Whole-Genome Sequencing Reveals Diversity of Carbapenem-Resistant Pseudomonas aeruginosa Collected through CDC's Emerging Infections Program, United States, 2016-2018

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

High-risk Pseudomonas aeruginosa clones harboring β-lactamases: 2024 update

Carbapenem-resistant Pseudomonas aeruginosa is defined by the World Health Organization as a "high priority" in developing new antimicrobials. Indeed, the emergence and spread of multidrug-resistant (MDR) or extensively drug-resistant (XDR) bacteria increase the morbidity and mortality risk of infected patients. Genomic variants of P. aeruginosa that display phenotypes of MDR/XDR have been defined as high-risk global clones. In this mini-review, we describe some international high-risk clones that carry β-lactamase genes that can produce chronic colonization and increase infected patients' morbidity and mortality rates.

Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models

Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate antibiotics' impact on microorganisms and predict microbial abundance. We examined the microbial abundances of various environmental soil samples treated with antibiotics. We developed 3 ML models: (Model 1) for predicting the most abundant bacterial classes in a specific treatment group; (Model 2) for predicting antibiotic treatment effects based on bacterial abundances; and (Model 3) for using data from short-term incubations to predict the data of community structure after stabilisation. In Model 1, the Random Forest model achieved the highest average accuracy, with a Coefficient of Variation mean of 0.05 and 0.14 in the training and test set. In Model 2, the accuracy of the random forest and SVM models have the highest accuracy (nearly 0.90). Model 3 demonstrates that the Random Forest can use data from short-term incubations to predict the abundance of bacterial communities after long-term stabilisation. This study highlights the potential of ML models as powerful tools for understanding microbial dynamics in response to antibiotic treatments. The code is publicly available at - https://github.com/DeweyYihengDu/ML_on_Microbiota.

In vitro activity assessment of cefiderocol against Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter spp., including β-lactam nonsusceptible molecularly characterized isolates, collected from 2020 to 2021 in the United States and European hospitals

This study reports the activity of cefiderocol against Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter spp. isolates collected from the United States and Europe, including Israel and Turkey, from 2020 to 2021. Among Enterobacterales, 2.8% were carbapenem nonsusceptible (CNSE); cefiderocol inhibited 96.6%/85.1% [Clinical Laboratory Standards Institute (CLSI)/European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints] of these isolates. Imipenem-relebactam, meropenem-vaborbactam, and ceftazidime-avibactam displayed susceptibilities lower than cefiderocol against CNSE isolates (67.4-84.6% susceptible, CLSI). Cefiderocol was the only agent active against CNSE isolates carrying metallo-β-lactamase (MBL) carbapenemase or multiple carbapenemase genes (84.6%-92.3% susceptible, CLSI). Approximately 18% of carbapenem-susceptible Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis carried extended-spectrum-β-lactamases and/or plasmid-borne AmpC-encoding genes; cefiderocol inhibited 99.8%-100.0% (CLSI) of these genotypic groups. Multi-drug resistance (MDR) phenotypes were observed in 16.9% and 52.5% of P. aeruginosa and A. baumannii-calcoaceticus isolates, respectively. Carbapenemase genes were rare (4.9%) among cephalosporin and/or carbapenem nonsusceptible P. aeruginosa, compared to 87.6% carriage in A. baumannii-calcoaceticus, respectively. Against the MDR and carbapenemase-carrying P. aeruginosa and A. baumannii-calcoaceticus subsets, cefiderocol was active against 98.6%/98.7% and 97.1%/97.4% (CLSI), respectively. Only 69 isolates (0.3%) across all species groups were identified as cefiderocol nonsusceptible per CLSI criteria (>4 mg/L). Cefiderocol was the most active agent in vitro against Enterobacterales, P. aeruginosa, and Acinetobacter spp., with uniform activity against all phenotypic- and genotypic-resistant subsets. Coupled with the low incidence of nonsusceptibility observed across species groups, these results demonstrate cefiderocol as an important option for treating infections caused by pathogens with diverse mechanisms of resistance in US and European hospitals.IMPORTANCEThe worldwide spread of multi-drug-resistant Pseudomonas aeruginosa and carbapenem-resistant Enterobacterales and Acinetobacter spp. poses a serious challenge in healthcare settings as infections caused by these organisms are commonly refractory to many frontline therapeutic agents. Multiple global health organizations highlighted these pathogens as critical priorities for new antibiotic development, thus necessitating continued surveillance of the activities of currently available antimicrobial agents and circulating mechanisms of resistance. To meet this need, this study phenotypically and genotypically characterized priority Gram-negative pathogens collected from patients in US and European hospitals to examine the activity of cefiderocol and other currently available treatment options, including carbapenems and β-lactam-β-lactamase inhibitor combinations. The results presented here provide a detailed perspective to healthcare practitioners of cefiderocol's broad applicability, manifested in high activity and low nonsusceptibility rates, across phenotypic and genotypic organism groups relative to other agents and further support its use against the most intransigent infections.

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests

BACKGROUND

Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST).

METHODS

The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099).

RESULTS

The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.

Impact of multidrug resistance on the virulence and fitness of Pseudomonas aeruginosa: a microbiological and clinical perspective

Pseudomonas aeruginosa is one of the most common nosocomial pathogens and part of the top emergent species associated with antimicrobial resistance that has become one of the greatest threat to public health in the twenty-first century. This bacterium is provided with a wide set of virulence factors that contribute to pathogenesis in acute and chronic infections. This review aims to summarize the impact of multidrug resistance on the virulence and fitness of P. aeruginosa. Although it is generally assumed that acquisition of resistant determinants is associated with a fitness cost, several studies support that resistance mutations may not be associated with a decrease in virulence and/or that certain compensatory mutations may allow multidrug resistance strains to recover their initial fitness. We discuss the interplay between resistance profiles and virulence from a microbiological perspective but also the clinical consequences in outcomes and the economic impact.

Whole-genome sequencing, multilocus sequence typing, and resistance mechanism of the carbapenem-resistant Pseudomonas aeruginosa in China

Pseudomonas aeruginosa is a significant pathogen responsible for severe multisite infections with high morbidity and mortality rates. This study analyzed carbapenem-resistant Pseudomonas aeruginosa (CRPA) at a tertiary hospital in Shandong, China, using whole-genome sequencing (WGS). The objective was to explore the mechanisms and molecular characteristics of carbapenem resistance. A retrospective analysis of 91 isolates from January 2022 to March 2023 was performed, which included strain identification and antimicrobial susceptibility testing. WGS was utilized to determine the genome sequences of these CRPA strains, and the species were precisely identified using average nucleotide identification (ANI), with further analysis on multilocus sequence typing and strain relatedness. Some strains were found to carry the ampD and oprD genes, while only a few harbored carbapenemase genes or related genes. Notably, all strains possessed the mexA, mexE, and mexX genes. The major lineage identified was ST244, followed by ST235. The study revealed a diverse array of carbapenem resistance mechanisms among hospital isolates, differing from previous studies in mainland China. It highlighted that carbapenem resistance is not due to a single mechanism but rather a combination of enzyme-mediated resistance, AmpC overexpression, OprD dysfunction, and efflux pump overexpression. This research provides valuable insights into the evolutionary mechanisms and molecular features of CRPA resistance in this region, aiding in the national prevention and control of CRPA, and offering references for targeting and developing new drugs.

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Nucleus-forming jumbophage PhiKZ therapeutically outcompetes non-nucleus-forming jumbophage Callisto

With the recent resurgence of phage therapy in modern medicine, jumbophages are currently under the spotlight due to their numerous advantages as anti-infective agents. However, most significant discoveries to date have primarily focused on nucleus-forming jumbophages, not their non-nucleus-forming counterparts. In this study, we compare the biological characteristics exhibited by two genetically diverse jumbophages: 1) the well-studied nucleus-forming jumbophage, PhiKZ; and 2) the newly discovered non-nucleus-forming jumbophage, Callisto. Single-cell infection studies further show that Callisto possesses different replication machinery, resulting in a delay in phage maturation compared to that of PhiKZ. The therapeutic potency of both phages was examined in vitro and in vivo, demonstrating that PhiKZ holds certain superior characteristics over Callisto. This research sheds light on the importance of the subcellular infection machinery and the organized progeny maturation process, which could potentially provide valuable insight in the future development of jumbophage-based therapeutics.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Molecular characterization of extensively drug-resistant hypervirulent Pseudomonas aeruginosa isolates in China

BACKGROUND

Recently, extensively drug-resistant Pseudomonas aeruginosa (XDR-PA) isolates have been increasingly detected and posed great challenges to clinical anti-infection treatments. However, little is known about extensively resistant hypervirulent P. aeruginosa (XDR-hvPA). In this study, we investigate its epidemiological characteristics and provide important basis for preventing its dissemination.

METHODS

Clinical XDR-PA isolates were collected from January 2018 to January 2023 and identified using matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry; antibiotic susceptibility testing was performed by broth microdilution method, and minimum inhibitory concentrations (MICs) were evaluated. Virulence was evaluated using the Galleria mellonella infection model; molecular characteristics, including resistance genes, virulence genes, and homology, were determined using whole-genome sequencing.

RESULTS

A total of 77 XDR-PA strains were collected; 47/77 strains were XDR-hvPA. Patients aged > 60 years showed a significantly higher detection rate of XDR-hvPA than of XDR-non-hvPA. Among the 47 XDR-hvPA strains, 24 strains carried a carbapenemase gene, including blaGES-1 (10/47), blaVIM-2 (6/47), blaGES-14 (4/47), blaIMP-45 (2/47), blaKPC-2 (1/47), and blaNDM-14 (1/47). ExoU, exoT, exoY, and exoS, important virulence factors of PA, were found in 31/47, 47/47, 46/47, and 29/47 strains, respectively. Notably, two XDR-hvPA simultaneously co-carried exoU and exoS. Six serotypes (O1, O4-O7, and O11) were detected; O11 (19/47), O7 (13/47), and O4 (9/47) were the most prevalent. In 2018-2020, O4 and O7 were the most prevalent serotypes; 2021 onward, O11 (16/26) was the most prevalent serotype. Fourteen types of ST were detected, mainly ST235 (14/47), ST1158 (13/47), and ST1800 (7/47). Five global epidemic ST235 XDR-hvPA carried blaGES and showed the MIC value of ceftazidime/avibactam reached the susceptibility breakpoint (8/4 mg/L).

CONCLUSIONS

The clinical detection rate of XDR-hvPA is unexpectedly high, particularly in patients aged > 60 years, who are seemingly more susceptible to contracting this infection. Clonal transmission of XDR-hvPA carrying blaGES, which belongs to the global epidemic ST235, was noted. Therefore, the monitoring of XDR-hvPA should be strengthened, particularly for elderly hospitalized patients, to prevent its spread.

Phenotypes of a Pseudomonas aeruginosa hypermutator lineage that emerged during prolonged mechanical ventilation in a patient without cystic fibrosis

Hypermutator lineages of Pseudomonas aeruginosa arise frequently during the years of airway infection experienced by patients with cystic fibrosis and bronchiectasis but are rare in the absence of chronic infection and structural lung disease. Since the onset of the COVID-19 pandemic, large numbers of patients have remained mechanically ventilated for extended periods of time. These patients are prone to acquire bacterial pathogens that persist for many weeks and have the opportunity to evolve within the pulmonary environment. However, little is known about what types of adaptations occur in these bacteria and whether these adaptations mimic those observed in chronic infections. We describe a COVID-19 patient with a secondary P. aeruginosa lung infection in whom the causative bacterium persisted for >50 days. Over the course of this infection, a hypermutator lineage of P. aeruginosa emerged and co-existed with a non-hypermutator lineage. Compared to the parental lineage, the hypermutator lineage evolved to be less cytotoxic and less virulent. Genomic analyses of the hypermutator lineage identified numerous mutations, including in the mismatch repair gene mutL and other genes frequently mutated in individuals with cystic fibrosis. Together, these findings demonstrate that hypermutator lineages can emerge when P. aeruginosa persists following acute infections such as ventilator-associated pneumonia and that these lineages have the potential to affect patient outcomes.IMPORTANCEPseudomonas aeruginosa may evolve to accumulate large numbers of mutations in the context of chronic infections such as those that occur in individuals with cystic fibrosis. However, these "hypermutator" lineages are rare following acute infections. Here, we describe a non-cystic fibrosis patient with COVID-19 pneumonia who remained mechanically ventilated for months. The patient became infected with a strain of P. aeruginosa that evolved to become a hypermutator. We demonstrate that hypermutation led to changes in cytotoxicity and virulence. These findings are important because they demonstrate that P. aeruginosa hypermutators can emerge following acute infections and that they have the potential to affect patient outcomes in this setting.

Direct prediction of carbapenem resistance in Pseudomonas aeruginosa by whole genome sequencing and metagenomic sequencing

Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P. aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa, including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa, the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa.

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multi-locus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

In host evolution of beta lactam resistance during active treatment for Pseudomonas aeruginosa bacteremia

Multidrug-resistant (MDR) Pseudomonas aeruginosa has been declared a serious threat by the United States Centers for Disease Control and Prevention. Here, we used whole genome sequencing (WGS) to investigate recurrent P. aeruginosa bloodstream infections in a severely immunocompromised patient. The infections demonstrated unusual, progressive increases in resistance to beta lactam antibiotics in the setting of active treatment with appropriate, guideline-directed agents. WGS followed by comparative genomic analysis of isolates collected over 44 days demonstrated in host evolution of a single P. aeruginosa isolate characterized by stepwise acquisition of two de-novo genetic resistance mechanisms over the course of treatment. We found a novel deletion affecting the ampC repressor ampD and neighboring gene ampE, which associated with initial cefepime treatment failure. This was followed by acquisition of a porin nonsense mutation, OprD, associated with resistance to carbapenems. This study highlights the potential for in-host evolution of P. aeruginosa during bloodstream infections in severely immunocompromised patients despite appropriate antimicrobial therapy. In addition, it demonstrates the utility of WGS for understanding unusual resistance patterns in the clinical context.

PorinPredict: In Silico Identification of OprD Loss from WGS Data for Improved Genotype-Phenotype Predictions of P. aeruginosa Carbapenem Resistance

The increasing integration of genomics into routine clinical diagnostics requires reliable computational tools to identify determinants of antimicrobial resistance (AMR) from whole-genome sequencing data. Here, we developed PorinPredict, a bioinformatic tool that predicts defects of the Pseudomonas aeruginosa outer membrane porin OprD, which are strongly associated with reduced carbapenem susceptibility. PorinPredict relies on a database of intact OprD variants and reports inactivating mutations in the coding or promoter region. PorinPredict was validated against 987 carbapenemase-negative P. aeruginosa genomes, of which OprD loss was predicted for 454 out of 522 (87.0%) meropenem-nonsusceptible and 46 out of 465 (9.9%) meropenem-susceptible isolates. OprD loss was also found to be common among carbapenemase-producing isolates, resulting in even further increased MICs. Chromosomal mutations in quinolone resistance-determining regions and OprD loss commonly co-occurred, likely reflecting the restricted use of carbapenems for multidrug-resistant infections as recommended in antimicrobial stewardship programs. In combination with available AMR gene detection tools, PorinPredict provides a robust and standardized approach to link P. aeruginosa phenotypes to genotypes. IMPORTANCE Pseudomonas aeruginosa is a major cause of multidrug-resistant nosocomial infections. The emergence and spread of clones exhibiting resistance to carbapenems, a class of critical last-line antibiotics, is therefore closely monitored. Carbapenem resistance is frequently mediated by chromosomal mutations that lead to a defective outer membrane porin OprD. Here, we determined the genetic diversity of OprD variants across the P. aeruginosa population and developed PorinPredict, a bioinformatic tool that enables the prediction of OprD loss from whole-genome sequencing data. We show a high correlation between predicted OprD loss and meropenem nonsusceptibility irrespective of the presence of carbapenemases, which are a second widespread determinant of carbapenem resistance. Isolates with resistance determinants to other antibiotics were disproportionally affected by OprD loss, possibly due to an increased exposure to carbapenems. Integration of PorinPredict into genomic surveillance platforms will facilitate a better understanding of the clinical impact of OprD modifications and transmission dynamics of resistant clones.

Comparison of Lateral Flow Immunochromatography and Phenotypic Assays to PCR for the Detection of Carbapenemase-Producing Gram-Negative Bacteria, a Multicenter Experience in Mexico

The identification of carbapenemase-producing Enterobacterales and Pseudomonas aeruginosa is important for treating and controlling hospital infections. The recommended methods for their identification require a long waiting time, technical training, and expertise. Lateral flow immunoassays such as NG-Test CARBA 5^® overcome these needs. We analyzed 84 clinical isolates of carbapenem-resistant Enterobacterales and P. aeruginosa from four different hospitals in a two-year period. Antimicrobial resistance patterns were confirmed with the broth dilution method. Evaluation of KPC, VIM, NDM, IMP, and OXA-48-like enzymes was performed and compared to NG-Test CARBA 5 and phenotypic assays. Enterobacterales represented 69% of isolates and P. aeruginosa represented 31%. Carbapenemase-producing strains were 51 (88%) of Enterobacterales and 23 (88.4%) of P. aeruginosa; 20 (34%) and 23 (88%) were Class B ß-lactamases, respectively. The NG-Test CARBA 5^® assay for Enterobacterales showed high sensitivity (98%), specificity (100%), and PPV (100%); however, it did not for P. aeruginosa. The Kappa concordance coefficient was 0.92 for Enterobacterales and 0.52 for P. aeruginosa. NG-Test CARBA 5^® is a fast and easy-to-use assay. In Enterobacterales, we found excellent agreement in our comparison with molecular tests. Despite the low agreement in P. aeruginosa, we suggest that this test could be used as a complementary tool.

ST11 Carbapenem-Resistant Klebsiella pneumoniae Clone Harboring blaNDM Replaced a blaKPC Clone in a Tertiary Hospital in China

The nosocomial spread of carbapenem-resistant Enterobacterales (CRE) is extremely common, resulting in severe burdens on healthcare systems. In particular, the high-risk Klebsiella pneumoniae ST11 strain has a wide endemic area in China. The current study describes the results of continuous monitoring of CRE genotypes and phenotypes in a tertiary hospital in North China from 2012 to 2020. A total of 160 isolates were collected, including 109 Klebsiella. pneumoniae (68.13%), 29 Escherichia coli (26.60%), 12 Enterobacter cloacae (7.50%), and 10 other strains (6.25%). A total of 149 carbapenemase genes were detected, of which blaKPC-2 (51.0%) was the most common, followed by blaNDM-1 (22.82%), and blaNDM-5 (23.49%). Based on multi-locus sequence typing, the ST11 strain (66.1%) dominates K. pneumoniae, followed by ST15 (13.8%). Interestingly, the proportion of blaNDM (22.2%, 16/72) in ST11 K. pneumoniae was significantly increased in 2018−2019. Hence, whole-genome sequencing was performed on ST11 K. pneumoniae. Growth curves and in vitro competition experiments showed that K. pneumoniae carrying blaNDM exhibited a stronger growth rate (p < 0.001) and competition index (p < 0.001) than K. pneumoniae carrying blaKPC. Moreover, K. pneumoniae carrying blaNDM had a stronger biofilm-forming ability than K. pneumoniae carrying blaKPC (t = 6.578; p < 0.001). K. pneumoniae carrying blaKPC exhibited increased defense against bactericidal activity than K. pneumoniae carrying blaNDM. Thus, ST11 K. pneumoniae carrying blaNDM has strong adaptability and can locally replace K. pneumoniae carrying blaKPC to become an epidemic strain. Based on these findings, infection control and preventive measures should focus on the high-risk ST11-K. pneumoniae strain.