Domestic travel as a driver for the dissemination of mcr-1 in healthy travelers in China: a prospective, genomic epidemiological and gut microbiome study.

Domestic travel as a driver for the dissemination of mcr-1 in healthy travelers in China: a prospective, genomic epidemiological and gut microbiome study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Antimicrob Agents Chemother . 2026 Apr 20;70(6):e01746-25. doi: 10.1128/aac.01746-25 Domestic travel as a driver for the dissemination of mcr-1 in healthy travelers in China: a prospective, genomic epidemiological and gut microbiome study Meina Wu 1, 2, 3, # , Yushun Chen 1, # , Minxuan Su 1, # , Hao Wu 4 , Li Luo 1 , Xiaojun Ao 1 , Chanjing Zhao 1 , Jingwei Shui 1 , Shuan Wen 1 , Jiehao Lin 1 , Jieying Pu 1, 3 , Jianming Zeng 1, 3 , Yueting Jiang 5 , Hoiman Ng 6 , Zhongde Zhang 1 , Mingli Hu 7 , Bin Huang 8 , Lingqing Xu 9, ✉ , Cha Chen 1, ✉ , Cong Shen 1, 2, 3, ✉, # Editor: Alessandra Carattoli 10 Author information Article notes Copyright and License information 1 Clinical Laboratory/State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, The Second Clinical Medical College, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China 2 State Key Laboratory of Dampness Syndrome of Chinese Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China 3 Guangdong Provincial Key Laboratory of Research on Emergency in TCM, Guangzhou, China 4 Department of Otolaryngology, Hospital of Honghe State Affiliated to Kunming Medical University, Southern Central Hospital of Yunnan Province, Mengzi, China 5 Clinical Laboratory, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China 6 Clinical Laboratory, Kiang Wu Hospital, Macau, China 7 Guangzhou University of Chinese Medicine, Science and Technology Innovation Center, Guangzhou, China 8 Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China 9 The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China 10 Universita degli studi di Roma La Sapienza, Rome, Italy ✉ Address correspondence to Cong Shen, shencong@gzucm.edu.cn ✉ Address correspondence to Cha Chen, chencha@gzucm.edu.cn ✉ Address correspondence to Lingqing Xu, lingqingxu2025@163.com # Meina Wu, Yushun Chen, Minxuan Su, and Cong Shen contributed equally to this article. Author order was determined in order of increasing effort on manuscript preparation. The authors declare no conflict of interest. # Contributed equally. Roles Alessandra Carattoli : Editor Received 2026 Jan 5; Accepted 2026 Mar 4; Collection date 2026 Jun. Copyright © 2026 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license . PMC Copyright notice PMCID: PMC13231915  PMID: 42008653 ABSTRACT The global spread of plasmid-mediated colistin resistance gene mcr-1 poses a significant threat to public health. Although international travel is a known driver of antimicrobial resistance, the role of domestic travel in high-prevalence settings remains unclear. We conducted a prospective cohort study of 81 healthy volunteers traveling in China (June–September 2022). Fecal samples collected before and after travel were screened for mcr-1 -positive Escherichia coli (MCRPEC). Antimicrobial resistance genes (ARGs), virulence factors (VFs), plasmid replicons, and gut microbial dynamics were investigated using whole-genome sequencing and 16S rRNA sequencing. Risk factors were analyzed using logistic regression analysis. Of the 81 participants who were negative for mcr-1 at baseline, 12 (14.8%) acquired mcr-1 after travel. Acquisition was associated with residence near poultry farms (odds ratio [OR] = 5.9, P = 0.04) and diarrhea during travel (OR = 11.22, P = 0.027). MCRPEC exhibited marked genetic diversity comprising 10 sequence types and the carriage of additional 23 ARGs and nine adherence-associated VFs. mcr-1 was located on IncI2, IncX4, IncHI2, or IncP plasmids, with 91.7% ( n = 11) transferable in conjugation assays. Gut microbiome analysis showed increased α -diversity, but a stable community structure, indicating colonization without major disruption. Our study demonstrated that domestic travel in China substantially contributes to the dissemination of mcr-1 . Poultry exposure and gastrointestinal disturbances are key risk factors. Genetic diversity, plasmid transferability, and co-carriage of resistance and virulence determinants highlight the risk of onward spread. Antimicrobial resistance surveillance should extend beyond international travel and incorporate domestic mobility within a “One Health” framework. KEYWORDS: mcr-1 , plasmid, domestic travel, colistin resistance, Escherichia coli , microbiome INTRODUCTION Colistin is considered a last-line antimicrobial for the treatment of multidrug-resistant gram-negative bacterial infections, particularly those caused by carbapenem-resistant Enterobacteriaceae (CRE) ( 1 , 2 ). However, the emergence and worldwide spread of plasmid-mediated colistin resistance, primarily mediated by mcr-1 , critically undermine the effectiveness of one of the few remaining therapeutic options for treating difficult infections ( 3 , 4 ). The mcr-1 gene is primarily located on plasmids and within the transposon Tn 6330 , which enhances the potential for horizontal gene transfer (HGT) and facilitates its spread across diverse bacterial hosts, resulting in global prevalence and dissemination of mcr-1 ( 5 , 6 ). International travel has been recognized as an essential driver of antimicrobial resistance (AMR) dissemination ( 7 – 11 ). Numerous studies have documented the high prevalence and frequent acquisition of extended-spectrum β -lactamases (ESBLs) producing Enterobacteriaceae and other significant pathogens among international travelers ( 9 , 12 ). Travelers exposed to novel microbial environments have an increased probability of acquiring resistant bacteria, which could subsequently be introduced into their home communities upon return ( 8 , 10 , 13 ). Recent research has revealed that a notable proportion of travelers returning from regions with a high prevalence of mcr-1 are carriers of mcr-1 -positive Enterobacterales, highlighting the potential risk associated with traveling to areas with a high prevalence of antimicrobial resistance genes (ARGs) ( 10 , 11 , 14 , 15 ). Growing evidence indicates the spread of AMR linked to international travel. However, surveillance systems and population-based data on AMR acquisition during domestic travel in China are limited, leaving the role of domestic travel in AMR transmission dynamics unclear, particularly in regions with a high prevalence of mcr-1 ( 7 , 8 ). The initial dissemination of mcr-1 was associated with animal reservoirs in China, and a subsequent decline in its prevalence was noted following the prohibition of colistin as a feed additive ( 16 , 17 ). However, the persistent detection of mcr-1 in human populations across various regions suggests that other factors, such as population mobility, may sustain residual circulation. We conducted a longitudinal study to investigate the acquisition of mcr-1 and its related risk factors in healthy volunteers who traveled within China. Furthermore, we characterized the genomic features of mcr-1 -positive Escherichia coli (MCRPEC) and analyzed gut microbiome dynamics in travelers who acquired MCRPEC. Our findings highlight the role of domestic travel in the dissemination of mcr-1 and emphasize the need to consider human mobility in AMR surveillance and control strategies. MATERIALS AND METHODS Study design and population A prospective longitudinal cohort study was conducted on healthy undergraduate students planning to travel within China during the summer vacation period (from June to September in 2022). The inclusion criteria for volunteers were no history of cardiopulmonary disease, metabolic syndrome, dyslipidemia, gastrointestinal disease, and no use of antimicrobials within the 3 months before the first sample collection. All participants completed the study with no loss to follow-up. Sample collection Volunteers provided fecal samples within 3 days before departure and within 3 days after returning from travel. Fecal samples were collected using a sterilized tube and transported to the laboratory within 1 h, where they were stored at −80°C until further processing. All volunteers completed a questionnaire after their return, which collected personal information, including age and gender, dietary habits, hospitalizations, antimicrobial use, living in proximity to poultry farms (<1 km) and other relevant factors ( Table S1 ). Purification and identification of MCRPEC MCRPEC was identified according to our previous studies ( 16 ). Samples were inoculated into 3 mL of brain heart infusion (BHI) broth (Oxoid, UK) supplemented with 2 mg/L colistin. The cultures were then incubated at 37°C for 18–24 h. Total DNA was extracted from 1 mL of the broth by the boiling method and screened for the presence of mcr-1 to mcr-5 and mcr-7 to mcr-9 by polymerase chain reaction (PCR), as previously described ( 16 , 18 ). mcr- Positive samples were then cultured on MacConkey agar supplemented with 2 mg/L colistin. Subsequently, colonies with distinct morphologies were all selected for species identification. When fewer than 10 morphological types were present, additional colonies were chosen to ensure that at least 10 colonies from each sample were examined. Species identification was conducted using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS, VITEK MS, bioMérieux, France). Antimicrobial susceptibility testing The minimum inhibitory concentrations (MICs) of the 12 antimicrobials were measured using the agar dilution method, whereas colistin and tigecycline were determined using the broth dilution method. The breakpoints used to define antimicrobial resistance were selected according to the Clinical and Laboratory Standards Institute (CLSI, M100-S34). The European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints v15.0 were selected for tigecycline. E. coli ATCC 25922 was used as a quality control strain. Plasmid conjugation assay A streptomycin-resistant E. coli C600 was used as the recipient strain. Donor and recipient isolates were cultured overnight and subcultured at a 1:100 ratio for 4 h at 37°C. The donor and recipient were then mixed at a 1:9 ratio, incubated stationary for 6 h at 37°C, and plated on Luria-Bertani (LB) agar plates supplemented with streptomycin (2,000 mg/L) and colistin (2 mg/L). Colonies were subjected to PCR and Sanger sequencing to detect the mcr-1 gene and ST. Colonies positive for mcr-1 , which also exhibited the same ST as the recipient strains, were identified as transconjugants ( 16 ). Whole-genome sequencing and bioinformatic analysis The genomic DNA of MCRPEC was extracted and sequenced using the Illumina HiSeq 4000 platform. Raw reads were assembled using SPAdes v3.14.0 with "--careful" parameter ( 19 ). In silico multilocus sequence typing (MLST) was identified by mlst v2.16.2 against PubMLST database ( https://pubmlst.org/ ). The antimicrobial resistance genes (ARGs), virulence factors (VFs), and plasmid replicons were identified by ABRicate v1.0.1 https://github.co ( https://github.com/tseemann/abricate ) against the Comprehensive Antimicrobial Resistance Database (CARD), Virulence Factor Database (VFDB), and PlasmidFinder Database, respectively. Core-genome SNPs were identified using Snippy with the E. coli K-12 substr. MG1655 ( GCA_000005845.2 ). A phylogenetic tree based on cgSNPs was generated using RAxML-ng under the general time-reversible + gamma rate heterogeneity model (GTR + G) and subsequently visualized via iTOL ( https://itol.embl.de/ ) ( 20 ). The structure of mcr-1 -harboring plasmids was reconstructed according to our previous study ( 21 ). Briefly, the contigs were mapped against reference plasmid sequences using BLASTn analysis, including IncI2 (pHNSHP45, GenBank accession: KP347127.1 ), IncX4 (pIBMC_ mcr1 , MF449287 ), IncHI2 (pHNSHP45-2, KU341381.1 ), and IncP (pHNEP124, MT667260.1 ). The contigs that met the criteria (coverage > 60% and identity > 90%) were considered as candidate sequences, which were subsequently aligned and ordered according to the corresponding reference plasmid. The gaps between contigs were filled by mapping of raw reads to the reference plasmid sequence using BWA and BCFtools ( 22 , 23 ). The reconstructed sequences were compared by BLASTn and visualized using Blast Ring Image Generator (BRIG). The genome assemblies of MCRPEC reported in this study have been deposited in the National Center for Biotechnology Information (NCBI) GenBank genomic DNA database under BioProject accession number PRJNA593695 . Gut microbiome analysis DNA was extracted from fecal samples using a QIAamp PowerFecal DNA Kit (QIAGEN, Hilden, Germany). The hypervariable V3 and V4 regions of the 16S rRNA gene were amplified and sequenced by Illumina MiSeq platform. Raw read processing and amplicon sequence variant (ASV) clustering were analyzed using QIIME2 v2021.11 ( 24 ). Taxonomic assignment of ASVs was performed on the SILVA 138 database. Shannon index and Bray-Curtis distance were estimated using the vegan package ( https://cran.r-project.org/web/packages/vegan/index.html ). The linear discriminant analysis effect size (LEfSe) method was implemented to identify differences in bacterial communities using the microeco package. Statistical analysis Univariate logistic regression was used to determine the factors associated with MCRPEC colonization by SPSS software v27.0. Factors with a P < 0.2 were selected to generate the final parsimony model. Odds ratios (ORs) and the corresponding 95% confidence intervals ( CIs ) were calculated. A P value less than 0.05 was considered statistically significant. RESULTS Acquisition of mcr-1 and associated risk factors Of the 89 participants in the study, individuals with pre-travel samples positive for mcr genes were excluded, including eight individuals carrying the mcr-1 , of whom two also carried the mcr-9 . The median age of the remaining 81 participants was 21 years (interquartile range [IQR]: 19–24), with 67.9% ( n = 55) being female ( Table S2 ). The participants' travel destinations displayed considerable geographic diversity, including seven individuals traveling within Guangdong Province and five visiting other provinces across China, including Anhui, Fujian, Hunan, and Sichuan ( Fig. 1 ). Of the 81 volunteers confirmed negative for mcr variants before travel, 14.8% ( n = 12) of them were found to be positive for the mcr-1 gene in their post-travel fecal samples. Fig 1. Open in a new tab Geographic locations of travel destinations in ( a ) 16 provinces and ( b ) Guangdong province among 81 volunteers. Green circles denote destinations visited by volunteers, while red stars indicate destinations with volunteers who were positive for mcr-1 in their post-travel fecal samples. The red dashed lines represent travel routes. Logistic regression analysis revealed two key risk factors significantly associated with acquiring mcr-1 , including living near poultry farms (OR = 5.90, 95% CI: 1.08–32.15, P =0.04) and having diarrhea during travel (OR = 11.22, 95% CI: 1.31–95.95, P = 0.027) ( Table 1 ). However, self-reported antimicrobial use and healthcare facility visits during travel were not significantly associated with the acquisition of mcr-1 , potentially due to the limited sample size, which reduced statistical power. TABLE 1. Risk factors linked to acquiring mcr-1 during travel a mcr-1 -Negative ( n = 69) mcr-1 -Positive ( n = 12) Unadjusted P value Adjusted P value OR (95% CI ) Gender Male 22 (31.88%) 4 (33.33%) 0.921 – – Female 47 (68.12%) 8 (66.66%) <18.5 8 (11.59%) 1 (8.33%) 0.815 – – BMI (kg/m²) 18.5 ≤ BMI ≤ 24.9 51 (73.91%) 11 (91.67%) ≥25.0 10 (14.49%) 0 (0.00%) Diarrhea 8 (11.59%) 5 (41.67%) 0.015 0.027 11.222 (1.312–95.954) Visiting health care facilities 22 (31.88%) 3 (25.00%) 0.635 – – Living in proximity to poultry farms 7 (10.14%) 3 (25.00%) 0.163 0.040 5.898 (1.082–32.153) Living with healthcare workers 17 (24.64%) 3 (25.00%) 0.979 – – Swimming 19 (27.54%) 5 (41.67%) 0.328 – – Contacting with pets 15 (21.74%) 1 (8.33%) 0.303 – – Antibiotic use 3 (4.35%) 1 (8.33%) 0.563 – – Gastrointestinal medications use 10 (14.49%) 4 (33.33%) 0.123 0.713 0.672 (0.081–5.578) Eating undercooked or raw food 33 (47.83%) 6 (50.00%) 0.889 – – Open in a new tab a Data are represented as n (%). Unadjusted P value: univariate tests. All those testing at P < 0.2 were selected for multivariable analysis. Adjusted P value: multivariable model includes diarrhea, living in proximity to poultry farms, gastrointestinal medications use. –, variables not included in the multivariable model, with no estimated odds ratio or P value. Species and antimicrobial susceptibility profiles of mcr-1 -positive isolates Through the purification and identification of mcr-1 -positive isolates, we identified all isolates from 12 volunteers as E. coli . All 12 MCRPEC isolates were multidrug-resistant, displaying a broad range of resistance patterns to various antimicrobial classes. All of the isolates were resistant to ampicillin and gentamicin, followed by ciprofloxacin (75.0%, n = 9), amikacin (33.3%, n = 4), fosfomycin (16.7%, n = 2), and cefotaxime (16.7%, n = 2). Conversely, ceftazidime resistance was detected in only a single isolate (8.3%). All 12 isolates remained susceptible to the critical last-resort antimicrobials, tigecycline, imipenem, meropenem, and nitrofurantoin ( Table 2 ; Table S3 ). TABLE 2. Antimicrobial resistance of MCRPEC isolates a Categories of antibiotics Antimicrobial agent R (%) Polymyxins CST 12 (100.00%) Tetracycline TGC 0 (0.00%) β-lactam AMP 12 (100.00%) CTX 2 (16.67%) CAZ 1 (8.33%) Carbapenems IPM 0 (0.00%) MEM 0 (0.00%) Fosfomycin FOS 2 (16.67%) Nitrofurans NIT 0 (0.00%) Aminoglycosides AMK 4 (33.33%) GEN 12 (100.00%) Quinolones CIP 9 (75.00%) Open in a new tab a CST, colistin; TGC, tigecycline; AMP, aminopenicillin; CTX, cefotaxime; CAZ, ceftazidime; IPM, imipenem; MEM, meropenem; FOS, fosfomycin; NIT, nitrofurantoin; AMK, amikacin; GEN, gentamicin; CIP, ciprofloxacin. The numbers in the table refer to the percentages of isolates showing resistance to each antimicrobial, which are represented as n (%). Genomic characterizations of MCRPEC isolates WGS analysis of 12 MCRPEC isolates revealed a highly diverse genetic landscape. Ten distinct STs were identified among the 12 isolates, including ST93 ( n = 3) and single isolates of ST10, ST95, ST101, ST117, ST165, ST746, ST906, ST2705, and ST9634. The sporadic distribution of these isolates in the phylogenetic tree further indicated significant genetic diversity of the host bacteria carrying the mcr-1 gene, suggesting a polyclonal population rather than the dominance of a single epidemic clone. While the host backgrounds were diverse, the phylogroup distribution was more concentrated in phylogroups A and B1 (83.4%, n = 10), which are commonly associated with community-acquired or commensal strains, suggesting that the acquisition of MCRPEC likely occurred in non-hospital settings ( Fig. 2 ) ( 10 ). Fig 2. Open in a new tab Phylogenetic tree and genomic characterizations of 12 MCRPEC isolates. The phylogenetic tree is located to the left of the plot, with different colors representing distinct phylogroups: red, yellow, blue, and green, corresponding to phylogenetic groups G, B2, B1, and A, respectively. The first column represents the strain names. The second column indicates the travel destinations. The third column represents ST. Blue, purple, red, and green stars represent the mcr-1 plasmids IncI2, IncX4, IncHI2, and IncP, respectively. Red and blue circles illustrate the distributions of the functions related to the ARGs and VFs for each strain, with circle size proportional to the number of genes present in this category. In addition to mcr-1 , the MCRPEC isolates demonstrated extensive co-occurrence of an additional 23 ARGs spanning 12 antimicrobial classes ( Table S4 ). Each isolate harbored a median of 13 ARGs (IQR: 12–14). All isolates possessed ARGs encoding resistance to sulfonamides ( sul and dfrA -like), macrolides ( mph- and emr -like), and aminoglycosides ( aph -, aac3 - and ant -like), as well as efflux pump genes associated with multidrug resistance ( acrAB-tolC and mdtABCD-tolC ), followed by phenicols (75%, n = 9, floR and cmlA1 ) and fluoroquinolones (66.7%, n = 8, qnrS1 and oqxAB ). VFDB analysis revealed that the isolates harbored 42 (median = 27, IQR: 26–35) virulence-associated genes attributed to five categories of functions that were involved in adhesion (21.4%, n = 9), motility and chemotaxis (11.9%, n = 5), immune modulation (19.1%, n = 8), iron acquisition (23.8%, n = 10), secretory system (14.3%, n = 6), and others (9.5%, n = 4) ( Table S5 ). All isolates harbored genes associated with adherence, including csg , fim , htpB , and ilpA , followed by ecp (91.7%, n = 11), aslA (83.3%, n = 10), fdeC (58.3%, n = 7), pap (25%, n = 3), and sinH (16.7%, n = 2). These findings indicate a high capacity for these isolates to breach mucosal barriers and maintain intestinal colonization. Genes associated with type III and II secretion systems were detected among the isolates, including esp (91.7%, n = 11) and members of the gsp family (66.7%, n = 8). In summary, these MCRPEC isolates demonstrated co-carriage of extensive ARGs and VFs, suggesting that their dissemination may also facilitate the co-dissemination of other ARGs and VFs. Diversity and transferability of mcr-1 -harboring plasmids The mcr-1 gene identified in all 12 isolates was carried on four distinct plasmid incompatibility groups. IncI2 and IncX4 plasmids were the most prevalent, each representing 33.3% ( n = 4) of the isolates, followed by IncHI2 (25.0%, n = 3) and IncP (8.3%, n = 1) ( Fig. 3 ). Comparative plasmid analysis revealed that IncI2 and IncX4 plasmids exhibited a high degree of structural conservation, with only minor variations in insertion sequences ( Fig. 3a and b ). In contrast, IncHI2 plasmids displayed substantial structural heterogeneity, likely attributable to extensive multidrug resistance regions within plasmid backbones ( Fig. 3c ). Fig 3. Open in a new tab Structural comparisons of mcr-1 -harboring plasmids. Comparisons of circular arrangements of mcr-1 -harboring plasmids with reference genomes of ( a ) IncI2 (pHNSHP45, accession KP347127.1 ), ( b ) IncX4 (pIBMC_mcr1, accession MF449287 ), ( c ) IncHI2 (pHNSHP45-2, accession KU341381.1 ), and ( d ) IncP (pHNEP124, accession MT667260.1 ). The outer circles represent genome annotations with different colors for functions, while the inner circles show the plasmids used in this study. Conjugation experiments demonstrated that 91.7% ( n = 11) of the isolates were capable of transferring mcr-1 –harboring plasmids to recipient strains ( Table S6 ). This transferability is likely driven by the presence of multiple plasmid-associated conjugation genes, highlighting the substantial potential for travelers to act as reservoirs and vectors for the dissemination of mcr-1 to other bacterial hosts. To further elucidate the genetic context of mcr-1 , the flanking regions were analyzed and compared with the reference transposon Tn 6330 (GenBank accession no. LC424783.1 ). The majority of isolates (91.7%, n = 11) carried an mcr-1-pap2 cassette lacking flanking IS Apl1 elements, whereas only a single IncHI2 plasmid retained one upstream IS Apl1 . Given the high frequency of conjugation observed and the absence of a complete composite Tn 6330 structure in most isolates, these findings suggest that plasmid-mediated horizontal transfer, rather than transposon-driven mobilization, is the predominant mechanism facilitating mcr-1 dissemination during travel. Changes in the gut microbiome of volunteers who acquired MCRPEC A within-individual analysis of gut microbiota dynamics was conducted in the volunteers who acquired MCRPEC. The Shannon diversity index significantly increased in the gut microbial community following travel ( P = 0.0465) ( Fig. 4a ). Despite this increase in α -diversity, no significant alterations were observed in the β -diversity of the overall gut microbiota structures during the travel period ( P = 0.589) ( Fig. 4b ). The dominant and core genera, including Bacteroides , Faecalibacterium , and Prevotella , remained stable throughout the study ( Fig. 4c ). Differential abundance analysis using LEfSe revealed four taxa with significant changes in their relative abundance. The abundance of the genus Lachnospira increased markedly following travel ( Fig. 4d ), which may be related to changes in dietary fiber intake during the journey ( 25 , 26 ). Conversely, a significant decrease was observed in the abundance of the phylum Acidobacteriota and family Peptostreptococcaceae ( Fig. 4d ). These observations collectively suggest that the successful acquisition of MCRPEC occurs, even when the overall stability of the gut microbiota is maintained, implying a high degree of adaptability and colonization potential of the newly acquired strains ( 9 ). Fig 4. Open in a new tab Gut microbiota dynamics in 12 volunteers who acquired MCRPEC. ( a ) Changes in Shannon index pre- and post-travel. ( b ) PCoA structure based on Bray-Curtis distance. P value was given by PERMANOVA. ( c ) Taxonomic compositions of the top 10 most abundant bacterial genera. ( d ) LDA score and the differentially abundant taxa identified by LEfSe. DISCUSSION This longitudinal investigation demonstrated that 14.8% of healthy individuals acquired the colistin resistance gene mcr-1 during domestic travel in China, highlighting the significant role of human mobility in the dissemination and persistence of mcr-1 , even in regions with restricted agricultural colistin usage ( 27 ). The proportion of travelers who acquired mcr-1 in this study markedly exceeded the 1.5–4.9% reported among European international travelers, including those traveling to regions with high prevalence rates, such as Southeast Asia (9.2%) and India (11%) ( 10 , 11 , 13 ). This discrepancy is likely attributable to the endemic presence of mcr-1 in these regions, potentially exacerbated by factors, such as travel conditions, food sanitation, and dietary habits, which differ from those typically encountered by travelers from other regions ( 28 ). While previous research has primarily shown that international travel contributes to the spread of antimicrobial resistance across regions with different prevalence levels, our results demonstrate that domestic travel within high-AMR areas is also a significant and frequently neglected risk factor, which emphasizes the urgent necessity to develop and enhance domestic AMR surveillance systems to complement current frameworks. Despite the ban on colistin as a feed additive in China, we identified a high odds ratio for living near poultry farms, which suggests that environmental or animal reservoirs remain a significant and persistent source of human exposure. Farm exposure may contribute to the enrichment of ARGs within the gut microbiome, thereby collectively elevating the risk of MCRPEC colonization through environmental pathways ( 29 ). Our findings highlight a critical need for "One Health" approaches to AMR surveillance, integrating data from human populations with continuous monitoring of agricultural and environmental settings. As in previous studies, experiencing diarrhea while traveling raises the risk of acquiring mcr-1 likely due to changes in diet and sanitation practices during travel ( 14 , 30 ). This condition can temporarily compromise the integrity of the gut barrier, thereby facilitating colonization by pathogenic microorganisms, which has also been described in the acquisition of ESBL-producing Enterobacteriaceae among travelers. In contrast, the lack of a significant correlation with antimicrobial use should be interpreted with caution due to the small sample size of users ( n = 4), which likely limits the statistical power to detect a true effect. A diverse polyclonal population of MCRPEC isolates comprised 10 distinct sequence types, which is in contrast with our pre-ban findings of ST10 dominance, indicating that the dissemination of the mcr-1 gene could not be attributed to the clonal expansion of a single successful clone ( 16 , 17 , 31 ). Interestingly, the predominance of community-associated phylogroups A and B1 aligns with acquisition patterns observed in international travel studies, suggesting common mechanisms, such as animal reservoirs and foodborne exposure ( 10 ). Notably, our observations indicate a high transferability of plasmids attributed to the presence of multiple genes associated with plasmid transfer functions, which suggests that the mcr-1 gene could be transferred to the host’s native gut microbiota, even in cases where the strain does not establish long-term colonization ( 4 , 6 ). These findings indicate that travelers act as vectors via two linked pathways: either helping diverse local MCRPEC isolates establish themselves or obtaining mcr-1 -harboring plasmids that can spread to their native commensal E. coli populations. This dual role complicates surveillance and control strategies that need to address both resistant strains and the mobility of resistance genes. mcr-1 acquired by travelers was located on diverse plasmid types, which reinforces the conclusion that these travelers were exposed to multiple independent acquisition sources at their destinations ( 3 ). The predominant plasmid types are IncI2 and IncX4, both of which are known for their stability and efficient transmission ( 16 , 31 ). Inconsistent with previous studies, we did not observe the composite Tn 6330 (IS Apl1-mcr-1-pap2 -IS Apl1 ) in this study ( 16 , 31 ). This loss may help stabilize the mcr-1 cassette and likely reflects that dissemination of mcr-1 is being driven primarily by plasmid transfer ( 5 , 6 ). The high conjugation success rate observed in our study highlights the potential for onward transfer to other gut bacteria, even within a short timeframe of domestic travel, which suggests that travelers are effective vectors of plasmid-mediated resistance across regions, thereby complicating containment efforts ( 11 , 15 ). Notably, the coexistence of a broad spectrum of ARGs, particularly those associated with mcr-1 -harboring plasmids, may facilitate co-selection and dissemination of other clinically relevant determinants ( 5 , 31 ). Although numerous ARGs were identified in the isolates, most strains remained phenotypically susceptible to commonly used antibiotics, which could be attributed to silent or poorly expressed ARGs that are not functionally active under standard conditions. Despite the observed susceptibility, these ARGs constitute a hidden reservoir that can be mobilized via horizontal gene transfer. Under antimicrobial selection pressure, such silent determinants may become activated or disseminated to other strains, potentially driving future resistance spread, which highlights the importance of integrating genomic and phenotypic data for comprehensive surveillance of antimicrobial resistance transmission risks. The extensive array of VFs, especially those involved in adhesion, iron acquisition, and motility, may enhance the capacity of these emerging strains to overcome ecological barriers and establish colonization within the host ( 31 , 32 ). Analysis of gut microbiome dynamics revealed that MCRPEC acquisition occurred without major disruption of the overall microbial community structure. Although Shannon diversity increased slightly after travel, the dominant genera remained stable, suggesting that MCRPEC can establish colonization within a resilient microbial ecosystem ( 33 ). This adaptability emphasizes the colonization potential of MCRPEC and raises concerns regarding the persistence of such strains in the community. This study had several limitations. First, our sample size was relatively small, which may have limited the statistical power to detect other potential risk factors. Second, the study focused on domestic travel within China, and the findings may not be generalizable to other countries with different travel patterns and mcr-1 epidemiology. Furthermore, the present study was unable to assess baseline AMR transmission within the community, as a control group of non-travelers was not included. Future investigations incorporating non-traveler cohorts would help to resolve it. Finally, the study design relies on a single post-travel sample, which does not provide information on the duration of carriage or the long-term impact on the host’s microbiome. Future research should focus on the duration and stability of mcr-1 carriage following travel, the frequency of secondary transmission to close contacts, and the precise molecular mechanisms that enable the successful colonization of these multidrug-resistant strains in a stable gut environment, which are essential for developing effective strategies to mitigate the ongoing spread of this critical resistance gene. Despite these limitations, our findings provide novel evidence that domestic travel within China plays a significant role in the spread of mcr-1 . Additionally, we emphasize the complex interactions between environmental exposure, host factors, and microbial ecology that facilitate the acquisition of mcr-1 . AMR surveillance programs should incorporate genomic surveillance, risk factor mitigation strategies, and microbiome-informed interventions to curtail the ongoing spread of mcr-1 . Such programs should expand their scope beyond international travel to encompass domestic mobility, especially in regions characterized by intensive animal agriculture. ACKNOWLEDGMENTS This work was supported by the National Major Science and Technology Projects of China (grant number 2024ZD0532800), Guangdong Basic and Applied Research Foundation (2022A1515111171), Research Fund for Qingmiao Talents of Guangdong Provincial Hospital of Chinese Medicine (SZ2024QN01), Key Project of State Key Laboratory of Traditional Chinese Medicine Syndrome (SKLKY2024B0003Z), Guangzhou Basic and Applied Foundation (2024A03J0028), and the China Postdoctoral Science Foundation (2023T160150 and 2022M720922). M.W., Y.C., and M.S. contributed equally to this study. C.S., C.C., and B.H. designed the study. C.S., M.W., and Y.C. performed literature searches. C.S., M.W., and Y.C. wrote the manuscript, which was reviewed and edited by C.C. and B.H. M.W., Y.C., H.N., and H.W. performed the experiments and produced the tables and figures. L.L., X.A., C.Z., and J.S. contributed to data analysis and interpretation. All authors (except H.N.) contributed to the sample collection and data collection. All authors reviewed, revised, and approved the final manuscript. Contributor Information Lingqing Xu, Email: lingqingxu2025@163.com. Cha Chen, Email: chencha@gzucm.edu.cn. Cong Shen, Email: shencong@gzucm.edu.cn. Alessandra Carattoli, Universita degli studi di Roma La Sapienza, Rome, Italy. DATA AVAILABILITY The raw reads and assembled genomes of evolved populations and evolved clones reported in this study have been deposited in the NCBI GenBank database under BioProject accession numbers PRJNA1336721 and PRJNA1336720 . ETHICS APPROVAL Ethical approval was obtained from the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (ZE2022-245-01). Individual consent forms were translated into Mandarin Chinese, and consent was obtained face-to-face from all volunteers. All the participants could withdraw from the study at any stage. SUPPLEMENTAL MATERIAL The following material is available online at https://doi.org/10.1128/aac.01746-25 . Fig. S1. aac.01746-25-s0001.pdf. ARGs and VFs of 12 MCRPEC isolates. aac.01746-25-s0001.pdf (280.1KB, pdf) DOI: 10.1128/aac.01746-25.SuF1 Table S1. aac.01746-25-s0002.xlsx. Questionnaire used in this study. aac.01746-25-s0002.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF2 Table S2. aac.01746-25-s0003.xlsx. Basic information of 81 volunteers. aac.01746-25-s0003.xlsx (17.6KB, xlsx) DOI: 10.1128/aac.01746-25.SuF3 Table S3. aac.01746-25-s0004.xlsx. MICs of 12 MCRPEC isolates for 12 antimicrobials. aac.01746-25-s0004.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF4 Table S4. aac.01746-25-s0005.xlsx. Distributions of ARGs for 12 MCRPEC isolates. aac.01746-25-s0005.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF5 Table S5. aac.01746-25-s0006.xlsx. Distributions of VFs for 12 MCRPEC isolates. aac.01746-25-s0006.xlsx (12.3KB, xlsx) DOI: 10.1128/aac.01746-25.SuF6 Table S6. aac.01746-25-s0007.xlsx. Transconjugation ability of mcr-1-harboring plasmids from 12 MCRPEC isolates. aac.01746-25-s0007.xlsx (10.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF7 ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse. REFERENCES 1. Binsker U, Käsbohrer A, Hammerl JA. 2022. Global colistin use: a review of the emergence of resistant Enterobacterales and the impact on their genetic basis. FEMS Microbiol Rev 46:fuab049. doi: 10.1093/femsre/fuab049 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. El-Sayed Ahmed MAE-G, Zhong L-L, Shen C, Yang Y, Doi Y, Tian G-B. 2020. Colistin and its role in the era of antibiotic resistance: an extended review (2000-2019). Emerg Microbes Infect 9:868–885. doi: 10.1080/22221751.2020.1754133 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Wang R, van Dorp L, Shaw LP, Bradley P, Wang Q, Wang X, Jin L, Zhang Q, Liu Y, Rieux A, Dorai-Schneiders T, Weinert LA, Iqbal Z, Didelot X, Wang H, Balloux F. 2018. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat Commun 9:1179. doi: 10.1038/s41467-018-03205-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Ogunlana L, Kaur D, Shaw LP, Jangir P, Walsh T, Uphoff S, MacLean RC. 2023. Regulatory fine-tuning of mcr-1 increases bacterial fitness and stabilises antibiotic resistance in agricultural settings. ISME J 17:2058–2069. doi: 10.1038/s41396-023-01509-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Mmatli M, Mbelle NM, Osei Sekyere J. 2022. Global epidemiology, genetic environment, risk factors and therapeutic prospects of mcr genes: a current and emerging update. Front Cell Infect Microbiol 12:941358. doi: 10.3389/fcimb.2022.941358 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Snesrud E, McGann P, Chandler M. 2018. The birth and demise of the ISApl1-mcr-1-ISApl1 composite transposon: the vehicle for transferable colistin resistance. mBio 9:e02381-17. doi: 10.1128/mBio.02381-17 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. D’Souza AW, Boolchandani M, Patel S, Galazzo G, van Hattem JM, Arcilla MS, Melles DC, de Jong MD, Schultsz C, COMBAT Consortium, Dantas G, Penders J. 2021. Destination shapes antibiotic resistance gene acquisitions, abundance increases, and diversity changes in Dutch travelers. Genome Med 13:79. doi: 10.1186/s13073-021-00893-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Worby CJ, Sridhar S, Turbett SE, Becker MV, Kogut L, Sanchez V, Bronson RA, Rao SR, Oliver E, Walker AT, Walters MS, Kelly P, Leung DT, Knouse MC, Hagmann SHF, Harris JB, Ryan ET, Earl AM, LaRocque RC. 2023. Gut microbiome perturbation, antibiotic resistance, and Escherichia coli strain dynamics associated with international travel: a metagenomic analysis. Lancet Microbe 4:e790–e799. doi: 10.1016/S2666-5247(23)00147-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Davies M, Galazzo G, Hattem JM, et al. 2022. Enterobacteriaceae and Bacteroidaceae provide resistance to travel-associated intestinal colonization by multidrug-resistant Escherichia coli. Gut Microbes 14:2060676. doi: 10.1080/19490976.2022.2060676 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Rondinaud E, Clermont O, Petitjean M, Ruppé E, Esposito-Farèse M, Nazimoudine A, VOYAG-R Study Group, Coignard B, Matheron S, Andremont A, Denamur E, Armand-Lefevre L. 2023. Acquisition of Enterobacterales carrying the colistin resistance gene mcr following travel to the tropics. J Travel Med 30:taac141. doi: 10.1093/jtm/taac141 [ DOI ] [ PubMed ] [ Google Scholar ] 11. von Wintersdorff CJH, Wolffs PFG, van Niekerk JM, Beuken E, van Alphen LB, Stobberingh EE, Oude Lashof AML, Hoebe C, Savelkoul PHM, Penders J. 2016. Detection of the plasmid-mediated colistin-resistance gene mcr-1 in faecal metagenomes of Dutch travellers. J Antimicrob Chemother 71:3416–3419. doi: 10.1093/jac/dkw328 [ DOI ] [ PubMed ] [ Google Scholar ] 12. Peng Y, Liang S, Poonsuk K, On H, Li SW, Maurin MMP, Chan CH, Chan CL, Sin ZY, Tun HM. 2021. Role of gut microbiota in travel-related acquisition of extended spectrum β-lactamase-producing Enterobacteriaceae. J Travel Med 28:taab022. doi: 10.1093/jtm/taab022 [ DOI ] [ PubMed ] [ Google Scholar ] 13. Schaumburg F, Sertic SM, Correa-Martinez C, Mellmann A, Köck R, Becker K. 2019. Acquisition and colonization dynamics of antimicrobial-resistant bacteria during international travel: a prospective cohort study. Clin Microbiol Infect 25:1287. doi: 10.1016/j.cmi.2019.03.002 [ DOI ] [ PubMed ] [ Google Scholar ] 14. Ruppé E, Armand-Lefèvre L, Estellat C, Consigny P-H, El Mniai A, Boussadia Y, Goujon C, Ralaimazava P, Campa P, Girard P-M, Wyplosz B, Vittecoq D, Bouchaud O, Le Loup G, Pialoux G, Perrier M, Wieder I, Moussa N, Esposito-Farèse M, Hoffmann I, Coignard B, Lucet J-C, Andremont A, Matheron S. 2015. High rate of acquisition but short duration of carriage of multidrug-resistant Enterobacteriaceae after travel to the tropics. Clin Infect Dis 61:593–600. doi: 10.1093/cid/civ333 [ DOI ] [ PubMed ] [ Google Scholar ] 15. Nakayama T, Kumeda Y, Kawahara R, Yamaguchi T, Yamamoto Y. 2018. Carriage of colistin-resistant, extended-spectrum β-lactamase-producing Escherichia coli harboring the mcr-1 resistance gene after short-term international travel to Vietnam. Infect Drug Resist 11:391–395. doi: 10.2147/IDR.S153178 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Shen C, Zhong L-L, Yang Y, Doi Y, Paterson DL, Stoesser N, Ma F, El-Sayed Ahmed MAE-G, Feng S, Huang S, Li H-Y, Huang X, Wen X, Zhao Z, Lin M, Chen G, Liang W, Liang Y, Xia Y, Dai M, Chen D-Q, Zhang L, Liao K, Tian G-B. 2020. Dynamics of mcr-1 prevalence and mcr-1-positive Escherichia coli after the cessation of colistin use as a feed additive for animals in China: a prospective cross-sectional and whole genome sequencing-based molecular epidemiological study. Lancet Microbe 1:e34–e43. doi: 10.1016/S2666-5247(20)30005-7 [ DOI ] [ PubMed ] [ Google Scholar ] 17. Wang Y, Xu C, Zhang R, Chen Y, Shen Y, Hu F, Liu D, Lu J, Guo Y, Xia X, et al. 2020. Changes in colistin resistance and mcr-1 abundance in Escherichia coli of animal and human origins following the ban of colistin-positive additives in China: an epidemiological comparative study. Lancet Infect Dis 20:1161–1171. doi: 10.1016/S1473-3099(20)30149-3 [ DOI ] [ PubMed ] [ Google Scholar ] 18. Liu J, Zhang Z, Feng Y, Hu H, Yu Y, Qiu L, Liu H, Guo Z, Huang J, Du C, Qiu J. 2020. Molecular detection of the mcr genes by multiplex PCR. Infect Drug Resist 13:3463–3468. doi: 10.2147/IDR.S256320 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi: 10.1089/cmb.2012.0021 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. 2019. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35:4453–4455. doi: 10.1093/bioinformatics/btz305 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Zhang L, Su M, Meng S, Zhang X, Wu H, Wu M, Ao X, Zhang X, Lin J, Yu S, Hong Y, Zeng X, Huang S, Zhang Y, Yang B, Zhang N, Jiang Y, Xu L, Zhang Z, Chen C, Shen C. 2025. Occurrence of vanA/vanM-positive vancomycin-resistant Enterococcus faecium in hospital wastewater associated with clinical infections in Guangzhou, China: a genomic epidemiological study. Int J Med Microbiol 319:151658. doi: 10.1016/j.ijmm.2025.151658 [ DOI ] [ PubMed ] [ Google Scholar ] 22. Danecek P, McCarthy SA. 2017. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics 33:2037–2039. doi: 10.1093/bioinformatics/btx100 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Jung Y, Han D. 2022. BWA-MEME: BWA-MEM emulated with a machine learning approach. Bioinformatics 38:2404–2413. doi: 10.1093/bioinformatics/btac137 [ DOI ] [ PubMed ] [ Google Scholar ] 24. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. doi: 10.1038/s41587-019-0209-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. De Filippis F, Pellegrini N, Vannini L, Jeffery IB, La Storia A, Laghi L, Serrazanetti DI, Di Cagno R, Ferrocino I, Lazzi C, Turroni S, Cocolin L, Brigidi P, Neviani E, Gobbetti M, O’Toole PW, Ercolini D. 2016. High-level adherence to a mediterranean diet beneficially impacts the gut microbiota and associated metabolome. Gut 65:1812–1821. doi: 10.1136/gutjnl-2015-309957 [ DOI ] [ PubMed ] [ Google Scholar ] 26. Manzano M, Talavera-Rodríguez A, Moreno E, Madrid N, Gosalbes MJ, Ron R, Dronda F, Pérez-Molina JA, Lanza VF, Díaz J, Moreno S, Navia B, Serrano-Villar S. 2022. Relationship of diet to gut microbiota and inflammatory biomarkers in people with HIV. Nutrients 14:1221. doi: 10.3390/nu14061221 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Walsh TR, Wu Y. 2016. China bans colistin as a feed additive for animals. Lancet Infect Dis 16:1102–1103. doi: 10.1016/S1473-3099(16)30329-2 [ DOI ] [ PubMed ] [ Google Scholar ] 28. Hassan J, Kassem II. 2020. Audacious hitchhikers: the role of travel and the international food trade in the global dissemination of mobile colistin-resistance (mcr) genes. Antibiotics (Basel) 9:370. doi: 10.3390/antibiotics9070370 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Sun J, Liao X-P, D’Souza AW, Boolchandani M, Li S-H, Cheng K, Luis Martínez J, Li L, Feng Y-J, Fang L-X, et al. 2020. Environmental remodeling of human gut microbiota and antibiotic resistome in livestock farms. Nat Commun 11:1427. doi: 10.1038/s41467-020-15222-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Boolchandani M, Blake KS, Tilley DH, Cabada MM, Schwartz DJ, Patel S, Morales ML, Meza R, Soto G, Isidean SD, Porter CK, Simons MP, Dantas G. 2022. Impact of international travel and diarrhea on gut microbiome and resistome dynamics. Nat Commun 13:7485. doi: 10.1038/s41467-022-34862-w [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Shen Y, Zhang R, Shao D, Yang L, Lu J, Liu C, Wang X, Jiang J, Wang B, Wu C, Parkhill J, Wang Y, Walsh TR, Gao GF, Shen Z. 2022. Genomic shift in population dynamics of mcr-1-positive Escherichia coli in human carriage. Genomics Proteomics Bioinformatics 20:1168–1179. doi: 10.1016/j.gpb.2022.11.006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Azam MW, Zarrilli R, Khan AU. 2023. Updates on the virulence factors produced by multidrug-resistant Enterobacterales and strategies to control their infections. Microorganisms 11:1901. doi: 10.3390/microorganisms11081901 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Langelier C, Graves M, Kalantar K, Caldera S, Durrant R, Fisher M, Backman R, Tanner W, DeRisi JL, Leung DT. 2019. Microbiome and antimicrobial resistance gene dynamics in international travelers. Emerg Infect Dis 25:1380–1383. doi: 10.3201/eid2507.181492 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Fig. S1. aac.01746-25-s0001.pdf. ARGs and VFs of 12 MCRPEC isolates. aac.01746-25-s0001.pdf (280.1KB, pdf) DOI: 10.1128/aac.01746-25.SuF1 Table S1. aac.01746-25-s0002.xlsx. Questionnaire used in this study. aac.01746-25-s0002.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF2 Table S2. aac.01746-25-s0003.xlsx. Basic information of 81 volunteers. aac.01746-25-s0003.xlsx (17.6KB, xlsx) DOI: 10.1128/aac.01746-25.SuF3 Table S3. aac.01746-25-s0004.xlsx. MICs of 12 MCRPEC isolates for 12 antimicrobials. aac.01746-25-s0004.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF4 Table S4. aac.01746-25-s0005.xlsx. Distributions of ARGs for 12 MCRPEC isolates. aac.01746-25-s0005.xlsx (11.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF5 Table S5. aac.01746-25-s0006.xlsx. Distributions of VFs for 12 MCRPEC isolates. aac.01746-25-s0006.xlsx (12.3KB, xlsx) DOI: 10.1128/aac.01746-25.SuF6 Table S6. aac.01746-25-s0007.xlsx. Transconjugation ability of mcr-1-harboring plasmids from 12 MCRPEC isolates. aac.01746-25-s0007.xlsx (10.2KB, xlsx) DOI: 10.1128/aac.01746-25.SuF7 Data Availability Statement The raw reads and assembled genomes of evolved populations and evolved clones reported in this study have been deposited in the NCBI GenBank database under BioProject accession numbers PRJNA1336721 and PRJNA1336720 . Articles from Antimicrobial Agents and Chemotherapy are provided here courtesy of American Society for Microbiology (ASM) ACTIONS View on publisher site PDF (1.5 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Developing Non-Communicable Disease Registries in Africa : A Step Towards Providing Quality Data for Improving Patient Outcomes#841661
• Scale-Dependent Drivers Shape Airborne Microbiomes across the Shanghai Subway Network#642750
• Analyzing human gut microbiome data from global populations: challenges and resources.#265410
• Bacterial Overgrowth Associated With Chronic Multisymptom Illness Complex#829387
• A Pilot Study Assessing Intestinal Microbiota Diversification and Changes After Travel to South(East) Asia From the US#279539