Introduction
The widespread prevalence of extended-spectrum β-lactamases (ESBLs) within Enterobacterales has led to the extensive utilization of carbapenems in clinical therapy.1 Consequently, this selective pressure has facilitated the emergence and spread of carbapenem-resistant Enterobacterales (CRE) through the transfer of plasmid-mediated genes, such as blaNDM-1, blaIMP, blaKPC, and blaOXA-48.2 Therefore, CRE has emerged as a predominant pathogen in clinical infectious diseases, posing a significant public health challenge.3 This shift towards carbapenem resistance not only prolongs hospital stays and increases mortality rates but also forces clinicians to rely on polymyxins, particularly colistin, as the last line of defense.4 Understanding the mechanisms and epidemiology of colistin resistance is therefore crucial to preserving the efficacy of this vital antibiotic and informing strategies to mitigate the spread of antimicrobial resistance.
Given the increasing prevalence of carbapenem resistance, polymyxin antibiotics, including polymyxin B and colistin, have emerged as a crucial last line of defense against CRE.4 These cationic compounds exert their antibacterial action by binding to the negatively charged lipid A component of lipopolysaccharide (LPS) in the bacterial cell membrane, thereby disrupting membrane integrity and inducing bacterial death. However, the emergence of resistance to polymyxins further complicates clinical treatment strategies.
One key mechanism of colistin resistance involves the acquisition of the mcr gene. The mcr gene encodes a phosphoethanolamine transferase that catalyzes the addition of phosphoethanolamine (PEtN) to the lipid A component of lipopolysaccharides (LPS) in the bacterial outer membrane.5 This modification reduces the net negative charge of the bacterial cell surface, consequently decreasing the binding affinity of the cationic peptide colistin and conferring resistance.
Intrinsic (chromosomally mediated) resistance to colistin primarily stems from modifications to lipid A that alter its charge. Key modifications include the addition of 4-amino-4-deoxy-L-arabinose (L-Ara4N) and PEtN to lipid A.6 These additions mask the negative charge of the cell envelope, thereby reducing the electrostatic interaction with polymyxins like colistin. Additionally, chromosomally encoded mutations, particularly in genes governing two-component regulatory systems (TCSs) such as PhoP-PhoQ and PmrA-PmrB, can lead to constitutive overexpression of LPS modification genes. These TCSs normally sense environmental cues (eg, low Mg2+ or Fe3+) to induce adaptive LPS changes; however, gain-of-function mutations disrupt this regulation, resulting in persistent LPS alterations and resistance. Notably, inactivation of the mgrB gene, a key negative regulator of the PhoP-PhoQ system, represents a common mutational pathway that derepresses the system and upregulates modification genes, thereby promoting colistin resistance.7
In contrast to these chromosomally mediated mechanisms, the plasmid-borne mcr gene, first identified in China in 2015, enables horizontally acquired resistance. It has since been reported globally, predominantly in Escherichia coli.8 To date, ten distinct variants of the mcr gene family (mcr-1 to mcr-10) have been identified worldwide. The horizontal transfer potential of these genes between diverse bacterial species represents a significant public health threat.9
Against this backdrop of global antimicrobial resistance challenges, Xuzhou, as a major medical hub in eastern China, is characterized by its abundant medical resources and high patient turnover, making it a high-risk area for the spread of CRE. Moreover, the extensive use of antimicrobial agents, especially during the COVID-19 pandemic, may have further exacerbated the drug resistance of CRE. Therefore, an in-depth investigation into the epidemiology and resistance mechanisms of CRE in Xuzhou is crucial for developing targeted public health strategies and antimicrobial stewardship programs. This study focuses on colistin-resistant CRE strains in Xuzhou, aiming to provide scientific evidence for regional prevention and control through epidemiological surveys and resistance mechanism analyses to address this increasingly severe public health challenge.
Materials and Methods
Bacterial Isolates
Between May 2016 and June 2022, 18 non-duplicated clinical isolates of colistin-resistant CRE were collected from inpatients at the Affiliated Hospital of Xuzhou Medical University. These isolates were specifically selected to investigate the prevalence and characteristics of colistin resistance among CRE strains in this clinical setting. Concurrently, detailed clinical information was gathered for each patient, including demographics (age and sex), underlying medical conditions, history of invasive procedures, duration of hospital stay, antimicrobial usage, and clinical outcomes. This comprehensive data collection provided a robust background for the study, facilitating a deeper understanding of the epidemiology and clinical impact of colistin-resistant strains.
For quality control, the colistin-sensitive K. pneumoniae wild strain (ATCC700603) was used, which has an intact and functionally normal two-component regulatory systems, such as PhoP-PhoQ and PmrA-B, serving as a reliable reference for susceptibility. Additionally, Salmonella enterica H9812 was employed as a reference strain for PFGE analysis due to its well-characterized genome size, which is crucial for accurate and reproducible results. The initial detection of colistin resistance was performed using the broth microdilution method according to CLSI guidelines (2024 edition).
Testing of Antimicrobial Susceptibility
Antimicrobial susceptibility was assessed using the VITEK-2 compact system (bio-Mérieux, Marcy-l’Étoile, France). Minimum Inhibitory Concentrations (MICs) were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) guidelines (CLSI M100, 2024).
Genotyping of Isolates
Whole-cell DNA from clinical strains was embedded in agarose gel plugs and subjected to pulsed-field gel electrophoresis (PFGE) using XbaI-digested genomic DNA. The PFGE conditions were as follows: pulse times of 6–36 seconds, pulse angle of 120°, voltage of 6 V/cm, and a running time of 20 hours. The Lambda Ladder PFG Marker (H9812) was used as the molecular weight marker. Clonal relationships were determined based on PFGE profiles, following the criteria proposed by report.10 Strains with PFGE profiles showing ≥85% similarity were considered clonally related, while those with <85% similarity were considered distinct clones.
Multilocus sequence typing (MLST) was also performed for these isolates using whole-genome sequencing (WGS) data, as previously described.11 Briefly, sequences of seven housekeeping genes (eg, gyrA, rpoB, recA, mdh, pgi, phoE, fumC) were extracted from the WGS data and compared to the MLST database to determine the sequence type (ST) of each isolate. This approach leverages the comprehensive coverage of WGS to provide accurate and detailed MLST results.
The genetic relatedness among the isolates was assessed by combining PFGE and MLST analyses. This dual approach provided a comprehensive understanding of the genetic diversity and clonal relationships among the isolates, and helped identify any potential clonal outbreaks.
Molecular Characterization and Related Genes Analysis
PCR was performed to identify carbapenem-resistant genes (blaKPC, blaNDM, blaVIM, blaIMP, and blaOXA-48) and colistin-resistant related genes (mcr-1, mgrB), based on a previous study12 (Table 1). The cycling conditions included an initial denaturation at 95°C for 5 minutes, followed by 35 cycles of 95°C for 30 seconds, 58°C for 30 seconds, and 72°C for 30 seconds, with a final extension at 72°C for 5 minutes. Positive PCR products were sequenced and compared to GenBank using BLAST (www.ncbi.nlm.nih.gov/GenBank).
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Table 1 Primers Used in This Study
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The expression of pmrA, pmrB, pmrC, pmrD, pmrE, pmrK, phoQ, phoP, and mgrB genes was analyzed using qRT-PCR. The selected genes (pmrA, pmrB, pmrC, pmrD, pmrE, pmrK, phoP, phoQ, and mgrB) were chosen because they play critical roles in lipopolysaccharide (LPS) modification and regulation of cell envelope stress responses, which are essential for colistin resistance. Specifically, the mgrB gene is involved in the regulation of the PhoP-PhoQ two-component system, which affects LPS modification and colistin binding affinity. The qRT-PCR assays were conducted using the ΔΔCt method for relative quantification, with the rpsL gene serving as the internal reference. The rpsL gene was chosen due to its stable expression across all isolates tested, which allows for accurate correction of sample-to-sample variations. The qRT-PCR cycling conditions were as follows: initial denaturation at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Each assay was performed in triplicate to ensure data reliability.13
The rpsL gene was chosen as the internal reference due to its stable expression across all isolates tested. rpsL encodes ribosomal protein S12 and has been shown to be highly stable in various bacterial species, making it suitable for normalizing gene expression levels. This stability allows for accurate correction of sample-to-sample variations, thereby enhancing the reliability of experimental results.14
mgrB Sequencing: The mgrB gene was sequenced using Sanger sequencing to identify specific mutations associated with colistin resistance. The sequencing results were analyzed using the CLC Main Workbench software.
Whole Genome Sequence Analysis
Whole-genome sequencing (WGS) was performed on selected strains to identify the presence of resistance genes and to understand the genetic context of colistin resistance. The coexistence of multiple resistance genes, such as mcr-1 and blaNDM-5, was observed in some strains, which may contribute to the observed resistance levels.
We selected Escherichia coli strain 104 as a representative of the same sequence type (ST) for detailed genetic analysis. The genome of this strain was sequenced using both PacBio RS Sequel II and Illumina HiSeq 4000 platforms at BGI Technology Service Co., Ltd. (Shenzhen, China) to elucidate the genetic environment surrounding the mcr-1 gene. The combined approach leverages the long-read capability of PacBio (average read length: 10,000 bp) and the high accuracy of Illumina (coverage: >100x), ensuring comprehensive and accurate genome assembly. The whole genome was submitted to GenBank (GenBank Accession Numbers: CP141581-CP141585).
BLAST was utilized to compare plasmid sequences, The genes associated with drug resistance were predicted using the ResFinder 4.1 database.
Plasmid genome circles were mapped using the CGView server (CGView – Overview), with input files in XML format. Plasmid replicon prediction, single nucleotide polymorphism (SNP)-based phylogenetic trees, and core genome multilocus sequence typing (cgMLST) were executed using BacWGSTdb (BacWGSTdb). The results were enhanced by TVBOT (TVBOT), which provides interactive visualization of phylogenetic trees. The specific parameters used for SNP analysis in BacWGSTdb included a minimum coverage of 10x and a core genome threshold of 90%. The phylogenetic trees were visualized using TVBOT’s drag-and-drop interface, allowing real-time updates and rendering of changes.
Statistical Analysis
Statistical analyses were performed using STATA version 17.0 (StataCorp LLC, College Station, TX, USA). For gene expression analysis by qRT-PCR, the ΔΔCt method was employed, and p-values were calculated using the Student’s t-test. A p-value of less than 0.05 was considered statistically significant.
Results
Baseline Information
A total of 18 non-duplicated clinical isolates of colistin-resistant CRE were collected from 18 adult patients admitted to the Affiliated Hospital of Xuzhou Medical University during the study period. Each isolate was derived from a unique patient, ensuring that the sample set represented a diverse range of clinical cases. These isolates included 14 colistin-resistant carbapenem-resistant Klebsiella pneumoniae (ColR-CRKP) strains and 4 colistin-resistant carbapenem-resistant Escherichia coli (ColR-CREC) strains.
The age of the patients ranged from 18 to 82 years. All patients had underlying infectious or chronic diseases, including sepsis, urinary tract infections, chronic cardiovascular diseases, and cerebrovascular diseases. They had undergone invasive procedures such as surgery, urinary catheterization, tracheal cannula insertion, tracheotomy, and central venous catheterization. The majority of the patients (11/18, 61.1%) had been admitted to the intensive care unit (ICU) and had received advanced antimicrobial agents targeting gram-negative bacteria. These agents included third-generation cephalosporins and enzyme inhibitors (17/18, 94.4%) and carbapenems (10/18, 55.6%). Notably, none of the patients had a history of polymyxin use before the first positive culture for CRE.
Among the 18 patients with colistin-resistant CRE infections, 7 (38.9%) succumbed to the infection, while 11 (61.1%) showed clinical improvement (Table 2).
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Table 2 Demographics and Clinical Characteristics of Patients with Colistin-Resistant K. Pneumoniae and E. Coli Infections
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Antimicrobial Susceptibility
All 18 colistin-resistant CRE strains exhibited multidrug resistance (Table 3). These strains were resistant to numerous antibiotics but showed selective sensitivity to amikacin, sulfamethoxazole, doxycycline, and tigecycline. Specifically, all 18 strains demonstrated high-level colistin resistance, with MIC values ranging from 4 to 16 mg/L.
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Table 3 Antimicrobial Resistance Profiles (MIC, mg/L) of 18 Colistin-Resistant CRE Strains
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Among them, the 4 ColR-CREC strains harboring the mcr-1 gene exhibited low-level resistance (MIC= 4 mg/L). The 14 ColR-CRKP strains with mgrB mutations had higher MIC values (≥8 mg/L).
Genotyping and Phylogenetic Analysis
Multilocus Sequence Typing (MLST)
MLST analysis revealed that ST11 (93.0%, 13/14) was the predominant sequence type among the 14 ColR-CRKP strains, indicating a high degree of clonality within this group.
Pulsed-Field Gel Electrophoresis (PFGE)
PFGE analysis further subdivided the 14 ColR-CRKP isolates into 9 distinct clusters (A-I), with clusters A and F being the most prevalent. This suggests the presence of multiple clonal lineages within the ST11 group. The four ColR-CREC strains were divided into two PFGE types: a type A clone (ST167) and a sporadic type B (ST405).
SNP-Based Phylogenetic Analysis
SNP-based phylogenetic analysis was performed using the CSI Phylogeny 1.4 tool with the following parameters: minimum coverage of 10x, minimum variant frequency of 0.75, and a core genome threshold of 90%. The resulting phylogenetic tree was constructed using the TVBOT online tool, chosen for its user-friendly interface and ability to visualize complex phylogenetic relationships. The tree was further visualized using iTOL (Interactive Tree of Life) to highlight key features and facilitate interpretation (Figure 1).
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Figure 1 PFGE analysis of colistin-resistant CRE strains. (A) Profiles of 14 ColR-CRKP strains showing 9 clusters (AI) with ≥85% similarity. (B) Profiles of 4 ColR-CREC strains divided into two types, (A) (ST167) and (B) (ST405).
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Sequence Comparison
The mgrB gene was sequenced using Sanger sequencing, and mutations were identified in all 14 K. pneumoniae strains. PCR and sequencing analysis of ColR-CRKP strains revealed point mutations within the mgrB gene, with no evidence of insertion sequences. Specifically, a missense mutation A55→T, resulting in the amino acid substitution S32C, was identified in 85.7% of the isolates (n = 12; including KP01-09, KP11-12, and KP14) among the ColR-CRKP strains with a 253 bp amplicon. Additional mutations were observed in KP10 and KP13 leading to significant alterations in the amino acid sequence (Figures 2 and 3).
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Figure 2 Sequence alignment of the mgrB gene among 14 ColR-CRKP strains. The sequence logos display the nucleotide composition at each position. Consensus sequence is indicated at the bottom, highlighting the conserved regions across all strains. WT (ColS-KPColS-KP-ATCC700603): colistin-sensitive Klebsiella pneumoniae.
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Figure 3 Alignment of the protein sequences of the mgrB genes in 14 ColR-CRKP strains. The sequence logos display the nucleotide composition at each position. Consensus sequence is indicated at the bottom, highlighting the conserved regions across all strains. WT (ColS-KPColS-KP-ATCC700603): colistin-sensitive Klebsiella pneumoniae.
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the mgrB gene mutations revealed that the A55→T mutation was present in 85.7% of isolates (n = 12), significantly higher than other mutation types. These results suggest that the A55→T mutation is a predominant factor driving colistin resistance in this region. Concurrently, the mcr-1 gene was detected in all ColR-CREC strains.
Expression Levels of Colistin-Resistant Related Genes
Quantitative real-time PCR (qRT-PCR) analysis was performed to investigate the expression levels of genes associated with colistin resistance in colistin-resistant Klebsiella pneumoniae (ColR-CRKP) strains. The selected genes included pmrA, pmrB, pmrC, pmrD, pmrE, pmrK, phoP, phoQ, and mgrB. These genes are known to play roles in lipopolysaccharide modification and regulation of cell envelope stress responses, which are critical for colistin resistance. The results revealed that the expression levels of pmrA, pmrB, pmrC, pmrD, pmrE, pmrK, phoP, and phoQ were significantly increased, while the expression of mgrB was decreased in all ColR-CRKP strains compared to the colistin-sensitive wild-type strain (WT) (Table 4). These findings suggest that alterations in the expression of these genes may be associated with colistin resistance in ColR-CRKP strains. Compared to the colistin-sensitive wild-type strain (WT), the expression levels of pmrA, pmrB, pmrC, pmrD, pmrE, pmrK, phoP, and phoQ were significantly increased (p < 0.05), while the expression of mgrB was decreased (p < 0.05) in all ColR-CRKP strains.
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Table 4 Expression of Genes in Clinical Colistin-Resistant Isolates and Colistin-Sensitive Strain
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Whole Genome Sequence Analysis
E. coli 104 was chosen for whole-genome sequencing due to its representative ST and the presence of multiple resistance genes, including mcr-1 and blaNDM-5. E. coli 104, subjected to whole genome sequencing (WGS), comprised a4799433bp chromosome and four distinct-sized plasmids (160 236 bp, 111954bp, 70829bp, and95917bp). We identified 65 virulence factors in the chromosome (accession number: CP141581) and 17 antimicrobial resistance-associated genes in 4 plasmids (accession number: CP141582-CP141585). ECO-104 plasmid p-1 (CP141582) contained several aminoglycoside-resistant genes including aph(3”)-Ib, aph(6)-Id, floR, and a β-lactam resistant gene (blaTEM-1), exhibiting a 98.39% identity with IncFIB (AP001918) and 96.56% identity with IncFII (pCoo) (p92944-mph, MG838205.1). ECO-104 plasmid p-2 (CP141583) carried the blaCTX-M-199 gene and exhibited 99.64% identity to IncFIB (pLF82-Phage Plasmid). ECO-104 plasmid p-3 (CP141583) contained aminoglycoside and β-lactam resistant genes (aadA2, blaNDM-5, and blaTEM-1), showing 100% similarity to IncFII of pNDM5-IBAC (KY463220.1) and pM217-FII (NZ_AP018147.1). ECO-104 plasmid p-4 (CP141584) contained a colistin resistance gene (mcr-1) and a β-lactam resistant gene (blaCTX-M-64), displaying 98.10% similar to the IncI2 (Delta) of pBA76-MCR-1 (KX013540.1) and p1108-MCR (MG825380.1) (Figure 4). BLASTn analysis revealed high similarity between ECO-104 plasmid p-3 and several other plasmids, namely p28078-NDM (MN156713.1), p_dm682b_NDM-5 (CP095639.1), FDAARGOS_440 plasmid (CP023923.1), and pMR0617ndm (CP024039.1). Additionally, ECO-104 plasmid p-4 displayed over 80% similarity to several plasmids from E. coli, such as pT28R-3 (CP049356.1), pHLJ179-167 (MN232211.1), pTBH7B1 (CP067343.1), and plasmid:1 (LR882930).
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Figure 4 Circle plots and comparative analysis of plasmids carried by E. coli 104. (A) Circle plots of ECO-104 plasmid p-1 (160,236bp). (B) Circle plots of ECO-104 plasmid p-2(111,954 bp). (C) Comparative analysis of ECO-104 plasmid p-3(70,829 bp) with other 4 plasmids (p28078-NDM, p_dm682b_NDM-5, FDAARGOS_440 plasmid, and pMR0617ndm). (D) Comparative analysis of ECO-104 plasmid p-4(9,5917 bp) with other 4 plasmids (pT28R-3, pHLJ179-167, pTBH7P1, and plasmid:1). The outermost circle represents plasmid sequences in this study.
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To further track bacterial sources, we analyzed E. coli 104 along with mcr-1-positive strains in BacWGSTdb. These strains underwent phylogenetic analysis based on SNP and cgMLST strategies, and a phylogenetic tree was constructed using the TVBOT online tool. The SNP-based phylogenetic tree revealed high similarity between clonal epidemic strains represented by E. coli 104 and ST167 strains isolated from Sichuan (CP025627, NGVI01, and WUBW01), Hangzhou (RIZW01, RIZV01, RIZU01, and RIZS01), and Shandong (RYCI01), China (Figure 5). The cgMLST-based phylogenetic tree illustrated ST167 as the predominant ST among E. coli isolates, followed by ST10, with distribution across humans, cows, pigs, dogs, chickens, and other species. Strains from different sources exhibited a close evolutionary relationship, suggesting the potential transmission of drug-resistant strains between humans and animals. Geographically, the ST167 epidemic strain was prevalent worldwide, especially in Latvia, India, China, and other developed countries in agriculture and animal husbandry. Although ST10 is also a global epidemic strain like ST167, it is mainly distributed in China and select developed European countries, such as Germany, the United Kingdom, and Italy (Figure 6).
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Figure 5 Phylogenetic tree based on the SNP strategy of E. coli 104. The same colors represent a closer evolutionary relationship. E. coli 104 in this study was assigned to category C.
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Figure 6 Phylogenetic tree based on the cgMLST strategy of E. coli 104 in this study with mcr-1-positive strains in the BacWGSTdb. (A) Phylogenetic tree based on host. Different colors represent different hosts. The numbers in the circle represent the ST types. (B) Phylogenetic tree based on region. Different colors represent different regions. The numbers in the circle represent the ST types.
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Discussion
Klebsiella pneumoniae stands out among gram-negative strains in hospitals, displaying a high detection rate and posing a significant challenge in clinical settings due to its carbapenems resistance. Polymyxin, the last line of defense against multi-drug-resistant K. pneumoniae, encounters resistance, and recent studies report the emergence of polymyxin resistance in K. pneumoniae in several countries, with the ST258 type being the most prevalent clonal type.15 Two-component regulatory systems play an important role in the modification of LPS, such as the inactivation of mgrB regulators caused by insertion sequence leading to the development of polymyxin resistance,16 which was speculated to be the main mechanism of K. pneumoniae.17
In our study, all 14 K. pneumoniae strains exhibited mutations in the mgrB gene. Nucleotide sequence comparisons revealed that mgrB gene mutations occurred mostly 85.7% (12/14) in A-base mutations to T at the 55 locus, resulting in an amino acid sequence change (S32C). The mgrB mutation (A55→T, resulting in S32C) identified in our study is consistent with global reports on polymyxin-resistant K. pneumoniae strains.18–20 This mutation significantly impacts colistin resistance by disrupting the PhoP-PhoQ regulatory system, leading to LPS modification and reduced polymyxin binding.7 The mutated strains showed significantly decreased expression of the mgrB gene compared to the wild-type strain, and the MIC of the corresponding strains to polymyxin increased to over 8 mg/L, indicating high-level polymyxin resistance.
Although mutations in bases are mostly random, certain triggers have been reported to increase the chance of mutation, such as the use of antimicrobial drugs. From the clinical data of patients infected with ColR-CRKP strains, it was observed that most of them had a history of antimicrobial drug use, such as third-generation cephalosporin and enzyme inhibitor (14/14, 100%), and carbapenems (8/14, 57.1%). This frequent exposure to antibiotics may have contributed to the selection of mgrB mutations, although a direct causal link remains elusive.21
Notably, In our study, all 14 K. pneumoniae strains exhibited mutations in the mgrB gene without inactivation by insertion sequences, differing from previous studies, especially in other countries.18 This difference may be attributed to the specific clinical practices and antibiotic usage patterns in our region, which warrant further investigation. Therefore, mutations in the mgrB gene were likely a result of these antimicrobial drugs; however, further validation of the mechanism is required.
Our study observed varying levels of colistin resistance among strains with different mgrB mutations. The A55T mutation, resulting in the S32C amino acid substitution, was associated with high-level resistance (MIC ≥ 16 mg/L) in 12 out of 14 isolates (85.7%), while other mutations, such as A55G, exhibited lower resistance levels (MIC = 8 mg/L) in the remaining 2 isolates (14.3%). revealed significant differences in colistin resistance levels among strains with different mgrB mutations. These findings highlight the need for further investigation into the specific impacts of different mgrB mutations on resistance mechanisms and are consistent with previous reports17, emphasizing the importance of mgrB mutations in colistin resistance.
While our study suggests that mgrB gene mutations may be associated with the use of antimicrobial drugs, as reported in other studies,7,18 a definitive cause-and-effect relationship has not been established. Although most patients with mgrB mutations had a history of antibiotic use, a direct causal link between antibiotic exposure and mgrB mutations remains elusive. However, studies have suggested that certain antibiotics, such as carbapenems, may indirectly promote mgrB mutations by inducing genetic changes or selecting for resistant strains.16,17 Further research is necessary to elucidate the specific mechanisms by which antibiotics contribute to the development of these mutations. This could involve longitudinal studies to track the emergence of mgrB mutations in patients receiving different antibiotic regimens, or experimental studies to investigate the genetic and environmental factors that drive these mutations.
Another significant mechanism of colistin resistance is the presence of the mcr gene. The discovery of mcr-1 as a plasmid-mediated determinant of colistin resistance in China has garnered global attention.22–24 While there are ten mcr gene family variants (mcr-1 to mcr-10), mcr-1 is typically considered the primary mechanism of polymyxin resistance, especially in E. coli.25–27 In our study, we collected four strains of polymyxin-resistant E. coli, including two strains from urine and two strains from blood. Moreover, these patients had no history of polymyxin use but had used other antibiotics. The presence of mcr-1 led to a low level of resistance to colistin (MIC = 4 mg/L), which is consistent with the literature worldwide reports on mcr-1-mediated polymyxin resistance in E. coli.28,29 However, further studies are needed to explore potential co-resistance to other antibiotics and other factors influencing resistance.
To place our findings within the broader global context of mcr-1-mediated resistance, a comparison with other studies is essential. Such comparisons will help us understand how our results fit into the global landscape of polymyxin resistance. The low-level resistance mediated by mcr-1 (MIC = 4 mg/L) has been widely reported globally.22 However, the low-level resistance observed in this study may be associated with the coexistence of other resistance genes, which warrants further investigation. Our study’s mcr-1 positive isolates exhibited high genetic similarity with strains from other regions, such as India and China. For example, E. coli 104 clustered closely with ST167 strains from these regions in the phylogenetic tree, indicating a shared evolutionary origin. This finding aligns with global reports on the spread of mcr-1 mediated resistance.24
Future studies should include multicenter investigations to validate the prevalence of mgrB mutations and mcr-1 genes in diverse clinical settings. Comparative genomic analyses of additional isolates, such as E. coli 101 and E. coli 104, are recommended to elucidate genetic differences and their functional implications. These efforts will enhance our understanding of the mechanisms underlying polymyxin resistance and inform targeted interventions. Given the limitations identified in our study, additional validation through whole-genome sequencing (WGS) and comprehensive genomic comparisons is essential. Such studies will not only validate the findings but also provide deeper insights into the genetic and functional differences between strains, ultimately guiding more effective strategies to combat antimicrobial resistance.
Our whole-genome sequencing (WGS) analysis of E. coli 104, a representative ST167 strain harboring mcr-1, revealed critical insights into the genetic architecture of colistin resistance. The strain carried four plasmids, each encoding distinct resistance determinants: p-1 (IncFIB/IncFII): Contained aph(3”)-Ib, aph(6)-Id, floR, and blaTEM-1, conferring resistance to aminoglycosides and β-lactams. p-2 (IncFIB): Encoded blaCTX-M-199, an extended-spectrum β-lactamase (ESBL). p-3 (IncFII): Harbored blaNDM-5 and blaTEM-1, mediating carbapenem and penicillin resistance. p-4 (IncI2): Carried mcr-1 and blaCTX-M-64, pivotal for colistin and cephalosporin resistance.
Notably, mcr-1 and blaNDM-5 were located on separate plasmids (IncI2 and IncFII, respectively), suggesting independent acquisition events. This genetic arrangement facilitates horizontal transfer of resistance genes across bacterial populations. Comparative analysis revealed >98% similarity between p-3 and globally circulating blaNDM-5-plasmids (eg, p28078-NDM [MN156713.1], pMR0617ndm [CP024039.1]), indicating transregional dissemination of carbapenem resistance. Similarly, p-4 (mcr-1-bearing IncI2 plasmid) shared high identity (>98%) with plasmids from clinical and environmental E. coli strains in China (eg, pBA76-MCR-1 [KX013540.1]), underscoring the role of mobile genetic elements in perpetuating colistin resistance.
Phylogenetic analysis further demonstrated that E. coli 104 clustered closely with ST167 strains from Sichuan, Hangzhou, and Shandong (China), as well as international isolates from Latvia and India. This clonal relatedness, supported by both SNP and cgMLST trees, highlights the potential for cross-regional and cross-species spread of mcr-1-harboring strains. The detection of ST167 in humans, livestock (cows, pigs, chickens), and companion animals (dogs) aligns with the One Health approach, emphasizing how agricultural antibiotic use and zoonotic transmission contribute to resistance dissemination.
These findings underscore two critical public health concerns: Co-resistance threats: The coexistence of mcr-1, blaNDM-5, and ESBL genes on plasmids creates pan-drug-resistant phenotypes, leaving few therapeutic options. Epidemiological linkages: Clonal expansion of ST167 E. coli across China and beyond necessitates enhanced surveillance to track resistance gene flow at the human-animal-environment interface.
Our phylogenetic analysis revealed close evolutionary relationships between mcr-1-positive strains from humans and animals, underscoring the importance of the One Health approach. The interplay between human health, animal health, and environmental factors likely facilitates the spread of resistant strains. For instance, the extensive use of antibiotics in agriculture and livestock farming may contribute to the emergence and dissemination of resistant bacteria. Future research should explore these interactions more thoroughly to develop comprehensive strategies for controlling antimicrobial resistance. This includes stringent antibiotic management measures in both clinical treatment and agricultural settings, as well as a deeper understanding of how these components interact to facilitate the spread of resistant strains.
Limitations
This study has several limitations that should be acknowledged. Firstly, the small sample size (n=18) may limit the generalizability of the findings. A larger sample would provide more robust statistical power and enhance the reliability of the results. Secondly, the lack of control strains from the environment means that we cannot fully account for potential external factors that may have influenced the outcomes. Lastly, the retrospective nature of the patient data may introduce biases related to data collection and recall, which could affect the accuracy and completeness of the information used in the analysis.
Conclusions
This study presents two distinct mechanisms contributing to colistin resistance in Enterobacterales. Mutations in the mgrB gene and the plasmid-carried mcr-1 gene account for colistin resistance in K. pneumoniae and E. coli, respectively. The acquisition of mcr-1 by K. pneumoniae with an existing mgrB gene mutation poses a substantial clinical challenge, as it results in a more resistant Enterobacterales. Whole-genome sequencing (WGS) revealed the coexistence of multiple resistance genes, such as mcr-1 and blaNDM-5, in some strains. This coexistence suggests that these strains may have a broader resistance profile, complicating treatment options. Additionally, the presence of these genes on plasmids indicates a potential for horizontal transfer, which could further spread resistance. Future studies should focus on understanding the genetic mechanisms underlying the coexistence of these resistance genes and their potential impact on clinical outcomes. Additionally, the phylogenetic analysis highlighted a close evolutionary relationship between mcr-1-positive strains isolated from both humans and animals, underscoring the potential for cross-species transmission. Given these findings, there is an urgent need to strengthen the epidemiological surveillance of drug-resistant bacteria and reinforce the management system of the rational use of antibiotics.
Data Sharing Statement
The sequence data have been submitted to NCBI database (accession number: CP141581-CP141585).
Ethics Approval and Consent to Participate
All strains were isolated from culture samples collected for routine clinical examinations of hospitalized patients admitted to the Affiliated Hospital of Xuzhou Medical University between May 2016 and June 2022. Any personally identifiable information was removed from this study. This study protocol, including the waiver of informed consent due to the use of anonymized data from routine clinical practice, was approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (XYFY2021-KL101-01). The research involved no more than minimal risk to subjects and no personal information was obtained. The research conformed to the principles of the Helsinki Declaration.
Acknowledgments
The authors would like to thank all the reviewers who participated in the review, as well as MJEditor (www.mjeditor.com) for providing English editing services during the preparation of this manuscript.
Funding
This research was funded by the six talent peaks project of Jiangsu Province (WSN-091); Scientific Research Project of Jiangsu Provincial Health Commission (Z2021009); Youth Innovation Project of Xuzhou Health Commission (XWKYHT20200062); Open project of Jiangsu Province Key Laboratory (XZSYSKF2020030); the Scientific Research Foundation of the Affiliated Hospital of Xuzhou Medical University (2021ZA43).
Disclosure
The authors declare no conflicts of interest in this work.
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