{"paper_id":"0444f533-db0f-4efe-9cd7-d276adece8b2","body_text":"Pan-genome analysis of Morganella Morganii reveals niche-specific selection of functional traits: Friend or Foe? | 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 Research Article Pan-genome analysis of Morganella Morganii reveals niche-specific selection of functional traits: Friend or Foe? Rajesh Pal, Bhagyashri J. Poddar, Prabhakar Pandit, Hemant Purohit, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6872201/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Archives of Microbiology → Version 1 posted 9 You are reading this latest preprint version Abstract Morganella morganii is a bacterium with open pangenomes, where genes move intra- and interspecies via horizontal gene transfer. Through pangenome analysis, the study maps three agriculture isolates; M. morganii with strong PGP activity, along with 78 publicly available genomes from clinical, food, wastewater, and animal sources. The analysis showed 23,829 gene clusters with only 8.52% core genes and discriminating distribution of 78.34% cloud genes across different niches. KEGG analysis showed 33, 12, and 38 genes related to nutrient solubilization in M. morganii isolates HM01, HM02, and HM04, respectively. Chemotaxis genes, crucial for stress response, were most abundant in HM04 (30), followed by HM01 (17) and HM02 (11). Additionally, numerous biosynthetic gene clusters encoding antibacterial and antifungal metabolites were identified. Clinical and wastewater isolates harboured a higher number of mobile genetic element (MGE) linked antimicrobial resistance (AMR) genes that confer resistance to 15 antibiotic classes. These AMR genes were predominantly plasmid-borne and found to transfer in M. morganii from clinical pathogens such as E. coli and A. baumannii . This study indicates that habitat pressure creates the scenario for selection of functional traits which enables the ecosystem specific survival of M. morganii . Together, the present investigation provides important insight into the genomic diversity and remarkable PGP potential of M. morganii strains for sustainable agriculture. The pangenome analysis proposes that detailed investigation is needed to confirm their efficacy as PGP bacterium and to distinguish them from pathogenic strains. Morganella pangenome antimicrobial resistance mobile genetic elements plant growth-promoting genes hypothetical proteins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction High genomic flexibility of the microorganisms makes them capable of inhabiting diverse ecological niches, such as soil, wastewater, and clinical communities. The adaptation of microorganisms is primarily determined by the gene acquisition and gene loss events that may be responsible for niche specialization, enabling bacteria to acquire traits requisite to thrive in specific ecosystems. Amidst versatile organisms, the genus Morganella , historically referred to as an opportunistic human pathogen, has been recently isolated from non-clinical habitats, prompting a re-examination of its ecological plasticity and niche-specific strategies. Regardless of its clinical relevance, recent studies demonstrate that Morganella may harbour plant growth-promoting (PGP) characteristics that make them to serve as a beneficial microorganism in environmental habitats (Chandarana and Amaresan 2024 ). In addition, several studies have reported its ability to immobilize the heavy metals from metal contaminated soil, suggesting their potential application for bioremediation purposes (Naqqash et al. 2022 , 2024a , 2024b ). Interestingly, certain species of Morganella are found to synthesize bioactive secondary metabolites as well as antimicrobial compounds that support its application in horticultural and medicinal fields (Kadhim et al. 2018 ). However, in clinical settings, the genus exhibited pathogenic characteristics, especially in immunocompromised hosts. Besides, the multidrug resistance strains of Morganella have been reported to cause nosocomial surgical wound infections, sepsis, plague, blood-stream infections, central nervous system infection, brucellosis, urinary tract infections, pneumonia, chorioamnionitis and systemic infections (Lebeaux et al. 2020 ). The dual nature of Morganella , as both a PGP bacterium and as an opportunistic pathogen, need further research to get deeper insight into its adaptation processes in different ecological niches and its genetic content that differentiate its environmental life cycle from clinical one. Horizontal gene transfer (HGT) events carried out by mobile genetic elements (MGEs) such as insertion elements, transposons, integrons, and plasmids, is responsible for microbial adaptation and their diversification at strain level. These events allow microorganisms to acquire niche-specific adaptive traits to perform essential functions for survival in complex habitats, such as resistance to heavy metals and antibiotic resistance genes (AMR), enhanced metabolic functionality, and host interactions. For instance, the broad dissemination of well-known pathogenic islands in Klebsiella pneumonia e is responsible for the emergence of its multidrug-resistant (MDRKP) and carbapenem-resistant hypervirulent (CR-hvKP) strain, aggravating the global concern in the treatment and prevention of pathogenic strains (Gao et al. 2025 ). On the other hand, the distribution of Tn MERI1 -like transposons encoding mer operon among diverse bacterial species, including Bacilli, emphasizes the niche-specific role of MGEs in mercury detoxification pathways (Matsui and Endo 2018 ). The common association of several MGEs on bacterial genomes was found to be responsible for intricate evolutionary interactions that drive adaptability as well as ecological success (Weisberg and Chang 2023 ). Previous studies reported the multiple HGT events in M. morganii , which have facilitated the acquisition of antimicrobial resistance, specifically in the clinical environment. A new transposon like Tn7376, enabled by IS26-mediated recombination was discovered in M. morganii that confers resistance to multidrug resistance genes (Luo et al. 2022 ). Genes such as blaKPC-2 and blaNDM-1 that provide resistance from carbapenem antibiotics are found to be transferred in M. morganii through IncL/M plasmids and IS26-mediated transposon activity (Yao et al. 2025 ). Moreover, inter-plasmid transposition of Tn4401a was found to facilitate the horizontal transfer of blaKPC-2 from Klebsiella pneumoniae to M. morganii through ColRNAI plasmids, enhancing resistance spread (Sugita et al. 2022 ). On the other side, genomic analysis of M. morganii found islands that encode for metal transporters, toxin proteins, stress proteins, and lipopolysaccharide virulence genes and chemotaxis (kdsA, cheY), biosynthetic gene clusters, and defence systems such as CRISPR-Cas (Sium et al. 2025). Although several studies have been carried out on clinical isolates of M. morganii , their genomic functions in environmental and plant habitat have not been fully investigated. The identification of genomic traits involved in PGP activity may give valuable information regarding their ecological role in agricultural fields outside of clinical settings. Further, the distribution and functional role of genes related to PGP activity, stress resistance, and AMR may provide a deeper understanding of microbial evolution, environmental survival, and their applicability in biotechnology and bioremediation. The aim of the study was to characterize the first-ever draft genome sequences of rhizosphere soil-isolated M. morganii. To do so, comparative genomic analysis of food, clinical, and wastewater M. morganii isolates was performed to provide insight into the genetic basis of niche-specific adaptations. Since our rhizosphere-associated M. morganii genome represents PGP characteristics, we sought to compare it with publicly available M. morganii genomes to find additional regions of DNA correlating with unique traits, i.e., heavy metal degradation in environmental isolates and AMR in clinical isolates. Specifically, the objectives of this study were: (i) to compare genome-wide structural variations using comparative genomic visualization and identify the niche-specialized genetic repertoire of the M. morganii across niche; (ii) to examine gene distribution related to AMR and role of MGEs in their dissemination, and (iii) to recognize genes linked to PGP activities in the rhizosphere-related M. morganii genome. By shedding light on the genetic adaptations of Morganella across various ecological habitat, this study provides new insights into its possible role as a PGP bacterium and advances our understanding of how bacteria manage the gene pool to support niche dependent survival. 2. Materials and methods 2.1 Isolation and identification of rhizosphere bacteria Rhizosphere soil samples from the chickpea ( Cicer arietinum ), maize ( Zea mays ), and wheat ( Triticum aestivum ) fields were collected in Panwadi, Nagpur, India. The samples were transported to the laboratory under sterile conditions and processed within 48 hrs of collection. The soil sample was serially diluted (10 − 1 to 10 − 4 ) in phosphate-buffered saline (PBS), and 100 µL of each dilution was spread onto nutrient agar medium under aseptic conditions (HiMedia, India). The plates were incubated at 30 ± 1°C for 24 hr. Following incubation, based on morphological characteristics, distinct bacterial colonies were selected and purified through successive re-streaking. The purified isolates were sent to HiMedia Laboratories, Thane, Mumbai, for species identification using Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectrometry. 2.2 In Vitro Assays for Plant Growth-Promoting Traits To assess the ability of microbial isolates to solubilize potassium, phosphorus, and zinc, 10 µL of overnight-grown culture from LB broth was PBS-washed and spot inoculated on a Pikovskaya’s, Aleksandrow, and Zinc solubilizing agar medium plate, respectively. The plates were incubated at 30 ± 1°C for 48 h. The solubilization index of the isolated strains was evaluated based on the solubilization zones observed on the medium plates, as shown in Eq. (1). $$\\:Solubilization\\:index=\\frac{Diameter\\:of\\:Solubilization\\:zone+Diameter\\:of\\:colony}{Diameter\\:of\\:colony}$$ …………………………… Eq. (1) The ability of isolates to produce indole-3-acetic acid (IAA) was assessed as per the protocol given by Lebrazi et al ( 2020 ). The nitrogen-fixation capacity of the isolates was measured by incubating NB-grown PBS-washed overnight cultures (normalized to OD 1.0) in a 1:1 mixture of NB and Burk's medium (50 mL each) at 30°C for 24 hours. Then, the cultures were transferred to 100 mL Burk's medium and incubated further for another 48 hours under the same incubation conditions. Nitrogen fixation capacity was measured indirectly by reading the OD of the cultures at 600 nm. For antifungal activity, an actively growing culture of phytopathogenic fungi was inoculated on one side of a petridish containing potato dextrose agar. Freshly grown culture of the soil bacterial isolates was streaked near the opposite edge of the plate. The plates were incubated for 7 days at 25°C, and zones of inhibition were observed to determine the antifungal activity. 2.3 Genomic DNA extraction, whole-genome sequencing, assembly, and taxonomic identification DNA extraction was carried out with the quick-DNA™ fecal/Soil Microbe Miniprep Kit (Zymo Research). DNA was checked for integrity using 1% agarose gel electrophoresis and quantified using a Qubit photometer. Library prep was done using Native Barcoding Kit 24 (Q20+) (SQK-NBD114.24) following the manufacturer's protocol (version NBE_9134_v112_revE_01Dec2021). The resulting barcoded library was loaded on one R10.4 flow cell, and sequencing was performed on a MinION sequencer (Mk1b) with MinKnow v23.04.3 (ONT). The generated raw reads were basecalled in high accuracy mode using Guppy v. 6.5.7 ( https://community.nanoporetech.com/downloads ) with the dna_r10.4.1_e8.2_400bps_hac.cfg model for R10.4 chemistry. Reads with lower than 200 bp length and a Phred quality score below 10 were removed from the dataset. De novo genome assembly was performed using Bacterial assembly and annotation workflow from EPI2ME labs v0.2.6 ( https://github.com/epi2me-labs/wf-bacterial-genomes ) with default parameters. Table S1 shows the workflow parameters and softwares used through the pipeline. Accurate species-level taxonomic assignment of the soil-isolated strains was achieved using an integrated approach incorporating several overall genome-relatedness indices (OGRI). Briefly, genome completeness score and contamination level were assessed using CheckM (Parks et al. 2015 ) that employs a set of clade-specific single-copy marker genes to confirm that the assembled genomes were of high quality and no gene had been missed. The Microbial Genome Atlas (MiGA) server was used to determine the genome quality by comparing the assembled genome to a closely related type strain based on average amino acid identity (AAI) and average nucleotide identity (ANI) (Rodriguez-R et al. 2018). The phylogenetic relationship of the isolates was performed by aligning their 16S rRNA gene sequence with those of related strains retrieved from the NCBI database, using the maximum likelihood method in MEGA X (Kumar et al. 2018 ). Furthermore, whole-genome phylogenetic analysis, including Rhizobium leguminosarum SM52 as an outgroup and a positive control for plant growth-promoting traits, was performed using the SpeciesTree v2.2.0 tool on the KBase platform (Arkin et al. 2018 ). M. morganii ATCC 25830 was used as a reference genome. Comparative analyses such as BLAST-based ANI (ANIb), MUMmer-based ANT (ANIm), and tetranucleotide frequency correlations (TETRA) were executed using JSpeciesWS (Richter et al. 2016 ). The digital DNA: DNA hybridization (dDDH) via the Genome-to-Genome Distance Calculator (GGDC) was calculated using Type (Strain) Genome Server (TYGS) (Meier-Kolthoff and Göker 2019 ), while the Orthologous Average Nucleotide Identity Tool (OAT) was used to construct the diagram illustrating ANI relationships (Lee et al. 2016 ). Basic genomic features and subsystem category distribution were obtained using the Rapid Annotations using Subsystems Technology tool kit (RASTtk) (Brettin et al. 2015 ). 2.4 Genome selection, annotation, and comparative analysis The genome sequences of the related Morganella strains were retrieved from the NCBI website ( https://www.ncbi.nlm.nih.gov/ ) by selecting entries with “assembly level-complete”. For assessing the genome quality, an OGRI was performed on the extracted genome as outlined in the above section to filter good-quality genomes. Metadata collection was carried out to obtain detailed information on each of the strains, such as isolation source, host, genome size, contig number, N50 and L50 values, GC content, and CheckM marker set scores. In addition, the completeness of the genome and contamination levels were determined to validate the precision and accuracy of the genomic data. These metadata were manually cross-checked and correlated with previous publications to ensure consistency and completeness. The complete and good-quality genomes were annotated using Prokka (Seemann 2014 ), which employs Prodigal to predict potential genes and proteins present within the genome. The resulting general feature format (GFF) output of Prokka was used as input for pan–core analysis using the Roary pipeline v3.11.2 (Page et al. 2015 ). The output of the pangenome analysis i.e. gene presence-absence matrix was used for principal coordinate analysis (PCoA) using Bray–Curtis dissimilarity, calculated via the vegdist() function from the vegan R package v4.4.3. The cmdscale() function was used to view the resultant matrix, and ggplot2 was employed to plot the first two components. Furthermore, BLAST Ring Image Generator (BRIG) plot analysis (Alikhan et al. 2011 ) was used to visualize the extra or unique DNA region and determine the genomic difference between the soil-isolated M. morganii and the distinct cluster that developed in response to the observed clustering pattern. The unique genes were annotated using the Basic Local Alignment Search Tool (BLAST) to infer potential function. For genes annotated as hypothetical proteins, their sequences were retrieved using BEDTools (Quinlan 2014 ) and the probable functional assignment was done using the InterProScan tool, which searches the protein sequences against databases like Pfam, PANTHER, CDD, NCBIfam, SMART, ProSiteProfiles, ProSitePatterns, Gene3D PRINTS, Superfamily, and TIGRFAM to detect conserved domains. The output obtained was probable functions, enzyme classes, and GO terms of hypothetical proteins. 2.5 Identification of AMR genes and mobile genetic elements To determine the distribution and mobility of AMR genes among Morganella isolates from distinct ecological niches, a multi-step bioinformatic pipeline was performed. The AMR gene detection was performed using AMRFinderPlus v3.10.5 (Feldgarden et al. 2021 ), and the resulting output was aggregated into a binary matrix representing gene presence or absence among isolates. This matrix was utilized to create a heatmap through the heatmap package in R, using hierarchical clustering to plot differences in AMR gene profiles between the distinct ecological niches. To investigate the genetic mobility of AMR genes, the genomic regions flanking ± 5 kb around each AMR gene were extracted using BEDTools intersect. Different tools were employed for detection of MGEs: ISEScan v1.7.2.3 (Xie and Tang 2017 ) was applied to find insertion sequences, MobileElementFinder v1.1.2 (Johansson et al. 2021 ) to identify transposons, and IntegronFinder v2.0 (Néron et al. 2022 ) to find active integrons, CALINs, and attC sites. In addition, the MOB-suite v3.1.9 (Robertson and Nash 2018 ) was applied to find plasmid-related AMR gene sequences. The results were combined and visualized in a Circos plot, giving an overall impression of AMR gene mobility and distribution. 2.6 Pathway analysis To determine the presence of PGP traits of the isolates, genes involved in direct as well as indirect PGP pathways were manually curated from the existing literature. These genes were compared against the relevant pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases ( https://www.genome.jp/kegg/pathway.html ) , and the full list of genes involved in specific pathways was extracted and compared with the annotated features of the isolates. Furthermore, detection of secondary metabolite biosynthetic gene clusters and bacteriocin were performed using antiSMASH v6.0 (Antibiotics and Secondary Metabolites Analysis Shell) and BAGEL4 tools, respectively (Heel et al. 2018 ; Blin et al. 2021 ). 2.7 Data access The genome sequences of HM01, HM02, and HM04 have been released in GenBank under the bioproject accession number PRJNA1266903. 3. Results and discussion 3.1 Phenotypic characterization and PGP activity Strains designated HM01, HM02, and HM04 were identified as M. morganii based on MALDI-TOF mass spectrometry analysis. These strains were isolated from the rhizosphere soils of chickpea, maize, and wheat fields, respectively. The isolates were further tested in detail for PGP characteristics through in vitro screening such as phosphate, potassium, and zinc solubilization, IAA production, and nitrogen fixation. All three Morganella isolates showed positive outcomes for PGP characteristics. Phosphate solubilization indices for HM01, HM02, and HM04 were found to be 4.5, 2.6, and 3.9. All the strains were able to solubilize potassium chloride as a sole source of potassium. The potassium solubilization indices were found to be 6.5 for HM01, 5.5 for HM02, and 4.2 for HM04. Zinc solubilization indices were reported to be 2.75, 4.5, and 5.1, respectively. IAA plays a major role in plant growth promotion and development, and it was observed that all the rhizosphere-associated M. morganii strains produced high levels of auxin when supplemented with L-tryptophan. The IAA concentrations were found to be 9.85, 10.36, and 9.29 µg/mL for HM01, HM02, and HM04, respectively. Nitrogen fixing ability was also confirmed from increased OD 600nm in Burk's medium, in which all three isolates proved to be positive. The OD was found to be increased from 0.1 OD/mL to 1.1, 0.8 and 0.6 OD/mL for HM01, HM02, and HM04, respectively. Additionally, the strain HM02 and HM04 exhibited antifungal activity. This suggest that these strains may produce bacteriocin (compounds with antimicrobial activity), that protects the plant by inhibiting the growth of phytopathogenic fungi as well as reduces the disease-related damage (Nazari and Smith 2020 ). Overall PGP potential of the isolates is provided in Table 1 . Table 1 PGP activities of Morganella strains isolated from rhizosphere soil of chickpea, maize, and wheat fields. Sr. No. PGP trait HM01 HM02 HM04 1 P-Solubilisation index 4.5 2.6 3.9 2 K-Solubilisation index 6.5 5.5 4.2 3 Zn-Solubilisation index 2.75 4.5 5.1 4 IAA production (µg/mL) 9.85 10.36 9.29 5 Nitrogen Fixation (OD) 1.1 0.8 0.6 6 Antifungal activity - + + 3.2 Genomic characterization The presence and distribution of single-copy marker genes in the genomes gives ideas about genome quality. Completeness and contamination levels of rhizosphere soil-isolated Morganella genomes were calculated based on the presence, absence, or possible duplication of these conserved marker genes. It indicates possible contamination added during the DNA extraction or sequencing step. The estimated genome completeness scores were found to be 100% for HM01, 99.37% for HM02, and 92.91% for HM04, which indicate the high-quality assembly for downstream analysis. 16S rRNA-based and whole-genome-based phylogenetic trees confirmed the close genetic relationship of HM01, HM02, and HM04 to Morganella morganii ATCC 25830. Furthermore, pairwise genomic similarity analyses using ANI and dDDH methods reinforced these findings. Particularly, ANI (based on OrthoANI), and GGDC estimates all indicated the greatest similarity among the three isolates and reference strains M. morganii ATCC 25830, thus validating their species-level identification and genetic similarity (Online Resource ESM_1) . The basic genomic characteristics of rhizosphere soil-isolated Morganella strains are depicted in Table 2 . Briefly, strain HM01 represents a complete circular chromosome with the highest assembly quality and an L50 value of 1. HM04 also showed a high-quality assembly, with only 13 contigs and an N50 value of 2,599,043 bp. In contrast, strain HM02 showed 330 contigs and a lower N50 value of 21,387 bp. The GC content of strains HM01, HM02, and HM04 was found to be 50.3, 41.7, and 50.6, respectively. The genome sizes of the three strains also varied from 3.8 to 4.3 Mb, with HM04 being the largest one. Corresponding with its size, HM04 showed a high count for predicted coding sequences (4,738) compared to HM01 (4,239) and HM02 (4,214). The RNA gene count was found to be 103, 94, and 103 in HM01, HM02, and HM04, respectively. Functional subsystem classification revealed a broader metabolic and functional repertoire in HM01 (333 subsystems) and HM04 (334), as opposed to HM02 (253), which could be due to its lower genome completeness or smaller genome size (Online Resource ESM_2) . In all the strain, a high abundance of genes involved in pathways known to promote plant growth was observed ( Table 2 ) . Specifically, strain HM01 and HM04 demonstrated significant enrichment in genes associated with phosphorus metabolism, potassium metabolism, iron acquisition and metabolism, motility and chemotaxis, and stress response. While in strain HM02, genes related to iron acquisition and metabolism and stress response were present in higher numbers. Collectively, these observations showed that rhizosphere-associated strains possess PGP traits for both direct mechanisms (such as nutrient solubilization and uptake) and indirect mechanisms (such as stress mitigation and competitive colonization). Table 2 Basic genomic details of rhizosphere soil-isolated Morganella strains. Features HM01 HM02 HM04 Taxonomy Bacteria; Pseudomonadati; Pseudomonadota; Gammaproteobacteria; Enterobacterales; Morganellaceae; Morganella; Morganella morganii Total data (Mb) 4.1 3.8 4.3 GC content (%) 50.3 41.7 50.6 Number of Contigs (with PEGs) 1 330 13 N50 (bp) 4155665 21387 2599043 L50 (bp) 1 30 1 Number of Coding Sequences 4239 4214 4738 Number of RNAs 103 94 103 Number of Subsystems 333 253 334 Subsystem Classification (Feature counts) phosphorus metabolism 25 3 25 potassium metabolism 14 4 15 sulphur metabolism 7 6 7 iron acquisition and metabolism 13 19 18 secondary metabolism 4 0 4 motility and chemotaxis 14 0 27 Stress response 74 50 64 For comparative genomic analysis, a total of 84 M. morganii genomes were extracted from NCBI along with the metadata annotation on basic genomic features. Based on the metadata information, genomes lacking source information (n = 4) and those with a low CheckM completeness score and a high number of contigs (n = 2) were excluded from the study. The resulting 78 genomes were included in the study and were derived from the diverse niche, including food (n = 11), wastewater (n = 3), human clinical samples (n = 48), and various animal hosts (n = 16). Across the niche, the sizes of the genomes varied from 3.8 to 4.3 Mb. The difference in genome size was found to be mainly due to the gain and loss of functional genes (Moulana et al. 2020 ). The greater sizes (4.0 to 4.3 Mb) of the food- and wastewater-derived genomes suggest the acquisition of accessory genes involved in nutrient uptake, stress tolerance, and environmental adaptation. In contrast, the smaller genome sizes (3.7 to 4.0 Mb) in human- and animal-derived genomes denote streamlined genomes harbouring specialized gene sets required for the adaptation to host-associated lifestyles. Relatively stable GC content, ranging between 50.5% and 51.5% among all isolates, indicates a conserved base composition across different niches. An overview of the genomic features of the NCBI-extracted Morganella genomes is provided in Supplementary File 1 . 3.3 Pan-core analysis To characterize the genetic diversity of the Morganella , a pan-genome of soil-isolated Morganella genomes (n = 3) was performed along with publicly available genomes (n = 78), and the distribution of genomic features was analysed. Pangenome analysis revealed the distinct genomic distance between the selected Morganella strains, with the core gene alignment showing three major phylogenetic groups ( Fig. 1 a ). The first group was the largest, well-defined cluster, consisting of 49 genomes, derived mostly from clinical and wastewater sources. The second group, consisting of soil-isolated strains HM01 and HM02, clustered separately with the food-derived isolates. Such phylogenetic allocation suggests that HM01 and HM02 could either have functional or evolutionary characteristics comparable to food-borne strains or could have analogous convergent adaptation or gene gain applicable to the food environment. This clade had a distinct block of accessory gene sets, well defined from the remaining two groups within the phylogenetic tree. Strain HM04, on the other hand, is grouped differently, closely related to the strains derived from both human as well as animal origins. The reason for this unexceptional placement is unclear; however, one possible explanation could be historical cross-environmental transmission or HGT events involving mobile genetic elements shared between host-associated environments. Based on homology, the total gene content of the genomes was classified into three categories: core (shared by all genomes), shell (present in some genomes), and cloud/unique (found in only one genome). Pangenome analysis calculated 23829 gene clusters, with only 8.52% (2031 genes) and 13.13% (3130 genes) being classified as core- and shell-genes, respectively ( Fig. 1 b ) . A higher number of cloud genes, representing 78.34% (18668 genes) of the total pangenome, indicates that high degree of genomic flexibility in Morganella is driven by acquiring the niche-specific traits (Zhong et al. 2021 ). Similarly, in the study of pangenome analysis of 59 M. morganii strains, Rahman et al. ( 2020 ) observed a similarly low percentage of core genes (6.83%) and a large number of cloud genes. The pan genome curve, which plots the number of genes against the number of genomes analysed, did not reach a plateau ( Fig. 1 c ) , again indicates the open-genome nature of M. morganii . Among the studied strains, HM01, HM02, and HM04 had a combined total of 4152 unique genes, substantially more than the remaining 78 strains. Despite having a smaller genome size, strain HM02 exhibited a significant number of cloud genes (2885), followed by HM04 (1081) and HM01 (186). The remarkably high percentage of cloud genes in these strains may reflect the HGT events that act as an evolutionary force and facilitate the selection of novel functions and increase the adaptive potential of bacteria (Woods et al. 2020 ). Investigation of bacterial traits involved in rhizosphere colonization revealed that HGT events play an important role in genome plasticity for rhizosphere adaptation (Lopes et al. 2016 ). Similarly, another study reported that bacterial strains that acquired genetic materials from genomic islands through HGT, displayed better symbiotic nitrogen fixation competency compared to the closely related strains that lacked these genomic elements (Cotta et al. 2025 ). A significant portion of these cloud genes encoded hypothetical proteins in HM01, HM02, and HM04 (134, 1204, and 770, respectively), which exist in an environment with a lack of any supporting evidence of in vivo expression. These conserved hypothetical proteins are encoded by a significant proportion of the bacterial genome that can act as biomarkers or essential signalling proteins in the adaptation process, including biotic and abiotic stress, interspecies communication, and environmental interactions (Chirgadze et al. 2022 ). Besides the hypothetical proteins, among the genes of interest, the unique genes identified in strains HM01, HM02, and HM04 were found to code for PGP activities, highlighting their potential ecological roles in the rhizosphere. Strain HM02 exhibited 19 unique PGP traits that include a wide range of functional categories needed for promoting plant growth and refining soil fertility. Pertaining to nutrient solubilization, strain HM02 contains a large repertoire of genes for phosphate solubilization (pstB3_1, pstB3_2, pstC1, and multiple pst genes) which facilitate the breakdown of inorganic phosphate and make it available for plant uptake. Besides, the strain also possesses genes for potassium solubilization (kup_1, kup_2), zinc solubilization (zupT), iron acquisition and metabolism ( dtxR , fur_2 ), siderophore production (fepC), assimilatory and dissimilatory nitrate/nitrite reduction ( napA , nirC , norG , norR_4 ), which help in the breakdown of essential macronutrients and micronutrients, further enhancing its PGP potential. Additionally, genes involved in chemotaxis (cheB) that enable the strain to move towards favourable environmental conditions are also annotated. Strain HM04 contains 34 distinct PGP genes, with most of the genes being chemotaxis-associated (32), indicative of its indirect plant growth modulation function through improved environmental sensing. Among the chemotaxis-related pathways, genes that code for chemotaxis signal transduction proteins (cheB, cheR, cheW, cheY, and cheZ) are detected. Genes for structural and motor flagellar components such as (fliD, fliE, fliF, fliG, fliH, fliI, fliJ, fliM, fliN, fliO, fliP, fliQ, fliR, fliS, fliT, and motA) were also detected. These genes represent the essential components of the bacterial two-component signalling system and are responsible for movement towards favourable stimuli (Colin et al. 2021 ). The presence of several subunits of the tsr genes associated with methyl-accepting chemotaxis proteins showed that strain HM04 can sense and react to the spectrum of chemical compounds in the rhizosphere (Colin et al. 2021 ). Furthermore, genes for assimilatory sulphate reduction (cysI and cysJ) were found, which suggest its possible function in sulfur assimilation and nutrient cycling as a part of plant health enhancement. No specific genes were present in strain HM01 directly related to plant growth. Yet, indirect plant development-influencing genes were present. These include genes for peptide uptake (sapD), starch synthesis (glgA), protein synthesis and stress tolerance (metZ), and a two-component regulatory system component facilitating environmental signal perception and response (rcsD_1). The occurrence of these cloud genes in rhizosphere bacteria indicates their possible functions in nutrient solubilization and acquisition, metabolic adjustment, and plant-microbe interaction, all of which are the essential elements of PGP mechanisms (Kumar et al. 2022a ). 3.4 Principal coordinate analysis (PCoA) To further analyse the genomic relationships observed by the pangenome analysis, a PCoA was performed. The PCoA plot based on the Bray-Cutis distance matrix demonstrated a well-defined cluster ( Fig. 2 a ) . This clustering pattern observed in the PCoA plot was consistent with those observed in the pangenome analysis based on gene composition. Genomes of different clusters were then plotted separately to study the genetic differentiation between each cluster, which reflects the degree of intra-cluster divergence along the PCA axis. In cluster 1 (Fig. 2 b), even though the clustering was tight, genomes from human, animal, and wastewater isolates created separate sub-clusters. Among human-derived Morganella genomes, Morganella from various clinical samples like blood, sputum, stool, wound, and urine showed a slight but distinct segregation on the PCoA axis. These minor spatial distinctions mirror localized genetic variations that may be motivated by site-specific selective pressures within the host (Chung et al. 2017 ). In addition, the co-occurrence of animal-derived genomes with those from humans indicates the overlapping gene pools and regular gene flow as a result of the selective pressure, e.g., exposure to antibiotics or HGT events (Jing et al. 2022 ). On the contrary, the food-derived genomes in cluster 2 ( Fig. 2 c ) formed a highly compact and distinct cluster, clearly separating them from the rhizosphere-associated strains. This is indicative of some crucial changes in these strains that may have occurred during their adaptation to specific habitat. In rhizosphere-associated strains, these changes might be the outcome of coevolution with plants (Iqbal et al. 2021 ). This observation further supports that a particular habitat, such as plant-associated habitat can exert a profound effect in determining the frequency and direction of HGT in the bacterial community. It also indicates a consistency between nucleotide sequence and hierarchical clustering patterns (Iqbal et al. 2021 ). These results are in line with the earlier research on Streptococcus spp., in which HGT was predominantly observed within strains that share the same habitat, further supporting the ides similar environmental pressure promote the exchange of genes between microbial communities that are suited to analogous ecological niches (Richards et al. 2014 ). This is indicative of a highly conserved core gene in rhizosphere-soil isolates suited for stress tolerance, heavy metal detoxification, and possibly plant-associated beneficial traits, including those related to nutrient solubilization or bioremediation. Like cluster 1, the genome in cluster 3 also showed spatial separation ( Fig. 2 d ) . Though rhizosphere-soil isolated strain HM04 is phylogenetically grouped among animal- and human-associated isolates, it is still genetically separate. This is an observation that reflects ecological pressure in the rhizosphere resulting in a specific genetic makeup, different from that noted among clinical samples. 3.5 BRIG-Based Genomic Alignment While strains HM02 and HM04 appeared more distantly clustered on the PCoA plot based on the cluster-separated analysis, strain HM01 remained closely associated with food-derived, and to a lesser extent, animal- and wastewater-derived genomes. This indicates the highly shared genetic pools, although HM01 retains unique genetic elements that set it slightly apart. Genomic flexibility analysis enables the recognition of regions that might be related to the adaptive processes of bacteria to their specific environment (Gomes et al. 2024 ). Therefore, to identify genomic differences within specific regions, the BRIG plot was used to examine divergence patterns ( Fig. 3 ) . The chromosomal backbone of the strain HM01 was found to be highly conserved and aligned with the reference strain M. morganii ATCC 25830. Other strains also showed large regions of similarity (98%) with the reference strain, however, the regions of dissimilarity were distributed intermittently across the genomes, which was visualized as gaps. The gaps consistently observed in the genomes, i.e., the genes present in HM01 but absent in others, were classified as major gaps (labeled 1–8) and minor gaps (a–w). The coordinates of the gap regions were manually curated and annotated using the GenBank (gbk) file derived from the Prokka tool. Specifically, the major gaps mainly contained clusters of hypothetical proteins and MGEs (prophage integrases (e.g., IntA and IntS) and transposases) and lysozyme-related genes (e.g., RrrD). Additionally, each major gap also contains a distinct set of genes. Gap 1 contained regulatory and metabolic elements such as LexA repressor and Na⁺-translocating NADH-quinone reductase subunit E. In Gap 2, there were genes for DNA damage response (UmuC and UmuD) and a mitochondrial ubiquinone biosynthesis methyltransferase, whereas Gap 3 coded for replication and DNA repair, such as replicative helicase, a putative HTH-type transcriptional regulator, and exodeoxyribonuclease VIII. This emphasizes the potential of HM01 for DNA damage repair and maintenance of metabolic processes under stress conditions (Fu et al. 2020 ; Stratton et al. 2022 ). Gap 4 has a membrane-bound lytic murein transglycosylase C and the toxin subunit YenB that is crucial to prevent the growth and colonization of phytopathogens in the rhizosphere (Panicker and Sayyed 2022 ). Gap 5 comprises carbohydrate metabolism-related genes, such as UDP-glucose modifying enzymes, glycogen synthase, teichoic acid transferase, and the sensor histidine kinase CpxA. CpxA is a sensor kinase primarily involved in envelope stress response by regulating the downstream genes of the CpxAR two-component system. Activation of this system triggers a cascade in response to envelope stress, such as antimicrobial exposure or environmental fluctuations (Cho et al. 2023 ). Stress response and regulatory elements, including the HipA toxin, FtsH metalloprotease, RNA polymerase-associated protein RapA, RecF, along with IS3 family transposases ISLad1 and IS911, were noted in Gap 6. HipA induces translation inhibition (Nashier 2025 ); FtsH maintains membrane protein homeostasis (Yi et al. 2022 ); RapA facilitates transcriptional recovery (Qayyum et al. 2021 ) RecF is involved in DNA damage repair (Sass et al. 2021 ); and IS elements enable the bacteria to acquire advantageous traits. Gap 7 was characterized by a full suite of arsenic resistance genes (ArsR2, ArsD, ATPase, efflux pump, and reductase) that are required to survive in an environment contaminated with heavy metals (Kumar et al. 2022b ). Besides, several minor gaps comprising fewer genes were annotated to contain a diverse array of functional genes that may play an important role in PGP activity. These genes included mannose-6-phosphate isomerase (gap a; carbohydrate metabolism), autoinducer-2 kinase (gap c; quorum sensing and interspecies communication), cell division protein DamX and a putative deoxyribonuclease (RhsC) (gap e; cellular growth and genome stability), threonine/homoserine exporter RhtA (gap h; amino acid transport), acetyltransferase (gap i; post-translational modification or detoxification); peptide transporter CstA (gap n; nutrient acquisition under stress), serine endoprotease DegS (gap o; stress-response), and serine acetyltransferase (gap r; sulfur metabolism). These genes are uniquely present in strain HM01 that help plant development by nutrient acquisition as well as in stress response. Additionally, genes related to AMR, virulence and MGEs are detected viz., phenazine antibiotic resistance protein EhpR (gap f; AMR resistance), actin cross-linking toxin VgrG1 (gap l; type VI secretion systems and bacterial virulence), IS3 family transposases (ISYps8, ISLad1, ISEc52) and a ribosomal RNA large subunit methyltransferase F (gap m; horizontal gene transfer and translation regulation), and multidrug resistance protein MdtB (gap w; AMR resistance). Genes with general cellular function, such as putative hydrolase YdeN and RNA pyrophosphohydrolase (gap u; nucleotide metabolism) and exonuclease DinG (gap b; DNA repair mechanism), were also detected. Collectively, the minor gaps reflect the broad range of genes involved in stress response, metabolism, AMR, and hence plant growth. Gaps d , g, j , k , p , q , t , and v were found to contain hypothetical proteins. This variation in gene content correlated with the finding that classifies Morganella as a highly recombinogenic bacterium that can frequently acquire genetic elements through HGT events (Jing et al. 2022 ). 3.6 Role of hypothetical proteins As revealed by the pangenome and the BRIG analysis, the majority of cloud genes present in the rhizosphere soil-derived Morganella genome were classified as hypothetical proteins. These hypothetical proteins, without any supporting evidence of in vivo expression, may act as biomarkers or essential signalling proteins in the adaptation process, including responses to biotic and abiotic stress, interspecies communication, and environmental interactions (Rahman et al., 2022 ). Hence, the predictive functional characterization of hypothetical proteins was performed to elucidate their potential biological role. Of the 134, 1204, and 770 hypothetical proteins, functional predictions for 56, 563, and 335 were successfully obtained in HM01, HM02, and HM04, respectively. Most hypothetical proteins annotated as endonucleases and reverse transcriptases that were associated with cellular functions such as DNA repair. Some proteins matched with DNA-binding motifs like lambda repressor-like, MerR-type, GntR-type, and winged helix-turn-helix (HTH) domains that are involved in putative regulatory activities. In all three strains, N-acetylmuramoyl-L-alanine amidases was predicted. These enzymes are peptidoglycan hydrolases that cleave the bond between N-acetylmuramic acid and L-alanine, and responsible for cell wall remodelling, cell separation, biofilm formation, and growth inhibition of phytopathogens (Guzmán-Moreno et al. 2022 ). Another major group of hypothetical proteins showed functional similarity with the Major Facilitator Superfamily (MFS), a group of integral membrane protein that takes part in several important processes of bacterial cell physiology. In PGP bacteria, MFS involved in nutrient transport, antimicrobial compound efflux, and cell-to-cell signalling, and contributes to increasing the plant resistance against pathogens via suppression of phytopathogens or modulation of plant defence (Pasqua et al. 2021 ). A similar functional role of MFS genes has been observed in plants. A genome-wide analysis of Populus trichocarpa identified 41 MFS genes (PtrMFSs) that were highly expressed in response to fungal infection ( Fusarium oxysporum ) (Diao et al. 2021 ). In addition, some of the hypothetical proteins were predicted to have stress response and virulence function-related domains, e.g., Salmonella virulence plasmid proteins encoded by the spv operon that play a role in systemic infection by increasing bacterial survival and replication inside host cells (Kang et al. 2024 ). Domains such as Colicin V synthesis proteins involved in bacteriocin production, phage tail fibre proteins for host recognition and virulence, and components of type IV secretion systems responsible for translocating effector proteins into host cells are also predicted (Nazari and Smith 2020 ). This reflects the strain’s ability to enhance bacterial competitiveness, host colonization, and adaptation to environmental stress. The complete results of the preliminary annotation of hypothetical proteins are provided in Supplementary File 2 . Hypothetical proteins with predicted role in plant growth and development were manually curated. In HM02, which harbours the largest group of hypothetical proteins, several were related to iron sequestration and metabolism (FeoA_2, iron siderophore/cobalamin periplasmic-binding domain profile, ferritin-like domain), heavy metal detoxification (heavy-metal-associated domain), and sulphur metabolism (sulfite exporter TauE/SafE). Additionally, some hypothetical proteins were predicted to be involved in stress response (glycine betaine/proline betaine transport system permease protein ProW, glyoxylase/bleomycin resistance protein/dioxygenase superfamily), cell wall modification and stress response (lipopolysaccharide choline phosphotransferase LicD, teichuronic acid biosynthesis protein TuaE), biofilm production (colanic acid biosynthesis UDP-glucose lipid carrier transferase), secondary metabolite production (S-adenosyl-L-methionine-dependent methyltransferases, acetyltransferase (GNAT) domain, radical SAM superfamily, alpha/beta hydrolase, PLP-dependent transferases), and antifungal properties (LysM domain). Furthermore, the major group of hypothetical proteins in HM02 was predicted to be involved in membrane transport. These proteins were found to be related to the sugar ABC transporter integral membrane protein, the phosphate transport system permease, the D-xylose-binding periplasmic protein, the maltose/maltodextrin transport system permease protein MalF, the PTS system sugar-specific permease components, the ECF transporter substrate-specific components, the C4-dicarboxylate anaerobic carrier, the N-acetylgalactosamine permease II component, and bacterial extracellular solute-binding proteins. These proteins help plants absorb nutrients that are beneficial to plants. In HM04, proteins are predicted to be involved in lipopolysaccharide biosynthesis (glycosyltransferase family 25, nucleotide-diphospho-sugar transferases), carbohydrate biosynthesis (glycosyltransferase and glycosyltransferase GT-D fold), polysaccharide biosynthesis (polysaccharide biosynthesis protein), membrane transport (bacterial extracellular solute-binding protein), secondary metabolite production (acyltransferase family and acetyltransferase (GNAT) family), stress response (cyclopropane-fatty-acyl-phospholipid synthase), substrate hydrolysis (alpha/beta-hydrolases), bacteriocin production (S-type pyocin and colicin D domains), and antifungal properties (lysozyme-like domains and polysaccharide lyase). In HM01, proteins related to biofilm production (epsG family), secondary metabolite production (acyltransferase family and acetyltransferase (GNAT) family), polysaccharide biosynthesis (polysaccharide biosynthesis protein), metal ion homeostasis and oxidative stress resistance (cupin fold metalloproteins), and carbon storage and energy regulation (glycogen phosphorylase B) were predicted. In a study, Guzmán-Moreno et al. ( 2022 ) identified several hypothetical proteins in Bacillus megaterium HgT21 that are involved in heavy metal degradation, phosphate solubilization, membrane transport and bacteriocin production. Similarly, Msimbira et al. ( 2022 ) reported that some hypothetical proteins in Lactobacillus helveticus (EL2006H) and Bacillus subtilis (EB2004S) were upregulated in response to changes in environmental conditions, such as pH. They concluded that these proteins may be expressed in response to specific environmental conditions and could play a major role in the adaptation process of bacteria. In a similar line, several studies identified a large repertoire of hypothetical proteins in strains associated with PGP activity (Guo et al. 2020 ; Balderas-Ruíz et al. 2020 ; Zhang et al. 2022 ). However, so far, it remains unclear whether the observed enhanced activity of the strains is due to the emergence of unique hypothetical protein or other biomolecules in PGP bacterium. Therefore, deeper insights into the predictive role of these significantly unique and potentially upregulated hypothetical proteins are required to better elucidate their involvement in plant growth and development. 3.7 AMR gene profiling The presence-absence heatmap for the AMR gene (ARGs) identified across 81 Morganella strains is illustrated in Fig. 4 . A total of 98 ARGs that confer resistance to 15 different antibiotic classes were identified. The classes include β-lactams, aminoglycosides, macrolides, lincosamides, streptogramins, fluoroquinolones, chloramphenicol, tetracyclines, trimethoprim, sulfonamides, rifampin, streptothricin, bleomycin, fosfomycin, and biocide resistance. The most prevalent ARGs were found to be from β-lactams class (27/98), followed by aminoglycosides (24/98) and macrolides (8/98). Human host-derived genomes that comprise the largest subset harbored numerous ARGs (83/98, 85%). Almost all the genomes in this niche exhibited multiple resistance determinants, with frequent detection of bla genes (blaCTX-M-15, blaCTX-M-65, blaTEM, blaSHV-12, blaKPC, blaKPC-2, blaNDM-5, blaIMP-27, blaVIM-1, blaOXA-10, blaDHA-5, blaDHA-27, and so on), conferring resistance to β-lactam antibiotics, such as penicillins, cephalosporins, Carbapenems, and Monobactams. ARGs within aminoglycoside resistance class (aac(3)-IIe, aac(6')-Ib, aac(6')-Ib3, aac(6')-Ib-cr, aadA5, aadA13, aph(3')-VIb, aph(3')-XV, rmtB1, rmtC), were present in approximately 95% of human-associated genomes. Presence of β -lactamases belonging to the AmpC b-lactamase (blaAmpC) family on the chromosome of Morganella leads to an intrinsic resistance to most β-lactam antibiotics as well as first and second- generation cephalosporins (Xiang et al. 2021 ). A recent study detected numerous carbapenemases in Morganella sp., including VIM-1, NDM-1, NDM-5, OXA-48, OXA-181, and OXA-641 (Bonnin et al. 2024 ). Additionally, in a study characterizing ARGs across 102 genomes of Morganella , Zhu et al. ( 2025 ) reported a total of 241 aminoglycoside phosphotransferase-related genes. Aminoglycoside along with β-lactam antibiotics have been extensively used to treat severe infection in human and animals from last 60 years. In recent years, these combination antibiotics have been used to treat tularemia, nosocomial surgical wound infections, sepsis, plague, blood-stream infections, central nervous system infection, endophthalmitis, endocarditis, brucellosis, urinary tract infections, pneumonia, chorioamnionitis and systemic infections caused by Morganella (Lebeaux et al. 2020 ). However, the long-term use of these antibiotics caused the acquisition and dissemination of aminoglycoside and β-lactam resistant genes in various clinical isolates, such as Escherichia, Acinetobacter, Salmonella, Klebsiella , as well as in Morganella (Wang et al. 2022 ). ARGs conferring resistant to trimethoprim (dfrA1, dfrA12, dfrA27, dfrA17, dfrA14, dfrA42, dfrA19), sulfonamide (sul1, sul2, sul3), tertracycline (tet(A), tet (B), tet(D)), chromaphenicol (catA1, catA2, catB3, catB8, floR, cmlA1, cmlA5, catB2), were also detected in 85% of the clinical genomes in present study. Bonnin et al. ( 2024 ) reported that intrinsic resistance to tetracycline was observed only in M. sibonii, while the tetracycline resistance genes was found in 42 M. morganii isolates in the present study. Other ARGs identified in ~ 70% of genomes included fosfomycin (fosA3), bleomycin (ble, bleO), macrolid (mph(A), mph(E), ere (B)), fluroquinolone (qnrS1), microlid-streptomycin B efflux genes (msr(E)). Genes conferring resistance to biocide (qacEdelta1, qacL), rifampin (arr, arr-2, arr-3), lincosamide (lnu(F), lnu(G)), and streptothricin (sat2) were also detected in some of the Morganella genomes which were isolated from the patients subjected to multiple antibiotic treatments. This observation is in line with the large-scale genomic study conducted on clinical isolates of Morganella which reported a high rate of ARGs and the emergence of multidrug-resistant strains (Zhu et al. 2025 ). The co-occurrence of multiple ARGs suggests a multidrug-resistant phenotype, which was driven by the intense use of antibiotics in healthcare settings. These indicate that Morganella from hospitalized patients had evolved to acquire many more ARGs to encounter complex and high selection of antimicrobials in the clinical environment. Genome clustering with respect to the ARG profiles was observed that indicates that each genome in the specific niche acquired different ARGs ( Fig. 4 ) . Among the 83 ARGs found in the human-derived genomes, 43 ARGs were shared with animal-derived ones (Online resource ESM_3) . In a similar line, Jing et al. ( 2022 ) reported 34 acquired ARGs shared between Morganella isolates from both sources, indicating a long history of acquisition and widespread dissemination of these genes within the genus. The ARGs dissemination between these habitats can occur through multiple routes such as food animals, direct contact between humans and animals, or through shared environmental resources, such as contaminated water, with latter being the most common source (Cao et al. 2022 ). Similarly, a present study observed that Morganella genomes which were derived from the wastewater shared 14 ARGs with human-derived genomes, 11 of which were also detected in animal-derived genomes, indicating that wastewater acts as a convergence point for ARGs dissemination between different habitats (Hutinel et al. 2022 ). Additionally, animal-derived genome exhibited 9 unique ARGs belonging to β-lactam (blaCTX-M-63), aminoglycoside (aph(3')-VI), trimethoprim (dfrA24, dfrA23, dfrA10), chloramphenicol (cmlA6), macrolide (erm(42)), biocide (qacE), fluoroquinolone (qepA). Two unique ARGs, namely aac(6')-Ie and tet(L) were also detected in wastewater. A total of 3 ARGs (blaDHA, catA2, and tet (D)), involved in resistance to 3 different categories of antimicrobials, were shared among Morganella isolates from all the niche, indicating a core set of resistance determinants that persist regardless of the environment ( Online resource ESM_3) . These were also the only ARGs detected in food and rhizosphere isolates, further suggesting that isolates from these habitats carry fewer ARGs compared to clinical or wastewater settings. Additionally, msr(C)—which confers resistance to macrolide was detected in the rhizosphere soil-derived HM02 strain. Macrolide, a critically important human medicine, enters into the soil through the application of biosolids that are applied as an organic fertilizer (Brown et al. 2022 ). These biosolids are frequently contaminated with pharmaceutical residues that persisted during wastewater treatment and partitioned into the organic phase. As a result, soil microbial communities may acquire resistance to macrolide to thrive in such contaminated environments. The low count of ARGs in food and rhizosphere isolates indicates lower selective pressure for resistance in these niches; however, the detection of any ARGs may raise concerns regarding food safety and environmental contamination. 3.8 Association between ARGs and mobile genetic elements Morganella accumulated both intrinsic and acquired ARGs, leading to a multidrug resistance strain. Different types of MGEs viz., insertion elements, transposons (composite transposons, unit transposons, and Miniature Inverted-repeat Transposable Elements (MITEs)), and integrons (integron-01 and integron-02) responsible for acquired ARGs were identified in the studied Morganella genomes ( Fig. 5 ) . Insertion sequences (IS) are found to be majorly involved in the mobilization and dissemination of different ARGs. Of the 32 types of insertion elements detected; the most frequent IS families found were: IS26, IS6100, IS5057, ISAba1, ISAba125, ISCfr1, ISEc59, ISSen9, ISVsa3, ISVsa5. Among them, IS26 found to be a predominant insertion sequence, primarily associated with ARGs conferring resistance to β-lactams and aminoglycosides. ISVsa5 was found to be strongly associated with the mobilization of tetracycline resistance genes, while ISVsa3 played a crucial role in facilitating HGT events contributing to resistance against sulfonamides and fluoroquinolones. Additionally, ISAba1, ISAba125, ISCfr1, ISEc59, and ISSen9 were frequently detected in the flanking regions of ARGs conferring resistance to fluoroquinolones, biocides, aminoglycosides, sulfonamides, and chloramphenicol, respectively. This widespread association of different IS families with various classes of ARGs indicates the important role of insertion elements in facilitating the horizontal dissemination and diversification of ARGs. Among integrons, integron-01 (characterized by the presence of the intl1 gene) represents a strong linkage with the ARGs flanking region. Major repertoire of genes linked to aminoglycoside resistance, were found to be horizontally disseminated by integron-01. Additionally, other resistance determinants such as those conferring resistance to sulphonamide, chromaphenicol, remapping, and biocide were also commonly associated with integron-01. Furthermore, trimethoprim resistance genes and quinolone resistance genes also displayed linkages with integrons, although at a lower frequency compared to aminoglycoside- and sulfonamide-resistance genes. Resistance genes against macrolides, phenicols, and rifampicin were also found to be associated with integrons-01, although these connections were less numerous. Furthermore, qacEdelta1 that is known as a multidrug efflux gene were flanked by the class 1 integron, further contributing to enhanced resistance ability. However, the role of integron-02 (characterized by the presence of the intl2 and intl3 genes) in mobilization of ARGs was limited and found to be associated with only few genes, such as catB3, qacEdelta1, and arr. Transposons were also found to be involved in the dissemination of various ARGs, although their associations were less frequent compared to insertion elements and integrons. Among the three types of transposons identified, composite transponsons are found to be more associated with the dissemination of ARGs. Composite transposons associated with IS26 were most frequent and associated with resistance genes against β-lactams, aminoglycosides, sulfonamide, bleomycin, and fosfomycin. Unit transposons, on the other hand, were mainly associated with resistance to macrolides and chloramphenicol, while MITEs were occasionally linked to β-lactam, aminoglycoside, and bleomycin resistance to a much lesser extent. It should be noteworthy that these MGEs are found in the flanking region of the ARGs which was predominant in the Morganella genomes isolated from human, animal, and wastewater. Interestingly, ARGs detected in the food and rhizosphere soil isolates were not found to be associated with any MGE. In the investigation of ARGs linked MGEs, a large repertoire of MGEs were detected in studied Morganella genomes. This analysis also revealed that more than one type of MGEs are involved in dissemination of resistance against particular ARG. In a study investigating the evolutionary trends of Morganella , Chen et al. ( 2024 ) reported that M. morganii undergoes evolution driven by MGEs, which significantly enhance its adaptability to environmental changes and the selective pressures imposed by clinical antimicrobial agents. Additionally, Xiang & Li, ( 2021 ) characterize two novel mobile genetic elements (Tn6835 and MMGI-1) in a Morganella strain isolated from fecal swab of healthy chicken and found that most of the ARGs were located on these MGEs which are responsible for pan-resistant nature of Morganella against all known antibiotics. Additionally, few studies have revealed new transposons like Tn7376 in Morganella and genomic islands that have multidrug resistance genes, like dfrA24, enabled by IS26-mediated recombination (Jing et al. 2022 ; Luo et al. 2022 ). Dissemination of blaKPC-2 and blaNDM-1 in Carbapenem-resistant M. morganii (CRMM) isolates was also found to be majorly facilitated by IncL/M plasmids and IS26-mediated transposon activity (Yao et al. 2025 ). In the circos plot, the color coding on the ring denotes the location of ARGs. Most of the ARGs associated with MGEs are found to be plasmid-borne. For those with are located on both plasmid and chromosome, their occurrence was less frequent on the chromosome. Plasmid-derived ARGs were identified from species such as Escherichia coli, Proteus mirabilis, Citrobacter freundii, Aeromonas rivipollensis, Pseudomonas aeruginosa, Salmonella enterica, Pasteurella aerogenes, Enterobacter cloacae , and Acinetobacter baumannii. AMR pangenome profiling of the 827 genomes from Enterobacteriaceae family, collected from livestock farms and wastewater, identified distinct dynamics for chromosomal and plasmid-borne ARGs. They found that plasmids carry a substantial burden of AMR genes and MGEs. Furthermore, AMR-gene-carrying plasmids appear to be under stronger selective pressure and are primarily responsible for conferring resistance to multiple antibiotics, specifically in clinical strain compared to non-clinical ones. These findings indicate that clinical isolates act as major reservoirs for the transfer of ARGs into Morganella species. Although direct evidence of ARG transmission from clinical isolates to M. morganii via mobile genetic elements is limited, several studies have reported the presence of clinically relevant resistance genes on plasmids and transposons in M. morganii isolates, suggesting potential acquisition through the clinical mobilome (Yao et al. 2025 ). Additionally, in a study by Sugita et al. ( 2022 ), it was found that inter-plasmid transposition of Tn4401a facilitates horizontal transfer of blaKPC-2 from Klebsiella pneumoniae to M. morganii through ColRNAI plasmids, enhancing resistance spread. On the other hand, ARGs that were located on the chromosome, generally not associated with MGEs and were primarily found in isolates originating from food and rhizosphere environments. This suggests that Morganella when present in non-clinical environment, such as food or rhizosphere, where the AMR pressure is low, lacks these ARGs and instead showed beneficial traits responsible for plant growth or environmental survival. 3.9 PGP pathway analysis The genome sequences of the rhizosphere-soil isolated strains were evaluated for their ability to improve plant development through both direct and indirect effects. The direct effects involve the pathways for nitrogen fixation, phytohormone production, and solubilization of nutrients such as phosphate, potassium, zinc, sulphate, and iron. In contrast, the indirect pathway involves the suppression of pathogenic growth and colonization, degradation of aromatic and toxic compounds, quorum sensing, biofilm formation, and the production of bacteriocins and secondary metabolites. Concerning the direct pathway, a total of 33, 12, and 38 genes related to nutrient solubilization were detected in strains HM01, HM02, and HM04, respectively ( Table 3 ) . The presence of phosphate, potassium, and zinc solubilization machinery in the strain correlated with the in-vitro activities. After the application of fertilizers in soil, a major portion of inorganic nutrients remains immobilized, leaving them unavailable for plants. Therefore, it is important for the soil microbial community to produce enzymes and organic acids to solubilize this poorly soluble mineral nutrients. Besides, these compounds must be transported across the plasma membrane before they may be used (Kumar et al. 2022a ). In rhizosphere-associated strain genes related to phosphate solubilization such as phnV (hydrolyse phosphonate into phosphate and alkane) and pstBACS (phosphate transporter) were detected. Moreover, genes involved in the potassium (kdp, kup), zinc (znt, znu, zup, zur) and sulphate (cys) transport and uptake were also annotated in these strains. High-affinity iron chelating compounds produced by the microbial community helps in collecting iron from the soil (Kumar et al. 2022a ). All strains were able to synthesis enterobactin sideophore (fep, fpt, ent) which are responsible for recovery of siderophore from the complex environment, while the protein (fhu) that help in transport and binding of iron are only detected in HM01 and HM04. Moreover, genes related to iron sequestration and metabolism were detected in all three strains (feo, efe, fur, fec, fpt, dtx). Chemotaxis genes, which play a major role in stress response, were most abundant in HM04 (30), followed by HM01 (17) and HM02 (11). These genes include clusters such as fli (fliDEFGHIJMNOPQRST), mot (motAB), che (cheABRWYZ), as well as aer, dppA, rbsB, tap, tar, tsr, and trg, all of which contribute to endophytic traits such as chemotactic movement and host attachment. Nitrogen metabolism represents another important pathway that promotes plant growth and biomass accumulation. Genes related to the nitrogen fixation pathway were not detected in any of the isolates. However, genes involved in the indirect nitrogen metabolism pathway, such as nitrification-denitrification ( narG , narH , narI , norG , norR , napA ) and assimilatory/dissimilatory nitrate reduction ( nasD , narG , narH , narI , narJ , narK , napA ), as well as nitrite reduction ( nirC ), were annotated. The overall presence-absence gene matrix for these three isolates involved in both direct and indirect PGP activity is shown in Table 3 . The antiSMASH analysis revealed a total of five, six, and five secondary metabolite biosynthetic gene clusters in strains HM01, HM02, and HM04, respectively (Online resource ESM_4) . Two clusters, namely the azole-containing RiPP (antimicrobial activities) and terpene precursor clusters (production of bioactive compounds for signaling and defense), were found in all three strains. The azole-containing RiPP was previously reported to produce diverse antimicrobial peptides that inhibit the growth of phytopathogenic fungi and bacteria (Thamvithayakorn et al. 2025 ). Terpene is generally considered to be a fungal and plant natural product. However, a study identified that bacterial genomes also harbor gene cluster encoding for terpenes and produce bioactive compounds for signaling and defense (Yamada et al. 2015 ). The β-lactone cluster that are known for antimicrobial and anticancer properties (Awolope et al. 2021 ) was detected in HM01 and HM04, while cyclic lactone autoinducer that induces quorum sensing and regulation of microbial community behaviours (Kapadia et al. 2022 ) was uniquely observed in HM02. Furthermore, four types of bacteriocins were annotated (Online resource ESM_5) . Microcin and Bottromycin were found in all three strains, whereas Colicin_E6 and Enterolysin_A were uniquely found in HM01 and HM02, respectively. These bacteriocins are potentially responsible for antagonizing the growth of phytopathogens, as reported previously (Nazari and Smith 2020 ). Table 3 Functional annotation of genes associated with PGP pathways in strain HM01, HM02, and HM04. Pathway Gene Function HM01 HM02 HM04 Phosphate solubilization phnV transport system permease protein responsible for 2-ami0ethylphosphonate ● ○ ● pstA Phosphate transport system permease protein pstA ● ○ ● pstB Phosphate-import ATP-binding protein pstB ● ● ● pstC Phosphate transport system permease protein pstC ● ● ● pstS Phosphate-binding protein pstS ● ○ ● potassium solubilization kdpA High-affinity K + transport protein A ● ○ ● kdpB High-affinity K + transport protein B (ATPase) ○ ○ ● kdpC High-affinity K + transport protein C ○ ○ ● kdpD Sensor histidine kinase (KdpD) ○ ○ ● kup Low-affinity K + transporter (Kup system) ○ ● ○ Zinc solubilization zntA Zinc/cadmium/lead-transporting P-type ATPase ○ ○ ● zntB Zinc transport protein ZntB ● ○ ● zntR HTH-type transcriptional regulator ZntR ● ○ ● znuA High-affinity zinc uptake system protein ZnuA ● ○ ● znuB High-affinity zinc uptake system membrane protein ZnuB ● ○ ● znuC Zinc import ATP-binding protein ZnuC ● ○ ● zur Zinc uptake regulation protein ● ○ ● zupT low-affinity zinc-uptake system ○ ● ○ Iron sequetration and metabolism efeO Iron uptake system component EfeO ○ ○ ● feoA Fe(2+) transport protein A ● ○ ● feoB Fe(2+) transporter FeoB ● ○ ● feoC putative [Fe-S]-dependent transcriptional repressor ● ○ ● fur Ferric uptake regulation protein ● ● ● yggX putative Fe(2+)-trafficking protein ● ● ● fecA Fe(3+) dicitrate transport protein FecA ● ○ ○ fptA Fe(3+)-pyochelin receptor ● ○ ○ dtxR Ferric uptake regulation protein ○ ● ○ siderophore production fepA Ferrienterobactin receptor ● ○ ● fhuC Iron (3+)-hydroxamate import ATP-binding protein ● ○ ● fptA Ferrienterobactin receptor ● ○ ○ fhuB Iron (3+)-hydroxamate import system permease protein ○ ○ ● entS Enterobactin exporter ○ ○ ● fepC Ferric enterobactin transport protein FepC ○ ● ○ Assimilatory sulphate reduction cysA Sulfate/thiosulfate import ATP-binding protein CysA ● ● ● cysE Serine acetyltransferase ● ○ ● cysG Siroheme synthase ● ○ ● cysI Sulfite reductase [NADPH] hemoprotein beta-component ● ○ ● cysL HTH-type transcriptional regulator CysL ● ○ ● cysP Thiosulfate-binding protein ● ● ● cysQ 3? (2? ),5? -bisphosphate nucleotidase CysQ ● ○ ● cysS CysteineÐtRNA ligase ● ○ ● cysT Sulfate transport system permease protein CysT ● ● ● cysW Sulfate transport system permease protein CysW ● ● ● cysZ gulator CysL cysZ S ● ○ ● cysJ Sulfite reductase [NADPH] flavoprotein alpha-component ○ ○ ● chemotaxis aer aerotaxis receptor ● ○ ● cheA two-component system, chemotaxis family, sensor kinase CheA ● ● ● cheB two-component system, chemotaxis family, protein-glutamate methylesterase/glutaminase ● ● ● cheR chemotaxis protein methyltransferase CheR ● ● ● cheW purine-binding chemotaxis protein CheW ● ● ● cheY two-component system, chemotaxis family, chemotaxis protein CheY ● ● ● cheZ chemotaxis protein CheZ ● ● ● dppA dipeptide transport system substrate-binding protein ● ○ ● fliG flagellar motor switch protein FliG ● ○ ● fliM flagellar motor switch protein FliM ● ○ ● fliN flagellar motor switch protein FliN ● ○ ● motA chemotaxis protein MotA ● ● ● motB chemotaxis protein MotB ● ● ● rbsB ribose transport system substrate-binding protein ● ○ ● tap methyl-accepting chemotaxis protein IV, peptide sensor receptor ● ○ ● tar methyl-accepting chemotaxis protein II, aspartate sensor receptor ● ● ● tsr methyl-accepting chemotaxis protein I, serine sensor receptor ● ● ● trg methyl-accepting chemotaxis protein III, ribose and galactose sensor receptor ○ ● ● fliD flagellar motor switch protein FliD ○ ○ ● fliE flagellar motor switch protein FliE ○ ○ ● fliF flagellar motor switch protein FliF ○ ○ ● fliH flagellar motor switch protein FliH ○ ○ ● fliT flagellar motor switch protein FliT ○ ○ ● fliJ flagellar motor switch protein FliJ ○ ○ ● fliO flagellar motor switch protein FliO ○ ○ ● fliI flagellar motor switch protein FliI ○ ○ ● fliP flagellar motor switch protein FliP ○ ○ ● fliQ flagellar motor switch protein FliQ ○ ○ ● fliR flagellar motor switch protein FliR ○ ○ ● fliS flagellar motor switch protein FliS ○ ○ ● Nitrification narG nitrate reductase / nitrite oxidoreductase, alpha subunit ● ○ ● narH nitrate reductase / nitrite oxidoreductase, beta subunit ○ ○ ● Denitrification narG nitrate reductase / nitrite oxidoreductase, alpha subunit ● ● ● narH nitrate reductase / nitrite oxidoreductase, beta subunit ○ ● ● narI nitrate reductase gamma subunit ● ● ● 0rG Regulator of nitric oxide reductase genes ○ ● ○ 0rR Transcriptional activator responding to nitric oxide ○ ● ○ napA nitrate reductase (cytochrome) ○ ● ○ Assimilatory nitrate reduction nasD nitrite reductase [NAD(P)H] large subunit ● ● ● Dissimilatory nitrate reduction narG Respiratory nitrate reductase 1 alpha chain ● ○ ● narH Respiratory nitrate reductase 1 beta chain ○ ○ ● narI Respiratory nitrate reductase 1 gamma chain ● ○ ● narJ Nitrate reductase molybdenum cofactor assembly chaperone narJ ● ○ ● narK Nitrate/nitrite transporter nark ● ○ ● napA nitrate reductase (cytochrome) ○ ● ○ Dissimilatory Nitrite Reduction nirC Nitrite transporter ○ ● ○ ● represent gene presence ○ represent gene absence 4. Conclusion The three M. morganii strains viz., HM01, HM02, and HM04 isolated from the rhizosphere of chickpea, maize and wheat field exhibited strong in-vitro PGP activity. Pangenome analysis, incorporating 78 publicly available M. morganii genomes, indicated an open pangenome with a high number of cloud genes, which indicated the likelihood of HGT events in the process of gaining niche-specific adaptive traits. A significant percentage of these cloud genes encode hypothetical proteins in the rhizosphere-associated strain that were found to be involved in plant growth and environmental survival. To further support this observation, genomic clustering using PCoA analysis showed that rhizosphere strain grouped with food-associated strain, whereas the genomes from human, animal, and wastewater aggregate to form a separate cluster. BRIG analysis, further identified and discriminated against unique genomic regions in rhizosphere strains from food associated isolates. Selection of gene pool specific to rhizosphere ecology was corroborated by KEGG pathway analysis that revealed PGP conferring genes involved in nutrient transport and uptake, chemotaxis, siderophore production and nitrogen metabolism. Additionally, numerous biosynthetic gene clusters encoding antibacterial and antifungal metabolites were identified. Pangenome mapping of AMR genes showed that rhizosphere and food-associated M. morganii have fewer AMR events, whereas clinical and wastewater isolates harboured a higher number of MGE linked-resistant gene, often plasmid-borne and derived from clinical isolates; may be required as one of the conditions for survival in new habitat. This study showed that while bacteria intelligently survive according to ecosystem conditions, the available genetic plasticity makes it potential sink for AMR genes. Overall, the study presents novel findings into the genomic adaptability of M. morganii in various ecological niches, highlighting its dual nature as both a PGP bacterium and an opportunistic pathogen. Caption for online resources (Electronic Supplementary Material) ESM_1 Heatmap illustrating a) average nucleotide identity by orthology (OrthoANI) and b) genome-to genome distance calculation (GGDC) between the rhizosphere soil-isolated Morganella genome and reference genome, represented by the type strains of the respective genera calculated by OAT software. High OrthoANI values and low GGDC values between the strains indicate greater genomic similarity. The \"ERR\" value in GGDC showed that the organism is genetically distant from the Morganella strains ESM_2 Subsystem category distribution and the biological function of M. morganii strain a) HM01, b) HM02, and c) HM04 as determined by RAST genome analysis. Gene distributions are depicted with different colours, and their corresponding genes are numerically shown within parentheses ESM_3 Venn diagrams illustrating the distribution of shared ARGs across the Morganella genomes isolated from different habitat viz., human, animal, wastwater, food, and rhizosphere soil ESM_4a Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated Morganella morganii strains HM01 ESM_4b Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated Morganella morganii strains HM02 ESM_4c Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated Morganella morganii strains HM04 ESM_5a Organization of bacteriocin in rhizosphere soil-isolated Morganella morganii strains HM01 ESM_5b Organization of bacteriocin in rhizosphere soil-isolated Morganella morganii strains HM02 ESM_5c Organization of bacteriocin in rhizosphere soil-isolated Morganella morganii strains HM04 Declarations The authors have no relevant financial or non-financial interests to disclose. 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Supplementary Files ESM1.jpg ESM2.jpg ESM3.jpg ESM4a.jpg ESM4b.jpg ESM4c.jpg ESM5a.jpg ESM5b.jpg ESM5c.jpg Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Archives of Microbiology → Version 1 posted Editorial decision: Revision requested 07 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 11 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6872201\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":470943140,\"identity\":\"b23f7625-097e-4108-bf44-b6adca27c1b5\",\"order_by\":0,\"name\":\"Rajesh Pal\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rajesh\",\"middleName\":\"\",\"lastName\":\"Pal\",\"suffix\":\"\"},{\"id\":470943142,\"identity\":\"5eb19903-a46b-4f5d-93fa-eafe73c75c5e\",\"order_by\":1,\"name\":\"Bhagyashri J. Poddar\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bhagyashri\",\"middleName\":\"J.\",\"lastName\":\"Poddar\",\"suffix\":\"\"},{\"id\":470943144,\"identity\":\"26b92ee9-062f-4249-8087-03e3b30b3ab0\",\"order_by\":2,\"name\":\"Prabhakar Pandit\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Prabhakar\",\"middleName\":\"\",\"lastName\":\"Pandit\",\"suffix\":\"\"},{\"id\":470943146,\"identity\":\"ffd72deb-57c9-4312-867e-1779f9b81d52\",\"order_by\":3,\"name\":\"Hemant Purohit\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAxDB2CDBwC/B2MDAwCbBwMfAQ5wWCckZUC1sRGphkDC4AWKxMRDWYi7dY/bh4w6LOuPbza0bPpRZyLOxnz38gaHGJhqXFss5Z4xnzjwjIWF252DbzRnnJAzbePLSJBiOpeU24HLYjRxjZt42oJYbiW23gQzGNoYcM6BTDxPWYjwDqOVvm4R9G/8b4w9EaTGQAGphbAOSEjkGEni13DlWzDizDRjGQIfd7Dknkdwm8S5NIgGfX243b2b42FbHzz8j/dmNH2V1tv38uYc/fKixwamFQQKraAIu5bi1jIJRMApGwShAAgAd6Vj2N/lLsAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hemant\",\"middleName\":\"\",\"lastName\":\"Purohit\",\"suffix\":\"\"},{\"id\":470943151,\"identity\":\"2c87de48-b4a3-47c3-ab1f-4b827406c110\",\"order_by\":4,\"name\":\"Rahul Warke\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rahul\",\"middleName\":\"\",\"lastName\":\"Warke\",\"suffix\":\"\"},{\"id\":470943153,\"identity\":\"773f0c9e-cff2-4946-8335-7c2b046788aa\",\"order_by\":5,\"name\":\"Gangadhar M. Warke\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"HiMedia Laboratories, Pvt. Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gangadhar\",\"middleName\":\"M.\",\"lastName\":\"Warke\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-11 13:08:36\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6872201/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6872201/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s00203-025-04566-y\",\"type\":\"published\",\"date\":\"2025-11-28T15:58:08+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":84680722,\"identity\":\"b1a6aea3-a4ec-4547-88ef-c110c0e5b306\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:19:37\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89597,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ea) Pangenome matrix diagram representing the presence of core, shell, and cloud genes in the blue color. The respective positions of the strains in the phylogenetic tree are based on genomic similarity to one another in the pan-genome. b) Pie chart representation of the gene distribution in the pangenome concerning the presence of genes in proportion of strains out of 81 strains. [Core genes (80 \\u0026lt; strains \\u0026lt;= 81), soft core genes (76 \\u0026lt;= strains \\u0026lt; 80), shell genes (12 \\u0026lt;= strains \\u0026lt; 76), and cloud genes (strains \\u0026lt;12)]. c) The pan genome curve of the total number of genes and conserved genes across the selected \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eM. morganii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e pangenome\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/b9f5b900a71ec2e1ea60ac6f.png\"},{\"id\":84679982,\"identity\":\"39777941-9ce8-4754-a1db-4da1cc5d3faa\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:36\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":55672,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ea) PCoA plot depicts Bray-Curtis distance based on presence and absence of genes among \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eMorganella\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003egenomes from diverse ecological niches. The confidence interval of the ellipses was set at 95%. Genomes derived from wastewater, animal host, and human host form distinct clusters from those derived from food and rhizosphere. b) Cluster 1 represents the genomes derived from wastewater, animal, and human hosts. c) Cluster 2 represents the genomes derived from food and rhizosphere. d) Cluster 3 represents the genomes derived from animal host, human host, and rhizosphere.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/891c81eb38e699e58e4d3e7f.png\"},{\"id\":84679977,\"identity\":\"bb63859d-2475-47dc-a1f8-1d169e4f908d\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:36\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":351675,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBRIG plot representing comparative genomic analysis of 20 \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eM. morganii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e strains (from cluster 2 of PCoA plot). The genome of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eM. morganii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003eATCC 25830 was used as the reference for graphical profiling. From the innermost to the outermost rings: Ring 1 represents GC content (black); Ring 2 represents GC skew (green/purple); Ring 3 represents \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eM. morganii\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e HM01, isolated from rhizosphere soil (red); Ring 4 represents strains isolated from food sources (blue); Ring 5 represents strains isolated from wastewater (green); and Ring 6 represents strains isolated from animal hosts (purple). The intensity of color in each ring corresponds to nucleotide sequence similarity based on BLAST analysis, with darker colors indicating higher similarity (~90%), and lighter colors reflecting lower similarity (below 70%). Major gaps were labelled 1–8, and minor gaps were labelled a–w.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/c6f9d89739b1084bab0861ea.png\"},{\"id\":84679981,\"identity\":\"4be5ea3e-4f7d-4677-815b-7dad57b40bef\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:36\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":110600,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHeatmap showing the distribution of ARGs detected across studied \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eM. \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003emorganii strains. Cluster analysis was performed based on the AMR profiles, with antimicrobial abbreviations shown on the horizontal axis and isolates from different niches displayed along the vertical axis. Red colour indicates presence, while white indicates absence of ARGs. The analysis revealed three distinct clusters, as identified in pangenome and PCoA analysis.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/d7681beef5d3b03a8b2be840.png\"},{\"id\":84680003,\"identity\":\"09e89e46-31ca-4ca5-89e5-b415e6c447d2\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:37\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":484115,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCircular visualization of MGE context surrounding ARGs according to their flanking region. The upper half of the plot represents the 98 ARGs found across \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eMorganella\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e genomes isolated from various niches. The lower half represents the mobile genetic elements detected in the flanking region of ARGs. Inner lines represent the connection between MGEs and ARGs. Colour coding in the outer ring (upper half) and inner ring (lower half) indicates the genomic location of the ARGs: green for chromosomal, blue for plasmid-borne, and red for ARGs found in both locations.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/ea1e793bfbf9512460196de5.png\"},{\"id\":97178476,\"identity\":\"de43e783-3fd7-44aa-b9e3-84ad32d3d522\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 16:10:27\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3690856,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/16ae0493-d311-459a-9265-67032ff530bb.pdf\"},{\"id\":84680721,\"identity\":\"031669d8-5824-4c99-b42d-f88a5d36bb84\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:19:36\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":508266,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/3900c3f264e67a1a9f2fead8.jpg\"},{\"id\":84679980,\"identity\":\"9b03bb43-6fbf-4ed8-b0d0-bdd6620e477d\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:36\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":181246,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/1371ea1391bf0e43d1c0a4db.jpg\"},{\"id\":84681657,\"identity\":\"1fa97016-d6ec-4a43-8af1-b508aaef2b09\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:27:37\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":44391,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/06af412f188c00d32c3cee07.jpg\"},{\"id\":84680004,\"identity\":\"278af060-fe0e-4ff4-bbaa-1c5939d0b43b\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:37\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":749305,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM4a.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/c0fda88dbd00c1d4dd7dd2d4.jpg\"},{\"id\":84679989,\"identity\":\"879b52ea-e42f-4720-8001-c5a78e1ea403\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:11:37\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":742666,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM4b.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/13b10afbd45b9b0c1165eb13.jpg\"},{\"id\":84680736,\"identity\":\"2d4d9cdf-7ae3-4878-92a0-7bb002d6c680\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:19:38\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":991488,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM4c.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/bd1f357d6d5f40507f5ff772.jpg\"},{\"id\":84680724,\"identity\":\"06eca9d6-fed4-4043-9deb-2ae39ac667ac\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:19:37\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":620856,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM5a.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/6f33f510521f528a41cf9145.jpg\"},{\"id\":84681660,\"identity\":\"4415850b-f94e-4c26-921f-73fc1a4a6126\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:27:37\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":628395,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM5b.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/80550a64a037300d0d5d7f9a.jpg\"},{\"id\":84682292,\"identity\":\"c6fdaf46-5f1f-47c2-a4bf-1e3c2851d5ce\",\"added_by\":\"auto\",\"created_at\":\"2025-06-16 08:35:37\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":414305,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ESM5c.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6872201/v1/91d57b02941a27bff2eed680.jpg\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Pan-genome analysis of Morganella Morganii reveals niche-specific selection of functional traits: Friend or Foe?\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eHigh genomic flexibility of the microorganisms makes them capable of inhabiting diverse ecological niches, such as soil, wastewater, and clinical communities. The adaptation of microorganisms is primarily determined by the gene acquisition and gene loss events that may be responsible for niche specialization, enabling bacteria to acquire traits requisite to thrive in specific ecosystems. Amidst versatile organisms, the genus \\u003cem\\u003eMorganella\\u003c/em\\u003e, historically referred to as an opportunistic human pathogen, has been recently isolated from non-clinical habitats, prompting a re-examination of its ecological plasticity and niche-specific strategies. Regardless of its clinical relevance, recent studies demonstrate that \\u003cem\\u003eMorganella\\u003c/em\\u003e may harbour plant growth-promoting (PGP) characteristics that make them to serve as a beneficial microorganism in environmental habitats (Chandarana and Amaresan \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In addition, several studies have reported its ability to immobilize the heavy metals from metal contaminated soil, suggesting their potential application for bioremediation purposes (Naqqash et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2024a\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e). Interestingly, certain species of \\u003cem\\u003eMorganella\\u003c/em\\u003e are found to synthesize bioactive secondary metabolites as well as antimicrobial compounds that support its application in horticultural and medicinal fields (Kadhim et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, in clinical settings, the genus exhibited pathogenic characteristics, especially in immunocompromised hosts. Besides, the multidrug resistance strains of \\u003cem\\u003eMorganella\\u003c/em\\u003e have been reported to cause nosocomial surgical wound infections, sepsis, plague, blood-stream infections, central nervous system infection, brucellosis, urinary tract infections, pneumonia, chorioamnionitis and systemic infections (Lebeaux et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The dual nature of \\u003cem\\u003eMorganella\\u003c/em\\u003e, as both a PGP bacterium and as an opportunistic pathogen, need further research to get deeper insight into its adaptation processes in different ecological niches and its genetic content that differentiate its environmental life cycle from clinical one.\\u003c/p\\u003e \\u003cp\\u003eHorizontal gene transfer (HGT) events carried out by mobile genetic elements (MGEs) such as insertion elements, transposons, integrons, and plasmids, is responsible for microbial adaptation and their diversification at strain level. These events allow microorganisms to acquire niche-specific adaptive traits to perform essential functions for survival in complex habitats, such as resistance to heavy metals and antibiotic resistance genes (AMR), enhanced metabolic functionality, and host interactions. For instance, the broad dissemination of well-known pathogenic islands in \\u003cem\\u003eKlebsiella pneumonia\\u003c/em\\u003ee is responsible for the emergence of its multidrug-resistant (MDRKP) and carbapenem-resistant hypervirulent (CR-hvKP) strain, aggravating the global concern in the treatment and prevention of pathogenic strains (Gao et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). On the other hand, the distribution of Tn\\u003cem\\u003eMERI1\\u003c/em\\u003e-like transposons encoding mer operon among diverse bacterial species, including Bacilli, emphasizes the niche-specific role of MGEs in mercury detoxification pathways (Matsui and Endo \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The common association of several MGEs on bacterial genomes was found to be responsible for intricate evolutionary interactions that drive adaptability as well as ecological success (Weisberg and Chang \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePrevious studies reported the multiple HGT events in \\u003cem\\u003eM. morganii\\u003c/em\\u003e, which have facilitated the acquisition of antimicrobial resistance, specifically in the clinical environment. A new transposon like Tn7376, enabled by IS26-mediated recombination was discovered in \\u003cem\\u003eM. morganii\\u003c/em\\u003e that confers resistance to multidrug resistance genes (Luo et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Genes such as blaKPC-2 and blaNDM-1 that provide resistance from carbapenem antibiotics are found to be transferred in \\u003cem\\u003eM. morganii\\u003c/em\\u003e through IncL/M plasmids and IS26-mediated transposon activity (Yao et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Moreover, inter-plasmid transposition of Tn4401a was found to facilitate the horizontal transfer of blaKPC-2 from \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e to \\u003cem\\u003eM. morganii\\u003c/em\\u003e through ColRNAI plasmids, enhancing resistance spread (Sugita et al. \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). On the other side, genomic analysis of \\u003cem\\u003eM. morganii\\u003c/em\\u003e found islands that encode for metal transporters, toxin proteins, stress proteins, and lipopolysaccharide virulence genes and chemotaxis (kdsA, cheY), biosynthetic gene clusters, and defence systems such as CRISPR-Cas (Sium et al. 2025).\\u003c/p\\u003e \\u003cp\\u003eAlthough several studies have been carried out on clinical isolates of \\u003cem\\u003eM. morganii\\u003c/em\\u003e, their genomic functions in environmental and plant habitat have not been fully investigated. The identification of genomic traits involved in PGP activity may give valuable information regarding their ecological role in agricultural fields outside of clinical settings. Further, the distribution and functional role of genes related to PGP activity, stress resistance, and AMR may provide a deeper understanding of microbial evolution, environmental survival, and their applicability in biotechnology and bioremediation. The aim of the study was to characterize the first-ever draft genome sequences of rhizosphere soil-isolated M. morganii. To do so, comparative genomic analysis of food, clinical, and wastewater \\u003cem\\u003eM. morganii\\u003c/em\\u003e isolates was performed to provide insight into the genetic basis of niche-specific adaptations. Since our rhizosphere-associated \\u003cem\\u003eM. morganii\\u003c/em\\u003e genome represents PGP characteristics, we sought to compare it with publicly available \\u003cem\\u003eM. morganii\\u003c/em\\u003e genomes to find additional regions of DNA correlating with unique traits, i.e., heavy metal degradation in environmental isolates and AMR in clinical isolates. Specifically, the objectives of this study were: (i) to compare genome-wide structural variations using comparative genomic visualization and identify the niche-specialized genetic repertoire of the \\u003cem\\u003eM. morganii\\u003c/em\\u003e across niche; (ii) to examine gene distribution related to AMR and role of MGEs in their dissemination, and (iii) to recognize genes linked to PGP activities in the rhizosphere-related \\u003cem\\u003eM. morganii\\u003c/em\\u003e genome. By shedding light on the genetic adaptations of \\u003cem\\u003eMorganella\\u003c/em\\u003e across various ecological habitat, this study provides new insights into its possible role as a PGP bacterium and advances our understanding of how bacteria manage the gene pool to support niche dependent survival.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Isolation and identification of rhizosphere bacteria\\u003c/h2\\u003e \\u003cp\\u003eRhizosphere soil samples from the chickpea (\\u003cem\\u003eCicer arietinum\\u003c/em\\u003e), maize (\\u003cem\\u003eZea mays\\u003c/em\\u003e), and wheat (\\u003cem\\u003eTriticum aestivum\\u003c/em\\u003e) fields were collected in Panwadi, Nagpur, India. The samples were transported to the laboratory under sterile conditions and processed within 48 hrs of collection. The soil sample was serially diluted (10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;4\\u003c/sup\\u003e) in phosphate-buffered saline (PBS), and 100 \\u0026micro;L of each dilution was spread onto nutrient agar medium under aseptic conditions (HiMedia, India). The plates were incubated at 30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1\\u0026deg;C for 24 hr. Following incubation, based on morphological characteristics, distinct bacterial colonies were selected and purified through successive re-streaking. The purified isolates were sent to HiMedia Laboratories, Thane, Mumbai, for species identification using Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectrometry.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 In Vitro Assays for Plant Growth-Promoting Traits\\u003c/h2\\u003e \\u003cp\\u003eTo assess the ability of microbial isolates to solubilize potassium, phosphorus, and zinc, 10 \\u0026micro;L of overnight-grown culture from LB broth was PBS-washed and spot inoculated on a Pikovskaya\\u0026rsquo;s, Aleksandrow, and Zinc solubilizing agar medium plate, respectively. The plates were incubated at 30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1\\u0026deg;C for 48 h. The solubilization index of the isolated strains was evaluated based on the solubilization zones observed on the medium plates, as shown in Eq.\\u0026nbsp;(1).\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:Solubilization\\\\:index=\\\\frac{Diameter\\\\:of\\\\:Solubilization\\\\:zone+Diameter\\\\:of\\\\:colony}{Diameter\\\\:of\\\\:colony}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip; Eq.\\u0026nbsp;(1)\\u003c/p\\u003e \\u003cp\\u003eThe ability of isolates to produce indole-3-acetic acid (IAA) was assessed as per the protocol given by Lebrazi et al (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The nitrogen-fixation capacity of the isolates was measured by incubating NB-grown PBS-washed overnight cultures (normalized to OD 1.0) in a 1:1 mixture of NB and Burk's medium (50 mL each) at 30\\u0026deg;C for 24 hours. Then, the cultures were transferred to 100 mL Burk's medium and incubated further for another 48 hours under the same incubation conditions. Nitrogen fixation capacity was measured indirectly by reading the OD of the cultures at 600 nm. For antifungal activity, an actively growing culture of phytopathogenic fungi was inoculated on one side of a petridish containing potato dextrose agar. Freshly grown culture of the soil bacterial isolates was streaked near the opposite edge of the plate. The plates were incubated for 7 days at 25\\u0026deg;C, and zones of inhibition were observed to determine the antifungal activity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Genomic DNA extraction, whole-genome sequencing, assembly, and taxonomic identification\\u003c/h2\\u003e \\u003cp\\u003eDNA extraction was carried out with the quick-DNA\\u0026trade; fecal/Soil Microbe Miniprep Kit (Zymo Research). DNA was checked for integrity using 1% agarose gel electrophoresis and quantified using a Qubit photometer. Library prep was done using Native Barcoding Kit 24 (Q20+) (SQK-NBD114.24) following the manufacturer's protocol (version NBE_9134_v112_revE_01Dec2021). The resulting barcoded library was loaded on one R10.4 flow cell, and sequencing was performed on a MinION sequencer (Mk1b) with MinKnow v23.04.3 (ONT). The generated raw reads were basecalled in high accuracy mode using Guppy v. 6.5.7 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://community.nanoporetech.com/downloads\\u003c/span\\u003e\\u003cspan address=\\\"https://community.nanoporetech.com/downloads\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e)\\u003c/span\\u003e with the dna_r10.4.1_e8.2_400bps_hac.cfg model for R10.4 chemistry. Reads with lower than 200 bp length and a Phred quality score below 10 were removed from the dataset. De novo genome assembly was performed using Bacterial assembly and annotation workflow from EPI2ME labs v0.2.6 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/epi2me-labs/wf-bacterial-genomes\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/epi2me-labs/wf-bacterial-genomes\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e)\\u003c/span\\u003e with default parameters. Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e shows the workflow parameters and softwares used through the pipeline.\\u003c/p\\u003e \\u003cp\\u003eAccurate species-level taxonomic assignment of the soil-isolated strains was achieved using an integrated approach incorporating several overall genome-relatedness indices (OGRI). Briefly, genome completeness score and contamination level were assessed using CheckM (Parks et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) that employs a set of clade-specific single-copy marker genes to confirm that the assembled genomes were of high quality and no gene had been missed. The Microbial Genome Atlas (MiGA) server was used to determine the genome quality by comparing the assembled genome to a closely related type strain based on average amino acid identity (AAI) and average nucleotide identity (ANI) (Rodriguez-R et al. 2018). The phylogenetic relationship of the isolates was performed by aligning their 16S rRNA gene sequence with those of related strains retrieved from the NCBI database, using the maximum likelihood method in MEGA X (Kumar et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Furthermore, whole-genome phylogenetic analysis, including \\u003cem\\u003eRhizobium leguminosarum\\u003c/em\\u003e SM52 as an outgroup and a positive control for plant growth-promoting traits, was performed using the SpeciesTree v2.2.0 tool on the KBase platform (Arkin et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). \\u003cem\\u003eM. morganii\\u003c/em\\u003e ATCC 25830 was used as a reference genome. Comparative analyses such as BLAST-based ANI (ANIb), MUMmer-based ANT (ANIm), and tetranucleotide frequency correlations (TETRA) were executed using JSpeciesWS (Richter et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The digital DNA: DNA hybridization (dDDH) via the Genome-to-Genome Distance Calculator (GGDC) was calculated using Type (Strain) Genome Server (TYGS) (Meier-Kolthoff and G\\u0026ouml;ker \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), while the Orthologous Average Nucleotide Identity Tool (OAT) was used to construct the diagram illustrating ANI relationships (Lee et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Basic genomic features and subsystem category distribution were obtained using the Rapid Annotations using Subsystems Technology tool kit (RASTtk) (Brettin et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Genome selection, annotation, and comparative analysis\\u003c/h2\\u003e \\u003cp\\u003eThe genome sequences of the related \\u003cem\\u003eMorganella\\u003c/em\\u003e strains were retrieved from the NCBI website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e)\\u003c/span\\u003e by selecting entries with \\u0026ldquo;assembly level-complete\\u0026rdquo;. For assessing the genome quality, an OGRI was performed on the extracted genome as outlined in the above section to filter good-quality genomes. Metadata collection was carried out to obtain detailed information on each of the strains, such as isolation source, host, genome size, contig number, N50 and L50 values, GC content, and CheckM marker set scores. In addition, the completeness of the genome and contamination levels were determined to validate the precision and accuracy of the genomic data. These metadata were manually cross-checked and correlated with previous publications to ensure consistency and completeness.\\u003c/p\\u003e \\u003cp\\u003eThe complete and good-quality genomes were annotated using Prokka (Seemann \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), which employs Prodigal to predict potential genes and proteins present within the genome. The resulting general feature format (GFF) output of Prokka was used as input for pan\\u0026ndash;core analysis using the Roary pipeline v3.11.2 (Page et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). The output of the pangenome analysis i.e. gene presence-absence matrix was used for principal coordinate analysis (PCoA) using Bray\\u0026ndash;Curtis dissimilarity, calculated via the vegdist() function from the vegan R package v4.4.3. The cmdscale() function was used to view the resultant matrix, and ggplot2 was employed to plot the first two components. Furthermore, BLAST Ring Image Generator (BRIG) plot analysis (Alikhan et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) was used to visualize the extra or unique DNA region and determine the genomic difference between the soil-isolated \\u003cem\\u003eM. morganii\\u003c/em\\u003e and the distinct cluster that developed in response to the observed clustering pattern. The unique genes were annotated using the Basic Local Alignment Search Tool (BLAST) to infer potential function. For genes annotated as hypothetical proteins, their sequences were retrieved using BEDTools (Quinlan \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) and the probable functional assignment was done using the InterProScan tool, which searches the protein sequences against databases like Pfam, PANTHER, CDD, NCBIfam, SMART, ProSiteProfiles, ProSitePatterns, Gene3D PRINTS, Superfamily, and TIGRFAM to detect conserved domains. The output obtained was probable functions, enzyme classes, and GO terms of hypothetical proteins.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Identification of AMR genes and mobile genetic elements\\u003c/h2\\u003e \\u003cp\\u003eTo determine the distribution and mobility of AMR genes among \\u003cem\\u003eMorganella\\u003c/em\\u003e isolates from distinct ecological niches, a multi-step bioinformatic pipeline was performed. The AMR gene detection was performed using AMRFinderPlus v3.10.5 (Feldgarden et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), and the resulting output was aggregated into a binary matrix representing gene presence or absence among isolates. This matrix was utilized to create a heatmap through the heatmap package in R, using hierarchical clustering to plot differences in AMR gene profiles between the distinct ecological niches. To investigate the genetic mobility of AMR genes, the genomic regions flanking\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5 kb around each AMR gene were extracted using BEDTools intersect. Different tools were employed for detection of MGEs: ISEScan v1.7.2.3 (Xie and Tang \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) was applied to find insertion sequences, MobileElementFinder v1.1.2 (Johansson et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) to identify transposons, and IntegronFinder v2.0 (N\\u0026eacute;ron et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) to find active integrons, CALINs, and attC sites. In addition, the MOB-suite v3.1.9 (Robertson and Nash \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) was applied to find plasmid-related AMR gene sequences. The results were combined and visualized in a Circos plot, giving an overall impression of AMR gene mobility and distribution.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Pathway analysis\\u003c/h2\\u003e \\u003cp\\u003eTo determine the presence of PGP traits of the isolates, genes involved in direct as well as indirect PGP pathways were manually curated from the existing literature. These genes were compared against the relevant pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.genome.jp/kegg/pathway.html\\u003c/span\\u003e\\u003cspan address=\\\"https://www.genome.jp/kegg/pathway.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e)\\u003c/span\\u003e, and the full list of genes involved in specific pathways was extracted and compared with the annotated features of the isolates. Furthermore, detection of secondary metabolite biosynthetic gene clusters and bacteriocin were performed using antiSMASH v6.0 (Antibiotics and Secondary Metabolites Analysis Shell) and BAGEL4 tools, respectively (Heel et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Blin et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 Data access\\u003c/h2\\u003e \\u003cp\\u003eThe genome sequences of HM01, HM02, and HM04 have been released in GenBank under the bioproject accession number PRJNA1266903.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Phenotypic characterization and PGP activity\\u003c/h2\\u003e \\u003cp\\u003eStrains designated HM01, HM02, and HM04 were identified as \\u003cem\\u003eM. morganii\\u003c/em\\u003e based on MALDI-TOF mass spectrometry analysis. These strains were isolated from the rhizosphere soils of chickpea, maize, and wheat fields, respectively. The isolates were further tested in detail for PGP characteristics through in vitro screening such as phosphate, potassium, and zinc solubilization, IAA production, and nitrogen fixation. All three \\u003cem\\u003eMorganella\\u003c/em\\u003e isolates showed positive outcomes for PGP characteristics. Phosphate solubilization indices for HM01, HM02, and HM04 were found to be 4.5, 2.6, and 3.9. All the strains were able to solubilize potassium chloride as a sole source of potassium. The potassium solubilization indices were found to be 6.5 for HM01, 5.5 for HM02, and 4.2 for HM04. Zinc solubilization indices were reported to be 2.75, 4.5, and 5.1, respectively. IAA plays a major role in plant growth promotion and development, and it was observed that all the rhizosphere-associated \\u003cem\\u003eM. morganii\\u003c/em\\u003e strains produced high levels of auxin when supplemented with L-tryptophan. The IAA concentrations were found to be 9.85, 10.36, and 9.29 \\u0026micro;g/mL for HM01, HM02, and HM04, respectively. Nitrogen fixing ability was also confirmed from increased OD\\u003csub\\u003e600nm\\u003c/sub\\u003e in Burk's medium, in which all three isolates proved to be positive. The OD was found to be increased from 0.1 OD/mL to 1.1, 0.8 and 0.6 OD/mL for HM01, HM02, and HM04, respectively. Additionally, the strain HM02 and HM04 exhibited antifungal activity. This suggest that these strains may produce bacteriocin (compounds with antimicrobial activity), that protects the plant by inhibiting the growth of phytopathogenic fungi as well as reduces the disease-related damage (Nazari and Smith \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Overall PGP potential of the isolates is provided in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\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\\u003ePGP activities of \\u003cem\\u003eMorganella\\u003c/em\\u003e strains isolated from rhizosphere soil of chickpea, maize, and wheat fields.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSr. No.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePGP trait\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHM01\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHM02\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHM04\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP-Solubilisation index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eK-Solubilisation index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eZn-Solubilisation index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIAA production (\\u0026micro;g/mL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNitrogen Fixation (OD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAntifungal activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e+\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e+\\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=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Genomic characterization\\u003c/h2\\u003e \\u003cp\\u003eThe presence and distribution of single-copy marker genes in the genomes gives ideas about genome quality. Completeness and contamination levels of rhizosphere soil-isolated \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes were calculated based on the presence, absence, or possible duplication of these conserved marker genes. It indicates possible contamination added during the DNA extraction or sequencing step. The estimated genome completeness scores were found to be 100% for HM01, 99.37% for HM02, and 92.91% for HM04, which indicate the high-quality assembly for downstream analysis. 16S rRNA-based and whole-genome-based phylogenetic trees confirmed the close genetic relationship of HM01, HM02, and HM04 to \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e ATCC 25830. Furthermore, pairwise genomic similarity analyses using ANI and dDDH methods reinforced these findings. Particularly, ANI (based on OrthoANI), and GGDC estimates all indicated the greatest similarity among the three isolates and reference strains \\u003cem\\u003eM. morganii\\u003c/em\\u003e ATCC 25830, thus validating their species-level identification and genetic similarity \\u003cb\\u003e(Online Resource ESM_1)\\u003c/b\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe basic genomic characteristics of rhizosphere soil-isolated \\u003cem\\u003eMorganella\\u003c/em\\u003e strains are depicted in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Briefly, strain HM01 represents a complete circular chromosome with the highest assembly quality and an L50 value of 1. HM04 also showed a high-quality assembly, with only 13 contigs and an N50 value of 2,599,043 bp. In contrast, strain HM02 showed 330 contigs and a lower N50 value of 21,387 bp. The GC content of strains HM01, HM02, and HM04 was found to be 50.3, 41.7, and 50.6, respectively. The genome sizes of the three strains also varied from 3.8 to 4.3 Mb, with HM04 being the largest one. Corresponding with its size, HM04 showed a high count for predicted coding sequences (4,738) compared to HM01 (4,239) and HM02 (4,214). The RNA gene count was found to be 103, 94, and 103 in HM01, HM02, and HM04, respectively. Functional subsystem classification revealed a broader metabolic and functional repertoire in HM01 (333 subsystems) and HM04 (334), as opposed to HM02 (253), which could be due to its lower genome completeness or smaller genome size \\u003cb\\u003e(Online Resource ESM_2)\\u003c/b\\u003e. In all the strain, a high abundance of genes involved in pathways known to promote plant growth was observed \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. Specifically, strain HM01 and HM04 demonstrated significant enrichment in genes associated with phosphorus metabolism, potassium metabolism, iron acquisition and metabolism, motility and chemotaxis, and stress response. While in strain HM02, genes related to iron acquisition and metabolism and stress response were present in higher numbers. Collectively, these observations showed that rhizosphere-associated strains possess PGP traits for both direct mechanisms (such as nutrient solubilization and uptake) and indirect mechanisms (such as stress mitigation and competitive colonization).\\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\\u003eBasic genomic details of rhizosphere soil-isolated \\u003cem\\u003eMorganella\\u003c/em\\u003e strains.\\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\\u003eFeatures\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHM01\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHM02\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHM04\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTaxonomy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBacteria; Pseudomonadati; Pseudomonadota; Gammaproteobacteria; Enterobacterales; Morganellaceae; Morganella; Morganella morganii\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal data (Mb)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGC content (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of Contigs (with PEGs)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e330\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN50 (bp)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4155665\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21387\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2599043\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eL50 (bp)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of Coding Sequences\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4239\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4214\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4738\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of RNAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e103\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of Subsystems\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e333\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e253\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e334\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSubsystem Classification (Feature counts)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ephosphorus metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epotassium metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esulphur metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eiron acquisition and metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esecondary metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emotility and chemotaxis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStress response\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e64\\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\\u003eFor comparative genomic analysis, a total of 84 \\u003cem\\u003eM. morganii\\u003c/em\\u003e genomes were extracted from NCBI along with the metadata annotation on basic genomic features. Based on the metadata information, genomes lacking source information (n\\u0026thinsp;=\\u0026thinsp;4) and those with a low CheckM completeness score and a high number of contigs (n\\u0026thinsp;=\\u0026thinsp;2) were excluded from the study. The resulting 78 genomes were included in the study and were derived from the diverse niche, including food (n\\u0026thinsp;=\\u0026thinsp;11), wastewater (n\\u0026thinsp;=\\u0026thinsp;3), human clinical samples (n\\u0026thinsp;=\\u0026thinsp;48), and various animal hosts (n\\u0026thinsp;=\\u0026thinsp;16). Across the niche, the sizes of the genomes varied from 3.8 to 4.3 Mb. The difference in genome size was found to be mainly due to the gain and loss of functional genes (Moulana et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The greater sizes (4.0 to 4.3 Mb) of the food- and wastewater-derived genomes suggest the acquisition of accessory genes involved in nutrient uptake, stress tolerance, and environmental adaptation. In contrast, the smaller genome sizes (3.7 to 4.0 Mb) in human- and animal-derived genomes denote streamlined genomes harbouring specialized gene sets required for the adaptation to host-associated lifestyles. Relatively stable GC content, ranging between 50.5% and 51.5% among all isolates, indicates a conserved base composition across different niches. An overview of the genomic features of the NCBI-extracted \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes is provided in \\u003cb\\u003eSupplementary File 1\\u003c/b\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Pan-core analysis\\u003c/h2\\u003e \\u003cp\\u003eTo characterize the genetic diversity of the \\u003cem\\u003eMorganella\\u003c/em\\u003e, a pan-genome of soil-isolated \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes (n\\u0026thinsp;=\\u0026thinsp;3) was performed along with publicly available genomes (n\\u0026thinsp;=\\u0026thinsp;78), and the distribution of genomic features was analysed. Pangenome analysis revealed the distinct genomic distance between the selected \\u003cem\\u003eMorganella\\u003c/em\\u003e strains, with the core gene alignment showing three major phylogenetic groups \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea\\u003cb\\u003e).\\u003c/b\\u003e The first group was the largest, well-defined cluster, consisting of 49 genomes, derived mostly from clinical and wastewater sources. The second group, consisting of soil-isolated strains HM01 and HM02, clustered separately with the food-derived isolates. Such phylogenetic allocation suggests that HM01 and HM02 could either have functional or evolutionary characteristics comparable to food-borne strains or could have analogous convergent adaptation or gene gain applicable to the food environment. This clade had a distinct block of accessory gene sets, well defined from the remaining two groups within the phylogenetic tree. Strain HM04, on the other hand, is grouped differently, closely related to the strains derived from both human as well as animal origins. The reason for this unexceptional placement is unclear; however, one possible explanation could be historical cross-environmental transmission or HGT events involving mobile genetic elements shared between host-associated environments.\\u003c/p\\u003e \\u003cp\\u003eBased on homology, the total gene content of the genomes was classified into three categories: core (shared by all genomes), shell (present in some genomes), and cloud/unique (found in only one genome). Pangenome analysis calculated 23829 gene clusters, with only 8.52% (2031 genes) and 13.13% (3130 genes) being classified as core- and shell-genes, respectively \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb\\u003cb\\u003e)\\u003c/b\\u003e. A higher number of cloud genes, representing 78.34% (18668 genes) of the total pangenome, indicates that high degree of genomic flexibility in \\u003cem\\u003eMorganella\\u003c/em\\u003e is driven by acquiring the niche-specific traits (Zhong et al. \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Similarly, in the study of pangenome analysis of 59 \\u003cem\\u003eM. morganii\\u003c/em\\u003e strains, Rahman et al. (\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) observed a similarly low percentage of core genes (6.83%) and a large number of cloud genes. The pan genome curve, which plots the number of genes against the number of genomes analysed, did not reach a plateau \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec\\u003cb\\u003e)\\u003c/b\\u003e, again indicates the open-genome nature of \\u003cem\\u003eM. morganii\\u003c/em\\u003e. Among the studied strains, HM01, HM02, and HM04 had a combined total of 4152 unique genes, substantially more than the remaining 78 strains. Despite having a smaller genome size, strain HM02 exhibited a significant number of cloud genes (2885), followed by HM04 (1081) and HM01 (186). The remarkably high percentage of cloud genes in these strains may reflect the HGT events that act as an evolutionary force and facilitate the selection of novel functions and increase the adaptive potential of bacteria (Woods et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Investigation of bacterial traits involved in rhizosphere colonization revealed that HGT events play an important role in genome plasticity for rhizosphere adaptation (Lopes et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Similarly, another study reported that bacterial strains that acquired genetic materials from genomic islands through HGT, displayed better symbiotic nitrogen fixation competency compared to the closely related strains that lacked these genomic elements (Cotta et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). A significant portion of these cloud genes encoded hypothetical proteins in HM01, HM02, and HM04 (134, 1204, and 770, respectively), which exist in an environment with a lack of any supporting evidence of in vivo expression. These conserved hypothetical proteins are encoded by a significant proportion of the bacterial genome that can act as biomarkers or essential signalling proteins in the adaptation process, including biotic and abiotic stress, interspecies communication, and environmental interactions (Chirgadze et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBesides the hypothetical proteins, among the genes of interest, the unique genes identified in strains HM01, HM02, and HM04 were found to code for PGP activities, highlighting their potential ecological roles in the rhizosphere. Strain HM02 exhibited 19 unique PGP traits that include a wide range of functional categories needed for promoting plant growth and refining soil fertility. Pertaining to nutrient solubilization, strain HM02 contains a large repertoire of genes for phosphate solubilization (pstB3_1, pstB3_2, pstC1, and multiple pst genes) which facilitate the breakdown of inorganic phosphate and make it available for plant uptake. Besides, the strain also possesses genes for potassium solubilization (kup_1, kup_2), zinc solubilization (zupT), iron acquisition and metabolism (\\u003cem\\u003edtxR\\u003c/em\\u003e, \\u003cem\\u003efur_2\\u003c/em\\u003e), siderophore production (fepC), assimilatory and dissimilatory nitrate/nitrite reduction (\\u003cem\\u003enapA\\u003c/em\\u003e, \\u003cem\\u003enirC\\u003c/em\\u003e, \\u003cem\\u003enorG\\u003c/em\\u003e, \\u003cem\\u003enorR_4\\u003c/em\\u003e), which help in the breakdown of essential macronutrients and micronutrients, further enhancing its PGP potential. Additionally, genes involved in chemotaxis (cheB) that enable the strain to move towards favourable environmental conditions are also annotated.\\u003c/p\\u003e \\u003cp\\u003eStrain HM04 contains 34 distinct PGP genes, with most of the genes being chemotaxis-associated (32), indicative of its indirect plant growth modulation function through improved environmental sensing. Among the chemotaxis-related pathways, genes that code for chemotaxis signal transduction proteins (cheB, cheR, cheW, cheY, and cheZ) are detected. Genes for structural and motor flagellar components such as (fliD, fliE, fliF, fliG, fliH, fliI, fliJ, fliM, fliN, fliO, fliP, fliQ, fliR, fliS, fliT, and motA) were also detected. These genes represent the essential components of the bacterial two-component signalling system and are responsible for movement towards favourable stimuli (Colin et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The presence of several subunits of the tsr genes associated with methyl-accepting chemotaxis proteins showed that strain HM04 can sense and react to the spectrum of chemical compounds in the rhizosphere (Colin et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Furthermore, genes for assimilatory sulphate reduction (cysI and cysJ) were found, which suggest its possible function in sulfur assimilation and nutrient cycling as a part of plant health enhancement.\\u003c/p\\u003e \\u003cp\\u003eNo specific genes were present in strain HM01 directly related to plant growth. Yet, indirect plant development-influencing genes were present. These include genes for peptide uptake (sapD), starch synthesis (glgA), protein synthesis and stress tolerance (metZ), and a two-component regulatory system component facilitating environmental signal perception and response (rcsD_1). The occurrence of these cloud genes in rhizosphere bacteria indicates their possible functions in nutrient solubilization and acquisition, metabolic adjustment, and plant-microbe interaction, all of which are the essential elements of PGP mechanisms (Kumar et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022a\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Principal coordinate analysis (PCoA)\\u003c/h2\\u003e \\u003cp\\u003eTo further analyse the genomic relationships observed by the pangenome analysis, a PCoA was performed. The PCoA plot based on the Bray-Cutis distance matrix demonstrated a well-defined cluster \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea\\u003cb\\u003e)\\u003c/b\\u003e. This clustering pattern observed in the PCoA plot was consistent with those observed in the pangenome analysis based on gene composition. Genomes of different clusters were then plotted separately to study the genetic differentiation between each cluster, which reflects the degree of intra-cluster divergence along the PCA axis. In cluster 1 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb), even though the clustering was tight, genomes from human, animal, and wastewater isolates created separate sub-clusters. Among human-derived \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes, \\u003cem\\u003eMorganella\\u003c/em\\u003e from various clinical samples like blood, sputum, stool, wound, and urine showed a slight but distinct segregation on the PCoA axis. These minor spatial distinctions mirror localized genetic variations that may be motivated by site-specific selective pressures within the host (Chung et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In addition, the co-occurrence of animal-derived genomes with those from humans indicates the overlapping gene pools and regular gene flow as a result of the selective pressure, e.g., exposure to antibiotics or HGT events (Jing et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOn the contrary, the food-derived genomes in cluster 2 \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec\\u003cb\\u003e)\\u003c/b\\u003e formed a highly compact and distinct cluster, clearly separating them from the rhizosphere-associated strains. This is indicative of some crucial changes in these strains that may have occurred during their adaptation to specific habitat. In rhizosphere-associated strains, these changes might be the outcome of coevolution with plants (Iqbal et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This observation further supports that a particular habitat, such as plant-associated habitat can exert a profound effect in determining the frequency and direction of HGT in the bacterial community. It also indicates a consistency between nucleotide sequence and hierarchical clustering patterns (Iqbal et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These results are in line with the earlier research on Streptococcus spp., in which HGT was predominantly observed within strains that share the same habitat, further supporting the ides similar environmental pressure promote the exchange of genes between microbial communities that are suited to analogous ecological niches (Richards et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). This is indicative of a highly conserved core gene in rhizosphere-soil isolates suited for stress tolerance, heavy metal detoxification, and possibly plant-associated beneficial traits, including those related to nutrient solubilization or bioremediation. Like cluster 1, the genome in cluster 3 also showed spatial separation \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed\\u003cb\\u003e)\\u003c/b\\u003e. Though rhizosphere-soil isolated strain HM04 is phylogenetically grouped among animal- and human-associated isolates, it is still genetically separate. This is an observation that reflects ecological pressure in the rhizosphere resulting in a specific genetic makeup, different from that noted among clinical samples.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 BRIG-Based Genomic Alignment\\u003c/h2\\u003e \\u003cp\\u003eWhile strains HM02 and HM04 appeared more distantly clustered on the PCoA plot based on the cluster-separated analysis, strain HM01 remained closely associated with food-derived, and to a lesser extent, animal- and wastewater-derived genomes. This indicates the highly shared genetic pools, although HM01 retains unique genetic elements that set it slightly apart. Genomic flexibility analysis enables the recognition of regions that might be related to the adaptive processes of bacteria to their specific environment (Gomes et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Therefore, to identify genomic differences within specific regions, the BRIG plot was used to examine divergence patterns \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. The chromosomal backbone of the strain HM01 was found to be highly conserved and aligned with the reference strain \\u003cem\\u003eM. morganii\\u003c/em\\u003e ATCC 25830. Other strains also showed large regions of similarity (98%) with the reference strain, however, the regions of dissimilarity were distributed intermittently across the genomes, which was visualized as gaps. The gaps consistently observed in the genomes, i.e., the genes present in HM01 but absent in others, were classified as major gaps (labeled 1\\u0026ndash;8) and minor gaps (a\\u0026ndash;w). The coordinates of the gap regions were manually curated and annotated using the GenBank (gbk) file derived from the Prokka tool. Specifically, the major gaps mainly contained clusters of hypothetical proteins and MGEs (prophage integrases (e.g., IntA and IntS) and transposases) and lysozyme-related genes (e.g., RrrD).\\u003c/p\\u003e \\u003cp\\u003eAdditionally, each major gap also contains a distinct set of genes. Gap 1 contained regulatory and metabolic elements such as LexA repressor and Na⁺-translocating NADH-quinone reductase subunit E. In Gap 2, there were genes for DNA damage response (UmuC and UmuD) and a mitochondrial ubiquinone biosynthesis methyltransferase, whereas Gap 3 coded for replication and DNA repair, such as replicative helicase, a putative HTH-type transcriptional regulator, and exodeoxyribonuclease VIII. This emphasizes the potential of HM01 for DNA damage repair and maintenance of metabolic processes under stress conditions (Fu et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Stratton et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Gap 4 has a membrane-bound lytic murein transglycosylase C and the toxin subunit YenB that is crucial to prevent the growth and colonization of phytopathogens in the rhizosphere (Panicker and Sayyed \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Gap 5 comprises carbohydrate metabolism-related genes, such as UDP-glucose modifying enzymes, glycogen synthase, teichoic acid transferase, and the sensor histidine kinase CpxA. CpxA is a sensor kinase primarily involved in envelope stress response by regulating the downstream genes of the CpxAR two-component system. Activation of this system triggers a cascade in response to envelope stress, such as antimicrobial exposure or environmental fluctuations (Cho et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Stress response and regulatory elements, including the HipA toxin, FtsH metalloprotease, RNA polymerase-associated protein RapA, RecF, along with IS3 family transposases ISLad1 and IS911, were noted in Gap 6. HipA induces translation inhibition (Nashier \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e); FtsH maintains membrane protein homeostasis (Yi et al. \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e); RapA facilitates transcriptional recovery (Qayyum et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) RecF is involved in DNA damage repair (Sass et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e); and IS elements enable the bacteria to acquire advantageous traits. Gap 7 was characterized by a full suite of arsenic resistance genes (ArsR2, ArsD, ATPase, efflux pump, and reductase) that are required to survive in an environment contaminated with heavy metals (Kumar et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022b\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBesides, several minor gaps comprising fewer genes were annotated to contain a diverse array of functional genes that may play an important role in PGP activity. These genes included mannose-6-phosphate isomerase (gap a; carbohydrate metabolism), autoinducer-2 kinase (gap c; quorum sensing and interspecies communication), cell division protein DamX and a putative deoxyribonuclease (RhsC) (gap e; cellular growth and genome stability), threonine/homoserine exporter RhtA (gap h; amino acid transport), acetyltransferase (gap i; post-translational modification or detoxification); peptide transporter CstA (gap n; nutrient acquisition under stress), serine endoprotease DegS (gap o; stress-response), and serine acetyltransferase (gap r; sulfur metabolism). These genes are uniquely present in strain HM01 that help plant development by nutrient acquisition as well as in stress response. Additionally, genes related to AMR, virulence and MGEs are detected viz., phenazine antibiotic resistance protein EhpR (gap f; AMR resistance), actin cross-linking toxin VgrG1 (gap l; type VI secretion systems and bacterial virulence), IS3 family transposases (ISYps8, ISLad1, ISEc52) and a ribosomal RNA large subunit methyltransferase F (gap m; horizontal gene transfer and translation regulation), and multidrug resistance protein MdtB (gap w; AMR resistance). Genes with general cellular function, such as \\u003cem\\u003eputative hydrolase YdeN\\u003c/em\\u003e and \\u003cem\\u003eRNA pyrophosphohydrolase (gap u;\\u003c/em\\u003e nucleotide metabolism) and exonuclease \\u003cem\\u003eDinG (gap b;\\u003c/em\\u003e DNA repair mechanism), were also detected. Collectively, the minor gaps reflect the broad range of genes involved in stress response, metabolism, AMR, and hence plant growth. Gaps \\u003cem\\u003ed\\u003c/em\\u003e, g, \\u003cem\\u003ej\\u003c/em\\u003e, \\u003cem\\u003ek\\u003c/em\\u003e, \\u003cem\\u003ep\\u003c/em\\u003e, \\u003cem\\u003eq\\u003c/em\\u003e, \\u003cem\\u003et\\u003c/em\\u003e, and \\u003cem\\u003ev\\u003c/em\\u003e were found to contain hypothetical proteins. This variation in gene content correlated with the finding that classifies \\u003cem\\u003eMorganella\\u003c/em\\u003e as a highly recombinogenic bacterium that can frequently acquire genetic elements through HGT events (Jing et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Role of hypothetical proteins\\u003c/h2\\u003e \\u003cp\\u003eAs revealed by the pangenome and the BRIG analysis, the majority of cloud genes present in the rhizosphere soil-derived \\u003cem\\u003eMorganella\\u003c/em\\u003e genome were classified as hypothetical proteins. These hypothetical proteins, without any supporting evidence of in vivo expression, may act as biomarkers or essential signalling proteins in the adaptation process, including responses to biotic and abiotic stress, interspecies communication, and environmental interactions (Rahman et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Hence, the predictive functional characterization of hypothetical proteins was performed to elucidate their potential biological role. Of the 134, 1204, and 770 hypothetical proteins, functional predictions for 56, 563, and 335 were successfully obtained in HM01, HM02, and HM04, respectively.\\u003c/p\\u003e \\u003cp\\u003eMost hypothetical proteins annotated as endonucleases and reverse transcriptases that were associated with cellular functions such as DNA repair. Some proteins matched with DNA-binding motifs like lambda repressor-like, MerR-type, GntR-type, and winged helix-turn-helix (HTH) domains that are involved in putative regulatory activities. In all three strains, N-acetylmuramoyl-L-alanine amidases was predicted. These enzymes are peptidoglycan hydrolases that cleave the bond between N-acetylmuramic acid and L-alanine, and responsible for cell wall remodelling, cell separation, biofilm formation, and growth inhibition of phytopathogens (Guzm\\u0026aacute;n-Moreno et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Another major group of hypothetical proteins showed functional similarity with the Major Facilitator Superfamily (MFS), a group of integral membrane protein that takes part in several important processes of bacterial cell physiology. In PGP bacteria, MFS involved in nutrient transport, antimicrobial compound efflux, and cell-to-cell signalling, and contributes to increasing the plant resistance against pathogens via suppression of phytopathogens or modulation of plant defence (Pasqua et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). A similar functional role of MFS genes has been observed in plants. A genome-wide analysis of \\u003cem\\u003ePopulus trichocarpa\\u003c/em\\u003e identified 41 MFS genes (PtrMFSs) that were highly expressed in response to fungal infection (\\u003cem\\u003eFusarium oxysporum\\u003c/em\\u003e) (Diao et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In addition, some of the hypothetical proteins were predicted to have stress response and virulence function-related domains, e.g., Salmonella virulence plasmid proteins encoded by the spv operon that play a role in systemic infection by increasing bacterial survival and replication inside host cells (Kang et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Domains such as Colicin V synthesis proteins involved in bacteriocin production, phage tail fibre proteins for host recognition and virulence, and components of type IV secretion systems responsible for translocating effector proteins into host cells are also predicted (Nazari and Smith \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). This reflects the strain\\u0026rsquo;s ability to enhance bacterial competitiveness, host colonization, and adaptation to environmental stress. The complete results of the preliminary annotation of hypothetical proteins are provided in \\u003cb\\u003eSupplementary File 2\\u003c/b\\u003e.\\u003c/p\\u003e \\u003cp\\u003eHypothetical proteins with predicted role in plant growth and development were manually curated. In HM02, which harbours the largest group of hypothetical proteins, several were related to iron sequestration and metabolism (FeoA_2, iron siderophore/cobalamin periplasmic-binding domain profile, ferritin-like domain), heavy metal detoxification (heavy-metal-associated domain), and sulphur metabolism (sulfite exporter TauE/SafE). Additionally, some hypothetical proteins were predicted to be involved in stress response (glycine betaine/proline betaine transport system permease protein ProW, glyoxylase/bleomycin resistance protein/dioxygenase superfamily), cell wall modification and stress response (lipopolysaccharide choline phosphotransferase LicD, teichuronic acid biosynthesis protein TuaE), biofilm production (colanic acid biosynthesis UDP-glucose lipid carrier transferase), secondary metabolite production (S-adenosyl-L-methionine-dependent methyltransferases, acetyltransferase (GNAT) domain, radical SAM superfamily, alpha/beta hydrolase, PLP-dependent transferases), and antifungal properties (LysM domain). Furthermore, the major group of hypothetical proteins in HM02 was predicted to be involved in membrane transport. These proteins were found to be related to the sugar ABC transporter integral membrane protein, the phosphate transport system permease, the D-xylose-binding periplasmic protein, the maltose/maltodextrin transport system permease protein MalF, the PTS system sugar-specific permease components, the ECF transporter substrate-specific components, the C4-dicarboxylate anaerobic carrier, the N-acetylgalactosamine permease II component, and bacterial extracellular solute-binding proteins. These proteins help plants absorb nutrients that are beneficial to plants.\\u003c/p\\u003e \\u003cp\\u003eIn HM04, proteins are predicted to be involved in lipopolysaccharide biosynthesis (glycosyltransferase family 25, nucleotide-diphospho-sugar transferases), carbohydrate biosynthesis (glycosyltransferase and glycosyltransferase GT-D fold), polysaccharide biosynthesis (polysaccharide biosynthesis protein), membrane transport (bacterial extracellular solute-binding protein), secondary metabolite production (acyltransferase family and acetyltransferase (GNAT) family), stress response (cyclopropane-fatty-acyl-phospholipid synthase), substrate hydrolysis (alpha/beta-hydrolases), bacteriocin production (S-type pyocin and colicin D domains), and antifungal properties (lysozyme-like domains and polysaccharide lyase). In HM01, proteins related to biofilm production (epsG family), secondary metabolite production (acyltransferase family and acetyltransferase (GNAT) family), polysaccharide biosynthesis (polysaccharide biosynthesis protein), metal ion homeostasis and oxidative stress resistance (cupin fold metalloproteins), and carbon storage and energy regulation (glycogen phosphorylase B) were predicted.\\u003c/p\\u003e \\u003cp\\u003eIn a study, Guzm\\u0026aacute;n-Moreno et al. (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) identified several hypothetical proteins in Bacillus megaterium HgT21 that are involved in heavy metal degradation, phosphate solubilization, membrane transport and bacteriocin production. Similarly, Msimbira et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reported that some hypothetical proteins in Lactobacillus helveticus (EL2006H) and Bacillus subtilis (EB2004S) were upregulated in response to changes in environmental conditions, such as pH. They concluded that these proteins may be expressed in response to specific environmental conditions and could play a major role in the adaptation process of bacteria. In a similar line, several studies identified a large repertoire of hypothetical proteins in strains associated with PGP activity (Guo et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Balderas-Ru\\u0026iacute;z et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). However, so far, it remains unclear whether the observed enhanced activity of the strains is due to the emergence of unique hypothetical protein or other biomolecules in PGP bacterium. Therefore, deeper insights into the predictive role of these significantly unique and potentially upregulated hypothetical proteins are required to better elucidate their involvement in plant growth and development.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.7 AMR gene profiling\\u003c/h2\\u003e \\u003cp\\u003eThe presence-absence heatmap for the AMR gene (ARGs) identified across 81 \\u003cem\\u003eMorganella\\u003c/em\\u003e strains is illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. A total of 98 ARGs that confer resistance to 15 different antibiotic classes were identified. The classes include β-lactams, aminoglycosides, macrolides, lincosamides, streptogramins, fluoroquinolones, chloramphenicol, tetracyclines, trimethoprim, sulfonamides, rifampin, streptothricin, bleomycin, fosfomycin, and biocide resistance. The most prevalent ARGs were found to be from β-lactams class (27/98), followed by aminoglycosides (24/98) and macrolides (8/98). Human host-derived genomes that comprise the largest subset harbored numerous ARGs (83/98, 85%). Almost all the genomes in this niche exhibited multiple resistance determinants, with frequent detection of \\u003cem\\u003ebla\\u003c/em\\u003e genes (blaCTX-M-15, blaCTX-M-65, blaTEM, blaSHV-12, blaKPC, blaKPC-2, blaNDM-5, blaIMP-27, blaVIM-1, blaOXA-10, blaDHA-5, blaDHA-27, and so on), conferring resistance to β-lactam antibiotics, such as penicillins, cephalosporins, Carbapenems, and Monobactams. ARGs within aminoglycoside resistance class (aac(3)-IIe, aac(6')-Ib, aac(6')-Ib3, aac(6')-Ib-cr, aadA5, aadA13, aph(3')-VIb, aph(3')-XV, rmtB1, rmtC), were present in approximately 95% of human-associated genomes. Presence of β -lactamases belonging to the AmpC b-lactamase (blaAmpC) family on the chromosome of \\u003cem\\u003eMorganella\\u003c/em\\u003e leads to an intrinsic resistance to most β-lactam antibiotics as well as first and second- generation cephalosporins (Xiang et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). A recent study detected numerous carbapenemases in \\u003cem\\u003eMorganella\\u003c/em\\u003e sp., including VIM-1, NDM-1, NDM-5, OXA-48, OXA-181, and OXA-641 (Bonnin et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Additionally, in a study characterizing ARGs across 102 genomes of \\u003cem\\u003eMorganella\\u003c/em\\u003e, Zhu et al. (\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) reported a total of 241 aminoglycoside phosphotransferase-related genes. Aminoglycoside along with β-lactam antibiotics have been extensively used to treat severe infection in human and animals from last 60 years. In recent years, these combination antibiotics have been used to treat tularemia, nosocomial surgical wound infections, sepsis, plague, blood-stream infections, central nervous system infection, endophthalmitis, endocarditis, brucellosis, urinary tract infections, pneumonia, chorioamnionitis and systemic infections caused by \\u003cem\\u003eMorganella\\u003c/em\\u003e (Lebeaux et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). However, the long-term use of these antibiotics caused the acquisition and dissemination of aminoglycoside and β-lactam resistant genes in various clinical isolates, such as \\u003cem\\u003eEscherichia, Acinetobacter, Salmonella, Klebsiella\\u003c/em\\u003e, as well as in \\u003cem\\u003eMorganella\\u003c/em\\u003e (Wang et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eARGs conferring resistant to trimethoprim (dfrA1, dfrA12, dfrA27, dfrA17, dfrA14, dfrA42, dfrA19), sulfonamide (sul1, sul2, sul3), tertracycline (tet(A), tet (B), tet(D)), chromaphenicol (catA1, catA2, catB3, catB8, floR, cmlA1, cmlA5, catB2), were also detected in 85% of the clinical genomes in present study. Bonnin et al. (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) reported that intrinsic resistance to tetracycline was observed only in M. sibonii, while the tetracycline resistance genes was found in 42 \\u003cem\\u003eM. morganii\\u003c/em\\u003e isolates in the present study. Other ARGs identified in ~\\u0026thinsp;70% of genomes included fosfomycin (fosA3), bleomycin (ble, bleO), macrolid (mph(A), mph(E), ere (B)), fluroquinolone (qnrS1), microlid-streptomycin B efflux genes (msr(E)). Genes conferring resistance to biocide (qacEdelta1, qacL), rifampin (arr, arr-2, arr-3), lincosamide (lnu(F), lnu(G)), and streptothricin (sat2) were also detected in some of the \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes which were isolated from the patients subjected to multiple antibiotic treatments. This observation is in line with the large-scale genomic study conducted on clinical isolates of \\u003cem\\u003eMorganella\\u003c/em\\u003e which reported a high rate of ARGs and the emergence of multidrug-resistant strains (Zhu et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The co-occurrence of multiple ARGs suggests a multidrug-resistant phenotype, which was driven by the intense use of antibiotics in healthcare settings. These indicate that \\u003cem\\u003eMorganella\\u003c/em\\u003e from hospitalized patients had evolved to acquire many more ARGs to encounter complex and high selection of antimicrobials in the clinical environment.\\u003c/p\\u003e \\u003cp\\u003eGenome clustering with respect to the ARG profiles was observed that indicates that each genome in the specific niche acquired different ARGs \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. Among the 83 ARGs found in the human-derived genomes, 43 ARGs were shared with animal-derived ones \\u003cb\\u003e(Online resource ESM_3)\\u003c/b\\u003e. In a similar line, Jing et al. (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reported 34 acquired ARGs shared between \\u003cem\\u003eMorganella\\u003c/em\\u003e isolates from both sources, indicating a long history of acquisition and widespread dissemination of these genes within the genus. The ARGs dissemination between these habitats can occur through multiple routes such as food animals, direct contact between humans and animals, or through shared environmental resources, such as contaminated water, with latter being the most common source (Cao et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Similarly, a present study observed that \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes which were derived from the wastewater shared 14 ARGs with human-derived genomes, 11 of which were also detected in animal-derived genomes, indicating that wastewater acts as a convergence point for ARGs dissemination between different habitats (Hutinel et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Additionally, animal-derived genome exhibited 9 unique ARGs belonging to β-lactam (blaCTX-M-63), aminoglycoside (aph(3')-VI), trimethoprim (dfrA24, dfrA23, dfrA10), chloramphenicol (cmlA6), macrolide (erm(42)), biocide (qacE), fluoroquinolone (qepA). Two unique ARGs, namely aac(6')-Ie and tet(L) were also detected in wastewater.\\u003c/p\\u003e \\u003cp\\u003eA total of 3 ARGs (blaDHA, catA2, and tet (D)), involved in resistance to 3 different categories of antimicrobials, were shared among \\u003cem\\u003eMorganella\\u003c/em\\u003e isolates from all the niche, indicating a core set of resistance determinants that persist regardless of the environment (\\u003cb\\u003eOnline resource ESM_3)\\u003c/b\\u003e. These were also the only ARGs detected in food and rhizosphere isolates, further suggesting that isolates from these habitats carry fewer ARGs compared to clinical or wastewater settings. Additionally, msr(C)\\u0026mdash;which confers resistance to macrolide was detected in the rhizosphere soil-derived HM02 strain. Macrolide, a critically important human medicine, enters into the soil through the application of biosolids that are applied as an organic fertilizer (Brown et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These biosolids are frequently contaminated with pharmaceutical residues that persisted during wastewater treatment and partitioned into the organic phase. As a result, soil microbial communities may acquire resistance to macrolide to thrive in such contaminated environments. The low count of ARGs in food and rhizosphere isolates indicates lower selective pressure for resistance in these niches; however, the detection of any ARGs may raise concerns regarding food safety and environmental contamination.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.8 Association between ARGs and mobile genetic elements\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003eMorganella\\u003c/em\\u003e accumulated both intrinsic and acquired ARGs, leading to a multidrug resistance strain. Different types of MGEs viz., insertion elements, transposons (composite transposons, unit transposons, and Miniature Inverted-repeat Transposable Elements (MITEs)), and integrons (integron-01 and integron-02) responsible for acquired ARGs were identified in the studied \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. Insertion sequences (IS) are found to be majorly involved in the mobilization and dissemination of different ARGs. Of the 32 types of insertion elements detected; the most frequent IS families found were: IS26, IS6100, IS5057, ISAba1, ISAba125, ISCfr1, ISEc59, ISSen9, ISVsa3, ISVsa5. Among them, IS26 found to be a predominant insertion sequence, primarily associated with ARGs conferring resistance to β-lactams and aminoglycosides. ISVsa5 was found to be strongly associated with the mobilization of tetracycline resistance genes, while ISVsa3 played a crucial role in facilitating HGT events contributing to resistance against sulfonamides and fluoroquinolones. Additionally, ISAba1, ISAba125, ISCfr1, ISEc59, and ISSen9 were frequently detected in the flanking regions of ARGs conferring resistance to fluoroquinolones, biocides, aminoglycosides, sulfonamides, and chloramphenicol, respectively. This widespread association of different IS families with various classes of ARGs indicates the important role of insertion elements in facilitating the horizontal dissemination and diversification of ARGs.\\u003c/p\\u003e \\u003cp\\u003eAmong integrons, integron-01 (characterized by the presence of the intl1 gene) represents a strong linkage with the ARGs flanking region. Major repertoire of genes linked to aminoglycoside resistance, were found to be horizontally disseminated by integron-01. Additionally, other resistance determinants such as those conferring resistance to sulphonamide, chromaphenicol, remapping, and biocide were also commonly associated with integron-01. Furthermore, trimethoprim resistance genes and quinolone resistance genes also displayed linkages with integrons, although at a lower frequency compared to aminoglycoside- and sulfonamide-resistance genes. Resistance genes against macrolides, phenicols, and rifampicin were also found to be associated with integrons-01, although these connections were less numerous. Furthermore, \\u003cem\\u003eqacEdelta1\\u003c/em\\u003e that is known as a multidrug efflux gene were flanked by the class 1 integron, further contributing to enhanced resistance ability. However, the role of integron-02 (characterized by the presence of the intl2 and intl3 genes) in mobilization of ARGs was limited and found to be associated with only few genes, such as catB3, qacEdelta1, and arr.\\u003c/p\\u003e \\u003cp\\u003eTransposons were also found to be involved in the dissemination of various ARGs, although their associations were less frequent compared to insertion elements and integrons. Among the three types of transposons identified, composite transponsons are found to be more associated with the dissemination of ARGs. Composite transposons associated with IS26 were most frequent and associated with resistance genes against β-lactams, aminoglycosides, sulfonamide, bleomycin, and fosfomycin. Unit transposons, on the other hand, were mainly associated with resistance to macrolides and chloramphenicol, while MITEs were occasionally linked to β-lactam, aminoglycoside, and bleomycin resistance to a much lesser extent. It should be noteworthy that these MGEs are found in the flanking region of the ARGs which was predominant in the \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes isolated from human, animal, and wastewater. Interestingly, ARGs detected in the food and rhizosphere soil isolates were not found to be associated with any MGE.\\u003c/p\\u003e \\u003cp\\u003eIn the investigation of ARGs linked MGEs, a large repertoire of MGEs were detected in studied \\u003cem\\u003eMorganella\\u003c/em\\u003e genomes. This analysis also revealed that more than one type of MGEs are involved in dissemination of resistance against particular ARG. In a study investigating the evolutionary trends of \\u003cem\\u003eMorganella\\u003c/em\\u003e, Chen et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) reported that \\u003cem\\u003eM. morganii\\u003c/em\\u003e undergoes evolution driven by MGEs, which significantly enhance its adaptability to environmental changes and the selective pressures imposed by clinical antimicrobial agents. Additionally, Xiang \\u0026amp; Li, (\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) characterize two novel mobile genetic elements (Tn6835 and MMGI-1) in a \\u003cem\\u003eMorganella\\u003c/em\\u003e strain isolated from fecal swab of healthy chicken and found that most of the ARGs were located on these MGEs which are responsible for pan-resistant nature of \\u003cem\\u003eMorganella\\u003c/em\\u003e against all known antibiotics. Additionally, few studies have revealed new transposons like Tn7376 in \\u003cem\\u003eMorganella\\u003c/em\\u003e and genomic islands that have multidrug resistance genes, like dfrA24, enabled by IS26-mediated recombination (Jing et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Luo et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Dissemination of blaKPC-2 and blaNDM-1 in Carbapenem-resistant \\u003cem\\u003eM. morganii\\u003c/em\\u003e (CRMM) isolates was also found to be majorly facilitated by IncL/M plasmids and IS26-mediated transposon activity (Yao et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn the circos plot, the color coding on the ring denotes the location of ARGs. Most of the ARGs associated with MGEs are found to be plasmid-borne. For those with are located on both plasmid and chromosome, their occurrence was less frequent on the chromosome. Plasmid-derived ARGs were identified from species such as \\u003cem\\u003eEscherichia coli, Proteus mirabilis, Citrobacter freundii, Aeromonas rivipollensis, Pseudomonas aeruginosa, Salmonella enterica, Pasteurella aerogenes, Enterobacter cloacae\\u003c/em\\u003e, and \\u003cem\\u003eAcinetobacter baumannii.\\u003c/em\\u003e AMR pangenome profiling of the 827 genomes from Enterobacteriaceae family, collected from livestock farms and wastewater, identified distinct dynamics for chromosomal and plasmid-borne ARGs. They found that plasmids carry a substantial burden of AMR genes and MGEs. Furthermore, AMR-gene-carrying plasmids appear to be under stronger selective pressure and are primarily responsible for conferring resistance to multiple antibiotics, specifically in clinical strain compared to non-clinical ones. These findings indicate that clinical isolates act as major reservoirs for the transfer of ARGs into \\u003cem\\u003eMorganella\\u003c/em\\u003e species. Although direct evidence of ARG transmission from clinical isolates to \\u003cem\\u003eM. morganii\\u003c/em\\u003e via mobile genetic elements is limited, several studies have reported the presence of clinically relevant resistance genes on plasmids and transposons in \\u003cem\\u003eM. morganii\\u003c/em\\u003e isolates, suggesting potential acquisition through the clinical mobilome (Yao et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Additionally, in a study by Sugita et al. (\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), it was found that inter-plasmid transposition of Tn4401a facilitates horizontal transfer of blaKPC-2 from \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e to \\u003cem\\u003eM. morganii\\u003c/em\\u003e through ColRNAI plasmids, enhancing resistance spread. On the other hand, ARGs that were located on the chromosome, generally not associated with MGEs and were primarily found in isolates originating from food and rhizosphere environments. This suggests that \\u003cem\\u003eMorganella\\u003c/em\\u003e when present in non-clinical environment, such as food or rhizosphere, where the AMR pressure is low, lacks these ARGs and instead showed beneficial traits responsible for plant growth or environmental survival.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.9 PGP pathway analysis\\u003c/h2\\u003e \\u003cp\\u003eThe genome sequences of the rhizosphere-soil isolated strains were evaluated for their ability to improve plant development through both direct and indirect effects. The direct effects involve the pathways for nitrogen fixation, phytohormone production, and solubilization of nutrients such as phosphate, potassium, zinc, sulphate, and iron. In contrast, the indirect pathway involves the suppression of pathogenic growth and colonization, degradation of aromatic and toxic compounds, quorum sensing, biofilm formation, and the production of bacteriocins and secondary metabolites. Concerning the direct pathway, a total of 33, 12, and 38 genes related to nutrient solubilization were detected in strains HM01, HM02, and HM04, respectively \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. The presence of phosphate, potassium, and zinc solubilization machinery in the strain correlated with the in-vitro activities. After the application of fertilizers in soil, a major portion of inorganic nutrients remains immobilized, leaving them unavailable for plants. Therefore, it is important for the soil microbial community to produce enzymes and organic acids to solubilize this poorly soluble mineral nutrients. Besides, these compounds must be transported across the plasma membrane before they may be used (Kumar et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022a\\u003c/span\\u003e). In rhizosphere-associated strain genes related to phosphate solubilization such as phnV (hydrolyse phosphonate into phosphate and alkane) and pstBACS (phosphate transporter) were detected. Moreover, genes involved in the potassium (kdp, kup), zinc (znt, znu, zup, zur) and sulphate (cys) transport and uptake were also annotated in these strains. High-affinity iron chelating compounds produced by the microbial community helps in collecting iron from the soil (Kumar et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022a\\u003c/span\\u003e). All strains were able to synthesis enterobactin sideophore (fep, fpt, ent) which are responsible for recovery of siderophore from the complex environment, while the protein (fhu) that help in transport and binding of iron are only detected in HM01 and HM04. Moreover, genes related to iron sequestration and metabolism were detected in all three strains (feo, efe, fur, fec, fpt, dtx).\\u003c/p\\u003e \\u003cp\\u003eChemotaxis genes, which play a major role in stress response, were most abundant in HM04 (30), followed by HM01 (17) and HM02 (11). These genes include clusters such as fli (fliDEFGHIJMNOPQRST), mot (motAB), che (cheABRWYZ), as well as aer, dppA, rbsB, tap, tar, tsr, and trg, all of which contribute to endophytic traits such as chemotactic movement and host attachment. Nitrogen metabolism represents another important pathway that promotes plant growth and biomass accumulation. Genes related to the nitrogen fixation pathway were not detected in any of the isolates. However, genes involved in the indirect nitrogen metabolism pathway, such as nitrification-denitrification (\\u003cem\\u003enarG\\u003c/em\\u003e, \\u003cem\\u003enarH\\u003c/em\\u003e, \\u003cem\\u003enarI\\u003c/em\\u003e, \\u003cem\\u003enorG\\u003c/em\\u003e, \\u003cem\\u003enorR\\u003c/em\\u003e, \\u003cem\\u003enapA\\u003c/em\\u003e) and assimilatory/dissimilatory nitrate reduction (\\u003cem\\u003enasD\\u003c/em\\u003e, \\u003cem\\u003enarG\\u003c/em\\u003e, \\u003cem\\u003enarH\\u003c/em\\u003e, \\u003cem\\u003enarI\\u003c/em\\u003e, \\u003cem\\u003enarJ\\u003c/em\\u003e, \\u003cem\\u003enarK\\u003c/em\\u003e, \\u003cem\\u003enapA\\u003c/em\\u003e), as well as nitrite reduction (\\u003cem\\u003enirC\\u003c/em\\u003e), were annotated. The overall presence-absence gene matrix for these three isolates involved in both direct and indirect PGP activity is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe antiSMASH analysis revealed a total of five, six, and five secondary metabolite biosynthetic gene clusters in strains HM01, HM02, and HM04, respectively \\u003cb\\u003e(Online resource ESM_4)\\u003c/b\\u003e. Two clusters, namely the azole-containing RiPP (antimicrobial activities) and terpene precursor clusters (production of bioactive compounds for signaling and defense), were found in all three strains. The azole-containing RiPP was previously reported to produce diverse antimicrobial peptides that inhibit the growth of phytopathogenic fungi and bacteria (Thamvithayakorn et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Terpene is generally considered to be a fungal and plant natural product. However, a study identified that bacterial genomes also harbor gene cluster encoding for terpenes and produce bioactive compounds for signaling and defense (Yamada et al. \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). The β-lactone cluster that are known for antimicrobial and anticancer properties (Awolope et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) was detected in HM01 and HM04, while cyclic lactone autoinducer that induces quorum sensing and regulation of microbial community behaviours (Kapadia et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) was uniquely observed in HM02. Furthermore, four types of bacteriocins were annotated \\u003cb\\u003e(Online resource ESM_5)\\u003c/b\\u003e. Microcin and Bottromycin were found in all three strains, whereas Colicin_E6 and Enterolysin_A were uniquely found in HM01 and HM02, respectively. These bacteriocins are potentially responsible for antagonizing the growth of phytopathogens, as reported previously (Nazari and Smith \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\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\\u003eFunctional annotation of genes associated with PGP pathways in strain HM01, HM02, and HM04.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathway\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGene\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFunction\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHM01\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHM02\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHM04\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ePhosphate solubilization\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ephnV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003etransport system permease protein responsible for 2-ami0ethylphosphonate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epstA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePhosphate transport system permease protein pstA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epstB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePhosphate-import ATP-binding protein pstB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epstC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePhosphate transport system permease protein pstC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epstS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePhosphate-binding protein pstS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003epotassium solubilization\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ekdpA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh-affinity K\\u0026thinsp;+\\u0026thinsp;transport protein A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ekdpB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh-affinity K\\u0026thinsp;+\\u0026thinsp;transport protein B (ATPase)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ekdpC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh-affinity K\\u0026thinsp;+\\u0026thinsp;transport protein C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ekdpD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSensor histidine kinase (KdpD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ekup\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLow-affinity K\\u0026thinsp;+\\u0026thinsp;transporter (Kup system)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e \\u003cp\\u003eZinc solubilization\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ezntA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eZinc/cadmium/lead-transporting P-type ATPase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ezntB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eZinc transport protein ZntB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ezntR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHTH-type transcriptional regulator ZntR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eznuA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh-affinity zinc uptake system protein ZnuA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eznuB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh-affinity zinc uptake system membrane protein ZnuB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eznuC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eZinc import ATP-binding protein ZnuC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ezur\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eZinc uptake regulation protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ezupT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003elow-affinity zinc-uptake system\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"8\\\" rowspan=\\\"9\\\"\\u003e \\u003cp\\u003eIron sequetration and metabolism\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eefeO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIron uptake system component EfeO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efeoA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFe(2+) transport protein A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efeoB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFe(2+) transporter FeoB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efeoC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eputative [Fe-S]-dependent transcriptional repressor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efur\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFerric uptake regulation protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eyggX\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eputative Fe(2+)-trafficking protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efecA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFe(3+) dicitrate transport protein FecA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efptA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFe(3+)-pyochelin receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003edtxR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFerric uptake regulation protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003esiderophore production\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efepA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFerrienterobactin receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efhuC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIron (3+)-hydroxamate import ATP-binding protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efptA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFerrienterobactin receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efhuB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIron (3+)-hydroxamate import system permease protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eentS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEnterobactin exporter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efepC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFerric enterobactin transport protein FepC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"11\\\" rowspan=\\\"12\\\"\\u003e \\u003cp\\u003eAssimilatory sulphate reduction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSulfate/thiosulfate import ATP-binding protein CysA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSerine acetyltransferase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSiroheme synthase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSulfite reductase [NADPH] hemoprotein beta-component\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHTH-type transcriptional regulator CysL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThiosulfate-binding protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysQ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3? (2? ),5? -bisphosphate nucleotidase CysQ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCysteine\\u0026ETH;tRNA ligase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSulfate transport system permease protein CysT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSulfate transport system permease protein CysW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysZ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003egulator CysL cysZ S\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ecysJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSulfite reductase [NADPH] flavoprotein alpha-component\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"29\\\" rowspan=\\\"30\\\"\\u003e \\u003cp\\u003echemotaxis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eaer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eaerotaxis receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003etwo-component system, chemotaxis family, sensor kinase CheA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003etwo-component system, chemotaxis family, protein-glutamate methylesterase/glutaminase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003echemotaxis protein methyltransferase CheR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003epurine-binding chemotaxis protein CheW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003etwo-component system, chemotaxis family, chemotaxis protein CheY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003echeZ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003echemotaxis protein CheZ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003edppA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003edipeptide transport system substrate-binding protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003emotA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003echemotaxis protein MotA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003emotB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003echemotaxis protein MotB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003erbsB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eribose transport system substrate-binding protein\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003etap\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emethyl-accepting chemotaxis protein IV, peptide sensor receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003etar\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emethyl-accepting chemotaxis protein II, aspartate sensor receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003etsr\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emethyl-accepting chemotaxis protein I, serine sensor receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003etrg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emethyl-accepting chemotaxis protein III, ribose and galactose sensor receptor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliO\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliQ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliQ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003efliS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eflagellar motor switch protein FliS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNitrification\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase / nitrite oxidoreductase, alpha subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase / nitrite oxidoreductase, beta subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eDenitrification\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase / nitrite oxidoreductase, alpha subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase / nitrite oxidoreductase, beta subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase gamma subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0rG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRegulator of nitric oxide reductase genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0rR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTranscriptional activator responding to nitric oxide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enapA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase (cytochrome)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAssimilatory nitrate reduction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enasD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrite reductase [NAD(P)H] large subunit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003eDissimilatory nitrate reduction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRespiratory nitrate reductase 1 alpha chain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRespiratory nitrate reductase 1 beta chain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRespiratory nitrate reductase 1 gamma chain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNitrate reductase molybdenum cofactor assembly chaperone narJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enarK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNitrate/nitrite transporter nark\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enapA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003enitrate reductase (cytochrome)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDissimilatory Nitrite Reduction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enirC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNitrite transporter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e○\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e●\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e○\\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● represent gene presence\\u003c/p\\u003e \\u003cp\\u003e○ represent gene absence\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Conclusion\",\"content\":\"\\u003cp\\u003eThe three \\u003cem\\u003eM. morganii\\u003c/em\\u003e strains viz., HM01, HM02, and HM04 isolated from the rhizosphere of chickpea, maize and wheat field exhibited strong in-vitro PGP activity. Pangenome analysis, incorporating 78 publicly available \\u003cem\\u003eM. morganii\\u003c/em\\u003e genomes, indicated an open pangenome with a high number of cloud genes, which indicated the likelihood of HGT events in the process of gaining niche-specific adaptive traits. A significant percentage of these cloud genes encode hypothetical proteins in the rhizosphere-associated strain that were found to be involved in plant growth and environmental survival. To further support this observation, genomic clustering using PCoA analysis showed that rhizosphere strain grouped with food-associated strain, whereas the genomes from human, animal, and wastewater aggregate to form a separate cluster. BRIG analysis, further identified and discriminated against unique genomic regions in rhizosphere strains from food associated isolates. Selection of gene pool specific to rhizosphere ecology was corroborated by KEGG pathway analysis that revealed PGP conferring genes involved in nutrient transport and uptake, chemotaxis, siderophore production and nitrogen metabolism. Additionally, numerous biosynthetic gene clusters encoding antibacterial and antifungal metabolites were identified. Pangenome mapping of AMR genes showed that rhizosphere and food-associated \\u003cem\\u003eM. morganii\\u003c/em\\u003e have fewer AMR events, whereas clinical and wastewater isolates harboured a higher number of MGE linked-resistant gene, often plasmid-borne and derived from clinical isolates; may be required as one of the conditions for survival in new habitat. This study showed that while bacteria intelligently survive according to ecosystem conditions, the available genetic plasticity makes it potential sink for AMR genes. Overall, the study presents novel findings into the genomic adaptability of \\u003cem\\u003eM. morganii\\u003c/em\\u003e in various ecological niches, highlighting its dual nature as both a PGP bacterium and an opportunistic pathogen.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eCaption for online resources (Electronic Supplementary Material)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_1\\u003c/b\\u003e Heatmap illustrating a) average nucleotide identity by orthology (OrthoANI) and b) genome-to genome distance calculation (GGDC) between the rhizosphere soil-isolated Morganella genome and reference genome, represented by the type strains of the respective genera calculated by OAT software. High OrthoANI values and low GGDC values between the strains indicate greater genomic similarity. The \\\"ERR\\\" value in GGDC showed that the organism is genetically distant from the \\u003cem\\u003eMorganella\\u003c/em\\u003e strains\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_2\\u003c/b\\u003e Subsystem category distribution and the biological function of \\u003cem\\u003eM. morganii\\u003c/em\\u003e strain a) HM01, b) HM02, and c) HM04 as determined by RAST genome analysis. Gene distributions are depicted with different colours, and their corresponding genes are numerically shown within parentheses\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_3\\u003c/b\\u003e Venn diagrams illustrating the distribution of shared ARGs across the Morganella genomes isolated from different habitat viz., human, animal, wastwater, food, and rhizosphere soil\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_4a\\u003c/b\\u003e Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM01\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_4b\\u003c/b\\u003e Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM02\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_4c\\u003c/b\\u003e Organization of secondary metabolite biosynthetic gene clusters in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM04\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_5a\\u003c/b\\u003e Organization of bacteriocin in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM01\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_5b\\u003c/b\\u003e Organization of bacteriocin in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM02\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eESM_5c\\u003c/b\\u003e Organization of bacteriocin in rhizosphere soil-isolated \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e strains HM04\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose. The study was conducted with available in-house funding for R\\u0026amp;D from the company. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRP and HJP conceptualized the study; RP and PP performed wet lab experiments including genome sequencing; BP and RP performed bioinformatics analyses and wrote the manuscript draft; HJP, RW and GW reviewed the draft and supervised complete study; RW and GW provided the internal funding and other resources to accomplish the study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAl Sium SM, Goswami B, Chowdhury SF et al (2025) An insight into the genome-wide analysis of bacterial defense mechanisms in a uropathogenic Morganella morganii isolate from Bangladesh. 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Front Cell Infect Microbiol 14:1464736\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSugita K, Aoki K, Komori K et al (2022) Molecular analysis of blaKPC-2-harboring plasmids: Tn4401a interplasmid transposition and Tn4401a-carrying ColRNAI plasmid mobilization from Klebsiella pneumoniae to Citrobacter europaeus and Morganella morganii in a single patient. mSPhere Vol.6, No.6\\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\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"archives-of-microbiology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"aomi\",\"sideBox\":\"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)\",\"snPcode\":\"203\",\"submissionUrl\":\"https://submission.nature.com/new-submission/203/3\",\"title\":\"Archives of Microbiology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Morganella, pangenome, antimicrobial resistance, mobile genetic elements, plant growth-promoting genes, hypothetical proteins\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6872201/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6872201/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e \\u003cem\\u003eMorganella morganii\\u003c/em\\u003e is a bacterium with open pangenomes, where genes move intra- and interspecies via horizontal gene transfer. Through pangenome analysis, the study maps three agriculture isolates; \\u003cem\\u003eM. morganii\\u003c/em\\u003e with strong PGP activity, along with 78 publicly available genomes from clinical, food, wastewater, and animal sources. The analysis showed 23,829 gene clusters with only 8.52% core genes and discriminating distribution of 78.34% cloud genes across different niches. KEGG analysis showed 33, 12, and 38 genes related to nutrient solubilization in \\u003cem\\u003eM. morganii\\u003c/em\\u003e isolates HM01, HM02, and HM04, respectively. Chemotaxis genes, crucial for stress response, were most abundant in HM04 (30), followed by HM01 (17) and HM02 (11). Additionally, numerous biosynthetic gene clusters encoding antibacterial and antifungal metabolites were identified. Clinical and wastewater isolates harboured a higher number of mobile genetic element (MGE) linked antimicrobial resistance (AMR) genes that confer resistance to 15 antibiotic classes. These AMR genes were predominantly plasmid-borne and found to transfer in \\u003cem\\u003eM. morganii\\u003c/em\\u003e from clinical pathogens such as \\u003cem\\u003eE. coli\\u003c/em\\u003e and \\u003cem\\u003eA. baumannii\\u003c/em\\u003e. This study indicates that habitat pressure creates the scenario for selection of functional traits which enables the ecosystem specific survival of \\u003cem\\u003eM. morganii\\u003c/em\\u003e. Together, the present investigation provides important insight into the genomic diversity and remarkable PGP potential of \\u003cem\\u003eM. morganii\\u003c/em\\u003e strains for sustainable agriculture. The pangenome analysis proposes that detailed investigation is needed to confirm their efficacy as PGP bacterium and to distinguish them from pathogenic strains.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Pan-genome analysis of Morganella Morganii reveals niche-specific selection of functional traits: Friend or Foe?\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-16 08:11:31\",\"doi\":\"10.21203/rs.3.rs-6872201/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-07-07T07:47:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-07-03T12:14:05+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-06-24T17:32:04+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"106427080619303892801304210434363545775\",\"date\":\"2025-06-13T13:25:33+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"333005888871849223510396081577579263418\",\"date\":\"2025-06-13T08:09:07+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-06-13T03:19:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-12T14:41:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-06-12T09:59:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Archives of Microbiology\",\"date\":\"2025-06-11T13:06:34+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"archives-of-microbiology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"aomi\",\"sideBox\":\"Learn more about [Archives of Microbiology](https://www.springer.com/journal/203)\",\"snPcode\":\"203\",\"submissionUrl\":\"https://submission.nature.com/new-submission/203/3\",\"title\":\"Archives of Microbiology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"d9eec580-994c-4e0d-8da2-4a0439a7f56d\",\"owner\":[],\"postedDate\":\"June 16th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-01T16:02:58+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6872201\",\"link\":\"https://doi.org/10.1007/s00203-025-04566-y\",\"journal\":{\"identity\":\"archives-of-microbiology\",\"isVorOnly\":false,\"title\":\"Archives of Microbiology\"},\"publishedOn\":\"2025-11-28 15:58:08\",\"publishedOnDateReadable\":\"November 28th, 2025\"},\"versionCreatedAt\":\"2025-06-16 08:11:31\",\"video\":\"\",\"vorDoi\":\"10.1007/s00203-025-04566-y\",\"vorDoiUrl\":\"https://doi.org/10.1007/s00203-025-04566-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6872201\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6872201\",\"identity\":\"rs-6872201\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}