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. 2026 Jun 23;12:100377. doi: 10.1016/j.bioflm.2026.100377

Genomic insights into activated antimicrobial resistance of in situ hospital-wastewater biofilm

Yusuke Ota a, Yoko Nukui b, Yoshiaki Gu c, Ryoichi Saito a,⁎
PMCID: PMC13320429  PMID: 42389745

Abstract

Antimicrobial resistance (AMR), particularly among carbapenemase-producing organisms, poses a major global health threat. Although hospital wastewater is considered an AMR hotspot, its functional contribution to resistance dynamics remains poorly defined. We developed in situ biofilms in hospital wastewater and applied integrated metagenomic, metatranscriptomic, and culture-based analyses to characterize community structure and gene expression. Biofilms exhibited greater biomass and higher contamination with extended-spectrum β-lactamase-producing Escherichia coli than planktonic wastewater. Biofilms were enriched in surface-adapted Flavobacteriaceae species and a broader array of carbapenemase genes, whereas wastewater showed higher abundance of gut-associated Bacteroidaceae species and virulence factors. Mobile genetic elements linked multiple AMR genes and showed increased expression in biofilms, including blaIMP family carbapenemases. Culture confirmed blaIMP-1 in four biofilm isolates and one wastewater isolate. These findings indicate that hospital-wastewater biofilms can serve as important reservoirs that promote the persistence and potential dissemination of clinically relevant carbapenem resistance.

Keywords: Hospital wastewater, Antimicrobial resistance, Biofilm, blaIMP-1, Tn3 family transposon

1. Introduction

Antimicrobial-resistant pathogens have become a critical global health threat, as they increasingly compromise the effectiveness of infectious disease treatments. Global burden estimates for antimicrobial resistance (AMR) in 2021 indicate 4.71 million AMR pathogen-related deaths worldwide, with projections suggesting that related mortality will reach approximately 8.22 million by 2050 [1]. Among antimicrobial-resistant pathogens, carbapenemase-producing organisms are particularly concerning because they exhibit a multidrug resistance phenotype and are associated with worse clinical outcomes, including high mortality, long durations of antibiotic therapy, and prolonged hospital stays, compared to those without this phenotype [2]. Clinically important AMR spreads through mobile genetic elements, including transposons and integrons, highlighting the need for comprehensive AMR monitoring to formulate effective control strategies [3].

A One Health approach is essential for combating AMR because antimicrobial-resistant organisms circulate across humans, animals, and various environments, requiring coordinated cross-sectoral action [4]. Hospital wastewater represents a critical environmental reservoir and transmission interface for AMR, reflecting selective pressures within healthcare settings and contributing to the dissemination of antimicrobial-resistant pathogens beyond clinical boundaries [5]. Consistently, hospital wastewater contains diverse antimicrobial-resistant organisms, including carbapenemase producers, as well as selective substances such as antimicrobial residues, chemical agents, and heavy metals that promote the persistence and dissemination of AMR [5,6]. Moreover, emerging forms of AMR have frequently been detected in environmental sources prior to their recognition in clinical settings, underscoring the utility of hospital wastewater for not only monitoring the current circulation of AMR but also identifying future epidemic trends [7].

Biofilms readily form in aquatic environments such as hospital wastewater, consisting of surface-attached, matrix-embedded microbial communities. Within biofilms, AMR is effectively sustained through impaired antibiotic penetration, reduced bacterial metabolic activity, and matrix-mediated physiological states that diminish antimicrobial effectiveness [8,9]. Furthermore, biofilms facilitate active horizontal gene transfer, mediated by mobile genetic elements, thereby accelerating the diversification and spread of AMR within microbial communities [10]. Environmental studies have demonstrated a marked increase in the uptake of extracellular DNA within biofilms, highlighting their enhanced capacity for horizontal gene acquisition [11]. Accordingly, a comprehensive understanding of environmental AMR requires not only conventional analyses of AMR abundance in planktonic communities but also evaluation of highly active genes, including AMR determinants and mobile genetic elements, within biofilms.

Plastics in aquatic environments act as active carriers and enrichment niches for antimicrobial-resistant bacteria and AMR genes, and the biofilms that form on their surfaces exhibit AMR profiles distinct from those in the surrounding water [12,13]. A recent study has shown that hospital wastewater contains substantial amounts of plastic debris, which can act as environmental vectors that facilitate the spread and evolution of AMR [14]. In this study, in situ biofilms were developed on plastic slides in hospital wastewater, and the distribution and expression levels of AMR genes were comprehensively characterized using metagenomic and metatranscriptomic analyses. Additionally, antimicrobial-resistant bacteria that exhibited high transcriptional levels were isolated through culture-based methods, enabling comparison of their genetic and phenotypic properties. This study provides insights into the mechanisms underlying the emergence and dissemination of biofilm-associated AMR, with important implications for understanding and mitigating the growing problem of AMR in the environment.

2. Methods

2.1. Biofilm development and sample collection

In situ biofilms were established in hospital wastewater following a previously described method [15]. Polystyrene slides (25 mm × 75 mm; thickness, 1.0 mm) (Kenis, Osaka, Japan) were placed in wastewater at the Institute of Science Tokyo Hospital, which is a teaching hospital with more than 800 beds, on October 7, 2024. On October 15, 2024, after 8 d of exposure to hospital wastewater, the polystyrene slides with in situ biofilms were retrieved, and the wastewater was collected in a sterile bottle (Fig. 1a). These samples were processed within 1 h of collection. The hospital wastewater was concentrated 10-fold by centrifugation. The slides with biofilms were rinsed with sterile phosphate-buffered saline (PBS). These slides were used as follows: one slide each for crystal-violet staining, fluorescent staining, bacterial isolation, and shotgun metagenomic sequencing analysis and three independent slides for evaluating the proportion of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and shotgun metatranscriptomic sequencing analysis. Slides for crystal-violet staining were stored in PBS, while slides for fluorescent staining were fixed in 3.7% formaldehyde for 1 h, washed with PBS, and air-dried. For DNA and RNA extraction, the biofilm was scraped off the slides, preserved in PBS for bacterial culture, and stored in DNA/RNA Shield (Zymo Research, Irvine, CA, USA).

Fig. 1.

Fig. 1

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Characteristics of in situ biofilm and hospital wastewater samples.

(a) Placement of plastic slides in hospital wastewater for in situ biofilm formation and collection of wastewater sample. (b) In situ biofilm samples before and after crystal-violet staining. (c) Fluorescent staining of in situ biofilm structure: green, red, and blue represent nucleic acids, proteins, and polysaccharides, respectively. (d) Comparison of contamination levels based on antimicrobial resistance between hospital wastewater and in situ biofilm. Statistical analysis using unpaired t-test; ∗∗∗p < 0.0001. DHL: deoxycholate hydrogen sulfide lactose, CTX: cefotaxime. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

2.2. Staining of biofilm components

For crystal-violet staining, the slides with biofilms were stained with 0.1% crystal violet (Muto Chemical, Tokyo, Japan) for 30 min and then washed with PBS. For fluorescence staining, nucleic acid was stained with cell-permeant SYTO 9 (Thermo Fisher Scientific, Waltham, MA, USA), proteins were stained with FilmTracer™ SYPRO™ Ruby Biofilm Matrix Stain (Thermo Fisher Scientific), and polysaccharides were stained with Concanavalin A conjugated to tetramethylrhodamine (Thermo Fisher Scientific). Fluorescent-stained slides were observed using a BZ-X810 microscope (KEYENCE, Osaka, Japan).

2.3. Bacterial isolation

To assess AMR contamination in environmental samples, we followed the World Health Organization Tricycle protocol and determined the prevalence of ESBL-producing E. coli [16]. Biofilm and wastewater samples were inoculated on deoxycholate hydrogen sulfide lactose agar plates containing 4 μg/mL cefotaxime and on plates without the antibiotic (control). Five presumptive E. coli colonies from each plate were selected and tested for indole production. ESBL production by these E. coli colonies was confirmed using the double-disc synergy test based on the Clinical and Laboratory Standards Institute (CLSI) M100-S32 guideline. The proportion of ESBL-producing E. coli among all E. coli isolates was compared across samples, and statistical significance was evaluated using an unpaired t-test based on biological triplicates. Next, the biofilm and wastewater samples were plated on CHROMagar selective agar (Kanto Chemical, Tokyo, Japan) for the isolation of methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, ESBL producers, carbapenemase producers, multidrug-resistant Pseudomonas aeruginosa, and multidrug-resistant Acinetobacter species. Plates were incubated under ambient air conditions at 37°C overnight. Colonies with distinct morphologies were identified through 16S rRNA gene sequencing based on the closest sequence match [17]. Selected colonies were tested for the presence of carbapenemase-encoding genes (i.e., blaIMP, blaVIM, blaKPC, blaGES, blaOXA-48-like, and blaNDM) following standard polymerase chain reaction (PCR) protocols, as previously described [18,19].

2.4. Shotgun metagenomic sequencing analysis

Genomic DNA was extracted from wastewater and biofilm samples using the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Hilden, Germany). Libraries were prepared using the QIAseq FX DNA Library Kit (QIAGEN) and the MGIEasy Universal Library Conversion Kit (MGI Tech, Shenzhen, China), and sequencing was performed on the DNBSEQ-G400 platform. Adapter trimming and low-quality read removal were performed using bbduk.sh (version 39.01; https://sourceforge.net/projects/bbmap/). Reads were mapped to a masked human reference genome using bbmap.sh (version 39.01; https://sourceforge.net/projects/bbmap/), and those aligning to the human genome were removed to eliminate human genome contamination. De novo assembly was performed using SPAdes (version 3.15.5) [20]. Contigs shorter than 200 bp were removed using seqkit (version 2.6.1) [21]. Binning was performed using MetaBAT2 (version 2.15) [22] and MaxBin2 (version 2.2.7) [23] to construct MAGs. The MAGs obtained through binning process were merged using MAGScoT (version 1.0.0) [24]. The completeness and contamination rate of MAGs were assessed using CheckM (version 1.1.3) [25]. Taxonomy of MAGs was assigned using GTDB-Tk (version 2.3.2) [26]. AMRFinderPlus (version 4.0.15) [27] was used to predict AMR genes, metal-resistance genes (MRGs), biocide-resistance genes (BRGs), and heat-resistance genes (HRGs). Plasmid-derived sequences were identified using Platon (version 1.6) [28]. The insertion sequences (ISs) from ISfinder [29] and the major integron sequences intI1 (AB709942), intI2 (JX469830), and intI3 (EF467661) [30] were retrieved from TnCentral [31] and the NCBI database [32], respectively (accessed on January 22, 2025), and these sequences were detected in shotgun genomic data using BLAST (version 2.15.0) [33]. A gene co-occurrence network was generated using Cytoscape (version 3.10.3) [34].

2.5. Shotgun metatranscriptomic sequencing analysis

Total RNA was extracted from hospital wastewater and biofilm samples, each analyzed in biological triplicate with independently prepared biofilm samples, using the RNeasy PowerMicrobiome Kit (QIAGEN). Ribosomal RNA was depleted using the QIAseq FastSelect–5S/16S/23S Kit (QIAGEN). Library preparation was performed using the QIAseq Stranded RNA Library Kit (QIAGEN) and the MGIEasy Universal Library Conversion Kit (MGI Tech), and the DNBSEQ-G400 platform (MGI Tech) was used for sequencing. Quality filtering was performed using Fastp (version 0.19.5) [35]. The obtained reads were mapped to the reference sequences of AMRFinderPlus [27], PLSDB [36], ISfinder [29], and major integrons [30] using HISAT2 (version 2.1.0) [37]. Primary mapped reads were extracted using Samtools (version 1.10) [38]. Read counts for each gene were obtained using featureCounts (version 2.0.3) [39] and CoverM (version 0.6.1) [40]. Gene expression values were normalized as transcripts per million (TPM) to account for differences in gene length and sequencing depth. Differential expression analysis between wastewater and biofilm samples was performed using an unpaired t-test for each gene to compare group means. To correct for multiple testing, false discovery rates (FDRs) were subsequently calculated using the “edgeR” package in R [41].

2.6. Whole-genome sequencing analysis

Bacterial DNA was extracted from colonies of each isolate using the NucleoBond HMW DNA Kit (Macherey-Nagel, Düren, Germany). Library preparation was performed for Illumina short reads using the Nextera DNA Flex Library Preparation Kit (Illumina, San Diego, California, USA). Whole-genome sequencing using paired-end reads was performed on the Illumina MiniSeq platform. Long-read DNA libraries were prepared using the Ligation Sequencing Kit and sequenced using the Nanopore MinION sequencer and R10.4.1 flow cells (Oxford Nanopore Technologies, Oxford, UK). Raw short reads were processed for filtering and trimming with Fastp (version 0.19.5) [35], while long reads were quality-filtered using Porechop (version 0.2.4) [42] and Filtlong (version 0.2.1) [43]. The filtered reads were assembled into hybrid genomes de novo using Unicycler (version 0.5.0) [44]. Sequencing depth of bacterial isolate genomes was assessed using seqkit (version 2.8.0) [21] and genome completeness was evaluated using BUSCO (version 6.0.0) [45]. Bacterial species were determined using GTDB-Tk Classify (version 1.7.0) [26] based on average nucleotide identity values. Sequence type was determined by multi-locus sequence typing [46]. Plasmid incompatibility type, AMR genes, and ISs were identified using PlasmidFinder (version 2.1) [47], AMRFinderPlus [27], and ISfinder [29], respectively. EasyFig (version 2.2.2) [48] was used to align assembled reads containing carbapenemase genes. Following annotation with Prokka (version 1.13) [49], pan-genome analysis was performed using Roary (version 3.13.0) [50], incorporating complete genome sequences of 20 Citrobacter species strains from previous studies [51,52]. A maximum-likelihood tree was built using IQ-TREE (version 2.3.0) [53] and visualized with iTOL (version 7) [54].

2.7. Antimicrobial susceptibility testing

The broth microdilution method was performed using the MicroScan Neg Combo EN5J (Enterobacterales and Aeromonas sp.) and NF2J (Pseudomonas sp.) panels (Beckman Coulter, Brea, CA, USA). Minimum inhibitory concentrations (MICs) of antibiotics for the bacterial isolates were determined using the MicroScan autoSCAN-4 system (Beckman Coulter). The breakpoints for antimicrobial susceptibility testing were interpreted according to the CLSI documents M100-Ed32 and M45-Ed3.

3. Results

3.1. Establishment and characterization of in situ biofilm in hospital wastewater

In situ biofilm was successfully established on polystyrene slides after 8 d of exposure to hospital wastewater (Fig. 1a). Crystal-violet staining confirmed substantial biofilm biomass on the slide surface (Fig. 1b). Fluorescence microscopy revealed a heterogeneous biofilm architecture with an extensive extracellular polymeric substance (EPS) matrix, characterized by filamentous structures and interconnected patterns that indicate a complex three-dimensional (3D) organization (Fig. 1c). The EPS contained multiple structural components, including nucleic acids, proteins, and polysaccharides, forming an intricate network that provided mechanical stability. Comparative analysis of AMR revealed the presence of ESBL-producing E. coli in both biofilm and hospital wastewater samples, with the mean ratio being markedly higher in biofilm samples (0.279) than in hospital wastewater samples (0.038) (Fig. 1d).

3.2. Taxonomic structure and functional gene distribution

Shotgun metagenomic sequencing revealed distinct taxonomic profiles between biofilm and hospital wastewater samples (Supplementary Table S1–S3). At the order level, Bacteroidales and Burkholderiales were dominant in hospital wastewater, whereas biofilms exhibited higher relative abundance of Burkholderiales and Flavobacteriales (Fig. 2a). At the family level, Burkholderiaceae was predominant in both the hospital wastewater and biofilm samples. Additionally, Bacteroidaceae and Flavobacteriaceae exhibited high relative abundance in hospital wastewater and biofilm samples, respectively. Functional annotation of metagenome-assembled genomes (MAGs) and contigs indicated widespread occurrence of AMR genes, MRGs, BRGs, and HRGs in the biofilm and hospital wastewater samples (Fig. 2c). The AMR genes conferring resistance to β-lactams, aminoglycosides, and macrolide-lincosamide-streptogramin were highly expressed in both sample types. Carbapenemase genes identified in biofilm samples included blaGES-5, blaIMP-1, blaIMP-11, blaKPC-2, blaOXA-23, blaOXA-58, and blaNDM-1, whereas hospital wastewater contained blaGES-5 and blaKPC-2 (Supplementary Tables S4 and S5). Virulence-associated genes were predominantly detected in hospital wastewater, including adhesion-related genes (eilA, fdeC, lpfA-O113, pap, and sfa), toxin and effector-related genes (cif, cnf1, esp, hlyA, nleB, tir, and vat), iron acquisition system-related genes (iro, iuc, and ybt), and other virulence factor genes (cvaC, iss, mchF, senB, sinH, and sslE). By contrast, only iss was detected in the biofilm sample.

Fig. 2.

Fig. 2

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Taxonomic and functional gene profiles of in situ biofilm and hospital wastewater based on shotgun metagenomic data.

Relative abundance of bacterial taxa at the order (a) and family (b) levels in in situ biofilm and hospital wastewater samples. (c) Distribution of genes associated with resistance to antibiotics, biocides, acids, heat, and metals, as well as virulence, shown at the metagenome-assembled genome (MAG) and contig levels for biofilm (BF) and hospital wastewater (HW). MLS: macrolide-lincosamide-streptogramin.

3.3. Co-occurrence structures of genomic components

Network diagrams were constructed to represent co-occurrence relationships among MAGs and functional genes. These networks exhibit centralized architecture dominated by a few highly connected hubs, notably Tn3 family transposons and ISs, in both in situ biofilm (Fig. 3a) and wastewater (Fig. 3b) samples. These hubs are strongly associated with multiple AMR genes and MAGs, forming dense clusters. Among carbapenemase genes detected in biofilm, blaIMP-11, blaOXA-23, and blaNDM-1 exhibited co-occurrence with MAGs and mobile genetic elements: blaIMP-11 with Bacteroidales MAG058 and Tn3; blaOXA-23 with Bacteroidales MAG053 and IS4; blaNDM-1 with Burkholderiales MAG025 and IS30 (Supplementary Table S2). In the wastewater sample, blaKPC-2 was associated with Psittacicellaceae MAG130 and Tn3/IS1182, whereas blaGES-5 was linked to unclassified MAG157 and Tn3 (Supplementary Table S3). We also evaluated the co-occurrence relationships among plasmid-derived contigs and functional genes (Supplementary Fig. S1). Tn3 served as a central hub, while IS6 and IS1595 exhibited multiple connections to plasmid-derived contigs and AMR genes. blaIMP-11 was associated with Tn3 and blaNDM-1 with IS30 in the biofilm samples, whereas blaKPC-2 was linked to Tn3/IS1182 in the hospital wastewater samples.

Fig. 3.

Fig. 3

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Network analysis of metagenome-assembled genomes, functional genes, and mobile genetic elements based on shotgun metagenomic data.

Networks of metagenome-assembled genomes (MAGs) and genes related to antimicrobial resistance (AMR), biocide and metal resistance, insertion sequences (ISs), integron, and virulence are depicted for biofilm (a) and hospital wastewater (b) samples. Nodes represent individual sequences and edges indicate co-occurrence within the same MAG or contig.

3.4. Comparative analysis of gene expression profiles

Metatranscriptomic comparison between the biofilm and hospital wastewater samples revealed clear differences in gene expression profiles (Supplementary Table S6). Across all detected genes, numerous BRGs and HRGs showed higher expression in biofilm, whereas AMR genes, IS, MRGs, and virulence genes exhibited higher expression in the hospital wastewater (Fig. 4a). When statistical significance was applied (p < 0.05), a distinct subset of genes was identified as significantly upregulated or downregulated in biofilms relative to that in hospital wastewater (Fig. 4b). The volcano plot further demonstrated that each gene exhibiting significant differences in expression was distributed across diverse functional categories (Fig. 4c). aac(6’)-IIc, mef(B), and tet(X2) were highly expressed in the wastewater, whereas blaIMP family, blaOXA-58 family, and msr(E) expression was markedly upregulated in the biofilm, with the blaIMP family showing a notable difference. Additionally, the expression of the blaIMP family and blaOXA-58-family was significantly upregulated in the in situ biofilm according to the FDR-adjusted analysis (Supplementary Table S7).

Fig. 4.

Fig. 4

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Differential gene expression profiles between biofilm and hospital wastewater based on metatranscriptomic data.

(a) Number of genes with higher (Up) or lower (Down) expression in biofilm (BF) compared to hospital wastewater (HW) across all genes. (b) Number of genes with significantly different expression (p < 0.05) between BF and HW. (c) Volcano plot showing genes with significant expression differences (p < 0.05) between BF and HW, where colors describe functional categories. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.5. Isolation and characterization of carbapenemase producers

Thirteen bacterial isolates were obtained from the biofilm and hospital wastewater samples (Supplementary Table S8). PCR screening for carbapenemase genes revealed the presence of blaIMP, blaGES, and blaKPC among the isolates. Consistent with its higher transcriptomic expression in the biofilm, blaIMP was found to be expressed in four biofilm isolates but in just one hospital-wastewater isolate. Whole-genome sequencing identified the biofilm isolates as Pseudomonas alcaligenes BFCARBA3 and Aeromonas caviae BFMDRP4, both carrying two blaIMP-1 genes, and Citrobacter freundii BFESBL3 and P. aeruginosa BFMDRP3, each carrying one blaIMP-1 gene (Table 1 and Supplementary Table S9). In addition to blaIMP-1, these four isolates harbored a diverse set of AMR genes, MRGs, BRGs, and HRGs. blaIMP-1 was found on the chromosome, IncFII plasmid, or on a plasmid of unknown incompatibility type. The A. caviae HWMDRP2 isolate obtained from hospital wastewater exhibited the same phylogenetic characteristics as A. caviae BFMDRP4 but lacked the 41,236-bp contig containing blaIMP-1. In antimicrobial susceptibility testing, C. freundii BFESBL3, P. alcaligenes BFCARBA3, and P. aeruginosa BFMDRP3 were resistant to carbapenems, whereas the A. caviae strains BFMDRP4 and HWMDRP2 were classified as susceptible; however, BFMDRP4 exhibited reduced susceptibility to meropenem (Table 2).

Table 1.

Genomic features of IMP-1-producing isolates from in situ biofilm and hospital wastewater samples.

Strain Species MLST Sequence component Plasmidfinder ARGs MRGs BRGs HRGs
BFESBL3
 Citrobacter freundii
 No match
 Contig1 4,859,215 Circular - blaCMY-65, qnrB38 fieF - -
Contig2 234,476 Circular IncFII(Yp) blaIMP-1, blaOXA-2, blaTEM-1, aac(6′)-Ib, aac(3)-Iid, aadA2, mph(A), sul1, dfrA12 merA, merD, merE, merR, merT qacEdelta1 -
Contig3 47,723 Circular IncP6 blaKPC-2 - - -
Contig4 45,455 Circular IncX3 qnrS1 - - -
Contig5
 33,416
 Circular
 IncX6
 -
 -
 -
 -
BFCARBA3 Pseudomonas
alcaligenes 		Not applicable Contig1 4,350,016 Liner - blaIMP-1 (2 copies), blaOXA-932, aadA1 (2 copies), aac(6′)-Ib, aac(6′)-Iic, aac(6′)-Il, sul1 (2 copies) - qacEdelta1 (2 copies) psi-GI, kefB-GI, trxLHR, hdeD-GI, yfdX2, yfdX1, shsP, clpK (2 copies)
BFMDRP3 Pseudomonas aeruginosa ST235 Contig1 6,915,056 Circular - blaIMP-1, blaOXA-1, blaOXA-488, blaPDC-35, aadA6, aac(6′)-Ib, aph(3′)-Iib, mexA, mexE, mexX, fosA, sul1 (2 copies), catB7 merA, merD, merE, merP, merR, merT (2 copies each) qacEdelta1 (2 copies) -
BFMDRP4
 Aeromonas caviae
 ST1061
 Contig1 4,630,085 Circular - blaOXA-1160, blaMOX-23, aadA2, aph(3′)-Ia, dfrA12, mph(A), sul1, tetE - qacEdelta1 -
Contig2 93,128 Circular - blaIMP-1 - - -
Contig3 41,236 Circular - blaIMP-1 - - -
Contig4
 39,460
 Circular
 -
 blaGES-24, aac(6′)-Il
 -
 -
 -
HWMDRP2 Aeromonas caviae ST1061 Contig1 4,628,224 Circular - blaOXA-1160, blaMOX-23, aadA2, dfrA12, mph(A), sul1, tetE - qacEdelta1 -
Contig2 93,128 Circular - blaIMP-1 - - -
Contig3 39,460 Circular - blaGES-24, aac(6′)-Il - - -
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MLST: multi-locus sequence typing, ARGs: antimicrobial resistance genes, MRGs: metal resistance genes, BRGs: biocide resistance genes, HRGs: heat resistance genes.

Table 2.

Antimicrobial susceptibility profiles of IMP-1-producing isolates from in situ biofilm and hospital wastewater samples.

Antibiotics BFESBL3 BFCARBA3 BFMDRP3 BFMDRP4 HWMDRP2
Ampicillin >16 (R) ND ND >16 >16
Piperacillin ND 16 (S) >64 (R) ND ND
Ampicillin/sulbactam >16/8 (R) ND ND >16/8 >16/8
Piperacillin/tazobactam >64/4 (R) >64/4 (R) >64/4 (R) ≤8/4 (S) ≤8/4 (S)
Ceftolozane/tazobactam >4/4 (R) ND ND >4/4 >4/4
Cefazolin >16 (R) ND ND >16 >16
Cefepime >8 (R) >16 (R) >16 (R) >8 (R) 4 (I)
Cefditoren >2 ND ND >2 >2
Ceftazidime >8 (R) >16 (R) >16 (R) >8 (R) >8 (R)
Ceftriaxone >2 (R) ND ND >2 (R) >2 (R)
Cefpodoxime >4 (R) ND ND >4 >4
Cefpodoxime/clavulanic acid >1 ND ND >1 >1
Cefozopran ND >16 >16 ND ND
Cefmetazole >32 (R) ND ND >32 >32
Flomoxef >32 ND ND >32 32
Latamoxef >8 ND ND >8 >8
Aztreonam >8 (R) >16 (R) 4(S) ≤4 (S) ≤4 (S)
Imipenem ND >8 (R) >8 (R) ND ND
Meropenem >2 (R) >8 (R) >8 (R) 1 (S) ≤0.12 (S)
Doripenem ND >8 >8 (R) ND ND
Faropenem >4 ND ND >4 >4
Colistin ≤1 (I) >4 ≤1 (I) ≤1 ≤1
Gentamicin >8 (R) ≤2 (S) >8 (R) ≤4 (S) ≤4 (S)
Amikacin ≤16 (S) ≤8 (S) 16 (S) ≤16 (S) ≤16 (S)
Tobramycin ND ≤2 (S) >8 (R) ND ND
Minocycline ≤4 (S) ≤2 (S) >8 ≤4 ≤4
Ciprofloxacin ND >2 (R) >2 (R) ND ND
Levofloxacin >1 (R) 2 (S) >4 (R) 0.5 (S) 0.5 (S)
Trimethoprim/sulfamethoxazole >2/38 (R) >2/38 (R) >2/38 >2/38 (R) >2/38 (R)
Fosfomycin ≤4 (S) ND ND >16 >16
Tigecycline ≤1 ≤0.5 >2 ≤1 ≤1
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S: susceptible, I: intermediate, R: resistance, ND: not determined.

3.6. Genetic context and phylogenetic relationship of blaIMP-1-harboring isolates

We compared the genomic contexts surrounding blaIMP-1 detected in the biofilm and hospital wastewater samples (Fig. 5). All blaIMP-1 genes isolated using culture-based methods were located adjacent to a Tn3 family transposon. In contig1 of P. alcaligenes BFCARBA3, the surrounding structures of the two blaIMP-1 genes showed high similarity, except that one of them additionally carried blaOXA-932. The genes surrounding blaIMP-1 in BFMDRP4 and HWMDRP2 exhibited highly conserved structures. The genomic contexts surrounding blaIMP-1 were not identified in the metagenomic contigs BFDNA_c177701 and HWDNA_c85536. The phylogenetic analysis showed that C. freundii BFESBL3 was closely related to C. freundii strain CFTMDU (SAMN23449392), previously detected in an upstream hospital sink that carried a blaKPC-2-horboring IncP6 plasmid [52] (Fig. 6). Comparative analysis of carbapenemase gene profiles indicated that C. freundii BFESBL3 additionally harbored blaIMP-1.

Fig. 5.

Fig. 5

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Linear comparison of genomic regions surrounding blaIMP-1.

Gray shading indicates similarity between sequences. Arrows represent gene orientation: red, lime green, and orange represent blaIMP-1, the Tn3 family sequence, and other genes, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6.

Fig. 6

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Maximum-likelihood phylogenetic tree of Citrobacter species, including isolates from in situ biofilm sample and publicly available genomes. The tree was constructed using core genome sequences and annotated with metadata including sequence type (ST), isolation source, country of origin, and β-lactamase gene profiles.

4. Discussion

Hospital wastewater is a major hotspot for AMR that contributes to the accumulation of clinically important organisms [5]. The presence of antibiotic residues, including ampicillin, sulbactam, levofloxacin, sulfamethoxazole, and trimethoprim, previously detected in hospital wastewater from the same facility, could exert selective pressure promoting the emergence and persistence of AMR [55]. Biofilms play a central role in sustaining and spreading AMR because their extracellular matrix and structured architecture protect microbes from antibiotics and promote horizontal gene transfer [8,9]. Crystal-violet staining revealed structurally cohesive biofilm accumulation on the surface of the polystyrene slides placed in hospital wastewater. Additionally, fluorescence microscopy exhibited a highly heterogeneous 3D matrix composed of abundant nucleic acids, polysaccharides, and proteins, in line with observations that hospital-wastewater biofilms are EPS enriched in these components [15]. Nucleic acids form dense structural scaffolds that restrict antibiotic penetration and support the transfer of resistance determinants within the biofilm community [8,9]. Polysaccharides generate highly viscous diffusion barriers that adsorb or neutralize antimicrobial compounds, effectively lowering their local concentrations [56]. Structural and enzymatic proteins further stabilize the matrix and modulate stress responses, enhancing persister cell formation and occasionally binding or inactivating antibiotics [57]. Consistent with these EPS-mediated mechanisms, our culture-based assessment showed markedly higher contamination with ESBL-producing E. coli in biofilm than in wastewater, reflecting the enhanced AMR-accumulation characteristic of biofilm communities.

A recent metagenomic study demonstrated that hospital wastewater harbors diverse bacterial communities and a wide range of AMR genes [6]. Taxonomic differences between in situ biofilms and hospital wastewater reflect distinct ecological conditions in these environments. Hospital wastewater is a dynamic, planktonic matrix continuously influenced by fecal and clinical inputs, favoring gut-associated taxa such as Bacteroidaceae [58,59]. By contrast, biofilms represent stable, surface-associated habitats that preferentially select Flavobacteriaceae, a group widely associated with the formation of biofilm and the degradation of complex organic substrates in aquatic environments [60]. These ecological differences are also reflected in AMR gene profiles, with biofilms harboring a broader diversity of carbapenemase genes than hospital wastewater, consistent with stable reservoirs in biofilms and transient release in wastewater [9]. Virulence-associated genes encoding adhesins, toxins, and components of iron acquisition systems were predominantly detected in hospital wastewater, indicating direct inputs from infected or colonized patients and the enrichment of pathogenic gram-negative bacteria [59,61]. The scarcity of virulence genes in biofilms suggests dominance of environmentally adapted or opportunistic bacteria with limited pathogenic potential, despite their function as reservoirs of AMR genes.

Carbapenemase genes such as blaKPC, blaIMP, and blaGES are commonly associated with Tn3-family composite transposons, which promote their duplication and horizontal dissemination across diverse chromosomal and plasmid backbones [62,63]. IS further enhances this process by introducing structural alterations or by disrupting local regulatory regions, and IS4, IS6, IS30, IS1182, and IS1595 have been reported in association with blaKPC, blaIMP, blaNDM, and blaOXA families [[64], [65], [66], [67]]. Differences in gene expression across environments reflect genomic context, as biofilm conditions preferentially activate resistance loci embedded in structurally dynamic regions [68,69]. Within biofilms, localized stress and close cell–cell proximity can promote DNA rearrangements around transposons and insertion sequences, increasing transcriptional responsiveness of the associated AMR genes. The strong transcriptional bias toward the blaIMP family is consistent with this interpretation, as blaIMP genes are typically embedded in Tn3-linked modules flanked by insertion sequences, which are highly responsive to structural rearrangement [62,63,66].

Culture-based isolation of carbapenemase-producing bacteria confirmed hospital-wastewater biofilms as reservoirs of clinically relevant AMR genes, with blaIMP-1 predominantly detected in biofilm. The detection of blaIMP-1 across phylogenetically distinct genera, including C. freundii, P. aeruginosa, P. alcaligenes, and A. caviae, highlights the broad host range of IMP-type carbapenemases and their capacity to circulate among environmental and opportunistic pathogens in hospital wastewater [3,70]. Whole-genome sequencing identified blaIMP-1 on chromosomes and on plasmids of multiple incompatibility types, pointing to its broad compatibility with diverse genomic backbones. A previous study has shown that IMP-type carbapenemases can exhibit variable carbapenem susceptibility patterns depending on the bacterial host and genetic background [71]. Similarly, OXA-type carbapenemases have been reported to exhibit altered resistance phenotypes in different genetic contexts [72]. Consistent with these observations, IMP-type carbapenemase-producing Aeromonas strains have been reported to remain susceptible to meropenem despite carrying carbapenemase genes [73]. Notably, two biofilm-derived isolates carried duplicate copies of blaIMP-1, and A. caviae BFMDRP4 was nearly genetically identical to A. caviae HWMDRP2 except for the acquisition of an additional blaIMP-1-encoding plasmid. Consistent with this difference, although both strains remained susceptible to meropenem, isolates with two blaIMP-1 exhibited lower susceptibility to meropenem than A. caviae HWMDRP2. Given that gene copy number is closely associated with expression level [74], the presence of isolates harboring multiple blaIMP copies may have contributed to elevated overall blaIMP activity within the in situ biofilm. Comparative analysis of blaIMP-1 genetic contexts revealed consistent localization adjacent to Tn3 family elements, suggesting the potential involvement of plasmids and transposons in the dissemination of blaIMP-1 within biofilms. The predominance of IMP-type carbapenemases reflects the Japanese epidemiology, where blaIMP is endemic in clinical and wastewater-associated isolates [75]. The IMP-type carbapenemase-producing C. freundii BFESBL3 strain also harbored a blaKPC-2-carrying IncP6 plasmid. The prior detection of C. freundii CFTMDU strain that was closely related at the whole-genome level and carried a blaKPC-2-harboring IncP6 plasmid in an upstream hospital sink suggests possible acquisition of blaIMP-1 within the hospital wastewater system [52]. These observations imply that the ecological success of IMP-type carbapenemases in biofilm-dominated environments may be associated with their genomic embedding within highly responsive mobile-element-associated regions that efficiently couple environmental stress to elevated activity at both molecular and phenotypic levels.

This study has some limitations. The single-facility design and limited number of samples restrict generalizability beyond this epidemiological setting, with the observed patterns potentially varying across healthcare institutions and sampling periods. Carbapenem resistance is shaped by distinct dissemination dynamics that differ from blaIMP-dominant contexts, potentially leading to alternative ecological outcomes [76]. Another limitation is the practical constraints of the approaches employed in this study. Isolation of carbapenemase producers relied on culture-based methods, which preferentially recover fast-growing, cultivable taxa and may underestimate AMR genes harbored by slow-growing or viable-but-nonculturable bacteria in wastewater and biofilm environments [77]. Additionally, metagenomic sequencing did not allow complete reconstruction of genomic regions surrounding blaIMP-1, limiting characterization of the local genetic context and comparability with culture-derived genomes. The co-occurrence analyses were based on co-detection patterns, and these analyses are useful for identifying potential associations, but do not necessarily indicate direct physical linkage, ecological interaction, or gene transfer. Furthermore, as the study was based on in situ environmental observations, the underlying gene transfer processes could not be directly evaluated. Future studies incorporating broader sampling alongside controlled experimental systems will be required to improve generalizability and clarify these processes.

This study demonstrates that in situ biofilms formed on plastic surfaces in hospital wastewater can act as important reservoirs of AMR. Biofilms harbored a broader diversity of carbapenemase determinants than planktonic wastewater and showed co-occurrence structures dominated by Tn3 family transposon and IS linking AMR genes to diverse microbial hosts. The metatranscriptomic analysis revealed higher expression of blaIMP-family genes and mobile genetic elements in biofilms, indicating conditions potentially favorable for resistance persistence and horizontal dissemination under biofilm conditions. Culture-based isolation and whole-genome sequencing confirmed the widespread distribution of blaIMP-1 across multiple genera and genomic contexts associated with Tn3 family transposons. These culture-based findings provided independent validation of the metagenomic and metatranscriptomic observations, particularly the presence of blaIMP-family genes in viable biofilm-associated bacteria. However, additional studies across multiple hospitals and sampling periods are needed to determine the broader generalizability of these patterns and to further clarify the implications of the observed co-occurrence relationships. Nonetheless, these findings underscore hospital-wastewater biofilms as critical interfaces for the persistence and dissemination of clinically relevant carbapenem resistance, highlighting the value of biofilm-focused approaches for early AMR detection and risk assessment within a One Health framework.

CRediT authorship contribution statement

Yusuke Ota: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Visualization, Writing – original draft. Yoko Nukui: Formal analysis, Writing – review & editing. Yoshiaki Gu: Formal analysis, Writing – review & editing. Ryoichi Saito: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We would like to thank Editage (www.editage.jp) for English language editing. This work was partially supported by the Japan Society for the Promotion of Science KAKENHI under Grant Numbers 23K28241, 24K02682, and 26K03093. These funders had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bioflm.2026.100377.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.docx (174KB, docx)
Multimedia component 2
mmc2.xlsx (2.4MB, xlsx)

Data availability

Data will be made available on request.

References

  • 1.GBD 2021 Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404:1199–1226. doi: 10.1016/S0140-6736(24)01867-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Budhram D.R., Mac S., Bielecki J.M., Patel S.N., Sander B. Health outcomes attributable to carbapenemase-producing Enterobacteriaceae infections: a systematic review and meta-analysis. Infect Control Hosp Epidemiol. 2020;41:37–43. doi: 10.1017/ice.2019.282. [DOI] [PubMed] [Google Scholar]
  • 3.Macesic N., Hawkey J., Vezina B., Wisniewski J.A., Cottingham H., Blakeway L.V., Harshegyi T., Pragastis K., Badoordeen G.Z., Dennison A., Spelman D.W., Jenney A.W.J., Peleg A.Y. Genomic dissection of endemic carbapenem resistance reveals metallo-beta-lactamase dissemination through clonal, plasmid and integron transfer. Nat Commun. 2023;14:4764. doi: 10.1038/s41467-023-39915-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.James R., Hardefeldt L.Y., Ierano C., Charani E., Dowson L., Elkins S., Thursky K. Antimicrobial stewardship from a one health perspective. Nat Rev Microbiol. 2026;24:146–162. doi: 10.1038/s41579-025-01233-3. [DOI] [PubMed] [Google Scholar]
  • 5.Lymperatou D., Konstantopoulou R., Mentsis M., Atzemoglou N., Diamanti C., Tzourtzos I., Naka K.K., Mitsis M., Konstantina G., Milionis H., Ntzani E., Christaki E. Hospital wastewater surveillance and antimicrobial resistance: a narrative review. Microorganisms. 2025;13:2739. doi: 10.3390/microorganisms13122739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kelly S.A., O'Connell N.H., Thompson T.P., Dillon L., Wu J., Creevey C., Kiely P., Slevin B., Powell J., Gilmore B.F., Dunne C.P. Large-scale characterization of hospital wastewater system microbiomes and clinical isolates from infected patients: profiling of multi-drug-resistant microbial species. J Hosp Infect. 2023;141:152–166. doi: 10.1016/j.jhin.2023.09.001. [DOI] [PubMed] [Google Scholar]
  • 7.Punch R., Azani R., Ellison C., Majury A., Hynds P.D., Payne S.J., Brown R.S. The surveillance of antimicrobial resistance in wastewater from a one health perspective: a global scoping and temporal review (2014–2024) One Health. 2025;21 doi: 10.1016/j.onehlt.2025.101139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Jia J., Liu Q., Zhao E., Li X., Xiong X., Wu C. Biofilm formation on microplastics and interactions with antibiotics, antibiotic resistance genes and pathogens in aquatic environment. Eco. Environ Health. 2024;3:516–528. doi: 10.1016/j.eehl.2024.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Liu H.Y., Prentice E.L., Webber M.A. Mechanisms of antimicrobial resistance in biofilms. npj Antimicrob Resist. 2024;2:27. doi: 10.1038/s44259-024-00046-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Ciofu O., Moser C., Jensen P.Ø., Høiby N. Tolerance and resistance of microbial biofilms. Nat Rev Microbiol. 2022;20:621–635. doi: 10.1038/s41579-022-00682-4. [DOI] [PubMed] [Google Scholar]
  • 11.Kittredge H.A., Dougherty K.M., Evans S.E. Dead but not forgotten: how extracellular DNA, moisture, and space modulate the horizontal transfer of extracellular antibiotic resistance genes in soil. Appl Environ Microbiol. 2022;88 doi: 10.1128/aem.02280-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wu X., Liu Z., Li M., Bartlam M., Wang Y. Integrated metagenomic and metatranscriptomic analysis reveals actively expressed antibiotic resistomes in the plastisphere. J Hazard Mater. 2022;430 doi: 10.1016/j.jhazmat.2022.128418. [DOI] [PubMed] [Google Scholar]
  • 13.Zhang X., Dong Z., Zhang S., Ma J., Liu S. Microplastic biofilm as hotspots of antibiotic resistance genes and potential pathogens. npj Biofilms Microbiomes. 2025;12:24. doi: 10.1038/s41522-025-00890-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kamani H., Ghayebzadeh M., Azari A., Ganji F. Characteristics of microplastics in a hospital wastewater treatment plant effluent and hazard risk assessment. Environ Process. 2024;11 doi: 10.1007/s40710-024-00694-7. [DOI] [Google Scholar]
  • 15.Buelow E., Dauga C., Carrion C., Mathé-Hubert H., Achaibou S., Gaschet M., Jové T., Chesneau O., Kennedy S.P., Ploy M.C., Da Re S., Dagot C. Hospital and urban wastewaters shape the matrix and active resistome of environmental biofilms. Water Res. 2023;244 doi: 10.1016/j.watres.2023.120408. [DOI] [PubMed] [Google Scholar]
  • 16.Milenkov M., Proux C., Rasolofoarison T.L., Rakotomalala F.A., Rasoanandrasana S., Rahajamanana V.L., Rafalimanana C., Ravaoarisaina Z., Ramahatafandry I.T., Westeel E., Petitjean M., Berti V., Marin J., Mullaert J., Han L., Clermont O., Raskine L., Endtz H., Andremont A., Denamur E., Komurian-Pradel F., Samison L.H., Armand-Lefevre L. Implementation of the WHO Tricycle protocol for surveillance of extended-spectrum beta-lactamase producing Escherichia coli in humans, chickens, and the environment in Madagascar: a prospective genomic epidemiology study. Lancet Microbe. 2024;5 doi: 10.1016/S2666-5247(24)00065-X. [DOI] [PubMed] [Google Scholar]
  • 17.Cole J.R., Wang Q., Fish J.A., Chai B., McGarrell D.M., Sun Y., Brown C.T., Porras-Alfaro A., Kuske C.R., Tiedje J.M. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642. doi: 10.1093/nar/gkt1244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Dallenne C., Da Costa A., Decré D., Favier C., Arlet G. Development of a set of multiplex PCR assays for the detection of genes encoding important beta-lactamases in Enterobacteriaceae. J Antimicrob Chemother. 2010;65:490–495. doi: 10.1093/jac/dkp498. [DOI] [PubMed] [Google Scholar]
  • 19.Wachino J., Yoshida H., Yamane K., Suzuki S., Matsui M., Yamagishi T., Tsutsui A., Konda T., Shibayama K., Arakawa Y. SMB-1, a novel subclass B3 metallo-beta-lactamase, associated with ISCR1 and a class 1 integron, from a carbapenem-resistant Serratia marcescens clinical isolate. Antimicrob Agents Chemother. 2011;55:5143–5149. doi: 10.1128/AAC.05045-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Bankevich A., Nurk S., Antipov D., Gurevich A.A., Dvorkin M., Kulikov A.S., Lesin V.M., Nikolenko S.I., Pham S., Prjibelski A.D., Pyshkin A.V., Sirotkin A.V., Vyahhi N., Tesler G., Alekseyev M.A., Pevzner P.A. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Shen W., Le S., Li Y., Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One. 2016;11 doi: 10.1371/journal.pone.0163962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Kang D.D., Li F., Kirton E., Thomas A., Egan R., An H., Wang Z. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7 doi: 10.7717/peerj.7359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wu Y.W., Simmons B.A., Singer S.W., MaxBin M.B. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–607. doi: 10.1093/bioinformatics/btv638. [DOI] [PubMed] [Google Scholar]
  • 24.Rühlemann M.C., Wacker E.M., Ellinghaus D., Franke A. MAGScoT: a fast, lightweight and accurate bin-refinement tool. Bioinformatics. 2022;38:5430–5433. doi: 10.1093/bioinformatics/btac694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Parks D.H., Imelfort M., Skennerton C.T., Hugenholtz P., Tyson G.W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–1055. doi: 10.1101/gr.186072.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chaumeil P.A., Mussig A.J., Hugenholtz P., Parks D.H. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36:1925–1927. doi: 10.1093/bioinformatics/btz848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Feldgarden M., Brover V., Gonzalez-Escalona N., Frye J.G., Haendiges J., Haft D.H., Hoffmann M., Pettengill J.B., Prasad A.B., Tillman G.E., Tyson G.H., Klimke W. AMRFinderPlus and the Reference Gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021;11 doi: 10.1038/s41598-021-91456-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Schwengers O., Barth P., Falgenhauer L., Hain T., Chakraborty T., Goesmann A. Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microb Genom. 2020;6:mgen000398. doi: 10.1099/mgen.0.000398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Siguier P., Perochon J., Lestrade L., Mahillon J., Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 2006;34:D32–D36. doi: 10.1093/nar/gkj014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Tavares R.D.S., Fidalgo C., Rodrigues E.T., Tacão M., Henriques I. Integron-associated genes are reliable indicators of antibiotic resistance in wastewater despite treatment- and seasonality-driven fluctuations. Water Res. 2024;258 doi: 10.1016/j.watres.2024.121784. [DOI] [PubMed] [Google Scholar]
  • 31.Ross K., Varani A.M., Snesrud E., Huang H., Alvarenga D.O., Zhang J., Wu C., McGann P., Chandler M. TnCentral: a prokaryotic transposable element database and web portal for transposon analysis. mBio. 2021;12 doi: 10.1128/mBio.02060-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Sayers E.W., Beck J., Bolton E.E., Brister J.R., Chan J., Connor R., Feldgarden M., Fine A.M., Funk K., Hoffman J., Kannan S., Kelly C., Klimke W., Kim S., Lathrop S., Marchler-Bauer A., Murphy T.D., O'Sullivan C., Schmieder E., Skripchenko Y., Stine A., Thibaud-Nissen F., Wang J., Ye J., Zellers E., Schneider V.A., Pruitt K.D. Database resources of the national center for biotechnology information in 2025. Nucleic Acids Res. 2025;53:D20–D29. doi: 10.1093/nar/gkae979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J. Basic local alignment search tool. J Mol Biol. 1990;215:403–410. doi: 10.1016/S0022-2836(05)80360-2. [DOI] [PubMed] [Google Scholar]
  • 34.Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–2504. doi: 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Chen S., Zhou Y., Chen Y., Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–i890. doi: 10.1093/bioinformatics/bty560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Schmartz G.P., Hartung A., Hirsch P., Kern F., Fehlmann T., Müller R., Keller A. PLSDB: advancing a comprehensive database of bacterial plasmids. Nucleic Acids Res. 2022;50:D273–D278. doi: 10.1093/nar/gkab1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kim D., Paggi J.M., Park C., Bennett C., Salzberg S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–915. doi: 10.1038/s41587-019-0201-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. 1000 genome project data processing subgroup, the sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079. doi: 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Liao Y., Smyth G.K., Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–930. doi: 10.1093/bioinformatics/btt656. [DOI] [PubMed] [Google Scholar]
  • 40.Aroney S.T.N., Newell R.J.P., Nissen J.N., Camargo A.P., Tyson G.W., Woodcroft B.J. CoverM: read alignment statistics for metagenomics. Bioinformatics. 2025;41 doi: 10.1093/bioinformatics/btaf147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Robinson M.D., McCarthy D.J., Smyth G.K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–140. doi: 10.1093/bioinformatics/btp616. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Wick R.R., Judd L.M., Gorrie C.L., Holt K.E. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb Genom. 2017;3 doi: 10.1099/mgen.0.000132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.De Coster W., D'Hert S., Schultz D.T., Cruts M., Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics. 2018;34:2666–2669. doi: 10.1093/bioinformatics/bty149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wick R.R., Judd L.M., Gorrie C.L., Holt K.E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13 doi: 10.1371/journal.pcbi.1005595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Simão F.A., Waterhouse R.M., Ioannidis P., Kriventseva E.V., Zdobnov E.M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–3212. doi: 10.1093/bioinformatics/btv351. [DOI] [PubMed] [Google Scholar]
  • 46.Larsen M.V., Cosentino S., Rasmussen S., Friis C., Hasman H., Marvig R.L., Jelsbak L., Sicheritz-Pontén T., Ussery D.W., Aarestrup F.M., Lund O. Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol. 2012;50:1355–1361. doi: 10.1128/JCM.06094-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Carattoli A., Zankari E., García-Fernández A., Voldby Larsen M., Lund O., Villa L., Møller Aarestrup F., Hasman H. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014;58:3895–3903. doi: 10.1128/AAC.02412-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Sullivan M.J., Petty N.K., Beatson S.A. Easyfig: a genome comparison visualizer. Bioinformatics. 2011;27:1009–1010. doi: 10.1093/bioinformatics/btr039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–2069. doi: 10.1093/bioinformatics/btu153. [DOI] [PubMed] [Google Scholar]
  • 50.Page A.J., Cummins C.A., Hunt M., Wong V.K., Reuter S., Holden M.T., Fookes M., Falush D., Keane J.A., Parkhill J. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3693. doi: 10.1093/bioinformatics/btv421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Xu H., Wang X., Yu X., Zhang J., Guo L., Huang C., Jiang X., Li X., Feng Y., Zheng B. First detection and genomics analysis of KPC-2-producing Citrobacter isolates from river sediments. Environ Pollut. 2018;235:931–937. doi: 10.1016/j.envpol.2017.12.084. [DOI] [PubMed] [Google Scholar]
  • 52.Ota Y., Prah I., Nukui Y., Koike R., Saito R. blaKPC−2-encoding IncP-6 plasmids in Citrobacter freundii and Klebsiella variicola strains from hospital sewage in Japan. Appl Environ Microbiol. 2022;88 doi: 10.1128/aem.00019-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Nguyen L.T., Schmidt H.A., von Haeseler A., Minh B.Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–274. doi: 10.1093/molbev/msu300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Letunic I., Bork P. Interactive tree of life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024;52:W78–W82. doi: 10.1093/nar/gkae268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Ota Y., Chen F., Prah I., Mahazu S., Watanabe K., Kinoshita T., Gu Y., Nukui Y., Saito R. Metatranscriptomic analysis reveals actively expressed antimicrobial-resistant genes and their hosts in hospital wastewater. Antibiotics. 2024;13:1122. doi: 10.3390/antibiotics13121122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Biswas S., Ahmed M., Das P., Niharika I.T., Das S.C., Shayla M.U., Mita M.A., Biswas S., Hasan M.R., Zaman S., Saleh M.A. Biofilms: mechanisms, quorum sensing, antibiotic resistance and implications in the food industry. Biologia. 2025;80:1803–1821. doi: 10.1007/s11756-025-01953-0. [DOI] [Google Scholar]
  • 57.Ugwu C.N., Ezeibe E.N., Emencheta S.C., Nwagwu C.S., Ogbonna K.O., Ejiofor C.V., Onugwu A.L., Berebon D.P., Attama A.A. Biofilms: structure, resistance mechanism, emerging control strategies, and applications. RSC Pharm. 2025;2:1376–1407. doi: 10.1039/d5pm00094g. [DOI] [Google Scholar]
  • 58.Buelow E., Bayjanov J.R., Majoor E., Willems R.J., Bonten M.J., Schmitt H., van Schaik W. Limited influence of hospital wastewater on the microbiome and resistome of wastewater in a community sewerage system. FEMS Microbiol Ecol. 2018;94 doi: 10.1093/femsec/fiy087. [DOI] [PubMed] [Google Scholar]
  • 59.Velazquez-Meza M.E., Galarde-López M., Cornejo-Juárez P., Bobadilla-Del-Valle M., Godoy-Lozano E., Aguilar-Vera E., Carrillo-Quiroz B.A., Ponce de León-Garduño A., Velazquez Acosta C., Alpuche-Aranda C.M. Bacterial communities and resistance and virulence genes in hospital and community wastewater: metagenomic analysis. Int J Mol Sci. 2025;26:2051. doi: 10.3390/ijms26052051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Pollet T., Berdjeb L., Garnier C., Durrieu G., Le Poupon C., Misson B., Jean-François B. Prokaryotic community successions and interactions in marine biofilms: the key role of Flavobacteriia. FEMS Microbiol Ecol. 2018;94 doi: 10.1093/femsec/fiy083. [DOI] [PubMed] [Google Scholar]
  • 61.Malik J., Naushad Z., Singh S., Shrivastav D., Verma V.V., Mishra M.K., Pal R.K., Sharma V.K. Emerging contaminants in healthcare effluents: antibiotic resistance and virulence factor dissemination. Folia Microbiol. 2025;70:1201–1216. doi: 10.1007/s12223-025-01356-3. [DOI] [PubMed] [Google Scholar]
  • 62.Nicolas E., Lambin M., Dandoy D., Galloy C., Nguyen N., Oger C.A., Hallet B. The Tn3-family of replicative transposons. Microbiol Spectr. 2015;3 doi: 10.1128/microbiolspec.MDNA3-0060-2014. [DOI] [PubMed] [Google Scholar]
  • 63.Partridge S.R., Kwong S.M., Firth N., Jensen S.O. Mobile genetic elements associated with antimicrobial resistance. Clin Microbiol Rev. 2018;31 doi: 10.1128/CMR.00088-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Ramsamy Y., Mlisana K.P., Allam M., Amoako D.G., Abia A.L.K., Ismail A., Singh R., Kisten T., Han K.S., Muckart D.J.J., Hardcastle T., Suleman M., Essack S.Y. Genomic analysis of carbapenemase-producing extensively drug-resistant Klebsiella pneumoniae isolates reveals the horizontal spread of p18-43_01 plasmid encoding blaNDM-1 in South Africa. Microorganisms. 2020;8:137. doi: 10.3390/microorganisms8010137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Gupta N., Angadi K., Jadhav S. Molecular characterization of carbapenem-resistant Acinetobacter baumannii with special reference to carbapenemases: a systematic review. Infect Drug Resist. 2022:7631–7650. doi: 10.2147/IDR.S386641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Shih Y.L., Bregente C.J.B., Chen P.K., Thuy T.T.D., Chen Y.-C., Kuo H.-Y., Lu H.-F., Kao C.-Y. First report of the chromosomal integration of carbapenemase gene blaIMP−19 in Acinetobacter baumannii AB322: the legacy of integron in phage-plasmid? Microbiol Spectr. 2024;12 doi: 10.1128/spectrum.00382-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Halder G., Chaudhuri B.N., Veeraraghavan B., Denny P., Dutta P., Chakraborty M., Khan U.R., Ganguly S.S., Mandal S., Upadhyaya Y.P., Biswas B., Chakraborty A., Maiti S., Mondal H., Pal S., Dutta S. Antimicrobial resistance and phylogenetic lineages of KPC-2-producing blood-borne Klebsiella pneumoniae subsp. pneumoniae from Kolkata, India during 2015–2024: emergence of Klebsiella pneumoniae subsp. pneumoniae with blaKPC−2, blaNDM, and blaOXA−48-like triple carbapenemases. Microbiol Spectr. 2025;13 doi: 10.1128/spectrum.00126-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Frost L.S., Leplae R., Summers A.O., Toussaint A. Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol. 2005;3:722–732. doi: 10.1038/nrmicro1235. [DOI] [PubMed] [Google Scholar]
  • 69.Stewart P.S., Franklin M.J. Physiological heterogeneity in biofilms. Nat Rev Microbiol. 2008;6:199–210. doi: 10.1038/nrmicro1838. [DOI] [PubMed] [Google Scholar]
  • 70.Grooters S.V., Mollenkopf D.F., Wittum T.E. The genetic context of blaIMP varies among bacterial families from one health sources. PLoS One. 2025;20 doi: 10.1371/journal.pone.0327200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Cañada-García J.E., Grippo N., de Arellano E.R., Bautista V., Lara N., Navarro A.M., Cabezas T., Martínez-Ramírez N.M., García-Cobos S., Calvo J., Cercenado E., Aracil B., Pérez-Vázquez M., Oteo-Iglesias J., Spanish IMP Study Group Phenotypic and molecular characterization of IMP-producing Enterobacterales in Spain: predominance of IMP-8 in Klebsiella pneumoniae and IMP-22 in Enterobacter roggenkampii. Front Microbiol. 2022;13 doi: 10.3389/fmicb.2022.1000787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Wu J., Thompson T.P., O'Connell N.H., McCracken K., Powell J., Gilmore B.F., Dunne C.P., Kelly S.A. More than just the gene: investigating expression using a non-native plasmid and host and its impact on resistance conferred by β-lactamase OXA-58 isolated from a hospital wastewater microbiome. Lett Appl Microbiol. 2024;77 doi: 10.1093/lambio/ovae097. [DOI] [PubMed] [Google Scholar]
  • 73.Neuwirth C., Siebor E., Robin F., Bonnet R. First occurrence of an IMP metallo-β-lactamase in Aeromonas caviae: IMP-19 in an isolate from France. Antimicrob Agents Chemother. 2007;51:4486–4488. doi: 10.1128/AAC.01462-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Suarez S.A., Martiny A.C. Gene amplification uncovers large previously unrecognized cryptic antibiotic resistance potential in E. coli. Microbiol Spectr. 2021;9 doi: 10.1128/Spectrum.00289-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Yano H., Hayashi W., Kawakami S., Aoki S., Anzai E., Zuo H., Kitamura N., Hirabayashi A., Kajihara T., Kayama S., Sugawara Y., Yahara K., Sugai M. Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan. Antimicrob Agents Chemother. 2024;68 doi: 10.1128/aac.01669-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Męcik M., Stefaniak K., Harnisz M., Korzeniewska E. Hospital and municipal wastewater as a source of carbapenem-resistant Acinetobacter baumannii and Pseudomonas aeruginosa in the environment: a review. Environ Sci Pollut Res. 2024;31:48813–48838. doi: 10.1007/s11356-024-34436-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Li L., Mendis N., Trigui H., Oliver J.D., Faucher S.P. The importance of the viable but non-culturable state in human bacterial pathogens. Front Microbiol. 2014;5:258. doi: 10.3389/fmicb.2014.00258. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

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Data will be made available on request.

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