Genomic and metabolomic characterization of Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10) reveal functional traits for plant stress alleviation and sustainable agriculture

Genomic and metabolomic characterization of Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10) reveal functional traits for plant stress alleviation and sustainable agriculture | 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 Genomic and metabolomic characterization of Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10) reveal functional traits for plant stress alleviation and sustainable agriculture Imen Ghazala, Naïma Sayahi, Abdelmalek Alioua, Valérie Cognat, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7900185/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Plant growth-promoting rhizobacteria (PGPR) enhance plant growth and development through diverse mechanisms, including phytohormone production, nutrient acquisition, and stress mitigation. This study describes the isolation and characterization of two bacterial strains, DT1 and S10, from the rhizospheres of Diplotaxis tenuifolia and Cynodon dactylon , respectively capable of solubilizing phosphate and zinc, fix nitrogen and produce indole acetic acid (IAA) and siderophores. Using whole genome sequencing and taxonomic analyses, these two strains were identified as Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10). Functional genomic annotation revealed numerous genes associated with key plant growth-promoting traits, including those involved in indole-3-acetic acid (IAA) ( trpABCDE , ipdC ), cytokinin ( miaABE ), and riboflavin biosynthesis, confirmed by targeted metabolomics. In addition, genes associated with nitrogen metabolism ( nirB , narGHI ) and phosphate solubilization ( gcd , phoARP , pstABCS , pqqEFG ) were identified and supported by phenotypic assays. Interestingly, biosynthetic gene clusters for the secondary metabolites enterobactin, bacillibactin, and staphyloferrin B, known to contribute to plant growth promotion, were identified in both genomes. Both strains also harbored genes encoding ACC deaminase, an enzyme known to enhance plant tolerance to abiotic stress. Furthermore, non-targeted metabolomic analysis revealed that DT1 and S10 produced a range of intracellular and extracellular metabolites associated with plant growth promotion and stress resilience, including cadaverine, biotin, arginine, and GABA. Collectively, these findings position DT1 and S10 as promising bioinoculant candidates, offering an integrative genomic and metabolic foundation for their application in next-generation sustainable agricultural strategies. Plant growth-promoting rhizobacteria (PGPR) Acinetobacter calcoaceticus Citrobacter braakii Whole-genome sequencing Metabolomic profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Plant growth-promoting rhizobacteria (PGPR) represent a functionally diverse group of beneficial root-associated bacteria that colonize the rhizosphere and stimulate plant growth and development through a variety of direct and indirect mechanisms (Mmotla et al. 2025 ). Since their introduction by Kloepper and Schroth in the late 1970s, PGPR have been extensively studied for their potential to enhance agricultural productivity while promoting environmentally sustainable and low-input farming practices. PGPR act via both direct and indirect mechanisms. Direct effects include atmospheric nitrogen fixation (Masood et al. 2020 ) and solubilization of soil inorganic phosphorus, both of which enhance nutrient availability, as well as the production of phytohormones such as indole-3-acetic acid (IAA), cytokinins, and gibberellins, which collectively promote plant growth and development (Joshi et al. 2023 ). Indirect effects include pathogen suppression through siderophores production (Gao et al. 2022 ), activation of 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity (Zafar-ul-Hye et al. 2019 ), emission of volatile organic compounds (VOCs) (Mhlongo et al. 2022 ) and synthesis of antimicrobial metabolites, leading to induced systemic resistance (ISR) (Li et al. 2024 ). Various bacterial genera exhibit plant growth-promoting (PGP) potential, including Pseudomonas (Comeau et al. 2021 ), Bacillus (Husna et al. 2023), Azospirillum (Sun et al. 2025 ), Citrobacter (Ajmal et al. 2022 ), and Acinetobacter (Josephine and Thomas 2021 ). Acinetobacter spp. are Gram-negative, oxidase-negative, aerobic bacteria that are non-motile due to the absence of flagella (Josephine and Thomas 2021 ). This genus is widely spread in nature and is frequently found in the rhizosphere of several plant species (Mujumdar et al. 2023 ). Previous research have demonstrated that certain Acinetobacter strains, including A. calcoaceticus , (Eswaran et al. 2024 ; Okla et al. 2025 ) possess PGP traits, including IAA production (Lin et al. 2018 ), inorganic phosphate solubilization (He and Wan 2021 ) and nitrogen fixation (Wu et al. 2022 ). Likewise, Citrobacter spp. are Gram-negative, facultatively anaerobic, oxidase-negative bacteria that are generally motile (Fanning et al. 2016 ). This genus has also been reported in several studies for its PGP potential (Ajmal et al. 2022 ; Fadiji et al. 2023 ). In recent years, advances in high-throughput genomics and metabolomics have significantly deepened our understanding of PGPR diversity, functionality and their complex interactions with host plants (for review see Paterson et al., 2017 ). Furthermore, whole-genome sequencing (WGS) facilitates the identification of key genetic determinants responsible for plant-beneficial traits, including genes involved in nitrogen fixation ( nir , nar ), phosphate solubilization ( gcd , pqq ), auxin biosynthesis ( ipdC ), and secondary metabolite production ( ent ) (Thamvithayakorn et al. 2025 ; Zhang et al. 2024a ; Li et al. 2024 ). In parallel, metabolomic profiles enable the identification of bioactive compounds produced by PGPR, including signaling molecules, growth regulators, and stress-related metabolites, thereby bridging genotypic potential with phenotypic expression (Mashabela et al. 2023 ). In this study we isolated two bacterial strains S10 and DT1 from the rhizospheres of Cynodon dactylon and Diplotaxis tenuifolia , respectively. These strains exhibited multiple PGP traits and were taxonomically classified, based on WGS analyses, as Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10). Functional genome annotation unveiled numerous genes associated with plant growth promotion, while targeted and non-targeted metabolomic profiling revealed a variety of metabolites implicated in growth stimulation and abiotic stress tolerance. Materials and methods Bacteria isolation Rhizospheres samples of Cynodon dactylon and Diplotaxis tenuifolia were collected from wild areas in the Sfax region, located in central Eastern coast of Tunisia (34°39'N, 10°43'E). One gram of rhizospheric soil from each plant was suspended in 10 mL of sterile water and serially diluted. Subsequently, 100 µL from the 10⁻⁶ and 10⁻⁷ dilutions of each suspension were plated onto a culture medium composed of: K₂HPO₄ (0.3 g/L), KH₂PO₄ (0.3 g/L), KCl (0.1 g/L), NaCl (1 g/L), CaCl₂ (0.1 g/L), yeast extract (1 g/L), peptone (5 g/L), glucose (C₆H₁₂O₆, 5 g/L), and agar (18 g/L) (Sayahi et al. 2022 ). After 48 hours of incubation at 30°C, isolated colonies were selected and subcultured onto fresh plates to ensure purity. The purified isolates (named hereafter S10 and DT1) were cultivated on Luria-Bertani (LB) medium at 37°C, maintained at 4°C prior to use, and preserved as glycerol stocks at − 80°C. Plant Growth‑Promoting (PGP) traits Siderophore production Siderophore production by S10 and DT1 isolates was evaluated using the Chrome Azurol S (CAS) agar method, as described by Schwyn and Neilands ( 1987 ), with slight modifications. Briefly, the CAS reagent was prepared by mixing 60.5 mg of Chrome Azurol S with an iron (III) solution (1 mM FeCl₃ in 10 mM HCl) and 72.9 mg of hexadecyltrimethylammonium bromide (HDTMA) to form a blue-colored complex. This solution was then added to a low-iron minimal medium at 10% (v/v) before pouring into Petri dishes. Bacterial isolates were streaked onto the CAS agar and incubated at 30°C for 24–48 hours. The appearance of an orange or yellow halo around the streak line indicated siderophore production. Phosphate and zinc solubilization The ability of S10 and DT1 isolates to solubilize insoluble phosphate and zinc compounds was evaluated using agar plate assays. For phosphate solubilization, isolates were spot-inoculated onto Pikovskaya’s agar medium containing tricalcium phosphate (Ca₃(PO₄)₂) as the sole phosphorus source (Pikovskaya 1948 ). For zinc solubilization, isolates were grown on Yeast Extract Dextrose (YED) medium supplemented with zinc oxide (ZnO) as the insoluble zinc sources (Saravanan et al. 2007 ). In both assays, plates were incubated at 30°C for 2 to 5 days. Solubilization was evidenced by the formation of clear halos around the bacterial colonies. All experiments were conducted in triplicate to ensure consistency and reproducibility. Indole-3-acetic acid (IAA) The production of indole-3-acetic acid (IAA) by S10 and DT1 isolates was investigated using Salkowski reagent. Each isolate was inoculated into 25 mL of LB medium supplemented with 5 mM of L-tryptophan and incubated at 30°C for 24 to 48 hours under shaking conditions (150 rpm). After incubation, cultures were centrifuged at 10,000 rpm for 10 minutes, and 1 mL of the cell-free supernatant was mixed with 2 mL of Salkowski reagent (2% 0.5M FeCl₃ in 35% perchloric acid). The mixture was incubated in the dark at room temperature for 30 minutes. A pink coloration indicated the presence of IAA, and absorbance was then measured at 530 nm using a spectrophotometer (Sobarzo et al. 2013 ). IAA concentration was determined by comparison with a standard curve of pure IAA. All experiments were performed in triplicate. Nitrogen fixation The nitrogen-fixing ability of S10 and DT1 was tested using the Nitrogen-Free Broth (NFB) medium as described by Siddikee et al. ( 2010 ). In brief, isolates were inoculated onto NFB plates and incubated at 30°C for 2 to 5 days. Bacterial growth on nitrogen-free medium was considered indicative of nitrogen fixation capacity. All experiments were conducted in triplicate. DNA extraction, genome sequencing and assembly Genomic DNA was extracted from overnight bacterial cultures using the Qiagen Genomic-tip 100/G kit (Qiagen, Cat. No. 10243). Approximately 10–20 µg of high molecular weight DNA was obtained per extraction, with concentrations measured using the Qubit™ dsDNA HS Assay Kit (Thermo Fisher Scientific). DNA purity was assessed by spectrophotometry, aiming for A260/280 ratios of ~ 1.8 and A260/230 ratios above 2.0. Oxford Nanopore libraries were prepared using the Native Barcoding Kit 24 V14 (SQK-NBD114.24, Oxford Nanopore Technologies) following the manufacturer’s protocol, using ~ 1 µg of input DNA. Sequencing was performed on R10.4.1 PromethION flow cells (FLO-PRO114M) with MinKNOW software version 22.07.9. The raw fast5 data generated by the MinKNOW software were basecalled and demultiplexed using Guppy v6.5.7 (Wick et al. 2019 ) with the super accuracy model dna_r10.4.1_e8.2_400bps_hac_prom and a minimum Q-score threshold of 7 to obtain the fastq files. Read quality was assessed with NanoPlot v0.32.1 (De Coster and Rademakers 2023 ), and the adapter sequences were trimmed with Porechop v0.2.4 ( https://github.com/rrwick/Porechop ). To evaluate potential contamination and obtain a preliminary taxonomic classification, reads were screened against the standard Kraken database using Kraken2 v2.0.9-beta (Wood et al. 2019 ). Genome assembly was performed with NextDenovo v2.5.2 (Hu et al. 2024 ) using default parameters, followed by polishing with NextPolish v1.4.1 (Hu et al. 2020 ) to correct base-level errors. Assemby quality and completeness were evaluated using QUAST v5.0.2 (Gurevich et al. 2013 ), including the BUSCO option to detect conserved orthologs and CheckM v1.2.2 (Parks et al. 2015 ). Final assemblies were reoriented at the dnaA gene using the Circlator fixstart tool v1.5.5 (Hunt et al. 2015 ). Genome annotation Genomic relatedness between strains was estimated based on the Average Nucleotide Identity (ANI) and digital DNA–DNA hybridization (dDDH). ANI values were calculated using the JSpecies Web Server (JSpeciesWS) (Richter et al. 2015 ), while dDDH values were obtained using the Type (Strain) Genome Server (TYGS) (Meier-Kolthoff and Göker 2019 ). Additionally, TYGS was used to construct a whole-genome phylogenetic tree based on the Genome Blast Distance Phylogeny (GBDP) approach. The annotated genomes were uploaded to the MicroScope platform ( https://mage.genoscope.cns.fr/microscope/home/index.php ) and analyzed using the integrated genome annotation tools available on the platform (Vallenet et al. 2020 ). Complementary annotation was performed using multiple pipelines such as RAST (Rapid Annotation using Subsystem Technology (Aziz et al. 2008 ), PROKKA (Galaxy Version 1.14.6) (Seemann 2014 ) implemented with default parameters via Galaxy, and KEGG (Kyoto Encyclopaedia of Genes and Genomes) Automatic Annotation Server v2.1 (Moriya et al. 2007 ) to study metabolic pathways within the assembled genome. Both genome sequences have been submitted to NCBI database under the submission references SUB15592691 (Genome assembly of Citrobacter braakii S10) and SUB15592887 (Genome assembly of Acinetobacter calcoaceticus DT1). Metabolomics analysis Chemicals Deionized water was filtered through a Direct-Q UV station (Millipore), isopropanol and methanol were purchased from Fisher Chemicals (Optima ® LC/MS grade). NaOH was obtained from Agilent Technologies, acetic acid formic acid from Sigma Aldrich. Deuterated abscissic acid ( 2 H 6 ABA) obtained from OlChemIm was used as an internal standard. Standards were used to develop the UHPLC-TQ-MS/MS methods: abscisic acid (ABA), 6-benzylaminopurine (BAP), indole-3-butyric acid potassium salt (IBA), salicylic acid (SA), trans zeatin (t-zea) and tryptophan were purchased from Sigma; benzoic acid (BA), and gibberellic acid (GA3) were purchased from Fluka; brassinolide (BR), cis-12-oxo-phytodienoic acid (OPDA), castasterone (CS), cathasterone (CT), dinor-12-oxo-phytodienoic acid (dnOPDA), gibberellins A1, A4, A7 and GA20 (GA1, GA4, GA7, GA20), jasmonic acid (JA), jasmonic acid-isoleucine (JA-lLE), jasmonic acid-phenylalanine (JA-Phe), jasmonic acid-valine (JA-Val), 12-hydroxy-jasmonic acid (12-0H­ JA), and orobanchol (Oro) were purchased from OlChemim; indole-3-acetic acid (lAA) was purchased from Serva, and 6-furfurylaminopurine (kinetin, KIN), 6-(y, y-dimethylallylamino) purine (2iP) were purchased from Duchefa. 12-hydroxy-jasmonic acid-isoleucine (12-0H-JA-Ile), 12-carboxy-jasmonic acid (12-COOH-JA) and 12-carboxy­ jasmonic acid-isoleucine (12-COOH-JA-Ile) were generous gifts from Dr. Patrick Wehrung. Sample preparation A culture of S10 and DT1 was prepared by inoculating 10 7 spores/mL in a 50 mL falcon containing 10 mL of LB medium. Cultures were grown 24 hours at 28°C under continuous shaking at 180 rpm and cells were pelleted (5500 rpm, 10min). Pellets (40–60 mg fresh weight) were extracted with 5 volumes of cold methanol spiked with 2 H 6 ABA (1µg/mL) and centrifuged (13,200 rpm; 10 min, 4°C) to recover the liquid phase for further analysis. The supernatant was prepared through solid phase extraction (5 mg, Oasis HLB, Waters) microplates. The phase was washed with 1 mL 80% MeOH and 1 mL 100% H 2 O, then 500 µL of acidified supernatant (0.1% formic acid) was applied. A wash was performed with 1mL 2% MeOH, 0.1% formic acid before elution of the samples with 250 µL of 80% MeOH, 0.1% formic acid. Non-Targeted Metabolomic Analysis Samples were analyzed using liquid chromatography coupled to high resolution mass spectrometry on an UltiMate 3000 system (Thermo) coupled to an Impact II (Bruker) quadrupole time-of-flight (Q-TOF) spectrometer. Chromatographic separation was performed on an Acquity UPLC ® HSS T3 C18 column (2.1x100 mm, 1.8 µm, Waters) equipped with and Acquity UPLC ® HSS T3 C18 pre-column (2.1x5 mm, 1.8 µm, Waters) using a gradient of solvents A (Water, 0.1% formic acid) and B (MeOH, 0.1% formic acid). Chromatography was carried out at 35°C with a flux of 0.3 mL.min − 1 , starting with 2% B for 2 min, reaching 100% B at 10 min, holding 100% for 3 min and coming back to the initial condition of 5% B in 2 min, for a total run time of 15 min. Samples were kept at 4°C, 10 µL were injected in full loop mode with a washing step after sample injection with 150 µL of wash solution (H 2 O/MeOH, 90/10, v/v). The spectrometer was equipped with an electrospray ionization (ESI) source and operated in positive ion mode on a mass range from 100 to 1000 Da with a spectra rate of 8 Hz in AutoMS/MS fragmentation mode. The end plate offset was set at 500 V, capillary voltage at 2500 V, nebulizer at 2 Bar, dry gas at 8 L.min − 1 and dry temperature at 200°C. The transfer time was set at 20–70 µs and MS/MS collision energy at 80–120% with a timing of 50–50% for both parameters. The MS/MS cycle time was set to 3 seconds, absolute threshold to 816 cts and active exclusion was used with an exclusion threshold at 3 spectra, release after 1 min and precursor ion was reconsidered if the ratio current intensity/previous intensity was higher than 5. A calibration segment was included at the beginning of the runs allowing the injection of a calibration solution from 0.05 to 0.25 min. The calibration solution used was a fresh mix of 50 mL isopropanol/water (50/50, v/v), 500 µL NaOH 1M, 75 µL acetic acid and 25 µL formic acid. The spectrometer was calibrated on the [M + H] + form of reference ions (57 masses from m/z 22.9892 to m/z 990.9196) in high precision calibration (HPC) mode with a standard deviation below 1 ppm before the injections for each polarity mode, and re-calibration of each raw data was performed after injection using the calibration segment. Raw data were processed in MetaboScape 4.0 software (Bruker): molecular features were considered and grouped into buckets containing one or several adducts and isotopes from the detected ions with their retention time and MS/MS information when available. The parameters used for bucketing are a minimum intensity threshold of 10000, a minimum peak length of 3 spectra, a signal-to-noise ratio (S/N) of 3 and a correlation coefficient threshold set at 0.8. The [M + H] + , [M + Na] + , [M + K] + , [M + NH4] + and [M-H 2 O + H] + ions were considered. The obtained list of buckets was annotated using NPA ( https://www.npatlas.org/ ), KNApSAcK ( http://www.knapsackfamily.com/ ), FooDB ( http://foodb.ca ), PlantCyc ( https://plantcyc.org/ ), PhenolExplorer ( http://phenol-explorer.eu/ ), ECMDB ( https://ecmdb.ca/ ) and YMDB ( https://www.ymdb.ca/ ) to achieve level 3 annotations on the Schymanski scale (DOI: 10.1021/es5002105 ) with a maximum mass deviation of 3ppm and a maximum mSigma value of 30 (assessing the good fit of the isotopic profile). Spectral libraries were used to achieve Schymanski level 2 annotations with a minimum score of 800. Targeted Metabolomic Analysis Samples were analyzed by ultrahigh-performance liquid chromatography (UHPLC) on the UltiMate 3000 UHPLC system (Thermo) coupled to an EvoQ Elite LC-TQMS/MS (Bruker) mass spectrometer equipped with an electrospray ionization (ESI) source in MS/MS mode. The samples were kept at 4 °C before injection of 5 µL in full loop mode, and chromatographic separation on an Acquity UPLC ® HSS T3 C18 column (2.1 X 100 mm, 1.8 µm, Waters) coupled to an Acquity UPLC HSS T3 C18 pre­column (2.1 x 5 mm, 1.8 µm, Waters). Samples were carried through the column following a gradient of solvent A (H 2 0; 0.1% formic acid) and B (methanol; 0.1% formic acid) at a flux of 0.300 mL min⁻¹ starting with 5% B for 2 min, reaching 100% B at 10 min, holding 100% B for 3 min and returning to 5% B in 2 min, for a total run time of 15 min. The column was operated at 35 °C. Nitrogen was generated from pressurized air by a Nitro 35 nitrogen generator (GenGaz) and used as cone gas (30 L h⁻¹), heated probe gas (30 L h⁻¹) and nebulizer gas (35 L h⁻¹). The cone and heated probe temperatures were 350 and 300°C, respectively, and the capillary voltage was set at 3.5 kV. Phytohormones and bioactive metabolites were analyzed by multiple reaction monitoring (MRM) after determining the retention time and mode (positive or negative) by scan and the cone voltage, daughter ion and collision energy using the MRM builder function on standards. Wash solvent (80% H 2 0, 20% MeOH) was used to wash the syringe. A mix of the different standards served as a positive control. Results Isolation and Evaluation of Plant Growth-Promoting Traits in Rhizospheric Bacterial Strains To identify PGPB strains adapted to harsh environments, rhizosphere samples of Cynodon dactylon and Diplotaxis tenuifolia were collected from wild arid areas. Pure bacterial cultures were obtained from the rhizospheric soils of both plants using the serial dilution technique. Among the thirty distinct isolates recovered on nutrient agar (NA), two strains (S10 and DT1 from Cynodon dactylon and Diplotaxis tenuifolia rhizospheres, respectively), were selected based on their distinct morphological characteristics. On nutrient agar, S10 produced smooth, greyish colonies, whereas DT1 produced opaque colonies. These two strains were subsequently used to evaluate their plant growth-promoting (PGP) potential. As shown in Table 1 , both S10 and DT1 were able to grow on nitrogen-free medium, indicating their nitrogen-fixing capacity. In phosphate solubilization assays, both strains produced clear halos on Pikovskaya’s inorganic phosphate medium, while only S10 was able to solubilize zinc (Table 1 , Fig. S1 ). The appearance of yellow halo around streak lines S10 and DT1 on Chrome Azurol S (CAS) agar showed their ability to produce siderophores (Table 1 , Fig. S1 c). Additionally, IAA production by strains S10 and DT1, in the presence of tryptophan, was quantified using Salkowski's reagent, yielding concentrations of 4.25 and 14.65 µg/mL, respectively (Table 1 ). Together, these results indicate that the newly isolated rhizobacterial strains S10 and DT1 exhibit promising plant growth-promoting traits. Table 1 Plant growth-promoting (PGP) traits of the selected bacterial isolates Citrobacter braakii S10 and Acinetobacter calcoaceticus DT1. Plant growth-promoting trait Citrobacter braakii S10 Acinetobacter calcoaceticus DT1 Phosphate solubilization + + Zinc solubilization + - Nitrogen fixation + + Siderophore production + + IAA production (µg/ml) 4.25 14.65 +, positive; -, negative result for the test General Genomic Features of S10 and DT1 To gain insight into the genomic architecture of the two selected PGPR strains, S10 and DT1, Whole Genome Sequencing (WGS) was performed. The sequencing output revealed that both strains possess a single circular chromosome (Fig. S2 ) and a summary of their genomic features is presented in Table 2 . The genome of strain S10 is notably larger than that of DT1, comprising a total size of 4,913,851 bp, compared to 3,930,652 bp for strain DT1. Consistently, the number of predicted protein-coding genes was also higher in S10 (4,774 genes) than in DT1 (3,818 genes). The G + C content differed substantially between the two strains, with S10 exhibiting a value of 52.04%, whereas DT1 displayed a markedly lower G + C content of 38.75%, indicating their distinct taxonomic origins. Non-coding RNA elements also highlighted the divergence between the two genomes. S10 harbored a higher number of tRNA genes (84 vs. 73) and rRNA genes (25 vs. 18) than DT1, while both genomes contained a single copy of the transfer-messenger RNA (tmRNA) gene. Additionally, the number of miscellaneous RNAs (misc-RNAs), potentially including regulatory small RNAs, was much higher in S10 (82) compared to DT1 (30), suggesting a more complex regulatory potential in this strain. Another notable difference between the two genomes lies in the number of predicted pseudogenes (66 pseudogenes in S10 vs. 5 in DT1). Together, these genomic features reflect the phylogenetic divergence between the two strains and provide a foundational framework for further functional annotation and comparative analysis of the PGP traits. Table 2 Genomic features of Citrobacter braakii S10 and Acinetobacter calcoaceticus DT1 Genomic Features Citrobacter braakii S10 Acinetobacter calcoaceticus DT1 Genome size (bp) 4,913,851 3,930,652 G + C content (%) 52.04 38.75 N50 4913851 3930652 Number of contigs 1 1 Gene number 4,774 3,818 tRNA 84 73 rRNA 25 18 tmRNA 1 1 Misc-RNA 82 30 Pseudogenes 66 5 Whole Genome-Based Phylogenomic Identification of S10 and DT1 To assess the taxonomic affiliation of strains S10 and DT1, whole-genome-based phylogenetic and comparative genomic analyses were carried out. A phylogenomic tree was generated using the TYGS server, incorporating both isolates alongside type strains of closely related species (Fig. 1 ). This analysis revealed that strain S10 was clustered with Citrobacter braakii ATCC 51113 (i.e., originally isolated from a snake in France (Brenner et al. 1993 )), with a digital DNA–DNA hybridization (dDDH) value of 90.7% (Table S1 -A), exceeding the 70% threshold for species delineation. Similarly, strain DT1 grouped with Acinetobacter calcoaceticus DSM 30006 (i.e., isolated from a quinate-enriched soil in the Netherlands), with a dDDH value of 73.5% (Table S2 -A). These taxonomic clusterings were further supported by pairwise Average Nucleotide Identity (ANI) analysis using the JSpeciesWS server (Fig. 2 ). Indeed, the ANI analysis revealed that S10 shared 99.04% ANI with Citrobacter braakii GTA-CB01 (Table S1 -B), and DT1 showed 96.83% ANI with Acinetobacter calcoaceticus DSM 30006 (Table S2 -B), both well above the species-level threshold of 95–96%. To further explore genomic conservation, a synteny analysis was conducted between the two genomes. Extensive collinear regions were observed (indicated by blue lines in Figure S3 ), indicating that large portions of the genome are organized in the same order and orientation. A smaller number of inverted regions (pink lines) were observed, indicating low levels of genomic rearrangements between strains (Fig. S3 ). Together, these results provide robust genomic evidence supporting the classification of S10 as C. braakii and DT1 as A. calcoaceticus and reveal high intra-genus genomic conservation within each lineage across diverse habitats. Functional Genome Annotation of S10 and DT1 Genome annotations for both strains were performed using the RAST, COG, and KEGG databases. The total number of predicted genes was 4,774 and 3,818 for S10 and DT1, respectively. Of these, 4,693 genes in S10 and 3,663 in DT1 were functionally annotated by RAST, leaving 81 and 155 genes unannotated, respectively (Fig. S4). COG annotated 4,459 genes in S10 and 3,487 in DT1, while KEGG assigned functions to 3,365 and 2,010 genes, respectively. Classification assigned the annotated genes into 384 subsystems in S10 (Fig. 3 a), and 310 subsystems in DT1 (Fig. 3 b). In S10, the most enriched categories were carbohydrate metabolism (408 genes; 8.70%), followed by amino acids and derivatives (370 genes; 7.88%), protein metabolism (262 genes; 5.58%), and cofactors, vitamins, prosthetic groups, and pigments (181 genes; 3.86%). In DT1, the most abundant genes were associated with amino acids and derivatives (297 genes; 7.95%), followed by protein metabolism (200 genes; 5.36%), carbohydrate metabolism (160 genes; 4.28%), and cofactors, vitamins, prosthetic groups, and pigments (143 genes; 3.83%). According to COG functional classification, metabolism was the most dominant process in both genomes, encompassing 2,065 genes (45.06%) in S10 (Fig. 4 a) and 1,518 genes (41.08%) in DT1 (Fig. 4 b). Genes involved in cellular processes and signaling accounted for 23.16% of the genes in S10 and 16.82% in DT1, covering functions such as cell wall/membrane/envelope biogenesis, signal transduction, cell motility, post-translational modification, intracellular trafficking, secretion, vesicular transport and protein turnover. Interestingly, the S10 genome contains numerous flagellar biosynthesis genes ( e.g ., fliADEFGHIJKKMNOPQRST , flhABCDE ) (Table S4), in line with the motile nature of most Citrobacter species. In contrast, such genes were absent in the DT1 genome, consistent with the non-motile nature of Acinetobacter (Table S3 ). About 17% of coding sequences in both genomes were related to information storage and processing, encompassing functions such as translation, ribosomal structure and biogenesis, transcription, replication, recombination, and repair. A substantial fraction of genes was classified as having unknown functions, with 999 genes (21.80%) in the S10 genome and 1,115 genes (30.16%) in DT1. Finally, KEGG pathway mapping reinforced the dominance of metabolic genes, with 1,356 genes in S10 and 1,128 in DT1 assigned to metabolism-related pathways (Fig. 5 ). Additional prominent categories included environmental information processing (S10: 458 genes; DT1: 169), genetic information processing (S10: 222; DT1: 194), and cellular processes (S10: 259; DT1: 115). Taken together, these annotations reveal that both genomes encode a broad repertoire of functional genes, with S10 showing greater metabolic diversity and DT1 exhibiting a higher proportion of genes with yet uncharacterized functions. Genome mining for PGP and Stress-related genes Functional genome comparison showed that both S10 and DT1 strains harbor a diverse repertoire of genes related to PGP traits, including auxin, cytokinin and siderophore biosynthesis, phosphate solubilization, nitrogen metabolism and ACC deaminase activity (Fig. 6 , Table S3 and Table S4). Both strains carried gene clusters involved in IAA biosynthesis, notably tyrB , trpABCDE , and ipdC , a key gene in the indole-3-pyruvate (IPA) pathway. Genes related to cytokinin biosynthesis, miaA , miaB , and miaE , were also found in both genomes (Fig. 6 ). Interestingly, aspC (aspartate aminotransferase) was specific to the S10 genome, whereas trpF (N-(5'-phosphoribosyl) anthranilate isomerase) was identified only in DT1. Although not directly involved in IAA biosynthesis, both genes contribute indirectly by supporting the availability of tryptophan, its primary precursor. In addition, key genes involved in the synthesis of riboflavin (vitamin B2) were also found in both the S10 and DT1 genomes, these include ribA (GTP cyclohydrolase II), ribB (3,4-dihydroxy-2-butanone 4-phosphate synthase), ribD (pyrimidine deaminase/reductase), ribE (lumazine synthase), and ribC (riboflavin synthase) (Fig. 6 ; Table S3 ; Table S4). These genes encode enzymes that convert one molecule of GTP and two molecules of ribulose-5-phosphate into one molecule of riboflavin, a crucial cofactor for plant metabolism, growth, and defense. Regarding phosphate solubilization, both genomes harbored the phosphate transporters gene pstB (Fig. 6 ), while pstACS genes were found only in S10 (Table S4). Core genes such as inorganic pyrophosphatase ( ppa ), exopolyphosphatase ( ppx ), polyphosphate kinase ( ppk ) and quinoprotein glucose dehydrogenase ( gcd ) were present in both strains. S10’s genome encoded uniquely phosphonate, and carbon-phosphorus (C-P) lyases ( phnCDEGHIJK ), whereas additional genes involved in phosphate sensing and cofactor synthesis were found DT1, including phosphate regulon sensor protein ( phoR ), phosphate specific transport system accessory protein ( phoU ) and pyrroloquinoline synthase ( pqqG ). For nitrogen fixation, no complete nitrogenase gene cluster was identified in either genome, but nitrogen metabolism potential was evident. DT1 carried the nirB gene, encoding nitrite reductase catalytic subunit, while the S10 genome harbored a more extensive set of nitrate reduction genes, including the narGHIVYZ operon (Fig. 6 , Table S3 ). Siderophore biosynthesis appeared to be under the control of several gene clusters, in S10 and DT1 genomes. Both strains carried genes for enterobactin ( entABCDEF ), and staphyloferrin B ( sbnABD ) biosynthesis. In addition, other siderophore-related genes including L-2,4-diaminobutyrate decarboxylase ( ddC ), diaminobutyrate-2-oxoglutarate aminotransferase ( dat ), 2,3-dihydroxybenzoate-AMP ligase ( dhbE ) were also identified, as well as genes related to iron storage ( bfr ) and transport ( feoABC ) (Fig. S6; Table S3 ; Table S4). Finally, genes involved in ACC deaminase activity were detected. The S10 genome contained rimI , rimK , rimL , rimM , rimO , rimP , and rimJ , while DT1 carried rimI , rimM , rimO , and rimP (Fig. 6 ). These genes are involved in the degradation of 1-aminocyclopropane-1-carboxylate (ACC), the immediate precursor of ethylene, a plant hormone that inhibits growth under abiotic stress conditions (Shekhawat et al. 2022 ). Taken together, these findings demonstrate that S10 and DT1 harbor complementary and overlapping sets of genes involved in phytohormone biosynthesis, nutrient acquisition, stress mitigation, siderophore production and stress mitigation, supporting their functional potential as robust PGPR candidates for sustainable agriculture. Genomic Analysis of Secondary Metabolism using AntiSMASH To identify gene clusters involved in secondary metabolite synthesis in S10 and DT1, genome mining was performed using antiSMASH (version 8.). The analysis revealed that the S10 genome harbored two biosynthetic gene clusters (BGCs): one encoding a thiopeptide (O-antigen-related) and another corresponding to an NRP-metallophore of the NRPS class, annotated as enterobactin (Table 3 ). In contrast, the DT1 genome harbored a broader diversity of secondary metabolite pathways, with eight predicted BGCs, including gene clusters for arylpolyenes, NI-siderophore, betalactone, redox-cofactor, NRP-metallophore, NRPS, and RiPP-like metabolites (Table 3 ). Comparison against the MIBiG reference database revealed varying degrees of similarity to characterized clusters. For instance, the enterobactin BGC from DT1 shared 55% similarity with a known orthologous cluster from Pseudomonas sp ., whereas the corresponding cluster in S10 exhibited full conservation (100%) with a reference Citrobacter cluster, suggesting potential divergence in siderophore structure, expression, or functionality between the two strains. Other BGCs in DT1 showed moderate similarity to clusters encoding APE VF, berninamycin variants, and mycosubtilin with 45%, 22%, and 20% similarity to clusters from Aliivibrio fischeri ES114, Streptomyces sp., and Bacillus subtilis subsp. spizizenii ATCC 6633, respectively. The remaining clusters showed less than 20% similarity to known BGCs, including 16% similarity with staphyloferrin B from Staphylococcus aureus subsp. aureus NCTC 8325, and 4% similarity with lagriene from Burkholderia gladioli . Altogether, these findings suggest that while S10 contains a limited set of conserved BGCs, DT1 exhibits a broader and more diverse secondary metabolite biosynthetic potential, likely reflecting an intrinsic genetic capacity for metabolic versatility, a trait commonly associated with microbial adaptability and survival in diverse or competitive environments (Dong et al. 2024 ). Table 3 Biosynthetic gene clusters (BGCs) for secondary metabolites identified in the genome of Acinetobacter calcoaceticus DT1 and Citrobacter braakii S10. Gene cluster prediction was performed using antiSMASH v8.0 and annotations were cross-referenced with the MIBiG database. Region Type From To Most similar know cluster MIBIG Accession (% Gene Similarity) Acinetobacter calcoaceticus DT1 1 NI-siderophore 1564594 1599021 staphyloferrin B* BGC0000943 (16%) 2 redox-cofactor, NRP-metallophore, NRPS 1916080 2023816 Enterobactin* BGC0000343 (55%) 3 betalactone 2367645 2396725 mycosubtilin BGC0001103 (20%) 4 RiPP-like 2507421 2518284 5 RiPP-like 2706268 2718466 6 arylpolyene 2981396 3022625 berninamycin K/berninamycin J/berninamycin A/berninamycin B BGC0002363 (22%) 7 arylpolyene 3321619 3365221 APE Vf BGC0000837 (45%) 8 NRPS, hserlactone 3750029 3793988 lagriene BGC0002455 (4%) Citrobacter braakii S10 1 Thiopeptide 3108899 3135189 O-antigen BGC0000781 (14%) 2 NRP-metallophore, NRPS 3430326 3485411 Enterobactin BGC0002476 (100%) NRP non-ribosomal peptide, NRPS non-ribosomal peptide synthase. * Genes associated with these clusters are presented in Fig. 6 Targeted and non-targeted metabolomic profiling of S10 and DT1 strains Given the presence of genes involved in phytohormone biosynthesis (Fig. 6 ), we performed targeted metabolomic analysis using Liquid Chromatography–Q-Tandem Mass Spectrometry (LC-MS/MS) to assess the production of key metabolites in S10 and DT1. Both intracellular (cell pellet) and extracellular (culture supernatant) fractions were analyzed to distinguish between metabolites synthesized in the bacteria and those actively or passively secreted into the environment, the latter being most relevant to plant-microbe interactions. This analysis confirmed the production of several phytohormones, including indole-3-acetic acid (IAA), D-abscisic acid, salicylic acid (SA), and the cytokinin 2-isopentenyladenine (Fig. 7 ). Among these, the most abundant hormone was IAA, especially in the supernatant of DT1 (confirming the data obtained in the assays using the Salkowski's reagent), indicating a high level of extracellular accumulation. In contrast, SA showed the lowest abundance in both supernatant and pellet fractions, but both strains produced comparable amounts of benzoic acid, a precursor of SA. Moreover, both strains produced equivalent amounts of riboflavin. Together with SA and benzoic acid, these metabolites are known to trigger both plant pathogen defense and abiotic stress tolerance (Azami-Sardooei et al. 2010 ; Senaratna et al. 2003 ). To further explore the metabolic diversity of S10 and DT1, non-targeted metabolomic profiling was conducted using Liquid Chromatography-High-Resolution Tandem Mass Spectrometry with Quadrupole Time-of-Flight (LC–HRMS QTOF). Both pellet and supernatant fractions were analyzed, and metabolite annotation was performed using publicly available databases including NPA, KNApSAcK, PlantCyc, FoodDB, phenolExplorer, YMDB, ECMDB, and Spectral libraries. To evaluate metabolic variations between intracellular and extracellular fractions of both strains, principal component analysis (PCA) was applied. The PCA score plot revealed a clear separation between the metabolic profiles of S10 and DT1, with the first principal component (PC1) accounting for 85.5% of the total variance (Fig. 8 ), indicating that strain identity is the main driver of metabolic differences. In contrast, the second principal component (PC2, 3% of the total variance) captured the subtler variation between intracellular and extracellular fractions, reflecting differences in metabolite accumulation and secretion. Differential metabolite analysis ( p -value 0,5|) identified 56 significantly different compounds between DT1 pellet and supernatant fractions. Among these, 45 were more abundant in the pellet ( e.g ., N.N-Dimethyldodecylamine N-oxide, Guanine, Adenosine 3'-monophosphate, alpha-Linolenic acid and Farnesylacetone), while 11 were more abundant in the supernatant ( e.g ., Perlolyrine, Radiosumin, Microcin SF608 and Maculosinin) (Table S5; Fig. 9 a). In S10, 31 metabolites were differently identified between fractions, 20 were more abundant in the pellet and 11 in the supernatant (Table S6; Fig. 9 a). Notably, no metabolites were shared among the differentially produced compounds in the supernatants of the two strains, whereas six metabolites were found in both pellets. Additionally, three overlaps were observed between the DT1 supernatants and the S10 pellet (Fig. 9 b). Clustering heatmaps of differential metabolites for DT1 (Fig. 10 a) and S10 (Fig. 11 a) confirmed distinct metabolite distribution patterns between intracellular and extracellular compartments. KEGG pathway enrichment analysis revealed that the detected metabolites were significantly associated with pathways involved in plant secondary metabolites biosynthesis, amino acid metabolism, phytohormone production, and ABC transporter-mediated environmental signal sensing (Fig. 10 b and 11 b). Overall, targeted and untargeted metabolomic analyses confirmed the production of phytoactive and stress-related metabolites in both strains, supporting their genomic potential and reinforcing their suitability as metabolically versatile PGPR candidates for plant health improvement. Discussion An increasing number of studies has emphasized the potential of PGPR to enhance plant health and productivity (Tripathi et al. 2024 ). In this context, we targeted the rhizosphere of two wild plant species, Cynodon dactylon and Diplotaxis tenuifolia , naturally thriving in arid environments, aiming to isolate bacterial strains potentially adapted to harsh conditions and endowed with PGP properties. From these rhizospheres, we isolated two strains, S10 and DT1, subsequently identified as Citrobacter braakii (S10) and Acinetobacter calcoaceticus (DT1), respectively. Although these species are best known for their opportunistic pathogenicity in clinical settings (Joly-Guillou 2005 ; Zhang et al. 2023 ), C. braakii has also been isolated from diverse environments including rhizospheric soil of rice grown in salt-affected areas (Nawaz et al. 2021 ). PGPR promote plant growth and stress resilience by producing or modulating phytohormones such as auxins, gibberellins, and cytokinins, as well as regulating ethylene levels through ACC deaminase activity (Tripathi et al. 2024 ). To explore the PGP potential of S10 and DT1, we performed whole-genome annotation, focusing on pathways related to phytohormone biosynthesis and metabolism. Both genomes contain the tryptophan biosynthesis operon trpABCDE , a key pathway indirectly linked to indole-3-acetic acid (IAA) production, a major phytohormone involved in plant development (Babalola et al. 2021 ). Moreover, both strains harbor the ipdC gene, encoding indole-3-pyruvate (IPyA) decarboxylase, a key enzyme in the IPyA pathway of IAA biosynthesis (Jiang et al. 2023 ). The functional relevance of ipdC in PGP activity has been demonstrated by Figueredo et al. ( 2023 ), who reported that its inactivation in Bacillus thuringiensis RZ2MS9 markedly reduced the strain’s capacity to promote maize growth. Consistent with these genomic findings, our targeted LC-MS/MS analysis confirmed substantial IAA production in both strains, particularly in the extracellular fraction of DT1 (Fig. 7 ). In addition to auxin production, S10 and DT1 also possess genes encoding ACC deaminase enzymes ( rimJ, rimK, rimL, rimM, rimO, rimP , and rimI ), which degrade 1-aminocyclopropane-1-carboxylate (ACC), the precursor of ethylene. By lowering ethylene levels, this activity helps mitigate growth inhibition under stress conditions such as drought and salinity, as observed in other PGPR including Phytobacter palmae WL65 (Thamvithayakorn et al. 2025 ). Cytokinins (CK) also play a critical role in plant development by regulating cell division, shoot initiation, and photosynthesis (Gao et al. 2022 ). For instance, cytokinin production by Rhizobium sp. WYJ-E13 promotes Curcuma root growth (Huang et al. 2022 ). In both S10 and DT1 genomes, we identified several genes involved in CK biosynthesis, including miaABE genes. These genes encode tRNA dimethylallyl transferases, which catalyze the biosynthesis of isopentenyladenosine (iPR), a key cytokinin precursor. Consistently, our targeted metabolomics confirmed the production of cytokinin 2-isopentenyladenine by S10 and DT1. Similar CK biosynthesis genes were reported to be active in Enterobacter mori AYS9, where miaA and miaB are involved in converting iPR into biologically active cytokinins (Fadiji et al. 2023 ). The functional relevance of cytokinin biosynthesis in PGPR has also been demonstrated by Asif et al. ( 2022 ), who showed that these hormones protect photosynthetic pigments like carotenoids and chlorophylls. Consistently, Priestia aryabhattai and Paenibacillus sp. strains harboring miaA and miaB genes were reported to enhance chlorophyll content in tomato plants (Almirón et al. 2025 ). Alongside cytokinins, riboflavin and its derivatives, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), are essential cofactors involved in photosynthesis, energy production, and redox metabolism (Sandoval et al. 2008 ). The beneficial impact of bacterial riboflavin production on plant health has been demonstrated by Ajeethan et al. ( 2023 ), who showed that inoculating kale seeds with Sinorhizobium meliloti 1021 ( i.e ., a strain secreting significant amounts of flavins) led to significant improvements in growth compared to a flavin-deficient mutant. In our study, both S10 and DT1 genomes harbored genes for riboflavin biosynthesis, suggesting a role in supporting plant health. Combined with cytokinin biosynthesis genes, these results underscore the diverse metabolic strategies of S10 and DT1 in promoting plant growth under stress conditions. Building on this genomic evidence, our targeted metabolite profiling further revealed that both strains produce not only cytokinins and riboflavin, but also D-abscisic acid and salicylic acid. Similarly, Priestia aryabhattai and Paenibacillus sp. isolates from the tomato rhizosphere have been reported as PGPR capable of producing various phytohormones (Almirón et al. 2025 ). These findings underscore the multiple phytohormone-mediated mechanisms underlying the PGP potential of S10 and DT1. Beyond targeted analyses, our comparative non-targeted metabolomic profiling revealed additional metabolites produced by S10 and DT1, with some accumulating differentially between intracellular and extracellular fractions (Figs. 10 and 11 ). Among these, cadaverine and biotin are known to enhance plant growth and stress tolerance (Jancewicz et al. 2016 ; Wang et al. 2020 ). Cadaverine has been linked to improved antioxidant defenses and tolerance to abiotic stresses (Gibbs et al. 2021 ; Ozmen et al. 2023 ). Biotin, another metabolite detected, supports plant development and resilience against carbonate stress (Wang et al. 2020 ). Notably, S10 produced arginine and trans-4-coumaric acid, compounds associated with photosynthetic enhancement and ROS signaling, respectively (Chen et al. 2022 ; Nkomo et al. 2019 ). DT1 produced indole-3-propionic acid (IPA), which promotes root development (Sun et al. 2024 ), as well as amino acids like valine, histidine, leucine, and proline, which support plant growth and stress adaptation (Ji et al. 2022 ; Liu et al. 2023 ; Renzetti et al. 2025 ; Sun et al. 2023 ). This strain also synthesized gamma-aminobutyric acid (GABA), known for enhancing tolerance to multiple stresses in plants (Golnari et al. 2021 ; Suhel et al. 2023 ). These diverse metabolites suggest significant potential for S10 and DT1 to promote plant growth and resilience under various environmental conditions. Notably, the co-production of plant-beneficial compounds such as phytohormones, riboflavin, siderophores, and polyamines like cadaverine may confer a broader functional capacity to modulate plant responses (Jancewicz et al. 2016 ; Olanrewaju et al. 2017 ; Vejan et al. 2016 ). Siderophores sequester iron from the environment, reducing availability to phytopathogens and contributing to plant health (Zhang et al. 2020 ). Our analyses revealed numerous genes associated with siderophore production, including entABCDEF, feoABC, dhbABCEF, efeBOU, sbnABD (Fig. 6 ). AntiSMASH analysis identified clusters for enterobactin, bacillibactin, and staphyloferrin B, highlighting a broad siderophore biosynthetic capacity (Table 3 ; Table S3 ; Fig. S5). These clusters are linked to biocontrol and plant health in other species like Bacillus amyloliquefaciens (Dimopoulou et al., 2021), Bacillus velezensis (Zhang et al. 2024a ; Zhang et al. 2024b ) and Methylobacterium aquaticum (Juma et al. 2022 ). Nitrogen fixation is a key PGPR trait enabling conversion of atmospheric nitrogen (N 2 ) into bioavailable forms (Masood et al. 2020 ). In our study, S10 and DT1 harbored the nirB gene, involved in nitrate reduction along with other nitrogen metabolism genes like narGHI . However, neither strain possessed the full nitrogenase cluster required for atmospheric nitrogen fixation, suggesting their role may be limited to nitrate assimilation rather than true nitrogen fixation (Guo et al. 2023 ). Phosphorus-solubilizing bacteria (PSB) play a key role in mineralizing organic phosphorus and mobilizing inorganic phosphorus (Pan and Cai 2023 ). Previous studies have shown that S10 can solubilize organic phosphate in vivo (El Ifa et al. 2024 ). Our genomic data confirms that S10 carries genes like gcd , phoARP , pstABCS , and pqqEFG (Fig. 6 ), consistent with its phosphate-solubilizing phenotype. Importantly, we provide new evidence that DT1 also possesses genes associated with phosphate solubilization, suggesting its potential as a PSB. Consistently, both S10 and DT1 demonstrated functional activity in vitro , as evidenced by their ability to produce siderophore on CAS agar, solubilize phosphate, and grow on nitrogen-free media (Table 2 ; Fig. S1 ). Together, these findings confirm the multifaceted PGP potential of S10 and DT1 and highlight their promise for application as bioinoculants in sustainable agriculture. In this context, a combined formulation of both strains may prove particularly effective, as it would leverage their complementary traits ( e.g ., zinc solubilization by S10 and the broader metabolic repertoire of DT1) to maximize plant growth promotion across varied environmental conditions. This strategy, which harnesses the functional complementarity of compatible PGPR, warrants further testing under greenhouse and field conditions. Conclusion This study highlights the multifunctional plant growth-promoting potential of two rhizobacterial strains, Citrobacter braakii S10 and Acinetobacter calcoaceticus DT1 isolated from the rhizospheres of wild plants thriving in arid habitats. Comprehensive genomic and metabolomic analyses revealed diverse genes and metabolites associated with key PGP traits, including pathways for nitrogen fixation, phosphate and zinc solubilization, siderophore and phytohormone production, and stress resilience. These genomic insights were corroborated by phenotypic assays and metabolomic profiling, confirming the functional capabilities of both strains. The capacity of S10 and DT1 to produce a range of bioactive compounds underscores their adaptability and potential to support plant growth and stress tolerance under challenging conditions. Together, these findings provide a strong foundation for the future application of these strains as bioinoculants in sustainable agriculture, offering eco-friendly alternatives to chemical fertilizers and supporting crop productivity in challenging environments. Future research should focus on evaluating their performance under greenhouse and field conditions to confirm their practical efficacy. Declarations Conflict of Interest The authors declare no conflict of interest. Funding This work was funded by grants provided by the Tunisian Ministry of Higher Education and Scientific Research (MESRS) and the French Ministry of Europe and Foreign Affairs (MEAE) and Ministry of Higher Education and Research (MESR) under the project PHC Maghreb 23MAG09. Author Contribution Conceptualization, IG, AB, MH and CE.; methodology, IG, NS (PGPR traits), AB, AA, VC (genome assembly) CV, DH, JZ (metabolomic); validation, CE, AB, MH; formal analysis, IG, NS, AA, VC, CV (genome assembly, annotation, genome analysis), CV, DH, JZ (metabolomic); writing—original draft preparation, IG, CE, AB.; writing—review and editing, IG, AB, MH.,CE; visualization, IG, NS.; supervision, CE, AB.; funding acquisition, AB, CE, MH. All authors have read and agreed to the published version of the manuscript. Acknowledgement The authors thank Drs. 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Int J Food Microbiol 410:110480. https://doi.org/10.1016/j.ijfoodmicro.2023.110480 Additional Declarations No competing interests reported. Supplementary Files SupTableANIandDDH.xlsx SuptablemetabolitesPGP.xlsx SuptablePGPtraitsce.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jan, 2026 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 19 Oct, 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. 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06:57:37","extension":"png","order_by":68,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56512,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/459bf973fe2fc20900db376f.png"},{"id":96052930,"identity":"b82077bc-1254-4de2-b1f4-134a3a0618c5","added_by":"auto","created_at":"2025-11-17 06:57:38","extension":"xml","order_by":69,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262763,"visible":true,"origin":"","legend":"","description":"","filename":"ba02eafd3dec42f7836c8dc8ecb2372b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/7e0ad02a06d172a6a1ab28fa.xml"},{"id":96052904,"identity":"bb4e0547-656a-4b02-82c0-45da276d11b8","added_by":"auto","created_at":"2025-11-17 06:57:37","extension":"html","order_by":70,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":280276,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/9f2f8d5c2ccb4da0930a72bf.html"},{"id":96246552,"identity":"0fc383e6-106b-4f5f-9b45-afa896485fb8","added_by":"auto","created_at":"2025-11-19 07:26:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234199,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-based phylogenetic positioning of \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 and \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10.\u003cstrong\u003e \u003c/strong\u003ePhylogenetic tree of DT1 (\u003cstrong\u003ea\u003c/strong\u003e) and S10 (\u003cstrong\u003eb\u003c/strong\u003e) inferred using FastME 2.1.6.1 from Genome BLAST Distance Phylogeny (GBDP) calculated from whole-genome sequences. Branch lengths are scaled according to the GBDP distance formula d5. Pseudo-bootstrap support values (\u0026gt; 60 %) are shown above branches and were calculated from 100 replicates, with an average branch support of 89.7 %. The trees are midpoint-rooted. The colored and/or scaled squares to the right of each taxon correspond to six genomic or taxonomic features, indexed as follows: 1. Species cluster (color-coded); 2. Subspecies cluster (color-coded); 3. G+C content (blue gradient : light blue = low, dark blue = high); 4. Delta statistics (brown gradient: darker squares = higher deviation from tree-likeness); 5. Genome size (square size proportional to total genome length); Protein-coding gene content (square size proportional to number of CDS). Strains DT1 and S10 are highlighted at the top of their respective trees and compared to reference genomes within the \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eCitrobacter\u003c/em\u003e genera.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/0a8f87ccf8fa0f9d3702822a.png"},{"id":96052846,"identity":"24732b0b-f1c2-42a5-bf43-bbaae25d82fe","added_by":"auto","created_at":"2025-11-17 06:57:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355529,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Nucleotide Identity (ANI) heatmaps of \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 and \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10.\u003cstrong\u003e \u003c/strong\u003eANI-based comparisons were performed to assess the genomic similarity of strain DT1 (\u003cstrong\u003ea\u003c/strong\u003e) with 13 \u003cem\u003eAcinetobacter\u003c/em\u003ereference genomes, and strain S10 (\u003cstrong\u003eb\u003c/strong\u003e) with 13 \u003cem\u003eCitrobacter\u003c/em\u003e genomes. Pairwise ANI values are visualized as heatmaps, where colors indicate percent identity at the nucleotide level. The gradient ranges from blue (low similarity) to yellow (high similarity), as shown in the accompanying color scale. These analyses were conducted using the JSpeciesWS server, with species-level boundaries typically considered at 95–96% ANI.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/b3f989cba9b6119fa16f059c.png"},{"id":96247266,"identity":"497d319b-855f-4641-8269-f7b4819a2ba5","added_by":"auto","created_at":"2025-11-19 07:27:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372377,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional subsystem category distribution in the genomes of \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1. were annotated using the RAST server and are grouped by major biological processes. The number of genes assigned to each subsystem is shown for S10 (\u003cstrong\u003ea\u003c/strong\u003e) and DT1 (\u003cstrong\u003eb\u003c/strong\u003e). Categories include core metabolic processes such as carbohydrate metabolism, amino acid synthesis, stress response, and cofactor production. Annotations were generated using RAST (Rapid Annotations using Subsystems Technology; accessed via \u003ca href=\"https://rast.nmpdr.org/\"\u003ehttps://\u003c/a\u003e\u003ca href=\"https://rast.nmpdr.org/\"\u003erast.nmpdr.org\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/46de050becf671cdb54f31c6.png"},{"id":96248073,"identity":"e7f114ba-acf6-4058-a103-edfe197e4cd2","added_by":"auto","created_at":"2025-11-19 07:28:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":252062,"visible":true,"origin":"","legend":"\u003cp\u003eCOG-based functional annotation of \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 genomes.\u003cstrong\u003e \u003c/strong\u003ePredicted protein-coding genes from S10 (\u003cstrong\u003ea\u003c/strong\u003e) and DT1 (\u003cstrong\u003eb\u003c/strong\u003e) were classified into Clusters of Orthologous Groups (COG) functional categories. Each bar represents the number of genes assigned to a specific functional group, reflecting major cellular processes, metabolism, and information storage and processing. Annotations were obtained using the COG database via the MicroScope platform (\u003ca href=\"https://mage.genoscope.cns.fr/microscope\"\u003ehttps://\u003c/a\u003e\u003ca href=\"https://mage.genoscope.cns.fr/microscope\"\u003emage.genoscope.cns.fr\u003c/a\u003e\u003ca href=\"https://mage.genoscope.cns.fr/microscope\"\u003e/microscope\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/bcbcf955f639dce2fe2bbb6f.png"},{"id":96052850,"identity":"b9c4645b-b494-4795-bbce-35221dae103a","added_by":"auto","created_at":"2025-11-17 06:57:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":212994,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG-based functional classification of genes in \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 genomes. Functional annotation results of KEGG database of \u003cem\u003eCitrobacter braakii S10 \u003c/em\u003egenome (a) and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 (b). The identified genes were classified into five major KEGG pathway categories: Metabolism, Genetic Information Processing, Environmental Information Processing, Cellular Processes, and Human Diseases. The bar plots reflect the number of genes assigned to each category, highlighting the predominance of metabolic and environmental processing functions in both strains.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/566a0fabb37a75bc503918af.png"},{"id":96052862,"identity":"d9227265-a9a6-4ce4-9843-8990dd41a767","added_by":"auto","created_at":"2025-11-17 06:57:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167220,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of genes related to plant growth-promoting (PGP) traits in \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 and \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10. This presence/absence matrix summarizes key functional genes associated with PGP activities, including phytohormone biosynthesis, phosphate solubilization, nitrogen metabolism, siderophore production, and stress resilience. Colored boxes indicate the presence of a given gene in the genome, while white boxes indicate its absence. The comparison highlights both shared and strain-specific genetic determinants supporting plant growth promotion.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/c796ef181f7d00725c6b633d.png"},{"id":96052855,"identity":"96c47745-ef2d-4d49-901f-fbcf0c642c11","added_by":"auto","created_at":"2025-11-17 06:57:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":121094,"visible":true,"origin":"","legend":"\u003cp\u003eQuantification of phytohormones in \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 strains.\u003cstrong\u003e \u003c/strong\u003eTargeted metabolomic profiling was conducted using Liquid Chromatography–Q-Tandem Mass Spectrometry (LC-MS/MS) to quantify phytohormones in both extracellular (supernatant) and intracellular (pellet) fractions of bacterial cultures. Violin plots show the signal intensities of indole-3-acetic acid (IAA), D-abscisic acid, salicylic acid (SA), and 2-isopentenyladenine (2-iP) for each strain.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/8b21d640dfad58b66c9fa558.png"},{"id":96052879,"identity":"a429c7ba-3e5f-43f3-bdc2-30eed97c1f3d","added_by":"auto","created_at":"2025-11-17 06:57:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":60425,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of the global metabolic profiles of \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1. Non-targeted metabolomic profiling was performed on intracellular (pellet) and extracellular (supernatant) fractions using Liquid Chromatography–High-Resolution Mass Spectrometry coupled with Quadrupole Time-of-Flight (LC–HRMS QTOF). PCA was computed using MetaboAnalyst 5.0 to visualize global metabolic differences between strains and sample types. Each point represents one replicate of intracellular or extracellular metabolite profile.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/b1437b52001136c43d71aa50.png"},{"id":96247812,"identity":"97109ac8-6350-413f-819a-0a4ca821f7d5","added_by":"auto","created_at":"2025-11-19 07:27:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":141541,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolite abundance and overlap between \u003cem\u003eCitrobacter braakii \u003c/em\u003eS10 and \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Stacked bar chart showing the number of significantly more abundant (blue) and less abundant (pink) metabolites identified in the intracellular (pellet) fractions of S10 and DT1 strains, based on LC–HRMS QTOF non-targeted metabolomics and statistical thresholds (fold change \u0026gt; |0,5|; p \u0026lt; 0.05). (\u003cstrong\u003eb) \u003c/strong\u003eFour-way Venn diagram illustrating the overlap of differentially abundant metabolites between pellet and supernatant of the two strains.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/1e9dcd76240fd671c43c152e.png"},{"id":96052909,"identity":"17a3cf62-5a30-45fc-8f0d-a12a82cb449a","added_by":"auto","created_at":"2025-11-17 06:57:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":497476,"visible":true,"origin":"","legend":"\u003cp\u003eIntracellular and extracellular metabolite profiles of \u003cem\u003eAcinetobacter calcoaceticus \u003c/em\u003eDT1 and associated functional pathways.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Hierarchical clustering heatmap of metabolites identified by LC–HRMS QTOF in the intracellular (pellet) and extracellular (supernatant) fractions of DT1, represented as log₂-transformed peak area values. (\u003cstrong\u003eb\u003c/strong\u003e) KEGG pathway enrichment analysis of the differentially produced metabolites from (\u003cstrong\u003ea\u003c/strong\u003e), highlighting biological processes associated with plant growth promotion and stress resilience.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/3fa78a33f4596b3c5600219e.png"},{"id":96247173,"identity":"a6ee6151-2981-4aa7-999a-da8a3b9bd030","added_by":"auto","created_at":"2025-11-19 07:27:14","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":443445,"visible":true,"origin":"","legend":"\u003cp\u003eIntracellular and extracellular metabolite profiles of \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10 and associated functional pathways. (\u003cstrong\u003ea\u003c/strong\u003e) Hierarchical clustering heatmap of metabolites identified by LC–HRMS QTOF in the intracellular (pellet) and extracellular (supernatant) fractions of S10, presented as log₂-transformed peak area values. (\u003cstrong\u003eb\u003c/strong\u003e) KEGG pathway enrichment analysis of the differentially abundant metabolites from (\u003cstrong\u003ea\u003c/strong\u003e), showing functional associations with plant growth-promoting and stress-responsive pathways.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/31a2abe545718a4268f54c2c.png"},{"id":96452778,"identity":"adf46dd8-f789-4e0a-bcb2-cd8f4e723448","added_by":"auto","created_at":"2025-11-21 09:44:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4234031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/8b689e72-dc21-4a89-8593-3af5f9f9e1b7.pdf"},{"id":96052861,"identity":"6aa45f7e-d149-4a36-9b57-82922c034b71","added_by":"auto","created_at":"2025-11-17 06:57:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17338,"visible":true,"origin":"","legend":"","description":"","filename":"SupTableANIandDDH.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/4ad8d7114f48fba26324e692.xlsx"},{"id":96052848,"identity":"07b2b634-b862-4a52-9689-743d9540cfab","added_by":"auto","created_at":"2025-11-17 06:57:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16834,"visible":true,"origin":"","legend":"","description":"","filename":"SuptablemetabolitesPGP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/9812b0b29b4b0a676cd29b0f.xlsx"},{"id":96248212,"identity":"1b102328-372f-423f-85af-0b708172256e","added_by":"auto","created_at":"2025-11-19 07:28:10","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19780,"visible":true,"origin":"","legend":"","description":"","filename":"SuptablePGPtraitsce.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7900185/v1/6de335696013251562028d3e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic and metabolomic characterization of Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10) reveal functional traits for plant stress alleviation and sustainable agriculture","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant growth-promoting rhizobacteria (PGPR) represent a functionally diverse group of beneficial root-associated bacteria that colonize the rhizosphere and stimulate plant growth and development through a variety of direct and indirect mechanisms (Mmotla et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Since their introduction by Kloepper and Schroth in the late 1970s, PGPR have been extensively studied for their potential to enhance agricultural productivity while promoting environmentally sustainable and low-input farming practices. PGPR act via both direct and indirect mechanisms. Direct effects include atmospheric nitrogen fixation (Masood et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and solubilization of soil inorganic phosphorus, both of which enhance nutrient availability, as well as the production of phytohormones such as indole-3-acetic acid (IAA), cytokinins, and gibberellins, which collectively promote plant growth and development (Joshi et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Indirect effects include pathogen suppression through siderophores production (Gao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), activation of 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity (Zafar-ul-Hye et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), emission of volatile organic compounds (VOCs) (Mhlongo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and synthesis of antimicrobial metabolites, leading to induced systemic resistance (ISR) (Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVarious bacterial genera exhibit plant growth-promoting (PGP) potential, including \u003cem\u003ePseudomonas\u003c/em\u003e (Comeau et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), \u003cem\u003eBacillus\u003c/em\u003e (Husna et al. 2023), \u003cem\u003eAzospirillum\u003c/em\u003e (Sun et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), \u003cem\u003eCitrobacter\u003c/em\u003e (Ajmal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and \u003cem\u003eAcinetobacter\u003c/em\u003e (Josephine and Thomas \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eAcinetobacter\u003c/em\u003e spp. are Gram-negative, oxidase-negative, aerobic bacteria that are non-motile due to the absence of flagella (Josephine and Thomas \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This genus is widely spread in nature and is frequently found in the rhizosphere of several plant species (Mujumdar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous research have demonstrated that certain \u003cem\u003eAcinetobacter\u003c/em\u003e strains, including \u003cem\u003eA. calcoaceticus\u003c/em\u003e, (Eswaran et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Okla et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) possess PGP traits, including IAA production (Lin et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), inorganic phosphate solubilization (He and Wan \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and nitrogen fixation (Wu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, \u003cem\u003eCitrobacter\u003c/em\u003e spp. are Gram-negative, facultatively anaerobic, oxidase-negative bacteria that are generally motile (Fanning et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This genus has also been reported in several studies for its PGP potential (Ajmal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fadiji et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, advances in high-throughput genomics and metabolomics have significantly deepened our understanding of PGPR diversity, functionality and their complex interactions with host plants (for review see Paterson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, whole-genome sequencing (WGS) facilitates the identification of key genetic determinants responsible for plant-beneficial traits, including genes involved in nitrogen fixation (\u003cem\u003enir\u003c/em\u003e, \u003cem\u003enar\u003c/em\u003e), phosphate solubilization (\u003cem\u003egcd\u003c/em\u003e, \u003cem\u003epqq\u003c/em\u003e), auxin biosynthesis (\u003cem\u003eipdC\u003c/em\u003e), and secondary metabolite production (\u003cem\u003eent\u003c/em\u003e) (Thamvithayakorn et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In parallel, metabolomic profiles enable the identification of bioactive compounds produced by PGPR, including signaling molecules, growth regulators, and stress-related metabolites, thereby bridging genotypic potential with phenotypic expression (Mashabela et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study we isolated two bacterial strains S10 and DT1 from the rhizospheres of \u003cem\u003eCynodon dactylon\u003c/em\u003e and \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e, respectively. These strains exhibited multiple PGP traits and were taxonomically classified, based on WGS analyses, as \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e (DT1) and \u003cem\u003eCitrobacter braakii\u003c/em\u003e (S10). Functional genome annotation unveiled numerous genes associated with plant growth promotion, while targeted and non-targeted metabolomic profiling revealed a variety of metabolites implicated in growth stimulation and abiotic stress tolerance.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBacteria isolation\u003c/h2\u003e\u003cp\u003eRhizospheres samples of \u003cem\u003eCynodon dactylon\u003c/em\u003e and \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e were collected from wild areas in the Sfax region, located in central Eastern coast of Tunisia (34\u0026deg;39'N, 10\u0026deg;43'E). One gram of rhizospheric soil from each plant was suspended in 10 mL of sterile water and serially diluted. Subsequently, 100 \u0026micro;L from the 10⁻⁶ and 10⁻⁷ dilutions of each suspension were plated onto a culture medium composed of: K₂HPO₄ (0.3 g/L), KH₂PO₄ (0.3 g/L), KCl (0.1 g/L), NaCl (1 g/L), CaCl₂ (0.1 g/L), yeast extract (1 g/L), peptone (5 g/L), glucose (C₆H₁₂O₆, 5 g/L), and agar (18 g/L) (Sayahi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After 48 hours of incubation at 30\u0026deg;C, isolated colonies were selected and subcultured onto fresh plates to ensure purity. The purified isolates (named hereafter S10 and DT1) were cultivated on Luria-Bertani (LB) medium at 37\u0026deg;C, maintained at 4\u0026deg;C prior to use, and preserved as glycerol stocks at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePlant Growth‑Promoting (PGP) traits\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eSiderophore production\u003c/h2\u003e\u003cp\u003eSiderophore production by S10 and DT1 isolates was evaluated using the Chrome Azurol S (CAS) agar method, as described by Schwyn and Neilands (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), with slight modifications. Briefly, the CAS reagent was prepared by mixing 60.5 mg of Chrome Azurol S with an iron (III) solution (1 mM FeCl₃ in 10 mM HCl) and 72.9 mg of hexadecyltrimethylammonium bromide (HDTMA) to form a blue-colored complex. This solution was then added to a low-iron minimal medium at 10% (v/v) before pouring into Petri dishes. Bacterial isolates were streaked onto the CAS agar and incubated at 30\u0026deg;C for 24\u0026ndash;48 hours. The appearance of an orange or yellow halo around the streak line indicated siderophore production.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhosphate and zinc solubilization\u003c/h3\u003e\n\u003cp\u003eThe ability of S10 and DT1 isolates to solubilize insoluble phosphate and zinc compounds was evaluated using agar plate assays. For phosphate solubilization, isolates were spot-inoculated onto Pikovskaya\u0026rsquo;s agar medium containing tricalcium phosphate (Ca₃(PO₄)₂) as the sole phosphorus source (Pikovskaya \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1948\u003c/span\u003e). For zinc solubilization, isolates were grown on Yeast Extract Dextrose (YED) medium supplemented with zinc oxide (ZnO) as the insoluble zinc sources (Saravanan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In both assays, plates were incubated at 30\u0026deg;C for 2 to 5 days. Solubilization was evidenced by the formation of clear halos around the bacterial colonies. All experiments were conducted in triplicate to ensure consistency and reproducibility.\u003c/p\u003e\n\u003ch3\u003eIndole-3-acetic acid (IAA)\u003c/h3\u003e\n\u003cp\u003eThe production of indole-3-acetic acid (IAA) by S10 and DT1 isolates was investigated using Salkowski reagent. Each isolate was inoculated into 25 mL of LB medium supplemented with 5 mM of L-tryptophan and incubated at 30\u0026deg;C for 24 to 48 hours under shaking conditions (150 rpm). After incubation, cultures were centrifuged at 10,000 rpm for 10 minutes, and 1 mL of the cell-free supernatant was mixed with 2 mL of Salkowski reagent (2% 0.5M FeCl₃ in 35% perchloric acid). The mixture was incubated in the dark at room temperature for 30 minutes. A pink coloration indicated the presence of IAA, and absorbance was then measured at 530 nm using a spectrophotometer (Sobarzo et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). IAA concentration was determined by comparison with a standard curve of pure IAA. All experiments were performed in triplicate.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eNitrogen fixation\u003c/h2\u003e\u003cp\u003eThe nitrogen-fixing ability of S10 and DT1 was tested using the Nitrogen-Free Broth (NFB) medium as described by Siddikee et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In brief, isolates were inoculated onto NFB plates and incubated at 30\u0026deg;C for 2 to 5 days. Bacterial growth on nitrogen-free medium was considered indicative of nitrogen fixation capacity. All experiments were conducted in triplicate.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction, genome sequencing and assembly\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from overnight bacterial cultures using the Qiagen Genomic-tip 100/G kit (Qiagen, Cat. No. 10243). Approximately 10\u0026ndash;20 \u0026micro;g of high molecular weight DNA was obtained per extraction, with concentrations measured using the Qubit\u0026trade; dsDNA HS Assay Kit (Thermo Fisher Scientific). DNA purity was assessed by spectrophotometry, aiming for A260/280 ratios of ~\u0026thinsp;1.8 and A260/230 ratios above 2.0. Oxford Nanopore libraries were prepared using the Native Barcoding Kit 24 V14 (SQK-NBD114.24, Oxford Nanopore Technologies) following the manufacturer\u0026rsquo;s protocol, using\u0026thinsp;~\u0026thinsp;1 \u0026micro;g of input DNA. Sequencing was performed on R10.4.1 PromethION flow cells (FLO-PRO114M) with MinKNOW software version 22.07.9.\u003c/p\u003e\u003cp\u003eThe raw fast5 data generated by the MinKNOW software were basecalled and demultiplexed using Guppy v6.5.7 (Wick et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with the super accuracy model dna_r10.4.1_e8.2_400bps_hac_prom and a minimum Q-score threshold of 7 to obtain the fastq files. Read quality was assessed with NanoPlot v0.32.1 (De Coster and Rademakers \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the adapter sequences were trimmed with Porechop v0.2.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rrwick/Porechop\u003c/span\u003e\u003cspan address=\"https://github.com/rrwick/Porechop\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To evaluate potential contamination and obtain a preliminary taxonomic classification, reads were screened against the standard Kraken database using Kraken2 v2.0.9-beta (Wood et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genome assembly was performed with NextDenovo v2.5.2 (Hu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using default parameters, followed by polishing with NextPolish v1.4.1 (Hu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to correct base-level errors. Assemby quality and completeness were evaluated using QUAST v5.0.2 (Gurevich et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), including the BUSCO option to detect conserved orthologs and CheckM v1.2.2 (Parks et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Final assemblies were reoriented at the \u003cem\u003ednaA\u003c/em\u003e gene using the Circlator fixstart tool v1.5.5 (Hunt et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenome annotation\u003c/h3\u003e\n\u003cp\u003eGenomic relatedness between strains was estimated based on the Average Nucleotide Identity (ANI) and digital DNA\u0026ndash;DNA hybridization (dDDH). ANI values were calculated using the JSpecies Web Server (JSpeciesWS) (Richter et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while dDDH values were obtained using the Type (Strain) Genome Server (TYGS) (Meier-Kolthoff and G\u0026ouml;ker \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, TYGS was used to construct a whole-genome phylogenetic tree based on the Genome Blast Distance Phylogeny (GBDP) approach.\u003c/p\u003e\u003cp\u003eThe annotated genomes were uploaded to the MicroScope platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mage.genoscope.cns.fr/microscope/home/index.php\u003c/span\u003e\u003cspan address=\"https://mage.genoscope.cns.fr/microscope/home/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and analyzed using the integrated genome annotation tools available on the platform (Vallenet et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Complementary annotation was performed using multiple pipelines such as RAST (Rapid Annotation using Subsystem Technology (Aziz et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), PROKKA (Galaxy Version 1.14.6) (Seemann \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) implemented with default parameters via Galaxy, and KEGG (Kyoto Encyclopaedia of Genes and Genomes) Automatic Annotation Server v2.1 (Moriya et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to study metabolic pathways within the assembled genome. Both genome sequences have been submitted to NCBI database under the submission references SUB15592691 (Genome assembly of \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10) and SUB15592887 (Genome assembly of \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMetabolomics analysis\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eChemicals\u003c/h2\u003e\u003cp\u003eDeionized water was filtered through a Direct-Q UV station (Millipore), isopropanol and methanol were purchased from Fisher Chemicals (Optima \u0026reg; LC/MS grade). NaOH was obtained from Agilent Technologies, acetic acid formic acid from Sigma Aldrich. Deuterated abscissic acid (\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003e6\u003c/sub\u003e ABA) obtained from OlChemIm was used as an internal standard. Standards were used to develop the UHPLC-TQ-MS/MS methods: abscisic acid (ABA), 6-benzylaminopurine (BAP), indole-3-butyric acid potassium salt (IBA), salicylic acid (SA), trans zeatin (t-zea) and tryptophan were purchased from Sigma; benzoic acid (BA), and gibberellic acid (GA3) were purchased from Fluka; brassinolide (BR), cis-12-oxo-phytodienoic acid (OPDA), castasterone (CS), cathasterone (CT), dinor-12-oxo-phytodienoic acid (dnOPDA), gibberellins A1, A4, A7 and GA20 (GA1, GA4, GA7, GA20), jasmonic acid (JA), jasmonic acid-isoleucine (JA-lLE), jasmonic acid-phenylalanine (JA-Phe), jasmonic acid-valine (JA-Val), 12-hydroxy-jasmonic acid (12-0H\u0026shy; JA), and orobanchol (Oro) were purchased from OlChemim; indole-3-acetic acid (lAA) was purchased from Serva, and 6-furfurylaminopurine (kinetin, KIN), 6-(y, y-dimethylallylamino) purine (2iP) were purchased from Duchefa. 12-hydroxy-jasmonic acid-isoleucine (12-0H-JA-Ile), 12-carboxy-jasmonic acid (12-COOH-JA) and 12-carboxy\u0026shy; jasmonic acid-isoleucine (12-COOH-JA-Ile) were generous gifts from Dr. Patrick Wehrung.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSample preparation\u003c/h2\u003e\u003cp\u003eA culture of S10 and DT1 was prepared by inoculating 10\u003csup\u003e7\u003c/sup\u003e spores/mL in a 50 mL falcon containing 10 mL of LB medium. Cultures were grown 24 hours at 28\u0026deg;C under continuous shaking at 180 rpm and cells were pelleted (5500 rpm, 10min). Pellets (40\u0026ndash;60 mg fresh weight) were extracted with 5 volumes of cold methanol spiked with \u003csup\u003e2\u003c/sup\u003eH\u003csub\u003e6\u003c/sub\u003e ABA (1\u0026micro;g/mL) and centrifuged (13,200 rpm; 10 min, 4\u0026deg;C) to recover the liquid phase for further analysis. The supernatant was prepared through solid phase extraction (5 mg, Oasis HLB, Waters) microplates. The phase was washed with 1 mL 80% MeOH and 1 mL 100% H\u003csub\u003e2\u003c/sub\u003eO, then 500 \u0026micro;L of acidified supernatant (0.1% formic acid) was applied. A wash was performed with 1mL 2% MeOH, 0.1% formic acid before elution of the samples with 250 \u0026micro;L of 80% MeOH, 0.1% formic acid.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eNon-Targeted Metabolomic Analysis\u003c/h2\u003e\u003cp\u003eSamples were analyzed using liquid chromatography coupled to high resolution mass spectrometry on an UltiMate 3000 system (Thermo) coupled to an Impact II (Bruker) quadrupole time-of-flight (Q-TOF) spectrometer. Chromatographic separation was performed on an Acquity UPLC \u0026reg; HSS T3 C18 column (2.1x100 mm, 1.8 \u0026micro;m, Waters) equipped with and Acquity UPLC \u0026reg; HSS T3 C18 pre-column (2.1x5 mm, 1.8 \u0026micro;m, Waters) using a gradient of solvents A (Water, 0.1% formic acid) and B (MeOH, 0.1% formic acid). Chromatography was carried out at 35\u0026deg;C with a flux of 0.3 mL.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, starting with 2% B for 2 min, reaching 100% B at 10 min, holding 100% for 3 min and coming back to the initial condition of 5% B in 2 min, for a total run time of 15 min. Samples were kept at 4\u0026deg;C, 10 \u0026micro;L were injected in full loop mode with a washing step after sample injection with 150 \u0026micro;L of wash solution (H\u003csub\u003e2\u003c/sub\u003eO/MeOH, 90/10, v/v). The spectrometer was equipped with an electrospray ionization (ESI) source and operated in positive ion mode on a mass range from 100 to 1000 Da with a spectra rate of 8 Hz in AutoMS/MS fragmentation mode. The end plate offset was set at 500 V, capillary voltage at 2500 V, nebulizer at 2 Bar, dry gas at 8 L.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and dry temperature at 200\u0026deg;C. The transfer time was set at 20\u0026ndash;70 \u0026micro;s and MS/MS collision energy at 80\u0026ndash;120% with a timing of 50\u0026ndash;50% for both parameters. The MS/MS cycle time was set to 3 seconds, absolute threshold to 816 cts and active exclusion was used with an exclusion threshold at 3 spectra, release after 1 min and precursor ion was reconsidered if the ratio current intensity/previous intensity was higher than 5. A calibration segment was included at the beginning of the runs allowing the injection of a calibration solution from 0.05 to 0.25 min. The calibration solution used was a fresh mix of 50 mL isopropanol/water (50/50, v/v), 500 \u0026micro;L NaOH 1M, 75 \u0026micro;L acetic acid and 25 \u0026micro;L formic acid. The spectrometer was calibrated on the [M\u0026thinsp;+\u0026thinsp;H]\u003csup\u003e+\u003c/sup\u003e form of reference ions (57 masses from m/z 22.9892 to m/z 990.9196) in high precision calibration (HPC) mode with a standard deviation below 1 ppm before the injections for each polarity mode, and re-calibration of each raw data was performed after injection using the calibration segment.\u003c/p\u003e\u003cp\u003eRaw data were processed in MetaboScape 4.0 software (Bruker): molecular features were considered and grouped into buckets containing one or several adducts and isotopes from the detected ions with their retention time and MS/MS information when available. The parameters used for bucketing are a minimum intensity threshold of 10000, a minimum peak length of 3 spectra, a signal-to-noise ratio (S/N) of 3 and a correlation coefficient threshold set at 0.8. The [M\u0026thinsp;+\u0026thinsp;H]\u003csup\u003e+\u003c/sup\u003e, [M\u0026thinsp;+\u0026thinsp;Na]\u003csup\u003e+\u003c/sup\u003e, [M\u0026thinsp;+\u0026thinsp;K]\u003csup\u003e+\u003c/sup\u003e, [M\u0026thinsp;+\u0026thinsp;NH4]\u003csup\u003e+\u003c/sup\u003e and [M-H\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;H]\u003csup\u003e+\u003c/sup\u003e ions were considered. The obtained list of buckets was annotated using NPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.npatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.npatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), KNApSAcK (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.knapsackfamily.com/\u003c/span\u003e\u003cspan address=\"http://www.knapsackfamily.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), FooDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://foodb.ca\u003c/span\u003e\u003cspan address=\"http://foodb.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PlantCyc (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plantcyc.org/\u003c/span\u003e\u003cspan address=\"https://plantcyc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PhenolExplorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://phenol-explorer.eu/\u003c/span\u003e\u003cspan address=\"http://phenol-explorer.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), ECMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecmdb.ca/\u003c/span\u003e\u003cspan address=\"https://ecmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and YMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ymdb.ca/\u003c/span\u003e\u003cspan address=\"https://www.ymdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to achieve level 3 annotations on the Schymanski scale (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/es5002105\u003c/span\u003e\u003cspan address=\"10.1021/es5002105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a maximum mass deviation of 3ppm and a maximum mSigma value of 30 (assessing the good fit of the isotopic profile). Spectral libraries were used to achieve Schymanski level 2 annotations with a minimum score of 800.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTargeted Metabolomic Analysis\u003c/h2\u003e\u003cp\u003eSamples were analyzed by ultrahigh-performance liquid chromatography (UHPLC) on the UltiMate 3000 UHPLC system (Thermo) coupled to an EvoQ Elite LC-TQMS/MS (Bruker) mass spectrometer equipped with an electrospray ionization (ESI) source in MS/MS mode.\u003c/p\u003e\u003cp\u003eThe samples were kept at 4 \u0026deg;C before injection of 5 \u0026micro;L in full loop mode, and chromatographic separation on an Acquity UPLC \u0026reg; HSS T3 C18 column (2.1 X 100 mm, 1.8 \u0026micro;m, Waters) coupled to an Acquity UPLC HSS T3 C18 pre\u0026shy;column (2.1 x 5 mm, 1.8 \u0026micro;m, Waters). Samples were carried through the column following a gradient of solvent A (H\u003csub\u003e2\u003c/sub\u003e0; 0.1% formic acid) and B (methanol; 0.1% formic acid) at a flux of 0.300 mL min⁻\u0026sup1; starting with 5% B for 2 min, reaching 100% B at 10 min, holding 100% B for 3 min and returning to 5% B in 2 min, for a total run time of 15 min. The column was operated at 35 \u0026deg;C. Nitrogen was generated from pressurized air by a Nitro 35 nitrogen generator (GenGaz) and used as cone gas (30 L h⁻\u0026sup1;), heated probe gas (30 L h⁻\u0026sup1;) and nebulizer gas (35 L h⁻\u0026sup1;). The cone and heated probe temperatures were 350 and 300\u0026deg;C, respectively, and the capillary voltage was set at 3.5 kV. Phytohormones and bioactive metabolites were analyzed by multiple reaction monitoring (MRM) after determining the retention time and mode (positive or negative) by scan and the cone voltage, daughter ion and collision energy using the MRM builder function on standards. Wash solvent (80% H\u003csub\u003e2\u003c/sub\u003e0, 20% MeOH) was used to wash the syringe. A mix of the different standards served as a positive control.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eIsolation and Evaluation of Plant Growth-Promoting Traits in Rhizospheric Bacterial Strains\u003c/h2\u003e\n \u003cp\u003eTo identify PGPB strains adapted to harsh environments, rhizosphere samples of \u003cem\u003eCynodon dactylon\u003c/em\u003e and \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e were collected from wild arid areas. Pure bacterial cultures were obtained from the rhizospheric soils of both plants using the serial dilution technique. Among the thirty distinct isolates recovered on nutrient agar (NA), two strains (S10 and DT1 from \u003cem\u003eCynodon dactylon\u003c/em\u003e and \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e rhizospheres, respectively), were selected based on their distinct morphological characteristics. On nutrient agar, S10 produced smooth, greyish colonies, whereas DT1 produced opaque colonies. These two strains were subsequently used to evaluate their plant growth-promoting (PGP) potential. As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, both S10 and DT1 were able to grow on nitrogen-free medium, indicating their nitrogen-fixing capacity. In phosphate solubilization assays, both strains produced clear halos on Pikovskaya\u0026rsquo;s inorganic phosphate medium, while only S10 was able to solubilize zinc (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The appearance of yellow halo around streak lines S10 and DT1 on Chrome Azurol S (CAS) agar showed their ability to produce siderophores (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Additionally, IAA production by strains S10 and DT1, in the presence of tryptophan, was quantified using Salkowski\u0026apos;s reagent, yielding concentrations of 4.25 and 14.65 \u0026micro;g/mL, respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Together, these results indicate that the newly isolated rhizobacterial strains S10 and DT1 exhibit promising plant growth-promoting traits.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePlant growth-promoting (PGP) traits of the selected bacterial isolates \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10 and \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlant growth-promoting trait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCitrobacter braakii\u003c/em\u003e S10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate solubilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc solubilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen fixation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiderophore production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIAA production (\u0026micro;g/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e+, positive; -, negative result for the test\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Genomic Features of S10 and DT1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo gain insight into the genomic architecture of the two selected PGPR strains, S10 and DT1, Whole Genome Sequencing (WGS) was performed. The sequencing output revealed that both strains possess a single circular chromosome (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e) and a summary of their genomic features is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The genome of strain S10 is notably larger than that of DT1, comprising a total size of 4,913,851 bp, compared to 3,930,652 bp for strain DT1. Consistently, the number of predicted protein-coding genes was also higher in S10 (4,774 genes) than in DT1 (3,818 genes). The G\u0026thinsp;+\u0026thinsp;C content differed substantially between the two strains, with S10 exhibiting a value of 52.04%, whereas DT1 displayed a markedly lower G\u0026thinsp;+\u0026thinsp;C content of 38.75%, indicating their distinct taxonomic origins. Non-coding RNA elements also highlighted the divergence between the two genomes. S10 harbored a higher number of tRNA genes (84 vs. 73) and rRNA genes (25 vs. 18) than DT1, while both genomes contained a single copy of the transfer-messenger RNA (tmRNA) gene. Additionally, the number of miscellaneous RNAs (misc-RNAs), potentially including regulatory small RNAs, was much higher in S10 (82) compared to DT1 (30), suggesting a more complex regulatory potential in this strain. Another notable difference between the two genomes lies in the number of predicted pseudogenes (66 pseudogenes in S10 vs. 5 in DT1). Together, these genomic features reflect the phylogenetic divergence between the two strains and provide a foundational framework for further functional annotation and comparative analysis of the PGP traits.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenomic features of \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10 and \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenomic Features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCitrobacter braakii\u003c/em\u003e S10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenome size (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,913,851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,930,652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u0026thinsp;+\u0026thinsp;C content (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4913851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3930652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of contigs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003etRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003erRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003etmRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMisc-RNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudogenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eWhole Genome-Based Phylogenomic Identification of S10 and DT1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;To assess the taxonomic affiliation of strains S10 and DT1, whole-genome-based phylogenetic and comparative genomic analyses were carried out. A phylogenomic tree was generated using the TYGS server, incorporating both isolates alongside type strains of closely related species (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This analysis revealed that strain S10 was clustered with \u003cem\u003eCitrobacter braakii\u003c/em\u003e ATCC 51113 (i.e., originally isolated from a snake in France (Brenner et al. \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e)), with a digital DNA\u0026ndash;DNA hybridization (dDDH) value of 90.7% (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-A), exceeding the 70% threshold for species delineation. Similarly, strain DT1 grouped with \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DSM 30006 (i.e., isolated from a quinate-enriched soil in the Netherlands), with a dDDH value of 73.5% (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e-A). These taxonomic clusterings were further supported by pairwise Average Nucleotide Identity (ANI) analysis using the JSpeciesWS server (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Indeed, the ANI analysis revealed that S10 shared 99.04% ANI with \u003cem\u003eCitrobacter braakii\u003c/em\u003e GTA-CB01 (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-B), and DT1 showed 96.83% ANI with \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DSM 30006 (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e-B), both well above the species-level threshold of 95\u0026ndash;96%. To further explore genomic conservation, a synteny analysis was conducted between the two genomes. Extensive collinear regions were observed (indicated by blue lines in Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e), indicating that large portions of the genome are organized in the same order and orientation. A smaller number of inverted regions (pink lines) were observed, indicating low levels of genomic rearrangements between strains (Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Together, these results provide robust genomic evidence supporting the classification of S10 as \u003cem\u003eC. braakii\u003c/em\u003e and DT1 as \u003cem\u003eA. calcoaceticus\u003c/em\u003e and reveal high intra-genus genomic conservation within each lineage across diverse habitats.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional Genome Annotation of S10 and DT1\u003c/h2\u003e\n \u003cp\u003eGenome annotations for both strains were performed using the RAST, COG, and KEGG databases. The total number of predicted genes was 4,774 and 3,818 for S10 and DT1, respectively. Of these, 4,693 genes in S10 and 3,663 in DT1 were functionally annotated by RAST, leaving 81 and 155 genes unannotated, respectively (Fig. S4). COG annotated 4,459 genes in S10 and 3,487 in DT1, while KEGG assigned functions to 3,365 and 2,010 genes, respectively. Classification assigned the annotated genes into 384 subsystems in S10 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), and 310 subsystems in DT1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). In S10, the most enriched categories were carbohydrate metabolism (408 genes; 8.70%), followed by amino acids and derivatives (370 genes; 7.88%), protein metabolism (262 genes; 5.58%), and cofactors, vitamins, prosthetic groups, and pigments (181 genes; 3.86%). In DT1, the most abundant genes were associated with amino acids and derivatives (297 genes; 7.95%), followed by protein metabolism (200 genes; 5.36%), carbohydrate metabolism (160 genes; 4.28%), and cofactors, vitamins, prosthetic groups, and pigments (143 genes; 3.83%). According to COG functional classification, metabolism was the most dominant process in both genomes, encompassing 2,065 genes (45.06%) in S10 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea) and 1,518 genes (41.08%) in DT1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Genes involved in cellular processes and signaling accounted for 23.16% of the genes in S10 and 16.82% in DT1, covering functions such as cell wall/membrane/envelope biogenesis, signal transduction, cell motility, post-translational modification, intracellular trafficking, secretion, vesicular transport and protein turnover. Interestingly, the S10 genome contains numerous flagellar biosynthesis genes (\u003cem\u003ee.g\u003c/em\u003e., \u003cem\u003efliADEFGHIJKKMNOPQRST\u003c/em\u003e, \u003cem\u003eflhABCDE\u003c/em\u003e) (Table S4), in line with the motile nature of most \u003cem\u003eCitrobacter\u003c/em\u003e species. In contrast, such genes were absent in the DT1 genome, consistent with the non-motile nature of \u003cem\u003eAcinetobacter\u003c/em\u003e (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). About 17% of coding sequences in both genomes were related to information storage and processing, encompassing functions such as translation, ribosomal structure and biogenesis, transcription, replication, recombination, and repair. A substantial fraction of genes was classified as having unknown functions, with 999 genes (21.80%) in the S10 genome and 1,115 genes (30.16%) in DT1. Finally, KEGG pathway mapping reinforced the dominance of metabolic genes, with 1,356 genes in S10 and 1,128 in DT1 assigned to metabolism-related pathways (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Additional prominent categories included environmental information processing (S10: 458 genes; DT1: 169), genetic information processing (S10: 222; DT1: 194), and cellular processes (S10: 259; DT1: 115).\u003c/p\u003e\n \u003cp\u003eTaken together, these annotations reveal that both genomes encode a broad repertoire of functional genes, with S10 showing greater metabolic diversity and DT1 exhibiting a higher proportion of genes with yet uncharacterized functions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eGenome mining for PGP and Stress-related genes\u003c/h2\u003e\n \u003cp\u003eFunctional genome comparison showed that both S10 and DT1 strains harbor a diverse repertoire of genes related to PGP traits, including auxin, cytokinin and siderophore biosynthesis, phosphate solubilization, nitrogen metabolism and ACC deaminase activity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e and Table S4). Both strains carried gene clusters involved in IAA biosynthesis, notably \u003cem\u003etyrB\u003c/em\u003e, \u003cem\u003etrpABCDE\u003c/em\u003e, and \u003cem\u003eipdC\u003c/em\u003e, a key gene in the indole-3-pyruvate (IPA) pathway. Genes related to cytokinin biosynthesis, \u003cem\u003emiaA\u003c/em\u003e, \u003cem\u003emiaB\u003c/em\u003e, and \u003cem\u003emiaE\u003c/em\u003e, were also found in both genomes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Interestingly, \u003cem\u003easpC\u003c/em\u003e (aspartate aminotransferase) was specific to the S10 genome, whereas \u003cem\u003etrpF\u003c/em\u003e (N-(5\u0026apos;-phosphoribosyl) anthranilate isomerase) was identified only in DT1. Although not directly involved in IAA biosynthesis, both genes contribute indirectly by supporting the availability of tryptophan, its primary precursor. In addition, key genes involved in the synthesis of riboflavin (vitamin B2) were also found in both the S10 and DT1 genomes, these include \u003cem\u003eribA\u003c/em\u003e (GTP cyclohydrolase II), \u003cem\u003eribB\u003c/em\u003e (3,4-dihydroxy-2-butanone 4-phosphate synthase), \u003cem\u003eribD\u003c/em\u003e (pyrimidine deaminase/reductase), \u003cem\u003eribE\u003c/em\u003e (lumazine synthase), and \u003cem\u003eribC\u003c/em\u003e (riboflavin synthase) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table S4). These genes encode enzymes that convert one molecule of GTP and two molecules of ribulose-5-phosphate into one molecule of riboflavin, a crucial cofactor for plant metabolism, growth, and defense. Regarding phosphate solubilization, both genomes harbored the phosphate transporters gene \u003cem\u003epstB\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), while \u003cem\u003epstACS\u003c/em\u003e genes were found only in S10 (Table S4). Core genes such as inorganic pyrophosphatase (\u003cem\u003eppa\u003c/em\u003e), exopolyphosphatase (\u003cem\u003eppx\u003c/em\u003e), polyphosphate kinase (\u003cem\u003eppk\u003c/em\u003e) and quinoprotein glucose dehydrogenase (\u003cem\u003egcd\u003c/em\u003e) were present in both strains. S10\u0026rsquo;s genome encoded uniquely phosphonate, and carbon-phosphorus (C-P) lyases (\u003cem\u003ephnCDEGHIJK\u003c/em\u003e), whereas additional genes involved in phosphate sensing and cofactor synthesis were found DT1, including phosphate regulon sensor protein (\u003cem\u003ephoR\u003c/em\u003e), phosphate specific transport system accessory protein (\u003cem\u003ephoU\u003c/em\u003e) and pyrroloquinoline synthase (\u003cem\u003epqqG\u003c/em\u003e). For nitrogen fixation, no complete nitrogenase gene cluster was identified in either genome, but nitrogen metabolism potential was evident. DT1 carried the \u003cem\u003enirB\u003c/em\u003e gene, encoding nitrite reductase catalytic subunit, while the S10 genome harbored a more extensive set of nitrate reduction genes, including the \u003cem\u003enarGHIVYZ\u003c/em\u003e operon (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Siderophore biosynthesis appeared to be under the control of several gene clusters, in S10 and DT1 genomes. Both strains carried genes for enterobactin (\u003cem\u003eentABCDEF\u003c/em\u003e), and staphyloferrin B (\u003cem\u003esbnABD\u003c/em\u003e) biosynthesis. In addition, other siderophore-related genes including L-2,4-diaminobutyrate decarboxylase (\u003cem\u003eddC\u003c/em\u003e), diaminobutyrate-2-oxoglutarate aminotransferase (\u003cem\u003edat\u003c/em\u003e), 2,3-dihydroxybenzoate-AMP ligase (\u003cem\u003edhbE\u003c/em\u003e) were also identified, as well as genes related to iron storage (\u003cem\u003ebfr\u003c/em\u003e) and transport (\u003cem\u003efeoABC\u003c/em\u003e) (Fig. S6; Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table S4).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFinally, genes involved in ACC deaminase activity were detected. The S10 genome contained \u003cem\u003erimI\u003c/em\u003e, \u003cem\u003erimK\u003c/em\u003e, \u003cem\u003erimL\u003c/em\u003e, \u003cem\u003erimM\u003c/em\u003e, \u003cem\u003erimO\u003c/em\u003e, \u003cem\u003erimP\u003c/em\u003e, and \u003cem\u003erimJ\u003c/em\u003e, while DT1 carried \u003cem\u003erimI\u003c/em\u003e, \u003cem\u003erimM\u003c/em\u003e, \u003cem\u003erimO\u003c/em\u003e, and \u003cem\u003erimP\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). These genes are involved in the degradation of 1-aminocyclopropane-1-carboxylate (ACC), the immediate precursor of ethylene, a plant hormone that inhibits growth under abiotic stress conditions (Shekhawat et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Taken together, these findings demonstrate that S10 and DT1 harbor complementary and overlapping sets of genes involved in phytohormone biosynthesis, nutrient acquisition, stress mitigation, siderophore production and stress mitigation, supporting their functional potential as robust PGPR candidates for sustainable agriculture.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eGenomic Analysis of Secondary Metabolism using AntiSMASH\u003c/h2\u003e\n \u003cp\u003eTo identify gene clusters involved in secondary metabolite synthesis in S10 and DT1, genome mining was performed using antiSMASH (version 8.). The analysis revealed that the S10 genome harbored two biosynthetic gene clusters (BGCs): one encoding a thiopeptide (O-antigen-related) and another corresponding to an NRP-metallophore of the NRPS class, annotated as enterobactin (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the DT1 genome harbored a broader diversity of secondary metabolite pathways, with eight predicted BGCs, including gene clusters for arylpolyenes, NI-siderophore, betalactone, redox-cofactor, NRP-metallophore, NRPS, and RiPP-like metabolites (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparison against the MIBiG reference database revealed varying degrees of similarity to characterized clusters. For instance, the enterobactin BGC from DT1 shared 55% similarity with a known orthologous cluster from \u003cem\u003ePseudomonas sp\u003c/em\u003e., whereas the corresponding cluster in S10 exhibited full conservation (100%) with a reference Citrobacter cluster, suggesting potential divergence in siderophore structure, expression, or functionality between the two strains. Other BGCs in DT1 showed moderate similarity to clusters encoding APE VF, berninamycin variants, and mycosubtilin with 45%, 22%, and 20% similarity to clusters from \u003cem\u003eAliivibrio fischeri\u003c/em\u003e ES114, \u003cem\u003eStreptomyces\u003c/em\u003e sp., and \u003cem\u003eBacillus subtilis\u003c/em\u003e subsp. \u003cem\u003espizizenii\u003c/em\u003e ATCC 6633, respectively. The remaining clusters showed less than 20% similarity to known BGCs, including 16% similarity with staphyloferrin B from \u003cem\u003eStaphylococcus aureus\u003c/em\u003e subsp. \u003cem\u003eaureus\u003c/em\u003e NCTC 8325, and 4% similarity with lagriene from \u003cem\u003eBurkholderia gladioli\u003c/em\u003e. Altogether, these findings suggest that while S10 contains a limited set of conserved BGCs, DT1 exhibits a broader and more diverse secondary metabolite biosynthetic potential, likely reflecting an intrinsic genetic capacity for metabolic versatility, a trait commonly associated with microbial adaptability and survival in diverse or competitive environments (Dong et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBiosynthetic gene clusters (BGCs) for secondary metabolites identified in the genome of \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1 and \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10. Gene cluster prediction was performed using antiSMASH v8.0 and annotations were cross-referenced with the MIBiG database.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMost similar know cluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMIBIG Accession (% Gene Similarity)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNI-siderophore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1564594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1599021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estaphyloferrin B*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0000943 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eredox-cofactor, NRP-metallophore, NRPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1916080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobactin*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0000343 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebetalactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2367645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2396725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emycosubtilin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0001103 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiPP-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2507421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2518284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiPP-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2706268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2718466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003earylpolyene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2981396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3022625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eberninamycin K/berninamycin J/berninamycin A/berninamycin B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0002363 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003earylpolyene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3321619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3365221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPE Vf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0000837 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRPS, hserlactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3750029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3793988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elagriene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0002455 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCitrobacter braakii\u003c/strong\u003e \u003cstrong\u003eS10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThiopeptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3108899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3135189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO-antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0000781 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRP-metallophore, NRPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3430326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3485411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobactin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBGC0002476 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNRP non-ribosomal peptide, NRPS non-ribosomal peptide synthase.\u003c/p\u003e\n \u003cp\u003e* Genes associated with these clusters are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eTargeted and non-targeted metabolomic profiling of S10 and DT1 strains\u003c/h2\u003e\n \u003cp\u003eGiven the presence of genes involved in phytohormone biosynthesis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), we performed targeted metabolomic analysis using Liquid Chromatography\u0026ndash;Q-Tandem Mass Spectrometry (LC-MS/MS) to assess the production of key metabolites in S10 and DT1. Both intracellular (cell pellet) and extracellular (culture supernatant) fractions were analyzed to distinguish between metabolites synthesized in the bacteria and those actively or passively secreted into the environment, the latter being most relevant to plant-microbe interactions. This analysis confirmed the production of several phytohormones, including indole-3-acetic acid (IAA), D-abscisic acid, salicylic acid (SA), and the cytokinin 2-isopentenyladenine (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Among these, the most abundant hormone was IAA, especially in the supernatant of DT1 (confirming the data obtained in the assays using the Salkowski\u0026apos;s reagent), indicating a high level of extracellular accumulation. In contrast, SA showed the lowest abundance in both supernatant and pellet fractions, but both strains produced comparable amounts of benzoic acid, a precursor of SA. Moreover, both strains produced equivalent amounts of riboflavin. Together with SA and benzoic acid, these metabolites are known to trigger both plant pathogen defense and abiotic stress tolerance (Azami-Sardooei et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Senaratna et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo further explore the metabolic diversity of S10 and DT1, non-targeted metabolomic profiling was conducted using Liquid Chromatography-High-Resolution Tandem Mass Spectrometry with Quadrupole Time-of-Flight (LC\u0026ndash;HRMS QTOF). Both pellet and supernatant fractions were analyzed, and metabolite annotation was performed using publicly available databases including NPA, KNApSAcK, PlantCyc, FoodDB, phenolExplorer, YMDB, ECMDB, and Spectral libraries.\u003c/p\u003e\n \u003cp\u003eTo evaluate metabolic variations between intracellular and extracellular fractions of both strains, principal component analysis (PCA) was applied. The PCA score plot revealed a clear separation between the metabolic profiles of S10 and DT1, with the first principal component (PC1) accounting for 85.5% of the total variance (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating that strain identity is the main driver of metabolic differences. In contrast, the second principal component (PC2, 3% of the total variance) captured the subtler variation between intracellular and extracellular fractions, reflecting differences in metabolite accumulation and secretion.\u003c/p\u003e\n \u003cp\u003eDifferential metabolite analysis (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; fold change\u0026thinsp;\u0026gt;\u0026thinsp;0,5|) identified 56 significantly different compounds between DT1 pellet and supernatant fractions. Among these, 45 were more abundant in the pellet (\u003cem\u003ee.g\u003c/em\u003e., N.N-Dimethyldodecylamine N-oxide, Guanine, Adenosine 3\u0026apos;-monophosphate, alpha-Linolenic acid and Farnesylacetone), while 11 were more abundant in the supernatant (\u003cem\u003ee.g\u003c/em\u003e., Perlolyrine, Radiosumin, Microcin SF608 and Maculosinin) (Table S5; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). In S10, 31 metabolites were differently identified between fractions, 20 were more abundant in the pellet and 11 in the supernatant (Table S6; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). Notably, no metabolites were shared among the differentially produced compounds in the supernatants of the two strains, whereas six metabolites were found in both pellets. Additionally, three overlaps were observed between the DT1 supernatants and the S10 pellet (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb). Clustering heatmaps of differential metabolites for DT1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea) and S10 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea) confirmed distinct metabolite distribution patterns between intracellular and extracellular compartments. KEGG pathway enrichment analysis revealed that the detected metabolites were significantly associated with pathways involved in plant secondary metabolites biosynthesis, amino acid metabolism, phytohormone production, and ABC transporter-mediated environmental signal sensing (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb and \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb). Overall, targeted and untargeted metabolomic analyses confirmed the production of phytoactive and stress-related metabolites in both strains, supporting their genomic potential and reinforcing their suitability as metabolically versatile PGPR candidates for plant health improvement.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn increasing number of studies has emphasized the potential of PGPR to enhance plant health and productivity (Tripathi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, we targeted the rhizosphere of two wild plant species, \u003cem\u003eCynodon dactylon\u003c/em\u003e and \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e, naturally thriving in arid environments, aiming to isolate bacterial strains potentially adapted to harsh conditions and endowed with PGP properties. From these rhizospheres, we isolated two strains, S10 and DT1, subsequently identified as \u003cem\u003eCitrobacter braakii\u003c/em\u003e (S10) and \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e (DT1), respectively. Although these species are best known for their opportunistic pathogenicity in clinical settings (Joly-Guillou \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), C. \u003cem\u003ebraakii\u003c/em\u003e has also been isolated from diverse environments including rhizospheric soil of rice grown in salt-affected areas (Nawaz et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePGPR promote plant growth and stress resilience by producing or modulating phytohormones such as auxins, gibberellins, and cytokinins, as well as regulating ethylene levels through ACC deaminase activity (Tripathi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To explore the PGP potential of S10 and DT1, we performed whole-genome annotation, focusing on pathways related to phytohormone biosynthesis and metabolism. Both genomes contain the tryptophan biosynthesis operon \u003cem\u003etrpABCDE\u003c/em\u003e, a key pathway indirectly linked to indole-3-acetic acid (IAA) production, a major phytohormone involved in plant development (Babalola et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, both strains harbor the \u003cem\u003eipdC\u003c/em\u003e gene, encoding indole-3-pyruvate (IPyA) decarboxylase, a key enzyme in the IPyA pathway of IAA biosynthesis (Jiang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional relevance of \u003cem\u003eipdC\u003c/em\u003e in PGP activity has been demonstrated by Figueredo et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported that its inactivation in \u003cem\u003eBacillus thuringiensis\u003c/em\u003e RZ2MS9 markedly reduced the strain\u0026rsquo;s capacity to promote maize growth. Consistent with these genomic findings, our targeted LC-MS/MS analysis confirmed substantial IAA production in both strains, particularly in the extracellular fraction of DT1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In addition to auxin production, S10 and DT1 also possess genes encoding ACC deaminase enzymes (\u003cem\u003erimJ, rimK, rimL, rimM, rimO, rimP\u003c/em\u003e, and \u003cem\u003erimI\u003c/em\u003e), which degrade 1-aminocyclopropane-1-carboxylate (ACC), the precursor of ethylene. By lowering ethylene levels, this activity helps mitigate growth inhibition under stress conditions such as drought and salinity, as observed in other PGPR including \u003cem\u003ePhytobacter palmae\u003c/em\u003e WL65 (Thamvithayakorn et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCytokinins (CK) also play a critical role in plant development by regulating cell division, shoot initiation, and photosynthesis (Gao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, cytokinin production by \u003cem\u003eRhizobium sp. WYJ-E13\u003c/em\u003e promotes \u003cem\u003eCurcuma\u003c/em\u003e root growth (Huang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In both S10 and DT1 genomes, we identified several genes involved in CK biosynthesis, including \u003cem\u003emiaABE\u003c/em\u003e genes. These genes encode tRNA dimethylallyl transferases, which catalyze the biosynthesis of isopentenyladenosine (iPR), a key cytokinin precursor. Consistently, our targeted metabolomics confirmed the production of cytokinin 2-isopentenyladenine by S10 and DT1. Similar CK biosynthesis genes were reported to be active in \u003cem\u003eEnterobacter mori\u003c/em\u003e AYS9, where \u003cem\u003emiaA\u003c/em\u003e and \u003cem\u003emiaB\u003c/em\u003e are involved in converting iPR into biologically active cytokinins (Fadiji et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional relevance of cytokinin biosynthesis in PGPR has also been demonstrated by Asif et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who showed that these hormones protect photosynthetic pigments like carotenoids and chlorophylls. Consistently, \u003cem\u003ePriestia aryabhattai\u003c/em\u003e and \u003cem\u003ePaenibacillus\u003c/em\u003e sp. strains harboring \u003cem\u003emiaA\u003c/em\u003e and \u003cem\u003emiaB\u003c/em\u003e genes were reported to enhance chlorophyll content in tomato plants (Almir\u0026oacute;n et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlongside cytokinins, riboflavin and its derivatives, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), are essential cofactors involved in photosynthesis, energy production, and redox metabolism (Sandoval et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The beneficial impact of bacterial riboflavin production on plant health has been demonstrated by Ajeethan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who showed that inoculating kale seeds with \u003cem\u003eSinorhizobium meliloti\u003c/em\u003e 1021 (\u003cem\u003ei.e\u003c/em\u003e., a strain secreting significant amounts of flavins) led to significant improvements in growth compared to a flavin-deficient mutant. In our study, both S10 and DT1 genomes harbored genes for riboflavin biosynthesis, suggesting a role in supporting plant health. Combined with cytokinin biosynthesis genes, these results underscore the diverse metabolic strategies of S10 and DT1 in promoting plant growth under stress conditions. Building on this genomic evidence, our targeted metabolite profiling further revealed that both strains produce not only cytokinins and riboflavin, but also D-abscisic acid and salicylic acid. Similarly, \u003cem\u003ePriestia aryabhattai\u003c/em\u003e and \u003cem\u003ePaenibacillus\u003c/em\u003e sp. isolates from the tomato rhizosphere have been reported as PGPR capable of producing various phytohormones (Almir\u0026oacute;n et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings underscore the multiple phytohormone-mediated mechanisms underlying the PGP potential of S10 and DT1.\u003c/p\u003e\u003cp\u003eBeyond targeted analyses, our comparative non-targeted metabolomic profiling revealed additional metabolites produced by S10 and DT1, with some accumulating differentially between intracellular and extracellular fractions (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Among these, cadaverine and biotin are known to enhance plant growth and stress tolerance (Jancewicz et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cadaverine has been linked to improved antioxidant defenses and tolerance to abiotic stresses (Gibbs et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ozmen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Biotin, another metabolite detected, supports plant development and resilience against carbonate stress (Wang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, S10 produced arginine and trans-4-coumaric acid, compounds associated with photosynthetic enhancement and ROS signaling, respectively (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nkomo et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). DT1 produced indole-3-propionic acid (IPA), which promotes root development (Sun et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as amino acids like valine, histidine, leucine, and proline, which support plant growth and stress adaptation (Ji et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Renzetti et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This strain also synthesized gamma-aminobutyric acid (GABA), known for enhancing tolerance to multiple stresses in plants (Golnari et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Suhel et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These diverse metabolites suggest significant potential for S10 and DT1 to promote plant growth and resilience under various environmental conditions. Notably, the co-production of plant-beneficial compounds such as phytohormones, riboflavin, siderophores, and polyamines like cadaverine may confer a broader functional capacity to modulate plant responses (Jancewicz et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Olanrewaju et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vejan et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSiderophores sequester iron from the environment, reducing availability to phytopathogens and contributing to plant health (Zhang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our analyses revealed numerous genes associated with siderophore production, including \u003cem\u003eentABCDEF, feoABC, dhbABCEF, efeBOU, sbnABD\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). AntiSMASH analysis identified clusters for enterobactin, bacillibactin, and staphyloferrin B, highlighting a broad siderophore biosynthetic capacity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Fig. S5). These clusters are linked to biocontrol and plant health in other species like \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e (Dimopoulou et al., 2021), \u003cem\u003eBacillus velezensis\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) and \u003cem\u003eMethylobacterium aquaticum\u003c/em\u003e (Juma et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNitrogen fixation is a key PGPR trait enabling conversion of atmospheric nitrogen (N\u003csub\u003e2\u003c/sub\u003e) into bioavailable forms (Masood et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study, S10 and DT1 harbored the \u003cem\u003enirB\u003c/em\u003e gene, involved in nitrate reduction along with other nitrogen metabolism genes like \u003cem\u003enarGHI\u003c/em\u003e. However, neither strain possessed the full nitrogenase cluster required for atmospheric nitrogen fixation, suggesting their role may be limited to nitrate assimilation rather than true nitrogen fixation (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhosphorus-solubilizing bacteria (PSB) play a key role in mineralizing organic phosphorus and mobilizing inorganic phosphorus (Pan and Cai \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies have shown that S10 can solubilize organic phosphate \u003cem\u003ein vivo\u003c/em\u003e (El Ifa et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our genomic data confirms that S10 carries genes like \u003cem\u003egcd\u003c/em\u003e, \u003cem\u003ephoARP\u003c/em\u003e, \u003cem\u003epstABCS\u003c/em\u003e, and \u003cem\u003epqqEFG\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e), consistent with its phosphate-solubilizing phenotype. Importantly, we provide new evidence that DT1 also possesses genes associated with phosphate solubilization, suggesting its potential as a PSB. Consistently, both S10 and DT1 demonstrated functional activity \u003cem\u003ein vitro\u003c/em\u003e, as evidenced by their ability to produce siderophore on CAS agar, solubilize phosphate, and grow on nitrogen-free media (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTogether, these findings confirm the multifaceted PGP potential of S10 and DT1 and highlight their promise for application as bioinoculants in sustainable agriculture. In this context, a combined formulation of both strains may prove particularly effective, as it would leverage their complementary traits (\u003cem\u003ee.g\u003c/em\u003e., zinc solubilization by S10 and the broader metabolic repertoire of DT1) to maximize plant growth promotion across varied environmental conditions. This strategy, which harnesses the functional complementarity of compatible PGPR, warrants further testing under greenhouse and field conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the multifunctional plant growth-promoting potential of two rhizobacterial strains, \u003cem\u003eCitrobacter braakii\u003c/em\u003e S10 and \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e DT1 isolated from the rhizospheres of wild plants thriving in arid habitats. Comprehensive genomic and metabolomic analyses revealed diverse genes and metabolites associated with key PGP traits, including pathways for nitrogen fixation, phosphate and zinc solubilization, siderophore and phytohormone production, and stress resilience. These genomic insights were corroborated by phenotypic assays and metabolomic profiling, confirming the functional capabilities of both strains. The capacity of S10 and DT1 to produce a range of bioactive compounds underscores their adaptability and potential to support plant growth and stress tolerance under challenging conditions. Together, these findings provide a strong foundation for the future application of these strains as bioinoculants in sustainable agriculture, offering eco-friendly alternatives to chemical fertilizers and supporting crop productivity in challenging environments. Future research should focus on evaluating their performance under greenhouse and field conditions to confirm their practical efficacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was funded by grants provided by the Tunisian Ministry of Higher Education and Scientific Research (MESRS) and the French Ministry of Europe and Foreign Affairs (MEAE) and Ministry of Higher Education and Research (MESR) under the project PHC Maghreb 23MAG09.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, IG, AB, MH and CE.; methodology, IG, NS (PGPR traits), AB, AA, VC (genome assembly) CV, DH, JZ (metabolomic); validation, CE, AB, MH; formal analysis, IG, NS, AA, VC, CV (genome assembly, annotation, genome analysis), CV, DH, JZ (metabolomic); writing\u0026mdash;original draft preparation, IG, CE, AB.; writing\u0026mdash;review and editing, IG, AB, MH.,CE; visualization, IG, NS.; supervision, CE, AB.; funding acquisition, AB, CE, MH. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Drs. Marie-Edith Chabout\u0026eacute; and Etienne Herzog, IBMP-CNRS Strasbourg and Prof. Mustafa Barakate, Universit\u0026eacute; Cadi Ayyad for their valuable advice.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGenome sequences have been submitted to NCBI database under the submission references SUB15592691 (Genome assembly of Citrobacter braakii S10) and SUB15592887 (Genome assembly of Acinetobacter calcoaceticus DT1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjeethan N, Yurgel SN, Abbey L (2023) Role of Bacteria-Derived Flavins in Plant Growth Promotion and Phytochemical Accumulation in Leafy Vegetables. 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Int J Food Microbiol 410:110480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijfoodmicro.2023.110480\u003c/span\u003e\u003cspan address=\"10.1016/j.ijfoodmicro.2023.110480\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Plant growth-promoting rhizobacteria (PGPR), Acinetobacter calcoaceticus, Citrobacter braakii, Whole-genome sequencing, Metabolomic profiling","lastPublishedDoi":"10.21203/rs.3.rs-7900185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7900185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant growth-promoting rhizobacteria (PGPR) enhance plant growth and development through diverse mechanisms, including phytohormone production, nutrient acquisition, and stress mitigation. This study describes the isolation and characterization of two bacterial strains, DT1 and S10, from the rhizospheres of \u003cem\u003eDiplotaxis tenuifolia\u003c/em\u003e and \u003cem\u003eCynodon dactylon\u003c/em\u003e, respectively capable of solubilizing phosphate and zinc, fix nitrogen and produce indole acetic acid (IAA) and siderophores. Using whole genome sequencing and taxonomic analyses, these two strains were identified as \u003cem\u003eAcinetobacter calcoaceticus\u003c/em\u003e (DT1) and \u003cem\u003eCitrobacter braakii\u003c/em\u003e (S10). Functional genomic annotation revealed numerous genes associated with key plant growth-promoting traits, including those involved in indole-3-acetic acid (IAA) (\u003cem\u003etrpABCDE\u003c/em\u003e, \u003cem\u003eipdC\u003c/em\u003e), cytokinin (\u003cem\u003emiaABE\u003c/em\u003e), and riboflavin biosynthesis, confirmed by targeted metabolomics. In addition, genes associated with nitrogen metabolism (\u003cem\u003enirB\u003c/em\u003e, \u003cem\u003enarGHI\u003c/em\u003e) and phosphate solubilization (\u003cem\u003egcd\u003c/em\u003e, \u003cem\u003ephoARP\u003c/em\u003e, \u003cem\u003epstABCS\u003c/em\u003e, \u003cem\u003epqqEFG\u003c/em\u003e) were identified and supported by phenotypic assays. Interestingly, biosynthetic gene clusters for the secondary metabolites enterobactin, bacillibactin, and staphyloferrin B, known to contribute to plant growth promotion, were identified in both genomes. Both strains also harbored genes encoding ACC deaminase, an enzyme known to enhance plant tolerance to abiotic stress. Furthermore, non-targeted metabolomic analysis revealed that DT1 and S10 produced a range of intracellular and extracellular metabolites associated with plant growth promotion and stress resilience, including cadaverine, biotin, arginine, and GABA. Collectively, these findings position DT1 and S10 as promising bioinoculant candidates, offering an integrative genomic and metabolic foundation for their application in next-generation sustainable agricultural strategies.\u003c/p\u003e","manuscriptTitle":"Genomic and metabolomic characterization of Acinetobacter calcoaceticus (DT1) and Citrobacter braakii (S10) reveal functional traits for plant stress alleviation and sustainable agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:57:29","doi":"10.21203/rs.3.rs-7900185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-23T21:13:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T14:39:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"645136017010997477729947669662665031","date":"2025-12-23T13:46:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T20:53:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T10:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T09:57:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2025-10-19T17:59:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d3a60763-b155-4347-acc2-32e5157c8c94","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T13:39:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:57:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7900185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7900185","identity":"rs-7900185","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}