Next Article in Journal
Repurposed Antipsychotics as Potential Anticancer Agents: Clozapine Efficacy and Dopaminergic Pathways in Neuroblastoma and Glioblastoma
Previous Article in Journal
Cardiovascular Remodeling and Potential Controversies in Master Endurance Athletes—A Narrative Review
Previous Article in Special Issue
Serological Distribution of Salmonella enterica subsp. Isolated from Feces of Domesticated Crested Gecko (Correlophus ciliates) in Busan Province, South Korea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Life
Volume 15
Issue 7
10.3390/life15071094
life-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hybrid Genome and Clinical Impact of Emerging Extensively Drug-Resistant Priority Bacterial Pathogen Acinetobacter baumannii in Saudi Arabia

by
J. Francis Borgio
1,2
1
Department of Genetic Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
2
Department of Epidemic Diseases Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Life 2025, 15(7), 1094; https://doi.org/10.3390/life15071094
Submission received: 8 June 2025 / Revised: 3 July 2025 / Accepted: 10 July 2025 / Published: 12 July 2025
(This article belongs to the Special Issue Latest Updates on Clinical Microbiology and Antimicrobial Resistant Infection)
Browse Figures
Versions Notes

Abstract

Acinetobacter baumannii is listed by the World Health Organization as an emerging bacterial priority pathogen, the prevalence and multidrug resistance of which have been increasing. This functional genomics study aimed to understand the drug-resistance mechanisms of an extensively drug-resistant (XDR) A. baumannii strain (IRMCBCU95U) isolated from a transtracheal aspirate sample from a female patient with end-stage renal disease in Saudi Arabia. The whole genome of IRMCBCU95U (4.3 Mbp) was sequenced using Oxford Nanopore long-read sequencing to identify and compare the antibiotic-resistance profile and genomic features of A. baumannii IRMCBCU95U. The antibiogram of A. baumannii IRMCBCU95U revealed resistance to multiple antibiotics, including cefepime, ceftazidime, ciprofloxacin, imipenem, meropenem and piperacillin/tazobactam. A comparative genomic analysis between IRMCBCU95U and A. baumannii K09-14 and ATCC 19606 identified significant genetic heterogeneity and mosaicism among the strains. This analysis also demonstrated the hybrid nature of the genome of IRMCBCU95U and indicates that horizontal gene transfer may have occurred between these strains. The IRMCBCU95U genome has a diverse range of genes associated with antimicrobial resistance and mobile genetic elements (ISAba1 and IS26) associated with the spread of multidrug resistance. The presence of virulence-associated genes that are linked to iron acquisition, motility and transcriptional regulation confirmed that IRMCBCU95U is a priority human pathogen. The plasmid fragment IncFIB(pNDM-Mar) observed in the strain is homologous to the plasmid in Klebsiella pneumoniae (439 bp; similarity: 99.09%), which supports its antimicrobial resistance. From these observations, it can be concluded that the clinical A. baumannii IRMCBCU95U isolate is an emerging extensively drug-resistant human pathogen with a novel combination of resistance genes and a plasmid fragment. The complex resistome of IRMCBCU95U highlights the urgent need for genomic surveillance in hospital settings in Saudi Arabia to fight against the spread of extensively drug-resistant A. baumannii.

1. Introduction

Acinetobacter baumannii has emerged as a priority pathogen in healthcare settings around the world [1,2,3]. A. baumannii is a significant nosocomial bacterial pathogen whose prevalence and multidrug resistance (MDR) have been increasing [1,4]. This Gram-negative coccobacillus poses a notable threat to patients who are immunocompromised and those who require prolonged hospitalization [1,4,5]. A. baumannii easily colonizes the skin and is found in high concentrations within the respiratory and oropharyngeal secretions of infected patients, which contributes to the widespread prevalence of nosocomial A. baumannii infections [2,6]. The rise in MDR A. baumannii infections is a major concern due to the limited options for treatment, high mortality and poor prognoses [1,2,3]. The World Health Organization (WHO) has recognized antimicrobial resistance as one of the most important challenges for human health [2,7]. A. baumannii is a key member of the ESKAPE pathogens (‘Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, A. baumannii, Pseudomonas aeruginosa and Enterobacter group’) due to its antibiotic resistance [2,8]. In the early 1970s, A. baumannii was susceptible to most antibiotics; however, it has recently been shown to exhibit extensive resistance to numerous first-line antibiotics [9]. A. baumannii has various resistance mechanisms including carbapenem enzyme production [10]. One of the key challenges is overcoming the ability of A. baumannii to acquire and utilize antimicrobial-resistance mechanisms. Resistance to antibiotics like tigecycline and polymyxins is being increasingly seen in clinical isolates. Understanding antibiotic-resistance mechanisms is critical for the use of antibiotics in clinics and for developing novel therapeutics. The ability of A. baumannii to rapidly develop various resistance mechanisms to antibiotics has increased the challenge of treating infected patients [4]. Beyond antibiotic resistance, A. baumannii has various virulence factors that contribute to its pathogenicity, including those involved in its ability to resist desiccation, form biofilms in various environments and express surface-adhesion proteins and secretion systems. These factors support the environmental persistence and host colonization of A. baumannii and contribute to its success as a nosocomial agent [1,2,3].
Genomic studies have revealed the genetic diversity within the clinical isolates of A. baumannii and their significant genetic divergence from laboratory strains [11]. These differences are clinically relevant as they can influence the expression of virulence factors and resistance genes in A. baumannii. The evolution of the pAB3 plasmid to pAB04, which has a higher number of resistance cassettes, is one of the best examples of adaptation in A. baumannii. Comparative genomics has unveiled significant genetic diversity among A. baumannii strains, with >2000 publicly available genomes with a genome size of nearly 12,000 genes. The accumulation of resistance mechanisms coupled with virulence factors contribute to the prevalence of A. baumannii as a nosocomial pathogen. In addition, the potential for A. baumannii to acquire toxin-related virulence factors similar to those found in other bacterial pathogens such as Vibrio cholerae and Clostridium difficile still remains a concern [3,12].
Recent studies have revealed that multidrug-resistant A. baumannii is prevalent across the Arabian Peninsula [13]. Studies from Saudi Arabia reported high mortality rates associated with MDR microbes, with A. baumannii being among the top three pathogenic microorganisms associated with high mortality [13]. In Saudi Arabia, A. baumannii has been isolated from various human samples including urine, blood and respiratory specimens [14,15,16]. There is an urgent need for alternative treatments against MDR A. baumannii such as phage therapy, metabolic interference, antimicrobial peptides and vaccines [3,17,18]. Recent clinical isolates of A. baumannii exhibit significant genetic differences from the type strains such as ATCC 17978 and ATCC 19606. These strains have served as primary research models for decades. However, these older strains were isolated nearly 70 years ago and may not accurately reflect the characteristics of clinical isolates recovered in the past three decades. Therefore, the utilization of modern techniques to assess clinical isolates is essential for obtaining more accurate and updated details of the molecular mechanisms underlying the survival and adaptation of A. baumannii in recent years. The use of advanced molecular and bioinformatics techniques may lead to the identification of novel and relevant virulence factors beyond the current knowledge on resistance mechanisms [3,19]. In the current study, the phenome and resistome of the clinical isolate A. baumannii IRMCBCU95U were characterized. This isolate was isolated from a transtracheal aspirate from a Saudi Arabian female patient with end-stage renal disease who was admitted to a tertiary care hospital in the Eastern Province of Saudi Arabia due to an acute condition requiring intubation.

2. Materials and Methods

2.1. Ethical Approval

The current study received ethical approval from the Institutional Review Board (IRB) of Imam Abdulrahman Bin Faisal University (Ref No.: IRB-2022-13-462). All experimental procedures were conducted in accordance with the principles outlined in the 1964 Declaration of Helsinki.

2.2. Bacterial Strain and DNA Isolation

A pathogenic bacterial strain was isolated from a transtracheal aspirates collected by the treating medical team at King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, as part of a standard clinical investigation. The transtracheal aspirate was obtained from a 31-year-old female patient of Arab origin. The patient presented with end-stage renal disease requiring hemodialysis and was hospitalized with an acute condition necessitating intubation at the time of sampling. The pathogenic strain (IRMCBCU95U) was isolated as elaborated in [20]. Antimicrobial susceptibility profiling of the clinical isolate IRMCBCU95U was performed using the VITEK® 2 automated system. This standardized assay enabled a quantitative assessment of the isolate’s resistance phenotypes for a panel of clinically relevant antibiotics and allowed for elucidation of the potential resistance mechanisms based on established criteria from the manufacturers. Genomic DNA was extracted from the isolate using the Puregene Yeast/Bact. Kit B (Qiagen, Hilden, Germany), following the manufacturer’s recommended protocol. This kit employs a salting-out procedure to selectively precipitate proteins and yield high-molecular-weight genomic DNA that is suitable for downstream sequencing applications. The purity and concentration of the extracted genomic DNA of IRMCBCU95U were evaluated using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The 260/280 nm and 260/230 nm absorbance ratios were assessed to determine the concentrations of protein and other contaminants. The DNA was quantified by measuring the absorbance at 260 nm, and was then used for the subsequent genomic analyses.

2.3. Nanopore Whole-Genome Sequencing and Data Processing

Whole-genome sequencing of the Acinetobacter clinical isolate IRMCBCU95U was performed using Oxford Nanopore long-read technology at the IRMC Genetics Laboratory. High-quality genomic DNA meeting the purity criteria of a 260/280 nm ratio between 1.8 and 2.0, a 260/230 nm ratio between 2.0 and 2.2, and a concentration of 100–500 ng in 50 µL was subjected to DNA repair and end preparation using NEBNext reagents. Following AMPure XP bead-based cleanup, the end-prepped DNA (>700 ng) was barcoded using the native barcoding kit and ligated. The barcoded samples were pooled to create a sample with a total of 700 ng of DNA and adapter ligation was performed using the NEBNext Quick Ligation Reaction Buffer and Adapter Mix II (Oxford Nanopore Technologies, Oxford, UK). The resulting library that was recovered contained approximately 430 ng of DNA and was cleaned using AMPure XP beads and optimized using long fragment buffer (LFB). The prepared library was loaded onto a primed Oxford Nanopore SpotON flow cell (Oxford Nanopore Technologies, Oxford, UK). Sequencing was conducted using the MinION Mk1C device (Oxford Nanopore Technologies, Oxford, UK) with the real-time data acquisition and base calling performed using the MinKNOW software (version 23.04.6) (Oxford Nanopore Technologies, Oxford, UK). The initial quality and coverage metrics were determined using the EPI2ME platform (EPI2ME Agent 3.5.7). Raw signal data were base called and converted into fastQ format for the downstream genomic analyses.

2.4. Bacterial Genome Analysis

The raw sequencing reads from the IRMCBCU95U sample were processed using the NanoForms pipeline. This pipeline included quality-assessment modules to evaluate various metrics of the sequencing run [21]. The long-read sequencing data for the Acinetobacter clinical isolate IRMCBCU95U were processed using several bioinformatics tools. Fastp [22], Nanoforms [21] and NanoFilt [23] were used for quality control and filtering and trimming of the reads, while Quast [24], Flye [25], the PATRIC (BV-BRC 3.28.5) software [26] and NanoGalaxy [27] were employed for de novo assembly, assembly quality assessment and genome annotation [28,29]. The taxonomic classification of the Acinetobacter isolate IRMCBCU95U was determined using a whole-genome phylogenetic tree. The average G+C content and the number of contigs were calculated. The predicted proteins were functionally annotated according to enzyme commission (EC) numbers [30], metabolic pathways [31], gene ontology (GO) [32], protein family classifications [33] and protein complex subsystems [34]. The genome of Acinetobacter IRMCBCU95U was also analyzed to identify genes associated with antibiotic-resistant genes [35], drug targets [36,37], virulence factors [38,39] and transport systems [40]. Established plasmid types were identified through plasmid typing [41]. K-mer-based methods [26] were employed to identify antimicrobial-resistance (AMR) genes. The phylogenetic analysis utilized 100 genes retrieved from the NCBI reference database supplemented with representative genomes selected via the Mash/MinHash algorithm (Mash v2.3) [42]. Genome alignment was performed using MUSCLE [43], with the statistical support assessed using bootstrapping [44,45]. The metagenomic reads were mapped against reference sequences in the VFDB (2019) and CARD (2020) databases [46].
The sequence of the IRMCBCU95U genome was queried against the VFDB core dataset using BLASTN (2.16.0). The BLASTN search was performed using the online interface provided by the VFDB database with the default parameters to identify homologous sequences to those of known virulence factors. Significant hits were defined as those with an E-value of 0.0. The results were analyzed and the identity percentage, score (bits), length, number of identities, gaps and strand information were extracted and compiled [47]. The genome sequence of IRMCBCU95U was analyzed using the PlasmidFinder 2.0 server to identify plasmid sequences. PlasmidFinder 2.0 is a tool designed for the in silico detection and typing of plasmids in bacterial genome sequences. The analysis was performed using the default parameters of the PlasmidFinder 2.0 server [41]. ResFinderFG (Version 2.0) was employed to predict the resistance phenotypes of IRMCBCU95U. Linezolid resistance determinants in this isolate were further investigated by identifying mutations in relevant genes using the LRE-Finder software (Version 1.0) [46,48]. Mobile genetic elements and pathogenic protein families related to antibiotic resistance were detected using PathogenFinder (Version 1.1) [49] and MGE [50]. The genome of the IRMCBCU95U strain was also subjected to an analysis for the presence of mycotoxin biosynthesis genes using the ToxFinder-1.0 tool. The input file was in FASTA format. ToxFinder-1.0 was used to screen for genes associated with the production of ochratoxin, fumonisin, trichothecene, citrinin, aflatoxin, ergot and patulin [46,51].

3. Results

This functional genomics study isolated a pathogenic bacterial strain that was named IRMCBCU95U. The isolate was obtained from a transtracheal aspirate specimen from a 31-year-old female patient of Saudi Arabian origin. The clinical history of the patient included end-stage renal disease managed with hemodialysis and, at the time of sampling, the patient had been admitted to a tertiary care hospital in the Eastern Province of Saudi Arabia with an acute condition requiring intubation.

3.1. Phenomics of A. baumannii Strain IRMCBCU95U

The antibiogram of the extensively drug-resistant A. baumannii strain IRMCBCU95U was characterized through antibiotic susceptibility testing (AST) and resistance mechanism detection utilizing the VITEK® 2 system. Table 1 provides the minimum inhibitory concentrations (MICs) and their corresponding interpretations (Resistant or Sensitive) for each antibiotic. The antibiotic susceptibility profile of A. baumannii strain IRMCBCU95U revealed that the isolate is resistant to several of the tested antibiotics including cefepime, ceftazidime, ciprofloxacin, imipenem, meropenem and piperacillin/tazobactam. The isolate is also sensitive to tigecycline (MIC = 2) and trimethoprim/sulfamethoxazole. The data indicate that ciprofloxacin-resistant isolates are likely to also be levofloxacin resistant.

3.2. Genomics of IRMCBCU95U

Quality of the Sequencing Data for IRMCBCU95U Genome

The NanoForms analysis indicated the successful processing of the sequencing genome data using long-read Nanopore sequencing, with the workflow completing without any errors. A total of 33,004 reads were generated for the IRMCBCU95U genomic DNA. The mean and median read lengths were 2665.9 and 1040.0, respectively, with an N50 read length of 7677, indicating a substantial proportion of longer reads. The total yield of the genome sequencing run was 87.98 million bases. The read quality assessment revealed that all the reads exceeded a quality threshold and 100% of the reads had a quality score above Q5. While 65.2% of the reads had a quality score above Q10, only 24.1% had a score exceeding Q12 and 0.3% had a quality score above Q15. This suggests a moderately high read quality with a reduction in the number of high-confidence base calls with more stringent quality filters (Supplementary Table S1). Figure 1 shows a detailed characterization of the read length distribution in the sequencing data and presents various metrics illustrating the quality and distribution of sequencing reads obtained for the IRMCBCU95U isolate.

3.3. Assembly of the IRMCBCU9SU Dataset

The assembly of the IRMCBCU9SU genome sequence using NanoForms resulted in a successful workflow (Supplementary Table S2). A total of 40 contigs were identified during the consensus assembly of the fastq files of the IRMCBCU9SU genome sequence. The N50 improved from the initial assembly (167,919 bp) to the polished genome (807,720 bp), but decreased in the consensus genome (405,948). This indicates that polishing initially increased the contig length, but the consensus step might have fragmented some contigs to improve accuracy. The largest contig was substantial, exceeding 1.4 Mbp in the initial assembly and polished genome. The total assembly length was approximately 4.3 Mbp across all versions. The GC content was around 40%. There were zero mismatches, suggesting a high degree of agreement within the assembly process.

3.4. Phylogenetic Tree Based on Whole Genomes of IRMCBCU95U and Other Acinetobacter Species

The phylogenetic tree was constructed using 99 whole reference genomes (Supplementary Table S3). The tree was used to elucidate the evolutionary relationships between Acinetobacter IRMCBCU95U and other Acinetobacter species. The tree was built using alignments of 100 single-copy orthologous genes, which are found in all 98 reference genomes and the genome of the clinical isolate (Supplementary Table S4). The amino acid sequences were aligned using MAFFT and the final tree was constructed using randomized accelerated maximum likelihood. The best-fit model for protein evolution was determined to be the LG substitution model. Branch support was assessed using randomized accelerated maximum likelihood fast bootstrapping. It was confirmed that the constructed phylogenetic tree includes various Acinetobacter species with Acinetobacter IRMCBCU95U positioned among them. The tree illustrates the close relationships between IRMCBCU95U and species such as A. baumannii strains K09-14 and ATCC 19606, and its divergence and relatedness to other Acinetobacter species (Figure 2).
A bootstrap value of 75 on a branch connected to Acinetobacter IRMCBCU95U was observed in the phylogenetic tree, which indicates moderate support for the evolutionary relationship. This suggests that, in 75% of the bootstrap replicates, Acinetobacter IRMCBCU95U and the A. baumannii strains K09-14 and ATCC 19606 clustered together. This provides reasonable evidence that the evolutionary relationship shown is likely correct.

3.5. Genomic Overview of A. baumannii IRMCBCU95U

The genome of A. baumannii IRMCBCU95U was comprehensively annotated, revealing a genome length of 4,340,742 base pairs (bp) distributed across 40 contigs. The average GC content of the genome was 40.46%. The assembly statistics indicated a contig N50 of 404,531 bp and a contig L50 of 4. Annotation of the genome identified a total of 7437 protein-coding sequences (CDSs). Additionally, the genome contains 89 transfer RNA (tRNA) genes and 17 ribosomal RNA (rRNA) genes. The annotation analysis also predicted 1737 hypothetical proteins and 5700 proteins with functional assignments. The functional annotation included the assignment of 1896 enzyme commission numbers, 1634 gene ontology terms and KEGG pathways for 1464 proteins. The initial analysis of the specialty genes within the genome revealed the presence of antibiotic-resistance genes. In addition, 50 and 10 genes were identified as potential drug targets (by DrugBank and TTD, respectively), 139 and 9 were identified as transporter genes (by TCDB and PATRIC_VF, respectively), and 14 (VFDB) and 40 (Victors) were identified as virulence factors. The annotated genome of A. baumannii IRMCBCU95U is displayed in Figure 3. The outer ring of the genome map displays the drug-resistance genes that were identified using a comprehensive antibiotic-resistance database (Figure 3, Figures S1 and S2). Based on the cgMLST analysis of the IRMCBCU95U genome and the A. baumannii reference genome, the sequence type (cgST) was determined to be ST436. Out of the 2133 loci examined, 631 alleles were called, which represent 29.58% of all the loci. When compared to the cgST, 624 alleles matched, indicating a 29.25% allele match with the cgST.

3.6. Genome Comparison

The genome alignment was performed using three A. baumannii strains: IRMCBCU95U, K09-14 and ATCC 19606. The analysis revealed the presence of specific genes from both the A. baumannii K09-14 and ATCC 19606 genomes within the IRMCBCU95U isolate genome (Figure 4). Specifically, the genes encoding FAD-dependent oxidoreductase and malonate-semialdehyde dehydrogenase (inositol) (EC 1.2.1.18) that are present in the A. baumannii K09-14 genome were also observed in the Acinetobacter IRMCBCU95U genome. However, these genes were absent in the A. baumannii ATCC 19606 genome. Genes in the A. baumannii ATCC 19606 genome, including those encoding a putative phage region, the phage repressor protein cI, a cold shock protein of the CSP family and the pdiff site/XerD/XerC-binding site, were found in the A. baumannii IRMCBCU95U genome but not in the A. baumannii K09-14 genome. The alignment revealed a mosaic pattern of the presence and absence of different genes across these strains, suggesting genetic diversity and potential horizontal gene transfer events.

3.7. Genome-Assisted Resistant Phenotype

Resistance Gene Identification
The resistome analysis of A. baumannii IRMCBCU95U revealed a genetic basis for its multidrug-resistant phenotype (Table 2). This clinical isolate harbors several acquired antimicrobial-resistance genes that confer resistance to multiple antibiotic classes. The armA gene was detected, which mediates resistance to aminoglycosides such as gentamicin, tobramycin, amikacin, isepamicin and netilmicin. The aph(3′)-Ia gene confers resistance to other aminoglycosides, including kanamycin, neomycin, lividomycin, paromomycin and ribostamycin. Beta-lactam resistance is attributed to the presence of blaTEM-1D, blaOXA-23, blaOXA-66, blaADC-25 and blaOXA-422 genes. BlaTEM-1D is associated with resistance to amoxicillin, ampicillin, piperacillin, ticarcillin and cephalothin. The presence of blaOXA-23 is particularly concerning as it confers resistance to carbapenems (imipenem and meropenem). The strain also carries msr(E) and mph(E) genes that contribute to macrolide resistance—specifically, to erythromycin and azithromycin—as well as resistance to streptogramin B antibiotics (quinupristin, pristinamycin Ia and virginiamycin S). The identification of these resistance genes aligns with the observed resistance phenotypes, confirming the genomic basis of the extensively drug-resistant profile of A. baumannii IRMCBCU95U (Table 2 and Table S5).
The IRMCBCU95U genome was also analyzed using KmerResistance-2.2 to identify antimicrobial-resistance genes (Table 3). The analysis revealed the presence of several genes associated with resistance to multiple classes of antibiotics. Genes conferring resistance to beta-lactams (blaOXA-23, blaOXA-66, blaTEM-1D and blaADC-25), aminoglycosides (aph(3′)-la and armA) and macrolides (msr(E) and mph(E)) were also detected using this method. The genome also showed significant similarity to the A. baumannii strain AB217-VUB. The findings using KmerResistance suggest that IRMCBCU95U is a multidrug-resistant strain and could pose a clinical challenge.

3.8. Virulence Factors

The BLASTN analysis using a virulence factor database revealed the presence of several genes in the IRMCBCU95U genome with high homology to known virulence factors. The top ten hits are listed in Table 4. A variety of virulence-associated genes were identified, including those involved in iron acquisition, motility and transcriptional regulation. Genes related to iron acquisition were prominently represented with several hits matching TonB-dependent receptors (VFG037497), porin family proteins (VFG037513), FecR domain-containing proteins (VFG037490) and transferrin-binding protein-like proteins (VFG037505). The identified factors are vital for the survival and proliferation of A. baumannii within a host. Iron is an essential nutrient and its availability is limited in hosts. Genes associated with Type IV pilus biogenesis and twitching motility (pilU (VFG050400) and pilT (VFG050386)) were also identified in the IRMCBCU95U genome. Type IV pili have significant roles in the adherence of bacteria to host cells and biofilm formation. The presence of genes encoding transcriptional regulators such as the LysR family transcriptional regulator (VFG037470) and the sigma-70 family RNA polymerase sigma factor (VFG037482) suggest that A. baumannii IRMCBCU95U possesses complex mechanisms that regulate the expression of virulence genes based on environmental signals. A gene involved in heme metabolism and biliverdin-producing heme oxygenase (hemO, VFG037529) was also identified in the A. baumannii IRMCBCU95U genome.

3.9. Mobile Elements in A. baumannii IRMCBCU95U

The genome of A. baumannii IRMCBCU95U was analyzed for the presence of mobile genetic elements (MGEs) using the MobileElementFinder tool. The analysis revealed a variety of insertion sequences (ISs) and composite transposons, highlighting the dynamic nature of its genome (Supplementary Table S6). Several distinct IS families were identified with the most prevalent being the IS4 family. Multiple copies of ISAba1 were found across different contigs. ISAba1 is known to be associated with the mobilization of antibiotic-resistance genes in A. baumannii. The results showed several entries for ISAba1 with varying positions in the contigs and a high sequence identity (generally > 99%) and coverage. Multiple copies of IS26 were also detected. IS26 is frequently associated with the formation of composite transposons and the spread of resistance genes. ISAba24, an insertion sequence belonging to the IS66 family, was also detected. The analysis also identified several putative composite transposons, which are formed when two IS elements flank a region of DNA. These are of particular concern due to their ability to mobilize larger DNA fragments, often including antibiotic-resistance genes. Composite transposons such as cn_3222_IS26, cn_5210_IS26, cn_2898_IS26, cn_45174_ISAba1, cn_11212_ISAba1, cn_20460_ISAba1 and cn_26891_ISAba1 were identified in the genome of A. baumannii strain IRMCBCU95U. The composite transposons cn_3222_IS26, cn_5210_IS26 and cn_2898_IS26 contain IS26 elements, while the composite transposons cn_45174_ISAba1, cn_11212_ISAba1, cn_20460_ISAba1 and cn_26891_ISAba1 contain ISAba1 elements.

3.10. Pathogenic Proteins

A. baumannii strain IRMCBCU95U was also analyzed using PathogenFinder 1 and PathogenFinder 2 to predict its pathogenic potential. The analysis from PathogenFinder 1 predicted Acinetobacter IRMCBCU95U to be a human pathogen (probability of 0.918). The input proteome coverage was 6.64% with 494 matched pathogenic families and zero matches with non-pathogenic families (Supplementary Table S7). Supplementary Table S7 details the identified genes associated with antimicrobial resistance and virulence determinants within the A. baumannii IRMCBCU95U genome, including the protein functions and genomic locations. While the table primarily focuses on gene identification and function, the protein ID and protein product of these genes is also provided to help understand the pathogenic potential of IRMCBCU95U. A total of 7437 sequences were analyzed with an average sequence length of 155.0 bp. The analysis identified several proteins with 100% identity to known proteins in other Acinetobacter strains including the exodeoxyribonuclease V beta chain, the two-component system sensor histidine kinase, an uncharacterized protein conserved in bacteria, an outer membrane component of the TAM transport system, an outer membrane autotransporter barrel domain, DNA internalization-related competence protein ComEC/Rec2, outer membrane receptor proteins, a predicted exporter, general secretion pathway protein D, an outer membrane receptor for monomeric catechols, an outer membrane receptor for ferric coprogen and ferric-rhodotorulic acid, a diguanylate cyclase/phosphodiesterase, a probable site-specific recombinase, a putative membrane protein, a Zn-dependent oligopeptidase, outer membrane vitamin B12 receptor BtuB, DNA primase DnaG, hypothetical proteins, a dipeptide ABC transporter, substrate-binding protein DppA, an uncharacterized siderophore S biosynthesis protein, an AcsD-like aminopeptidase N family protein, a poly (glycerol-phosphate) alpha-glucosyltransferase, a putative permease, a SAM-dependent methyltransferase, cytoskeleton protein RodZ, a putative threonine efflux protein, a lytic transglycosylase, transcriptional regulators, 5-formyltetrahydrofolate cyclo-ligase, outer membrane protein W precursor, UPF0301 protein YqgE, a conserved hypothetical protein, NADH ubiquinone oxidoreductase chain A, a predicted 3-hydroxylacyl-(acyl carrier protein) dehydratase, a signal peptide, protein GlcG, DNA-binding protein Fis, phospholipid ABC transporter-binding protein MlaB and Z-ring-associated protein ZapA. PathogenFinder 2 also predicted Acinetobacter IRMCBCU95U to be a human pathogen. The mean prediction score was 0.8737 ± 0.1079 based on the predictions from four neural networks: neural network 1 (0.8276), neural network 2 (0.7188), neural network 3 (0.9619) and neural network 4 (0.9863).

3.11. Mycotoxin-Producing Genes

The genome of IRMCBCU95U was analyzed using the ToxFinder-1.0 tool to identify potential mycotoxin-producing genes. The analysis revealed no significant hits for the genes involved in the biosynthesis of ochratoxin, fumonisin, trichothecene, citrinin, aflatoxin, ergot and patulin. This suggests that IRMCBCU95U does not possess the genetic potential to produce these mycotoxins.

3.12. Plasmid Analysis in IRMCBCU95U

The PlasmidFinder 2.0 analysis of the A. baumannii strain IRMCBCU95U genome revealed the presence of a plasmid fragment identified as IncFIB(pNDM-Mar) (Supplementary Table S8). This plasmid fragment showed a high identity (100%) over a query/template length of 439 bp. The 439 bp fragment in the IRMCBCU95U genome is identical to Klebsiella pneumoniae plasmid pNDM-MAR (GenBank accession number JN420336.1). This fragment was located on the plus strand at position 29,199 to 29,637. The 439 bp fragment in the IRMCBCU95U genome partially encodes a RepB family plasmid replication initiator protein, a protein that can initiate the replication of certain plasmids.

4. Discussion

The prevalence of multidrug-resistant A. baumannii is a significant concern across the nine countries of the Arabian Peninsula where it is frequently the most prevalent MDR bacteria, demonstrating its ability to persist in regions with hot and dry summers [13]. The initial identification of A. baumannii and other reports from various regions of the Kingdom of Saudi Arabia reported the presence of this pathogen in various human samples such as urine, blood cultures, respiratory specimens, skin and soft tissue specimens, sterile body fluids, wound swabs, rectal swabs and sputum [14,15,16]. Studies from Saudi Arabia have reported high mortality rates associated with MDR microbes, with A. baumannii, Mycobacterium tuberculosis and Pseudomonas aeruginosa being the top three organisms [13]. Analyses of A. baumannii strains isolated from Saudi Arabia have revealed the presence of diverse antimicrobial-resistance genes [52,53]. Core genome multilocus sequence typing (cgMLST) of the IRMCBCU95U isolate yielded a cgMLST436 profile, which is distinct from that of the previously characterized A. baumannii isolate AB-JZ67 (cgMLST785) from the same country [53] and other strains characterized that were as cgMLST785, cgMLST909, cgMLST1295, cgMLST906, cgMLST779, cgMLST556, cgMLST406 [54], cgMLST604, cgMLST45 and cgMLST2 [55].
The presence of multiple IS elements (ISAba1 and IS26) and composite transposons in the IRMCBCU95U genome is significant as the abundance of MGEs clearly indicates a high degree of genome plasticity in the IRMCBCU95U genome. This enables A. baumannii to rapidly adapt to changing environments. These MGEs and composite transposons are key factors in the accumulation of antibiotic-resistance genes in the IRMCBCU95U genome. There may be a high risk of horizontal transfer of resistance genes and antibiotic resistance spread due to the presence of MGEs in IRMCBCU95U. MGEs carry genes involved in virulence and could potentially contribute to the pathogenicity of IRMCBCU95U. An earlier study from a healthcare facility in the Western Region of Saudi Arabia that reported the detection of A. baumannii isolates, including the Ab27-HEnv isolate, and the present study on A. baumannii IRMCBCU95U highlight the significant presence of mobile genetic elements associated with antimicrobial resistance [56]. The previous study from Saudi Arabia identified seven MGEs in the Ab27-HEnv isolate, which are composed of five insertion sequences and two composite transposons with resistance genes for aminoglycosides (aph(6)-Id, aph(3″)-Ib, aph(3′)-Via and armA) and macrolides (mphE) and are present on the contigs with MGEs. IS26, ISVsa3, ISAba24 and ISAba26 were also identified in the clinical isolates of a Saudi strain from the previous study [56]. Similarly, the isolate studied in the present study (IRMCBCU95U) revealed a variety of insertion sequences and composite transposons in its genome, confirming the resistance A. baumannii strains. Insertion sequences IS26 and ISAba24 that were found in a previously reported isolate (Ab27-HEnv) were also detected in IRMCBCU95U. ISAba1 is associated with the mobilization of antibiotic-resistance genes in A. baumannii; there were multiple copies of this sequence in different contigs from the IRMCBCU95U genome. The prevalence of the IS4 family and the identification of multiple copies of ISAba1 and IS26 in the present study suggest that these elements may play a particularly important role in genome plasticity and resistance gene dissemination for the IRMCBCU95U strain. Consistent with the observations in a previous study from a healthcare facility in the Western Region of Saudi Arabia, the present study conducted in the Eastern Region of Saudi Arabia also detected the aminoglycoside-resistance genes aph(3′)-Ia and armA in an A. baumannii strain. This concordance underscores the importance of nationwide genomic surveillance in Saudi Arabia. A significant finding in the two studies was the identification of composite transposons that are capable of mobilizing larger DNA fragments containing antibiotic-resistance genes. The presence of both IS26- and ISAba1-containing composite transposons strengthens the importance of IS elements in the spread of resistance genes in Saudi Arabia [54,56]. The identification of similar MGEs and the prevalence of ISAba1 suggest common mechanisms of resistance genes and their spread in this species.
Isolates from a neighboring country (Yemen) contain naturally occurring blaOXA-51 and carbapenemase-encoding blaOXA-23 genes. An A. baumannii isolate was reported to have the acetyltransferase aac(6′-Ib) gene associated with aminoglycoside resistance. All isolates from the study had the fluoroquinolone-resistance-associated GyrA gene with a Ser83Leu substitution [57]. These regional findings align with the comparative genomic analysis of IRMCBCU95U that revealed significant genetic heterogeneity and mosaicism among A. baumannii strains. The hybrid genome observed in the studied IRMCBCU95U isolate contains genes from both K09-14 and ATCC 19606, indicating the importance of horizontal gene transfer during the evolution of A. baumannii. The detection of diverse antimicrobial-resistance genes in IRMCBCU95U is consistent with the extensive resistance profiles reported in clinical isolates from Yemen, which may be contributing to the challenges in treating A. baumannii infections in the region [13,57].
The genome alignment revealed a mosaic pattern of the presence and absence of difference genes across the K09-14, ATCC 19606 and IRMCBCU95U strains, suggesting genetic diversity and potential horizontal gene transfer events. The hybrid genome of the IRMCBCU95U strain contains genes specific to both K09-14 and ATCC 19606, indicating that IRMCBCU95U has acquired genetic material from both strains. IRMCBCU95U shares genes encoding FAD-dependent oxidoreductase and malonate-semialdehyde dehydrogenase (inositol) with K09-14 but these are absent in ATCC 19606, suggesting potential differences in metabolic capabilities between the strains. IRMCBCU95U also shares genes related to a putative phage repressor protein cI, cold shock protein (CSP family) and the pdiff site/XerD/XerC-binding site with ATCC 19606 which are absent in K09-14.
The identification of a broad range of virulence-associated genes in IRMCBCU95U using the VFDB database provides valuable insights into the mechanisms underlying its pathogenicity [47]. The presence of multiple iron’ systems supports its survival and pathogenesis. Similarly, the genes related to pilus formation contribute to host cell interactions and biofilm development. The identification of transcriptional regulators emphasizes the adaptability of A. baumannii to different host environments. It is important to note that this in silico analysis only provides a prediction of potential virulence factors. Further experimental studies are necessary to confirm the expression and functional roles of these genes in the pathogenesis of A. baumannii IRMCBCU95U. The genome data of IRMCBCU95U lay a solid foundation for future investigations into the molecular mechanisms of virulence in this clinically relevant isolate.
The KmerResistance analysis of the IRMCBCU95U genome provides evidence for its potential multidrug-resistant nature, which was reported in earlier studies [46,58]. The identification of several key antimicrobial-resistance genes highlights the ability of IRMCBCU95U to withstand the effects of multiple antibiotics and has significant implications for its clinical management and potential spread. The detection of beta-lactamase genes including blaOXA-23, blaOXA-66, blaTEM-1D and blaADC-25 are particularly concerning for Saudi Arabia [54,56]. The blaOXA genes frequently found in A. baumannii are associated with resistance to carbapenems. The identification of the aminoglycoside-resistance genes aph(3′)-la and armA indicate that IRMCBCU95U is resistant to aminoglycoside antibiotics, which was observed in a previous study in Saudi Arabia [56]. The presence of the macrolide-resistance genes msr(E) and mph(E) adds another layer of complexity to IRMCBCU95U. The high similarity of the A. baumannii IRMCBCU95U and AB217-VUB genomes highlights the importance of further research. A. baumannii is believed to be an opportunistic pathogen and is known for its ability to acquire and spread resistance genes.
Cold shock proteins are involved in stress responses and pdiff/XerD/XerC sites are involved in recombination. The sharing of genes between IRMCBCU95U and the other two strains (K09-14 and ATCC 19606) indicate that they have the ability to rapidly acquire new traits, potentially contributing to their antibiotic resistance and adaptation to different environments. The identification of the IncFIB(pNDM-Mar) plasmid fragment in A. baumannii IRMCBCU95U is important. IncFIB plasmids are commonly found in Enterobacteriaceae and are known to be involved in the dissemination of antimicrobial-resistance genes, including those encoding carbapenemases such as NDM (New Delhi Metallo-beta-lactamase) [59,60,61]. The presence of this plasmid fragment suggests that A. baumannii IRMCBCU95U might have the potential to acquire the resistance genes associated with this type of plasmid. It is important to note that the identified fragment of the IncFIB(pNDM-Mar) plasmid may have been partially integrated into the chromosome and suggests interspecies horizontal gene transfer from K. pneumoniae to A. baumannii in clinical settings. This gene transfer highlights an epidemiological concern regarding resistance genes, as they can readily cross species barriers, accelerating the spread of MDR and creating a hospital resistance reservoir. Understanding these transfers is crucial for targeted interventions against pan-drug-resistant bacteria. Further investigation such as Sanger sequencing-based assays are needed to determine the complete structure and context of this plasmid. Finding an IncFIB-related sequence from Enterobacteriaceae in A. baumannii highlights the importance of horizontal gene transfer in the evolution of antimicrobial resistance across different bacterial species.

5. Conclusions

The comparative genomic analysis of A. baumannii strains K09-14, ATCC 19606 and IRMCBCU95U revealed significant genetic heterogeneity and mosaicism. The studied isolate IRMCBCU95U was found to have a hybrid genome with genes from K09-14 and ATCC 19606, indicating a history of horizontal gene transfers which could potentially influence clinically relevant traits. The resistance gene analysis found that IRMCBCU95U possesses antimicrobial-resistance genes that confer resistance to multiple antibiotic classes. These findings indicate the importance of comprehensive surveillance and characterization of resistance factors in the bacterial pathogens that are prevalent in Saudi Arabian hospital settings to guide the selection of effective treatment strategies and prevent the spread of antimicrobial resistance. Further studies on phenotypic-resistance testing in correlation with detailed genomic analysis are necessary to fully elucidate the resistance mechanisms and the clinical significance. The genome of A. baumannii IRMCBCU95U exhibits a diverse set of mobile genetic elements and insertion sequences. The presence of specific MGEs (ISAba1 and IS26) associated with resistance genes highlight the importance of genomic surveillance for monitoring the emergence and spread of multidrug-resistant A. baumannii strains. Several potential virulence-associated genes in the A. baumannii IRMCBCU95U genome provide valuable insights into the genetic determinants of its pathogenicity, including iron acquisition, motility and transcriptional regulation. Further research is necessary to elucidate the mechanisms and functional consequences of the observed genetic exchange. The presence of pathogenic proteins confirmed the pathogenicity of Acinetobacter IRMCBCU95U, and it was predicted to be a human pathogen. The identification of an IncFIB(pNDM-Mar) plasmid fragment in A. baumannii IRMCBCU95U emphasizes the importance of horizontal gene transfer in the spread of antimicrobial resistance across bacterial species and suggests that this strain may possess the capacity to acquire resistance genes. However, further investigation is needed to elucidate the complete structure and context of this plasmid within the genome.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life15071094/s1. Figure S1: Genome map of Acinetobacter IRMCBCU95U genome, with selected genomic regions projected to indicate the location of mutated genes; Figure S2. Acinetobacter IRMCBCU95U Genome map with selected genomic regions indicating the location of mutated genes associated with Mupirocin (Top panel), amoxicillin (Middle panel) and rifampicin (Bottom panel); Table S1: Comprehensive overview of the quality of the sequencing data generated from the IRMCBCU95U sample using the NanoForms platform.; Table S2: Assembly statistics for the IRMCBCU95U genome; Table S3: List of genomes used for constructing phylogenetic tree of whole genome of IRMCBCU95U; Table S4: Genome tree analysis statistics for constructing phylogenetic tree of whole genome of IRMCBCU95U; Table S5: Genome-predicted resistance phenotype in extensively drug-resistant Acinetobacter baumannii IRMCBCU95U; Table S6: List of mobile elements identified in extensively drug-resistant Acinetobacter baumannii IRMCBCU95U; Table S7: Pathogenic Proteins identified in the genome IRMCBCU95U; Table S8: Fragment of plasmid sequence identified in IRMCBCU95U.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Institutional Review Board (IRB) of Imam Abdulrahman Bin Faisal University (IRB approval number: IRB-2022-13-462, 15 November 2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors thank Noor B. Almandil and Sayed AbdulAzeez for their critical review of the manuscript. The authors thank Ranilo M. Tumbaga, Horace T. Pacifico, Reem AlJindan, Nehal Mahmoud, Sarah Almofty and Dana Almohazey for their support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shi, J.; Cheng, J.; Liu, S.; Zhu, Y.; Zhu, M. Acinetobacter baumannii: An Evolving and Cunning Opponent. Front. Microbiol. 2024, 15, 1332108. [Google Scholar] [CrossRef] [PubMed]
  2. Howard, A.; O’Donoghue, M.; Feeney, A.; Sleator, R.D. Acinetobacter baumannii: An Emerging Opportunistic Pathogen. Virulence 2012, 3, 243–250. [Google Scholar] [CrossRef]
  3. Harding, C.M.; Hennon, S.W.; Feldman, M.F. Uncovering the Mechanisms of Acinetobacter baumannii Virulence. Nat. Rev. Microbiol. 2018, 16, 91–102. [Google Scholar] [CrossRef]
  4. Arshad, N.; Azzam, W.; Zilberberg, M.D.; Shorr, A.F. Acinetobacter baumannii Complex Infections: New Treatment Options in the Antibiotic Pipeline. Microorganisms 2025, 13, 356. [Google Scholar] [CrossRef]
  5. Zhang, R.; Li, D.; Fang, H.; Xie, Q.; Tang, H.; Chen, L. Iron-Dependent Mechanisms in Acinetobacter baumannii: Pathogenicity and Resistance. JAC Antimicrob. Resist. 2025, 7, dlaf039. [Google Scholar] [CrossRef]
  6. Sebeny, P.J.; Riddle, M.S.; Petersen, K. Acinetobacter baumannii Skin and Soft-Tissue Infection Associated with War Trauma. Clin. Infect. Dis. 2008, 47, 444–449. [Google Scholar] [CrossRef]
  7. Bassetti, M.; Ginocchio, F.; Mikulska, M. New Treatment Options against Gram-Negative Organisms. Crit. Care 2011, 15, 215. [Google Scholar] [CrossRef] [PubMed]
  8. Rice, L.B. Federal Funding for the Study of Antimicrobial Resistance in Nosocomial Pathogens: No ESKAPE. J. Infect. Dis. 2008, 197, 1079–1081. [Google Scholar] [CrossRef] [PubMed]
  9. Fournier, P.-E.; Vallenet, D.; Barbe, V.; Audic, S.; Ogata, H.; Poirel, L.; Richet, H.; Robert, C.; Mangenot, S.; Abergel, C.; et al. Comparative Genomics of Multidrug Resistance in Acinetobacter baumannii. PLoS Genet. 2006, 2, e7. [Google Scholar] [CrossRef]
  10. Iovleva, A.; Fowler, V.G.; Doi, Y. Treatment Approaches for Carbapenem-Resistant Acinetobacter baumannii Infections. Drugs 2025, 85, 21–40. [Google Scholar] [CrossRef]
  11. Wright, M.S.; Iovleva, A.; Jacobs, M.R.; Bonomo, R.A.; Adams, M.D. Genome Dynamics of Multidrug-Resistant Acinetobacter baumannii during Infection and Treatment. Genome Med. 2016, 8, 26. [Google Scholar] [CrossRef] [PubMed]
  12. Chan, A.P.; Sutton, G.; DePew, J.; Krishnakumar, R.; Choi, Y.; Huang, X.-Z.; Beck, E.; Harkins, D.M.; Kim, M.; Lesho, E.P.; et al. A Novel Method of Consensus Pan-Chromosome Assembly and Large-Scale Comparative Analysis Reveal the Highly Flexible Pan-Genome of Acinetobacter baumannii. Genome Biol. 2015, 16, 143. [Google Scholar] [CrossRef]
  13. Borgio, J.F.; Rasdan, A.S.; Sonbol, B.; Alhamid, G.; Almandil, N.B.; AbdulAzeez, S. Emerging Status of Multidrug-Resistant Bacteria and Fungi in the Arabian Peninsula. Biology 2021, 10, 1144. [Google Scholar] [CrossRef]
  14. Alamri, A.M.; Alsultan, A.A.; Ansari, M.A.; Alnimr, A.M. Biofilm-Formation in Clonally Unrelated Multidrug-Resistant Acinetobacter baumannii Isolates. Pathogens 2020, 9, 630. [Google Scholar] [CrossRef]
  15. Al-Anazi, K.A.; Abdalhamid, B.; Alshibani, Z.; Awad, K.; Alzayed, A.; Hassan, H.; Alsayiegh, M. Acinetobacter baumannii Septicemia in a Recipient of an Allogeneic Hematopoietic Stem Cell Transplantation. Case Rep. Transpl. 2012, 2012, 646195. [Google Scholar] [CrossRef]
  16. Aljindan, R.; Elhadi, N. Genetic Relationship of Multi-Resistant Acinetobacter baumannii Isolates in Kingdom of Saudi Arabia. J. Pure Appl. Microbiol. 2018, 12, 1951–1958. [Google Scholar] [CrossRef]
  17. Bhuiyan, M.S.; Ellett, F.; Murray, G.L.; Kostoulias, X.; Cerqueira, G.M.; Schulze, K.E.; Mahamad Maifiah, M.H.; Li, J.; Creek, D.J.; Lieschke, G.J.; et al. Acinetobacter baumannii Phenylacetic Acid Metabolism Influences Infection Outcome through a Direct Effect on Neutrophil Chemotaxis. Proc. Natl. Acad. Sci. USA 2016, 113, 9599–9604. [Google Scholar] [CrossRef] [PubMed]
  18. García-Quintanilla, M.; Pulido, M.R.; López-Rojas, R.; Pachón, J.; McConnell, M.J. Emerging Therapies for Multidrug Resistant Acinetobacter baumannii. Trends Microbiol. 2013, 21, 157–163. [Google Scholar] [CrossRef]
  19. Jacobs, A.C.; Thompson, M.G.; Black, C.C.; Kessler, J.L.; Clark, L.P.; McQueary, C.N.; Gancz, H.Y.; Corey, B.W.; Moon, J.K.; Si, Y.; et al. AB5075, a Highly Virulent Isolate of Acinetobacter baumannii, as a Model Strain for the Evaluation of Pathogenesis and Antimicrobial Treatments. mBio 2014, 5, e01076-14. [Google Scholar] [CrossRef]
  20. AlJindan, R.; Mahmoud, N.; AlEraky, D.M.; Almandil, N.B.; AbdulAzeez, S.; Borgio, J.F. Phenomics and Genomic Features of Enterococcus Avium IRMC1622a Isolated from a Clinical Sample of Hospitalized Patient. J. Infect. Public Health 2024, 17, 102463. [Google Scholar] [CrossRef]
  21. Czmil, A.; Wronski, M.; Czmil, S.; Sochacka-Pietal, M.; Cmil, M.; Gawor, J.; Wołkowicz, T.; Plewczynski, D.; Strzalka, D.; Pietal, M. NanoForms: An Integrated Server for Processing, Analysis and Assembly of Raw Sequencing Data of Microbial Genomes, from Oxford Nanopore Technology. PeerJ 2022, 10, e13056. [Google Scholar] [CrossRef] [PubMed]
  22. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. Fastp: An Ultra-Fast All-in-One FASTQ Preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  23. De Coster, W.; D’Hert, S.; Schultz, D.T.; Cruts, M.; Van Broeckhoven, C. NanoPack: Visualizing and Processing Long-Read Sequencing Data. Bioinformatics 2018, 34, 2666–2669. [Google Scholar] [CrossRef]
  24. Mikheenko, A.; Prjibelski, A.; Saveliev, V.; Antipov, D.; Gurevich, A. Versatile Genome Assembly Evaluation with QUAST-LG. Bioinformatics 2018, 34, i142–i150. [Google Scholar] [CrossRef]
  25. Lin, Y.; Yuan, J.; Kolmogorov, M.; Shen, M.W.; Chaisson, M.; Pevzner, P.A. Assembly of Long Error-Prone Reads Using de Bruijn Graphs. Proc. Natl. Acad. Sci. USA 2016, 113, E8396–E8405. [Google Scholar] [CrossRef] [PubMed]
  26. Wattam, A.R.; Davis, J.J.; Assaf, R.; Boisvert, S.; Brettin, T.; Bun, C.; Conrad, N.; Dietrich, E.M.; Disz, T.; Gabbard, J.L.; et al. Improvements to PATRIC, the All-Bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res. 2017, 45, D535–D542. [Google Scholar] [CrossRef]
  27. de Koning, W.; Miladi, M.; Hiltemann, S.; Heikema, A.; Hays, J.P.; Flemming, S.; van den Beek, M.; Mustafa, D.A.; Backofen, R.; Grüning, B.; et al. NanoGalaxy: Nanopore Long-Read Sequencing Data Analysis in Galaxy. Gigascience 2020, 9, giaa105. [Google Scholar] [CrossRef]
  28. AlJindan, R.; AlEraky, D.M.; Farhat, M.; Almandil, N.B.; AbdulAzeez, S.; Borgio, J.F. Genomic Insights into Virulence Factors and Multi-Drug Resistance in Clostridium Perfringens IRMC2505A. Toxins 2023, 15, 359. [Google Scholar] [CrossRef]
  29. Borgio, J.F.; Alhujaily, R.; Alquwaie, R.; Alabdullah, M.J.; AlHasani, E.; Alothman, W.; Alaqeel, R.K.; Alfaraj, A.S.; Kaabi, A.; Alhur, N.F.; et al. Mining the Nanotube-Forming Bacillus Amyloliquefaciens MR14M3 Genome for Determining Anti-Candida Auris and Anti-Candida Albicans Potential by Pathogenicity and Comparative Genomics Analysis. Comput. Struct. Biotechnol. J. 2023, 21, 4261–4276. [Google Scholar] [CrossRef]
  30. Schomburg, I.; Chang, A.; Ebeling, C.; Gremse, M.; Heldt, C.; Huhn, G.; Schomburg, D. BRENDA, the Enzyme Database: Updates and Major New Developments. Nucleic Acids Res. 2004, 32, D431–D433. [Google Scholar] [CrossRef]
  31. Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a Reference Resource for Gene and Protein Annotation. Nucleic Acids Res. 2016, 44, D457–D462. [Google Scholar] [CrossRef] [PubMed]
  32. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene Ontology: Tool for the Unification of Biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25–29. [Google Scholar] [CrossRef]
  33. Davis, J.J.; Gerdes, S.; Olsen, G.J.; Olson, R.; Pusch, G.D.; Shukla, M.; Vonstein, V.; Wattam, A.R.; Yoo, H. PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database. Front. Microbiol. 2016, 7, 118. [Google Scholar] [CrossRef] [PubMed]
  34. Overbeek, R.; Begley, T.; Butler, R.M.; Choudhuri, J.V.; Chuang, H.-Y.; Cohoon, M.; de Crécy-Lagard, V.; Diaz, N.; Disz, T.; Edwards, R.; et al. The Subsystems Approach to Genome Annotation and Its Use in the Project to Annotate 1000 Genomes. Nucleic Acids Res. 2005, 33, 5691–5702. [Google Scholar] [CrossRef]
  35. McArthur, A.G.; Waglechner, N.; Nizam, F.; Yan, A.; Azad, M.A.; Baylay, A.J.; Bhullar, K.; Canova, M.J.; De Pascale, G.; Ejim, L.; et al. The Comprehensive Antibiotic Resistance Database. Antimicrob. Agents Chemother. 2013, 57, 3348–3357. [Google Scholar] [CrossRef]
  36. Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; et al. DrugBank 4.0: Shedding New Light on Drug Metabolism. Nucleic Acids Res. 2014, 42, D1091–D1097. [Google Scholar] [CrossRef]
  37. Zhu, F.; Han, B.; Kumar, P.; Liu, X.; Ma, X.; Wei, X.; Huang, L.; Guo, Y.; Han, L.; Zheng, C.; et al. Update of TTD: Therapeutic Target Database. Nucleic Acids Res. 2010, 38, D787–D791. [Google Scholar] [CrossRef] [PubMed]
  38. Chen, L.; Zheng, D.; Liu, B.; Yang, J.; Jin, Q. VFDB 2016: Hierarchical and Refined Dataset for Big Data Analysis--10 Years On. Nucleic Acids Res. 2016, 44, D694–D697. [Google Scholar] [CrossRef]
  39. Mao, C.; Abraham, D.; Wattam, A.R.; Wilson, M.J.C.; Shukla, M.; Yoo, H.S.; Sobral, B.W. Curation, Integration and Visualization of Bacterial Virulence Factors in PATRIC. Bioinformatics 2015, 31, 252–258. [Google Scholar] [CrossRef]
  40. Saier, M.H.; Reddy, V.S.; Tsu, B.V.; Ahmed, M.S.; Li, C.; Moreno-Hagelsieb, G. The Transporter Classification Database (TCDB): Recent Advances. Nucleic Acids Res. 2016, 44, D372–D379. [Google Scholar] [CrossRef]
  41. Carattoli, A.; Zankari, E.; García-Fernández, A.; Voldby Larsen, M.; Lund, O.; Villa, L.; Møller Aarestrup, F.; Hasman, H. In Silico Detection and Typing of Plasmids Using PlasmidFinder and Plasmid Multilocus Sequence Typing. Antimicrob. Agents Chemother. 2014, 58, 3895–3903. [Google Scholar] [CrossRef] [PubMed]
  42. Ondov, B.D.; Treangen, T.J.; Melsted, P.; Mallonee, A.B.; Bergman, N.H.; Koren, S.; Phillippy, A.M. Mash: Fast Genome and Metagenome Distance Estimation Using MinHash. Genome Biol. 2016, 17, 132. [Google Scholar] [CrossRef]
  43. Edgar, R.C. MUSCLE: Multiple Sequence Alignment with High Accuracy and High Throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef]
  44. Stamatakis, A. RAxML Version 8: A Tool for Phylogenetic Analysis and Post-Analysis of Large Phylogenies. Bioinformatics 2014, 30, 1312–1313. [Google Scholar] [CrossRef]
  45. Stamatakis, A.; Hoover, P.; Rougemont, J. A Rapid Bootstrap Algorithm for the RAxML Web Servers. Syst. Biol. 2008, 57, 758–771. [Google Scholar] [CrossRef] [PubMed]
  46. Clausen, P.T.L.C.; Aarestrup, F.M.; Lund, O. Rapid and Precise Alignment of Raw Reads against Redundant Databases with KMA. BMC Bioinform. 2018, 19, 307. [Google Scholar] [CrossRef]
  47. Zhou, S.; Liu, B.; Zheng, D.; Chen, L.; Yang, J. VFDB 2025: An Integrated Resource for Exploring Anti-Virulence Compounds. Nucleic Acids Res. 2025, 53, D871–D877. [Google Scholar] [CrossRef] [PubMed]
  48. Hasman, H.; Clausen, P.T.L.C.; Kaya, H.; Hansen, F.; Knudsen, J.D.; Wang, M.; Holzknecht, B.J.; Samulioniené, J.; Røder, B.L.; Frimodt-Møller, N.; et al. LRE-Finder, a Web Tool for Detection of the 23S rRNA Mutations and the optrA, Cfr, Cfr(B) and poxtA Genes Encoding Linezolid Resistance in Enterococci from Whole-Genome Sequences. J. Antimicrob. Chemother. 2019, 74, 1473–1476. [Google Scholar] [CrossRef]
  49. 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, e77302. [Google Scholar] [CrossRef]
  50. Johansson, M.H.K.; Bortolaia, V.; Tansirichaiya, S.; Aarestrup, F.M.; Roberts, A.P.; Petersen, T.N. Detection of Mobile Genetic Elements Associated with Antibiotic Resistance in Salmonella Enterica Using a Newly Developed Web Tool: MobileElementFinder. J. Antimicrob. Chemother. 2021, 76, 101–109. [Google Scholar] [CrossRef]
  51. Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and Applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef] [PubMed]
  52. Yasir, M.; Shah, M.W.; Jiman-Fatani, A.A.; Almasaudi, S.B.; Ahmad, H.; Alawi, M.; Azhar, E.I. Draft Genome Sequence of a Clinical Acinetobacter baumannii Isolate of New Sequence Type ST1688 from Saudi Arabia. J. Glob. Antimicrob. Resist. 2019, 18, 151–152. [Google Scholar] [CrossRef]
  53. Al-Zahrani, I.A.; Brek, T.M. Comprehensive Genome Analysis of Colistin-Only-Sensitive KPC-2 and NDM1-1-Coproducing Klebsiella pneumoniae ST11 and Acinetobacter baumannii ST2 From a Critically Ill Patient With COVID-19 in Saudi Arabia: Whole Genome Sequencing (WGS) of K. pneumoniae ST11 and A. baumannii ST2. Int. J. Microbiol. 2024, 2024, 9233075. [Google Scholar] [CrossRef]
  54. Brek, T.M.; Muhajir, A.A.; Alkuwaity, K.K.; Haddad, M.A.; Alattas, E.M.; Eisa, Z.M.; Al-Thaqafy, M.S.; Albarraq, A.M.; Al-Zahrani, I.A. Genomic Insights of Predominant International High-Risk Clone ST2 Acinetobacter baumannii Isolates in Saudi Arabia. J. Glob. Antimicrob. Resist. 2025, 42, 243–252. [Google Scholar] [CrossRef] [PubMed]
  55. Okdah, L.; AlDosary, M.S.; AlMazyed, A.; Alkhurayb, H.M.; Almossallam, M.; Al Obaisi, Y.S.; Marie, M.A.; Abdelrahman, T.; Diene, S.M. Genomic Characterization of Colistin-Resistant Isolates from the King Fahad Medical City, Kingdom of Saudi Arabia. Antibiotics 2022, 11, 1597. [Google Scholar] [CrossRef]
  56. Yasir, M.; Subahi, A.M.; Shukri, H.A.; Bibi, F.; Sohrab, S.S.; Alawi, M.; Sindi, A.A.; Jiman-Fatani, A.A.; Azhar, E.I. Bacterial Community and Genomic Analysis of Carbapenem-Resistant Acinetobacter baumannii Isolates from the Environment of a Health Care Facility in the Western Region of Saudi Arabia. Pharmaceuticals 2022, 15, 611. [Google Scholar] [CrossRef] [PubMed]
  57. Bakour, S.; Alsharapy, S.A.; Touati, A.; Rolain, J.-M. Characterization of Acinetobacter baumannii Clinical Isolates Carrying Bla(OXA-23) Carbapenemase and 16S rRNA Methylase armA Genes in Yemen. Microb. Drug Resist. 2014, 20, 604–609. [Google Scholar] [CrossRef]
  58. Clausen, P.T.L.C.; Zankari, E.; Aarestrup, F.M.; Lund, O. Benchmarking of Methods for Identification of Antimicrobial Resistance Genes in Bacterial Whole Genome Data. J. Antimicrob. Chemother. 2016, 71, 2484–2488. [Google Scholar] [CrossRef]
  59. Shankar, C.; Vasudevan, K.; Jacob, J.J.; Baker, S.; Isaac, B.J.; Neeravi, A.R.; Sethuvel, D.P.M.; George, B.; Veeraraghavan, B. Hybrid Plasmids Encoding Antimicrobial Resistance and Virulence Traits Among Hypervirulent Klebsiella pneumoniae ST2096 in India. Front. Cell. Infect. Microbiol. 2022, 12, 875116. [Google Scholar] [CrossRef]
  60. Zeng, Z.; Lei, L.; Li, L.; Hua, S.; Li, W.; Zhang, L.; Lin, Q.; Zheng, Z.; Yang, J.; Dou, X.; et al. In Silico Characterization of blaNDM-Harboring Plasmids in Klebsiella pneumoniae. Front. Microbiol. 2022, 13, 1008905. [Google Scholar] [CrossRef]
  61. Fomda, B.A.; Khan, A.; Zahoor, D. NDM-1 (New Delhi Metallo Beta Lactamase-1) Producing Gram-Negative Bacilli: Emergence & Clinical Implications. Indian J. Med. Res. 2014, 140, 672–678. [Google Scholar] [PubMed]
Life 15 01094 g001
Figure 1. Characterization of Nanopore read lengths from the A. baumannii IRMCBCU95U sequencing run. (A) Frequency distribution of different read lengths from the IRMCBCU95U sequencing run (logarithmic scale), highlighting the range and most common lengths of the raw reads. (B) Total number of bases contributed by reads with a specific length, providing insight into the overall IRMCBCU95U sequencing yield for different read sizes. (C) Total base yield per read length of the IRMCBCU95U sequencing run, with the x-axis transformed logarithmically to better visualize the distribution across a wide range of read lengths. (D) Proportion of the total sequencing yield (bases) as a function of increasing minimum read length of the IRMCBCU95U sequencing run, indicating how many data were retained when filtering for longer reads.
Figure 1. Characterization of Nanopore read lengths from the A. baumannii IRMCBCU95U sequencing run. (A) Frequency distribution of different read lengths from the IRMCBCU95U sequencing run (logarithmic scale), highlighting the range and most common lengths of the raw reads. (B) Total number of bases contributed by reads with a specific length, providing insight into the overall IRMCBCU95U sequencing yield for different read sizes. (C) Total base yield per read length of the IRMCBCU95U sequencing run, with the x-axis transformed logarithmically to better visualize the distribution across a wide range of read lengths. (D) Proportion of the total sequencing yield (bases) as a function of increasing minimum read length of the IRMCBCU95U sequencing run, indicating how many data were retained when filtering for longer reads.
Life 15 01094 g001
Life 15 01094 g002
Figure 2. Phylogenetic tree based on whole genomes of Acinetobacter IRMCBCU95U (pointed with blue square) and 98 additional Acinetobacter strains (see Supplementary Table S3 for a comprehensive list of the genomes and corresponding strain designations). The alignment of 100 core single-copy orthologous genes comprising 31,071 amino acid residues (equivalent to 93,213 nucleotide bases) was generated using the MAFFT multiple sequence alignment program (Supplementary Table S4). The resulting alignment served as the basis for the phylogenetic tree construction employing the randomized accelerated maximum likelihood algorithm with rapid bootstrap analysis to assess branch support.
Figure 2. Phylogenetic tree based on whole genomes of Acinetobacter IRMCBCU95U (pointed with blue square) and 98 additional Acinetobacter strains (see Supplementary Table S3 for a comprehensive list of the genomes and corresponding strain designations). The alignment of 100 core single-copy orthologous genes comprising 31,071 amino acid residues (equivalent to 93,213 nucleotide bases) was generated using the MAFFT multiple sequence alignment program (Supplementary Table S4). The resulting alignment served as the basis for the phylogenetic tree construction employing the randomized accelerated maximum likelihood algorithm with rapid bootstrap analysis to assess branch support.
Life 15 01094 g002
Life 15 01094 g003
Figure 3. Graphical representation of the annotated A. baumannii IRMCBCU95U genome. The outermost ring illustrates the drug-resistance genes identified through the Comprehensive Antibiotic Resistance Database (CARD). Top panel: circular map of the A. baumannii IRMCBCU95U genome. Bottom panel: selected regions of the Acinetobacter IRMCBCU95U genome projected to indicate the location of mutated genes.
Figure 3. Graphical representation of the annotated A. baumannii IRMCBCU95U genome. The outermost ring illustrates the drug-resistance genes identified through the Comprehensive Antibiotic Resistance Database (CARD). Top panel: circular map of the A. baumannii IRMCBCU95U genome. Bottom panel: selected regions of the Acinetobacter IRMCBCU95U genome projected to indicate the location of mutated genes.
Life 15 01094 g003
Life 15 01094 g004
Figure 4. Genome alignment of A. baumannii IRMCBCU95U, K09-14 and ATCC 19606.
Figure 4. Genome alignment of A. baumannii IRMCBCU95U, K09-14 and ATCC 19606.
Life 15 01094 g004
Table 1. Antibiotic susceptibility profile of extensively drug-resistant A. baumannii IRMCBCU95U.
Table 1. Antibiotic susceptibility profile of extensively drug-resistant A. baumannii IRMCBCU95U.
AntibioticInterpretationMIC (µg/mL)
CefepimeResistantNaN
CeftazidimeResistantNaN
CiprofloxacinResistantNaN
ImipenemResistantNaN
MeropenemResistantNaN
Piperacillin/TazobactamResistantNaN
TigecyclineSensitive2.0
Trimethoprim/SulfamethoxazoleSensitiveNaN
MIC: minimum inhibitory concentration; NaN: MIC value was not determined.
Table 2. Acquired AMR genes identified in A. baumannii IRMCBCU95U.
Table 2. Acquired AMR genes identified in A. baumannii IRMCBCU95U.
Resistance GeneIdentity (%)Alignment LengthPosition in ReferenceContig or DepthPosition in ContigResistant PhenotypeNotes
aph(3′)-Ia99.518181...816contig_25_segment0 contig_25:1.0-336501.085910...86726(‘kanamycin’, ‘neomycin’, ‘lividomycin’, ‘paromomycin’, ‘ribostamycin’)
armA99.357741...772contig_5_segment0 contig_5:1.0-977170.0774608...775381(‘gentamicin’, ‘tobramycin’, ‘amikacin’, ‘isepamicin’, ‘netilmicin’)
blaOXA-6699.768251...824contig_2_segment0 contig_2:1.0-102924.085778...86602(‘unknown beta-lactam’)Class D; OXA-51-like; naturally occurring in A. baumannii
blaTEM-1D99.778631...861contig_25_segment0 contig_25:1.0-336501.079447...80309(‘amoxicillin’, ‘ampicillin’, ‘piperacillin’, ‘ticarcillin’, ‘cephalothin’)Class A
blaADC-2599.5711531...1152contig_5_segment2 contig_5:979486.0-1449651.020168...21319(‘unknown beta-lactam’)Chromosomal
blaOXA-2399.888221...822contig_6_segment1 contig_6:253978.0-804087.0537440...538260(‘imipenem’, ‘meropenem’)Class D; OXA-23-like; naturally occurring in Acinetobacter radioresistens; alternative name: blaARI-1
blaOXA-42299.888221...822contig_6_segment1 contig_6:253978.0-804087.0537440...538260(‘unknown beta-lactam’)Class D; OXA-23-like; natural occurring in A. baumannii
msr(E)99.8014771...1476contig_5_segment0 contig_5:1.0-977170.0770829...772303(‘erythromycin’, ‘azithromycin’, ‘quinupristin’, ‘pristinamycin ia’, ‘virginiamycin s’)ABC transporter
mph(E)99.778861...885contig_5_segment0 contig_5:1.0-977170.0769889...770773(‘erythromycin’)Macrolide phosphotransferase
Table 3. Antimicrobial-resistance genes in IRMCBCU95U genome associated with resistance to multiple classes of antibiotics.
Table 3. Antimicrobial-resistance genes in IRMCBCU95U genome associated with resistance to multiple classes of antibiotics.
TemplateScoreTemplate LengthQ Valuep ValueTemplate CoverageQuery CoverageDepthDepth Corr
NZ_CP091346.1 A. baumannii strain AB217-VUB chromosome, complete genome137,198152,863137,185.021 × 102689.6686.570.900.6321
aph(3′)-Ia_7_X62115806816800.341 × 1026100.1299.881.000.6708
armA_1_AY220558759774753.591 × 1026100.2699.741.000.6708
blaOXA-23_1_AY795964821822815.311 × 102699.88100.121.000.6708
blaOXA-66_1_AY750909819825813.281 × 1026100.1299.881.000.6708
blaTEM-1D_1_AF188200858861852.071 × 1026100.2399.771.000.6708
blaADC-25_1_EF016355113911521131.401 × 1026100.00100.001.000.6708
msr(E)_1_FR751518146814761458.761 × 102699.93100.071.000.6708
mph(E)_1_DQ839391881885874.931 × 1026100.00100.001.000.6708
Table 4. Top virulence factors from standard nucleotide BLAST analysis of A. baumannii IRMCBCU95U genome.
Table 4. Top virulence factors from standard nucleotide BLAST analysis of A. baumannii IRMCBCU95U genome.
VFDB IDGene Name/DescriptionIdentity (%)E-ValueScore (bits)LengthIdentitiesGapsStrand
VFG037497(ACICU_RS04580) TonB-dependent receptor940481131052943/31087/3108+/+
VFG037513(ACICU_RS04590) porin family protein940226814671386/14684/1468+/+
VFG050400(pilU) PilT/Pilu family type 4a pilus ATPase990214111191108/11191/1119+/−
VFG050386(pilT) type IV pilus twitching motility protein PilT990203010381038/10402/1040+/−
VFG037490(ACICU_RS04575) FecR domain-containing protein94015841017966/10203/1020+/+
VFG037470(ACICU_RS04565) LysR family transcriptional regulator9401392891839/887N/A+/+
VFG037505(ACICU_RS04585) transferrin-binding protein-like9601332792763/7921/792+/+
VFG037521(ACICU_RS04595) TonB family protein9201279921853/9185/918+/+
VFG037529(hemo) biliverdin-producing heme oxygenase9701035600585/6023/602+/+
VFG037482(ACICU_RS04570) sigma-70 family RNA polymerase sigma factor970924522508/522N/A+/+
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Borgio, J.F. Hybrid Genome and Clinical Impact of Emerging Extensively Drug-Resistant Priority Bacterial Pathogen Acinetobacter baumannii in Saudi Arabia. Life 2025, 15, 1094. https://doi.org/10.3390/life15071094

AMA Style

Borgio JF. Hybrid Genome and Clinical Impact of Emerging Extensively Drug-Resistant Priority Bacterial Pathogen Acinetobacter baumannii in Saudi Arabia. Life. 2025; 15(7):1094. https://doi.org/10.3390/life15071094

Chicago/Turabian Style

Borgio, J. Francis. 2025. "Hybrid Genome and Clinical Impact of Emerging Extensively Drug-Resistant Priority Bacterial Pathogen Acinetobacter baumannii in Saudi Arabia" Life 15, no. 7: 1094. https://doi.org/10.3390/life15071094

APA Style

Borgio, J. F. (2025). Hybrid Genome and Clinical Impact of Emerging Extensively Drug-Resistant Priority Bacterial Pathogen Acinetobacter baumannii in Saudi Arabia. Life, 15(7), 1094. https://doi.org/10.3390/life15071094

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop