npj Antimicrobials and Resistance

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.

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.

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.

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.

Small open reading frames (sORFs) are a potentially rich, yet error-prone, source of antimicrobial-peptide (AMP) candidates: short sequences are readily prioritized by AMP classifiers but may derive from incomplete gene calls. We developed a genome-context-aware discovery workflow that separates AMP-like sequence properties from evidence for a complete, recurrent coding locus. From 649,653 RefSeq assemblies representing 327 clinically relevant bacterial species, species-aware clustering and length filtering yielded 4,442,548 representative 10-100-aa sequences. AmpScanner v2, Macrel and AMPlify identified 585 non-haemolytic records supported by all three models. However, genome-context auditing of 11,918 mapped candidates showed that 529 of 536 mapped consensus candidates were supported exclusively by partial ORFs near contig termini. By contrast, 3,382 candidates had at least one complete non-edge occurrence; 1,069 recurred in [≥]2 assemblies and 251 in [≥]10 assemblies. We therefore assembled a 20-peptide panel through two explicitly labelled routes: sequence/structure-led selection (n=8) and genome-supported selection (n=12). Broth microdilution against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 25923 identified low-micromolar activity in both routes. CAND_04141, a recurrent complete non-edge candidate, had the strongest combined profile (MICs of 4 and 2 M, respectively), while CAND_07825 and CAND_04265 were also active at low micromolar concentrations. In plate-count MBC assays, all three advanced peptides achieved [≥]3-log10 reductions at 128 M. These findings show that high classifier agreement is not a substitute for genomic evidence and provide an auditable framework for prioritizing both synthetic AMP-like sequences and candidate genome-encoded peptides.

Aminoglycosides are critical antibiotics with partially elucidated mechanisms of uptake and action in Gram-negative bacteria. Importantly, although energy-dependent uptake across the inner membrane has been well-established, the specific molecular mechanisms involved have not been definitively identified. To deepen understanding of genetic factors influencing susceptibility and resistance to streptomycin, we applied transposon insertion sequencing in Escherichia coli K-12. This approach identified both known and novel genes whose disruption increased susceptibility, including those involved in respiration, protein export, cell division, and uncharacterised functions. Notably, voltage-sensitive membrane dye-based assays revealed that many susceptible mutants did not display inner membrane hyperpolarisation as often assumed. Conversely, disruption of certain genes, such as the inner membrane antimicrobial peptide transporter sbmA, conferred low-level resistance, with sbmA overexpression increasing streptomycin sensitivity, suggesting its role in aminoglycoside uptake. These findings refine the model of aminoglycoside interaction with various pathways and highlight potential targets for adjuvant therapies to combat antimicrobial resistance.

The global spread of antimicrobial-resistant (AMR) bacteria represents one of the greatest threats to modern medicine, making the development of novel therapeutic strategies increasingly urgent. Phage therapy, which exploits bacteriophages (phages, viruses that specifically infect and kill bacteria) has regained attention as a therapeutic approach for multidrug-resistant infections. One critical determinant of treatment outcome is the capacity of phages to sustain bacterial growth suppression; however, no metric has previously existed to directly quantify the duration of effective lytic activity. Here, we propose the Sustainability Index (SusI), a novel metric that quantifies both the duration and extent of phage-mediated bacterial growth suppression, which is restricted to the primary lysis period from lysis initiation to resistance emergence. Evaluation of individual phages and two-phage cocktails against both laboratory and clinical isolates of Escherichia coli demonstrated that SusI provides information independent of the Virulence Index, which primarily reflects bactericidal activity during the initial phase of infection, and serves as a complementary metric to the Suppression Index, which may incorporate behavior beyond primary lysis. Cocktails composed of phages targeting different receptors specificities consistently exhibited higher SusI values, consistent with the notion that multifaceted selective pressure delays resistance emergence. Furthermore, in a mouse model of systemic infection established by intraperitoneal administration, cocktails with higher SusI values demonstrated superior therapeutic efficacy. These results confirm SusI as a practical metric for rational phage cocktail design. As phage therapy advances toward clinical implementation, standardized quantitative metrics such as SusI are expected to facilitate evidence-based selection of therapeutic phages across diverse pathogens and infection conditions. ImportanceThe global spread of antimicrobial-resistant bacteria is making bacterial infections increasingly difficult to treat. Phage therapy, which uses bacteriophages (viruses that specifically infect bacteria), has re-emerged as a therapeutic alternative; however, reliable methods to determine in advance which phages will be therapeutically effective remain limited. Current evaluation metrics are well-suited for quantifying how rapidly phages kill bacteria but were not designed to directly measure how long lytic activity is sustained before resistant bacteria emerge. Here, we developed the Sustainability Index (SusI), a novel metric that specifically quantifies the duration of effective bacterial growth suppression. Evaluation of multiple phages and their combinations (cocktails) against both laboratory and clinical bacterial isolates demonstrated that SusI can distinguish phage combinations that existing metrics fail to differentiate. Moreover, in a mouse model of lethal bacterial infection, higher SusI values correlated with improved therapeutic outcomes. SusI has potential as a practical tool for selecting phages with greater likelihood of therapeutic success.

Collateral sensitivity (CS) - resistance to one antibiotic inducing hypersensitivity to another - offers an evolutionary trap for multidrug-resistant pathogens. A strongly connected component (SCC) in the directed CS graph is a closed cycle in which every drug is reachable from every other. Prior evidence for CS SCCs is exclusively in vitro. We mined 104,337 susceptibility records from BV-BRC spanning four WHO critical-priority pathogens (Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa; 18,821 isolates), using Fisher's exact tests with Benjamini-Hochberg FDR correction, Tarjan's algorithm, and permutation testing (n = 1,000). Two species yielded qualifying SCCs. In K. pneumoniae (4,286 isolates), a 3-node SCC - imipenem, meropenem, tetracycline - was detected (empirical p = 0.001); both carbapenem-tetracycline edges are bidirectional (OR = 1.81-1.82, q < 0.002, n > 850 per edge). In E. coli (6,720 isolates), a bidirectional 2-node SCC links colistin and cefotaxime (OR = 10.13, 95% CI 2.82-46.12, q = 0.042, n = 87; permutation p = 0.008); with a fragility index of 1, we report it as a hypothesis, not an established effect size. The carbapenem signal is tetracycline-specific: tigecycline shows co-resistance (OR < 0.35), as its distinct RamA/AcrAB-TolC mechanism predicts. ORs of 2.2-2.7 persisted across independent year bands (2009-2014). S. aureus returned no qualifying SCC, but that null is power-limited: only 8% of testable pairs could detect the K. pneumoniae effect size. Prior clinical analyses characterised pairwise and three-way collateral effects; to our knowledge these are the first closed CS cycles identified in clinical surveillance data, motivating experimental follow-up.

Antibiotic tolerance enables bacteria to survive bactericidal antibiotic exposure and has been linked to resistance evolution in laboratory systems and individual infections, but its role in plasmid-mediated resistance evolution in clinical populations remains unclear. Here, we analyzed a longitudinal collection of more than 800 clinical Klebsiella pneumoniae isolates spanning 1997-2020. Among 779 minimum inhibitory concentration (MIC)-defined ertapenem-susceptible isolates, 137 (17.6%) displayed hidden ertapenem tolerance, defined by enhanced survival after 6 h at 30 times the isolate-specific ertapenem MIC, mostly without extended lag time or reduced growth rate. Tolerance was detected before local ertapenem introduction and was enriched among ertapenem-resistant isolates, supporting a population-level association between pre-existing tolerance and the emergence of carbapenem resistance. Genomic and plasmid-curing analyses separated plasmid-mediated carbapenem resistance from plasmid-independent antibiotic tolerance. Moreover, tolerant recipient backgrounds enhanced resistance plasmid acquisition, preserved viable recipients following antibiotic exposure and accelerated ceftazidime-avibactam resistance evolution. A phylogeny-guided variant-enrichment analysis further identified the uhpABC regulatory operon as a candidate tolerance-associated locus, and coordinated expression of the complete operon increased ertapenem survival. Together, these findings identify clinical antibiotic tolerance as a pre-existing, MIC-hidden phenotype that can facilitate plasmid-mediated carbapenem resistance evolution in K. pneumoniae.

Multidrug-resistant (MDR) Klebsiella pneumoniae is a leading cause of healthcare-associated mortality in Africa, yet genomic prediction of resistance has relied almost exclusively on resistance-gene detection validated under random data splits. Whether genomic context lineage, capsule and O-locus background, and virulence loci, with all resistance determinants excluded can predict aggregate MDR status, and whether such signal survives geographic transport, remains untested. As a feasibility study, we built an explainable machine-learning framework with leave-one-country-out (LOCO) cross-validation. Phenotypic linkage proved extremely scarce: only 231 of 9,505 strict-K. pneumoniae African NCBI records (2.43%) carry submitter-supplied antibiograms, necessitating a rule-based genotypic MDR proxy label. Country-sufficiency analysis showed LOCO is feasible on the current snapshot 8 countries at n >= 200 genomes but not on any previously published cohort. In a stratified pilot (175 species-confirmed genomes, 9 countries), tree ensembles reached pooled AUROC 0.85 under random splitting but only 0.66 - 0.69 under LOCO; this ~0.15 AUROC geographic-generalization gap suggests that pooled accuracy overstates transportability, though at pilot fold sizes (n <= 20 test genomes) confidence intervals are wide and overlapping. SHAP attributions implicated the ybt virulence locus and O-serotype background, indicating models exploit lineage-associated population structure. The substantive contribution is a leakage-controlled, fully reproducible pipeline indicating that geographic validation, not pooled accuracy, is the operative test for genomic AMR surveillance models.

Bacterial outbreak publications outline the key factors involved in the uncontrolled spread of infection. Such factors include the environment, pathogens, hosts, and antimicrobial resistance genes (ARGs). Individually, each paper published in this area gives a glimpse into the devastating impact drug resistant infections have on healthcare, agriculture, and livestock. When examined together, these publications provide contextual information on ARG transmission, from the discovery of new resistance genes to their dissemination to different pathogens, hosts, and environments. We have extracted this information from publications in PubMed by using the biomedical deep-learning language model, BioBERT. We trained BioBERT on two tasks: entity recognition to identify AMR-relevant terms (i.e., ARGs, taxonomy, environments, geographical locations, etc.) and relation extraction to determine which terms identified through entity recognition contextualize ARGs. By collating results from 204,094 antimicrobial resistance publications worldwide, we have generated interpretable results about the sources where genes are commonly found. To visualize the dataset, we have created two pipelines to analyze transmission patterns of ARGs across agriculture, environments, and human populations using a Confusogram and Uniform Manifold Approximation and Projection. Overall, we have taken a large-scale approach to collect antimicrobial resistance data from a commonly overlooked resource, i.e., the systematic examination of the large body of AMR literature and have visualized how scientific literature can be used to assess transmission patterns of ARGs.

Antimicrobial resistance (AMR) continues to pose a major public health threat across Africa, yet available surveillance data remain highly fragmented across private, public, and academic sources. This study analysed continent-wide AMR surveillance patterns by integrating datasets from multiple independent repositories and visualising them through interactive Geographic Information System (GIS) dashboards. The objective was to generate an integrated evidence base that highlights resistance patterns, surveillance disparities, reporting gaps, and opportunities for improved AMR monitoring across Africa. Data were compiled from major private AMR surveillance programmes including Pfizers ATLAS, GSKs SOAR, Johnson & Johnsons DREAM, Venatorxs GEARS, and Shionogis SIDERO-WT covering the period 2004-2022. Public datasets from the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Fleming Funds Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) were incorporated for 2016-2020, together with published AMR studies conducted between 2010 and 2024. Datasets were harmonised to align key variables including bacterial species, isolate identifiers, antibiotics tested, surveillance source, geographical location, and categorical AMR outcomes while preserving the original structure of the contributing datasets. Interactive dashboards were developed using R Shiny to support spatial visualisation and dynamic analytical exploration of resistance patterns, temporal trends, species distribution, and country-level surveillance coverage. Descriptive analyses including means, standard deviations, medians, interquartile ranges (IQR), frequency distributions, Gini coefficients, Shannon entropy, Herfindahl-Hirschman Index (HHI), and Lorenz curves were used to assess inequality and concentration in country-level AMR reporting across surveillance systems. The integrated analyses revealed substantial heterogeneity and concentration in AMR surveillance reporting across Africa, reflecting major differences in surveillance intensity, laboratory infrastructure, reporting systems, and diagnostic capacity across countries. Private datasets demonstrated broader antibiotic panels and longer temporal coverage, whereas public datasets exhibited substantial gaps in country participation and pathogen-antibiotic representation. Published AMR studies additionally highlighted important surveillance information absent from formal surveillance databases. By integrating multiple streams of AMR evidence, this study demonstrates the value of interactive GIS dashboards as exploratory and updateable surveillance-support tools for improving visibility of fragmented AMR datasets, identifying surveillance disparities, supporting geographically informed interpretation of resistance trends, and strengthening future AMR surveillance harmonisation efforts across Africa.

ObjectiveCarbapenem-resistant Klebsiella pneumoniae harboring blaNDM poses a serious threat to public health; however, blaNDM-15 remains poorly characterized outside the dominant epidemic lineages. MethodsWe characterized K. pneumoniae strain ETFK6090, isolated from a perianal surveillance swab of an 11-month-old immunocompromised child in a paediatric intensive care unit. Investigations included antimicrobial susceptibility testing, broth conjugation, S1 nuclease PFGE with Southern blotting, complete genome sequencing, and comparative genomic analysis against 465 curated blaNDM-positive K. pneumoniae genomes from 37 countries. ResultsETFK6090 belonged to ST580 and exhibited resistance to carbapenems, ceftazidime-avibactam, broad-spectrum cephalosporins, fluoroquinolones, gentamicin, chloramphenicol and trimethoprim-sulfamethoxazole; amikacin and fosfomycin retained low MICs. The complete genome comprised one chromosome and five plasmids, blaNDM-15 was localized on a 46,161-bp IncX3 plasmid, confirmed by Southern blotting. Conjugation into Escherichia coli EC600 transferred carbapenem and cephalosporin resistance, confirming in vitro mobility. The blaNDM-15 genetic environment retained a conserved blaNDM module, with IS-mediated rearrangements at the downstream boundary. In the global comparison, blaNDM-1 and blaNDM-5 predominated, the ST580-blaNDM-15 combination was exceedingly rare, and ETFK6090 constituted a distinct branch apart from major epidemic lineages. ConclusionsA transferable IncX3-blaNDM-15 plasmid can emerge in an uncommon ST580 background, underscoring the necessity to extend genomic surveillance of carbapenem-resistant K. pneumoniae beyond dominant epidemic clones, particularly in high-risk paediatric and intensive-care settings.

Adverse drug event (ADE) evidence is produced across patient-generated, clinical, and scientific settings that differ in language, documentation purpose, terminology, and degree of standardization. These differences shape both which adverse experiences become visible to pharmacovigilance systems and how readily they can be linked to curated drug-safety knowledge. We examine these relationships across five corpora representing distinct data-production settings: ADE Corpus V2 (medical case reports), SMM4H-2026 Task 1 (multi-lingual user-generated health content), CADEC V2 (patient-forum narratives), the Dutch ADE Corpus (EHR clinical notes), and TwiMed-PubMed (biomedical literature). A shared BERTopic analysis of ADE-positive texts concerning antidepressants and antihypertensives across the four English-language corpora identified nine interpretable topics. CADEC V2 contained a more differentiated distribution of symptom-specific themes, including sexual effects, suicidal or panic-related thoughts, vivid dreams, and memory difficulties, whereas SMM4H-2026, TwiMed-PubMed, and ADE Corpus V2 were dominated by a broader medication, sleep, tiredness, and pain theme. These patterns indicate that data-production context shapes what adverse experiences are expressed and standardized, with patient-generated narratives surfacing subjective, symptom-specific experience largely absent from clinical and scientific sources. We further show that this context shapes how readily real-world drug mentions can be linked to curated pharmacovigilance knowledge. Using SIDER 4.1 as a retrieval resource, we find substantial cross-corpus mismatches between real-world drug mentions and SIDER's predominantly English, generic-name vocabulary: CADEC V2 achieved only 9.5% exact-match coverage, with unmatched mentions frequently involving brand names, misspellings, and language-specific variants, compared to 91.0% coverage in TwiMed-PubMed's formally standardized biomedical literature. To probe how these representational differences interact with automated detection, we compare corpus-specific QLoRA fine-tuning of Llama-3.2-3B with retrieval-augmented inference using Llama-3.1-70B and Llama-3.1-405B grounded in SIDER-retrieved evidence. QLoRA-Llama-3B achieved the highest micro-averaged F1 scores on ADE Corpus V2 (0.91), CADEC V2 (0.88), and SMM4H-2026 (0.80), whereas SIDER-grounded inference with Llama-3.1-405B achieved the highest scores on Dutch ADE (0.95) and TwiMed-PubMed (0.91); these corpus-dependent patterns should not be interpreted as a controlled comparison of adaptation strategies, since model scale, task formulation, and available supervision differ across datasets. Together, our findings indicate that data-production context influences what adverse experiences are expressed, how they are standardized, and how readily they can be retrieved and computationally detected. Pharmacovigilance systems should therefore combine source-sensitive supervision with external knowledge grounding while explicitly monitoring gaps between real-world language and curated drug-safety resources.

Carbapenemase-producing Gram-negative bacteria carrying Verona integron-encoded metallo-{beta}-lactamase (VIM) pose a persistent global threat, yet the mechanisms driving their worldwide dissemination remain poorly resolved. We analysed 5,617 blaVIM-positive genomes collected from 73 countries or regions across six continents between 1999 and 2025 to reconstruct the global epidemiology and transmission architecture of VIM carbapenemases. Forty VIM variants were identified across 16 bacterial genera, revealing marked host preferences and temporal shifts. VIM-2 dominated global circulation, whereas VIM-1 and VIM-4 remained prominent among Enterobacterales. Strikingly, VIM spread followed distinct host-specific evolutionary strategies. In Pseudomonas aeruginosa, dissemination was largely clone-driven, with high-risk lineages ST111 and ST235 supporting long-term persistence through lineage expansion and stable inheritance. By contrast, Enterobacterales were dominated by horizontal transmission through broad-host-range IncHI2A, IncA, and IncC plasmids, although only a limited subset of plasmid-VIM combinations achieved intercontinental spread. Among 2,828 loci with sufficient flanking sequence, 96.2% were embedded within integrative genetic elements, frequently nested with insertion sequences and phage-related elements, revealing a multilayered mobile-element network underlying VIM persistence. Structural analyses showed a highly conserved metallo-{beta}-lactamase scaffold but recurrent diversification near substrate-interacting residues, particularly positions 224 and 228. Shared genetic clusters between human-associated and environmental isolates further suggested cross-niche circulation. Together, these findings establish a hierarchical, host-dependent framework for global VIM dissemination, integrating clonal expansion, plasmid transfer, and nested mobile genetic elements. ImportanceBy analysing 5,617 blaVIM-positive genomes worldwide, this study reveals a host-dependent transmission architecture of VIM carbapenemases. In P. aeruginosa, VIM dissemination is mainly driven by expansion of successful high-risk clones, particularly ST111 and ST235, whereas in Enterobacterales it is primarily mediated by broad-host-range plasmids enabling cross-species transfer. Global VIM spread is not driven by a single dominant pathway but by a limited number of successful clone-plasmid-mobile element combinations, while most genetic backgrounds remain regionally restricted. These findings define a hierarchical transmission model of VIM evolution and provide insights for genomic surveillance and targeted control of metallo-{beta}-lactamase-mediated antimicrobial resistance.

Drug repositioning has emerged as an attractive strategy to accelerate the development of new antimalarial therapies, particularly by evaluating compounds already used against pathologies co-endemic with malaria. This approach offers the advantage of leveraging existing pharmacokinetic, toxicological, and safety data, thereby potentially shortening the drug development pipeline. However, transposing a compound from its original therapeutic indication to an antimalarial use is far from straightforward: differences in target biology, parasite stage specificity, pharmacodynamic requirements, and host-parasite interactions can result in a loss of efficacy despite promising in vitro or structural rationale. Rigorous in vivo validation therefore remains indispensable before any repositioning hypothesis can be considered translationally relevant. In this context, we evaluated the blood-stage antimalarial activity of triclabendazole, an antihelminthic drug used against co-endemic fascioliasis, together with its metabolite triclabendazole sulfoxide, and sutezolide, an oxazolidinone antibiotic, in a murine model of Plasmodium berghei ANKA infection following oral administration. None of the three compounds demonstrated significant antimalarial activity under these experimental conditions, contradicting a previously published repositioning hypothesis. Beyond these specific findings, our study is deliberately framed within the 3Rs principles (Replacement, Reduction, Refinement) governing animal experimentation. We argue that publishing negative in vivo results is not only scientifically legitimate but ethically necessary: sharing such data allows research teams working on similar preclinical models to build on existing knowledge, avoid unnecessary experimental duplication, and ultimately reduce the number of animal procedures performed across the field. We advocate for wider dissemination of negative outcomes in antimalarial drug repositioning research as a concrete contribution to more responsible and efficient use of animal models in preclinical pharmacology. Graphical Abstract

Skeletal rearrangement drives the immense structural complexity of terpene, yet predicting it remains a formidable challenge due to sequence-function decoupling in terpene synthases. Here, we established TRACER (terpene rearrangement annotation via co-attentive enzyme-product representation), a multimodal framework mapping the latent associations between sequence-derived enzyme representations and product chemotypes. Retrospective validation proved TRACERs exceptional precision in predicting compound classes and discriminating skeletal rearrangement (SR) from non-skeletal rearrangement (NSR) pathways. TRACER-guided genome mining characterized two bifunctional synthases, FsPS and AcPS, uncovering four unprecedented carbon skeletons. Density functional theory calculations deciphered these cyclization cascades, pinpointing a critical 5/6/11 tricyclic intermediate as the key branching node for scaffold diversification. Mutagenesis and molecular dynamics simulations suggested that E305 in FsPS enables rearrangement by maintaining active-site water exclusion, whereas its alanine mutation causes premature carbocation quenching. Collectively, this work establishes a predictive paradigm for the rational discovery and mechanistic elucidation of complex terpene architectures.

We present a robust and versatile in vitro transcription (IVT) assay based on an optimized Broccoli RNA aptamer sequence. When paired with the fluorophore DFHBI-1T, this system enables real-time monitoring of multi-round transcription over several hours. To facilitate streamlined promoter analysis, we developed the pIVT3 plasmid backbone. The system was validated using both the single-subunit T7 RNA polymerase and the multi-subunit Escherichia coli RNA polymerase; notably, the activity of the E. coli enzyme remained strictly dependent on the presence of a {sigma} factor and a cognate promoter. To optimize the signal-to-noise ratio, we incorporated two rrnBT1 terminators upstream of the promoter of interest. This modification effectively eliminated background transcription for weak promoters (PlivJ) and prevented interference from read-through transcription in strong synthetic promoters (Ptrc*). Furthermore, we demonstrated the assays utility for drug discovery by characterizing the time- and dose-dependent inhibitory kinetics of rifampicin. Collectively, these results establish the Broccoli-based IVT system as a highly adaptable platform for quantifying promoter strength and screening small-molecule inhibitors of bacterial transcription. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/744185v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1e0c991org.highwire.dtl.DTLVardef@d154aeorg.highwire.dtl.DTLVardef@10e95fcorg.highwire.dtl.DTLVardef@98ea80_HPS_FORMAT_FIGEXP M_FIG C_FIG

Human-targeted non-antibiotic drugs are increasingly recognized for their intrinsic antibacterial activity, yet Gram-negative pathogens such as Acinetobacter baumannii exhibit substantial tolerance to these compounds. This tolerance is largely attributed to restricted outer membrane permeability and the activity of multidrug efflux systems. While Resistance Nodulation Division (RND) transporters have been extensively studied, the contribution of individual Major Facilitator Superfamily (MFS) transporters to non-antibiotic drug tolerance remains poorly understood. Here, we investigated H0N29_04330, designated Drug Resistance Transporter A (DrtA), a Bcr/CflA subfamily MFS transporter, to define its substrate specificity and contribution to antibiotic and non-antibiotic drug tolerance. DrtA was highly conserved across the A. calcoaceticus-baumannii complex and exhibited broad substrate specificity when heterologously expressed in an efflux-deficient Escherichia coli background, conferring resistance to benzalkonium, ethidium bromide, phenicols, and the antineoplastic agent mitomycin C. Intriguingly, drtA expression increased E. coli susceptibility to the antifolate compounds methotrexate and aminopterin, suggesting that DrtA may recognize folate-related metabolites rather than function as a dedicated antifolate transporter. In contrast, loss of drtA in its native A. baumannii host primarily impaired tolerance to mitomycin C, highlighting a context-dependent physiological role influenced by the extensive functional redundancy among A. baumannii efflux systems. Site-directed mutagenesis further identified M18 and the membrane-embedded protonatable residue D26 as critical determinants of DrtA transport activity and substrate recognition. Together with previous characterization of CraA, our findings demonstrate that Bcr/CflA subfamily MFS transporters contribute to protection against structurally diverse human-targeted compounds and expand the functional landscape of efflux-mediated intrinsic tolerance beyond conventional antibiotic resistance.