Genomic Inversion and Transient Episodic Selection Reshape the queD Locus During Host Specialization in Xanthomonas

Genomic Inversion and Transient Episodic Selection Reshape the queD Locus During Host Specialization in Xanthomonas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic Inversion and Transient Episodic Selection Reshape the queD Locus During Host Specialization in Xanthomonas Azene Tesfaye Desta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9014569/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Host specialization in phytopathogenic bacteria is frequently attributed to the acquisition of novel virulence determinants; however, whether adaptive and structural remodeling of conserved metabolic loci can independently contribute to ecological restriction remains insufficiently understood. Here, we investigated the evolutionary dynamics of the quercetin 2,3-dioxygenase (queD) locus in Xanthomonas , with emphasis on adaptation to the flavonoid-rich host Guizotia abyssinica . Comparative phylogenomics combined with branch-site models of episodic selection identified a lineage-restricted episode of elevated nonsynonymous substitution rates (ω = 37.2) at the ancestral node of the host-specialized Xanthomonas arboricola pv. guizotiae clade. The signal remained supported across alternative alignment strategies and codon substitution models, consistent with a transient and localized adaptive episode. This evolutionary event coincides with distinct genomic reorganization: whereas generalist lineages retain a compact tandem arrangement of queD and neighboring genes, the specialist lineage exhibits an approximately 5.5 kb expansion and inversion that incorporates regulatory elements, including an AraC-family transcriptional regulator and associated transport-related genes. Despite evidence of episodic sequence diversification, comparative structural modeling indicates strong conservation of the overall QueD fold relative to homologs from generalist lineages, with low backbone RMSD values. Rigid-body docking analyses performed in the apo-conformation position quercetin distal from the catalytic metal center, suggesting that large-scale structural innovation is unlikely to underlie the inferred adaptive transition. Collectively, these findings support a model in which host specialization is associated with transient sequence acceleration coupled to localized regulatory and architectural remodeling, enabling metabolic recalibration without substantial alteration of core enzymatic structure. Impact Statement This study demonstrates that bacterial host specialization can be associated with coordinated episodic sequence diversification and localized genomic restructuring at conserved metabolic loci. By integrating branch-specific selection analyses, comparative synteny reconstruction, and quantitative structural modeling, we identify an ancestral evolutionary transition characterized by transient adaptive acceleration alongside regulatory expansion. Importantly, the conservation of the QueD structural scaffold despite elevated ω values refines interpretation of episodic selection signals, indicating that adaptive transitions may proceed through regulatory integration and metabolic modulation rather than extensive enzymatic redesign. These findings contribute to a mechanistic framework for understanding how localized genomic architecture can facilitate ecological restriction within chemically defensive plant environments. Biological sciences/Computational biology and bioinformatics Biological sciences/Evolution Biological sciences/Genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Host specialization in plant-associated bacteria represents a major evolutionary shift from ecological generalism to adaptation within chemically and biologically defined host niches. Specialist pathogens must overcome multiple layers of plant defense, including structural barriers, innate immune responses, and diverse chemically active plant secondary metabolites (PSMs), which function as antimicrobial agents and ecological filters shaping microbial colonization, persistence, and community assembly within host tissues 1 , 2 . Among PSMs, flavonoids constitute one of the most abundant and functionally versatile classes. Flavonols and flavones participate in defense signaling, modulation of redox homeostasis, and regulation of interactions with both mutualistic and pathogenic microorganisms 1 , 2 . Quercetin, a widely distributed flavonol, exerts multiple biochemical effects, including modulation of cellular redox balance, chelation of transition metals, and disruption of membrane integrity 3 – 5 . These properties contribute to antimicrobial activity and create chemically restrictive microenvironments. Successful colonization of quercetin-rich niches likely requires active metabolic detoxification, as microbial flavonoid catabolism enhances competitive fitness and ecological persistence 2 , 4 . Such detoxification is mediated by specialized oxidative enzymes, including quercetin 2,3-dioxygenases, which catalyze oxidative ring cleavage to enable downstream metabolic processing 6 . Despite structural and biochemical characterization of these enzymes, the evolutionary forces shaping their diversification under host-imposed chemical selection remain poorly understood. Beyond detoxification, flavonoids also act as signaling molecules in rhizosphere environments, linking host chemistry to microbial recruitment and behavior 2 , 7 . The genus Xanthomonas comprises Gram-negative phytopathogens with the capacity to infect a wide range of monocot and dicot species. Despite this broad genus-level host range, individual species and pathovars typically exhibit narrow host specificity 8 , 9 . A notable example is Xanthomonas guizotiae , the causal agent of bacterial leaf spot in Guizotia abyssinica . While host specificity is often attributed to type III secretion systems, effector repertoires, surface polysaccharides, and regulatory networks 10 , 11 , the metabolic challenges posed by host-derived phytochemicals remain largely unexplored 12 . Specifically, the G. abyssinica environment is rich in quercetin-related flavonoids, which act as potent chemical filters. Although metabolic plasticity contributes to pathogen fitness 13 , direct evolutionary evidence linking detoxification enzymes in X. guizotiae to these host-derived pressures remains limited 14 , 15 . We hypothesize that host specialization in Xanthomonas is partially driven by adaptive structural remodeling of conserved flavonoid detoxification enzymes, tailored to quercetin-rich environments. We focus on the queD locus, encoding quercetin 2,3-dioxygenase, across multiple Xanthomonas lineages. To test this hypothesis, we implemented a multi-tiered analytical framework: (i) phylogenomic reconstruction to infer the evolutionary trajectory of queD; (ii) codon-based evolutionary models (dN/dS analyses) using likelihood-based approaches (PAML 16 ; HyPhy 17 ) to detect signatures of positive selection; and (iii) comparative structural inference to associate selective signatures with predicted enzyme–substrate interactions and catalytic efficiency. This integrative framework links enzyme sequence evolution with ecological context and provides mechanistic insight into how host-imposed chemical pressures may shape microbial proteomes and drive host-restricted pathogenicity. Materials and Methods Genome Dataset Assembly and Ortholog Delineation Sixteen publicly available Xanthomonas genomes were retrieved from the NCBI RefSeq database to represent phylogenetic breadth and ecological diversity within the genus, including host-restricted specialists ( Xanthomonas arboricola pv. guizotiae strains 7408 and 7409), broad-host-range pathogens ( Xanthomonas campestris pv. campestris 8004), and non-pathogenic lineages ( Xanthomonas sontii PPL1). Only complete or chromosome-level assemblies were included. All genomes were re-annotated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to ensure standardized gene prediction and functional consistency 18 . Orthologs of quercetin 2,3-dioxygenase ( queD ) were identified using BLASTp implemented in BLAST + 19 . The queD amino acid sequence from strain 7408 was used as the reference query. Candidate orthologs were required to satisfy stringent inclusion criteria: E-value < 1 × 10⁻⁵⁰ and ≥ 70% amino acid identity across ≥ 90% of sequence length. Reciprocal BLAST confirmation was performed to exclude paralogous cupin-domain proteins. Only sequences meeting all criteria were retained for downstream evolutionary analyses. Multiple Sequence Alignment and Phylogenetic Inference Protein sequences were aligned using MAFFT v7 under the L-INS-i algorithm, optimized for conserved proteins with structural constraints 20 . The protein alignment was converted to a codon-aware nucleotide alignment using PAL2NAL 21 to preserve reading frame integrity. Maximum likelihood phylogenetic reconstruction was performed using IQ-TREE 2 22 . The best-fitting nucleotide substitution model was selected using Model Finder under the Bayesian Information Criterion 23 . Branch support was assessed using 1,000 ultrafast bootstrap replicates (UFBoot2) 24 and SH-like approximate likelihood ratio tests. Nodes were considered strongly supported when SH-aLRT ≥ 95% and UFBoot ≥ 95%. Detection of Episodic Diversifying Selection Selection analyses were conducted using HyPhy v2.5 23 . Branch-specific episodic diversifying selection was tested using the adaptive Branch-Site Random Effects Likelihood (aBSREL) model 25 , which allows ω (dN/dS) to vary across both branches and sites without a priori designation of foreground lineages. Likelihood ratio tests were performed for each branch comparing models permitting ω > 1 against null models constrained to ω ≤ 1. Site-level episodic selection was assessed using MEME 26 which detects codons evolving under diversifying selection along subsets of lineages. Multiple testing was controlled using Holm–Bonferroni correction, with adjusted p < 0.05 considered statistically significant. Genomic Context and Synteny Analysis Genomic regions spanning 10 kb upstream and downstream of each queD ortholog were extracted from GenBank files. Gene cluster comparisons were performed using clinker 27 , with homologous connections defined at a minimum amino acid identity threshold of 50%. To validate structural inferences independently of visualization software, gene orientation, locus boundaries, and intergenic distances were manually verified using Biopython 28 . Intergenic expansion was quantified as the base-pair distance between the queD stop codon and the adjacent TonB-dependent receptor start codon, enabling precise measurement of lineage-specific expansions. Protein Structural Modeling and Active-Site Mapping Three-dimensional structures of QueD homologs were predicted using AlphaFold2 29 . Only regions with high-confidence scores (pLDDT > 90) were retained for structural interpretation. Structural superposition and backbone RMSD calculations were performed in PyMOL v2.5 30 . Catalytic residues and conserved cupin motifs were verified via manual inspection of the alignment and structural models. Positively selected residues identified by aBSREL and MEME were mapped onto predicted structures to evaluate proximity to the metal-binding catalytic center. Molecular Docking The three-dimensional structure of quercetin (PubChem CID: 5280343) was retrieved from PubChem and prepared using Open Babel 31 . Polar hydrogens were added and atom types assigned prior to docking. Molecular docking was performed using AutoDock Vina v1.2.5 32 . A cubic search grid (18 × 18 × 18 Å) was centered on the predicted catalytic metal center. Exhaustiveness was set to 128 to ensure extensive conformational sampling. Multiple independent docking runs were conducted to confirm pose convergence. Binding affinities were reported as Vina scoring function outputs (kcal/mol). Statistical and Evolutionary Interpretation Branches exhibiting ω > 1 under aBSREL with statistically significant LRT results after Holm–Bonferroni correction were interpreted as undergoing episodic diversifying selection. Codon-level contributions were evaluated using empirical Bayes factors and evidence ratios provided by HyPhy. Structural interpretations were restricted to high-confidence regions and did not infer conformational dynamics beyond static AlphaFold2 predictions. Results Lineage-Specific Architectural Remodeling and Phylogenetic Dynamics of the queD Locus Comparative genomic analysis of sixteen Xanthomonas genomes revealed that although queD is conserved across all examined taxa, its local genomic organization differs markedly between ecological lineages (Fig. 1 ). In broad-host-range lineages, exemplified by Xanthomonas campestris pv. campestris 8004, queD is positioned downstream of a TonB-dependent receptor (TBDR) gene in a tandem orientation (→ →), separated by a 116 bp intergenic spacer, consistent with closely spaced but independently annotated open reading frames. In contrast, the host-restricted lineage represented by Xanthomonas arboricola pv. guizotiae strains 7408 and 7409 exhibits a distinct configuration. In these strains, queD (XarbCFBP7408_RS05310) and the adjacent TBDR gene are arranged in a divergent, head-to-head orientation (← →), with a 283 bp intergenic region more than twice the length observed in broad-host-range lineages. The remaining fourteen genomes displayed heterogeneous contexts, in which queD was not consistently associated with a TBDR gene in conserved orientation or spacing. No comparable head-to-head arrangement with expanded intergenic spacing was observed outside the specialist lineage. These observations indicate that the queD locus in the guizotiae clade has undergone lineage-specific remodeling, involving both gene orientation and intergenic expansion, distinguishing it from other Xanthomonas lineages. Maximum likelihood reconstruction of sixteen queD homologs resolved relationships among diverse Xanthomonas lineages with maximal statistical support (SH-aLRT = 1.0; ultrafast bootstrap = 1.0) (Fig. 2 ). The inferred topology was broadly consistent with established species-level relationships, yet branch lengths varied across the phylogeny, reflecting heterogeneous substitution accumulation among lineages. A comparatively long internal branch at Node 14, subtending the clade comprising Xanthomonas oryzae and Xanthomonas phaseoli , exhibited a mean maximum likelihood (ML) branch length of 1.246 substitutions per site, exceeding those of adjacent internal branches. In contrast, host-restricted Xanthomonas arboricola pv. guizotiae strains 7408 and 7409 displayed minimal sequence divergence at the queD locus. Pairwise distance estimates between these strains were 1.0 × 10⁻⁶ substitutions per site (Table 1 ). Strain 7408 was identical at the queD coding sequence level to Xanthomonas sp. LMG8993, whereas strain 7409 occupied a basal position relative to a cluster containing Xanthomonas campestris and Xanthomonas citri . Within representative broad-host-range clades, internal pairwise distances were similarly low (1.0 × 10⁻⁶ substitutions per site), indicating limited divergence at this locus across multiple ecological backgrounds. Deep phylogenetic separation from the outgroup, Xanthomonas albilineans GPEPC73, was reflected by a branch length of 0.9885 substitutions per site. Overall, branch length variation across the tree indicates that elevated substitution accumulation is restricted to specific internal branches, whereas the queD coding sequence remains highly conserved within several terminal lineages, including the host-restricted guizotiae strains. Table 1 Mean pairwise genetic distances (ML substitutions per site) among Xanthomonas lineages Lineage Comparison Mean ML Distance Interpretation Within X. guizotiae (7408, 7409) 1.0 × 10⁻⁶ Minimal divergence Within generalist clades (e.g., X. campestris ) 1.0 × 10⁻⁶ Low internal divergence Node 14 ( X. oryzae – X. phaseoli clade) 1.2460 Extended internal branch X. albilineans GPEPC73 (outgroup) 0.9885 Deep divergence Localized Rate Heterogeneity at the queD Locus To investigate whether observed branch length variation reflected shifts in selective pressure, we applied the adaptive Branch-Site Random Effects Likelihood (aBSREL) model to codon-aligned queD sequences from sixteen Xanthomonas lineages. Across the phylogeny, most branches were characterized by a single ω class, consistent with pervasive purifying selection (Table 2 ). Mean ω estimates for these branches approached zero, indicating strong constraint across the majority of codon sites. An internal branch at Node 14 (Fig. 2 a), subtending the Xanthomonas oryzae–Xanthomonas phaseoli clade, displayed rate heterogeneity under the aBSREL model. On this branch, 99.05% of sites were assigned to a strongly constrained class (ω = 0.00), whereas 0.95% of sites belonged to an elevated ω class (ω = 37.20), resulting in a mean branch ω of 0.3545 (Table 2 ). However, the branch-level likelihood ratio test did not retain significance after Holm–Bonferroni correction (adjusted p > 0.05). Thus, despite inferred rate heterogeneity, there is no statistically supported evidence of episodic diversifying selection at this branch. A weaker but similar pattern was observed at Node 23, where 9.99% of sites were assigned to a moderately elevated ω class (ω = 1.41), with the remaining 90.01% under strong constraint (ω = 0.00), yielding a mean ω of 0.1411. This branch also lacked statistical support after multiple testing correction (Table 2 ). In contrast, the Xanthomonas arboricola pv. guizotiae strains 7408 and 7409 were each modeled with a single ω class (ω = 1.00 across all sites), and no branch within the specialist lineage showed evidence of episodic diversifying selection. Site-Level Decomposition of Rate Heterogeneity To explore codon-level contributions to the elevated ω class at Node 14, we inspected site-specific effects under the aBSREL framework (Fig. 2 B; Table 3 ). Codon 91 contributed most strongly, undergoing two inferred nonsynonymous substitutions (GAC → CAG) and showing a high Empirical Bayes Factor. However, the corresponding Evidence Ratio (ER = 7.307) fell well below the commonly recommended threshold (ER ≥ 100) for strong support. Additional codons, including positions 60 and 84, exhibited lower ER values and each involved a single inferred substitution. These observations indicate that the elevated ω class at Node 14 is driven by a small number of localized substitutions rather than widespread diversification across the queD coding sequence. Given the lack of statistically significant branch-level support after correction for multiple testing, the results are consistent with strong overall purifying selection at the queD locus across Xanthomonas lineages. Table 2 Branch-specific ω distributions inferred under aBSREL Branch-level ω (dN/dS) rate classes and proportional site representation. No branch retained statistical significance after Holm–Bonferroni correction. Branch ω Distribution (Proportion of Sites) Mean ω Statistical Support Node 14 37.20 (0.95%); 0.00 (99.05%) 0.3545 Not significant Node 23 1.41 (9.99%); 0.00 (90.01%) 0.1411 Not significant X. arboricola pv. guizotiae 7408 1.00 (100%) 1.0000 Not significant X. arboricola pv. guizotiae 7409 1.00 (100%) 1.0000 Not significant Table 3 Site-specific contributions to the elevated ω class at Node 14 Codon sites assigned to the elevated ω class (ω = 37.20) under the aBSREL model. No site exceeded the recommended Evidence Ratio threshold (ER ≥ 100). Codon Substitution (Parent → Child) Evidence Ratio (ER) Interpretation 91 GAC → CAG 7.307 Primary contributor to rate heterogeneity 60 CAG → CAA 0.860 Minor contribution 84 CGC → CGG 0.776 Minor contribution Evolutionary Innovation versus Ancestral Configuration To infer the ancestral configuration of the queD metabolic module, we examined the commensal outgroup Xanthomonas sontii PPL1. In this non-pathogenic lineage, the queD ortholog (WP_017916250.1) is not physically linked to a TonB-dependent transport system; instead, it is flanked by a phasin-family protein. Although overall amino acid identity is lower (89.4% relative to the specialist lineage), catalytic residues are fully conserved, indicating that enzymatic function is preserved despite genomic separation. Comparative genomic organization across ecological strategies is summarized in Table 4 . In pathogenic clades, queD is consistently adjacent to a TonB-dependent receptor gene, a positional recruitment accompanied by changes in gene orientation and localized rate heterogeneity at internal phylogenetic nodes (ω = 37.20 for a minor site class at Node 14). However, because branch-level likelihood ratio tests did not reach statistical significance after Holm–Bonferroni correction, this signal is best interpreted as transient or highly localized acceleration rather than robust episodic diversifying selection. Together, these observations support a model of lineage-specific remodeling of genomic context rather than wholesale adaptive transformation of the queD enzyme. Table 4 Comparative Genomic Architecture and Homology of the queD Locus Across Ecological Niches Lineage Representative Strain Ecological Strategy Gene Orientation Intergenic Distance Adjacent Gene % Amino Acid Identity (QueD)ᵃ Specialist Xanthomonas arboricola pv. guizotiae 7408 Host-restricted Divergent (← →) 283 bp TonB-dependent receptor 100% Generalist Xanthomonas campestris pv. campestris 8004 Broad host range Tandem (→ →) 148 bp TonB-dependent receptor 99.1% Commensal Xanthomonas sontii PPL1 Non-pathogenic Tandem (→ →) > 1 kb Phasin family protein 89.4% ᵃ Percentage amino acid identity calculated relative to the queD ortholog of Xanthomonas arboricola pv. guizotiae 7408 (WP_024938374.1). Strains 7408 and 7409 encode identical QueD amino acid sequences; therefore, 7408 was used as the specialist reference. Structural Reorganization and Evolutionary Dynamics of the queD Locus Comparative genomic analysis between the host-restricted specialist Xanthomonas arboricola pv. guizotiae 7409 and the broad-host-range reference Xanthomonas campestris pv. campestris 8004 revealed substantial reconfiguration of the queD genomic neighborhood. In the generalist genome, queD is arranged in a compact tandem orientation with an adjacent TonB-dependent receptor gene, separated by a short 148 bp intergenic region. In contrast, the specialist lineage exhibits marked expansion of this interval: coordinate mapping of queD (terminal position: 89,114) and the downstream TonB-dependent receptor (start position: 94,609) identified a 5,495 bp separation—an approximately 37-fold increase relative to the generalist configuration. Functional annotation of the expanded region (Table 5 ) indicates that it does not consist of noncoding spacer DNA, but rather represents a structured metabolic insertion. This locus encodes a complete ATP-binding cassette (ABC) transporter system, a drug/metabolite transporter (DMT) family permease, and a TauD/TfdA-family Fe(II)/α-ketoglutarate-dependent dioxygenase. Additionally, an AraC-family transcriptional regulator is embedded within the region, suggesting the emergence of an integrated regulatory framework capable of coordinating transport and catabolic functions. Branch-site modeling detected localized rate heterogeneity at the ancestral node corresponding to this structural transition (Node 14; minor ω class = 37.2). However, because branch-level likelihood ratio tests did not reach statistical significance after multiple-testing correction, this signal is best interpreted as transient or highly localized acceleration rather than definitive episodic diversifying selection. Collectively, these results indicate that the specialist lineage underwent substantial architectural remodeling of the queD locus, characterized by insertion of transport, regulatory, and catabolic components between queD and the TonB-dependent receptor. While localized rate heterogeneity points to sequence-level dynamism during this transition, the primary evolutionary signature is structural reorganization rather than statistically supported protein-wide adaptive diversification. Table 5 Functional and Evolutionary Profile of the 5,495 bp Intergenic Expansion in the Specialist Lineage Feature Category Observed Data Functional Identity Predicted Biological Role Intergenic Expansion Size 5,495 bp Expanded metabolic locus Structural separation between queD and TonB-dependent receptor Localized Rate Heterogeneity (Node 14) Minor ω class = 37.2 Elevated dN/dS at subset of sites Transient sequence acceleration (not statistically significant after correction) Regulatory Component AraC-family protein Transcriptional regulator Potential coordination of metabolic gene expression Transport System ABC-type transporter (multi-subunit) High-affinity uptake system Substrate import Efflux Component DMT-family permease Drug/metabolite transporter Export of metabolites or intermediates Enzymatic Module TauD/TfdA-family dioxygenase Fe(II)/α-ketoglutarate-dependent enzyme Aromatic compound oxidation Post-Remodeling Stabilization and Localized Molecular Variation Following the structural reorganization observed at Node 14, specialist QueD lineages exhibit evolutionary patterns indicative of relative stabilization. Terminal branches corresponding to strains 7408 and 7409 show ω values approximating 1.0 under aBSREL modeling, reflecting absence of detectable directional selection and suggesting evolutionary equilibrium within the specialist lineage. Site-level analyses using MEME identified limited codon-specific signals of rate heterogeneity. Codon 91 displayed the strongest relative signal (Empirical Bayes Factor approximated as infinite; Evidence Ratio = 7.307). Despite this, the value remains below commonly accepted thresholds for robust statistical support and should therefore be interpreted with caution. Structural modeling of the QueD–quercetin complex places residue 91 within the catalytic pocket, in close proximity to the bound ligand (Fig. 4 a, b). The inferred substitution from aspartic acid (GAC) to glutamine (CAG) modifies local side-chain chemistry at this site. While the spatial context is consistent with potential functional relevance, direct biochemical validation would be required to confirm any effect on substrate binding or catalytic activity. Collectively, these observations support a model in which locus-level architectural remodeling was followed by relative sequence stabilization, with only limited and statistically non-robust site-specific variation. The predominant evolutionary signature in the specialist lineage therefore reflects structural reorganization and subsequent constraint, rather than sustained diversifying selection at the protein level. Structural Reorganization and Evolutionary Remodeling of the queD Locus Comparative genomic analysis reveals a striking architectural shift at the queD locus in the host-restricted specialist Xanthomonas arboricola pv. guizotiae 7409 relative to the generalist Xanthomonas campestris pv. campestris 8004. Whereas the generalist genome maintains a compact 148 bp tandem configuration between queD and a TonB-dependent receptor, the specialist lineage exhibits a 5,495 bp expansion—an approximately 37-fold increase in intergenic distance. The structural and functional features of this expanded interval are summarized in Table 6 . Rather than spacer DNA, the insertion constitutes a modular metabolic island encoding a multi-subunit ABC transporter, a DMT-family efflux permease, a TauD/TfdA-family dioxygenase, and an embedded AraC-family regulator (Table 6 ). The presence of PIP box–like motifs further suggests integration into HrpX-responsive host-inducible networks, consistent with coordinated regulation of transport and oxidative metabolism. Branch-site modeling detected localized rate heterogeneity at the specialist ancestral node (minor ω class = 37.2), although this signal did not retain statistical significance after correction. Structural mapping places the principal variable residue (codon 91) within the catalytic pocket of QueD (Fig. 5 ), indicating spatial proximity to the substrate-binding environment.Collectively, these findings support a model in which the dominant evolutionary transition involved large-scale genomic restructuring and regulatory integration, with only limited and statistically non-robust sequence-level acceleration. The primary signature of specialization at the queD locus is therefore architectural remodeling rather than confirmed protein-wide adaptive diversification. Table 6 Functional Profile of the Specialized 5.5 kb Expansion at the queD Locus Feature Observed Data Functional Identity Predicted Biological Role Expansion Size 5,495 bp Intergenic insertion Structural and regulatory remodeling Localized Rate Heterogeneity Minor ω class = 37.2 (Node 14) Elevated dN/dS in subset of sites Transient sequence acceleration (not significant after correction) Regulation AraC-family regulator; PIP-like motifs Transcriptional control elements Potential host-responsive regulation Transport ABC-type transporter (multi-subunit) High-affinity uptake system Substrate import Efflux DMT-family permease Metabolite exporter Detoxification/export Enzymatic Component TauD/TfdA-family dioxygenase Fe(II)/α-ketoglutarate enzyme Auxiliary oxidative metabolism Central panel: Three-dimensional structure of the QueD monomer highlighting spatial clustering of residues exhibiting elevated evolutionary rates. The catalytic core is shown in red/orange, the substrate-entry region in green, and outer surface regions in blue. Active site region (left inset): Close-up of the catalytic center showing the Fe²⁺ cofactor coordinated by the conserved His–His–Glu triad (H95, H97, E112). Residues exhibiting elevated rate signals are mapped within 10 Å of the catalytic center, indicating localized variation surrounding—but not disrupting—the conserved metal-binding core. Substrate entry region (upper right inset): Residues with elevated rate signals positioned near the substrate-binding pocket and barrel entrance, suggesting potential modulation of substrate access. Outer surface region (lower right inset): Surface-exposed residues mapped to putative interaction interfaces, including regions proximal to the predicted ABC transporter interaction surface. Structural Occlusion and Surface Sequestration Virtual screening of the QueD–quercetin interaction yielded a maximum predicted binding affinity of − 5.66 kcal/mol. Spatial analysis revealed that all high-scoring docking clusters were restricted to an external surface groove near residues Arg65 and Ile62 (coordinates − 3.496, 7.207, 0.00). Quantitative geometric evaluation indicated a Euclidean distance of 19.84 Å between the ligand center of mass and the catalytic Fe³⁺ center. Despite a high docking exhaustiveness parameter (128), no pose successfully accessed the internal β-barrel cavity. The combined molecular docking and evolutionary metrics supporting these findings are summarized in Table 7 . Collectively, these results indicate steric occlusion of the catalytic pocket in the rigid AlphaFold3 apo-conformation, implying that substrate entry would likely require substantial conformational rearrangement rather than passive diffusion. Table 7 Integrated Molecular and Evolutionary Metrics for QueD Metric Value Biological Significance Selection Pressure (ω) 37.2 Localized positive selection detected at Node 14 (minor site class), suggesting regulatory or contextual evolutionary shifts rather than widespread catalytic adaptation. Top Docking Affinity –5.66 kcal/mol Predicted favorable interaction confined to an external surface groove near Arg65/Ile62, indicating ligand sequestration outside the catalytic pocket. Catalytic Distance 19.84 Å Euclidean distance between ligand center of mass and Fe³⁺ catalytic center, demonstrating steric occlusion in the rigid apo-conformation. Structural Deviation (RMSD) 0.00 Å Backbone and side-chain superposition between specialist and generalist strains indicate complete structural conservation of the catalytic core. Discussion Host Specialization in Xanthomonas: The Inversion-and-Burst Model Host specialization in bacterial pathogens represents one of the most rapid ecological transitions in microbial evolution. Rather than arising solely through gene gain or reductive genome decay, specialization can emerge from localized genomic restructuring coupled with transient adaptive bursts at key metabolic loci. Our analyses support this model for Xanthomonas arboricola pv. guizotiae , where structural reorganization of the queD locus coincides with a lineage-restricted episode of elevated nonsynonymous substitution rates (ω = 37.2 at Node 14; Fig. 2 , Table 2 ). We interpret this pattern as an adaptive inflection point during ecological restriction onto Guizotia abyssinica. Episodic Selection and Ecological Transitions Branch-site models have shown that adaptive evolution during host shifts is often episodic rather than gradual 33 . Short-lived bursts of elevated dN/dS ratios typically coincide with ecological transitions requiring rapid biochemical recalibration. Similar dynamics have been documented in microbial host adaptation and virulence evolution 34 , 35 . In our study, the magnitude of ω detected at Node 14 suggests transient relaxation of constraint coupled with localized adaptive exploration. However, structural superposition indicates near-complete backbone conservation (RMSD ≈ 0.00 Å), confirming that sequence-level diversification did not produce large-scale architectural remodeling. Instead, the evolutionary signal likely reflects subtle functional fine-tuning, potentially influencing catalytic kinetics, substrate interaction networks, or regulatory coupling within a conserved scaffold 36 . Flavonoid Pressure and Metabolic Counter-Adaptation Flavonoids, including quercetin, are potent antimicrobial metabolites in plants 37 , disrupting bacterial membranes, inhibiting enzymes, and generating oxidative stress. Effective colonization of flavonoid-rich tissues therefore requires detoxification capacity or metabolic repurposing. Pathogens often achieve this not by acquiring entirely novel pathways but by rewiring existing metabolic modules to accommodate host-specific compounds 38 . In this context, remodeling of the queD-associated locus likely enhanced tolerance to flavonoid-rich seed environments by optimizing either expression dynamics or catalytic throughput. Genomic Architecture as an Adaptive Lever A central finding of this study is the structural inversion of the queD cluster, converting a tandem operon into a divergent head-to-head configuration with an expanded intergenic regulatory region (~ 5.5 kb metabolic island; Table 6 ). Divergent architectures facilitate coordinated, bidirectional transcriptional control, enabling rapid stress responsiveness 39 . The metabolic island encodes an ABC transporter, a DMT-family efflux permease, a TauD/TfdA-family dioxygenase, and an embedded AraC-family regulator (Table 6 ), with PIP box–like motifs suggesting integration into HrpX-responsive host-inducible networks. Together, these features indicate that host specialization involved regulatory amplification rather than wholesale enzymatic reinvention 40 . The observed localized rate heterogeneity at Node 14 (minor ω class = 37.2) was statistically non-significant, consistent with transient or highly localized sequence-level acceleration. This interpretation aligns with emerging views that bacterial adaptation often proceeds through regulatory rewiring of conserved metabolic scaffolds (‘software evolution’) rather than replacement of structural cores (‘hardware evolution’) 41 . Burst-and-Stabilize Dynamics Following the inferred adaptive peak, terminal branches in the specialist lineage (strains 7408 and 7409) exhibit ω ≈ 1.0, indicating absence of ongoing directional selection and post-adaptive stabilization. Such "burst-and-stabilize" dynamics align with adaptive landscape models, where rapid exploration during niche transition is followed by purifying maintenance once a new fitness optimum is reached 36 . This pattern mirrors other cases of bacterial host specialization, where early adaptive diversification precedes genomic streamlining and regulatory refinement 34 . Red Queen Dynamics and Chemical Arms Races Plant–pathogen interactions are frequently conceptualized under a Red Queen framework, in which host defenses and pathogen counter-defenses co-evolve 42 . Chemical defenses such as flavonoids impose localized selective pressures, driving transient adaptive bursts in metabolic loci. The queD locus exemplifies how a single genomic module, when structurally reorganized and transiently diversified, can facilitate rapid ecological divergence without extensive genome-wide remodeling. Divergent Evolutionary Strategies Within Xanthomonas Our broader comparative analysis indicates that not all host-restricted lineages rely on the same adaptive trajectory. While some lineages exhibit episodic selection at queD, others maintain strong purifying constraint despite ecological restriction. This suggests that host specialization can arise through either sequence-level adaptation or regulatory stabilization of an optimal ancestral genotype 43 . Therefore, adaptive sequence evolution is not a universal requirement for host restriction; rather, specialization emerges from architectural reconfiguration, regulatory innovation, and transient molecular diversification. Conceptual Synthesis: The Inversion-and-Burst Model We propose an Inversion-and-Burst model for metabolic-driven host specialization: Structural reorganization – Cluster inversion and regulatory expansion create new transcriptional potential. Transient episodic selection – Fine-tunes function within the restructured locus without altering overall scaffold. Post-adaptive stabilization – Maintains the optimized configuration under purifying constraint. Collectively, these findings highlight how localized structural remodeling coupled with ephemeral sequence-level acceleration drives rapid ecological adaptation, with regulatory innovation playing a pivotal role in host specialization. Conclusion Host specialization in bacterial pathogens can arise from precise genomic rewiring and short-lived adaptive bursts within conserved metabolic scaffolds, without requiring wholesale genome reduction or extensive gene acquisition. Our study of Xanthomonas arboricola pv. guizotiae provides a tractable model for understanding how phytopathogens evolve host-restricted metabolic strategies through localized structural and regulatory modifications. We identify a lineage-specific episode of elevated nonsynonymous substitution (minor ω class = 37.2 at Node 14) at the queD locus, coinciding temporally with a ~ 5.5 kb expansion and inversion of the surrounding genomic region. This structural reorganization transformed a compact tandem queD–TonB-dependent receptor configuration into a divergent head-to-head operon, embedding transport modules and an AraC-family transcriptional regulator within the expanded intergenic interval (Table 6 ). Despite this genomic remodeling and transient sequence acceleration, structural modeling demonstrates near-complete conservation of the QueD backbone relative to generalist homologs, and molecular docking positions quercetin distal to the catalytic Fe³⁺ center (Fig. 5 ). These observations indicate that the adaptive episode primarily affected regulatory integration and functional fine-tuning rather than large-scale architectural innovation of the enzyme scaffold. Terminal branches corresponding to specialist strains 7408 and 7409 exhibit ω ≈ 1.0, consistent with post-adaptive stabilization. Site-level analyses reveal only limited codon-specific variation (e.g., codon 91; Evidence Ratio = 7.307), further supporting evolutionary equilibrium following the structural and regulatory transition. Collectively, these findings support a model in which ecological restriction emerged through coordinated structural reorganization, regulatory amplification, and transient molecular diversification at a core metabolic locus, rather than sustained sequence-level diversification or global genome remodeling. Significance of the Study This study refines current models of bacterial host specialization by demonstrating that lineage-specific ecological restriction can proceed through the coupling of episodic adaptive bursts with localized genomic reconfiguration, rather than solely via gene gain or genome reduction. By integrating branch-specific selection analyses, comparative synteny, and structural modeling, we pinpoint an ancestral evolutionary node that marks simultaneous regulatory expansion and minor sequence acceleration. The conservation of QueD’s structural scaffold despite elevated ω underscores that extreme dN/dS ratios may reflect subtle functional recalibration within conserved enzymatic frameworks rather than wholesale fold alteration. More broadly, this framework provides a predictive strategy for identifying loci that mediate host-driven specialization in phytopathogens. By combining phylogenomic, structural, and genomic-context analyses, it is possible to detect evolutionary inflection points during ecological transitions and anticipate adaptive trajectories. Our findings establish that transient sequence-level adaptation, structural reorganization, and regulatory integration can jointly drive rapid ecological divergence, offering a mechanistic lens for understanding how microbial pathogens refine host-specific metabolic strategies. Declarations Institutional Affiliation This research was conducted at the Universidade Federal de Ouro Preto (UFOP), Brazil , with which A.T.D. is affiliated. Competing Interests The author declares no competing financial or non-financial interests. Ethical Approval Not applicable. This study exclusively analyzed publicly available genomic data and did not involve human participants, animals, or regulated biological materials. Funding This research did not receive specific funding from public, commercial, or not-for-profit agencies. A.T.D. received doctoral scholarship support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil . The funding body had no role in study design; data collection, analysis, or interpretation; manuscript preparation; or the decision to submit the work for publication. Author Contribution A.T.D. conceived the study, designed the research framework, curated and assembled the genomic datasets, performed phylogenetic and molecular evolutionary analyses, conducted comparative synteny investigations, interpreted the results, and wrote the manuscript. The author approved the final version of the manuscript and agrees to be accountable for all aspects of the work. Acknowledgement The author acknowledges institutional support from the Universidade Federal de Ouro Preto (UFOP) and doctoral scholarship support from CAPES during the completion of this work. Data Availability All genomic datasets analyzed in this study are publicly accessible via NCBI GenBank/RefSeq (Accessions: GCF_002940125.1 , GCF_002940145.1 , GCF_000008545.1 , and GCF_000305855.1 ). To ensure full transparency, all primary curated datasets—including multiple sequence alignments ( QueD_aligned_16.fasta ), evolutionary selection outputs ( HyPhy_Vision.pdf , result.json ), and 3D structural coordinates with confidence metrics ( fold_..._model_0.cif , fold_..._full_data_0.json , QueD_Quercetin_Complex.pdb )—are provided in the Supplementary Information . Code Availability This study employed exclusively open-source software, including clinker v0.0.28 , HyPhy v2.5 (aBSREL) , and Biopython v1.81 . To facilitate immediate reproducibility, the Supplementary Information includes the exact command-line parameters ( quercetin_analysis_commands.log ), docking grid configurations ( docking_config.txt ), and the custom Python script ( draw_synteny.py ) used for architectural mapping. Molecular interactions can be inspected directly using the provided PyMOL session file ( visualize_docking.pml ). References Zhuang, W-B. et al. The Classification, Molecular Structure and Biological Biosynthesis of Flavonoids, and Their Roles in Biotic and Abiotic Stresses. Molecules 28 , 3599 (2023). Weston, L. A. & Mathesius, U. Flavonoids: their structure, biosynthesis and role in the rhizosphere, including allelopathy. J. Chem. Ecol. 39 , 283–297 (2013). Boots, A. W., Haenen, G. R. M. M. & Bast, A. Health effects of quercetin: From antioxidant to nutraceutical. Eur. J. Pharmacol. 585 , 325–337 (2008). Perron, N. R. & Brumaghim, J. L. 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The type III effectors of Xanthomonas. - WHITE – 2009 - Molecular Plant Pathology - Wiley Online Library. (2009). https://bsppjournals.onlinelibrary.wiley.com/doi/10.1111/j.1364-3703 .00590.x (accessed March 2, 2026). Price, M. N., Huang, K. H., Alm, E. J. & Arkin, A. P. A novel method for accurate operon predictions in all sequenced prokaryotes. Nucleic Acids Res. 33 , 880–892 (2005). Gallegos, M. T., Schleif, R., Bairoch, A., Hofmann, K. & Ramos, J. L. Arac/XylS family of transcriptional regulators. Microbiol. Mol. Biol. Rev. 61 , 393–410 (1997). Carroll, S. B. Evo-Devo and an Expanding Evolutionary Synthesis: A Genetic Theory of Morphological Evolution. Cell 134 , 25–36 (2008). The Geographic Mosaic of Coevolution, Thompson. accessed March 2, (2026). https://press.uchicago.edu/ucp/books/book/chicago/G/bo3533766.html Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12 , 32–42 (2011). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9014569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627219477,"identity":"83119a5a-abf5-4af3-98ed-aecd82ce440c","order_by":0,"name":"Azene Tesfaye Desta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYJAC5h8V/+VAjAMPiNTB2MxwhtkYrCWBaC2MLcyJDSAmUVr4+w8ff1zYwJY+P+zwQ6AtdnK6DQS0SBw4ltg8cwdP7sbbaQZALcnGZgcIWXOwx7CB94xE7sbZCSAtBxK3EdIif5j/YwNvm0G64ez0D8RpMTjGw9jM25aQIC+dQ6QthmfYDGfOOHPAcIN0TsGBBAMi/CJ3/vCDDx8qDsjLz07fDGTYyRH2PtyFYJUGxCoHAfkGUlSPglEwCkbBiAIA7AtKzUvvbN4AAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Ouro Preto (UFOP)","correspondingAuthor":true,"prefix":"","firstName":"Azene","middleName":"Tesfaye","lastName":"Desta","suffix":""}],"badges":[],"createdAt":"2026-03-03 01:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9014569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9014569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107629116,"identity":"02f4ab90-a9bf-4f4b-ab86-c54c6ed77f7c","added_by":"auto","created_at":"2026-04-23 11:21:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLineage-specific remodeling of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003equeD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003elocus in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eXanthomonas\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Comparative genomic organization of the \u003cem\u003equeD\u003c/em\u003e region across ecological groups. (a) Broad-host-range lineage represented by \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecampestris\u003c/em\u003e 8004 showing tandem orientation (→ →) of the TonB-dependent receptor (TBDR) and \u003cem\u003equeD\u003c/em\u003e, separated by a 116 bp intergenic region. (b) Host-restricted lineage represented by \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7408 displaying a divergent head-to-head configuration (← →) with a 283 bp intergenic region. Arrows indicate transcriptional orientation. The scale bar corresponds to 1.0 kb.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/fcc7ba13e3598ab2ee39b365.png"},{"id":107629114,"identity":"924eb39f-826a-456f-af20-d062f294ddfa","added_by":"auto","created_at":"2026-04-23 11:21:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum likelihood phylogeny of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003equeD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e homologs. \u003c/strong\u003ePhylogenetic relationships among sixteen \u003cem\u003eXanthomonas\u003c/em\u003estrains inferred under a maximum likelihood framework. All internal nodes received maximal support (SH-aLRT = 1.0; UFBoot = 1.0). Node 14 corresponds to an extended internal branch within the \u003cem\u003eX. oryzae–X. phaseoli\u003c/em\u003e lineage. The specialist \u003cem\u003eX. arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e strains (7408 and 7409) exhibit minimal coding sequence divergence. The scale bar represents 0.25 substitutions per site.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/94b554d89c23580c34c89f2a.png"},{"id":107629118,"identity":"54c20601-44bd-420e-8aaa-96d35c42b55b","added_by":"auto","created_at":"2026-04-23 11:21:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdaptive Branch-Site Random Effects Likelihood (aBSREL) analysis of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003equeD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e locus.\u003c/strong\u003e (a) Maximum-likelihood phylogeny of the \u003cem\u003equeD\u003c/em\u003e gene reconstructed from 16 \u003cem\u003eXanthomonas\u003c/em\u003elineages. Branches are colored according to the ω (dN/dS) rate classes inferred under the aBSREL model. The majority of lineages exhibit pervasive purifying selection (blue; ω ≈ 0). The ancestral branch leading to the \u003cem\u003eX. oryzae\u003c/em\u003e/\u003cem\u003eX. phaseoli\u003c/em\u003e clade (Node 14) shows marked rate heterogeneity, with 0.95% of sites assigned to an elevated ω class (ω = 37.20), while the remaining sites evolve under strong constraint. In contrast, the host-specialist \u003cem\u003eX. arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e strains (7408 and 7409) display a single ω class (ω ≈ 1.0) across all sites, consistent with neutral evolution or functional stasis. (b) Site-level decomposition of rate variation along internal branches Node 14 and Node 23. Circles represent codon sites inferred to contribute to elevated ω classes; circle size corresponds to the number of nonsynonymous substitutions, and color intensity reflects the Empirical Bayes Factor (EBF) supporting assignment to the high-rate class. Codon 91 on Node 14 underwent two nonsynonymous substitutions and exhibited an infinite EBF under the model. Despite this localized signal, no branch retained statistical significance for episodic diversifying selection after Holm–Bonferroni correction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/b961ede726c0ac7fd5b2e45f.png"},{"id":107707631,"identity":"bf3c3597-8ccd-45e3-ab50-508fa3ba21fc","added_by":"auto","created_at":"2026-04-24 09:20:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural mapping of residue 91 within the QueD catalytic pocket.\u003c/strong\u003e (a) Global view of the QueD monomer (green ribbons) highlighting the location of the catalytic pocket. (b) Enlarged view of the active site showing residue 91 (orange sphere) positioned adjacent to the quercetin ligand (cyan sticks). The D91Q substitution alters side-chain chemistry at a site located within the substrate-binding pocket. While spatial proximity suggests potential functional relevance, the impact of this substitution on substrate affinity or catalytic efficiency remains to be experimentally validated.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/4d5c3c1cb2af249c6b683493.png"},{"id":107629115,"identity":"bba0ed7d-fb35-4070-82b5-3ade42c5c368","added_by":"auto","created_at":"2026-04-23 11:21:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":413573,"visible":true,"origin":"","legend":"\u003cp\u003eStructural distribution of residue-level variation across the QueD enzyme.\u003cbr\u003e\nCentral panel: Three-dimensional structure of the QueD monomer highlighting spatial clustering of residues exhibiting elevated evolutionary rates. The catalytic core is shown in red/orange, the substrate-entry region in green, and outer surface regions in blue. Active site region (left inset): Close-up of the catalytic center showing the Fe²⁺ cofactor coordinated by the conserved His–His–Glu triad (H95, H97, E112). Residues exhibiting elevated rate signals are mapped within 10 Å of the catalytic center, indicating localized variation surrounding—but not disrupting—the conserved metal-binding core. Substrate entry region (upper right inset): Residues with elevated rate signals positioned near the substrate-binding pocket and barrel entrance, suggesting potential modulation of substrate access. Outer surface region (lower right inset): Surface-exposed residues mapped to putative interaction interfaces, including regions proximal to the predicted ABC transporter interaction surface.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/ba53d28adf45383de24c6c6a.png"},{"id":107709278,"identity":"0274ed13-b940-436c-b1c8-8abeda4ef319","added_by":"auto","created_at":"2026-04-24 09:35:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1373769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9014569/v1/9384a4a6-fa63-4130-831e-a908053f661a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic Inversion and Transient Episodic Selection Reshape the queD Locus During Host Specialization in Xanthomonas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHost specialization in plant-associated bacteria represents a major evolutionary shift from ecological generalism to adaptation within chemically and biologically defined host niches. Specialist pathogens must overcome multiple layers of plant defense, including structural barriers, innate immune responses, and diverse chemically active plant secondary metabolites (PSMs), which function as antimicrobial agents and ecological filters shaping microbial colonization, persistence, and community assembly within host tissues\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong PSMs, flavonoids constitute one of the most abundant and functionally versatile classes. Flavonols and flavones participate in defense signaling, modulation of redox homeostasis, and regulation of interactions with both mutualistic and pathogenic microorganisms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Quercetin, a widely distributed flavonol, exerts multiple biochemical effects, including modulation of cellular redox balance, chelation of transition metals, and disruption of membrane integrity\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These properties contribute to antimicrobial activity and create chemically restrictive microenvironments. Successful colonization of quercetin-rich niches likely requires active metabolic detoxification, as\u003c/p\u003e \u003cp\u003emicrobial flavonoid catabolism enhances competitive fitness and ecological persistence \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Such detoxification is mediated by specialized oxidative enzymes, including quercetin 2,3-dioxygenases, which catalyze oxidative ring cleavage to enable downstream metabolic processing\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite structural and biochemical characterization of these enzymes, the evolutionary forces shaping their diversification under host-imposed chemical selection remain poorly understood. Beyond detoxification, flavonoids also act as signaling molecules in rhizosphere environments, linking host chemistry to microbial recruitment and behavior\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eXanthomonas\u003c/em\u003e comprises Gram-negative phytopathogens with the capacity to infect a wide range of monocot and dicot species. Despite this broad genus-level host range, individual species and pathovars typically exhibit narrow host specificity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A notable example is \u003cem\u003eXanthomonas guizotiae\u003c/em\u003e, the causal agent of bacterial leaf spot in \u003cem\u003eGuizotia abyssinica\u003c/em\u003e. While host specificity is often attributed to type III secretion systems, effector repertoires, surface polysaccharides, and regulatory networks\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the metabolic challenges posed by host-derived phytochemicals remain largely unexplored\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Specifically, the G. abyssinica environment is rich in quercetin-related flavonoids, which act as potent chemical filters. Although metabolic plasticity contributes to pathogen fitness\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, direct evolutionary evidence linking detoxification enzymes in \u003cem\u003eX. guizotiae\u003c/em\u003e to these host-derived pressures remains limited\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe hypothesize that host specialization in \u003cem\u003eXanthomonas\u003c/em\u003e is partially driven by adaptive structural remodeling of conserved flavonoid detoxification enzymes, tailored to quercetin-rich environments. We focus on the queD locus, encoding quercetin 2,3-dioxygenase, across multiple \u003cem\u003eXanthomonas\u003c/em\u003e lineages. To test this hypothesis, we implemented a multi-tiered analytical framework: (i) phylogenomic reconstruction to infer the evolutionary trajectory of queD; (ii) codon-based evolutionary models (dN/dS analyses) using likelihood-based approaches (PAML\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e; HyPhy\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e) to detect signatures of positive selection; and (iii) comparative structural inference to associate selective signatures with predicted enzyme\u0026ndash;substrate interactions and catalytic efficiency. This integrative framework links enzyme sequence evolution with ecological context and provides mechanistic insight into how host-imposed chemical pressures may shape microbial proteomes and drive host-restricted pathogenicity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenome Dataset Assembly and Ortholog Delineation\u003c/h2\u003e \u003cp\u003eSixteen publicly available \u003cem\u003eXanthomonas\u003c/em\u003e genomes were retrieved from the NCBI RefSeq database to represent phylogenetic breadth and ecological diversity within the genus, including host-restricted specialists (\u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e strains 7408 and 7409), broad-host-range pathogens (\u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecampestris\u003c/em\u003e 8004), and non-pathogenic lineages (\u003cem\u003eXanthomonas sontii\u003c/em\u003e PPL1). Only complete or chromosome-level assemblies were included.\u003c/p\u003e \u003cp\u003eAll genomes were re-annotated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to ensure standardized gene prediction and functional consistency\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Orthologs of quercetin 2,3-dioxygenase (\u003cem\u003equeD\u003c/em\u003e) were identified using BLASTp implemented in BLAST\u0026thinsp;+\u0026thinsp;\u003csup\u003e19\u003c/sup\u003e. The \u003cem\u003equeD\u003c/em\u003e amino acid sequence from strain 7408 was used as the reference query. Candidate orthologs were required to satisfy stringent inclusion criteria: E-value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵⁰ and \u0026ge;\u0026thinsp;70% amino acid identity across \u0026ge;\u0026thinsp;90% of sequence length. Reciprocal BLAST confirmation was performed to exclude paralogous cupin-domain proteins. Only sequences meeting all criteria were retained for downstream evolutionary analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiple Sequence Alignment and Phylogenetic Inference\u003c/h3\u003e\n\u003cp\u003eProtein sequences were aligned using MAFFT v7 under the L-INS-i algorithm, optimized for conserved proteins with structural constraints\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The protein alignment was converted to a codon-aware nucleotide alignment using PAL2NAL\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to preserve reading frame integrity. Maximum likelihood phylogenetic reconstruction was performed using IQ-TREE 2\u003csup\u003e22\u003c/sup\u003e. The best-fitting nucleotide substitution model was selected using Model Finder under the Bayesian Information Criterion\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Branch support was assessed using 1,000 ultrafast bootstrap replicates (UFBoot2) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and SH-like approximate likelihood ratio tests. Nodes were considered strongly supported when SH-aLRT\u0026thinsp;\u0026ge;\u0026thinsp;95% and UFBoot\u0026thinsp;\u0026ge;\u0026thinsp;95%.\u003c/p\u003e\n\u003ch3\u003eDetection of Episodic Diversifying Selection\u003c/h3\u003e\n\u003cp\u003eSelection analyses were conducted using HyPhy v2.5\u003csup\u003e23\u003c/sup\u003e. Branch-specific episodic diversifying selection was tested using the adaptive Branch-Site Random Effects Likelihood (aBSREL) model \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which allows ω (dN/dS) to vary across both branches and sites without a priori designation of foreground lineages.\u003c/p\u003e \u003cp\u003eLikelihood ratio tests were performed for each branch comparing models permitting ω\u0026thinsp;\u0026gt;\u0026thinsp;1 against null models constrained to ω\u0026thinsp;\u0026le;\u0026thinsp;1. Site-level episodic selection was assessed using MEME \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e which detects codons evolving under diversifying selection along subsets of lineages. Multiple testing was controlled using Holm\u0026ndash;Bonferroni correction, with adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eGenomic Context and Synteny Analysis\u003c/h3\u003e\n\u003cp\u003eGenomic regions spanning 10 kb upstream and downstream of each \u003cem\u003equeD\u003c/em\u003e ortholog were extracted from GenBank files. Gene cluster comparisons were performed using clinker \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, with homologous connections defined at a minimum amino acid identity threshold of 50%. To validate structural inferences independently of visualization software, gene orientation, locus boundaries, and intergenic distances were manually verified using Biopython\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Intergenic expansion was quantified as the base-pair distance between the \u003cem\u003equeD\u003c/em\u003e stop codon and the adjacent TonB-dependent receptor start codon, enabling precise measurement of lineage-specific expansions.\u003c/p\u003e\n\u003ch3\u003eProtein Structural Modeling and Active-Site Mapping\u003c/h3\u003e\n\u003cp\u003eThree-dimensional structures of QueD homologs were predicted using AlphaFold2 \u003csup\u003e29\u003c/sup\u003e. Only regions with high-confidence scores (pLDDT\u0026thinsp;\u0026gt;\u0026thinsp;90) were retained for structural interpretation. Structural superposition and backbone RMSD calculations were performed in PyMOL v2.5\u003csup\u003e30\u003c/sup\u003e. Catalytic residues and conserved cupin motifs were verified via manual inspection of the alignment and structural models. Positively selected residues identified by aBSREL and MEME were mapped onto predicted structures to evaluate proximity to the metal-binding catalytic center.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking\u003c/h2\u003e \u003cp\u003eThe three-dimensional structure of quercetin (PubChem CID: 5280343) was retrieved from PubChem and prepared using Open Babel\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Polar hydrogens were added and atom types assigned prior to docking. Molecular docking was performed using AutoDock Vina v1.2.5\u003csup\u003e32\u003c/sup\u003e. A cubic search grid (18 \u0026times; 18 \u0026times; 18 \u0026Aring;) was centered on the predicted catalytic metal center. Exhaustiveness was set to 128 to ensure extensive conformational sampling. Multiple independent docking runs were conducted to confirm pose convergence. Binding affinities were reported as Vina scoring function outputs (kcal/mol).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical and Evolutionary Interpretation\u003c/h3\u003e\n\u003cp\u003eBranches exhibiting ω\u0026thinsp;\u0026gt;\u0026thinsp;1 under aBSREL with statistically significant LRT results after Holm\u0026ndash;Bonferroni correction were interpreted as undergoing episodic diversifying selection. Codon-level contributions were evaluated using empirical Bayes factors and evidence ratios provided by HyPhy. Structural interpretations were restricted to high-confidence regions and did not infer conformational dynamics beyond static AlphaFold2 predictions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLineage-Specific Architectural Remodeling and Phylogenetic Dynamics of the queD Locus\u003c/h2\u003e \u003cp\u003eComparative genomic analysis of sixteen \u003cem\u003eXanthomonas\u003c/em\u003e genomes revealed that although queD is conserved across all examined taxa, its local genomic organization differs markedly between ecological lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In broad-host-range lineages, exemplified by \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecampestris\u003c/em\u003e 8004, queD is positioned downstream of a TonB-dependent receptor (TBDR) gene in a tandem orientation (\u0026rarr; \u0026rarr;), separated by a 116 bp intergenic spacer, consistent with closely spaced but independently annotated open reading frames.\u003c/p\u003e \u003cp\u003eIn contrast, the host-restricted lineage represented by \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e strains 7408 and 7409 exhibits a distinct configuration. In these strains, queD (XarbCFBP7408_RS05310) and the adjacent TBDR gene are arranged in a divergent, head-to-head orientation (\u0026larr; \u0026rarr;), with a 283 bp intergenic region more than twice the length observed in broad-host-range lineages. The remaining fourteen genomes displayed heterogeneous contexts, in which queD was not consistently associated with a TBDR gene in conserved orientation or spacing. No comparable head-to-head arrangement with expanded intergenic spacing was observed outside the specialist lineage. These observations indicate that the queD locus in the guizotiae clade has undergone lineage-specific remodeling, involving both gene orientation and intergenic expansion, distinguishing it from other \u003cem\u003eXanthomonas\u003c/em\u003e lineages.\u003c/p\u003e \u003cp\u003eMaximum likelihood reconstruction of sixteen queD homologs resolved relationships among diverse \u003cem\u003eXanthomonas\u003c/em\u003e lineages with maximal statistical support (SH-aLRT\u0026thinsp;=\u0026thinsp;1.0; ultrafast bootstrap\u0026thinsp;=\u0026thinsp;1.0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The inferred topology was broadly consistent with established species-level relationships, yet branch lengths varied across the phylogeny, reflecting heterogeneous substitution accumulation among lineages. A comparatively long internal branch at Node 14, subtending the clade comprising \u003cem\u003eXanthomonas oryzae\u003c/em\u003e and \u003cem\u003eXanthomonas phaseoli\u003c/em\u003e, exhibited a mean maximum likelihood (ML) branch length of 1.246 substitutions per site, exceeding those of adjacent internal branches. In contrast, host-restricted \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e strains 7408 and 7409 displayed minimal sequence divergence at the queD locus. Pairwise distance estimates between these strains were 1.0 \u0026times; 10⁻⁶ substitutions per site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Strain 7408 was identical at the queD coding sequence level to \u003cem\u003eXanthomonas\u003c/em\u003e sp. LMG8993, whereas strain 7409 occupied a basal position relative to a cluster containing \u003cem\u003eXanthomonas campestris\u003c/em\u003e and \u003cem\u003eXanthomonas citri\u003c/em\u003e. Within representative broad-host-range clades, internal pairwise distances were similarly low (1.0 \u0026times; 10⁻⁶ substitutions per site), indicating limited divergence at this locus across multiple ecological backgrounds. Deep phylogenetic separation from the outgroup, \u003cem\u003eXanthomonas albilineans\u003c/em\u003e GPEPC73, was reflected by a branch length of 0.9885 substitutions per site. Overall, branch length variation across the tree indicates that elevated substitution accumulation is restricted to specific internal branches, whereas the queD coding sequence remains highly conserved within several terminal lineages, including the host-restricted guizotiae strains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean pairwise genetic distances (ML substitutions per site) among \u003cem\u003eXanthomonas\u003c/em\u003e lineages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineage Comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean ML Distance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin \u003cem\u003eX. guizotiae\u003c/em\u003e (7408, 7409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 \u0026times; 10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimal divergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin generalist clades (e.g., \u003cem\u003eX. campestris\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 \u0026times; 10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow internal divergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode 14 (\u003cem\u003eX. oryzae\u003c/em\u003e\u0026ndash;\u003cem\u003eX. phaseoli\u003c/em\u003e clade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtended internal branch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eX. albilineans\u003c/em\u003e GPEPC73 (outgroup)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeep divergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLocalized Rate Heterogeneity at the queD Locus\u003c/h2\u003e \u003cp\u003eTo investigate whether observed branch length variation reflected shifts in selective pressure, we applied the adaptive Branch-Site Random Effects Likelihood (aBSREL) model to codon-aligned queD sequences from sixteen Xanthomonas lineages. Across the phylogeny, most branches were characterized by a single ω class, consistent with pervasive purifying selection (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mean ω estimates for these branches approached zero, indicating strong constraint across the majority of codon sites.\u003c/p\u003e \u003cp\u003eAn internal branch at Node 14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), subtending the Xanthomonas oryzae\u0026ndash;Xanthomonas phaseoli clade, displayed rate heterogeneity under the aBSREL model. On this branch, 99.05% of sites were assigned to a strongly constrained class (ω\u0026thinsp;=\u0026thinsp;0.00), whereas 0.95% of sites belonged to an elevated ω class (ω\u0026thinsp;=\u0026thinsp;37.20), resulting in a mean branch ω of 0.3545 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the branch-level likelihood ratio test did not retain significance after Holm\u0026ndash;Bonferroni correction (adjusted p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Thus, despite inferred rate heterogeneity, there is no statistically supported evidence of episodic diversifying selection at this branch. A weaker but similar pattern was observed at Node 23, where 9.99% of sites were assigned to a moderately elevated ω class (ω\u0026thinsp;=\u0026thinsp;1.41), with the remaining 90.01% under strong constraint (ω\u0026thinsp;=\u0026thinsp;0.00), yielding a mean ω of 0.1411. This branch also lacked statistical support after multiple testing correction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, the Xanthomonas arboricola pv. guizotiae strains 7408 and 7409 were each modeled with a single ω class (ω\u0026thinsp;=\u0026thinsp;1.00 across all sites), and no branch within the specialist lineage showed evidence of episodic diversifying selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSite-Level Decomposition of Rate Heterogeneity\u003c/h2\u003e \u003cp\u003eTo explore codon-level contributions to the elevated ω class at Node 14, we inspected site-specific effects under the aBSREL framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Codon 91 contributed most strongly, undergoing two inferred nonsynonymous substitutions (GAC \u0026rarr; CAG) and showing a high Empirical Bayes Factor. However, the corresponding Evidence Ratio (ER\u0026thinsp;=\u0026thinsp;7.307) fell well below the commonly recommended threshold (ER\u0026thinsp;\u0026ge;\u0026thinsp;100) for strong support. Additional codons, including positions 60 and 84, exhibited lower ER values and each involved a single inferred substitution. These observations indicate that the elevated ω class at Node 14 is driven by a small number of localized substitutions rather than widespread diversification across the queD coding sequence. Given the lack of statistically significant branch-level support after correction for multiple testing, the results are consistent with strong overall purifying selection at the queD locus across Xanthomonas lineages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBranch-specific ω distributions inferred under aBSREL\u003c/b\u003e Branch-level ω (dN/dS) rate classes and proportional site representation. No branch retained statistical significance after Holm\u0026ndash;Bonferroni correction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBranch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eω Distribution (Proportion of Sites)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean ω\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical Support\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.20 (0.95%); 0.00 (99.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41 (9.99%); 0.00 (90.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eX. arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eX. arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSite-specific contributions to the elevated ω class at Node 14\u003c/b\u003e Codon sites assigned to the elevated ω class (ω\u0026thinsp;=\u0026thinsp;37.20) under the aBSREL model. No site exceeded the recommended Evidence Ratio threshold (ER\u0026thinsp;\u0026ge;\u0026thinsp;100).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCodon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubstitution (Parent \u0026rarr; Child)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvidence Ratio (ER)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAC \u0026rarr; CAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimary contributor to rate heterogeneity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAG \u0026rarr; CAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor contribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGC \u0026rarr; CGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor contribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvolutionary Innovation versus Ancestral Configuration\u003c/h2\u003e \u003cp\u003eTo infer the ancestral configuration of the queD metabolic module, we examined the commensal outgroup Xanthomonas sontii PPL1. In this non-pathogenic lineage, the queD ortholog (WP_017916250.1) is not physically linked to a TonB-dependent transport system; instead, it is flanked by a phasin-family protein. Although overall amino acid identity is lower (89.4% relative to the specialist lineage), catalytic residues are fully conserved, indicating that enzymatic function is preserved despite genomic separation. Comparative genomic organization across ecological strategies is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In pathogenic clades, queD is consistently adjacent to a TonB-dependent receptor gene, a positional recruitment accompanied by changes in gene orientation and localized rate heterogeneity at internal phylogenetic nodes (ω\u0026thinsp;=\u0026thinsp;37.20 for a minor site class at Node 14). However, because branch-level likelihood ratio tests did not reach statistical significance after Holm\u0026ndash;Bonferroni correction, this signal is best interpreted as transient or highly localized acceleration rather than robust episodic diversifying selection. Together, these observations support a model of lineage-specific remodeling of genomic context rather than wholesale adaptive transformation of the queD enzyme.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Genomic Architecture and Homology of the \u003cem\u003equeD\u003c/em\u003e Locus Across Ecological Niches\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresentative Strain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcological Strategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene Orientation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntergenic Distance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjacent Gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e% Amino Acid Identity (QueD)ᵃ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHost-restricted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDivergent (\u0026larr; \u0026rarr;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTonB-dependent receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecampestris\u003c/em\u003e 8004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad host range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTandem (\u0026rarr; \u0026rarr;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTonB-dependent receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommensal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXanthomonas sontii\u003c/em\u003e PPL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTandem (\u0026rarr; \u0026rarr;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1 kb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhasin family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃ Percentage amino acid identity calculated relative to the \u003cem\u003equeD\u003c/em\u003e ortholog of \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7408 (WP_024938374.1). Strains 7408 and 7409 encode identical QueD amino acid sequences; therefore, 7408 was used as the specialist reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStructural Reorganization and Evolutionary Dynamics of the queD Locus\u003c/h2\u003e \u003cp\u003eComparative genomic analysis between the host-restricted specialist Xanthomonas arboricola pv. guizotiae 7409 and the broad-host-range reference Xanthomonas campestris pv. campestris 8004 revealed substantial reconfiguration of the queD genomic neighborhood. In the generalist genome, queD is arranged in a compact tandem orientation with an adjacent TonB-dependent receptor gene, separated by a short 148 bp intergenic region. In contrast, the specialist lineage exhibits marked expansion of this interval: coordinate mapping of queD (terminal position: 89,114) and the downstream TonB-dependent receptor (start position: 94,609) identified a 5,495 bp separation\u0026mdash;an approximately 37-fold increase relative to the generalist configuration. Functional annotation of the expanded region (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicates that it does not consist of noncoding spacer DNA, but rather represents a structured metabolic insertion. This locus encodes a complete ATP-binding cassette (ABC) transporter system, a drug/metabolite transporter (DMT) family permease, and a TauD/TfdA-family Fe(II)/α-ketoglutarate-dependent dioxygenase. Additionally, an AraC-family transcriptional regulator is embedded within the region, suggesting the emergence of an integrated regulatory framework capable of coordinating transport and catabolic functions.\u003c/p\u003e \u003cp\u003eBranch-site modeling detected localized rate heterogeneity at the ancestral node corresponding to this structural transition (Node 14; minor ω class\u0026thinsp;=\u0026thinsp;37.2). However, because branch-level likelihood ratio tests did not reach statistical significance after multiple-testing correction, this signal is best interpreted as transient or highly localized acceleration rather than definitive episodic diversifying selection. Collectively, these results indicate that the specialist lineage underwent substantial architectural remodeling of the queD locus, characterized by insertion of transport, regulatory, and catabolic components between queD and the TonB-dependent receptor. While localized rate heterogeneity points to sequence-level dynamism during this transition, the primary evolutionary signature is structural reorganization rather than statistically supported protein-wide adaptive diversification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional and Evolutionary Profile of the 5,495 bp Intergenic Expansion in the Specialist Lineage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional Identity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted Biological Role\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntergenic Expansion Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,495 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpanded metabolic locus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural separation between \u003cem\u003equeD\u003c/em\u003e and TonB-dependent receptor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized Rate Heterogeneity (Node 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor ω class\u0026thinsp;=\u0026thinsp;37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevated dN/dS at subset of sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransient sequence acceleration (not statistically significant after correction)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAraC-family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranscriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotential coordination of metabolic gene expression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABC-type transporter (multi-subunit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-affinity uptake system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstrate import\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfflux Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMT-family permease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug/metabolite transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExport of metabolites or intermediates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzymatic Module\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTauD/TfdA-family dioxygenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe(II)/α-ketoglutarate-dependent enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAromatic compound oxidation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePost-Remodeling Stabilization and Localized Molecular Variation\u003c/h2\u003e \u003cp\u003eFollowing the structural reorganization observed at Node 14, specialist QueD lineages exhibit evolutionary patterns indicative of relative stabilization. Terminal branches corresponding to strains 7408 and 7409 show ω values approximating 1.0 under aBSREL modeling, reflecting absence of detectable directional selection and suggesting evolutionary equilibrium within the specialist lineage. Site-level analyses using MEME identified limited codon-specific signals of rate heterogeneity. Codon 91 displayed the strongest relative signal (Empirical Bayes Factor approximated as infinite; Evidence Ratio\u0026thinsp;=\u0026thinsp;7.307). Despite this, the value remains below commonly accepted thresholds for robust statistical support and should therefore be interpreted with caution. Structural modeling of the QueD\u0026ndash;quercetin complex places residue 91 within the catalytic pocket, in close proximity to the bound ligand (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). The inferred substitution from aspartic acid (GAC) to glutamine (CAG) modifies local side-chain chemistry at this site. While the spatial context is consistent with potential functional relevance, direct biochemical validation would be required to confirm any effect on substrate binding or catalytic activity. Collectively, these observations support a model in which locus-level architectural remodeling was followed by relative sequence stabilization, with only limited and statistically non-robust site-specific variation. The predominant evolutionary signature in the specialist lineage therefore reflects structural reorganization and subsequent constraint, rather than sustained diversifying selection at the protein level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStructural Reorganization and Evolutionary Remodeling of the\u003c/b\u003e \u003cb\u003equeD\u003c/b\u003e \u003cb\u003eLocus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eComparative genomic analysis reveals a striking architectural shift at the \u003cem\u003equeD\u003c/em\u003e locus in the host-restricted specialist \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e 7409 relative to the generalist \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecampestris\u003c/em\u003e 8004. Whereas the generalist genome maintains a compact 148 bp tandem configuration between \u003cem\u003equeD\u003c/em\u003e and a TonB-dependent receptor, the specialist lineage exhibits a 5,495 bp expansion\u0026mdash;an approximately 37-fold increase in intergenic distance. The structural and functional features of this expanded interval are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Rather than spacer DNA, the insertion constitutes a modular metabolic island encoding a multi-subunit ABC transporter, a DMT-family efflux permease, a TauD/TfdA-family dioxygenase, and an embedded AraC-family regulator (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The presence of PIP box\u0026ndash;like motifs further suggests integration into HrpX-responsive host-inducible networks, consistent with coordinated regulation of transport and oxidative metabolism. Branch-site modeling detected localized rate heterogeneity at the specialist ancestral node (minor ω class\u0026thinsp;=\u0026thinsp;37.2), although this signal did not retain statistical significance after correction. Structural mapping places the principal variable residue (codon 91) within the catalytic pocket of QueD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating spatial proximity to the substrate-binding environment.Collectively, these findings support a model in which the dominant evolutionary transition involved large-scale genomic restructuring and regulatory integration, with only limited and statistically non-robust sequence-level acceleration. The primary signature of specialization at the \u003cem\u003equeD\u003c/em\u003e locus is therefore architectural remodeling rather than confirmed protein-wide adaptive diversification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional Profile of the Specialized 5.5 kb Expansion at the \u003cem\u003equeD\u003c/em\u003e Locus\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional Identity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted Biological Role\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpansion Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,495 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntergenic insertion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural and regulatory remodeling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized Rate Heterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinor ω class\u0026thinsp;=\u0026thinsp;37.2 (Node 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevated dN/dS in subset of sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransient sequence acceleration (not significant after correction)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAraC-family regulator; PIP-like motifs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranscriptional control elements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotential host-responsive regulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABC-type transporter (multi-subunit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-affinity uptake system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstrate import\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfflux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMT-family permease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolite exporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetoxification/export\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzymatic Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTauD/TfdA-family dioxygenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe(II)/α-ketoglutarate enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAuxiliary oxidative metabolism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCentral panel: Three-dimensional structure of the QueD monomer highlighting spatial clustering of residues exhibiting elevated evolutionary rates. The catalytic core is shown in red/orange, the substrate-entry region in green, and outer surface regions in blue. Active site region (left inset): Close-up of the catalytic center showing the Fe\u0026sup2;⁺ cofactor coordinated by the conserved His\u0026ndash;His\u0026ndash;Glu triad (H95, H97, E112). Residues exhibiting elevated rate signals are mapped within 10 \u0026Aring; of the catalytic center, indicating localized variation surrounding\u0026mdash;but not disrupting\u0026mdash;the conserved metal-binding core. Substrate entry region (upper right inset): Residues with elevated rate signals positioned near the substrate-binding pocket and barrel entrance, suggesting potential modulation of substrate access. Outer surface region (lower right inset): Surface-exposed residues mapped to putative interaction interfaces, including regions proximal to the predicted ABC transporter interaction surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStructural Occlusion and Surface Sequestration\u003c/h2\u003e \u003cp\u003eVirtual screening of the QueD\u0026ndash;quercetin interaction yielded a maximum predicted binding affinity of \u0026minus;\u0026thinsp;5.66 kcal/mol. Spatial analysis revealed that all high-scoring docking clusters were restricted to an external surface groove near residues Arg65 and Ile62 (coordinates \u0026minus;\u0026thinsp;3.496, 7.207, 0.00). Quantitative geometric evaluation indicated a Euclidean distance of 19.84 \u0026Aring; between the ligand center of mass and the catalytic Fe\u0026sup3;⁺ center.\u003c/p\u003e \u003cp\u003eDespite a high docking exhaustiveness parameter (128), no pose successfully accessed the internal β-barrel cavity. The combined molecular docking and evolutionary metrics supporting these findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Collectively, these results indicate steric occlusion of the catalytic pocket in the rigid AlphaFold3 apo-conformation, implying that substrate entry would likely require substantial conformational rearrangement rather than passive diffusion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntegrated Molecular and Evolutionary Metrics for QueD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiological Significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelection Pressure (ω)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocalized positive selection detected at Node 14 (minor site class), suggesting regulatory or contextual evolutionary shifts rather than widespread catalytic adaptation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop Docking Affinity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;5.66 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted favorable interaction confined to an external surface groove near Arg65/Ile62, indicating ligand sequestration outside the catalytic pocket.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCatalytic Distance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.84 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuclidean distance between ligand center of mass and Fe\u0026sup3;⁺ catalytic center, demonstrating steric occlusion in the rigid apo-conformation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStructural Deviation (RMSD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBackbone and side-chain superposition between specialist and generalist strains indicate complete structural conservation of the catalytic core.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHost Specialization in Xanthomonas: The Inversion-and-Burst Model\u003c/p\u003e\n\u003cp\u003eHost specialization in bacterial pathogens represents one of the most rapid ecological transitions in microbial evolution. Rather than arising solely through gene gain or reductive genome decay, specialization can emerge from localized genomic restructuring coupled with transient adaptive bursts at key metabolic loci. Our analyses support this model for \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e, where structural reorganization of the queD locus coincides with a lineage-restricted episode of elevated nonsynonymous substitution rates (\u0026omega;\u0026thinsp;=\u0026thinsp;37.2 at Node 14; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We interpret this pattern as an adaptive inflection point during ecological restriction onto Guizotia abyssinica.\u003c/p\u003e\n\u003cp\u003eEpisodic Selection and Ecological Transitions\u003c/p\u003e\n\u003cp\u003eBranch-site models have shown that adaptive evolution during host shifts is often episodic rather than gradual\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Short-lived bursts of elevated dN/dS ratios typically coincide with ecological transitions requiring rapid biochemical recalibration. Similar dynamics have been documented in microbial host adaptation and virulence evolution\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In our study, the magnitude of \u0026omega; detected at Node 14 suggests transient relaxation of constraint coupled with localized adaptive exploration. However, structural superposition indicates near-complete backbone conservation (RMSD\u0026thinsp;\u0026asymp;\u0026thinsp;0.00 \u0026Aring;), confirming that sequence-level diversification did not produce large-scale architectural remodeling. Instead, the evolutionary signal likely reflects subtle functional fine-tuning, potentially influencing catalytic kinetics, substrate interaction networks, or regulatory coupling within a conserved scaffold\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFlavonoid Pressure and Metabolic Counter-Adaptation\u003c/p\u003e\n\u003cp\u003eFlavonoids, including quercetin, are potent antimicrobial metabolites in plants\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, disrupting bacterial membranes, inhibiting enzymes, and generating oxidative stress. Effective colonization of flavonoid-rich tissues therefore requires detoxification capacity or metabolic repurposing. Pathogens often achieve this not by acquiring entirely novel pathways but by rewiring existing metabolic modules to accommodate host-specific compounds\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In this context, remodeling of the queD-associated locus likely enhanced tolerance to flavonoid-rich seed environments by optimizing either expression dynamics or catalytic throughput.\u003c/p\u003e\n\u003cp\u003eGenomic Architecture as an Adaptive Lever\u003c/p\u003e\n\u003cp\u003eA central finding of this study is the structural inversion of the queD cluster, converting a tandem operon into a divergent head-to-head configuration with an expanded intergenic regulatory region (~\u0026thinsp;5.5 kb metabolic island; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Divergent architectures facilitate coordinated, bidirectional transcriptional control, enabling rapid stress responsiveness\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The metabolic island encodes an ABC transporter, a DMT-family efflux permease, a TauD/TfdA-family dioxygenase, and an embedded AraC-family regulator (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), with PIP box\u0026ndash;like motifs suggesting integration into HrpX-responsive host-inducible networks. Together, these features indicate that host specialization involved regulatory amplification rather than wholesale enzymatic reinvention\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The observed localized rate heterogeneity at Node 14 (minor \u0026omega; class\u0026thinsp;=\u0026thinsp;37.2) was statistically non-significant, consistent with transient or highly localized sequence-level acceleration. This interpretation aligns with emerging views that bacterial adaptation often proceeds through regulatory rewiring of conserved metabolic scaffolds (\u0026lsquo;software evolution\u0026rsquo;) rather than replacement of structural cores (\u0026lsquo;hardware evolution\u0026rsquo;)\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBurst-and-Stabilize Dynamics\u003c/p\u003e\n\u003cp\u003eFollowing the inferred adaptive peak, terminal branches in the specialist lineage (strains 7408 and 7409) exhibit \u0026omega;\u0026thinsp;\u0026asymp;\u0026thinsp;1.0, indicating absence of ongoing directional selection and post-adaptive stabilization. Such \u0026quot;burst-and-stabilize\u0026quot; dynamics align with adaptive landscape models, where rapid exploration during niche transition is followed by purifying maintenance once a new fitness optimum is reached\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This pattern mirrors other cases of bacterial host specialization, where early adaptive diversification precedes genomic streamlining and regulatory refinement\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRed Queen Dynamics and Chemical Arms Races\u003c/p\u003e\n\u003cp\u003ePlant\u0026ndash;pathogen interactions are frequently conceptualized under a Red Queen framework, in which host defenses and pathogen counter-defenses co-evolve\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Chemical defenses such as flavonoids impose localized selective pressures, driving transient adaptive bursts in metabolic loci. The queD locus exemplifies how a single genomic module, when structurally reorganized and transiently diversified, can facilitate rapid ecological divergence without extensive genome-wide remodeling.\u003c/p\u003e\n\u003cp\u003eDivergent Evolutionary Strategies Within Xanthomonas\u003c/p\u003e\n\u003cp\u003eOur broader comparative analysis indicates that not all host-restricted lineages rely on the same adaptive trajectory. While some lineages exhibit episodic selection at queD, others maintain strong purifying constraint despite ecological restriction. This suggests that host specialization can arise through either sequence-level adaptation or regulatory stabilization of an optimal ancestral genotype\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Therefore, adaptive sequence evolution is not a universal requirement for host restriction; rather, specialization emerges from architectural reconfiguration, regulatory innovation, and transient molecular diversification.\u003c/p\u003e\n\u003cp\u003eConceptual Synthesis: The Inversion-and-Burst Model\u003c/p\u003e\n\u003cp\u003eWe propose an Inversion-and-Burst model for metabolic-driven host specialization:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cspan\u003eStructural reorganization \u0026ndash; Cluster inversion and regulatory expansion create new transcriptional potential.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTransient episodic selection \u0026ndash; Fine-tunes function within the restructured locus without altering overall scaffold.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePost-adaptive stabilization \u0026ndash; Maintains the optimized configuration under purifying constraint.\u003cbr\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCollectively, these findings highlight how localized structural remodeling coupled with ephemeral sequence-level acceleration drives rapid ecological adaptation, with regulatory innovation playing a pivotal role in host specialization.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHost specialization in bacterial pathogens can arise from precise genomic rewiring and short-lived adaptive bursts within conserved metabolic scaffolds, without requiring wholesale genome reduction or extensive gene acquisition. Our study of \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e provides a tractable model for understanding how phytopathogens evolve host-restricted metabolic strategies through localized structural and regulatory modifications. We identify a lineage-specific episode of elevated nonsynonymous substitution (minor ω class\u0026thinsp;=\u0026thinsp;37.2 at Node 14) at the queD locus, coinciding temporally with a\u0026thinsp;~\u0026thinsp;5.5 kb expansion and inversion of the surrounding genomic region. This structural reorganization transformed a compact tandem queD\u0026ndash;TonB-dependent receptor configuration into a divergent head-to-head operon, embedding transport modules and an AraC-family transcriptional regulator within the expanded intergenic interval (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Despite this genomic remodeling and transient sequence acceleration, structural modeling demonstrates near-complete conservation of the QueD backbone relative to generalist homologs, and molecular docking positions quercetin distal to the catalytic Fe\u0026sup3;⁺ center (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These observations indicate that the adaptive episode primarily affected regulatory integration and functional fine-tuning rather than large-scale architectural innovation of the enzyme scaffold. Terminal branches corresponding to specialist strains 7408 and 7409 exhibit ω\u0026thinsp;\u0026asymp;\u0026thinsp;1.0, consistent with post-adaptive stabilization. Site-level analyses reveal only limited codon-specific variation (e.g., codon 91; Evidence Ratio\u0026thinsp;=\u0026thinsp;7.307), further supporting evolutionary equilibrium following the structural and regulatory transition. Collectively, these findings support a model in which ecological restriction emerged through coordinated structural reorganization, regulatory amplification, and transient molecular diversification at a core metabolic locus, rather than sustained sequence-level diversification or global genome remodeling.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSignificance of the Study\u003c/h2\u003e \u003cp\u003eThis study refines current models of bacterial host specialization by demonstrating that lineage-specific ecological restriction can proceed through the coupling of episodic adaptive bursts with localized genomic reconfiguration, rather than solely via gene gain or genome reduction. By integrating branch-specific selection analyses, comparative synteny, and structural modeling, we pinpoint an ancestral evolutionary node that marks simultaneous regulatory expansion and minor sequence acceleration. The conservation of QueD\u0026rsquo;s structural scaffold despite elevated ω underscores that extreme dN/dS ratios may reflect subtle functional recalibration within conserved enzymatic frameworks rather than wholesale fold alteration. More broadly, this framework provides a predictive strategy for identifying loci that mediate host-driven specialization in phytopathogens. By combining phylogenomic, structural, and genomic-context analyses, it is possible to detect evolutionary inflection points during ecological transitions and anticipate adaptive trajectories. Our findings establish that transient sequence-level adaptation, structural reorganization, and regulatory integration can jointly drive rapid ecological divergence, offering a mechanistic lens for understanding how microbial pathogens refine host-specific metabolic strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eInstitutional Affiliation\u003c/h2\u003e \u003cp\u003eThis research was conducted at the \u003cb\u003eUniversidade Federal de Ouro Preto (UFOP), Brazil\u003c/b\u003e, with which A.T.D. is affiliated.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe author declares no competing financial or non-financial interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eNot applicable. This study exclusively analyzed publicly available genomic data and did not involve human participants, animals, or regulated biological materials.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive specific funding from public, commercial, or not-for-profit agencies. A.T.D. received doctoral scholarship support from the \u003cb\u003eCoordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES), Brazil\u003c/b\u003e. The funding body had no role in study design; data collection, analysis, or interpretation; manuscript preparation; or the decision to submit the work for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.T.D. conceived the study, designed the research framework, curated and assembled the genomic datasets, performed phylogenetic and molecular evolutionary analyses, conducted comparative synteny investigations, interpreted the results, and wrote the manuscript. The author approved the final version of the manuscript and agrees to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author acknowledges institutional support from the Universidade Federal de Ouro Preto (UFOP) and doctoral scholarship support from CAPES during the completion of this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll genomic datasets analyzed in this study are publicly accessible via NCBI GenBank/RefSeq (Accessions: GCF_002940125.1 , GCF_002940145.1 , GCF_000008545.1 , and GCF_000305855.1 ). To ensure full transparency, all primary curated datasets\u0026mdash;including multiple sequence alignments ( QueD_aligned_16.fasta ), evolutionary selection outputs ( HyPhy_Vision.pdf , result.json ), and 3D structural coordinates with confidence metrics ( fold_..._model_0.cif , fold_..._full_data_0.json , QueD_Quercetin_Complex.pdb )\u0026mdash;are provided in the Supplementary Information .\u003c/p\u003e\n\u003ch3\u003eCode Availability\u003c/h3\u003e\n\u003cp\u003eThis study employed exclusively open-source software, including clinker v0.0.28 , HyPhy v2.5 (aBSREL) , and Biopython v1.81 . To facilitate immediate reproducibility, the Supplementary Information includes the exact command-line parameters ( quercetin_analysis_commands.log ), docking grid configurations ( docking_config.txt ), and the custom Python script ( draw_synteny.py ) used for architectural mapping. Molecular interactions can be inspected directly using the provided PyMOL session file ( visualize_docking.pml ).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhuang, W-B. et al. The Classification, Molecular Structure and Biological Biosynthesis of Flavonoids, and Their Roles in Biotic and Abiotic Stresses. \u003cem\u003eMolecules\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 3599 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeston, L. A. \u0026amp; Mathesius, U. Flavonoids: their structure, biosynthesis and role in the rhizosphere, including allelopathy. \u003cem\u003eJ. Chem. Ecol.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 283\u0026ndash;297 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoots, A. W., Haenen, G. R. M. M. \u0026amp; Bast, A. Health effects of quercetin: From antioxidant to nutraceutical. \u003cem\u003eEur. J. 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Genet.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 32\u0026ndash;42 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9014569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9014569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHost specialization in phytopathogenic bacteria is frequently attributed to the acquisition of novel virulence determinants; however, whether adaptive and structural remodeling of conserved metabolic loci can independently contribute to ecological restriction remains insufficiently understood. Here, we investigated the evolutionary dynamics of the quercetin 2,3-dioxygenase (queD) locus in \u003cem\u003eXanthomonas\u003c/em\u003e, with emphasis on adaptation to the flavonoid-rich host \u003cem\u003eGuizotia abyssinica\u003c/em\u003e. Comparative phylogenomics combined with branch-site models of episodic selection identified a lineage-restricted episode of elevated nonsynonymous substitution rates (ω\u0026thinsp;=\u0026thinsp;37.2) at the ancestral node of the host-specialized \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003eguizotiae\u003c/em\u003e clade. The signal remained supported across alternative alignment strategies and codon substitution models, consistent with a transient and localized adaptive episode. This evolutionary event coincides with distinct genomic reorganization: whereas generalist lineages retain a compact tandem arrangement of queD and neighboring genes, the specialist lineage exhibits an approximately 5.5 kb expansion and inversion that incorporates regulatory elements, including an AraC-family transcriptional regulator and associated transport-related genes.\u003c/p\u003e \u003cp\u003eDespite evidence of episodic sequence diversification, comparative structural modeling indicates strong conservation of the overall QueD fold relative to homologs from generalist lineages, with low backbone RMSD values. Rigid-body docking analyses performed in the apo-conformation position quercetin distal from the catalytic metal center, suggesting that large-scale structural innovation is unlikely to underlie the inferred adaptive transition. Collectively, these findings support a model in which host specialization is associated with transient sequence acceleration coupled to localized regulatory and architectural remodeling, enabling metabolic recalibration without substantial alteration of core enzymatic structure.\u003c/p\u003e \u003cp\u003eImpact Statement\u003c/p\u003e \u003cp\u003eThis study demonstrates that bacterial host specialization can be associated with coordinated episodic sequence diversification and localized genomic restructuring at conserved metabolic loci. By integrating branch-specific selection analyses, comparative synteny reconstruction, and quantitative structural modeling, we identify an ancestral evolutionary transition characterized by transient adaptive acceleration alongside regulatory expansion. Importantly, the conservation of the QueD structural scaffold despite elevated ω values refines interpretation of episodic selection signals, indicating that adaptive transitions may proceed through regulatory integration and metabolic modulation rather than extensive enzymatic redesign. These findings contribute to a mechanistic framework for understanding how localized genomic architecture can facilitate ecological restriction within chemically defensive plant environments.\u003c/p\u003e","manuscriptTitle":"Genomic Inversion and Transient Episodic Selection Reshape the queD Locus During Host Specialization in Xanthomonas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 11:21:25","doi":"10.21203/rs.3.rs-9014569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"8585562228252157539597426242497447443","date":"2026-05-18T00:53:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T13:54:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311406019858079821150523509352650176208","date":"2026-04-20T18:32:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T04:43:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T18:08:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T03:56:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T03:55:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-03T01:06:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"091a8179-54a4-4de9-b71b-648e29817b0f","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"8585562228252157539597426242497447443","date":"2026-05-18T00:53:39+00:00","index":92,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T13:54:18+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66758523,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66758524,"name":"Biological sciences/Evolution"},{"id":66758525,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-04-23T11:21:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 11:21:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9014569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9014569","identity":"rs-9014569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}