npj Antimicrobials and Resistance

Antimicrobial resistance is an urgent global health threat, with over 2.8 million multidrug-resistant infections killing over 35,000 annually in the US. Machine Learning (ML) has emerged as a potential solution to improve efficiency of antibiotic high-throughput screens (HTS). We report ML-guided high-throughput screening against E. coli. Large-scale Learning-to-Rank models were trained on public and proprietary datasets to maximize phenotypic inhibition and minimize human cell cytotoxicity. We evaluated several pre-plated compound libraries and a set of "cherry-picked", structurally novel compounds. We screened against a hyperpermeable lptD- mutant, followed by hit confirmation, profiling, cytotoxicity counter-screening, and MOA determination. Results demonstrated a doubled hit rate and 3X fewer toxic hits. Additionally, activity improved against both Wild Type E. coli and the lptD- mutant. ML models showed robust predictive power on structurally dissimilar compounds. The combination of large-scale HTS, ML innovation, and both library-wise selection and cherry-picking strategies distinguishes this study in the antibiotic discovery field.

Background: Tuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, remains a leading cause of infectious death worldwide. The rapid accumulation of whole-genome sequencing (WGS) data had spurred numerous computational methods for predicting antimicrobial resistance in Mycobacterium tuberculosis. However, heterogeneous datasets, preprocessing pipelines, and evaluation protocols have made fair comparisons impossible and have hindered clinical translation. A critical yet missing resource is a large-scale, unified benchmark to systematically assess and compare existing methods. Methods: We curated an integrated MTB WGS--phenotypic drug susceptibility testing (pDST) dataset from three sources: the CRyPTIC dataset (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium), a published multi-study compilation, and newly curated literature-derived datasets. The final benchmark contains 54,364 paired WGS-pDST records with broad geographic, lineage, and drug coverage. After harmonizing phenotypes and generating standardized variant features, we evaluated seven models (including classical machine learning and deep learning architectures) across 18 drug-level and six clinical resistance category prediction tasks. Results: XGBoost achieved the highest mean drug-level AUPRC (0.674) and F1-score (0.620) and ranked first in AUPRC for 11 of 18 drugs, whereas WDNN achieved the highest mean AUROC. Random forest yielded the highest mean specificity (0.956) and accuracy (0.933), whereas logistic regression achieved the highest mean recall (0.774), highlighting distinct clinical trade-offs. Drug-level difficulty was highly heterogeneous: rifampicin and isoniazid were predicted robustly, whereas bedaquiline, delamanid, linezolid, and clofazimine remained persistently difficult. In clinical resistance category evaluation, RR-TB, MDR-TB, and pan-susceptibility were well predicted, but XDR-TB and other resistance categories constituted major bottlenecks. Conclusions: Under the largest unified benchmark to date, classical machine-learning methods, particularly XGBoost, provided the strongest precision--recall and F1 performance overall, while neural models remained competitive by AUROC. Emerging drugs (bedaquiline, delamanid, linezolid, clofazimine) and XDR cases remain persistently difficult to predict, identifying key bottlenecks for future method development. This benchmark can serve as a community standard for evaluating MTB resistance prediction and the provided evaluation pipeline offers an actionable baseline for regulatory qualification and clinical decision support system validation, accelerating the translation of WGS-based resistance prediction into practice.

Multidrug-resistant pathogens pose a major threat to One Health. Within the past decade, CRISPR-Cas systems have been explored as sequence-specific antimicrobials. While chromosomal injury has been considered the primary mechanism underlying pathogen killing by CRISPR-Cas antimicrobials, the synergistic role of gene disruption together with chromosomal injuries remains poorly understood. In this study, we characterized a new class of CRISPR-Cas antimicrobials that simultaneously cotarget essential and defensive genes to enhance potency against the clinically relevant pathogen Staphylococcus aureus. High-throughput CRISPR screening identified top-performing guide RNAs for twenty functionally diverse essential and defensive genes across the S. aureus genome. CRISPR-Cas antimicrobials were modularly formulated to target single or multiple gene loci and packaged in phage-like particles for specific delivery. By engineering an S. aureus production host with a chromosomally integrated anti-CRISPR protein, we demonstrated efficient production of CRISPR-Cas antimicrobials targeting any S. aureus chromosomal locus without self-targeting. Characterization of CRISPR-Cas antimicrobials with single guide RNA designs revealed that potency varied according to targeted gene function, achieving up to a 4-log10 reduction in viability and outperforming traditional antibiotics. Multiplexed configurations were consistently more effective than single-targeting designs, with the top-performing design demonstrating a 4.7-log10 reduction in viability. Cotargeting essential and defensive genes revealed synergies that led to improved lethality and attenuated resistance, with enhanced activity in biofilms compared to traditional antibiotics. Genes involved in signaling and stress responses were important defensive targets for developing cotargeting CRISPR-Cas antimicrobials. Overall, this study establishes design principles for synergistic CRISPR-Cas antimicrobials applicable to next-generation precision antimicrobial development. SIGNIFICANCEThe ability to effectively combat multidrug-resistant pathogens is of primary importance to One Health. This study develops a generalizable design principle for formulating potent CRISPR-Cas antimicrobials that exploit synergistic cotargeting strategies for enhanced pathogen killing. In addition to chromosomal injuries, we found that disruption of gene function plays a crucial role in determining the lethality of CRISPR-Cas antimicrobials, providing a generalizable framework for effective CRISPR-Cas antimicrobial design. The development of a CRISPR-Cas antimicrobial production host with stable, chromosomally integrated anti-CRISPR genes greatly expands the modularity, adaptability, and efficiency of formulating CRISPR-Cas antimicrobials and enables deeper insights into the molecular mechanisms involved in eliminating multidrug-resistant pathogens.

Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spread. Although many previous works have explored the use of tree-based machine learning (ML) models to predict resistance, the field lacks a systematic evaluation of the training pipeline across a variety of pathogenic species and antibiotics. Results: Using nine clinically relevant species-antibiotic combinations from the NCBI antimicrobial susceptibility testing database, we present a detailed analysis of the ML pipeline and identify key factors affecting model performance and evaluation. We begin by relabelling all isolates using current CLSI minimum inhibitory concentration breakpoints to resolve inconsistencies and increase available data, resulting in up to a 19% label swap and 56% data enlargement per species-antibiotic combination. We identify several key training parameters including k-mer length, which can increase classification F1 scores by over 20 points compared to commonly used k-values, feature matrix truncation, which can induce polynomial time reductions with limited performance reduction, and ML model class. By comparing 5-fold cross-validation with evaluation on an unseen clinical dataset, we show that random cross-validation splits--often criticized as overly optimistic--can act as a strong proxy for downstream clinical performance, yielding closer F1 scores than phylogeny-aware splits in all cases. We finally present an interpretability study which shows that over 95% of k-mers used by our models are associated with identifiable genomic features. Our results highlight the importance of feature design, evaluation protocol, and biological analysis in genomic AMR prediction, and support tree-based models as a robust and interpretable method.

Triculamin is a potent antibiotic lasso peptide first isolated in 1967. Previous studies have demonstrated that its biosynthesis follows a non-canonical logic unlike any other lasso peptide. In this study, we investigate the role of the unusual follower peptide and demonstrate that it is essential for efficient biosynthesis. Using structural prediction and targeted mutations of key conserved residues, we hypothesize that the interactions between the follower peptide and the macrocyclase create an enzyme-substrate complex that ensures delivery of the core peptide to the enzyme active site. Moreover, we demonstrate that analogs of the lasso peptide can be produced by modifying the core peptide, highlighting the substrate promiscuity of the lasso macrocyclase and identifying lysine-3 in the lasso peptide ring as the site of acetylation. Lastly, we achieve successful heterologous expression in Burkholderia sp. FERM 3421, which proves to be a superior heterologous host.

Laboratory-based experimental evolution is widely used to investigate how antimicrobial resistance (AMR) emerges and to identify resistance-associated trade-offs that could inform treatment strategies. However, there is limited understanding of how in vitro AMR evolution reflects the complexity of resistance evolution within the human host, where selective pressures, and therefore evolutionary pathways, are more variable. Here, we investigated the effect of population bottleneck size and growth environment on the evolution of piperacillin-tazobactam (TZP) resistance in Klebsiella grimontii and compared this to resistance evolution observed during a recurrent bloodstream infection. Three clonal K. grimontii isolates cultured from one patient over four months included a TZP-susceptible ancestor and a within-patient evolved TZP-resistant isolate. The susceptible ancestor was evolved under TZP selection using either a small 0.1% bottleneck or a larger 5% bottleneck, and under a second environment, LB supplemented with 5% sheep blood, using a 0.1% bottleneck. Evolved isolates were assessed for TZP susceptibility, {beta}-lactamase activity, fitness, and genomic changes. A single nucleotide polymorphism (SNP) in the promoter region of the chromosomally located {beta}-lactamase gene blaOXY-6-4 was identified in the within-patient evolved isolate and was replicated in all 0.1% bottleneck lineages across both environments. In contrast, the larger 5% bottleneck lineages exhibited greater phenotypic variation and genetic diversity, including multiple blaOXY-6-4 promoter variants and variable TZP MICs. These findings show that laboratory evolution can reproduce key within-patient resistance mechanisms, but that bottleneck size strongly shapes the resistance phenotypes and mutational landscapes observed in vitro. ImportanceAdaptive laboratory evolution is increasingly used to predict how antimicrobial resistance emerges and to identify trade-offs associated with resistance acquisition that could inform future treatment strategies. Here, we directly compared piperacillin-tazobactam resistance evolution in the laboratory with resistance that emerged within a patient during a recurrent bloodstream infection. We show that a small population bottleneck reproducibly selected the same blaOXY-6-4 promoter mutation observed in the patient, whereas a larger bottleneck produced more diverse evolutionary outcomes. These findings build on previous work showing that experimental conditions shape laboratory evolution outcomes and highlight population bottleneck size as an important experimental parameter when designing laboratory evolution studies that intend to model clinically relevant resistance evolution.

DNA gyrase is an essential bacterial enzyme and a clinically validated target for the treatment of tuberculosis. However, the discovery of new inhibitors remains limited by the many challenges regarding the manipulation on pathogenic mycobacteria. This study validates Corynebacterium glutamicum (Cglu) as a safe, non-pathogenic surrogate for Mycobacterium tuberculosis (Mtb) to investigate DNA gyrase and facilitate the identification of new inhibitors. Using Cglu as a target allows for fast whole-cell screening under safe conditions while ensuring efficient drug uptake. Cglu shares key physiological features with Mtb, including genome size, complex cell wall structure, and a single type I and type II topoisomerase. Structural and functional comparisons emphasize the similarity of Cglu and Mtb gyrases, which share 70% sequence identity and show comparable catalytic properties and responsiveness to known inhibitors. Thus, the cryo-EM structure of the Cglu gyrase-DNA complex at 3.2 [A] resolution reveals highly conserved drug-binding pockets for known anti-gyrase inhibitors and the genetic depletion of gyrA or gyrB in Cglu causes severe growth and morphological defects, mirroring the effects of chemical inhibition and allowing to link gyrase function to cellular phenotypes. Comparative imaging of different inhibitor classes (fluoroquinolones, aminocoumarins, NBTIs) uncovers distinct morphological signatures that reflect each compounds mode of action. Finally, cross-species complementation confirms functional conservation but also highlights subtle structural differences affecting efficiency. Together, these findings establish Cglu as a robust and biosafe model for dissecting gyrase function, visualizing DNA topology dynamics, and accelerating the discovery of gyrase-targeting antimicrobials. More generally, our studies demonstrate the feasibility of using Cglu as a cell-based screening platform to discover new anti-tuberculous compounds targeting conserved mechanisms, not only for validated TB drug targets such as DNA gyrase but also for new, yet to be identified, targets.

Carbapenem-resistant Klebsiella pneumoniae (CRKp) is a critical global healthcare threat driven by high-risk multidrug-resistant (MDR) clones that acquire hypervirulence genes. Although resistance-virulence co-occurrence is extensively documented, the plasmid-level mechanisms facilitating this convergence remain unclear. In this study, we utilized hybrid short- and long-read whole-genome sequencing of 376 clinical CRKp strains to define the evolutionary trajectories and structural plasmid dynamics of three predominant high-risk clones: ST147 (n=157), ST231 (n=108), and ST2096 (n=111). Carbapenemase genes were present in 90% of isolates, predominantly blaOXA-48-like and blaNDM-5 co-harbored with blaCTX-M-15. Virulence profiling indicated high aerobactin (iuc) prevalence (62.7%), while salmochelin and colibactin were undetected. Hypermucoviscosity occurred infrequently (6.6%) and was independent of rmpA/rmpA2, confirming a clear genotype-phenotype discordance. Comparative plasmid mapping revealed three distinct, lineage-specific plasmid configurations underlying this intermediate convergent pathotype: ST147 exhibited dynamic, mosaic hybrid IncFIB-IncHI1B plasmids; ST2096 showed structurally stabilized hybrids; and ST231 retained virulence and resistance determinants on separate, segregated plasmids. These findings show that convergence is regulated by multiple, clone-specific evolutionary routes rather than a single path, highlighting the critical need for more in-depth genomic surveillance capable of identifying convergent plasmids along with high-risk lineages

Directed evolution consisting of iterative rounds of diversification, selection, and counter-selection, underlies modern protein and antibody engineering, yet small-molecule drug design still advances largely through high-throughput screening and medicinal-chemistry intuition. Transformer softmax attention is mathematically identical to the Boltzmann distribution that governs molecular binding at thermal equilibrium1, an isomorphism that prescribes a sequence-native Specificity Foundation Model (SFM)2. This framework was recently applied across seven molecular recognition domains3,4 and scaled into the drug-target SFM (dtSFM), the first to pair a full-scale encoder with a generative decoder5. Whether such a model can be driven, iteratively and under selection, to optimize leads rather than sample them once has not been shown. Here we present GenLoop, a closed generative drug design loop that turns single-pass generation into directed evolution of chemistry. dtSFM generates target-conditioned molecules and reranks them by their thermodynamic compatibility score. An orthogonal structural verifier, AlphaFold 3, is used that shares no architecture or training data with dtSFM. Cheminformatics filters enforce developability, and generative evolution is performed on the structurally verified candidates, selecting for predicted binders and counter-selecting against off-target chemistry. Applied across twelve drug targets spanning pharmacologically distinct mechanism classes, GenLoop produced AlphaFold 3-verified designs that reached the structural confidence of the approved drug for five of the twelve targets, with the best designs at interface iPTM 0.93-0.98 and PAE 0.8-2.0 [A], as well as resolving paralog selectivity across nine targets. Two full disease campaigns followed. For the cystic-fibrosis transmembrane conductance regulator, GenLoop designed nine developability-filtered and structurally novel lead candidates (iPTM up to 0.93, interface PAE 2.3 [A]) targeting all three orthogonal sites of the approved drug Trikafta. For the GLP-1 receptor family, dtSFM engineered tunable single-, dual-, and triple-receptor incretin designs, yielding 23 central-pocket candidates that are structurally novel at median iPTM 0.89 and interface PAE 1.95 [A]. GenLoop with dtSFM brings directed evolution to small molecules through computational-thermodynamic selection; wet-lab validation is the immediate next step.

Companion animals are increasingly recognised within One Health antimicrobial-resistance (AMR) surveillance, yet the global genomic structure of dog- and cat-associated Klebsiella pneumoniae remains poorly defined. We assembled a global dataset of 712 domestic dog- and cat-derived K. pneumoniae genomes to define lineage diversity, AMR burden, host-associated patterns and overlap with human-associated populations. Companion-animal isolates comprised 263 sequence types (STs), but recurrent high-risk lineages, including ST307, ST11, ST15 and ST147, were prominent. Among 706 isolates from 25 countries, extended-spectrum {beta}-lactamase (ESBL) and carbapenemase genes were widely distributed, detected in 303 (42.9%) and 98 (13.9%) isolates, respectively. Cat-derived isolates showed higher multidrug-resistance (MDR) prevalence than dog-derived isolates; 80.0% versus 56.3%, partly reflecting enrichment of epidemic clones, especially ST147. MDR was not confined to infection-associated samples, indicating that colonisation may represent an important reservoir state. Comparison with 38,106 human-associated K. pneumoniae isolates revealed extensive ST overlap, with 71.1% of companion-animal STs and 87.2% of companion-animal isolates belonging to STs also detected in humans. Focused recombination-filtered phylogenomics of ST147 identified a recent host-spanning MDR sub-lineage linking cat-, dog- and human-associated genomes. Together, these findings show that domestic dogs and cats are not epidemiologically separate from the wider K. pneumoniae AMR landscape, but harbour globally circulating human-associated lineages and high-risk AMR clones.

Antimicrobial resistance represents a continuing threat to clinical infection management, with methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Escherichia coli identified by the World Health Organization as priority pathogens. This study evaluated the antimicrobial activity, synergistic potential, and chemical composition of six plant-derived preparations (three ethanolic extracts: nettle, thyme, rosemary; and three essential oils: lavender, lemongrass, doTERRA Peace blend) against MRSA, methicillin-sensitive S. aureus (MSSA), and E. coli K-12 by disc diffusion, broth microdilution, post-exposure culturability, antimicrobial interactions assessed by checkerboard assay, and GC-MS profiling. Disc diffusion produced no interpretable zones of inhibition for any plant preparation tested; however, broth microdilution revealed reproducible inhibitory activity within published ranges across the panel. Three essential oils achieved a median Minimum Inhibitory Concentration (MIC) of 0.39 mg/mL against MRSA despite presenting compositionally distinct chemotypes: lavender was linalool-dominated (61% combined), lemongrass was citral-dominated (76%), and the doTERRA blend was sesquiterpene-rich. Rosemary ethanolic extract achieved the same potency (0.39 mg/mL) against MSSA. No preparation produced a bactericidal reduction (>=3 log10 CFU/mL) at any timepoint, with all reductions transient and recovering by 24 hours. Checkerboard combinations of plant preparations with vancomycin and ciprofloxacin were uniformly classified, according to the Fractional Inhibitory Concentration Index (FICI), as indifference/no interaction, attributable in part to inoculum-mediated effects on vancomycin MIC. To analyse the relationship between chemical composition and antimicrobial outcomes, we introduce a Chemotype Similarity Index (CSI), a chemometric framework quantifying pairwise compositional similarity between essential oils by Pearson correlation and relating it to log2-MIC differences across strains. CSI revealed a strain-dependent chemistry-activity relationship, convergent against MRSA, monotonic against MSSA, and absent against E. coli, indicating that compositional similarity predicts antimicrobial outcomes on a strain-specific basis. The convergence of three chemotypically divergent essential oils with the same anti-MRSA potency suggested a shared membrane-disrupting mechanism operating through distinct chemical routes. Although exploratory at this scale, the CSI framework provides a reusable analytical scaffold for linking phytochemical composition to antimicrobial activity, and identifies the MRSA convergence as a specific direction for mechanistic investigation into the development of plant-derived antimicrobial adjuncts.

Background: Antimicrobial resistance poses a major threat to global public health. Large language models (LLMs) offer new possibilities for optimizing antibiotic prescribing decisions, but the capabilities of general-purpose versus domain-specific medical LLMs under different prompting strategies remain to be clarified. Methods: This double-blind, randomized-sequence evaluation used a 2X2 factorial design comparing four AI conditions-the domain-specific model MedGo and the general-purpose model DeepSeek V3.5, each under standard direct prompting and chain-of-thought (CoT) prompting-alongside real physician prescriptions across 59 complex inpatient infection cases. Five parallel regimens were generated per case and independently evaluated by three senior clinicians (1-5 comprehensive score and five domain sub-scores). ChatGPT 5.2 was additionally assessed as an automated evaluation tool. Results: Score ranking: real physicians > MedGo-CoT > DeepSeek-CoT > MedGo> DeepSeek (Friedman test, p<0.001). In base mode, MedGo significantly outperformed DeepSeek (Holm-adjusted p=0.040). CoT improved both models (Holm-adjusted p<0.001 for DeepSeek; p=0.024 for MedGo) and reduced score dispersion. MedGo-CoT significantly outperformed DeepSeek-CoT in individualized adjustment (adjusted p<0.001) and dosing precision (adjusted p=0.005). ChatGPT-expert correlation was negligible (overall Kendall {tau}=0.153, p=0.003; subgroup {tau}=0.06-0.20, all p>0.05). Conclusions: Domain-specific medical LLMs enhanced by CoT approach the antibiotic decision-making level of real physicians, with advantages in individualization and dosing precision. However, notable deficiencies persist in antimicrobial stewardship ecological awareness and automated evaluation reliability, underscoring the continued indispensability of senior clinical expertise.

SynopsisO_ST_ABSBackgroundC_ST_ABSThe main advantages of Time Kill Curves (TKCs) in antimicrobial drug development are the ability to track bacterial kill and regrowth over time and with varying drug concentrations. Whilst there are guideline documents in place, such as M26-A in CLSI, there remains scope for individual laboratory differences in practice. Here we evaluated several factors which potentially influenced data generated in TKCs. MethodsFirstly, E. coli ATCC 25922 was used to determine optimum sampling volume, culture vessel volume, CFU enumeration variance factors and static versus agitated cultures in a single laboratory. Secondly, a ring test comprising of TKCs was performed by six laboratories focusing on: standardised inoculum, static culture and two culture vessel sizes 10 mL and 200 {micro}L. Data analysis was performed to determine consistency within centres and between them. ResultsConsistently accurate inocula could be achieved by use of: larger sampling volumes between 100 {micro}L > 20 mL; larger culture vessels volumes (10 mL > 100 {micro}L) and higher inocula (10 8 > 1.5x10 5 CFU). Culture agitation during the TKC experiment resulted in reduced killing compared to static cultures. Reproducibility of TKCs was best between centres when they were performed in 10 mL culture vessels. There was more variability per site when performing TKC in 96 well trays. ConclusionsTechnical factors such as preparation of inocula, agitation, vessel size and enumeration of cultures are important variables in performing TKCs that need to be standardised in drug development programmes involving multiple laboratory centres.

Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot keep pace with expanding evidence. We assessed whether an LLM-assisted workflow could generate a rapid, repeatable, and policy-relevant living evidence base for climate-sensitive foodborne disease. We combined structured PubMed searches (2010-2023), gold-standard human labelling, and iterative refinement of a GPT?4?Turbo?based auto-labeller within the SysRev platform. Pathogens of public-health importance in England were selected a priori. Model performance was evaluated against human reviewers using recall, precision, specificity, accuracy, and balanced accuracy. The refined inclusion model achieved 89{middle dot}2% recall, 59{middle dot}2% precision, 84{middle dot}5% specificity, and 85{middle dot}4% accuracy across 1,044 screened abstracts, identifying 436 studies for inclusion. Post-hoc re-evaluation of discordant abstracts showed that records excluded by the model but included during initial human screening did not meet the refined inclusion criteria. Frequently identified climate exposures included rainfall, temperature, seasonality, and humidity; norovirus, Salmonella, Campylobacter, and Cryptosporidium were the most common pathogens. An LLM-assisted workflow can generate living evidence for climate-sensitive foodborne disease with high recall and improved screening consistency. The approach is scalable, auditable, and suitable for secure institutional environments, supporting horizon scanning and climate-health risk assessment.

Insecticide-based vector control remains the cornerstone of malaria prevention, averting approximately 1.2 billion cases between 2000 and 2025. These interventions primarily reduce transmission by killing mosquitoes; however, widespread reliance on a limited number of compounds has driven the emergence of insecticide resistance. This has prompted the development of new insecticides with novel modes of action. Notably, the pyrrole insecticide chlorfenapyr has been shown to affect both the mosquito vector and the malaria parasite, suggesting that compounds with dual activity could provide an additional strategy to suppress transmission. Here, we present a medium-throughput discovery pipeline that integrates in vitro Plasmodium sporozoite motility assays with machine-learning-based analysis, alongside in vivo exposure of infected Anopheles mosquitoes and quantification of parasite development. Screening 32 insecticidal chemistries identified five compounds that significantly impaired sporozoite motility, including three avermectin endectocides, the mitochondrial complex III inhibitor hydramethylnon, and tralopyril, the active form of chlorfenapyr. Several compounds transiently increased motility, indicating that parasite physiology is frequently influenced by insecticide exposure. In vivo exposure to abamectin reduced parasite numbers in both the haemolymph and salivary glands and impaired productive motility. Importantly, this inhibition was confirmed in Plasmodium falciparum-infected mosquitoes, where exposure significantly reduced salivary gland invasion. These findings reveal that parasite-directed activity among insecticides may be more common than previously appreciated and demonstrate a scalable approach to identify compounds capable of simultaneously killing mosquitoes and suppressing parasite transmission. Significance StatementVector control relies heavily on insecticides that kill mosquitoes, yet rising resistance threatens their effectiveness. Here we show that several insecticides also affect the malaria parasite itself. Using a scalable screening pipeline combining machine learning-assisted sporozoite motility analysis with mosquito infection assays, we found that 15% of tested insecticides significantly impaired parasite motility, including compounds with distinct modes of action. Among these hits, the avermectin abamectin reduced parasite dissemination in mosquitoes and limited salivary gland invasion in both Plasmodium berghei and the human malaria parasite P. falciparum. These findings reveal that parasite-directed activity among insecticides may be more widespread than expected and highlight the potential to develop vector control tools that simultaneously kill mosquitoes and block parasite transmission.

Macrolides are key agents for treating infections caused by non-tuberculous mycobacteria (NTM). Nevertheless, chromosomal erm genes conferring inducible macrolide resistance are described in some NTM species, such as Mycobacterium abscessus and M. fortuitum, whereas M. chelonae had long been considered as lacking a functional erm. Recent descriptions from the USA and Japan of a new plasmid-borne erm(55) (erm(55)P) in M. chelonae and other rapidly growing mycobacteria (RGM) have challenged this assumption. We investigated erm(55)P occurrence in clinical RGM referred to the French National Reference Centre for Mycobacteria between 2012 and 2026 by genome screening and erm(55)P specific real-time PCR. Positive isolates underwent long-read whole genome sequencing (GridIon, Oxford Nanopore Technologies). Clarithromycin (CLR) minimum inhibitory concentration (MIC) was determined by broth microdilution (RAPMYCO and FRATMYC, Thermo Fisher) and read up to 14 days. Five clinical isolates showing inducible CLR resistance (MIC range <0.25-64 mg/L on day 3-4 and 128 - >128 mg/L on day 14) were positive for erm(55)P: one M. chelonae, three M. neoaurum, and one M. parafortuitum. erm(55)P-positive M. chelonae genomes from this and previous descriptions did not cluster together in the phylogenetic analysis of 263 genomes. The assembled plasmids showed high similarity to previously reported erm(55)-carrying plasmids, especially within the erm(55)P region. The upstream sequence of erm(55)P showed a secondary structure compatible with a possible translation attenuation mechanism. These findings document the first report of a plasmid-borne erm(55) in Europe in M. chelonae and other RGM and raise concern about the emergence of plasmid macrolide resistance in NTM.

Machine learning (ML) has accelerated molecular discovery, yet training models to generalize to out-of-distribution (OOD) chemical spaces remains fundamentally constrained by the high cost of experimental validation. In antibiotic discovery, where whole-cell phenotypic high throughput screening (HTS) is resource-intensive, iterative ML-guided compound selection, or Active Learning (AL), offers a pathway to efficiently navigate available chemical spaces. However, the algorithmic tradeoffs between prioritizing compound novelty (exploration), predicted bioactivity (exploitation), and their impact on OOD generalizability remain unresolved for noisy, whole-cell biological systems. In this work, we systematically evaluate three AL strategies for whole-cell bacterial bioactivity and benchmark their effects on model accuracy, hit rate, and OOD performance. Using retrospective simulations on Mycobacterium tuberculosis HTS data, we identify an optimal AL strategy that balances predicted hit/non-hit novelty with overall hit rate. We then integrate the strategy in a closed-loop Borrelia burgdorferi antibiotic discovery HTS campaign. The AL-guided approach successfully increased the experimental screening hit rate five-fold (from a 0.2% rate within investigator-selected plates to 1.0%). Further, when the trained model was applied in prospective in silico selection of highly diverse compounds across multiple bacterial species, the AL-trained whole-cell inhibition predictor demonstrates 53-fold enrichment over investigator-directed screening (11.0% experimental validation of predicted hits). Of these, 100% demonstrated the intended narrow spectrum activity for Borrelia burgdorferi. These results demonstrate that calibrated AL strategies can overcome data acquisition bottlenecks and train generalizable property predictors able to extrapolate to OOD molecules.

Achromobacter species are emerging multidrug-resistant (MDR) pathogens in people with cystic fibrosis. Their increasing resistance has grown an interest in phage therapy as an alternative treatment strategy. However, the factors governing phage susceptibility remain poorly understood, thereby limiting the rational selection of phage candidates. Using 15 strictly lytic Achromobacter phages and 7 clinical cystic fibrosis isolates representing Achromobacter insolitus and Achromobacter xylosoxidans, we demonstrate substantial variation in infection efficiency across all 105 phage-host combinations, variation that could not be discerned from qualitative plaque assays alone. We integrated complete bacterial and phage genomes with quantitative efficiency-of-plating (EOP) assays and lineage-aware Bayesian mixed-effects modelling to show that phage infectivity in Achromobacter is governed predominantly by bacterial lineage and strain identity, accounting for 90% of total variance in log-normalised EOP, with individual strains varying substantially in permissiveness irrespective of species membership. After accounting for this lineage structure, no individual defence system, antimicrobial resistance gene class, or phage tail cluster retained a statistically significant independent or interaction association with infectivity. Together, these findings demonstrate that bacterial strain identity is the primary driver of infection outcome. Host defence systems and phage tail-associated genes remain biologically plausible contributors; their independent effect could not be resolved after accounting for lineage structure, indicating that infection outcomes are largely strain-dependent. This work shifts the question from which individual traits predict infection to how strain lineage and specific host-phage combinations jointly determine infectivity, and argues that quantitative phenotyping of individual phage-host pairs is essential for guiding phage candidate selection and supporting rational cocktail design against multidrug-resistant Achromobacter infections in cystic fibrosis. Impact statementChronic Achromobacter infections in cystic fibrosis are increasingly difficult to treat due to multidrug resistance and biofilm formation. Although phage therapy is a promising alternative, its development is limited by poorly understood and highly variable infectivity. Here, we show that infectivity within a phage host range spans a broad quantitative continuum spanning several orders of magnitude that cannot be captured by qualitative plaque assays. These infection efficiencies are primarily structured by bacterial lineage and strain identity, while the contributions of individual genomic features remain unresolved, given the current sample size. This work provides a framework for predicting phage-host compatibility and supports a shift from empirical screening toward rational, evidence-based phage selection for MDR Achromobacter infections.

Microbial libraries remain an important resource for natural product discovery; however, constructing taxonomically and chemically diverse collections remains a challenge. Advances in dereplication strategies, including molecular networking, have reduced the rediscovery of known bioactive molecules and facilitated the identification of novel chemical scaffolds, but these approaches are typically applied after library construction or to existing repositories. Furthermore, many dereplication workflows require scaled fermentation and extraction, increasing the time needed to assess a microbes metabolite profile. Here, we integrate matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) into the bioinformatics platform IDBac, enabling streamlined characterization of microbial taxonomic identity, metabolite production potential, and preliminary metabolite annotation through GNPS2 molecular networking. This miniaturized high-content workflow facilitates strain prioritization by providing metabolite annotations directly from single microbial colonies prior to scale-up and extraction. Application of this approach to marine actinomycetes enabled the annotation of lavanducyanin and multiple napyradiomycin analogs. Subsequent investigation led to the discovery of napyradiomycin B8 from marine Streptomyces sp. CNZ-289, which was confirmed by 1D and 2D NMR spectroscopy and MALDI-MS/MS. Expanding this workflow to an untargeted analysis of 25 commensal marine vertebrate-derived bacterial isolates resulted in the annotation of several known bioactive natural products, including surugamides, antimycins, desferrioxamine siderophores, and the isolation and elucidation of harmane derivatives using NMR. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/733640v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@c92cfforg.highwire.dtl.DTLVardef@1a9522borg.highwire.dtl.DTLVardef@151b309org.highwire.dtl.DTLVardef@c1531f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Antimicrobial resistance databases are central to genomic surveillance, but resistance determinants remain distributed across resources with different scopes, structures, and annotations. We developed PanRes, a curated resistance database of 11,717 genes integrating acquired and latent determinants of antibiotic, biocide, and metal resistance within a unified ontology. We predicted representative protein structures and clustered them by structural similarity, grouping proteins into 598 structurally conserved clusters coherent despite sequence divergence. Their structure-guided alignments were used to build Hidden Markov Models (HMMs) for remote homology search. In wastewater metagenomes from seven European cities, PanRes 3D-based HMMs expanded detection beyond high-confidence BLAST, with 35.2% of retained hits identified only by the HMMs and generally showing greater divergence from known proteins. For beta-lactamases, several proteins retained beta-lactamase-like folds and catalytic geometry despite weak sequence similarity. PanRes is available through an interactive web platform (https://panres.rambio.dk/), a structure-informed resource for exploring the whole resistome.