Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia Using Oxford Nanopore Technology

Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia Using Oxford Nanopore Technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia Using Oxford Nanopore Technology Francis Omega Somah, Fahn M. Taweh, Sianne Tokpa, Julius S.M Gilayeneh, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8542744/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Multidrug resistance (MDR) is a key driver of antimicrobial resistance (AMR) in all of sub-Saharan Africa; however, genomic AMR data from Liberia are unavailable. To fill the gap, this study utilized Oxford Nanopore long-read sequencing to produce draft genomes of seven multidrug-resistant isolates from four bacterial species ( Escherichia coli, Enterobacter hormaechei, Proteus mirabilis , and Shigella flexneri ) of diarrheal samples at Liberia’s National Public Health Reference Laboratory (NPHRL). The sequencing was of moderate quality (8–13×), long reads and BUSCO coverage (75 to 93 percent), thus enabling relevant de novo assembly and standardized annotation through PGAP. In the isolates, AMR determinants were found to confer resistance to β-lactams (including blaTEM variants and blaACT -16), fluoroquinolones ( qnrS 1 and mutations in gyr A, gyr B, par C and par E), aminoglycosides, sulfonamides, tetracyclines, phencyclines, and polymyxins, as well as RND/MFS efflux systems and global regulatory genes, including marA, soxS and mdtE. PlasmidFinder found IncFIA, IncFIB(K), and Col-type replicons in two isolates but no plasmid-encoded AMR genes, which suggests that the resistance of this collection is mainly chromosomally located. MLST showed that only partial allele profiles and the nearest-sequence-type matches were possible, indicating a great quantity of genetic diversity and region-specific E. coli lineages circulating in Liberia. These findings provide the initial genomic baseline of MDR Gram-negative pathogens in Liberia and show that whole-genome sequencing with Nanopores can be successfully performed in portable technology, which is likely to increase the need to consider higher-resolution genomic surveillance to direct stewardship and public health interventions. Antimicrobial resistance Whole-genome sequencing Gram-negative bacteria Oxford Nanopore sequencing Multidrug resistance and Genomic surveillance Figures Figure 1 INTRODUCTION Antimicrobial resistance (AMR) remains one of the most urgent global health challenges, threatening the effective treatment of common infectious diseases ( 1 ). Adding to this challenge is the rapid emergence of multidrug-resistant (MDR) bacteria which poses a growing clinical and public health risk ( 2 )( 3 ). Numerous studies demonstrate that gram-negative bacteria including Esherichia coli , Klebsiella , Enterobacter , Proteus and Shigella have acquired multiple antimicrobial-resistance mechanism, resulting in MDR phenotypes which is very concerning as these pathogens are frequently associated with emerging and re-emerging infections ( 4 )( 5 )( 6 ). The growing public health burden posed by Gram-negative bacteria is reflected in global surveillance and mortality trends ( 7 ). The World Health Organization’s 2024 Bacterial Priority Pathogens List highlights several Gram-negative species including those mentioned previously, critical threats due to their resistance to last-resort antibiotics ( 8 ). Recent global estimates indicate a significant rise in deaths associated with carbapenem-resistant Gram-negative bacteria, increasing from approximately 619,000 in 1990 to about 1.03 million in 2021. During the same period, the number of deaths directly attributable to these pathogens more than doubled, from roughly 127,000 to 216,000, underscoring their expanding role in the antimicrobial resistance crisis worldwide ( 9 ). Despite recent advancement in sequencing technologies and significant progress in genomic surveillance across high-income regions, data from low-and-middle-income countries remain scare ( 10 ). The application of whole-genome sequencing (WGS) provides an unprecedented opportunity to understand the genetic basis of resistance, track the evolution of pathogens, and inform local AMR control strategies ( 11 ). However, the lack of genomic data from Liberia’s clinical isolates poses a major barrier to understanding the local and regional spread of AMR genes and mobile genetic elements. Traditional phenotypic methods commonly used in Liberia’s clinical laboratories offer limited insight into the genetic mechanisms underlying resistance or the potential for gene transfer among bacterial species ( 12 ). The generation of genomic data from Liberia’s bacterial isolates is therefore critical not only for surveillance but also for the development of novel, easy-to-use diagnostic assays and the design of effective antibiotics and therapeutics at both individual and population levels. Leveraging Oxford Nanopore long-read sequencing, we performed whole-genome sequencing and draft genome assembly of multidrug-resistant Escherichia coli , Enterobacter hormaechei , Proteus mirabilis , and Shigella flexneri isolated from diarrhea samples referred to the National Public Health Reference Laboratory (NPHRL) for surveillance testing. This study represents one of the first efforts to generate and publicly release complete genomic information for clinically relevant Gram-negative bacteria from Liberia, establishing a foundation for future genomic surveillance and comparative studies in the region. MATERIALS AND METHOD Sample Collection This study was conducted at the Bacteriology and Genomic Sequencing Unit of the National Public Health Reference Laboratory (NPHRL), under the National Public Health Institute of Liberia (NPHIL). A total of ten clinical isolates were selected from stool specimens received at NPHRL for routine diarrhea surveillance in September 2025. The isolates were chosen based on their multidrug-resistant (MDR) phenotypes, identified during routine antimicrobial susceptibility testing. Only bacterial isolates were included in the study; no patient-identifying information or associated clinical metadata were used. All isolates were collected as part of ongoing national surveillance activities; therefore, no direct patient sampling or additional ethical approval was required. Authorization for the use of these isolates was obtained from the National Public Health Institute of Liberia (NPHIL) through the NPHRL. Bacterial isolates and identification A loopful of each stool specimen was inoculated onto Xylose Lysine Deoxycholate (XLD) agar, MacConkey agar, and Salmonella–Shigella (SS) agar (HiMedia Laboratories, India). The inoculated plates were incubated aerobically at 37°C for 18–24 hours. Bacterial colonies exhibiting distinct morphological characteristics were subjected to Gram staining for preliminary identification. Biochemical identification was performed using the Hi25™ Enterobacteriaceae Identification Kit (KB003, HiMedia, India) according to the manufacturer’s instructions. Colonies exhibiting morphological features suggestive of Shigella spp. were further confirmed using slide agglutination tests with polyvalent antisera (Becton, Dickinson and Company, MD, USA). The appearance of visible agglutination with the Group A, B, C, or D polyvalent antisera indicated Shigella dysenteriae, S. flexneri, S. boydii, and S. sonnei, respectively. Isolates demonstrating multidrug-resistant (MDR) profiles were subcultured on Tryptic Soy Agar (TSA) to obtain pure cultures. Pure isolates were preserved in Tryptic Soy Broth (TSB) supplemented with 20% glycerol and stored at − 20°C for subsequent molecular analysis. Antibiotic Susceptibility Testing The antibiotic susceptibility test was done by the Kirby-Bauer disk diffusion method in Muller-Hinton agar following standard procedures ( 13 ). To summarize, a sterile cotton swab was used to wipe the whole surface of Mueller Hinton agar (Oxoid) with a MacFarland 0.5 standardized suspension of the bacteria in 0.8% sterile saline. The inoculated surface was subsequently covered with discs (HiMedia, India) that contained single concentrations of each antimicrobial agent. Using a straight-line ruler, the clear zones created by the antimicrobial suppression of bacterial growth were measured in millimeters following an overnight incubation at 37°C and they were interpreted as sensitive, intermediate sensitive, and resistant (CLSI, 2014). The antibiotic susceptibility pattern of six antibiotics, namely Chloramphenicol (CHL, 30 mcg), Co-trimoxazole (COT, 25 mcg), Tetracycline (TET, 10 mcg), Ciprofloxacin (CIP, 10 mcg), Azithromycin (AZM, 30 mcg), Ceftriaxone (CTR, 30 mcg). Quality control was set up using an Escherichia coli strain (ATCC 25922) which was susceptible to all the tested drugs. DNA extraction and quantification Bacterial isolates preserved in 20% glycerol stocks were subcultured on Tryptic Soy Agar (TSA) and incubated at 37°C for 18–24 hours to obtain fresh colonies. Genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Germany) according to the manufacturer’s protocol, with minor modifications optimized for Gram-negative bacteria. Briefly, 3–5 well-isolated colonies were suspended in phosphate-buffered saline (PBS), lysed with Proteinase K and Buffer AL, and incubated at 56°C for 10 minutes. DNA was bound to silica columns, washed with Buffers AW1 and AW2, and eluted in 50–100 µL of EDTA-free Buffer AE. DNA concentration and purity were assessed using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA). All DNA extracts exceeded 20 ng/µL, meeting the minimum input requirement for Oxford Nanopore Technologies (ONT) MinION sequencing. Purified DNA was stored at − 20°C until library preparation. Library Preparation and sequencing Sequencing libraries were prepared using the Oxford Nanopore Technologies (ONT) DNA Ligation Sequencing Kit (SQK-NBD114-96) according to the manufacturer’s protocol. Library preparation was performed over two days. On day one, extracted genomic DNA was subjected to DNA repair, end-preparation, and native barcoding, followed by purification. On day two, adapter ligation and final library clean-up were performed prior to sequencing. The final pooled library was loaded onto an R10.4.1 (FLO-MN114) flow cell and sequenced on an ONT MinION Mk1C device. Basecalling was performed using Guppy (v6.6.1, ONT) in high-accuracy (HAC) mode. Sequencing quality metrics, including read length and Q-scores, were monitored in real time using MinKNOW (v23.10.7). Raw FASTQ reads were exported for downstream analysis. Read Quality Assessment and Species identification Following basecalling, raw FASTQ reads were uploaded to the BUGSEQ web-based metagenomic analysis platform (BugSeq Bioinformatics, Canada) for preliminary species identification and quality assessment. The platform employs long-read alignment and classification algorithms to confirm taxonomic identity at the species level. Additionally, Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis was performed within BUGSEQ to estimate genome completeness and assess sequencing quality. BUSCO completeness scores were used to evaluate dataset suitability and to select high-quality isolates for downstream genome assembly and analysis. Genome Assembly and Annotation Raw FASTQ reads were processed to remove sequencing adapters and barcode sequences using Porechop (v0.2.4). Read quality filtering was performed with NanoQ using a minimum quality score threshold of Q10 and a minimum read length of 1,000 bp to eliminate low-quality and short reads. Filtered reads were assembled de novo using Dragonflye (v5.32.1), a long-read assembler optimized for Oxford Nanopore data. The resulting assemblies were polished through one round of Racon followed by Medaka (v1.11.3) to improve consensus accuracy. Assembly statistics, including total genome length, GC content, number of contigs, and N50, were generated as part of the Dragonflye output. Genome annotation was carried out using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to predict coding sequences (CDS), rRNAs, tRNAs, and other genomic features. Genomic Characterization of Antimicrobial Resistance and Plasmid Replicon The assembled genomes were screened for antimicrobial resistance (AMR) determinants using both the ResFinder 4.7.1 and Comprehensive Antibiotic Resistance Database (CARD) platforms. ResFinder analysis was performed through the Center for Genomic Epidemiology (CGE) web server with default parameters, while CARD-based resistance gene identification was conducted using the Resistance Gene Identifier (RGI, v6.0.3) tool. Only hits with sequence identity and coverage above 90% were considered valid. Plasmid replicons were identified using PlasmidFinder (CGE) with a minimum identity threshold of 95% and default coverage parameters. Plasmid contigs identified by PlasmidFinder were extracted from assembled genomes and re-analyzed with ResFinder to determine plasmid-associated AMR genes. This allowed differentiation between chromosomally encoded and plasmid-borne resistance determinants. Multilocus sequence typing (MLST) was evaluated using the MLST 2.0 tool available at the Center for Genomic Epidemiology (CGE, Technical University of Denmark). Assembled genome FASTA files were queried against the E. coli MLST database, and allele matches were determined automatically based on exact sequence identity and coverage. For isolates with incomplete locus matches, the nearest sequence types (STs) were assigned based on the highest overall allelic similarity. Isolates exhibiting less than 100% identity in one or more loci were interpreted as either potential new ST variants or unresolved due to minor sequence variation. Ethical Consideration This study was conducted under the Public Health Sample Collection and Testing Policy of the Government of Liberia, which authorizes the use of de-identified clinical isolates for public health surveillance and research purposes. All bacterial isolates included in this study were obtained from stool specimens submitted to the National Public Health Reference Laboratory (NPHRL) for routine diarrheal disease surveillance and antimicrobial resistance monitoring. No patient-identifying information or associated clinical metadata were used in the analysis. Therefore, no additional ethical approval was required, as all procedures were performed in compliance with national laboratory surveillance regulations and ethical guidelines established by the National Public Health Institute of Liberia (NPHIL). RESULTS Sequencing Output and Read Quality A total of ten bacterial isolates were successfully sequenced using the Oxford Nanopore MinION platform, generating raw read yields ranging from approximately 30,760 kb to 303,799 kb (Table 1 ). Read quality and depth varied across isolates, reflecting minor differences in extraction purity and library preparation efficiency. After quality evaluation, seven isolates were retained for downstream analyses, each meeting the minimum inclusion thresholds of mean genome coverage above 8×, BUSCO completeness exceeding 75%, and fewer than 200 contigs. Across the retained datasets, the median read length ranged from 601 bp to 1,361 bp, with most samples showing well-distributed long reads suitable for de novo assembly. The read N50 values, representing the length above which half of the total bases are contained, varied between 73.8 kbp and 3400 kbp, indicating moderate to high sequencing quality across isolates. These metrics collectively suggest that the sequencing runs yielded sufficiently long and high-quality reads for reliable downstream assembly and genomic characterization. Table 1 Sequencing yield and read quality metrics for ten Gram-negative bacterial isolates sequenced using Oxford Nanopore Technology. Isolate ID Species Total Yield (kb) Median Read Length (bp) N50 (kb) Genome coverage Selected for assembly (Yes/No) ABD_002 Proteus mirabilis 120948 1024 3400 14 Yes SF_001_LIB Shigella Flexneri 1548607 1361 111.9 33 Yes ABD_004 Enterobacter hormaechei 93623 677 305.5 9 Yes ABD_003 Escherichia coli 96582 1125 3087.8 13 Yes ABD_005 Escherichia coli 192269 601 206.2 9 Yes ABD_006 Escherichia coli 68256 656 73.8 9 Yes ABD_007 Escherichia coli 56254 822 120.1 8 Yes N/A Escherichia coli 30760 505 27.4 4 No N/A Escherichia coli 138749 262 10 4 No N/A Proteus mirabilis 68559 580 36 5 No Genome Assembly and Annotation High-quality filtered reads from seven isolates were de novo assembled using the Dragonflye pipeline, optimized for Oxford Nanopore long-read data. The assemblies produced genome sizes ranging from approximately 3.8 Mbp to 4.83 Mbp, which are consistent with typical genome sizes reported for Escherichia coli , Proteus mirabilis , Enterobacter hormaechei , and Shigella flexneri . The number of contigs per assembly varied between 4 and 194, with N50 values ranging from 73.8 kbp to 3.4 Mbp, indicating moderate to high assembly contiguity. Mean GC content among the isolates ranged from 36.63% to 54.37%, aligning with the expected species-specific profiles. BUSCO completeness scores of 76–88% further confirmed that the assembled genomes captured a substantial portion of the expected single-copy orthologs, supporting their suitability for comparative genomic and resistance gene analysis (Table 2 a). Genome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), a standardized and curated pipeline for bacterial genome annotation. PGAP applies a best-placed reference protein set in combination with GeneMarkS-2 + for gene prediction and functional assignment. Detailed information about the PGAP workflow is available through the National Center for Biotechnology Information (NCBI) genome annotation resource. Annotated genomic features included protein-coding sequences (CDS), ribosomal RNA (rRNA) genes, transfer RNA (tRNA) genes, and non-coding RNA (ncRNA) elements (Table 2 b). Table 2 a. Draft genome assembly statistics and BUSCO completeness for seven multidrug-resistant Gram-negative isolates. Isolate ID Species Genome length (bp) Number of contigs Largest contig (bp) GC content (%) BUSCO complete (%) BUSCO missing (%) ABD_002 Proteus mirabilis 3888178 4 3368269 36.63 93.1 6.9 SF_001_LIB Shigella flexneri 4512242 105 328887 50.64 77.6 22.4 ABD_004 Enterobacter hormaechei 4838533 107 366542 54.37 87.1 12.9 ABD_003 Escherichia coli 4829463 32 1475657 50.87 88.7 11.3 ABD_005 Escherichia coli 4508363 90 343666 50.51 83.6 16.4 ABD_006 Escherichia coli 4503962 194 203411 50.63 75.0 25.0 ABD_007 Escherichia coli 4409889 117 283053 50.71 79.3 20.7 Table 2 b. Structural genome annotation features of multidrug-resistant Gram-negative bacterial isolates generated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). Isolate ID Species Total genes Total CDS Genes (coding) Genes (RNA) tRNA genes ncRNAs Pseudo genes (total) ABD_002 Proteus mirabilis 4302 4199 2102 103 77 4 2097 SF_001_LIB Shigella flexneri 5604 5498 1750 106 83 7 3748 ABD_004 Enterobacter hormaechei 6457 6355 2536 102 84 6 3819 ABD_003 Escherichia coli 5771 5655 1865 116 84 11 3790 ABD_005 Escherichia coli 5890 5789 1903 101 74 11 3886 ABD_006 Escherichia coli 6293 6192 2288 101 71 9 3904 ABD_007 Escherichia coli 6097 6003 2240 94 74 7 3763 Genomic Characterization of Antimicrobial Resistance and Plasmid Replicon Screening of the seven selected isolates using ResFinder and CARD-RGI revealed diverse AMR determinants associated with multiple antibiotic classes. Across all isolates, genes conferring resistance to β-lactams, fluoroquinolones, aminoglycosides, sulfonamides, tetracyclines, and macrolides were frequently detected, reflecting the multidrug-resistant phenotype previously observed phenotypically. ResFinder revealed multiple horizontally acquired AMR genes, while CARD-RGI identified chromosomally encoded efflux systems and regulatory mutations linked to broad resistance phenotypes. The E. coli isolates carried a combination of plasmid-mediated β-lactamase genes ( blaTEM variants) and quinolone resistance determinants ( qnrS1 ), consistent with extended-spectrum β-lactamase (ESBL) and fluoroquinolone-resistant profiles (Table 3 a and b). Enterobacter hormaechei harbored the AmpC-type β-lactamase blaACT-16 , which confers intrinsic resistance to penicillins and first-generation cephalosporins, and the fosA gene mediating fosfomycin resistance. Proteus mirabilis encoded both tet(J) and cat , supporting its phenotypic tetracycline and chloramphenicol resistance. Shigella flexneri carried tet(B) and dfrA14 , conferring tetracycline and trimethoprim resistance, respectively. Efflux-related determinants were widespread among isolates, including RND-family genes ( AcrAB-TolC , mdtE , emrA ) and transcriptional regulators ( marA , soxS ), indicating a potential basal multidrug-resistance mechanism even in isolates lacking clear plasmid-encoded AMR genes. Table 3 a. Summary of Antimicrobial Resistance Genes Identified by ResFinder. Isolate ID Species Number of AMR genes Key resistance genes Predicted Resistance Classes Acquired Chromosomal mutation ABD_002 Proteus mirabilis 2 Cat, tet(J) - Phenicols, tetracyclines SF_001_LIB Shigella flexneri 2 tet(B), dfrA14 - Tetracyclines, trimethoprim ABD_004 Enterobacter hormaechei 2 blaACT-16 , fosA - β-lactams (cephalosporins, penicillins), fosfomycin ABD_003 Escherichia coli 7 catA1 gyrA , gyrB , parC , parE , pmrA , pmrB Chloramphenicol, fluoroquinolones, polymyxins ABD_005 Escherichia coli 1 blaTEM-143 - Extended-spectrum β-lactamase (ESBL) ABD_006 Escherichia coli 6 blaTEM-164 , aph ( 6 ) -Id , aph(3″)-Ib , sul2 , qnrS1 - β-lactams, aminoglycosides, sulfonamides, fluoroquinolones ABD_007 Escherichia coli 0 - - No AMR gene detected Table 3 b. Summary of CARD-RGI Results for Selected Isolates Isolate ID Species Perfect Hits Strict Hits Major AMR Mechanisms Detected Representative Genes/Models ABD_002 Proteus mirabilis 0 5 Efflux and target alteration rsmA , CRP , catA4 , vanG SF_001_LIB Shigella flexneri 0 5 Efflux, target alteration and reduced permeability KpnE , KpnH , emrE , soxS , leuO ABD_004 Enterobacter hormaechei 0 2 Efflux and target alteration rsmA, AcrAB-TolC ABD_003 Escherichia coli 1 6 Multidrug efflux (RND/MFS), reduced permeability mdtE , AcrS , emrA , marA , qacJ ABD_004 Escherichia coli 0 3 Efflux and target alteration qacJ , bacA , soxS ABD_006 Escherichia coli 0 2 Efflux and target alteration soxR , bacA ABD_007 Escherichia coli 0 0 - - Figure 1 . Hierarchical clustering and distribution of antimicrobial resistance (AMR) genes among multidrug-resistant Gram-negative isolates sequenced in this study. The dendrogram (top panel) shows clustering of isolates based on binary presence–absence patterns of AMR genes identified through ResFinder and CARD-RGI analyses. The heatmap (bottom panel) visualizes the distribution of key resistance determinants (1 = gene present, 0 = absent). Distinct clustering patterns were observed among Escherichia coli, Enterobacter hormaechei, Proteus mirabilis , and Shigella flexneri isolates, reflecting both species-specific and shared resistance gene profiles. PlasmidFinder analysis identified multiple plasmid replicons among the sequenced isolates. Two isolates including Enterobacter hormaechei (ABD_004) and Escherichia coli (ABD_003), harbored distinct replicon types. In E. hormaechei , two replicons, IncFIB(K) and Col(pHAD28) , were detected on contig 79 (positions 13,712–14,266) and contig 17 (positions 879–1,007), respectively. In E. coli , three replicons, IncFIA , Col156 , and Col(pEc648) , were identified on contig 20 (positions 5,795–6,179), contig 31 (positions 6,489–6,642), and contig 33 (positions 1,166–1,860), respectively. Subsequent analysis of the extracted plasmid sequences using ResFinder and CARD-RGI revealed no AMR genes associated with these replicons. This suggests that the AMR determinants detected in the current dataset are likely chromosomally encoded rather than plasmid-mediated. Inc-type plasmids, particularly IncFIA and IncFIB(K) , have been widely reported in Enterobacteriaceae for their role in the horizontal transfer of extended-spectrum β-lactamase (ESBL) genes such as bla CTX-M and bla TEM ( 14 )( 15 ). However, their absence in the present plasmid-resident regions may indicate either loss of mobile AMR cassettes or the presence of non-resistance plasmids functioning in replication, stability, or conjugation maintenance. Multi-Locus Sequence Typing MLST analysis was performed on the E. coli isolates using the CGE MLST 2.0 pipeline. Four isolates (ABD_003, ABD_005, ABD_006, ABD_007) generated partial allele profiles that could be compared against the E. coli MLST database. ABD_003 showed closest similarity to ST632, with a perfect (100%) allele identity for the putP locus. ABD_005 was most similar to ST65 and ST7, while Barcode 65 and 66 shared allelic relatedness with multiple STs (ST210, ST155, ST681, ST1332 for ABD_006; ST974, ST441, ST1737, ST294, ST1137, ST1770 for ABD_007). The incomplete MLST profiles across most isolates likely result from the draft nature of the genome assemblies, which contained fragmented contigs and missing loci rather than true allelic absence. Such assembly gaps can interrupt the recovery of full housekeeping gene sequences needed for exact ST assignment. Nonetheless, the allelic diversity and partial matches observed across the isolates highlight underlying genetic heterogeneity and possibly regionally distinct E. coli genotypes circulating in Liberia. DISCUSSION To provide a more holistic MDR surveillance data on Gram-negative bacteria isolates from clinical samples in Liberia, we integrated phenotypic susceptibility testing with genome assembly, annotation, antimicrobial resistance profiling, plasmid replicon analysis, and MLST inference. Our findings provide critical baseline genomic data for a region where such information remains extremely limited. The draft genome assemblies characterized here were of sufficient quality to identify clinically relevant AMR determinants despite moderate coverage (8–13×). Although fragmented assemblies can limit complete recovery of some loci, they were adequate for robust AMR characterization. This is consistent with other studies demonstrating that long-read sequencing at moderate coverage can successfully identify resistance genes and mobile elements in Enterobacterales ( 16 )( 17 )( 18 ). The AMR gene profiles observed across the isolates reflect resistance to multiple antibiotic classes commonly used in clinical practice, including β-lactams, fluoroquinolones, aminoglycosides, sulfonamides, tetracyclines, and phenicols. The high prevalence of multidrug-resistant Gram-negative pathogens in this study complements regional evidence indicating that Escherichia coli , Klebsiella spp., and other Enterobacterales are dominant contributors to antimicrobial resistance in West Africa. A recent meta-analysis reported that gram-negative bacteria accounted for over 60% of MDR isolates in healthcare and community settings in West Africa, with an overall MDR prevalence of approximately 59% (95% CI: 48–69%) across studies from 13 countries ( 19 ). Another systematic review of hospital wastewater studies underscored the high occurrence of resistant E. coli and resistance genes such as bla TEM and bla SHV in West African environments ( 20 ). Our genomic data align with these findings, revealing multiple β-lactamase genes (including bla TEM, and others) and fluoroquinolone resistance determinants ( qnrS ) across isolates, consistent with the patterns reported in clinical and environmental studies in the region. While phenotypic surveillance studies from West Africa emphasize high MDR rates, genomic data remain sparse. Our findings add depth by characterizing the genetic basis of resistance and showing the diversity of AMR mechanisms in isolates from Liberia. In Shigella flexneri , meta-analyses across Africa indicate an ESBL and Carbapenem Resistance of ~ 41.2% ( 21 ). Although our Shigella isolates did not exhibit carbapenemase genes, the presence of β-lactam resistance correlates with continental patterns, underscoring the clinical significance of ESBLs in this species. Importantly, CARD-RGI analysis revealed a broad repertoire of efflux systems and regulatory genes, indicating that multidrug resistance in these isolates is not solely driven by acquired resistance genes. This mirrors global observations that chromosomally encoded efflux and regulatory mechanisms form a foundational resistance backbone in Enterobacteriaceae, upon which horizontally acquired genes further expand resistance phenotypes. PlasmidFinder analysis identified IncFIA, IncFIB(K), and Col-type replicons in two isolates, consistent with plasmid families commonly reported among Enterobacteriaceae worldwide ( 22 )( 23 ). IncF plasmids, in particular, are well recognized as major vectors in the dissemination of extended-spectrum β-lactamase (ESBL) genes, including bla CTX-M and bla TEM, especially in Escherichia coli and related species ( 24 ). However, targeted extraction of plasmid-associated contigs followed by ResFinder and CARD-RGI screening revealed no AMR genes within these replicon regions. This finding suggests that, in the present dataset, resistance determinants are predominantly chromosomally encoded rather than plasmid-borne. Similar patterns have been reported in global studies, where prolonged antibiotic selective pressure has been associated with the stable chromosomal integration of resistance genes, even in the presence of plasmids lacking AMR determinants ( 25 )( 26 ). The presence of plasmids without detectable resistance genes may nonetheless provide a genetic backbone capable of acquiring and mobilizing AMR determinants in the future especially from the One health perspective, underscoring the importance of continued genomic surveillance in under-sampled settings ( 27 )( 28 ). MLST analysis revealed partial allele profiles across all E. coli isolates, with none achieving full sequence-type assignment. The absence of 100% locus identity is most plausibly explained by assembly fragmentation rather than true gene absence, a known limitation of draft genomes generated at moderate coverage. Nevertheless, the identification of nearest STs spanning multiple lineages, including ST632, ST65, ST7, and others, highlights substantial genetic diversity among the isolates. ( 29 )( 30 ). This pattern likely reflects the presence of novel or regionally distinct Escherichia coli genotypes circulating in Liberia, a phenomenon increasingly recognized in genomic studies from previously under-sampled regions. Expanded population-level genomic surveillance, incorporating higher sequencing depth and complete genome resolution, will be essential to confirm these findings and to better define the evolutionary and epidemiological dynamics of these lineages. From a public health perspective, the coexistence of multiple resistance mechanisms, combined with evidence of mobile genetic elements and diverse genetic backgrounds, underscores the complexity of AMR in Liberia. The lack of plasmid-associated AMR genes in this study should not be interpreted as reduced transmission risk, as chromosomally encoded resistance can persist stably and plasmids may rapidly acquire resistance cassettes under selective pressure. This study demonstrates the feasibility and value of implementing whole-genome sequencing for AMR surveillance in resource-limited settings using portable Nanopore platforms. Future work should prioritize increased sequencing depth, hybrid short- and long-read approaches, inclusion of clinical metadata, and longitudinal sampling to track resistance evolution and transmission dynamics. Expanding genomic surveillance across Liberia and neighboring countries will be essential for informing antibiotic stewardship, guiding empirical therapy, and strengthening regional and global AMR monitoring efforts. CONCLUSION This study provides the first publicly available whole-genome data for multidrug-resistant Gram-negative bacteria isolated through routine surveillance in Liberia. Long-read sequencing revealed diverse resistance determinants that were predominantly chromosomally encoded, alongside incomplete MLST profiles suggestive of novel or regionally distinct Escherichia coli lineages. These findings highlight both the genetic diversity of antimicrobial resistance in an under-sampled setting and the critical need to expand genomic surveillance capacity in West Africa to better inform local and global AMR control efforts. Declarations Competing interests The authors declare that they have no competing interests. Data Availability The whole-genome sequencing data generated in this study have been deposited in the DDBJ/ENA/GenBank databases. Five genome assemblies are available under BioProject accession number PRJNA1346421, entitled “Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia.” In addition, two genome assemblies have been deposited separately with accession numbers JBQXYK000000000 and JBREJJ000000000. All data are publicly accessible and may be used for comparative genomic and antimicrobial resistance studies. Funding This work received no specific external funding. Reagents, sequencing consumables, and infrastructure support were provided through routine laboratory operations and partner support at the National Public Health Reference Laboratory of Liberia. Author Contribution F.O. Somah, F.M. Taweh, J.SM. Gilayeneh and M. Sarmie conceptualized the study and drafted the manuscript. S. Tokpa, H. Tarwoe, J. George, A. Momolu, A. Wuo, S.O. Nyilah, C. Johnson, and A. Boakai performed DNA extraction, library preparation, and sequencing. E. Tiawroh, R. Koon, and R. Yeaney isolated bacterial strains from stool specimens. D. Kollie and F.O. Somah conducted the bioinformatic analyses. All authors reviewed and approved the final manuscript. Declaration of Interest All authors declare no competing interests. The authors received no financial support, grants, personal fees, or non-financial benefits related to the conduct of this study, its analysis, or the preparation of this manuscript. Acknowledgement The authors acknowledge the National Public Health Institute of Liberia (NPHIL) and the Ministry of Health (MoH) for their leadership and support of public health surveillance in Liberia. We are grateful to our partners, including the Africa Centres for Disease Control and Prevention (Africa CDC) and the World Health Organization (WHO), for their technical and logistical support toward the establishment and strengthening of the Genomic Sequencing Unit at the National Public Health Reference Laboratory (NPHRL), including the provision of reagents and capacity-building through training and fellowships. We also thank the medical and laboratory staff involved in sample collection and processing, as well as the sample transport riders whose efforts ensured timely delivery of specimens to the NPHRL. References Ahmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA, et al. Antimicrobial resistance: Impacts, challenges, and future prospects. J Med Surg Public Health. 2024;2:100081. Sharma S, Chauhan A, Ranjan A, Mathkor DM, Haque S, Ramniwas S, et al. Emerging challenges in antimicrobial resistance: implications for pathogenic microorganisms, novel antibiotics, and their impact on sustainability. Front Microbiol. 2024;15:1403168. Rankin DA, Stahl A, Sabour S, Khan MA, Armstrong T, Huang JY, et al. Changes in Carbapenemase-Producing Carbapenem-Resistant Enterobacterales, 2019 to 2023. Ann Intern Med. 2025;178(12):1818–21. Elbaiomy RG, El-Sappah AH, Guo R, Luo X, Deng S, Du M, et al. Antibiotic Resistance: A Genetic and Physiological Perspective. MedComm. 2025;6(11):e70447. Breijyeh Z, Jubeh B, Karaman R. Resistance of Gram-Negative Bacteria to Current Antibacterial Agents and Approaches to Resolve It. Molecules. 2020;25(6):1340. Birlutiu V, Birlutiu RM. An Overview of the Epidemiology of Multidrug Resistance and Bacterial Resistance Mechanisms: What Solutions Are Available? A Comprehensive Review. Microorganisms 2025 Sept 19;13(9):2194. Zha L, Li S, Guo J, Hu Y, Pan L, Wang H, et al. Global and regional burden of bloodstream infections caused by carbapenem-resistant Gram-negative bacteria in 2019: A systematic analysis from the MICROBE database. Int J Infect Dis. 2025;153:107769. WHO Bacterial Priority Pathogens List. 2024: Bacterial Pathogens of Public Health Importance, to Guide Research, Development, and Strategies to Prevent and Control Antimicrobial Resistance. 1st ed. Geneva: World Health Organization; 2024. 1 p. Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet. 2024 Sept;404(10459):1199–226. Getchell M, Wulandari S, De Alwis R, Agoramurthy S, Khoo YK, Mak TM et al. Pathogen genomic surveillance status among lower resource settings in Asia. Nat Microbiol 2024 Sept 24;9(10):2738–47. Köser CU, Ellington MJ, Peacock SJ. Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 2014 Sept;30(9):401–7. Corona F, Martinez J. Phenotypic Resistance to Antibiotics. Antibiotics. 2013;2(2):237–55. Bauer AW, Kirby WM, Sherris JC, Turck M. Antibiotic susceptibility testing by a standardized single disk method. Am J Clin Pathol. 1966;45(4):493–6. Rozwandowicz M, Brouwer MSM, Fischer J, Wagenaar JA, Gonzalez-Zorn B, Guerra B, et al. Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae. J Antimicrob Chemother. 2018;73(5):1121–37. Liu H, Fan S, Zhang X, Yuan Y, Zhong W, Wang L, et al. Antibiotic-resistant characteristics and horizontal gene transfer ability analysis of extended-spectrum β-lactamase-producing Escherichia coli isolated from giant pandas. Front Vet Sci. 2024 July;26:11:1394814. Rose R, Nolan DJ, Ashcraft D, Feehan AK, Velez-Climent L, Huston C, et al. Comparing antimicrobial resistant genes and phenotypes across multiple sequencing platforms and assays for Enterobacterales clinical isolates. BMC Microbiol. 2023;23(1):225. Lerminiaux N, Fakharuddin K, Mulvey MR, Mataseje L. Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. Can J Microbiol. 2024;70(5):178–89. Landman F, Jamin C, De Haan A, Witteveen S, Bos J, Van Der Heide HGJ, et al. Genomic surveillance of multidrug-resistant organisms based on long-read sequencing. Genome Med. 2024;16(1):137. Diop M, Bassoum O, Ndong A, Wone F, Ghogomu Tamouh A, Ndoye M, et al. Prevalence of multidrug-resistant bacteria in healthcare and community settings in West Africa: systematic review and meta-analysis. BMC Infect Dis. 2025;25(1):292. Hotor P, Kotey FCN, Donkor ES. Antibiotic resistance in hospital wastewater in West Africa: a systematic review and meta-analysis. BMC Public Health. 2025;25(1):1364. Somda NS, Nyarkoh R, Tankoano A, Bonkoungou OJI, Tetteh-Quarcoo PB, Donkor ES. Molecular epidemiology of extended-spectrum beta-lactamases and carbapenemases-producing Shigella in Africa: a systematic review and meta-analysis. BMC Infect Dis. 2025;25(1):81. De Souza HCA, Panzenhagen P, Dos Santos AMP, Portes AB, Almeida ACDO, Conte Junior CA. Understanding the Association of Plasmid Incompatibility Groups With Variable Antimicrobial Resistance Genotypes in Bacteria. MicrobiologyOpen. 2025;14(6):e70187. Carattoli A. Resistance Plasmid Families in Enterobacteriaceae . Antimicrob Agents Chemother. 2009 June;53(6):2227–38. Stein M, Brinks E, Loop J, Habermann D, Cho GS, Franz CMAP. Antibiotic resistance plasmids in Enterobacteriaceae isolated from fresh produce in northern Germany. Oladeinde A, editor. Microbiol Spectr. 2024;12(11):e00361-24. Coluzzi C, Rocha EPC. The Spread of Antibiotic Resistance Is Driven by Plasmids Among the Fastest Evolving and of Broadest Host Range. Samhita L, editor. Molecular Biology and Evolution. 2025;42(3):msaf060. Baharoglu Z, Garriss G, Mazel D. Multiple Pathways of Genome Plasticity Leading to Development of Antibiotic Resistance. Antibiotics. 2013;2(2):288–315. Castañeda-Barba S, Top EM, Stalder T. Plasmids, a molecular cornerstone of antimicrobial resistance in the One Health era. Nat Rev Microbiol. 2024;22(1):18–32. Che Y, Yang Y, Xu X, Břinda K, Polz MF, Hanage WP, et al. Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proc Natl Acad Sci USA. 2021;118(6):e2008731118. Denton JF, Lugo-Martinez J, Tucker AE, Schrider DR, Warren WC, Hahn MW. Extensive Error in the Number of Genes Inferred from Draft Genome Assemblies. Guigo R, editor. PLoS Comput Biol. 2014;10(12):e1003998. Huebner R, Mugabi R, Hetesy G, Fox L, De Vliegher S, De Visscher A et al. Characterization of genetic diversity and population structure within Staphylococcus chromogenes by multilocus sequence typing. Robinson DA, editor. PLoS ONE. 2021;16(3):e0243688. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:13:35","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133161,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8542744/v1/943cc2e9b095e95989adef53.html"},{"id":100267449,"identity":"24ca56f7-fe78-4192-89f0-caf7f029a4f2","added_by":"auto","created_at":"2026-01-14 18:47:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104348,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering and distribution of antimicrobial resistance (AMR) genes among multidrug-resistant Gram-negative isolates sequenced in this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8542744/v1/633e6712cf033dcf325c8d03.png"},{"id":100383961,"identity":"60973c2b-82af-409b-bb8f-8cabfff846a2","added_by":"auto","created_at":"2026-01-16 10:48:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1317779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8542744/v1/9c371bc1-0527-49b0-86dc-44a7d337758d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia Using Oxford Nanopore Technology","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAntimicrobial resistance (AMR) remains one of the most urgent global health challenges, threatening the effective treatment of common infectious diseases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Adding to this challenge is the rapid emergence of multidrug-resistant (MDR) bacteria which poses a growing clinical and public health risk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Numerous studies demonstrate that gram-negative bacteria including \u003cem\u003eEsherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e and \u003cem\u003eShigella\u003c/em\u003e have acquired multiple antimicrobial-resistance mechanism, resulting in MDR phenotypes which is very concerning as these pathogens are frequently associated with emerging and re-emerging infections (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe growing public health burden posed by Gram-negative bacteria is reflected in global surveillance and mortality trends (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The World Health Organization\u0026rsquo;s \u003cem\u003e2024 Bacterial Priority Pathogens List\u003c/em\u003e highlights several Gram-negative species including those mentioned previously, critical threats due to their resistance to last-resort antibiotics (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Recent global estimates indicate a significant rise in deaths associated with carbapenem-resistant Gram-negative bacteria, increasing from approximately 619,000 in 1990 to about 1.03\u0026nbsp;million in 2021. During the same period, the number of deaths directly attributable to these pathogens more than doubled, from roughly 127,000 to 216,000, underscoring their expanding role in the antimicrobial resistance crisis worldwide (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite recent advancement in sequencing technologies and significant progress in genomic surveillance across high-income regions, data from low-and-middle-income countries remain scare (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The application of whole-genome sequencing (WGS) provides an unprecedented opportunity to understand the genetic basis of resistance, track the evolution of pathogens, and inform local AMR control strategies (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, the lack of genomic data from Liberia\u0026rsquo;s clinical isolates poses a major barrier to understanding the local and regional spread of AMR genes and mobile genetic elements. Traditional phenotypic methods commonly used in Liberia\u0026rsquo;s clinical laboratories offer limited insight into the genetic mechanisms underlying resistance or the potential for gene transfer among bacterial species (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The generation of genomic data from Liberia\u0026rsquo;s bacterial isolates is therefore critical not only for surveillance but also for the development of novel, easy-to-use diagnostic assays and the design of effective antibiotics and therapeutics at both individual and population levels.\u003c/p\u003e \u003cp\u003eLeveraging Oxford Nanopore long-read sequencing, we performed whole-genome sequencing and draft genome assembly of multidrug-resistant \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eEnterobacter hormaechei\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, and \u003cem\u003eShigella flexneri\u003c/em\u003e isolated from diarrhea samples referred to the National Public Health Reference Laboratory (NPHRL) for surveillance testing. This study represents one of the first efforts to generate and publicly release complete genomic information for clinically relevant Gram-negative bacteria from Liberia, establishing a foundation for future genomic surveillance and comparative studies in the region.\u003c/p\u003e"},{"header":"MATERIALS AND METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003eThis study was conducted at the Bacteriology and Genomic Sequencing Unit of the National Public Health Reference Laboratory (NPHRL), under the National Public Health Institute of Liberia (NPHIL). A total of ten clinical isolates were selected from stool specimens received at NPHRL for routine diarrhea surveillance in September 2025. The isolates were chosen based on their multidrug-resistant (MDR) phenotypes, identified during routine antimicrobial susceptibility testing. Only bacterial isolates were included in the study; no patient-identifying information or associated clinical metadata were used.\u003c/p\u003e \u003cp\u003eAll isolates were collected as part of ongoing national surveillance activities; therefore, no direct patient sampling or additional ethical approval was required. Authorization for the use of these isolates was obtained from the National Public Health Institute of Liberia (NPHIL) through the NPHRL.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBacterial isolates and identification\u003c/h3\u003e\n\u003cp\u003eA loopful of each stool specimen was inoculated onto Xylose Lysine Deoxycholate (XLD) agar, MacConkey agar, and Salmonella\u0026ndash;Shigella (SS) agar (HiMedia Laboratories, India). The inoculated plates were incubated aerobically at 37\u0026deg;C for 18\u0026ndash;24 hours. Bacterial colonies exhibiting distinct morphological characteristics were subjected to Gram staining for preliminary identification. Biochemical identification was performed using the Hi25\u0026trade; Enterobacteriaceae Identification Kit (KB003, HiMedia, India) according to the manufacturer\u0026rsquo;s instructions. Colonies exhibiting morphological features suggestive of Shigella spp. were further confirmed using slide agglutination tests with polyvalent antisera (Becton, Dickinson and Company, MD, USA). The appearance of visible agglutination with the Group A, B, C, or D polyvalent antisera indicated Shigella dysenteriae, S. flexneri, S. boydii, and S. sonnei, respectively.\u003c/p\u003e \u003cp\u003eIsolates demonstrating multidrug-resistant (MDR) profiles were subcultured on Tryptic Soy Agar (TSA) to obtain pure cultures. Pure isolates were preserved in Tryptic Soy Broth (TSB) supplemented with 20% glycerol and stored at \u0026minus;\u0026thinsp;20\u0026deg;C for subsequent molecular analysis.\u003c/p\u003e\n\u003ch3\u003eAntibiotic Susceptibility Testing\u003c/h3\u003e\n\u003cp\u003eThe antibiotic susceptibility test was done by the Kirby-Bauer disk diffusion method in Muller-Hinton agar following standard procedures (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). To summarize, a sterile cotton swab was used to wipe the whole surface of Mueller Hinton agar (Oxoid) with a MacFarland 0.5 standardized suspension of the bacteria in 0.8% sterile saline. The inoculated surface was subsequently covered with discs (HiMedia, India) that contained single concentrations of each antimicrobial agent. Using a straight-line ruler, the clear zones created by the antimicrobial suppression of bacterial growth were measured in millimeters following an overnight incubation at 37\u0026deg;C and they were interpreted as sensitive, intermediate sensitive, and resistant (CLSI, 2014). The antibiotic susceptibility pattern of six antibiotics, namely Chloramphenicol (CHL, 30 mcg), Co-trimoxazole (COT, 25 mcg), Tetracycline (TET, 10 mcg), Ciprofloxacin (CIP, 10 mcg), Azithromycin (AZM, 30 mcg), Ceftriaxone (CTR, 30 mcg).\u003c/p\u003e \u003cp\u003eQuality control was set up using an \u003cem\u003eEscherichia coli\u003c/em\u003e strain (ATCC 25922) which was susceptible to all the tested drugs.\u003c/p\u003e\n\u003ch3\u003eDNA extraction and quantification\u003c/h3\u003e\n\u003cp\u003eBacterial isolates preserved in 20% glycerol stocks were subcultured on Tryptic Soy Agar (TSA) and incubated at 37\u0026deg;C for 18\u0026ndash;24 hours to obtain fresh colonies. Genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Germany) according to the manufacturer\u0026rsquo;s protocol, with minor modifications optimized for Gram-negative bacteria. Briefly, 3\u0026ndash;5 well-isolated colonies were suspended in phosphate-buffered saline (PBS), lysed with Proteinase K and Buffer AL, and incubated at 56\u0026deg;C for 10 minutes. DNA was bound to silica columns, washed with Buffers AW1 and AW2, and eluted in 50\u0026ndash;100 \u0026micro;L of EDTA-free Buffer AE.\u003c/p\u003e \u003cp\u003eDNA concentration and purity were assessed using a Qubit 4 Fluorometer (Thermo Fisher Scientific, USA). All DNA extracts exceeded 20 ng/\u0026micro;L, meeting the minimum input requirement for Oxford Nanopore Technologies (ONT) MinION sequencing. Purified DNA was stored at \u0026minus;\u0026thinsp;20\u0026deg;C until library preparation.\u003c/p\u003e\n\u003ch3\u003eLibrary Preparation and sequencing\u003c/h3\u003e\n\u003cp\u003eSequencing libraries were prepared using the Oxford Nanopore Technologies (ONT) DNA Ligation Sequencing Kit (SQK-NBD114-96) according to the manufacturer\u0026rsquo;s protocol. Library preparation was performed over two days. On day one, extracted genomic DNA was subjected to DNA repair, end-preparation, and native barcoding, followed by purification. On day two, adapter ligation and final library clean-up were performed prior to sequencing.\u003c/p\u003e \u003cp\u003eThe final pooled library was loaded onto an R10.4.1 (FLO-MN114) flow cell and sequenced on an ONT MinION Mk1C device. Basecalling was performed using Guppy (v6.6.1, ONT) in high-accuracy (HAC) mode. Sequencing quality metrics, including read length and Q-scores, were monitored in real time using MinKNOW (v23.10.7). Raw FASTQ reads were exported for downstream analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRead Quality Assessment and Species identification\u003c/h2\u003e \u003cp\u003eFollowing basecalling, raw FASTQ reads were uploaded to the BUGSEQ web-based metagenomic analysis platform (BugSeq Bioinformatics, Canada) for preliminary species identification and quality assessment. The platform employs long-read alignment and classification algorithms to confirm taxonomic identity at the species level.\u003c/p\u003e \u003cp\u003eAdditionally, Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis was performed within BUGSEQ to estimate genome completeness and assess sequencing quality. BUSCO completeness scores were used to evaluate dataset suitability and to select high-quality isolates for downstream genome assembly and analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome Assembly and Annotation\u003c/h3\u003e\n\u003cp\u003eRaw FASTQ reads were processed to remove sequencing adapters and barcode sequences using Porechop (v0.2.4). Read quality filtering was performed with NanoQ using a minimum quality score threshold of Q10 and a minimum read length of 1,000 bp to eliminate low-quality and short reads.\u003c/p\u003e \u003cp\u003eFiltered reads were assembled de novo using Dragonflye (v5.32.1), a long-read assembler optimized for Oxford Nanopore data. The resulting assemblies were polished through one round of Racon followed by Medaka (v1.11.3) to improve consensus accuracy. Assembly statistics, including total genome length, GC content, number of contigs, and N50, were generated as part of the Dragonflye output.\u003c/p\u003e \u003cp\u003eGenome annotation was carried out using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to predict coding sequences (CDS), rRNAs, tRNAs, and other genomic features.\u003c/p\u003e\n\u003ch3\u003eGenomic Characterization of Antimicrobial Resistance and Plasmid Replicon\u003c/h3\u003e\n\u003cp\u003eThe assembled genomes were screened for antimicrobial resistance (AMR) determinants using both the ResFinder 4.7.1 and Comprehensive Antibiotic Resistance Database (CARD) platforms. ResFinder analysis was performed through the Center for Genomic Epidemiology (CGE) web server with default parameters, while CARD-based resistance gene identification was conducted using the Resistance Gene Identifier (RGI, v6.0.3) tool. Only hits with sequence identity and coverage above 90% were considered valid.\u003c/p\u003e \u003cp\u003ePlasmid replicons were identified using PlasmidFinder (CGE) with a minimum identity threshold of 95% and default coverage parameters. Plasmid contigs identified by PlasmidFinder were extracted from assembled genomes and re-analyzed with ResFinder to determine plasmid-associated AMR genes. This allowed differentiation between chromosomally encoded and plasmid-borne resistance determinants.\u003c/p\u003e \u003cp\u003eMultilocus sequence typing (MLST) was evaluated using the MLST 2.0 tool available at the Center for Genomic Epidemiology (CGE, Technical University of Denmark). Assembled genome FASTA files were queried against the E. coli MLST database, and allele matches were determined automatically based on exact sequence identity and coverage. For isolates with incomplete locus matches, the nearest sequence types (STs) were assigned based on the highest overall allelic similarity. Isolates exhibiting less than 100% identity in one or more loci were interpreted as either potential new ST variants or unresolved due to minor sequence variation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical Consideration\u003c/h2\u003e \u003cp\u003eThis study was conducted under the Public Health Sample Collection and Testing Policy of the Government of Liberia, which authorizes the use of de-identified clinical isolates for public health surveillance and research purposes. All bacterial isolates included in this study were obtained from stool specimens submitted to the National Public Health Reference Laboratory (NPHRL) for routine diarrheal disease surveillance and antimicrobial resistance monitoring.\u003c/p\u003e \u003cp\u003eNo patient-identifying information or associated clinical metadata were used in the analysis. Therefore, no additional ethical approval was required, as all procedures were performed in compliance with national laboratory surveillance regulations and ethical guidelines established by the National Public Health Institute of Liberia (NPHIL).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSequencing Output and Read Quality\u003c/h2\u003e \u003cp\u003eA total of ten bacterial isolates were successfully sequenced using the Oxford Nanopore MinION platform, generating raw read yields ranging from approximately 30,760 kb to 303,799 kb (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Read quality and depth varied across isolates, reflecting minor differences in extraction purity and library preparation efficiency. After quality evaluation, seven isolates were retained for downstream analyses, each meeting the minimum inclusion thresholds of mean genome coverage above 8\u0026times;, BUSCO completeness exceeding 75%, and fewer than 200 contigs.\u003c/p\u003e \u003cp\u003eAcross the retained datasets, the median read length ranged from 601 bp to 1,361 bp, with most samples showing well-distributed long reads suitable for de novo assembly. The read N50 values, representing the length above which half of the total bases are contained, varied between 73.8 kbp and 3400 kbp, indicating moderate to high sequencing quality across isolates. These metrics collectively suggest that the sequencing runs yielded sufficiently long and high-quality reads for reliable downstream assembly and genomic characterization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSequencing yield and read quality metrics for ten Gram-negative bacterial isolates sequenced using Oxford Nanopore Technology.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Yield (kb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian Read Length (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN50 (kb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenome coverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected for assembly (Yes/No)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF_001_LIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella Flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1548607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3087.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenome Assembly and Annotation\u003c/h2\u003e \u003cp\u003eHigh-quality filtered reads from seven isolates were de novo assembled using the Dragonflye pipeline, optimized for Oxford Nanopore long-read data. The assemblies produced genome sizes ranging from approximately 3.8 Mbp to 4.83 Mbp, which are consistent with typical genome sizes reported for \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003eEnterobacter hormaechei\u003c/em\u003e, and \u003cem\u003eShigella flexneri\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe number of contigs per assembly varied between 4 and 194, with N50 values ranging from 73.8 kbp to 3.4 Mbp, indicating moderate to high assembly contiguity. Mean GC content among the isolates ranged from 36.63% to 54.37%, aligning with the expected species-specific profiles. BUSCO completeness scores of 76\u0026ndash;88% further confirmed that the assembled genomes captured a substantial portion of the expected single-copy orthologs, supporting their suitability for comparative genomic and resistance gene analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eGenome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), a standardized and curated pipeline for bacterial genome annotation. PGAP applies a best-placed reference protein set in combination with GeneMarkS-2\u0026thinsp;+\u0026thinsp;for gene prediction and functional assignment. Detailed information about the PGAP workflow is available through the National Center for Biotechnology Information (NCBI) genome annotation resource. Annotated genomic features included protein-coding sequences (CDS), ribosomal RNA (rRNA) genes, transfer RNA (tRNA) genes, and non-coding RNA (ncRNA) elements (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Draft genome assembly statistics and BUSCO completeness for seven multidrug-resistant Gram-negative isolates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenome length (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of contigs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLargest contig (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGC content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBUSCO complete (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBUSCO missing (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3888178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3368269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF_001_LIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4512242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e328887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4838533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e366542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4829463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1475657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4508363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e343666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4503962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4409889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Structural genome annotation features of multidrug-resistant Gram-negative bacterial isolates generated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal CDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes (coding)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenes (RNA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003etRNA genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003encRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePseudo genes (total)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF_001_LIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Characterization of Antimicrobial Resistance and Plasmid Replicon\u003c/h2\u003e \u003cp\u003eScreening of the seven selected isolates using \u003cem\u003eResFinder\u003c/em\u003e and CARD-RGI revealed diverse AMR determinants associated with multiple antibiotic classes. Across all isolates, genes conferring resistance to β-lactams, fluoroquinolones, aminoglycosides, sulfonamides, tetracyclines, and macrolides were frequently detected, reflecting the multidrug-resistant phenotype previously observed phenotypically. \u003cem\u003eResFinder\u003c/em\u003e revealed multiple horizontally acquired AMR genes, while \u003cem\u003eCARD-RGI\u003c/em\u003e identified chromosomally encoded efflux systems and regulatory mutations linked to broad resistance phenotypes. The \u003cem\u003eE. coli\u003c/em\u003e isolates carried a combination of plasmid-mediated β-lactamase genes (\u003cem\u003eblaTEM\u003c/em\u003e variants) and quinolone resistance determinants (\u003cem\u003eqnrS1\u003c/em\u003e), consistent with extended-spectrum β-lactamase (ESBL) and fluoroquinolone-resistant profiles (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEnterobacter hormaechei\u003c/em\u003e harbored the AmpC-type β-lactamase \u003cem\u003eblaACT-16\u003c/em\u003e, which confers intrinsic resistance to penicillins and first-generation cephalosporins, and the \u003cem\u003efosA\u003c/em\u003e gene mediating fosfomycin resistance. \u003cem\u003eProteus mirabilis\u003c/em\u003e encoded both \u003cem\u003etet(J)\u003c/em\u003e and \u003cem\u003ecat\u003c/em\u003e, supporting its phenotypic tetracycline and chloramphenicol resistance. \u003cem\u003eShigella flexneri\u003c/em\u003e carried \u003cem\u003etet(B)\u003c/em\u003e and \u003cem\u003edfrA14\u003c/em\u003e, conferring tetracycline and trimethoprim resistance, respectively.\u003c/p\u003e \u003cp\u003eEfflux-related determinants were widespread among isolates, including RND-family genes (\u003cem\u003eAcrAB-TolC\u003c/em\u003e, \u003cem\u003emdtE\u003c/em\u003e, \u003cem\u003eemrA\u003c/em\u003e) and transcriptional regulators (\u003cem\u003emarA\u003c/em\u003e, \u003cem\u003esoxS\u003c/em\u003e), indicating a potential basal multidrug-resistance mechanism even in isolates lacking clear plasmid-encoded AMR genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Summary of Antimicrobial Resistance Genes Identified by ResFinder.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIsolate ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of AMR genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eKey resistance genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted Resistance Classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcquired\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChromosomal mutation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCat, tet(J)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhenicols, tetracyclines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF_001_LIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003etet(B), dfrA14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTetracyclines, trimethoprim\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eblaACT-16\u003c/em\u003e, \u003cem\u003efosA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ-lactams (cephalosporins, penicillins), fosfomycin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ecatA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003egyrA\u003c/em\u003e, \u003cem\u003egyrB\u003c/em\u003e, \u003cem\u003eparC\u003c/em\u003e, \u003cem\u003eparE\u003c/em\u003e, \u003cem\u003epmrA\u003c/em\u003e, \u003cem\u003epmrB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChloramphenicol, fluoroquinolones, polymyxins\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eblaTEM-143\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtended-spectrum β-lactamase (ESBL)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eblaTEM-164\u003c/em\u003e, \u003cem\u003eaph\u003c/em\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003cem\u003e-Id\u003c/em\u003e, \u003cem\u003eaph(3\u0026Prime;)-Ib\u003c/em\u003e, \u003cem\u003esul2\u003c/em\u003e, \u003cem\u003eqnrS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ-lactams, aminoglycosides, sulfonamides, fluoroquinolones\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo AMR gene detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Summary of CARD-RGI Results for Selected Isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerfect Hits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrict Hits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMajor AMR Mechanisms Detected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRepresentative Genes/Models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEfflux and target alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ersmA\u003c/em\u003e, \u003cem\u003eCRP\u003c/em\u003e, \u003cem\u003ecatA4\u003c/em\u003e, \u003cem\u003evanG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF_001_LIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEfflux, target alteration and reduced permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKpnE\u003c/em\u003e, \u003cem\u003eKpnH\u003c/em\u003e, \u003cem\u003eemrE\u003c/em\u003e, \u003cem\u003esoxS\u003c/em\u003e, \u003cem\u003eleuO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfflux and target alteration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ersmA, AcrAB-TolC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultidrug efflux (RND/MFS), reduced permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003emdtE\u003c/em\u003e, \u003cem\u003eAcrS\u003c/em\u003e, \u003cem\u003eemrA\u003c/em\u003e, \u003cem\u003emarA\u003c/em\u003e, \u003cem\u003eqacJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEfflux and target alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eqacJ\u003c/em\u003e, \u003cem\u003ebacA\u003c/em\u003e, \u003cem\u003esoxS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEfflux and target alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003esoxR\u003c/em\u003e, \u003cem\u003ebacA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABD_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Hierarchical clustering and distribution of antimicrobial resistance (AMR) genes among multidrug-resistant Gram-negative isolates sequenced in this study. The dendrogram (top panel) shows clustering of isolates based on binary presence\u0026ndash;absence patterns of AMR genes identified through ResFinder and CARD-RGI analyses. The heatmap (bottom panel) visualizes the distribution of key resistance determinants (1\u0026thinsp;=\u0026thinsp;gene present, 0\u0026thinsp;=\u0026thinsp;absent). Distinct clustering patterns were observed among \u003cem\u003eEscherichia coli, Enterobacter hormaechei, Proteus mirabilis\u003c/em\u003e, and \u003cem\u003eShigella flexneri\u003c/em\u003e isolates, reflecting both species-specific and shared resistance gene profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePlasmidFinder analysis identified multiple plasmid replicons among the sequenced isolates. Two isolates including \u003cem\u003eEnterobacter hormaechei\u003c/em\u003e (ABD_004) and \u003cem\u003eEscherichia coli\u003c/em\u003e (ABD_003), harbored distinct replicon types. In \u003cem\u003eE. hormaechei\u003c/em\u003e, two replicons, \u003cem\u003eIncFIB(K)\u003c/em\u003e and \u003cem\u003eCol(pHAD28)\u003c/em\u003e, were detected on contig 79 (positions 13,712\u0026ndash;14,266) and contig 17 (positions 879\u0026ndash;1,007), respectively. In \u003cem\u003eE. coli\u003c/em\u003e, three replicons, \u003cem\u003eIncFIA\u003c/em\u003e, \u003cem\u003eCol156\u003c/em\u003e, and \u003cem\u003eCol(pEc648)\u003c/em\u003e, were identified on contig 20 (positions 5,795\u0026ndash;6,179), contig 31 (positions 6,489\u0026ndash;6,642), and contig 33 (positions 1,166\u0026ndash;1,860), respectively.\u003c/p\u003e \u003cp\u003eSubsequent analysis of the extracted plasmid sequences using \u003cem\u003eResFinder\u003c/em\u003e and \u003cem\u003eCARD-RGI\u003c/em\u003e revealed no AMR genes associated with these replicons. This suggests that the AMR determinants detected in the current dataset are likely chromosomally encoded rather than plasmid-mediated.\u003c/p\u003e \u003cp\u003eInc-type plasmids, particularly \u003cem\u003eIncFIA\u003c/em\u003e and \u003cem\u003eIncFIB(K)\u003c/em\u003e, have been widely reported in Enterobacteriaceae for their role in the horizontal transfer of extended-spectrum β-lactamase (ESBL) genes such as \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;CTX-M\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;and \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;TEM\u0026lt;/sub\u0026gt; (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, their absence in the present plasmid-resident regions may indicate either loss of mobile AMR cassettes or the presence of non-resistance plasmids functioning in replication, stability, or conjugation maintenance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMulti-Locus Sequence Typing\u003c/h2\u003e \u003cp\u003eMLST analysis was performed on the \u003cem\u003eE. coli\u003c/em\u003e isolates using the CGE MLST 2.0 pipeline. Four isolates (ABD_003, ABD_005, ABD_006, ABD_007) generated partial allele profiles that could be compared against the \u003cem\u003eE. coli\u003c/em\u003e MLST database. ABD_003 showed closest similarity to ST632, with a perfect (100%) allele identity for the \u003cem\u003eputP\u003c/em\u003e locus. ABD_005 was most similar to ST65 and ST7, while Barcode 65 and 66 shared allelic relatedness with multiple STs (ST210, ST155, ST681, ST1332 for ABD_006; ST974, ST441, ST1737, ST294, ST1137, ST1770 for ABD_007).\u003c/p\u003e \u003cp\u003eThe incomplete MLST profiles across most isolates likely result from the draft nature of the genome assemblies, which contained fragmented contigs and missing loci rather than true allelic absence. Such assembly gaps can interrupt the recovery of full housekeeping gene sequences needed for exact ST assignment. Nonetheless, the allelic diversity and partial matches observed across the isolates highlight underlying genetic heterogeneity and possibly regionally distinct \u003cem\u003eE. coli\u003c/em\u003e genotypes circulating in Liberia.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo provide a more holistic MDR surveillance data on Gram-negative bacteria isolates from clinical samples in Liberia, we integrated phenotypic susceptibility testing with genome assembly, annotation, antimicrobial resistance profiling, plasmid replicon analysis, and MLST inference. Our findings provide critical baseline genomic data for a region where such information remains extremely limited. The draft genome assemblies characterized here were of sufficient quality to identify clinically relevant AMR determinants despite moderate coverage (8\u0026ndash;13\u0026times;). Although fragmented assemblies can limit complete recovery of some loci, they were adequate for robust AMR characterization. This is consistent with other studies demonstrating that long-read sequencing at moderate coverage can successfully identify resistance genes and mobile elements in \u003cem\u003eEnterobacterales\u003c/em\u003e (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AMR gene profiles observed across the isolates reflect resistance to multiple antibiotic classes commonly used in clinical practice, including β-lactams, fluoroquinolones, aminoglycosides, sulfonamides, tetracyclines, and phenicols. The high prevalence of multidrug-resistant Gram-negative pathogens in this study complements regional evidence indicating that \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., and other Enterobacterales are dominant contributors to antimicrobial resistance in West Africa. A recent meta-analysis reported that gram-negative bacteria accounted for over 60% of MDR isolates in healthcare and community settings in West Africa, with an overall MDR prevalence of approximately 59% (95% CI: 48\u0026ndash;69%) across studies from 13 countries (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another systematic review of hospital wastewater studies underscored the high occurrence of resistant \u003cem\u003eE. coli\u003c/em\u003e and resistance genes such as \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;TEM\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;and \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;SHV\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;in West African environments (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Our genomic data align with these findings, revealing multiple β-lactamase genes (including \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;TEM\u0026lt;/sub\u0026gt;, and others) and fluoroquinolone resistance determinants (\u003cem\u003eqnrS\u003c/em\u003e) across isolates, consistent with the patterns reported in clinical and environmental studies in the region.\u003c/p\u003e \u003cp\u003eWhile phenotypic surveillance studies from West Africa emphasize high MDR rates, genomic data remain sparse. Our findings add depth by characterizing the genetic basis of resistance and showing the diversity of AMR mechanisms in isolates from Liberia. In \u003cem\u003eShigella flexneri\u003c/em\u003e, meta-analyses across Africa indicate an ESBL and Carbapenem Resistance of ~\u0026thinsp;41.2% (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Although our Shigella isolates did not exhibit carbapenemase genes, the presence of β-lactam resistance correlates with continental patterns, underscoring the clinical significance of ESBLs in this species. Importantly, CARD-RGI analysis revealed a broad repertoire of efflux systems and regulatory genes, indicating that multidrug resistance in these isolates is not solely driven by acquired resistance genes. This mirrors global observations that chromosomally encoded efflux and regulatory mechanisms form a foundational resistance backbone in Enterobacteriaceae, upon which horizontally acquired genes further expand resistance phenotypes.\u003c/p\u003e \u003cp\u003ePlasmidFinder analysis identified IncFIA, IncFIB(K), and Col-type replicons in two isolates, consistent with plasmid families commonly reported among Enterobacteriaceae worldwide (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). IncF plasmids, in particular, are well recognized as major vectors in the dissemination of extended-spectrum β-lactamase (ESBL) genes, including \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;CTX-M\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;and \u003cem\u003ebla\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;TEM\u0026lt;/sub\u0026gt;, especially in \u003cem\u003eEscherichia coli\u003c/em\u003e and related species (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, targeted extraction of plasmid-associated contigs followed by ResFinder and CARD-RGI screening revealed no AMR genes within these replicon regions. This finding suggests that, in the present dataset, resistance determinants are predominantly chromosomally encoded rather than plasmid-borne.\u003c/p\u003e \u003cp\u003eSimilar patterns have been reported in global studies, where prolonged antibiotic selective pressure has been associated with the stable chromosomal integration of resistance genes, even in the presence of plasmids lacking AMR determinants (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The presence of plasmids without detectable resistance genes may nonetheless provide a genetic backbone capable of acquiring and mobilizing AMR determinants in the future especially from the One health perspective, underscoring the importance of continued genomic surveillance in under-sampled settings (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMLST analysis revealed partial allele profiles across all \u003cem\u003eE. coli\u003c/em\u003e isolates, with none achieving full sequence-type assignment. The absence of 100% locus identity is most plausibly explained by assembly fragmentation rather than true gene absence, a known limitation of draft genomes generated at moderate coverage. Nevertheless, the identification of nearest STs spanning multiple lineages, including ST632, ST65, ST7, and others, highlights substantial genetic diversity among the isolates. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This pattern likely reflects the presence of novel or regionally distinct \u003cem\u003eEscherichia coli\u003c/em\u003e genotypes circulating in Liberia, a phenomenon increasingly recognized in genomic studies from previously under-sampled regions. Expanded population-level genomic surveillance, incorporating higher sequencing depth and complete genome resolution, will be essential to confirm these findings and to better define the evolutionary and epidemiological dynamics of these lineages.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, the coexistence of multiple resistance mechanisms, combined with evidence of mobile genetic elements and diverse genetic backgrounds, underscores the complexity of AMR in Liberia. The lack of plasmid-associated AMR genes in this study should not be interpreted as reduced transmission risk, as chromosomally encoded resistance can persist stably and plasmids may rapidly acquire resistance cassettes under selective pressure. This study demonstrates the feasibility and value of implementing whole-genome sequencing for AMR surveillance in resource-limited settings using portable Nanopore platforms. Future work should prioritize increased sequencing depth, hybrid short- and long-read approaches, inclusion of clinical metadata, and longitudinal sampling to track resistance evolution and transmission dynamics. Expanding genomic surveillance across Liberia and neighboring countries will be essential for informing antibiotic stewardship, guiding empirical therapy, and strengthening regional and global AMR monitoring efforts.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides the first publicly available whole-genome data for multidrug-resistant Gram-negative bacteria isolated through routine surveillance in Liberia. Long-read sequencing revealed diverse resistance determinants that were predominantly chromosomally encoded, alongside incomplete MLST profiles suggestive of novel or regionally distinct \u003cem\u003eEscherichia coli\u003c/em\u003e lineages. These findings highlight both the genetic diversity of antimicrobial resistance in an under-sampled setting and the critical need to expand genomic surveillance capacity in West Africa to better inform local and global AMR control efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome sequencing data generated in this study have been deposited in the DDBJ/ENA/GenBank databases. Five genome assemblies are available under BioProject accession number PRJNA1346421, entitled \u003cem\u003e\u0026ldquo;Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia.\u0026rdquo;\u003c/em\u003e In addition, two genome assemblies have been deposited separately with accession numbers JBQXYK000000000 and JBREJJ000000000. All data are publicly accessible and may be used for comparative genomic and antimicrobial resistance studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no specific external funding. Reagents, sequencing consumables, and infrastructure support were provided through routine laboratory operations and partner support at the National Public Health Reference Laboratory of Liberia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.O. Somah, F.M. Taweh, J.SM. Gilayeneh and M. Sarmie conceptualized the study and drafted the manuscript. S. Tokpa, H. Tarwoe, J. George, A. Momolu, A. Wuo, S.O. Nyilah, C. Johnson, and A. Boakai performed DNA extraction, library preparation, and sequencing. E. Tiawroh, R. Koon, and R. Yeaney isolated bacterial strains from stool specimens. D. Kollie and F.O. Somah conducted the bioinformatic analyses. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests. The authors received no financial support, grants, personal fees, or non-financial benefits related to the conduct of this study, its analysis, or the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the National Public Health Institute of Liberia (NPHIL) and the Ministry of Health (MoH) for their leadership and support of public health surveillance in Liberia. We are grateful to our partners, including the Africa Centres for Disease Control and Prevention (Africa CDC) and the World Health Organization (WHO), for their technical and logistical support toward the establishment and strengthening of the Genomic Sequencing Unit at the National Public Health Reference Laboratory (NPHRL), including the provision of reagents and capacity-building through training and fellowships. We also thank the medical and laboratory staff involved in sample collection and processing, as well as the sample transport riders whose efforts ensured timely delivery of specimens to the NPHRL.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA, et al. Antimicrobial resistance: Impacts, challenges, and future prospects. J Med Surg Public Health. 2024;2:100081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Chauhan A, Ranjan A, Mathkor DM, Haque S, Ramniwas S, et al. Emerging challenges in antimicrobial resistance: implications for pathogenic microorganisms, novel antibiotics, and their impact on sustainability. Front Microbiol. 2024;15:1403168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRankin DA, Stahl A, Sabour S, Khan MA, Armstrong T, Huang JY, et al. 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J Antimicrob Chemother. 2018;73(5):1121\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Fan S, Zhang X, Yuan Y, Zhong W, Wang L, et al. Antibiotic-resistant characteristics and horizontal gene transfer ability analysis of extended-spectrum β-lactamase-producing Escherichia coli isolated from giant pandas. Front Vet Sci. 2024 July;26:11:1394814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose R, Nolan DJ, Ashcraft D, Feehan AK, Velez-Climent L, Huston C, et al. Comparing antimicrobial resistant genes and phenotypes across multiple sequencing platforms and assays for Enterobacterales clinical isolates. BMC Microbiol. 2023;23(1):225.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLerminiaux N, Fakharuddin K, Mulvey MR, Mataseje L. Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. 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Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proc Natl Acad Sci USA. 2021;118(6):e2008731118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenton JF, Lugo-Martinez J, Tucker AE, Schrider DR, Warren WC, Hahn MW. Extensive Error in the Number of Genes Inferred from Draft Genome Assemblies. Guigo R, editor. PLoS Comput Biol. 2014;10(12):e1003998.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuebner R, Mugabi R, Hetesy G, Fox L, De Vliegher S, De Visscher A et al. Characterization of genetic diversity and population structure within Staphylococcus chromogenes by multilocus sequence typing. Robinson DA, editor. PLoS ONE. 2021;16(3):e0243688.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, Whole-genome sequencing, Gram-negative bacteria, Oxford Nanopore sequencing, Multidrug resistance, and Genomic surveillance","lastPublishedDoi":"10.21203/rs.3.rs-8542744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8542744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultidrug resistance (MDR) is a key driver of antimicrobial resistance (AMR) in all of sub-Saharan Africa; however, genomic AMR data from Liberia are unavailable. To fill the gap, this study utilized Oxford Nanopore long-read sequencing to produce draft genomes of seven multidrug-resistant isolates from four bacterial species (\u003cem\u003eEscherichia coli, Enterobacter hormaechei, Proteus mirabilis\u003c/em\u003e, and \u003cem\u003eShigella flexneri\u003c/em\u003e) of diarrheal samples at Liberia\u0026rsquo;s National Public Health Reference Laboratory (NPHRL). The sequencing was of moderate quality (8\u0026ndash;13\u0026times;), long reads and BUSCO coverage (75 to 93 percent), thus enabling relevant de novo assembly and standardized annotation through PGAP. In the isolates, AMR determinants were found to confer resistance to β-lactams (including \u003cem\u003eblaTEM\u003c/em\u003e variants and \u003cem\u003eblaACT\u003c/em\u003e-16), fluoroquinolones (\u003cem\u003eqnrS\u003c/em\u003e1 and mutations in \u003cem\u003egyr\u003c/em\u003eA, \u003cem\u003egyr\u003c/em\u003eB, \u003cem\u003epar\u003c/em\u003eC and \u003cem\u003epar\u003c/em\u003eE), aminoglycosides, sulfonamides, tetracyclines, phencyclines, and polymyxins, as well as RND/MFS efflux systems and global regulatory genes, including marA, soxS and mdtE. PlasmidFinder found IncFIA, IncFIB(K), and Col-type replicons in two isolates but no plasmid-encoded AMR genes, which suggests that the resistance of this collection is mainly chromosomally located. MLST showed that only partial allele profiles and the nearest-sequence-type matches were possible, indicating a great quantity of genetic diversity and region-specific E. coli lineages circulating in Liberia. These findings provide the initial genomic baseline of MDR Gram-negative pathogens in Liberia and show that whole-genome sequencing with Nanopores can be successfully performed in portable technology, which is likely to increase the need to consider higher-resolution genomic surveillance to direct stewardship and public health interventions.\u003c/p\u003e","manuscriptTitle":"Whole-Genome Sequencing of Multidrug-Resistant Gram-Negative Bacteria Isolated from Clinical Samples in Liberia Using Oxford Nanopore Technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 18:47:42","doi":"10.21203/rs.3.rs-8542744/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T18:35:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T15:57:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107959810330846530263287709665652950779","date":"2026-03-10T15:26:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T12:17:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231944214635515239028207850842662330073","date":"2026-01-31T11:24:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T18:23:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T11:54:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T11:49:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-01-07T14:25:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd69916a-8fc5-4169-a151-6f6bcdabaa80","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T18:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 18:47:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8542744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8542744","identity":"rs-8542744","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}