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Infections caused by carbapenemase-producing organisms are a global health threat. IMP carbapenemases are one of the key drivers of these infections but little is known regarding their global epidemiology. We analyse three decades of bla IMP gene spread using sequence data from 4556 genomes collected between 1996–2023. A total of 52 bla IMP variants were identified across 93 bacterial species. We reconstruct the historical emergence and variant-specific epidemiologies of bla IMP genes and showed how key variants ( bla IMP-1 , bla IMP-4 , bla IMP-7 , bla IMP-8 and bla IMP-13 ) achieved global endemicity, while bla IMP-26 and bla IMP-27 became regionally endemic in Southeast Asia and North America, respectively. Dissemination was driven predominantly by horizontal gene transfer facilitated by mobile genetic elements such as class 1 integrons and insertion sequences. These elements mobilised bla IMP genes into 52 distinct plasmid clusters (predominantly IncHI2A, IncN, IncL/M, and IncC), enabling broad inter-species transmission. Despite limited overall cross-source transmission, spillover primarily occurred between human and environmental reservoirs. Structural analysis revealed conserved IMP carbapenemase structure (mean lDDT 0.977) with convergent missense mutations at seven catalytically relevant sites. Our analysis provides a framework for understanding bla IMP dissemination, highlighting their emergence as an important, yet under-recognised, public health threat. Carbapenemase blaIMP genes between 1996-2023 were analysed across 4,556 genomes, revealing variant-specific epidemiologies and endemicities. Horizontal gene transfer enabled broad inter-species transmission, along with expansion of successful clones.

Infections by multidrug-resistant bacteria (MDRB) remain a leading cause of morbidity and mortality after liver transplantation (LT). Gut dysbiosis characteristic of end-stage liver disease may predispose patients to intestinal MDRB colonization and infection, in turn exacerbating dysbiosis. However, relationships between MDRB colonization and dysbiosis after LT remain unclear. We prospectively recruited 177 adult patients undergoing LT at a single tertiary care center. 16 S V3-V4 rRNA sequencing was performed on 723 fecal samples collected pre-LT and periodically until one-year post-LT to test whether MDRB colonization was associated with decreased microbiome diversity. In multivariate linear mixed-effect models, MDRB colonization predicts reduced Shannon α-diversity, after controlling for underlying liver disease, antibiotic exposures, and clinical complications. Importantly, pre-LT microbial markers predict subsequent colonization by MDRB. Our results suggest MDRB colonization as a major, previously unrecognized, marker of persistent dysbiosis. Therapeutic approaches accounting for microbial and clinical factors are needed to address post-transplant microbiome health. In a large prospective cohort of liver transplantation (LT) recipients, the authors identify associations between colonization by multidrug-resistant bacteria (MDRB) and microbiome dysbiosis pre- and post-LT, suggesting colonizing MDRB as an important target for microbiome-informed therapeutic approaches post-LT.

Infections caused by metallo-beta-lactamase-producing organisms (MBLs) are a global health threat. Our understanding of transmission dynamics and how MBLs establish endemicity remains limited. We analysed two decades of bla IMP-4 evolution in a hospital using sequence data from 270 clinical and environmental isolates (including 169 completed genomes) and identified the bla IMP-4 gene across 7 Gram-negative genera, 68 bacterial strains and 7 distinct plasmid types. We showed how an initial multi-species outbreak of conserved IncC plasmids (95 genomes across 37 strains) allowed endemicity to be established through the ability of bla IMP-4 to disseminate in successful strain-genetic setting pairs we termed propagators, in particular Serratia marcescens and Enterobacter hormaechei . From this reservoir, bla IMP-4 persisted through diversification of genetic settings that resulted from transfer of bla IMP-4 plasmids between bacterial hosts and of the integron carrying bla IMP-4 between plasmids. Our findings provide a framework for understanding endemicity and spread of MBLs and may have broader applicability to other carbapenemase-producing organisms. Resistance to carbapenems, a class of last-line antibiotics, is a global health threat. This study analysed a two-decade history of carbapenem resistance and identified complex, multi-level (bacterial strain, plasmid, gene) transmission dynamics.

Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics. Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P =  0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P =  0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance. IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.

Pseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

Recent reports have established the escalating threat of carbapenem-resistant Enterobacter cloacae complex (CREC). Here, we demonstrate that CREC has evolved as a highly antibiotic-resistant rather than highly virulent nosocomial pathogen. Applying genomics and Bayesian phylogenetic analyses to a 7-year collection of CREC isolates from a northern Manhattan hospital system and to a large set of publicly available, geographically diverse genomes, we demonstrate clonal spread of a single clone, ST171. We estimate that two major clades of epidemic ST171 diverged prior to 1962, subsequently spreading in parallel from the Northeastern to the Mid-Atlantic and Midwestern United States and demonstrating links to international sites. Acquisition of carbapenem and fluoroquinolone resistance determinants by both clades preceded widespread use of these drugs in the mid-1980s, suggesting that antibiotic pressure contributed substantially to its spread. Despite a unique mobile repertoire, ST171 isolates showed decreased virulence in vitro . While a second clone, ST78, substantially contributed to the emergence of CREC, it encompasses diverse carbapenemase-harboring plasmids, including a potentially hypertransmissible IncN plasmid, also present in other sequence types. Rather than heightened virulence, CREC demonstrates lineage-specific, multifactorial adaptations to nosocomial environments coupled with a unique potential to acquire and disseminate carbapenem resistance genes. These findings indicate a need for robust surveillance efforts that are attentive to the potential for local and international spread of high-risk CREC clones. IMPORTANCE Carbapenem-resistant Enterobacter cloacae complex (CREC) has emerged as a formidable nosocomial pathogen. While sporadic acquisition of plasmid-encoded carbapenemases has been implicated as a major driver of CREC, ST171 and ST78 clones demonstrate epidemic potential. However, a lack of reliable genomic references and rigorous statistical analyses has left many gaps in knowledge regarding the phylogenetic context and evolutionary pathways of successful CREC. Our reconstruction of recent ST171 and ST78 evolution represents a significant addition to current understanding of CREC and the directionality of its spread from the Eastern United States to the northern Midwestern United States with links to international collections. Our results indicate that the remarkable ability of E. cloacae to acquire and disseminate cross-class antibiotic resistance rather than virulence determinants, coupled with its ability to adapt under conditions of antibiotic pressure, likely led to the wide dissemination of CREC. Carbapenem-resistant Enterobacter cloacae complex (CREC) has emerged as a formidable nosocomial pathogen. While sporadic acquisition of plasmid-encoded carbapenemases has been implicated as a major driver of CREC, ST171 and ST78 clones demonstrate epidemic potential. However, a lack of reliable genomic references and rigorous statistical analyses has left many gaps in knowledge regarding the phylogenetic context and evolutionary pathways of successful CREC. Our reconstruction of recent ST171 and ST78 evolution represents a significant addition to current understanding of CREC and the directionality of its spread from the Eastern United States to the northern Midwestern United States with links to international collections. Our results indicate that the remarkable ability of E. cloacae to acquire and disseminate cross-class antibiotic resistance rather than virulence determinants, coupled with its ability to adapt under conditions of antibiotic pressure, likely led to the wide dissemination of CREC.

Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen Klebsiella pneumoniae and improve recovery from human stool samples. We found our enrichment method to significantly outperform traditional methods, generating far more data originating from our target genes. Additionally, we developed new computational techniques to further enhance the analysis, providing a thorough method for characterizing pathogens from complex biological samples.

Background Third-generation cephalosporin-resistant Gram-negatives (3GCR-GN) and vancomycin-resistant enterococci (VRE) are common causes of multi-drug resistant healthcare-associated infections, for which gut colonisation is considered a prerequisite. However, there remains a key knowledge gap about colonisation and infection dynamics in high-risk settings such as the intensive care unit (ICU), thus hampering infection prevention efforts. Methods We performed a three-month prospective genomic survey of infecting and gut-colonising 3GCR-GN and VRE among patients admitted to an Australian ICU. Bacteria were isolated from rectal swabs ( n  = 287 and n  = 103 patients ≤2 and > 2 days from admission, respectively) and diagnostic clinical specimens between Dec 2013 and March 2014. Isolates were subjected to Illumina whole-genome sequencing ( n  = 127 3GCR-GN, n  = 41 VRE). Multi-locus sequence types (STs) and antimicrobial resistance determinants were identified from de novo assemblies. Twenty-three isolates were selected for sequencing on the Oxford Nanopore MinION device to generate completed reference genomes (one for each ST isolated from ≥2 patients). Single nucleotide variants (SNVs) were identified by read mapping and variant calling against these references. Results Among 287 patients screened on admission, 17.4 and 8.4% were colonised by 3GCR-GN and VRE, respectively. Escherichia coli was the most common species ( n  = 36 episodes, 58.1%) and the most common cause of 3GCR-GN infection. Only two VRE infections were identified. The rate of infection among patients colonised with E. coli was low, but higher than those who were not colonised on admission ( n  = 2/33, 6% vs n  = 4/254, 2%, respectively, p  = 0.3). While few patients were colonised with 3GCR- Klebsiella pneumoniae or Pseudomonas aeruginosa on admission ( n  = 4), all such patients developed infections with the colonising strain. Genomic analyses revealed 10 putative nosocomial transmission clusters (≤20 SNVs for 3GCR-GN, ≤3 SNVs for VRE): four VRE, six 3GCR-GN , with epidemiologically linked clusters accounting for 21 and 6% of episodes, respectively (OR 4.3, p  = 0.02). Conclusions 3GCR- E. coli and VRE were the most common gut colonisers. E. coli was the most common cause of 3GCR-GN infection, but other 3GCR-GN species showed greater risk for infection in colonised patients. Larger studies are warranted to elucidate the relative risks of different colonisers and guide the use of screening in ICU infection control.

Liver transplant (LT) recipients have high rates of Clostridioides difficile infection (CDI), which has been associated with poor outcomes, including graft-related complications and mortality, in prior studies. Susceptibility to CDI is known to increase following perturbations in intestinal commensal bacteria that enable germination of C. difficile spores and bacterial overgrowth. Clostridioides difficile infection (CDI) has a higher incidence in solid organ transplant recipients than other hospitalized patients and can lead to poor outcomes. Perturbations to the intestinal microbiome are common in patients undergoing liver transplant (LT); however, the impacts of microbial diversity and composition on risk of CDI in this patient population is incompletely understood. Here, we assessed patients in an established, longitudinal LT cohort for development of CDI within 1 year of transplant. Clinical data were compared for patients with and without CDI using univariable models. 16S rRNA sequencing of fecal samples was performed at multiple pre- and posttransplant time points to compare microbiome α- and β-diversity and enrichment of specific taxa in patients with and without CDI. Of 197 patients who underwent LT, 18 (9.1%) developed CDI within 1 year. Pre-LT Child-Pugh class C liver disease, postoperative biliary leak, and use of broad-spectrum antibiotics were significantly associated with CDI. Patients who developed CDI had significantly lower α-diversity than patients without CDI overall and in samples collected at months 1, 3, and 6. Microbial composition (β-diversity) differed between patients with and without CDI and across sampling time points, particularly later in their posttransplant course. We also identified 15 (8%) patients with toxigenic C. difficile colonization who did not develop CDI and may have had additional protective factors. In summary, clinical and microbiome factors are likely to converge to impart CDI risk. Along with enhanced preventive measures, there may be a role for microbiome modulation to restore microbial diversity in high-risk LT patients. IMPORTANCE Liver transplant (LT) recipients have high rates of Clostridioides difficile infection (CDI), which has been associated with poor outcomes, including graft-related complications and mortality, in prior studies. Susceptibility to CDI is known to increase following perturbations in intestinal commensal bacteria that enable germination of C. difficile spores and bacterial overgrowth. In LT patients, changes in the intestinal microbiome resulting from advanced liver disease, surgery, and other clinical factors is common and most pronounced during the early posttransplant period. However, the relationship between microbiome changes and CDI risk after LT remains unclear. In this study, we investigated clinical and microbiome factors associated with development of CDI within the first year after LT. The importance of this work is to identify patients with high-risk features that should receive enhanced preventive measures and may benefit from the study of novel strategies to reconstitute the intestinal microbiome after LT.

Multidrug-resistant Gram-negative bacterial infections cause significant morbidity and mortality globally. These pathogens easily acquire antimicrobial resistance (AMR), further highlighting their clinical significance. Third-generation cephalosporin-resistant and carbapenem-resistant Enterobacterales (eg, Escherichia coli and Klebsiella spp), multidrug-resistant Pseudomonas aeruginosa, and carbapenem-resistant Acinetobacter baumannii are the most problematic and have been identified as priority pathogens. In response, several new diagnostic technologies aimed at rapidly detecting AMR have been developed, including biochemical, molecular, genomic, and proteomic techniques. The last decade has also seen the licensing of multiple antibiotics that have changed the treatment landscape for these challenging infections.