Orthogroup Expression Profiling Under Biotic Stress: Evolutionary Insights and Translational Applications

Abstract

This comprehensive review explores the emerging field of orthogroup expression profiling in plant biotic stress responses, integrating evolutionary genomics with functional validation. We examine how comparative transcriptomics across diverse species reveals conserved defense pathways, species-specific adaptations, and core stress-responsive gene networks. The article details methodological frameworks for orthogroup analysis, addresses computational and experimental challenges in cross-species prediction, and validates findings through functional studies. For researchers and drug development professionals, this synthesis provides critical insights into evolutionary constraints on immune responses and identifies orthogroups with translational potential for developing broad-spectrum disease resistance strategies in crops and model systems.

Evolutionary Foundations: How Orthogroup Analysis Reveals Conserved Biotic Stress Pathways

Orthogroups, defined as sets of genes descended from a single gene in the last common ancestor of the species being considered, provide a fundamental framework for comparative transcriptomics. This framework enables researchers to trace the evolutionary history of gene families, differentiate between conserved and lineage-specific traits, and identify molecular adaptations to environmental stresses. By grouping homologous genes across multiple species, orthogroups control for evolutionary divergence and facilitate meaningful cross-species comparisons of gene expression patterns. This guide examines current methodologies for orthogroup inference, their applications in stress response research, and practical considerations for implementing orthogroup-based analyses in transcriptomic studies, with a special focus on biotic stress adaptation in plants.

Orthogroups represent sets of genes that descend from a single ancestral gene in the last common ancestor of the species under consideration, forming the foundational unit for comparative genomic and transcriptomic analyses. Unlike simpler orthology concepts that consider pairwise relationships between genes, orthogroups accommodate complex evolutionary histories including gene duplications and losses across multiple species. This comprehensive approach enables researchers to distinguish between conserved genetic elements and lineage-specific adaptations, providing critical evolutionary context for interpreting transcriptomic data.

The theoretical foundation of orthogroups rests on understanding that genes in different species share evolutionary relationships that can be categorized as orthologs (descended from a common ancestor without duplication) and paralogs (related through gene duplication events). Orthogroups encapsulate both relationships by including all descendants of an ancestral gene, thus representing complete gene families. This classification is particularly valuable for comparative transcriptomics because genes within the same orthogroup often retain similar functions despite sequence divergence, allowing for meaningful cross-species expression comparisons.

In the context of biotic stress research, orthogroup-based analysis enables identification of conserved defense pathways across species while also revealing lineage-specific adaptations. For example, a study of anion channels in barley identified 43 anion channel proteins through genome-wide analysis, which were then classified into orthogroups including CLC, ALMT, VDAC, and MSL gene families [1]. This orthogroup framework facilitated cross-species comparisons and revealed that different cultivars possess diverse anion channel genes responding to drought stress, demonstrating how orthogroup classification can illuminate stress response mechanisms.

Methodological Framework for Orthogroup Inference

Computational Tools and Algorithms

Orthogroup inference relies on sophisticated algorithms that cluster genes into families based on sequence similarity and phylogenetic relationships. The standard workflow begins with all-vs-all sequence comparisons between proteomes of interested species, typically performed using tools like BLAST, DIAMOND, or MMseqs2. These sequence comparisons generate similarity scores that serve as input for clustering algorithms.

The most widely used tools for orthogroup inference include:

• OrthoFinder: Employs a graph-based clustering approach using the MCL algorithm and subsequently resolves gene trees within clusters to identify orthologs [2]
• OrthoMCL: Uses Markov Clustering algorithm on similarity graphs to group putative orthologs and paralogs
• BUSCO: Assesses orthogroup completeness by benchmarking against universal single-copy ortholog sets [3]
• GET_HOMOLOGUES-EST: Clusters gene models using BLASTN hits to drive Markov clustering of CDS sequences [4]

These tools differ in their underlying algorithms, scalability, and accuracy. A comparative analysis of their performance characteristics is summarized in Table 1.

Table 1: Performance Comparison of Orthogroup Inference Tools

Tool	Clustering Algorithm	Scalability	Key Strength	Limitations
OrthoFinder	MCL + Gene Tree-Species Tree reconciliation	Medium to Large Datasets	Accurate ortholog identification	Computationally intensive for very large datasets
OrthoMCL	Markov Clustering	Medium Datasets	Robust to parameter variation	Less accurate for distant homologs
BUSCO	Profile hidden Markov models	Quality Assessment	Orthogroup completeness assessment	Limited to conserved single-copy genes
GET_HOMOLOGUES-EST	Markov clustering of CDS	Transcriptome Data	Suitable for expressed sequences	Requires high-quality transcriptomes

Experimental Workflow for Orthogroup Definition

The process of defining orthogroups follows a structured workflow that integrates genomic and transcriptomic data. The following diagram illustrates the key steps in orthogroup inference and their application to transcriptomic studies:

Orthogroup Inference and Application Workflow

The process begins with input proteomes from the species of interest. For species lacking high-quality genome assemblies, transcriptome assemblies can serve as proxies, though this may reduce completeness. The next step involves all-vs-all sequence similarity search using tools like BLAST or DIAMOND, which generates pairwise similarity scores between all proteins across species. These scores then inform similarity graph construction, where genes are nodes and edges represent significant sequence similarity. The similarity graph undergoes graph clustering using the MCL algorithm or similar approaches, which partitions the graph into densely connected subgraphs that represent orthogroups.

The resulting orthogroup sets serve as the foundation for downstream analyses, including evolutionary analysis of gene family expansion/contraction, expression profiling across conditions or species, and functional annotation transfer. A study on Citrus species demonstrated this approach by clustering genes from 11 Citrus assemblies using GET_HOMOLOGUES-EST with a minimum alignment coverage of 90%, successfully identifying presence-absence variations (PAVs) and species-specific genes [4].

Orthogroup Applications in Comparative Transcriptomics

Evolutionary Pattern Analysis Across Taxa

Orthogroups enable systematic investigation of gene expression evolution across large evolutionary distances. By comparing expression patterns of orthogroups across species, researchers can distinguish conserved genetic programs from lineage-specific adaptations. A landmark study examining ~7,000 highly conserved orthogroups shared between vertebrates and insects revealed that sequence and expression similarities were significantly correlated between these distantly related clades, suggesting that predisposition to molecular diversification is an intrinsic property of each orthogroup [5].

This research demonstrated that protein sequences are generally more conserved in vertebrates compared to insects, while expression profiles across tissues showed similar conservation levels between the two clades [5]. The positive correlation between sequence and expression conservation indicates that genes under strong selective constraint tend to experience limited diversification at both sequence and expression levels. These patterns were particularly strong for vertebrates, likely influenced by their historical whole-genome duplication events.

Another study on Fagaceae tree species identified 11,749 single-copy orthologous genes to compare seasonal gene expression patterns [2]. The research revealed that rhythmic gene expression with significant periodic oscillations was more prevalent in buds (51.9%) than in leaves (40.6%), with most rhythmic genes exhibiting annual periodicity. Furthermore, seasonal peaks of rhythmic genes were highly synchronized across species in winter but diverged during the growing season, reflecting species-specific timing of leaf flushing and flowering.

Stress Response Profiling in Plants

Orthogroup-based transcriptomic analyses have revealed conserved molecular networks underlying plant responses to biotic and abiotic stresses. By comparing expression patterns of orthogroups across species under stress conditions, researchers can identify core stress response pathways as well as lineage-specific adaptations.

A comprehensive analysis of anion channel gene families in barley identified 43 anion channel proteins classified into CLC, ALMT, VDAC, and MSL orthogroups [1]. Under drought treatment, 17 of these genes showed significant expression changes, with different cultivars exhibiting diverse anion channel gene responses. The study demonstrated that plants with high transcripts of these anion channel genes demonstrated stronger tolerance to drought stress and maintained better element content (e.g., potassium, calcium), suggesting these orthogroups play crucial roles in stress adaptation [1].

Similarly, a transcriptomic atlas of macaúba palm revealed organ-specific transcripts and stress-related pathways by comparing expression patterns across seven organs [6]. The study identified expanded gene families in macaúba palm compared to oil palm, date palm, rice, and banana, with these expanded families enriched for functions in stress response and lipid metabolism. This orthogroup-based comparative approach helped identify genetic adaptations underlying macaúba's resilience to irregular precipitation and other environmental challenges.

Table 2: Orthogroup Expression Patterns Under Biotic Stress in Plants

Study System	Stress Condition	Key Orthogroups Identified	Expression Pattern	Functional Significance
Barley [1]	Drought	CLC, ALMT, VDAC, MSL	Upregulation in drought-tolerant cultivars	Ion homeostasis, osmotic balance
Citrus species [4]	Huanglongbing disease	Germin-like proteins (GLPs)	Differential expression in tolerant vs susceptible varieties	Disease resistance, cell wall modification
Solanaceae family [7]	Developmental stress	YABBY, MADS-box	Flower/fruit-specific expression	Reproductive development, stress response
Fagaceae trees [2]	Seasonal fluctuations	Rhythmic genes	Annual periodicity, winter synchronization	Phenological adaptation

Orthogroup Expression Profiling Under Biotic Stress

Experimental Design Considerations

Effective orthogroup expression profiling requires careful experimental design to control for biological and technical variability. For comparative transcriptomics under biotic stress, researchers should include multiple species or genotypes with varying stress tolerance, appropriate control conditions, and sufficient biological replication. The selection of reference species for orthogroup inference significantly impacts results, with ideal references having high-quality genome annotations and phylogenetic proximity to target species.

For temporal studies of stress response, sampling at multiple time points is essential to capture dynamic expression patterns. A study on seasonal gene expression in Fagaceae trees collected 483 transcriptome profiles every four weeks over two years, revealing distinct temporal expression patterns between leaf and bud tissues [2]. Similarly, a multifactorial experimental design examining Mesotaenium endlicherianum responses to temperature and light gradients generated 128 transcriptomes across 42 different conditions, providing comprehensive insights into stress response networks [3].

Proper normalization across species and experiments is critical for reliable comparisons. A study on Primula veris highlighted that commonly used housekeeping genes are often not constitutively expressed across tissues, recommending instead the identification of species-specific constitutively expressed genes through RNA-seq data [8]. For orthogroup expression analysis, normalization should account for both within-species and between-species variations.

Signaling Pathway Mapping

Orthogroup-based analysis facilitates the reconstruction of conserved signaling pathways across species. The following diagram illustrates a generalized stress response signaling pathway identified through orthogroup comparison:

Conserved Stress Response Pathway Across Species

This pathway framework highlights key components identified through orthogroup comparisons across multiple studies. Stress perception involves membrane receptors and sensory mechanisms that detect biotic stressors. Signal transduction encompasses conserved pathways including hormone signaling (e.g., abscisic acid, jasmonic acid) and reactive oxygen species (ROS) signaling, which have been identified as core hubs in stress response networks across land plants and streptophyte algae [3].

Transcriptional regulation involves transcription factor networks such as ERF, YABBY, and MADS-box families that show conserved expression patterns under stress conditions [7]. Cellular responses include activation of anion channels, production of defensive compounds, and cell wall modifications. For example, the GLP (germin-like protein) gene family in Citrus showed differential expression under Huanglongbing disease and citrus canker conditions, with specific genes (CsGLPs1-2 and CsGLPs8-4) displaying significant upregulation in disease-tolerant varieties [4]. These cellular changes ultimately lead to physiological adaptations that enhance stress tolerance.

Research Reagent Solutions for Orthogroup Studies

Table 3: Essential Research Reagents and Resources for Orthogroup Analysis

Resource Type	Specific Examples	Application in Orthogroup Studies	Key Considerations
Reference Genomes	Barley (Hordeum vulgare) [1], Citrus species [4], Quercus glauca [2]	Orthogroup inference, synteny analysis	Genome completeness, annotation quality
Transcriptome Atlases	Macaúba palm (22,703 transcripts across 7 organs) [6], Primula veris (26,338 expressed genes) [8]	Expression conservation analysis, tissue-specificity	Tissue coverage, replication, normalization
Software Tools	OrthoFinder [2], GET_HOMOLOGUES-EST [4], BUSCO [3]	Orthogroup inference, completeness assessment	Algorithm selection, parameter optimization
Sequence Databases	Pfam, KEGG [9], OrthoDB	Functional annotation, pathway mapping	Taxonomic coverage, annotation quality
Experimental Protocols	RNA extraction (TRIzol) [9], library prep (Illumina) [3], qRT-PCR validation [1]	Data generation, experimental validation	Protocol standardization, cross-species compatibility

Orthogroup analysis provides an essential evolutionary framework for comparative transcriptomics, enabling researchers to distinguish conserved genetic programs from lineage-specific adaptations. By grouping genes into evolutionarily meaningful clusters, orthogroups facilitate robust cross-species comparisons of expression patterns under biotic stress conditions. Current methodologies have established standardized workflows for orthogroup inference, though careful consideration of algorithm selection and parameters remains crucial.

Future developments in orthogroup analysis will likely focus on improving scalability for large multi-species datasets and integrating additional dimensions of molecular evolution such as rates of positive selection. As demonstrated across multiple studies, orthogroup-based expression profiling has already revealed conserved stress response networks across diverse plant species, from barley and citrus to primitive streptophyte algae [1] [4] [3]. These conserved molecular pathways represent promising targets for crop improvement strategies aimed at enhancing biotic stress tolerance.

The integration of pangenome concepts with orthogroup analysis represents another emerging frontier, enabling researchers to capture presence-absence variations within species while maintaining evolutionary context across species [4]. As comparative transcriptomics continues to expand across the tree of life, orthogroups will remain indispensable for interpreting expression patterns within an evolutionary framework, ultimately deepening our understanding of how genetic networks evolve in response to environmental challenges.

Plant immunity relies on a sophisticated innate system where nucleotide-binding site leucine-rich repeat (NBS-LRR) genes, also known as NLR genes, play a pivotal role as intracellular immune receptors. These genes constitute one of the largest and most diverse gene families in plant genomes, functioning primarily in effector-triggered immunity (ETI) by recognizing pathogen-derived molecules and initiating robust defense responses [10] [11]. The NBS-LRR proteins are characterized by a conserved nucleotide-binding site (NBS) domain and a C-terminal leucine-rich repeat (LRR) region, with variations in N-terminal domains enabling classification into distinct subfamilies: TNL (TIR-NBS-LRR), CNL (CC-NBS-LRR), and RNL (RPW8-NBS-LRR) [12] [13]. Recent research has revealed that these resistance genes are not randomly distributed but are organized into evolutionarily conserved groups known as orthogroups—sets of genes descended from a single gene in the last common ancestor of the species being compared.

The study of NBS-LRR genes through an orthogroup lens provides unprecedented insights into the evolutionary arms race between plants and their pathogens. This comparative framework allows researchers to trace the evolutionary history of resistance genes across plant lineages, identify core defense mechanisms conserved through millions of years of evolution, and discover species-specific innovations that could be harnessed for crop improvement. As genomic resources have expanded, systematic analyses have uncovered remarkable diversity in NBS-LRR gene content, organization, and expression patterns across the plant kingdom, from early land plants like mosses to highly derived monocots and dicots [14] [15]. This article provides a comprehensive comparison of NBS-LRR orthogroups across plant lineages, synthesizing current understanding of their genomic distribution, evolutionary dynamics, and functional specialization in response to biotic stress.

Methodology: Computational and Experimental Frameworks for Orthogroup Analysis

Genome-Wide Identification of NBS-LRR Genes

The foundation of orthogroup analysis begins with the comprehensive identification of NBS-LRR genes across multiple plant genomes. Standard protocols employ a dual approach combining Hidden Markov Model (HMM) searches and BLAST-based methods to ensure high coverage. The typical workflow begins with HMMER software using the conserved NB-ARC domain (Pfam: PF00931) as query with stringent E-value cutoffs (typically < 1e-20) [10] [12] [13]. Candidate sequences identified through this process are subsequently validated through domain architecture analysis using tools like InterProScan and NCBI's Conserved Domain Database to confirm the presence of characteristic NBS-LRR domains [13].

Additional domains are identified using specialized approaches: TIR domains (PF01582) are detected through HMM searches, while coiled-coil (CC) domains require tools like Paircoil2 with P-score cutoffs of 0.03, as they cannot be identified through conventional Pfam searches [10]. LRR domains (PF00560, PF07723, PF07725, PF12799) are identified through iterative database searches. For irregular NBS-LRR genes that lack complete domain structures, additional BLAST searches against curated NBS-LRR databases help identify partial genes that may represent pseudogenes or rapidly evolving functional genes [10] [12]. This multi-tiered approach ensures comprehensive identification of both typical and irregular NBS-LRR genes, providing the raw data for subsequent orthogroup analysis.

Orthogroup Delineation and Evolutionary Analysis

Orthogroup analysis employs specialized algorithms to cluster NBS-LRR genes into evolutionarily meaningful groups. The standard pipeline utilizes OrthoFinder v2.5.1 or higher, which employs DIAMOND for fast sequence similarity searches and the MCL clustering algorithm for gene grouping [14] [15]. Protein sequences from multiple species are aligned using MAFFT 7.0, and phylogenetic trees are constructed using maximum likelihood methods implemented in FastTreeMP or MEGA6-MEGA7 with 1000 bootstrap replicates to assess node support [14] [10] [15].

The classification of NBS-LRR genes into orthogroups enables several critical analyses: First, it allows identification of core orthogroups that are conserved across multiple species and likely represent fundamental immune components. Second, it reveals species-specific orthogroups that may underlie specialized defense adaptations. Third, it facilitates the detection of tandem duplication events that drive the rapid expansion of particular resistance gene families in specific lineages [14]. Additional evolutionary analyses often include synteny mapping to identify conserved genomic contexts and calculations of non-synonymous to synonymous substitution rates (dN/dS) to detect positive selection acting on specific orthogroups.

Expression Profiling Under Biotic Stress

Functional validation of NBS-LRR orthogroups typically involves comprehensive expression profiling under various biotic stress conditions. Standard protocols extract RNA-seq data from public databases (IPF database, NCBI BioProjects) or generate new sequence data from infected tissue [14] [15]. Data processing includes quality control, adapter trimming, read alignment, and quantification of expression values (typically FPKM or TPM). Differential expression analysis is performed using tools like DESeq2 or edgeR, with genes considered significantly differentially expressed if they meet thresholds of |log2 fold change| > 1 and adjusted p-value < 0.05.

Experiments are typically designed to capture temporal dynamics of orthogroup expression, with samples collected at multiple time points post-infection. The expression patterns are then categorized into: (1) tissue-specific expression (leaf, stem, root, flower), (2) abiotic stress-responsive expression (drought, salt, temperature extremes), and (3) biotic stress-responsive expression (fungal, bacterial, viral, nematode challenges) [14] [15]. For key candidate genes, validation often proceeds through virus-induced gene silencing (VIGS) and transgenic overexpression to confirm functional roles in disease resistance [14] [16] [17].

Figure 1: Computational workflow for identifying and validating NBS-LRR orthogroups, integrating genomic, transcriptomic, and functional approaches.

Comparative Analysis of NBS-LRR Orthogroups Across Plant Lineages

Genomic Distribution and Evolutionary Dynamics

The comprehensive analysis of NBS-LRR genes across land plants reveals striking variation in gene family size and organization. A recent landmark study examining 34 species from mosses to monocots and dicots identified 12,820 NBS-domain-containing genes classified into 168 distinct architectural classes, encompassing both classical patterns (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific configurations (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf) [14]. Orthogroup analysis resolved these genes into 603 orthogroups, with a subset identified as core orthogroups (OG0, OG1, OG2) conserved across multiple species and others categorized as unique orthogroups (OG80, OG82) specific to particular lineages [14].

The distribution of NBS-LRR genes follows distinct phylogenetic patterns. TNL-type genes are predominantly found in dicots, with complete absence observed in most monocots [11] [13]. For instance, the tung tree (Vernicia montana) possesses 12 TNL genes, while its close relative Vernicia fordii has completely lost this subclass [16]. In garden asparagus (Asparagus officinalis), a significant contraction of the NLR gene family has occurred during domestication, with only 27 NLR genes identified compared to 63 in its wild relative Asparagus setaceus [13]. This genomic reduction potentially explains the increased disease susceptibility observed in cultivated asparagus.

The genomic organization of NBS-LRR genes exhibits a strong tendency toward clustered arrangements across plant species. In cassava, approximately 63% of 327 NBS-LRR genes occur in 39 clusters distributed across the chromosomes [10]. These clusters are predominantly homogeneous, containing genes derived from recent duplication events, though heterogeneous clusters with phylogenetically distant NBS-LRR genes also occur [10]. This clustering facilitates rapid evolution through unequal crossing over and gene conversion, enabling plants to generate novel resistance specificities in response to evolving pathogen populations.

Table 1: Comparative Genomic Distribution of NBS-LRR Genes Across Plant Species

Plant Species	Total NBS-LRR Genes	TNL Genes	CNL Genes	Other/Partial	Genomic Organization
Arabidopsis thaliana (Reference)	~200	~70	~110	~20	Clustered arrangement
Manihot esculenta (Cassava)	327	34	128	165	63% in 39 clusters [10]
Nicotiana benthamiana (Tobacco)	156	5	25	126	3 major phylogenetic clades [12]
Rosa chinensis (Rose)	96 (TNL only)	96	0	0	Dominantly expressed in leaves [11]
Vernicia montana (Tung tree)	149	12	98	39	Includes CC-TIR-NBS hybrids [16]
Vernicia fordii (Tung tree)	90	0	49	41	Complete TNL absence [16]
Asparagus setaceus (Wild)	63	Not specified	Not specified	Not specified	Significant contraction in domestication [13]
Asparagus officinalis (Cultivated)	27	Not specified	Not specified	Not specified	Reduced disease resistance [13]

Expression Profiling Under Biotic Stress

Transcriptomic analyses across multiple plant species have revealed that specific NBS-LRR orthogroups show consistent induction patterns in response to biotic challenges. In a comprehensive study of cotton leaf curl disease (CLCuD) response, orthogroups OG2, OG6, and OG15 demonstrated significant upregulation across different tissues in both susceptible and tolerant cotton accessions under various biotic and abiotic stresses [14]. Genetic variation analysis between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified substantially more unique variants in NBS genes of the tolerant line (6,583 variants) compared to the susceptible cultivar (5,173 variants), suggesting that sequence polymorphism in these orthogroups contributes to resistance phenotypes [14].

In rose plants challenged with fungal pathogens, systematic expression analysis of 96 TNL genes identified RcTNL23 as showing particularly strong responses to three hormones (gibberellin, jasmonic acid, salicylic acid) and three pathogens (Botrytis cinerea, Podosphaera pannosa, and Marssonina rosae) [11]. This pattern suggests that certain NBS-LRR orthogroups function as integration nodes for multiple signaling pathways, enabling coordinated immune responses. Similarly, in soybean, the TNL gene GmTNL16 (Glyma.16G135500) was shown to confer resistance to Phytophthora sojae through a regulatory pair with gma-miR1510, with manipulation of this pair activating both jasmonate and salicylic acid defense pathways [17].

Cross-species transcriptomic analyses have revealed both conserved and divergent expression patterns among orthologous NBS-LRR genes. A study comparing Arabidopsis, rice, and barley responses to oxidative stress and hormone treatments found that 15-34% of orthologous differentially expressed genes showed opposite expression patterns between species, highlighting fundamental differences in immune regulation despite gene conservation [18]. This transcriptional divergence underscores the evolutionary flexibility of plant immune systems and the challenge of translating resistance mechanisms across distant plant lineages.

Table 2: Expression Profiles of Key NBS-LRR Orthogroups Under Biotic Stress

Orthogroup / Gene	Plant Species	Pathogen/Stress	Expression Response	Proposed Function
OG2	Gossypium hirsutum (Cotton)	Cotton leaf curl disease (CLCuD)	Upregulation in tolerant line	Virus tittering; VIGS validation confirmed role [14]
OG6, OG15	Gossypium hirsutum (Cotton)	Various biotic/abiotic stresses	Putative upregulation	Core defense response [14]
RcTNL23	Rosa chinensis (Rose)	Multiple fungi & hormones	Strong upregulation	Defense pathway integration node [11]
GmTNL16	Glycine max (Soybean)	Phytophthora sojae	Induced upon infection	Activates JA/SA pathways; regulated by miR1510 [17]
Vf11G0978-Vm019719	Vernicia species (Tung tree)	Fusarium wilt	Differential between species	Downregulated in susceptible, upregulated in resistant [16]
Preserved NLRs	Asparagus officinalis	Phomopsis asparagi	Unchanged or downregulated	Functional impairment in domestication [13]

Functional Validation of Defense-Related Orthogroups

Protein Interaction and Signaling Mechanisms

Functional characterization of NBS-LRR orthogroups has revealed intricate interaction networks with both host proteins and pathogen effectors. Protein-ligand and protein-protein interaction studies in cotton demonstrated strong binding between specific NBS proteins and ADP/ATP, highlighting the fundamental nucleotide-dependent conformational switching mechanism that underlies NBS-LRR activation [14]. Additionally, these studies identified direct interactions between putative NBS proteins and core proteins of the cotton leaf curl disease virus, suggesting recognition specificity mediated through both direct and indirect mechanisms [14].

The signaling pathways activated by NBS-LRR orthogroups typically involve hormonal crosstalk between salicylic acid (SA) and jasmonic acid (JA) pathways. In soybean, RNA sequencing of plants with manipulated GmTNL16 expression revealed enriched differentially expressed genes in "plant-pathogen interaction" and "plant hormone signal transduction" pathways, with specific induction of JAZ, COI1, TGA, and PR genes [17]. This pattern indicates that effective TNL-mediated resistance requires coordinated activation of both SA and JA signaling branches, potentially enabling broad-spectrum resistance against diverse pathogens.

NBS-LRR proteins localize to multiple subcellular compartments, including the cytoplasm, plasma membrane, and nucleus, reflecting their diverse recognition and signaling functions [12]. For example, in tobacco, subcellular localization predictions of 156 NBS-LRR homologs indicated 121 in cytoplasm, 33 in plasma membrane, and 12 in nucleus, suggesting distinct functional specializations for different NBS-LRR types [12]. This compartmentalization enables sophisticated surveillance strategies, with some NBS-LRR proteins functioning at infection sites while others operate within the nuclear compartment to regulate defense-related transcription.

Functional Assessment Through Genetic Manipulation

Virus-induced gene silencing (VIGS) has emerged as a powerful tool for functional characterization of NBS-LRR orthogroups, particularly in non-model species. In resistant cotton, silencing of GaNBS (a member of OG2) compromised resistance to cotton leaf curl disease, demonstrating its essential role in virus restriction [14]. Similarly, in tung tree, VIGS-mediated knockdown of Vm019719—a gene specifically upregulated in resistant Vernicia montana during Fusarium infection—significantly compromised resistance, while its allelic counterpart in susceptible Vernicia fordii (Vf11G0978) showed ineffective defense responses due to a promoter deletion affecting WRKY transcription factor binding [16].

Transgenic approaches have complemented VIGS studies by enabling overexpression of candidate NBS-LRR genes. In soybean, overexpression of GmTNL16 in hairy roots significantly reduced Phytophthora sojae biomass compared to controls, confirming its functional role in pathogen restriction [17]. Similarly, in apple, overexpression of MdTNL1 enhanced resistance to Glomerella leaf spot, with a single nucleotide polymorphism in this gene distinguishing resistant and susceptible germplasms [11]. These validation studies demonstrate that orthogroup analysis successfully identifies functional resistance genes with potential for crop improvement.

Figure 2: NBS-LRR mediated immunity signaling pathway, showing receptor activation, nucleotide exchange, hormone crosstalk, and defense outputs.

Table 3: Essential Research Resources for NBS-LRR Orthogroup Analysis

Resource Category	Specific Tools/Databases	Primary Function	Application Example
Genomic Databases	Phytozome, NCBI Genome, Plaza	Genome assemblies & annotations	Retrieving gene models and sequences [10] [15]
Domain Databases	Pfam, SMART, CDD	Domain architecture analysis	Identifying NBS, TIR, CC, LRR domains [10] [12]
Orthogroup Software	OrthoFinder, DIAMOND, MCL	Clustering genes into orthogroups	Defining core and lineage-specific orthogroups [14] [15]
Expression Databases	IPF Database, CottonFGD, NCBI BioProject	RNA-seq data retrieval	Expression profiling under stress [14] [15]
Phylogenetic Tools	MEGA, FastTreeMP, Clustal Omega	Evolutionary relationship inference	Constructing phylogenetic trees [14] [12] [13]
Functional Validation	VIGS vectors, CRISPR-Cas9	Gene silencing/editing	Testing gene function in resistant/susceptible lines [14] [16]
Cis-element Analysis	PlantCARE, MEME	Promoter motif identification	Finding regulatory elements [11] [12] [13]

The comparative analysis of NBS-LRR orthogroups across plant lineages reveals both conserved defense principles and lineage-specific innovations. Core orthogroups represent ancestral immune components maintained across diverse species, while rapidly evolving, species-specific orthogroups reflect adaptive responses to specialized pathogen pressures. This evolutionary perspective provides a rational framework for prioritizing candidate resistance genes for crop improvement programs.

The functional validation of orthogroups through VIGS, transgenic approaches, and expression analyses has demonstrated that orthogroup membership often predicts functional conservation, enabling knowledge transfer between species. However, the observed transcriptional divergence among orthologous genes also highlights important species-specific regulatory adaptations that must be considered when engineering resistance across plant lineages. Future research integrating pan-genomic approaches with detailed orthogroup analysis will further illuminate the dynamic evolutionary processes shaping plant immune systems and identify durable resistance combinations capable of withstanding rapidly evolving pathogens.

The systematic characterization of NBS-LRR orthogroups represents a powerful paradigm for understanding plant immunity across evolutionary timescales. By revealing both conserved and divergent elements of plant defense systems, this approach accelerates the identification of functional resistance genes and informs strategic breeding for enhanced crop resilience in the face of evolving pathogen threats.

Phylogenetic Distribution of Stress-Responsive Orthogroups

Orthogroups, which consist of genes descended from a single ancestral gene in a common ancestor, provide a powerful framework for understanding the evolutionary history and functional diversification of gene families. In plant biotic stress research, analyzing the phylogenetic distribution of stress-responsive orthogroups is essential for identifying conserved defense mechanisms and lineage-specific adaptations. Recent studies have harnessed pan-genomic approaches and functional genomic techniques to delineate how orthogroups have expanded, contracted, and diversified across plant phylogenies in response to pathogenic pressures. This guide objectively compares current methodologies and findings in this field, synthesizing experimental data to provide researchers with a clear overview of the key orthogroups involved in plant immunity and the techniques used to characterize them.

Comparative Analysis of Key Stress-Responsive Orthogroups

Table 1: Key Stress-Responsive Orthogroups and Their Functional Characteristics

Orthogroup ID	Gene Family	Species Studied	Stress Response	Expression Pattern	Functional Role
OG2	NBS-domain	Gossypium hirsutum (cotton)	Cotton Leaf Curl Disease (CLCuD)	Upregulated in tolerant accession Mac7	Putative role in virus tittering; silencing increases viral load [14]
OG6	NBS-domain	Gossypium hirsutum (cotton)	Various biotic and abiotic stresses	Upregulation in different tissues	Defense response against pathogens [15]
OG15	NBS-domain	Gossypium hirsutum (cotton)	Various biotic and abiotic stresses	Upregulation in different tissues	Defense response against pathogens [15]
Core NAC	NAC TFs	A. hypochondriacus, A. thaliana, O. sativa, B. vulgaris, C. quinoa	Multiple abiotic stresses	Conserved across species	Ancestral stress-responsive functions [19]
Core HvNAC	NAC TFs	Barley pan-genome (20 accessions)	Salt stress	Diverse tissue-specific patterns	Evolutionary conservation under purifying selection [20]

Table 2: Genomic Features and Evolutionary Dynamics of Stress-Responsive Orthogroups

Orthogroup Category	Representative Genes	Genomic Features	Selection Pressure	Duplication Events	Genetic Variation
Core Orthogroups	27 NAC genes shared across 5 species [19]	Conserved genomic loci	Strong purifying selection [20]	Primarily segmental/whole-genome duplication	Lower variation; shared across accessions
Lineage-Specific Orthogroups	3 NAC genes unique to A. hypochondriacus and C. quinoa [19]	Species-specific structural patterns	Relaxed selection constraints [20]	Tandem and transposed duplications	Higher variation; presence/absence variation
NBS-domain OG2	GaNBS in cotton [14]	Classical NBS-LRR architecture	Not specified	Tandem duplications	6583 unique variants in tolerant Mac7 vs 5173 in susceptible Coker312 [15]
Shell/Lineage-Specific	HvNACs in barley [20]	Dynamic loci with PAVs and CNVs	Relaxed selection	TE-mediated rearrangements	High structural variation driven by TEs

Experimental Protocols for Orthogroup Characterization

Genome-Wide Identification and Classification

The foundational step in orthogroup analysis involves comprehensive identification of gene family members across multiple species. For NBS-domain genes, researchers typically use PfamScan with the NB-ARC domain (PF00931) hidden Markov model (HMM) at a stringent E-value cutoff (1.1e-50) to identify candidate genes from genomic data [15]. Similarly, for NAC transcription factors, the NAM domain (PF02365) HMM is employed with an E-value threshold of 1E-5 and minimum domain score of 20 [20]. Additional validation through NCBI's Conserved Domain Database (CDD), SMART database, and BLASTP analysis with minimum 70% query coverage ensures only genuine family members are included [20]. Domain architecture classification follows established systems that group genes with similar domain arrangements into distinct classes, revealing both classical and species-specific structural patterns [15].

Orthogroup Delineation and Evolutionary Analysis

Orthologous groups are inferred using OrthoFinder v2.5.1, which employs DIAMOND for rapid sequence similarity searches and the MCL algorithm for clustering [15]. For pan-genome analyses, CD-HIT is used to cluster orthologous genes across multiple accessions, categorizing them into core (shared across all accessions), soft-core, shell, and lineage-specific orthogroups [20]. Multiple sequence alignment is performed using MAFFT 7.0, with phylogenetic trees constructed via maximum likelihood algorithms in FastTreeMP with 1000 bootstrap replicates [15]. Evolutionary dynamics are assessed through calculation of non-synonymous (Ka) to synonymous (Ks) substitution rates, where Ka/Ks ratios <1 indicate purifying selection, >1 indicate positive selection, and ≈1 suggest neutral evolution [19].

Expression Profiling Under Biotic Stress

RNA-seq data from public databases (e.g., IPF database, Cotton Functional Genomics Database) or newly generated datasets are processed through standardized transcriptomic pipelines. Reads are mapped to reference genomes or transcriptomes using appropriate aligners, with expression levels quantified as FPKM (Fragments Per Kilobase of transcript per Million mapped reads) or TPM (Transcripts Per Million) values [15]. Data are categorized into tissue-specific, abiotic stress-specific, and biotic stress-specific expression profiles. For biotic stress experiments, susceptible and tolerant genotypes are compared under pathogen challenge conditions, such as cotton leaf curl disease (CLCuD) infection [15]. Differential expression analysis identifies significantly upregulated or downregulated orthogroups, with validation through RT-qPCR for selected candidates [19].

Functional Validation Through Genetic Approaches

Virus-induced gene silencing (VIGS) serves as a key functional validation method, particularly for difficult-to-transform species. For example, silencing of GaNBS (OG2) in resistant cotton demonstrated its role in limiting viral titers of cotton leaf curl disease [14]. Protein-ligand and protein-protein interaction studies through molecular docking show how NBS proteins interact with pathogen effectors, such as the strong binding observed between putative NBS proteins and core proteins of the cotton leaf curl disease virus [15]. Genetic variation analysis between susceptible and tolerant accessions identifies unique variants associated with resistance phenotypes, providing candidates for marker-assisted breeding [15].

Visualization of Research Workflows and Signaling Pathways

Orthogroup Analysis Workflow

Orthogroup Analysis Workflow

NBS-Mediated Defense Signaling Pathway

NBS-Mediated Defense Signaling

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for Orthogroup Expression Studies

Reagent/Resource	Function/Application	Example Use Case	Key Features
Pfam HMM Profiles	Identification of gene family members using conserved domains	NBS-domain (PF00931) and NAC (PF02365) identification [15] [20]	Curated multiple sequence alignments and profile HMMs for protein domains
OrthoFinder	Orthogroup inference and comparative genomics	Delineation of orthogroups across multiple species [15]	Accurate orthogroup inference, phylogenetic analysis of orthogroups
PlantTFDB	Database of plant transcription factors and binding motifs	Classification and functional prediction of NAC TFs [19]	Comprehensive TF repository with functional annotations
Virus-Induced Gene Silencing (VIGS) System	Functional characterization through transient gene silencing	Validation of GaNBS (OG2) role in virus resistance [14]	Rapid functional assessment without stable transformation
IPF Database	Repository for plant RNA-seq data under various conditions	Expression profiling of NBS genes across tissues and stresses [15]	Curated expression data from multiple studies and treatments
NCBI CDD/SMART	Protein domain analysis and functional annotation	Validation of NAC domain presence and structure [19] [20]	Integrated domain databases with interactive tools
MEME Suite	Motif discovery and sequence analysis	Identification of conserved motifs in NAC proteins [19]	Discovers novel, ungapped motifs in nucleotide or protein sequences

Conserved vs. Species-Specific Expression Patterns in Pathogen Response

Understanding how organisms respond to pathogens is fundamental to ecology, evolutionary biology, and drug discovery. Orthogroup expression profiling—comparing the expression of groups of evolutionarily related genes across different species—provides a powerful framework for disentangling conserved defense mechanisms from species-specific adaptations. Under biotic stress, such as pathogen infection, selective pressures can lead to both the conservation of core immune pathways and the diversification of specific response elements. This comparative guide examines the evidence for these shared and unique expression patterns, synthesizing experimental data and methodologies critical for researchers and drug development professionals working at the intersection of genomics, immunology, and evolutionary biology. The central thesis is that while a core set of defense orthogroups exhibits conserved expression dynamics, the pathogen response landscape is significantly shaped by species-specific evolutionary trajectories and ecological contexts.

Comparative Analysis of Expression Patterns

Analysis of pathogen response across species reveals a complex interplay of conserved and lineage-specific gene expression. The following table synthesizes key findings from comparative studies.

Table 1: Conserved vs. Species-Specific Pathogen Response Patterns

Aspect of Response	Conserved Patterns (Common Across Species)	Species-Specific Patterns (Divergent Across Species)
Core Immune Pathway Activation	Upregulation of innate immune pathways (e.g., Toll, Imd) and antimicrobial peptides (AMPs) like defensins in insects [21].	Differential expression of specific AMPs (e.g., hymenoptaecin) and immune genes (e.g., vago) linked to survival in specific species [21].
Pathogen Sensing	Recognition of general pathogen-associated molecular patterns (PAMPs) by conserved receptor families [21].	Expansion or diversification of specific receptor families (e.g., NOD-like receptors) in certain lineages.
Evolutionary Significance	Maintains essential, non-redundant functions for survival against a broad range of pathogens; often under purifying selection.	Reflects adaptive evolution and local adaptation to specific pathogen pressures or ecological niches [22] [21].
Expression Dynamics	Rapid, systemic induction upon pathogen challenge.	Variable timing, magnitude, and tissue specificity of expression, influenced by evolutionary history [21].
Implications for Drug Discovery	High-value targets for broad-spectrum therapeutic interventions.	Targets for highly specific drugs, biologics, or personalized medicine approaches; explains differential drug responses [23].

Experimental Data from a Model System

A study on honey bees (Apis mellifera) provides a compelling model for examining conserved and specific responses within a single species under different selection regimes. The study compared pathogen dynamics and immune gene expression between managed colonies and feral colonies, which survive in the wild without human intervention [21].

Table 2: Experimental Data from Feral vs. Managed Honey Bee Colonies

Parameter Measured	Findings in Feral Colonies	Findings in Managed Colonies	Implication
Deformed Wing Virus (DWV) Load	Significantly higher levels, but with high temporal variability [21].	Lower levels compared to feral colonies [21].	Feral colonies persist despite high pathogen burdens, suggesting adaptation.
Immune Gene Expression	Higher expression in 5 out of 6 immune genes tested (defensin-1, hymenoptaecin, pgrp-lc, pgrp-s2, argonaute-2, vago) for at least one sampling period [21].	Lower baseline expression of most immune genes [21].	Feralization alters host immune responses, leading to upregulated gene expression.
Association with Survival	Differential expression of hymenoptaecin and vago increased the odds of overwintering survival [21].	Differential expression of hymenoptaecin and vago increased the odds of overwintering survival [21].	Specific immune genes, not just general pathway activation, are critical for fitness, representing a conserved survival mechanism.

Detailed Experimental Protocols

Protocol 1: Orthogroup Identification and Expression Profiling

This protocol is used to identify groups of orthologous genes and compare their expression across species under biotic stress.

• Sample Collection & RNA Extraction: Collect tissue from multiple species under control and pathogen-challenged conditions. Preserve samples immediately in RNAlater or flash-freeze in liquid nitrogen. Extract total RNA using a commercial kit with DNase treatment to remove genomic DNA contamination.
• RNA Sequencing & Quality Control: Prepare stranded mRNA-seq libraries and sequence on an Illumina platform to a minimum depth of 30 million paired-end reads per sample. Assess raw read quality using FastQC and trim adapters and low-quality bases with Trimmomatic.
• De Novo Transcriptome Assembly & Annotation:* For non-model organisms, perform *de novo transcriptome assembly using Trinity. Assess assembly completeness with BUSCO. Annotate transcripts using BLAST against Swiss-Prot and InterProScan for domain identification.
• Orthogroup Inference: Use OrthoFinder with translated transcriptomes from all studied species to identify orthogroups (groups of orthologous genes).
• Expression Quantification & Normalization: Map reads from each sample back to their respective transcriptome using Salmon to obtain transcript-level counts. Use the tximport package in R to summarize counts to the gene level.
• Differential Expression Analysis: For each species, perform differential expression analysis between challenged and control groups using DESeq2. Orthogroups with significantly adjusted p-values (p-adj < 0.05) are considered differentially expressed.
• Comparative Expression Analysis: Categorize orthogroups as follows:
  • Conserved Response: Orthogroups that are differentially expressed in the same direction (e.g., upregulated) in all or most species.
  • Species-Specific Response: Orthogroups that are differentially expressed in only one or a subset of species.

Protocol 2: qPCR Validation of Immune Gene Expression

This protocol, derived from the honey bee study, provides a targeted method for validating expression of specific immune genes [21].

• Field Sampling: Identify and sample pairs of organisms from different groups (e.g., feral and managed honey bee colonies) within a controlled geographic radius to minimize site-specific variation. Collect individuals from each colony/group during consistent seasons (e.g., spring and fall) over multiple years.
• cDNA Synthesis: Quantify RNA concentration and integrity. Synthesize cDNA from a standardized amount of total RNA (e.g., 1 µg) using a High-Capacity cDNA Reverse Transcription Kit with random hexamers.
• Primer Design: Design and validate quantitative PCR (qPCR) primers for target immune genes (e.g., defensin-1, hymenoptaecin, vago) and stable reference genes (e.g., actin, GAPDH). Ensure primer efficiency is between 90–110%.
• Quantitative PCR: Perform qPCR reactions in triplicate for each sample using a SYBR Green master mix on a real-time PCR detection system. Use a standard thermal cycling protocol (e.g., 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min).
• Data Analysis: Calculate relative gene expression using the comparative Cq method (2^−ΔΔCq). Normalize the Cq values of target genes to the geometric mean of the reference genes. Use statistical tests (e.g., linear mixed models) to compare gene expression between groups and correlate expression levels with survival outcomes.

Visualizing the Comparative Analysis Framework

The following diagram illustrates the logical workflow and key concepts for analyzing conserved and species-specific expression patterns.

Visualizing the Experimental Workflow

This diagram outlines the key steps in the qPCR validation protocol for measuring immune gene expression in a comparative context.

The Scientist's Toolkit: Essential Research Reagents

The following table details key reagents and materials essential for conducting orthogroup expression profiling and validation experiments.

Table 3: Research Reagent Solutions for Expression Profiling

Reagent / Material	Function / Application	Example Use Case
RNAlater Stabilization Solution	Preserves RNA integrity in fresh tissues immediately after collection, preventing degradation.	Field sampling of multiple species from diverse environments for transcriptomic studies [21].
Poly(A) mRNA Selection Beads	Isolates messenger RNA from total RNA for strand-specific RNA-seq library preparation.	Enriching for protein-coding transcripts during library prep for orthogroup expression analysis.
OrthoFinder Software	Infers orthogroups and orthologs from whole-genome or transcriptome data.	Identifying groups of orthologous genes across the species included in a comparative study.
DESeq2 R Package	Performs differential expression analysis of RNA-seq count data, using shrinkage estimation.	Statistically determining which orthogroups are significantly upregulated or downregulated under biotic stress.
SYBR Green qPCR Master Mix	Fluorescent dye that binds double-stranded DNA for real-time quantification of PCR products.	Validating the expression levels of specific immune genes (e.g., defensins) identified in RNA-seq data [21].
High-Capacity cDNA Reverse Transcription Kit	Converts RNA into stable cDNA for downstream applications like qPCR.	Synthesizing cDNA from RNA samples extracted from feral and managed organisms for targeted validation [21].

Ancient Gene Duplications and Their Impact on Modern Immune Repertoires

Gene duplication is a fundamental evolutionary mechanism driving genomic innovation and functional diversification. In the context of the immune system, these duplication events have served as a primary source of raw genetic material for the development of sophisticated defense mechanisms across the tree of life. This review examines how ancient gene duplications have shaped modern immune repertoires, with particular emphasis on comparative analysis of experimental approaches and findings across diverse biological systems. Understanding these evolutionary processes provides critical insights for biomedical research and therapeutic development, particularly in the field of orthogroup expression profiling under biotic stress.

Evolutionary Mechanisms and Patterns

Gene duplications occur through various mechanisms, each with distinct evolutionary implications. Whole-genome duplication (WGD) events and small-scale duplications, including tandem, segmental, and transposon-mediated duplications, provide the raw material for immune system evolution [24] [25]. The fate of duplicated genes follows several pathways: nonfunctionalization (pseudogenization), neofunctionalization (acquiring novel functions), subfunctionalization (partitioning ancestral functions), and dosage effects (increased expression) [25] [26].

Recent spatial transcriptomics studies in diverse plant species (Arabidopsis thaliana, soybean, orchid, maize, and barley) reveal that duplication mechanisms preserving cis-regulatory landscapes yield paralogs with more conserved expression profiles [25]. Genes originating from segmental or whole-genome duplication and tandem duplication events often exhibit greater expression levels and are expressed in more cell types compared to genes derived from dispersed duplications [25].

The evolutionary trajectory of duplicated genes is significantly influenced by gene function constraints. Dosage-sensitive genes involved in basic cellular processes display highly preserved expression profiles due to selection pressures maintaining stoichiometric balance, while genes involved in specialized processes such as immune responses diverge more rapidly [25].

Case Studies in Immune Gene Evolution

Vertebrate Adaptive Immune System

The adaptive immune system, shared by all vertebrates, originated approximately 500 million years ago following a genome-wide duplication event [27]. Critical insight came from studies of shark genomes, where researchers characterized a protein from a modern shark gene (UrIg2) that explains the evolution of adaptive immunity [27]. This research identified the candidate gene that catalyzed the initiation of the adaptive immune system when a mobile genetic element from a microbe—a recombination-activating gene (RAG) transposon—inserted itself into and split a gene in a eukaryotic cell [27]. This random event led to the generation of innumerable new proteins through gene rearrangement, forming the foundation of antibody-based immunity [27].

Primate Antiviral Restriction Factors

Primates have extensively used gene duplication to combat lentiviruses, including HIV. Many restriction factors belong to larger protein families arising through gene duplication events [26]. The evolution of these genes demonstrates several functional outcomes:

• CCL3L1 gene duplications are associated with reduced risk of HIV-1 acquisition, reduced viral loads, and slowed CD4+ T cell decline through a dosage effect [26].
• IFITM proteins show specialization after duplication, with IFITM1 restricting HIV-1 strains from macrophages at the cell surface, while IFITM2 and IFITM3 restrict CXCR4-tropic viruses in endosomal compartments [26].
• TRIM5-CypA fusion in owl monkeys represents neofunctionalization, where retrotransposition of cyclophilin A into the TRIM5 locus created a novel restriction factor against HIV-1 [26].

SAMD9/9L Gene Family in Antiviral Defense

Human SAMD9 and SAMD9L are duplicated genes encoding innate immune proteins that restrict poxviruses and lentiviruses [28]. Evolutionary characterization reveals these genes have remarkable copy number variations across mammals, with genomic signatures of evolutionary arms races [28]. Researchers discovered that bonobos experienced a recent SAMD9 gene loss still segregating in the population, while chimpanzee and bonobo SAMD9L genes show enhanced anti-HIV-1 functions compared to human orthologs [28]. These adaptations resulted from strong viral selective pressures, particularly from lentiviruses, and contribute to lentiviral resistance in bonobos [28].

Plant Immune Gene Expansions

Plants have massively expanded innate immune genes through duplication events. A comprehensive study of nucleotide-binding site (NBS) domain genes identified 12,820 NBS-containing genes across 34 land plant species, classified into 168 classes with numerous novel domain architectures [24]. This expansion represents an adaptive recruitment of tandemly duplicated genes that provide discriminatory responses to different pathogens [24]. Expression profiling demonstrated that specific NBS gene orthogroups (OG2, OG6, and OG15) show upregulated expression in various tissues under biotic and abiotic stresses in cotton plants with varying susceptibility to cotton leaf curl disease [24].

Table 1: Comparative Analysis of Gene Duplication in Immune Systems Across Species

Organismic Group	Key Duplicated Gene Families	Evolutionary Outcome	Experimental Evidence
Vertebrates	Immunoglobulin superfamily	Adaptive immunity	Shark UrIg2 structure [27]
Primates	TRIM, IFITM, CCL3L1	Antiretroviral defense	HIV susceptibility studies [26]
Mammals	SAMD9/9L	Antiviral restriction	Lentiviral resistance in bonobos [28]
Plants	NBS domain genes	Pathogen recognition	Orthogroup expression under stress [24]
Invertebrates	Innate immune genes	Pathogen defense	Oyster transcriptome analysis [29]

Experimental Approaches and Methodologies

Genomic Identification and Classification

Comparative genomics approaches enable researchers to identify and classify duplicated genes across species. Standard protocols include:

• Domain-based identification: Using tools like PfamScan with HMM search scripts (e-value 1.1e-50) to identify genes containing specific domains (e.g., NBS, Zf-BED) [24] [30].
• Classification systems: Categorizing genes based on domain architecture, with similar domain-architecture-bearing genes placed under the same classes [24].
• Orthogroup analysis: Employing OrthoFinder with DIAMOND for sequence similarity searches and MCL clustering algorithm for grouping paralogous and orthologous genes [24] [30].

Evolutionary Analysis

Phylogenetic analyses using maximum likelihood algorithms (e.g., IQ-TREE, FastTreeMP) with bootstrap validation reconstruct evolutionary relationships [24] [28]. Population genomics approaches identify signatures of positive selection and gene loss [28]. Structural similarity searches using tools like Foldseek complement sequence-based methods, revealing deep evolutionary relationships [28].

Functional Characterization

Gene Expression Profiling

RNA-seq data from various databases (e.g., IPF database, NCBI BioProjects) provide expression values (FPKM) across tissues and stress conditions [24]. Data are categorized into tissue-specific, abiotic stress-specific, and biotic stress-specific responses to identify differentially expressed genes [24].

Functional Validation

• Virus-Induced Gene Silencing (VIGS): Used in plants to demonstrate the role of specific genes (e.g., GaNBS in cotton) in disease resistance [24].
• Protein-ligand and protein-protein interaction studies: Reveal strong interactions between immune proteins and pathogen molecules (e.g., NBS proteins with cotton leaf curl disease virus core proteins) [24].
• X-ray crystallography: Determines three-dimensional protein structures (e.g., shark UrIg2 at ALS Beamline 5.0.2) [27].

The following diagram illustrates the evolutionary fates of duplicated immune genes and the corresponding experimental approaches used to characterize them:

Quantitative Comparative Analysis

Table 2: Gene Family Expansions Associated with Lifespan and Immunity in Mammals

Gene Category	Association with Maximum Lifespan	Association with Brain Size	Functional Enrichment	Study Reference
Immune-related gene families	Significant expansion (236 families)	Significant expansion (360 families)	Immune system functions	[31]
Total protein-coding genes	No significant association	Not reported	Not applicable	[31]
SAMD9/9L genes	Not directly studied	Not directly studied	Antiviral defense against poxviruses and lentiviruses	[28]
NBS domain genes	Not studied in mammals	Not studied in mammals	Plant pathogen recognition	[24]

Research across 46 mammalian species revealed significant gene family size expansions associated with maximum lifespan potential and relative brain size, but not with gestation time, age of sexual maturity, or body mass [31]. Extended lifespan was specifically associated with expanding gene families enriched in immune system functions, suggesting a connection between gene duplication in immune-related gene families and the evolution of longer lifespans in mammals [31].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Studying Duplicated Immune Genes

Reagent/Resource	Function/Application	Example Use Cases
T2T-CHM13 reference genome	Provides gapless sequence of all autosomes and chromosome X	Identifying human-specific duplicated genes [32]
PfamScan with HMM search	Identification of domain-containing genes	Screening for NBS, Zf-BED domains [24] [30]
OrthoFinder with DIAMOND	Orthogroup inference and phylogenetic analysis	Clustering paralogous and orthologous genes [24]
RNA-seq datasets (FPKM values)	Gene expression quantification	Profiling expression under biotic stress [24]
Virus-Induced Gene Silencing (VIGS)	Functional validation of gene roles	Demonstrating GaNBS role in virus resistance [24]
Protein crystallography	Three-dimensional structure determination	Shark UrIg2 structure analysis [27]

Ancient gene duplications have fundamentally shaped the evolution of immune systems across kingdoms. From the origin of adaptive immunity in vertebrates to the expansion of pathogen recognition systems in plants, gene duplication provides evolutionary innovation that enhances organismal survival in hostile environments. The comparative analysis presented here demonstrates consistent patterns of immune gene expansion followed by functional diversification through various evolutionary fates. Future research integrating spatial transcriptomics, structural biology, and functional genomics will further illuminate how these ancient genetic events continue to influence modern immune repertoires and disease responses. Understanding these evolutionary principles provides valuable insights for manipulating immune responses in therapeutic contexts and anticipating future host-pathogen coevolution.

Methodological Frameworks: From Orthogroup Identification to Functional Prediction

Computational Pipelines for Orthogroup Identification and Annotation

Orthogroup analysis has become a fundamental methodology in comparative genomics, enabling researchers to identify groups of genes descended from a single ancestral gene in the last common ancestor of the species being analyzed [33]. This approach is particularly valuable for evolutionary studies, functional annotation, and expression profiling across multiple species or genotypes. In the context of biotic stress research, orthogroup analysis provides a framework for understanding how conserved gene networks respond to pathogens and environmental challenges, allowing for cross-species comparisons that would otherwise be complicated by gene duplication events and taxonomic divergence [34] [35].

The accuracy and completeness of orthogroup inference directly impact downstream biological interpretations, making the choice of computational pipeline critical for reliable research outcomes. With the continuous advancement of sequencing technologies and the increasing availability of genomic data for non-model organisms, robust orthogroup analysis has become indispensable for uncovering evolutionary relationships and functional conservation in stress response pathways [36]. This guide provides a comprehensive comparison of current orthogroup identification and annotation pipelines, with particular emphasis on their application to biotic stress research.

Comparative Analysis of Orthogroup Inference Methods

Performance Metrics and Benchmarking Approaches

The evaluation of orthogroup inference methods requires careful benchmarking against curated reference datasets. The Orthobench benchmark database, which contains 70 expert-curated reference orthogroups (RefOGs) spanning the Bilateria, provides a gold standard for assessing inference accuracy [33]. A recent phylogenetic revision of Orthobench found that 44% of RefOGs required membership updates, with 34% needing major revisions affecting phylogenetic extent, highlighting the importance of using current benchmarks that leverage improved tree inference algorithms [33].

Comparative performance studies have revealed significant differences in orthogroup inference accuracy across methods. When benchmarked against the updated Orthobench reference, OrthoFinder consistently demonstrates high accuracy, though all methods show variability in performance depending on the specific biological challenges presented by different gene families [33]. The standard benchmark provides an open-source testing suite to support future development and evaluation of orthogroup inference methods.

Pipeline Comparison and Technical Specifications

Table 1: Comparative Analysis of Orthogroup Inference Pipelines

Pipeline	Core Algorithm	Scalability	Key Features	Best Applications
OrthoFinder	Graph-based clustering with DendroBLAST [37]	Medium to large datasets	Species tree inference, gene duplication analysis	Evolutionary studies, comparative genomics [33]
OrthoMCL	Markov Cluster (MCL) algorithm [38] [35]	Small to medium datasets	Handling of in-paralogs, resolution of complex families	Taxonomic comparisons with moderate divergence [35]
M1CR0B1AL1Z3R 2.0	Optimized OrthoMCL variant with batch processing [38]	Large datasets (up to 2000 genomes)	Genome completeness analysis, orphan gene detection, ANI calculation	Microbial comparative genomics, pan-genome studies [38]
CoRMAP	OrthoMCL with de novo assembly [35]	Reference-free transcriptomes	Cross-species expression comparison, meta-analysis of RNA-Seq data	Comparative transcriptomics, non-model organisms [35]
PEPPAN	Modified PanTools algorithm [39]	Large pangenome datasets	Efficient pangenome graph construction, variant detection	Pathogen evolution, virulence factor identification [39]

Table 2: Performance Characteristics in Biotic Stress Research Contexts

Method	Stress-Responsive OG Detection	Expression Integration	Cross-Species Applicability	Technical Requirements
OrthoFinder	High (validated in plant stress studies [37])	Moderate (requires additional steps)	Excellent for phylogenetically diverse species [37]	Moderate computational resources
OrthoMCL	Moderate (depends on annotation quality)	Limited (primarily genomic)	Good for closely related species [35]	Lower memory requirements
M1CR0B1AL1Z3R 2.0	Specialized for microbial pathogenesis [38]	Integrated with expression analysis	Optimized for bacterial datasets	High-performance computing for large datasets
CoRMAP	High (designed for comparative transcriptomics [35])	Native integration of expression data	Excellent for non-model organisms	Large-memory server for de novo assembly
GENESPACE	High (used in wheat pan-transcriptome [36])	Compatible with expression data	Designed for synteny analysis across cultivars	Moderate to high resources for polyploid genomes

Experimental Protocols for Orthogroup Analysis

Standardized Workflow for Orthogroup Identification and Annotation

The following diagram illustrates a comprehensive experimental workflow for orthogroup analysis, integrating elements from multiple established pipelines:

Short Title: Orthogroup Analysis Workflow

Detailed Methodological Protocols

Input Data Preparation and Quality Control

The initial phase requires meticulous data curation and quality assessment. For genomic data, this involves assembly evaluation using metrics such as N50 and completeness assessment with BUSCO (Benchmarking Universal Single-Copy Orthologs) [40] [36]. The wheat pan-transcriptome study demonstrated the importance of this step, achieving over 99.8% completeness of BUSCO genes through rigorous quality control [36]. For transcriptomic data, tools like Trim Galore! and FastQC perform quality control, adapter trimming, and read filtering [35]. For cross-species comparisons, particular attention must be paid to normalization methods and sequencing depth consistency to avoid technical artifacts in downstream analyses.

Orthogroup Inference and Quality Assessment

The core inference process begins with sequence similarity searches using tools such as MMseqs2 or HMMER, followed by clustering with algorithms like MCL (Markov Cluster algorithm) or graph-based approaches [38] [35]. M1CR0B1AL1Z3R 2.0 employs an optimized version of OrthoMCL with an inflation parameter of 1.5 to balance cluster granularity and cohesiveness [38]. For large datasets, batch processing strategies can be implemented where the input is divided into smaller batches, with orthogroups inferred within each batch before a final integration step [38]. Quality assessment should include evaluation of orthogroup size distribution, species representation, and comparison to known reference sets when available.

Expression Integration for Stress Response Profiling

For biotic stress studies, expression data integration is crucial. The CoRMAP pipeline exemplifies this approach by using orthogroup assignments to compare gene expression levels across species and experimental conditions [35]. This involves mapping RNA-Seq reads to assemblies, quantifying expression using methods like RSEM (RNA-Seq by Expectation-Maximization), and normalizing across samples [35]. In the wheat pan-transcriptome analysis, researchers identified conserved expression patterns in a core set of homeologous genes while also detecting widespread changes in subgenome expression bias between cultivars, demonstrating the power of integrated orthogroup-expression analysis [36].

Applications in Biotic Stress Research

Case Studies in Plant-Pathogen Interactions

Orthogroup analysis has revealed critical insights into plant defense mechanisms. A transcriptomic atlas of macaúba palm identified organ-specific gene expression and stress-related pathways, providing a valuable genomic resource for understanding molecular mechanisms underlying environmental adaptability [6]. The study generated 34,293 transcripts from seven organs, enabling the identification of organ-specific transcripts and analysis of expression patterns in key biological processes [6].

In wheat, a comprehensive pan-transcriptome analysis utilizing orthogroup approaches examined variation in the prolamin superfamily and immune-reactive proteins across cultivars [36]. This study demonstrated how orthogroup analysis can identify cultivar-specific genes and define core and dispensable genomes, with cloud and shell genes (those present in one or a subset of cultivars) frequently associated with disease resistance and environmental adaptation [36]. The research revealed that while core genes tend to be more highly expressed across all subgenomes and tissues, shell genes showed enrichment for stress response functions, highlighting their potential importance in biotic stress resistance.

Identification of Conserved Stress Response Pathways

Phylotranscriptomic analysis using orthogroup approaches has successfully identified conserved transcriptional regulators in stress responses. One study analyzing five eudicot species identified 35 high-confidence conserved cold-responsive transcription factor orthogroups (CoCoFos), including well-known regulators like CBFs, HSFC1, ZAT6/10, and CZF1 [34]. This orthogroup-based framework enabled researchers to distinguish between species-specific expansions and conserved stress response pathways, demonstrating the utility of this approach for dissecting complex stress response networks.

The following diagram illustrates how orthogroup analysis reveals conserved and lineage-specific stress response mechanisms:

Short Title: Stress Response Orthogroup Discovery

Microbial Pathogenesis Studies

In microbial research, orthogroup analysis has been instrumental in understanding pathogen evolution and virulence mechanisms. A comparative pan-genomic study of Vibrio parahaemolyticus clinical and environmental isolates used OrthoFinder to analyze genomic differences between pathogenic and non-pathogenic strains [39]. The research revealed that environmental isolates possessed a higher number of core genes, while clinical isolates showed enrichment of virulence-associated genes [39]. This approach identified specific genetic elements associated with pathogenicity, demonstrating how orthogroup analysis can pinpoint potential targets for therapeutic intervention and vaccine development.

Table 3: Research Reagent Solutions for Orthogroup Analysis

Category	Tool/Resource	Function	Application Context
Genome Annotation	BRAKER [40]	Automated gene prediction	Structural annotation without manual curation
	EVidenceModeler [40]	Evidence-weighted annotation consolidation	Integrating multiple evidence sources
	AUGUSTUS [40]	Ab initio gene prediction	Annotation when limited evidence available
Quality Assessment	BUSCO [40] [36]	Completeness assessment	Evaluating annotation quality using universal orthologs
	OMArk [36]	Consistency and completeness evaluation	Quality control for gene sets
	GeneValidator [40]	Problem identification in gene predictions	Detecting questionable gene models
Orthogroup Inference	OrthoFinder [37] [33]	Primary orthogroup inference	General comparative genomics
	OrthoMCL [38] [35]	Ortholog clustering with MCL algorithm	Handling in-paralogs and complex families
	PEPPAN [39]	Pangenome orthogroup inference	Pathogen evolution studies
Expression Integration	Trinity [35]	De novo transcriptome assembly	Reference-free transcriptomics
	RSEM [35]	Transcript abundance estimation	Quantifying expression levels
	CoRMAP [35]	Cross-species expression comparison	Meta-analysis of RNA-Seq data
Functional Analysis	eggNOG [39]	Functional annotation	Orthology assignment and functional inference
	KEGG [38] [39]	Pathway analysis	Mapping genes to biological pathways
	GO [39]	Gene Ontology enrichment	Functional categorization

Orthogroup identification and annotation pipelines have become indispensable tools for comparative genomics, particularly in the context of biotic stress research. The continuing development of these methods focuses on improving scalability to handle increasingly large datasets, enhancing accuracy through better algorithms and benchmarking, and deepening functional integration with expression and phenotypic data. The emergence of pan-genome and pan-transcriptome analyses represents a significant advancement, enabling researchers to move beyond single reference genomes to capture the full genetic diversity within species [36].

For biotic stress research, orthogroup analysis provides a powerful framework for distinguishing conserved defense mechanisms from lineage-specific adaptations, with important implications for developing durable disease resistance strategies in agriculture and understanding host-pathogen coevolution. As these methods continue to evolve, they will undoubtedly yield new insights into the genetic architecture of stress responses across the tree of life.

The quest to understand the genetic basis of stress responses across species has positioned cross-species transcriptomic integration as a cornerstone of modern comparative genomics. Within the specific context of orthogroup expression profiling under biotic stress, this approach enables researchers to distinguish evolutionary conserved defense mechanisms from species-specific adaptations [34] [15]. Such differentiation is critical for translating findings from model organisms to crop species and for identifying fundamental biological principles that transcend taxonomic boundaries.

The analytical challenge lies in successfully integrating datasets separated by millions of years of evolution, which creates substantial transcriptomic divergence that can obscure true biological relationships [41]. This guide objectively compares the performance of prevailing integration methods, providing the experimental data and protocols necessary for researchers to select and implement optimal strategies for their specific biological questions, particularly within biotic stress research.

Benchmarking Integration Strategies: A Quantitative Comparison

The effectiveness of cross-species integration strategies must be evaluated based on their ability to balance two competing objectives: achieving sufficient species mixing to enable comparison while conserving meaningful biological heterogeneity within cell types or conditions. Benchmarking studies have systematically assessed these factors across diverse biological contexts.

Performance Metrics for Integration Quality

Rigorous benchmarking employs multiple quantitative metrics to evaluate integration performance [41]:

• Species Mixing Metrics: Assess how well homologous cell types from different species cluster together in the integrated space. Key metrics include:
  • Alignment score: Quantifies the percentage of cross-species neighbors
  • Batch effect removal: Measures elimination of technical and species-specific effects
• Biology Conservation Metrics: Evaluate preservation of biological variance after integration:
  • Accuracy Loss of Cell type Self-projection (ALCS): Specifically developed to quantify overcorrection that obscures cell type distinguishability
  • Conservation of biological variance: Maintains meaningful biological heterogeneity

Comparative Performance of Integration Methods

A comprehensive benchmark evaluation of 28 integration strategies—spanning 4 gene homology mapping approaches and 10 integration algorithms—revealed significant performance differences across multiple biological scenarios [41]. The table below summarizes the quantitative performance of top-performing methods:

Table 1: Performance Comparison of Cross-Species Integration Methods

Method	Algorithm Type	Species Mixing Score	Biology Conservation Score	Integrated Score	Optimal Use Case
scANVI	Probabilistic/semi-supervised	High	High	High	General purpose; one-to-one homologous cell types
scVI	Probabilistic generative model	High	High	High	Large datasets; multiple conditions
SeuratV4	CCA/RPCA-based anchoring	High	High	High	Tissues with clear cell type correspondence
SAMap	Reciprocal BLAST-based	N/A*	N/A*	N/A*	Evolutionarily distant species; challenging homology
SATURN	Gene sequence-based	Not reported	Not reported	Not reported	Broad evolutionary studies; multiple taxonomic levels
scGen	Generative model	Not reported	Not reported	Not reported	Closely related species (within class)

Note: Standard batch correction metrics are not applicable to SAMap due to its distinct methodology; performance is assessed via alignment score and visual inspection [41] [42].

Performance evaluations across 16 integration tasks revealed that method performance varies significantly by biological context. Methods excelling in pancreas data integration showed different performance characteristics than those optimal for hippocampus or heart tissue, emphasizing the importance of context-specific method selection [41].

Experimental Protocols for Cross-Species Integration

Implementing robust cross-species transcriptomic analysis requires careful attention to experimental design, preprocessing, and integration steps. The following protocols detail established methodologies from key studies in the field.

Sample Preparation and Sequencing Considerations

Effective multi-species transcriptomics begins with strategic sample preparation to address the inherent abundance imbalances between organisms [43]:

• Proportional Composition Assessment: Prior to full sequencing, use qRT-PCR or test sequencing to determine the relative RNA proportions of each organism. This informs the required sequencing depth and enrichment strategy.
• Enrichment Strategies: When the target organism represents a small fraction of total RNA, employ:
  • rRNA depletion: Using kits like Illumina Ribo-Zero or NEBNext rRNA depletion kit
  • Poly(A) depletion: For prokaryotic targets or non-polyadenylated transcripts
  • Targeted capture: Custom probe hybridization for extreme abundance disparities (e.g., enriching Wolbachia endosymbiont reads 2242-fold)
• Sequencing Depth Calculation: Ensure sufficient reads for the minor organism—benchmarks suggest approximately 850 reads/gene enable robust differential expression analysis [43].

The BENGAL Pipeline for Integration and Assessment

The BENchmarking strateGies for cross-species integrAtion of singLe-cell RNA sequencing data (BENGAL) pipeline provides a standardized framework for cross-species integration [41]:

Table 2: Key Steps in the BENGAL Integration Pipeline

Step	Process	Implementation Details
1. Input Preparation	Quality control and annotation	Perform input-specific QC and cell ontology curation
2. Gene Homology Mapping	Ortholog translation	Use ENSEMBL multiple species comparison tool; concatenate raw count matrices
3. Integration Algorithm Application	Data integration	Feed concatenated matrix to chosen algorithm (e.g., scANVI, SeuratV4, SAMap)
4. Output Assessment	Multi-metric evaluation	Compute species mixing and biology conservation scores
5. Annotation Transfer	Cross-species label transfer	Train classifier on one species to annotate cell types in another

The pipeline systematically evaluates integration quality using multiple metrics, with particular emphasis on the ALCS metric to detect overcorrection that might obscure biologically meaningful species-specific cell types [41].

Orthogroup Analysis for Biotic Stress Studies

For biotic stress research, orthogroup analysis enables the identification of conserved transcriptional responses across species [34] [15]:

• Orthogroup Construction: Cluster homologous genes from multiple species into orthogroups using tools like OrthoFinder with the MCL clustering algorithm [15]
• Differential Expression Analysis: Identify stress-responsive orthogroups showing significant expression changes across species
• Response Classification: Categorize orthogroups as commonly responsive (same direction) or oppositely responsive (different directions) across species
• Functional Validation: Select candidate genes for experimental validation (e.g., virus-induced gene silencing) to confirm functional importance in stress responses [15]

This approach successfully identified 35 high-confidence conserved cold-responsive transcription factor orthogroups (CoCoFos) across eudicots, including both known regulators and novel candidates [34].

Visualization of Integration Workflows and Relationships

Effective cross-species transcriptomic analysis requires understanding both the computational workflow and biological relationships. The following diagrams illustrate these critical aspects.

Cross-Species Integration and Assessment Workflow

Diagram 1: BENGAL Cross-Species Integration Pipeline. This workflow illustrates the standardized process for integrating transcriptomic data across species, from quality control through final assessment [41].

Orthogroup Expression Profiling Under Biotic Stress

Diagram 2: Orthogroup Profiling for Biotic Stress. This workflow shows the process for identifying conserved and divergent transcriptional responses to biotic stress across species using orthogroup analysis [34] [15].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Successful cross-species transcriptomic analysis requires specific computational tools and experimental resources. The following table catalogs essential solutions referenced in benchmarking studies.

Table 3: Research Reagent Solutions for Cross-Species Transcriptomics

Category	Tool/Reagent	Function	Application Context
Gene Homology Mapping	ENSEMBL Comparative Tool	Ortholog translation between species	Pre-integration gene mapping [41]
	Reciprocal BLAST	De novo gene-gene homology graph construction	SAMap workflow for distant species [41]
Integration Algorithms	SeuratV4 (CCA/RPCA)	Canonical correlation analysis for dataset alignment	General purpose integration [41] [44]
	scANVI/scVI	Probabilistic modeling with deep neural networks	Large-scale integration with complex batch effects [41]
	SAMap	Iterative cell-cell and gene-gene mapping	Whole-body atlases; evolutionarily distant species [41]
Experimental Kits	Illumina Ribo-Zero	rRNA depletion for prokaryote-containing samples	Enriching bacterial transcriptomes [43]
	NEBNext Poly(A) Magnetic Module	Poly(A) enrichment for eukaryotic transcripts	Standard eukaryotic mRNA sequencing [43]
Validation Tools	Virus-Induced Gene Silencing (VIGS)	Functional validation of candidate genes	Confirm role in biotic stress response [15]
	qRT-PCR	Expression validation and marker gene verification	Treatment efficacy confirmation [18]

Cross-species transcriptomic data integration represents a powerful approach for advancing our understanding of orthogroup expression profiling under biotic stress. The benchmarking data presented here reveals that method selection must be guided by specific biological context—with scANVI, scVI, and SeuratV4 achieving superior balance for standard comparisons, while SAMap offers unique advantages for evolutionarily distant species.

Successful implementation requires meticulous attention to experimental design, particularly regarding abundance imbalances between species and appropriate homology mapping strategies. By leveraging the standardized protocols, visualization workflows, and reagent solutions outlined in this guide, researchers can robustly integrate transcriptomic data across species boundaries to uncover both conserved and divergent biological responses to biotic stress.

Machine Learning Approaches for Predicting Stress-Responsive Orthogroups

The identification of stress-responsive genes is paramount for understanding the molecular mechanisms that underpin plant resilience. Traditionally, this process relied on differential expression analysis of individual genes under specific stress conditions. However, the focus has progressively shifted towards analyzing evolutionarily related groups of genes, or orthogroups, which can provide deeper insights into conserved stress response mechanisms across species. The integration of machine learning (ML) has revolutionized this field, enabling the high-throughput prediction and prioritization of key genetic elements with remarkable accuracy. This guide objectively compares the performance of various ML approaches used to predict stress-responsive orthogroups, detailing their experimental protocols, data requirements, and performance outcomes to aid researchers in selecting the most appropriate methodology for their work.

Experimental Protocols for Predicting Stress-Responsive Orthogroups

Hybrid Feature Selection and Classifier-Based Identification

This protocol, adapted from a study on Arabidopsis thaliana, combines multiple feature selection techniques with classifier algorithms to identify a core set of stress-responsive genes [45].

• Data Acquisition and Preprocessing: Download a microarray dataset (e.g., GSE41935 from GEO). The dataset includes 207 samples and 30,380 genes from Arabidopsis subjected to salt, heat, cold, high-light, and flagellin stresses. Identify Differentially Expressed Genes (DEGs) using the limma R package with a False Discovery Rate (FDR) threshold of 0.01, yielding 439 DEGs [45].
• Addressing Class Imbalance: The dataset contained 32 control and 175 stress samples, creating a class imbalance. Apply the Synthetic Minority Oversampling Technique (SMOTE) in WEKA software to generate synthetic samples in the minority (control) class, using default parameters (nearest neighbors K=5, percentage=400) [45].
• Feature Selection for Orthogroup Prioritization:
  • Information Gain (IG) and ReliefF: Use these filter-based methods in WEKA to rank all genes (features) based on their predictive power for stress response. Select the top 20 genes from each method for further analysis [45].
  • LASSO Regression: Apply LASSO regression using R to perform feature selection from the entire set of 30,380 genes. This method shrinks the coefficients of less important features to zero, identifying 42 key genes [45].
• Classifier Training and Evaluation: Train multiple classifiers (BayesNet, logistic, multilayer perceptron, SMO, and Random Forest) on the datasets generated by the different feature selection methods. Use k-fold cross-validation (e.g., 10-fold) to evaluate performance. The Random Forest classifier achieved the highest accuracy, 97.91% with IG features and 98.51% with ReliefF features [45].
• Identification of a Core Gene Signature: Analyze the top genes from each feature selection method to find common candidates. The study identified a three-gene signature (AT5G44050, AT2G47180, AT1G70700). Validate this signature's predictive power on an independent RNA-seq dataset using a Random Forest or XGBoost classifier [45].

Machine Learning on Gene Family Size for Cross-Species Prediction

This approach, demonstrated in yeasts, uses gene family size within orthogroups as a feature to predict complex phenotypic traits like oxidative stress resistance across an entire subphylum [46].

• Phenotypic Data Collection: Measure the relative growth of 285 yeast species under oxidative stress induced by tert-butyl hydroperoxide (TBOOH). Classify the poorest-growing 20% of species in 1 mM TBOOH as "ROS-sensitive" and the best-growing 20% in 2 mM TBOOH as "ROS-resistant" [46].
• Orthogroup Matrix Construction: Use a pre-computed orthogroup matrix for the Saccharomycotina subphylum. The features for the ML model are the number of genes (gene family size) each species possesses in each orthogroup [46].
• Model Training and Feature Selection: Train a Random Forest classifier to predict the ROS resistance classification based on gene family sizes. Use the RF feature selection algorithm to identify the 50 most important orthogroups based on Gini importance [46].
• Model Interpretation and Validation: Use SHapley Additive exPlanations (SHAP) values to estimate each feature's contribution to the prediction for each species. This local interpretation guides experimental validation. For example, overexpress a top-predictive gene (e.g., old yellow enzyme reductase) in a susceptible species like Kluyveromyces lactis to confirm it increases ROS resistance [46].

Meta-Analysis and Multi-Model Prioritization in Crops

This protocol, applied in maize, integrates transcriptomic data from multiple independent studies (meta-analysis) and uses an ensemble of ML models to prioritize candidate genes from orthogroups [47].

• Data Collection and Integration: Search public repositories (e.g., NCBI SRA) for all RNA-seq datasets related to biotic and abiotic stresses in the crop of interest. Map raw sequence reads to the reference genome (e.g., B73 NAM 5.0 for maize) using HISAT2 and generate count data with FeatureCounts [47].
• Batch Effect Correction and Normalization: Normalize raw count data using the "normalize.quantiles" function in R. Correct for batch effects introduced by different experimental conditions using the ComBat function in the SVA package, which employs an empirical Bayes method [47].
• Multi-Algorithm Model Training: Split the normalized data into training (80%) and testing (20%) sets. Apply seven different ML algorithms to the training data:
  • Support Vector Machine (SVM)
  • Partial Least Squares Discriminant Analysis (PLS-DA)
  • K-Nearest Neighbors (KNN)
  • Gradient Boosting Machine (GBM)
  • Random Forest (RF)
  • Naïve Bayes (NB)
  • Decision Tree (DT) Use model-specific methods (e.g., Recursive Feature Elimination for SVM, Variable Importance in Projection for PLS-DA) to extract a list of top-ranked genes from each algorithm [47].
• Identification of High-Confidence Candidates: Compare the gene lists from all models to identify a common set of high-priority candidate genes. Further analyze these genes through weighted gene co-expression network analysis (WGCNA) to identify hub genes within key modules [47].

Performance Comparison of Machine Learning Approaches

The following tables summarize the performance, data requirements, and optimal use cases of the different ML approaches discussed.

Table 1: Performance Metrics of Featured ML Approaches

Study Organism	ML Approach	Key Features	Best-Performing Classifier	Reported Accuracy/Performance
Arabidopsis thaliana [45]	Hybrid Feature Selection	Gene expression from microarrays	Random Forest	98.51% accuracy
Yeasts (Saccharomycotina) [46]	Gene Family Size Prediction	Orthogroup gene count	Random Forest	AUC-ROC > 0.90
Maize [47]	Meta-analysis & Multi-Model	Integrated RNA-seq data	Multiple (SVM, PLSDA, RF, etc.)	235 unique candidate genes identified
Rice [48]	Meta-analysis & PLS-DA	Microarray gene expression	PLS-DA / Random Forest	100% classification accuracy (abiotic vs. biotic)

Table 2: Data Requirements and Applications of ML Approaches

ML Approach	Data Input Requirements	Computational Complexity	Ideal Use Case
Hybrid Feature Selection [45]	Gene expression matrix (e.g., from microarrays/RNA-seq)	Medium	Identifying a minimal, highly predictive gene signature from a single species.
Gene Family Size Prediction [46]	Orthogroup matrix and phenotypic data across many species	High	Discovering evolutionary genetic mechanisms of a trait across diverse species.
Meta-Analysis & Multi-Model [47]	Integrated transcriptomic datasets from multiple studies	High	Robustly prioritizing candidate genes by leveraging large, public datasets.

Visualizing the Predictive Workflow

The diagram below illustrates a generalized, high-level workflow for predicting stress-responsive orthogroups using machine learning, integrating common elements from the protocols above.

Generalized ML Workflow for Stress-Responsive Orthogroup Prediction

Successful implementation of these ML approaches relies on a suite of computational tools and biological resources.

Table 3: Key Research Reagents and Computational Tools

Item Name	Type	Function in Workflow	Example Source / Package
Reference Genome	Biological Data	Provides the coordinate system for aligning sequencing reads and assigning features.	B73 NAM 5.0 (maize), IRGSP 1.0 (rice) [47] [49]
Orthogroup Matrix	Computational Data	Defines groups of orthologous genes across species, enabling comparative analysis.	OrthoFinder output; pre-computed matrices [46]
Gene Expression Omnibus (GEO)	Data Repository	Primary public archive for functional genomics data sets, including microarray and RNA-seq data.	NCBI [45]
Sequence Read Archive (SRA)	Data Repository	Stores raw sequencing data from high-throughput sequencing platforms.	NCBI [47]
limma	R Package	Performs differential expression analysis for microarray and RNA-seq data.	Bioconductor [45]
SMOTE	Algorithm	Corrects for class imbalance in datasets by generating synthetic minority class samples.	WEKA software [45]
Random Forest	Algorithm	A versatile classifier used for both prediction and feature importance ranking.	Scikit-learn (Python), randomForest (R) [45] [46] [47]
SHAP	Interpretation Tool	Explains the output of any ML model by quantifying the contribution of each feature.	SHAP Python library [46]

Topological Data Analysis for Visualizing Expression Networks

Topological Data Analysis (TDA) has emerged as a powerful mathematical framework for analyzing complex, high-dimensional biological datasets, particularly gene expression networks. Unlike traditional statistical methods that often impose linear or locally constrained assumptions, TDA captures the intrinsic geometric and topological structure of data, enabling researchers to detect features such as clusters, loops, and voids that conventional approaches may overlook [50]. This capability is particularly valuable for visualizing expression networks in orthogroup expression profiling under biotic stress conditions, where understanding the shape and connectivity of gene expression patterns can reveal critical regulatory mechanisms and stress response pathways.

The application of TDA to gene expression data represents a paradigm shift from traditional reductionist approaches to a more holistic perspective that preserves the global organization and hidden structures within transcriptomic data [50]. By leveraging mathematical tools from algebraic topology, TDA provides a robust, scale-invariant framework for uncovering hidden organization within the complex expression patterns that emerge during biotic stress responses. This approach is especially suited for orthogroup expression profiling, as it can identify conserved and divergent expression modules across species and stress conditions, potentially revealing core stress response mechanisms that might be missed by conventional differential expression analysis.

TDA Versus Traditional Methods for Expression Network Analysis

Comparative Analysis of Methodologies

When evaluating Topological Data Analysis against traditional bioinformatics approaches for expression network visualization, distinct advantages and limitations emerge across multiple dimensions. The table below provides a systematic comparison of these methodologies:

Table 1: Performance comparison between TDA and traditional methods for expression network analysis

Analytical Dimension	Topological Data Analysis (TDA)	Traditional Methods (PCA, t-SNE, UMAP)
Underlying Mathematical Foundation	Algebraic topology (persistent homology, Mapper algorithm) [50]	Linear algebra (PCA) or neighbor graphs (t-SNE, UMAP) [50]
Data Structure Assumptions	Model-independent; no predefined data distribution assumptions [50]	Linear (PCA) or locally constrained embeddings (t-SNE) [50]
Handling of Nonlinear Structures	Excellent capture of nonlinear relationships, loops, and voids [50]	Limited ability; may distort global nonlinear structures [50]
Multiscale Analysis Capability	Native multiscale analysis via persistent homology [50]	Typically single-scale; parameters fixed by user
Detection of Rare Cell States	High sensitivity for rare or transitional states [50]	Often obscured by dominant populations
Interpretability of Features	Topological features (Betti numbers, persistence diagrams) [50]	Statistical clusters or reduced dimensions
Application to Orthogroup Expression	Identifies expression backbone and modular connections [51]	Focuses on variance maximization or local similarities

Empirical Performance Metrics

Recent studies have demonstrated the superior capability of TDA in identifying biologically meaningful patterns in gene expression data that traditional methods might miss. In a landmark study applying TDA to flowering plants, researchers discovered a core gene expression backbone that defines form and function across diverse species [51]. The TDA-based Mapper graphs formed well-defined gradients of tissues from leaves to seeds and from healthy to stressed samples, revealing distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses [51].

The quantitative advantage of TDA becomes particularly evident when analyzing expression networks under biotic stress conditions. Where traditional PCA might collapse subtle but biologically important variations, TDA preserves transitional states and rare expression patterns that often represent critical regulatory checkpoints in stress response pathways. Genes identified through TDA as correlating with tissue lens functions show significant enrichment in central processes such as photosynthetic activity, growth and development, housekeeping functions, and stress responses [51].

Experimental Protocols for TDA in Orthogroup Expression Profiling

Sample Preparation and RNA Sequencing

Orthogroup expression profiling under biotic stress begins with careful experimental design and sample preparation. For comparative analysis across species, researchers must obtain tissue samples from organisms subjected to controlled biotic stress conditions (pathogen infection, herbivory, etc.) alongside appropriate unstressed controls. The protocol below outlines key steps:

Table 2: Essential research reagents for orthogroup expression profiling under biotic stress

Research Reagent	Specification/Function	Application in Orthogroup Profiling
RNA Stabilization Solution	RNase-inhibiting buffers (e.g., TRIzol)	Preserves RNA integrity during stress treatment and tissue collection
mRNA Enrichment Kit	PolyA selection or rRNA depletion	Ensures high-quality transcriptome data
cDNA Synthesis Kit	Reverse transcription with unique molecular identifiers	Creates stable cDNA libraries for sequencing
Cross-Species Hybridization Probes	Designed against conserved regions	Enables comparative analysis across species
Normalization Controls	Spike-in RNAs or housekeeping genes	Corrects for technical variation in cross-species comparisons
Structural Alignment Tools	FoldSeek or similar software [52]	Clusters proteins by structural similarity for orthogroup definition

• Stress Application: Apply standardized biotic stress treatments to experimental organisms. For plant studies, this may include pathogen inoculation (e.g., fungal spray application, bacterial infiltration) or herbivore exposure. Include appropriate control groups treated with sterile water or mock treatments.

• Tissue Collection: Harvest tissues at multiple time points post-stress application (e.g., 0, 6, 12, 24, 48 hours) to capture dynamic expression changes. Flash-freeze samples immediately in liquid nitrogen and store at -80°C until RNA extraction.

• RNA Extraction and Quality Control: Extract total RNA using standardized protocols. Assess RNA quality using capillary electrophoresis (e.g., Bioanalyzer RNA Integrity Number > 8.0). Quantify RNA concentration using fluorometric methods.

• Library Preparation and Sequencing: Prepare sequencing libraries using platform-specific kits (e.g., Illumina TruSeq). For cross-species comparisons, employ 3' end sequencing protocols to handle sequence divergence. Sequence libraries on appropriate platforms to achieve sufficient depth (>20 million reads per sample).

Computational Analysis Pipeline

• Read Processing and Quality Control:

  Process raw sequencing reads through quality control (FastQC), adapter trimming (Trimmomatic, Cutadapt), and filtering to remove low-quality sequences.

• Cross-Species Read Mapping and Quantification: For orthogroup expression profiling, map reads to reference genomes or transcriptomes using splice-aware aligners (STAR, HISAT2). Quantify expression at the gene level using transcript-compatibility counting methods that handle multi-mapping reads across species.

• Orthogroup Definition: Utilize both sequence-based and structure-based approaches for orthogroup definition:

  • Sequence-based orthogroups: Use OrthoFinder [52] or similar tools with default parameters on translated peptide sequences to identify groups of orthologous genes.
  • Structure-based orthogroups: Employ FoldSeek [52] to perform all-versus-all TM-score structural comparisons of AlphaFold-predicted structures, then cluster using GreedySet algorithm to generate structural cluster groups as a shared feature space.

• Expression Matrix Construction: Construct consolidated expression matrices where rows represent orthogroups and columns represent samples. Normalize using cross-species normalization methods (e.g., TMM normalization on single-copy orthologs) [53] to account for technical variations across experiments and species.

TDA Implementation for Expression Network Visualization

Topological Data Analysis Workflow

The application of TDA to orthogroup expression data involves several methodical steps to transform expression matrices into topological networks that reveal the underlying structure of stress responses:

• Distance Metric Selection: Calculate pairwise distances between samples using appropriate metrics (e.g., correlation distance, Euclidean distance) based on orthogroup expression profiles.

• Filter Function Definition: Define lens functions that capture meaningful biological variation. For biotic stress studies, this may include:

  • Tissue-specific expression patterns: Identify genes with differential expression across tissue types.
  • Stress response gradients: Capture expression variation along a stress intensity or time gradient.
  • Developmental trajectories: Map expression changes across developmental stages affected by stress.

• Mapper Graph Construction: Apply the Mapper algorithm [50] to create a combinatorial graph representation of the data:

  • Covering: Create a covering of the data space using the lens function and predefined resolution parameters.
  • Clustering: Perform clustering within each covering element using algorithms like DBSCAN or hierarchical clustering.
  • Graph formation: Connect clusters that share data points to form the Mapper graph.

• Topological Feature Analysis: Compute persistent homology [50] to identify robust topological features (connected components, loops, voids) that persist across multiple scales, distinguishing true biological signals from noise.

Interpretation of TDA Results

Interpreting TDA output requires understanding the topological features in biological context. In the study of flowering plants, TDA revealed a well-defined gradient of tissues from leaves to seeds and from healthy to stressed samples, depending on the lens function [51]. This suggests distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to stresses.

Key interpretative elements include:

• Connected Components: Represent distinct expression states or cell types. Under biotic stress, new components may emerge indicating novel expression programs.
• Loops and Cycles: Often represent cyclic biological processes (e.g., circadian rhythms) or continuous transitions between states.
• Branches and Divergence Points: Indicate alternative expression trajectories or cell fate decisions in response to stress.

For orthogroup expression profiling under biotic stress, TDA can identify conserved expression modules that represent core stress response pathways, as well as divergent modules that may represent species-specific adaptations.

Case Study: TDA Reveals Core Expression Backbone in Plant Stress Responses

A recent groundbreaking study demonstrated the power of TDA for analyzing complex gene expression data across flowering plants [51]. Researchers implemented TDA to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, they created a topological representation of the shape of gene expression across plant evolution, development, and environment for phylogenetically diverse flowering plants.

The study revealed several key findings:

• Conserved Expression Backbone: Despite 125 million years of evolution, flowering plants share a core gene expression backbone that defines form and function [51]. The TDA-based Mapper graphs formed well-defined gradients of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function.

• Stress Response Signatures: The topological approach successfully separated expression patterns associated with different stress responses, identifying both conserved and lineage-specific stress response modules.

• Functional Enrichment: Genes that correlated with the tissue lens function were enriched in central processes such as photosynthesis, growth and development, housekeeping, or stress responses [51].

This case study demonstrates how TDA can integrate expression data across evolutionary timescales to reveal fundamental principles of biological organization that remain obscured when analyzing individual species or experiments in isolation.

Table 3: Key topological features identified in plant expression atlas study

Topological Feature	Biological Interpretation	Functional Enrichment
Leaf-to-Seed Gradient	Continuous developmental transition	Photosynthesis, storage proteins, developmental regulators
Health-to-Stress Axis	Progressive stress response activation	Pathogenesis-related proteins, redox regulators, signaling components
Tissue-Specific Branches	Organ-specific specialization	Tissue-specific metabolic pathways, structural proteins
Evolutionary Loops	Alternative regulatory strategies	Species-specific adaptations, gene family expansions

Integration with Multi-Omics Approaches for Comprehensive Stress Response Profiling

The true power of TDA emerges when integrated with multi-omics approaches, creating a comprehensive framework for understanding biotic stress responses. Combining transcriptomics with proteomics, metabolomics, ionomics, interactomics, and phenomics simplifies the study of plant resistance mechanisms [54]. This comprehensive approach enables the development of regulatory networks and pathway maps, identifying potential targets for improving resistance through genetic engineering or breeding strategies [54].

For orthogroup expression profiling under biotic stress, TDA can serve as an integrative framework that connects different molecular layers:

• Transcriptome-Proteome Integration: By applying TDA to both transcriptomic and proteomic data, researchers can identify conserved topological features that persist across molecular layers, indicating robust regulatory modules.

• Cross-Species Alignment: Structural similarity metrics from protein structure predictions [52] can inform the distance metrics used in TDA, creating more biologically meaningful topological representations.

• Dynamic Network Analysis: Time-series expression data during stress progression can be analyzed using persistent homology to track the emergence and dissolution of expression modules throughout the stress response.

The integration of TDA with multi-omics data represents a powerful strategy for enhancing plant resilience against biotic stresses [54]. By leveraging insights from genomics, transcriptomics, proteomics, metabolomics, and phenomics, researchers can develop a comprehensive understanding of plant resilience mechanisms, paving the way for innovative solutions that improve crop performance under challenging environmental conditions [54].

The nucleotide-binding site (NBS) domain gene family represents one of the most extensive and crucial plant resistance (R) gene families, serving as fundamental components of the plant immune system [15]. These genes encode intracellular receptors that recognize pathogen effectors and initiate effector-triggered immunity (ETI), providing protection against diverse biotic stresses [55]. Understanding the evolutionary dynamics and functional characteristics of NBS genes across multiple plant species offers invaluable insights into plant adaptation mechanisms and provides genetic resources for breeding disease-resistant crops [56]. This case study examines a comprehensive analysis of 12,820 NBS-domain-containing genes identified across 34 plant species, from mosses to monocots and dicots, with particular emphasis on orthogroup expression profiling under biotic stress conditions [15].

Methodology

Genome-Wide Identification of NBS-Encoding Genes

The identification of NBS-encoding genes followed a standardized bioinformatics pipeline to ensure consistency across species. Researchers downloaded latest genome assemblies from publicly available databases including NCBI, Phytozome, and Plaza [15]. To screen for NBS domain-containing genes, they employed PfamScan.pl HMM search script with default e-value (1.1e-50) using the background Pfam-A_hmm model [15]. The essential NB-ARC domain (PF00931) from the Pfam database served as the primary query [57] [58]. All genes containing the NB-ARC domain were considered NBS genes and filtered for further analysis. Additional validation was performed using the NCBI Conserved Domain Database (CDD) to confirm the presence of associated domains, with only genes containing verified domains retained for subsequent analysis [57].

Classification and Domain Architecture Analysis

The classified NBS genes based on their domain composition into distinct subfamilies. The primary classification system categorized genes according to their N-terminal domains:

• TNL: Genes containing Toll/interleukin-1 receptor (TIR), NBS, and LRR domains
• CNL: Genes containing coiled-coil (CC), NBS, and LRR domains
• RNL: Genes containing resistance to powdery mildew 8 (RPW8), NBS, and LRR domains [56] [58]

Additionally, partial NBS genes lacking complete domains were classified separately (e.g., TN, CN, NL) [58]. Domain architecture was determined using Pfam domains (PF01582 for TIR, PF00560, PF07723, PF07725, PF12779, PF13306, PF13516, PF13855, PF14580, PF03382, PF01030, PF05725 for LRR) and CC domains confirmed via NCBI CDD [57].

Evolutionary and Orthogroup Analysis

For evolutionary analysis, researchers employed OrthoFinder v2.5.1 package tools to identify orthogroups across species [15]. The DIAMOND tool was used for fast sequence similarity searches among NBS sequences, while the MCL clustering algorithm facilitated gene clustering [15]. Orthologs and orthogrouping were carried out with DendroBLAST, and multiple sequence alignment was performed using MAFFT 7.0 [15]. A gene-based phylogenetic tree was constructed using the maximum likelihood algorithm in FastTreeMP with a 1000 bootstrap value [15]. Selection pressures were quantified by calculating non-synonymous (Ka) and synonymous (Ks) substitution rates with KaKs_Calculator 2.0 using the Nei-Gojobori (NG) evolutionary model [57].

Transcriptomic Analysis Under Biotic Stress

Expression profiling of NBS genes under biotic stress conditions involved retrieving RNA-seq data from public databases including the IPF database, Cotton Functional Genomics Database (CottonFGD), and Cottongen database [15]. Researchers extracted FPKM values and categorized them into biotic stress conditions. For differential expression analysis, raw sequencing files from SRA accessions were converted to FASTQ format using fastq-dump v2.6.3 [57]. Read quality control was performed using Trimmomatic v0.36 with minimum read length of 90 bp [57]. Cleaned data were mapped to reference genomes using Hisat2, and transcript quantification was conducted with Cufflinks v2.2.1 with FPKM normalization [57]. Differentially expressed genes (DEGs) were identified through Cuffdiff [57].

Genetic Variation and Functional Validation

Genetic variation between susceptible and tolerant accessions was identified through whole-genome resequencing and variant calling [15]. For functional validation, virus-induced gene silencing (VIGS) was employed to knock down candidate NBS genes in resistant plants, followed by pathogen challenge assays to confirm gene function [15]. Quantitative real-time PCR (qRT-PCR) validated expression patterns of selected NBS genes under stress conditions [55] [58].

Table 1: Summary of NBS-Encoding Genes Across Representative Plant Species

Plant Species	Total NBS Genes	TNL	CNL	RNL	Reference
Arabidopsis thaliana	164	80	84	0	[58]
Brassica rapa	212	103	109	0	[58]
Brassica oleracea	244	124	120	0	[58]
Raphanus sativus (Radish)	225	134	91	0	[58]
Brassica napus (Oilseed rape)	464	191	273	0	[59]
Nicotiana tabacum (Tobacco)	603	73	224	-	[57]
Lathyrus sativus (Grass pea)	274	124	150	0	[55]
Gossypium hirsutum (Cotton)	641	-	-	-	[59]
Hordeum vulgare (Barley)	43*	-	-	-	[1]

Note: *Barley study identified anion channel genes rather than NBS-encoding genes

Results and Discussion

Genomic Distribution and Evolutionary Patterns

The comprehensive analysis identified 12,820 NBS-domain-containing genes across 34 plant species, which were classified into 168 distinct classes based on domain architecture [15]. Researchers observed both classical structural patterns (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [15].

Orthogroup analysis revealed 603 orthogroups (OGs), including both core orthogroups (OG0, OG1, OG2) present across multiple species and unique orthogroups (OG80, OG82) specific to particular lineages [15]. The evolutionary analysis indicated that NBS genes have undergone dynamic birth-and-death evolution, with significant variation in gene numbers across species due to frequent gene duplications and losses [56]. In allopolyploid species like Brassica napus, researchers observed rapid evolution of NBS genes after polyploidization, with the C subgenome showing greater diversification (273 genes) compared to the progenitor B. oleracea (146 genes) [59].

Table 2: Evolutionary Patterns of NBS-LRR Genes Across Plant Families

Plant Family	Species	Evolutionary Pattern	Key Characteristics	Reference
Rosaceae	Rosa chinensis	"Continuous expansion"	Progressive increase in NBS gene number	[56]
Rosaceae	Rubus occidentalis	"First expansion then contraction"	Initial gene duplication followed by loss	[56]
Rosaceae	Fragaria vesca	"Expansion-contraction-expansion"	Complex fluctuation in gene family size	[56]
Brassicaceae	Brassica napus	"Rapid diversification"	Significant gene birth/death after polyploidization	[59]
Fabaceae	Medicago truncatula	"Consistent expansion"	Progressive increase in NBS gene number	[56]
Poaceae	Rice, Maize, Sorghum	"Contracting"	Net loss of NBS genes over time	[56]
Solanaceae	Potato	"Consistent expansion"	Progressive increase in NBS gene number	[56]
Solanaceae	Tomato	"Expansion then contraction"	Initial expansion followed by selective loss	[56]
Solanaceae	Pepper	"Shrinking"	Net reduction in NBS gene number	[56]

Orthogroup Expression Profiling Under Biotic Stress

Phylotranscriptomic analysis across multiple eudicot species identified 35 high-confidence conserved cold-responsive transcription factor orthogroups (CoCoFos), demonstrating the power of orthogroup-based expression analysis [34]. In the NBS gene study, expression profiling revealed putative upregulation of specific orthogroups (OG2, OG6, OG15) in different tissues under various biotic stresses in cotton accessions with varying susceptibility to cotton leaf curl disease (CLCuD) [15].

In radish, RNA-seq analysis identified 75 NBS-encoding genes that contributed to resistance against Fusarium wilt, with qRT-PCR validation showing that RsTNL03 and RsTNL09 expression positively regulated resistance, while RsTNL06 acted as a negative regulator [58]. Similarly, in Brassica oleracea, studies of combined heat and fungal stress identified 17 NBS-encoding genes from heat-shock treated plants, with eight genes highly induced in resistant cultivars infected with F. oxysporum [60].

In Brassica napus, 60% of NBS-encoding genes showed highest expression levels in root tissues, and 204 NBS-encoding genes were located within 71 resistance QTL intervals against three major diseases: blackleg, clubroot, and Sclerotinia stem rot [59]. The majority co-localized with QTLs against a single disease, while 47 genes were associated with two diseases and three genes with all three diseases, highlighting their potential role in broad-spectrum resistance [59].

Figure 1: Experimental workflow for NBS gene family identification and orthogroup expression analysis

NBS Gene Clustering and Genomic Distribution

NBS-encoding genes typically display non-random distribution across plant genomes, with a strong tendency to form clusters. In radish, 72% of the 202 chromosomally mapped NBS-encoding genes were grouped into 48 clusters distributed in 24 crucifer blocks, with the U block on chromosomes R02, R04, and R08 containing the most NBS genes (48), followed by R (24), D (23), E (23), and F (17) blocks [58]. These clusters were predominantly homogeneous, containing NBS genes derived from recent common ancestors [58].

Both tandem and segmental duplications contribute significantly to NBS gene expansion. Radish genomes showed 15 tandem duplication events and 20 segmental duplication events within the NBS family [58]. In tobacco, whole-genome duplication was found to contribute significantly to the expansion of NBS gene families, with 76.62% of NBS members in Nicotiana tabacum traceable to parental genomes [57].

Functional Mechanisms and Disease Resistance

NBS genes function as intracellular immune receptors that recognize pathogen effectors directly or indirectly through detection of effector-induced modifications in host proteins [55]. Upon pathogen recognition, the LRR domain binds to pathogen effectors, causing conformational changes in the NBS domain that lead to multimerization of the TIR or CC domain and activation of immune signaling [56]. The central NBS domain acts as a molecular switch, with its nucleotide-binding state regulating R protein activity [59], while the LRR domain is responsible for specific pathogen recognition [57].

Functional studies demonstrated the critical role of NBS genes in disease resistance. In cotton, silencing of an NBS-LRR gene reduced resistance to Verticillium dahliae [57], while heterologous expression of a maize NBS-LRR gene improved resistance to Pseudomonas syringae in Arabidopsis [57]. Similarly, VIGS-mediated silencing of GaNBS (OG2) in resistant cotton demonstrated its putative role in virus tittering against cotton leaf curl disease [15].

Figure 2: Domain architecture and functional mechanisms of major NBS protein subfamilies

The Scientist's Toolkit

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

Tool/Resource	Category	Function	Application in NBS Studies
PF00931 (NB-ARC)	HMM Profile	NBS domain identification	Primary query for identifying NBS genes	[57] [58]
PfamScan	Bioinformatics Tool	Domain architecture analysis	Classifying NBS genes into subfamilies	[15]
OrthoFinder	Evolutionary Analysis	Orthogroup identification	Grouping NBS genes into orthologous groups	[15]
KaKs_Calculator	Selection Pressure Analysis	Ka/Ks calculation	Measuring evolutionary selection on NBS genes	[57]
Cufflinks/Cuffdiff	Transcriptomic Analysis	Differential expression	Identifying stress-responsive NBS genes	[57]
VIGS	Functional Validation	Gene silencing	Testing NBS gene function in disease resistance	[15]
Plant GARDEN	Database	Genomic resource portal	Accessing plant genome information	[61]
NCBI CDD	Database	Conserved domain verification	Validating NBS and associated domains	[57] [58]

This case study demonstrates the power of comparative genomic approaches for understanding the evolution and function of the NBS gene family across diverse plant species. The analysis of 12,820 NBS genes from 34 species reveals remarkable diversity in gene number, domain architecture, and evolutionary patterns, influenced by both shared evolutionary constraints and lineage-specific adaptations. Orthogroup expression profiling under biotic stress highlights conserved functional modules while revealing species-specific innovations. The integration of genomic, transcriptomic, and functional validation approaches provides a comprehensive framework for identifying key NBS genes involved in disease resistance, offering valuable resources for future crop improvement programs. The continued expansion of plant genomic resources and development of sophisticated bioinformatics tools will further enhance our ability to decipher the complex evolutionary dynamics of this crucial gene family and harness its potential for sustainable agriculture.

Overcoming Analytical Challenges in Cross-Species Orthogroup Profiling

Addressing Technical Variability in Cross-Study Comparisons

In the field of orthogroup expression profiling, researchers increasingly rely on integrating data from multiple studies to enhance the statistical power and generalizability of their findings, particularly in the context of biotic stress response. However, this integrative approach is fraught with challenges, as technical variability introduced by differing laboratory protocols, sequencing platforms, and data processing methods can significantly obscure true biological signals. This guide objectively compares the performance of various computational strategies designed to mitigate these technical artifacts, providing researchers with a clear framework for selecting appropriate methods to improve the reliability and reproducibility of their cross-study comparisons.

Methodologies for Bias Correction

Several computational strategies have been developed to address technical variability. The table below summarizes the core approaches of three distinct methods.

Method Name	Core Methodology	Primary Application Context
DEBIAS-M [62]	Uses machine learning to estimate and correct for protocol-specific processing biases for each microbe in a sample batch.	Microbiome-based prediction models; integrates data from studies using different DNA extraction and sequencing protocols.
Statistical Framework for CV [63]	Provides an unbiased framework to assess the impact of cross-validation setups (e.g., number of folds) on the statistical significance of model accuracy differences.	Rigorous comparison of neuroimaging-based machine learning classification models.
Single-Copy Orthologs Filtering [2]	Uses evolutionarily conserved, single-copy orthologous genes as a stable basis for cross-species transcriptomic comparisons.	Evolutionary studies of gene expression, such as molecular phenology across different tree species.

Experimental Protocols for Cross-Study Integration

Protocol for DEBIAS-M Application in Microbiome Studies

DEBIAS-M is designed to correct for technical biases arising from different laboratory protocols in microbiome data, thereby improving the performance of cross-study predictive models [62].

• Data Collection and Processing Bias Estimation: The model is trained on large, publicly available microbiome datasets where the same samples may have been processed with multiple protocols. It computationally infers "bias-correction factors" for each microbe within each batch of samples. These factors quantify the estimated effect of a specific laboratory protocol (e.g., a particular DNA extraction kit) on the observed abundance of a microbe [62].
• Bias Correction and Model Integration: The inferred bias-correction factors are then applied to new datasets. DEBIAS-M simultaneously minimizes these batch effects while enhancing the detection of true biological associations between microbial profiles and clinical phenotypes of interest, such as colorectal cancer or cervical neoplasia [62].
• Performance Validation: Model performance is validated by integrating data from multiple independent studies and assessing the improvement in the predictive accuracy of microbiome-based classification models (e.g., for disease state) compared to models using data corrected with traditional methods like ComBat or voom-SNM [62].

Protocol for Orthogroup Expression Profiling Under Biotic Stress

Investigating the molecular response to stresses like drought requires a robust framework for cross-species and cross-study comparison. The following workflow, detailed in a study on barley anion channels, outlines this process [1].

• Genome-Wide Identification: The process begins with a genome-wide scan to identify all members of a gene family of interest. For example, a study in barley identified 43 anion channel proteins from the CLC, ALMT, VDAC, and MSL families [1].
• Phylogenetic and Evolutionary Analysis: The identified genes are analyzed to understand their evolutionary relationships. This often involves constructing a phylogenetic tree and examining gene structure and conserved protein domains [1].
• Expression Profiling: The expression patterns of the genes are analyzed across multiple tissues and in response to biotic stress. This utilizes data from public transcriptome databases and is verified experimentally. For instance, expression of 17 anion channel genes was confirmed by qRT-PCR in different barley cultivars under drought stress [1].
• Functional Validation: The role of key candidate genes in stress tolerance is validated. Transgenic plants with high expression of these genes demonstrated stronger drought tolerance and maintained better ion homeostasis (e.g., potassium and calcium content) [1].

Comparative Performance Data

The following table summarizes quantitative data on the performance of different approaches, highlighting their effectiveness in managing technical variability.

Method / Approach	Key Performance Metric	Reported Outcome	Comparative Advantage
DEBIAS-M [62]	Predictive performance in cross-study integration.	Outperformed traditional batch-correction methods (ComBat, voom-SNM) in improving predictive models for HIV, colorectal cancer, and cervical neoplasia.	Provides stable, interpretable bias-correction factors linked to specific experimental protocols.
Anion Channel Gene Family Analysis [1]	Identification of drought-responsive genes.	17 out of 43 identified anion channel genes showed confirmed expression changes under drought stress in barley.	Links gene expression patterns to a tangible physiological outcome: stronger drought tolerance and element content homeostasis in plants with high gene transcripts.
Statistical CV Framework [63]	Likelihood of detecting significant model differences.	Demonstrated that the probability of finding significant differences is highly sensitive to cross-validation configurations and data properties.	Highlights the risk of "p-hacking" and underscores the need for rigorous, unbiased statistical practices in model comparison.

Success in cross-study expression profiling relies on a suite of key reagents and genomic resources.

Tool / Resource	Function in Research
High-Quality Reference Genome	Provides the essential map for accurate gene identification, annotation, and read mapping in transcriptomic studies [1] [2].
Single-Copy Orthologous Genes	Serves as a stable, evolutionarily conserved set for reliable cross-species gene expression comparisons, minimizing ambiguity [2].
Pan-genomic/Transcriptomic Resources	Captures the full genetic diversity within a species, which is critical for understanding genotype-dependent responses in gene-family studies [1].
Public RNA-Seq Databases	Source of baseline and stress-condition expression profiles for initial hypothesis generation and comparative analysis [1].
Bias-Corrected Integrated Datasets	Data processed with tools like DEBIAS-M to minimize technical noise, enabling more robust meta-analyses and machine learning model training [62].

Signaling Pathway Integration in Biotic Stress

The response to biotic stress often involves complex signaling pathways. The diagram below synthesizes the role of anion channels in the drought stress response in plants, as identified in barley [1].

• Pathway Activation: Drought stress triggers the production of signaling molecules, including abscisic acid (ABA), reactive oxygen species (ROS), and calcium (Ca²⁺) [1].
• Anion Channel Regulation: These signals activate various anion channels at the plasma membrane and in organelles. For example, ALMT transporters are rapid-activating channels influenced by NO, ABA, Ca²⁺, and ROS [1].
• Coordinated Physiological Response: The activation of these channels orchestrates key tolerance mechanisms. This includes regulating stomatal closure to reduce water loss, maintaining ion homeostasis by sequestering toxic ions like Cl⁻ into vacuoles, and controlling osmotic balance through the transport of metabolites like malate [1]. The network of CLCs, ALMTs, VDACs, and MSLs works in concert to regulate these processes and enhance the plant's resilience to drought [1].

Limitations of Model Organism Knowledge for Non-Model Species

The use of model organisms such as laboratory mice (Mus musculus), fruit flies (Drosophila melanogaster), and the plant Arabidopsis thaliana has enabled tremendous advances in our understanding of fundamental biological processes [64]. These species provide standardized, genetically tractable systems for studying everything from embryonic development to disease mechanisms. However, a fundamental challenge emerges when attempting to extrapolate findings from these established model systems to the vast diversity of non-model species, particularly in evolutionarily distant taxa [64] [65]. This limitation is especially critical in the context of orthogroup expression profiling under biotic stress, where the assumption of functional conservation may lead to incomplete or misleading conclusions.

The very characteristics that make model organisms convenient—standardized laboratory lines, extensive annotation, and available reagents—also represent their greatest limitation for comparative biology [65]. As noted in a 2023 perspective, "the current handful of highly standardized model organisms cannot represent the complexity of all biological principles in the full breadth of biodiversity" [64]. This review systematically examines the technical limitations, methodological considerations, and emerging solutions for extending biological knowledge beyond traditional model systems, with particular emphasis on gene expression studies in biotic stress research.

Fundamental Limitations in Genetic and Molecular Biology

Genetic Divergence and Functional Variation

Laboratory model organisms typically represent a minuscule fraction of the genetic diversity present in natural populations, even within the same species [65]. DNA sequencing has revealed substantial genetic variation in natural populations of model organism species: 0.3–0.8% in S. cerevisiae, 0.5–1% in D. melanogaster, and 0.1–0.4% in C. elegans [65]. This variation includes surprising numbers of nonsense mutations segregating in natural populations, suggesting that even essential gene functions may be more variable than previously assumed [65].

When extending analyses to non-model species, divergence increases dramatically, creating substantial challenges for orthology assignment and functional inference. Transcriptome assembly and read mapping performance decline significantly when genetic divergence between reference and query sequences exceeds 15% [66]. This problem is particularly acute for gene expression studies using RNA-seq, where mapping short reads to incomplete or divergent genome sequences "entails both reduced power (reads that fail to align) and false-positive errors (reads that align uniquely to a partial genome sequence but arise from elsewhere)" [67].

Biochemical and Physiological Context Dependence

Molecular functions identified in model organisms may not translate directly to non-model species due to fundamental differences in cellular environments and metabolic networks. Research on wildlife species has revealed "cancer resistance mechanisms detected in naked mole rats" that "do not appear to exist in mice" [64], highlighting how protective mechanisms can be species-specific. Similarly, studies have identified "hyperglycemia that characterizes birds but without the adverse effects observed in type 2 diabetics" [64], suggesting identical molecular markers may have different functional consequences across species.

The limitations of extrapolation are particularly evident in stress response pathways, where "simplified systems as model organisms do not always appropriately reflect the complexity of more complex systems such as the human body and its specific interactions with its long-life adapted microbiota" [64]. Indeed, plants and animals must be considered as holobionts, comprising both their own cells and those of their associated microorganisms [64], a complexity rarely captured in standard model systems.

Table 1: Documented Cases of Failed Extrapolation from Model Organisms

Finding in Model Organism	Non-Model System Result	Implication
TGN1412 safe in animal models	Life-threatening immune response in humans [64]	Critical failure in toxicity prediction
Standard mouse aging mechanisms	Different mechanisms in long-lived bats [64]	Limited understanding of aging diversity
Laboratory mouse muscle atrophy	Bears maintain muscle mass during hibernation [64]	Species-specific adaptations overlooked
Standard cancer pathways	Novel cancer resistance in naked mole rats [64]	Protective mechanisms missing from models

Technical Limitations in Genomic and Transcriptomic Analyses

Challenges in Transcriptome Assembly and Orthology Inference

For non-model organisms, transcriptome inference presents substantial technical hurdles that are not encountered with well-characterized model organisms. The two primary approaches—de novo assembly and genome-guided assembly—each have significant limitations when applied to non-model species [66]. De novo assembly is sensitive to issues including "alignment error due to paralogs and multigene families; production of artefactual chimeras; problems reconstructing transcript length, and potentially misestimating allelic diversity" [66]. Meanwhile, genome-guided approaches performance declines dramatically when sequence divergence exceeds 15% [66].

Orthology inference is particularly challenging for data sets using transcriptomes or low-coverage genomes that "often contain misassemblies and partial or missing sequences" [68]. The complexities of these data types make it difficult to distinguish "recently duplicated copies from allelic variations, splice variants, and misassemblies" [68]. Methods based on reciprocal similarity criteria frequently fail with incomplete sequences, gene and genome duplication, and molecular rate heterogeneity [68].

Methodological Comparisons for Non-Model Organisms

Alternative methods have been developed specifically to address the limitations of standard RNA-seq approaches for non-model organisms. The EDGE approach (EcoP15I-tagged Digital Gene Expression) uses 27-bp cDNA fragments that uniquely tag corresponding genes, allowing direct quantification of transcript abundance without requiring a high-quality assembled reference genome [67]. This method demonstrates particular advantages for non-model organisms where reference genomes are unavailable or incomplete.

Table 2: Performance Comparison of Gene Expression Methods for Non-Model Organisms

Method	Key Features	Advantages for Non-Models	Limitations
Standard RNA-seq	Random shearing, alignment to reference	Comprehensive transcript information	Requires high-quality reference genome; performance declines >15% divergence [67] [66]
EDGE	27-bp tags from restriction sites	Assays >99% of genes; minimal technical noise; no transcript length bias [67]	Limited to expression quantification; less transcript isoform information [67]
Blastn-guided assembly	Similarity-based read assignment	Outperforms mapping at high divergence; recovers 92.6% genes at 30% divergence [66]	Computationally intensive; requires related reference transcriptome [66]

Experimental Protocols for Non-Model Organism Transcriptomics

EDGE (EcoP15I-tagged Digital Gene Expression) Protocol

The EDGE methodology provides an optimized approach for gene expression profiling in non-model organisms [67]:

Sample Requirements and RNA Processing:

• Input: 1–2 μg total RNA
• mRNA enrichment using paramagnetic oligo(dT) beads
• Directional tagging via 27-bp sequence beginning with 4-bp NlaIII restriction site
• Generation of 23 bp immediately downstream by type III restriction endonuclease EcoP15I

Theoretical and Practical Considerations:

• Theoretically, each tag begins with the NlaIII site closest to poly(A) tail
• In practice, several-fold more tags observed than transcripts due to partial NlaIII cleavage
• NlaIII sites present in >99% of mouse or human cDNA sequences
• Application captures ~90% of >20,000 RefSeq genes

Sequencing and Analysis:

• Ultra-high-throughput sequencing of 27-bp tags
• For organisms with annotated genomes: unique alignment to reference transcriptome
• For non-model organisms: tag-to-gene assignment via comparative genomics or de novo transcriptomes

Performance Characteristics:

• Saturation after 6–8 million reads
• 86% uniquely aligned to transcriptome (compared to 60% for RNA-seq)
• Very little technical noise (mean correlation between technical replicates: 0.975)
• Large (10⁶) dynamic range of gene expression [67]

Blastn-Based Transcriptome Assembly Protocol

For organisms with high divergence from available references, a blastn-based assembly approach outperforms traditional mapping methods [66]:

Read Processing Pipeline:

• Filter for over-representation (PCR duplicates, over-expression)
• Merge overlapping paired-end reads using Pear (parameters: minimum overlap -v 8, scoring method -s 2, p-value -p 0.01)
• Quality filtering: trim 5' and 3' ends until Phred score ≥13 encountered
• Read rejection criteria:
  • One base with Phred quality score <5
  • >10% of bases with Phred quality score <13
  • Mean Phred quality score <20 for entire read
  • Length shorter than 30 bases

Read Assignment and Homology Inference:

• Nucleotide BLAST (blastn) against reference transcriptome
• Parameters: Word-size = 9, HSP >70%, similarity >70%
• Assignment to gene-id corresponding to best hit
• All-by-all BLAST followed by Markov clustering (MCL) for homology inference
• Multiple sequence alignment and phylogenetic tree inference for each cluster
• Pruning of spurious branches from deep paralogs, misassembly, frameshifts, or recombination

Orthology Inference:

• Tree-based orthology inference without requiring known species tree
• Accommodation of gene and genome duplications
• Four alternative strategies for obtaining orthologs from cutting homolog trees [66] [68]

Visualization of Key Methodological Approaches

EDGE Molecular Biology Workflow

Orthology Inference Pipeline for Non-Model Organisms

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools for Non-Model Organism Studies

Reagent/Tool	Function	Application Context
Paramagnetic Oligo(dT) Beads	mRNA enrichment from total RNA	EDGE protocol; polyA-tailed RNA selection [67]
NlaIII Restriction Enzyme	Creates 4-bp anchoring site	EDGE tag generation [67]
EcoP15I Restriction Enzyme	Type III enzyme generating 23-bp tags	EDGE protocol; directional cDNA tagging [67]
Blastn Algorithm	Nucleotide similarity search	Read assignment in divergent species [66]
Markov Clustering (MCL)	Sequence clustering based on connectivity	Homology inference from BLAST hits [68]
PEAR Software	Paired-end read merger	Pre-assembly read processing [66]
OrthoMCL	Orthology inference pipeline	Phylogenomic dataset construction [68]
Selective Reaction Monitoring (SRM)	Targeted protein quantification	Proteomic validation in non-model species [69]

The limitations of model organism knowledge for understanding non-model species represent both a challenge and an opportunity for biological research. While established model organisms will continue to provide important insights into fundamental mechanisms, embracing methodological approaches specifically designed for non-model systems is essential for comprehensive understanding of biological diversity [64] [70]. The development of methods like EDGE for gene expression profiling [67] and blastn-guided assembly for transcriptome inference [66] provides powerful tools for overcoming the technical barriers associated with genetic divergence and limited genomic resources.

For researchers studying orthogroup expression profiling under biotic stress, these approaches offer pathways to generate reliable data without relying on potentially problematic extrapolations from model systems. As technological advances continue to make genomic, transcriptomic, and proteomic tools more accessible [64], the research community can increasingly move beyond the constraints of traditional model organisms to explore the vast functional diversity present in nature. This transition will be particularly critical for understanding species-specific responses to biotic stress and developing comprehensive models of gene expression evolution across the tree of life.

Optimizing Orthogroup Resolution for Tandemly Duplicated Genes

In the field of plant biotic stress research, orthogroup expression profiling has emerged as a powerful technique for identifying conserved transcriptional networks across species. However, a significant challenge arises from the presence of tandemly duplicated genes, which often exhibit high sequence similarity and can distort orthogroup inference. Tandem duplications are particularly prevalent in stress-responsive gene families, as they provide a mechanism for rapid adaptation to environmental pressures [71]. The expansion of gene families through tandem duplication is strongly associated with responses to environmental stimuli and biotic stress conditions [71] [72]. This methodological guide compares current approaches for optimizing orthogroup resolution when working with tandemly duplicated genes, providing researchers with experimental data and protocols to enhance their transcriptional profiling studies under biotic stress conditions.

Orthogroup Inference Methods: A Comparative Analysis

Table 1： Comparison of orthogroup inference methods for tandemly duplicated genes

Method/Strategy	Key Features	Advantages	Limitations	Reported Accuracy
Orthogroup (OG) Analysis with Phylotranscriptomics	Identifies orthogroups across multiple species; integrates RNA-seq data from stress treatments [34]	Identifies conserved stress-responsive regulators; high-confidence orthogroup assignment	May miss lineage-specific expansions; requires multiple species	35 high-confidence conserved cold-responsive TF orthogroups identified in eudicots [34]
Pan-Genome Orthogroup Analysis	Constructs species-wide gene inventory; identifies presence-absence variation (PAV) and copy number variation (CNV) [72]	Captures full genetic diversity within species; identifies structural variation	Computationally intensive; requires multiple high-quality genomes	225 LRR-RLKs identified across 146 Arabidopsis ecotypes with significant PAV [72]
Lineage-Specific Expansion Analysis	Classifies genes into orthologous groups (OGs) between species; examines functional bias of retained duplicates [71]	Reveals functional bias in retention; identifies stress-responsive tandem duplicates	Focused on evolutionary patterns rather than immediate resolution	Tandem duplicates in expanded OGs significantly enriched in genes up-regulated by biotic stress (P<0.05) [71]
Cross-Species Transcriptomic Orthogroups	Constructs orthogroups for comparative transcriptome analysis; identifies common/opposite responses [18]	Enables translation of gene functions between species; identifies conserved responses	May not resolve recent tandem duplications	15-34% of orthologous DEGs showed opposite responses between species despite orthology [18]

Experimental Protocols for Orthogroup Resolution

Protocol 1: Phylotranscriptomic Orthogroup Analysis

This protocol, adapted from the identification of conserved cold-responsive transcription factor orthogroups (CoCoFos), enables researchers to resolve orthogroups with tandem duplicates across multiple plant species under stress conditions [34].

• Step 1: Orthogroup Identification

  • Collect protein sequences from five or more representative species relevant to your study system
  • Use OrthoFinder or similar software to identify orthogroups across species
  • Parameters: Minimum of 10,549 orthogroups expected for five eudicot species [34]

• Step 2: Transcriptomic Data Integration

  • Obtain RNA-seq data from stress-treated seedlings or tissues
  • Include multiple stress time points and replicates (3+ biological replicates recommended)
  • Map reads to reference genomes and calculate normalized expression values (TPM or FPKM)

• Step 3: Phylogenetic Reconciliation

  • Construct gene trees for each orthogroup containing tandem duplicates
  • Reconcile gene trees with species trees to identify duplication events
  • Use tools like Notung or RANGER-DTL for tree reconciliation

• Step 4: Conservation Scoring

  • Identify orthogroups with conserved stress responses across species
  • Apply statistical thresholds (e.g., FDR < 0.05, log2FC > 1)
  • Validate with experimental approaches (e.g., mutant analysis)

Protocol 2: Pan-Genome Orthogroup Construction

This protocol enables researchers to account for presence-absence variation in tandemly duplicated genes across multiple accessions of a species, particularly relevant for stress-responsive gene families [72].

• Step 1: Multi-Accession Genome Collection

  • Collect genome sequences for 20+ accessions of the target species
  • Ensure representation of diverse ecotypes with varied stress adaptations

• Step 2: Gene Prediction and Annotation

  • Predict protein sequences using combination of homology, de novo, and transcript-based methods
  • Annotate genes of interest using conserved domain databases (Pfam, SMART, CDD)

• Step 3: Orthogroup Clustering

  • Cluster genes into orthogroups using CD-HIT or similar tools with E-value threshold of 1E-5 and minimum domain score of 20 [73]
  • Classify genes into core (shared across all accessions), soft-core, shell, and lineage-specific categories

• Step 4: Structural Variation Analysis

  • Identify presence-absence variation (PAV) and copy number variation (CNV) across accessions
  • Associate structural variation with tandem duplication events
  • Correlate variation with stress adaptation phenotypes

Visualization of Orthogroup Resolution Workflow

The following diagram illustrates the integrated workflow for resolving orthogroups containing tandemly duplicated genes, combining phylotranscriptomic and pan-genome approaches:

The Scientist's Toolkit: Essential Research Reagents

Table 2： Key research reagents and computational tools for orthogroup analysis

Category	Specific Tool/Reagent	Function/Application	Key Features
Orthogroup Inference	OrthoFinder [74]	Infers orthogroups from protein sequences	Accurate, scalable, species tree estimation
	CD-HIT [73]	Clusters protein sequences into orthogroups	Fast, memory-efficient, handles large datasets
Gene Family Identification	HMMER [73]	Identifies protein domains in gene sequences	Pfam domain detection (e.g., NAM domain)
	MCScanX	Identifies tandem and segmental duplications	Genome-wide duplication history inference
Sequence Annotation	Pfam Database [73]	Curated database of protein families	Domain architecture analysis
	SMART Database [73]	Protein domain annotation	Identification of structural domains
	NCBI CDD [73]	Conserved domain detection	Comprehensive domain annotation
Expression Analysis	RNA-seq Pipelines [74]	Transcript quantification from RNA-seq data	TPM calculation, differential expression
	SRA Toolkit [74]	Access to public RNA-seq datasets	Dataset retrieval and processing
Pan-Genome Analysis	Pan-genome Browsers [72]	Visualization of structural variation	PAV/CNV analysis across accessions
Validation	qRT-PCR [18]	Experimental validation of gene expression	Marker gene verification, stress responses

Discussion and Future Perspectives

Optimizing orthogroup resolution for tandemly duplicated genes remains a critical challenge in plant stress research, particularly given the prominent role of tandem duplication in expanding stress-responsive gene families [71] [72]. The integration of phylotranscriptomic approaches with pan-genome analyses represents a powerful strategy to overcome these challenges, enabling researchers to distinguish genuine orthologous relationships from recently duplicated paralogs. Future methodological developments should focus on improving computational efficiency for handling large pan-genome datasets and enhancing integration of multi-omics data to resolve complex gene families. As demonstrated in recent studies, these approaches are already yielding valuable insights into the evolution of stress response mechanisms and identifying conserved regulatory networks that can be targeted for crop improvement strategies [34] [73] [72].

Statistical Approaches for Differentiating Noise from Biological Signal

In the field of biological research, particularly in studies involving orthogroup expression profiling under biotic stress, distinguishing true biological signals from background noise is a fundamental challenge. High-dimensional data from modern omic technologies contain substantial noise that can obscure biologically relevant signals, making robust statistical approaches essential for accurate interpretation [75]. This guide compares statistical methodologies used to address this critical issue, evaluating their performance in extracting meaningful patterns from complex biological datasets, with a focus on applications in plant biotic stress research.

Comparative Analysis of Statistical Methodologies

Machine Learning for Assay Interference Identification

• Technology-Specific Models: Specialized machine learning models trained on assay technology-specific experimental data demonstrate superior performance in identifying technology-specific assay interference compared to global models. Random forest and multi-layer perceptron classifiers developed for fluorescence-based assay interference achieved Matthews correlation coefficients (MCC) of up to 0.47 on holdout data and 0.45 on external test sets [76].

• Data Utilization Strategy: The most effective approach utilizes large bioactivity data matrices from global models while maintaining assay technology awareness through specialized models and novel compound labeling approaches [76].

Horizontal, Vertical, and Mosaic Data Integration

Biological data integration employs three primary frameworks for noise reduction and signal enhancement, each with distinct advantages for orthogroup expression studies:

Table 1: Data Integration Frameworks for Noise Reduction

Integration Type	Data Relationship	Application in Orthogroup Studies	Noise Reduction Capability
Horizontal	Connects replicate batches with overlapping homologous features	Technical replication analysis	Medium
Vertical	Connects different features across replicate sets of individuals	Multi-omic data integration (genome, transcriptome, proteome)	High
Mosaic	Joint embedding of datasets without matching individuals/features	Cross-species comparative transcriptomics	High

Vertical and mosaic integration approaches are particularly valuable for evolutionary biologists studying phenotypic variation, as they connect data from multiple levels of organization and techniques without requiring identical sample sets [75].

Cross-Species Transcriptomic Analysis

A comparative transcriptome analysis of Arabidopsis, rice, and barley under oxidative stress and hormone treatment revealed that 15-34% of orthologous differentially expressed genes (DEGs) showed opposite responses between species despite orthology, highlighting both conserved and species-specific signaling networks [18]. This approach enables researchers to:

• Identify conserved response networks across species
• Distinguish species-specific adaptations from core biological signals
• Filter evolutionary noise from essential stress response pathways

Experimental Protocols for Orthogroup Expression Profiling

Orthogroup Identification and Classification Protocol

• Identification Method: Screen for domain-containing genes using PfamScan.pl HMM search script with default e-value (1.1e-50) against background Pfam-A_hmm model [15].
• Classification System: Classify genes following domain architecture methods, grouping similar domain-architecture-bearing genes into the same classes [15].
• Orthogrouping: Utilize OrthoFinder v2.5.1 package with DIAMOND tool for sequence similarity searches and MCL clustering algorithm for ortholog identification [15].

Transcriptomic Profiling Under Biotic Stress

• Data Collection: Retrieve RNA-seq data from specialized databases (IPF database, Cotton Functional Genomics Database, Cottongen database) using gene accession query IDs [15].
• Data Categorization: Organize expression data into three categories:
  • Tissue-specific (leaf, stem, flower, pollen, seed)
  • Abiotic stress-specific (dehydration, cold, drought, heat, salt)
  • Biotic stress-specific (pathogen infections, nematodes, bacterial strains) [15]
• Expression Validation: Employ virus-induced gene silencing (VIGS) to functionally validate putative stress-responsive genes, as demonstrated with GaNBS (OG2) in cotton, which confirmed its role in virus titering [15].

Visualization of Statistical Workflows

Multi-Omic Data Integration Pathway

Orthogroup Expression Analysis Workflow

Research Reagent Solutions for Orthogroup Expression Studies

Table 2: Essential Research Reagents for Orthogroup Expression Profiling

Reagent/Resource	Application Function	Example Use Case
Pfam-A_hmm Model	Domain identification and classification	Identifying NBS-domain-containing genes across species [15]
OrthoFinder v2.5.1	Orthogroup clustering and analysis	Determining evolutionary relationships among NBS genes [15]
VIGS Vectors	Functional gene validation through silencing	Testing role of GaNBS (OG2) in virus resistance [15]
RNA-seq Databases	Expression data retrieval and comparison	IPF database, CottonFGD for stress expression profiling [15]
qRT-PCR Reagents	Marker gene validation and expression confirmation	Verifying efficacy of stress treatments across species [18]

Performance Comparison in Biological Context

Efficacy in Plant Biotic Stress Research

The application of these statistical approaches in plant nucleotide-binding site (NBS) domain gene research demonstrates their practical utility:

• Comprehensive Orthogroup Identification: Analysis across 34 plant species identified 12,820 NBS-domain-containing genes classified into 168 classes with both classical and species-specific structural patterns [15].

• Expression Profiling Precision: Statistical differentiation of expression patterns revealed putative upregulation of orthogroups OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in cotton plants with varying susceptibility to cotton leaf curl disease [15].

• Genetic Variation Analysis: Statistical comparison between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified 6,583 unique variants in NBS genes of tolerant Mac7 versus 5,173 in susceptible Coker312 [15].

Cross-Species Transcriptomic Consistency

The cross-species analysis of Arabidopsis, rice, and barley following oxidative stress and hormone treatment revealed that orthologous DEGs with common responses to multiple treatments across all three species correlated strongly with experimental data confirming their functional importance in stress responses [18]. This demonstrates the power of comparative statistical approaches to distinguish conserved biological signals from species-specific noise.

Statistical approaches for differentiating noise from biological signal in orthogroup expression profiling have evolved significantly, with machine learning methods, sophisticated data integration frameworks, and cross-species comparative analyses providing powerful tools for extracting meaningful biological insights. The integration of these computational approaches with experimental validation through methods like VIGS creates a robust framework for identifying genuine stress-responsive genes and pathways. As omic technologies continue to generate increasingly complex datasets, these statistical methodologies will remain essential for advancing our understanding of orthogroup dynamics under biotic stress conditions.

Batch Effect Correction in Multi-Species Transcriptomic Datasets

The integration of transcriptomic data across different species—a process known as orthogroup expression profiling—provides unparalleled insights into evolutionary biology, stress response mechanisms, and disease pathways. When studying biotic stress responses, researchers can identify conserved defense mechanisms and specialized adaptations by comparing gene expression patterns across multiple species. However, this powerful approach is substantially complicated by batch effects, which are technical variations introduced during sample processing, sequencing, or data generation that can obscure true biological signals [77]. These effects are particularly pronounced in multi-species studies where biological and technical variations are often confounded [78] [79].

Substantial batch effects emerge from various sources in multi-species experiments. Differences in sequencing technologies (single-cell RNA-seq versus single-nuclei RNA-seq), sample types (primary tissue versus organoids), and biological systems (mouse versus human) all introduce systematic variations that can exceed the biological differences of interest [78]. When batch effects are strongly confounded with biological factors of interest—such as when all samples from one species are processed in a different batch—traditional correction methods often fail or inadvertently remove biological signal [79]. This challenge is especially relevant in biotic stress research, where researchers aim to distinguish genuine evolutionary adaptations from technical artifacts.

Comparative Analysis of Batch Effect Correction Methods

Batch effect correction methods employ diverse strategies to disentangle technical artifacts from biological signals. Linear regression-based approaches like removeBatchEffect (limma) and ComBat use statistical models to adjust for known batch variables [80] [81]. Deep learning methods such as conditional variational autoencoders (cVAE) learn complex nonlinear projections of high-dimensional gene expression data into integrated lower-dimensional spaces [78] [77]. Reference-based approaches leverage commonly profiled reference materials to enable ratio-based scaling, which is particularly effective in confounded scenarios [79]. Nearest-neighbor methods identify mutual nearest neighbors across batches to align cellular populations without requiring identical cell type compositions [81] [82].

Table 1: Classification of Batch Effect Correction Methods

Method Type	Representative Methods	Key Principles	Best-Suited Scenarios
Linear Model-Based	ComBat, removeBatchEffect, limma	Empirical Bayes framework, linear regression	Bulk RNA-seq, known batch variables, balanced designs
Deep Learning	scVI, sysVI, SCVI	Variational autoencoders, latent space integration	Large-scale single-cell data, nonlinear batch effects
Reference-Based	Ratio-based scaling, ComBat-ref	Scaling relative to reference materials	Confounded designs, multi-omics studies
Nearest-Neighbor	Harmony, MNN, BBKNN, Seurat	Mutual nearest neighbors, graph-based integration	Single-cell data, heterogeneous cell compositions
Tree-Based	BERT (Batch-Effect Reduction Trees)	Hierarchical batch correction, matrix dissection	Incomplete omic profiles, large-scale integration

Performance Comparison in Multi-Species Context

Evaluating batch correction methods requires assessing both technical effect removal and biological signal preservation. Performance metrics include Local Inverse Simpson's Index (LISI) for batch mixing, Average Silhouette Width (ASW) for biological preservation, and k-nearest neighbor Batch Effect Test (kBET) for local batch effect assessment [83] [80]. In multi-species integration, the critical challenge is maintaining evolutionary-related biological variation while removing technically-induced differences.

Table 2: Performance Comparison of Methods for Multi-Species Integration

Method	Batch Correction Strength (iLISI)	Biological Preservation (ASW/ARI)	Multi-Species Performance	Key Limitations
Harmony	High	High	Consistently performs well; minimal artifacts [82]	Requires normalized count matrix
sysVI (VAMP + CYC)	High	High	Effectively integrates cross-species; improves biological signals [78]	Complex implementation; computational intensity
ComBat-ref	Medium-High	Medium-High	Superior statistical power for differential expression [84]	Requires reference batch selection
Ratio-Based Scaling	High	High	Effective in confounded scenarios; broadly applicable [79]	Requires reference materials
BERT	High	Medium-High	Handles incomplete data; retains numeric values [85]	Newer method; less extensively validated
Seurat	High	Medium	Good for cross-species atlas-level integration [82]	May introduce artifacts; alters count matrix
scVI	High	Medium	Scalable to large datasets; nonlinear correction [78]	May remove biological signal; requires tuning

Recent benchmarking studies reveal that method performance significantly depends on data characteristics and integration challenges. For cross-species integration involving human and mouse pancreatic islets, sysVI—which employs VampPrior and cycle-consistency constraints—successfully integrated datasets while preserving biological signals for downstream interpretation of cell states and conditions [78]. Harmony consistently demonstrates robust performance across multiple evaluation frameworks, effectively removing batch effects while minimizing the introduction of artifacts [82]. Ratio-based methods show particular promise in completely confounded scenarios where biological groups and batches are perfectly aligned, as is common in multi-species experiments [79].

Experimental Protocols for Method Evaluation

Benchmarking Workflow for Multi-Species Integration

Robust evaluation of batch correction methods requires a structured workflow that assesses both technical effect removal and biological signal preservation. The following protocol outlines a comprehensive benchmarking approach suitable for multi-species transcriptomic datasets:

Sample Preparation and Data Collection:

• Select at least two model organisms relevant to biotic stress research (e.g., Arabidopsis thaliana and Solanum lycopersicum for plant studies)
• Process each species in separate batches to deliberately confound biological and technical variation
• Include replicate samples (minimum n=3 per condition) for statistical power
• Sequence all samples using comparable library preparation and sequencing depths
• Generate orthogonal validation data (qPCR, RNA-FISH) for key genes to establish ground truth

Data Preprocessing:

• Perform standard quality control (QC) including read mapping, count quantification, and normalization
• Filter low-quality cells or samples based on established QC metrics
• Subset all datasets to common orthologous genes using orthogroup mappings
• Apply variance stabilization transformations appropriate for each correction method

Batch Correction Application:

• Apply multiple correction methods in parallel to the same preprocessed data
• Use consistent downstream analysis pipelines for all methods
• Employ both negative controls (samples where no correction should occur) and positive controls (samples with known batch effects)

Performance Quantification:

• Calculate LISI scores to evaluate batch mixing while accounting for local neighborhoods [83]
• Compute ASW with respect to species identity to assess biological signal preservation
• Perform differential expression analysis between species to quantify false positives/negatives
• Conduct clustering analysis (e.g., ARI) to evaluate cell type identification accuracy

Case Study: Cross-Species Retina Integration

A recent investigation integrated single-cell RNA-seq datasets from human and mouse retinal tissues to identify conserved cell type-specific expression patterns [78]. The experimental protocol provides a template for orthogroup expression profiling under biotic stress:

Dataset Characteristics:

• Human samples: 65 samples, 192,203 cells
• Mouse samples: 8 datasets, 52 samples, 263,140 cells
• Sequencing protocols: Combination of single-cell and single-nuclei RNA-seq
• Cell types: Photoreceptors, bipolar cells, ganglion cells, amacrine cells, Müller glia

Integration Workflow:

• Orthogroup mapping: Identified 15,327 one-to-one orthologous genes between human and mouse
• Quality control: Removed low-quality cells and genes with limited cross-species expression
• Batch correction: Applied sysVI with VampPrior and cycle-consistency constraints
• Downstream analysis: Performed differential expression, gene ontology enrichment, and trajectory analysis

Validation Approach:

• Compared expression patterns of known conserved marker genes (e.g., RHO, OPN1SW)
• Assessed preservation of specialized neuronal subtype distinctions
• Evaluated consistency with independent bulk RNA-seq datasets
• Verified findings through literature mining and orthogonal data sources

The study demonstrated that sysVI successfully integrated cross-species datasets while preserving subtle biological differences between neuronal subtypes that were obscured by other methods [78].

Visualization of Batch Correction Workflows

Figure 1: Workflow for Multi-Species Batch Correction. This diagram outlines the key steps in batch effect correction for cross-species transcriptomic studies, highlighting critical decision points and alternative methodological approaches.

Successful batch effect correction in multi-species transcriptomics requires both computational tools and experimental resources. The following toolkit provides essential components for designing robust cross-species studies:

Table 3: Essential Research Toolkit for Multi-Species Batch Correction

Tool/Resource	Function	Application Context	Examples/Sources
Reference Materials	Enable ratio-based correction; quality control	Confounded designs; multi-omics studies	Quartet Project reference materials [79]
Orthogroup Databases	Map genes across species for comparable features	Evolutionary studies; cross-species integration	OrthoDB, Ensembl Compara, OrthoFinder
Batch Correction Software	Implement computational correction algorithms	All integration studies	Harmony, sysVI, ComBat-ref, BERT
Evaluation Metrics	Quantify correction quality and biological preservation	Method selection; quality assurance	LISI, ASW, kBET, ARI [83] [80]
Visualization Tools	Explore data structure before and after correction	Quality control; result interpretation	UMAP, t-SNE, PCA plots [80] [86]

Reference Materials are particularly valuable for severe batch effects or completely confounded designs. The Quartet Project provides multi-omics reference materials derived from four immortalized B-lymphoblastoid cell lines from a monozygotic twin family, enabling robust batch effect correction through ratio-based scaling [79]. When physical reference materials are unavailable, computational approaches like sysVI's VampPrior can create reference points from the data itself [78].

Orthogroup Databases are fundamental to multi-species transcriptomics as they define evolutionarily related genes across species. These databases provide the feature alignment necessary for cross-species comparison, ensuring that expression profiles compare orthologous genes rather than unrelated genes with similar sequences.

Batch effect correction in multi-species transcriptomic datasets remains challenging yet essential for advancing evolutionary biology and comparative transcriptomics. The integration of diverse methodologies—including emerging deep learning approaches like sysVI, robust linear methods like ComBat-ref, and innovative reference-based strategies—provides researchers with powerful tools to disentangle technical artifacts from biological signals. For orthogroup expression profiling under biotic stress, method selection should be guided by the specific experimental design, degree of confounding, and analytical priorities. When biological and technical factors are completely confounded, reference-based methods offer particular promise. As multi-species studies continue to expand in scale and complexity, the development of specifically tailored batch correction methods will be crucial for extracting meaningful biological insights from integrated datasets.

Validation Strategies and Comparative Insights Across Plant Kingdoms

In modern plant biology, particularly in the study of biotic stress responses, researchers face the critical challenge of moving from correlative expression data to causal gene function validation. The integration of large-scale transcriptomic analyses with targeted functional validation techniques represents a powerful framework for identifying key genetic players in plant defense mechanisms. Among these techniques, Virus-Induced Gene Silencing (VIGS) has emerged as a particularly versatile tool for rapid functional characterization of candidate genes identified through expression profiling [87].

This guide provides a comprehensive comparison of experimental approaches that connect genome-wide expression studies with VIGS-based functional validation, focusing specifically on their application in plant immunity research. We objectively evaluate the performance, scalability, and technical requirements of these integrated methodologies, providing researchers with the experimental data and protocols necessary to design effective functional genomics studies.

Theoretical Foundation: Orthogroup Expression Profiling in Biotic Stress Research

Orthogroup Classification and Comparative Genomics

The concept of orthogroups—sets of genes descended from a single gene in the last common ancestor of the species being compared—provides an evolutionary framework for cross-species transcriptomic analyses. Large-scale comparative studies have identified numerous orthogroups with potential roles in biotic stress responses. For instance, a comprehensive analysis of nucleotide-binding site (NBS) domain genes across 34 plant species identified 12,820 NBS-domain-containing genes classified into 168 architectural classes, with 603 orthogroups showing distinct evolutionary patterns [15].

Key orthogroups with demonstrated expression in biotic stress responses include:

• OG0, OG1, OG2: Core orthogroups with widespread distribution across species
• OG2, OG6, OG15: Orthogroups showing significant upregulation in tolerant cotton accessions under cotton leaf curl disease (CLCuD) pressure [15]
• OG80, OG82: Species-specific orthogroups potentially contributing to specialized defense mechanisms

Expression Profiling Methodologies for Orthogroup Analysis

Table 1: Comparative Analysis of Expression Profiling Methods for Orthogroup Studies

Method	Throughput	Key Applications in Orthogroup Analysis	Technical Considerations	Compatibility with Downstream VIGS
RNA-seq	High	Genome-wide differential expression, isoform detection	Requires high-quality references, computational resources	Excellent—directly identifies candidate genes for silencing
qRT-PCR	Medium	Targeted validation of specific orthogroups	Limited to known genes, requires primer design	Good for preliminary confirmation before VIGS
Microarrays	Medium	Cross-species comparison with defined probe sets	Limited to pre-designed probes, lower dynamic range	Moderate—may miss novel candidates
Single-Cell RNA-seq	Emerging	Cell-type-specific orthogroup expression	Technical challenges, high cost	Limited currently due to tissue-specific requirements

Integrated Experimental Workflow: From Profiling to Validation

Comprehensive Experimental Pipeline

The following diagram illustrates the integrated workflow connecting expression profiling to functional validation through VIGS:

Expression Profiling and Orthogroup Analysis Phase

The initial phase focuses on identifying candidate genes through comprehensive transcriptome analysis under biotic stress conditions. Key methodological considerations include:

Sample Collection and RNA Sequencing:

• Collect tissue samples from both susceptible and tolerant genotypes under controlled stress conditions
• Ensure appropriate biological replication (minimum n=3) and randomization
• Extract high-quality RNA using validated kits (e.g., RNAprep Pure Kit)
• Prepare sequencing libraries compatible with the platform (Illumina recommended)
• Sequence to sufficient depth (typically 20-40 million reads per sample) [15]

Bioinformatic Analysis for Orthogroup Identification:

• Quality control (FastQC) and adapter trimming (Trimmomatic)
• Read alignment to reference genome (HISAT2, STAR)
• Read counting (featureCounts, HTSeq)
• Differential expression analysis (edgeR, DESeq2)
• Orthogroup classification (OrthoFinder) using predefined databases [15]

Candidate Gene Selection Criteria:

• Significant differential expression (FDR < 0.05, log2FC > 1)
• Membership in stress-associated orthogroups
• Expression patterns correlated with tolerant phenotypes
• Domain architecture relevant to defense (e.g., NBS-LRR domains) [15]

VIGS Implementation for Functional Validation

VIGS Mechanism and Vector Systems

VIGS operates by hijacking the plant's natural RNA interference (RNAi) machinery. When a recombinant virus containing a fragment of a plant gene is introduced, the plant's defense system processes the viral RNA into small interfering RNAs (siRNAs) that target both the virus and corresponding endogenous mRNAs for degradation, resulting in gene silencing [87].

Table 2: Comparison of Major VIGS Vector Systems

Vector System	Virus Type	Key Features	Optimal Host Species	Silencing Duration	Efficiency
TRV (Tobacco Rattle Virus)	RNA virus	Broad host range, mild symptoms, meristem penetration	Solanaceae, Arabidopsis, Cotton	3-6 weeks	High (70-90%) [87] [88]
CLCrV (Cotton Leaf Crumple Virus)	DNA virus (Geminivirus)	Efficient in cotton, stable silencing	Cotton, Malvoideae species	4-8 weeks	High (>90% in cotton) [89]
BBWV2 (Broad Bean Wilt Virus 2)	RNA virus	Effective in legumes, pepper	Legumes, Pepper	3-5 weeks	Medium-High
CMV (Cucumber Mosaic Virus)	RNA virus	Extensive host range	Cucurbits, Arabidopsis	2-4 weeks	Medium
All-in-One Systems (VS/VS2)	DNA/RNA chimeric	Simplified T-DNA delivery, unified cloning	Multiple species	3-6 weeks	High [89]

VIGS Experimental Protocol

Vector Selection and Insert Design:

• Select vector system based on target species (TRV for Solanaceae, CLCrV for cotton)
• Amplify 200-300 bp target gene fragment from cDNA
• Verify fragment specificity using SGN VIGS Tool or similar
• Ensure <40% similarity to non-target genes to minimize off-target effects [88]
• Clone fragment into appropriate VIGS vector using restriction enzymes or recombination-based cloning

Agroinfiltration Procedure:

• Transform constructs into Agrobacterium tumefaciens strains (GV3101 recommended)
• Culture in YEB medium with appropriate antibiotics (kanamycin, rifampicin)
• Induce with acetosyringone (200 μM) in infiltration medium (10 mM MES, 10 mM MgCl₂)
• Adjust bacterial density to OD₆₀₀ = 0.9-1.0 for leaf infiltration [88]
• For recalcitrant tissues, use specialized methods (e.g., pericarp cutting immersion) [88]

Infiltration Methods Comparison:

• Leaf infiltration: Standard for tender tissues, high efficiency
• Vacuum infiltration: Whole seedling treatment, more uniform
• Shoot apex infiltration: For meristematic tissues
• Fruit infiltration: Specialized methods for recalcitrant tissues [88]

Optimization Parameters:

• Plant developmental stage (varies by species)
• Temperature (20-25°C optimal for most systems)
• Light intensity (moderate levels preferred)
• Agroinoculum concentration (OD₆₀₀ = 0.3-1.0) [87]

Case Studies: Integrated Orthogroup Profiling and VIGS Validation

Cotton Leaf Curl Disease (CLCuD) Resistance

A comprehensive study exemplifies the integrated approach, identifying NBS-encoding genes through comparative genomics and expression profiling across 34 plant species [15].

Key Experimental Findings:

• 12,820 NBS-domain genes identified with 168 domain architecture patterns
• Expression profiling revealed upregulation of OG2, OG6, and OG15 orthogroups in CLCuD-tolerant cotton
• Genetic variation analysis identified 6,583 unique variants in tolerant accession Mac7
• VIGS validation of GaNBS (OG2 member) demonstrated its role in virus tittering [15]

Methodological Details:

• RNA-seq data from IPF database and NCBI BioProjects (PRJNA490626, PRJNA594268)
• FPKM values categorized into tissue-specific, abiotic, and biotic stress responses
• OrthoFinder v2.5.1 used for orthogroup classification with MCL clustering
• TRV-based VIGS system for functional validation [15]

Soybean Mosaic Virus (SMV) Resistance

Another study identified Glyma02g13380 as a candidate gene conferring resistance to SC4 and SC20 SMV strains in soybean cultivar Kefeng-1 [90].

Integrated Validation Approach:

• Linkage mapping confined resistance to 253 kb (SC4) and 375 kb (SC20) regions
• Association analysis identified SNP11692903 as most significant marker
• qRT-PCR confirmed expression patterns
• VIGS provided functional validation of the candidate gene [90]

Salt Stress Response in Cotton

Transcriptome analysis of salt-tolerant and sensitive cotton genotypes identified 8,182 differentially expressed genes, with GhSAP6 emerging as a negative regulator of salt tolerance [91].

Validation Pipeline:

• WGCNA identified co-expression modules associated with salt tolerance
• VIGS silencing of GhSAP6 enhanced salt tolerance
• Yeast two-hybrid and LCI assays revealed interaction with RAD23C protein
• Demonstrated role in ubiquitin-mediated regulation [91]

Technical Considerations and Optimization Strategies

Enhancing VIGS Efficiency

Table 3: Troubleshooting Common VIGS Challenges

Challenge	Potential Causes	Solutions	Expected Outcome
Weak or transient silencing	Low viral titer, improper plant stage, suboptimal conditions	Optimize OD₆₀₀, infiltrate at 4-6 leaf stage, maintain 20-22°C	Extended silencing duration (3+ weeks)
Non-uniform silencing	Uneven agroinfiltration, vascular limitations	Include surfactant (Silwet L-77), use multiple infiltration sites	Consistent phenotype across tissue
Viral symptoms interfere	Overly aggressive viral vector, high titer	Use milder vectors (TRV), reduce bacterial density	Clearer distinction between viral and silencing phenotypes
Poor efficiency in recalcitrant tissues	Lignified tissues, limited viral movement	Use specialized infiltration (cutting immersion, shoot infusion) [88]	Effective silencing in woody tissues

Advanced VIGS Applications

Recent technological advances have expanded VIGS applications:

All-in-One Vector Systems:

• Simplified T-DNA delivery for bipartite viruses
• VS system (CLCrV-based) and VS2 system (TRV-based)
• Unified cloning system with 2×BsaI-containing MCS
• Enabled VIGS, VOX (virus-mediated overexpression), and VIGE (virus-induced genome editing) [89]

High-Throughput Screening:

• Proto-VIGS system for rapid validation in protoplasts
• Dual-luciferase reporter system (pVSr)
• 2-day screening vs. 2-3 weeks for conventional VIGS [89]

Multiple Gene Manipulation:

• Tandem arrangement of multiple silencing fragments
• Simultaneous VIGS and VOX in single T-DNA
• Enables study of gene families and redundant pathways [89]

Research Reagent Solutions Toolkit

Table 4: Essential Research Reagents for Orthogroup-to-VIGS Pipeline

Reagent Category	Specific Products/Systems	Primary Function	Key Features/Benefits
VIGS Vectors	pTRV1/pTRV2, pCLCrV-VA/VB, pNC-TRV2, All-in-one VS/VS2	Delivery of silencing constructs	Species-specific optimization, modular cloning systems [87] [89]
Agrobacterium Strains	GV3101, LBA4404	Delivery of T-DNA to plant cells	Disarmed strains, high transformation efficiency
Selection Antibiotics	Kanamycin, Rifampicin, Spectinomycin	Selection of transformed bacteria	Vector-specific resistance markers
Induction Compounds	Acetosyringone, MES buffer	Induction of vir genes for T-DNA transfer	Enhanced transformation efficiency
RNA Isolation Kits	RNAprep Pure Kit (Tiangen)	High-quality RNA extraction	DNA-free RNA for sensitive applications
cDNA Synthesis Kits	Reverse transcription systems (Yeasen)	First-strand cDNA synthesis	High efficiency, full-length transcripts
Cloning Systems	Nimble Cloning, Gateway, Golden Gate	Vector construction	Efficiency for toxic inserts, modular assembly [89]

The integration of orthogroup expression profiling with VIGS validation represents a powerful strategy for advancing our understanding of plant immunity mechanisms. This comparative guide demonstrates that success in functional genomics relies on selecting appropriate profiling methods matched with optimized validation systems tailored to specific plant species and biological questions.

Future developments in this field will likely focus on several key areas:

• Single-cell transcriptomics integrated with tissue-specific VIGS
• Multiplexed VIGS systems for studying gene networks and redundant functions
• CRISPR-VIGS hybrids for more precise functional dissection
• Computational prediction of optimal silencing fragments to enhance efficiency

The continued refinement of these integrated approaches will accelerate the identification and validation of key genetic determinants of biotic stress responses, ultimately contributing to the development of more resilient crop varieties.

Cross-Species Conservation of Defense Response Networks

Plant survival in natural environments depends on the activation of complex molecular defense networks in response to biotic stressors such as insect herbivores. Understanding the conservation and divergence of these response mechanisms across species provides crucial insights into plant evolution and adaptation strategies. This guide compares defense response networks across multiple plant species, focusing on orthogroup expression profiling under biotic stress conditions. We synthesize experimental data from comparative transcriptomic studies to objectively analyze conserved and species-specific defense mechanisms, providing researchers with a framework for understanding the evolutionary patterns of plant stress responses.

Comparative Analysis of Defense Response Mechanisms

Experimental Models and Systems

Recent comparative transcriptomics studies have utilized closely related plant species with different ecological adaptations to dissect conserved and divergent defense networks. Key model systems include:

Datura Genus Species: Four species (D. stramonium, D. pruinosa, D. inoxia, and D. wrightii) with contrasting developmental patterns and chemical phenotypes (alkaloid accumulation) subjected to herbivory by specialist insect Lema trilineata daturaphila [92]. These species represent an ideal system for studying evolutionary adaptations in plant-insect interactions.

Brassicaceae Relatives: Pachycladon cheesemanii, a New Zealand native species growing under extreme environmental conditions, compared with its relative Arabidopsis thaliana under multiple stress conditions (cold, salt, and UV-B radiation) [93]. This system reveals adaptations to harsh environments through comparative transcriptomics.

Land Plant Species: A broad evolutionary analysis across 35 land plant species, from algae to angiosperms, focusing on Zinc finger-BED transcription factor genes involved in defense responses [30]. This comprehensive approach identifies evolutionary patterns in defense-related transcription factors.

Quantitative Comparison of Defense Responses

Table 1: Defense Gene Expression Profiles Across Plant Species

Species Comparison	Stress Condition	Conserved Responses	Species-Specific Responses	Key Regulators Identified
Datura species [92]	Specialist herbivore feeding	JA-signaling pathway activation	Differential modulation of terpene and tropane alkaloid genes	Species-specific transcription factors
P. cheesemanii vs. A. thaliana [93]	Cold, salt, UV-B radiation	Core environmental stress response genes	Glycosinolate metabolism (cold), proline biosynthesis (salt), anthocyanin production (UV-B)	Species-specific regulatory networks
Land plants (35 species) [30]	Evolutionary analysis	Zf-BED domain conservation	Class-specific domain architectures; novel functional domains (WRKY, NBS)	Diverse transcription factor classes

Table 2: Experimental Methodologies for Cross-Species Defense Analysis

Methodology	Application in Defense Studies	Key Parameters	Data Output
RNA-seq transcriptome profiling [92] [93]	Gene expression changes under stress	3+ biological replicates; 5h treatment duration; 27,655-122.11 GB sequence data	Differential expression analysis; gene co-expression networks
Phylogenomic analysis [92]	Evolutionary relationships	OrthoFinder with DIAMOND sequence similarity; MCL clustering	Orthogroups; phylogenetic trees
Comparative genomics [30]	Transcription factor evolution	Pfam domain scanning (e-value: 1.1e-50); domain architecture classification	Gene family size variation; functional domain prediction

Orthogroup Expression Profiling Under Biotic Stress

Transcriptional Regulation in Datura-Herbivore Interactions

The response of Datura species to specialist herbivore feeding reveals complex regulatory networks governing specialized metabolism. Comparative transcriptome profiling identified three key patterns: (1) common gene expression in species with similar chemical profiles, (2) species-specific responses of specialized metabolism proteins characterized by constant gene expression levels coupled with transcriptional rearrangement, and (3) herbivory-induced transcriptional rearrangement of major terpene and tropane alkaloid genes [92].

The experimental protocol involved:

• Plant Material: Four Datura species with contrasting alkaloid accumulation patterns grown under controlled conditions
• Herbivory Treatment: Infestation with three-lined potato beetle (Lema trilineata daturaphila), a specialist folivore
• Transcriptome Analysis: RNA sequencing of damaged and control tissues at two developmental stages
• Data Analysis: Differential gene expression analysis focused on specialized metabolite classes (tropane alkaloids, terpenes), jasmonate signaling, and transcription factors

The study demonstrated differential modulation of terpene and tropane metabolism linked to jasmonate signaling and specific transcription factors, suggesting plastic adaptive responses to cope with herbivory [92].

Multi-Stress Response Conservation in Brassicaceae

The comparison between P. cheesemanii and A. thaliana under multiple stressors (cold, salt, and UV-B radiation) revealed both conserved and species-specific response pathways. Researchers treated six-week-old A. thaliana and nine-week-old P. cheesemanii plants at 4°C, 250 mM NaCl, or 5.2 μmol m⁻² s⁻¹ UV-B radiation, collecting samples after 5 hours to detect early transcriptional changes [93].

The experimental workflow included:

• Treatment Conditions: Controlled application of cold (4°C), salt (250 mM NaCl), and UV-B (5.2 μmol m⁻² s⁻¹) stresses
• RNA Sequencing: 12 samples per species (3 biological replicates for each stress and control)
• Comparative Analysis: Identification of stress-specific and conserved gene expression patterns

Notably, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were exclusively enriched in P. cheesemanii, while salt stress was associated with cuticle alteration in A. thaliana and proline biosynthesis in P. cheesemanii. Anthocyanin production was identified as a potentially important strategy for UV-B radiation tolerance in P. cheesemanii [93].

Figure 1: Conserved Defense Signaling Pathway in Datura Species. This diagram illustrates the core jasmonate-mediated defense activation pathway in response to herbivory, showing the sequence from stress perception to metabolic output.

Evolutionary Conservation of Defense-Related Transcription Factors

The genome-wide comparative evolutionary analysis of Zinc finger-BED transcription factor genes across 35 land plant species revealed significant diversity in defense-related transcription factors. The study identified 750 Zf-BED-encoding genes categorized into 22 classes based on domain architectures [30].

Key findings included:

• Class Distribution: Class I (Zf-BEDDUF-domainDimerTnphAT) was most common in majority of land plants, with some classes being family-specific and others species-specific
• Novel Domains: Prediction of several novel functional domains including WRKY and nucleotide-binding site (NBS)
• Expression Profiling: Differential expression of Zf-BED genes in different tissues and under various stress conditions in cotton species
• Evolutionary Patterns: Clear footprint of polyploidization in Gossypium species, demonstrating how genome duplication events contribute to defense gene evolution

This comprehensive analysis demonstrated that Zf-BED proteins play crucial roles in plant growth, development, and responses to biotic and abiotic stresses, with significant evolutionary diversity among plant species [30].

Research Reagent Solutions for Defense Response Studies

Table 3: Essential Research Reagents for Orthogroup Expression Profiling

Reagent/Resource	Application	Specifications	Research Function
RNA-seq Libraries	Transcriptome profiling	2×150-bp paired-end reads; 122.11 GB total sequence [92] [93]	Genome-wide expression quantification under stress conditions
Phytohormone Elicitors	Defense signaling studies	Methyl-jasmonate; concentration-dependent responses [92]	Activation of defense pathways; identification of regulated genes
Specialist Herbivores	Biotic stress application	Lema trilineata daturaphila for Datura species [92]	Ecologically relevant biotic stressor for defense response studies
Pfam Domain Databases	Protein classification	Local installation; pfamScan.pl algorithm (e-value 1.1e−50) [30]	Identification and classification of defense-related protein domains
OrthoFinder Software	Evolutionary analysis	DIAMOND sequence similarity; MCL clustering; DendroBLAST [30]	Orthogroup identification and comparative genomics across species
Abiotic Stress Chambers	Controlled stress application	Precise temperature, salinity, and UV-B regulation [93]	Standardized application of environmental stresses for comparison

Cross-species analysis of defense response networks reveals both deeply conserved and lineage-specific mechanisms for biotic stress adaptation. The conserved jasmonate signaling core operates across species, while specialized metabolic pathways and transcription factor families display significant evolutionary diversification. Orthogroup expression profiling provides a powerful framework for identifying key regulatory components that may be targeted for crop improvement strategies. Future research should expand these comparative approaches to include more diverse plant families and additional biotic stressors to fully elucidate the evolutionary principles governing plant defense networks.

Opposite Expression Patterns in Orthologous Genes Under Stress

Orthologous genes, which originate from a common ancestral gene and perform similar functions in different species, do not always exhibit conserved expression patterns under stress conditions. Cross-species transcriptomic analyses reveal that a significant proportion of orthologous genes display opposite expression patterns—where a gene is upregulated in one species but downregulated in another under identical stress treatments. This phenomenon highlights the complex evolutionary divergence in gene regulatory networks and presents both challenges and opportunities for translational genomics in crop improvement strategies.

The accurate prediction of gene function across species often relies on identifying orthologs—genes in different species that evolved from a common ancestral gene through speciation events. Conventional annotation methods typically assume that orthologous genes retain conserved functions and, by extension, conserved expression patterns. However, with the increasing availability of comparative transcriptomic data, this assumption is frequently challenged. Research has demonstrated that orthologous genes can show divergent expression profiles across species in response to the same stimuli, a phenomenon with critical implications for functional genomics [94].

Several evolutionary processes contribute to this divergence. Following gene or genome duplication events, paralogous genes may undergo subfunctionalization (partitioning of ancestral functions) or neofunctionalization (acquisition of new functions), leading to expression pattern variations. Furthermore, cis-regulatory element evolution and changes in transcription factor networks can rewire gene expression responses independently in different lineages [94]. In the context of biotic and abiotic stress responses, identifying these opposite patterns is essential for understanding species-specific adaptation and for accurately transferring gene function knowledge from model organisms to crops.

Quantitative Evidence of Opposite Expression Patterns

Comprehensive cross-species transcriptomic analyses provide direct evidence for widespread opposite expression patterns in orthologous genes. A systematic study comparing the model plant Arabidopsis thaliana with the crop species rice (Oryza sativa) and barley (*Hordeum vulgare *) under six different treatments revealed significant divergences [18].

Table 1: Incidence of Opposite Expression Patterns in Orthologous DEGs [18]

Treatment Comparison	Percentage of Orthologous DEGs with Opposite Patterns
Across all six treatments	15% to 34%
Hormone Treatments (ABA, SA)	Significant subset
Oxidative Stress (3AT, MV)	Significant subset
Respiration Inhibitor (Antimycin A)	Significant subset
Genetic Damage (UV Radiation)	Significant subset

The same study reported that a remarkable 70% of Differentially Expressed Genes (DEGs) overlapped with at least one other treatment within a species, indicating highly interconnected response networks. However, between species, a substantial fraction of orthologous genes responded in opposite directions. This suggests that, despite shared ancestry, the regulatory circuitry governing stress responses has significantly diverged [18].

Further evidence comes from a detailed analysis of anion channel gene families in barley. Among the 43 identified anion channel proteins, including CLC, ALMT, VDAC, and MSL families, expression profiling under drought stress showed that different cultivars exhibited diverse expression of these genes. This indicates that even within a species, genetic background can influence expression patterns, and orthology does not guarantee identical responses [1].

Experimental Protocols for Profiling Ortholog Expression

Cross-Species Transcriptomic Analysis

The identification of opposite expression patterns relies on robust comparative transcriptomics. The following methodology, adapted from a study on Arabidopsis, rice, and barley, outlines a standard approach [18].

• Treatment Application: Subjects plants to a panel of hormonal and stress treatments. Standard treatments include:
  • Hormones: Abscisic Acid (ABA, 100 µM) and Salicylic Acid (SA, 500 µM).
  • Oxidative Stress Inducers: 3-amino-1,2,4-triazole (3AT, 10 mM) and Methyl Viologen (MV, 10 µM).
  • Respiration Inhibitor: Antimycin A (AA, 10 µM).
  • Genetic Damage Inducer: Ultraviolet Radiation (UV).
• Tissue Harvesting and RNA Extraction: Collects leaf or root tissue from treated and control plants after a standardized period (e.g., 3 hours). Uses a validated RNA extraction kit to obtain high-quality, genomic DNA-free total RNA.
• Library Preparation and Sequencing: Prepares mRNA libraries (e.g., poly-A selected) and sequences them on a high-throughput platform (e.g., Illumina) to generate paired-end reads.
• Bioinformatic Processing:
  • Read Mapping and Quantification: Trims raw reads for quality and maps them to the respective reference genomes (e.g., Araport11 for Arabidopsis, IRGSP-1.0 for rice, and MorexV3 for barley) using tools like HISAT2 or STAR. Estimates gene expression levels (e.g., as Transcripts Per Million, TPM).
  • Differential Expression Analysis: Uses software packages like DESeq2 or edgeR to identify statistically significant DEGs for each treatment-species combination (common thresholds: adjusted p-value < 0.05, absolute log2 fold change > 1).
  • Ortholog Identification and Comparison: Defines orthogroups across the studied species using tools such as OrthoMCL [94] or OrthoFinder. For each orthogroup, the expression patterns (upregulated, downregulated, non-responsive) of all genes under a given treatment are compared to identify common and opposite responses.

Functional Validation of Expressologs

To confirm that expression pattern similarity predicts functional conservation, a complementation assay can be employed, as demonstrated in a study on Arabidopsis lyrata and Arabidopsis thaliana [94].

• Candidate Selection: From orthology analysis, select an A. thaliana gene and its multiple putative orthologs (co-orthologs) in A. lyrata.
• Expression Profiling: Analyze organ-dependent and stress-induced expression patterns of all candidates in both species using microarrays or RNA-seq.
• Expressolog Prediction: Designate the A. lyrata co-ortholog with the most similar expression pattern to the A. thaliana gene as the "expressolog." Others are classified as non-expressologs.
• Cloning and Transformation: Clone the coding sequences of the A. lyrata expressolog and non-expressologs into plant transformation vectors under the control of a constitutive promoter.
• Plant Material and Complementation: Introduce each construct into A. thaliana loss-of-function mutants for the target gene via Agrobis tumefaciens-mediated transformation.
• Phenotypic Scoring: Assess the restoration of the wild-type phenotype in the transgenic lines under specific stress conditions. The study confirmed that the predicted expressologs successfully complemented the mutant phenotype, while non-expressologs did not, validating expression similarity as a strong predictor of functional orthology [94].

Signaling Pathways and Expression Divergence

The divergence in expression patterns often stems from evolutionary rewiring of stress-responsive signaling pathways. The diagram below illustrates a generalized model of how orthologous transcription factors in two species might be regulated by different upstream signals, leading to opposite expression patterns of their target genes under stress.

This model shows that in Species 1, a stress signal primarily activates a pathway that positively regulates a transcription factor (SP1TF), which then induces the expression of the orthologous gene. Conversely, in Species 2, the same stress might more strongly activate a different pathway (Pathway 2) that induces a repressor transcription factor (SP2TF), leading to the downregulation of the same orthologous gene. This divergence in regulatory networks explains how opposite expression patterns can evolve [18] [94].

The Scientist's Toolkit: Key Research Reagents and Platforms

Successful research in orthogroup expression profiling relies on a suite of bioinformatic tools, databases, and experimental reagents.

Table 2: Essential Resources for Orthologous Gene Expression Studies

Resource Name	Type	Primary Function in Research
OrthoMCL / OrthoFinder	Bioinformatics Software	Identifies groups of orthologous and paralogous genes across multiple species [94].
GEO2R	Bioinformatics Tool	Web-based tool for comparing sample groups in the Gene Expression Omnibus (GEO) to identify differentially expressed genes [95].
GEPIA2	Bioinformatics Platform	Interactive web server for analyzing RNA-seq expression data, including differential expression and survival analysis [96].
DESeq2 / edgeR	R/Bioconductor Packages	Statistical analysis of differential gene expression from RNA-seq count data [18].
Abscisic Acid (ABA)	Chemical Reagent	Phytohormone used to simulate abiotic stress response pathways in experimental treatments [18].
Methyl Viologen (Paraquat)	Chemical Reagent	Herbicide that generates superoxide radicals, used to induce oxidative stress in plants [18].
Antimycin A	Chemical Reagent	Inhibitor of mitochondrial electron transport chain, used to induce mitochondrial retrograde signaling [18].
Virus-Induced Gene Silencing (VIGS)	Experimental Protocol	A rapid method for transiently knocking down gene expression in plants to validate gene function [97].

The phenomenon of opposite expression patterns in orthologous genes under stress underscores a critical layer of complexity in comparative genomics. Relying solely on sequence-based orthology for functional inference can be misleading, as the regulatory context in each species plays a decisive role. Integrating transcriptomic data to identify "expressologs" significantly improves the prediction of functional orthologs, as demonstrated by successful genetic complementation experiments [94]. Future research leveraging single-cell transcriptomics [97] and pangenomic resources [1] will further refine our understanding of conserved and divergent regulatory programs, ultimately enhancing our ability to engineer stress-resilient crops.

Tissue-Specific and Developmentally Regulated Orthogroup Expression

In the field of plant and animal biology, understanding gene expression patterns across species is fundamental to deciphering evolutionary adaptations, particularly in response to biotic stresses such as pathogens. Orthogroup expression profiling has emerged as a powerful comparative framework that enables researchers to identify functionally conserved gene networks across multiple species by grouping genes descended from a single gene in a common ancestor [98]. This approach provides a systematic method for comparing transcriptional responses across evolutionary distances where direct gene-to-gene comparisons are problematic due to sequence divergence and gene duplication events [52].

The integration of orthogroup analysis with tissue-specific and developmental expression data allows researchers to identify core regulatory programs that are conserved across species, as well as lineage-specific adaptations. This is particularly valuable in biotic stress research, where understanding the evolutionary conservation of defense mechanisms can inform breeding strategies and therapeutic interventions. Studies have demonstrated that orthogroup-based comparisons can reveal both shared and species-specific expression patterns in response to pathogens, providing insights into the evolution of immune systems across plants and animals [15] [99].

Methodological Framework for Orthogroup Analysis

Orthogroup Inference and Construction

The foundation of reliable cross-species expression comparison lies in accurate orthogroup inference. OrthoFinder has become the tool of choice for this purpose, as it solves fundamental biases in whole genome comparisons and dramatically improves orthogroup inference accuracy compared to earlier methods [98]. The standard workflow involves:

• Protein Sequence Collection: Proteomes for each species are downloaded from databases such as Phytozome or Ensembl [100] [73].
• Sequence Similarity Search: Reciprocal DIAMOND searches (a faster alternative to BLAST) are performed for all protein sequences across all species [100].
• Orthogroup Clustering: The Markov Cluster (MCL) algorithm is used to cluster sequences into orthogroups based on their sequence similarity scores [100] [98].

A critical innovation in OrthoFinder is its novel score transformation that eliminates gene length bias in orthogroup detection. Traditional methods using BLAST scores showed strong dependencies between performance characteristics and gene length, with short sequences suffering from low recall rates and long sequences suffering from low precision [98]. OrthoFinder's normalization approach ensures that orthogroup inference is not biased by gene length or phylogenetic distance between species.

Table 1: Key Tools for Orthogroup Inference and Expression Analysis

Tool Name	Primary Function	Key Features	Applicability
OrthoFinder [98]	Orthogroup Inference	Solves gene-length bias, fast, scalable	Genome-wide cross-species comparisons
OrthoMCL [101]	Orthogroup Inference	Early widely-used method, MCL clustering	Legacy datasets, specific pipelines
CoRMAP [101]	Comparative Transcriptomics	Reference-independent, standardized workflow	RNA-Seq meta-analysis across diverse taxa
Diamond [100]	Sequence Alignment	BLAST-like, faster than BLAST	Large-scale proteome comparisons
Trinity [101]	Transcriptome Assembly	De novo assembly without reference genome	Non-model organisms, cross-study comparisons

Expression Quantification and Normalization

Once orthogroups are established, expression data must be mapped to these groups for cross-species comparison. The typical workflow involves:

• Expression Quantification: RNA-Seq reads are mapped to reference genomes or transcriptomes, and expression values are calculated using metrics such as TPM (Transcripts Per Million) or FPKM (Fragments Per Kilobase Million) [100] [101].
• Orthogroup Expression Summarization: For orthogroups where a species has multiple genes (paralogs), the expression values of all genes in that orthogroup are summed. For single-copy orthologs, the raw expression value is used directly [100].
• Data Transformation: Expression data are typically log2 or z-score transformed to normalize the distribution and facilitate comparison across experiments and species [100].
• Batch Effect Correction: Methods such as Surrogate Variable Analysis (SVA) are employed to characterize and correct for unmodeled technical variables that might confound biological interpretations [100].

The Comparative RNA-Seq Metadata Analysis Pipeline (CoRMAP) provides a standardized framework for such analyses, implementing orthology searches to create orthologous gene groups and enabling comparison of expression levels of these groups between species and experimental conditions [101]. This approach is particularly valuable when comparing data from different studies or sequencing platforms.

Experimental Design and Applications in Biotic Stress

Tissue-Specific Expression Profiling

Tissue-specific orthogroup expression analysis has revealed fundamental insights into functional specialization across species. Two primary approaches have been developed for tissue expression profiling:

• Whole Tissue Profiling: This approach involves transcriptional profiling of entire organs or tissues to rapidly build comprehensive expression databases. It is particularly useful for identifying genes with highly restricted or tissue-specific expression patterns [102].
• Tissue Fractionation: Organs are subdivided into morphological or developmental components through methods such as physical microdissection, laser capture microdissection, or enzymatic dispersion followed by cell purification. This provides dramatically increased resolution of the tissue transcriptome [102].

Research on the mouse epididymis demonstrated the power of tissue fractionation, where transcriptional profiling of 10 individual segments increased the number of probe sets identified by 44% compared to whole epididymis analysis. When this segmented transcriptome was compared to a database of 23 whole mouse tissues, the number of probe sets specific to the epididymis increased by approximately 100% [102].

Investigating Developmental Regulation

Orthogroup analysis across developmental stages has uncovered evolutionarily conserved transcriptional programs. Studies on gestational and postnatal organ development have elucidated dramatic changes in gene expression that occur during development [102]. When combined with appropriate bioinformatics tools, fractionation of tissues along developmental progressions provides a powerful mechanism to study the precise relative expression patterns of single genes or gene families associated with specific biological mechanisms and pathways [102].

For example, pan-genome analysis of NAC transcription factors in barley revealed diverse tissue expression patterns across different developmental stages, with core genes showing strong purifying selection while shell and lineage-specific genes were under relaxed selection constraints, suggesting functional diversification [73].

Applications in Biotic Stress Response

Orthogroup-based analysis has proven particularly valuable in understanding responses to biotic stresses. Studies of the Nucleotide-binding site-Leucine-rich repeat (NBS-LRR) gene family, which represents a major class of plant resistance genes, have utilized orthogroup approaches to identify conserved defense mechanisms [15] [99].

Table 2: Orthogroup Expression in Biotic Stress Studies

Study System	Orthogroup Focus	Key Findings	Reference
Rosaceae-Valsa canker [99]	NLR genes	Identified 13 NLR members in key nodes of differentially expressed genes; specific orthogroups showed significant upregulation after infection	[99]
Cotton leaf curl disease [15]	NBS-domain genes	Expression profiling revealed upregulation of OG2, OG6, and OG15 in different tissues under biotic stress; silencing of GaNBS (OG2) demonstrated its role in virus resistance	[15]
Barley pan-genome [73]	NAC transcription factors	Revealed diverse expression patterns under stress conditions; identified orthogroups with strong purifying selection (core) versus those with diversifying selection (lineage-specific)	[73]
Grain amaranth [103]	NAC transcription factors	Comparative genomics showed conserved and lineage-specific expansions; transcriptome analysis under stress identified condition-specific and multi-stress responsive NAC genes	[103]

In Rosaceae species affected by Valsa canker, researchers identified 3,718 NLR genes from 19 plant species and classified them into 15 clades. The expression of some NLR members was low under normal growth conditions but significantly enhanced after Valsa canker infection. Co-expression network analysis revealed that 13 NLR members were distributed in key nodes of differentially expressed genes, suggesting their role as critical regulators of canker resistance [99].

Similarly, in cotton leaf curl disease research, comparative analysis of NBS-domain genes across 34 species identified 12,820 genes classified into 168 classes with several novel domain architectures. Expression profiling showed putative upregulation of specific orthogroups (OG2, OG6, and OG15) in different tissues under various biotic stresses. Functional validation through virus-induced gene silencing of GaNBS (OG2) demonstrated its putative role in virus resistance [15].

Comparative Analysis of Orthogroup Expression Methodologies

Traditional Sequence-Based vs. Emerging Structure-Based Approaches

Most current orthogroup inference methods rely on sequence similarity to identify homologous genes across species. However, emerging approaches are exploring alternative strategies:

• Sequence-Based Orthology: This traditional approach uses tools like OrthoFinder and OrthoMCL to cluster genes based on sequence similarity. While highly effective, these methods can fail to detect remote homology, particularly for rapidly evolving genes involved in biotic stress responses [52].
• Structure-Based Clustering: Recent investigations have explored protein structure predictions from databases such as AlphaFold as an alternative basis for comparing gene expression across species. The hypothesis is that protein structure similarity might better capture functional conservation than sequence similarity across larger evolutionary distances [52].
• Protein Language Models: Methods like SATURN use protein language models to encode protein sequences, enabling direct comparison and clustering of proteins in embedded space. Preliminary results suggest this approach may effectively merge cells from diverse species [52].

In a comparative study of single-cell RNA-seq data from mouse, frog, and zebrafish brain samples, protein structural clusters preserved dataset structure but did not merge homologous cell types across species better than sequence homology-based methods [52]. This suggests that while structure-based approaches show promise, sequence-based orthogroup inference remains the most reliable method for cross-species expression comparisons.

Pan-Genome vs. Single Reference Genome Approaches

Traditional gene family identification relying on a single reference genome fails to capture the full extent of genetic diversity within species, particularly gene presence/absence variation (gPAV) [73]. Pan-genome analysis addresses this limitation by integrating genomic data from multiple accessions to reconstruct a species' complete gene repertoire [73].

In barley NAC transcription factor analysis, pan-genome examination of 20 accessions identified a range of 127 to 149 HvNACs in each genome, compared to the 105-154 typically identified in single-genome studies of other species. These were classified into 201 orthogroups, stratified into core (102), soft-core (18), shell (25), and lineage-specific (56) categories [73]. This approach revealed that core genes have undergone strong purifying selection, while shell and lineage-specific genes were under relaxed selection constraints, highlighting the dynamic nature of stress-responsive gene families.

Essential Research Tools and Reagents

Successful orthogroup expression analysis requires a comprehensive toolkit of bioinformatics resources and experimental reagents. The following table summarizes key solutions used in the studies reviewed:

Table 3: Research Reagent Solutions for Orthogroup Expression Studies

Resource Category	Specific Tools/Databases	Application in Research	Key Features
Orthogroup Inference	OrthoFinder [100] [98], OrthoMCL [101]	Identifying groups of orthologous genes across species	Handles large datasets, corrects for gene length bias
Sequence Databases	Phytozome [100], Ensembl [73], NCBI	Source of reference genomes and proteomes	Curated genomic data for multiple species
Expression Analysis	CoRMAP [101], Trinity [101], EdgeR/DESeq2	RNA-Seq processing and differential expression	Standardized workflows, statistical robustness
Domain Annotation	Pfam [103] [15], SMART [73], CDD [103]	Identifying conserved protein domains	Hidden Markov Models, curated domain databases
Visualization	iTOL [99], ggplot2, ComplexHeatmaps	Phylogenetic trees, expression patterns	Publication-quality figures, customizable

Signaling Pathways and Experimental Workflows

The following diagram illustrates a generalized workflow for orthogroup-based expression analysis, integrating key steps from multiple methodologies described in the research:

Orthogroup Expression Analysis Workflow

For biotic stress response, the following diagram illustrates key molecular interactions in plant defense mechanisms identified through orthogroup analysis:

Plant Defense Mechanism Orthogroups

Orthogroup-based expression analysis has revolutionized our ability to compare transcriptional programs across species, tissues, and developmental stages, particularly in the context of biotic stress responses. The integration of orthogroup inference with tissue-specific expression profiling has enabled researchers to identify conserved defense mechanisms while also revealing species-specific adaptations.

Future advancements in this field will likely come from several directions:

• Improved orthology inference through integration of structural information and protein language models [52]
• Pan-genome scale analyses that capture the full extent of genetic diversity within species [73]
• Single-cell orthogroup expression profiling to resolve cellular heterogeneity in stress responses [52]
• Machine learning approaches to predict gene function and regulatory networks from orthogroup expression patterns

As these methodologies continue to evolve, orthogroup-based expression analysis will remain a cornerstone of comparative genomics, providing critical insights into the evolution of transcriptional regulation and its role in biotic stress responses across diverse species.

Comparative Analysis of Orthogroup Responses to Multiple Stressors

In the face of global climate change and its associated biotic and abiotic challenges, understanding the molecular basis of plant stress adaptation has become a critical research imperative. A significant limitation in translating findings from model species to crops lies in the inherent complexity of distinguishing true functional orthologs from lineage-specific expansions of gene families. Orthogroup analysis, which clusters homologous genes originating from a single gene in the last common ancestor, provides a powerful framework for comparative genomics. By defining groups of orthologous genes across multiple species, this approach enables researchers to identify conserved stress-response mechanisms and species-specific adaptations. Within the context of biotic stress research, orthogroup expression profiling offers unprecedented resolution for tracking the evolutionary conservation and diversification of defense pathways across phylogenetically diverse species, thereby revealing core regulatory networks that can be targeted for crop improvement strategies.

Orthogroup Methodologies for Cross-Species Comparative Transcriptomics

Fundamental Concepts and Analytical Pipelines

Orthogroup analysis represents a refinement of traditional orthology prediction methods by grouping genes into sets of descendants from a single gene in the last common ancestor of the species being compared. This approach effectively handles the challenges posed by gene duplication events, which create complex families of co-orthologs and in-paralogs that can obscure evolutionary relationships [34] [104]. According to Fitch's seminal definition, orthologs are homologous genes that diverged via speciation events, while paralogs result from gene duplications [104]. From an operational perspective, orthologs typically share conserved functions, whereas paralogs often exhibit functional diversification through mutations or domain recombinations [104].

The technical implementation of orthogroup analysis typically involves sophisticated software pipelines such as OrthoFinder and OrthoInspector, which employ a combination of sequence similarity searches and clustering algorithms to infer orthology relationships [104] [74] [15]. These tools begin with all-versus-all BLAST searches of protein sequences across multiple genomes, followed by the application of clustering algorithms like MCL to group sequences into orthogroups based on sequence similarity metrics [104] [15]. The OrthoInspector algorithm, for instance, incorporates a three-step process: (1) parsing BLAST results to identify best hits and create preliminary inparalog groups; (2) comparing inparalog groups across organisms in pairwise fashion to define potential orthologs; and (3) detecting and resolving contradictory best hits that conflict with established orthology relationships [104].

Experimental Design Considerations for Stress Studies

When applying orthogroup analysis to stress response studies, several experimental design factors require careful consideration. The selection of species should represent an appropriate phylogenetic spread to distinguish conserved responses from lineage-specific adaptations, as demonstrated in studies incorporating species from mosses to higher plants [15] or across diverse angiosperm lineages [74]. Tissue sampling should account for developmental stage and tissue specificity of stress responses, while stress treatments must be standardized to enable meaningful cross-species comparisons [18]. For transcriptomic analyses, normalization across experiments and species is critical, often achieved through the use of conserved low-copy orthologs as internal references [74]. The identification of such conserved ortholog sets—6,328 orthologous low-copy genes across 54 plant species in one large-scale study—provides a stable framework for comparative expression analyses by minimizing noise introduced by comparing rapidly evolving gene families [74].

Table 1: Key Bioinformatics Tools for Orthogroup Analysis

Tool	Methodology	Key Features	Applications in Stress Studies
OrthoFinder [13] [74] [15]	Graph-based clustering using sequence similarity	Identifies orthogroups and gene duplication events; provides phylogenetic dating	Used in NAC gene family analysis in barley pan-genome [73] and NLR gene study in asparagus [13]
OrthoInspector [104]	BLAST all-versus-all searches with inparalog validation	Rapid detection of orthology/in-paralogy relations; command line and GUI interfaces	Improves detection sensitivity with minimal specificity loss for large datasets
DIAMOND [15]	Double Index Alignment of Next-Generation Sequencing Data	Accelerated sequence similarity searches; faster than BLAST	Used in large-scale NBS gene analysis across 34 plant species [15]
CD-HIT [73]	Cluster Database at High Identity with Tolerance	Rapid clustering of similar proteins	Applied in pan-genome analysis of NAC transcription factors in barley [73]

Key Studies in Orthogroup Responses to Multiple Stressors

Conserved Cold Response Networks Across Eudicots

A groundbreaking phylotranscriptomic analysis of cold-treated seedlings across five representative eudicots identified 35 high-confidence conserved cold-responsive transcription factor orthogroups (CoCoFos) from 10,549 initial orthogroups [34]. This systematic approach leveraged orthogroup analysis to overcome the challenges posed by gene gain and loss events that create complex communities of co-orthologs and in-paralogs between and within species. The identified CoCoFos included well-known cold-responsive regulators such as CBFs, HSFC1, ZAT6/10, and CZF1, alongside novel candidates [34]. Functional validation of Arabidopsis BBX29, one of the identified CoCoFos, revealed that its cold induction occurs independently of CBF and abscisic acid pathways, and it functions as a negative regulator of cold tolerance by repressing a suite of cold-induced transcription factors including ZAT12, PRR9, RVE1, and MYB96 [34]. This study demonstrates how orthogroup analysis can disentangle complex regulatory networks and identify core conserved components of stress response pathways across phylogenetically diverse species.

Cross-Species Transcriptomic Signatures of Oxidative Stress

A comprehensive cross-species transcriptomic analysis of oxidative stress and hormone responses in Arabidopsis, rice, and barley revealed both conserved and species-specific response patterns [18]. Researchers analyzed transcriptome responses to six treatments including hormones (abscisic acid and salicylic acid), oxidative stress inducers (3-amino-1,2,4-triazole and methyl viologen), a respiration inhibitor (antimycin A), and a genotoxic stressor (ultraviolet radiation). The study found that 15-34% of orthologous differentially expressed genes showed opposite responses between species, highlighting significant diversification in stress response regulation despite gene orthology [18]. However, the mitochondrial dysfunction response emerged as highly conserved across all three species, both in terms of responsive genes and regulation via the mitochondrial dysfunction element [18]. This research provides a valuable resource for knowledge translation between model and crop species, while highlighting fundamental differences in stress response networks that must be considered when extrapolating findings across species.

Evolutionary Dynamics of Disease Resistance Gene Families

Comparative genomic analyses of nucleotide-binding site leucine-rich repeat (NLR) genes—key components of plant immune systems—across asparagus species revealed marked contraction of NLR genes during domestication, with wild relative Asparagus setaceus containing 63 NLR genes compared to just 27 in cultivated A. officinalis [13]. Orthologous gene analysis identified only 16 conserved NLR gene pairs between these species, representing the core immune repertoire preserved during domestication [13]. Pathogen inoculation assays demonstrated that the domesticated asparagus was susceptible while the wild relative remained asymptomatic, with most preserved NLR orthologs in the cultivated species showing either unchanged or downregulated expression following fungal challenge [13]. This suggests that artificial selection for yield and quality traits during domestication may have compromised disease resistance mechanisms, both through gene loss and altered regulation of retained NLR orthologs.

Table 2: Summary of Key Orthogroup Stress Response Studies

Study Focus	Species Analyzed	Orthogroups Identified	Key Findings	Reference
Cold Response	Five eudicots	35 conserved cold-responsive orthogroups from 10,549 total	Identified CoCoFos including BBX29 as a negative regulator; Response mechanisms conserved across species	[34]
Oxidative Stress & Hormone Response	Arabidopsis, rice, barley	Orthologous DEGs across species	15-34% of orthologous DEGs showed opposite responses; Mitochondrial dysfunction response highly conserved	[18]
NLR Gene Family Evolution	Asparagus officinalis, A. kiusianus, A. setaceus	16 conserved NLR orthogroups	Domesticated asparagus shows NLR contraction (27 genes) vs. wild relative (63 genes); Expression alteration in conserved orthologs	[13]
NAC Transcription Factor Family	20 barley accessions	201 orthogroups (102 core, 18 soft-core, 25 shell, 56 lineage-specific)	Core genes under purifying selection; Shell/lineage-specific genes under relaxed selection with functional diversification	[73]

Experimental Protocols for Orthogroup Stress Response Analysis

Orthogroup Construction and Differential Expression Analysis

A standardized protocol for orthogroup-based comparative transcriptomics begins with the retrieval of genomic and transcriptomic data from public databases such as NCBI, Phytozome, or organism-specific databases [13] [73] [15]. For gene family studies, Hidden Markov Model (HMM) searches using conserved domain profiles (e.g., PF00931 for NB-ARC domains or PF02365 for NAC domains) are performed to identify candidate genes, followed by validation using tools like InterProScan, NCBI's CDD, or SMART to confirm domain architecture [13] [73] [15]. Orthogroup clustering is then performed using OrthoFinder or similar tools, which employ DIAMOND for sequence similarity searches and the MCL algorithm for clustering [15]. For transcriptomic studies, RNA-seq data processing includes quality control, adapter trimming, read alignment, and quantification of expression levels (typically as TPM or FPKM values) [74] [15]. Differential expression analysis is performed using appropriate statistical methods, with orthogroup information enabling direct cross-species comparison of expression patterns for orthologous genes [74] [18].

Validation and Functional Characterization

Following identification of stress-responsive orthogroups, functional validation typically employs both computational and experimental approaches. Phylogenetic reconstruction using maximum likelihood methods with tools like MEGA or FastTreeMP provides evolutionary context, while promoter analysis using PlantCARE identifies cis-regulatory elements associated with stress responses [13] [15]. Gene silencing approaches such as Virus-Induced Gene Silencing (VIGS) have proven valuable for functional validation, as demonstrated in a study where silencing of GaNBS (OG2) in resistant cotton increased susceptibility to cotton leaf curl disease, confirming its role in virus defense [15]. Protein-ligand and protein-protein interaction studies can further elucidate molecular mechanisms, with some studies demonstrating strong interactions between NBS proteins and ADP/ATP or viral core proteins [15]. For temporal expression profiling, qRT-PCR analyses at multiple time points following stress treatment can reveal dynamic expression patterns of orthogroup members [18].

Figure 1: Experimental workflow for orthogroup analysis of stress responses, integrating bioinformatics and functional validation approaches.

Computational Tools and Databases

The implementation of orthogroup analysis requires specialized bioinformatics tools and databases. OrthoFinder has emerged as a widely-used tool for orthogroup inference, employing DIAMOND for accelerated sequence similarity searches and the MCL algorithm for clustering [15]. OrthoInspector provides an alternative approach with enhanced sensitivity for detecting orthology/in-paralogy relationships and offers both command-line and graphical interfaces for data exploration [104]. For domain identification and classification, InterProScan, NCBI's Conserved Domain Database (CDD), and the SMART database are indispensable resources [13] [73]. Genomic data repositories such as NCBI, Phytozome, Plant GARDEN, and organism-specific databases provide the essential raw data for comparative analyses [13] [73] [15]. For transcriptomic studies, databases like the IPF database, Cotton Functional Genomics Database (CottonFGD), and Cottongen offer curated expression data across various tissues and stress conditions [15].

Experimental Reagents and Biological Materials

Functional validation of stress-responsive orthogroups requires specific experimental reagents and biological materials. For gene silencing studies, Virus-Induced Gene Silencing (VIGS) vectors provide a powerful tool for rapid functional characterization, as demonstrated in the validation of GaNBS in cotton defense against cotton leaf curl disease [15]. For stress treatments, specific chemicals are employed to induce particular stress responses: methyl viologen and 3-amino-1,2,4-triazole for oxidative stress, antimycin A for mitochondrial inhibition, and salicylic acid and abscisic acid for hormone responses [18]. The use of resurrected historical populations, such as Daphnia magna lineages revived from sedimentary archives, provides unique opportunities to study evolutionary responses to environmental stressors over time [105]. For plant studies, diverse germplasm collections—including wild relatives, landraces, and modern cultivars—enable comparative analyses of orthogroup evolution and diversification under selection pressure [13] [73].

Table 3: Essential Research Reagent Solutions for Orthogroup Stress Studies

Category	Specific Tools/Reagents	Function/Application	Examples from Literature
Bioinformatics Software	OrthoFinder, OrthoInspector	Orthogroup identification and analysis	OrthoFinder used in barley pan-genome analysis [73]; OrthoInspector for eukaryotic orthology analysis [104]
Domain Databases	Pfam, CDD, SMART, InterProScan	Protein domain identification and classification	Used for NLR and NAC gene identification [13] [73] [15]
Expression Databases	IPF, CottonFGD, Cottongen	Source of tissue/stress expression data	IPF database used for NBS gene expression profiling [15]
Functional Validation Tools	VIGS vectors, CRISPR-Cas9	Gene silencing/editing for functional characterization	VIGS used to validate GaNBS role in cotton leaf curl disease resistance [15]
Stress Inducers	Methyl viologen, Antimycin A, SA, ABA	Induction of specific stress responses	Used in cross-species transcriptomic analyses [18]
Biological Materials	Resurrected populations, Pan-genome collections	Evolutionary and diversity studies	Daphnia resurrection ecology [105]; Barley pan-genome [73]

Emerging Applications and Future Perspectives

Pan-Genome Orthogroup Analysis

The integration of orthogroup analysis with pan-genome approaches represents a cutting-edge methodology for capturing species-wide genetic diversity. A study of NAC transcription factors across 20 barley accessions identified 201 orthogroups stratified into core (102), soft-core (18), shell (25), and lineage-specific (56) categories [73]. This analysis revealed that core genes have undergone strong purifying selection, while shell and lineage-specific genes experience relaxed selection constraints, suggesting functional diversification in barley [73]. Genomic variation in NAC loci, including presence-absence variations and copy number variations, appeared largely driven by transposable elements, highlighting the dynamic nature of this important transcription factor family [73]. Such pan-genome orthogroup analyses provide unprecedented resolution of within-species evolutionary dynamics and enable identification of conserved core genes that may represent valuable targets for breeding programs.

Topological Data Analysis for Pattern Recognition

Topological Data Analysis (TDA) has emerged as a powerful approach for visualizing and interpreting high-dimensional transcriptomic data across species. Application of TDA to gene expression data from 54 angiosperm species revealed distinct topological shapes when viewed through lenses that delineate different tissue types or stress responses [74]. The resulting Mapper graphs formed well-defined gradients from leaves to seeds or from healthy to stressed samples, suggesting conserved expression patterns across angiosperms that define plant form and function [74]. Genes correlating with tissue-specific expression patterns showed enrichment for central biological processes including photosynthesis, growth and development, housekeeping functions, and stress responses [74]. This approach demonstrates how orthogroup analysis can be enhanced through advanced mathematical frameworks to reveal higher-order organization in cross-species transcriptomic data.

Figure 2: Conserved stress response signaling pathways integrating orthogroup responses across species, highlighting key transcription factor orthogroups identified in comparative studies.

Conclusion

Orthogroup expression profiling represents a paradigm shift in understanding plant biotic stress responses, revealing both deeply conserved defense mechanisms and rapidly evolving species-specific adaptations. The integration of evolutionary context with transcriptomic data enables identification of core stress-responsive networks with significant translational potential. Future research should prioritize expanding phylogenetic coverage, developing more sophisticated cross-species prediction models, and leveraging orthogroup insights for engineering durable disease resistance. For biomedical and clinical research, these plant-based evolutionary findings offer analogical frameworks for understanding host-pathogen interactions across biological systems, suggesting conserved principles in immune response organization that transcend kingdom boundaries.

References

• 1. Genome‐Wide Identification, Molecular Evolution, and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11928749/]
• 2. The evolution of gene expression in seasonal environments [https://elifesciences.org/reviewed-preprints/107309]
• 3. Environmental gradients reveal stress hubs pre-dating ... [https://www.nature.com/articles/s41477-023-01491-0]
• 4. Comparative genomics profiling of Citrus species reveals the ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-025-06397-x]
• 5. Evolutionary diversification of ancestral genes across ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03699-z]
• 6. A transcriptomic atlas of macaúba palm reveals organ- ... [https://www.sciencedirect.com/science/article/pii/S2452014425002638]
• 7. Comparative Transcriptome Reveals Conserved Gene ... [https://www.mdpi.com/1422-0067/26/8/3568]
• 8. Comparative transcriptomics reveals commonalities and ... [https://www.nature.com/articles/s41598-022-25132-2]
• 9. Comparative Transcriptomics of Olfactory Rosettes Reveals ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12466535/]
• 10. Identification and distribution of the NBS-LRR gene family in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4422547/]
• 11. Genome-Wide Identification and Expression Analysis ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10045381/]
• 12. Genome-wide identification and characterization of NBS ... [https://www.nature.com/articles/s41598-025-03507-5]
• 13. Comparative analysis of the NLR gene family in ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1681919/full]
• 14. Comparative analysis, diversification, and functional ... [https://pubmed.ncbi.nlm.nih.gov/38789717/]
• 15. Comparative analysis, diversification, and functional ... [https://www.nature.com/articles/s41598-024-62876-5]
• 16. Functional characterization of NBS-LRR genes reveals an ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10898138/]
• 17. A novel TIR-NBS-LRR gene regulates immune response to ... [https://www.sciencedirect.com/science/article/pii/S2214514122000733]
• 18. Cross-species transcriptomic analyses reveals common and ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-021-03406-7]
• 19. Genome-wide analysis of NAC transcription factors in grain ... [https://www.nature.com/articles/s41598-025-23630-7]
• 20. The evolution, variation, and expression patterns under ... [https://www.frontiersin.org/articles/10.3389/fpls.2025.1635416]
• 21. The Role of Pathogen Dynamics and Immune Gene ... [https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2020.594263/full]
• 22. Conservation Evolutionary Biology: A Unified Framework ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12203002/]
• 23. A quantum machine learning framework for predicting drug ... [https://www.nature.com/articles/s41598-025-06544-2]
• 24. Comparative analysis, diversification, and functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11126693/]
• 25. Spatial transcriptomics illuminates evolutionary fates - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12622374/]
• 26. The more the merrier? Gene duplications in ... [https://www.sciencedirect.com/science/article/abs/pii/S1879625723000500]
• 27. Sharks Shed Light on Origins of Adaptive Immune System [https://als.lbl.gov/sharks-shed-light-on-origins-of-adaptive-immune-system/]
• 28. Evolutionary characterization of antiviral SAMD9/9L across ... [https://www.nature.com/articles/s41559-025-02845-x]
• 29. Massive expansion and functional divergence of innate ... [https://pubmed.ncbi.nlm.nih.gov/25732911/]
• 30. A genome-wide comparative evolutionary analysis of zinc ... [https://www.nature.com/articles/s41598-022-16602-8]
• 31. Maximum lifespan and brain size in mammals are ... [https://www.nature.com/articles/s41598-025-98786-3]
• 32. Human-specific gene expansions contribute to brain ... [https://www.sciencedirect.com/science/article/abs/pii/S0092867425007391]
• 33. Benchmarking Orthogroup Inference Accuracy [https://pmc.ncbi.nlm.nih.gov/articles/PMC7738749/]
• 34. Orthogroup and phylotranscriptomic analyses identify ... [https://www.sciencedirect.com/science/article/pii/S2590346223002122]
• 35. Comparative transcriptomics analysis pipeline for the meta ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04972-9]
• 36. De novo annotation reveals transcriptomic complexity ... [https://www.nature.com/articles/s41467-025-64046-1]
• 37. A genome-wide comparative evolutionary analysis of zinc ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9296551/]
• 38. M1CR0B1AL1Z3R 2.0: an enhanced web server for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12230721/]
• 39. Comparative pan-genomic analysis reveals pathogenic ... [https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1574627/full]
• 40. Tips for improving genome annotation quality [https://www.maxapress.com/article/id/67e36fa6fa6c5866a7a3e810]
• 41. Benchmarking strategies for cross-species integration of ... [https://www.nature.com/articles/s41467-023-41855-w]
• 42. Benchmarking cross-species single-cell RNA-seq data ... [https://www.rna-seqblog.com/benchmarking-cross-species-single-cell-rna-seq-data-integration-methods/]
• 43. Best practices on the differential expression analysis of multi ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02337-8]
• 44. Integrating single-cell transcriptomic data across different ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6700744/]
• 45. A Novel Machine-Learning Approach to Predict Stress ... [https://www.mdpi.com/1999-4893/16/9/407]
• 46. Machine learning reveals genes impacting oxidative stress ... [https://www.nature.com/articles/s41467-025-60189-3]
• 47. Predictive prioritization of genes significantly associated ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1611784/full]
• 48. Machine Learning Approaches Distinguish Multiple Stress ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3875824/]
• 49. Predicting transcriptional responses to heat and drought ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1212073/full]
• 50. Topological data analysis in single cell biology - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12436302/]
• 51. Topological data analysis reveals a core gene expression ... [https://par.nsf.gov/biblio/10513269-topological-data-analysis-reveals-core-gene-expression-backbone-defines-form-function-across-flowering-plants]
• 52. Comparing gene expression across species based on ... [https://research.arcadiascience.com/pub/idea-structure-based-expression-comparison/release/1/]
• 53. Amalgamated cross-species transcriptomes reveal organ ... [https://www.nature.com/articles/s41467-020-18090-8]
• 54. Multi-Omics Approaches Against Abiotic and Biotic Stress ... [https://www.mdpi.com/2223-7747/14/6/865]
• 55. Identification, characterization, and validation of NBS ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10318170/]
• 56. Genome-wide analysis of NBS-LRR genes in Rosaceae ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1052191/full]
• 57. Genome-Wide Identification of NBS-LRR Family in Three ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12192410/]
• 58. Genome-wide identification and characterization of NBS ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-020-02803-8]
• 59. NBS-Encoding Genes in Brassica napus Evolved Rapidly ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.00026/full]
• 60. Response of NBS encoding resistance genes linked to ... [https://www.sciencedirect.com/science/article/abs/pii/S0981942814003428]
• 61. Plant GARDEN: a portal website for cross-searching between ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-023-04392-8]
• 62. New Machine Learning Model Handles Technical Variability ... [https://www.cancer.columbia.edu/news/new-machine-learning-model-handles-technical-variability-accelerate-microbiome-based-cancer-detection]
• 63. Statistical variability in comparing accuracy of neuroimaging ... [https://pubmed.ncbi.nlm.nih.gov/40769991/]
• 64. Diversifying the concept of model organisms in the age of [https://www.nature.com/articles/s42003-023-05458-x]
• 65. The Power of Natural Variation for Model Organism Biology [https://www.sciencedirect.com/science/article/abs/pii/S0168952515002218]
• 66. Challenges and advances for transcriptome assembly in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5607178/]
• 67. Digital gene expression for non-model organisms - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3205575/]
• 68. Orthology Inference in Nonmodel Organisms Using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4209138/]
• 69. Multi-Omics Approaches Against Abiotic and Biotic Stress—A ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11944711/]
• 70. Experimental ecology and the balance between realism and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12134181/]
• 71. Importance of Lineage-Specific Expansion of Plant ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2556807/]
• 72. A species-wide inventory of receptor-like kinases in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12382012/]
• 73. The evolution, variation, and expression patterns under ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1635416/full]
• 74. Topological data analysis reveals a core gene expression ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10723737/]
• 75. Navigating Through the Noise: A Roadmap for Combining ... [https://link.springer.com/article/10.1007/s11692-025-09655-w]
• 76. Statistical approaches enabling technology-specific assay ... [https://www.sciencedirect.com/science/article/pii/S2667318524000060]
• 77. Are batch effects still relevant in the age of big data? [https://www.sciencedirect.com/science/article/pii/S0167779922000361]
• 78. Integrating single-cell RNA-seq datasets with substantial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12577435/]
• 79. Correcting batch effects in large-scale multiomics studies ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03047-z]
• 80. Why You Must Correct Batch Effects in Transcriptomics Data? [https://www.metwarebio.com/transcriptomics-batch-effect-correction/]
• 81. Chapter 1 Correcting batch effects | Multi-Sample Single- ... [https://bioconductor.org/books/3.14/OSCA.multisample/integrating-datasets.html]
• 82. Batch correction methods used in single-cell RNA sequencing ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12315870/]
• 83. A benchmark of batch-effect correction methods for single- ... [https://www.rna-seqblog.com/a-benchmark-of-batch-effect-correction-methods-for-single-cell-rna-sequencing-data/]
• 84. Highly effective batch effect correction method for RNA-seq ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11718288/]
• 85. High performance data integration for large-scale analyses ... [https://www.nature.com/articles/s41467-025-62237-4]
• 86. Batch correcting single-cell spatial transcriptomics count data ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03479-9]
• 87. Virus-Induced Gene Silencing (VIGS) in Functional Genomics [https://www.mdpi.com/2311-7524/11/11/1297]
• 88. Development of a robust and efficient virus-induced gene ... [https://plantmethods.biomedcentral.com/articles/10.1186/s13007-024-01320-1]
• 89. An all-in-one plant virus-based vector toolkit ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12365833/]
• 90. Identification and functional validation of a new gene ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11811538/]
• 91. Comparative transcriptome analysis and functional ... [https://www.sciencedirect.com/science/article/abs/pii/S098194282401074X]
• 92. Comparative transcriptome profiling reveals distinct regulatory ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11231941/]
• 93. Comparative Transcriptomics of Multi-Stress Responses in ... [https://www.mdpi.com/1422-0067/24/14/11323]
• 94. Expression Pattern Similarities Support the Prediction ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4972257/]
• 95. GEO2R - GEO - NCBI - NIH [https://www.ncbi.nlm.nih.gov/geo/geo2r/]
• 96. GEPIA (Gene Expression Profiling Interactive Analysis) [http://gepia.cancer-pku.cn/]
• 97. Comparative single-cell transcriptomic map reveals ... [https://www.sciencedirect.com/science/article/pii/S2090123225002565]
• 98. OrthoFinder: solving fundamental biases in whole genome ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0721-2]
• 99. Phylogeny, Expression Profiling, and Coexpression ... [https://www.mdpi.com/2311-7524/9/3/345]
• 100. Comparing expression across species [https://bio-protocol.org/exchange/minidetail?id=16954244&type=30]
• 101. Comparative transcriptomics analysis pipeline for the meta- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9547434/]
• 102. Gene Expression Profiling and its Practice in Drug ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2430683/]
• 103. Genome-wide analysis of NAC transcription factors in grain ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12618503/]
• 104. OrthoInspector: comprehensive orthology analysis and visual ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-11]
• 105. Evolutionary mechanisms underpinning fitness response to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8549616/]