Genomic Convergence of Hypervirulence, Pan-Drug Resistance, and Phage Defense in a High-Risk Klebsiella pneumoniae from Pharmaceutical Wastewater in Bangladesh

Genomic Convergence of Hypervirulence, Pan-Drug Resistance, and Phage Defense in a High-Risk Klebsiella pneumoniae from Pharmaceutical Wastewater in Bangladesh | 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 Article Genomic Convergence of Hypervirulence, Pan-Drug Resistance, and Phage Defense in a High-Risk Klebsiella pneumoniae from Pharmaceutical Wastewater in Bangladesh Md Firoz Ahmed, Md Murshed Hasan Sarkar, Kaniz Mehzabin, MD Ismail Hossain, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8281161/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 22 You are reading this latest preprint version Abstract K. pneumoniae strains that combine multidrug resistance, hypervirulence and persistence are spreading worldwide, causing a severe threat to public health. Unraveling the genetics that underlies these high-risk clones is critical for the development of countermeasures. We isolated K. pneumoniae JU-BAEC-01 from treated effluent of antibiotic-manufacturing pharmaceutical facilities in Bangladesh. Herein, we report a comprehensive genomic analysis of the K. pneumoniae strain JU-BAEC-01 using whole-genome sequencing, comparative genomics, and various bioinformatics tools, including CARD, ResFinder, VFDB, PADLOC, Defense Finder, and CRISPRCas Finder, to outline its phylogenetic position, antibiotic resistance profile, virulence potential, mobile genetic elements, and antiviral defense systems. JU-BAEC-01 belongs to a phylogenetically distinct lineage, serotype O3b:KL150, unrelated to globally dominant high-risk clones. This isolate shows resistance to nearly all clinically relevant antibiotic classes except carbapenems and colistin, mediated by an extensive acquired resistome, including tmexCD3-toprJ3 (tigecycline), armA, aac(6')-Ib-cr, qnrB4, oqxAB, blaDHA-1, blaSHV-182, and blaTEM-1B, mostly carried on conjugative IncC, IncFIB, IncHI1B, and IncR plasmids. Classical hypervirulence markers are present: complete aerobactin (iucABCD-iutA) and salmochelin (iroBCDEN) clusters, rmpA2, type 1 and type 3 fimbriae, T6SS, and pgaABCD. Six prophage regions and multiple insertion elements further enhance genomic plasticity. Notably, the strain encodes one of the most elaborate anti-phage defense arsenals reported in Klebsiella to date, comprising functional Type I-E, III-A, and IV-A CRISPR-Cas systems, multiple restriction-modification systems, BREX Type I, abortive infection systems (AbiE, AbiU), and additional novel defenses that coexist with phage-derived anti-CRISPR (AcrIE9) and anti-restriction (ArdA) proteins. Klebsiella pneumoniae JU-BAEC-01 is a "perfect storm" pathogen that combines pan-drug resistance (PDR), hypervirulence, and a multilayered, highly developed defense against bacteriophages. This genomic convergence confounds treatment options and emphasizes the evolutionary capability of this priority pathogen to resist both the antimicrobial and natural predatory pressures. The presence of phage anti-defense systems underlines a dynamic co-evolutionary arms race with significant implications for the potential failure of phage therapy against such robustly defended isolates. Biological sciences/Microbiology Biological sciences/Molecular biology Klebsiella pneumoniae Pan-drug resistance Hypervirulence Phage defense systems CRISPR-Cas BREX system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction AMR has emerged as one of the greatest threats to global public health in the 21st century. Since the discovery of penicillin in 1928 and the subsequent "golden era" of antibiotic development, the selective pressure imposed by widespread clinical, agricultural, and veterinary use of antibiotics has driven the rapid evolution and dissemination of resistant bacterial populations [ 1 , 2 ]. The World Health Organization now estimates that AMR could cause 10 million deaths annually by 2050 if current trends continue [ 3 ]. Of particular concern are Gram-negative pathogens exhibiting multidrug resistance or extensive drug resistance, many of which are resistant to last-resort carbapenems and colistin [ 4 ]. Among these critical-priority pathogens, K. pneumoniae exhibits extraordinary genomic plasticity and has been known to acquire both antibiotic resistance and hypervirulence determinants [ 5 , 6 ]. Traditionally, K. pneumoniae strains were divided into classical K. pneumoniae (cKP), mainly associated with nosocomial infections in immunocompromised hosts, and hypervirulent K. pneumoniae (hvKP), which had the capability of causing severe community-acquired infections in healthy individuals [ 7 , 8 ]. The hypervirulence is largely attributed to enhanced siderophore production, particularly aerobactin, hyper mucoviscosity conferred by rmpA/rmpA2, and other virulence plasmids [ 9 ]. The worrying convergence of carbapenem resistance with hypervirulence factors has generated multidrug-resistant-hvKP strains that combine near pan-drug resistance with extreme pathogenicity and high transmissibility [ 10 , 11 ]. Such "superbugs" have caused fatal outbreaks worldwide and represent a nightmare for modern medicine. Horizontal gene transfer mediated by plasmids, transposons, integrative conjugative elements, and even bacteriophages facilitate the rapid spread of resistance and virulence genes in K. pneumoniae [ 12 , 13 ]. Pharmaceutical wastewater treatment plants receiving effluents from bulk-drug manufacturing facilities are extreme selective environments, with mg/L-level antibiotic concentrations, several orders of magnitude higher than in typical hospital or municipal wastewaters [ 14 , 15 ]. The hotspots thus created promote the emergence, persistence, and dissemination of antibiotic-resistant bacteria and antibiotic-resistant genes (ARG) into receiving water bodies and ultimately the broader environment [ 16 , 17 ]. Despite the increase in global awareness, pharmaceutical wastewater remains poorly regulated and under-characterized in many low and middle income countries. In Bangladesh, a major hub for generic drug manufacturing, high concentrations of active pharmaceutical ingredients have been detected in industrial effluents, raising serious concerns about environmental contamination and selection for high-risk clones [ 18 ]. The present study thus aimed to characterize the antibiotic residues, resistant bacterial populations, and associated resistance determinants in pharmaceutical wastewater from Bangladesh. Here, we provide a comprehensive analysis of this critical pollution source through the employment of physicochemical analysis, HPLC, culture-based isolation, antimicrobial susceptibility testing, 16S rRNA sequencing, and whole-genome sequencing. Significantly, we describe in detail a carbapenem-resistant hypervirulent K. pneumoniae isolate, JU-BAEC-01, which epitomizes the convergence of pan-drug resistance, hypervirulence, and an unprecedented multi-layered anti-phage defense system that calls for urgent attention to improve the standards of wastewater treatment and for the development of novel therapeutic strategies against such evolving pathogens. Results Antibiotic Residues and Antibiotic-Resistant Bacteria in Pharmaceutical Wastewater Ten wastewater samples, including five influents and five effluents, were collected from antibiotic-manufacturing facilities in Dhaka and Gazipur (Fig. 1 a). Influents of the wastewater were significantly polluted, characterized by mean (± SD) concentrations of COD 1,248 ± 412 mg/L, BOD₅ 468 ± 156 mg/L, and TDS 2,156 ± 786 mg/L (Table 1 ). After conventional activated-sludge treatment, COD decreased to < 68 mg/L and BOD₅ to 118 ± 24 mg/L (p < 0.01). However, TDS declined only partly and remained above the limit in several treated effluents, as high as 2,978 mg/L. Through HPLC quantification, no ciprofloxacin or penicillin G residues above the limit of quantification (0.05 µg/mL) could be detected, indicating their effective removal or degradation by the specific treatment. From the wastewater samples analyzed, 150 distinct bacterial isolates were obtained. Biochemical characterization of the obtained bacteria revealed their identity (Fig. 1 b): Klebsiella spp . (26%), Acinetobacter baumannii (23%), Escherichia coli (17%), Enterobacter spp . (13%), Pseudomonas aeruginosa (7%), Aeromonas spp . (7%), and other genera, which accounted for 7%. Susceptibility testing by the disk-diffusion method showed high rates of resistance among the collection: 91.6% for amoxicillin/clavulanic acid, 89.2% for erythromycin, 64.7% for trimethoprim, 60.9% for kanamycin, 56.3% for chloramphenicol, 46.1% for streptomycin, and 19.7% for ciprofloxacin (Fig. 1 c). Table 1 Physicochemical characteristics of influent and effluent wastewater samples collected from five pharmaceutical factories (n = 5 each) Parameter Influent (mean ± SD) Effluent (mean ± SD) p-value pH 7.1 ± 0.4 7.3 ± 0.2 n.s. Temperature (°C) 28.4 ± 1.1 28.1 ± 0.9 n.s. TDS (mg/L) 2,156 ± 786 1,247 ± 912 < 0.05 COD (mg/L) 1,248 ± 412 < 68 < 0.01 BOD₅ (mg/L) 468 ± 156 118 ± 24 < 0.01 Thirty MDR isolates (resistance to ≥ 3 antimicrobial classes) were further tested by the VITEK® 2 Compact system against a panel of 17 antibiotics (Fig. 1 d). Resistance among MDR isolates was universal to amoxicillin/clavulanic acid (100%), and highly prevalent to trimethoprim/sulfamethoxazole (80%) and ciprofloxacin (80%). Resistance to cefuroxime was observed for 60% of the isolates, while 40% were resistant to piperacillin/tazobactam, amikacin, tigecycline, ceftriaxone, and gentamicin. Of note, all MDR isolates were susceptible to carbapenems, colistin, and cefoperazone/sulbactam. This Klebsiella pneumoniae was designated as K. pneumoniae JU-BAEC-01, representing an isolate with the broadest observed phenotypic resistance profile selected for WGS to investigate its resistome, virulence determinants, and potential mobile genetic elements. Genome Assembly and Taxonomic Identification The genome of Klebsiella pneumoniae JU-BAEC-01 was assembled using the PATRIC Comprehensive Genome Analysis service and yielded a draft genome consisting of 5,769,218 bp in 343 contigs, with a G + C content of 56.79%. The assembly was visualized using Bakta (Fig. 2 a). Assembly metrics are excellent, with a contig N50 of 32,077 and L50 of 51, indicating a good-quality draft genome (Table 2 ). Table 2 Assembly Details Feature Value Contigs 343 GC Content 56.79% Contig L50 51 Genome Length 5,769,218 bp Contig N50 32,077 Taxonomic classification of the sequencing reads was performed using EDGE bioinformatics tools, including gottcha-speDB-b, gottcha2-speDB-b, gottcha-speDB-v, kraken2, pangia, and centrifuge. Each tool uses different algorithms and databases that give a comprehensive view of the taxonomic composition of the dataset. Figure 2 b shows the output in a heatmap format where a darker red cell means the species is more abundant, indicating that the JU-BAEC-01 sample contains Klebsiella pneumoniae as its most abundant species. This result was further confirmed by whole-genome sequencing and multi-locus sequence typing (MLST), which identified Klebsiella pneumoniae with 100% confidence. The evolutionary relationship of different species of Klebsiella , including Klebsiella JU-BAEC-01, was determined using the TYGS platform. The phylogenetic tree obtained indicates that Klebsiella JU-BAEC-01 belongs to the Klebsiella pneumoniae family, a group of bacteria commonly associated with pneumonia (Fig. 2 c). Interestingly, Klebsiella JU-BAEC-01 shares a close genetic relationship with Klebsiella pneumoniae strains ATCC 13883 and DSM 30104. Identification of Klebsiella pneumoniae Lineage In the bacterial assembly "JU-BAEC-01," Kaptive analysis identified three main loci: KL150, KL107-D1, and O3b. Thus, the KL150 locus had a high match confidence with 98.19% coverage and 99.51% identity, disclosing 19 out of the 21 expected genes, hence indicating a nearly complete capsular polysaccharide synthesis region (Fig. 3 a). On the other hand, KL107-D1 showed low confidence with 72.73% coverage and 76.74% identity, revealing only 4 genes out of the 13 genes expected and therefore suggesting the presence of a partial or modified capsular locus. Furthermore, the O3b locus presented a high match confidence with 99.43% coverage and 98.85% identity, where 7 out of the 8 expected genes were present, indicating a well-preserved O-antigen biosynthesis region. The data reveal genetic diversity and a potential virulence mechanism within the bacterial strain, which are important for understanding its pathogenicity and treatment strategies. The Average Nucleotide Identity (ANI) analysis provided additional insights into the genetic relationships among the strains. Klebsiella JU-BAEC-01 exhibited the highest ANI similarity (99.60%) with Klebsiella pneumoniae HS11286, confirming its close evolutionary relationship within the K. pneumoniae species complex. ANI values above 95% typically indicate that genomes belong to the same species, supporting the classification of JU-BAEC-01 as a strain of K. pneumoniae . JU-BAEC-01 also showed high ANI values with Klebsiella variicola F2R9T (94.32%) and Klebsiella africana 200023 (94.96%), indicating close evolutionary relationships, though more distant than with K. pneumoniae (Fig. 3 b). In contrast, ANI values with other species, such as Klebsiella aerogenes (84.26%) and Klebsiella grimontii (83.07%), were substantially lower, reflecting greater genetic divergence. To determine the genetic relatedness and phylogenetic placement of Klebsiella pneumoniae isolate JU-BAEC-01, a cgMLST analysis was performed using the BacWGSTdb 2.0 platform. Its whole-genome sequence was queried against all publicly available K. pneumoniae genomes. Phylogenetic relationships were subsequently visualized using a Minimum Spanning Tree obtained by GrapeTree. The cgMLST-based phylogeny placed JU-BAEC-01 in a long, distinct branch of the MST, indicative of a highly diverged genetic background. Its closest genomic neighbor was isolate SU13_RCGF01(Fig. 3 c). However, it was genetically quite distant, with 576 allelic differences between the two cgMLST loci. This degree of genetic divergence far exceeds accepted thresholds for clonal or outbreak-related isolates (≤ 10–25 allelic differences), which confirms that JU-BAEC-01 and SU13_RCGF01 belong to different transmission networks or recent evolutionary lineages. Collectively, these findings indicate that K. pneumoniae JU-BAEC-01 is genetically unique and represents a distinct lineage of the species. Genome Annotation and Functional Insights Whole-genome annotation of Klebsiella pneumoniae isolate JU-BAEC-01 provided an in-depth view of its structural and functional potential. The RASTtk pipeline identified 6,062 protein-coding sequences (CDSs), out of which 4,975 were functionally characterized, while 1,087 CDSs were designated as hypothetical proteins. The well-structured genome assembly contained 49 tRNA and 4 rRNA genes, with no partial CDSs, miscellaneous RNA, or repeat regions. Similarly, a considerable functional diversity as depicted by 5,771 genus-specific and 5,862 cross-genus protein families was identified (Table 3 ). Table 3 Annotated Genome Features Feature Count CDS 6,062 tRNA 49 rRNA 4 Hypothetical proteins 1,087 Proteins with functional assignments 4,975 Proteins with EC number assignments 1,515 Proteins with GO assignments 1,251 Proteins with Pathway assignments 1,097 Proteins with PATRIC genus-specific family (PLfam) assignments 5,771 Proteins with PATRIC cross-genus family (PGfam) assignments 5,862 Functional categorization by RASTtk subsystem technology assigned 2,876 protein-coding sequences to 28 distinct functional categories (Fig. 4 a), revealing a metabolic profile typical of a versatile, facultative anaerobic bacterium. The genome was significantly enriched in core metabolic processes, with the most heavily represented subsystems including Carbohydrates (414 genes) and Amino Acids and Derivatives (412 genes), underpinning a high capacity for nutrient acquisition and biosynthesis. This extensive metabolic network was further underpinned by significant genetic investments in Protein Metabolism (219 genes), Cofactors, Vitamins, Prosthetic Groups, Pigments (202 genes), and Respiration (116 genes). Prominent genes were associated with pathogenicity and environmental interaction. For example, the subsystems Virulence, Disease and Defense accounted for 71 genes, while 33 genes were dedicated to Iron acquisition and metabolism—a critical virulence factor. Further, 35 genes were associated with Cell Wall and Capsule biosynthesis. This genetic landscape also reflected considerable genomic plasticity and adaptability—evidenced by 20 features categorized under Phages, Prophages, Transposable elements, and Plasmids, 101 genes associated with Stress Response, and 77 genes for Regulation and Cell signaling. On the contrary, the absence of genes for Photosynthesis and Motility and Chemotaxis was consistent with the established biology of K. pneumoniae. COG classification likewise supported these observations, with a strong overrepresentation of metabolism and cellular processes (Fig. 4 b). Main functional categories represented were Carbohydrate transport and metabolism (661 genes), Amino acid transport and metabolism (583 genes), Energy production and conversion (352 genes) and Transcription (558 genes). The genome also contained a remarkably high number of genes related to Defense mechanisms (165) and the mobilome (226), indicating active evolutionary forces. General or unknown functions were assigned to 584 genes, pointing to a pool of untapped genetic potential. KEGG pathway mapping further outlined the metabolic potential of JU-BAEC-01, as shown in Fig. 4 c. The major category represented was metabolism, dominated by Carbohydrate metabolism (637 genes), Amino acid metabolism (382 genes), and Energy metabolism (215 genes). It had pathways for Xenobiotic degradation (145 genes) and Membrane transport (391 genes) for environmental resilience and nutrient uptake. Clinical perspectives were also posed through KEGG, highlighting genes associated with Antimicrobial drug resistance (76) and Infectious diseases (37). Collectively, this integrated genomic analysis using RASTtk, COG, and KEGG shows that K. pneumoniae JU-BAEC-01 possesses a metabolically versatile and genetically resilient genome. The degree of its biosynthetic machinery, intricate stress-response networks, and adaptations for pathogenicity and horizontal gene transfer underpin its survival in diverse ecological and potentially clinical contexts, representing a distinct and robust lineage within the species. Pathogenicity, Antibiotic Resistance and Virulence Island Further analysis with the IslandPath-DIMOB tool revealed a diverse array of proteins, enzymes, transporters, and regulatory elements in Klebsiella JU-BAEC-01 isolates; most of these were directly or indirectly related to resistance mechanisms, mobile genetic elements, and other vital bacterial processes. Using bioinformatic tools such as SIGI-HMM, which uses Hidden Markov Models to detect genomic islands, phage, and resistance genes, this work has been able to highlight several important genomic features, as shown in Fig. 5 a. The isolate possesses a robust arsenal of virulence genes that definitively classify it as hypervirulent (hvKp). Among the most important are its elaborated siderophore systems; it carries the aerobactin receptor gene iutA, one of the main hallmarks of hvKp, and the complete salmochelin cluster iroBCDEN, which modifies enterobactin to evade host immunity. The combination provides exceptional iron acquisition that is key to systemic infection. This isolate also carries a complete and complex Type VI Secretion System (T6SS), which is a molecular weapon for invading host cells and competing with other bacteria. The presence of entire fim (Type 1 fimbriae) and mrk (Type 3 fimbriae) operons, alongside the pgaABCD locus for poly-N-acetylglucosamine synthesis, promotes adhesion and biofilm development. Adhesion properties are further provided by the genes for the E. coli common pilus, ECP (ecpA-R). Immune evasion is facilitated by the presence of genes for extensive capsular (CPS) and lipopolysaccharide (LPS) synthesis, which is controlled by rcsAB, and the arn operon for resistance to cationic antimicrobial peptides (CAMPs) (Fig. 5 b). The resistome of this isolate includes an extensive range of antimicrobial resistance mechanisms that make it resistant to nearly all major classes of antibiotics. Of particular concern is the defense against last-resort agents. Resistance to tigecycline is mediated through the powerful RND-type efflux system TmexCD3-toprJ3. High-level, broad-spectrum aminoglycoside resistance is conferred by the 16S rRNA methyltransferase armA. In addition, fosfomycin resistance proceeds through a two-fold mechanism that includes both the glutathione transferase gene fosA6 and a mutation in the uhpT fosfomycin transporter. The combination of an AmpC-type cephalosporinase, DHA-1, with an extended-spectrum beta-lactamase, SHV-11, and a penicillinase, TEM-1, ensures a broad-spectrum beta-lactam resistance profile. Fluoroquinolone resistance is multi-pronged, with contributions from plasmid-mediated target protection, qnrB4; target site mutations in DNA gyrase, gyrA S83I, and topoisomerase IV, parC S80I; and efflux pumps, OqxAB. The presence of aac(6')-Ib-cr further contributes by acetylating and inactivating ciprofloxacin. Other core resistances consist of genes for macrolides [mph(A), mph(E), msr(E)], sulfonamides (sul1), trimethoprim (dfrA12), and chloramphenicol (through OqxAB efflux). Importantly, resistance mechanisms against peptide antibiotics such as polymyxins were identified: the arnT and eptB genes, which modify lipid A, and impaired permeability through OmpA and OmpK37. The combined presence of various efflux systems, including AcrAB-TolC, OqxAB, TmexCD3-toprJ3, and various regulatory mutations, such as those in marR, ensure a high degree of baseline multidrug tolerance (Fig. 5 c). Several heavy metal resistance genes were detected, indicating the ability of the isolate to survive under contaminated environments. These included: ArsA, ArsB, and ArsH, which take part in arsenic detoxification, while CzcD confers resistance to cobalt, zinc, and cadmium. Copper resistance is mediated by CusA, CusB, CusR, and PcoE, which facilitate detoxification and subsequent efflux of copper ions out of the cells. Additionally, MerD, MerE, and MerT take part in mercury detoxification, thus showing the potential of an isolate for survival in environments under toxic metal stress. In addition, copper and silver efflux systems such as CusC, CusA, and CusR further enforce the ability of the isolate to handle metal toxicity (Fig. 5 d). Bacteriophage Diversity, Mobile Genetic Elements and Horizontal Gene Transfer MobileOG-db classification of the K. pneumoniae isolate JU-BAEC-01 identified 637 protein families that could be assigned to five major functional categories of MGEs: IE, with 138 protein families; RRR, with 174 protein families; P, with 124 protein families; STD, with 81 protein families; and T, with 120 protein families. The representation of such a wide range of protein families indicates that the isolate probably carries an array of MGEs, including conjugative plasmids, transposons, and bacteriophages, which are probably contributing to MDR and virulence. Further study on specific protein families obtained from mobileOG-db will be required for understanding the mechanisms involved in these processes (Fig. 6 a). A detailed bacteriophage analysis using PHASTER revealed a diverse array of bacteriophages with varied structural and functional properties. PHASTER analysis identified six prophage regions, including a questionable 19.9 Kb region linked to PHAGE_Shigel_SfIV, and intact regions of 36 Kb, 27.1 Kb, and 24.1 Kb associated with PHAGE_Edward_GF_2, PHAGE_Klebsi_ST512_KPC3phi13.2, and PHAGE_Pseudo_phiPSA1, respectively. Additionally, two incomplete prophage regions (18.6 Kb and 11.2 Kb) were linked to PHAGE_Escher_phiV10 and PHAGE_Escher_HK639. These findings suggest that the isolate may possess a significant number of prophages that could play a role in the horizontal transfer of resistance and virulence genes, thus enhancing the pathogenic potential of the strain (Fig. 6 b). These analyses were followed by plasmid replicon detection within the isolate using the MobileElementFinder and PlasmidFinder tools. Multiple plasmid types, such as IncC, IncFIB(pNDM-Mar), IncHI1B(pNDM-MAR), IncR, IncY, and repB(R1701), were detected. These replicons are known to be associated with the spread of antibiotic resistance genes. Application of the geNomad workflow in scaffold analysis revealed several plasmids carrying important resistance genes, including *qnrB4, blaDHA-1, blaTEM-1B, blaSHV-182, OqxA, OqxB, fosA, mph(A), qacE, aac(6')-Ib-cr, mph(E), msr(E), dfrA12,* and aadA2. This is evidence of resistance to various classes of antibiotics, including quinolones, beta-lactams, and aminoglycosides. Virulence factors such as iutA, mrkA, terC, and fimH were also found in this isolate, showing enhanced pathogenicity potential (Fig. 6 c). Defense Mechanisms against Phage Infections The bacterial defense systems of Klebsiella JU-BAEC-01 were comprehensively characterized using three complementary bioinformatics tools: PADLOC, DefenseFinder, and CRISPRCasFinder 4.2.20. These analyses revealed a diverse and multi-layered defense strategy involving CRISPR-Cas systems, restriction-modification (RM) systems, abortive infection (Abi) mechanisms, and less characterized defense elements, along with phage-encoded antidefense systems, which highlight the ongoing evolutionary dynamics between bacteria and bacteriophages. CRISPR-Cas systems were detected consistently by both DefenseFinder (Fig. 7 a) and PADLOC (Fig. 7 b), which identified a Class 1, Subtype IV-A CRISPR-Cas system known for targeting phage DNA. CRISPR arrays were also confirmed by CRISPRCasFinder (Fig. 7 c), with a particularly strong prediction on Contig 41 (evidence level 3), suggesting a likely functional CRISPR-Cas defense. Additionally, Cas proteins necessary for viral DNA targeting were found across multiple contigs, including Type III-A on Contig 71 and Type U on Contig 41, indicating that Klebsiella JU-BAEC-01 harbors several functional CRISPR-Cas systems. Both PADLOC and DefenseFinder identified multiple RM systems, including Type I and Type II, which are crucial for the protection of bacterial genomes through the cleavage of foreign DNA and methylation of specific sequences. These are among the essential components of the arsenal of Klebsiella JU-BAEC-01 against bacteriophages, enhancing its capacity for resistance against phage infection. In addition to CRISPR-Cas and RM systems, the BREX (I) system was detected by both PADLOC and DefenseFinder, suggesting that Klebsiella JU-BAEC-01 employs this non-cleaving phage defense mechanism to inhibit viral replication. The detection of abortive infection (Abi) systems, including AbiE and AbiU, further adds to the multilayered nature of the bacterial defense, providing an additional fail-safe mechanism by inducing programmed cell death in infected cells to prevent the spread of phages. Furthermore, both PADLOC and DefenseFinder identified several other less-studied or novel defense systems. PADLOC detected PD-T7-1, PDC-S12, and VSPR systems, along with BrxL and BrxC proteins associated with the BREX system, whereas DefenseFinder highlighted the GAPS1 system and the Mok-Hok-Sok toxin-antitoxin system, which may contribute to bacterial stress responses and phage resistance. Of particular interest was the identification by DefenseFinder (Fig. 6 a) of phage-encoded antidefense systems, including Anti-RM systems (e.g., Ard proteins) and Anti-CRISPR systems (e.g., acrIE9), which allow phages to evade bacterial immune defenses. This highlights the ongoing evolutionary arms race between Klebsiella JU-BAEC-01 and bacteriophages, as the phages continue to evolve mechanisms to overcome bacterial defense strategies. Methods Sample Collection and Physicochemical Characterization Wastewater samples were collected from five pharmaceutical industries located in Dhaka and Gazipur, Bangladesh, between January and March 2023. Each industry was a bulk producer of β-lactam and fluoroquinolone antibiotics. Five influent (raw wastewater) and five effluent (post-treatment) samples were collected aseptically in sterile 1-L polypropylene bottles. Samples were transported at 4°C and processed within 6 h of collection. Temperature and pH were measured on-site using a portable meter (Hanna Instruments, USA). Total dissolved solids (TDS), chemical oxygen demand (COD), and biochemical oxygen demand (BOD₅ at 20°C) were determined according to the standard methods [ 19 ]. Determination of Antibiotic Residues by High-Performance Liquid Chromatography (HPLC) Antibiotic residues were quantified using a Hitachi LaChrome Elite HPLC system, UV detector, and reversed-phase Nucleosil 120-5 C18 column (250 × 4.6 mm, 5 µm). The mobile phase consisted of a mixture of acetonitrile and 0.024 M orthophosphoric acid buffer (pH 3.0 ± 0.1 adjusted with triethylamine), at a ratio of 13:87 (v/v). The flow rate was 1.5 mL/min, detection wavelength 278 nm, column temperature 40°C and injection volume 10 µL. Ciprofloxacin and penicillin G were used as external standards. Quantification was based on peak area using six-point calibration curves (0.1–100 µg/mL; R² > 0.999). LOQ was 0.05 µg/mL for both antibiotics [ 11 , 18 ]. Bacterial Isolation, Enumeration, and Preservation Serial ten-fold dilutions of wastewater were plated onto nutrient agar (Oxoid, UK), cetrimide agar (Condalab, Spain) and mFC agar (HiMedia, India) for enumeration of total heterotrophic bacteria, Pseudomonas spp . and fecal coliforms, respectively. Plates were incubated at 37°C for 24–48 h (44.5°C for mFC agar). Morphologically distinct colonies were purified and preserved at − 80°C in tryptic soy broth containing 20% glycerol. Antimicrobial Susceptibility Testing Antibiotic susceptibility was determined by the Kirby–Bauer disk diffusion method on Mueller–Hinton agar (Oxoid, UK) in accordance with the EUCAST 2023 guidelines [ 20 ]. A total of 26 antibiotics belonging to 12 classes were tested. Isolates resistant to at least one agent in ≥ 3 antibiotic classes were classified as multidrug-resistant (MDR) [ 21 ]. MICs of selected MDR isolates were determined using the VITEK® 2 Compact automated system (bioMérieux, France) with AST-GN83 cards according to the manufacturer’s recommendations. Genomic DNA Extraction and Whole-Genome Sequencing of K. pneumoniae JU-BAEC-01 High-molecular-weight genomic DNA from an overnight culture of K. pneumoniae JU-BAEC-01 was extracted with the DNeasy Blood & Tissue Kit (Qiagen, Germany), as instructed by the manufacturer’s protocol for Gram-negative bacteria. Library preparation was carried out using the Nextera XT DNA Library Preparation Kit (Illumina, USA). Paired-end sequencing (2×150 bp) was carried out on an Illumina MiniSeq platform, generating > 100× average coverage. Genome Assembly High-quality reads were assembled into a draft genome using the integrated assembly pipelines of the BV-BRC [ 22 ] and the EDGE bioinformatics platform [ 23 ]. These services utilize robust algorithms like SPAdes [ 24 ] to perform contig generation, scaffolding, and quality assessment. Taxonomic and Phylogenetic Analysis The isolate was confirmed as K. pneumoniae , an MLST scheme that defines the strain type based on seven housekeeping genes through the PubMLST [ 25 ] database. Capsular polysaccharide (K-locus) and O-antigen (O-locus) were typed with high confidence to KL150/KL107-D1 and O3b, respectively, by Kaptive [ 26 ]. Phylogenetic placement was evaluated by the Type (Strain) Genome Server (TYGS) [ 27 ]. Additionally, a reference genome SNP approach and the cgMLST scheme through the BacWGSTdb 2.0 database [ 28 ] were used in an effort to build phylogenetic trees, generating neighbor-joining and minimum spanning trees that help explain genetic relationships among the isolates. Pangenome Analysis A pangenome analysis was performed with the aim of determining genetic variability. Fourteen reference genome sequences of the Klebsiella genus have been downloaded from the NCBI database. The analysis via the Integrated Prokaryotes Genome and Pan-genome Analysis tool [ 29 ] confirmed that there is a high degree of genetic relatedness between JU-BAEC-01 and other K. pneumoniae strains. Genome Annotation and Functional Analysis Extensive genome annotation was performed to identify and characterize genetic elements. Primary annotation was carried out using the Genome Annotation Service on BV-BRC and Prokka [ 30 ] for rapid gene prediction. Functional insights were achieved by annotating genes with GO terms and KEGG pathways using the gcType database [ 31 ]. For subsystem-based annotation, RASTtk (RAST tool kit) [ 32 ] was used. The genome was visualized using Proksee and Bakta [ 33 , 34 ], which provided a way to map the genome in a circular fashion and to highlight features like CDSs, RNA genes, and AMR loci. Analysis of Antibiotic Resistance and Virulence Factors ARGs were identified using CARD [ 35 ] and ResFinder [ 36 ], and results were visualized using ProbioMinServer [ 37 ]. Resistance genes for metals and biocides were detected using the BacMet database [ 38 ]. Virulence factors and pathogenic islands were visualized using the dedicated analysis pipelines available within IslandViewer 4 [ 54 ] and EDGE Bioinformatics. Identification of mobile genetic elements and bacteriophages MGEs were characterized with the use of Mobile Element Finder [ 39 , 41 ] and Plasmid Finder [ 40 , 41 ] for the identification of insertion sequences, transposons, and plasmid replicons. Putative plasmid sequences within assembled scaffolds were further analyzed using geNomad [ 42 ]. Bacteriophage-derived sequences and prophage regions were identified and classified using PHASTER [ 43 ]. Prediction of Antiviral Defense Systems Bacterial defense systems against viral infections, such as CRISPRCas and abortive infection systems, were identified using a suite of bioinformatics tools: DefenseFinder [ 44 ], PADLOC [ 45 ], and CRISPRCasFinder [ 46 ]. Discussion The escalating crisis of antimicrobial resistance (AMR) has been widely recognized as one of the most serious global public health threats of the 21st century, with current estimates of 700,000 annual deaths projected to rise to 10 million by 2050 [ 3 ]. Pharmaceutical wastewater treatment plants (PWWTPs) that receive effluents from bulk-drug manufacturing facilities constitute extreme selective environments in which antibiotic concentrations can reach the mg/L range—orders of magnitude higher than in municipal or hospital wastewaters [ 14 , 47 ]. Such hotspots have repeatedly been shown to act as crucibles for the emergence and dissemination of multidrug-resistant (MDR) and extensively drug-resistant pathogens, as well as reservoirs of mobilizable antibiotic resistance genes (ARGs) [ 48 , 49 ]. Here, we report the first whole genomic characterization of a high-risk K. pneumoniae isolate, JU-BAEC-01, recovered from treated pharmaceutical effluent in Bangladesh-a major hub of generic antibiotic production. Although conventional activated-sludge treatment had substantially reduced the organic load (COD and BOD₅), and ciprofloxacin and penicillin G were not detectable by HPLC in the effluents, MDR bacteria were still viable in numbers ranging from 10³ to 10⁶ CFU/mL. This suggests that current treatment processes are inadequate for removal of highly adapted resistant populations and is in agreement with findings from related PWWTPs in India and China [ 47 , 15 ]. Phenotypic screening of 150 isolates showed alarmingly high resistance rates to many antibiotics (> 90% to amoxicillin/clavulanic acid and erythromycin, > 60% to trimethoprim and kanamycin), including 20% that were classified as MDR. Of 30 MDR isolates analyzed by VITEK® 2, the predominant species was K. pneumoniae at 30%, followed by Acinetobacter baumannii and Escherichia coli . Notably, all retained susceptibility to carbapenems and colistin-preserving these last-resort agents for now but underscoring the selective pressure exerted by non-carbapenem, non-polymyxin antibiotics in these environments. Whole-genome sequencing of the most resistant isolate, K. pneumoniae JU-BAEC-01, uncovered a paradigm of convergent evolution rarely documented thus far in environmental isolates. It combines an extended acquired resistome conferring resistance to nearly all major antibiotic classes except carbapenems and colistin, with bona fide hypervirulence determinants characteristic of hvKp and one of the most elaborate multi-layered anti-phage defense systems reported in Klebsiella thus far. In particular, the resistome of JU-BAEC-01 is alarming. It bears the newly emerged tmexCD3-toprJ3 RND efflux cluster mediating high-level tigecycline resistance that has recently spread with remarkable success globally in mobile plasmids [ 50 ]. Other relevant elements included armA mediating high-level aminoglycoside resistance, the bifunctional aminoglycoside/fluoroquinolone acetyltransferase aac(6')-Ib-cr, plasmid-mediated qnrB4 and oqxAB [ 43 ], and chromosomal quinolone resistance-conferring mutations -QRDR gyrA S83I, parC S80I-. Multiple β-lactamases -blaDHA-1, blaSHV-182, blaTEM-1B- completed a profile making the strain effectively pan-resistant to first- and second-line therapies. Most of such determinants were plasmid-borne-IncC, IncFIB, IncHI1B, IncR-pointing out the role of horizontal gene transfer in assembling such a complex resistome under intense selective pressure. Meanwhile, JU-BAEC-01 harbors undisputed hypervirulence markers: complete aerobactin (iucABCD-iutA) and salmochelin (iroBCDEN) siderophore systems, rmpA2 (hypermucoviscosity), intact Type 1 and Type 3 fimbriae, T6SS, and pgaABCD biofilm operon-highly associated with severe community-acquired invasive infections, such as pyogenic liver abscess and metastatic spread [ 8 , 26 ]. Convergence of carbapenem-susceptible yet extensively MDR profiles with hvKp determinants in an environmental isolate is decidedly alarmist, since their acquisition of carbapenemases-for example, via a single plasmid transfer event-could instantly yield untreatable CR-hvKp "superbugs" of the kind responsible for fatal nosocomial outbreaks [ 15 , 11 ]. A third, hitherto underappreciated dimension of JU-BAEC-01 success is the extraordinarily sophisticated arsenal of anti-phage defense systems that it possesses. Utilizing DefenseFinder, PADLOC, and CRISPRCasFinder, we have identified functional Type I-E, IIIA, and IVA CRISPR-Cas systems; multiple Type I and II restriction-modification systems; BREX Type I; abortive infection systems AbiE, AbiU; toxin-antitoxin modules; and several poorly characterized systems: PD-T7-1, VSPR, GAPS1. Cooccurrence of phage-encoded anti-defense proteins AcrIE9 and ArdA within prophage regions highlights an active evolutionary arms race. The presence of this multi-layered defense can be a plausible explanation for the ecological dominance of the strain within phage-rich environments such as wastewater and serves as a major obstacle to phage therapy-an otherwise very promising alternative against MDR infections [ 51 ]. Successful therapeutic phages, therefore, will necessarily need to elude or suppress several independent systems simultaneously, a challenge potentially requiring highly engineered or cocktail-based approaches. Such extensive innate immunity should, therefore, coexist with a finely tuned evolutionary trade-off-a selective relaxation of adaptive immunity, for example, limited CRISPR activity against resistance/virulence plasmids, with retention of robust innate systems that block lytic phages but allow access to beneficial gene acquisition [ 52 , 53 ]. This approach mirrors that adopted in globally successful high-risk clones and likely contributes to the rapid assembly of dangerous phenotypes in polluted environments. In all, K. pneumoniae JU-BAEC-01 represents the "perfect storm" of bacterial evolution under anthropogenic pressure-a genetically unique, environmentally sourced clone that has combined pan-drug resistance, hypervirulence, and phage-hardened defenses into a single resilient lineage. Its emergence in treated pharmaceutical effluent underlines the urgent need for (i) improved wastewater treatment technologies capable of removing viable MDR pathogens and ARGs, (ii) stringent regulation of antibiotic emissions from manufacturing, and (iii) accelerated development of novel therapeutics-including phage cocktails, anti-virulence agents, and CRISPR-based approaches-capable of overcoming such superbugs. In the absence of effective intervention, environmental reservoirs such as that described herein will continue to seed the next generation of clinically untreatable pathogens. The present study demonstrates that even efficiently treated pharmaceutical wastewater in Bangladesh remains a reservoir of highly multidrug-resistant pathogens. K. pneumoniae JU-BAEC-01 represents a genetically distinct environmental clone that has converged pan-drug resistance, hypervirulence, and one of the most elaborate anti-phage defense systems yet reported in Klebsiella. This “perfect storm” pathogen severely constrains conventional and alternative therapies and underscores the urgent need for advanced wastewater treatment, stringent discharge regulations, and continuous genomic surveillance of industrial effluents to prevent the dissemination of such high-risk clones into clinical and community settings. Declarations Declaration of interests All the contributing author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. They declare they have no conflict of interest regarding contribution, submission and publication. Funding Not applicable Author Contribution FA. contributed to the design and execution of the research experiments, ensuring alignment with the study’s objectives. MMHS. performed comprehensive genomic data analyses and carried out all major bioinformatics interpretations. MIH. led the sampling activities and conducted statistical analyses, ensuring high-quality and reliable datasets. MB. actively participated in data analysis and manuscript drafting, ensuring clarity, logical flow, and coherence. KM. provided essential research assistance and played a key role in sampling and coordination of field activities. SFC. conducted sample preparation and genome extraction, maintaining strict quality control throughout the process. SRN. managed sample preparation and sequencing workflows, ensuring accuracy and consistency in data generation. TM. secured funding, supervised project management, and facilitated collaboration among team members and institutions. MOF. developed the research plan, managed the project’s data framework, and led the manuscript writing, ensuring comprehensive documentation of the study. All authors reviewed the manuscript. Acknowledgement The authors would like to acknowledge all participants who took part in this study. Authors are grateful to Dr. Kamruzzaman Pramanik head of Microbiology Industrial Irradiation Division (MIID); Dr. Zahid Hasan of MIID under the Institute of Food and Radiation Biology (IFRB), Bangladesh Atomic Energy Commission (BAEC) and Department of Microbiology, Jahangirnagar University for the logistics support for this study. 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1","display":"","copyAsset":false,"role":"figure","size":607164,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical context, species distribution, and antimicrobial susceptibility profile of bacterial isolates.\u003c/strong\u003e A complete overview of the studied isolates, their origin, phenotypic resistance, and the diversity within the species. (a) Map showing the geographical location of the sample collection site. (b) Representative cultural morphology of isolated bacteria grown on non-selective Nutrient Agar (NA) after 24 hours of incubation at 37°C, demonstrating colonial diversity. (c) Phenotypic antibiotic susceptibility testing by the Kirby-Bauer disk diffusion method on Mueller-Hinton Agar (MHA). A clear zone of inhibition (ZOI) around the antibiotic disks indicates sensitivity, while confluent growth up to the disk edge indicates resistance. (d) Bar chart summarizing the antimicrobial susceptibility test results, showing the percentage of isolates classified as Sensitive, Intermediate, and Resistant for a panel of common antimicrobial agents. (e) Pie chart showing the percentage distribution of identified bacterial species by an automated system such as VITEK.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/3a12c7556cf3897a176d9890.png"},{"id":98826096,"identity":"e4e0aa38-5cbc-4e38-9c98-77ac49d46cea","added_by":"auto","created_at":"2025-12-22 18:51:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome assembly, taxonomic composition, and phylogenetic placement of Klebsiella pneumoniae JU-BAEC-01.\u003c/strong\u003e Initial genomic characterization confirms the species identity of the isolate. (a) Circular genome map of the draft assembly (5.77 Mbp, 343 contigs) annotated with Bakta, showing from outer to inner: COG functional classifications of CDS on forward and reverse strands, RNA genes, GC content (black), and GC skew (purple/green). (b) Heat map of taxonomic classification for JU-BAEC-01 sequencing dataset. Red color scale indicates relative abundance, and K. pneumoniae was identified as the predominant species. (c) TYGS-based phylogenetic tree indicating the evolutionary position of JU-BAEC-01, highlighted in red, within the genus Klebsiella. It clustered together with the two reference strains, namely, K. pneumoniae ATCC 13883 and DSM 30104.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/245c109588b3af18bef04905.png"},{"id":98826100,"identity":"d9d3e834-77c8-4670-afb8-0fcfc2e43c6e","added_by":"auto","created_at":"2025-12-22 18:51:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic characterization and phylogenetic analysis of a distinct Klebsiella pneumoniae lineage.\u003c/strong\u003e Strain JU-BAEC-01 represents a unique genetic lineage within the species complex. (a) Kaptive analysis characterizing the capsular (K)-and O-antigen loci, identifying a high-confidence KL150 K-locus, a partial KL107-D1 locus, and a conserved O3b O-antigen locus. (b) Heatmap depicting Average Nucleotide Identity values, which indicated that JU-BAEC-01 shares 99.60% identity with K. pneumoniae HS11286, thus confirming its species-level classification, whereas it shared lower identity with K. variicola and K. africana. (c) Minimum spanning tree based on core-genome MLST, placing JU-BAEC-01 on a long, distinct branch with 576 allelic differences from its closest relative (SU13_RCGF01).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/92b355138a30f047357a568a.jpeg"},{"id":99307675,"identity":"e47dc52b-133e-4fb1-8d61-2828050bf126","added_by":"auto","created_at":"2025-12-31 16:06:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":500209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional annotation and metabolic profiling of the Klebsiella pneumoniae JU-BAEC-01 genome.\u003c/strong\u003e The genome is enriched with metabolic functions and pathways related to environmental adaptation. (a) Bar plot representing the distribution of protein-coding sequences across 28 functional subsystems as annotated by RASTtk. (b) Chart of the Clusters of Orthologous Groups (COG) functional classification, showing strong representation in Metabolism, Cellular Processes and Signaling, and Information Storage and Processing. (c) Histogram of KEGG pathway mapping, which shows the counts of genes in major pathway categories and enrichment in metabolic pathways.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/04350be55d853a721c2dbdde.png"},{"id":99307625,"identity":"7afe0011-4648-4a26-8b0a-84f28d4b41e4","added_by":"auto","created_at":"2025-12-31 16:06:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":751334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirulence potential, antibiotic resistance, and genomic islands of Klebsiella pneumoniae JU-BAEC-01.\u003c/strong\u003e The genome harbors a diverse arsenal of virulence factors, resistance determinants, and horizontally acquired elements. (a) Circular genome map showing genomic islands as predicted by IslandPath-DIMOB and SIGI-HMM methods indicating putative horizontal gene transfer events. (b) Circular genome map showing the distribution of major virulence gene categories, including adhesion factors (adhesins), T2SS components, and genes for LPS biosynthesis. Color intensity represents the number of the respective genes. (c) Distribution of antibiotic resistance genes detected across major drug classes by CARD and ResFinder, considering efflux pumps, enzymatic inactivation, and target modification mechanisms. (d) Circular genome map showing the locations of heavy metal resistance operons for arsenic (ars), copper (cus, pco), mercury (mer), and cobalt-zinc-cadmium (czc).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/605aa1711359da7aacc29420.png"},{"id":98826098,"identity":"1f5be76c-6443-46f9-b66b-98cec397eb62","added_by":"auto","created_at":"2025-12-22 18:51:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":374897,"visible":true,"origin":"","legend":"\u003cp\u003eMobile genetic elements and bacteriophage diversity in Klebsiella pneumoniae JU-BAEC-01. The genome is equipped with a rich repertoire of mobile elements, including prophages and plasmids. (a) Circular genome map showing 637 mobile genetic element protein families classified by MobileOG-db, including functions for Integration/Excision, Replication/Repair, Phage, Stability/Transfer/Defense, and Conjugation. (b) Circular representation of the genome showing the location, size and completeness of six identified prophage regions. (c) Schematic of detected plasmid replicons, highlighting their associated antibiotic resistance genes and virulence factors.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/e5f02e4880bc500ed01626e1.png"},{"id":99307783,"identity":"2a38413e-afa5-463e-9a93-131d8cf0cce4","added_by":"auto","created_at":"2025-12-31 16:06:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":288029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive analysis of antiviral defense systems in Klebsiella pneumoniae JU-BAEC-01.\u003c/strong\u003e It possesses a multilayered arsenal of antiviral defense mechanisms. (a) Circular genome map from DefenseFinder, showing the locations of identified defense systems. These include the CRISPR-Cas, restriction-modification, abortive infection, and BREX systems. (b) Genome map representation of defense systems identified by PADLOC. These include a CRISPR-Cas Type IV-A system, several restriction-modification systems, and BREX modules. Colour intensity is indicative of prediction confidence. (c) Detailed visualization of CRISPR arrays and associated Cas proteins identified by CRISPRCasFinder, showing the multi-component architecture of the CRISPR-Cas systems.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/c0985d8cc2942af00396812b.png"},{"id":105754943,"identity":"a7bb4149-f455-4dde-b277-3d1f5da2b7b1","added_by":"auto","created_at":"2026-03-30 16:23:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4330072,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8281161/v1/30a28bb8-41c5-41d1-af0b-cdc061f59916.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenomic Convergence of Hypervirulence, Pan-Drug Resistance, and Phage Defense in a High-Risk Klebsiella pneumoniae from Pharmaceutical Wastewater in Bangladesh\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAMR has emerged as one of the greatest threats to global public health in the 21st century. Since the discovery of penicillin in 1928 and the subsequent \"golden era\" of antibiotic development, the selective pressure imposed by widespread clinical, agricultural, and veterinary use of antibiotics has driven the rapid evolution and dissemination of resistant bacterial populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The World Health Organization now estimates that AMR could cause 10\u0026nbsp;million deaths annually by 2050 if current trends continue [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Of particular concern are Gram-negative pathogens exhibiting multidrug resistance or extensive drug resistance, many of which are resistant to last-resort carbapenems and colistin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong these critical-priority pathogens, \u003cem\u003eK. pneumoniae\u003c/em\u003e exhibits extraordinary genomic plasticity and has been known to acquire both antibiotic resistance and hypervirulence determinants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Traditionally, \u003cem\u003eK. pneumoniae\u003c/em\u003e strains were divided into classical \u003cem\u003eK. pneumoniae\u003c/em\u003e (cKP), mainly associated with nosocomial infections in immunocompromised hosts, and hypervirulent \u003cem\u003eK. pneumoniae\u003c/em\u003e (hvKP), which had the capability of causing severe community-acquired infections in healthy individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The hypervirulence is largely attributed to enhanced siderophore production, particularly aerobactin, hyper mucoviscosity conferred by rmpA/rmpA2, and other virulence plasmids [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The worrying convergence of carbapenem resistance with hypervirulence factors has generated multidrug-resistant-hvKP strains that combine near pan-drug resistance with extreme pathogenicity and high transmissibility [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Such \"superbugs\" have caused fatal outbreaks worldwide and represent a nightmare for modern medicine.\u003c/p\u003e \u003cp\u003eHorizontal gene transfer mediated by plasmids, transposons, integrative conjugative elements, and even bacteriophages facilitate the rapid spread of resistance and virulence genes in \u003cem\u003eK. pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Pharmaceutical wastewater treatment plants receiving effluents from bulk-drug manufacturing facilities are extreme selective environments, with mg/L-level antibiotic concentrations, several orders of magnitude higher than in typical hospital or municipal wastewaters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The hotspots thus created promote the emergence, persistence, and dissemination of antibiotic-resistant bacteria and antibiotic-resistant genes (ARG) into receiving water bodies and ultimately the broader environment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the increase in global awareness, pharmaceutical wastewater remains poorly regulated and under-characterized in many low and middle income countries. In Bangladesh, a major hub for generic drug manufacturing, high concentrations of active pharmaceutical ingredients have been detected in industrial effluents, raising serious concerns about environmental contamination and selection for high-risk clones [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The present study thus aimed to characterize the antibiotic residues, resistant bacterial populations, and associated resistance determinants in pharmaceutical wastewater from Bangladesh. Here, we provide a comprehensive analysis of this critical pollution source through the employment of physicochemical analysis, HPLC, culture-based isolation, antimicrobial susceptibility testing, 16S rRNA sequencing, and whole-genome sequencing. Significantly, we describe in detail a carbapenem-resistant hypervirulent \u003cem\u003eK. pneumoniae\u003c/em\u003e isolate, JU-BAEC-01, which epitomizes the convergence of pan-drug resistance, hypervirulence, and an unprecedented multi-layered anti-phage defense system that calls for urgent attention to improve the standards of wastewater treatment and for the development of novel therapeutic strategies against such evolving pathogens.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAntibiotic Residues and Antibiotic-Resistant Bacteria in Pharmaceutical Wastewater\u003c/h2\u003e \u003cp\u003eTen wastewater samples, including five influents and five effluents, were collected from antibiotic-manufacturing facilities in Dhaka and Gazipur (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Influents of the wastewater were significantly polluted, characterized by mean (\u0026plusmn;\u0026thinsp;SD) concentrations of COD 1,248\u0026thinsp;\u0026plusmn;\u0026thinsp;412 mg/L, BOD₅ 468\u0026thinsp;\u0026plusmn;\u0026thinsp;156 mg/L, and TDS 2,156\u0026thinsp;\u0026plusmn;\u0026thinsp;786 mg/L (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After conventional activated-sludge treatment, COD decreased to \u0026lt;\u0026thinsp;68 mg/L and BOD₅ to 118\u0026thinsp;\u0026plusmn;\u0026thinsp;24 mg/L (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, TDS declined only partly and remained above the limit in several treated effluents, as high as 2,978 mg/L. Through HPLC quantification, no ciprofloxacin or penicillin G residues above the limit of quantification (0.05 \u0026micro;g/mL) could be detected, indicating their effective removal or degradation by the specific treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the wastewater samples analyzed, 150 distinct bacterial isolates were obtained. Biochemical characterization of the obtained bacteria revealed their identity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb): \u003cem\u003eKlebsiella spp\u003c/em\u003e. (26%), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (23%), \u003cem\u003eEscherichia coli\u003c/em\u003e (17%), \u003cem\u003eEnterobacter spp\u003c/em\u003e. (13%), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (7%), \u003cem\u003eAeromonas spp\u003c/em\u003e. (7%), and other genera, which accounted for 7%. Susceptibility testing by the disk-diffusion method showed high rates of resistance among the collection: 91.6% for amoxicillin/clavulanic acid, 89.2% for erythromycin, 64.7% for trimethoprim, 60.9% for kanamycin, 56.3% for chloramphenicol, 46.1% for streptomycin, and 19.7% for ciprofloxacin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\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\u003ePhysicochemical characteristics of influent and effluent wastewater samples collected from five pharmaceutical factories (n\u0026thinsp;=\u0026thinsp;5 each)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluent (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffluent (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2,156\u0026thinsp;\u0026plusmn;\u0026thinsp;786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,247\u0026thinsp;\u0026plusmn;\u0026thinsp;912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1,248\u0026thinsp;\u0026plusmn;\u0026thinsp;412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD₅ (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e468\u0026thinsp;\u0026plusmn;\u0026thinsp;156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003eThirty MDR isolates (resistance to \u0026ge;\u0026thinsp;3 antimicrobial classes) were further tested by the VITEK\u0026reg; 2 Compact system against a panel of 17 antibiotics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Resistance among MDR isolates was universal to amoxicillin/clavulanic acid (100%), and highly prevalent to trimethoprim/sulfamethoxazole (80%) and ciprofloxacin (80%). Resistance to cefuroxime was observed for 60% of the isolates, while 40% were resistant to piperacillin/tazobactam, amikacin, tigecycline, ceftriaxone, and gentamicin. Of note, all MDR isolates were susceptible to carbapenems, colistin, and cefoperazone/sulbactam. This \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was designated as \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01, representing an isolate with the broadest observed phenotypic resistance profile selected for WGS to investigate its resistome, virulence determinants, and potential mobile genetic elements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome Assembly and Taxonomic Identification\u003c/h3\u003e\n\u003cp\u003eThe genome of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e JU-BAEC-01 was assembled using the PATRIC Comprehensive Genome Analysis service and yielded a draft genome consisting of 5,769,218 bp in 343 contigs, with a G\u0026thinsp;+\u0026thinsp;C content of 56.79%. The assembly was visualized using Bakta (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Assembly metrics are excellent, with a contig N50 of 32,077 and L50 of 51, indicating a good-quality draft genome (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eAssembly Details\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContig L50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,769,218 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContig N50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,077\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\u003eTaxonomic classification of the sequencing reads was performed using EDGE bioinformatics tools, including gottcha-speDB-b, gottcha2-speDB-b, gottcha-speDB-v, kraken2, pangia, and centrifuge. Each tool uses different algorithms and databases that give a comprehensive view of the taxonomic composition of the dataset. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows the output in a heatmap format where a darker red cell means the species is more abundant, indicating that the JU-BAEC-01 sample contains \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e as its most abundant species. This result was further confirmed by whole-genome sequencing and multi-locus sequence typing (MLST), which identified \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e with 100% confidence.\u003c/p\u003e \u003cp\u003eThe evolutionary relationship of different species of \u003cem\u003eKlebsiella\u003c/em\u003e, including \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01, was determined using the TYGS platform. The phylogenetic tree obtained indicates that \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 belongs to the \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e family, a group of bacteria commonly associated with \u003cem\u003epneumonia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Interestingly, \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 shares a close genetic relationship with \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e strains ATCC 13883 and DSM 30104.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of\u003c/b\u003e \u003cb\u003eKlebsiella pneumoniae\u003c/b\u003e \u003cb\u003eLineage\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the bacterial assembly \"JU-BAEC-01,\" Kaptive analysis identified three main loci: KL150, KL107-D1, and O3b. Thus, the KL150 locus had a high match confidence with 98.19% coverage and 99.51% identity, disclosing 19 out of the 21 expected genes, hence indicating a nearly complete capsular polysaccharide synthesis region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). On the other hand, KL107-D1 showed low confidence with 72.73% coverage and 76.74% identity, revealing only 4 genes out of the 13 genes expected and therefore suggesting the presence of a partial or modified capsular locus. Furthermore, the O3b locus presented a high match confidence with 99.43% coverage and 98.85% identity, where 7 out of the 8 expected genes were present, indicating a well-preserved O-antigen biosynthesis region. The data reveal genetic diversity and a potential virulence mechanism within the bacterial strain, which are important for understanding its pathogenicity and treatment strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Average Nucleotide Identity (ANI) analysis provided additional insights into the genetic relationships among the strains. \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 exhibited the highest ANI similarity (99.60%) with \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e HS11286, confirming its close evolutionary relationship within the \u003cem\u003eK. pneumoniae\u003c/em\u003e species complex. ANI values above 95% typically indicate that genomes belong to the same species, supporting the classification of JU-BAEC-01 as a strain of \u003cem\u003eK. pneumoniae\u003c/em\u003e. JU-BAEC-01 also showed high ANI values with \u003cem\u003eKlebsiella variicola\u003c/em\u003e F2R9T (94.32%) and \u003cem\u003eKlebsiella africana\u003c/em\u003e 200023 (94.96%), indicating close evolutionary relationships, though more distant than with \u003cem\u003eK. pneumoniae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In contrast, ANI values with other species, such as \u003cem\u003eKlebsiella aerogenes\u003c/em\u003e (84.26%) and \u003cem\u003eKlebsiella grimontii\u003c/em\u003e (83.07%), were substantially lower, reflecting greater genetic divergence.\u003c/p\u003e \u003cp\u003eTo determine the genetic relatedness and phylogenetic placement of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolate JU-BAEC-01, a cgMLST analysis was performed using the BacWGSTdb 2.0 platform. Its whole-genome sequence was queried against all publicly available \u003cem\u003eK. pneumoniae\u003c/em\u003e genomes. Phylogenetic relationships were subsequently visualized using a Minimum Spanning Tree obtained by GrapeTree. The cgMLST-based phylogeny placed JU-BAEC-01 in a long, distinct branch of the MST, indicative of a highly diverged genetic background. Its closest genomic neighbor was isolate SU13_RCGF01(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). However, it was genetically quite distant, with 576 allelic differences between the two cgMLST loci. This degree of genetic divergence far exceeds accepted thresholds for clonal or outbreak-related isolates (\u0026le;\u0026thinsp;10\u0026ndash;25 allelic differences), which confirms that JU-BAEC-01 and SU13_RCGF01 belong to different transmission networks or recent evolutionary lineages. Collectively, these findings indicate that \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 is genetically unique and represents a distinct lineage of the species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGenome Annotation and Functional Insights\u003c/h3\u003e\n\u003cp\u003eWhole-genome annotation of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e isolate JU-BAEC-01 provided an in-depth view of its structural and functional potential. The RASTtk pipeline identified 6,062 protein-coding sequences (CDSs), out of which 4,975 were functionally characterized, while 1,087 CDSs were designated as hypothetical proteins. The well-structured genome assembly contained 49 tRNA and 4 rRNA genes, with no partial CDSs, miscellaneous RNA, or repeat regions. Similarly, a considerable functional diversity as depicted by 5,771 genus-specific and 5,862 cross-genus protein families was identified (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnotated Genome Features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothetical proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with functional assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with EC number assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with GO assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with Pathway assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with PATRIC genus-specific family (PLfam) assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins with PATRIC cross-genus family (PGfam) assignments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,862\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\u003eFunctional categorization by RASTtk subsystem technology assigned 2,876 protein-coding sequences to 28 distinct functional categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), revealing a metabolic profile typical of a versatile, facultative anaerobic bacterium. The genome was significantly enriched in core metabolic processes, with the most heavily represented subsystems including Carbohydrates (414 genes) and Amino Acids and Derivatives (412 genes), underpinning a high capacity for nutrient acquisition and biosynthesis. This extensive metabolic network was further underpinned by significant genetic investments in Protein Metabolism (219 genes), Cofactors, Vitamins, Prosthetic Groups, Pigments (202 genes), and Respiration (116 genes).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProminent genes were associated with pathogenicity and environmental interaction. For example, the subsystems Virulence, Disease and Defense accounted for 71 genes, while 33 genes were dedicated to Iron acquisition and metabolism\u0026mdash;a critical virulence factor. Further, 35 genes were associated with Cell Wall and Capsule biosynthesis. This genetic landscape also reflected considerable genomic plasticity and adaptability\u0026mdash;evidenced by 20 features categorized under Phages, Prophages, Transposable elements, and Plasmids, 101 genes associated with Stress Response, and 77 genes for Regulation and Cell signaling. On the contrary, the absence of genes for Photosynthesis and Motility and Chemotaxis was consistent with the established biology of K. pneumoniae.\u003c/p\u003e \u003cp\u003eCOG classification likewise supported these observations, with a strong overrepresentation of metabolism and cellular processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Main functional categories represented were Carbohydrate transport and metabolism (661 genes), Amino acid transport and metabolism (583 genes), Energy production and conversion (352 genes) and Transcription (558 genes). The genome also contained a remarkably high number of genes related to Defense mechanisms (165) and the mobilome (226), indicating active evolutionary forces. General or unknown functions were assigned to 584 genes, pointing to a pool of untapped genetic potential.\u003c/p\u003e \u003cp\u003eKEGG pathway mapping further outlined the metabolic potential of JU-BAEC-01, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec. The major category represented was metabolism, dominated by Carbohydrate metabolism (637 genes), Amino acid metabolism (382 genes), and Energy metabolism (215 genes). It had pathways for Xenobiotic degradation (145 genes) and Membrane transport (391 genes) for environmental resilience and nutrient uptake. Clinical perspectives were also posed through KEGG, highlighting genes associated with Antimicrobial drug resistance (76) and Infectious diseases (37). Collectively, this integrated genomic analysis using RASTtk, COG, and KEGG shows that \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 possesses a metabolically versatile and genetically resilient genome. The degree of its biosynthetic machinery, intricate stress-response networks, and adaptations for pathogenicity and horizontal gene transfer underpin its survival in diverse ecological and potentially clinical contexts, representing a distinct and robust lineage within the species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePathogenicity, Antibiotic Resistance and Virulence Island\u003c/h3\u003e\n\u003cp\u003eFurther analysis with the IslandPath-DIMOB tool revealed a diverse array of proteins, enzymes, transporters, and regulatory elements in \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 isolates; most of these were directly or indirectly related to resistance mechanisms, mobile genetic elements, and other vital bacterial processes. Using bioinformatic tools such as SIGI-HMM, which uses Hidden Markov Models to detect genomic islands, phage, and resistance genes, this work has been able to highlight several important genomic features, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe isolate possesses a robust arsenal of virulence genes that definitively classify it as hypervirulent (hvKp). Among the most important are its elaborated siderophore systems; it carries the aerobactin receptor gene iutA, one of the main hallmarks of hvKp, and the complete salmochelin cluster iroBCDEN, which modifies enterobactin to evade host immunity. The combination provides exceptional iron acquisition that is key to systemic infection. This isolate also carries a complete and complex Type VI Secretion System (T6SS), which is a molecular weapon for invading host cells and competing with other bacteria. The presence of entire fim (Type 1 fimbriae) and mrk (Type 3 fimbriae) operons, alongside the pgaABCD locus for poly-N-acetylglucosamine synthesis, promotes adhesion and biofilm development. Adhesion properties are further provided by the genes for the \u003cem\u003eE. coli\u003c/em\u003e common pilus, ECP (ecpA-R). Immune evasion is facilitated by the presence of genes for extensive capsular (CPS) and lipopolysaccharide (LPS) synthesis, which is controlled by rcsAB, and the arn operon for resistance to cationic antimicrobial peptides (CAMPs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe resistome of this isolate includes an extensive range of antimicrobial resistance mechanisms that make it resistant to nearly all major classes of antibiotics. Of particular concern is the defense against last-resort agents. Resistance to tigecycline is mediated through the powerful RND-type efflux system TmexCD3-toprJ3. High-level, broad-spectrum aminoglycoside resistance is conferred by the 16S rRNA methyltransferase armA. In addition, fosfomycin resistance proceeds through a two-fold mechanism that includes both the glutathione transferase gene fosA6 and a mutation in the uhpT fosfomycin transporter.\u003c/p\u003e \u003cp\u003eThe combination of an AmpC-type cephalosporinase, DHA-1, with an extended-spectrum beta-lactamase, SHV-11, and a penicillinase, TEM-1, ensures a broad-spectrum beta-lactam resistance profile. Fluoroquinolone resistance is multi-pronged, with contributions from plasmid-mediated target protection, qnrB4; target site mutations in DNA gyrase, gyrA S83I, and topoisomerase IV, parC S80I; and efflux pumps, OqxAB. The presence of aac(6')-Ib-cr further contributes by acetylating and inactivating ciprofloxacin.\u003c/p\u003e \u003cp\u003eOther core resistances consist of genes for macrolides [mph(A), mph(E), msr(E)], sulfonamides (sul1), trimethoprim (dfrA12), and chloramphenicol (through OqxAB efflux). Importantly, resistance mechanisms against peptide antibiotics such as polymyxins were identified: the arnT and eptB genes, which modify lipid A, and impaired permeability through OmpA and OmpK37. The combined presence of various efflux systems, including AcrAB-TolC, OqxAB, TmexCD3-toprJ3, and various regulatory mutations, such as those in marR, ensure a high degree of baseline multidrug tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eSeveral heavy metal resistance genes were detected, indicating the ability of the isolate to survive under contaminated environments. These included: ArsA, ArsB, and ArsH, which take part in arsenic detoxification, while CzcD confers resistance to cobalt, zinc, and cadmium. Copper resistance is mediated by CusA, CusB, CusR, and PcoE, which facilitate detoxification and subsequent efflux of copper ions out of the cells. Additionally, MerD, MerE, and MerT take part in mercury detoxification, thus showing the potential of an isolate for survival in environments under toxic metal stress. In addition, copper and silver efflux systems such as CusC, CusA, and CusR further enforce the ability of the isolate to handle metal toxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eBacteriophage Diversity, Mobile Genetic Elements and Horizontal Gene Transfer\u003c/h3\u003e\n\u003cp\u003eMobileOG-db classification of the \u003cem\u003eK. pneumoniae\u003c/em\u003e isolate JU-BAEC-01 identified 637 protein families that could be assigned to five major functional categories of MGEs: IE, with 138 protein families; RRR, with 174 protein families; P, with 124 protein families; STD, with 81 protein families; and T, with 120 protein families. The representation of such a wide range of protein families indicates that the isolate probably carries an array of MGEs, including conjugative plasmids, transposons, and bacteriophages, which are probably contributing to MDR and virulence. Further study on specific protein families obtained from mobileOG-db will be required for understanding the mechanisms involved in these processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA detailed bacteriophage analysis using PHASTER revealed a diverse array of bacteriophages with varied structural and functional properties. PHASTER analysis identified six prophage regions, including a questionable 19.9 Kb region linked to PHAGE_Shigel_SfIV, and intact regions of 36 Kb, 27.1 Kb, and 24.1 Kb associated with PHAGE_Edward_GF_2, PHAGE_Klebsi_ST512_KPC3phi13.2, and PHAGE_Pseudo_phiPSA1, respectively. Additionally, two incomplete prophage regions (18.6 Kb and 11.2 Kb) were linked to PHAGE_Escher_phiV10 and PHAGE_Escher_HK639. These findings suggest that the isolate may possess a significant number of prophages that could play a role in the horizontal transfer of resistance and virulence genes, thus enhancing the pathogenic potential of the strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThese analyses were followed by plasmid replicon detection within the isolate using the MobileElementFinder and PlasmidFinder tools. Multiple plasmid types, such as IncC, IncFIB(pNDM-Mar), IncHI1B(pNDM-MAR), IncR, IncY, and repB(R1701), were detected. These replicons are known to be associated with the spread of antibiotic resistance genes. Application of the geNomad workflow in scaffold analysis revealed several plasmids carrying important resistance genes, including *qnrB4, blaDHA-1, blaTEM-1B, blaSHV-182, OqxA, OqxB, fosA, mph(A), qacE, aac(6')-Ib-cr, mph(E), msr(E), dfrA12,* and aadA2. This is evidence of resistance to various classes of antibiotics, including quinolones, beta-lactams, and aminoglycosides. Virulence factors such as iutA, mrkA, terC, and fimH were also found in this isolate, showing enhanced pathogenicity potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDefense Mechanisms against Phage Infections\u003c/h2\u003e \u003cp\u003eThe bacterial defense systems of \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 were comprehensively characterized using three complementary bioinformatics tools: PADLOC, DefenseFinder, and CRISPRCasFinder 4.2.20. These analyses revealed a diverse and multi-layered defense strategy involving CRISPR-Cas systems, restriction-modification (RM) systems, abortive infection (Abi) mechanisms, and less characterized defense elements, along with phage-encoded antidefense systems, which highlight the ongoing evolutionary dynamics between bacteria and bacteriophages.\u003c/p\u003e \u003cp\u003eCRISPR-Cas systems were detected consistently by both DefenseFinder (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) and PADLOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), which identified a Class 1, Subtype IV-A CRISPR-Cas system known for targeting phage DNA. CRISPR arrays were also confirmed by CRISPRCasFinder (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), with a particularly strong prediction on Contig 41 (evidence level 3), suggesting a likely functional CRISPR-Cas defense. Additionally, Cas proteins necessary for viral DNA targeting were found across multiple contigs, including Type III-A on Contig 71 and Type U on Contig 41, indicating that \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 harbors several functional CRISPR-Cas systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBoth PADLOC and DefenseFinder identified multiple RM systems, including Type I and Type II, which are crucial for the protection of bacterial genomes through the cleavage of foreign DNA and methylation of specific sequences. These are among the essential components of the arsenal of \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 against bacteriophages, enhancing its capacity for resistance against phage infection.\u003c/p\u003e \u003cp\u003eIn addition to CRISPR-Cas and RM systems, the BREX (I) system was detected by both PADLOC and DefenseFinder, suggesting that \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 employs this non-cleaving phage defense mechanism to inhibit viral replication. The detection of abortive infection (Abi) systems, including AbiE and AbiU, further adds to the multilayered nature of the bacterial defense, providing an additional fail-safe mechanism by inducing programmed cell death in infected cells to prevent the spread of phages.\u003c/p\u003e \u003cp\u003eFurthermore, both PADLOC and DefenseFinder identified several other less-studied or novel defense systems. PADLOC detected PD-T7-1, PDC-S12, and VSPR systems, along with BrxL and BrxC proteins associated with the BREX system, whereas DefenseFinder highlighted the GAPS1 system and the Mok-Hok-Sok toxin-antitoxin system, which may contribute to bacterial stress responses and phage resistance. Of particular interest was the identification by DefenseFinder (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) of phage-encoded antidefense systems, including Anti-RM systems (e.g., Ard proteins) and Anti-CRISPR systems (e.g., acrIE9), which allow phages to evade bacterial immune defenses. This highlights the ongoing evolutionary arms race between \u003cem\u003eKlebsiella\u003c/em\u003e JU-BAEC-01 and bacteriophages, as the phages continue to evolve mechanisms to overcome bacterial defense strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection and Physicochemical Characterization\u003c/h2\u003e \u003cp\u003eWastewater samples were collected from five pharmaceutical industries located in Dhaka and Gazipur, Bangladesh, between January and March 2023. Each industry was a bulk producer of β-lactam and fluoroquinolone antibiotics. Five influent (raw wastewater) and five effluent (post-treatment) samples were collected aseptically in sterile 1-L polypropylene bottles. Samples were transported at 4\u0026deg;C and processed within 6 h of collection. Temperature and pH were measured on-site using a portable meter (Hanna Instruments, USA). Total dissolved solids (TDS), chemical oxygen demand (COD), and biochemical oxygen demand (BOD₅ at 20\u0026deg;C) were determined according to the standard methods [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of Antibiotic Residues by High-Performance Liquid Chromatography (HPLC)\u003c/h2\u003e \u003cp\u003eAntibiotic residues were quantified using a Hitachi LaChrome Elite HPLC system, UV detector, and reversed-phase Nucleosil 120-5 C18 column (250 \u0026times; 4.6 mm, 5 \u0026micro;m). The mobile phase consisted of a mixture of acetonitrile and 0.024 M orthophosphoric acid buffer (pH 3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 adjusted with triethylamine), at a ratio of 13:87 (v/v). The flow rate was 1.5 mL/min, detection wavelength 278 nm, column temperature 40\u0026deg;C and injection volume 10 \u0026micro;L. Ciprofloxacin and penicillin G were used as external standards. Quantification was based on peak area using six-point calibration curves (0.1\u0026ndash;100 \u0026micro;g/mL; R\u0026sup2; \u0026gt; 0.999). LOQ was 0.05 \u0026micro;g/mL for both antibiotics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBacterial Isolation, Enumeration, and Preservation\u003c/h2\u003e \u003cp\u003eSerial ten-fold dilutions of wastewater were plated onto nutrient agar (Oxoid, UK), cetrimide agar (Condalab, Spain) and mFC agar (HiMedia, India) for enumeration of total heterotrophic bacteria, \u003cem\u003ePseudomonas spp\u003c/em\u003e. and fecal coliforms, respectively. Plates were incubated at 37\u0026deg;C for 24\u0026ndash;48 h (44.5\u0026deg;C for mFC agar). Morphologically distinct colonies were purified and preserved at \u0026minus;\u0026thinsp;80\u0026deg;C in tryptic soy broth containing 20% glycerol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAntimicrobial Susceptibility Testing\u003c/h2\u003e \u003cp\u003eAntibiotic susceptibility was determined by the Kirby\u0026ndash;Bauer disk diffusion method on Mueller\u0026ndash;Hinton agar (Oxoid, UK) in accordance with the EUCAST 2023 guidelines [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A total of 26 antibiotics belonging to 12 classes were tested. Isolates resistant to at least one agent in \u0026ge;\u0026thinsp;3 antibiotic classes were classified as multidrug-resistant (MDR) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. MICs of selected MDR isolates were determined using the VITEK\u0026reg; 2 Compact automated system (bioM\u0026eacute;rieux, France) with AST-GN83 cards according to the manufacturer\u0026rsquo;s recommendations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenomic DNA Extraction and Whole-Genome Sequencing of K. pneumoniae JU-BAEC-01\u003c/h2\u003e \u003cp\u003eHigh-molecular-weight genomic DNA from an overnight culture of \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 was extracted with the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Germany), as instructed by the manufacturer\u0026rsquo;s protocol for Gram-negative bacteria. Library preparation was carried out using the Nextera XT DNA Library Preparation Kit (Illumina, USA). Paired-end sequencing (2\u0026times;150 bp) was carried out on an Illumina MiniSeq platform, generating\u0026thinsp;\u0026gt;\u0026thinsp;100\u0026times; average coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenome Assembly\u003c/h2\u003e \u003cp\u003eHigh-quality reads were assembled into a draft genome using the integrated assembly pipelines of the BV-BRC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the EDGE bioinformatics platform [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These services utilize robust algorithms like SPAdes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to perform contig generation, scaffolding, and quality assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic and Phylogenetic Analysis\u003c/h2\u003e \u003cp\u003eThe isolate was confirmed as \u003cem\u003eK. pneumoniae\u003c/em\u003e, an MLST scheme that defines the strain type based on seven housekeeping genes through the PubMLST [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] database. Capsular polysaccharide (K-locus) and O-antigen (O-locus) were typed with high confidence to KL150/KL107-D1 and O3b, respectively, by Kaptive [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Phylogenetic placement was evaluated by the Type (Strain) Genome Server (TYGS) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, a reference genome SNP approach and the cgMLST scheme through the BacWGSTdb 2.0 database [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were used in an effort to build phylogenetic trees, generating neighbor-joining and minimum spanning trees that help explain genetic relationships among the isolates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePangenome Analysis\u003c/h2\u003e \u003cp\u003eA pangenome analysis was performed with the aim of determining genetic variability. Fourteen reference genome sequences of the Klebsiella genus have been downloaded from the NCBI database. The analysis via the Integrated Prokaryotes Genome and Pan-genome Analysis tool [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] confirmed that there is a high degree of genetic relatedness between JU-BAEC-01 and other \u003cem\u003eK. pneumoniae\u003c/em\u003e strains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenome Annotation and Functional Analysis\u003c/h2\u003e \u003cp\u003eExtensive genome annotation was performed to identify and characterize genetic elements. Primary annotation was carried out using the Genome Annotation Service on BV-BRC and Prokka [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] for rapid gene prediction. Functional insights were achieved by annotating genes with GO terms and KEGG pathways using the gcType database [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For subsystem-based annotation, RASTtk (RAST tool kit) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was used. The genome was visualized using Proksee and Bakta [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which provided a way to map the genome in a circular fashion and to highlight features like CDSs, RNA genes, and AMR loci.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Antibiotic Resistance and Virulence Factors\u003c/h2\u003e \u003cp\u003eARGs were identified using CARD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and ResFinder [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and results were visualized using ProbioMinServer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Resistance genes for metals and biocides were detected using the BacMet database [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Virulence factors and pathogenic islands were visualized using the dedicated analysis pipelines available within IslandViewer 4 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and EDGE Bioinformatics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of mobile genetic elements and bacteriophages\u003c/h2\u003e \u003cp\u003eMGEs were characterized with the use of Mobile Element Finder [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and Plasmid Finder [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] for the identification of insertion sequences, transposons, and plasmid replicons. Putative plasmid sequences within assembled scaffolds were further analyzed using geNomad [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Bacteriophage-derived sequences and prophage regions were identified and classified using PHASTER [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Antiviral Defense Systems\u003c/h2\u003e \u003cp\u003eBacterial defense systems against viral infections, such as CRISPRCas and abortive infection systems, were identified using a suite of bioinformatics tools: DefenseFinder [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], PADLOC [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and CRISPRCasFinder [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe escalating crisis of antimicrobial resistance (AMR) has been widely recognized as one of the most serious global public health threats of the 21st century, with current estimates of 700,000 annual deaths projected to rise to 10\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Pharmaceutical wastewater treatment plants (PWWTPs) that receive effluents from bulk-drug manufacturing facilities constitute extreme selective environments in which antibiotic concentrations can reach the mg/L range\u0026mdash;orders of magnitude higher than in municipal or hospital wastewaters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Such hotspots have repeatedly been shown to act as crucibles for the emergence and dissemination of multidrug-resistant (MDR) and extensively drug-resistant pathogens, as well as reservoirs of mobilizable antibiotic resistance genes (ARGs) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we report the first whole genomic characterization of a high-risk \u003cem\u003eK. pneumoniae\u003c/em\u003e isolate, JU-BAEC-01, recovered from treated pharmaceutical effluent in Bangladesh-a major hub of generic antibiotic production. Although conventional activated-sludge treatment had substantially reduced the organic load (COD and BOD₅), and ciprofloxacin and penicillin G were not detectable by HPLC in the effluents, MDR bacteria were still viable in numbers ranging from 10\u0026sup3; to 10⁶ CFU/mL. This suggests that current treatment processes are inadequate for removal of highly adapted resistant populations and is in agreement with findings from related PWWTPs in India and China [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhenotypic screening of 150 isolates showed alarmingly high resistance rates to many antibiotics (\u0026gt;\u0026thinsp;90% to amoxicillin/clavulanic acid and erythromycin, \u0026gt;\u0026thinsp;60% to trimethoprim and kanamycin), including 20% that were classified as MDR. Of 30 MDR isolates analyzed by VITEK\u0026reg; 2, the predominant species was \u003cem\u003eK. pneumoniae\u003c/em\u003e at 30%, followed by \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e. Notably, all retained susceptibility to carbapenems and colistin-preserving these last-resort agents for now but underscoring the selective pressure exerted by non-carbapenem, non-polymyxin antibiotics in these environments.\u003c/p\u003e \u003cp\u003eWhole-genome sequencing of the most resistant isolate, \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01, uncovered a paradigm of convergent evolution rarely documented thus far in environmental isolates. It combines an extended acquired resistome conferring resistance to nearly all major antibiotic classes except carbapenems and colistin, with bona fide hypervirulence determinants characteristic of hvKp and one of the most elaborate multi-layered anti-phage defense systems reported in \u003cem\u003eKlebsiella\u003c/em\u003e thus far.\u003c/p\u003e \u003cp\u003eIn particular, the resistome of JU-BAEC-01 is alarming. It bears the newly emerged tmexCD3-toprJ3 RND efflux cluster mediating high-level tigecycline resistance that has recently spread with remarkable success globally in mobile plasmids [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Other relevant elements included armA mediating high-level aminoglycoside resistance, the bifunctional aminoglycoside/fluoroquinolone acetyltransferase aac(6')-Ib-cr, plasmid-mediated qnrB4 and oqxAB [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and chromosomal quinolone resistance-conferring mutations -QRDR gyrA S83I, parC S80I-. Multiple β-lactamases -blaDHA-1, blaSHV-182, blaTEM-1B- completed a profile making the strain effectively pan-resistant to first- and second-line therapies. Most of such determinants were plasmid-borne-IncC, IncFIB, IncHI1B, IncR-pointing out the role of horizontal gene transfer in assembling such a complex resistome under intense selective pressure.\u003c/p\u003e \u003cp\u003eMeanwhile, JU-BAEC-01 harbors undisputed hypervirulence markers: complete aerobactin (iucABCD-iutA) and salmochelin (iroBCDEN) siderophore systems, rmpA2 (hypermucoviscosity), intact Type 1 and Type 3 fimbriae, T6SS, and pgaABCD biofilm operon-highly associated with severe community-acquired invasive infections, such as pyogenic liver abscess and metastatic spread [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Convergence of carbapenem-susceptible yet extensively MDR profiles with hvKp determinants in an environmental isolate is decidedly alarmist, since their acquisition of carbapenemases-for example, via a single plasmid transfer event-could instantly yield untreatable CR-hvKp \"superbugs\" of the kind responsible for fatal nosocomial outbreaks [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA third, hitherto underappreciated dimension of JU-BAEC-01 success is the extraordinarily sophisticated arsenal of anti-phage defense systems that it possesses. Utilizing DefenseFinder, PADLOC, and CRISPRCasFinder, we have identified functional Type I-E, IIIA, and IVA CRISPR-Cas systems; multiple Type I and II restriction-modification systems; BREX Type I; abortive infection systems AbiE, AbiU; toxin-antitoxin modules; and several poorly characterized systems: PD-T7-1, VSPR, GAPS1. Cooccurrence of phage-encoded anti-defense proteins AcrIE9 and ArdA within prophage regions highlights an active evolutionary arms race. The presence of this multi-layered defense can be a plausible explanation for the ecological dominance of the strain within phage-rich environments such as wastewater and serves as a major obstacle to phage therapy-an otherwise very promising alternative against MDR infections [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Successful therapeutic phages, therefore, will necessarily need to elude or suppress several independent systems simultaneously, a challenge potentially requiring highly engineered or cocktail-based approaches.\u003c/p\u003e \u003cp\u003eSuch extensive innate immunity should, therefore, coexist with a finely tuned evolutionary trade-off-a selective relaxation of adaptive immunity, for example, limited CRISPR activity against resistance/virulence plasmids, with retention of robust innate systems that block lytic phages but allow access to beneficial gene acquisition [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This approach mirrors that adopted in globally successful high-risk clones and likely contributes to the rapid assembly of dangerous phenotypes in polluted environments.\u003c/p\u003e \u003cp\u003eIn all, \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 represents the \"perfect storm\" of bacterial evolution under anthropogenic pressure-a genetically unique, environmentally sourced clone that has combined pan-drug resistance, hypervirulence, and phage-hardened defenses into a single resilient lineage. Its emergence in treated pharmaceutical effluent underlines the urgent need for (i) improved wastewater treatment technologies capable of removing viable MDR pathogens and ARGs, (ii) stringent regulation of antibiotic emissions from manufacturing, and (iii) accelerated development of novel therapeutics-including phage cocktails, anti-virulence agents, and CRISPR-based approaches-capable of overcoming such superbugs. In the absence of effective intervention, environmental reservoirs such as that described herein will continue to seed the next generation of clinically untreatable pathogens.\u003c/p\u003e \u003cp\u003eThe present study demonstrates that even efficiently treated pharmaceutical wastewater in Bangladesh remains a reservoir of highly multidrug-resistant pathogens. \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 represents a genetically distinct environmental clone that has converged pan-drug resistance, hypervirulence, and one of the most elaborate anti-phage defense systems yet reported in \u003cem\u003eKlebsiella.\u003c/em\u003e This \u0026ldquo;perfect storm\u0026rdquo; pathogen severely constrains conventional and alternative therapies and underscores the urgent need for advanced wastewater treatment, stringent discharge regulations, and continuous genomic surveillance of industrial effluents to prevent the dissemination of such high-risk clones into clinical and community settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eDeclaration of interests\u003c/strong\u003e \u003cp\u003eAll the contributing author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. They declare they have no conflict of interest regarding contribution, submission and publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFA. contributed to the design and execution of the research experiments, ensuring alignment with the study\u0026rsquo;s objectives. MMHS. performed comprehensive genomic data analyses and carried out all major bioinformatics interpretations. MIH. led the sampling activities and conducted statistical analyses, ensuring high-quality and reliable datasets. MB. actively participated in data analysis and manuscript drafting, ensuring clarity, logical flow, and coherence. KM. provided essential research assistance and played a key role in sampling and coordination of field activities. SFC. conducted sample preparation and genome extraction, maintaining strict quality control throughout the process. SRN. managed sample preparation and sequencing workflows, ensuring accuracy and consistency in data generation. TM. secured funding, supervised project management, and facilitated collaboration among team members and institutions. MOF. developed the research plan, managed the project\u0026rsquo;s data framework, and led the manuscript writing, ensuring comprehensive documentation of the study. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge all participants who took part in this study. Authors are grateful to Dr. Kamruzzaman Pramanik head of Microbiology Industrial Irradiation Division (MIID); Dr. Zahid Hasan of MIID under the Institute of Food and Radiation Biology (IFRB), Bangladesh Atomic Energy Commission (BAEC) and Department of Microbiology, Jahangirnagar University for the logistics support for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw sequencing reads generated from this study will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDavies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74(3):417-433. doi:10.1128/MMBR.00016-10\u003c/li\u003e\n\u003cli\u003eMacLean RC, San Millan A. The evolution of antibiotic resistance. Science. 2019;365(6458):1082-1083. doi:10.1126/science.aax3879\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Neill J. Tackling drug-resistant infections globally: final report and recommendations. 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Comput Struct Biotechnol J. 2024;23:3575-3583. doi:10.1016/j.csbj.2024.09.018\u003c/li\u003e\n\u003cli\u003eCamargo AP, Roux S, Schulz F, et al. Identification of mobile genetic elements with geNomad. Nat Biotechnol. 2024;42(8):1303-1312. doi:10.1038/s41587-023-01953-y\u003c/li\u003e\n\u003cli\u003eArndt D, Grant JR, Marcu A, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44(W1):W16-W21. doi:10.1093/nar/gkw387\u003c/li\u003e\n\u003cli\u003eGautreau G, Bruto M, Cassan C, et al. DefenseFinder: a tool for discovering bacterial defense systems. Bioinformatics. 2020;36(15):4671-4673. doi:10.1093/bioinformatics/btaa366\u003c/li\u003e\n\u003cli\u003ePayne LJ, Meaden S, Mestre MR, et al. PADLOC: a web server for the identification of antiviral defence systems in microbial genomes. Nucleic Acids Res. 2022;50(W1):W541-W550. doi:10.1093/nar/gkac398\u003c/li\u003e\n\u003cli\u003eGrissa I, Vergnaud G, Pourcel C. 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Global dissemination of the tmexCD-toprJ resistance pump. Nat Microbiol. 2023;8(6):1179-1189. doi:10.1038/s41564-023-01394-6\u003c/li\u003e\n\u003cli\u003eStrathdee SA, Hatfull GF, Mutalik VK, Schooley RT. Phage therapy: from biological mechanisms to future directions. Cell. 2023;186(1):17-31. doi:10.1016/j.cell.2022.11.001\u003c/li\u003e\n\u003cli\u003eGophna U, Brodt A. The effect of CRISPR-Cas systems on horizontal gene transfer and microbial evolution. Trends Microbiol. 2018;26(6):525-535. doi:10.1016/j.tim.2018.01.004\u003c/li\u003e\n\u003cli\u003eWheatley R, MacLean RC. CRISPR-Cas systems restrict horizontal gene transfer in clinical Staphylococcus aureus strains. Nat Commun. 2021;12:4562. doi:10.1038/s41467-021-24688-2\u003c/li\u003e\n\u003cli\u003eBertelli C, Laird MR, Williams KP, et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 2017;45(W1):W30-W35. doi:10.1093/nar/gkx343\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Klebsiella pneumoniae, Pan-drug resistance, Hypervirulence, Phage defense systems, CRISPR-Cas, BREX system","lastPublishedDoi":"10.21203/rs.3.rs-8281161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8281161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eK. pneumoniae\u003c/em\u003e strains that combine multidrug resistance, hypervirulence and persistence are spreading worldwide, causing a severe threat to public health. Unraveling the genetics that underlies these high-risk clones is critical for the development of countermeasures. We isolated \u003cem\u003eK. pneumoniae\u003c/em\u003e JU-BAEC-01 from treated effluent of antibiotic-manufacturing pharmaceutical facilities in Bangladesh. Herein, we report a comprehensive genomic analysis of the \u003cem\u003eK. pneumoniae\u003c/em\u003e strain JU-BAEC-01 using whole-genome sequencing, comparative genomics, and various bioinformatics tools, including CARD, ResFinder, VFDB, PADLOC, Defense Finder, and CRISPRCas Finder, to outline its phylogenetic position, antibiotic resistance profile, virulence potential, mobile genetic elements, and antiviral defense systems. JU-BAEC-01 belongs to a phylogenetically distinct lineage, serotype O3b:KL150, unrelated to globally dominant high-risk clones. This isolate shows resistance to nearly all clinically relevant antibiotic classes except carbapenems and colistin, mediated by an extensive acquired resistome, including tmexCD3-toprJ3 (tigecycline), armA, aac(6')-Ib-cr, qnrB4, oqxAB, blaDHA-1, blaSHV-182, and blaTEM-1B, mostly carried on conjugative IncC, IncFIB, IncHI1B, and IncR plasmids. Classical hypervirulence markers are present: complete aerobactin (iucABCD-iutA) and salmochelin (iroBCDEN) clusters, rmpA2, type 1 and type 3 fimbriae, T6SS, and pgaABCD. Six prophage regions and multiple insertion elements further enhance genomic plasticity. Notably, the strain encodes one of the most elaborate anti-phage defense arsenals reported in Klebsiella to date, comprising functional Type I-E, III-A, and IV-A CRISPR-Cas systems, multiple restriction-modification systems, BREX Type I, abortive infection systems (AbiE, AbiU), and additional novel defenses that coexist with phage-derived anti-CRISPR (AcrIE9) and anti-restriction (ArdA) proteins. Klebsiella pneumoniae JU-BAEC-01 is a \"perfect storm\" pathogen that combines pan-drug resistance (PDR), hypervirulence, and a multilayered, highly developed defense against bacteriophages. This genomic convergence confounds treatment options and emphasizes the evolutionary capability of this priority pathogen to resist both the antimicrobial and natural predatory pressures. The presence of phage anti-defense systems underlines a dynamic co-evolutionary arms race with significant implications for the potential failure of phage therapy against such robustly defended isolates.\u003c/p\u003e","manuscriptTitle":"Genomic Convergence of Hypervirulence, Pan-Drug Resistance, and Phage Defense in a High-Risk Klebsiella pneumoniae from Pharmaceutical Wastewater in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 18:51:44","doi":"10.21203/rs.3.rs-8281161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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