https://orcid.org/0009-0006-9929-7730
Figures
Abstract
This study investigates the bioremediation potential of bacterial isolates from diesel-contaminated soils in Dhaka, Bangladesh. A total of 34 morphologically distinct bacterial strains were isolated from hydrocarbon-polluted sites, with Acinetobacter baumannii emerging as the dominant species (41.2%), followed by Pseudomonas otitidis and Klebsiella pneumoniae (11.8% each). Genetic screening revealed that 32.35% of isolates harbored the alkB gene (alkane hydroxylase), while 58.82% carried catE (catechol-2,3-dioxygenase), indicating a strong predisposition for aromatic hydrocarbon degradation. Following turbidimetric screening of 34 bacterial isolates, 11 demonstrating superior growth were selected for gravimetric degradation assessment. Among these, isolate MB 1002 (Pseudomonas nitroreducens) demonstrated the highest degradative capability at 46.92%, followed by MB 751 (Acinetobacter baumannii) at 41.18% and MB 750 (Pseudomonas aeruginosa) at 39.16%. The MB 1002 and MB 751 both were positive for alkB. FTIR analysis revealed that both MB 751 and MB 1002 contribute to diesel degradation, with MB 751 showing stronger oxidation patterns due to the presence of carboxyl functional groups. Due to its superior oxidative capability, MB 751 (designated A. baumannii DUEMBL6) was selected for whole-genome sequencing. The sequence analysis of A. baumannii DUEMBL6 (deposited as JBLODW000000000) revealed: (1) hydrocarbon degradation genes (alkB, ssuD, catechol dioxygenases); (2) 7 biosynthetic gene clusters including siderophores (100% similarity to baumannoferrin); and (3) complete xenobiotic degradation pathways for aliphatic/aromatic compounds. Despite its bioremediation potential, A. baumannii DUEMBL6 harbored 26 antibiotic resistance genes (e.g., blaOXA-338, adeABC efflux pumps) and 33 virulence factors (e.g., csu pilus, biofilm genes), with an 86.1% pathogenicity probability. These findings highlight A. baumannii DUEMBL6’s dual role as a promising bioremediation agent and a potential public health risk, necessitating careful strain selection for environmental applications.
Citation: Uddin N, Prova MI, Billaha M, Anik TA, Uzzaman R, Haider N, et al. (2026) Genomic analysis of Acinetobacter baumannii DUEMBL6 reveals diesel bioremediation potential and biosafety concerns. PLoS One 21(1): e0339456. https://doi.org/10.1371/journal.pone.0339456
Editor: Ranjit Gurav, UPES: University of Petroleum and Energy Studies, INDIA
Received: May 2, 2025; Accepted: December 6, 2025; Published: January 20, 2026
Copyright: © 2026 Uddin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files. Additionally, the whole genome sequence of A. baumannii DUEMBL6 has been deposited in the DDBJ/ENA/GenBank databases under BioProject PRJNA1217114, BioSample SAMN46479258, and Accession no. JBLODW000000000.
Funding: The research was funded by the Ministry of Science and Technology, and University Grant Commission, Bangladesh. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Petroleum hydrocarbons remain indispensable to global energy systems despite international efforts to transition toward renewable alternatives. The United Nations and International Energy Agency (IEA) have advocated for reduced crude oil dependence through biofuel adoption (IEA, 2022), yet petroleum products continue to dominate energy markets, particularly in developing economies. From running industries to managing household chores, crude oil in various forms like petrol, gasoline, diesel, and kerosene is widely used. Crude oil is a well-known environmental pollutant, threatening the lives of marine and soil ecosystems. Once introduced into the environment, as the oil starts weathering, it produces recalcitrant and xenobiotic molecules such as polycyclic aromatic hydrocarbons (PAHs), halogenated and nitro-aromatic compounds, pentachlorophenol, benzene, toluene, etc., which alter the physicochemical properties of local habitats, plants, and the microbial consortium of that environment [1–4]. Certain hydrocarbons found in oil, specifically aromatic compounds, possess toxicity [5] and exhibit resistance to degradation, resulting in their persistence in soil [6]. As a consequence, the ecology is greatly impacted. Oxygen and water deficiency, along with nitrogen and phosphorus scarcity, restrict the growth of living entities. The microbial ecology changes as many microbes gradually vanish due to nutrient deficiency, while others capable of degrading oil thrive in that environment, ultimately altering the composition of soil or water. This phenomenon is particularly concerning in the case of oil spills, which often occur accidentally during transportation. Many countries, including Bangladesh, have witnessed major oil spills in ecological hotspots. In 2014, due to the collision between an oil-containing tank and a vessel, 350,000 liters of black furnace oil spilled into a river [7] in a restricted area of the Sundarbans, reportedly disturbing the local ecology of that environment.
These oil spill events are becoming more frequent, with rising energy demands. Particularly, the demand for diesel and heating oil in Bangladesh has reached record highs in recent years. In 2023, the country consumed approximately 95.49 thousand barrels per day of diesel and heating oil, up from 90.19 thousand barrels per day in 2022. This marks the highest level ever recorded for Bangladesh, with the long-term average from 1980 to 2023 being 41.5 thousand barrels per day [8]. Diesel is the dominant liquid fuel in the country, accounting for the largest share of petroleum product use, particularly in the transport sector (62%), followed by agriculture (17%), power generation (13%), and industry (5%) as of the 2021−22 fiscal year [9]. As a refined crude oil product, diesel consists mainly of naphthalene and paraffinic hydrocarbons and is a major pollutant of soil and water [10,11]. Its environmental consequences include reduced plant growth, impaired germination, and poor soil aeration due to clogging of pore spaces [12].
While physical and chemical techniques are employed for cleaning up spilled oil, these methods are often costly and impractical in low-and-middle-income countries (LMIC) like Bangladesh. Therefore, bioremediation is recommended over the former methods. Bioremediation facilitates the degradation of oil spills through the interaction of microorganisms, which utilize their metabolic and enzymatic properties. Microorganisms typically contain oil-degrading genes that produce enzymes for various metabolic pathways, converting the oil into a soluble form. Bioaugmentation and biostimulation are key factors here, as microbes act as natural supplements to the environment. Various bacteria such as Pseudomonas, Acinetobacter, Rhodococcus, Klebsiella, and Nocardia have different approaches to degrading oil components. Rhodococcus has previously been documented as an efficient diesel oil degrader [13,14]. Some of these bacteria employ adaptive strategies such as biofilm formation and biosurfactant production, which function synergistically to reduce interfacial tension and increase hydrocarbon bioavailability [15], while simultaneously protecting microbial communities from environmental stressors [16].
Given Bangladesh’s escalating diesel consumption and vulnerability to oil spills, this study sought to evaluate the bioremediation potential of indigenous soil microorganisms from contaminated sites in Dhaka. We employed a multi-method approach to: (1) isolate and characterize native diesel-degrading bacteria, (2) quantify their degradation efficiency through gravimetric and FTIR analyses, and (3) genomically profile the most promising isolate for both metabolic potential and biosafety risks.
Materials and methods
Sample collection and processing
Samples of soil contaminated with petroleum hydrocarbons were collected from four filling stations in Dhaka (23° 43’ 56.43’‘ N/90° 23’ 7.51’‘ E, 23° 45’ 38.43’‘ N/90° 22’ 23.69’‘ E, 23° 47’ 18.68’‘ N/90° 19’ 33.38’‘ E, and 23° 50’ 2.03’‘ N/90° 15’ 35.01’‘ E). The filling stations were selected based on higher usage and greater hydrocarbon contamination observed at these sites. A total of 40 soil samples were collected from four filling stations in Dhaka, with 10 samples from each location. The soil samples were taken from a depth of 5–10 cm in areas with potential fuel spillage and near fuel dispensers. The samples were collected in sterile bottles to avoid contamination and were immediately transported to the laboratory in controlled conditions. Upon arrival, the samples were processed promptly to prevent any alteration in the soil’s microbial community. One gram of soil from each sample was inoculated into 100 mL of freshly prepared Bushnell Haas medium (BHM) supplemented with 1% diesel in a sterile flask. The cultures were incubated at 30°C with shaking at 180 rpm for 7 days to enrich diesel-degrading bacteria. After incubation, 1 mL of each culture was centrifuged at 14000 rpm for 10 minutes to pellet the cells, and the diesel-containing supernatant was discarded. The pellet was resuspended in 1 mL of sterile saline (0.85% NaCl), and the optical density (OD) of the suspension was measured at 600 nm using sterile saline as the blank. Cultures with an OD600 > 0.5 were serially diluted (10 ⁻ ¹ to 10 ⁻ ⁶) in sterile saline, and 100 µL of each dilution was spread onto BHM agar plates supplemented with 1% (w/v) diesel. Plates were incubated at 30°C for 5–7 days, and morphologically distinct colonies were selected and streaked onto fresh BHM-diesel agar plates to obtain pure cultures.
Identification of isolates
The isolated pure bacterial cultures were identified via 16S rRNA gene sequencing. Genomic DNA was extracted using the QIAamp DNA Mini Kit (QIAGEN, Germany) and quantified with a Colibri Microvolume Spectrometer (Titertek-Berthold, Germany). Universal primers 16S F (5’-AGAGTTTGATCCTGGCTCAG-3’) and 16S R (5’-TACCTTGTTTTACGACTC-3’) [17] were used for PCR amplification. Reactions were run in a Thermo Fisher Veriti 96-well Thermal Cycler with 35 cycles at 94 °C (1 min), 47 °C (1 min), and 74 °C (1 min), followed by a final extension at 72 °C for 10 minutes.
Determination of biofilm-forming ability
Biofilm production was assessed through complementary qualitative and quantitative approaches. Qualitative screening utilized Congo Red Agar (CRA) plates containing Brain Heart Infusion agar (37 g/L), sucrose (50 g/L), and filter-sterilized Congo Red (0.8 g/L), inoculated with fresh bacterial cultures and incubated at 30°C for 48 hours; biofilm-forming isolates were identified by black, dry, crystalline colonies indicative of exopolysaccharide (EPS) production, while non-producers appeared pink/red with smooth morphology. For quantitative analysis, biofilm biomass was measured via a standardized microtiter plate assay [18], where bacterial suspensions (OD (600 nm) = 0.1 in Bushnell Haas Broth + 1% diesel) were incubated statically in 96-well polystyrene plates at 30°C for 48 hours. After removing non-adherent cells through PBS washing, biofilms were fixed with methanol, stained with 0.1% crystal violet, and solubilized in 33% glacial acetic acid for spectrophotometric quantification at 600 nm. The optical density cut-off value (ODc) was set as three standard deviations (SD) above the mean of the optical density (OD) of the negative control as shown in the following formula: ODc = average OD of negative control + (3 × SD of negative control). The optical density (OD) was determined by calculating the mean of all the replicates. The results were summarized into four categories based on their optical densities: (1) strong biofilm producer (4 × ODc < OD); (2) medium biofilm producer (2 × ODc < OD ≤ 4 × ODc); (3) weak biofilm producer (ODc < OD ≤ 2 × ODc); and (4) non-biofilm producer (OD ≤ ODc).
Detection of oil-degrading genes by PCR
Two oil-degrading genes, including, alkB and catE were detected in the isolates by PCR using primers listed in Table 1. PCR cycling conditions were initial denaturation 95 °C (5 min), followed by 35 cycles of denaturation 95 °C (15 s), Tm for annealing (15 s) and extension at 72 °C (1 min). Final extension at 72 °C for 5 min. PCR products were visualized using Gel Doc (BioRad, USA) following electrophoresis through 1.5% agarose gel stained with ethidium bromide.
Determination of bacterial biodegradation activity by turbidimetric method
For biodegradation activity assessment, pure cultures were inoculated into 5 mL of Bushnell Haas Broth (BHB) supplemented with 1% (w/v) diesel and incubated at 30°C with shaking at 180 rpm for 15 days. Bacterial growth was monitored by measuring OD at 600 nm every 2 days after centrifuging 1 mL of culture at 14,000 rpm for 10 minutes, discarding the supernatant, and resuspending the pellet in 1 mL sterile saline to remove diesel interference.
Determination of diesel oil degradation using the gravimetric analysis
The level of diesel degradation was determined using the gravimetric analysis [21]. Initially, bacterial cultures were inoculated in 100 ml of Bushnell-Haas broth supplemented with 1% diesel oil as a carbon source in an Erlenmeyer flask and incubated at 30°C for 7 days. After incubation, the cell pellet was separated by centrifugation at 14000 rpm for 10 minutes, resulting in a pellet of cell debris and a supernatant containing residual oil. The remaining diesel was extracted using hexane. Equal volumes of hexane and the cell-free supernatant were mixed to separate the diesel from the aqueous phase, which remained in the organic phase of hexane. The organic layer was then transferred to a separate falcon tube. For oil degradation estimation, 5 ml of n-hexane was added to the original flasks, and the contents were transferred to a separating funnel. This extraction was performed three times to ensure complete recovery of the oil. The hexane containing the remaining oil was left in a fume hood. The amount of residual oil was measured after extraction of oil from the medium. Negative controls (BHB + 1% diesel without bacteria) and positive controls (Pseudomonas putida) were included to account for non-biological losses and validate the protocol. The volume of extracted oil was deducted from the previously weighed beaker.
The percentage of diesel oil degraded was calculated as follows:
Here, Y = Percentage of oil degradation
Characterization of residual crude oil
Fourier Transform Infrared Spectroscopy (FTIR) is a powerful analytical technique used to identify and quantify various chemical compounds in a sample based on their molecular vibrations [22]. A total of 3 samples (original diesel oil for control and residual oil 1,2 after degradation) were selected for FTIR analysis. Sample preparation and processing for FTIR analysis were made up following the guidelines of the Center for Advanced Research & Sciences, Dhaka University.
Whole genome sequencing (WGS) of selected strain
Whole-genome sequencing of the target isolate was performed using the NextSeq 2000 Illumina platform at the Biotech Concern. The quality of the raw FASTQ files was assessed using FastQC (v0.12.1) [23], and low-quality bases and adapter sequences were trimmed using Trimmomatic (v0.39) [24] with parameters SLIDINGWINDOW:4:20, MINLEN:50, and ILLUMINACLIP for adapter removal. After trimming, the good-quality reads were subjected to de novo assembly using SPAdes v4.0.0 [25], and the assembled genome was taxonomically identified using the KmerFinder V3.2 tool [26]. To further confirm species identity and assess genomic similarity, FastANI [27] was used to calculate the Average Nucleotide Identity (ANI) between the target isolate and reference genomes from the NCBI database. Multilocus sequence typing (MLST) was performed using the MLST tool from the Center for Genomic Epidemiology (CGE) [28] which identified sequence types (STs) based on conserved housekeeping genes. A graphical map of the circular genome was generated using the Proksee server [29]. The assembled sequence undergone annotation through Prokka [30], RAST [31] and Prokaryotic genome annotation pipeline (PGAP) of NCBI [32]. Antibiotic resistance genes, virulence factors, and plasmid-associated genes were identified using ABRicate (v1.0.1) [33] with the ANNOT [34], NCBI AMR [35], CARD (v3.2.4) [36], VFDB (2023 release) [37] and PlasmidFinder databases [38]. The biosynthetic gene clusters were determined through antiSMASH7.0 tool [39]. Analysis of different subsystems and functional metabolic pathways was carried out through the Patric server [40]. Pathogenicity profiling and prediction of the pathogenicity of all the isolates towards human hosts was carried out through PathogenFinder (v1.1) [41] webtool of Center for Genomic Epidemiology. The alkB protein is essential for the oil degradation pathway [42]. Therefore, we conducted mutation analysis to identify variations within the alkB gene of Acinetobacter sp.
Phenotypic characterization of antibiotic resistance
Antibiotic susceptibility testing (AST) was carried out on the isolates by using Mueller-Hinton agar by the Kirby-Bauer disk diffusion method [43]. The results were categorized as sensitive, intermediate, or resistant according to the Clinical and Laboratory Standards Institute’s (CLSI) M100-Ed35:2024 guidelines. The following antibiotic discs were used in the assay: ampicillin, ceftazidime, cefotaxime, cefepime, imipenem, meropenem, gentamycin, tobramycin, kanamycin, ciprofloxacin, levofloxacin, tetracycline, doxycycline, azithromycin, and chloramphenicol.
Results and discussion
Isolation and identification of potential oil-degrading bacteria
Oil spills pose a significant environmental threat globally, with Bangladesh being particularly vulnerable. Frequent spills occur at Chittagong’s port-centric shipyards and fuel stations, often due to negligence or inadequate safety practices. The Sundarbans, the world’s largest mangrove forest, suffered severe ecological damage from a major oil spill in recent years [44,45]. In Dhaka, routine leaks at fuel stations and industrial discharge—including oils and toxic chemicals—have led to critical water pollution. Studies reveal oil contaminants in 65% of water samples collected near industrial areas [46]. Hence, from Bangladesh’s standpoint, spillage of crude oil onto soil and its dispersal into the environment is inevitable, and thus we need to act with a state-of-the-art bioremediation approach specifically focusing on the genomic potential of the indigenous isolates. In this study, we identified a wide range of bacteria from oil-contaminated sites of Dhaka. A total of 34 morphologically distinct bacterial isolates were recovered from diesel-enriched Bushnell Haas medium inoculated with soil samples collected from four filling stations in Dhaka. Following incubation and purification on diesel-supplemented agar plates, these isolates demonstrated visible growth under selective conditions, suggesting potential diesel-degrading capability. 16S rRNA sequencing identified the bacterial composition as follows: Acinetobacter baumannii was the most prevalent species, with 14 isolates (41.17%), followed by Pseudomonas otitidis and Klebsiella pneumoniae, each with 4 isolates (11.76%) (Table 2). Pseudomonas aeruginosa was represented by 3 isolates (8.82%), while Pseudomonas nitroreducens and Capriavidus gilardi each had 2 isolates (5.88%). Pseudomonas and Acinetobacter species are consistently effective in oil degradation across studies, though efficiency varies with environmental conditions and microbial partnerships (e.g., co-cultures achieved higher degradation rates) [47]. Additionally, Cellulomonas hominis, Stenotrophomonas maltophilia, Glutamicibacter urotoxydans, Pseudomonas mendocina, and Pseudomonas taiwanensis were each represented by 1 isolate (3%). The qualitative biofilm formation assay using CRA plates revealed that only a subset of isolates formed black, dry, crystalline colonies indicative of strong biofilm production. These included Capriavidus gilardi (MB 756), A. baumannii (MB 503, MB 504, and MB 505), P. otitidis (MB 257 and MB 253), P. aeruginosa (MB 752), Klebsiella pneumoniae (ML 025), and Pseudomonas mendocina (ML 075). The quantitative microtiter plate assay revealed that among the 34 isolates, 3 were strong biofilm-formers, 12 were moderate, 9 were weak, and the remaining 10 showed no detectable biofilm formation. The strong biofilm-formers were from A. baumannii (MB 503 and MB 505) and P. aeruginosa (MB 752). The ability to form biofilms underpins the ecological fitness of these strains in hydrocarbon-rich environments, facilitating adherence to hydrophobic substrates and enhancing pollutant breakdown efficiency.
These isolates were screened for the presence of alkB and catE genes. Out of 34 isolates, 32.35% (11 isolates) were positive for alkB, whereas 58.82% (20 isolates) tested positive for catE, indicating a higher prevalence of catechol-2,3-dioxygenase producers compared to alkane hydroxylase producers. The alkB gene was detected in K. pneumoniae (ML 50, ML 100), P. mendocina (ML 075), P. nitroreducens (MB 502 and MB 1002), A. baumannii (MB 503, MB 505, MB 751, MB 755, MB 1001, MB 1003). The catE gene was detected in P. otitidis (MB 253), P. aeruginosa (MB 252, MB 254, MB 752), A. baumannii (MB 255, MB 256, MB 501, MB 503, MB 504, MB 505, MB 506, MB 751, MB 754, MB 755, MB 1000, MB 1001, MB 1003), P. nitroreducens (MB 750), C. hominis (MB 507), and C. gilardi (MB 500). The prevalence of catE in these isolates aligns with global findings that catechol dioxygenases are critical for aromatic hydrocarbon breakdown [48,49]. Previous studies have shown that alkB-encoded alkane hydroxylases are widely used as biomarkers for alkane degradation in marine and soil environments [50,51], while catE-encoded catechol-2,3-dioxygenases play a central role in aromatic compound degradation and have been applied in the bioremediation of oil-contaminated soils and groundwater [52,53]. All 34 isolates were then evaluated for degrading ability via turbidimetric analysis, and 11 isolates that exhibited higher growth rates were selected for further assessment using gravimetric analysis. Among these, isolate MB 1002 (P. nitroreducens) demonstrated the highest degradative capability at 46.92%, followed by MB 751 (Acinetobacter baumannii) at 41.18% and MB 750 (Pseudomonas aeruginosa) at 39.16% (Fig 1). Previous studies have reported variable diesel degradation efficiencies under differing experimental conditions, as summarized in Table 3. For instance, the diesel-degrading efficiency of A. baumannii has been shown to reach up to 99% at 37 °C in controlled laboratory settings [54]. In contrast, our isolates demonstrated substantial degradation efficiency at 30 °C, which more closely reflects typical environmental conditions, underscoring their potential applicability in natural or field-based bioremediation efforts.
P. nitroreducens MB1002 showed the highest degradation efficiency (46.92%), followed by A. baumannii MB751 (41.18%) and P. aeruginosa MB750 (39.16%).
The MB 1002 and MB 751 both were positive for alkB. This suggests a potential role of alkB in the oil-degrading ability of these isolates. The biodegradation observed by MB 1002 and MB 751 were subjected to Fourier-transform infrared spectroscopy (FTIR) analysis.
Fourier-transform infrared spectroscopy (FTIR) analysis
The FTIR analysis of diesel oil and its degraded forms by bacterial strains MB 1002 and MB 751 reveals significant structural changes in hydrocarbon composition, indicating biodegradation (Fig 2). The original diesel oil exhibits strong peaks at 732.95 cm ⁻ ¹, 1458.18 cm ⁻ ¹, 2858.51 cm ⁻ ¹, and 2922.16 cm ⁻ ¹, corresponding to methylene (-CH₂-) and methyl (-CH₃) groups (Table 4). These peaks are still present in the residual oils (MB 1002 and MB 751), but their intensities have diminished, suggesting partial degradation. Additionally, MB 1002 and MB 751 show new peaks around 881.47 cm ⁻ ¹, 1043.49 cm ⁻ ¹, and 1130.29 cm ⁻ ¹, indicating the presence of aromatic C-H and alkene groups. The detection of aromatic C-H (881 cm ⁻ ¹) aligns with India’s FTIR data [58]. Notably, MB 1002 showed new hydroxyl group formation (3373.5 cm ⁻ ¹), suggesting alcohol production similar to Mariobacter spp. Pathways [59]. Meanwhile, MB 751 developed a distinct carboxyl peak at 1712.79 cm ⁻ ¹, indicative of ketone, aldehyde, or carboxylic acid formation, consistent with Marinobacter-mediated oxidative degradation [49]. The appearance of these oxygen-containing functional groups, absent in the original diesel, confirms that microbial activity has altered the chemical structure of diesel hydrocarbons. The results suggest that both MB 1002 and MB 751 contribute to diesel degradation, with MB 751 showing stronger oxidation patterns due to the presence of carboxyl functional groups. Comparative analysis revealed that P. nitroreducens (MB 1002) outperforms cold-adapted Oleispira (32% PAH degradation) [59] but lags behind Sphingomonas consortia [60]. Nonetheless, the degradation efficiency of these strains remains moderate compared to Bacillus spp. from China (66%) [61] and optimized consortia from India (95%) [49].
Spectra show (A) unmodified diesel, (B) residual oil after degradation by P. nitroreducens MB1002, and (C) residual oil after degradation by A. baumannii MB751. Reduced intensities of hydrocarbon peaks (732–2922 cm ⁻ ¹) indicate partial degradation, while new bands (881–1130 cm ⁻ ¹) reflect aromatic and alkene groups. Hydroxyl (3373 cm ⁻ ¹) and carboxyl (1712 cm ⁻ ¹) peaks suggest oxidative transformation of hydrocarbons, with stronger oxidation in MB751.
Whole genome sequence analysis, annotation and identification
Among the tested strains, MB 751 (A. baumannii) was selected for whole-genome sequencing (WGS) based on its superior and more complete degradation efficiency. Sequence-based identification through KmerFinder further confirmed the selected isolate to be A. baumannii. Multilocus sequence typing (MLST) analysis revealed an unknown sequence type, suggesting that the isolate represents a novel strain of A. baumannii. Afterwards, the isolate was designated as “Acinetobacter baumannii DUEMBL6”. Further analysis revealed a 97.93% Average Nucleotide Identity (ANI) with another A. baumannii strain (GCA_016746035.1), which provides strong evidence for the accurate identification of the isolate. The assembled whole genome sequence was deposited in GenBank of NCBI under the accession number JBLODW000000000. The analysis of the genome indicated good quality of the genome considering the completeness (97.33%) and low contamination (0.83%). Additionally, the genome size and GC content of the sequenced isolates aligned well with the established genomes of A. baumannii. Following the process of de novo assembly and annotation, the CDS ratio of 0.946 provided evidence of a well-executed and successful assembly. This conclusion is based on the fact that the standard CDS ratio endorsed by NCBI typically falls between 0.8 and 1.2 [62]. The genome annotation and mapping revealed significant features of the genome which are listed in (Table 5 and Fig 3).
Circular representation of the A. baumannii DUEMBL6 genome showing coding sequences (CDS), GC content, and GC skew.
Prevalence of hydrocarbon degrading genes
Functional annotation of A. baumannii DUEMBL6 revealed the presence of multiple genes associated with hydrocarbon degradation, including alkane 1-monooxygenase (alkB), which catalyzes the initial oxidation of alkanes [42]; alkanesulfonate monooxygenase (ssuD), involved in the degradation of sulfonated alkanes [63]; catechol O-methyltransferase (COMT), which plays a role in the metabolism of aromatic compounds [64]; and catechol 1,2-dioxygenase, which cleaves catechol rings during aromatic hydrocarbon degradation [65]. These genes are known to play crucial roles in the breakdown of both aliphatic and aromatic hydrocarbons, highlighting the strain’s potential for bioremediation. Mutation analysis of the alkB protein in A. baumannii DUEMBL6 revealed two specific mutations, I138F and A334T, when compared to other closely related strains. These mutations may indicate a variant of alkB with enhanced metabolic activity, potentially leading to more efficient degradation of hydrocarbons [58].
Identification of biosynthetic gene clusters
The genomic analysis of this diesel-degrading bacterium using antiSMASH revealed a diverse array of secondary metabolite biosynthetic gene clusters (BGCs), highlighting their potential ecological and biotechnological significance. A total of 7 secondary metabolite BGCs were identified under relaxed detection settings, spanning multiple functional categories (Fig 4). Among these, the NRP-metallophore biosynthetic cluster (Region 23.1) exhibited 83% similarity to the well-characterized acinetobactin cluster, a known siderophore involved in iron acquisition [66]. Additionally, a beta lactone-producing cluster (Region 39.1) showed 20% similarity to the mycosubtilin cluster, which is associated with antimicrobial activity [67]. Furthermore, arylpolyene biosynthetic clusters (Regions 79.1 and 211.1) were detected, with potential roles in oxidative stress resistance and biofilm formation [68]. Notably, Region 81.1 (NI-siderophore cluster) displayed 100% similarity to the Baumannoferrin A/B biosynthetic pathway, suggesting a significant contribution to metal ion sequestration and environmental adaptation [69].
Genome mining using antiSMASH revealed seven secondary metabolite biosynthetic gene clusters, including NRP-metallophore, beta-lactone, arylpolyene, and NI-siderophore clusters.
The identification of these BGCs in a diesel-degrading bacterium suggests that secondary metabolites may play a crucial role in the organism’s survival and competitiveness in hydrocarbon-rich environments [70]. For instance, the production of siderophores (e.g., NI-siderophore) could enhance the bacterium’s ability to acquire iron, a limiting nutrient in many environments, while the production of antimicrobial compounds (e.g., betalactone) could provide a competitive advantage against other microorganisms [71]. These findings underscore the ecological and biotechnological potential of A. baumannii DUEMBL6, not only as a hydrocarbon degrader but also as a producer of bioactive secondary metabolites that could be harnessed for industrial and environmental applications.
Pathway and subsystem analysis
The analysis of metabolic pathways revealed thirteen distinct classes involved in a range of essential biological processes, including carbohydrate metabolism, amino acid metabolism, glycan biosynthesis and metabolism, signal transduction, lipid metabolism, energy metabolism, biosynthesis of polyketides, secondary metabolite biosynthesis, and xenobiotics biodegradation and metabolism (Fig 5). The identified pathway class xenobiotics degradation and metabolism is particularly important in biotechnology due to its role in breaking down environmental pollutants and synthetic compounds [72,73]. We identified pathways for degrading diverse xenobiotics, including 1,4-dichlorobenzene degradation, 1- and 2-methylnaphthalene degradation, 2,4-dichlorobenzoate degradation, atrazine degradation, benzoate degradation via hydroxylation, bisphenol A degradation, caprolactam degradation, DDT degradation, drug metabolism (cytochrome P450 and other enzymes), ethylbenzene degradation, fluorobenzoate degradation, geraniol degradation, metabolism of xenobiotics by cytochrome P450, naphthalene and anthracene degradation, styrene degradation, tetrachloroethene degradation, toluene and xylene degradation, trinitrotoluene degradation, and γ-hexachlorocyclohexane degradation. Each of these pathways comprises multiple enzymes that contribute to the degradation of diesel components, including both aromatic hydrocarbons and aliphatic compounds (Fig 6). Catechol 1,2-dioxygenase and protocatechuate 3,4-dioxygenase are critical for cleaving aromatic rings in polycyclic aromatic hydrocarbons (PAHs) like naphthalene and phenanthrene, converting them to tricarboxylic acid (TCA) cycle intermediates [74,75]. Benzoate 1,2-dioxygenase initiates the breakdown of monoaromatic compounds such as toluene and xylene, common diesel constituents [76,77]. Enzymes like 3-hydroxyacyl-CoA dehydrogenase and enoyl-CoA hydratase participate in β-oxidation pathways, degrading straight-chain alkanes into acetyl-CoA [78]. Cyclohexanone monooxygenase facilitates the degradation of cyclic alkanes, while benzaldehyde dehydrogenase and alcohol dehydrogenase metabolize intermediate aldehydes and alcohols generated during alkane oxidation [79,80]. These pathways align with established mechanisms for microbial diesel degradation, as demonstrated in Pseudomonas and Acinetobacter species adapted to hydrocarbon-rich environments [81,82].
Metabolic pathway analysis revealed thirteen major classes involved in core cellular and environmental functions, including carbohydrate, amino acid, lipid, and energy metabolism, as well as secondary metabolite and xenobiotic degradation pathways.
Identified xenobiotic degradation pathways include 1,4-dichlorobenzene, toluene, xylene, naphthalene, and bisphenol A degradation, among others. These pathways involve enzymes such as catechol 1,2-dioxygenase and benzoate 1,2-dioxygenase, highlighting the strain’s potential for biodegradation of aromatic and aliphatic hydrocarbons.
We also analyzed the subsystems of A. baumannii DUEMBL6, which were categorized into eleven superclasses and twenty-six functional classes (Fig 7). These subsystems are involved in essential biological processes, including metabolism, protein processing, stress response and defense, virulence, maintenance of cell envelope integrity, respiration, membrane transport, cellular processes, and regulation and cell signaling. Further classification revealed a total of seventy-seven distinct subclasses. This subsystem analysis highlights that A. baumannii DUEMBL6 harbors a diverse array of genes contributing to multiple functional categories critical for survival, adaptation, and potential pathogenicity.
Subsystem annotation revealed eleven superclasses and twenty-six functional classes encompassing metabolism, stress response, virulence, membrane transport, and regulatory processes. These findings illustrate the genetic versatility and adaptive capacity of A. baumannii DUEMBL6.
Antibiotic resistance genes and mobile genetic elements
The presence of antibiotic resistance genes in isolates showing bio remediation ability is not desirable since they can serve as a source of resistance for other pathogens. To assess this risk, we investigated the antibiotic resistance genes in the isolate by screening multiple databases, including ResFinder, NCBI AMR, MEGARes, CARD, and ARG-ANNOT, using ABRicate. The results suggest the bacteria as a multi-drug resistant isolate with the presence of 26 resistance genes (S1 Table). The β-lactamase genes detected included blaOXA-338, blaOXA-781, blaOXA-800, blaADC-25, blaADC-2, blaADC-158, and blaA1, which contribute to resistance against β-lactam antibiotics, including cephalosporins and carbapenems. Among them, blaOXA-type genes are associated with carbapenem hydrolysis [83], while blaADC genes encode class C β-lactamases that can degrade cephalosporins [83,84]. Phenotypically, the isolate displayed resistance to ampicillin, ceftazidime, cefotaxime, cefepime, imipenem, and meropenem (Table 6), consistent with the genomic detection of OXA and ADC enzymes. Aminoglycoside resistance was conferred by ant(3’‘)-IIa, which encodes an aminoglycoside nucleotidyltransferase enzyme, modifying aminoglycosides such as gentamicin, tobramycin, and kanamycin, reducing their efficacy [85,86]. This correlated well with the phenotypic resistance observed against gentamicin, tobramycin, and kanamycin in our susceptibility assays. A review highlights that ARGs, particularly β-lactamases, are abundant in petroleum-contaminated soils due to co-selection pressures from hydrocarbons and metals, which favor resistant strains [87].
Multiple efflux pump-related genes were also detected, including adeT1, adeT2, adeS, adeR, adeA, adeB, abeS, abeM, adeK, adeJ, adeI, adeH, adeG, adeF, and adeL. The AdeABC efflux system (adeA, adeB, and adeC) is known for mediating resistance to fluoroquinolones, aminoglycosides, and tigecycline, while AbeM and AbeS contribute to multidrug resistance in A. baumannii [88,89]. Regulatory elements adeR and adeS were also present, suggesting potential overexpression of efflux pumps [90,91]. Consistently, the strain exhibited phenotypic resistance to ciprofloxacin, levofloxacin, doxycyline and tetracycline, supporting the functional relevance of these efflux systems. Additionally, MexT, a transcriptional regulator, was identified, which is known to upregulate the MexEF-OprN efflux pump in Pseudomonas aeruginosa, leading to resistance against fluoroquinolones and chloramphenicol [92]. The presence of abaQ suggests a role in additional efflux mechanisms, potentially affecting aminoglycoside resistance [88,93]. The amvA gene, encoding a major facilitator superfamily (MFS) efflux pump, was also detected, further supporting the isolate’s ability to expel various antibiotics, including macrolides [88,93]. Phenotypic resistance to azithromycin and chloramphenicol further reflected the genomic predictions for these efflux mechanisms.
The combination of β-lactamase production, aminoglycoside-modifying enzymes, and multiple efflux pumps suggests a robust antibiotic resistance mechanism in the isolate. However, no plasmid-associated genes were identified after screening the PlasmidFinder database using ABRicate, indicating that the organism likely has a limited ability to spread resistance genes horizontally. This finding suggests that while the isolate possesses intrinsic resistance mechanisms, the absence of plasmid-mediated resistance reduces the risk of widespread dissemination of these genes to other pathogens in the environment.
Virulence factor, metal resistance genes, and pathogenicity
The presence of virulence factors indicates both the bioremediation potential and pathogenic risk. In A. baumannii DUEMBL6, a total of 33 virulence genes were identified, as listed in S2 Table. The csuA/B, csuA, csuB, csuC, csuD, and csuE genes, which encode the chaperone-usher pili assembly system, were detected, highlighting their potential role in surface adherence and biofilm formation [94,95]. The presence of pgaA, pgaB, pgaC, and pgaD suggests that the bacterium has the ability to produce poly-β-(1,6)-N-acetylglucosamine (PNAG), a key biofilm matrix component that enhances environmental persistence and antibiotic resistance [94,95]. The regulatory genes bfmR and bfmS, which control biofilm maturation, were also identified, suggesting a robust biofilm-forming capability [94,95]. The presence of phospholipase genes plc and plcD indicates the potential for membrane disruption and tissue invasion, contributing to the pathogenicity of the strain [86,87]. Iron acquisition systems were well-represented, with bauA, bauB, bauC, bauD, bauE, bauF, basA, basB, basC, basD, basF, basG, basH, basJ, barA, and barB detected. These genes encode proteins involved in the biosynthesis and transport of acinetobactin and other siderophores, which are critical for survival in iron-limited environments such as the human host [94,95]. The presence of entE further supports the strain’s ability to sequester iron, enhancing its virulence. Outer membrane protein (ompA) and regulatory genes such as abaR were also detected, which are known to play roles in immune evasion and antimicrobial resistance [95,96].
Along with virulence and antimicrobial resistance genes, several metal resistance genes were also detected in the genome of A. baumannii DUEMBL6, suggesting adaptation to metal-contaminated environments. ZitB and YeiR encode zinc efflux transporters, regulating intracellular zinc levels to prevent toxicity [97]. CzcA and CzcB form a cobalt-zinc-cadmium efflux system, crucial for heavy metal resistance [97]. The FtsH zinc-dependent protease aids in stress response by degrading misfolded proteins [93]. mntP regulates manganese efflux, balancing metal homeostasis [98]. KefF, KefC, and KdpC contribute to potassium transport, protecting against acidic stress and osmotic imbalance [99]. FdhD supports anaerobic respiration, while TusA is essential for iron-sulfur cluster biogenesis [100]. GlpE facilitates sulfur detoxification, and CorC and CorA mediate magnesium and cobalt efflux, maintaining cation homeostasis [97,100]. These genes enhance bacterial survival in metal- and hydrocarbon-contaminated environments, potentially contributing to cross-resistance between metals and antibiotics [101].
Since the bacterium has a large repertoire of antibiotic resistance genes and virulence genes that can promote persistence in the human host, we estimated the pathogenic potential using the PathogenFinder. It predicts a probability of 86.1% that the A. baumannii DUEMBL6 is a human pathogen, with 614 matches to known pathogenic families and none to non-pathogenic ones. While the A. baumannii DUEMBL6 might be a promising candidate for bioremediation, the pathogenic potential raises public health concerns during its application in environmental cleanup efforts. The use of bacteria with pathogenic potential in bioremediation raises concerns about the risk of spreading antibiotic resistance genes and causing infections. This is particularly relevant for A. baumannii, given its history of hospital outbreaks and ability to survive on surfaces [102]. To mitigate these risks, future work should evaluate strategies such as employing non-pathogenic, functionally redundant microbial consortia in place of pathogenic isolates, genetically attenuating virulence factors or resistance determinants to reduce pathogenicity, and using immobilization or encapsulation techniques to confine bacterial activity and prevent uncontrolled environmental spread. Incorporating such biosafety measures would be essential before considering field deployment of A. baumannii or related isolates for bioremediation.
Conclusion
The study underscores the efficacy of indigenous bacterial isolates, particularly A. baumannii DUEMBL6, in diesel degradation, driven by enzymatic pathways like alkane hydroxylation and aromatic ring cleavage. While A. baumannii DUEMBL6’s genomic repertoire (e.g., alkB mutations, siderophore production) enhances its adaptability to hydrocarbon-rich environments, its multidrug resistance and virulence traits pose significant challenges for safe bioremediation deployment. It is important to note that conclusions regarding the functional advantage of the observed alkB mutations (I138F, A334T) remain speculative. Future research should therefore include enzyme activity assays, transcriptomics, and proteomics to experimentally validate their role in hydrocarbon degradation, as well as targeted deletion or inactivation of key genes to confirm their contribution to biodegradation. In addition, engineering approaches to remove virulence and antibiotic resistance determinants, and functional assays to test the activity of biosynthetic pathways, represent promising directions. Efforts should also prioritize the use of non-pathogenic, genetically stable strains or microbial consortia to mitigate ecological and health risks. This research contributes to the growing body of knowledge on microbial bioremediation in low-resource settings, emphasizing the need for balanced strategies that harness microbial potential while minimizing unintended consequences.
Supporting information
S1 Table. List of antimicrobial resistance genes detected in A. baumannii DUEMBL6.
https://doi.org/10.1371/journal.pone.0339456.s001
(PDF)
S2 Table. List of virulence genes detected in A. baumannii DUEMBL6.
https://doi.org/10.1371/journal.pone.0339456.s002
(PDF)
References
- 1. Katayama A, Bhula R, Burns GR, Carazo E, Felsot A, Hamilton D, et al. Bioavailability of xenobiotics in the soil environment. Rev Environ Contam Toxicol. 2010;203:1–86. pmid:19957116
- 2. Dechesne A, Badawi N, Aamand J, Smets BF. Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications. Front Microbiol. 2014;5:667. pmid:25538691
- 3. Mamy L, Barriuso E, Gabrielle B. Prediction of the fate of organic compounds in the environment from their molecular properties: A review. Agron Sustain Dev. 2015;35(4).
- 4. Megharaj M, Ramakrishnan B, Venkateswarlu K, Sethunathan N, Naidu R. Bioremediation approaches for organic pollutants: a critical perspective. Environ Int. 2011;37(8):1362–75. pmid:21722961
- 5. Wang S, Wang D, Yu Z, Dong X, Liu S, Cui H, et al. Advances in research on petroleum biodegradability in soil. Environ Sci Process Impacts. 2021;23(1):9–27. pmid:33393551
- 6. Andreoni V, Gianfreda L. Bioremediation and monitoring of aromatic-polluted habitats. Appl Microbiol Biotechnol. 2007;76(2):287–308. pmid:17541581
- 7. Sunny AR. Impact of oil spill in the Bangladesh Sundarbans. Int J Fish Aquat Stud. 2017;5(4):263–9.
- 8.
TheGlobalEconomy.com. Bangladesh diesel and heating oil consumption [Internet]. 2023 [cited 2025 May 1]. Available from: https://www.theglobaleconomy.com/Bangladesh/diesel_fuel_consumption/
- 9.
Hydrocarbon Unit, Bangladesh. Energy scenario of Bangladesh 2020-21 [Internet]. 2023 [cited 2025 May 1]. Available from: http://www.hcu.org.bd
- 10. Fahid M, Arslan M, Shabir G, Younus S, Yasmeen T, Rizwan M, et al. Phragmites australis in combination with hydrocarbons degrading bacteria is a suitable option for remediation of diesel-contaminated water in floating wetlands. Chemosphere. 2020;240:124890. pmid:31726588
- 11. Ahmed F, Fakhruddin ANM. A review on environmental contamination of petroleum hydrocarbons and its biodegradation. Int J Environ Sci Nat Res. 2018;11(3):1–8.
- 12. Nwinyi OC, Kanu IA, Tunde A, Ajanaku KO. Characterization of diesel degrading bacterial species from contaminated tropical ecosystem. Braz Arch Biol Technol. 2014;57(5):789–96.
- 13.
Cho KS, Ryu HW, Lee EH, Kang YS. Bioremediation of diesel-contaminated soils by natural attenuation, biostimulation and bioaugmentation. In: Proceedings of the 14th Asia Pacific Confederation of Chemical Engineering Congress. Singapore; 2012. p. 783–4.
- 14. Menezes Bento F, de Oliveira Camargo FA, Okeke BC, Frankenberger WT Jr. Diversity of biosurfactant producing microorganisms isolated from soils contaminated with diesel oil. Microbiol Res. 2005;160(3):249–55. pmid:16035236
- 15. Lin M, Liu Y, Chen W, Wang H, Hu X. Use of bacteria-immobilized cotton fibers to absorb and degrade crude oil. Int Biodeterior Biodegrad. 2014;88:8–12.
- 16. Singh P, Patil Y, Rale V. Biosurfactant production: emerging trends and promising strategies. J Appl Microbiol. 2019;126(1):2–13. pmid:30066414
- 17. Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173(2):697–703. pmid:1987160
- 18. O’Toole GA. Microtiter dish biofilm formation assay. J Vis Exp. 2011;47:e2437.
- 19. Vomberg A, Klinner U. Distribution of alkB genes within n-alkane-degrading bacteria. J Basic Microbiol. 2001;41(2):99–106.
- 20. Tahya AM, Fakhruddin ANM, Abdullah SRS. Catechol 2,3-dioxygenase gene detection in crude oil biodegradation by Pseudomonas aeruginosa. J Environ Chem Eng. 2019;7(5):103384.
- 21. Latha R, Kalaivani KR. Bacterial degradation of crude oil by gravimetric analysis. Adv Appl Sci Res. 2012.
- 22.
Smith BC. Fundamentals of Fourier transform infrared spectroscopy. 2nd ed. Boca Raton (FL): CRC Press; 2011. p. 210.
- 23. Ward CM, To TH, Pederson SM. ngsReports: a Bioconductor package for managing FastQC reports and other NGS related log files. Bioinformatics. 2020;36(8):2587–8.
- 24. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.
- 25. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77. pmid:22506599
- 26. Kirstahler P, Bjerrum SS, Friis-Møller A, la Cour M, Aarestrup FM, Westh H, et al. Genomics-Based Identification of Microorganisms in Human Ocular Body Fluid. Sci Rep. 2018;8(1):4126. pmid:29515160
- 27. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114. pmid:30504855
- 28. Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, et al. Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol. 2012;50(4):1355–61. pmid:22238442
- 29. Grant JR, Enns E, Marinier E, Mandal A, Herman K, Chen CY. Proksee: In-depth characterization and visualization of bacterial genomes. Nucleic Acids Research. 2023;51(W1):W484-92.
- 30. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9.
- 31. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75. pmid:18261238
- 32. Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44(14):6614–24. pmid:27342282
- 33. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012;67(11):2640–4. pmid:22782487
- 34. Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2014;58(1):212–20. pmid:24145532
- 35. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, et al. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob Agents Chemother. 2019;63(11):e00483-19. pmid:31427293
- 36. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020;48(D1):D517–25. pmid:31665441
- 37. Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 2019;47(D1):D687-92.
- 38. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O, Villa L, et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58(7):3895–903. pmid:24777092
- 39. Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47(W1):W81-7.
- 40. Gillespie JJ, Wattam AR, Cammer SA, Gabbard JL, Shukla MP, Dalay O, et al. PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species. Infect Immun. 2011;79(11):4286–98. pmid:21896772
- 41. Cosentino S, Voldby Larsen M, Møller Aarestrup F, Lund O. PathogenFinder--distinguishing friend from foe using bacterial whole genome sequence data. PLoS One. 2013;8(10):e77302. pmid:24204795
- 42. Guo X, Zhang J, Han L, Lee J, Williams SC, Forsberg A, et al. Structure and mechanism of the alkane-oxidizing enzyme AlkB. Nat Commun. 2023;14(1):2180. pmid:37069165
- 43.
Hudzicki J. Kirby-Bauer Disk Diffusion Susceptibility Test Protocol. 2012.
- 44.
ReliefWeb. Sundarbans oil spill – Bangladesh [Internet]. [cited 2025 May 1]. Available from: https://reliefweb.int/report/bangladesh/sundarbans-oil-spill
- 45.
SkyTruth. [Updated] Bangladesh – oil spill in the Sundarbans National Park [Internet]. [cited 2025 May 1]. Available from: https://skytruth.org/2014/12/sunderbans-spill/
- 46. Mukti KNK. Study of sources of pollution and its effects on water quality and human habitat: a case study of Bansi River, Savar, Dhaka, Bangladesh. UITS J. 2023;5(1):35–45.
- 47. Wu B, Xiu J, Yu L, Huang L, Yi L, Ma Y. Degradation of crude oil in a co-culture system of Bacillus subtilis and Pseudomonas aeruginosa. Front Microbiol. 2023;14.
- 48. Mehboob M, Rehman A, Naz I, Shuja MN, Farooq AS, Khattak B. Bioremediation Potential of Indigenous Bacterial Isolates for Treating Petroleum Hydrocarbons-Induced Environmental Pollution. ACS Omega. 2025;10(3):2501–16. pmid:39895734
- 49. Taskar K, Gupta S. Exploiting bacterial isolates for diesel degrading potential under in vitro conditions. Plant Sci Today. 2022;9(2):123–30.
- 50. van Beilen JB, Funhoff EG. Alkane hydroxylases involved in microbial alkane degradation. Appl Microbiol Biotechnol. 2007.
- 51. Wang W, et al. alkB gene as a functional marker for assessing hydrocarbon-degrading bacterial communities in oil-contaminated soils. Appl Environ Microbiol. 2011.
- 52. Harayama S, Kasai Y, Hara A. Microbial communities in oil-contaminated seawater. Curr Opin Biotechnol. 1999.
- 53. Jiao S, et al. Biodegradation of aromatic hydrocarbons in groundwater by microbial communities. Front Microbiol. 2016.
- 54. Palanisamy N, Ramya J, Kumar S, Vasanthi N, Chandran P, Khan S. Diesel biodegradation capacities of indigenous bacterial species isolated from diesel contaminated soil. J Environ Health Sci Eng. 2014;12(1):142. pmid:25530870
- 55. Abdulrasheed M, Zakaria NN, Ahmad Roslee AF, Shukor MY, Zulkharnain A, Napis S. Biodegradation of diesel oil by cold-adapted bacterial strains of Arthrobacter spp. from Antarctica. Antarct Sci. 2020;32(5):341–53.
- 56. Zhang N, Li Y, Jiang Z, Zhou H, Zhou M, Zhang R, et al. Construction and application of an efficient diesel degrading bacterial consortium for oily wastewater bioremediation. World J Microbiol Biotechnol. 2025;41(8):299. pmid:40768007
- 57. Ghanem K, Al-Zahrani M. Bioremediation of diesel fuel by fungal consortium using statistical experimental designs. Polish Journal of Environmental Studies. 2016;25(1):97–106.
- 58. Cook-Libin S, Sykes EME, Kornelsen V, Kumar A. Iron Acquisition Mechanisms and Their Role in the Virulence of Acinetobacter baumannii. Infect Immun. 2022;90(10):e0022322. pmid:36066263
- 59. Murphy SMC, Bautista MA, Cramm MA, Hubert CRJ. Diesel and crude oil biodegradation by cold-adapted microbial communities in the Labrador Sea. Applied and Environmental Microbiology. 2021;87(20):e01234-21.
- 60. Jabur AR, Abbas LK, Moosa SA. Antibacterial Activity of Electrospun Silver Nitrate /Nylon 6 Polymeric Nanofiber Water Filtration Mats. Al-Khwarizmi Engineering Journal. 2017;13(1):84–93.
- 61. Sun W, Ali I, Liu J, Dai M, Cao W, Jiang M. Isolation, identification, and characterization of diesel-oil-degrading bacterial strains indigenous to Changing oil field, China. J Basic Microbiol. 2019;59(7):723–34.
- 62. Goldfarb T, Kodali VK, Pujar S, Brover V, Robbertse B, Farrell CM, et al. NCBI RefSeq: reference sequence standards through 25 years of curation and annotation. Nucleic Acids Res. 2025;53(D1):D243-57.
- 63. Robbins JM, Ellis HR. Identification of critical steps governing the two-component alkanesulfonate monooxygenase catalytic mechanism. Biochemistry. 2012;51(32):6378–87. pmid:22775358
- 64. Tsao D, Liu S, Dokholyan NV. Regioselectivity of Catechol O-Methyltransferase Confers Enhancement of Catalytic Activity. Chem Phys Lett. 2011;506(4–6):135–8. pmid:21731105
- 65. Liu Y, Wei F, Xu R, Cheng T, Ma Y. Insights into the Binding Interaction of Catechol 1,2-Dioxygenase with Catechol in Achromobacter xylosoxidans DN002. Appl Biochem Biotechnol. 2023;195(1):298–313. pmid:36074236
- 66. Staude MW, Leonard DA, Peng JW. Expanded substrate activity of OXA-24/40 in carbapenem-resistant Acinetobacter baumannii involves enhanced binding loop flexibility. Biochemistry. 2016;55(47):6535–44.
- 67. Eijkelkamp BA, Hassan KA, Paulsen IT, Brown MH. Investigation of the human pathogen Acinetobacter baumannii under iron limiting conditions. BMC Genomics. 2011;12:126. pmid:21342532
- 68. Artuso I, Poddar H, Evans BA, Visca P. Genomics of Acinetobacter baumannii iron uptake. Microb Genom. 2023;9(8):mgen001080. pmid:37549061
- 69. Sheldon JR, Skaar EP. Acinetobacter baumannii can use multiple siderophores for iron acquisition, but only acinetobactin is required for virulence. PLoS Pathog. 2020;16(10):e1008995. pmid:33075115
- 70. Freitas JF, Silva DFL, Silva BS, Castro JNF, Felipe MBMC, Silva-Portela RCB, et al. Genomic and phenotypic features of Acinetobacter baumannii isolated from oil reservoirs reveal a novel subspecies specialized in degrading hazardous hydrocarbons. Microbiol Res. 2023;273:127420. pmid:37270893
- 71. Monowar T, Rahman MS, Bhore SJ, Raju G, Sathasivam KV. Secondary Metabolites Profiling of Acinetobacter baumannii Associated with Chili (Capsicum annuum L.) Leaves and Concentration Dependent Antioxidant and Prooxidant Properties. Biomed Res Int. 2019;2019:6951927. pmid:30868071
- 72. Mishra S, Lin Z, Pang S, Zhang W, Bhatt P, Chen S. Recent advanced technologies for the characterization of xenobiotic-degrading microorganisms and microbial communities. Front Bioeng Biotechnol. 2021;9:632059.
- 73. Miglani R, Parveen N, Kumar A, Ansari MA, Khanna S, Rawat G, et al. Degradation of Xenobiotic Pollutants: An Environmentally Sustainable Approach. Metabolites. 2022;12(9):818. pmid:36144222
- 74. Liu Y, Wei F, Xu R, Cheng T, Ma Y. Insights into the Binding Interaction of Catechol 1,2-Dioxygenase with Catechol in Achromobacter xylosoxidans DN002. Appl Biochem Biotechnol. 2023;195(1):298–313. pmid:36074236
- 75. Al-Hakim M, Hasan MH, Hasan R, Ali MH, Rabbee MF, Marufatuzzahan , et al. In-silico characterization and homology modeling of catechol 1,2-dioxygenase involved in processing of catechol—an intermediate of aromatic compound degradation pathway. Global Journal of Science Frontier Research: C Biological Science. 2023;23(2):15–23.
- 76. Banerjee S, Bedics A, Tóth E, Kriszt B, Soares AR, Bóka K, et al. Isolation of Pseudomonas aromaticivorans sp. nov from a hydrocarbon-contaminated groundwater capable of degrading benzene-, toluene-, m- and p-xylene under microaerobic conditions. Front Microbiol. 2022;13:929128. pmid:36204622
- 77. Wolfe MD, Altier DJ, Stubna A, Popescu CV, Münck E, Lipscomb JD. Benzoate 1,2-dioxygenase from Pseudomonas putida: single turnover kinetics and regulation of a two-component Rieske dioxygenase. Biochemistry. 2002;41(30):9611–26. pmid:12135383
- 78. Pirnik MP, Atlas RM, Bartha R. Hydrocarbon metabolism by Brevibacterium erythrogenes: normal and branched alkanes. J Bacteriol. 1974;119(3):868–78. pmid:4852318
- 79. Yachnin BJ, McEvoy MB, MacCuish RJD, Morley KL, Lau PCK, Berghuis AM. Lactone-bound structures of cyclohexanone monooxygenase provide insight into the stereochemistry of catalysis. ACS Chem Biol. 2014;9(12):2843–51. pmid:25265531
- 80. Musat F, Wilkes H, Behrends A, Woebken D, Widdel F. Microbial nitrate-dependent cyclohexane degradation coupled with anaerobic ammonium oxidation. ISME Journal. 2010;4(10):1290–301.
- 81. Varjani SJ. Microbial degradation of petroleum hydrocarbons. Bioresour Technol. 2017;223:277–86.
- 82. Varjani SJ, Upasani VN. A new look on factors affecting microbial degradation of petroleum hydrocarbon pollutants. International Biodeterioration & Biodegradation. 2017;120:71–83.
- 83. Luo TL, Rickard AH, Srinivasan U, Kaye KS, Foxman B. Association of blaOXA-23 and bap with the persistence of Acinetobacter baumannii within a major healthcare system. Front Microbiol. 2015;6.
- 84. Hernández-Alomía F, Bastidas-Caldes C, Ballesteros I, Tenea GN, Jarrín-V P, Molina CA, et al. Beta-Lactam Antibiotic Resistance Genes in the Microbiome of the Public Transport System of Quito, Ecuador. Int J Environ Res Public Health. 2023;20(3):1900. pmid:36767267
- 85. Cox G, Stogios PJ, Savchenko A, Wright GD. Structural and molecular basis for resistance to aminoglycoside antibiotics by the adenylyltransferase ANT(2″)-Ia. mBio. 2015;6(1).
- 86. Zhang G, Leclercq SO, Tian J, Wang C, Yahara K, Ai G, et al. A new subclass of intrinsic aminoglycoside nucleotidyltransferases, ANT(3")-II, is horizontally transferred among Acinetobacter spp. by homologous recombination. PLoS Genet. 2017;13(2):e1006602. pmid:28152054
- 87. Cunningham CJ, Kuyukina MS, Ivshina IB, Konev AI, Peshkur TA, Knapp CW. Potential risks of antibiotic resistant bacteria and genes in bioremediation of petroleum hydrocarbon contaminated soils. Environ Sci Process Impacts. 2020;22(5):1110–24. pmid:32236187
- 88. Cain AK, Hamidian M. Portrait of a killer: Uncovering resistance mechanisms and global spread of Acinetobacter baumannii. PLoS Pathog. 2023;19(8):e1011520. pmid:37561719
- 89. Sun C, Yu Y, Hua X. Resistance mechanisms of tigecycline in Acinetobacter baumannii. Front Cell Infect Microbiol. 2023;13.
- 90. Roy S, Chatterjee S, Bhattacharjee A, Chattopadhyay P, Saha B, Dutta S, et al. Overexpression of Efflux Pumps, Mutations in the Pumps’ Regulators, Chromosomal Mutations, and AAC(6’)-Ib-cr Are Associated With Fluoroquinolone Resistance in Diverse Sequence Types of Neonatal Septicaemic Acinetobacter baumannii: A 7-Year Single Center Study. Front Microbiol. 2021;12:602724. pmid:33776950
- 91. Lari AR, Ardebili A, Hashemi A. AdeR-AdeS mutations & overexpression of the AdeABC efflux system in ciprofloxacin-resistant Acinetobacter baumannii clinical isolates. Indian J Med Res. 2018;147(4):413–21. pmid:29998878
- 92. Castanheira M, Mendes RE, Gales AC. Global Epidemiology and Mechanisms of Resistance of Acinetobacter baumannii-calcoaceticus Complex. Clin Infect Dis. 2023;76(Suppl 2):S166–78. pmid:37125466
- 93. Lee CR, Lee JH, Park M, Park KS, Bae IK, Kim YB. Biology of Acinetobacter baumannii: Pathogenesis, Antibiotic Resistance Mechanisms, and Prospective Treatment Options. Front Cell Infect Microbiol. 2017;7.
- 94. Harding CM, Hennon SW, Feldman MF. Uncovering the mechanisms of Acinetobacter baumannii virulence. Nat Rev Microbiol. 2018;16(2):91–102. pmid:29249812
- 95. Shadan A, Pathak A, Ma Y, Pathania R, Singh RP. Deciphering the virulence factors, regulation, and immune response to Acinetobacter baumannii infection. Front Cell Infect Microbiol. 2023;13:1053968. pmid:36968113
- 96. Marszalik A, Sidor K, Kraśnicka A, Wróblewska M, Skirecki T, Jagielski T. Acinetobacter baumannii – virulence factors and epidemiology of infections. Postępy Mikrobiologii - Advancements of Microbiology. 2021;60(4):267–79.
- 97. Alquethamy SF, Adams FG, Maharjan R, Delgado NN, Zang M, Ganio K. The Molecular Basis of Acinetobacter baumannii Cadmium Toxicity and Resistance. Applied and Environmental Microbiology. 2021;87(22):e0171821.
- 98. Al-Jabri Z, Zamudio R, Horvath-Papp E, Ralph JD, Al-Muharrami Z, Rajakumar K, et al. Integrase-Controlled Excision of Metal-Resistance Genomic Islands in Acinetobacter baumannii. Genes (Basel). 2018;9(7):366. pmid:30037042
- 99. Wang H, Wang J, Yu P, Ge P, Jiang Y, Xu R, et al. Identification of antibiotic resistance genes in the multidrug-resistant Acinetobacter baumannii strain, MDR-SHH02, using whole-genome sequencing. Int J Mol Med. 2017;39(2):364–72. pmid:28035408
- 100. Monem S, Furmanek-Blaszk B, Łupkowska A, Kuczyńska-Wiśnik D, Stojowska-Swędrzyńska K, Laskowska E. Mechanisms Protecting Acinetobacter baumannii against Multiple Stresses Triggered by the Host Immune Response, Antibiotics and Outside-Host Environment. Int J Mol Sci. 2020;21(15):5498. pmid:32752093
- 101. Williams CL, Neu HM, Alamneh YA, Reddinger RM, Jacobs AC, Singh S. Characterization of Acinetobacter baumannii copper resistance reveals a role in virulence. Front Microbiol. 2020;11.
- 102. Alsan M, Klompas M. Acinetobacter baumannii: An Emerging and Important Pathogen. J Clin Outcomes Manag. 2010;17(8):363–9. pmid:26966345