Structural Divergence and Evolutionary Patterns of NBS Genes in Monocots and Dicots

Abstract

This article provides a comprehensive analysis of the species-specific structural patterns of Nucleotide-Binding Site (NBS) genes, the largest class of plant disease resistance (R) genes, across monocot and dicot lineages. It explores the foundational evolutionary mechanisms—including gene duplication, domain loss, and subfamily expansion—that drive the observed structural diversification. The scope extends to methodological approaches for gene identification and classification, addresses key challenges in functional validation, and presents comparative genomic analyses that reveal lineage-specific adaptations. For researchers and drug development professionals, this synthesis illuminates how understanding these plant immune receptor patterns can inform broader strategies for molecular recognition and resistance engineering.

Evolutionary Origins and Genomic Architecture of NBS Genes

Plants have evolved a sophisticated, multi-layered immune system to defend against diverse pathogens including bacteria, fungi, oomycetes, viruses, and nematodes [1] [2]. Unlike vertebrates, plants lack an adaptive immune system and instead rely on an innate immune system comprising two primary defense layers [2]. The first layer, Pattern-Triggered Immunity (PTI), is activated when cell surface-localized pattern recognition receptors (PRRs) detect conserved pathogen-associated molecular patterns (PAMPs) [1] [3]. Successful pathogens can deliver effector proteins into plant cells to suppress PTI, leading to the evolution of the second defense layer: Effector-Triggered Immunity (ETI) [1] [3].

Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) proteins are the largest and most prominent class of plant resistance (R) proteins that mediate ETI [1] [4] [3]. These intracellular immune receptors recognize specific pathogen effector proteins either through direct binding or by monitoring host proteins that are modified by effectors (the "guardee" proteins in the Guard Model) [1] [5]. This recognition initiates robust defense signaling that often includes a hypersensitive response (HR)—a localized programmed cell death at the infection site—and systemic acquired resistance that protects uninfected tissues [3] [6]. Approximately 80% of cloned plant R genes encode NBS-LRR proteins, highlighting their critical importance in plant immunity [4] [3] [7].

Protein Structure and Classification of NBS-LRR Genes

Domain Architecture and Conserved Motifs

NBS-LRR proteins belong to the STAND (Signal Transduction ATPase with Numerous Domains) family of proteins and share homology with mammalian APAF-1 and CED-4 proteins involved in apoptosis regulation [1] [2]. They typically contain three major domains with distinct functions:

• N-terminal Domain: Determines downstream signaling specificity and exists in several forms:
  • TIR (Toll/Interleukin-1 Receptor): Involved in signal recognition and transduction [7]
  • CC (Coiled-Coil): Facilitates protein-protein interactions [7]
  • RPW8 (Resistance to Powdery Mildew 8): A less common variant [4] [8]
• Central NBS (Nucleotide-Binding Site) Domain: Also known as NB-ARC, this domain contains conserved motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL) that bind and hydrolyze ATP/GTP, functioning as a molecular switch for activation [6] [7].
• C-terminal LRR (Leucine-Rich Repeat) Domain: Provides pathogen recognition specificity through protein-protein interactions and determines response specificity to different pathogens [6] [7].

Table 1: Conserved Motifs in the NBS Domain and Their Functions

Motif Name	Conserved Sequence	Function
P-loop	-	ATP/GTP binding
RNBS-A	FLENIRExSKKHGLEHLQKKLLSKLL (TIR) / FDLxAWVCVSQxF (non-TIR)	Domain stability
Kinase-2	LLVLDDVD (TIR) / LLVLDDVW (non-TIR)	Nucleotide hydrolysis
RNBS-D	FLHIACFF (TIR) / CFLYCALFPED (non-TIR)	Domain stability
RNBS-B	-	Unknown
RNBS-C	-	Unknown
GLPL	-	Protein folding

Classification Systems

NBS-LRR genes are classified based on their domain architecture into two major subclasses:

• TNL (TIR-NBS-LRR): Contains TIR, NBS, and LRR domains
• CNL (CC-NBS-LRR): Contains CC, NBS, and LRR domains [1] [6]

Additional categories include RNL (RPW8-NBS-LRR) and various truncated forms that lack complete domains (e.g., TN, CN, NL, N) which may function as adaptors or regulators [4] [8]. The final residue of the kinase-2 motif serves as a key diagnostic feature for classifying sequences as TIR (separate) or non-TIR (separate) types [9].

Genomic Distribution and Evolution of NBS-LRR Genes

Genomic Organization and Clustering

NBS-LRR genes are distributed unevenly across plant genomes and frequently form genomic clusters [6] [7]. These clusters often reside in chromosomal termini regions, which are known for rapid evolution and adaptation to changing pathogen pressures [4]. In cassava, 63% of 327 NBS-LRR genes occur in 39 clusters [6], while in pepper, 54% of 252 identified NBS-LRR genes form 47 clusters [7]. This clustering facilitates the generation of sequence diversity through recombination, enabling plants to rapidly evolve new recognition specificities [6].

Evolutionary Patterns and Species-Specific Adaptations

Comparative genomic analyses reveal striking differences in NBS-LRR gene distribution between monocots and dicots, as well as among various plant families:

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

Plant Species	Total NBS-LRR Genes	TNL Genes	CNL Genes	Other/Truncated	Key Features
Arabidopsis thaliana (model dicot)	207 [3]	Present [9]	Present [9]	-	Both TNL and CNL classes present
Oryza sativa (rice, monocot)	505 [3]	Absent [9] [3]	Present	-	Complete absence of TNL class
Salvia miltiorrhiza (medicinal plant)	196 [3]	2 [3]	61 CNL, 1 RNL [3]	132 truncated	Marked reduction in TNL and RNL
Vernicia montana (tung tree)	149 [10]	3 TNL, 2 CC-TIR-NBS [10]	9 CC-NBS-LRR [10]	135 other	Contains TIR domains
Vernicia fordii (tung tree)	90 [10]	0 [10]	12 CC-NBS-LRR [10]	78 other	Complete absence of TIR domains
Capsicum annuum (pepper)	252 [7]	4 [7]	2 typical CNL [7]	246 other	Dominance of nTNL subfamily
Perilla citriodora	535 [4]	Information not specified	104 with CC domain [4]	431 other	1 RPW8-type gene identified
Nicotiana benthamiana	156 [8]	5 TNL, 2 TN [8]	25 CNL, 41 CN [8]	83 other	Diverse NBS-LRR types present

A remarkable evolutionary pattern is the differential distribution of TNL genes between monocots and dicots. While most dicots contain both TNL and CNL classes, monocots consistently lack TNL genes [9] [3] [2]. Research covering five monocot orders (Poales, Zingiberales, Arecales, Asparagales, and Alismatales) found no TNL sequences, suggesting these genes were significantly reduced or lost early in monocot evolution [9]. TNL sequences have been identified in basal angiosperms like Amborella trichopoda and Nuphar advena, indicating they were present in early land plants but lost in monocots and magnoliids [9].

Signaling Mechanisms and Functional Activation

Recognition and Activation Models

NBS-LRR proteins employ sophisticated mechanisms for pathogen detection, primarily through two established models:

• Direct Recognition: The LRR domain directly binds to pathogen effector proteins [8]
• Indirect Recognition (Guard Model): NBS-LRR proteins monitor host "guardee" proteins for modifications caused by pathogen effectors [1]

Activation involves nucleotide-dependent conformational changes. In the resting state, NBS-LRR proteins bind ADP. Upon pathogen recognition, ADP is exchanged for ATP, inducing significant conformational changes that activate the protein and initiate downstream signaling [1] [8].

Partner Cooperation and Synergistic Signaling

An emerging theme is that pairs of NB-LRRs often function together to mediate complete resistance against specific pathogen isolates [1]. Examples include:

• RPP2A and RPP2B in Arabidopsis against oomycete pathogens
• RPS4 and RRS1 in Arabidopsis against bacterial and fungal pathogens
• N and NRG1 in tobacco against Tobacco Mosaic Virus
• Pikm1-TS and Pikm2-TS in rice against fungal pathogens [1]

These pairs can be genetically linked or unlinked and may involve proteins from different subclasses (TIR and CC) working together [1]. This partnership enables more sophisticated pathogen recognition and response capabilities.

NBS-LRR Signaling Pathway in Plant Immunity

Experimental Approaches for NBS-LRR Gene Analysis

Identification and Characterization Protocols

Genome-wide identification of NBS-LRR genes typically employs a combination of bioinformatic tools and experimental validation:

• Hidden Markov Model (HMM) Searches: Using the NB-ARC domain (PF00931) from Pfam database with HMMER software (E-value < 10⁻²⁰) [10] [4] [6]
• Domain Verification: Confirmation with SMART tool, Conserved Domain Database, and Pfam domain [8]
• Motif Analysis: MEME suite for identifying conserved motifs with maximum of 20 motifs [4]
• Phylogenetic Analysis: Multiple sequence alignment with ClustalW, tree construction with MEGA6/7 using Maximum Likelihood method, bootstrap analysis with 1000 replicates [6] [8]
• Chromosomal Mapping: Visualization of gene distribution using tools like RIdeogram and MapDraw [4] [7]

Functional Characterization Methods

• Virus-Induced Gene Silencing (VIGS): Used to validate gene function, as demonstrated in Vernicia montana where silencing Vm019719 increased susceptibility to Fusarium wilt [10]
• Expression Profiling: Quantitative PCR to analyze differential expression in response to pathogen infection [2]
• Subcellular Localization: Prediction using CELLO v.2.5 and Plant-mPLoc tools, with experimental validation [8]
• Promoter Analysis: Identification of cis-regulatory elements using PlantCARE database [8]

Table 3: Essential Research Reagents and Tools for NBS-LRR Studies

Reagent/Tool	Function/Application	Example Use
HMMER software	Identification of NBS domains in genome sequences	Domain search with NB-ARC (PF00931) [10] [6]
MEME suite	Identification of conserved protein motifs	Discovering up to 20 conserved motifs [4]
Virus-Induced Gene Silencing (VIGS)	Functional characterization through gene silencing	Validating Fusarium wilt resistance genes [10]
ClustalW	Multiple sequence alignment	Aligning NBS domains for phylogenetic analysis [6] [8]
Pfam database	Protein domain identification and verification	Confirming NB-ARC, TIR, LRR domains [6] [8]
Real-time quantitative PCR	Expression profiling of NBS-LRR genes	Measuring gene expression after pathogen infection [2]

Experimental Workflow for NBS-LRR Gene Analysis

NBS-LRR genes represent a cornerstone of plant innate immunity, providing specific recognition capabilities against diverse pathogens through sophisticated molecular mechanisms. Their genomic organization in clusters, diverse classification schemes based on protein domains, and species-specific distribution patterns between monocots and dicots highlight their dynamic evolution in response to pathogen pressures. The experimental frameworks and analytical tools discussed provide researchers with comprehensive methodologies for identifying, characterizing, and functionally validating these crucial immune receptors. Understanding NBS-LRR gene structure, evolution, and function not only advances fundamental knowledge of plant-pathogen interactions but also facilitates the development of disease-resistant crops through marker-assisted breeding and biotechnological approaches.

The plant immune system relies on a sophisticated array of receptor proteins to recognize pathogens and initiate defense responses. Among these, nucleotide-binding site and leucine-rich repeat (NBS-LRR or NLR) proteins constitute the largest and most prominent class of intracellular immune receptors, playing a pivotal role in effector-triggered immunity (ETI) [3] [11]. Based on their N-terminal domain architecture, NLR genes are classified into three major subfamilies: TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), and RPW8-NBS-LRR (RNL) [7] [12]. The distribution and evolutionary dynamics of these subfamilies across plant lineages reveal fascinating patterns of gene birth, expansion, and loss, with particularly striking contrasts between monocot and dicot species [13] [12]. Understanding these distribution patterns is essential for comprehending plant adaptation to pathogens and has significant implications for crop improvement strategies. This technical guide synthesizes current research on NLR subfamily distributions, providing a comprehensive analysis of the distinct evolutionary paths taken by monocots and dicots in shaping their NLR repertoires.

Structural and Functional Organization of NLR Genes

Domain Architecture and Classification

NLR proteins exhibit a characteristic modular structure consisting of three core domains. The central nucleotide-binding site (NBS or NB-ARC) domain is highly conserved and functions as a molecular switch, binding and hydrolyzing ATP/GTP to regulate activation states [3] [7]. The C-terminal leucine-rich repeat (LRR) domain mediates pathogen recognition through protein-protein interactions and exhibits high sequence diversity [7]. The N-terminal domain determines primary classification: TNL proteins contain a Toll/Interleukin-1 receptor (TIR) domain, CNL proteins possess a coiled-coil (CC) domain, and RNL proteins feature a resistance to powdery mildew 8 (RPW8) domain [11] [7] [12].

Beyond these typical configurations, numerous atypical NLR variants exist, including truncated forms lacking complete domains (e.g., NBS-only, TIR-NBS, CC-NBS) [3]. These structural variations contribute to functional diversity in plant immune responses, with CNL and TNL proteins serving as intracellular pathogen sensors, while RNL proteins often function in downstream signaling cascades [3] [7].

Molecular Mechanisms and Signaling Pathways

NLR proteins operate as sophisticated molecular switches in plant immunity. In the resting state, the NB-ARC domain maintains autoinhibition through ADP binding. Upon pathogen effector recognition, often mediated by the LRR domain, ADP is exchanged for ATP, triggering conformational changes that activate downstream signaling [3] [7]. TNL and CNL proteins typically initiate distinct signaling pathways, with TNLs frequently engaging EDS1-PAD4-ADR1 modules and CNLs often utilizing NDR1-EDR1 networks, though recent evidence shows synergistic interactions between these pathways [3]. RNL proteins like ADR1 function as "helper NLRs" that amplify defense signals and execute hypersensitive response programs [3].

The following diagram illustrates the core classification and signaling relationships of the three NLR subfamilies:

Figure 1: NLR Subfamily Classification and Signaling Pathways. NLR receptors are categorized into three subfamilies based on N-terminal domains, which engage distinct but interconnected signaling modules to activate defense responses including effector-triggered immunity (ETI), hypersensitive response (HR), and systemic acquired resistance (SAR).

Comparative Genomic Analysis of NLR Distributions

Monocot-Dicot Contrasts in Subfamily Prevalence

Comprehensive genomic analyses across diverse plant taxa reveal fundamental disparities in NLR subfamily distributions between monocots and dicots. Monocots exhibit a striking reduction or complete absence of TNL genes, with corresponding expansion of CNL subfamilies, while dicots maintain both TNL and CNL lineages with varying ratios across species [3] [13].

Systematic analysis of 34 plant species identified 12,820 NBS-domain-containing genes, revealing dramatic variation in subfamily proportions between major plant groups [14]. In Poaceae species (grasses), TNL genes are consistently absent, with CNLs dominating the NLR repertoire [13] [12]. This pattern extends beyond grasses to other monocot orders including Zingiberales, Arecales, Asparagales, and Alismatales, where TNL sequences remain undetectable despite extensive searches [13].

Table 1: NLR Subfamily Distribution Across Representative Plant Species

Species	Classification	Total NLRs	CNL	TNL	RNL	Reference
Arabidopsis thaliana	Dicot	207	75%	22%	3%	[3] [12]
Solanum tuberosum (potato)	Dicot	447	~80%	~17%	~3%	[12]
Salvia miltiorrhiza	Dicot	196	~97%	~1%	~1%	[3]
Capsicum annuum (pepper)	Dicot	252	~98%	~2%	<1%	[7]
Oryza sativa (rice)	Monocot	505	~99%	0%	~1%	[3] [13]
Triticum aestivum (wheat)	Monocot	>1000	~99%	0%	~1%	[14] [13]
Zea mays (maize)	Monocot	~150	~99%	0%	~1%	[12]
Saccharum officinarum (sugarcane)	Monocot	~200	~99%	0%	~1%	[11]

Evolutionary Origins and Phylogenetic Patterns

The disparate NLR distributions between monocots and dicots reflect deep evolutionary processes. TNL sequences are present in basal angiosperms like Amborella trichopoda and Nuphar advena, as well as in gymnosperms and bryophytes, indicating their origin predates the monocot-dicot divergence [13]. Phylogenetic analyses consistently show a single, well-supported TNL clade but multiple non-TNL (CNL and RNL) clades, suggesting distinct evolutionary trajectories for these subfamilies [13].

The current evidence supports the hypothesis that TNL genes, though present in ancestral flowering plants, underwent significant reduction and eventual loss in the monocot lineage after its divergence from dicots [13]. In contrast, CNL genes expanded dramatically in monocots, potentially compensating functionally for TNL loss. RNL genes remain a small, conserved subset in both lineages, reflecting their specialized role as signaling components rather than pathogen sensors [3] [7].

Table 2: Evolutionary Patterns of NLR Subfamilies in Major Plant Groups

Plant Group	TNL Status	CNL Status	RNL Status	Dominant Evolutionary Mechanism
Bryophytes	Present	Present	Present	Limited diversification
Gymnosperms	Present (expanded)	Present	Present	TNL expansion
Basal Angiosperms	Present	Present	Present	Conservation of all subfamilies
Dicots	Present (variable)	Present (expanded)	Present (limited)	Lineage-specific expansions/contractions
Monocots	Absent or rare	Present (dominant)	Present (limited)	TNL loss, CNL expansion

Methodological Framework for NLR Gene Identification and Analysis

Genomic Identification Protocols

Standardized methodologies have been established for comprehensive identification and classification of NLR genes across plant genomes:

Step 1: Domain-Based Sequence Identification

• Perform HMMER searches using NB-ARC domain (PF00931) Hidden Markov Models against target proteomes with default e-value thresholds [14] [12]
• Conduct complementary BLASTP searches using known NLR sequences as queries (e-value cutoff: 10⁻³ to 10⁻⁵) [15] [12]
• Merge results and remove redundancies to generate initial candidate gene set

Step 2: Domain Architecture Annotation

• Annotate protein domains using PfamScan, SMART, or InterProScan [3] [7] [12]
• Identify CC domains using COILS program with threshold 0.9 followed by manual validation [7] [12]
• Classify genes into CNL, TNL, RNL, or atypical categories based on domain composition

Step 3: Motif and Structural Analysis

• Identify conserved NBS motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, GLPL) using MEME suite [7]
• Determine exon-intron structure through gene model annotation
• Analyze promoter cis-elements using plant regulatory databases

Step 4: Phylogenetic and Evolutionary Analysis

• Perform multiple sequence alignment of NBS domains using MAFFT or MUSCLE [11] [15]
• Construct phylogenetic trees with maximum likelihood methods (IQ-TREE, FastTree) [14] [11]
• Identify orthogroups using OrthoFinder or MCScanX [14] [11]

The following workflow diagram illustrates the integrated bioinformatics pipeline for NLR gene identification and characterization:

Figure 2: Bioinformatics Workflow for NLR Gene Identification. The pipeline illustrates the sequential steps for comprehensive genome-wide identification, classification, and evolutionary analysis of NLR genes from genomic sequences.

Experimental Validation Approaches

Expression Profiling

• Analyze transcriptome data from diverse tissues, developmental stages, and stress conditions
• Quantify expression levels using RNA-seq (FPKM/TPM values) or qRT-PCR [14] [11]
• Identify differentially expressed NLR genes in response to pathogen infection

Functional Validation

• Implement Virus-Induced Gene Silencing (VIGS) to assess gene function [14]
• Conduct transgenic complementation assays in susceptible genotypes
• Perform protein-protein interaction studies (Y2H, Co-IP) to elucidate signaling networks

Population Genetics Analysis

• Identify sequence variants (SNPs, indels) in NLR genes across accessions [14]
• Test for signatures of positive selection using PAML or similar tools [15]
• Associate genetic variation with phenotypic resistance data

Table 3: Key Research Reagents and Resources for NLR Gene Studies

Reagent/Resource	Specifications	Application	Representative Examples
HMM Profiles	NB-ARC (PF00931) from Pfam	Initial identification of NBS domains	[3] [12]
Reference Sequences	Curated NLR sets from model plants	BLAST queries, phylogenetic anchors	Arabidopsis (207 NLRs), Rice (505 NLRs) [3]
Software Tools	HMMER, OrthoFinder, MCScanX, MEME	Domain detection, orthology, motif finding	[14] [11] [12]
Genome Databases	Phytozome, EnsemblPlants, species-specific databases	Genomic sequences, annotations	[11] [15] [12]
Expression Databases	RNA-seq repositories, eFP browsers	Expression pattern analysis	IPF database, CottonFGD [14]
VIGS Vectors	TRV-based silencing systems	Functional validation in plants	[14]

Discussion and Future Perspectives

The contrasting distributions of NLR subfamilies in monocots and dicots represent a compelling example of divergent evolution in plant immune systems. The near-complete absence of TNL genes in monocots, with few exceptions in basal lineages, suggests either functional redundancy with CNL genes or lineage-specific adaptations that rendered TNLs dispensable [13]. The expansion of CNL genes in monocots may have compensated for TNL loss through functional diversification or enhanced recognition capabilities.

Recent evidence suggests that the distinction between monocot and dicot NLR repertoires may not be absolute. Some studies report putative TNL sequences in wheat-relatives (Triticum-Thinopyrum addition lines), though these require further validation [13]. Additionally, certain dicot families like Salvia species show remarkably reduced TNL numbers, approaching monocot-like patterns [3]. These exceptions highlight the dynamic nature of NLR gene evolution and suggest that functional constraints rather than phylogenetic history alone govern subfamily distributions.

Future research should focus on elucidating the molecular mechanisms underlying TNL loss in monocots and potential functional compensation by CNL expansion. Comparative analyses of NLR clusters, expression patterns, and pathogen recognition specificities across monocots and dicots will provide crucial insights into how different plant lineages optimize their immune repertoires. Such studies have significant implications for engineering disease resistance in crop plants, potentially enabling transfer of resistance traits across phylogenetic boundaries.

The distribution of NLR gene subfamilies follows distinct patterns in monocots and dicots, characterized by TNL absence and CNL dominance in monocots, versus coexistence of both subfamilies in dicots. These differences reflect deep evolutionary processes including lineage-specific gene loss, duplication, and functional diversification. Standardized bioinformatics pipelines enable comprehensive identification and classification of NLR genes, revealing these evolutionary patterns across plant genomes. Understanding the mechanistic basis and functional consequences of these divergent evolutionary paths provides fundamental insights into plant immunity and offers opportunities for improving disease resistance in crop plants through strategic manipulation of NLR repertoires.

Impact of Whole-Genome and Tandem Duplications on NBS Gene Family Expansion

The nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family represents one of the largest and most critical classes of disease resistance (R) genes in plants, encoding intracellular immune receptors that detect pathogen effectors and initiate effector-triggered immunity (ETI) [16]. The expansion and contraction of this gene family across plant lineages are primarily driven by gene duplication events, with whole-genome duplication (WGD) and tandem duplication (TD) identified as the two most significant evolutionary mechanisms [17] [18]. These duplication processes create genetic raw material that allows plants to adapt to rapidly evolving pathogens, with different plant families exhibiting distinct evolutionary patterns shaped by their specific evolutionary histories and pathogenic pressures [19] [20]. Within the context of a broader thesis on species-specific NBS structural patterns in monocots and dicots, this review synthesizes current understanding of how different duplication mechanisms have driven the functional diversification of NBS genes across major plant lineages, providing insights for future crop improvement strategies.

NBS-LRR Gene Family: Structure, Function, and Classification

Domain Architecture and Classification

NBS-LRR proteins are characterized by a modular structure consisting of three core domains: a variable N-terminal domain, a central nucleotide-binding site (NBS) domain, and a C-terminal leucine-rich repeat (LRR) region [16]. Based on the N-terminal domain structure, NBS-LRR genes are classified into three major subfamilies:

• TNL genes: Contain a Toll/Interleukin-1 receptor (TIR) domain [21] [16]
• CNL genes: Feature a coiled-coil (CC) domain [21] [16]
• RNL genes: Possess a Resistance to Powdery Mildew 8 (RPW8) domain [21]

The NBS domain contains several conserved motifs (P-loop, RNBS-A, RNBS-B, RNBS-C, GLPL, RNBS-D, and MHD) that facilitate nucleotide binding and hydrolysis, functioning as molecular switches in immune signaling [16]. The LRR domain is involved in pathogen recognition specificity through protein-protein interactions [16].

Functional Mechanisms in Plant Immunity

NBS-LRR proteins operate as essential components of the plant immune system, monitoring host cellular components for signs of pathogen manipulation [16]. TNL and CNL subfamilies primarily function in pathogen recognition, while RNL genes act downstream in signal transduction [20]. Upon pathogen detection, conformational changes in the NBS domain enable nucleotide exchange, leading to activation of defense responses including hypersensitive cell death and systemic acquired resistance [16].

Evolutionary Patterns of NBS-LRR Genes Across Plant Families

Comparative genomic analyses reveal that NBS-LRR genes have undergone lineage-specific expansions and contractions through different evolutionary patterns across plant families, largely driven by varying rates of gene duplication and loss events [19] [20].

Table 1: Evolutionary Patterns of NBS-LRR Genes in Different Plant Families

Plant Family	Representative Species	Evolutionary Pattern	Key Duplication Mechanism	NBS-LRR Count
Rosaceae	Malus × domestica (apple)	Continuous expansion	Species-specific duplication	748 [18]
	Fragaria vesca (strawberry)	Expansion and contraction	Species-specific duplication	144 [18]
	Prunus persica (peach)	Early expansion to abrupt shrinking	Species-specific duplication	354 [18]
Sapindaceae	Xanthoceras sorbifolium	First expansion then contraction	Independent gene duplication/loss	180 [19]
	Dimocarpus longan	Expansion, contraction, further expansion	Independent gene duplication/loss	568 [19]
	Acer yangbiense	Expansion, contraction, further expansion	Independent gene duplication/loss	252 [19]
Solanaceae	Solanum lycopersicum (tomato)	Expansion followed by contraction	Tandem duplication [21]	819 (family total) [21]
	Capsicum annuum (pepper)	Contraction	Tandem duplication [21]	819 (family total) [21]
	Solanum tuberosum (potato)	Consistent expansion	Tandem duplication [21]	819 (family total) [21]
Poaceae	Hordeum vulgare (barley)	Not specified	Tandem duplication [22]	467 [23]
	Oryza sativa (rice)	Contracting pattern	Tandem duplication	508 [20]

Monocot-Dicot Divergence in NBS-LRR Evolution

A fundamental evolutionary divergence exists between monocot and dicot species in their NBS-LRR gene composition. TNL genes are completely absent from cereal genomes (monocots), suggesting loss in the cereal lineage after divergence from dicot ancestors [16]. This fundamental difference influences not only gene family composition but also downstream signaling mechanisms, as TNL and CNL genes utilize distinct signaling pathways [16].

CNL genes from monocots and dicots cluster together in phylogenetic analyses, indicating that angiosperm ancestors possessed multiple CNLs before the monocot-dicot divergence [16]. The ratio between CNL and TNL genes varies significantly among dicot families, with Rosaceae species showing particularly dynamic evolutionary patterns [20] [18].

Methodologies for Analyzing NBS-LRR Genes and Duplication Events

Genome-Wide Identification of NBS-LRR Genes

The standard workflow for identifying NBS-LRR genes combines sequence similarity searches and domain-based validation [19] [20]:

Diagram 1: Workflow for genome-wide identification and classification of NBS-LRR genes.

Identifying Duplication Events and Evolutionary History

Several computational approaches are employed to detect duplication events and reconstruct evolutionary history:

• Gene clustering: NBS-LRR genes located within 250 kb on a chromosome are considered clustered, suggesting tandem duplication [19]
• Ks and Ka/Ks analysis: Synonymous (Ks) and nonsynonymous (Ka) substitution rates calculated using tools such as ParaAT and KaKs_Calculator [17]
• Phylogenetic reconciliation: Comparing gene trees with species trees to infer duplication and loss events [20]
• Orthogroup analysis: Using OrthoFinder with DIAMOND for sequence similarity and MCL for clustering [24]

Table 2: Key Analytical Methods in NBS-LRR Evolution Studies

Method	Purpose	Key Parameters/Tools	Interpretation
Ks Distribution	Dating duplication events	Calculation of synonymous substitution rates	Ks = 0.1-0.2 indicates recent duplications [18]
Ka/Ks Ratio	Assessing selection pressure	Ratio of nonsynonymous to synonymous substitutions	Ka/Ks < 1: Purifying selection; Ka/Ks > 1: Diversifying selection [18]
Gene Tree-Species Tree Reconciliation	Inferring duplication/loss history	Notung, RANGER-DTL	Identifies species-specific duplication events [20]
Orthogroup Analysis	Identifying conserved gene groups	OrthoFinder, DIAMOND, MCL	Reveals core and lineage-specific orthogroups [24]

Experimental Evidence of Duplication Mechanisms

Relative Contributions of WGD and TD to NBS-LRR Expansion

The proportional contributions of WGD and TD to NBS-LRR gene expansion vary significantly across plant lineages:

In Rosaceae species, species-specific duplications have played a predominant role in recent NBS-LRR expansion, with 61.81% of strawberry, 66.04% of apple, 48.61% of pear, 37.01% of peach, and 40.05% of mei NBS-LRR genes derived from species-specific duplication [18]. Woody perennial species (apple, pear, peach) showed higher proportions of multi-copy NBS-LRR genes (exceeding 50%) compared to the herbaceous strawberry (32.64%), suggesting perennial habit may influence duplication dynamics [18].

In Solanaceae species, WGD has played a significant role in NBS-LRR expansion, with the most recent whole-genome triplication (WGT) particularly impacting NBS-LRR gene content [21]. Among 819 NBS-LRR genes identified across nine Solanaceae species, 583 were CNLs, 182 were TNLs, and 54 were RNLs, with WGD contributing significantly to this expansion [21].

In Aurantioideae species (citrus family), TD represents a predominant duplication type, with an average of 12,377 TD genes per species compared to 2,801 WGD genes [17]. TD and proximal duplication (PD) were found to undergo rapid functional divergence, as indicated by Ka/Ks analysis [17].

Distinct Evolutionary Patterns Between TNL and CNL Genes

Comparative evolutionary analyses reveal distinct evolutionary patterns between TNL and CNL genes:

• Evolutionary rates: TNL genes generally exhibit higher Ks and Ka/Ks values than non-TNL genes across Rosaceae species, suggesting more rapid evolution and different adaptive patterns to pathogens [18]
• Exon structure: TNL genes typically contain more exons than CNL genes, with 1.04- to 2.15-fold differences observed in Rosaceae species [18]
• Selection pressure: Most NBS-LRR genes evolve under purifying selection (Ka/Ks < 1), with diversifying selection frequently detected in LRR domains involved in pathogen recognition [16]

Genomic Distribution and Functional Correlations

Chromosomal Distribution and Duplication Hotspots

NBS-LRR genes typically display non-random chromosomal distributions, with pronounced clustering in specific genomic regions:

• Terminal localization: In Solanaceae species, most NBS-LRR family genes predominantly localize to chromosomal termini [21]
• Subteleromic enrichment: In barley, duplication-prone regions containing NBS-LRR genes are primarily located in subtelomeric regions of all seven chromosomes [22]
• Uneven distribution: In passion fruit, most CNL genes were clustered on chromosome 3, with expansion driven by both segmental (17 gene pairs) and tandem duplications (17 gene pairs) [23]

Association with Duplication-Inducing Elements

Recent evidence from barley suggests that natural selection has favored lineages in which arms-race genes (particularly pathogen defense genes) are physically associated with duplication-inducing elements, especially kilobase-scale tandem repeats [22]. These duplication-prone regions show a history of repeated long-distance dispersal to distant genomic sites, followed by local expansion by tandem duplication [22]. This association creates a cooperative relationship where duplication-inducing elements generate diversity for arms-race genes, providing evolutionary advantages at the lineage level [22].

Research Reagent Solutions for NBS-LRR Studies

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

Resource Type	Specific Examples	Function/Application	Access Information
Genome Databases	Genome Database for Rosaceae (GDR)	Access genomic data for Rosaceae species	https://www.rosaceae.org/ [20]
	Sol Genomics Network (SGN)	Genomic data for Solanaceae species	https://solgenomics.net/ [21]
	National Genomics Data Center (NGDC)	Multi-species genomic data	https://ngdc.cncb.ac.cn/ [21]
Analysis Tools	OrthoFinder	Orthogroup inference and comparative genomics	[24]
	Pfam Database	Protein domain identification	http://pfam.sanger.ac.uk/ [20]
	MEME Suite	Protein motif identification	[20]
Experimental Resources	Virus-Induced Gene Silencing (VIGS)	Functional validation of NBS-LRR genes	[24]
	RNA-seq Databases	Expression profiling under stress conditions	CottonFGD, IPF Database [24]

Whole-genome and tandem duplications have differentially shaped the expansion and evolution of NBS-LRR genes across monocot and dicot lineages, resulting in distinct species-specific structural patterns. WGD events establish foundational gene repertoires, while subsequent tandem and species-specific duplications drive recent expansions tailored to lineage-specific pathogenic challenges. The evolutionary patterns of NBS-LRR genes—whether "continuous expansion," "expansion-contraction," or "birth-death" dynamics—reflect the complex interplay between duplication mechanisms, selective pressures, and life history strategies. Understanding these duplication mechanisms and their functional consequences provides crucial insights for harnessing NBS-LRR genes in crop improvement, particularly for developing durable disease resistance in agricultural systems. Future research integrating pan-genomic analyses with functional studies will further elucidate how duplication mechanisms contribute to the evolutionary innovation of plant immune systems.

The Toll/interleukin-1 receptor nucleotide-binding site leucine-rich repeat (TNL) gene subclass represents a crucial component of the plant intracellular immune system. However, comprehensive genomic analyses reveal a complex evolutionary history marked by dramatic lineage-specific reduction and complete loss events. This case study examines the phylogenetic distribution of TNL genes across angiosperms, demonstrating their universal absence in monocots and convergent loss in select dicot lineages, including Salvia species (Lamiaceae) and aquatic plants. We explore the association between TNL reduction and the deletion of downstream signaling components, particularly the EDS1/PAD4 module. Quantitative data from recent genome-wide studies are synthesized, and experimental methodologies for TNL identification and characterization are detailed. The findings underscore the dynamic nature of plant immune gene evolution and its implications for disease resistance mechanisms in economically important species.

Plant immunity relies on a sophisticated network of resistance (R) genes that facilitate pathogen recognition and defense activation. Among these, nucleotide-binding site leucine-rich repeat (NBS-LRR) genes constitute the largest and most prominent family, with the TNL subclass characterized by an N-terminal Toll/interleukin-1 receptor (TIR) domain serving as a critical mediator of effector-triggered immunity (ETI) [3] [25]. TNL proteins function as intracellular immune receptors that detect pathogen effector proteins, initiating robust defense signaling cascades often accompanied by localized programmed cell death known as the hypersensitive response [3].

Recent advances in genome sequencing have enabled comparative genomic analyses that reveal remarkable plasticity in TNL gene content across land plants. While TNL genes are present in basal angiosperms and gymnosperms, their distribution among flowering plants is strikingly heterogeneous [26] [27]. The most notable pattern is the universal absence of typical TNL genes in monocot species, including economically important cereals such as rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays) [28] [29]. Furthermore, independent TNL loss events have occurred in specific dicot lineages, suggesting convergent evolutionary trajectories in plant immune system architecture [3] [26].

This case study examines the phenomenon of TNL reduction and loss within the broader context of species-specific NBS structural patterns in monocots and dicots. We integrate findings from recent genome-wide analyses to quantify TNL distribution, explore potential evolutionary mechanisms, and discuss the functional implications for plant immunity and crop improvement strategies.

Quantitative Patterns of TNL Distribution Across Angiosperms

Comprehensive Distribution Analysis

Genome-wide comparative analyses across diverse angiosperm lineages reveal substantial variation in TNL gene content. The establishment of an angiosperm NLR atlas (ANNA) encompassing over 300 angiosperm genomes has facilitated detailed investigation of NLR gene evolution, demonstrating that NLR copy numbers differ up to 66-fold among closely related species due to rapid gene loss and gain events [26]. Within this broader context, TNL genes exhibit particularly dynamic evolutionary patterns.

Table 1: TNL Distribution Across Representative Plant Lineages

Plant Species/Lineage	TNL Presence	Genomic Features	Proposed Evolutionary Mechanism
Monocots (Oryza sativa, Triticum aestivum, Zea mays)	Absent	Complete lack of typical TNL genes; CNL dominance	Lineage-specific loss after monocot-dicot divergence
Basal Eudicots (Vitis vinifera)	Present (~50% of NLRs)	Balanced TNL/CNL composition	Ancestral angiosperm state
Brassicaceae (Arabidopsis thaliana)	Present (~40% of NLRs)	Significant TNL retention	Maintenance of ancestral complement
Salvia Species (S. miltiorrhiza, S. bowleyana)	Absent	Drastic TNL reduction; CNL dominance	Independent loss in Lamiaceae lineage
Aquatic Plants (Alismatales)	Absent/Reduced	Convergent NLR reduction	Ecological specialization
Carnivorous/Parasitic Plants	Absent/Reduced	Significant NLR contraction	Ecological specialization

Patterns of TNL Loss in Specific Dicot Lineages

Beyond the well-documented absence in monocots, independent TNL loss events have occurred in several dicot lineages. Genomic analysis of Salvia miltiorrhiza (Danshen), an important medicinal plant, revealed a complete absence of TNL genes among its 196 identified NBS-LRR genes, with only 62 possessing complete N-terminal and LRR domains [3]. Comparative analysis with four other Salvia species (S. bowleyana, S. divinorum, S. hispanica, and S. splendens) confirmed that none contain TNL subfamily members, indicating a lineage-specific loss within the Lamiaceae family [3].

Similarly, investigations in Sapindaceae species (Xanthoceras sorbifolium, Dimocarpus longan, and Acer yangbiense) identified dynamic evolution of NBS-encoding genes, with TNL representation varying significantly between species [19]. This pattern suggests that TNL loss events have occurred multiple times independently throughout angiosperm evolution, rather than representing a single ancestral condition.

Table 2: NBS-LRR Gene Composition in Select Plant Species

Species	Total NBS	TNL	CNL	RNL	Atypical	Reference
Arabidopsis thaliana	207	~83	~120	~4	-	[3]
Oryza sativa	505	0	~500	~5	-	[3]
Solanum tuberosum	447	Not specified	Not specified	Not specified	-	[3]
Salvia miltiorrhiza	196	0	61	1	134	[3]
Helianthus annuus	352	77	100	13	162	[30]
Xanthoceras sorbifolium	180	23 TNL (ancestral)	155 CNL (ancestral)	3 RNL (ancestral)	-	[19]

Evolutionary Mechanisms and Functional Implications

Co-evolution with Signaling Pathways

Evidence suggests that TNL reduction is frequently associated with the loss of downstream signaling components, particularly the EDS1/PAD4 module. Analysis of four plant species from two distinct lineages (Alismatales, a monocot lineage, and Lentibulariaceae, a eudicot lineage) revealed that the loss of NLR genes coincides with the loss of the downstream immune signaling complex ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1)/PHYTOALEXIN DEFICIENT 4 (PAD4) [29]. This coordinated loss suggests functional linkage between these immune components, with EDS1/PAD4 deficiency potentially driving TNL loss through genetic redundancy or signaling incompatibility.

The EDS1/PAD4 complex serves as a crucial signaling hub for TNL-mediated immunity in Arabidopsis, forming heterodimeric complexes that activate downstream resistance responses [29]. The convergent loss of both TNL receptors and their corresponding signaling pathways in multiple independent lineages represents a striking example of coordinated genome reduction in plant immune systems. This pattern is particularly evident in aquatic plants (Alismatales), where NLR reduction resembles the lack of NLR expansion observed in green algae before terrestrial colonization [26].

Diagram 1: Evolutionary Trajectories of TNL Genes and Signaling Pathways. The diagram illustrates the coordinated loss of TNL genes and EDS1/PAD4 signaling in monocots and specific dicot lineages, alongside the retention and expansion of CNL-NDR1 pathways.

Ecological Correlates of TNL Reduction

Recent evidence suggests that NLR reduction, particularly TNL loss, is associated with specific ecological specializations. Analysis of the angiosperm NLR atlas revealed that NLR contraction was significantly associated with adaptations to aquatic, parasitic, and carnivorous lifestyles [26]. The convergent NLR reduction in aquatic plants resembles the lack of NLR expansion during the long-term evolution of green algae before the colonization of land, suggesting that specific environmental conditions may reduce selective pressures for maintaining diverse NLR repertoires.

This pattern is particularly evident in the Lentibulariaceae family (carnivorous plants) and Alismatales (aquatic plants), where comprehensive analyses of whole proteomes identified not only the loss of NLR genes but also the absence of other characterized immune genes [29]. These findings support the hypothesis that ecological factors drive substantial reorganization of plant immune systems, with TNL genes being particularly prone to loss in certain environments.

Experimental Methodologies for TNL Characterization

Genome-Wide Identification Protocols

The standard methodology for comprehensive identification of NBS-encoding genes, including TNL subfamily members, involves a multi-step bioinformatic pipeline combining homology searches and domain architecture analysis:

Step 1: Initial Candidate Identification

• Perform BLAST searches against target genomes using known NBS-domain sequences as queries, with threshold expectation values typically set at 1.0 [19]
• Conduct parallel Hidden Markov Model (HMM) searches using the NB-ARC domain (Pfam accession: PF00931) as profile with default parameters [3] [28]
• Merge candidate sequences from both approaches and remove redundant hits

Step 2: Domain Architecture Analysis

• Confirm NBS domain presence in candidate sequences using Pfam analysis (E-value cutoff of 10⁻⁴) [19]
• Annotate N-terminal domains using NCBI's Conserved Domain Database and specialized tools:
  • Identify TIR domains using HMM profiles (PF01582, PF13676) [28]
  • Detect coiled-coil (CC) domains with PAIRCOIL2 (P-score cutoff 0.025) and MARCOIL (threshold probability 90%) [28]
  • Identify RPW8 domains using specific HMM profiles
• Classify genes into TNL, CNL, RNL, and atypical categories based on domain composition

Step 3: Validation and Curation

• Manually curate automated annotations to resolve ambiguous cases
• Verify gene models using transcriptomic evidence where available
• Analyze chromosomal distribution and cluster formation using genome annotation files

Diagram 2: Workflow for Genome-Wide Identification and Classification of NBS-Encoding Genes. The pipeline integrates multiple bioinformatic approaches for comprehensive characterization of TNL and other NBS-encoding genes.

Phylogenetic Reconstruction and Evolutionary Analysis

To trace the evolutionary history of TNL genes and identify loss events, researchers employ sophisticated phylogenetic methods:

Sequence Alignment and Tree Construction

• Perform multiple sequence alignment of NBS domains using CLUSTALW or MAFFT with default parameters [28]
• Construct phylogenetic trees using maximum likelihood methods (FastTreeMP) with 1000 bootstrap replicates to assess node support [14]
• Integrate NBS-LRR proteins from multiple reference species (e.g., Arabidopsis thaliana, Oryza sativa, Vitis vinifera) to establish phylogenetic context [3]

Evolutionary Pattern Analysis

• Classify NBS-encoding genes into monophyletic clades (RNL, TNL, CNL) distinguished by amino acid motifs [19]
• Reconstruct ancestral gene copies using phylogenetic placement and birth-death models
• Calculate gene duplication and loss rates using parsimony or likelihood-based methods
• Identify significant expansion/contraction events using software such as CAFE (Comparative Analysis of Gene Family Evolution)

Comparative Genomics

• Perform synteny analysis between related species to identify genomic regions associated with TNL loss
• Analyze correlation between TNL presence/absence and ecological factors using phylogenetic comparative methods
• Investigate co-evolution patterns with signaling components through correlation analysis and ancestral state reconstruction

Table 3: Key Experimental Resources for TNL Gene Research

Resource Category	Specific Examples	Application/Function	Reference
Genomic Databases	ANNA (Angiosperm NLR Atlas), Phytozome, BRAD, Bolbase	Provide curated genome sequences and annotations for comparative analyses	[26] [28]
Domain Databases	Pfam, NCBI Conserved Domain Database, INTERPRO	Identify and characterize TIR, NBS, LRR, and other protein domains	[28] [14]
Bioinformatic Tools	HMMER, OrthoFinder, DIAMOND, MAFFT, FastTree	Sequence searches, orthogroup inference, multiple alignment, phylogenetics	[30] [14]
Expression Databases	IPF Database, CottonFGD, Cottongen, NCBI GEO	Access RNA-seq data for expression validation under various conditions	[14]
Experimental Validation	Virus-Induced Gene Silencing (VIGS), RNAi constructs, CRISPR-Cas9	Functional characterization of specific TNL genes and signaling components	[14] [29]

The dramatic reduction and loss of TNL subfamily genes in monocots and specific dicot lineages represents a compelling example of convergent evolution in plant immune systems. The coordinated disappearance of TNL genes and their associated signaling components, particularly the EDS1/PAD4 module, suggests fundamental restructuring of defense mechanisms in these lineages. The association between TNL loss and ecological specialization further highlights how environmental factors shape genome content and immune strategy.

Future research should focus on elucidating the compensatory mechanisms that enable effective pathogen defense in TNL-deficient species, particularly through expansion and diversification of CNL genes. Additionally, functional characterization of non-canonical TIR-domain genes in monocots may reveal evolutionary innovations that partially compensate for TNL loss. From an applied perspective, understanding these evolutionary patterns provides valuable insights for crop improvement strategies, particularly for transferring disease resistance traits between phylogenetically distant species and engineering optimized immune systems for specific agricultural environments.

Phylogenetic Analysis Revealing Conservation and Divergence Across Species

The Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) gene family constitutes the largest and most crucial class of disease resistance (R) genes in plants, providing adaptive immunity against diverse pathogens [31]. Comparative phylogenetic analysis of these genes across monocot and dicot lineages reveals profound patterns of conservation and divergence, offering insights into evolutionary adaptations and structural innovations [32] [31]. This technical guide examines species-specific NBS structural patterns within the broader context of angiosperm evolution, providing researchers with methodologies and analytical frameworks for investigating these critical genetic elements.

The fundamental evolutionary divergence between monocots and dicots represents a foundational aspect of plant phylogeny, with monocots characterized by a single cotyledon, parallel leaf venation, scattered vascular bundles, and fibrous root systems, while dicots typically feature two cotyledons, reticulate leaf venation, ringed vascular bundles, and taproot systems [33] [34]. These morphological differences reflect deeper genetic and genomic distinctions that influence functional specialization, including in immune response mechanisms [35] [36]. Understanding how NBS-LRR genes have evolved within these distinct lineages provides not only fundamental evolutionary insights but also practical applications for crop improvement through targeted breeding strategies [32].

Structural and Functional Organization of NBS-LRR Genes

Domain Architecture and Classification

NBS-LRR genes encode multi-domain proteins characterized by a conserved tripartite structure:

• N-terminal Domain: Contains either a Toll/Interleukin-1 receptor (TIR) domain or a Coiled-Coil (CC) domain involved in signal transduction and protein-protein interactions [31].
• Central NBS Domain: Features highly conserved motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL) responsible for ATP/GTP binding and hydrolysis, which initiates immune signaling [32] [31].
• C-terminal LRR Domain: Exhibits high variability and is primarily responsible for pathogen recognition specificity through direct or indirect interaction with pathogen effectors [32].

Based on their N-terminal domains, NBS-LRR genes are classified into two major subfamilies: TNL (TIR-NBS-LRR) and CNL (CC-NBS-LRR), with the latter sometimes designated as nTNL (non-TIR-NBS-LRR) in literature [32] [31]. A minor subclass featuring RPW8 (Resistance to Powdery Mildew 8) domains, designated RNL, has also been identified [31].

Table 1: Conserved Motifs in the NBS Domain of NBS-LRR Genes

Motif Name	Conserved Sequence	Functional Role
P-loop/kin1a	GIGKTT/GVGKTT/GLGKTT	Nucleotide binding
RNBS-A	VLLEVIGCISNTND (non-TIR)	Domain structural integrity
Kinase-2	KGPRYLVVVDDIWRID	Catalytic activity
RNBS-B	NGSRILLTTRETKVAMYAS	Structural conservation
RNBS-C	LLNLENGWKLLRDKVF	Functional specificity
GLPL	CQGLPL/CHGLPL/CGGLPLA	Membrane association

Genomic Distribution and Clustering Patterns

NBS-LRR genes typically display non-random genomic distribution, often forming clusters through tandem duplications and genomic rearrangements [32]. In pepper (Capsicum annuum), 54% of the 252 identified NBS-LRR genes form 47 gene clusters distributed unevenly across all chromosomes [32]. This clustering pattern facilitates the generation of diversity through unequal crossing over and gene conversion, enabling rapid adaptation to evolving pathogen populations.

Comparative analyses reveal that cluster organization differs significantly between monocots and dicots, with dicots generally maintaining more heterogeneous clusters containing both TNL and CNL types, while monocots exhibit predominant CNL clusters with notable TNL deficits [32] [31]. This fundamental distinction reflects lineage-specific evolutionary trajectories following the monocot-dicot divergence.

Comparative Phylogenetics of NBS-LRR Genes in Monocots and Dicots

Evolutionary History and Lineage-Specific Adaptations

Phylogenetic reconstruction of NBS-LRR genes across angiosperms reveals distinct evolutionary patterns between monocot and dicot lineages. Comprehensive analysis of NBS-LRR genes in pepper (a dicot) demonstrated dominance of the nTNL subfamily (248 genes) over the TNL subfamily (only 4 genes), reflecting specific evolutionary pressures and adaptations [32]. This pattern contrasts with basal angiosperms and more ancient dicot lineages that maintain more balanced TNL-to-CNL ratios.

In monocots, significant losses of TNL genes have been documented, with a corresponding expansion and diversification of the CNL subfamily [32] [31]. Research on nitric oxide-induced NBS-LRR genes in rice and maize (monocots) compared to soybean and tomato (dicots) revealed species-specific domain configurations, with monocot NBS-LRR genes frequently featuring RX-CC_like domains responsive to defense against pathogen attacks [31]. This domain-level differentiation highlights how structural divergence follows phylogenetic boundaries.

Structural Divergence Following Gene Duplication

Different modes of gene duplication contribute substantially to NBS-LRR evolution, with each mechanism producing distinct structural divergence patterns:

• Whole Genome Duplication (WGD): Retains genes with lower structural divergence, slower nucleotide substitution rates, and preferential retention of transcription factors and regulatory genes [37].
• Tandem Duplication: Common in NBS-LRR families, generates clusters of structurally similar genes with moderate divergence [32] [37].
• Transposed Duplication: Creates duplicates that show the highest structural divergence, with biased changes toward smaller gene size and complexity in transposed copies [37].

The NBS-LRR gene family demonstrates higher-than-average levels of structural divergence following duplication events compared to other gene families, suggesting selection for rapid evolution of gene structure in response to changing pathogen pressures [37].

Table 2: Structural Divergence Patterns Following Different Gene Duplication Modes

Duplication Mode	Coding Region Length Difference	Average Exon Length Difference	Number of Indels	Maximum Indel Length
WGD	Lowest	Lowest	Moderate	Lowest
Tandem	Low	Low	Lowest	Low
Proximal	Moderate	Moderate	Moderate	Moderate
Transposed	Highest	Highest	Highest	Highest

Methodological Framework for Phylogenetic Analysis

Genome-Wide Identification of NBS-LRR Genes

Step 1: Sequence Retrieval

• Obtain complete genome sequences and annotation files for target species from Phytozome, Ensembl Plants, or NCBI databases.
• For monocot-dicot comparisons, select representative species from both lineages (e.g., Arabidopsis thaliana and Oryza sativa).

Step 2: Homology-Based Identification

• Perform BLAST searches using known NBS-LRR sequences as queries with relaxed e-value thresholds (e<0.01) to capture divergent homologs.
• Execute Hidden Markov Model (HMM) searches against proteome datasets using Pfam models for NBS (NB-ARC, PF00931), TIR (PF01582), CC, and LRR (PF00560, PF07723, PF07725, PF12799, PF13306, PF13516, PF13855) domains [32] [31].

Step 3: Domain Structure Annotation

• Validate putative NBS-LRR genes using domain analysis tools (Pfam, SMART, INTERPRO).
• Classify genes into TNL and CNL subfamilies based on N-terminal domain presence.
• Identify atypical domain architectures (e.g., NL, NLL, NN, NLN, NLNLN) that may represent lineage-specific innovations [32].

Figure 1: Workflow for Phylogenetic Analysis of NBS-LRR Genes

Phylogenetic Reconstruction and Divergence Analysis

Step 4: Multiple Sequence Alignment

• Extract NBS domain sequences (approximately 300 amino acids) to ensure homologous comparison.
• Employ alignment algorithms suitable for divergent sequences (MUSCLE, MAFFT, or PRANK) with default parameters.
• Manually inspect and refine alignments to remove poorly aligned regions.

Step 5: Phylogenetic Tree Construction

• Implement multiple phylogenetic inference methods:
  • Maximum Likelihood (ML) using RAxML or IQ-TREE with best-fit model selection (e.g., LG+G+I).
  • Bayesian Inference (BI) using MrBayes for posterior probability support values.
  • Neighbor-Joining (NJ) for initial tree assessment.
• Assess branch support with 1000 bootstrap replicates for ML and NJ analyses.

Step 6: Divergence and Selection Analysis

• Calculate non-synonymous (dN) and synonymous (dS) substitution rates using PAML or similar packages.
• Identify sites under positive selection using branch-site and site-specific models.
• Correlate structural divergence (indels, exon structure changes) with phylogenetic patterns.

Figure 2: Domain Architecture of NBS-LRR Resistance Proteins

Experimental Protocols for Functional Characterization

Gene Expression Analysis Under Nitric Oxide Induction

Protocol 1: Nitric Oxide Treatment and RNA Extraction

• Infiltrate leaves of 4-6 week old plants with 1mM S-nitrosocysteine (CysNO) solution using needleless syringe.
• Include control treatments with infiltration buffer alone.
• Harvest leaf samples at multiple time points (e.g., 0, 2, 6, 12, 24 hours post-infiltration) with three biological replicates.
• Extract total RNA using TRIzol reagent or commercial kits with DNase I treatment to remove genomic DNA contamination.
• Assess RNA quality using spectrophotometry (A260/280 ratio ≥1.8) and agarose gel electrophoresis.

Protocol 2: Transcriptome Sequencing and Differential Expression

• Prepare stranded mRNA sequencing libraries from 1μg high-quality total RNA.
• Sequence on Illumina platform to generate 30-50 million 150bp paired-end reads per sample.
• Process raw reads: quality control (FastQC), adapter trimming (Trimmomatic), and alignment to reference genome (HISAT2/STAR).
• Quantify gene expression levels (featureCounts) and identify differentially expressed NBS-LRR genes (DESeq2, edgeR) with threshold of |log2FC|>1 and FDR<0.05.
• Validate RNA-seq results for key targets using qRT-PCR with SYBR Green chemistry and gene-specific primers.

Protein-Protein Interaction and S-Nitrosylation Assays

Protocol 3: Yeast Two-Hybrid Screening

• Clone full-length and domain-specific NBS-LRR sequences into pGBKT7 (DNA-BD vector) and pGADT7 (AD vector).
• Co-transform bait and prey constructs into yeast strain AH109 using lithium acetate method.
• Screen for interactions on selective medium lacking leucine, tryptophan, histidine, and adenine.
• Quantify interaction strength using β-galactosidase liquid assays with ONPG as substrate.

Protocol 4: S-Nitrosylation Site Prediction and Validation

• Predict potential S-nitrosylation sites using computational tools (GPS-SNO, iSNO-PseAAC) based on cysteine flanking sequences.
• Validate predictions using biotin-switch technique:
  • Block free thiols with methyl methanethiosulfonate (MMTS).
  • Reduce S-nitrosylated thiols with ascorbate.
  • Label newly reduced thiols with biotin-HPDP.
  • Capture biotinylated proteins with streptavidin-agarose and detect by immunoblotting.
• Mutate candidate cysteine residues to serine to confirm functional significance.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagent Solutions for NBS-LRR Gene Analysis

Reagent/Category	Specific Examples	Function/Application
Domain Detection Tools	Pfam, SMART, INTERPRO, COILS	Identification and annotation of protein domains (TIR, CC, NBS, LRR)
Phylogenetic Software	RAxML, IQ-TREE, MrBayes, MEGA	Construction of phylogenetic trees and evolutionary inference
Sequence Alignment Tools	MUSCLE, MAFFT, PRANK	Multiple sequence alignment for comparative analysis
Selection Analysis Packages	PAML, HyPhy, Datamonkey	Detection of sites under positive selection (dN/dS analysis)
NBS-Domain Specific Primers	Kin1a, Kin2, GLPL conserved primers	Amplification of NBS-LRR gene fragments for resistance gene analog (RGA) identification
NO Donors & Inhibitors	S-nitrosocysteine (CysNO), cPTIO	Modulation of nitric oxide signaling pathways in plant immunity
Yeast Two-Hybrid System	pGBKT7, pGADT7, AH109 strain	Protein-protein interaction screening for immune signaling complexes

Discussion and Future Perspectives

The comparative phylogenetic framework presented here reveals fundamental insights into the evolutionary dynamics of NBS-LRR genes across monocot and dicot lineages. The pronounced structural divergence observed between these lineages, particularly the differential retention and expansion of TNL versus CNL subfamilies, underscores how immune system evolution has followed distinct paths in these major angiosperm groups [32] [31]. These differences likely reflect both historical evolutionary contingencies and adaptation to distinct ecological pressures.

Future research directions should prioritize functional characterization of lineage-specific NBS-LRR innovations, particularly through heterologous expression systems and gene editing approaches. The development of synthetic NBS-LRR genes that combine conserved functional modules with variable recognition domains represents a promising strategy for engineering broad-spectrum disease resistance in crop plants. Additionally, integrating structural biology approaches with phylogenetic analysis will elucidate how sequence variation translates into functional differences in pathogen recognition and signaling activation.

The methodological advances in genome-wide analysis now enable unprecedented resolution in tracking the evolutionary history of plant immune genes [31]. As more high-quality genomes become available, particularly from basal angiosperms and early-diverging monocot and dicot lineages, we will gain further insights into the ancestral state of plant immunity and the key innovations that have shaped the diversification of NBS-LRR genes. This knowledge will ultimately enhance our ability to develop durable disease resistance in agricultural systems through informed manipulation of these critical genetic components.

Advanced Techniques for NBS Gene Identification and Classification

The identification of protein domains is a fundamental task in bioinformatics, enabling researchers to infer function, understand evolutionary relationships, and decipher biological mechanisms. For plant biology, this is particularly critical in the study of large gene families involved in immunity, such as the Nucleotide-Binding Site (NBS)-encoding gene family. These genes, which are major contributors to plant disease resistance, display significant diversity and species-specific structural patterns across monocots and dicots [14]. Hidden Markov Model (HMM) profiles and Pfam scanning constitute a core bioinformatics pipeline for the accurate annotation of these domains. This whitepaper provides a technical guide for employing these pipelines, framed within the context of researching species-specific NBS domain architectures in monocots and dicots. The methodologies outlined are designed for use by researchers, scientists, and drug development professionals seeking to characterize protein families at scale.

Theoretical Foundations: HMMs and the Pfam Database

Hidden Markov Models in Bioinformatics

A Hidden Markov Model (HMM) is a statistical model for representing a system that is assumed to be a Markov process with unobserved (hidden) states. In bioinformatics, HMMs are exceptionally well-suited for modeling protein families and domains because they can capture the conservation and variation of amino acids at each position in a multiple sequence alignment [38].

The model consists of:

• A set of hidden states: These often represent different match, insert, or delete states for a position in a multiple sequence alignment.
• Transition probabilities: The probabilities of moving from one state to another.
• Emission probabilities: The probabilities of emitting a particular amino acid from a given state.

For domain identification, a profile HMM is built from a curated multiple sequence alignment of a known protein domain. This profile HMM encapsulates the consensus sequence and the tolerated variations, creating a powerful probabilistic template for identifying the same domain in novel protein sequences [38].

The Pfam Database

Pfam is a widely-used database of protein families, each represented by multiple sequence alignments and HMMs [39]. It classifies protein regions into families, domains, repeats, and motifs. The core data in Pfam includes:

• Pfam-A: A high-quality, manually curated set of families.
• HMM Profiles: Statistical models for each family, used for sequence searching.

As of 2021, the Pfam website has been integrated into the InterPro platform, which consolidates information from multiple protein family databases. While the original Pfam site remains as a static page, all data searches and analyses are now redirected to InterPro, which provides a unified interface for functional annotation [39] [40].

Table 1: Key Terminology for HMMs and Pfam

Term	Definition	Relevance to Domain Identification
Hidden Markov Model (HMM)	A statistical model representing a system with hidden states.	Models the consensus and variation of a protein domain.
Profile HMM	An HMM constructed from a multiple sequence alignment of a protein family.	Serves as a template for detecting distant homologs in sequence searches.
Pfam	A database of protein families and their HMM representations.	Provides a comprehensive collection of curated domain models.
InterPro	An integrated resource consolidating Pfam and other protein signature databases.	A one-stop platform for running HMM scans and integrating annotations.
HMMER	A software suite for sequence analysis using profile HMMs.	The primary tool for scanning sequences against Pfam HMMs.

Technical Workflow for Domain Identification

The standard pipeline for identifying protein domains, such as the NBS domain, using HMM profiles and Pfam involves several key stages, from data preparation to final annotation.

The diagram below illustrates the logical flow and data transformations in a typical HMMER and Pfam scanning pipeline.

Detailed Methodological Steps

Step 1: Data Collection and Preparation

• Genome Assemblies: Download the latest genome assemblies and predicted protein sequences from public databases such as NCBI, Phytozome, or Plaza [14]. For a study on monocots and dicots, select species representing both lineages.
• Sequence Formatting: Ensure all protein sequences are in a single FASTA file format for processing.

Step 2: HMM Profile Selection

• Identify the relevant Pfam HMM for your domain of interest. For NBS domain identification, the canonical model is the NB-ARC (PF00931) domain [41]. The HMM profile can be obtained from the Pfam website, which redirects to InterPro for download.

Step 3: Running the HMM Scan

• Use the hmmscan program from the HMMER suite to search your protein sequences against the Pfam HMM library.
• A typical command is:

• Critical parameters include:
  • E-value (-E or --domE): The expectation value threshold for reporting hits. A stricter value (e.g., 1e-50) ensures high-confidence domain calls, as used in recent NBS studies [14].
  • Output Format (--domtblout): Generates a parseable domain table output, which is essential for downstream analysis.

Step 4: Post-processing and Filtering

• Parse the domtblout file to extract significant domain hits. Filter results based on the E-value threshold and the bit score.
• For a robust analysis, consider only domains that meet stringent statistical criteria. The study on NBS genes, for instance, used a default E-value of 1.1e-50 to ensure only high-confidence NBS genes were retained [14].

Step 5: Domain Architecture Analysis

• After identifying individual domains, determine the full domain architecture for each protein. This involves mapping all identified domains (e.g., TIR, CC, LRR) onto the protein sequence in their correct order.
• Classify genes into architectural classes (e.g., TIR-NBS-LRR, CC-NBS-LRR, NBS-LRR) based on the combination and order of domains present [14]. This step is crucial for identifying species-specific patterns in monocots versus dicots.

Case Study: Genome-Wide NBS Domain Identification in Plants

A 2024 study in Scientific Reports provides a exemplary model for applying this pipeline to investigate species-specific NBS patterns across 34 plant species, from mosses to monocots and dicots [14].

Experimental Protocol and Reagents

Table 2: Research Reagent Solutions for NBS Domain Identification

Research Reagent / Tool	Type	Function in the Experiment
PfamScan.pl	Software Script	A wrapper script for HMMER3, used to scan protein sequences against the Pfam HMM library.
Pfam-A.hmm	Database File	The curated library of profile HMMs from the Pfam database.
HMMER (v3.1b2)	Software Suite	The core software used for the sequence homology search using profile HMMs.
NB-ARC Domain (PF00931)	HMM Profile	The specific Hidden Markov Model used to identify the nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4.
Custom Perl/Python Scripts	Software Scripts	Used for post-processing HMMER output, filtering results, and classifying domain architectures.

Detailed Protocol from the Case Study:

• Identification of NBS-Domain-Containing Genes:

  • The PfamScan.pl HMM search script was used with a stringent E-value cutoff of 1.1e-50 and the background Pfam-A.hmm model to scan the proteomes of 34 species.
  • All genes containing an NB-ARC domain (PF00931) were considered NBS genes and selected for further analysis [14].

• Classification and Comparative Analysis:

  • The domain architecture for each identified NBS gene was determined. Genes with similar domain patterns were grouped into the same class.
  • A comprehensive comparison of these architectural classes was conducted across all land plants, from bryophytes to angiosperms, to trace evolutionary patterns.

• Validation and Functional Analysis:

  • The study employed additional methods to validate and characterize the identified NBS genes, including:
    • Orthogroup analysis using OrthoFinder to understand evolutionary relationships.
    • Transcriptomic analysis using RNA-seq data to study expression under stress.
    • Virus-Induced Gene Silencing (VIGS) to functionally validate the role of a specific NBS gene (GaNBS) in virus resistance [14].

Key Quantitative Findings

The application of this pipeline led to significant quantitative findings, summarized in the table below.

Table 3: Quantitative Results from Genome-Wide NBS Analysis in 34 Plant Species

Analysis Metric	Result	Biological Significance
Total NBS Genes Identified	12,820	Highlights the massive expansion of this gene family in plants.
Number of Architectural Classes	168	Demonstrates extensive structural diversification beyond canonical NLRs.
Unique Variants in Tolerant vs. Susceptible Cotton	Mac7: 6,583; Coker312: 5,173	Suggests a genetic basis for disease tolerance linked to NBS diversity.
Example Species-Specific Architecture	TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf	Illustrates novel domain integrations that may confer specialized immune functions.

Advanced Applications and Integrative Analysis

Orthogroup and Evolutionary Analysis

After domain identification, clustering NBS genes into orthogroups (OGs) helps trace their evolutionary history and conservation. The case study used OrthoFinder, which employs DIAMOND for fast sequence similarity and the MCL algorithm for clustering [14]. This identified 603 orthogroups, including:

• Core OGs: Common across many species (e.g., OG0, OG1, OG2).
• Unique OGs: Highly specific to certain species (e.g., OG80, OG82), potentially underlying species-specific resistance traits [14].

Structural Bioinformatics and Annotation

While sequence-based HMM scanning is powerful, structural annotation can reveal domains missed due to low sequence conservation. A 2024 study demonstrated this by creating a structural database of Pfam domains and using Foldseek for ultra-fast structural alignment [42]. This approach annotated over 400 new domains in the Trypanosoma brucei proteome that were missed by sequence-based Pfam tools. Integrating such structural methods can further refine NBS domain annotation, especially for divergent sequences.

The relationship between primary sequence annotation and higher-level structural and functional analysis is a critical pathway for comprehensive gene characterization.

The pipeline of HMM profiles and Pfam scanning represents a robust, reliable, and essential method for the large-scale identification of protein domains. When applied to the study of NBS domain genes in monocots and dicots, it unveils a remarkable landscape of diversity, innovation, and adaptation in the plant immune system. The integration of this core annotation workflow with advanced evolutionary, expression, and structural analyses—as demonstrated in the cited case studies—provides a comprehensive framework for understanding gene family evolution and function. For drug development and agricultural biotechnology, these insights and methodologies are invaluable for identifying and engineering new sources of disease resistance.

In the innate immune systems of plants, nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins serve as critical intracellular sentinels against pathogen invasion. The functional dynamics of these proteins are governed by a set of highly conserved structural motifs that work in concert to regulate nucleotide-dependent molecular switching, protein-protein interactions, and signal transduction. This technical guide provides an in-depth structural and functional annotation of four principal motifs—P-loop, RNBS, Kinase-2, and GLPL—within the broader context of species-specific NBS structural patterns across monocot and dicot plant lineages. Understanding the architectural constraints and evolutionary variations of these motifs is fundamental to elucidating the mechanistic basis of plant immunity and for engineering novel disease resistance traits in crop species. Recent genomic analyses have revealed that NBS-LRR genes constitute one of the largest and most diverse gene families in plants, with approximately 150 members in Arabidopsis thaliana and over 400 in Oryza sativa [43]. The conserved motifs addressed in this work form the operational core of these essential immune receptors.

Structural and Functional Annotation of Core Motifs

P-loop Motif

Structure and Consensus: The P-loop (phosphate-binding loop), also known as the Walker A motif, is a glycine-rich structural element with the conserved sequence pattern G-x(4)-GK-[T/S], where 'x' denotes any amino acid [44]. This motif forms a flexible loop between a beta strand and an alpha helix, creating a phosphate-sized concavity where the main chain NH groups point inward to coordinate the beta-phosphate of nucleotides [44]. The conserved lysine (K) residue is particularly crucial for nucleotide binding [44].

Functional Role: As the primary nucleotide-binding site, the P-loop facilitates binding to ATP or GTP in NBS-LRR proteins [45]. This motif is a hallmark feature of the STAND (signal transduction ATPases with numerous domains) family of ATPases, which function as molecular switches in disease signaling pathways [43]. Specific binding and hydrolysis of ATP has been experimentally demonstrated for the NBS domains of tomato CNLs I2 and Mi, with ATP hydrolysis driving conformational changes that regulate downstream signaling [43].

Table 1: Characteristic Features of the P-loop Motif

Feature	Description
Consensus Pattern	G-x(4)-GK-[T/S]
Structural Context	Positioned between beta strand and alpha helix
Key Residues	Glycine-rich sequence, conserved Lysine
Primary Function	Nucleotide (ATP/GTP) binding and hydrolysis
Role in NBS-LRR	Molecular switch for activation signaling

RNBS Motifs

Structural Context: The RNBS (Resistance Nucleotide Binding Site) motifs are conserved sequence blocks within the larger NB-ARC (NOD-LRR proteins, APAF-1, R proteins, and CED-4) domain, which spans approximately 300 amino acids [46]. Eight conserved NBS motifs have been identified in Arabidopsis through MEME analysis, with RNBS-A, RNBS-C, and RNBS-D serving as key discriminators between TNL and CNL subfamilies [43].

Subfamily Specificity: The sequence variation in RNBS motifs provides a molecular basis for differentiating between the two major NBS-LRR subfamilies: TIR-NBS-LRR (TNL) and CC-NBS-LRR (CNL) [43]. This distinction is not merely structural but extends to signaling pathways, with TNLs and CNLs utilizing different downstream signaling components [43]. Phylogenetic analyses consistently separate TNL and CNL proteins into distinct clades based on their NBS domain sequences [43].

Table 2: RNBS Motif Characteristics in NBS-LRR Subfamilies

Motif	TNL Characteristics	CNL Characteristics	Functional Significance
RNBS-A	Subfamily-specific sequence	Distinct conserved sequence	Contributes to subfamily-specific structure
RNBS-C	TIR-associated signature	CC-associated signature	Differentiation between TNL and CNL
RNBS-D	Conserved TNL pattern	Conserved CNL pattern	Evolutionary distinction
Overall NBS	Binds and hydrolyzes ATP	Binds and hydrolyzes ATP	Molecular switch function

Kinase-2 Motif

Structure and Conservation: The Kinase-2 motif represents another highly conserved element within the NB-ARC domain. While the search results do not provide extensive specific details about the Kinase-2 motif in NBS-LRR proteins, it is known from broader literature that this motif often contains a conserved aspartic acid or asparagine residue and contributes to the nucleotide-binding pocket.

Functional Implications: In STAND ATPases, which include NBS-LRR proteins, motifs analogous to Kinase-2 typically participate in coordinating magnesium ions and facilitating phosphotransfer reactions [43]. The precise conformation of this motif is likely influenced by the nucleotide-bound state (ATP vs. ADP), thereby contributing to the molecular switching mechanism that controls NBS-LRR activation and signaling.

GLPL Motif

Conservation and Significance: The GLPL motif, with the conserved sequence G-L-P-L, is a signature element within the NB-ARC domain of plant NBS-LRR proteins. Its functional importance is underscored by direct experimental evidence showing that a single amino acid substitution (G→E) in this motif completely abolishes resistance function, as demonstrated in a spontaneous rust-susceptible mutant of the flax P2 resistance gene [47].

Evolutionary Context: The GLPL motif exhibits remarkable evolutionary conservation across kingdoms, being present not only in plant NBS-LRR proteins but also in animal cell death regulators APAF-1 and CED-4 [47]. This phylogenetic conservation indicates a fundamental role in the nucleotide-dependent regulation of cell death signaling pathways.

Comparative Structural Analysis: Monocots vs. Dicots

The genomic distribution and structural composition of NBS-LRR proteins reveal significant evolutionary divergence between monocot and dicot plant lineages. Comparative analysis of these structural patterns provides insights into lineage-specific adaptations in plant immunity.

TNL Distribution: A fundamental phylogenetic distinction exists in the presence of TNL proteins, which are completely absent from cereal genomes [43]. This observation suggests that early angiosperm ancestors possessed few TNLs, which were subsequently lost in the cereal lineage [43]. In contrast, dicot species typically harbor both TNL and CNL subfamilies.

CNL Conservation: CC-NBS-LRR proteins from both monocots and dicots cluster together in phylogenetic analyses, indicating that angiosperm ancestors contained multiple CNLs that have been maintained in both lineages [43]. This conservation suggests essential functions fulfilled by CNL proteins across angiosperms.

Motif Conservation Patterns: While the core motifs (P-loop, RNBS, Kinase-2, GLPL) maintain their fundamental architecture across plant lineages, subtle sequence variations in these motifs contribute to functional diversification. The LRR domains, in contrast, exhibit substantial diversity driven by diversifying selection, particularly in solvent-exposed residues [43].

Diagram 1: Domain architecture of plant NBS-LRR proteins showing the relative position of conserved motifs within the overall structure.

Experimental Protocols for Motif Analysis

Site-Directed Mutagenesis for Functional Characterization

Objective: To determine the functional contribution of specific residues within conserved motifs to pathogen recognition specificity and signal transduction.

Methodology:

• Gene Isolation: Clone full-length NBS-LRR genes from target plant species using PCR-based approaches with degenerate primers targeting conserved NBS domains [43].
• Mutagenesis Design: Introduce specific amino acid substitutions into motif residues (e.g., conserved lysine in P-loop, GLPL residues) using overlap extension PCR or commercial mutagenesis kits.
• Plant Transformation: Construct chimeric genes where specific motif regions are swapped between closely related NBS-LRR proteins with different recognition specificities [47].
• Functional Assay: Transform mutant constructs into susceptible plant genotypes and challenge with corresponding pathogens to assess complementation of resistance function.
• Biochemical Analysis: Express and purify recombinant mutant proteins for in vitro nucleotide binding and hydrolysis assays [43].

Key Experimental Evidence: Chimeric gene constructs between flax P and P2 resistance specificities demonstrated that just six amino acid changes confined to the beta-strand/beta-turn motif of LRR units are sufficient to alter recognition specificity [47].

Genomic Identification and Annotation Pipeline

Objective: To systematically identify and classify NBS-LRR genes carrying target motifs from plant genome sequences.

Methodology:

• HMMER Scanning: Perform initial searches of predicted proteomes using Hidden Markov Models corresponding to Pfam NBS (NB-ARC) family (PF00931) with E-value cutoff < 1×10⁻²⁰ [46].
• Domain Annotation: Identify associated domains (TIR, CC, LRR) using hmmpfam comparison to respective Pfam models (TIR: PF01582, LRR: PF00560) [46].
• Motif Identification: Extract NB-ARC domains and identify conserved motifs (P-loop, RNBS, Kinase-2, GLPL) using multiple sequence alignment and motif discovery tools such as MEME [43].
• Phylogenetic Analysis: Construct maximum likelihood trees from aligned NB-ARC domains to classify sequences into TNL and CNL subfamilies and identify orthologous relationships [46].
• Genomic Mapping: Visualize chromosomal distribution and identify gene clusters using genetic map positions [46].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for NBS-LRR Motif Analysis

Reagent/Resource	Function/Application	Specifications/Alternatives
HMMER Suite	Identification of NBS domains in genomic sequences	Uses Pfam NB-ARC (PF00931) HMM profile [46]
MEME Suite	Discovery of conserved motifs in protein sequences	Identifies RNBS and other conserved patterns [43]
ClustalW	Multiple sequence alignment of NB-ARC domains	Default parameters for initial alignment [46]
Phytozome	Access to annotated plant genomes	Source for cassava and other crop genomes [46]
Paircoil2	Prediction of coiled-coil domains in CNL proteins	P-score cutoff of 0.03 [46]
MEGA6	Phylogenetic tree construction and analysis	Maximum Likelihood method with WAG model [46]

Species-Specific Patterns and Evolutionary Dynamics

The evolution of NBS-LRR genes follows a birth-and-death model characterized by frequent gene duplication and loss, resulting in significant interspecific variation [43]. This evolutionary dynamic has produced distinctive structural patterns across plant lineages.

Motif Evolution Heterogeneity: Different domains of NBS-LRR proteins experience distinct selective pressures. The NBS domain, containing the conserved motifs addressed here, is predominantly under purifying selection with limited gene conversion events [43]. In contrast, the LRR domain exhibits diversifying selection, particularly in solvent-exposed residues that directly interact with pathogen components [43].

Genomic Organization: NBS-LRR genes are frequently organized in clusters resulting from both segmental and tandem duplications [43] [46]. In cassava, 63% of 327 identified NBS-LRR genes occur in 39 chromosomal clusters that are predominantly homogeneous, containing genes derived from recent common ancestors [46]. This clustering facilitates rapid evolution through unequal crossing-over and sequence exchange.

Lineage-Specific Expansions: Different plant families exhibit distinct patterns of NBS-LRR diversification. Independent expansions have occurred in legumes, Solanaceae, and Asteraceae, resulting in family-specific subfamilies not found in other lineages [43]. The spectrum of NBS-LRR proteins in one species is not representative of the diversity in other plant families [43].

Diagram 2: Evolutionary dynamics of NBS-LRR genes in monocot and dicot lineages, highlighting the loss of TNLs in cereals and the role of gene clustering in diversification.

The structural annotation of conserved motifs in NBS-LRR proteins reveals a sophisticated molecular machinery underlying plant immunity. The P-loop, RNBS, Kinase-2, and GLPL motifs form an integrated functional core that governs nucleotide-dependent molecular switching, while surrounding domains mediate pathogen recognition and signal transduction. The species-specific patterns observed between monocots and dicots, particularly the complete absence of TNL proteins in cereals, highlight the dynamic evolutionary processes that have shaped plant immune systems. Future research focusing on structural determinations of full-length NBS-LRR proteins and continued comparative genomic analyses will further elucidate how variations in these conserved motifs contribute to functional specialization across plant lineages. This knowledge provides a foundation for developing novel strategies to enhance crop disease resistance through informed engineering of these essential immune receptors.

Orthogroup Analysis and Clustering to Trace Evolutionary Relationships

Orthogroup analysis represents a fundamental methodology in comparative genomics for inferring evolutionary relationships among genes across multiple species. An orthogroup is defined as the set of genes that descended from a single ancestral gene in the last common ancestor of all species being considered, encompassing both orthologs and paralogs [48]. This approach provides a coherent framework for tracing gene evolution, facilitating functional annotation transfer, and understanding the genetic basis of phenotypic diversity. Within plant genomics, orthogroup analysis has become particularly valuable for investigating species-specific patterns and evolutionary dynamics between major plant groups such as monocots and dicots, offering insights into how gene family expansions, contractions, and functional diversification contribute to lineage-specific characteristics [49] [50].

The application of orthogroup analysis to study NBS structural patterns – referring to nucleotide-binding site domains often associated with plant disease resistance genes – enables researchers to trace the evolutionary history of these critical genetic components across plant lineages. By clustering genes into orthogroups, scientists can distinguish between conserved disease resistance mechanisms shared across monocots and dicots and lineage-specific adaptations that may confer specialized resistance capabilities. This analytical framework provides the phylogenetic context necessary for interpreting structural variations in NBS domains and their functional implications for plant immunity systems [50].

Fundamental Principles and Benchmarking

Key Concepts and Definitions

Orthogroup analysis relies on several foundational concepts that distinguish it from pairwise orthology inference methods. Orthologs are genes in different species that evolved from a common ancestral gene by speciation, while paralogs are genes related by duplication events within a genome [48]. An orthogroup represents a more comprehensive concept that includes all genes descended from a single ancestral gene in the last common ancestor of the species being analyzed, thus providing a complete set of orthologs and paralogs for that gene family [51] [48]. This approach is particularly valuable for comparative genomics as it offers a natural unit for comparing gene families across multiple species, enabling researchers to trace duplication events and functional diversification through evolutionary history.

The accuracy of orthogroup inference methods is critically evaluated using benchmark datasets such as Orthobench, which contains expert-curated reference orthogroups (RefOGs) that span the Bilateria and cover a range of different challenges for orthogroup inference [51]. Recent re-evaluation of these reference sets using improved phylogenetic methods revealed that approximately 44% required revision, with 34% needing major changes affecting phylogenetic extent, highlighting both the importance and challenges of accurate orthogroup delineation [51]. These benchmarks have demonstrated that methods like OrthoFinder significantly improve inference accuracy by addressing fundamental biases in whole genome comparisons, outperforming other commonly used methods by between 8% and 33% [48].

Performance Benchmarks of Orthogroup Inference Methods

Table 1: Performance Characteristics of Orthogroup Inference Methods

Method	Approach	Key Innovation	Reported Accuracy	Limitations
OrthoFinder	Orthogroup delimitation	Gene length bias correction	8-33% higher accuracy than other methods [48]	Computational intensity for very large datasets
OrthoMCL	Graph-based clustering	MCL algorithm on BLAST scores	Suffers from gene length bias [48]	Low recall for short genes, low precision for long genes
OMA	Pairwise orthology inference	Pairwise relationships extended to multiple species	High precision for orthologue pairs [48]	Low recall for complete orthogroups due to duplication events
Hieranoid	Hierarchical inference	Uses species tree information	Not specifically benchmarked	Complex implementation for non-model species
SonicParanoid	Fast orthology inference	Optimized for speed	Not specifically benchmarked	Potential trade-off between speed and accuracy

The performance characteristics of orthogroup inference methods have been quantitatively assessed using benchmark datasets. OrthoMCL, despite its widespread adoption, demonstrates significant gene length bias in orthogroup detection, resulting in low recall rates for short sequences and low precision for long sequences [48]. This bias stems from fundamental properties of BLAST scores, which inherently favor longer sequences regardless of their true evolutionary relationships. OrthoFinder addresses this limitation through a novel score normalization approach that eliminates gene length dependency, resulting in more accurate orthogroup assignments across the full spectrum of gene lengths [48]. When evaluated on the OrthoBench dataset, this approach demonstrated substantially improved precision over the entire range of sequence lengths without compromising recall rates.

Methodological Approaches and Workflows

Standard Orthogroup Inference Pipeline

A standard orthogroup inference pipeline involves sequential computational steps that transform raw protein sequences into evolutionarily meaningful clusters. The foundation of this process relies on sequence similarity searches using tools like BLAST or HMMER, followed by sophisticated clustering algorithms that group genes based on their evolutionary relationships [51] [48]. For the initial sequence similarity search, researchers typically employ an all-versus-all BLAST search of protein sequences across the target species, which generates raw similarity scores that form the basis for subsequent analysis [48]. The critical innovation in modern methods like OrthoFinder involves transforming these BLAST bit scores to eliminate gene length bias – a significant confounder in orthogroup inference where longer sequences artificially receive higher similarity scores regardless of their true evolutionary relationships [48].

Following sequence similarity analysis, the transformed scores undergo clustering procedures to delineate orthogroups. The OrthoFinder algorithm employs a graph-based approach where sequences represent nodes and similarity scores represent weighted edges, applying the MCL (Markov Cluster) algorithm to identify strongly connected components that constitute putative orthogroups [48]. This method specifically uses reciprocal best hits based on length-normalized scores (RBNH) as a high-precision method for identifying orthologous gene pairs prior to clustering, which significantly improves overall accuracy compared to approaches that rely solely on unprocessed BLAST scores [48]. For plant-specific applications with large gene families, researchers often incorporate iterative phylogenetic analysis to refine orthogroup boundaries, particularly for complex families like GELP-type esterases/lipases where automatic methods may produce inaccurate clustering [50].

Figure 1: Orthogroup Inference Workflow. The standard computational pipeline for orthogroup analysis involves four major phases: input data preparation, sequence analysis, orthogroup delineation, and evolutionary interpretation.

Experimental Protocol for Orthogroup Analysis

A comprehensive orthogroup analysis requires careful execution of sequential computational steps with specific parameter considerations at each stage. The following protocol outlines the key procedures based on established methodologies from recent literature:

• Input Data Preparation: Collect protein sequences for all species of interest in FASTA format. For flowering plants, include representative monocot and dicot species to facilitate comparative analysis. Ensure proteome annotations are current, as updates to genome annotations can significantly impact orthogroup inference accuracy [51]. The Orthobench re-evaluation study utilized the latest versions of proteomes for the original 12 species, which were downloaded and made publicly available to ensure reproducibility [51].

• Sequence Similarity Search: Perform all-versus-all BLAST searches for all protein sequences across the target species. Use BLASTP with an e-value cutoff of 1e-5 as a starting parameter. For more sensitive detection of distant homologs, consider using HMMER with hidden Markov models as queries, applying liberal e-value inclusion thresholds (e.g., three times more permissive than the worst e-value of known members) to ensure comprehensive coverage while accepting that false positives will be filtered in subsequent steps [51].

• Score Normalization and Transformation: Apply gene length normalization to BLAST bit scores to eliminate sequence length bias. OrthoFinder implements an automated approach for this by analyzing the top 5% of hits in length-based bins and fitting a linear model in log-log space to normalize scores across different sequence lengths [48]. This step is critical for equalizing scoring between short and long sequences and for normalizing phylogenetic distance between species comparisons.

• Orthogroup Delineation: Cluster sequences into orthogroups using normalized similarity scores. OrthoFinder applies the MCL algorithm to the graph of normalized scores with an inflation parameter of 1.5 as default [48]. For plant-specific applications with large gene families, consider using an iterative phylogenetic approach as implemented in GELP family analysis, where global phylogenies are constructed and well-supported clusters are successively removed in each iteration to resolve complex relationships [50].

• Phylogenetic Validation: For critical orthogroups, particularly those showing species-specific patterns in NBS genes, perform multiple sequence alignment using MAFFT L-INS-i algorithm followed by phylogenetic inference with IQ-TREE under the best-fitting model of sequence evolution [51]. Manually curate orthogroup boundaries based on phylogenetic evidence, as this process altered the membership of 31 out of 70 reference orthogroups in the Orthobench dataset, with 24 requiring extensive revision [51].

Specialized Workflow for Plant Single-Cell Transcriptomics

The Orthologous Marker Gene Groups (OMG) method represents a specialized application of orthogroup analysis for cell type identification in plant single-cell RNA sequencing data. This approach addresses the challenge of comparing cell types across diverse plant species where marker genes have diverged due to gene family expansions and duplication events [49]. The OMG method operates through three key stages: first, identifying top marker genes (typically N=200) for each cell cluster in each species using standard tools like Seurat; second, generating orthologous gene groups across multiple plant species using OrthoFinder; and third, performing pairwise comparisons using overlapping OMGs between clusters in query and reference species with statistical testing (Fisher's exact test) to identify significant similarities [49]. This method successfully identified 14 dominant groups with substantial conservation in shared cell-type markers across monocots and dicots, demonstrating the utility of orthogroup-based approaches for cross-species comparisons in plant biology [49].

Orthogroup Applications in Monocot and Dicot Research

Analyzing NBS Structural Patterns Across Plant Lineages

Orthogroup analysis provides a powerful framework for investigating the evolution of nucleotide-binding site (NBS) domain architectures across monocot and dicot lineages. By clustering NBS-encoding genes into orthogroups based on phylogenetic relationships rather than sequence similarity alone, researchers can distinguish between conserved structural patterns maintained across both lineages and species-specific innovations that may confer specialized functions. This approach has revealed distinctive evolutionary dynamics in large plant gene families, with some orthogroups expanding through tandem duplications while others are maintained as single copies, reflecting different selective pressures and functional constraints [50].

The application of orthogroup analysis to the GDSL-type esterase/lipase (GELP) family in flowering plants demonstrated how this method can elucidate lineage-specific evolutionary patterns. Through iterative phylogenetic analysis of representative angiosperm genomes, researchers identified 10 main clusters subdivided into 44 orthogroups, revealing dicot-specific clusters and specific amplifications in monocots [50]. This systematic classification enables accurate transfer of functional annotations between model and non-model species, facilitating the identification of candidate genes for crop improvement. For NBS gene research, a similar orthogroup-based classification can help researchers determine whether particular structural variants represent ancestral states shared across monocots and dicots or derived states specific to particular lineages.

Cross-Species Cell Type Identification Using OMGs

The Orthologous Marker Gene Groups (OMG) method exemplifies how orthogroup analysis enables comparative biology across monocot and dicot species. When applied to single-cell transcriptomic data from Arabidopsis (dicot) and rice (monocot) roots, the OMG method identified significant similarities between 14 pairs of cell clusters, 13 of which represented orthologous cell types [49]. In contrast, methods relying solely on one-to-one orthologous genes identified only 8 pairs of similar clusters, with just 3 representing true orthologous cell types [49]. This demonstrates the superior performance of orthogroup-based approaches for cross-species comparisons in plants, where gene family expansions and duplications complicate one-to-one orthology relationships.

Table 2: OMG Method Performance in Cross-Species Cell Type Identification

Species Comparison	Cluster Pairs with Significant Similarity	Orthologous Cell Type Matches	Methodological Advantage
Arabidopsis vs Tomato (dicot-dicot)	24 pairs (FDR < 0.01) [49]	12 exact matches, 1 partial match, 2 functional matches [49]	Identified exodermis clusters in tomato as functionally similar to endodermis in Arabidopsis
Arabidopsis vs Rice (dicot-monocot)	14 pairs (FDR < 0.01) [49]	13 out of 14 pairs from orthologous cell types [49]	Superior to one-to-one ortholog approach which identified only 3 orthologous cell types
15 plant species integration	14 dominant conserved cell type groups [49]	Conservation across monocots and dicots [49]	Enabled mapping of 1 million cells, 268 clusters across diverse species

The OMG method's success stems from its ability to account for the complex orthology relationships characteristic of plant genomes. By using orthogroups rather than one-to-one orthologs as the unit of comparison, the method accommodates gene family expansions and duplications that have occurred since the divergence of monocots and dicots approximately 200 million years ago. This approach revealed 14 dominant groups with substantial conservation in shared cell-type markers across monocots and dicots, providing evidence for deep conservation of developmental programs despite extensive sequence divergence [49]. For researchers studying NBS genes, this demonstrates the value of orthogroup-based comparisons for identifying functionally equivalent genes between monocot and dicot species.

Essential Research Tools and Reagents

Computational Tools for Orthogroup Analysis

Table 3: Essential Computational Tools for Orthogroup Analysis

Tool/Resource	Primary Function	Application in NBS Research	Key Features
OrthoFinder	Orthogroup inference	Phylogenetic delineation of NBS gene families [48]	Gene length bias correction, species tree inference, scalable to thousands of genomes
Orthobench	Benchmarking	Accuracy assessment of NBS orthogroup inferences [51]	70 expert-curated reference orthogroups, standardized evaluation framework
OMA	Orthology inference	Pairwise ortholog identification for functional transfer [48]	High precision for orthologue pairs, non-transitive approach
OrthoMCL	Orthogroup clustering	Legacy method for comparative analysis [48]	MCL algorithm on BLAST scores, widely used but with gene length bias
MAFFT	Multiple sequence alignment	Aligning NBS domain sequences [51]	L-INS-i algorithm for accurate alignment, handles large datasets
IQ-TREE	Phylogenetic inference	Gene tree construction for orthogroup validation [51]	Model selection, high computational efficiency, parallelization
HMMER	Sequence similarity search	Identifying distant NBS homologs [51]	Profile hidden Markov models, sensitive detection of remote homologs

Successful orthogroup analysis requires both computational tools and curated biological data resources. For plant-specific research, particularly focusing on NBS genes in monocots and dicots, several essential reagents and data sources enable comprehensive analysis:

• Reference Proteomes: High-quality protein sequences for representative monocot (e.g., rice, maize) and dicot (e.g., Arabidopsis, tomato) species are fundamental for orthogroup analysis. These should be obtained from authoritative sources such as Ensembl Plants, Phytozome, or NCBI, with attention to version consistency across species [51]. The Orthobench re-evaluation study emphasized the importance of using updated proteome versions, as annotations improve over time and significantly impact orthogroup inference accuracy [51].

• Benchmark Datasets: The Orthobench dataset provides 70 expert-curated reference orthogroups that span the Bilateria and cover a range of different challenges for orthogroup inference [51]. While not plant-specific, these benchmarks offer a gold standard for evaluating orthogroup inference methods applied to plant genes. For plant-specific validation, the OMG method used promoter-GFP lines in tomato as a gold-standard validation for cell-type identity [49].

• Functional Annotation Resources: Gene Ontology (GO) databases and specialized resources like the Plant Omics Data Center provide functional annotations that help interpret the biological significance of orthogroup analysis results. In the OMG method, GO functional enrichment analysis revealed that clusters with ambiguous orthology relationships were enriched for ribosomal genes characteristic of meristematic cell identities [49].

• Curated Gene Family Collections: For large gene families like NBS genes, pre-compiled collections such as the GELP family classification [50] provide valuable starting points for orthogroup analysis. These resources typically include manually curated gene models, functional annotations, and phylogenetic classifications that facilitate more accurate orthogroup delineation and functional inference.

Orthogroup analysis represents a powerful phylogenetic framework for tracing evolutionary relationships and investigating species-specific patterns in gene families. The application of this methodology to study NBS structural patterns in monocots and dicots enables researchers to distinguish conserved mechanistic elements from lineage-specific innovations, providing crucial insights into the evolution of plant immune systems. Through benchmarked computational workflows incorporating gene length bias correction and phylogenetic validation, orthogroup analysis overcomes limitations of simpler similarity-based approaches, delivering more accurate evolutionary inferences. As plant genomics continues to expand with increasing numbers of sequenced genomes from both monocot and dicot lineages, orthogroup-based comparative approaches will remain essential for translating sequence information into biological understanding, ultimately supporting crop improvement efforts through identification of evolutionarily conserved functional modules and lineage-specific genetic adaptations.

Leveraging RNA-seq Data for Expression Profiling Across Tissues and Stresses

In contemporary plant genomics, leveraging RNA sequencing (RNA-seq) data has become a cornerstone for understanding complex gene expression patterns activated by developmental cues and environmental challenges. This technical guide focuses on the application of RNA-seq for expression profiling, framed within a broader thesis investigating the species-specific structural patterns of Nucleotide-Binding Site-Leucine Rich Repeat (NBS-LRR) genes between monocots and dicots. The NBS-LRR gene family constitutes the largest class of plant resistance (R) proteins, serving as intracellular immune receptors that recognize pathogen effectors and trigger robust immune responses [3] [14]. Recent genome-wide studies across diverse species reveal that the composition and expansion of NBS-LRR subfamilies have undergone lineage-specific evolution, marked by a significant reduction or complete loss of certain subfamilies, such as TNL (TIR-NBS-LRR), in monocot species [3] [14]. This whitepaper provides researchers and drug development professionals with a comprehensive framework for designing and executing RNA-seq experiments to unravel the expression dynamics of these critical immune genes across different tissues and stress conditions, thereby contributing to the development of disease-resistant crops.

RNA-seq Experimental Design for Profiling NBS-LRR Genes

Key Considerations for Study Design

A well-designed RNA-seq experiment is paramount for generating reliable and biologically meaningful data. When profiling NBS-LRR genes, which can show rapid, tissue-specific, and stress-induced expression changes, several factors must be prioritized:

• Tissue Selection: Choose tissues relevant to the pathogen or stressor under investigation. For comprehensive profiling, include multiple organs (e.g., root, leaf, stem). Studies on Salvia miltiorrhiza and rat models highlight the importance of selecting key tissues—such as liver and adrenal glands for stress response—to capture the full spectrum of regulatory mechanisms [3] [52].
• Replication: Incorporate a minimum of three biological replicates per condition to account for biological variability and ensure statistical robustness in subsequent differential expression analysis [53].
• Stress Application and Timing: For time-course experiments, clearly define the stress intensity, duration, and recovery periods. For instance, a heat stress study in rats defined specific exposure times (30, 60, and 120 minutes) to track temporal gene expression patterns [52]. Similarly, profiling Chenopodium quinoa under Cercospora disease stress required careful timing to capture the progressive immune response [54].

The RNA-seq Wet-Lab Workflow

The following diagram illustrates the standard workflow from sample to sequenced library, which is consistent across numerous studies [52] [53].

Diagram 1: RNA-seq experimental workflow.

Computational Analysis of RNA-seq Data

Primary Data Processing and Quality Control

Upon generating raw sequencing reads (FastQ files), the first computational step involves rigorous quality control and alignment. The standard pipeline, as employed in a meta-analysis of rainbow trout stress responses, involves several key steps [53]:

• Quality Assessment: Tools like FastQC provide initial read quality metrics.
• Trimming and Adapter Removal: Programs like Trim Galore and Cutadapt are used to remove low-quality bases and adapter sequences.
• Alignment to a Reference Genome: High-quality reads are aligned to a species-specific reference genome (e.g., Omyk_1.1 for rainbow trout) using splice-aware aligners like HISAT2 [53]. In the context of NBS-LRR studies, using the most recent genome assembly (e.g., Salvia miltiorrhiza bh-27 inbred line genome) is critical for accurate identification and quantification [3].
• Quantification of Transcript Abundance: Aligned reads are assembled into transcripts and their abundance is estimated using tools like StringTie. The output is typically expressed as FPKM (Fragments Per Kilobase of transcript per Million mapped reads) or TPM (Transcripts Per Million), which normalizes for gene length and sequencing depth [53].

Identification of Differentially Expressed NBS-LRR Genes

Differential expression analysis identifies genes whose expression levels change significantly between conditions (e.g., control vs. stress). The DESeq2 package in R is widely used for this purpose, as demonstrated in studies on rats and rainbow trout [52] [53]. This tool applies a negative binomial model to normalized read counts to test for statistical significance. Genes are typically considered differentially expressed if they pass a threshold of adjusted p-value < 0.05 (to control for false discoveries) and an absolute log2 fold change > 1 [53]. For a focused analysis on NBS-LRR genes, a list of gene identifiers from a prior genome-wide identification can be used to extract and filter expression data [54] [14].

Functional Interpretation of Expression Results

Once a set of differentially expressed NBS-LRR genes is identified, functional analysis provides biological context.

• Cis-Element Analysis: Promoter analysis of NBS-LRR genes, as performed in S. miltiorrhiza, can reveal an abundance of cis-acting elements related to plant hormones (e.g., jasmonic acid, salicylic acid) and abiotic stress, linking expression patterns to potential regulatory mechanisms [3].
• Gene Ontology (GO) and Pathway Enrichment: Tools for GO and pathway analysis (e.g., KEGG) can determine if the up-regulated NBS-LRR genes are enriched in specific biological processes or pathways, such as "defense response" or "plant-pathogen interaction" [54] [53].
• Co-expression and Interaction Networks: Protein-protein interaction analysis can suggest functional partnerships between NBS-LRR proteins and other cellular components, as seen in studies of cotton NBS proteins and viral pathogens [14].

Case Studies: NBS-LRR Expression Profiling in Monocots and Dicots

Expression Profiling in Dicots

Table 1: Summary of NBS-LRR Expression Profiling in Dicot Plants

Plant Species	Stress Condition	Key Findings on NBS-LRR Expression	Reference
Salvia miltiorrhiza (Danshen)	Hormonal treatments, Abiotic stress	Expression of SmNBS-LRR genes is closely associated with secondary metabolism. Promoters are enriched with hormone and stress-responsive cis-elements.	[3]
Chenopodium quinoa (Quinoa)	Cercospora cf. chenopodii (Fungal pathogen)	24 NBS genes showed progressive upregulation under disease stress, confirming their dynamic role in plant immunity.	[54]
Gossypium hirsutum (Upland Cotton)	Cotton Leaf Curl Disease (Viral pathogen)	NBS genes in orthogroups OG2, OG6, OG15 were upregulated. VIGS silencing of GaNBS (OG2) confirmed its role in virus resistance.	[14]
Capsicum annuum (Pepper)	Not Specified (Genome-wide profiling)	54% of NBS-LRR genes (136 genes) were physically clustered in 47 clusters on chromosomes, indicating tandem duplication as a key evolutionary mechanism.	[7]

Expression Profiling in Monocots and Cross-Species Comparisons

Table 2: Summary of NBS-LRR Expression and Evolutionary Patterns in Monocots

Plant Species / Group	Context	Key Findings on NBS-LRR Evolution/Expression	Reference
Oryza sativa (Rice)	Phylogenetic Comparison	Genome contains 505 NBS-LRR proteins. Comparative analysis revealed a complete loss of the TNL subfamily in monocots.	[3]
Monocots (e.g., Rice, Wheat, Maize)	Comparative Genomics	Typical TNL and RNL subfamilies are completely lost in monocotyledonous species, a defining structural difference from dicots.	[3] [14]
Angiosperms (Broad Survey)	Evolutionary Analysis	A broad analysis of 34 species confirmed a greater prevalence of nTNL (CNL) genes in angiosperms, with significant TNL loss in monocots.	[14]

The case studies underscore a fundamental evolutionary divergence between monocots and dicots. While dicots like pepper and quinoa utilize a diverse repertoire of NBS-LRR genes that are often clustered and stress-responsive, monocots like rice have undergone a major evolutionary shift by completely losing the entire TNL subfamily [3] [14]. This structural difference necessitates tailored approaches for expression profiling and functional validation in the two plant groups.

Table 3: Research Reagent Solutions for RNA-seq Based Expression Profiling

Item / Reagent	Function / Application	Example from Literature
High-Quality RNA Isolation Kits	Extraction of intact, pure total RNA without genomic DNA contamination, which is critical for library prep.	Used in rat model study for RNA from blood, liver, and adrenal glands [52].
Stranded mRNA-Seq Library Prep Kits	Construction of sequencing libraries that preserve the strand orientation of transcripts, improving annotation accuracy.	Standard protocol for Illumina sequencing in multiple studies [52] [53].
Reference Genome Sequence	A high-quality, annotated genome assembly for read alignment, gene model identification, and quantification.	Salvia miltiorrhiza bh-27 genome [3]; Omyk_1.1 for rainbow trout [53].
DESeq2 R Package	Statistical software for identifying differentially expressed genes from raw read count data.	Used for differential expression analysis in rainbow trout meta-analysis [53].
Virus-Induced Gene Silencing (VIGS) Vectors	Functional validation tool to knock down the expression of candidate NBS-LRR genes and assess phenotypic changes.	Used to confirm the role of GaNBS in cotton resistance to leaf curl disease [14].

Advanced Applications and Integrated Workflows

The integration of RNA-seq with other genomic technologies provides a more comprehensive view of gene regulation and function. A powerful example is the combination of Optical Genome Mapping (OGM) and RNA-seq for detecting and interpreting structural variants (SVs) in human neurodevelopmental disorders [55]. While OGM excels at detecting large SVs in non-coding regions, RNA-seq confirms the pathogenicity of these variants by revealing their functional consequences on transcription, such as altered gene expression or disrupted splicing [55].

This integrated approach is highly applicable to plant NBS-LRR research. Complex genomic rearrangements and SVs are known to drive the evolution of R gene clusters. The following diagram illustrates how OGM and RNA-seq can be combined to unravel the structure and function of NBS-LRR genes.

Diagram 2: Multi-omics approach for R gene analysis.

Data Visualization and Interpretation

Effective visualization is critical for interpreting the complex data generated from RNA-seq experiments. Principles of effective data visualization recommend choosing geometries that accurately represent the underlying data and avoiding misleading representations like bar plots for mean values without distributional information [56].

For RNA-seq data, key visualizations include:

• Heatmaps: Ideal for displaying the expression matrix of multiple NBS-LRR genes (rows) across different samples or conditions (columns). Clustering can reveal co-expressed gene modules [14].
• Volcano Plots: Used in differential expression analysis to visualize the relationship between statistical significance (-log10(p-value)) and magnitude of change (log2 fold change), highlighting strongly deregulated genes [53].
• PCA Plots: Useful for assessing overall sample similarity and identifying batch effects before differential expression analysis [53].

Adhering to these best practices in data visualization ensures that the expression patterns of NBS-LRR genes are communicated clearly and accurately, facilitating deeper insights into their roles in plant immunity.

Integrating Genomic and Transcriptomic Data to Predict Gene Function

The quest to elucidate gene function represents a central challenge in modern biology, with profound implications for understanding disease mechanisms, improving crop resilience, and advancing therapeutic development. While genomic data provides a static blueprint of an organism's DNA sequence, it often fails to fully predict dynamic gene function and regulatory complexity. Transcriptomic data, which captures the dynamic expression of genes across tissues, developmental stages, and environmental conditions, provides crucial intermediate phenotypes that bridge the gap between genotype and final organismal traits. The integration of these complementary data layers has emerged as a powerful paradigm for advancing functional genomics.

This technical guide examines current methodologies and applications for integrating genomic and transcriptomic data to predict gene function, with particular emphasis on species-specific structural patterns of Nucleotide-Binding Site (NBS) genes in monocots and dicots. NBS genes constitute the largest class of plant disease resistance (R) proteins and display remarkable structural diversity across plant lineages, making them an ideal model system for studying the genetic architecture of adaptive traits [14] [3]. The integration of multi-omics data is particularly valuable for deciphering the complex regulatory mechanisms governing these important gene families.

Technical Foundations of Data Integration

Genomic and Transcriptomic Data Types

Genomic data typically encompasses whole-genome sequencing (WGS) and single nucleotide polymorphisms (SNPs), providing a comprehensive map of genetic variation [57] [58]. Transcriptomic data includes RNA sequencing (RNA-seq) that quantifies gene expression levels, alternative splicing events, and isoform usage [58]. More specialized transcriptomic approaches also profile non-coding RNAs, such as microRNAs (miRNAs), which can regulate the expression of target genes, including NBS-LRR genes [57] [14].

Statistical Models for Data Integration

Several statistical frameworks have been developed to integrate genomic and transcriptomic data, each addressing specific analytical challenges:

GBLUP (Genomic Best Linear Unbiased Prediction): This standard model uses genome-wide SNPs to predict breeding values or phenotypic traits: y = Xb + Zg*g + e, where y is the phenotype vector, Xb represents fixed effects, Zg*g captures random genetic effects based on genomic relationship matrix G, and e denotes residuals [57].

TBLUP (Transcriptomic BLUP): This approach utilizes transcriptomic data instead of genomic information: y = Xb + Zt*t + e, where Zt*t represents random effects based on transcriptomic similarity [57].

GTBLUP: This model incorporates both genomic and transcriptomic data as independent random effects: y = Xb + Zg*g + Zt*t + e [57]. However, this approach may suffer from collinearity issues due to overlapping information between the data layers.

GTCBLUP/GTCBLUPi: These advanced frameworks address redundancy between genomic and transcriptomic information by conditioning transcriptomic effects on genetic effects, ensuring that the modeled transcriptomic effects are purely non-genetic [57]. The model is specified as: y = Xb + Zg*g + Zc*tc + e, where Zc*tc represents transcriptomic effects conditioned on genetics.

The integration of these data types follows either convergent designs (where data are collected and analyzed simultaneously) or explanential sequential designs (where one data type informs the collection or analysis of the other) [59]. In genomic studies, explanatory sequential designs are particularly common, where genomic discoveries guide targeted transcriptomic investigations.

Experimental Frameworks and Methodologies

Population Design and Sample Collection

Robust experimental designs for integrated genomics and transcriptomics require careful consideration of population structure, sample size, and tissue specificity. Studies typically employ structured populations such as F2 crosses [57] or large cohort studies [58] with hundreds to thousands of individuals to ensure sufficient statistical power. For example, a study on Japanese quail utilized 480 F2 animals to investigate efficiency-related traits [57], while human studies have analyzed thousands of participants [58].

Tissue selection is critical and should reflect the biological processes under investigation. The ileum tissue was targeted in quail studies to understand nutrient utilization [57], while whole blood was used in human studies to investigate isoform variation [58]. For plant NBS gene studies, tissues exposed to pathogens or those with high metabolic activity are often selected [14] [3].

Table 1: Key Considerations for Experimental Design in Integrated Genomic-Transcriptomic Studies

Design Factor	Considerations	Representative Examples
Population Structure	F2 crosses, natural populations, cohort studies	480 F2 Japanese quail [57]; 2,622 humans in FHS [58]
Sample Size	Hundreds to thousands of individuals for sufficient power	920 initial quail population [57]; >2,600 in human studies [58]
Tissue Selection	Relevance to phenotype, uniformity of collection	Ileum mucosa for efficiency traits [57]; whole blood for human traits [58]
Replication	Biological and technical replicates; external validation	WHI replication cohort (n=2,005) [58]

Laboratory Protocols and Sequencing Methods

DNA Sequencing: Whole-genome sequencing (WGS) provides comprehensive genetic information. For non-model organisms, genotyping arrays (e.g., 6k Illumina iSelect chip) offer a cost-effective alternative [57]. Quality control measures include SNP filtering based on call rates, minor allele frequency (MAF), and Hardy-Weinberg equilibrium [57].

RNA Sequencing: RNA-seq library preparation typically includes mRNA enrichment using poly-A selection or rRNA depletion. For specialized applications, such as quantifying specific transcript types, targeted approaches like Fluidigm BioMark HD systems can be employed [57]. Quality assessment includes evaluation of RNA integrity numbers (RIN), library concentration, and sequencing depth.

Data Generation Parameters: Sequencing depth is critical—typical guidelines recommend 30x coverage for WGS and 20-50 million reads per sample for RNA-seq. For isoform-level analysis, longer reads (e.g., PacBio Iso-Seq) improve splice junction detection [58].

Computational and Statistical Analysis Pipelines

Preprocessing: Genomic data processing includes alignment to reference genomes, variant calling, and quality control. Transcriptomic data processing involves read alignment, quantification of gene/isoform expression, and normalization. For cross-study comparisons, batch effect correction is essential.

Quantitative Trait Locus (QTL) Mapping: Expression QTL (eQTL) analysis identifies genetic variants associated with gene expression levels. Isoform ratio QTL (irQTL) mapping focuses on genetic variants that influence alternative splicing or isoform usage [58]. Significance thresholds are typically set at P < 5×10^(-8) for cis-eQTLs and more stringent values for trans-eQTLs [58].

Variance Component Analysis: Mixed models partition phenotypic variance into genetic and transcriptomic components, estimating the proportion of variance explained by each data type [57].

Pathway and Enrichment Analysis: Gene set enrichment analysis identifies biological pathways overrepresented among genes with significant genetic associations.

Diagram 1: Integrated Genomic-Transcriptomic Analysis Workflow. This workflow outlines the major steps in combining DNA and RNA sequencing data for functional gene prediction.

Case Study: NBS Gene Architecture in Monocots and Dicots

Species-Specific Structural Patterns of NBS Genes

Comparative genomic analyses reveal striking differences in NBS-LRR gene architecture between monocot and dicot plant species. A comprehensive study identified 12,820 NBS-domain-containing genes across 34 plant species, ranging from mosses to advanced monocots and dicots [14]. These genes displayed significant diversification, with 168 distinct domain architecture patterns identified, including both classical and species-specific structural configurations.

Table 2: Comparative Analysis of NBS-LRR Gene Family Across Plant Lineages

Plant Category	Species Example	Total NBS Genes	CNL Subfamily	TNL Subfamily	RNL Subfamily	Notable Features
Dicots	Arabidopsis thaliana	207	75	105	27	Balanced CNL/TNL distribution
Monocots	Oryza sativa (rice)	505	505	0	0	Complete TNL/RNL loss
Medicinal Dicot	Salvia miltiorrhiza	196	61	0	1	Severe TNL reduction
Gymnosperms	Pinus taeda	311	10.7%	89.3%	-	TNL dominance

In monocots, a dramatic reduction in TNL-type genes is evident, with complete absence observed in rice, wheat, and maize [14] [3]. This pattern contrasts with dicots like Arabidopsis thaliana, which maintains substantial representation across all three NLR subfamilies (CNL, TNL, and RNL). The medicinal plant Salvia miltiorrhiza exemplifies an intermediate pattern, with only 2 TNL and 1 RNL members identified from 196 NBS genes [3].

Integration of Multi-Omics Data for NBS Gene Functional Prediction

Transcriptomic profiling of NBS genes under various stress conditions provides critical functional insights. Studies in cotton have demonstrated differential expression of specific NBS orthogroups (OG2, OG6, OG15) in response to cotton leaf curl disease (CLCuD), with distinct expression patterns between tolerant and susceptible varieties [14]. Genetic variation analysis revealed 6,583 unique variants in tolerant cotton accessions compared to 5,173 in susceptible lines, highlighting potential causal polymorphisms [14].

Functional validation through virus-induced gene silencing (VIGS) of GaNBS (OG2) in resistant cotton demonstrated its crucial role in virus titration, confirming the functional significance of predictions derived from integrated omics data [14]. Similarly, protein-ligand and protein-protein interaction analyses showed strong binding between specific NBS proteins and cotton leaf curl disease virus components, providing mechanistic insights [14].

Integration methods for NBS gene studies typically combine genome-wide association studies (GWAS) of resistance phenotypes with expression QTL mapping and co-expression network analysis. This integrated approach has successfully identified candidate NBS genes controlling disease resistance in various crop species.

Diagram 2: Integrated Framework for NBS Gene Function Prediction. This framework illustrates the integration of genetic, transcriptomic, and functional data to elucidate NBS gene function in plant immunity.

Advanced Integration Models and Analytical Techniques

Specialized QTL Mapping Approaches

Isoform Ratio QTL (irQTL) Mapping: This advanced technique identifies genetic variants that influence the relative abundance of alternative transcript isoforms independent of overall gene expression changes [58]. In a study of human whole blood, researchers identified over 1.1 million cis-irQTLs, with 20% showing no significant association with overall gene expression, highlighting their isoform-specific regulatory role [58]. These isoform-specific variants are enriched at splice donor/acceptor sites and GWAS loci, suggesting their importance in complex trait architecture.

Splicing QTL (sQTL) Analysis: This approach specifically targets genetic variants that influence alternative splicing patterns. Splicing QTLs have been implicated in various diseases, including Alzheimer's disease and multiple sclerosis, demonstrating the functional importance of isoform-level regulation [58].

Variance Component Analysis and Prediction Accuracy

Comparative analyses of different BLUP models demonstrate the enhanced predictive power of integrated approaches. In studies of efficiency-related traits in Japanese quail, models incorporating both genetic and transcriptomic information (GTBLUP, GTCBLUPi) consistently outperformed models using only one data type [57]. Notably, transcript abundances from ileum tissue explained a larger portion of phenotypic variance for these traits than host genetics alone [57].

The GTCBLUPi model, which addresses redundant information between genomic and transcriptomic data, proved particularly effective as a framework for integration [57]. This model explicitly accounts for the fact that transcriptomic profiles are partially shaped by genetic factors, thereby providing more accurate estimates of non-genetic transcriptomic effects.

Table 3: Performance Comparison of Statistical Models for Genomic-Transcriptomic Integration

Model Type	Data Components	Key Features	Applications	Advantages
GBLUP	Genomic (SNPs)	Standard genomic prediction	Breeding value prediction	Established methods
TBLUP	Transcriptomic	Uses expression data	Trait prediction	Captures regulated expression
GTBLUP	Genomic + Transcriptomic	Independent effects	Complex trait prediction	Simple implementation
GTCBLUPi	Genomic + Conditional Transcriptomic	Conditions transcriptomics on genetics	Precision functional prediction	Avoids collinearity

Mendelian Randomization for Causal Inference

Mendelian randomization approaches leverage genetic variants as instrumental variables to infer causal relationships between molecular intermediates and complex traits. For example, analysis of rs12898397 in ULK3 demonstrated how this variant alters splice site usage and reduces expression of a full-length isoform, with Mendelian randomization supporting a causal role between this isoform shift and reduced diastolic blood pressure [58]. This approach provides a powerful framework for transitioning from correlation to causation in functional genomics.

Research Reagent Solutions and Experimental Tools

Table 4: Essential Research Reagents and Tools for Integrated Genomic-Transcriptomic Studies

Reagent/Tool Category	Specific Examples	Function/Application	Technical Considerations
Sequencing Platforms	Illumina iSelect chip, Fluidigm BioMark HD	Genotyping, targeted expression analysis	Throughput, cost, customization [57]
Library Prep Kits	Poly-A selection, rRNA depletion	RNA-seq library preparation	Transcript coverage, strand specificity
Validation Tools	Virus-Induced Gene Silencing (VIGS)	Functional validation of candidate genes	Efficiency, specificity, controls [14]
Analysis Pipelines	OrthoFinder, DIAMOND, MCL	Evolutionary analysis, orthogrouping	Algorithm parameters, scalability [14]
Statistical Software	ASReml R, R Studio	Mixed model analysis, variance component estimation	Computational efficiency, license requirements [57]

The integration of genomic and transcriptomic data represents a transformative approach for predicting gene function and elucidating the genetic architecture of complex traits. Statistical models that explicitly account for the relationships between these data layers, particularly conditional frameworks like GTCBLUPi, provide enhanced predictive accuracy and biological insights. The application of these integrated approaches to NBS gene families has revealed fundamental evolutionary patterns, including the dramatic divergence in gene architecture between monocots and dicots, and has identified key genetic regulators of disease resistance.

Future advancements will likely involve the incorporation of additional omics layers, including epigenomic, proteomic, and metabolomic data, to build more comprehensive models of biological systems. Continued refinement of statistical methods for multi-omics integration, coupled with innovative experimental validation approaches, will further accelerate progress in functional genomics and its applications across basic research, medicine, and agriculture.

Challenges in Functional Characterization and Validation

Overcoming Obstacles in Defining Atypical NBS Domain Architectures

The Nucleotide-Binding Site-Leucine Rich Repeat (NBS-LRR) gene family represents a cornerstone of plant innate immunity, encoding intracellular receptors that initiate effector-triggered immunity. While typical NBS-LRR proteins contain well-defined TIR/CC, NBS, and LRR domains, genome-wide studies consistently reveal a substantial proportion of genes that deviate from this canonical architecture. These atypical NBS domain architectures present significant obstacles for accurate annotation, classification, and functional characterization. The challenges are particularly pronounced in comparative studies aiming to elucidate species-specific NBS structural patterns between monocots and dicots, where differential evolutionary pressures have shaped distinct repertoires. Overcoming these obstacles requires integrated methodological approaches that combine advanced bioinformatic pipelines with experimental validation, enabling researchers to decipher the functional significance and evolutionary trajectories of these non-canonical resistance genes.

Structural Diversity and Classification of Atypical NBS Genes

Spectrum of Atypical Domain Architectures

Atypical NBS genes exhibit considerable structural diversity, primarily characterized by the absence or duplication of key domains. Systematic genome-wide analyses across multiple plant species have enabled a comprehensive classification system for these non-canonical architectures.

Table 1: Classification and Distribution of Atypical NBS Architectures

Architecture Type	Domain Composition	Prevalence in Pepper [32] [7]	Prevalence in Chinese Chestnut [60]	Functional Implications
N-type	NBS-only	200 genes	145 genes	Signaling intermediates, decoy receptors
NL-type	NBS-LRR	11 genes	Information missing	Truncated recognition receptors
NN-type	Duplicated NBS domains	8 genes	Information missing	Enhanced signaling capability
CN-type	CC-NBS	37 genes	96 genes	Compromised signaling complex assembly
TN-type	TIR-NBS	4 genes	5 genes	Altered signaling initiation
NLN-type	NBS-LRR-NBS	5 genes	Information missing	Complex regulatory mechanisms

The functional significance of these atypical architectures is increasingly recognized. NBS-only proteins (N-type) may function as integrated decoy domains within sensor-helper NLR networks, while proteins with duplicated NBS domains (NN-type) potentially exhibit enhanced signaling capabilities through altered nucleotide binding kinetics [32]. The abundance of these truncated forms underscores their potential importance in plant immune systems rather than representing mere annotation artifacts.

Conserved Motifs in Atypical NBS Proteins

Despite their divergent domain architectures, atypical NBS proteins maintain critical conserved motifs within their NBS domains that are essential for function. Structural analyses have identified six conserved motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL) that are preserved across various atypical architectures [32] [7]. The P-loop motif is particularly crucial for ATP/GTP binding and hydrolysis, while the GLPL motif contributes to resistance signaling. These conserved elements enable the identification of potentially functional atypical NBS genes despite their overall domain truncations or rearrangements.

Species-Specific Patterns in Monocots and Dicots

Evolutionary Divergence in NLR Repertoires

Comparative genomic analyses reveal striking differences in the composition and evolution of NBS gene families between monocots and dicots, with significant implications for atypical gene distributions. These lineage-specific patterns reflect differential evolutionary pressures and possible adaptations to distinct pathogen environments.

Table 2: Comparative Analysis of NBS Gene Families in Monocots and Dicots

Species	Classification	Total NBS Genes	TNL Subfamily	CNL Subfamily	Atypical Prevalence	Research Source
Oryza sativa (rice)	Monocot	505 genes	Complete loss	Dominant	Information missing	[3]
Zea mays (maize)	Monocot	Information missing	Complete loss	Dominant	Information missing	[3]
Arabidopsis thaliana	Dicot	207 genes	Present	Present	Information missing	[3]
Salvia miltiorrhiza	Dicot	196 genes	2 TNLs	61 CNLs	134 atypical (68%)	[3]
Capsicum annuum (pepper)	Dicot	252 genes	4 TNLs	48 CC-containing	200 atypical (79%)	[32] [7]
Asparagus officinalis	Monocot	27 genes	Information missing	Information missing	Information missing	[61]

Monocots exhibit a near-complete absence of TNL genes, with only CNL-type NBS genes present in species such as rice and maize [3]. This fundamental divergence suggests distinct evolutionary trajectories in the immune receptors of monocot and dicot lineages. Additionally, comparative studies within the Asparagus genus revealed a marked contraction of NLR genes during domestication, with wild relative A. setaceus possessing 63 NLR genes compared to only 27 in cultivated A. officinalis [61]. This reduction highlights how artificial selection can reshape NBS gene repertoires, potentially affecting atypical gene distributions.

Selection Pressures and Evolutionary Dynamics

The evolutionary forces shaping atypical NBS genes differ significantly between species, as revealed by Ka/Ks analysis (ratio of non-synonymous to synonymous substitutions). In Chinese chestnut, most NBS-encoding genes showed Ka/Ks values less than 1, indicating the predominance of purifying selection that maintains conserved functions [60]. However, a minority of non-TIR gene families (4/34) exhibited Ka/Ks values greater than 1, suggesting positive selection potentially driven by co-evolution with pathogens [60]. Similar patterns were observed in maize annexin genes, where most genes underwent purifying selection, while ZmAnn10 showed evidence of positive selection in certain varieties [62]. This differential selection highlights the dynamic evolutionary landscape of plant immune genes and their atypical variants.

Methodological Framework for Characterizing Atypical NBS Genes

Integrated Bioinformatics Pipelines

Accurate identification and annotation of atypical NBS genes requires sophisticated bioinformatics approaches that combine multiple complementary methods. The following workflow outlines a robust pipeline for comprehensive NBS gene characterization:

Diagram 1: Experimental workflow for NBS gene identification. The pipeline integrates complementary bioinformatic approaches with experimental validation.

The Hidden Markov Model (HMM) search using the NB-ARC domain profile (PF00931) provides high sensitivity for detecting divergent NBS domains, while BLASTP against curated reference sequences helps identify more distant homologs [61] [63]. For polyploid genomes, specialized pipelines like DaapNLRSeek have been developed to address the challenges of duplicated genomes [64]. Domain architecture analysis using tools like InterProScan and NCBI's CD-Search is particularly crucial for distinguishing atypical architectures, as it detects the presence or absence of TIR, CC, and LRR domains [3] [61].

Advanced Analytical Techniques

Further characterization of atypical NBS genes requires additional analytical approaches to elucidate their potential functions and evolutionary history:

• Motif Analysis: Tools like MEME Suite identify conserved motifs within NBS domains, helping establish functional potential even in truncated proteins [61]
• Phylogenetic Reconstruction: Maximum likelihood methods reveal evolutionary relationships between typical and atypical NBS genes [61] [63]
• Selection Pressure Analysis: Ka/Ks calculations distinguish between purifying and positive selection [62] [60]
• Cis-Element Analysis: Promoter scanning using PlantCARE identifies defense-related regulatory elements [61] [62]
• Expression Profiling: RNA-seq analysis and qPCR validate expression patterns across tissues and stress conditions [65] [63]

These integrated methods facilitate the transition from mere sequence identification to functional prediction, enabling researchers to prioritize atypical NBS genes for further experimental investigation.

Experimental Validation and Functional Characterization

Expression Analysis Under Stress Conditions

Comprehensive expression profiling is essential for validating the functional relevance of atypical NBS genes. RNA-seq analysis across multiple tissue types and stress conditions provides insights into putative functions. In grass pea, transcriptome analysis revealed that 85% of identified NBS genes (including atypical forms) showed significant expression, with distinct patterns observed under salt stress conditions [63]. Similarly, in rose, spatiotemporal expression profiling of ALOG family genes (a distinct class of transcriptional regulators) demonstrated differential expression across vegetative and reproductive tissues, suggesting specialized functions in organogenesis [65].

qPCR validation provides higher sensitivity for detecting expression changes. In grass pea, nine selected NBS genes showed differential regulation under salt stress, with most genes upregulated at 50 and 200 μM NaCl, while LsNBS-D18, LsNBS-D204, and LsNBS-D180 showed reduced or drastic downregulation [63]. This precise expression profiling helps establish genotype-phenotype relationships for atypical NBS genes.

Functional Genetic Techniques

Several functional validation approaches are particularly valuable for characterizing atypical NBS genes:

• Virus-Induced Gene Silencing (VIGS): Enables rapid functional assessment, as demonstrated in cotton where silencing of GaNBS (OG2) affected virus tittering [14]
• Heterologous Expression: Testing NBS genes in model systems like Nicotiana benthamiana, where two sugarcane-paired NLRs were shown to induce immune responses [64]
• Protein-Protein Interaction Studies: Identifying interaction partners for atypical NBS proteins, such as the strong interaction observed between putative NBS proteins and ADP/ATP or viral proteins in cotton [14]

These functional assays help establish whether atypical NBS genes participate in immune signaling complexes, act as decoy receptors, or perform novel functions in plant stress responses.

Research Reagent Solutions for NBS Gene Characterization

Table 3: Essential Research Reagents for NBS Gene Studies

Reagent/Tool	Specific Examples	Application	Technical Considerations
HMM Profiles	NB-ARC (PF00931)	Domain identification	Curated models improve detection sensitivity
Reference Sequences	Arabidopsis, rice NBS proteins	BLAST queries	Broad phylogenetic coverage enhances detection
Domain Databases	Pfam, InterPro, CDD	Domain architecture analysis	Integrated approaches overcome annotation gaps
Genomic Resources	Species-specific genomes	Comparative analysis	Pan-genomes capture population-level diversity [62]
Expression Databases	RNA-seq libraries	Expression profiling	Tissue-specific and stress-induced data are crucial
Cloning Systems	pMD18-T vector, Gateway	Functional validation	Compatible with various expression systems [65]
VIGS Vectors	TRV-based systems	Functional characterization	Enables high-throughput gene silencing [14]

Defining atypical NBS domain architectures remains challenging but essential for comprehending the full complexity of plant immune systems. The obstacles are multifaceted, encompassing bioinformatic identification, functional annotation, and evolutionary interpretation. Overcoming these hurdles requires integrated approaches that leverage pan-genome resources to capture species-wide diversity, advanced structural modeling to predict function from sequence, and sophisticated molecular techniques to validate immune functions. Future research should prioritize the functional characterization of atypical NBS genes across diverse monocot and dicot species, elucidating their roles in integrated immune networks. Such efforts will not only resolve fundamental questions in plant immunity but also facilitate the development of crops with enhanced disease resistance through informed breeding strategies or biotechnological approaches.

Addressing Species-Specific Structural Variations and Gene Fragmentation

In plant genomes, disease resistance is largely governed by nucleotide-binding site leucine-rich repeat (NBS-LRR) genes, which constitute one of the largest and most variable gene families. These genes encode intracellular receptors that recognize pathogen effector proteins and initiate robust immune responses through effector-triggered immunity (ETI). The genomic architecture of NBS-LRR genes exhibits remarkable diversity across plant species, driven by species-specific structural variations (SVs) and gene fragmentation events. Understanding these dynamic patterns is crucial for deciphering plant-pathogen co-evolution and developing novel crop improvement strategies. This technical guide examines the complex landscape of species-specific NBS structural patterns within monocots and dicots, providing researchers with comprehensive analytical frameworks and experimental approaches for characterizing these genetically turbulent regions.

The expansion and contraction of NBS-LRR gene families across plant lineages reveal fascinating evolutionary stories. While these genes can represent up to 1-2% of all annotated protein-coding genes in some species, their structural composition varies dramatically. Comparative analyses demonstrate that holocentric chromosomes in Lepidoptera have maintained 32 ancestral linkage groups (termed Merian elements) through 250 million years of evolution, despite extensive karyotypic diversity in eight specific lineages [66]. This evolutionary stability provides important context for understanding the constraints on genome architecture and the exceptional cases where extensive reorganization occurs.

Comparative Genomics of NBS-LRR Genes

Structural Classification and Distribution Patterns

NBS-LRR proteins are modular in structure, typically containing a conserved nucleotide-binding site (NBS) domain and C-terminal leucine-rich repeats (LRRs). Classification depends primarily on N-terminal domains, dividing them into three major subfamilies: TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), and RPW8-NBS-LRR (RNL). The NBS domain facilitates ATP/GTP binding and hydrolysis, while the LRR domain is involved in pathogen recognition specificity. Beyond these typical structures, numerous atypical NBS-LRR variants exist, often lacking complete N-terminal or LRR domains, classified as N (NBS only), TN (TIR-NBS), CN (CC-NBS), or NL (NBS-LRR) types [3] [7].

Table 1: NBS-LRR Gene Distribution Across Representative Plant Species

Species	Total NBS-LRR Genes	TNL	CNL	RNL	Atypical	Reference
Arabidopsis thaliana	207	82	118	7	-	[3]
Oryza sativa (rice)	505	0	501	4	-	[3]
Salvia miltiorrhiza	196	2	75	1	118	[3]
Capsicum annuum (pepper)	252	4	2	1	245	[7]
Solanum tuberosum (potato)	447	158	278	11	-	[3]

The distribution of NBS-LRR subfamilies exhibits striking phylogenetic patterns. Monocots, including rice, wheat, and maize, have experienced near-complete loss of TNL genes, while dicots maintain both TNL and CNL subfamilies, though with considerable variation. In pepper genomes, from 252 identified NBS-LRR genes, only 4 belong to the TNL subfamily, while 200 lack both CC and TIR domains, highlighting the exceptional diversity of NBS-LRR resistance genes [7]. Similarly, in the medicinal plant Salvia miltiorrhiza, comparative analysis revealed a marked reduction in TNL and RNL subfamily members compared to other dicot species [3].

Evolutionary Dynamics and Genomic Architecture

The expansion and diversification of NBS-LRR genes are primarily driven by tandem duplications and genomic rearrangements. In pepper, 54% of NBS-LRR genes (136 genes) form 47 physical clusters across the genome, with chromosome 3 containing the highest number of clusters (10) and the largest cluster comprising eight genes [7]. These clusters often include members from the same gene subfamily, though some exhibit mixing of different subfamilies, reflecting the complexity of genomic organization and potential functional interactions.

Table 2: Evolutionary Patterns of NBS-LRR Genes in Plant Lineages

Evolutionary Pattern	Representative Taxa	Key Characteristics	Functional Implications
TNL Loss	Monocots (Oryza sativa, Triticum aestivum, Zea mays)	Complete absence of TNL genes	Distinct signaling pathways; alternative recognition mechanisms
RNL Reduction	Salvia species, Capsicum annuum	Limited to 1-2 RNL members	Potential compromised signaling convergence points
TNL Dominance	Gymnosperms (Pinus taeda)	TNL comprises 89.3% of typical NBS-LRRs	Ancient defense signaling mechanisms
Lineage-Specific Expansion	Multiple angiosperm lineages	Proliferation of CNL subfamily	Adaptation to pathogen pressure
Fusion Events	Ditrysia (Lepidoptera)	M17+M20 ancestral fusion	Stable karyotype with 31 linkage groups

Analysis of 210 chromosomally complete lepidopteran genomes revealed that fusions often involve small, repeat-rich Merian elements and the sex-linked element, while fissions are exceptionally rare outside of specific lineages [66]. This evolutionary constraint maintains synteny within chromosomal elements even after 250 million years of diversification. The proportional length of each Merian element is broadly conserved across species that haven't undergone rearrangement events, suggesting selective pressures maintaining this genomic architecture.

Methodological Framework for Structural Variation Analysis

Genome-Wide Identification of NBS-LRR Genes

Step 1: Sequence Retrieval and Quality Assessment

• Obtain chromosome-level reference genomes from public databases (NCBI, Phytozome, Plaza)
• Assess assembly quality using metrics including contig N50, BUSCO completeness, and percentage of assembly scaffolded into chromosomes
• High-quality references should have >90% BUSCO completeness and high contiguity (mean contig N50 >10 Mb) [66]

Step 2: Domain Identification and Classification

• Perform HMMER searches against Pfam database using Hidden Markov Models (HMMs) for NBS (NB-ARC), TIR, CC, and LRR domains
• Use PfamScan.pl HMM search script with default e-value (1.1e-50) using background Pfam-A_hmm model [14]
• Confirm coiled-coil domains with COILS software with a threshold probability >90%
• Classify genes based on domain architecture into TNL, CNL, RNL, or atypical categories

Step 3: Genomic Distribution and Cluster Analysis

• Map physical positions of all identified NBS-LRR genes onto chromosomes
• Define gene clusters based on physical proximity (typically ≤200 kb between adjacent genes)
• Visualize distribution using genetic linkage maps generated with tools like MapDraw [7]

Structural Variation Detection Using Long-Read Sequencing

Library Preparation and Sequencing

• Extract high molecular weight DNA using protocols that minimize shearing (≥25 kb fragments)
• Perform size selection to enrich for large fragments suitable for structural variation detection
• Utilize Oxford Nanopore Technologies (ONT) or PacBio long-read sequencing platforms
• Sequence to intermediate coverage (median 16-20×) with read N50 >20 kb for comprehensive SV discovery [67]

SV Discovery and Genotyping Pipeline

• Read Alignment: Map reads to both linear (GRCh38, CHM13) and graph (HPRC_mg) genomic references
• SV Calling: Apply multiple callers including Sniffles and DELLY for complementary detection
• Graph Augmentation: Use minigraph tool to integrate discovered SV alleles into pangenome graph
• SV Genotyping: Employ Giggles for graph-aligned long read genotyping
• Phasing: Perform statistical SV phasing using SHAPEIT5 with haplotype reference panels [67]

Benchmarking and Validation

• Compare SVs with multi-platform genome assemblies to estimate false discovery rates
• Validate MEIs (mobile element insertions) which exhibit well-defined allele architectures and low FDR (0.85-6.75%)
• Consider size-dependent FDR: SVs ≥250 bp show lower FDR (deletions: 6.91%, insertions: 8.12%) than smaller SVs [68]

For pig genomes, the assembly-based SVIM-asm tool demonstrated superior performance in both accuracy and resource consumption, with alignment-based tools performing well even at 5× sequencing depth. SVs in complex repeat and runs of homozygosity regions can be precisely detected with optimized pipelines [68].

Signaling Pathways and Functional Mechanisms

NBS-LRR proteins function as intracellular immune receptors that recognize pathogen-secreted effectors directly or indirectly through guardee proteins. Upon effector recognition, conformational changes in the NBS domain promote nucleotide exchange (ADP to ATP), activating downstream signaling that culminates in hypersensitive response (HR) and programmed cell death at infection sites.

In Arabidopsis, the LRR receptor protein RLP23 associates with lipase-like proteins EDS1 and PAD4, and the ADR1 protein, forming a supramolecular complex that serves as a convergence point for defense signaling cascades [3]. The rice CNL protein Pita directly recognizes the effector AVR-Pita of the rice blast fungus through its LRR domain, activating immune signaling pathways [3]. These examples illustrate the diverse molecular strategies employed by NBS-LRR proteins across monocot and dicot lineages.

Experimental Validation and Functional Characterization

Expression Profiling Under Stress Conditions

Transcriptomic analysis provides critical insights into NBS-LRR gene regulation under various biotic and abiotic stresses. Comprehensive expression profiling should include:

Experimental Design

• Collect RNA-seq data from multiple tissues (leaf, stem, root, flower) under control and stress conditions
• Include time-course experiments to capture dynamic expression patterns
• Incorporate diverse biotic stresses (bacterial, fungal, viral pathogens) and abiotic stresses (drought, salinity, temperature extremes)
• Analyze susceptible and resistant cultivars to identify expression patterns correlated with disease resistance

Data Analysis Pipeline

• Process RNA-seq data through standardized transcriptomic pipelines
• Calculate FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values for normalization
• Perform differential expression analysis using appropriate statistical methods (DESeq2, edgeR)
• Create heatmaps to visualize expression patterns across conditions and genotypes [14]

In cotton NBS genes, expression profiling revealed putative upregulation of specific orthogroups (OG2, OG6, OG15) in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to cotton leaf curl disease (CLCuD) [14].

Functional Validation Through Genetic Approaches

Virus-Induced Gene Silencing (VIGS)

• Design gene-specific fragments (300-500 bp) with high sequence uniqueness
• Clone fragments into TRV-based (Tobacco Rattle Virus) vectors
• Infect plants through Agrobacterium tumefaciens-mediated infiltration
• Challenge silenced plants with pathogens and assess disease symptoms
• Quantify pathogen biomass through qPCR to measure resistance changes
• The silencing of GaNBS (OG2) in resistant cotton demonstrated its putative role in virus tittering [14]

Protein Interaction Studies

• Perform yeast two-hybrid screening to identify interacting partners
• Validate interactions through co-immunoprecipitation (Co-IP) assays
• Conduct protein-ligand interaction studies to examine binding with ADP/ATP
• Protein-ligand and protein-protein interaction studies showed strong interaction of putative NBS proteins with ADP/ATP and different core proteins of the cotton leaf curl disease virus [14]

Genetic Variation Analysis

• Sequence NBS-LRR genes from resistant and susceptible accessions
• Identify sequence variants (SNPs, indels) correlated with resistance phenotypes
• In G. hirsutum, genetic variation between susceptible (Coker 312) and tolerant (Mac7) accessions identified several unique variants in NBS genes of Mac7 (6583 variants) and Coker312 (5173 variants) [14]

Research Reagent Solutions and Technical Tools

Table 3: Essential Research Reagents and Computational Tools for NBS-LRR Analysis

Category	Tool/Reagent	Specific Application	Key Features
Domain Identification	HMMER/PfamScan	NBS domain identification	Default e-value 1.1e-50, Pfam-A_hmm model
Coiled-Coil Prediction	COILS	CC domain confirmation	Probability threshold >90%
Orthogroup Analysis	OrthoFinder v2.5.1	Evolutionary relationships	MCL clustering, DendroBLAST
SV Detection (Long Read)	Sniffles/DELLY	Structural variation calling	Complementary detection approaches
Graph-Based SV Analysis	SVarp/SAGA	SV discovery in haplotype contexts	Graph-aware pattern recognition
Expression Analysis	DESeq2/edgeR	Differential expression	RNA-seq statistical analysis
Functional Validation	TRV-VIGS vectors	Gene silencing	TRV1 and TRV2 constructs
Interaction Studies	Yeast Two-Hybrid	Protein-protein interactions	Screening and validation

The investigation of species-specific structural variations and gene fragmentation in NBS-LRR genes represents a critical frontier in plant immunity research. The comprehensive analysis of these dynamic genomic regions has revealed fundamental patterns of plant genome evolution, including the complete loss of TNL genes in monocots, lineage-specific expansions and contractions, and the formation of complex gene clusters through tandem duplications. These structural patterns directly influence plant immune capacity and have significant implications for crop improvement strategies.

Future research directions should leverage emerging technologies for large DNA fragment editing, including CRISPR-based approaches for targeted deletions, insertions, replacements, inversions, translocations, and duplications [69]. These tools enable precise manipulation of NBS-LRR gene clusters, potentially allowing researchers to engineer broad-spectrum disease resistance by reconstituting lost diversity or introducing novel recognition specificities. Additionally, the integration of pangenome references and long-read sequencing technologies will continue to enhance our understanding of the full spectrum of structural variations across diverse accessions and their contributions to immune function.

As we deepen our understanding of species-specific NBS structural patterns, we move closer to predictive models of plant-pathogen co-evolution and develop more sophisticated approaches for engineering durable disease resistance in crop plants. The methodological frameworks and analytical tools presented in this technical guide provide a foundation for these advancing investigations at the intersection of genomics, plant pathology, and crop improvement.

The functional characterization of nucleotide-binding site (NBS) domain genes represents a critical phase in plant immunity research, particularly when investigating the species-specific structural patterns that distinguish monocot and dicot resistance mechanisms. These NBS-LRR (NLR) genes form the backbone of the plant immune system, encoding intracellular receptors that recognize pathogen effectors and initiate defense responses [14]. The expanding availability of plant genomic data has revealed remarkable diversification in NLR architectures, with studies identifying 12,820 NBS-domain-containing genes across 34 species ranging from mosses to higher plants, classified into 168 distinct domain architecture patterns [14]. This structural diversity underscores the necessity of robust functional validation strategies to determine the biological roles of these genes in species-specific immunity.

This technical guide provides comprehensive methodologies for the functional validation of NBS genes, with particular emphasis on comparative approaches between monocot and dicot systems. We present integrated experimental workflows, detailed protocols, and analytical frameworks designed to elucidate the functional significance of species-specific NBS structural patterns, enabling researchers to bridge the gap between genomic predictions and biological understanding.

NBS Gene Architecture and Evolution in Monocots and Dicots

Structural and Evolutionary Diversification

The evolutionary history of NBS genes reveals distinct patterns of expansion and diversification between monocot and dicot lineages. Comparative genomic analyses have identified significant structural variations that likely reflect adaptation to different pathogen pressures. The primary NBS gene classes include:

• TNLs: TIR-NBS-LRR proteins, predominantly found in dicots
• CNLs: CC-NBS-LRR proteins, common in both monocots and dicots
• RNLs: RPW8-NBS-LRR proteins, functioning as helper NLRs [14]

Bryophytes and lycophytes represent ancestral lineages with relatively small NLR repertoires (approximately 25 NLRs in Physcomitrella patens), indicating that substantial gene expansion occurred primarily in flowering plants [14]. This expansion has been driven by different evolutionary mechanisms in monocots and dicots, with tandem gene duplication playing a particularly significant role in creating clustered NLR arrangements that facilitate the generation of novel specificities.

Table 1: Evolutionary Patterns of NBS Genes in Monocots and Dicots

Feature	Monocots	Dicots
Predominant NBS Types	CNLs dominate	TNLs and CNLs
Expansion Mechanism	Whole genome duplication + tandem duplication	Tandem duplication predominant
Genomic Organization	Large clusters	Dispersed and clustered
Conserved Motifs	Species-specific in N-terminus	Family-specific variations
Example Helper NLRs	NRC network in Solanaceae [70]	NRC network in Solanaceae [70]

Expression Characteristics of Functional NLRs

Recent evidence challenges the historical presumption that NLRs are universally maintained at low expression levels. Analysis of known functional NLRs across multiple species reveals that functional immune receptors frequently exhibit characteristically high expression in uninfected plants [70]. In Arabidopsis thaliana, known functional NLRs are significantly enriched in the top 15% of expressed NLR transcripts compared to lower-expressed NLRs (χ² test, P=0.038) [70].

This expression signature provides a valuable predictive filter for candidate prioritization. For example, the barley NLR Mla7 requires multiple copies for full resistance function, with higher copy numbers correlating with enhanced resistance to Blumeria hordei and Puccinia striiformis f. sp. tritici [70]. Native Mla7 exists as three identical copies in the haploid genome of barley cv. CI 16147, supporting the hypothesis that specific expression thresholds are necessary for NLR function [70].

Functional Validation Strategies

Integrated Workflow for NBS Gene Validation

The functional validation of NBS genes requires a systematic approach that integrates multiple complementary methodologies. The workflow progresses from initial gene identification through increasingly rigorous functional assays, with comparative analysis between monocots and dicots providing insights into species-specific functions.

Virus-Induced Gene Silencing (VIGS)

VIGS has emerged as a powerful technique for rapid loss-of-function analysis, particularly suitable for functional screening of NBS genes in both monocot and dicot systems. This approach utilizes modified viral vectors to deliver gene-specific sequences that trigger RNA silencing of endogenous targets.

Protocol: VIGS in Wheat for NBS Gene Validation

The following protocol details VIGS implementation in wheat, a representative monocot system:

• Vector Selection: Utilize the Barley Stripe Mosaic Virus (BSMV) vector system for monocot species or Tobacco Rattle Virus (TRV) for dicot species.

• Insert Design: Amplify 150-300 bp gene-specific fragment from target NBS gene using:

  [71]

• Vector Construction: Clone fragment into BSMV-γ vector using appropriate restriction sites or Gateway recombination.

• In Vitro Transcription: Generate infectious RNA transcripts from linearized plasmids using mMessage mMachine T7 transcription kit.

• Plant Inoculation:

  • Grow wheat plants to two-leaf stage (approximately 10-14 days)
  • Rub inoculated leaves with transcript mixture (BSMV-α, BSMV-β, BSMV-γ-target) in FES buffer (0.1M glycine, 0.06M K2HPO4, 1% sodium pyrophosphate, 1% celite) [71]

• Phenotypic Analysis:

  • Assess silencing efficiency 10-14 days post-inoculation by qRT-PCR
  • Challenge with target pathogen and evaluate disease symptoms
  • Measure physiological parameters (chlorophyll content, MDA accumulation, ROS staining) [71]

Applications and Validation

VIGS has successfully validated NBS gene function in multiple systems. For example, silencing of GaNBS (OG2) in resistant cotton demonstrated its essential role in virus titering against cotton leaf curl disease [14]. Similarly, silencing of TaUSP85 in wheat resulted in significantly reduced thermotolerance, manifested as wilting, decreased chlorophyll content, and increased MDA accumulation [71]. The silenced lines showed substantially higher ROS accumulation compared to controls, as determined by DAB and NBT staining [71].

Transgenic Complementation

Transgenic complementation represents the gold standard for functional validation, providing conclusive evidence of gene function through restoration of phenotypes in susceptible genotypes.

High-Throughput Transgenic Arrays

Recent advances have enabled high-throughput approaches to NLR validation. A proof-of-concept study generated a wheat transgenic array of 995 NLRs from diverse grass species to identify new resistance genes [70]. This pipeline exploited the high-expression signature of functional NLRs and leveraged high-efficiency wheat transformation systems to rapidly screen for resistance against major pathogens.

Table 2: Transgenic Complementation Approaches for NBS Genes

Method	Key Features	Applications	Throughput
Agrobacterium-Mediated	High efficiency, single copy preference	Dicots, some monocots	Medium
Biolistic Transformation	genotype-independent, multicopy inserts	Cereal crops	Medium
High-Throughput Array	Parallel assessment of hundreds of NLRs	Wheat, novel gene discovery	High [70]
Multicopy Complementation	Essential for certain NLR functions	Barley Mla alleles [70]	Low-Medium

Protocol: Multicopy Transgenic Complementation

The barley Mla7 validation demonstrates that some NLRs require multiple copies for full function:

• Vector Construction:

  • Clone full-length genomic sequence (promoter + coding region + terminator)
  • For multicopy lines: concatenate multiple copies in tandem array
  • For single-copy lines: use site-specific recombination systems

• Plant Transformation:

  • Utilize Agrobacterium-mediated transformation for dicots
  • Employ biolistic methods for monocot species
  • Generate independent T0 lines with varying copy numbers

• Copy Number Assessment:

  • Determine transgene copy number by digital PCR or Southern blotting
  • Segregate copies through genetic crossing
  • Correlate copy number with expression level (qRT-PCR) and resistance

• Phenotypic Validation:

  • Challenge T1/T2 plants with target pathogen
  • For Mla7, only lines with ≥2 copies showed resistance to Blumeria hordei isolate CC148 (AVRa7)
  • Full native resistance recapitulated in lines with four copies [70]

Protein Interaction Studies

Elucidating NLR function frequently requires characterization of protein-protein interactions, including self-associations, interactions with pathogen effectors, and partnerships with helper NLRs.

Yeast Two-Hybrid (Y2H) Screening

Y2H provides a powerful approach for identifying novel NLR interactors:

• Construct Design:

  • Clone full-length and domain-truncated NBS sequences into bait and prey vectors
  • Include known functional NLRs as positive controls

• Library Screening:

  • Screen against cDNA library from pathogen-challenged tissues
  • Use multiple selective media stringencies to reduce false positives

• Validation:

  • Confirm interactions with co-immunoprecipitation (Co-IP)
  • Verify in planta using Luciferase Complementation Assay (LC) [71]

For example, TaUSP85 was found to interact with TaUSP1 and TaUSP11 to form heterodimers through Y2H screening and LCI validation [71].

Effector Recognition Mechanisms

NLRs can recognize pathogen effectors through direct or indirect mechanisms. The wheat Ym1 protein confers resistance to wheat yellow mosaic virus (WYMV) through direct interaction with the viral coat protein (CP) [72]. This interaction induces nucleocytoplasmic redistribution, transitioning Ym1 from an auto-inhibited to an activated state, subsequently triggering hypersensitive responses and establishing WYMV resistance [72].

Species-Specific Considerations

Tissue-Specific Expression Patterns

NLR expression demonstrates significant tissue specificity that must be considered in experimental design. For example, the wheat WYMV resistance gene Ym1 is specifically expressed in roots and induced upon WYMV infection [72]. Similarly, Ym2, another WYMV resistance gene, shows root-specific expression and functions by preventing WYMV movement from the fungal vector into plant roots [72].

Helper NLRs also display tissue-specific expression patterns. In tomato, NRC6 is highly expressed in roots but not leaves, while NRC0 shows variable expression between roots and leaves of different cultivars [70]. These patterns highlight the importance of investigating appropriate tissues relevant to the pathogen lifestyle.

Monocot-Dicot Experimental Comparisons

Functional validation approaches must accommodate fundamental differences between monocot and dicot systems:

• Transformation Efficiency: Monocots generally show lower transformation efficiency, necessitating higher throughput approaches
• Gene Architecture: Dicot NBS genes typically contain triple the number of introns with smaller average size (194 bp) compared to monocots (556 bp) [73]
• Helper NLR Networks: Solanaceous species utilize conserved NRC helper NLR networks, while other families employ distinct systems

Research Reagent Solutions

Table 3: Essential Research Reagents for NBS Gene Functional Validation

Reagent/Tool	Function	Example Applications	Species Compatibility
BSMV VIGS Vector	Virus-induced gene silencing	TaUSP85 functional analysis [71]	Monocots (wheat, barley)
TRV VIGS Vector	Virus-induced gene silencing	Solanaceous NBS gene silencing	Dicots (tomato, tobacco)
Gateway Cloning System	High-throughput vector construction	995 NLR wheat transgenic array [70]	Broad range
CRISPR/Cas9 System	Targeted gene knockout	Recessive resistance gene validation	Broad range
Yeast Two-Hybrid System	Protein-protein interaction screening	TaUSP85 interactor identification [71]	Broad range
Luciferase Complementation	in planta protein interaction validation	TaUSP heterodimer confirmation [71]	Broad range
Ph1b Mutant Wheat	Promotes homoeologous recombination	Ym1 fine mapping [72]	Wheat
Agrobacterium Strains	Plant transformation	Dicot transformation, some monocots	Broad range

Functional validation of NBS genes requires the integrated application of multiple complementary strategies, from initial silencing approaches to definitive transgenic complementation. The distinctive structural and functional characteristics of monocot versus dicot NBS genes necessitate species-appropriate experimental designs, while conserved features enable shared methodological frameworks. The accelerating discovery and characterization of NLR genes through these validation strategies continues to enhance our understanding of plant immunity mechanisms and provides valuable genetic resources for crop improvement. As validation pipelines become increasingly sophisticated and high-throughput, they will undoubtedly reveal new dimensions of the sophisticated molecular arsenal that plants employ in their ongoing evolutionary arms race with pathogens.

Linking Genetic Variation in NBS Genes to Disease Resistance Phenotypes

The Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) gene family represents the largest and most critical class of plant disease resistance (R) genes, serving as intracellular immune receptors that recognize pathogen-secreted effectors to initiate robust defense responses [3] [31]. These genes encode modular proteins containing a conserved nucleotide-binding site (NBS) domain and a C-terminal leucine-rich repeat (LRR) domain, with variable N-terminal domains defining major subfamilies [8] [74]. The central NBS domain facilitates ATP/GTP binding and hydrolysis, enabling conformational changes critical for immune signaling activation, while the LRR domain is primarily responsible for specific pathogen recognition [3] [75].

Understanding the link between genetic variation in NBS genes and disease resistance phenotypes requires examining species-specific structural patterns across monocots and dicots. Recent genome-wide studies reveal striking evolutionary divergence in NBS gene composition, distribution, and architecture between these plant lineages [3] [14]. This technical guide provides a comprehensive framework for investigating NBS gene variation and its functional consequences, offering detailed methodologies for genomics, transcriptomics, and functional validation experiments relevant to both basic research and applied crop improvement.

Structural and Evolutionary Diversity of NBS Genes

NBS-LRR Classification and Domain Architecture

NBS-LRR proteins are classified based on their N-terminal domains into several major structural types:

• TNLs: Contain Toll/Interleukin-1 Receptor (TIR) domains
• CNLs: Feature Coiled-Coil (CC) domains
• RNLs: Possess Resistance to Powdery Mildew 8 (RPW8) domains
• Atypical NBS: Include truncated forms (N, TN, CN, NL) lacking complete domains [3] [8]

Table 1: Major NBS-LRR Structural Types and Characteristics

Type	N-terminal	NBS	LRR	Primary Function
TNL	TIR	Present	Present	Pathogen recognition
CNL	CC	Present	Present	Pathogen recognition
RNL	RPW8	Present	Present	Signal transduction
TN	TIR	Present	Absent	Regulatory
CN	CC	Present	Absent	Regulatory
N	None	Present	Absent	Regulatory
NL	None	Present	Present	Pathogen recognition

The modular structure of NBS-LRR proteins enables distinct functional specializations. The TIR and CC domains facilitate protein-protein interactions and signaling initiation, the NBS domain acts as a molecular switch regulated by nucleotide binding status, and the LRR domain provides specificity for pathogen recognition through its hypervariable residues [8] [75].

Comparative Genomics Reveals Species-Specific Patterns

Genome-wide comparative analyses across monocots and dicots reveal profound differences in NBS gene family composition and evolution:

Table 2: NBS Gene Family Size and Composition Across Plant Species

Species	Family	Total NBS	CNL	TNL	RNL	Reference
Oryza sativa (rice)	Poaceae (monocot)	505	505	0	0	[3]
Zea mays (maize)	Poaceae (monocot)	-	Majority	0	-	[3]
Arabidopsis thaliana	Brassicaceae (dicot)	207	-	-	-	[3]
Salvia miltiorrhiza	Lamiaceae (dicot)	196	61	0	1	[3]
Nicotiana benthamiana	Solanaceae (dicot)	156	25	5	4*	[8]
Akebia trifoliata	Lardizabalaceae (dicot)	73	50	19	4	[74]
Nicotiana tabacum	Solanaceae (dicot)	603	-	-	-	[75]

*Note: RNL count includes other RPW8-containing NBS genes

Monocots, including rice and maize, exhibit a complete absence of TNL genes, with their NBS repertoires composed exclusively of CNL-type genes [3]. In contrast, most dicots maintain both TNL and CNL lineages, though with substantial variation in relative proportions. For instance, Salvia species show marked reduction in TNL and RNL subfamilies, while Akebia trifoliata maintains significant TNL representation (19 of 73 genes) [3] [74].

These structural patterns directly influence disease resistance mechanisms. TNL proteins typically signal through ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1)/PHYTOALEXIN DEFICIENT 4 (PAD4) complexes, while CNL proteins often activate signaling via NON-RACE SPECIFIC DISEASE RESISTANCE 1 (NDR1), creating divergent defense signaling pathways between monocots and dicots [3].

Diagram 1: NBS domain architecture in monocots versus dicots

Experimental Approaches for NBS Gene Identification and Characterization

Genome-Wide Identification Pipeline

A standardized bioinformatics workflow enables comprehensive identification and classification of NBS-LRR genes:

Step 1: HMMER-based Domain Identification

• Retrieve NB-ARC domain (PF00931) HMM profile from Pfam database
• Perform HMMER search (hmmsearch) against proteome with E-value cutoff < 1×10⁻²⁰ [8] [75]
• Command: hmmsearch --domtblout output.txt Pfam_NB-ARC.hmm proteome.fasta

Step 2: Domain Architecture Analysis

• Scan candidate sequences against multiple domain databases:
  • Pfam for TIR (PF01582), RPW8 (PF05659), LRR (PF08191, PF00560, PF07723, PF07725, PF12779, PF13306, PF13516, PF13855, PF14580) domains [75]
  • NCBI Conserved Domain Database (CDD) for additional validation
  • Coiled-coil prediction tools (e.g., COILS, DeepCoil) with threshold 0.5 [74]

Step 3: Phylogenetic Reconstruction

• Multiple sequence alignment using MUSCLE v3.8.31 or MAFFT v7.0
• Phylogenetic tree construction via Maximum Likelihood method in MEGA11 or FastTreeMP with 1000 bootstrap replicates [14] [75]
• Subfamily classification based on domain presence and phylogenetic clustering

Step 4: Gene Structure and Motif Analysis

• Exon-intron structure visualization from GFF3 annotations using TBtools [8] [74]
• Conserved motif identification with MEME Suite (10 motifs, width 6-50 amino acids) [74]

Expression Profiling Under Stress Conditions

Transcriptomic analyses reveal NBS gene regulation during pathogen challenge:

RNA-seq Experimental Design:

• Tissue collection from multiple organs (root, stem, leaf, flower) across developmental stages
• Pathogen inoculation using standardized methods (spray inoculation, injection, vacuum infiltration)
• Time-series sampling (0, 6, 12, 24, 48, 72 hours post-inoculation) to capture early and late responses
• Inclusion of mock-treated controls and biological replicates (≥3) [3] [14]

Data Analysis Pipeline:

• Read quality control with Trimmomatic v0.36
• Alignment to reference genome using HISAT2
• Transcript quantification and FPKM normalization with Cufflinks v2.2.1
• Differential expression analysis using Cuffdiff or DESeq2 [75]
• Co-expression network analysis to identify regulatory modules

In Salvia miltiorrhiza, expression profiling of 196 NBS genes revealed specific members with pathogen-responsive expression patterns, with some genes showing constitutive expression while others were induced following challenge [3]. Similar studies in cotton identified NBS genes with elevated expression in tolerant varieties during cotton leaf curl disease infection [14].

Linking Genetic Variation to Disease Resistance

Variation Detection and Analysis

Identifying functionally relevant polymorphisms in NBS genes requires multiple approaches:

Sequence-Based Variation Detection:

• Whole-genome resequencing of resistant and susceptible genotypes
• Variant calling pipeline (BWA-MEM alignment, GATK variant discovery)
• Focus on nonsynonymous substitutions in NBS and LRR domains
• Structural variant analysis for gene presence/absence polymorphisms [14]

Selection Pressure Analysis:

• Calculation of nonsynonymous (Ka) and synonymous (Ks) substitution rates using KaKs_Calculator 2.0
• Identification of positive selection (Ka/Ks > 1) in LRR domains indicating co-evolution with pathogens
• Detection of purifying selection (Ka/Ks < 1) in NBS domains suggesting functional constraint [75]

In a comparative analysis of cotton NBS genes, researchers identified 6,583 unique variants in tolerant genotypes versus 5,173 in susceptible lines, with significant enrichment of nonsynonymous mutations in LRR domains of resistant accessions [14].

Functional Validation Approaches

Virus-Induced Gene Silencing (VIGS):

• Target sequence selection (200-300 bp gene-specific fragment)
• TRV-based vector construction (pTRV1, pTRV2 derivatives)
• Agrobacterium tumefaciens-mediated delivery in young leaves
• Pathogen challenge 2-3 weeks post-silencing
• Disease assessment and pathogen quantification [14]

A VIGS study in cotton demonstrated that silencing of specific NBS genes (GaNBS from orthogroup OG2) significantly increased viral titers and disease susceptibility, confirming functional roles in resistance [14].

Heterologous Expression:

• Gateway cloning into binary vectors (pEarleyGate, pBIN19)
• Agrobacterium-mediated transformation in model systems (Nicotiana benthamiana, Arabidopsis)
• Pathogen inoculation assays with quantitative disease scoring
• Hypersensitive response characterization upon effector co-expression [75]

Protein-Protein Interaction Studies:

• Yeast two-hybrid screening for immune signaling complexes
• Bimolecular fluorescence complementation (BiFC) for in planta validation
• Co-immunoprecipitation to confirm complex formation [31]

Diagram 2: Experimental workflow for NBS gene characterization

Table 3: Key Research Reagents and Computational Tools for NBS Gene Studies

Category	Resource	Specification/Function	Application Example
Domain Databases	Pfam (PF00931)	NB-ARC domain HMM profile	Initial gene identification [8]
	NCBI CDD	Conserved domain verification	Domain architecture classification [75]
Software Tools	HMMER v3.1b2	Hidden Markov Model search	NBS domain identification [75]
	MEME Suite	Motif discovery	Conserved motif analysis [74]
	MEGA11	Phylogenetic analysis	Evolutionary relationships [75]
	TBtools	Genomic data visualization	Gene structure diagrams [8]
Experimental Materials	TRV VIGS vectors	Virus-Induced Gene Silencing	Functional validation [14]
	Gateway-compatible vectors	Heterologous expression	Functional characterization [75]
Biological Resources	Plant materials	Resistant/susceptible genotypes	Variation analysis [14]
	Pathogen isolates	Defined virulence spectra	Phenotypic screening [3]

The integration of comparative genomics, expression profiling, and functional validation provides a powerful framework for linking genetic variation in NBS genes to disease resistance phenotypes. The distinctive architectural patterns between monocots and dicots highlight the evolutionary plasticity of plant immune systems and underscore the necessity of lineage-specific research approaches.

Future research directions should include pan-genomic analyses to capture full NBS gene diversity within species, structural biology approaches to understand how specific polymorphisms affect receptor function, and genome editing applications to engineer novel resistance specificities. The continuing decline in sequencing costs and advancement of gene editing technologies will accelerate both fundamental understanding and practical applications of NBS genes in crop improvement.

As demonstrated across multiple systems, a multidisciplinary approach combining computational prediction with experimental validation enables researchers to move from sequence variation to mechanistic understanding of disease resistance, ultimately supporting the development of durable disease control strategies in agricultural systems.

Optimizing Protocols for Protein-Ligand and Protein-Protein Interaction Studies

The precise analysis of protein-ligand and protein-protein interactions (PPIs) represents a cornerstone of modern biological research, with particular significance for understanding plant immune responses. These interactions regulate critical cellular processes, including signal transduction, transcriptional regulation, and defense mechanisms against pathogens [76]. In plants, nucleotide-binding site-leucine rich repeat (NBS-LRR) proteins constitute the largest class of disease resistance (R) genes, providing specialized immune recognition capabilities through their specific structural configurations [14] [7]. The optimization of interaction studies is therefore essential for deciphering the molecular basis of plant immunity and for translating these insights into practical applications in crop improvement and drug discovery.

The structural and functional diversification of NBS-LRR genes across plant species presents both challenges and opportunities for interaction studies. These proteins can be broadly classified into two major subfamilies based on their N-terminal domains: TIR-NBS-LRR (TNL) and non-TIR-NBS-LRR (nTNL), with the latter encompassing coiled-coil (CC) domain-containing CNL proteins [7]. Recent genomic analyses have revealed significant species-specific patterns in the distribution of these subfamilies. Notably, monocots exhibit a substantial reduction or complete absence of TNL genes, while dicots maintain both TNL and nTNL types [9] [7]. This evolutionary divergence underscores the necessity for tailored experimental approaches that account for structural variations across species. This technical guide provides optimized protocols for investigating protein interactions within the context of these species-specific NBS structural patterns, integrating computational and experimental methodologies to advance research in plant immunity and beyond.

Biological Background: Species-Specific NBS Structural Patterns

Genomic Distribution and Structural Classification of NBS-LRR Genes

Comprehensive genomic surveys across land plants have identified extensive diversification in NBS-LRR genes. Studies analyzing 34 species from mosses to monocots and dicots have identified 12,820 NBS-domain-containing genes classified into 168 distinct architectural classes [14]. These encompass both classical configurations (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns, highlighting the remarkable evolutionary plasticity of this gene family [14].

In pepper (Capsicum annuum L.), a representative dicot, genomic analysis has identified 252 NBS-LRR resistance genes unevenly distributed across all chromosomes, with 54% forming 47 gene clusters [7]. These clusters arise primarily from tandem duplications and genomic rearrangements, driving the expansion and diversification of resistance genes. Classification of these genes revealed 248 nTNLs and only 4 TNLs, with further subcategorization based on domain architecture [7]. This structural diversity necessitates customized approaches for protein interaction studies that account for domain-specific characteristics.

Evolutionary Divergence Between Monocots and Dicots

Phylogenetic analyses provide compelling evidence for significant evolutionary divergence in NBS-LRR genes between monocots and dicots. Research spanning multiple plant orders (Poales, Zingiberales, Arecales, Asparagales, and Alismatales) has consistently demonstrated the rarity of TIR-NBS-LRR sequences in monocots, while these sequences remain prevalent in dicots and basal angiosperms [9]. This distribution pattern suggests that although TIR sequences were present in early land plants, they have been significantly reduced in monocots and magnoliids [9].

The structural basis for classifying NBS-LRR proteins resides in conserved motifs within the NBS domain. The final residue of the kinase-2 motif is particularly diagnostic—aspartic acid (D) in TIR-type sequences and tryptophan (W) in non-TIR-type sequences [9]. This fundamental structural difference likely influences protein interaction capabilities and must be considered when designing interaction studies.

Table 1: Conserved Motifs in NBS Domain for Gene Classification

Gene Class	RNBS-A Motif	Kinase-2 Motif	RNBS-D Motif
TIR-NBS-LRR	FLENIRExSKKHGLEHLQKKLLSKLL	LLVLDDVD	FLHIACFF
Non-TIR-NBS-LRR	FDLxAWVCVSQxF	LLVLDDVW	CFLYCALFPED

Note: The diagnostic residue in the kinase-2 motif is bolded and underlined. Source: [9]

Computational Protocols for Interaction Prediction

Deep Learning Approaches for Protein-Protein Interaction Prediction

Deep learning has revolutionized computational prediction of PPIs by enabling automatic feature extraction from complex biological data. Several core architectures have demonstrated particular efficacy for PPI analysis:

Graph Neural Networks (GNNs) excel at modeling graph-structured data inherent to protein interaction networks. Specific variants include:

• Graph Convolutional Networks (GCNs) aggregate information from neighboring nodes using convolutional operations, ideal for node classification and graph embedding tasks [76].
• Graph Attention Networks (GATs) incorporate attention mechanisms to adaptively weight neighboring nodes based on relevance, enhancing flexibility for diverse interaction patterns [76].
• GraphSAGE utilizes neighbor sampling and feature aggregation to reduce computational complexity, making it suitable for large-scale graph processing [76].

Convolutional Neural Networks (CNNs) effectively process grid-structured data and can be adapted for sequence-based interaction prediction. Advanced architectures incorporate residual connectivity, dense connectivity, and dilation convolution to enhance training depth and stability [76].

Multi-modal frameworks that integrate sequence information, structural data, and gene expression profiles have demonstrated improved accuracy by capturing complementary aspects of protein interactions [76]. The AG-GATCN framework, which integrates GAT and temporal convolutional networks, provides particular robustness against noise interference in PPI analysis [76].

Diagram 1: Deep Learning Framework for PPI Prediction

Protein-Ligand Interaction Modeling

Accurate prediction of protein-ligand interactions is essential for understanding NBS protein function, particularly their binding to nucleotides and signaling molecules. Recent advances in machine learning have improved docking predictions, though important limitations remain:

Classical docking algorithms like GOLD consistently outperform newer ML-based methods in recovering critical chemical interactions such as hydrogen bonds, as their scoring functions are explicitly designed to reward these connections [77].

ML-based docking models including DiffDock-L often identify physically plausible poses with low root-mean-square deviation but frequently miss key interactions that classical methods successfully identify [77].

Cofolding models that simultaneously predict protein and ligand structures represent a promising direction. Models like Boltz-2 show significant progress in addressing the binding affinity problem by estimating absolute binding free energies without relying on experimental crystal structures [77].

Table 2: Performance Comparison of Protein-Ligand Docking Methods

Method Type	Representative Tools	Strengths	Limitations
Classical Docking	GOLD	High recovery of key chemical interactions (e.g., hydrogen bonds)	Requires experimental crystal structures; Computationally intensive
ML-Based Docking	DiffDock-L	Fast pose prediction; Low RMSD values	Often misses key chemical interactions
Cofolding Models	Boltz-2	Predicts binding affinity without crystal structures; Adaptive protein conformation	Nascent technology; Performance still improving

Source: [77]

Experimental Protocols for Interaction Validation

Identification and Classification of NBS Domain Genes

Protocol for Genome-Wide Identification of NBS-LRR Genes:

• Data Acquisition: Obtain latest genome assemblies from publicly available databases (NCBI, Phytozome, Plaza) [14].
• Domain Screening: Use PfamScan.pl HMM search script with default e-value (1.1e-50) against the Pfam-A_hmm model to identify NB-ARC domains [14].
• Gene Filtering: Retain all genes containing NB-ARC domains as putative NBS genes for further analysis.
• Architecture Classification: Identify associated domains (TIR, CC, LRR) using domain architecture analysis following established classification systems [14].
• Orthogroup Analysis: Utilize OrthoFinder v2.5.1 with DIAMOND for sequence similarity searches and MCL clustering algorithm for gene clustering [14].

Species-Specific Considerations:

• For monocot species: Primarily focus on non-TIR-type sequences, with verification of absence of TIR-types using kinase-2 motif analysis [9].
• For dicot species: Include both TNL and nTNL types, noting potential expansion of specific subfamilies.
• For basal angiosperms: Include broader sampling to capture ancestral diversity.

Diagram 2: NBS Gene Identification Workflow

Functional Validation Through Genetic and Molecular Approaches

Virus-Induced Gene Silencing (VIGS) Protocol for Functional Validation:

• Gene Selection: Prioritize candidate NBS genes based on expression profiles under pathogen challenge and genetic variation between resistant and susceptible genotypes [14].
• Vector Construction: Clone fragments (200-300 bp) of target NBS genes into appropriate VIGS vectors (e.g., TRV-based vectors).
• Plant Infiltration: Introduce vectors into resistant plants through Agrobacterium-mediated infiltration.
• Phenotypic Assessment: Challenge silenced plants with pathogens and monitor for loss of resistance.
• Molecular Verification: Quantify gene silencing efficiency through qRT-PCR and measure pathogen titers [14].

Application Example: Silencing of GaNBS (OG2) in resistant cotton demonstrated its putative role in virus tittering, validating its function in disease resistance [14].

Expression Profiling Under Stress Conditions:

• Data Collection: Retrieve RNA-seq data from specialized databases (IPF database, Cotton Functional Genomics Database, Cottongen) [14].
• Stress Categorization: Classify expression data into tissue-specific, abiotic stress-specific, and biotic stress-specific categories.
• Differential Expression: Process RNA-seq data through standardized transcriptomic pipelines to identify significantly regulated NBS genes.
• Validation: Confirm expression patterns of key candidates through qRT-PCR.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for Protein Interaction Studies in NBS Research

Reagent/Material	Function	Application Examples
Pfam HMM Models	Identification of NB-ARC domains in protein sequences	Genome-wide identification of NBS genes [14]
OrthoFinder Software	Orthogroup inference and phylogenetic analysis	Evolutionary studies of NBS gene families [14]
VIGS Vectors	Transient gene silencing in plants	Functional validation of NBS gene candidates [14]
Co-immunoprecipitation Kits	Capture of protein complexes	Experimental validation of NBS protein interactions [76]
Classical Docking Software	Prediction of protein-ligand interactions	Analysis of nucleotide binding to NBS domains [77]
Deep Learning Frameworks	PPI prediction from sequence and structural data	Mapping NBS protein interaction networks [76]

The optimization of protein-ligand and protein-protein interaction studies requires careful consideration of species-specific structural patterns, particularly the divergent evolution of NBS-LRR genes between monocots and dicots. Integrating the computational and experimental protocols outlined in this guide provides a comprehensive framework for advancing research in plant immunity and beyond. The continued refinement of deep learning approaches for interaction prediction, coupled with robust experimental validation methods, will enhance our understanding of the molecular mechanisms underlying disease resistance and facilitate the development of improved crop varieties through targeted breeding strategies. As protein interaction modeling technologies continue to evolve, particularly in the realm of cofolding models and affinity prediction, researchers are poised to make significant strides in bridging the gap between computational predictions and biological function.

Cross-Species Comparative Genomics and Functional Validation

Comparative Analysis of NBS Gene Clusters and Genomic Distribution

Nucleotide-binding site (NBS) genes represent one of the largest and most critical gene families in plant innate immunity, encoding proteins that function as intracellular immune receptors in effector-triggered immunity. These genes, particularly those belonging to the NBS-leucine rich repeat (NBS-LRR) superfamily, are crucial for recognizing diverse pathogen effectors and initiating defense responses. The genomic organization of NBS genes into clusters represents a fundamental aspect of their evolution and functional diversification, with significant differences observed between monocot and dicot species. This review provides a comprehensive analysis of NBS gene cluster distribution patterns, structural characteristics, and evolutionary dynamics across plant lineages, with particular emphasis on the comparative genomics between monocots and dicots.

NBS Gene Family Classification and Structural Diversity

Major NBS Gene Subclasses

NBS-LRR genes are classified based on their N-terminal domains into several major subclasses:

• TNL genes: Characterized by an N-terminal Toll/Interleukin-1 receptor (TIR) domain
• CNL genes: Contain an N-terminal coiled-coil (CC) domain
• RNL genes: Feature an N-terminal RPW8 (resistance to powdery mildew 8) domain
• Other variants: Include genes with only NBS domains (N-type) or various combinations of domains (NL, NN, CN, etc.) [32]

The central NBS (NB-ARC) domain contains several conserved motifs critical for function, including the P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL motifs, which facilitate nucleotide binding and act as molecular switches for immune signaling [32] [78].

Structural Diversity in Monocots and Dicots

Comparative analyses reveal substantial structural differences in NBS genes between monocots and dicots. In pepper (Capsicum annuum, a dicot), researchers identified 252 NBS-LRR genes classified into 10 structural subclasses, with the majority (248 genes) belonging to the nTNL (non-TIR-NBS-LRR) category and only 4 classified as TNL genes [32]. This distribution contrasts with monocot species like rye (Secale cereale), where from 582 identified NBS-LRR genes, 581 were CNLs and only one was an RNL, with complete absence of TNL genes [79]. This pattern of TNL absence is consistent across Poaceae species, indicating a lineage-specific loss in monocots [80].

Table 1: Comparative NBS-LRR Gene Distribution in Selected Plant Species

Species	Family	Monocot/Dicot	Total NBS-LRR	TNL	CNL	RNL	Reference
Capsicum annuum (pepper)	Solanaceae	Dicot	252	4	248	0	[32]
Secale cereale (rye)	Poaceae	Monocot	582	0	581	1	[79]
Saccharum spontaneum (sugarcane)	Poaceae	Monocot	585	0	584	1	[11]
Manihot esculenta (cassava)	Euphorbiaceae	Dicot	228	34	128	66*	[6]
Cucumis sativus (cucumber)	Cucurbitaceae	Dicot	63	Not specified	Not specified	Not specified	[81]

Note: *In cassava, 66 genes were partial NBS genes not classified into TNL or CNL categories [6].

Genomic Distribution and Cluster Organization

Chromosomal Distribution Patterns

NBS-LRR genes are distributed unevenly across plant chromosomes, with notable clustering in specific genomic regions. In pepper, NBS-LRR genes are present on all chromosomes, with 54% (136 genes) organized into 47 clusters [32]. Similarly, in cassava, 63% of the 327 identified NBS-LRR and partial NBS genes were clustered in 39 groups across the chromosomes [6].

Chromosome-specific enrichment patterns vary between species. In rye, chromosome 4 contains the largest number of NBS-LRR genes, a pattern similar to the A genome of wheat but different from barley and the B and D genomes of wheat [79]. Synteny analysis suggests that more NBS-LRR genes on chromosome 4 were inherited from a common ancestor by rye and wheat genome A than by wheat genomes B and D [79].

Cluster Characteristics and Evolution

NBS-LRR gene clusters predominantly arise through tandem duplications and genomic rearrangements [32] [82]. These clusters are mostly homogeneous, containing NBS-LRRs derived from a recent common ancestor, though heterogeneous clusters also exist [6]. The size of NBS-LRR clusters shows a positive correlation with the total number of NBS-LRR genes in a genome [80].

Recent studies in barley have identified Long Duplication-Prone Regions (LDPRs) that are statistically associated with arms-race genes, including NBS-LRRs [82]. These LDPRs, characterized by elevated levels of duplicated sequences, are enriched in subtelomeric regions and show a history of repeated long-distance dispersal to distant genomic sites followed by local expansion by tandem duplication [82].

Table 2: NBS-LRR Gene Cluster Characteristics Across Species

Species	Total NBS Genes	Clustered Genes	Number of Clusters	Cluster Type	Main Evolutionary Mechanism
Capsicum annuum	252	136 (54%)	47	Homogeneous	Tandem duplications [32]
Manihot esculenta	327	206 (63%)	39	Homogeneous	Tandem duplications [6]
Barley	Not specified	Enriched in LDPRs	1,199 LDPRs	Mixed	Tandem repeats, NAHR [82]
Saccharum spontaneum	585	Not specified	Not specified	Homogeneous	Whole genome duplication [11]

Comparative Evolutionary Analysis

Lineage-Specific Expansion and Contraction

Phylogenetic analyses reveal dynamic evolutionary patterns of NBS-LRR genes across plant lineages. A study of 34 plant species identified 12,820 NBS-domain-containing genes classified into 168 classes with both classical and species-specific structural patterns [14]. Orthogroup analysis identified 603 orthogroups, with some core orthogroups (OG0, OG1, OG2) conserved across multiple species and unique orthogroups specific to particular lineages [14].

Research in rye, barley, and Triticum urartu suggests that at least 740 NBS-LRR lineages were present in their common ancestor, with only 65 preserved in all three species [79]. The rye genome inherited 382 of these ancestral NBS-LRR lineages, 120 of which have been lost in both barley and T. urartu [79]. This pattern indicates extensive lineage-specific gene loss and retention following species divergence.

Evolutionary Mechanisms Driving Diversity

Several mechanisms contribute to the evolution of NBS gene clusters:

• Tandem duplications: Localized gene duplications leading to homogeneous clusters [32] [6]
• Whole genome duplication (WGD): Polyploidization events contributing to NBS-LRR expansion, particularly evident in sugarcane [11]
• Non-allelic homologous recombination (NAHR): Facilitates gene conversion and domain shuffling [82]
• Transposable element activity: Associated with duplication-prone regions [82]
• Birth-and-death evolution: Continuous gene duplication and loss [78]

A study of 23 plant species revealed that whole genome duplication, gene expansion, and allele loss significantly affect NBS-LRR gene numbers, with WGD likely being the main driver in sugarcane [11]. Additionally, a progressive trend of positive selection on NBS-LRR genes was observed, supporting their role in adapting to evolving pathogens [11].

Methodologies for NBS Gene Identification and Analysis

Genomic Identification Pipelines

Standardized pipelines have been developed for genome-wide identification of NBS-LRR genes:

NBS Gene Identification Workflow

The typical workflow involves:

• HMMER-based domain identification: Using Hidden Markov Model profiles (e.g., NB-ARC domain PF00931) to scan protein sequences [79] [6]
• Domain architecture analysis: Identifying associated domains (TIR, CC, LRR) using tools like PfamScan, COILS, and TMHMM [32] [80]
• Manual curation: Verifying domain integrity and removing false positives [6]
• Classification: Categorizing genes into subclasses based on domain composition [32]

Evolutionary and Expression Analysis Methods

• Phylogenetic analysis: Multiple sequence alignment of NB-ARC domains followed by maximum likelihood tree construction [79]
• Synteny analysis: Identifying conserved genomic blocks and lineage-specific rearrangements [81] [79]
• Expression profiling: Using RNA-seq data to assess transcriptional responses to biotic and abiotic stresses [81] [14]
• Genetic variation analysis: Identifying sequence variants between resistant and susceptible genotypes [14]

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

Reagent/Tool	Category	Function	Example/Reference
HMMER Suite	Bioinformatics	Domain identification	[79] [6]
NB-ARC HMM (PF00931)	Database	NBS domain detection	Pfam database [6]
OrthoFinder	Bioinformatics	Orthogroup identification	[14]
MEME Suite	Bioinformatics	Motif discovery	[79]
MCScanX	Bioinformatics	Synteny analysis	[11]
Virus-Induced Gene Silencing (VIGS)	Functional validation	Gene function analysis	[14]

Functional Implications and Expression Dynamics

Expression Patterns and Regulation

NBS-LRR genes exhibit specific transcriptional responses and genotype/tissue-dependent expression variations under biotic and abiotic stresses [81]. Research in cucumber and wild relatives demonstrated that NLR genes from various genotypes and tissues show distinct expression patterns over time under different stress conditions [81].

Studies in cotton revealed differential expression of NBS orthogroups (OG2, OG6, OG15) in different tissues under various biotic and abiotic stresses in susceptible and tolerant accessions [14]. Genetic variation analysis between susceptible (Coker 312) and tolerant (Mac7) cotton accessions identified unique variants in NBS genes, with Mac7 containing 6583 variants compared to 5173 in Coker312 [14].

miRNA-Mediated Regulation

A tight association exists between NBS-LRR diversity and miRNA regulation, with miRNAs typically targeting highly duplicated NBS-LRRs [78]. Diverse miRNA families (e.g., miR482/2118) target conserved regions of NBS-LRRs, particularly the P-loop motif [78]. This regulatory mechanism potentially balances the benefits and costs of maintaining large NBS-LRR repertoires, as high expression of these genes can be lethal to plant cells [78].

The comparative analysis of NBS gene clusters reveals fundamental aspects of plant genome organization and evolution. The distinct distribution patterns between monocots and dicots, particularly the absence of TNL genes in Poaceae species, highlight lineage-specific evolutionary trajectories. The clustering of NBS genes in duplication-prone genomic regions represents an evolutionary strategy for generating diversity in genes involved in arms races with pathogens. Advanced genomic technologies and comparative approaches continue to uncover the complex dynamics of NBS gene evolution, providing insights for crop improvement and understanding plant-pathogen co-evolution. Future research leveraging pan-genome approaches and functional studies will further elucidate the relationship between genomic organization and disease resistance functionality.

The evolutionary split between monocotyledons (monocots) and dicotyledons (dicots) represents a fundamental divergence in angiosperm history, leading to distinct structural and physiological traits [83] [84]. Beyond the classical morphological differences in seed, leaf, and root architecture, recent molecular evidence reveals profound lineage-specific adaptations in their immune systems. The Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) gene family, which constitutes the largest class of plant disease resistance (R) genes, exhibits particularly striking evolutionary patterns between these two groups [32] [20]. These genes encode intracellular immune receptors that recognize pathogen effectors and initiate effector-triggered immunity (ETI), playing a critical role in plant survival against diverse pathogens [85]. Understanding the divergent evolution of this gene family between monocots and dicots provides not only fundamental insights into plant adaptation but also practical tools for engineering broad-spectrum disease resistance in crop species.

Structural and Functional Basis of NBS-LRR Genes

Domain Architecture and Classification

NBS-LRR proteins are characterized by a conserved tripartite domain structure. The central Nucleotide-Binding Site (NBS) domain is responsible for ATP/GTP binding and hydrolysis, while the C-terminal Leucine-Rich Repeat (LRR) domain mediates protein-protein interactions and determines pathogen recognition specificity [32] [85]. The N-terminal domain defines two major subclasses: TIR-NBS-LRR (TNL) proteins contain a Toll/Interleukin-1 Receptor domain, while CC-NBS-LRR (CNL) proteins possess a coiled-coil domain [20]. A third, smaller subclass called RNL contains an RPW8 domain at the N-terminus [20].

The NBS domain itself contains several highly conserved motifs essential for function, including the P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL motifs [32]. Structural analyses of these motifs reveal both conservation and variation that correlate with functional specialization across plant lineages.

Signaling Mechanisms and Immune Activation

NBS-LRR proteins function as central components of the plant immune system through two primary mechanisms. TNL proteins generally signal through the ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) pathway, while CNL proteins typically utilize the NON-RACE-SPECIFIC DISEASE RESISTANCE (NDR1) pathway [20]. Upon pathogen recognition, NBS-LRR proteins undergo conformational changes that trigger a robust defense response, often including a hypersensitive response (HR) characterized by localized cell death at the infection site, preventing pathogen spread [85]. This initial response is frequently followed by systemic acquired resistance (SAR), which provides long-lasting protection against broader pathogen spectra [85].

Figure 1: NBS-LRR-mediated immune signaling pathway. Pathogen effectors are recognized by NBS-LRR receptors, triggering hypersensitive response and systemic acquired resistance.

Comparative Genomic Analysis of NBS-LRR Genes

Lineage-Specific Distribution of TNL and CNL Genes

Comparative genomic analyses reveal striking differences in the distribution of NBS-LRR subclasses between monocots and dicots. Studies in pepper (Capsicum annuum), a dicot, identified 252 NBS-LRR genes with a predominance of the nTNL (non-TIR NBS-LRR) subfamily, which includes CNL-type genes [32]. Remarkably, only 4 TNL genes were identified compared to 248 nTNL genes, representing a dramatically skewed distribution of 1.6% TNL versus 98.4% nTNL [32].

This pattern contrasts with earlier observations in Arabidopsis and other dicots, suggesting complex evolutionary dynamics. Meanwhile, comprehensive analysis of Rosaceae species (dicots) revealed 2,188 NBS-LRR genes across 12 species, with ancestral reconstruction estimating 102 ancestral genes (7 RNLs, 26 TNLs, and 69 CNLs) prior to lineage diversification [20].

Table 1: NBS-LRR Gene Distribution in Monocot and Dicot Species

Species	Classification	Total NBS-LRR	TNL Genes	CNL/nTNL Genes	TNL Percentage	Reference
Pepper (Capsicum annuum)	Dicot	252	4	248	1.6%	[32]
Arabidopsis (Arabidopsis thaliana)	Dicot	~200*	~90*	~110*	~45%*	[20]
Maize (Zea mays)	Monocot	129	Minimal	Predominant	<1%*	[20] [85]
Rice (Oryza sativa)	Monocot	508	Minimal	Predominant	<1%*	[20]
Rosaceae species (average)	Dicot	182	26*	156*	14.3%*	[20]

Note: Values marked with asterisk () are estimates based on contextual information from the search results.*

Genomic Distribution and Clustering Patterns

NBS-LRR genes typically display non-random genomic distributions, often forming clusters resulting from tandem duplications. In pepper, 54% of NBS-LRR genes are organized into 47 gene clusters distributed unevenly across all chromosomes [32]. This clustering pattern, driven by tandem duplications and genomic rearrangements, underscores the dynamic evolution of resistance genes and provides a mechanism for rapid generation of novel recognition specificities.

The evolutionary patterns of NBS-LRR genes vary significantly across plant families. In Rosaceae, different species exhibit distinct evolutionary patterns: Rubus occidentalis, Potentilla micrantha, Fragaria iinumae, and Gillenia trifoliata display "first expansion and then contraction"; Rosa chinensis exhibits "continuous expansion"; while F. vesca shows "expansion followed by contraction, then a further expansion" [20]. This diversity in evolutionary trajectories highlights the complex interplay between lineage-specific selective pressures and genomic constraints.

Case Studies in Monocot-Dicot Comparisons

Pepper (Dicot): Capsicum annuum NBS-LRR Diversity

Compressive analysis of the pepper genome reveals remarkable structural diversity among its 252 NBS-LRR genes. These genes were classified into multiple structural subclasses based on domain architecture [32]:

• N-class: Containing only the NB-ARC domain (172 genes)
• NL-class: Containing NB-ARC and LRR_8 domains (11 genes)
• NLN-class: Containing NB-LRR and NB-ARC domains (7 genes)
• NLL-class: Composed of NB-ARC and two LRR_8 domains (2 genes)
• NN-class: Containing two NB-ARC domains (8 genes)
• CN-class: With CC and NB-ARC domains (37 genes)
• CNL-class: Typical CC-NBS-LRR structure (2 genes)

The extraordinary diversity in domain architecture suggests functional specialization and evolutionary innovation in pepper's immune system. Notably, the NLNLN subclass is represented by only a single gene, making it the rarest among all subclasses [32].

Maize (Monocot): ZmNBS25 Functional Characterization

The maize NBS-LRR gene ZmNBS25 provides an excellent example of monocot NBS-LRR function and cross-species transfer potential. ZmNBS25 responds to pathogen inoculation and salicylic acid (SA) treatment, and transient overexpression induces hypersensitive response in tobacco [85]. Functional analysis demonstrates that ZmNBS25 overexpression in Arabidopsis and rice results in higher SA levels and enhanced resistance to Pseudomonas syringae pv. tomato DC3000 and sheath blight disease, respectively [85].

Notably, ZmNBS25-OE rice lines showed little change in grain size and 1000-grain weight compared to controls, suggesting that enhanced resistance doesn't necessarily compromise yield traits [85]. This finding has significant implications for crop improvement programs, highlighting the potential of NBS-LRR genes for engineering broad-spectrum resistance without yield penalties.

Rosaceae Family (Dicot): Diverse Evolutionary Patterns

Comparative analysis of 12 Rosaceae species provides insights into dicot NBS-LRR evolution. The study identified 2,188 NBS-LRR genes with distinct evolutionary patterns across species [20]:

• Prunus species (P. armeniaca, P. avium, P. persica) and Maleae species (P. betulifolia, M. baccata, M. domestica): "Early sharp expanding to abrupt shrinking" pattern
• Rosa chinensis: "Continuous expansion" pattern
• Fragaria vesca: "Expansion followed by contraction, then further expansion" pattern

These diverse evolutionary trajectories reflect species-specific interactions with pathogens and demonstrate how related dicot species have employed different genomic strategies to adapt to their respective pathogenic environments.

Methodologies for NBS-LRR Gene Analysis

Genome-Wide Identification Pipeline

The identification and characterization of NBS-LRR genes follows a standardized bioinformatics workflow:

Figure 2: Bioinformatics workflow for genome-wide identification and classification of NBS-LRR genes.

Functional Characterization Protocols

Gene Expression Analysis under Pathogen Challenge

• Plant Materials and Growth Conditions: Maize CMT030 plants are grown in greenhouse at 28°C under 16h light/8h dark cycle [85]
• Pathogen Inoculation: Healthy seedlings at 3-leaf stage are treated with B. maydis spore suspension (10⁵ mL⁻¹) by spray inoculation, with covered plastic film maintaining moisture for 24h [85]
• Salicylic Acid Treatment: 1 mM SA solution sprayed onto seedlings not subject to pathogen infection [85]
• Tissue Harvesting: Leaves collected at 0, 12, 24, 48, and 60h post-inoculation or 0, 1, 6, 12, and 24h post-SA treatment [85]
• RNA Extraction and Analysis: Six plants collected per treatment with three biological replicates [85]

Transient Expression and Hypersensitive Response Assay

• Vector Construction: Full-length coding sequence without termination codon cloned into pCAMBIA1301 to generate 35S::ZmNBS25 [85]
• Agrobacterium Transformation: Construct transformed into A. tumefaciens strain GV3101 [85]
• Infiltration: Approximately 100 μL of A. tumefaciens suspensions injected into 4-week-old N. benthamiana leaves [85]
• Cell Death Assessment: Hypersensitive response monitored 24-72h post-infiltration [85]

Table 2: Essential Research Reagents for NBS-LRR Functional Analysis

Reagent/Resource	Specifications	Application	Key Features
Vector System	pCAMBIA1301	Protein expression and localization	35S promoter, suitable for monocots and dicots
Agrobacterium Strain	GV3101	Plant transformation	High transformation efficiency, suitable for transient expression
Plant Growth Medium	PDA (Potato Dextrose Agar)	Fungal culture and spore production	Standard for culturing B. maydis and similar pathogens
Treatment Solution	1 mM Salicylic Acid	Defense pathway induction	Prepared in deionized water, filter-sterilized
Pathogen Strain	Bipolaris maydis	Disease resistance assays	Causes southern leaf blight, maintained on PDA
Analysis Software	MEME Suite	Conserved motif identification	Identifies overrepresented motifs in protein sequences
Phylogenetic Tool	MEGA6	Evolutionary relationship analysis	Neighbor-joining method with bootstrap validation

Evolutionary Dynamics and Selection Pressures

Gene Duplication Mechanisms

Gene duplication plays a fundamental role in NBS-LRR gene family expansion and evolution. Five primary duplication mechanisms have been identified [17]:

• Whole Genome Duplication (WGD): Results from polyploidization events, providing substantial genetic novelty
• Tandem Duplication (TD): Generates clustered gene arrays through unequal crossing-over
• Proximal Duplication (PD): Occurs between closely located genes, potentially through different mechanisms than TD
• Transposed Duplication (TRD): Involves relocation of gene copies to new chromosomal positions
• Dispersed Duplication (DSD): Creates duplicated genes distributed across the genome

Analysis of Aurantioideae species shows that tandem duplication is the predominant type, contributing significantly to NBS-LRR gene family expansion and functional diversification [17]. These duplication events are predominantly under purifying selection (Ka/Ks < 1), with TD and PD genes experiencing particularly rapid functional divergence [17].

Lineage-Specific Evolutionary Patterns

Monocots and dicots exhibit distinct evolutionary patterns in their NBS-LRR gene repertoires. Most monocots show significant reduction or complete loss of TNL genes, with dominance of CNL-type genes [32] [20]. In contrast, dicots generally maintain both TNL and CNL classes, though with considerable variation in relative proportions between species [32] [20].

These lineage-specific patterns reflect different evolutionary strategies for pathogen recognition and defense signaling. The preferential retention of CNL-type genes in monocots may reflect adaptations to specific pathogen pressures or compatibility with monocot-specific signaling components.

The comparative analysis of NBS-LRR genes between monocots and dicots reveals profound lineage-specific adaptations in plant immune systems. The dramatic differences in TNL/CNL distribution, gene clustering patterns, and evolutionary trajectories highlight how shared ancestral genetic material can diverge through distinct evolutionary paths. These differences reflect adaptations to lineage-specific pathogen pressures and likely contribute to the morphological and physiological distinctions between monocots and dicots.

Future research should focus on several key areas:

• Functional characterization of lineage-specific NBS-LRR genes to elucidate their roles in pathogen recognition and defense signaling
• Structural studies of monocot-specific NBS-LRR domains to understand molecular mechanisms of pathogen recognition
• Engineering synthetic NBS-LRR genes with broad-spectrum recognition capabilities for crop improvement
• Evolutionary analyses tracing the origin and diversification of NBS-LRR subclasses across angiosperm phylogeny

The knowledge gained from these comparative studies not only advances our fundamental understanding of plant evolution but also provides critical tools for developing sustainable disease management strategies in both monocot and dicot crops. The successful transfer of ZmNBS25 from maize to rice and Arabidopsis demonstrates the potential for leveraging lineage-specific adaptations for crop improvement across taxonomic boundaries [85].

Validating NBS Gene Function in Disease Resistance Through VIGS and Mutant Analysis

Plant nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes constitute one of the largest and most critical gene families responsible for disease resistance in plants, encoding intracellular immune receptors that directly or indirectly recognize pathogen effector proteins to initiate effector-triggered immunity (ETI) [7]. These genes represent approximately 80% of the characterized disease resistance (R) genes in plants and provide resistance to a wide spectrum of pathogens including bacteria, fungi, oomycetes, viruses, and nematodes [7]. The typical structure of an NBS-LRR resistance gene includes three main domains: a Toll/Interleukin-1 receptor (TIR) or coiled-coil (CC) domain at the N-terminus, an NBS domain in the middle, and an LRR domain at the C-terminus [7]. Based on differences in their N-terminal domains, NBS-LRR resistance genes are classified into two principal subclasses: TIR-NBS-LRR (TNL) and CC-NBS-LRR (CNL), also referred to as non-TIR-NBS-LRR (nTNL) [7].

The NBS domain contains several conserved motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL) essential for ATP/GTP binding and hydrolysis, which are crucial for initiating immune signaling [7]. In contrast, the LRR domain is highly variable, enabling pathogen-specific recognition [7]. The significant structural and functional diversification of NBS-LRR genes across plant species, particularly between monocots and dicots, necessitates robust functional validation methods to characterize their roles in disease resistance pathways. This technical guide provides comprehensive methodologies for validating NBS gene function through virus-induced gene silencing (VIGS) and mutant analysis, with emphasis on species-specific structural patterns in monocots and dicots.

Structural and Evolutionary Diversity of NBS-LRR Genes

Comparative Genomic Distribution Across Species

Recent advances in sequencing technologies have facilitated genome-wide identification of NBS-LRR genes across numerous plant species, revealing substantial variation in family size and composition. The structural diversity of NBS-LRR genes extends beyond the typical TNL and CNL classifications, with numerous irregular types lacking complete domain structures yet playing crucial regulatory roles in plant immunity [8].

Table 1: NBS-LRR Gene Family Size and Composition Across Plant Species

Plant Species	Total NBS-LRR Genes	TNL Genes	CNL Genes	Other/ Irregular Types	Reference
Arabidopsis thaliana	189	68	121	-	[31]
Vernicia fordii (tung tree)	90	0	49	41	[10]
Vernicia montana (tung tree)	149	12	98	39	[10]
Nicotiana benthamiana	156	5	25	126	[8]
Capsicum annuum (pepper)	252	4	48	200	[7]
Triticum aestivum (wheat)	2151	-	-	-	[31]
Populus trichocarpa	402	-	-	-	[31]

Species-Specific Structural Patterns in Monocots and Dicots

Comprehensive comparative analyses have revealed significant structural differences in NBS-LRR genes between monocots and dicots. A striking pattern is the preferential loss of TNL genes in monocots, with numerous monocot species exhibiting complete absence of TNL-type genes [10]. In dicots, both TNL and CNL subtypes are generally present, though their relative proportions vary considerably between species [7].

The LRR domain, responsible for pathogen recognition specificity, also shows species-specific variations. In tung trees, for instance, Vernicia montana displays four types of LRR domains (LRR1, LRR3, LRR4, and LRR8), while the susceptible Vernicia fordii lacks LRR1 and LRR4 domains, suggesting domain loss events during evolution that may contribute to differences in disease resistance [10].

Genome-wide studies have identified additional domain architectural patterns beyond the classical NBS-LRR structure, including TN (TIR-NBS), CN (CC-NBS), NL (NBS-LRR), N (NBS-only), and more complex multi-domain arrangements [8]. These irregular types often function as adaptors or regulators for typical NBS-LRR proteins [8].

Virus-Induced Gene Silencing (VIGS) for NBS Gene Validation

Fundamental Principles of VIGS

Virus-induced gene silencing is a powerful post-transcriptional gene silencing (PTGS)-based technique that exploits the natural defense mechanisms plants employ against viruses [86]. The methodology involves using modified viral genomes as vectors to deliver fragments of plant target genes, triggering sequence-specific degradation of complementary mRNA transcripts [86].

The VIGS process initiates when a recombinant virus containing a fragment of the target plant gene is introduced into plant cells. The viral RNA replication produces double-stranded RNA intermediates, which are recognized by plant DICER-like enzymes and processed into 21-25 nucleotide small interfering RNAs (siRNAs). These siRNAs are incorporated into the RNA-induced silencing complex (RISC), which identifies and cleaves complementary cellular mRNAs, resulting in targeted gene silencing [86].

VIGS Vector Systems for Monocots and Dicots

The selection of appropriate VIGS vectors is critical for successful gene silencing and varies between monocot and dicot species due to differences in viral host ranges and silencing efficiencies.

Table 2: VIGS Vector Systems for Functional Analysis of NBS Genes

Vector System	Host Range	Key Features	Example Applications
Tobacco Rattle Virus (TRV)	Broad dicot range	Mild symptoms, spreads to meristem, high efficiency	Nicotiana benthamiana, tomato, pepper, rose [86]
Barley Stripe Mosaic Virus (BSMV)	Monocots, especially cereals	Efficient in wheat and barley, moderate symptoms	Functional analysis of abiotic stress genes in wheat and barley [86]
Satellite Virus-Based Systems	Specific to helper virus	Reduced viral symptoms, strong silencing	Tomato yellow leaf curl china virus with DNAβ satellite [86]

For dicotyledonous plants, the Tobacco rattle virus (TRV)-based vector is the most widely used system due to its ability to infect a broad host range, systemic spread throughout the plant including meristematic tissues, and minimal virus-associated symptoms [86]. TRV is a positive-sense single-stranded RNA virus with a bipartite genome (RNA1 and RNA2). The RNA1 component encodes RNA-dependent RNA polymerase, movement protein, and a cysteine-rich protein, while RNA2 is modified to contain the coat protein gene and cloning sites for insertion of target gene fragments [86].

In monocotyledonous plants, the Barley stripe mosaic virus (BSMV)-based vector has emerged as the most effective system for functional genomics studies [86]. BSMV-based VIGS has been successfully implemented for characterizing abiotic stress-responsive genes in wheat and barley, demonstrating its utility for NBS gene validation in cereal crops [86].

Experimental Protocol: VIGS-Mediated Silencing of NBS Genes

Step 1: Target Gene Fragment Selection and Vector Construction

• Identify a 300-500 bp fragment of the target NBS gene with no off-target similarity to other genes (validate using tools like RNAiScan)
• Amplify the fragment using gene-specific primers with appropriate restriction sites
• Clone the fragment into the multiple cloning site of the chosen VIGS vector
• Transform the recombinant vector into Agrobacterium tumefaciens strains (e.g., GV3101)

Step 2: Plant Inoculation

• Grow plants under standard conditions until 2-3 true leaf stage
• For TRV-based vectors: Inoculate by agroinfiltration using needleless syringe
• Prepare Agrobacterium cultures (OD600 = 0.4-1.0) in infiltration medium (10 mM MgCl2, 10 mM MES, 150 μM acetosyringone)
• For BSMV in monocots: Rub-inoculate with in vitro transcribed RNA or viral RNA preparations
• Include empty vector controls and positive silencing controls (e.g., PDS for photobleaching)

Step 3: Phenotypic and Molecular Analysis

• Monitor plants for development of silencing phenotypes (typically 2-4 weeks post-inoculation)
• Challenge silenced plants with target pathogen
• Assess disease symptoms and pathogen proliferation
• Confirm silencing efficiency through qRT-PCR or Western blot analysis
• Document hypersensitive response (HR) and programmed cell death alterations

A successful application of this protocol was demonstrated in tung trees, where VIGS of Vm019719 (an NBS-LRR gene) compromised resistance to Fusarium wilt in the normally resistant Vernicia montana, validating its essential role in disease resistance [10].

Mutant Analysis for NBS Gene Functional Validation

Mutant Generation and Screening Strategies

Mutant analysis provides complementary approaches to VIGS for validating NBS gene function, offering stable genetic materials for comprehensive phenotypic characterization. Multiple mutant generation strategies are available, each with distinct advantages and limitations.

Table 3: Mutant Resources for NBS Gene Functional Analysis

Mutant Type	Generation Method	Key Features	Applications in NBS Research
T-DNA Insertion	Agrobacterium-mediated transformation	Stable insertion, easy identification of flanking sequences	Large-scale mutant collections in Arabidopsis, rice [86]
Chemical/Physical Mutagenesis	EMS, fast neutron, gamma irradiation	High mutation density, broad applicability	Forward genetic screens for disease susceptibility [86]
Transposon Tagging	Endogenous or heterologous transposons	Potential for reversible mutations, gene trapping	Maize, Antirrhinum, and other species with active transposons [86]
CRISPR/Cas9	Targeted genome editing	Precise gene knockout or modification	Direct validation of specific NBS gene function [14]

Protocol: Functional Analysis of NBS Genes Using Mutant Lines

Step 1: Mutant Identification and Genotyping

• Screen available mutant collections (e.g., Arabidopsis Biological Resource Center) for insertions in target NBS genes
• Design genotyping primers: gene-specific primers and insertion-specific primers
• Confirm homozygous mutant lines through PCR-based genotyping
• For EMS mutants, employ TILLING (Targeting Induced Local Lesions IN Genomes) platforms

Step 2: Pathogenicity Assays

• Grow wild-type and mutant plants under controlled conditions
• Inoculate with pathogen using appropriate method (spray inoculation, root dipping, vacuum infiltration)
• Include resistant and susceptible controls
• Monitor disease progression and score symptoms using standardized scales
• Quantify pathogen biomass through culture-based methods or qPCR

Step 3: Molecular and Biochemical Characterization

• Analyze expression of defense-related marker genes (PR1, PDF1.2, etc.)
• Measure reactive oxygen species (ROS) burst and callose deposition
• Assess phytohormone levels (salicylic acid, jasmonic acid, ethylene)
• Evaluate hypersensitive response (HR) cell death following pathogen recognition

An illustrative example comes from the functional analysis of the Arabidopsis NBS-LRR gene L3 (At1g15890), where heterologous expression in E. coli caused significant bacterial death, enabling genetic screens in bacteria to identify host factors modifying NBS protein function [87]. This creative approach identified nupG and yedZ as mediators of L3 toxicity in E. coli, with subsequent validation in plants showing that NupG affects peroxidase activity and suppresses cell death induced by the NBS-LRR protein RPM1(D505V) in N. benthamiana [87].

Integrated Validation Framework and Technical Considerations

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Research Reagent Solutions for NBS Gene Functional Analysis

Reagent/Resource	Function/Application	Examples/Specifications
TRV-Based VIGS Vectors	Gene silencing in dicots	pTRV1 (RNA1), pTRV2 (RNA2 with MCS) [86]
BSMV VIGS System	Gene silencing in monocots	BSMV α, β, γ components; γ vector with MCS [86]
Agrobacterium Strains	Delivery of VIGS constructs	GV3101, LBA4404, AGL1 [86]
Pathogen Isolates	Disease resistance assays	Well-characterized strains with known avirulence genes
Antibodies	Protein detection and localization	Custom antibodies against specific NBS protein domains
siRNA Detection Kits	Validation of silencing efficiency	Northern blot or sequencing-based approaches

Species-Specific Technical Considerations

Functional validation of NBS genes requires careful consideration of species-specific biological factors, particularly when comparing monocots and dicots. Dicot species generally offer more established and efficient VIGS protocols, with TRV-based systems working effectively across numerous families [86]. In contrast, monocot species present greater challenges for VIGS implementation, with BSMV remaining the most reliable vector for cereals despite more variable efficiency [86].

The distinct evolutionary patterns of NBS-LRR genes between monocots and dicots also necessitate different experimental approaches. The near-complete absence of TNL genes in many monocots means functional studies focus predominantly on CNL-type genes, while dicot research must account for both major subclasses [10] [7]. Furthermore, the clustering of NBS-LRR genes in plant genomes creates challenges for specific gene silencing or mutation, as closely related paralogs may confer functional redundancy [7].

Recent research has revealed that nitric oxide (NO) signaling regulates NBS-LRR activity, with 29 NO-induced NBS-LRR genes identified in Arabidopsis [31]. This regulatory dimension should be incorporated into functional studies through monitoring of NO bursts and S-nitrosylation events during pathogen recognition.

The functional validation of NBS genes through VIGS and mutant analysis provides critical insights into plant immune mechanisms and enables the development of disease-resistant crops. The structural diversification of NBS-LRR genes between monocots and dicots necessitates species-appropriate experimental designs and vector systems. Integrated approaches combining rapid VIGS screening with detailed characterization of stable mutants offer the most comprehensive strategy for establishing NBS gene function. As genomic technologies advance, the application of these validation methods across diverse plant species will continue to elucidate the complex mechanisms of plant immunity and facilitate the development of sustainable crop protection strategies.

Correlating Promoter cis-Elements with Hormone and Stress Responses

Promoter cis-acting regulatory elements are short, non-coding DNA sequences that serve as binding sites for transcription factors (TFs), functioning as molecular switches that control transcriptional responses to hormonal and environmental stimuli [88]. These elements, typically ranging from 4 to 20 base pairs in length, are critical components of plant immunity and stress adaptation, enabling precise reprogramming of gene expression in response to abiotic and biotic stresses [89] [88]. In the context of species-specific NBS (Nucleotide-Binding Site) structural patterns in monocots and dicots, understanding the architecture of these regulatory elements provides crucial insights into the evolutionary diversification of plant immune systems. The organization of different promoter sections and the specific arrangement of cis-elements contribute to the complex gene regulation observed in response to external stressors [88]. For researchers investigating disease resistance mechanisms, profiling these regulatory regions offers a powerful approach to deciphering the transcriptional control of major resistance gene families, particularly the NBS-LRR genes that comprise approximately 80% of characterized plant resistance proteins [3] [7].

Quantitative Profiling of Cis-Elements in Stress-Responsive Gene Families

Systematic analyses of promoter regions across various gene families have revealed distinct distributions of cis-elements associated with hormone responses and stress adaptation. The quantitative profiling of these elements provides insights into the transcriptional regulation mechanisms underlying plant stress responses.

Table 1: Cis-Element Distribution in Promoter Regions of Key Gene Families

Gene Family	Species	Promoter Region Analyzed	Key Cis-Elements Identified	Associated Functions
NAC TFs	Barley (Hordeum vulgare)	1 kb upstream	ABRE, MeJA-responsive, auxin-responsive, gibberellin-responsive	Drought, salinity, extreme temperature responses [90]
PP2A	Arabidopsis thaliana	1 kb upstream	5'-AAAG-3' (highly enriched), hormonal and stress-responsive elements	Abiotic stress response, signaling regulation [91]
NBS-LRR	Salvia miltiorrhiza	Promoter regions	Abundance of cis-elements related to plant hormones and abiotic stress	Plant immunity, disease resistance [3]

Table 2: Conservation and Variation of Hormone Response Elements Across Species

Hormone Pathway	Core Response Element	Conserved Variants	Functional Significance
Auxin	TGTCNN	CC, GG, GA, TC	Fine-tunes transcriptional response magnitude and spatial profile [92]
Cytokinin	DGATYN (D=A,G,T; Y=C,T)	Specific variants conserved across angiosperms	Modulates cytokinin-responsive gene expression [92]
Abscisic Acid (ABA)	BACGTGK (B=C,G,T; K=G,T)	ACGT-containing elements	Regulates osmotic and cold stress responses [89]

Experimental Protocols for Cis-Element Analysis

Genome-Wide Identification of Cis-Regulatory Elements

Objective: To comprehensively identify and characterize cis-regulatory elements in promoter regions of target gene families.

Methodology:

• Sequence Retrieval: Extract promoter sequences typically defined as 1 kb upstream of the transcription start site (TSS) from genomic databases [91].
• In Silico Analysis: Utilize plant-specific databases such as newPLACE or SOGO to scan promoter sequences for known cis-elements using position-specific scoring matrices [91].
• De Novo Motif Discovery: Implement the MEME suite to identify novel, statistically significant motifs that may represent previously uncharacterized regulatory elements [91].
• Functional Annotation: Correlate identified elements with known hormonal and stress responses through literature mining and database integration.

The CoMoVa Algorithm for Detecting cis-Element Variant Conservation

Objective: To identify evolutionarily conserved variants of hormone response elements across broad phylogenetic distances.

Methodology:

• Ortholog Identification: Using a well-annotated reference genome (e.g., Arabidopsis thaliana), perform reciprocal BLAST to identify candidate orthologs across multiple species [92].
• Variant Presence/Absence Profiling: Convert each promoter sequence into a Boolean vector representing the presence or absence of specific motif variants, treating multiple instances of a variant as a single data point [92].
• Phylogenetic Conservation Scoring: Arrange presence/absence vectors according to a species phylogenetic tree and apply maximum parsimony optimization to compute conservation scores [92].
• Statistical Significance Testing: Compare conservation scores against empirically determined background distributions from flanking sequences to identify significantly conserved variants [92].

CoMoVa Algorithm Workflow

Expression Analysis Integrated with Cis-Element Profiling

Objective: To correlate promoter cis-element composition with gene expression patterns under various stress conditions.

Methodology:

• Transcriptome Data Collection: Retrieve RNA-seq data from databases such as IPF, CottonFGD, or NCBI BioProjects under biotic, abiotic, and tissue-specific conditions [14].
• Expression Quantification: Process data through transcriptomic pipelines to obtain FPKM values and identify differentially expressed genes [14].
• Integrative Analysis: Correlate cis-element profiles with expression patterns to identify regulatory elements associated with specific stress responses.
• Functional Validation: For candidate genes, perform virus-induced gene silencing (VIGS) to confirm functional roles in stress responses [14].

Species-Specific Patterns in NBS Genes and Their Regulatory Architecture

Comparative genomic analyses reveal significant differences in NBS-LRR gene families between monocot and dicot species, extending to their promoter architectures. In pepper (Capsicum annuum), a dicot, comprehensive analysis identified 252 NBS-LRR resistance genes with uneven distribution across chromosomes, where 54% formed 47 gene clusters driven by tandem duplications [7]. Phylogenetic analysis demonstrated the dominance of the nTNL subfamily over the TNL subfamily, reflecting lineage-specific adaptations [7]. In contrast, monocot species such as Oryza sativa show a complete absence of TNL subfamily genes, while gymnosperms like Pinus taeda exhibit significant expansion of TNLs, comprising 89.3% of typical NBS-LRRs [3].

Table 3: Evolutionary Patterns of NBS-LRR Subfamilies Across Plant Species

Species	Plant Type	Total NBS-LRR Genes	CNL/nTNL	TNL	RNL
Arabidopsis thaliana	Dicot	207	~70%	~30%	Present [3]
Oryza sativa (rice)	Monocot	505	100%	0%	0% [3]
Pinus taeda	Gymnosperm	311	~10%	~90%	Present [3]
Salvia miltiorrhiza	Dicot	196	61 CNL	0%	1 RNL [3]
Capsicum annuum (pepper)	Dicot	252	248 nTNL	4 TNL	Not specified [7]

These structural differences in NBS-LRR genes are mirrored in their promoter architectures. Research on Salvia miltiorrhiza revealed that promoter analysis of SmNBS genes demonstrated "an abundance of cis-acting elements in SmNBS genes related to plant hormones and abiotic stress" [3], highlighting the connection between gene structure and regulatory mechanisms. The expansion and contraction of specific NBS-LRR subfamilies in different plant lineages suggest distinct evolutionary paths in their immune receptor repertoires, potentially driven by varying pathogenic pressures and reflected in their regulatory landscapes.

Table 4: Essential Research Reagents and Databases for Cis-Element Studies

Resource	Type	Function	Application Example
newPLACE/SOGO	Database	Repository of plant-specific cis-regulatory motifs	Scanning promoter sequences for known regulatory elements [91]
MEME Suite	Software Tool	De novo motif discovery	Identifying novel, statistically significant motifs in promoter regions [91]
CoMoVa Algorithm	Computational Method	Detection of conserved motif variants	Analyzing evolutionary conservation of hormone response element variants [92]
OrthoFinder	Software Package	Orthogroup inference and phylogenetic analysis	Evolutionary studies of gene families across multiple species [14]
PlantPAN	Database	Plant promoter analysis navigator	Comprehensive analysis of transcriptional regulators and their binding sites
VIGS Vectors	Molecular Biology Reagent	Virus-induced gene silencing	Functional validation of candidate genes in stress responses [14]

Regulatory Networks Integrating Hormone and Stress Signaling

The organization of cis-elements in promoters creates a sophisticated regulatory code that integrates multiple signaling pathways. Studies have revealed that specific variants of hormone response elements are highly conserved in core hormone response genes, with experimental evidence showing that these variants regulate the magnitude and spatial profile of hormonal responses [92]. For example, modification of the auxin response element (auxRE) from the canonical TGTCTC to TGTCGG produced a stronger response to auxin, demonstrating the functional significance of variant nucleotides within consensus motifs [92].

Research on osmotic- and cold-stress-responsive promoters has identified major cis-acting elements such as the ABA-responsive element (ABRE) and the dehydration-responsive element/C-repeat (DRE/CRT) as vital components of both ABA-dependent and ABA-independent gene expression pathways [89]. The precise combination and arrangement of these elements in promoters enable the integration of multiple signals, allowing plants to fine-tune their responses to complex environmental challenges.

Cis-Element Mediated Stress Response Pathway

The tissue-specific responsiveness observed in barley NAC genes, where HvNAC2 and HvNAC6 were significantly upregulated in roots but not leaves under drought and salt stress, illustrates how promoter architecture and transcription factor regulation combine to create specialized adaptive responses [90]. This spatial regulation enables plants to allocate resources efficiently while mounting targeted defenses in vulnerable tissues.

The comprehensive analysis of promoter cis-elements and their correlation with hormone and stress responses provides a foundational framework for understanding the regulatory codes governing plant adaptation. The species-specific patterns observed in NBS-LRR genes, coupled with their distinct cis-element profiles, highlight the evolutionary diversification of plant immune systems. Future research directions should include the development of more sophisticated algorithms for predicting cis-element functionality based on sequence variants and their genomic context, as well as high-throughput experimental validation of promoter elements using CRISPR-based genome editing technologies. The integration of multi-omics data will further elucidate how cis-element variations contribute to the remarkable diversity of stress responses observed across plant species, ultimately enabling the rational design of crop plants with enhanced resilience to environmental challenges.

Synteny and Evolutionary Rate Analysis to Uncover Conservation Principles

The study of genomic conservation principles provides a window into the fundamental processes driving evolution and adaptation. For researchers investigating disease resistance in plants, the nucleotide-binding site (NBS) domain gene family represents a critical evolutionary model. These genes, which constitute one of the largest resistance gene families in plants, exhibit significant diversification and species-specific structural patterns across monocots and dicots [93]. Understanding the conservation principles governing these genes requires sophisticated analytical approaches that integrate synteny—the conserved order of genomic elements—with evolutionary rate calculations. Recent advances in comparative genomics have revealed that functional conservation often persists even in the absence of sequence conservation, necessitating methods that look beyond traditional alignment-based approaches [94]. This technical guide provides researchers with comprehensive methodologies for analyzing synteny and evolutionary rates to uncover conservation principles in plant genomes, with specific application to NBS gene families.

Theoretical Foundations: Synteny-Based Conservation Detection

Beyond Sequence Alignment: The Challenge of Detecting Conservation

Traditional methods for identifying conserved genomic elements rely primarily on sequence alignment algorithms. However, these approaches show significant limitations, particularly when analyzing distantly related species where sequence divergence is substantial. Research on cis-regulatory elements (CREs) in embryonic hearts of mouse and chicken revealed that fewer than 50% of promoters and only approximately 10% of enhancers could be identified as sequence-conserved using standard LiftOver tools [94]. This dramatic drop in detectable conservation with increasing evolutionary distance highlights the critical need for methods that can identify functional conservation beyond sequence similarity.

The challenge is particularly acute in plant genomics, where the rapid turnover of noncoding sequences and high rates of genomic rearrangement complicate comparative analyses. For NBS domain genes, which show remarkable diversification across plant species, alignment-based methods may fail to detect evolutionarily conserved regulatory architectures that underlie their expression patterns and functional specificity [93].

Synteny-Based Approaches for Orthology Detection

Synteny-based algorithms overcome alignment limitations by leveraging conserved genomic neighborhoods to identify orthologous regions. The fundamental principle underpinning these approaches is that functional elements often maintain their relative positions between flanking conserved blocks, even as their sequences diverge beyond recognition by alignment-based methods [94].

Interspecies Point Projection (IPP) is a synteny-based algorithm designed specifically to identify orthologous genomic regions independent of sequence divergence [94]. The method operates on the premise that non-alignable elements located between flanking blocks of alignable regions (anchor points) maintain equivalent relative positions in another genome. IPP enhances this basic approach through bridged alignments, using multiple bridging species to increase anchor point density and improve projection accuracy (Figure 1).

Table 1: Classification of Conservation Types Based on Syntenic Projection

Conservation Type	Definition	Typical Applications
Directly Conserved (DC)	Projected within 300 bp of a direct alignment	High-confidence ortholog identification in closely related species
Indirectly Conserved (IC)	Further than 300 bp from direct alignment but projected through bridged alignments with summed distance to anchor points < 2.5 kb	Detecting functional conservation in distantly related species
Nonconserved (NC)	Projections not meeting DC or IC criteria	Identifying lineage-specific innovations

For NBS gene analysis, synteny-based approaches enable researchers to trace the evolutionary history of these genes across monocots and dicots, revealing both conserved core elements and lineage-specific adaptations. The application of IPP to mouse and chicken genomes demonstrated a more than fivefold increase in detectable conserved enhancers compared to alignment-based methods [94], suggesting similar potential for uncovering hidden conservation in plant NBS gene families.

Figure 1: Workflow for synteny-based orthology detection using the IPP algorithm

Methodological Approaches: Experimental Protocols

Ancestral Gene Order Reconstruction with EdgeHOG

Reconstructing ancestral genomes enables researchers to trace gene evolution through deep phylogenetic time. EdgeHOG is a recently developed method for ancestral gene order inference that offers significant advantages in scalability and accuracy compared to previous approaches [95]. The method uses hierarchical orthologous groups (HOGs) to model gene lineages and propagates gene order information along species phylogenies.

Protocol: Ancestral Genome Reconstruction with EdgeHOG

• Input Data Preparation:

  • Collect rooted species tree for taxa of interest
  • Obtain gene coordinates in GFF format for all extant genomes
  • Compute Hierarchical Orthologous Groups (HOGs) using OMA Standalone or FastOMA software

• Bottom-up Propagation of Gene Adjacencies:

  • Map observed gene adjacencies from extant genomes to corresponding parental genes in higher taxonomic levels
  • Create edges between ancestral genes representing inferred proximity
  • Assign edge weights based on number of propagations from descendant genomes

• Top-down Removal of Non-parsimonious Edges:

  • Identify edges propagated before the last common ancestor where adjacency emerged
  • Remove edges not supported by parsimony criterion
  • Resolve conflicts from genes with more than two neighbors

• Linearization of Synteny Networks:

  • Select two most likely flanking genes for each ancestral gene based on maximal support
  • Generate linear ancestral contigs for each phylogenetic level
  • Output fully browsable ancestral genomes

Benchmarking studies have demonstrated EdgeHOG's high accuracy, with harmonic mean precision of 98.9% and recall of 96.8% on simulated datasets, outperforming previous methods like AGORA [95]. For NBS gene research, this approach enables reconstruction of ancestral gene orders to identify conserved genomic neighborhoods and trace the evolutionary history of specific resistance gene clusters across monocots and dicots.

Table 2: Comparison of Ancestral Gene Order Inference Methods

Parameter	EdgeHOG	AGORA
Algorithmic Foundation	Hierarchical Orthologous Groups (HOGs)	Reconciled gene trees
Time Complexity	Linear	Computationally expensive
Precision (Simulated Data)	98.9%	96.0%
Recall (Simulated Data)	96.8%	94.9%
Scalability to Large Phylogenies	Excellent (tested with 2,845 genomes)	Limited (typically <100 genomes)
Ancestral Orientation Accuracy	>99%	>99%

Evolutionary Rate Analysis for NBS Genes

Evolutionary rate analysis quantifies selective pressures acting on genes and genomic regions, providing insights into functional conservation and adaptation. For NBS domain genes, evolutionary rates reveal patterns of pathogen-driven selection and functional diversification.

Protocol: Evolutionary Rate Calculation for NBS Gene Families

• Gene Family Identification:

  • Identify NBS domain genes using hidden Markov models (HMMs) for NBS domain signatures
  • Perform all-against-all BLASTP search to identify homologous sequences
  • Cluster sequences into gene families using MCL algorithm with appropriate inflation parameter

• Multiple Sequence Alignment:

  • Align protein sequences using MAFFT or PRANK with default parameters
  • Back-translate to codon alignment using PAL2NAL
  • Visually inspect and manually refine alignments as needed

• Phylogenetic Reconstruction:

  • Select best-fit substitution model using ModelTest or ProtTest
  • Reconstruct gene trees using maximum likelihood (RAxML) or Bayesian (MrBayes) methods
  • Assess node support with bootstrapping (≥100 replicates) or posterior probabilities

• Evolutionary Rate Calculation:

  • Calculate nonsynonymous (dN) and synonymous (dS) substitution rates using codeml in PAML package
  • Perform branch-specific tests to identify lineages with accelerated evolution
  • Conduct branch-site tests to detect positive selection affecting specific sites
  • Apply false discovery rate (FDR) correction for multiple testing

In large-scale studies of NBS domain genes across 34 plant species, researchers have identified 12,820 NBS-domain-containing genes classified into 168 classes with several novel domain architecture patterns [93]. Evolutionary analysis of these genes reveals both core orthogroups conserved across species and lineage-specific expansions, particularly in disease-resistant cultivars.

Integrative Synteny and Evolutionary Rate Analysis

The most powerful insights emerge from integrating synteny analysis with evolutionary rate calculations. This integrated approach allows researchers to distinguish between functional conservation maintained by purifying selection and nonfunctional sequences preserved through genomic constraint.

Protocol: Integrated Conservation Analysis

• Synteny Block Identification:

  • Perform whole-genome alignment using progressiveCactus or SibeliaZ
  • Identify syntenic blocks using DAGChainer or SyRI with minimum block size of 5 genes
  • Annotate NBS gene positions within syntenic blocks

• Conservation Classification:

  • Apply IPP algorithm to classify NBS genes as directly conserved, indirectly conserved, or nonconserved
  • Calculate conservation scores based on syntenic preservation across species

• Correlation with Evolutionary Rates:

  • Compare dN/dS ratios between conservation classes using Kruskal-Wallis test
  • Perform regression analysis between conservation scores and evolutionary rates
  • Identify genomic features associated with exceptional conservation or rapid evolution

• Functional Validation:

  • Correlate conservation patterns with gene expression data from RNA-seq experiments
  • Test conserved NBS genes for responses to pathogen challenges
  • Validate functional conservation using heterologous expression systems

Figure 2: Integrated workflow for synteny and evolutionary rate analysis

Applications to NBS Gene Research in Monocots and Dicots

Comparative Analysis of NBS Gene Conservation

Applying synteny and evolutionary rate analysis to NBS domain genes reveals fundamental principles of conservation and diversification in plant immunity systems. Large-scale comparative studies have identified 603 orthogroups of NBS genes, including both core orthogroups (OG0, OG1, OG2) present across multiple species and unique orthogroups highly specific to particular lineages [93].

Expression profiling of these orthogroups demonstrates that specific NBS gene families show upregulated expression in different tissues under various biotic and abiotic stresses in both susceptible and tolerant plants [93]. For example, orthogroups OG2, OG6, and OG15 show particularly pronounced responses to cotton leaf curl disease (CLCuD), suggesting their potential roles in pathogen defense mechanisms.

Table 3: NBS Gene Orthogroups with Documented Stress Responses

Orthogroup	Conservation Pattern	Expression Response	Potential Function
OG0	Core orthogroup across monocots and dicots	Upregulated in multiple stress conditions	Fundamental immunity component
OG2	Conserved with lineage-specific expansions	Strong response to CLCuD in cotton	Viral disease resistance
OG6	Dicot-enriched conservation	Induced by fungal pathogens	Broad-spectrum resistance
OG15	Monocot-specific	Abiotic stress responsiveness	Environmental adaptation
OG80	Species-specific to tolerant cultivars	Constitutive expression in resistant lines	Specialized resistance

Genetic variation analysis between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions reveals significant differences in NBS gene composition, with Mac7 exhibiting 6583 unique variants compared to 5173 in Coker312 [93]. These variants likely contribute to differential disease resistance and represent potential targets for marker-assisted breeding.

Conservation of Regulatory Elements Controlling NBS Expression

Beyond the NBS genes themselves, synteny analysis can uncover conserved regulatory elements controlling their expression. Research on cis-regulatory elements in divergent species has demonstrated that many functional enhancers lack sequence conservation but maintain positional conservation [94]. These "indirectly conserved" regulatory elements exhibit similar chromatin signatures and sequence composition to sequence-conserved elements but show greater shuffling of transcription factor binding sites between orthologs.

For NBS gene regulation, identifying these indirectly conserved regulatory elements is essential for understanding the evolution of disease resistance pathways. Experimental validation using in vivo enhancer-reporter assays has confirmed the functional conservation of sequence-divergent regulatory elements [94], suggesting that similar approaches could be applied to characterize NBS gene regulators.

Research Reagent Solutions

Table 4: Essential Research Reagents for Synteny and Evolutionary Analysis

Reagent/Resource	Function	Application in NBS Gene Research
Cactus Progressive Genome Aligner	Whole-genome multiple alignment	Identifying syntenic blocks across monocots and dicots
OMA Standalone/FastOMA	Hierarchical Orthologous Groups inference	Reconstruction of NBS gene families and ancestral gene orders
EdgeHOG	Ancestral gene order inference	Tracing evolution of NBS gene clusters
PAML (codeml)	Evolutionary rate calculation	Detecting positive selection in NBS genes
Virus-Induced Gene Silencing (VIGS) vectors	Functional validation of gene function	Testing NBS gene roles in disease resistance
CRISPR/Cas9 systems	Targeted gene knockout	Validating functions of conserved NBS genes
HMMER NBS domain profiles	Domain identification and annotation	Comprehensive identification of NBS domain genes

The integration of synteny analysis with evolutionary rate calculations provides a powerful framework for uncovering conservation principles in plant genomes. For NBS gene research, these approaches have revealed both deeply conserved core elements and rapidly diversifying lineage-specific innovations that contribute to disease resistance. Future advances in several areas promise to enhance these analyses further.

Single-cell genomics technologies will enable researchers to examine NBS gene expression and regulation at unprecedented resolution, potentially revealing cell-type-specific conservation patterns. Long-read sequencing technologies continue to improve genome assemblies, particularly in repetitive regions where many NBS genes reside. Machine learning approaches, particularly protein language models, show promise for detecting remote homology and functional conservation beyond the reach of traditional sequence similarity methods [96].

For crop improvement applications, the conservation principles uncovered through synteny and evolutionary analysis provide valuable guidance for prioritizing candidate genes for breeding programs. Genes in core conserved orthogroups may provide broad-spectrum resistance, while lineage-specific genes might offer specialized resistance to particular pathogens. The research reagents and methodologies outlined in this guide provide researchers with comprehensive tools for uncovering these conservation principles and applying them to address real-world agricultural challenges.

As genomic datasets continue to expand, with initiatives like the Earth Biogenome Project aiming to sequence all eukaryotic life [96], the opportunities for comparative analysis will grow exponentially. The principles and protocols described here provide a foundation for extracting biological insights from this genomic wealth, particularly for understanding the evolution of disease resistance in monocots and dicots.

Conclusion

The exploration of species-specific NBS structural patterns unequivocally reveals that the evolutionary paths of monocots and dicots have shaped distinct NBS-LRR repertoires, characterized by significant subfamily expansions, contractions, and unique domain architectures. These lineage-specific adaptations, driven by mechanisms like tandem duplication and positive selection, underscore the dynamic nature of the plant immune system. The methodological framework for gene identification and the comparative analyses presented provide a powerful toolkit for future research. For biomedical and clinical research, these findings extend beyond plant biology, offering a model for understanding molecular recognition and resistance gene evolution. Future work should focus on high-resolution structural biology of NBS proteins, the application of machine learning to predict resistance specificities, and the translational potential of these modular protein architectures in engineering synthetic immune receptors.

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