npj Antimicrobials and Resistance

The rapid rise of multidrug-resistant (MDR) bacterial infections has severely limited treatment options, particularly for Gram-negative pathogens such as Klebsiella pneumoniae, a leading contributor to pneumonia, bloodstream, urinary tract, and surgical-site infections. One strategy to restore antibiotic efficacy is the use of resistance-mitigating agents (RMAs), compounds that re-sensitize bacteria to existing antibiotics without displaying independent antibacterial activity. Herein, we report the results of a high-throughput screen of a 3,200-compound fragment-based library against an MDR K. pneumoniae isolate in the presence of subinhibitory ciprofloxacin. This screen identified a tetrahydrocarbazole-containing compound, 1, as a ciprofloxacin potentiator. Subsequent structure-activity relationship studies yielded a difluorinated analog, compound 5, which potentiated multiple antibiotic classes in MDR K. pneumoniae, reducing MICs up to [≥]16 fold. Further testing demonstrated synergistic interactions between compound 5 and ciprofloxacin, ceftriaxone, cefoxitin, and tetracycline across four genetically diverse MDR K. pneumoniae strains. These findings suggest that tetrahydrocarbazole-containing compounds constitute a promising new class of RMAs with potential for future development as therapies against MDR K. pneumoniae infections.

Antimicrobial resistance generates a vast, rapidly growing literature, yet no resource offers a comprehensive, evidence-linked repository of AMR findings at scale. We present ResLit, an automated pipeline and public database that mines the AMR literature for resistance genes, mutations, organisms, and mechanisms. From 2 million candidate PubMed records, BioMistral-7B screened abstracts to 356,000 relevant papers; multi-tier retrieval yielded 117,000 full texts, from which Qwen3-30B performed two-step extraction. ResLit contains 3,120 genes and 13,593 mutations, cross-linked to CARD, ResFinder, and NCBI Reference Gene Catalog across four evidence tiers. It further supports community-driven curation of automated outputs and reference databases. Freely available at www.reslit.info.

Background Antimicrobial-resistant Gram-negative bacteria pose a major threat to global health. In the WHO Bacterial Priority Pathogen List (BPPL) 2024, several species were classified as critical or high priority for research and development of new antimicrobial therapeutics and diagnostics. However, access to well-characterised and clinically relevant bacterial isolate panels remains an unmet need for development and validation. Methods We constructed five panels of Gram-negative bacterial isolates, cultured from samples originally referred to the UK Health Security Agency Antimicrobial Resistance and Healthcare-Associated Infections Reference Unit for analysis between 2014 and 2025. For each isolate, antimicrobial susceptibilities were determined against a range of clinically relevant antibiotics, including third-generation cephalosporins, carbapenems, colistin and other last-line antibiotics. WGS was performed on all isolates, and previously described resistance determinants were characterised. Results The five panels consist of 145 isolates and represent 81 sequence types across Escherichia coli (30 isolates), Klebsiella pneumoniae (30 isolates), carbapenem-resistant Acinetobacter baumannii (30 isolates), carbapenem-resistant Pseudomonas aeruginosa (30 isolates), and a mixed panel of other healthcare-associated Enterobacterales species (25 isolates). Panels are highly representative of globally relevant strains, including those currently in circulation and on the WHO BPPL 2024, and of resistance mechanisms of public health importance. Conclusion The panels provide a diverse, comprehensive set of multidrug-resistant Gram-negative bacterial isolates, representative of strains currently in circulation globally. Selected isolates carry a breadth of important resistance mechanisms and are highly relevant to the current epidemiological landscape. The isolates and associated metadata are available to industry, academia, and other laboratories for use in developing novel antimicrobial compounds and diagnostic assays.

The emergence of drug resistance in Mycobacterium tuberculosis (M.tb) presents a major challenge to drug discovery, as compounds active against drug-susceptible (DS) isolates may not retain comparable inhibitory activity against drug-resistant (DR) isolates. Here, we developed an AI/ML workflow to independently prioritize small molecules separately against DS and DR isolates of M.tb and predict MIC, MIC90, and MIC99 as distinct quantitative endpoints. Following curation of data retrieved from the ChEMBL repository, molecular-activity datasets comprised 25,645 and 3,749 compounds tested against whole cell DS and DR isolates respectively. Molecular representations were generated using RDKit descriptors and Morgan fingerprints, along with embeddings from ChemBERTa, MolCLR, and Uni-Mol, and evaluated for binary activity classification and endpoint-specific regression. The best-performing DS classifier combined RDKit/Morgan features with ExtraTrees (balanced accuracy 0.782; AUROC 0.864; AUPRC 0.870), while histogram gradient boosting using the same features performed best for DR classification (balanced accuracy 0.762; AUROC 0.836; AUPRC 0.880). Similarly, RDKit/Morgan features combined with tree-based regressors achieved the best overall performance across all six DS/DR regression endpoints, with pooled out-of-fold RMSE values ranging from 0.604 to 0.784 pMIC units. To facilitate broader application, we developed MtbAIM (https://github.com/jasdeep002/MtbAIM), a Google Colab-based prediction platform that enables users to assess and prioritize small molecules against DS and DR isolates of M.tb.

Fosfomycin is increasingly used to treat multidrug-resistant Gram-negative infections, yet resistance surveillance largely relies on homology-based detection of known fos genes. Here, we identify Gfa, a previously unrecognized integron-borne fosfomycin resistance determinant uncovered through functional screening of 129 integron gene cassettes. Gfa confers high-level fosfomycin resistance across Enterobacterales and Pseudomonas aeruginosa and is resistant to phosphonoformate inhibition. Structural modelling and mutagenesis reveal a conserved cysteine-rich, zinc-organized architecture essential for activity and distinct from canonical Fos enzymes. Crude-extract assays confirm glutathione-dependent fosfomycin inactivation under metal-chelating conditions. Distant Gfa homologs also conferred resistance, although activity was restricted to a subset of proteins tested. Together, these findings establish Gfa as a previously unrecognized fosfomycin resistance family and highlight the limitations of homology-based mining for discovering novel resistance determinants and predicting which family members are functional.

Klebsiella pneumoniae is a WHO critical-priority pathogen for which strain-specific bacteriophages are being explored as precision antimicrobials, yet rapid phage-host matching remains a major barrier to therapeutic deployment. We constructed a comprehensive interaction atlas comprising 84 taxonomically diverse phages and 101 globally sourced, clinically representative K. pneumoniae strains, including multidrug-resistant isolates. Systematic pairwise profiling produced 8,484 interaction measurements, of which 2,656 (31.3%) scored positive for bacterial clearance. Genus was the dominant phage-side determinant of host range, while capsule K-serotype was the strongest host-side determinant of susceptibility; aggregate defense, prophage, plasmid, and antimicrobial-resistance features contributed comparatively little. A genome-guided machine learning model predicted interactions without curated host annotations (AUROC, 0.882; AUPR, 0.765), outperforming a model based only on phage genus and K-serotype and modestly exceeding a curated genomic baseline. The model recovered capsule- and lipopolysaccharide-biosynthesis genes, canonical receptors and defense-associated features as major predictors using SHAP analysis. Feasibility tests of expert- and model-selected cocktails exposed a translational constraint. Although all formulations suppressed growth in vitro, only the specific cocktail whose phages replicated robustly within the murine gut reduced colonization, suggesting in vivo amplification rather than predicted host range as the limiting factor for therapeutic efficacy. Together with the activity of a model-selected cocktail built for an isolate completely excluded from training, these results provide a species-wide resource for K. pneumoniae phage matching and support a hybrid workflow combining genome-based ranking with targeted phenotypic validation.

Nitrofurantoin is commonly prescribed as a first-line treatment for urinary tract infections (UTIs) and is effective against most uropathogens; however, resistance among Klebsiella spp. remains high. In this study, we investigated synergistic drug combinations--defined as pairs of drugs whose combined effect exceeds that of each agent alone--to enhance treatment efficacy against drug-resistant Klebsiella pneumoniae. A high-throughput drug screen identified six and two small molecules that exhibited >50% synergistic activity in tested strains in combination with nitrofurantoin and ciprofloxacin, respectively. We further validated the top three candidates using nitrofurantoin-susceptible and resistant clinical isolates. Notably, the combination of nitrofurantoin and dequalinium demonstrated strong synergistic activity, observed in 68% of susceptible Escherichia coli strains and 94% of resistant Klebsiella pneumoniae strains tested. Combined inhibition of the citric acid cycle by nitrofurantoin and F1-ATPase by dequalinium resulted in a significant reduction in ATP levels compared to either treatment alone. Importantly, decreased ATP levels did not increase persister cell formation, and dequalinium alone reduced persister cell populations more effectively than nitrofurantoin. However, nitrofurantoin is processed into a poorly understood reactive intermediate whose specific metabolic targets have not been fully elucidated. Using metabolomic analyses, we identified aconitase and isocitrate dehydrogenase--key enzymes in the citric acid cycle--as altered in response to nitrofurantoin. Together, these findings demonstrate that dual targeting of bacterial metabolism by nitrofurantoin and dequalinium represents a promising therapeutic strategy for treating drug-resistant Klebsiella spp. in UTIs.

Screening of the open-access CRESTdb small-molecule library identified the imidazothiazole carboxamide sCQG200 (1) as an initial hit, with activity against H37Rv and Erdman Mycobacterium tuberculosis strains in cholesterol-containing medium (MIC90= 6.88 uM and 4.48 uM, respectively) and against intracellular Mtb (EC50= 3.80 uM). sCQG200 retained activity against drug-resistant Mtb strains, including clinical MDR/XDR isolates. SAR optimization led to analogs 21 and 24 with substantially improved antimycobacterial potency. Mouse pharmacokinetic studies following oral and intravenous administration showed 62.1% and 29.5% oral bioavailability for 21 and 24, respectively. This structurally differentiated chemotype provides a promising starting point for further optimization as antitubercular agent.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a leading global health threat, especially due to multidrug-resistant strains. The first-line drug isoniazid (INH) and second-line drugs ethionamide (ETH) and prothionamide (PTH), are prodrugs that target the essential enoyl-ACP reductase InhA, a key enzyme involved in mycolic acid biosynthesis. Resistance to these drugs primarily arises from genetic mutations that inactivate the enzymatic function of their respective bioactivating enzymes. While the recent development of chemical boosters has shown promising results in restoring drug susceptibility in these mutants, genetic tools to dynamically modulate these pathways and fine-tune resistance could be extremely valuable to develop the next-generation of anti-InhA drugs. In this context, we leverage CRISPR interference (CRISPRi) to develop inducible and reversible genetic systems to fine-tune antibiotic susceptibility in Mycobacterium smegmatis (Msmeg) and Mtb. By using our CRISPRi-based approach we first generated an inducible inhA hypomorph, an essential target required for mycolic acid production and bacterial growth, therefore confirming that our method is fully functional. Then, we developed genetic tools to selectively target katG, ethA, and the transcriptional repressor ethR, in order to generate conditional hypomorphs with altered antibiotic susceptibility profiles. Antibiotic susceptibility assays confirmed that the conditional hypomorphs selectively displayed high-level resistance or hypervulnerability to INH or ETH upon CRISPRi induction. Altogether, this work establishes CRISPRi as a powerful genetic platform for selectively fine-tuning mycobacterial drug susceptibility, with potential applications in developing next-generation anti-TB therapies.

The standard numbering scheme for class B beta-lactamases (metallo-beta-lactamases, MBLs), the BBL scheme, was proposed by Galleni et al. in 2001 and updated by Garau et al. in 2004 from the small number of MBL structures then solved. Hundreds of additional class B sequences have since been catalogued, but most have never been structurally solved, so whether BBL correspondences hold across this larger diversity has not been directly tested. Following the precedent set for the sibling class D enzymes, we used ColabFold (AlphaFold2/MMseqs2) to model 87 class B families across subclasses B1, B2, and B3 lacking an experimental structure, after validating the pipeline against an eight-enzyme benchmark spanning all three subclasses (RMSD to experimental structures 0.35-0.46 A). Applying BBL zinc-ligand numbering to the 87 models via paired sequence- and structure-based residue-transfer methods, the canonical zinc-coordination motif transferred cleanly in the large majority of families, and 11 of the 16 sequence-level exceptions were accounted for by a literature-supported biological cause (a subclass-appropriate substitution or a previously proposed, unconfirmed fourth subgroup), while two motif-retaining families showed predicted local structural divergence at the active-site loop, flagged by structural superposition, that could not be resolved to exact zinc-ligand numbering and warrants direct experimental follow-up. These results show that the BBL scheme, formulated before metagenomic sequencing or deep-learning structure prediction existed, generalizes robustly into the structural space these technologies have since opened, and identify a small set of variants whose active sites merit experimental attention.

We previously identified a series of heterobenzamides (HBAs) with potent growth inhibitory activity against Mycobacterium tuberculosis in axenic culture. We also provided evidence that these target QcrB, a component of the terminal cytochrome oxidase in the electron transport chain. We expanded our studies to look at the full microbiological profile: key molecules from the series were tested for activity under different conditions and against additional strains. HBA analogs were active against intracellular bacteria where they exhibited bacteriostatic activity. A strain of M. tuberculosis with a mutation in QcrB (T313I) was resistant to HBAs in both axenic culture and inside macrophages. HBAs retained potency against lineages and mono-resistant strains of M. tuberculosis. HBAs had a narrow spectrum of activity, since they were not active against the ESKAPEE pathogens. Combination of the key HBA with bedaquiline was synergistic, as expected for a QcrB inhibitor, but there was no strong synergy with other drugs. Exposure of M. tuberculosis to the key HBA led to ATP depletion and boosted the oxygen consumption rate. This effect was specific to M. tuberculosis, since human THP-1 macrophage-like cells were unaffected by exposure to the HBA. HBA did not induce the production of reactive oxygen species or affect membrane potential but did affect pH homeostasis. Taken together, these data provide further evidence to support the identification of QcrB as the target and indicate that they are suitable for further drug development.

CRISPR-Cas12a-based diagnostics are promising for field detection yet are often constrained by two step workflows or laborious optimization of one-pot reactions. Herein, we present a universal one-pot RPA/CRISPR-Cas12a strategy using base-mismatched crRNA in the seed region (positions 1-6). Introducing of single-nucleotide mismatches (complementary or error) into crRNA suppresses cis-cleavage activity, enabling amplification and detection in a single tube without PAM re-design or costly modified crRNA. Molecular dynamics (MD) simulations indicated that moderate potential energy (PE) variation correlates with improved screening performance, providing guidance for the selection of effective mismatches. This strategy successfully converted established two-step assays for Burkholderia pseudomallei and Brucella into one-pot formats, achieving detection of targets at 10 copies/L within 30 min with good specificity. Lyophilized pellets supported field testing on lithium-ion battery-powered devices, and experiments with simulated soil samples confirm the robustness of the approach. Together, this cost-effective, adaptable strategy can streamline RPA-CRISPR diagnostics for infectious disease surveillance.

Background: Patients with intracerebral hemorrhage (ICH) in intensive care units (ICUs) frequently receive antibiotics early in critical illness. Such exposure reflects suspected infection, physiological deterioration, monitoring intensity, and clinical management and may vary across institutions and electronic health record databases. We evaluated target-domain adaptation for modeling sustained antibiotic exposure in critically ill patients with ICH. Methods: We analyzed derived ICH cohorts from MIMIC-IV and the eICU Collaborative Research Database. MIMIC-IV included 2,024 ICU stays and served as the source cohort. The eICU cohort comprised 1,277 stays and was divided, at the ICU-stay level, into a development subset (766 stays) and a held-out evaluation subset (511 stays). The eICU outcome was sustained antibiotic exposure on at least two ICU days. Candidate models incorporated demographic, comorbidity, physiological, laboratory, neurologic, respiratory, temporal, and measurement-process features. Timestamped observations for static and temporal tabular families were censored at the recorded first-antibiotic time when available, whereas the compact sequence component and explicit measurement-density features used the full first 24 hours. For base-model families using eICU development data, meta-learner inputs were generated by five-fold stratified cross-fitting. A 42-input L2-penalized logistic meta-learner combined 16 model probabilities with model-summary, clinical, availability, and measurement-density features. Results: The held-out eICU subset contained 48 positive outcomes (9.39%). The adapted ensemble achieved an area under the receiver operating characteristic curve (AUROC) of 0.841 (95% CI 0.775-0.899) and an average precision (AP) of 0.540 (95% CI 0.399-0.676), with a Brier score of 0.063 and expected calibration error of 0.029. The MIMIC-IV source-only stack achieved an AUROC of 0.782; the paired AUROC difference was 0.059 (95% CI 0.003-0.112). In a separate patient-grouped five-fold out-of-fold sensitivity analysis of 503 linked eICU stays, AUROC was 0.807 (95% CI 0.691-0.910). Conclusions: Target-domain adaptation improved held-out discrimination relative to a source-only stack for sustained antibiotic exposure. Because some model components and measurement-process features used information from the broader first 24 hours, the model should be interpreted as a first-24-hour treatment-association model rather than a strictly prospective pre-treatment infection predictor.

Streptococcus pyogenes remains clinically susceptible to {beta}-lactam antibiotics, its first-line treatment. Yet recent reports from the US, Iceland, and Japan have identified isolates with PBP2x (a primary {beta}-lactam target) substitutions that reduced antibiotic susceptibility. To determine whether these PBP2x substitutions occurred in S. pyogenes from England and Wales, we re-analysed 2,970 invasive genomes from 2001 to 2015. We found 42 PBP2x sequence mutants in 34% (996/2,970) of the genomes, revealing an unexpected diversity in this antibiotic target. Nine emm12 isolates carried a novel double substitution, PBP2x G600A_P601H, that co-occurred with macrolide resistance genes. Experimentally, these isolates showed 2- to 4-fold elevated minimum inhibitory concentrations for four {beta}-lactam antibiotics, alongside erythromycin resistance. Other PBP2x substitutions, found across seven emm types without reduced antibiotic susceptibility, appeared driven by emm lineage expansion rather than antibiotic pressure. These findings establish a baseline for pbp2x variation, reinforcing the case for sustained genomic surveillance globally.

In this technical report, we introduce NO_SCPLOWESSOC_SCPLOW-1, a coarse-grained cofolding framework for binding- affinity prediction. NO_SCPLOWESSOC_SCPLOW-1 requires[~] 1 second per prediction on a single GPU. This offers more than one order of magnitude speed-up over the leading open-source baseline, Boltz-2, which significantly expands the regions of chemical space that can be explored during high-throughput virtual screening. Importantly, NO_SCPLOWESSOC_SCPLOW-1 matches or surpasses the accuracy of Boltz-2 over the same benchmarks adopted in their study--which we show reflect in-distribution scenarios--as well as over more challenging out-of-distribution data encompassing the OpenBind affinity benchmark and 25 internal biochemical assays. Notably, NO_SCPLOWESSOC_SCPLOW-1 maintains robust predictive accuracy even on assays with extremely low similarity to the training data. Moreover, we highlight examples where NO_SCPLOWESSOC_SCPLOW-1 demonstrates meaningful selectivity, separating the binding affinities of identical compounds between on-targets and related off-targets. Nonetheless, zero-shot generalization to real- world medicinal chemistry remains an inherently challenging task; consequently, we acknowledge specific assays where the models performance is limited. We open-source NO_SCPLOWESSOC_SCPLOW-1: code and weights are available at https://github.com/recursionpharma/nesso

Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.

The incidence of infections caused by multidrug-resistant bacterial agents has increased at an alarming rate worldwide. With the aim of identifying novel antimicrobial peptides (AMPs), we screened bacteria isolated from various environmental samples. Our hypothesis was that the sequence space of natural AMPs belonging to known AMP classes is far from saturation. We used a classical pipeline of bacterial culturing, overlay assays, extraction and fractionation to purify AMPs for identification. Using LC/MS analysis, the structure of AMPs isolated from a strain of Paenibacillus was narrowed down and a new subclass of polymyxins was uncovered. These harbor three aliphatic residues within the cyclic C-terminal, and in certain cases Ser replaces Thr at position A2. Four discrete polymyxins fitting the new class, but not identical to the natural polymyxins were synthesized and tested against 29 human pathogenic bacteria. Each displayed an antibacterial spectrum different from that of colistin, with the best candidate surpassing it in potency against ten bacterial strains, but underperforming against three others. These results demonstrate that current screening of natural bacterial isolates can still permit the design of novel antimicrobial peptide variants without the substantial threat of diminishing returns.

Understanding how molecular structure encodes biological function remains a grand challenge in drug discovery. Here, we present PubCheF-1, a deep learning model that predicts literature-derived biological function directly from chemical structure. PubCheF-1 was trained on a dataset linking molecules to labels derived from the scientific articles in which they appear, a strategy that connects disparate compounds through the language used to describe their functionalities. When tasked with identifying inhibitors of {beta}-lactamases, including enzymes considered largely refractory to inhibition, PubCheF-1 predicted structurally distinct compounds that collectively have activity against all {beta}-lactamase classes. Furthermore, hit compounds directly bind the enzyme active site, restore antibiotic efficacy in multidrug-resistant high-priority pathogens, and demonstrate potent activity in animal infection models. Together, these findings establish that machine learning-based prediction of biological function derived from the language of scientific literature allows the identification of bioactive molecules at high hit rates, thereby accelerating therapeutic discovery.

Ribosomally synthesized host defense peptides (HDPs) are promising candidates for novel antibiotics. However, non-lytic HDPs, which target intracellular machinery, remain underexploited due to limitations including low potency in serum and narrow activity spectra. To enhance their therapeutic profile, we fused non-lytic HDPs generating "chimerophores" with multimodal mechanisms of action (MOAs). Using a high-throughput self-screening platform (Mex), we synthesized and evaluated a combinatorial library of 99,235 variants, identifying over 30,300 active chimerophores, vastly expanding the functional space of chimeric HDPs. Functional screening of 18 chimerophores revealed candidates with potent, broad-spectrum activity displaying serum-tolerance, low cytotoxicity, orthogonal uptake pathways and multimodality, such as simultaneously targeting of ribosomes and DNA. Integrating these distinct mechanisms into a single molecule allowed lead candidate cp9 to suppress the emergence of resistance in Pseudomonas aeruginosa, establishing a scalable platform for the systematic engineering of next-generation multimodal antibiotics.

The global CTX-M pandemic is considered a plasmid-driven phenomenon, yet chromosomal integration of blaCTX-M has been reported in various settings. Screening 451,174 publicly available E. coli genomes and retaining 2,269 CTX-M-E. coli long-read or hybrid assemblies, we found that 33.5% carried at least one chromosomally integrated blaCTX-M. Chromosomal integration was strongly structured by E. coli lineage and CTX-M variant, reaching 68.5% in ST38 and 43.9% in ST131. In the latter, integration rates varied between subclades with modelling, suggesting recent acceleration within the ST131-C2 subclade. Additional resistance genes were frequently co-integrated with blaCTX-M (33%), with architectures largely inherited from plasmid-borne structures. Integrations were mainly ISEcp1- or IS26-mediated (77.3% and 18.9%, respectively) and occurred across the entire E. coli chromosome without spared zones. blaCTX-M chromosomal integration is a widespread, lineage-structured component of the CTX-M pandemic, providing stable vertical inheritance that complements or may supplant plasmid-mediated dissemination in successful ESBL-E. coli clones.