Comparative phylogenetic analysis of bacterial acds genes reveals a combination of evolutionary patterns and ecological adaptation

Introduction

The primary challenge to the sustainability of global agricultural output is the abrupt and unpredictable shift in climate, in addition to population growth (Oyebamiji et al., 2024) [37] . Since they are sessile, plants must adjust to the variety of environmental pressures they face. There are two types of environmental stresses: biotic and abiotic. Drought, flooding, salinity, heat, cold, injury, and exposure to xenobiotics, heavy metals, and ultraviolet radiation are examples of abiotic stressors. In addition, biotic stressors that affect plants include microbial infections including fungi, bacteria, and nematodes. These stressors have the potential to negatively impact plant development and productivity, either separately or in combination. In response to biotic and abiotic stressors, the plant releases reactive oxygen species, activates a variety of ion channels, and accumulates hormones including salicylic acid, ethylene, jasmonic acid, and abscisic acid (Sachdev et al., 2021) [41] . All plants generate ethylene, an important hormone that modulates many kinds of responses and developmental processes. Since the introduction of many stress signals, including mechanical wounding, salt, drought, water logging, severe temperatures, pathogenic infection, and pollutants, speeds up the synthesis of this hormone, it is also known as a stress hormone (Morgan and Drew, 1997) [33] . On the other hand, excessive ethylene generated during seed germination or by various environmental stressors may retard plant growth, particularly in the case of root growth retardation (Li, J. et al., 2015) [27] . There are two steps in the entire ethylene manufacturing process: -Sadenosylmethionine (S-AdoMet) is converted by ACC synthase (ACS, S-adenosyl-L-methionine methylthioadenosine-lyase) into 1-aminocyclopropane-1-carboxylic acid (ACC) and 5-Methylthioadenosine (MTA). During high rates of ethylene synthesis, the 5-Methylthioadenosine (MTA) is then recycled back to methionine to restore the cell's concentration of methionine. The second step involves oxidizing 1-Aminocyclopropane-1-Carboxylic Acid (ACC) to ethylene and cyanoformate ion [NCCO2] by ACC oxidase in the presence of oxygen (Yang and Hoffman, 1984). A schematic representation of ethylene biosynthesis is shown in Figure 1. One strategy to mitigate the harmful accumulation of stress-induced ethylene involves exploiting plant-microbe interactions, which play a central role in sustainable agriculture. This interaction can be symbiotic (bacteria living inside root nodules), endophytic (bacteria inside plant tissues), rhizospheric (bacteria binding to root surface), or phyllospheric (bacteria binding on leaf or stem) (Compant et al., 2010) [10] . Compared to fertilizers, pesticides, and insecticides based on inorganic chemicals, such microbe-based approaches are theoretically and economically practical (Daniel et al., 2022) [12] . Plants produce ethylene in direct proportion to the amount of 1-aminocyclopropane-1-carboxylic acid (ACC), which is the precursor of ethylene. By cleaving ACC to aketobutyrate and ammonium ion, ACC deaminase, which is encode by the acdS gene in various bacteria that promote plant growth, can lower the amount of ethylene produced by stressed plants. The mechanism of enzymatic deamination of ACC by ACC deaminase is schematically illustrated in Fig. 2. By reducing the amount of stress-induced ethylene in the plants, the plant growth-promoting bacteria (PGPR) with 1-Aminocyclopropane-1-Carboxylate (ACC) deaminase potential provide resistance to a variety of abiotic stress conditions, including high temperature, flood, drought, salinity, and acidity (Singh et al., 2015) [44] . Despite this well-established functional significance, the evolutionary history of the acdS gene across bacterial lineages remains incompletely understood. Even though ACC deaminase's physiological role is well-known, the context presented by its evolutionary distribution is more complex.

Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Actinobacteria, and Firmicutes are among the taxonomically diverse bacterial groups that have been identified with the acdS gene (Hontzeas et al., 2005, Blaha et al., 2006) [4,21] . The consistent clustering of acdS sequences with recognised taxonomic relationships in certain lineages suggests vertical inheritance. Others have questioned gene mobility and evolutionary reshaping due to incongruence between 16S rRNA trees and acdS-based phylogenies (Hontzeas et al., 2005) [21] . In contrast to housekeeping genes like 16S rRNA, functional genes frequently evolve under different selective pressures. Therefore, it is possible to assess whether the evolutionary trajectory of acdS reflects lineage history, ecological adaptation, or genetic exchange mechanisms by comparing its phylogeny with established taxonomic frameworks (Hontzeas et al., 2005, Blaha et al., 2006) [4,21] . Extensive in silico comparative analyses are now possible due to the growing availability of publicly accessible sequence databases. These methods make it possible to quantitatively evaluate genetic distance relationships across a wide range of taxa and to reconstruct gene-based phylogenies in a systematic manner. Therefore, patterns of conservation, divergence, and possible incongruence with respect to conventional phylogenetic markers can be clarified by integrating acdS sequence data from various bacterial representatives. In this context, the present study is based on an in silico comparative analysis of publicly available acdS gene sequences. This review is aimed at analyzing the phylogenetic relationships among diverse representatives of bacterial taxa with the acdS gene as a molecular marker. The resulting acdS-based phylogenetic trees and their respective distance matrices are analyzed, and a comparison with established taxonomic (16S rRNAbased) phylogenies is made in an effort to establish the degree of concordance between them. Any incongruences that may be observed will be discussed in detail, focusing on the possible role of evolutionary and genetic factors such as horizontal gene transfer, gene duplication, and adaptive divergence. Hence, the present study aims to give a better understanding of how the acdS gene has changed over bacterial lineages and how its distribution reflects the relationship between function, taxonomy, and evolutionary dynamics by combining phylogenetic, genetic, and comparative insights.

Materials and methods

Retrieval of acds gene sequences

An extensive set of acdS gene sequences were retrieved from the NCBI database as of December 2025. A wide range of bacterial taxa representing different genera and families were considered for analysis. From each species, a single representative strain was selected to avoid redundancy. Selection was based on the criterion that the selected sequence is found to be a representative of all the sequences of different strains of same species after aligning using the MUSCLE algorithm in MEGA software version 12 and care was taken to ensure all chosen acdS sequences were of comparable length to ensure reliable alignment and downstream phylogenetic analysis.

Taxonomic data collection

Taxonomic details of the selected bacterial species were obtained from the List of Prokaryotic names with Standing in Nomenclature (LPSN) database (https://lpsn.dsmz.de). These taxonomic assignments were used as the reference framework for comparison with the acdS gene-based phylogenetic tree.

Multiple sequence alignment

All analyses were performed using MEGA version 12. All the acdS gene sequences were aligned using the MUSCLE algorithm in MEGA software. The results were then examined and trimmed to remove any poorly aligned regions found at the ends of the sequences. These aligned and trimmed sequences were used for further phylogenetic and distance matrix analyses.

Genetic distance analysis

Pairwise genetic distances among the aligned acdS sequences were calculated using the Kimura 2-parameter (K2P) model. The generated distance matrix was used to assess genetic divergence of acdS gene among various taxa and to support phylogenetic interpretations.

Phylogenetic tree construction

Phylogenetic analysis was carried out using the Maximum Likelihood method based on the Kimura 2-parameter model in MEGA software. The statistical robustness of the inferred phylogenetic relationships was evaluated using bootstrap analysis with 1000 replications. The resulting acdS gene phylogeny was subsequently compared with the established taxonomic framework to evaluate congruence and incongruence patterns. The Kimura 2-parameter model was selected as it accounts for differential rates of transition and transversion substitutions, while the Maximum Likelihood method was used for its robustness in phylogenetic inference. Similarity between the acdS gene phylogeny and the taxonomic framework was assessed by comparing clustering patterns of acdS phylogeny at the genus and family levels.

Results

Overview of retrieved acds gene sequences

A total of 177 acdS gene sequences were retrieved from NCBI database as of December 2025. The details of these sequences are provided in Supplementary Table S1. For further phylogenetic analysis, a total of 107 sequences were selected so that there were maximum of one sequence as representative for that species. Organism names are presented in abbreviated form for clarity in the phylogenetic tree. The list of selected sequences for further analysis along with their corresponding species names and accession numbers is provided in Table 1.

Taxonomic profile of selected bacterial species

The taxonomic classification of the selected bacterial species was compiled based on data obtained from the LPSN database. These classifications were used as a reference for comparison with the acdS gene-based phylogenetic tree. The detailed taxonomic information of all selected species is provided in Table 2.

Phylogenetic clustering and diversity of acds sequences

A circular maximum likelihood phylogenetic tree based on acdS gene sequences is presented in Figure 3, showing the evolutionary relationships among bacterial taxa. Phylogenetic analysis based on acdS gene sequences revealed various clustering patterns that were mostly similar with established bacterial taxonomy. Seven major clades were identified. The phylogenetic tree with highlighted clades is presented in Supplementary Figure 1, and the clade assignment of each organism is provided in Table 1 ), and Bacilli (Priestia aryabhattai) were found in another mixed cluster (Cluster E), emphasising the deviation between acdS phylogeny and taxonomic classification. A complex evolutionary trajectory of the gene could be explained by the observed inconsistency between acdSbased phylogeny and taxonomic classification. The acdS gene's complex evolutionary history among bacterial taxa have been emphasised by the acdS-based phylogenetic tree, which overall showed both conserved lineage-specific clustering and extensive taxonomic mixing.

Bootstrap support across major acds clusters

Bootstrap analysis (1000 replicates) was used to assess the robustness of the acdS-based phylogenetic groupings, and support values differed among the main clusters (A-G). Cluster A, a taxonomically mixed cluster that includes Bacilli, Actinomycetes, Betaproteobacteria, and Gammaproteobacteria showed moderate to high bootstrap support and internal node values typically ranged from 59% to 99%, indicating a stable but evolutionarily diverse grouping. Cluster B (the Burkholderiaceae-dominated cluster) showed moderate bootstrap support with values generally ranging from 60% to 84%, indicating the family's general congruence with some internal divergence. With bootstrap values ranging from 73% to 100%, Cluster C (Achromobacter cluster) showed strong support, indicating that Achromobacter species are grouped together in the same clade based on the acdS gene. This suggests that the gene is likely inherited through vertical evolution and is an inherent part of their genome, rather than being acquired through horizontal transfer. Herbaspirillum sp. Os23 is part of Cluster D, which is dominated by actinomycetes. Despite taxonomic deviation, Cluster D showed moderate to high bootstrap support, with values ranging from 64% to 100%. The acdS sequences in the mixed cluster E (Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Bacilli) showed evolutionary connection across diverse taxa, with moderate bootstrap support values primarily between 43% and 99%. With the majority of nodes consistenly supported by high bootstrap values between 75% and 100%, Cluster F (Alphaproteobacteria-dominated cluster) confirms strong acdS gene conservation within this group. With high bootstrap support ranging from 72% to 100%, Cluster G (Bacilli cluster, which includes Bacillus, Priestia, and Lysinibacillus) established a well-resolved grouping that was different from other Bacilli in the tree. Overall, clusters dominated by taxonomically related groups exhibited higher bootstrap support, whereas mixed clusters showed moderate support, reflecting functional conservation of the acdS gene across phylogenetically diverse bacteria.

Evolutionary distance analysis

In addition to showing numerous instances of taxonomic similarity and mismatch, the distance matrix mostly confirmed the phylogenetic clustering identified in the Maximum Likelihood tree. Overall, taxa belonging to the same major clusters showed low evolutionary distances, while clusters representing different bacterial classes confirmed high genetic divergence. Within their respective clusters, the alphaproteobacteria (Cluster F) and Burkholderiaceae (Cluster B) showed comparatively short pairwise distances, suggesting conserved acdS sequences. Comparisons of Actinomycetes, Bacilli, and Alphaproteobacteria, on the other hand, revealed significantly greater distances, which is consistent with deep evolutionary divergence. In several taxonomic groups, there was evident genetic congruence. Alphaproteobacteria showed concordance between taxonomy and acdS-based relationships by clustering together with low intra-group distances. Additionally, Achromobacter species (Cluster C) revealed strong internal coherence and low evolutionary distances. Similar to this, the majority of Burkholderiaceae members showed relatively low divergence within Cluster B, indicating concordance at the genus and family levels. On the other hand, various taxa revealed notable intrageneric diversity. Significant divergence of acdS within the genus has been confirmed by the broad evolutionary distances and multi-cluster distribution of Pseudomonas species. Similar patterns were seen for Enterobacter and Bacillus, where species from the same genus segregated into distinct clusters and exhibited significant genetic distances. Bacillus species' division into two separate clusters emphasises intra-genus diversity even more. Within mixed clusters, especially Cluster A, taxonomically unrelated bacteria were found to have unexpectedly low evolutionary distances.

Members of Bacilli, Actinomycetes, Betaproteobacteria, and Gammaproteobacteria were found in this cluster, and their genetic similarity was closer than what taxonomy would have predicted. Likewise, Herbaspirillum sp. Os23 (Betaproteobacteria) showed lower evolutionary distance to actinomycetes than to other Betaproteobacteria.

Correlation between clades and environmental stress, ecological origin, and geographic distribution

The metadata of the sequences, including the stress conditions under which the bacteria were studied, their ecological sources, and geographical details, are presented in Table 1. Partial similarity as well as significant differences across clusters were found when acdS-based clades were compared with environmental stress, sample source, and country of isolation. Several clades (notably A, B, and C) included strains associated with abiotic stress mitigation, such as drought, salinity, heavy metal stress, and ethylene-mediated stress, indicating functional similarity across genetically related acdS sequences. Similarity between gene-based clustering and stress-related function can be observed by the genetically similar acdS sequences across various genera and classes within these clades. However, identical stress types were distributed across multiple clades, and individual clades frequently contained sequences linked to diverse or multiple stresses, demonstrating stress-related inconsistency and functional diversification within clusters. Ecological origin analysis revealed moderate concordance, with a number of clades enriched in plant-associated niches, especially isolates derived from the rhizosphere, roots, nodules, and endosphere. These isolates also showed relatively low intra-clade divergence, indicating that acdS is conserved in plant-interactive environments. However, some clades presented ecological incongruence as isolates from different ecological sources, such as rhizosphere soil, root nodules, phyllosphere, river estuaries, fungal associations, and non-plant environments, were often grouped together within those same clades. Genetic diversity within the same genus was evident from the frequent and significant divergence observed among members isolated from different ecological niches. While strains from the same country were distributed across multiple clades, indicating strong geographic divergence, isolates from different countries and continents clustered together with low genetic divergence, indicating the weakest correlation, according to geographic analysis. No clade was confined to a particular area. Overall, the findings show that acdS sequences from bacteria that are geographically and taxonomically different from one another are similar, but they are also diverse within the same genera or classes. This suggests that functional conservation and diversification, rather than strict alignment with taxonomy, ecology, or geographic origin, have shaped the evolution of acdS. [39] 13.

Halomonas sp. strain XB-RS-PIA-11

2009) [36] 25 2014) [7] 58 [15] 72. F MG495925.1

Methylobacterium tardum strain RB677

1 Mb_tardum -Russia -(Ekimova et al., 2018) [15] 73. F MF124309.1

Methylobacterium goesingense strain iEII3

1 Mb_goesin -Russia -(Ekimova et al., 2018) [15] 74. F MF124312.1

Methylobacterium marchantiae strain JT1

1 Mb_marchan -Russia -(Ekimova et al., 2018) [15] 75. F KY289001. [22] 103 G MK652706 Bacillus subtilis strain ACCD-SF48 1 B_subtil rhizosphere soil India drought (Gowtham et al., 2020) [19] 104 G PV166437. [2,3,7,18,24,20,31,38,42] .

Our phylogenetic analysis showed that several major acdS clades show close correspondence with accepted bacterial taxonomy. Specifically, members of the Burkholderiaceae and subsets of Alphaproteobacteria formed well-supported clusters, indicating that vertical inheritance played a dominant role in acdS evolution within these lineages.

Comparative sequencing studies show that the acdS gene can be stably inherited when it becomes part of the main genome. This is supported by the fact that different strains from the same genus often have very similar acdS sequences, even when they come from different environments (Blaha et al., 2006) [4] . On the other hand, significant phylogenetic deviation throughout the tree at times contradicted with this vertical inheritance. Despite being clearly separated in 16S rRNA phylogenies, taxonomically distant genera clustered together based on acdS sequence similarity. Rhizobium sp. TAL1145 Zhihengliuella somnathii JG05 Herbaspirillum sp. Os23, Burkholderia sp. BL25, Gammaproteobacteria, Enterobacter, Pseudomonas and Bacillus are the major instances which were not placed in the phylogenetic tree similar to their taxonomy. This pattern is extensively inferred as strong confirmation for Horizontal Gene Transfer (HGT). Multiple independent studies have experimentally and phylogenetically reported this incongruence, demonstrating that the acdS phylogeny is inconsistent with evolutionary relationships, suggesting frequent sharing of genes across distant groups (Hontzeas et al., 2005; Blaha et al., 2006) [4,21] . This pattern appears frequently across several clades in our analysis, proving that acdS mobility is an aspect of its evolutionary history rather than an uncommon or isolated occurrence. Another possible explanation for the clustering of acdS sequences disagreement from the taxonomic classification maybe gene loss followed by re-acquisition. Gene loss can occur due to reduced selective pressure or genome streamlining, leading to the absence of functional genes in certain lineages (Mira et al., 2001) [32] . These genes may later be re-acquired through horizontal gene transfer, restoring functional capabilities under changing environmental conditions (Soucy et al., 2015) [46] . Such gene gain-loss dynamics contribute to genome plasticity and evolutionary adaptation in bacteria (Koonin et al., 2001) [23] . Consequently, these processes can result in phylogenetic inconsistencies, where some organisms cluster with different genera rather than their expected taxonomic groups in the acdS phylogenetic tree (Boto, 2009) [5] . This intra-generic diversity correlates with results from culture-based surveys that showed significant differences in gene sequences and acdS activity levels between strains of the same species or genus. Blaha et al. (2006) [4] reported significant sequence heterogeneity and functional variation among acdS-positive bacteria isolated from similar environments, suggesting that microevolutionary processes like recombination, differential regulation, and controlled selection pressures play a role in post-acquisition diversification. The persistence of functional acdS appears to be influenced by the environment despite its mobility. In the data we've collected, closely related acdS sequences were found in bacteria isolated from bulk soils, endophytic tissues, rhizosphere soils, and habitats under environmental stress. Essentially, these niches link with conditions in which ethylene-mediated stress responses play a significant role, either directly in plants or indirectly through microbial adaptation to stress. Functional ACC deaminase-producing bacteria have been recovered from rhizospheres, internal plant tissues, contaminated soils, saline soils, and metal-polluted sites in isolation-based studies, which provide evidence to this wider ecological distribution (Glick, 2014) [16] . All these results suggest that ecological conditions influence acdS genes in a way that allows horizontally transferred genes to remain functional rather than undergoing neutral degeneration. (Hontzeas et al., 2005) [21] . Strong research evidence for the adaptive significance of the acdS gene in plant-bacterial interactions is supported by gene mutation studies. Chemically induced ACC deaminase-deficient mutants in Pseudomonas putida GR12-2 were unable to use ACC and, contrary to the wild type, were unable to induce canola root elongation under gnotobiotic conditions (Glick et al., 1994) [17] . This made it significantly evident that the promotion of plant growth was directly inhibited by the loss of ACC deaminase activity. Similar to this, Enterobacter cloacae UW4's targeted acdS gene replacement produced a mutant strain with significantly reduced ACC deaminase activity and being unable to stimulate root elongation. More recently, precise genome editing in Bradyrhizobium verified that while overexpression increased enzyme activity, deletion of acdS significantly decreased ACC degradation capacity (Wang et al., 2025) [49] . Furthermore, in our analysis, acdS clustering was not significantly impacted by geographic origin. Low biogeographic structuring was suggested by the frequent grouping of isolates from different continents with high bootstrap support. This observation corresponds to worldwide sampling studies that indicates ecological function has a stronger influence on the distribution of acdS than geographic isolation. Blaha et al. (2006) [4] suggested that acdS-positive bacteria from geographically distant regions frequently shared closely related gene sequences, confirming that dispersal and horizontal transfer can overcome geographic barriers when selective conditions are favourable. In addition, methodological caution is crucial when comprehending acdS diversity. Li, Z. et al. (2015) [28] explicitly confirmed that overestimation of gene distribution and incorrect identification of ACC deaminase-producing bacteria were resulting from non-specific amplification of acdS homologs. Their study showed that key active-site residues such as Glu295 and Leu322 distinguish true ACC deaminase from closely related homologs, including Dcysteine desulfhydrase. Similarly, established experimentally that ACC deaminase can be modified into Dcysteine desulfhydrase and vice versa by making minor amino acid substitutions. Together, these results demonstrate that phylogenetic placement alone is not enough for accurate identification; in order to correct any misannotation, residue-level analysis and biochemical validation of enzyme activity are supplemental prerequisites.

Conclusion

This review is an attempt to comprehend how a single functional gene can reflect both ancestry and adaptation. With strong bootstrap support, our phylogenetic reconstruction identified seven major clades (A-G), displaying patterns of significant incongruence in some groups and evident lineage conservation in others. Sequences from different environmental stress often clustered together, while closely related taxa occasionally split into separate clades. These explain the vertical inheritance and horizontal gene transfer. Overall, the acdS gene shows up as a dynamic evolutionary marker influenced by both taxonomy and ecological pressure, rather than just as a trait linked to stress.

Supplementary Table S1

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53. EU886317.1 Burkholderia kururiensis strain M130 55 DQ125252.1 Burkholderia phenazinium strain LMG2247 56 EU886303.1 Burkholderia phenoliruptrix strain LMG 22037 57 EU886304.1 Burkholderia silvatlantica strain PPCR-2 58 KR154507 Burkholderia sp. BL25 I3R1 59 HQ728595.1 Burkholderia sp. CZ152S 60 KU517660.1 Burkholderia sp. D1SS235 61 EU886319.1 Burkholderia stabilis strain LMG 14294 62 EU886307.1 Burkholderia terricola strain LMG 20594 63 DQ125254.1 Burkholderia tropica strain BM273 64 EU886308.1 Burkholderia unamae strain CAC-98 65 EU886309.1 Burkholderia vietnamiensis strain TVV75 66 EU886312.1 Burkholderia xenovorans strain CAC-124 67 JX233794.1 Cupriavidus sp. SaCR1 68 KY305933 Cupriavidus sp. strain XDPOP-RS-TSA-2 69 KR080559.1 Delftia sp. 17(2015) 70 AY604544 Enterobacter aerogenes strain Cal3 71 AF047840 Enterobacter cloacae CAL2 72 KM501058.2 Enterobacter cloacae strain SBP8 73 AF047710 Enterobacter cloacae UW4 74 KY352309 Enterobacter hormaechei strain DR16 75 MH588718.1 Enterobacter sp. strain 7K 76 MH492008.1 Enterobacter sp. strain KA36 77 KY305930 Halomonas sp. strain XB-RS-PIA-11 78 HQ728588.1 Herbaspirillum sp. Os23 79 FJ357241.1 Klebsiella oxytoca strain Rs-5 80 JN625725.1 Klebsiella pneumoniae AcdSPB2 81 JN625725.1 Klebsiella pneumoniae AcdSPB2 82 PV166433.1 Lysinibacillus fusiformis strain UA14-1 83 KP116956 Marmoricola sp. LR61 84 JQ013380.1 Mesorhizobium albiziae 85 JQ013381.1 Mesorhizobium chacoense 86 JQ013382.1 Mesorhizobium ciceri 87 EF495226.2 Mesorhizobium ciceri 88 JQ013383.1 Mesorhizobium loti 89 KC143169.1 Mesorhizobium loti strain Le2 90 JQ013384.1 Mesorhizobium mediterraneum 91 JQ013385.1 Mesorhizobium plurifarium 92 KP117016.1 Mesorhizobium septentrionale strain CCBAU 83284 93 JQ013399.1 Mesorhizobium sp. 101-Evora 94 JQ013398.1 Mesorhizobium sp. 6b-Beja 95 KU745729.1 Mesorhizobium sp. ACMP18 96 KU745724.1 Mesorhizobium sp. AG1 97 JQ013390.1 Mesorhizobium sp. BR-8-Braganca 98 KR154567 Mesorhizobium sp. MM5343 99 KR154562 Mesorhizobium sp. MM5357 100 KR154565 Mesorhizobium sp. MM5462 101 KR154563 Mesorhizobium sp. MM5675 102 KR154566 Mesorhizobium sp. OD119 103 JQ013387.1 Mesorhizobium tarimense 104 JQ013389.1 Mesorhizobium thiogangeticum 105 JQ013388.1 Mesorhizobium tianshanense 106 EU430449.1 Methylobacterium fujisawaense strain KACC 10744 107 MF124309.1 Methylobacterium goesingense strain iEII3 108 MF124310.1 Methylobacterium gregans strain 002-074 109 MF124311.1 Methylobacterium longum strain 440 110 MF124312.1 Methylobacterium marchantiae strain JT1 111 MF124313.1 Methylobacterium mesophilicum strain A47 112 EU430439.1 Methylobacterium oryzae strain CBMB110 113 EU430438.1 Methylobacterium phyllosphaerae strain CBMB27 114 FJ624149 Methylobacterium radiotolerans strain CoLR1 115 MG495925.1 Methylobacterium tardum strain RB677 116 OR958551.1 Methylorubrum sp. strain C44s 117 KP116955.1 Microbacterium sp. GLR25 118 KP116952.1 Mycobacterium sp. GDR13
54. 119 KX622567.1 Paraburkholderia tropica strain S39-2 120 EF452620.1 Phyllobacterium brassicacearum 121 MH939181.1 Priestia aryabhattai strain EWR29 122 PV166436.1 Priestia megaterium strain UA18-1 123 AY604528 Pseudomonas brassicacearum strain Am3 124 KC430107.1 Pseudomonas brassicacearum strain KY5404 125 MF040748 Pseudomonas fluorescens strain DPB15 126 KY451713 Pseudomonas fluorescens strain DR11 127 KY352308 Pseudomonas fluorescens strain DR7 128 FJ465155 Pseudomonas fluorescens strain FY32 129 MW328565.1 Pseudomonas fluorescens strain GL-RE-29 130 AY604542 Pseudomonas marginalis 131 KY451712 Pseudomonas migulae strain DR35 132 PP208627.1 Pseudomonas mucidolens 133 JF741959.1 Pseudomonas oryzihabitans strain LP11 134 MF040747 Pseudomonas palleroniana strain DPB13 135 MF040749 Pseudomonas palleroniana strain DPB16 136 >KF612321.1 Pseudomonas psychrotolerans strain TPs-04 137 AY604533 Pseudomonas putida strain Bm3 138 MN065177.1 Pseudomonas putida strain PSE-3 139 AY823987 Pseudomonas putida strain UW4 140 MT779781.1 Pseudomonas rhodesiae strain GRC093 141 M73488 Pseudomonas sp. 142 KR080557.1 Pseudomonas sp. 12(2015) 143 OR227281.1 Pseudomonas sp. strain BApK9 144 MN232215.1 Pseudomonas sp. strain Q1 145 KY305919 Pseudomonas sp. strain SYC-RS-TSA-3 146 LT795007.1 Pseudomonas sp. VKh13 147 AY604545 Pseudomonas syringae strain GR12-2 148 KU517662.1 Ralstonia sp. S1SM82 149 EF525234 Rhizobium gallicum strain PB2 150 AF421376 Rhizobium leguminosarum 151 JN110340.1 Rhizobium leguminosarum bv. phaseoli strain KA-PH7-10 152 JN110344.1 Rhizobium leguminosarum bv. trifolii strain KA-F-12 153 JF937709.1 Rhizobium leguminosarum bv. viciae strain KA-CP11 154 AY604535 Rhizobium leguminosarum strain 99A1 155 JN110342.1 Rhizobium leguminosarum strain KA-P-28 156 EF525255 Rhizobium leguminosarum strain PB165 157 KU248370.1 Rhizobium sp. OA132 158 KU517661.1 Rhizobium sp. S1SS148