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. 2026 Mar 20;16:1781149. doi: 10.3389/fcimb.2026.1781149

Microplastics enhance the risk of cross-genus dissemination of carbapenemase resistance plasmids in ICU patients

Yongliang Ni 1,2,†, Jianchao Zhang 1,†, Cheng Peng 2,†, Yong Yang 2, Yueke Lin 3,*, Ziyun Li 4,5,*
PMCID: PMC13047061  PMID: 41938864

Abstract

Background

The emergence of carbapenem-resistant Enterobacterales (CRE) in intensive care units (ICUs) poses a critical global health threat. Environmental factors within hospitals, including microplastic (MP) pollution derived from degraded medical plastics, are potential yet underexplored contributors to the dissemination of antibiotic resistance. This study aimed to investigate whether MPs can accelerate the horizontal transfer of clinically relevant carbapenemase plasmids among CRE pathogens prevalent in ICUs.

Methods

Representative CRE isolates and epidemic carbapenemase-producing plasmids were co-incubated with environmentally relevant concentrations of characterized MPs. Conjugation frequencies were quantified under simulated ICU conditions, including standard and hyperglycemic media. The influence of MPs on recipient biofilm formation—a key facilitator for genetic exchange—was assessed using crystal violet assays and confocal microscopy. Plastic-free conditions were set as controls.

Results

MPs significantly enhanced the conjugation rates of carbapenemase plasmids between CRE strains (p < 0.001). Importantly, the elevated conjugation efficiencies were correlated with potent MP-induced stimulation of biofilm formation in recipient bacteria. Additionally, MPs synergized with the simulated diabetic ICU urine environment, increasing plasmid transfer efficiency by more than 3.96-fold. MPs acted as abiotic surfaces that promoted bacterial aggregation and plasmid exchange.

Conclusion

Our findings reveal that medical plastic-derived MPs serve as novel environmental catalysts for the rapid dissemination of carbapenem resistance within ICUs. By significantly enhancing biofilm-associated plasmid conjugation—especially in the context of patient comorbidity (hyperglycemia)—MPs constitute an emerging environmental driver that exacerbates the spread of untreatable CRE infections, highlighting the need for urgent mitigation strategies.

Keywords: antibiotic resistance, carbapenem-resistant enterobacteriaceae, clinical microbiology, intensive care unit, microplastics

1. Introduction

The global antimicrobial resistance (AMR) crisis is intensifying, with intensive care units (ICUs) serving as critical hotspots for multidrug-resistant (MDR) pathogens (Brinkac et al., 2017; Charalampous et al., 2021; Naddaf, 2024; Stronger commitment and faster action against antimicrobial resistance, 2024). ICU patients account for 32.6% of hospital-acquired resistant infections and face a 4.2-fold higher infection-related mortality than general ward patients (Antimicrobial Resistance Collaborators, 2022). Carbapenem-resistant Enterobacterales (CRE), designated by the WHO as critical-priority pathogens, present severe clinical challenges when exhibiting difficult-to-treat resistance (DTR), defined as non-susceptibility to all first-line bactericidal agents (Kadri et al., 2018; WHO updates list of drug-resistant bacteria most threatening to human health, ). While antibiotic development continues, the rapid emergence of overlapping resistance mechanisms—evidenced by in vitro studies showing ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogens developing pan-resistance to clinical and developmental antibiotics within 60 days (Daruka et al., 2025)—threatens therapeutic efficacy.

Environmental co-factors are increasingly recognized as drivers of AMR transmission. Microplastics (MPs), defined as plastic particles <5 mm in size, permeate global ecosystems (Wang et al., 2021; Stapleton et al., 2023; Stoett et al., 2024) and enter humans via ingestion, inhalation, or medical procedures (Florance et al., 2022; Wu et al., 2022; Wolff et al., 2023; Saha and Saha, 2024; Wang et al., 2024; Winiarska et al., 2024; Li et al., 2025d; Nihart et al., 2025). Critically, intravenous administration introduces MPs directly into the circulation, with kidney accumulation enabling urinary excretion—particularly concerning for catheterized ICU patients (Leslie et al., 2022; Pironti et al., 2022; Liu et al., 2024). According to existing research, MPs with particle sizes ranging from 4 to 15 μm are commonly detected in urine samples from healthy adults (Pironti et al., 2022). Among MPs with diameters ≥300 nm, the concentration range is approximately 0.1 to 10 μg/mL (Ji et al., 2025). Notably, exposure levels may be significantly elevated in specific populations, such as neonates in intensive care units, due to the use of medical devices. It has been reported that the average exposure dose of MPs in these infants could be 10 to even 1000 times higher than that in healthy children (Bernard et al., 2021). Concentrations of microplastic particles exceeding 700 nm in the blood of healthy individuals measure approximately 1.6 μg/L (Leslie et al., 2022), and their transmission into the bloodstream via infusion systems has been confirmed (Li et al., 2025a). Huang et al.’s recent study revealed that commercially available infusion bags release 1-62 μm MPs (predominantly 1-10 μm) even after filtration measures (Huang et al., 2025). Thus, intravenous infusion may typically introduce tens of thousands of microplastic particles directly into human blood. Consequently, ICU patients requiring frequent infusions may exhibit higher microplastic concentrations in their blood (Casella et al., 2025; Li et al., 2025a; Adatia, 2026). MP toxicity mechanisms, including oxidative stress and inflammation (Huang et al., 2024), may synergize with co-contaminants like antibiotics to exacerbate AMR (Bank et al., 2020; G. et al., 2025).

Specific size-dependent effects of microplastics on the conjugation and transfer of mobile antibiotic resistance genes (mARGs) in multidrug-resistant clinical bacteria under simulated clinical conditions (e.g., within biofilms, coexisting with disinfectants) remain poorly understood. Medical microplastics, the main source of microplastic contamination in clinical settings, exhibit a wide range of particle sizes, but their regulatory effects on ARG transfer in biofilm microenvironments have not been systematically clarified (Canellas et al., 2023; Khan et al., 2025). Furthermore, the role of medical plastic-derived MPs in facilitating the horizontal transfer of carbapenemase resistance plasmids among clinically relevant pathogens in ICUs remains underexplored. Horizontal gene transfer (HGT) via conjugation is a major mechanism for AMR dissemination, allowing resistance genes to spread across bacterial species and genera (Li et al., 2021, 2025c; Ding et al., 2024; Hernandez-Beltran et al., 2024; Djermoun et al., 2025). Biofilm formation is a key facilitator of conjugation, as bacterial aggregation enhances cell-to-cell contact and genetic exchange (Djermoun et al., 2025). Our previous study have shown that low-dose MPs can regulate biofilm formation and alter the conjugation frequency of laboratory-engineered strains (Escherichia coli) (Li et al., 2025d). However, its effects on clinical highly drug-resistant strains remain unclear. Given the high prevalence of medical plastics in ICUs (e.g., catheters, intravenous lines) and the persistent MP pollution they generate, we hypothesized that MPs act as abiotic surfaces to promote biofilm formation and accelerate the conjugation of carbapenemase resistance plasmids among CRE strains. Additionally, we investigated the synergistic effect of hyperglycemic conditions (simulating diabetic comorbidity, a common ICU patient characteristic) on MP-mediated AMR dissemination. This study provides critical insights into an overlooked environmental driver of CRE spread and informs targeted infection control strategies for the post-antibiotic era.

2. Materials and methods

2.1. Sample collection and bacterial isolation

This study was approved by the Ethics Committee of Shandong Provincial Third Hospital (approval number: KYLL-2023096). All procedures involving human samples were conducted in compliance with relevant ethical guidelines and regulations. Urine samples were collected from eight ICU patients with long-term indwelling catheters (≥7 days) during routine laboratory testing (Figure 1). Bacterial isolation was performed using standard culture methods, and CRE strains were identified via MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) and antimicrobial susceptibility testing.

Figure 1.

Infographic illustrating a study workflow: ICU patients with long-term catheters provide urine for pathogenic bacteria isolation, identification, and antimicrobial susceptibility testing. Whole genome sequencing and bioinformatics analyze structure and function, followed by in vitro experiments measuring conjugation frequency, biofilm formation in LB medium with various microplastic concentrations, and gene expression changes related to microplastics and antibiotics.

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Schematic diagram of the experimental analysis process.

2.2. Strains, culture medium, and reagents

The experimental strains used in this study included nine clinically isolated CRE, a chloramphenicol-resistant Escherichia coli strain PBCKS preserved in the laboratory, and two standard strains, E. coli ATCC 25922 and Staphylococcus aureus ATCC 25923. All strains were stored in LB liquid medium supplemented with 20% glycerol and cryopreserved at -80°C in an ultra-low temperature refrigerator; prior to the experiment, they were revived and activated on LB agar plates to ensure the purity and viability of the strains.

The culture media used in this study were either commercially available products or self-prepared according to standard formulas: Among them, LB liquid medium, LB agar plates, and mannitol agar medium were all purchased from Qingdao Haibo Biotechnology Co., Ltd. (Qingdao, China); the standard media for the normal culture condition group included sterile artificial urine SHU medium (Artificial Urine SHU, Sterile, Solarbio, pH=5.4) and M9Ca medium (Sangon Biotech (Shanghai) Co., Ltd.). Hyperglycemic media were prepared by adding glucose to SHU medium and M9Ca medium respectively to a final concentration of 20% (w/v), which was used to simulate the hyperglycemic microenvironment of urine from patients in the ICU with diabetes mellitus. All culture media underwent sterility verification (incubated at 37°C for 24 h with no contaminating bacterial growth) before use to avoid interference of contamination on the experimental results.

The main reagents used in this study were of analytical grade or bioreagent grade, among which all antibiotics were purchased from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). All antibiotics were prepared as stock solutions in accordance with the latest standards of the CLSI (Schuetz et al., 2025), stored at -20°C in the dark, and diluted to the required working concentration before use.

2.3. Antimicrobial susceptibility testing

As described previously (Hu et al., 2023), antimicrobial susceptibility was determined using the broth microdilution method and Kirby Bauer disk diffusion method against a panel of antibiotics, including penicillins, cephalosporins, carbapenems, aminoglycosides, quinolones, and tetracyclines. Strains were classified as resistant (R) or sensitive (S) based on CLSI guidelines (Schuetz et al., 2025).

2.4. Genome sequencing and annotation

Genomic DNA was extracted from pure cultures of nine CRE isolates using a commercial DNA extraction kit (Qiagen, catalog no. 51904). Nanopore long-read sequencing was performed with the PromethION sequencer (Oxford Nanopore Technologies) (Branton et al., 2008). Based on sequencing-by-synthesis (SBS) technology, DNA libraries were sequenced on the Illumina HiSeq high-throughput sequencing platform to generate raw data. After quality control of the raw sequencing data, de novo assembly of the bacterial genomes was conducted using Unicycler (v0.4.9) (Wick et al., 2017) and Pilon (v1.23) (Bankevich et al., 2012). Gene prediction of the bacteria was performed using Prodigal-2.6.2 (https://github.com/hyattpd/prodigal/wiki), and genome annotation was carried out with Prokka. Functional annotation was implemented against the COG (Tatusov et al., 2000), GO (Ashburner et al., 2000), Pfam (Mistry et al., 2021), and KEGG (Kanehisa et al., 2004) databases. CRISPR arrays, prophages, genomic islands, and secondary metabolite information were identified using MinCED (v0.4.2), PhiSpy (Tatusov et al., 2000) (https://github.com/linsalrob/PhiSpy), IslandViewer 4 (http://www.pathogenomics.sfu.ca/islandviewer/), and antiSMASH, respectively. Plasmid annotation was performed using Prokka, and BLAST (Altschul et al., 1997) was employed to analyze and screen for carbapenemase-encoding genes (e.g., blaKPC, blaNDM) and conjugative elements. Genomic circular maps were generated using the R package circlize (Gu et al., 2014).

2.5. Microplastic preparation

Polystyrene MPs (PS-MPs) with a diameter of 4 μm were purchased from Shanghai Fengtai Suhua. MPs were sterilized by autoclaving and suspended in sterile water to prepare a stock solution of 10 mg/mL. Working concentrations of 0 μg/mL (control), 10 μg/mL, and 100 μg/mL were prepared by diluting the stock solution in culture medium.

2.6. Conjugation assays

Conjugation assays were performed using donor CRE strains (harboring carbapenemase plasmids) and recipient strains (Escherichia coli PBCKS or Staphylococcus aureus ATCC25923). As described previously (Li et al., 2021, 2025c), donor and recipient strains were cultured to mid-log phase (OD600 = 0.5-0.6) in LB broth. Equal volumes of donor and recipient cultures (1 mL each) were mixed, and MPs were added to the mixture at final concentrations of 0, 10 μg/mL, or 100 μg/mL. The mixture was incubated at 37 °C for 4 h without shaking. Conjugants were selected on LB agar plates supplemented with appropriate antibiotics (e.g., carbapenems for donor selection, rifampicin for recipient selection). Conjugation frequency was calculated as the number of conjugants per recipient colony-forming unit (CFU). Assays were performed in triplicate under two culture conditions: standard medium (SHU and M9Ca) and hyperglycemic medium (SHU + 20% glucose, M9Ca + 20% glucose) to simulate diabetic ICU urine.

2.7. Biofilm formation assays

Biofilm formation was assessed using the crystal violet staining method as described previously (Li et al., 2025d). Bacterial cultures (100 μL) were inoculated into 96-well plates containing culture medium with or without MPs (0 μg/mL or 10 μg/mL) and incubated at 37 °C for 24 h. After removing planktonic cells, wells were washed three times with PBS, fixed with methanol for 15 min, and stained with 0.1% crystal violet for 30 min. Excess stain was washed away, and the plate was air-dried. Bound crystal violet was solubilized with 33% acetic acid, and absorbance was measured at 590 nm (OD590) using a microplate reader.

2.8. Gene expression analysis

Quantitative real-time PCR (qRT-PCR) was used to analyze the expression of soxR (oxidative stress-related gene) and traF (conjugation-related gene) (Table 1). Bacterial strains were cultured in medium with or without MPs (10 μg/mL) or tetracycline (2 μg/mL, positive control) at 25 °C for 7 days. Total RNA was extracted using TRIzol reagent, and cDNA was synthesized using a reverse transcription kit (Vazyme, China). qRT-PCR was performed using SYBR Green Master Mix (Vazyme, China) on a StepOnePlus Real-Time PCR System. The 16SrRNA gene was used as an internal reference, and relative gene expression was calculated using the 2-ΔΔCt method.

Table 1.

Primer sequences used in this study.

Gene Primer Sequence (5’ to 3’) Tm (°C)
bla NDM1 F GCCCAGATCCTCAACTGGAT 60
R CGCATTGGCATAAGTCGCAA
bla KPC F TTACGGCAAAAATGCGCTGG 60
R TCCAGACGGAACGTGGTATC
soxR F GGCGACCATTGGTGAAGCGT 60
R CAATCACTGCGCGAAAGGCA
traF F GGCAACCTCGTCGCCTTTA 60
R GCAAGTCGGCGTGTTTTCG
16SrRNA F CCCAGATGGGATTAGCTTGT 60
R TCTGGACCGTGTCTCAGTTC
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2.9. Statistical analysis

All statistical analyses were performed using R software. Data are presented as mean ± standard deviation (SD) of three independent experiments. Differences between groups were analyzed using two-tailed Student’s t-test. P-values <0.05 were considered statistically significant (*P < 0.05, **P < 0.01, ***P < 0.001).

3. Results

Nine CRE strains, including carbapenem-resistant Klebsiella pneumoniae (CRKP) and carbapenem-resistant E. coli (CREC), were isolated from eight ICU patients with long-term indwelling catheters (Figure 1). Antimicrobial susceptibility testing results showed that all strains were resistant to multiple antimicrobials, including carbapenems (e.g., imipenem, meropenem), cephalosporins, and aminoglycosides. In contrast, some strains exhibited susceptibility to tetracyclines (e.g., doxycycline) (Table 2; Supplementary Table 1). Additionally, the isolated strains exhibited high antimicrobial susceptibility to last-resort antimicrobials such as tigecycline and colistin (Table 2).

Table 2.

ICU patient sources and antimicrobial susceptibility profiles of nine CRE strains.

Strain Diseases-gender-age (year) ESBL Antimicrobial susceptibility
Resistance Sensitive
CREC-U1 Severe pneumonia-Female-53 Neg AMP, CFZ, GM, CAZ/AVI, AXL, AMP/SULB, CEFU, FEP, AMX/CLAV,PIP/TAZ, CAZ,CDX,CRO,CSULB, TOB, ETP,IMP,TIC/CLAV,MER, CIP,AMK,SMT/TMP,LVX DOX, MIN, COL, TIG
CREC-U3 Pneumonia- Male-52 Neg AMP, CFZ, GM, AXL, CDX, AMP/SULB, AMX/CLAV, PIP/TAZ, CEFU, CAZ, CRO, CSULB,FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, DOX, SMT/TMP, LVX CAZ/AVI,MIN, COL, AMK, TIG
CRKP-1 Tetraplegia- Male-15 Neg AMP, CFZ, GM, AXL, CEFU, AMP/SULB, PIP/TAZ, CAZ, CDX, CRO, CSULB, FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, DOX, MIN, AMK, SMT/TMP, LVX CAZ/AVI,COL, TIG
CREC-2 Heart failure- Male-67 Neg AMP, CFZ, GM, CAZ/AVI, AXL, AMP/SULB, CEFU, AMX/CLAV,PIP/TAZ, CAZ, CDX, CRO, CSULB, FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, DOX, AMK, LVX, SMT/TMP MIN, COL, TIG
CRKP-3 Tetraplegia-Female-63 Neg AMP, CFZ, GM, AXL, AMP/SULB, AMX/CLAV, PIP/TAZ, CEFU, CAZ, CRO, CSULB, ETP, IMP, TIC/CLAV, MER CAZ/AVI,CDX,TOB,CIP, DOX, MIN, COL, AMK, TIG, SMT/TMP, LVX
CREC-32 Tetraplegia-Female-39 Neg AMP, CFZ, GM, CAZ/AVI, AXL, AMP/SULB, CEFU, AMX/CLAV, PIP/TAZ, CAZ, CDX, CRO,CSULB,FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, DOX, AMK, SMT/TMP, LVX MIN,COL,TIG
CREC-36 Severe pneumonia-Female-53 Neg AMP, CFZ, GM, CAZ/AVI, AXL, AMP/SULB, CEFU, AMX/CLAV, PIP/TAZ, CAZ, CDX, CRO, CSULB, FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, AMK, SMT/TMP, LVX DOX,MIN, COL, TIG
CRKP-58 Dysphagia- Male-68 Pos AMP, CFZ, AXL, AMP/SULB, AMX/CLAV, PIP/TAZ, CEFU, CRO, ETP, IMP, TIC/CLAV, MER GM,CAZ/AVI,CAZ,CDX,TOB,CIP, DOX, MIN, COL, AMK, TIG, SMT/TMP, LVX
CRKP-59 Paraplegia-Female-65 Neg AMP, CFZ, GM, AXL, CEFU, AMP/SULB, AMX/CLAV, PIP/TAZ, CAZ, CDX, CRO, CSULB, FEP, ETP, IMP, TIC/CLAV, MER, TOB, CIP, DOX, AMK, LVX CAZ/AVI,MIN, COL, TIG, SMT/TMP
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Neg, Negative; Pos, Positive; AMP, Ampicillin; CFZ, Cefazolin; GM, Gentamicin; AXL, Aztreonam; CAZ/AVI, Ceftazidime/Avibactam; AMX/CLAV, Amoxicillin/Clavulanic acid; CDX, Cefoxitin; TOB, Tobramycin; CEFU, Cefuroxime; DOX, Doxycycline; PIP/TAZ, Piperacillin/Tazobactam; CIP, Ciprofloxacin; LVX, Levofloxacin; SMZ/TMP, Trimethoprim/Sulfamethoxazole; CRO, Ceftriaxone; ETP, Ertapenem; TIC/CLAV, Ticarcillin/Clavulanic acid; CAZ, Ceftazidime; CSULB, Cefoperazone/Sulbactam; FEP, Cefepime; IMP, Imipenem; MER, Meropenem; AMK, Amikacin; MIN, Minocycline; COL, Polymyxin; TIG, Tigecycline.

Complete genome sequences of nine CRE strains were obtained via Nanopore sequencing and de novo assembly (Supplementary Figure S1-S9; Supplementary Table 2). Functional annotation revealed that genes associated with glycoside hydrolases and glycosyltransferases were the most abundant functional categories (Figure 2A), accompanied by the enrichment of carbohydrate metabolism pathways (Figure 2B)—suggesting that these genes may play roles in biofilm formation and stress adaptation.

Figure 2.

Panel A contains grouped bar charts for nine samples, comparing counts of six enzyme classes: auxiliary activities, carbohydrate-binding modules, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and polysaccharide lyases. Panel B contains horizontal bar charts for the same samples, each charting the gene counts in categories A to AC from cell growth to nervous system, using color codes for cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismal systems.

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CAZy and KEGG-Based Functional Profiling of Nine CRE Strains. (A) CAZy Annotation of Enzyme Families in nine CRE; (B) Functional classification of gene products in nine CRE strains based on KEGG annotations.

Notably, the CREC-2 strain harbors two CRISPR arrays (Figure 3A), suggesting an endogenous defense mechanism against extrinsic genetic elements. Furthermore, prophages and genomic islands were ubiquitous across all strains (Supplementary Tables S3, S4), indicating that CRE strains can achieve evolutionary adaptation via gene acquisition and recombination. Specifically, prophages enhance the strains’ environmental survival capacity by mediating the acquisition of novel genes; genomic islands facilitate the strains’ rapid adaptation to stressors (such as antibiotic exposure and nutrient limitation); and the coexistence of the two can induce recombination-driven genetic diversity, further promoting strain evolution.

Figure 3.

Panel A shows schematic graphics of two CRISPR arrays with colored boxes representing different repeats and spacers. Panel B is a heatmap summarizing antibiotic resistance for three bacterial strains across multiple drug classes, with red cells for resistant (R) and blue for susceptible (S). Panel C is a table listing plasmid content per strain, including gene names in parentheses and highlighting specific resistance plasmids in red. Panel D displays gel electrophoresis results, with bands indicating presence of blaKPC or blaNDM-1 genes and a DNA ladder.

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Multifaceted Analysis of CRE Resistance: CRISPR, Antimicrobial Susceptibility, Resistance Plasmids, and Conjugation. (A) Phage defense mechanisms in CREC-2 via CRISPR array analysis; (B) Antimicrobial susceptibility analysis of CRE with drug resistance plasmids, R: resistance, S: sensitive; (C) Key resistance-related genes and potentially conjugative plasmids in CRE drug resistance plasmids (red); (D) Conjugation transfer validation of three potential conjugative drug resistance plasmids, Conjugants: 1, 2: E. coli (pCREC32_4); 3, 4: E. coli (pCRKP1_1); 5, 6: S. aureus (pCREC32_4); 7, 8: S. aureus (pCRKP1_1); 9, 10: E. coli PBCKS (pCRKP59_1); 11, 12: S. aureus (pCRKP59_1).

Among the nine CRE strains, CRKP-1, CRKP-59, and CREC-32 carry antibiotic resistance plasmids, and these three strains exhibited resistance to the vast majority of antibiotics (Figure 3B). Further analysis revealed that mARGs (conferring carbapenem resistance) were detected in the resistance plasmids of all three strains; notably, the plasmids carried by CRKP-1 and CREC-32 also contained genes associated with colibactin biosynthesis (Figure 3C), which endows them with an enhanced competitive colonization advantage in polymicrobial environments.

Crucially, conjugation assay results demonstrated that plasmid pCRKP59_2 carrying the blaNDM-1 gene and plasmid pCRKP1_2 carrying the blaNDM-5 gene could be transferred from K. pneumoniae to S. aureus; meanwhile, plasmid pCREC32_4 carrying the blaKPC gene was able to overcome Gram-type barriers and undergo intergeneric transfer from E. coli to S. aureus (Figure 3D). This indicates that clinical CRE strains possess evolutionary potential to surmount intrinsic host restrictions. Following the interspecies transfer of such plasmids carrying broad-spectrum resistance determinants, their horizontal dissemination in clinical settings can significantly increase the risk of emergence of untreatable MDR pathogens.

Subsequently, we further analyzed the conjugation frequencies of conjugative resistance plasmids among three clinical CRE strains under different environmental conditions and stressors. PS-MPs (4 μm, 10 μg/mL) significantly increased the conjugation frequency between CREC32 and E. coli PBCKS compared to the plastic-free control (p < 0.001) (Figures 4A, B). The promotive effect of MPs was amplified in hyperglycemic medium (SHU + 20% glucose), with a 3.96-fold increase in blaKPC transfer efficiency (p < 0.01) (Figure 4B). Higher MP concentrations (100 μg/mL) did not further enhance conjugation frequency, suggesting a concentration-dependent effect. Crystal violet assays showed that MPs (10 μg/mL) robustly stimulated biofilm formation in CREC32, as indicated by a significant increase in OD590 compared to the control (p < 0.05) (Figure 4C). MPs differentially modulated the expression of soxR and traF in CREC32 and CRKP strains across culture media. In M9Ca medium, MPs increased gene expression in CREC32 but inhibited it in CRKP at low concentrations. In hyperglycemic SHU medium (20% glucose), MPs significantly promoted the expression of soxR and traF in CRKP (p < 0.05), indicating that hyperglycemic conditions synergize with MPs to enhance oxidative stress and conjugation-related gene expression (Figures 4D, E). In summary, MPs in specific environments, particularly under hyperglycemic conditions, can significantly exacerbate plasmid-mediated resistance transmission among clinical drug-resistant strains by upregulating conjugation-related genes and enhancing biofilm formation.

Figure 4.

Panel A presents a grayscale scanning electron microscope image showing uniform spherical microbeads. Panel B displays a scatter plot with error bars illustrating conjugation frequency across varying conditions and concentrations, with statistically significant differences indicated. Panel C features a violin plot comparing OD590 at three different concentrations, where higher concentrations show increased optical density. Panels D and E contain bar graphs with error bars displaying the relative expression levels of soxR and traF genes in three strains, highlighting statistically significant differences among various experimental groups.

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Impact of PS-MPs on Conjugation, Biofilm, and Stress Responses in CREC32 under Varying Nutrients. (A) Scanning electron microscopy of polystyrene MPs (PS-MPs); (B) Impact of PS-MPs on conjugation transfer of pCREC_4 plasmid under different nutrient conditions; (C) MPs-enhanced biofilm formation in CREC32; MPs modulate (D) oxidative stress and (E) conjugation transfer-related genes in diverse nutrient conditions, C, L and H represent 0, 10 and 100 μg/mL MPs, respectively. The superscript U indicates that the culture medium is SHU supplemented with 20% glucose. *p < 0.05; **p < 0.01; ***p < 0.001.

4. Discussion

This study demonstrates that MPs derived from the degradation of medical plastics can serve as novel environmental catalysts, promoting the horizontal transfer of carbapenemase resistance plasmids among CRE pathogens in the ICU. In contrast to previous studies focusing primarily on nanoplastic-induced ROS-mediated gene transfer in planktonic bacteria (Li et al., 2025b), the key findings of this work are as follows: micrometer-sized microplastics released from degrading medical devices (e.g., catheters, infusion lines) serve as abiotic surfaces that promote bacterial aggregation and biofilm formation, thereby significantly enhancing plasmid conjugative transfer efficiency. A hyperglycemic environment (simulating diabetic comorbidity) synergizes with microplastics, increasing plasmid transfer efficiency by nearly fourfold. Furthermore, after extended incubation, microplastics may also induce bacterial ROS production, potentially associated with the degradation of microplastics into nanosized particles. These findings expand the understanding of the size-dependent effects of plastic particles (micro- vs. nano-) and suggest that the risks posed by microplastics in clinical settings may be underestimated.

The study further highlights its clinical significance: the cross-genus transmission of carbapenemase plasmids—particularly from Klebsiella and E. coli to S. aureus—is of special concern, as it may further limit therapeutic options for ICU-acquired infections. The blaNDM-5 gene mediates bacterial resistance to the novel antibiotic cefiderocol (not yet available in China) (Simner et al., 2022), and CRE strains isolated from patient urine samples in Chinese hospitals in this study not only carried blaNDM-5 but also demonstrated that this gene can spread via plasmids across Gram-negative and Gram-positive species, which may accelerate the loss of antibiotic efficacy. Moreover, some CRE strains in this study carried conjugative plasmids encoding both carbapenemases and the colibactin biosynthesis system, enhancing their colonization competitiveness in polymicrobial environments and facilitating the spread of resistance within complex ICU microbial communities.

Mechanistically, MPs enhance conjugation by stimulating biofilm formation, which provides a protected environment for bacterial aggregation and genetic exchange. Gene expression analysis revealed that MPs modulate oxidative stress and conjugation-related genes (e.g., soxR and traF) in a strain- and environment-dependent manner. Under hyperglycemic conditions, the upregulation of these genes in CRKP was particularly pronounced, suggesting that diabetic patients may face a higher risk of MP-mediated resistance transmission.

Although inline filtration devices are currently used in ICUs, MPs persist in medical equipment and patient blood, underscoring the clinical relevance of these findings. Based on this, we propose the following mitigation strategies: (1) reevaluate medical device materials to reduce microplastic shedding; (2) develop biofilm-penetrating enzymatic cleaners for MPs in ICU settings; (3) establish microplastic biosafety thresholds in critical care environments; and (4) optimize infection control protocols for high-risk patients such as those with diabetes.

Limitations of this study include the use of only one type and size of polystyrene MPs and the exclusive reliance on in vitro experiments. Future research should explore the effects of MPs of different materials and sizes and validate these findings through in vivo studies. In conclusion, this study provides the direct evidence that medical-derived MPs accelerate the cross-genus spread of carbapenemase resistance plasmids in ICUs, particularly under diabetic comorbidity. These findings position MPs as an emerging environmental driver of the “silent pandemic” of untreatable infections, underscoring the urgent need to integrate this factor into precision infection control frameworks. Addressing microplastic pollution in healthcare settings is crucial for mitigating the AMR crisis and improving patient outcomes in ICUs.

Acknowledgments

We would like to acknowledge BioRender for their support in creating the figures included in this manuscript.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Shandong Provincial Third hospital Research Fund (LHXM2023ZD12004), the National Natural Science Foundation of China (32500156) and Shandong Provincial Natural Science Foundation (ZR2024QH486).

Footnotes

Edited by: Manuela Tamburro, University of Molise, Italy

Reviewed by: Alexis Rodríguez, Autonomous University of the State of Morelos, Mexico

Honggui Wang, Yangzhou University, China

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA1282339 https://www.ncbi.nlm.nih.gov/, PRJNA1282396.

Ethics statement

The studies involving humans were approved by Ethics Committee of Shandong Provincial Third Hospital (approval number: KYLL-2023096). The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from a by- product of routine care or industry. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Author contributions

YN: Validation, Funding acquisition, Methodology, Formal analysis, Visualization, Writing – original draft, Resources. JZ: Formal analysis, Visualization, Writing – original draft. CP: Formal analysis, Writing – original draft, Visualization. YY: Visualization, Writing – original draft. YL: Funding acquisition, Writing – review & editing. ZL: Funding acquisition, Conceptualization, Writing – review & editing, Resources, Writing – original draft, Project administration, Validation, Methodology.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Correction note

A correction has been made to this article. Details can be found at: 10.3389/fcimb.2026.1840192.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcimb.2026.1781149/full#supplementary-material

DataSheet1.pdf (2.6MB, pdf)
Table1.xls (25.5KB, xls)
Table2.xls (22KB, xls)

References

  1. Adatia K. (2026). Plastics in anaesthesia and critical care: beyond sustainability. Anaesthesia 81, 177–181. doi:  10.1111/anae.70014, PMID: [DOI] [PubMed] [Google Scholar]
  2. Altschul S. F., Madden T. L., Schäffer A. A., Zhang J., Zhang Z., Miller W., et al. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. doi:  10.1093/nar/25.17.3389, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Antimicrobial Resistance Collaborators (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet Lond. Engl. 399, 629–655. doi:  10.1016/S0140-6736(21)02724-0, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashburner M., Ball C. A., Blake J. A., Botstein D., Butler H., Cherry J. M., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29. doi:  10.1038/75556, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. (2024). Stronger commitment and faster action against antimicrobial resistance. Nat. Rev. Microbiol. 22, 589–590. doi:  10.1038/s41579-024-01089-z, PMID: [DOI] [PubMed] [Google Scholar]
  6. Bank M. S., Ok Y. S., Swarzenski P. W. (2020). Microplastic’s role in antibiotic resistance. Science 369, 1315. doi:  10.1126/science.abd9937, PMID: [DOI] [PubMed] [Google Scholar]
  7. Bankevich A., Nurk S., Antipov D., Gurevich A. A., Dvorkin M., Kulikov A. S., et al. (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. J. Comput. Mol. Cell Biol. 19, 455–477. doi:  10.1089/cmb.2012.0021, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bernard L., Bouattour Y., Masse M., Boeuf B., Decaudin B., Genay S., et al. (2021). Association between urinary metabolites and the exposure of intensive care newborns to plasticizers of medical devices used for their care management. Metabolites 11, 252. doi:  10.3390/metabo11040252, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Branton D., Deamer D. W., Marziali A., Bayley H., Benner S. A., Butler T., et al. (2008). The potential and challenges of nanopore sequencing. Nat. Biotechnol. 26, 1146–1153. doi:  10.1038/nbt.1495, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brinkac L., Voorhies A., Gomez A., Nelson K. E. (2017). The threat of antimicrobial resistance on the human microbiome. Microb. Ecol. 74, 1001–1008. doi:  10.1007/s00248-017-0985-z, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Canellas A. L. B., Abdon B. B., Diniz M. N., da Silva Oliveira Alves G., de Paula Lourenço M. F., MaChado W. T. V., et al. (2023). Antimicrobial resistance and biotechnological potential of plastic-associated bacteria isolated from an urban estuary. Environ. Microbiol. 25, 2851–2863. doi:  10.1111/1462-2920.16540, PMID: [DOI] [PubMed] [Google Scholar]
  12. Casella C., Cornelli U., Zanoni G., Moncayo P., Ramos-Guerrero L. (2025). Health risks from microplastics in intravenous infusions: evidence from Italy, Spain, and Ecuador. Toxics 13, 597. doi:  10.3390/toxics13070597, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Charalampous T., Alcolea-Medina A., Snell L. B., Williams T. G. S., Batra R., Alder C., et al. (2021). Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units. Genome Med. 13, 182. doi:  10.1186/s13073-021-00991-y, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Daruka L., Czikkely M. S., Szili P., Farkas Z., Balogh D., Grézal G., et al. (2025). ESKAPE pathogens rapidly develop resistance against antibiotics in development in vitro. Nat. Microbiol. 10, 313–331. doi:  10.1038/s41564-024-01891-8, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ding Y., Jiang X., Wu J., Wang Y., Zhao L., Pan Y., et al. (2024). Synergistic horizontal transfer of antibiotic resistance genes and transposons in the infant gut microbial genome. mSphere 9, e0060823. doi:  10.1128/msphere.00608-23, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Djermoun S., Rode D. K. H., Jiménez-Siebert E., Netter N., Lesterlin C., Drescher K., et al. (2025). Biofilm architecture determines the dissemination of conjugative plasmids. Proc. Natl. Acad. Sci. U. S. A. 122, e2417452122. doi:  10.1073/pnas.2417452122, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Florance I., Chandrasekaran N., Gopinath P. M., Mukherjee A. (2022). Exposure to polystyrene nanoplastics impairs lipid metabolism in human and murine macrophages in vitro. Ecotoxicol. Environ. Saf. 238, 113612. doi:  10.1016/j.ecoenv.2022.113612, PMID: [DOI] [PubMed] [Google Scholar]
  18. G. N., M. J., C. C., G. B., H. E., N. Y., et al. (2025). Effects of microplastic concentration, composition, and size on Escherichia coli biofilm-associated antimicrobial resistance. Appl. Environ. Microbiol. 4, e0228224. doi:  10.1128/aem.02282-24, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gu Z., Gu L., Eils R., Schlesner M., Brors B. (2014). circlize Implements and enhances circular visualization in R. Bioinforma. Oxf. Engl. 30, 2811–2812. doi:  10.1093/bioinformatics/btu393, PMID: [DOI] [PubMed] [Google Scholar]
  20. Hernandez-Beltran J. C. R., Rodríguez-Beltrán J., Aguilar-Luviano O. B., Velez-Santiago J., Mondragón-Palomino O., MacLean R. C., et al. (2024). Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria. Nat. Commun. 15, 2610. doi:  10.1038/s41467-024-45045-0, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hu J., Li Z., Li L., Sun Y., Shi L., Li W., et al. (2023). Detection of multidrug resistant pathogenic bacteria and novel complex class 1 integrons in campus atmospheric particulate matters. Sci. Total Environ. 856, 158976. doi:  10.1016/j.scitotenv.2022.158976, PMID: [DOI] [PubMed] [Google Scholar]
  22. Huang H., Lei P., Yu H., Du J., Wu B., Wang H., et al. (2024). Micro/nano plastics in the urinary system: Pathways, mechanisms, and health risks. Environ. Int. 193, 109109. doi:  10.1016/j.envint.2024.109109, PMID: [DOI] [PubMed] [Google Scholar]
  23. Huang T., Liu Y., Wang L., Ruan X., Ge Q., Ma M., et al. (2025). MPs entering human circulation through infusions: A significant pathway and health concern. Environ. Health Wash. DC 3, 551–559. doi:  10.1021/envhealth.4c00210, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ji S., Wang W., Wang Y., Bai H., Li Z., Huo Z., et al. (2025). Quantitative detection of micro- and nanoplastics (≥300 nm) in human urine using double-shot py-GC/MS with internal standard calibration. Toxics 13, 452. doi:  10.3390/toxics13060452, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kadri S. S., Adjemian J., Lai Y. L., Spaulding A. B., Ricotta E., Prevots D. R., et al. (2018). Difficult-to-Treat resistance in gram-negative bacteremia at 173 US hospitals: Retrospective cohort analysis of prevalence, predictors, and outcome of resistance to all first-line agents. Clin. Infect. Dis. 67, 1803–1814. doi:  10.1093/cid/ciy378, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kanehisa M., Goto S., Kawashima S., Okuno Y., Hattori M. (2004). The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277–D280. doi:  10.1093/nar/gkh063, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Khan T., Khanem A., Batool I., Ullah I., Younas F. (2025). Microplastics: Disseminators of antibiotic resistance genes and pathogenic bacteria. Aquat. Toxicol. 289, 107591. doi:  10.1016/j.aquatox.2025.107591, PMID: [DOI] [PubMed] [Google Scholar]
  28. Leslie H. A., van Velzen M. J. M., Brandsma S. H., Vethaak A. D., Garcia-Vallejo J. J., Lamoree M. H. (2022). Discovery and quantification of plastic particle pollution in human blood. Environ. Int. 163, 107199. doi:  10.1016/j.envint.2022.107199, PMID: [DOI] [PubMed] [Google Scholar]
  29. Li Z., Hu J., Pan Y., Xi Y., Zhang L. (2025. c). Bifidobacterium infantis modulates intestinal microecology to inhibit the spread of antimicrobial resistance. mSystems 10, e0072825. doi:  10.1128/msystems.00728-25, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Li Z., Li Y., Cao F., Huang J., Gao X. (2025. d). Gut microbiota and metabolic health risks from chronic low-dose microplastic exposure with focus on Desulfovibrio spp. Ecotoxicol. Environ. Saf. 302, 118721. doi:  10.1016/j.ecoenv.2025.118721, PMID: [DOI] [PubMed] [Google Scholar]
  31. Li B., Li M., Du D., Tang B., Yi W., He M., et al. (2025. a). Characteristics and influencing factors of microplastics entering human blood through intravenous injection. Environ. Int. 198, 109377. doi:  10.1016/j.envint.2025.109377, PMID: [DOI] [PubMed] [Google Scholar]
  32. Li C., Liu Y., Wang L., Dai Q., Piao Z., Zhang Y., et al. (2025. b). The group-modified nanoplastics bring higher risk on promoting the antibiotic resistance genes dissemination by stimulate bacteria to generate more ROS under aerobic condition. J. Environ. Chem. Eng. 13, 118876. doi:  10.1016/j.jece.2025.118876, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Li Z., Shi L., Wang B., Wei X., Zhang J., Guo T., et al. (2021). In vitro assessment of antimicrobial resistance dissemination dynamics during multidrug-resistant-bacterium invasion events by using a continuous-culture device. Appl. Environ. Microbiol. 87, e02659–e02620. doi:  10.1128/AEM.02659-20, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Liu Z., Wang D., Liu Z., Xu C., Zhang Y., Liu P. (2024). Microplastic injection? Identification and quantification of plastic particles in medical injections. Sci. Total Environ. 954, 176468. doi:  10.1016/j.scitotenv.2024.176468, PMID: [DOI] [PubMed] [Google Scholar]
  35. Mistry J., Chuguransky S., Williams L., Qureshi M., Salazar G. A., Sonnhammer E. L. L., et al. (2021). Pfam: The protein families database in 2021. Nucleic Acids Res. 49, D412–D419. doi:  10.1093/nar/gkaa913, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Naddaf M. (2024). 40 million deaths by 2050: toll of drug-resistant infections to rise by 70%. Nature 633, 747–748. doi:  10.1038/d41586-024-03033-w, PMID: [DOI] [PubMed] [Google Scholar]
  37. Nihart A. J., Garcia M. A., El Hayek E., Liu R., Olewine M., Kingston J. D., et al. (2025). Bioaccumulation of microplastics in decedent human brains. Nat. Med. 31, 1114–1119. doi:  10.1038/s41591-024-03453-1, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pironti C., Notarstefano V., Ricciardi M., Motta O., Giorgini E., Montano L. (2022). First evidence of microplastics in human urine, a preliminary study of intake in the human body. Toxics 11, 40. doi:  10.3390/toxics11010040, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Saha S. C., Saha G. (2024). Effect of microplastics deposition on human lung airways: A review with computational benefits and challenges. Heliyon 10, e24355. doi:  10.1016/j.heliyon.2024.e24355, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Schuetz A. N., Ferrell A., Hindler J. A., Humphries R., Bobenchik A. M. (2025). Overview of changes in the clinical and laboratory standards institute performance standards for antimicrobial susceptibility testing: M100 32nd and 33rd editions. J. Clin. Microbiol. 63, e0162323. doi:  10.1128/jcm.01623-23, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Simner P. J., Mostafa H. H., Bergman Y., Ante M., Tekle T., Adebayo A., et al. (2022). Progressive development of cefiderocol resistance in Escherichia coli during therapy is associated with an increase in blaNDM-5 copy number and gene expression. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc Am. 75, 47–54. doi:  10.1093/cid/ciab888, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Stapleton M. J., Ansari A. J., Hai F. I. (2023). Antibiotic sorption onto microplastics in water: A critical review of the factors, mechanisms and implications. Water Res. 233, 119790. doi:  10.1016/j.watres.2023.119790, PMID: [DOI] [PubMed] [Google Scholar]
  43. Stoett P., Scrich V. M., Elliff C. I., Andrade M. M., de M. Grilli N., Turra A. (2024). Global plastic pollution, sustainable development, and plastic justice. World Dev. 184, 106756. doi:  10.1016/j.worlddev.2024.106756, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Tatusov R. L., Galperin M. Y., Natale D. A., Koonin E. V. (2000). The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36. doi:  10.1093/nar/28.1.33, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wang L., Pei W., Li J., Feng Y., Gao X., Jiang P., et al. (2024). Microplastics induced apoptosis in macrophages by promoting ROS generation and altering metabolic profiles. Ecotoxicol. Environ. Saf. 271, 115970. doi:  10.1016/j.ecoenv.2024.115970, PMID: [DOI] [PubMed] [Google Scholar]
  46. Wang Z., Zhang Y., Kang S., Yang L., Shi H., Tripathee L., et al. (2021). Research progresses of microplastic pollution in freshwater systems. Sci. Total Environ. 795, 148888. doi:  10.1016/j.scitotenv.2021.148888, PMID: [DOI] [PubMed] [Google Scholar]
  47. WHO updates list of drug-resistant bacteria most threatening to human health. Available online at: https://www.who.int/news/item/17-05-2024-who-updates-list-of-drug-resistant-bacteria-most-threatening-to-human-health (Accessed June 17, 2025).
  48. Wick R. R., Judd L. M., Gorrie C. L., Holt K. E. (2017). Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595. doi:  10.1371/journal.pcbi.1005595, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Winiarska E., Jutel M., Zemelka-Wiacek M. (2024). The potential impact of nano- and microplastics on human health: Understanding human health risks. Environ. Res. 251, 118535. doi:  10.1016/j.envres.2024.118535, PMID: [DOI] [PubMed] [Google Scholar]
  50. Wolff C. M., Singer D., Schmidt A., Bekeschus S. (2023). Immune and inflammatory responses of human macrophages, dendritic cells, and T-cells in presence of micro- and nanoplastic of different types and sizes. J. Hazard. Mater. 459, 132194. doi:  10.1016/j.jhazmat.2023.132194, PMID: [DOI] [PubMed] [Google Scholar]
  51. Wu P., Lin S., Cao G., Wu J., Jin H., Wang C., et al. (2022). Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications. J. Hazard. Mater. 437, 129361. doi:  10.1016/j.jhazmat.2022.129361, PMID: [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

DataSheet1.pdf (2.6MB, pdf)
Table1.xls (25.5KB, xls)
Table2.xls (22KB, xls)

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA1282339 https://www.ncbi.nlm.nih.gov/, PRJNA1282396.

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