetic variants within NBS genes of each accession.
• Key Finding: The tolerant Mac7 accession possessed 6,583 unique variants in its NBS genes, while the susceptible Coker312 had 5,173 unique variants [9]. This suggests that the tolerant line may have a more diversified repertoire of resistance gene alleles, potentially contributing to its ability to recognize and respond to the virus.
• Orthogroup Context: Expression profiling highlighted specific orthogroups (OG2, OG6, OG15) that were upregulated in response to biotic stress. Furthermore, functional validation via virus-induced gene silencing (VIGS) of a gene from OG2 (GaNBS) confirmed its role in reducing viral titer, demonstrating the functional importance of variants within this orthogroup [9].

Case Study 2: Chilling Stress in Walnut

Research on walnut (Juglans regia L.) tolerance to chilling stress demonstrates the application of WGS outside the context of NBS genes, focusing on general stress tolerance mechanisms.

• Cultivar Comparison: The cold-tolerant 'Qingxiang' was compared to the cold-sensitive 'Liaoning No.8' [90].
• Genomic Data Generation: WGS at approximately 16x coverage identified millions of variants in each cultivar.
• Variant Identification and Correlation:
  • The study identified 2.73–2.78 million SNPs and 378–382k InDels per cultivar [90].
  • Twenty candidate genes associated with chilling stress response were found to contain sequence variants. A significant positive correlation (r = 0.62, P < 0.01) was observed between the mutation density in these genes and their transcriptional response under cold stress [90].
  • A specific gene (XM_018985465.2) lacking SNPs in the tolerant 'Liaoning No.8' cultivar showed 4.2 times higher expression, suggesting that cis-regulatory variations are critical for stress adaptation [90].

Table 1: Summary of Genetic Variants Identified in Case Studies

Case Study	Tolerant Cultivar	Unique Variants in Tolerant Cultivar	Susceptible Cultivar	Unique Variants in Susceptible Cultivar	Key Candidate Genes/Orthogroups
Cotton Leaf Curl Disease [9]	Mac7	6,583 variants in NBS genes	Coker 312	5,173 variants in NBS genes	OG2, OG6, OG15
Chilling Stress in Walnut [90]	Qingxiang	2.78M SNPs, 382k InDels	Liaoning No.8	2.73M SNPs, 378k InDels	XM_018985465.2 (among others)

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful execution of genetic variation analysis requires a suite of specialized reagents, computational tools, and biological materials.

Table 2: Key Research Reagent Solutions for Genetic Variation Analysis

Category	Item	Specific Example / Tool	Function in the Workflow
Plant Materials & Growth	Cultivars with Contrasting Phenotypes	Badia (susceptible) vs. Kavir (tolerant) barley [91]	Provides the biological source of genetic variation for comparison.
	Controlled Stress Application System	iPOT system with automatic irrigation [89]	Mimics field-relevant drought stress in a reproducible manner.
Molecular Biology	DNA Extraction & Library Prep Kits	Commercial kits for NGS library prep [90]	Prepares high-quality genomic DNA for sequencing.
	Sequencing Platform	Illumina (PE150 configuration) [90]	Generates high-throughput short-read sequencing data.
Bioinformatics	Read Alignment Tool	BWA-MEM [90]	Maps sequencing reads to a reference genome.
	Variant Caller	SAMtools mpileup, BreakDancer, CNVnator [90]	Identifies SNPs, InDels, SVs, and CNVs from aligned reads.
	Orthogroup Analysis Software	OrthoFinder [9]	Clusters genes into orthogroups across multiple cultivars/species.
	Motif Analysis Tool	MEME Suite [36]	Discovers conserved protein motifs in candidate genes.
Validation	Functional Validation Tool	Virus-Induced Gene Silencing (VIGS) [9]	Tests the functional role of a candidate gene in planta.

The integration of whole-genome resequencing with orthogroup analysis provides a powerful, systematic framework for pinpointing genetic variants that underlie important agronomic traits. By focusing on well-annotated gene families like the NBS genes, researchers can efficiently filter millions of genomic variations to identify the functionally significant differences between tolerant and susceptible cultivars. The case studies in cotton and walnut demonstrate that this approach can successfully identify thousands of unique variants and correlate them with gene expression and function, ultimately leading to the discovery of key candidate genes for crop improvement. As sequencing technologies continue to advance and the annotation of plant genomes improves, this methodology will become increasingly central to unlocking the genetic basis of stress tolerance and accelerating the development of resilient crop varieties.

Functional characterization of genes and their products represents a critical phase in modern biological research, bridging the gap between genomic sequences and biological meaning. This is particularly true for the study of nucleotide-binding site (NBS) genes, which play crucial roles in plant defense mechanisms against pathogens. The integration of orthogroup analysis—which groups genes into families based on evolutionary relationships—has revolutionized how researchers identify candidate genes for detailed functional study. Within this framework, techniques ranging from virus-induced gene silencing (VIGS) to sophisticated protein-ligand interaction studies provide complementary approaches for validating gene function and understanding molecular mechanisms. This technical guide examines these core methodologies, their applications in NBS gene research, and their integration into a comprehensive functional characterization pipeline.

Orthogroup Analysis of NBS Genes: Establishing the Functional Framework

Orthogroup analysis provides an evolutionary foundation for functional studies by identifying groups of genes descended from a single ancestral gene in a common ancestor. When applied to NBS genes, this approach reveals important patterns in the expansion and diversification of plant immune receptors.

Identification and Classification of NBS Genes

The initial step in orthogroup analysis involves the systematic identification of NBS-domain-containing genes across multiple species. A recent comprehensive study identified 12,820 NBS-domain-containing genes across 34 plant species, ranging from mosses to monocots and dicots [9]. These genes were classified into 168 distinct classes based on their domain architecture, revealing both classical patterns (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural variations (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf) [9].

Table 1: Classification of NBS Genes Based on Domain Architecture

Domain Architecture Class	Prevalence	Representative Species	Postulated Functional Role
NBS-LRR	Widespread	Arabidopsis, Cotton	Pathogen recognition
TIR-NBS-LRR	Eudicots	Tomato, Potato	Effector-triggered immunity
CC-NBS-LRR	Angiosperms	Barley, Wheat	Immune signaling
TIR-NBS-TIR-Cupin_1	Species-specific	Cotton	Specialized resistance
NBS-only	Multiple lineages	Various	Regulatory functions

The use of Hidden Markov Model (HMM) searches with the Pfam NBS family (NB-ARC domain PF00931) model represents the standard approach for identifying these genes, typically employing an E-value cut-off of 10⁻⁶⁰ for initial screening [36]. Subsequent validation with tools like NCBI's Conserved Domains Database ensures the presence of a complete NBS domain, while additional domain identification (TIR, LRR, CC) enables precise classification [36].

Orthogroup Delineation and Evolutionary Analysis

OrthoFinder and similar tools enable the clustering of NBS genes into orthogroups based on sequence similarity. Research has identified 603 orthogroups containing NBS genes, with some core orthogroups (OG0, OG1, OG2) being widely distributed across species, while others (OG80, OG82) appear species-specific [9]. This phylogenetic framework guides functional characterization efforts by highlighting which gene families are conserved across species and which represent recent, lineage-specific expansions.

Tandem duplication emerges as a primary mechanism driving NBS gene expansion, with complex clusters containing at least ten NBS genes showing patterns of repeated duplication and divergence [36]. The study of orthologous relationships further reveals that most NBS genes arise from duplication of paralogues within a limited set of ancestral orthologous groups [36].

Virus-Induced Gene Silencing (VIGS) for Functional Validation

VIGS has emerged as a powerful technique for rapid functional characterization of genes, particularly in species resistant to stable genetic transformation.

Mechanism and Workflow of VIGS

VIGS operates by exploiting the natural antiviral defense mechanisms of plants. When a virus engineered to carry a fragment of a plant gene infects the host, it triggers post-transcriptional gene silencing that targets both the viral RNA and the corresponding endogenous mRNA for degradation [92]. The key steps in this process include:

• Vector Construction: A 300-500 bp fragment of the target gene is cloned into a modified viral genome [92].
• Plant Infection: The recombinant virus is introduced into plants via Agrobacterium tumefaciens-mediated transient transformation or other inoculation methods [92].
• Viral Replication and dsRNA Formation: The viral RNA replicates, forming double-stranded RNA intermediates [92].
• siRNA Generation: DICER-like enzymes process dsRNA into 21-25 nucleotide small interfering RNAs (siRNAs) [92].
• Target mRNA Degradation: siRNAs guide the RNA-induced silencing complex (RISC) to cleave complementary mRNA sequences [92].

Table 2: Commonly Used VIGS Vectors and Their Applications

Vector System	Virus Type	Host Range	Key Features	Applications in NBS Research
TRV (Tobacco Rattle Virus)	RNA virus	Broad dicot range	Mild symptoms, meristem invasion	Solanaceous species, Arabidopsis
BSMV (Barley Stripe Mosaic Virus)	RNA virus	Monocots, especially cereals	Efficient silencing in grasses	Barley, wheat NBS genes [93]
DNAβ Satellite + TYLCCNV	DNA virus	Limited host range	Reduced viral symptoms	Tomato stress response genes

The following workflow diagram illustrates the typical VIGS experimental process:

Application to NBS Gene Functional Characterization

VIGS has proven particularly valuable for characterizing NBS genes involved in disease resistance. In barley, BSMV-based VIGS was used to validate the role of the Brassinosteroid Insensitive 1 (BRI1) receptor in leaf resistance to Fusarium infection [93]. The protocol involves:

• Plant Material Preparation: 10-14 day old barley seedlings grown under controlled conditions [93].
• In Vitro Transcription: BSMV RNAs α, β, and γ are transcribed from linearized plasmids.
• Vector Inoculation: The γ plasmid containing the target gene fragment is mixed with α and β plasmids and applied to carbonundum-dusted leaves [93].
• Phenotypic Assessment: Disease symptoms are evaluated 7-14 days post-inoculation [93].

In a recent study applying orthogroup analysis to cotton NBS genes, VIGS was used to validate the role of GaNBS (OG2) in resistance to cotton leaf curl disease [9]. Silencing of this gene in resistant cotton led to increased viral titers, confirming its functional role in disease resistance [9].

Protein-Ligand Interaction Studies for Molecular Mechanism Elucidation

While VIGS establishes gene-phenotype relationships, protein-ligand interaction studies reveal the molecular mechanisms underlying these relationships, particularly for NBS proteins whose function often involves nucleotide binding and conformational changes.

Fundamentals of Protein-Ligand Interactions

Protein-ligand interactions are governed by complementary structural features and physicochemical forces. The binding process can be described by the equation:

[ P + L \rightleftharpoons^{k{on}}{k_{off}} PL ]

where ( P ) represents the protein, ( L ) the ligand, and ( PL ) the protein-ligand complex [94]. The binding affinity is quantified by the dissociation constant:

[ Kd = \frac{[P][L]}{[PL]} = \frac{k{off}}{k_{on}} ]

The standard binding free energy (( \Delta G^\circ )) relates to ( K_d ) through:

[ \Delta G^\circ = -RT \ln \left( \frac{1}{K_d} \right) ]

where ( R ) is the gas constant and ( T ) is temperature [94]. This free energy change comprises both enthalpic (( \Delta H )) and entropic (( -T\Delta S )) components:

[ \Delta G = \Delta H - T\Delta S ]

Several conceptual models describe the binding mechanism, including the "lock-and-key" model (pre-existing complementarity), "induced fit" model (conformational changes upon binding), and "conformational selection" model (selection of pre-existing conformations from an ensemble) [94].

Experimental Methods for Studying Protein-Ligand Interactions

Multiple experimental approaches are available for characterizing protein-ligand interactions, each with distinct advantages and applications.

Isothermal Titration Calorimetry (ITC)

ITC directly measures the heat change associated with binding, providing complete thermodynamic characterization (( \Delta G ), ( \Delta H ), ( \Delta S ), and binding stoichiometry) in a single experiment [94]. The protocol typically involves:

• Sample Preparation: Purified protein and ligand solutions in matched buffers.
• Titration: Sequential injections of ligand into the protein solution.
• Data Analysis: Integration of heat peaks and fitting to appropriate binding models.

Surface Plasmon Resonance (SPR)

SPR measures binding kinetics in real-time without requiring labeling. The key steps include:

• Immobilization: One binding partner (typically the protein) is immobilized on a sensor chip.
• Association: The analyte (ligand) flows over the surface, and binding is monitored.
• Dissociation: Buffer flow allows monitoring of complex dissociation.
• Data Analysis: Sensorgrams are fitted to determine ( k{on} ), ( k{off} ), and ( K_d ) [94].

High-Throughput Peptide-Centric Local Stability Assay (HT-PELSA)

A recent advancement, HT-PELSA, enables high-throughput detection of protein-ligand interactions by monitoring ligand-induced changes in protein stability [95]. The method:

• Processes 400 samples per day (100-fold improvement over previous methods) [95].
• Works directly with complex samples (crude cell lysates, tissues, bacterial lysates).
• Enables study of previously inaccessible targets, including membrane proteins (60% of known drug targets) [95].

The workflow involves proteome-wide digestion with trypsin, separation of cleaved peptides from intact proteins, and quantitative mass spectrometry to identify protein regions stabilized by ligand binding [95].

The following diagram illustrates the relationship between different protein-ligand interaction techniques and the information they provide:

Integrated Research Framework: From Orthogroup to Function

The power of these techniques is magnified when integrated into a cohesive research pipeline. The following workflow represents an integrated approach for functional characterization of NBS genes:

Case Study: NBS Gene Characterization in Cotton

A recent study exemplifies this integrated approach. Researchers identified NBS genes across 34 plant species, performed orthogroup analysis, and identified 603 orthogroups [9]. Expression profiling revealed putative upregulation of specific orthogroups (OG2, OG6, OG15) in different tissues under various biotic and abiotic stresses in cotton accessions with contrasting responses to cotton leaf curl disease [9]. Genetic variation analysis identified 6583 unique variants in tolerant accessions versus 5173 in susceptible lines [9]. Protein-ligand and protein-protein interaction studies demonstrated strong binding between putative NBS proteins and ADP/ATP, as well as core proteins of the cotton leaf curl disease virus [9]. Finally, VIGS-mediated silencing of GaNBS (OG2) confirmed its role in reducing viral titers [9].

Essential Research Reagent Solutions

Successful implementation of these functional characterization techniques requires specific research reagents and tools. The following table summarizes key solutions for studying NBS genes and their functions:

Table 3: Research Reagent Solutions for Functional Characterization Studies

Reagent/Tool Category	Specific Examples	Function/Application	Technical Notes
VIGS Vectors	TRV, BSMV, DNAβ satellite virus	Gene silencing in dicots/monocots	BSMV particularly valuable for cereal crops [93] [92]
Cloning Systems	Gateway, Golden Gate, TA cloning	Insertion of target fragments	300-500 bp inserts optimize silencing efficiency [92]
Agrobacterium Strains	GV3101, LBA4404	Delivery of VIGS constructs	Critical for high-efficiency transformation
Protein Purification Systems	His-tag, GST-tag, MBP-tag	Recombinant protein production	Essential for structural and binding studies
Binding Assay Platforms	ITC, SPR, FP instruments	Quantitative binding measurements	HT-PELSA enables high-throughput screening [95]
Bioinformatic Tools	HMMER, OrthoFinder, MEME	Identification, classification, motif discovery	Pfam NB-ARC domain (PF00931) as search model [36]

The functional characterization of NBS genes has been transformed by the integration of evolutionary frameworks like orthogroup analysis with powerful experimental techniques spanning from VIGS to protein-ligand interaction studies. VIGS provides a rapid, cost-effective method for validating gene function in planta, while protein-ligand interaction techniques reveal the molecular mechanisms underlying these functions. The continuing development of high-throughput methods like HT-PELSA promises to further accelerate our understanding of how NBS genes function at the molecular level. For researchers investigating the complex landscape of plant immune genes, this integrated approach offers a powerful pathway from gene sequence to biological function, with important implications for crop improvement and sustainable agriculture.

In the field of plant genomics, the pursuit of disease resistance is a cornerstone of crop improvement. Nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes constitute the largest family of plant disease resistance (R) genes and play a pivotal role in the innate immune system of plants, enabling them to recognize diverse pathogens [96]. These genes are characterized by a conserved NBS domain that facilitates nucleotide binding and signaling, coupled with highly variable LRR domains responsible for pathogen recognition [96]. The NBS-encoding resistance genes are one of the most numerous gene families in plants, with counts ranging from approximately 50 in some species to over 650 in others [96].

Orthogroup analysis has emerged as a powerful comparative genomics approach for identifying evolutionarily conserved gene families across multiple species. This method clusters genes into groups of orthologs and paralogs descended from a single gene in the last common ancestor, providing a framework for understanding gene family evolution and identifying conserved functional units [97]. When applied to disease resistance gene research, orthogroup analysis enables researchers to trace the evolutionary history of R genes, identify species-specific expansions, and pinpoint conserved candidates with potential broad-spectrum resistance functions.

This case study examines the application of orthogroup-based discovery for identifying resistance genes in two economically important plant groups: cotton (Gossypium species) and tung trees (Vernicia fordii). Cotton represents a globally significant fiber crop, while tung tree is a valuable oil-producing woody plant. Both face substantial threats from fungal pathogens and nematodes, necessitating the identification of durable resistance mechanisms. By integrating genomic resources and orthogroup analysis, this study provides a framework for efficient resistance gene discovery with implications for molecular breeding programs.

High-quality genome assemblies and annotations are fundamental prerequisites for effective orthogroup analysis. Recent advances in sequencing technologies have enabled the development of comprehensive genomic resources for both cotton and tung trees, providing the foundation for comparative studies.

The genus Gossypium encompasses diploid and tetraploid species with varying genome sizes and compositions. For diploid cotton, the Gossypium raimondii genome has served as a key resource for systematic analysis of NBS-encoding genes, with 355 identified members [98]. This study revealed that cotton NBS genes are characterized by a high proportion of non-regular NBS architecture and diverse N-terminal domains, with gene duplication playing a significant role in functional diversification [98].

For tetraploid cottons, multiple genome assemblies are now available:

Table 1: Genomic Resources for Tetraploid Cotton

Species/Accession	Genome Size	Assembly Status	Gene Number	Key Features
G. barbadense Pima-S6	2.30 Gb	Chromosome-level	75,419	Fusarium wilt resistance source [99]
G. barbadense Pima 3-79	~2.3 Gb	Chromosome-level	74,561	Reference genome [99]
G. barbadense Hai7124	~2.3 Gb	Chromosome-level	75,071	Asian cultivar [99]
G. hirsutum TM-1	~2.3 Gb	Chromosome-level	75,376	Upland cotton reference [99]

The Pima-S6 genome is of particular interest for resistance gene discovery, as it serves as the source of resistance to highly pathogenic Fusarium oxysporum f. sp. vasinfectum (FOV) race 4, a soil-borne fungal pathogen that causes devastating wilt disease in cotton [99]. Comparative analyses have revealed important differences in chromosome structure and annotated protein content between different G. barbadense assemblies, highlighting the need for species-specific references for accurate gene discovery [99].

The tung tree (Vernicia fordii) genome has been sequenced and assembled to chromosome-scale, enabling comprehensive analysis of its resistance gene repertoire. The genome assembly spans 1.12 Gb, with over 95% anchored to 11 pseudochromosomes, and contains 28,422 predicted genes [100].

Table 2: Tung Tree Genome Assembly Statistics

Assembly Parameter	Value
Estimated genome size	1.31 Gb
Total assembly size	1.12 Gb
Sequences anchored to Hi-C map	1.06 Gb (95.15%)
N50 of scaffolds after Hi-C assembly	87.15 Mb
Number of genes	28,422
Repeat content	73.34%
Number of NBS resistance genes	88 [100]

The tung tree genome provides crucial insights into the evolution of disease resistance genes in the Euphorbiaceae family. As a member of this family along with other economically important species like cassava, castor bean, and rubber tree, comparative analysis within this group can reveal conserved resistance mechanisms [100]. The genome has undergone an ancient triplication event shared by core eudicots but no recent whole-genome duplication, providing a distinctive evolutionary context for resistance gene analysis [100].

Orthogroup Analysis Methodology

Orthogroup analysis provides a systematic framework for identifying and classifying gene families across multiple species. The methodology involves several key steps, from data preparation to functional annotation.

Workflow for Orthogroup Identification

The following diagram illustrates the comprehensive workflow for orthogroup analysis of resistance genes:

Orthogroup Analysis Workflow

Key Computational Tools and Databases

The orthogroup analysis pipeline relies on several specialized bioinformatics tools and databases:

• OrthoFinder: This tool performs orthogroup inference from protein sequence data using a graph-based algorithm with the MCL clustering method [97]. It constructs a graph where vertices represent sequences from all species, and edges represent sequence similarity (typically BLAST hits). The MCL algorithm then clusters this graph into orthogroups.

• PfamScan and HMMER: These tools are used for domain annotation, specifically identifying NBS (NB-ARC, PF00931), TIR (PF01582), CC (Coiled-coil), and LRR (PF00560, PF07723, PF07725, PF12799, PF13306, PF13516, PF13855, PF14580) domains using hidden Markov models [97].

• MAFFT and FastTree: Used for multiple sequence alignment and phylogenetic tree construction, respectively [97]. These tools help reconstruct evolutionary relationships within orthogroups.

• Gene Ontology and KEGG: Functional annotation databases that help assign biological roles to genes within orthogroups [100].

Experimental Validation Approaches

Following computational identification, candidate resistance genes require experimental validation:

• Expression Analysis: RNA-seq data from various tissues and stress conditions can reveal expression patterns associated with defense responses. For example, in tung trees, RNA-seq data from roots infected with Fusarium oxysporum f. sp. fordiis (Fof-1) has been used to identify differentially expressed resistance genes [101].

• Transgenic Validation: Hairy root transformation systems or stable plant transformation can test gene function. For instance, overexpression of VfUGT90A2 in tung tree hairy roots significantly enhanced resistance to Fof-1, demonstrating the role of this UDP-glycosyltransferase in Fusarium defense [101].

• Biochemical Assays: Enzyme activity assays, protein-protein interaction studies, and metabolite profiling can elucidate molecular mechanisms. The root extract from transgenic VfUGT90A2 tung tree roots showed inhibitory effects on Fof-1 mycelium growth, linked to increased flavonoids like quercitrin and myricitrin [101].

Case Study 1: NBS Gene Orthogroups in Cotton

Genomic Distribution and Evolution

The systematic analysis of NBS-encoding genes in Gossypium raimondii identified 355 NBS genes, revealing several important evolutionary patterns [98]. These genes display a non-random distribution across the genome, with evidence that gene duplication has played a significant role in expanding the functional diversity of the NBS gene family in cotton.

Phylogenetic comparisons revealed that NBS-encoding genes with TIR domains follow distinct evolutionary patterns compared to non-TIR types, and also exhibit species-specific characteristics that differentiate them from TIR genes in other plants [98]. This suggests lineage-specific evolutionary pressures shaping the TIR-NBS-LRR repertoire in cotton.

Analysis of the correlation between disease resistance quantitative trait loci (QTL) and NBS-encoding genes showed that more than half of the disease resistance QTL are associated with NBS-encoding genes in cotton [98]. This finding aligns with previous studies establishing that more than half of plant resistance genes are NBS-encoding, validating the importance of this gene family in disease resistance.

Orthogroup Analysis Across Gossypium Species

Comparative analysis of NBS genes across multiple Gossypium species enables the identification of conserved orthogroups with potential broad-spectrum resistance functions. The following diagram illustrates the evolutionary relationships and NBS gene content in cotton species:

Cotton Evolution and NBS Genes

G. longicalyx, a rare African diploid species with F-genome, represents a particularly valuable resource for resistance gene discovery as it possesses immunity to reniform nematode [102]. This species is phylogenetically sister to the A-genome cottons and provides insights into the evolution of resistance genes in the Gossypium genus. Orthogroup analysis comparing G. longicalyx with A-genome and D-genome species enables identification of conserved nematode resistance mechanisms.

Application to Fusarium Wilt Resistance

Orthogroup analysis has proven valuable for identifying candidate genes for Fusarium wilt resistance in cotton. The Pima-S6 genome, which contains sources of FOV4 resistance, provides a platform for identifying orthogroups associated with this trait [99]. By comparing orthogroups across resistant and susceptible varieties, researchers can pinpoint specific NBS-LRR genes that may confer FOV4 resistance.

Comparative genomics between G. barbadense Pima-S6 and other Gossypium genomes has revealed important differences in chromosome structure, including major inversions in chromosomes A09, A13, and D05 [99]. These structural variations may have reshaped the NBS-LRR gene repertoire and contributed to resistance phenotypes in Pima-S6.

Case Study 2: Resistance Gene Orthogroups in Tung Trees

NBS Gene Family in Vernicia fordii

Genome-wide analysis of the tung tree (Vernicia fordii) identified 88 NBS resistance genes, a number relatively small compared to some angiosperms but consistent with other members of the Euphorbiaceae family [100]. These genes represent the core NBS-LRR repertoire that can be organized into orthogroups for comparative analysis with related species.

Orthogroup analysis of tung tree NBS genes with other Euphorbiaceae species (castor bean, cassava, rubber tree, and physic nut) enables the identification of conserved resistance gene families within this economically important plant family. This comparative approach can reveal orthogroups maintained across multiple species, which may represent fundamental resistance mechanisms against common pathogens.

The tung tree genome provides particular insight into the evolution of NBS genes in woody perennials compared to herbaceous annuals. As a woody species with a long generation time, tung trees may exhibit different evolutionary dynamics in their resistance gene repertoire compared to annual crops.

Integration of NBS and Non-NBS Resistance Mechanisms

Orthogroup analysis in tung trees has revealed that disease resistance involves not only NBS-LRR genes but also other gene families that contribute to defense responses:

• UDP-glycosyltransferases (UGTs): Genome-wide analysis identified 153 UGT genes in tung trees, with VfUGT90A2 emerging as a key player in Fusarium resistance [101]. This gene is highly induced in response to Fusarium oxysporum f. sp. fordiis (Fof-1) infection and enhances resistance when overexpressed in transgenic hairy roots.

• PR-4 genes: Pathogenesis-related protein-4 genes contribute to disease resistance in tung trees, with two VfPR-4 genes (VF16136 and VF16135) identified and classified as Class II and Class I, respectively [103]. These genes respond to hormonal treatments and show high expression in leaves and early seed development stages.

• Zinc finger-BED genes: A comprehensive evolutionary analysis identified Zf-BED genes across land plants, with some classes containing NBS domains, suggesting their potential role in disease resistance [97].

The following diagram illustrates the integrated resistance mechanisms in tung trees:

Tung Tree Resistance Mechanisms

Orthogroup-Based Discovery of Fusarium Resistance

Orthogroup analysis has facilitated the identification of candidate genes for controlling Fusarium wilt in tung trees. A systematic analysis of NBS-LRR genes identified a candidate gene that can be utilized for marker-assisted breeding to control this devastating disease [101]. This finding demonstrates the practical application of orthogroup analysis for crop improvement.

The integration of orthogroup analysis with functional genomics approaches has provided insights into the molecular mechanisms of Fusarium resistance in tung trees. Metabolomic and transcriptomic analyses of transgenic hairy roots overexpressing VfUGT90A2 revealed increased levels of flavonoids such as quercitrin and myricitrin, which have inhibitory effects on Fof-1 mycelium growth [101]. This demonstrates how orthogroup analysis can identify key players in defense-related metabolic pathways.

The Scientist's Toolkit: Research Reagent Solutions

Orthogroup-based discovery of resistance genes relies on specialized research reagents and computational resources. The following table provides a comprehensive list of essential tools for conducting such analyses:

Table 3: Research Reagent Solutions for Orthogroup Analysis

Reagent/Resource	Type	Function in Research	Example Applications
Genome Assemblies	Data Resource	Reference sequences for gene prediction and annotation	G. raimondii (diploid cotton), G. barbadense Pima-S6 (tetraploid cotton), V. fordii (tung tree) [98] [100] [99]
OrthoFinder	Software Tool	Orthogroup inference from protein sequences	Identifying conserved gene families across multiple species [97]
Pfam Database	Data Resource	Protein family and domain annotations	Identifying NBS, TIR, CC, and LRR domains in predicted proteins [97]
RNA-seq Data	Data Resource	Gene expression profiling	Identifying differentially expressed genes under stress conditions [101] [99]
Hairy Root Transformation	Experimental Method	Functional validation of candidate genes	Testing Fusarium resistance of VfUGT90A2 in tung trees [101]
qRT-PCR	Experimental Method	Quantitative gene expression analysis	Validating expression patterns of VfPR-4 genes in tung trees [103]
CottonFGD	Database	Cotton Functional Genomics Database	Gene expression analysis under abiotic stresses [97]
PlantCARE	Database	cis-acting regulatory element analysis	Identifying stress-responsive promoter elements [97]

Discussion and Future Perspectives

Orthogroup analysis has emerged as a powerful framework for discovering resistance genes in crop plants, as demonstrated by the case studies in cotton and tung trees. This approach leverages comparative genomics to identify evolutionarily conserved resistance mechanisms while also revealing species-specific innovations that may contribute to specialized defense responses.

The integration of orthogroup analysis with functional genomics data provides a multidimensional understanding of resistance gene function. For example, in tung trees, combining orthogroup analysis with expression profiling under Fusarium infection identified VfUGT90A2 as a key hub gene in the defense response [101]. Similarly, in cotton, orthogroup analysis combined with QTL mapping has revealed associations between NBS-encoding genes and disease resistance loci [98].

Future applications of orthogroup analysis in resistance gene discovery will likely benefit from several technological advances:

• Pan-genome Resources: Expanding orthogroup analysis to encompass pan-genomes representing the full diversity of crop species will enable identification of core and accessory resistance gene repertoires.

• Machine Learning Approaches: Integrating orthogroup data with machine learning models, similar to those developed for predicting cold-stress responsive genes in cotton [104], may enhance the prediction of resistance gene function.

• Single-Cell Genomics: Applying orthogroup analysis to single-cell transcriptome data could reveal cell-type-specific expression of resistance genes, providing insights into spatial organization of plant immunity.

• Synthetic Biology: Orthogroup analysis can inform synthetic biology approaches by identifying optimal gene combinations for engineering durable resistance in crop plants.

As genomic resources continue to expand for non-model crops, orthogroup analysis will play an increasingly important role in bridging fundamental knowledge from model systems to applied crop improvement. The integration of evolutionary insights with functional validation will accelerate the development of disease-resistant cultivars, reducing reliance on chemical pesticides and enhancing global food security.

Nucleotide-binding domain and Leucine-Rich Repeat (NLR) proteins represent a cornerstone of the innate immune system across multiple kingdoms of life. These intracellular immune receptors function as critical sentinels that detect pathogen-derived molecules and initiate robust defense responses [6] [105]. Despite their independent evolutionary origins in different kingdoms, NLRs exhibit remarkable convergent evolution in their modular tripartite structure, typically consisting of a variable N-terminal domain, a central nucleotide-binding (NB) domain, and a C-terminal ligand-sensing domain [106] [4].

The genomic organization of NLR genes has emerged as a crucial factor influencing their evolution, diversity, and functional specialization. This technical review synthesizes current research on NLR genomic architecture across fungi, animals, and plants, with particular emphasis on insights derived from orthogroup analyses of nucleotide-binding site genes. Understanding the principles governing NLR genomic organization provides valuable frameworks for predicting immune receptor function and engineering disease resistance across diverse biological systems [106] [107].

NLR Genomic Organization Across Kingdoms

Plant NLR Genomic Organization

Plant NLRs exhibit the most extensive characterized diversity among eukaryotes, with genomes encoding dozens to thousands of NLR genes [106]. Table 1 summarizes the quantitative variation in NLR repertoires across representative plant species.

Table 1: NLR Repertoire Size Variation in Selected Plant Species

Species	Common Name	NLR Count	TNLs	CNLs	References
Arabidopsis thaliana	Thale cress	151	94	55	[105]
Oryza sativa	Rice	458	0	274	[105]
Zea mays	Maize	95	0	71	[105]
Vitis vinifera	Wine grape	459	97	215	[105]
Glycine max	Soybean	319	116	20	[105]
Utricularia gibba	Bladderwort	~0.003% of all genes	-	-	[106]
Malus domestica	Apple	~2% of all genes	-	-	[106]

Plant NLR genes are frequently organized in clusters within genomes, a configuration that facilitates rapid diversification through unequal crossing over and gene conversion [106]. Comparative genomic analyses reveal that NLR genes can be found in pairs, often in head-to-head arrangement, where sensing and signaling functions are uncoupled into distinct "sensor" and "helper" NLR proteins encoded by closely linked genes [6] [4]. These helper NLRs, such as the ADR1 and NRG1 gene families in Arabidopsis thaliana, function as essential signaling hubs for multiple sensor NLRs [108].

The expansion of NLR repertoires in flowering plants appears to be facilitated by microRNA-mediated transcriptional suppression, which may compensate for fitness costs associated with maintaining large NLR inventories by preventing autoimmunity [105]. Pan-genome studies have revealed extensive intraspecific NLR diversity, with significant variation in NLR complements between individuals of the same species [106].

Fungal NLR Genomic Organization

Fungal NLRs display distinctive genomic organizational patterns that illuminate fundamental aspects of NLR evolution. A comprehensive analysis of 82 Sordariales fungi identified 4,613 NLRs, revealing striking variability in NLR counts between species that was independent of mechanisms defending against genomic repeat elements [6] [4]. Similar to plants, fungal NLRs are frequently organized in clusters, with a strong correlation between the total number of NLRs and the number of NLR clusters, suggesting that clustering promotes repertoire diversification [4].

Table 2: NLR Diversity in Sordariales Fungi

Parameter	Findings	Implications
Total NLRs Identified	4,613 across 82 taxa	Extensive NLR diversity in fungi
NLR Cluster Organization	Present in majority of taxa	Similar to plant NLR genomic organization
NB Domain Types	NACHT domains of both NAIP-like and TLP1-like types	Similar to animal NLRs
Correlation	Strong correlation between NLR number and cluster number	Clustering may drive diversification

Fungal NLRs are characterized by exceptional variability in their domain architectures, with Wojciechowski et al. identifying 27 different N-terminal annotations clustered in 17 domain families [4]. The NB domains of fungal NLRs belong to either the NACHT or NB-ARC types, with recent improvements in annotation revealing that fungi possess NACHT domains of both NAIP-like and TLP1-like varieties, similar to animals [6] [4]. This finding bridges an important phylogenetic gap in our understanding of NLR evolution across kingdoms.

Animal NLR Genomic Organization

Animal NLR repertoires typically exhibit more constrained diversity compared to plants and fungi, with vertebrate genomes generally encoding approximately 20 NLR genes [105]. However, significant expansions have occurred in specific lineages, including sea urchins (Strongylocentrotus purpuratus) with 206 NLRs and sea squirts (Ciona intestinalis) with 203 NLRs [105]. Figure 1 illustrates the structural similarities and differences between NLR architectures across kingdoms.

Recent research on lophotrochozoans (including molluscs, segmented worms, and flatworms) has revealed intriguing patterns in animal NLR genomic organization. Most molluscan genomes encode no more than three NLRs, but certain taxa such as Polychaeta and Pteriidae exhibit species-specific expansions [109]. Notably, molluscan NLRs generally do not show upregulated expression following pathogen challenge, but incomplete NLR (incNLR) genes—encoding proteins lacking the NACHT domain—demonstrate induced expression during infection, suggesting alternative immune recognition strategies [109].

Figure 1: Comparative NLR Architecture Across Kingdoms. NLR proteins in plants, fungi, and animals share a common tripartite modular structure but differ in their specific domain compositions. All NLRs originate from a common bacterial ancestor of the STAND (Signal Transduction ATPases with Numerous Domains) superfamily.

Orthogroup Analysis Methodologies for NLR Genes

Orthogroup analysis has emerged as a powerful computational framework for classifying NLR genes into evolutionarily meaningful groups and tracing their diversification across lineages. This approach facilitates systematic comparisons of NLR repertoires across multiple species, enabling insights into evolutionary patterns and functional specialization [9].

Standardized Orthogroup Analysis Pipeline

A robust orthogroup analysis pipeline for NLR genes typically includes the following key steps, as implemented in studies of land plants and fungi [9] [4]:

• NLR Identification: Hidden Markov Model (HMM) searches using conserved NB-ARC (PF00931) or NACHT domain profiles, combined with BLASTp analyses against reference NLR sequences with stringent E-value cutoffs (typically 1e-10) [12] [9].

• Domain Architecture Characterization: Validation of candidate NLR sequences using InterProScan and NCBI's Batch CD-Search, with retention of sequences containing the NB-ARC or NACHT domains (E-value ≤ 1e-5) [12] [107].

• Orthogroup Clustering: Implementation of OrthoFinder or similar algorithms using DIAMOND for sequence similarity searches and the MCL clustering algorithm for orthogroup assignment [12] [9].

• Phylogenetic Reconstruction: Multiple sequence alignment using Clustal Omega or MAFFT followed by maximum likelihood tree construction with tools such as IQ-TREE or MEGA with bootstrap validation [12] [107].

• Evolutionary Analysis: Identification of duplication events, synteny analysis using MCScanX, and selection pressure analysis [107].

Figure 2 illustrates a representative orthogroup analysis workflow for NLR genes.

Figure 2: Orthogroup Analysis Workflow for NLR Genes. The pipeline begins with comprehensive NLR identification using complementary methods, followed by domain validation, clustering into orthogroups, and evolutionary and functional analyses.

Key Findings from Orthogroup Analyses

Orthogroup analyses have revealed fundamental principles of NLR evolution across kingdoms. Studies of land plants have identified both core orthogroups (widely conserved across species) and unique orthogroups (species-specific), with tandem duplication emerging as a primary driver of NLR family expansion [9]. For example, in pepper (Capsicum annuum), tandem duplication accounts for 18.4% of NLR genes, with particularly high density on chromosomes 08 and 09 [107].

In fungi, orthogroup analysis of Sordariales revealed 77,480 orthogroups, of which 2,367 were single-copy orthogroups present in >80% of genomes, providing a robust framework for phylogenetic analysis [4]. These approaches have demonstrated that NLRs are frequently organized in clusters across kingdoms, suggesting convergent evolutionary strategies for maintaining diverse immune repertoires [6] [4].

Experimental Protocols for NLR Characterization

Genomic Identification and Annotation

Comprehensive NLR identification requires a multi-faceted approach combining similarity searches and domain validation [12] [9]:

• HMMER-based searches using HMM profiles for canonical NLR domains (NB-ARC, PF00931; or NACHT) against target proteomes with E-value cutoff of 1e-5.

• BLASTp analyses against curated NLR reference sequences from model organisms (e.g., Arabidopsis thaliana, Oryza sativa) with E-value cutoff of 1e-10.

• Domain architecture validation using InterProScan and NCBI's Conserved Domain Database (CDD) with manual curation to remove false positives.

• Classification based on N-terminal domains (TIR, CC, RPW8 for plants; diverse domains for fungi) and C-terminal architectures.

Transcriptional Regulation Analysis

Protocols for assessing NLR transcriptional dynamics include [108]:

• RNA-seq experiments under various biotic (pathogen infection) and abiotic (temperature, drought) stress conditions.

• Differential expression analysis using tools such as DESeq2 with thresholds of |log2FoldChange| ≥ 1 and FDR < 0.05.

• cis-regulatory element prediction by analyzing promoter regions (typically 2 kb upstream of transcription start sites) using PlantCARE or similar databases.

• Co-expression network analysis to identify coordinated expression patterns among NLR genes and potential regulatory modules.

Functional Validation Approaches

Key experimental methods for NLR functional characterization include [9] [107]:

• Virus-Induced Gene Silencing (VIGS) to assess the role of candidate NLRs in disease resistance.

• Protein-protein interaction studies using yeast two-hybrid systems or co-immunoprecipitation to identify signaling components.

• Heterologous expression in susceptible genotypes to validate resistance function.

• Protein structure modeling using SWISS-MODEL or similar platforms to predict functional domains and potential ligand-binding sites.

Table 3: Essential Research Reagents and Computational Tools for NLR Studies

Category	Resource/Tool	Specific Application	Key Features	References
Bioinformatics Tools	HMMER v3.3.2	NLR identification using HMM profiles	PF00931 (NB-ARC) domain detection	[107]
	OrthoFinder v2.5.1	Orthogroup clustering	MCL algorithm for clustering	[9]
	MCScanX	Synteny and duplication analysis	Identifies tandem and segmental duplications	[107]
	MEME Suite	Motif discovery in NLR genes	Identifies conserved sequence motifs	[12]
Databases	Pfam	Protein domain annotation	Curated HMM profiles for NLR domains	[12]
	PlantCARE	cis-regulatory element prediction	Identifies hormone-responsive elements	[107]
	PRGdb 4.0	Plant resistance gene database	Curated collection of known R genes	[12]
	STRING	Protein-protein interactions	Predicts NLR interaction networks	[107]
Experimental Methods	VIGS	Functional validation in plants	Transient gene silencing	[9]
	RNA-seq	Transcriptome profiling	Expression analysis under stress	[108] [107]
	InterProScan	Domain architecture analysis	Integrates multiple domain databases	[12]

Discussion and Future Perspectives

Cross-kingdom comparisons of NLR genomic organization reveal both striking convergent evolutionary patterns and kingdom-specific adaptations. The recurrent emergence of cluster-based genomic organization across plants, fungi, and animals suggests this arrangement provides selective advantages for immune receptor diversification, potentially through facilitated gene conversion and unequal crossing over [6] [4]. Similarly, the independent expansion of NLR repertoires in multiple lineages indicates common evolutionary pressures driving immune system complexity.

Orthogroup analysis has emerged as an indispensable framework for contextualizing NLR diversity within evolutionary frameworks. This approach enables researchers to distinguish between conserved, core NLR lineages maintained across broad phylogenetic distances and rapidly evolving, lineage-specific NLRs that may mediate specialized immune functions [9]. The integration of orthogroup analysis with functional data is increasingly enabling predictive frameworks for assigning NLR function based on sequence and genomic context [106].

Future research directions include developing more sophisticated classification systems that integrate genomic organization, phylogenetic relationships, and functional attributes; elucidating the mechanisms governing NLR transcriptional regulation in response to biotic and abiotic stresses [108]; and harnessing comparative genomic insights to engineer broad-spectrum disease resistance in crop plants [107]. As genomic resources continue to expand across diverse lineages, cross-kingdom NLR comparisons will undoubtedly yield additional fundamental insights into the evolution of immune systems.

Conclusion

Orthogroup analysis provides a powerful evolutionary framework for deciphering the vast complexity of NBS gene families, revealing core conserved genes and lineage-specific innovations driven by duplication and selection. The integration of robust bioinformatic methodologies with functional validation, such as VIGS and expression profiling, is crucial for moving from genomic predictions to biological insights. Future directions will be shaped by the rise of multi-omics and AI-powered analytics, enabling the integration of genomic, transcriptomic, and epigenomic data from the same sample. This will dramatically accelerate the identification of master regulator orthogroups for crop improvement and offer novel perspectives on nucleotide-binding domain function across biomedical research, from plant pathology to human innate immunity. The ongoing development of specialized databases and genomic language models will further empower the discovery of functional elements, pushing the boundaries of what we can learn from genomic sequences alone.

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