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.

Carbapenem-resistant Gram-negative bacteria (CRGNB) infections remain difficult to manage because treatment decisions must balance heterogeneous patient risk, limited antibiotic options, potential toxicity and emerging resistance. Clinical care in this setting requires not only single-endpoint risk prediction, but also decision-support frameworks that can jointly enable prognosis assessment, result interpretation, and individualized treatment comparison. Here we present Dr.BUG, an interactive clinical AI agent for personalized decision support in CRGNB infection. Dr.BUG integrates stable feature-set selection, multi-task prognostic modelling, interpretability analysis and model-based simulation of antibiotic regimen recommendation into a unified workflow. Using a development cohort, a temporally independent validation cohort, and external cohorts from the MIMIC-IV dataset, we developed and validated models for four clinically relevant tasks: clinical efficacy, survival outcome, polymyxin resistance and treatment duration. Model inputs were derived primarily from routinely available and relatively low-cost clinical variables, supporting translational feasibility. Across the major tasks, selected-feature models matched or exceeded the performance of their full-feature counterparts while using fewer variables, as reflected in 82.0% of optimized-metric comparisons in the development cohort, and remained robust in both temporal and external validation. Dr.BUG further provided both population-level and patient-level interpretability and generated individualized rankings of candidate antibiotic regimens. In the retrospective analysis of non-survivors, clinician review suggested that regimens recommended by Dr.BUG might be associated with higher predicted survival probabilities. These findings support a broader role for clinical AI in complex drug-resistant infections, extending its utility from offline risk prediction to interpretable, deployable, and personalized decision support.

Antimicrobial resistance (AMR) is a growing global threat to human health, and rapid methods for characterising emerging antimicrobial resistance genes (ARGs) are needed. Here, we develop a semi-automated workflow using cell-free gene expression (CFE) systems to measure the activity of two ARGs encoded on plasmid DNA that produce rifampicin-inactivating and gentamicin-inactivating enzymes. We validated the use of a small benchtop Myra liquid handling system compared to manual pipetting, with no statistical differences observed. After optimising the pre-incubation time of ARGs and dispensing protocol, expression of aac(3)-IIa increased the half-maximal inhibition concentration (IC50) of gentamicin by over 150-fold, while arr-3 increased the IC50 of rifampicin by approximately 20-fold compared to controls. Future work could extend this platform to characterise novel ARGs identified through genomic surveillance or rapidly profile activity of new or derivative antibiotics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/720151v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1a61fe3org.highwire.dtl.DTLVardef@1778eadorg.highwire.dtl.DTLVardef@380be4org.highwire.dtl.DTLVardef@194bb63_HPS_FORMAT_FIGEXP M_FIG C_FIG

In vitro methods to characterize drug combinations typically involve phenotypic screenings using checkerboard assays (CBA) or, more recently, DiaMOND. Such approaches rely on the Fractional Inhibitory Concentration Index (FICI), a fixed-time measurement of growth inhibition that, nonetheless, necessitates secondary validation by time-kill assays (TKA). Longitudinal time-kinetics of bacterial killing are considered the gold standard in vitro proxy for antimicrobial activity, but they required increased assay complexity, particularly against the slow growing Mycobacterium tuberculosis. Here, we developed a new methodology named OPTIKA (Optimized Time Kill Assays) that enhances the capacity of traditional TKA by over 1000-fold. This allows for easy and dynamic examination of n-way drug interactions by simultaneously monitoring bactericidal and sterilizing capacities in a longitudinal manner. We then replicated previous DiaMOND studies and performed comparisons using CBA and OPTIKA methodologies. We demonstrate that selection of the efficacy parameters (either routed on bacteriostatic, bactericidal or sterilizing properties) affects the interpretation of in vitro drug interactions and, consequently, its potential translational value. The increased assay throughput provided by OPTIKA offers a novel framework for developing tuberculosis treatment regimens. TeaserOPTIKA is a new methodology that increases time-kill assay performance against Mycobacterium tuberculosis by over 1,000-fold

Tuberculosis (TB) remains a leading cause of infectious disease mortality, and the continued emergence of drug-resistant Mycobacterium tuberculosis (MTB) strains threatens the effectiveness of standard treatment regimens. Culture-based antibiotic susceptibility testing (AST) remains the clinical reference standard for resistance determination but typically requires six to eight weeks, delaying initiation of optimized therapy for patients with drug-resistant disease. Whole-genome sequencing (WGS)-based approaches provide a rapid alternative for predicting antimicrobial resistance directly from genomic data and are increasingly being incorporated into diagnostic workflows. This survey reviews computational approaches for genomic resistance prediction in MTB, focusing on two major classes of methods: catalog-based tools that identify established resistance-conferring variants, and de novo machine learning approaches that infer resistance from genome-wide sequence features. We examine the strengths and limitations of these approaches with respect to interpretability, scalability, computational requirements, and concordance with phenotypic testing. We further discuss emerging directions in quantitative minimum inhibitory concentration (MIC) prediction, challenges in pyrazinamide susceptibility testing, and the limited availability of resistant isolates for newer and repurposed drugs used in multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens. Continued expansion of paired phenotypic and genomic datasets, standardized MIC testing protocols, and rigorous lineage-aware evaluation frameworks will be essential for improving the clinical reliability and global deployment of genomic resistance prediction for tuberculosis diagnostics.

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.

We present BIOTIA-DX RESISTANCE (BDXR), our submission to the CAMDA 2026 AMR Challenge. This work extends our CAMDA 2025 submission [1] to a new set of six species-drug pairs and adds k-mer-based feature engineering (both targeted and whole-genome) for pairs where the 2025 gene-presence base model underperforms. BDXR achieved a mean accuracy of 86.1% across the six pairs on the CAMDA 2026 test set, ranking first on four pairs, tied for first on Streptococcus pneumoniae (penicillin), and second on Campylobacter jejuni (nalidixic acid); per-pair test accuracy ranged from 69.9% (C. jejuni, nalidixic acid) to 98.8% (S. pneumoniae, penicillin). We refer the reader to our 2025 preprint [1] for the underlying workflow, dataset curation, and clinical motivation; this preprint focuses on the results and methodological changes that are new in 2026.

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.

Microbial whole-genome sequence data is now generated at scale, including to support antimicrobial resistance (AMR) surveillance and understand resistance mechanisms, yet analytical infrastructure for systematically linking AMR genotypes to measured phenotypes remains fragmented. Here we present AMRgen, an R package to support systematic AMR genotype-phenotype analysis. AMRgen imports and harmonises genotypic data from common bioinformatics tools, alongside phenotypic data from automated antimicrobial susceptibility testing instruments and public repositories. It supports common analyses linking data to reference distributions, modelling associations, quantifying concordance, and producing publication-ready visualisations including UpSet plots that jointly display genotypic marker combination frequencies and associated phenotypic distributions. We demonstrate AMRgens utility using publicly available surveillance data for World Health Organization priority AMR pathogens, Neisseria gonorrhoeae, Klebsiella pneumoniae, Escherichia coli and Salmonella enterica. AMRgen, available free and open-source at https://AMRgen.org, provides a reproducible end-to-end foundation for genotype-phenotype research in AMR genomics, clinical microbiology, and public health surveillance.

Microbial cultivation optimization remains labor-intensive and inefficient, requiring extensive experimental screening to identify suitable growth conditions. Traditional one-factor-at-a-time approaches are particularly ineffective for exploring complex, multidimensional nutrient parameter spaces. We present MicroGrowAgents, an AI-driven, agent-based system that automates the design of optimized growth media through integration of knowledge graphs, metabolic modeling, and optimal experimental design. The system employs 28 specialized agents and 50 skills that query structured biological knowledge (KG-Microbe: 864,363 validated species), mine literature evidence (245+ papers), perform genome-guided design (57 genomes, 667,000+ annotated features), and generate statistically optimal experimental designs using the MaxPro algorithm. We applied the approach to Methylorubrum extorquens AM1 by cultivating 70 designed conditions in quadruplicate and assessing three concurrent objectives: biomass (OD600 at 740 nm), redox activity (Abs590 Biolog proxy), and lanthanide uptake (residual Nd measured by arsenazo III). Monte-Carlo resampling of the replicate-level uncertainty (1000 iterations) identified a single stable Pareto-optimal medium, MPOB_058 (membership frequency 0.99), together with two borderline candidates and six rare appearers, providing a robust anchor set for subsequent rounds of design-build-test-learn. The integration of chemical similarity search (208,000+ embeddings), metabolic gap analysis, and multi-modal reasoning enables evidence-based hypothesis generation that reduces experimental burden while accelerating discovery of growth-promoting conditions. MicroGrowAgents provides complete provenance tracking with cryptographic checksums and 90.5% literature citation coverage, advancing reproducible, data-driven approaches to microbial cultivation. Author SummaryGrowing microbes in the laboratory is like figuring out the right recipe: too much or too little of any nutrient and they barely grow. Scientists have traditionally tested ingredients one at a time, an approach that is slow, expensive, and poorly suited to the dozens of interacting nutrients that real microbes need. We built MicroGrowAgents, an AI system that acts like a team of specialist scientists working together. It consults structured biological databases, reads the published literature, inspects microbial genomes, and uses statistical experimental design to recommend nutrient combinations worth testing in the laboratory. Applied to Methylorubrum extorquens AM1, a methanol-eating bacterium of interest for capturing rare-earth elements, the system designed 70 growth conditions and identified one robust winner that performed well across cell growth, metabolism, and lanthanide uptake. The software is free and open-source, helping any laboratory adopt these tools.

Carbapenemase-producing Enterobacterales represent a growing global threat due to their extensive antimicrobial resistance and rapid dissemination. This study characterized the phenotypic and genomic features of Escherichia coli and Klebsiella pneumoniae isolates collected between 2020 and 2022 from four healthcare institutions in Lima, Peru. A total of 320 non-redundant isolates (61 E. coli and 259 K. pneumoniae) were analyzed through antimicrobial susceptibility testing, polymerase chain reaction, and whole-genome sequencing. The most frequent carbapenemase gene was blaNDM (69%), followed by blaKPC (16.9%) and blaOXA-48-like (4.6%). Eleven K. pneumoniae isolates co-produced NDM and KPC, and one E. coli isolate co-harbored NDM and OXA-48-like. All isolates were multidrug resistant, and 5% were pandrug resistant. Novel {beta}-lactam/{beta}-lactamase inhibitor combinations such as aztreonam/avibactam and cefiderocol showed complete activity against all classes of carbapenemases. Genomic analysis revealed predominant E. coli sequence types ST167 and ST410 and K. pneumoniae lineages ST147, ST15, ST45, and ST273. The blaNDM-5 allele was detected for the first time in Peru, mostly in E. coli ST167, carried on multireplicon IncF-type plasmids. In K. pneumoniae, ST147 was identified as a dominant clone associated with blaNDM-1, indicating sustained local dissemination of high-risk clonal groups. The coexistence of multiple carbapenemases and plasmid backbones highlights the ongoing evolution of resistance mechanisms. These findings provide actionable evidence to guide treatment strategies in settings with high prevalence of metallo-{beta}-lactamases and underscores the need for continuous genomic surveillance and antimicrobial stewardship to mitigate their clinical and epidemiological impact.

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.

Klebsiella pneumoniae (Kp) is a common antibiotic-resistant pathogen that colonizes the gastrointestinal tract and can disseminate to peripheral sites, causing a range of infections including bacteremia, urinary tract infections, and pneumonia. Intestinal colonization with Kp is a risk factor for subsequent infection, as the colonizing strain frequently corresponds to the infecting isolate. Accordingly, targeting Kp prior to dissemination at the site of colonization through decolonization strategies offers a promising approach to mitigate infection risk. In this study, we evaluated the repurposing of existing drugs with previously uncharacterized antibacterial activity as candidates for Kp decolonization. To this end, we screened an antiviral compound library for their activity against Kp. We identified and validated six compounds with previously uncharacterized activity against Kp. Then, we screened a library of clinical Kp strains against a subset of these compounds and found that their activity was strain-specific to degrees that differed based on the compound. Finally, we tested the activity of these compounds in conditions relevant to the human gut. We determined the activity of these candidates was dependent on biological context. Collectively, these findings support further investigation of antiviral drugs as potential gut decolonization therapies for Kp.

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.

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) remains a potent threat to global public health. Moreover, the alarming surge in the number of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mtb strains will continue to imperil TB control efforts. Thus, the discovery of new TB agents with novel modes of action or resistance-reversing therapeutics is a pressing priority. In this study, we report the generation of spontaneous Mtb mutants exhibiting bedaquiline (BDQ) resistance and the subsequent evaluation of natural product-derived efflux inhibitors (EIs) that restored the antimicrobial efficacy of BDQ against the mutants. BDQ-resistant mutants were successfully isolated, and colonies were observed on agar plates with concentrations up to 100x the minimum inhibitory concentration (MIC). Upon screening against BDQ, the resistant strains exhibited MIC values ranging from 0.098 M to 3.136 M, corresponding to 1-32-fold increases relative to the wild type, with higher resistance observed on 50x- and 100x-selection plates. Genetic analysis identified point mutations and frameshifts in key resistance-related genes, including Rv0678, pepQ, and atpE. Notably, combining BDQ with EIs such as berberine (BER), reserpine (RES), piperine (PIP), and lyoniresinol (LYO) remarkably lowered the MIC in the selected mutant strain. Synergistic effects were observed for BDQ+BER (FICI = 0.188; 16-fold MIC reduction) and BDQ+RES (FICI = 0.37; 8-fold MIC reduction). For BDQ+LYO, the FICI could not be calculated because LYO did not have an MIC at the highest concentration tested; however, this combination produced the strongest effect, restoring susceptibility with a 64-fold MIC reduction and exhibiting bactericidal activity. These results highlight the role of efflux pumps in BDQ resistance and support the use of natural product-derived EIs as potential supplementary therapies against drug-resistant Mtb.

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.

Mycobacterium tuberculosis possesses a flexible metabolic system helping it to survive inside the host. The type II NADH dehydrogenase, composed of Ndh and NdhA, essential for bacilli, is a promising drug target. Based on ATP depletion values, two quinoline scaffolds were shortlisted after screening of a library of drug like molecules. Structurally, both 64-9C and 64-9D carry ester moieties at the 5- and 8-positions of the quinoline core, respectively. Ease to re-synthesise 64-9D resulted in synthesis of a focused library of compounds, with MIC values of 0.25-4 g/mL, consistent with ATP depletion. These compounds exhibited bactericidal activity against non-replicating mycobacteria, and showed potent efficacy against multidrug-resistant isolates. Altered, intracellular NADH/NAD+ ratio and reduced respiration was indicative of oxidative phosphorylation inhibition. Inhibition of the purified recombinant NDH protein uncompetitively, SNPs in gene encoding NDH-2 for selected one step mutants and, molecular modelling of 4FQN and 2FQN validated NDH-2 as a target for these compounds. The derivative 2FQN exhibited dose-dependent bactericidal efficacy in mice, underscoring the potential of the series as a promising anti-tuberculosis candidates.

Long COVID affects millions of people worldwide, yet no disease-modifying treatment has been approved, and existing interventions have shown only modest and inconsistent benefits. A key reason for this limited progress is that current computational drug repurposing pipelines do not match well with the clinical reality of Long COVID. These patients often have persistent, multi-systemic symptoms and may already be taking multiple medications, making treatment safety a primary concern. However, most repurposing workflows still treat safety as a downstream filter and rely on disease-associated targets rather than causal drivers. They also assume that the findings of one analysis would generalize across the diverse presentations of Long COVID. We introduce SPLIT, a safety-first repurposing framework that addresses these limitations. SPLIT prioritizes safety at the start of the candidate evaluation, integrates complementary causal inference strategies to identify likely driver genes, and uses a counterfactual substitution design to compare drugs within specific cohort contexts. When applied to cognitive and respiratory Long COVID cohorts, SPLIT revealed three main findings. First, drugs with similar predicted efficacy could have very different predicted safety profiles. Second, the drugs flagged as unfavorable were often different between the two cohorts, showing that drug prioritization is phenotype-specific. Third, SPLIT flagged 18 drugs currently under active investigation in Long COVID trials as having unfavorable predicted profiles. SPLIT provides a practical framework to identify safer, more context-appropriate candidates earlier in the process, supporting more targeted and better-tolerated treatment strategies for Long COVID.

The rise of antimicrobial resistance warrants renewed attention to established but overlooked antibiotics such as nitroxoline (NTX). Here, we systematically dissect NTXs mode of action and investigate the contribution of its first-pass metabolites, NTX-sulphate and NTX-glucuronide. We identified metallophore-mediated cellular iron deprivation as the principal antibacterial mechanism of NTX, characterized by induction of iron acquisition pathways and Fe-S cluster proteins, and concomitant loss of protein-bound iron. In contrast, NTX metabolites were biologically inactive and lacked metal-chelating properties. Ex vivo assays demonstrated that clinically relevant uropathogens, including Escherichia coli and Klebsiella pneumoniae, efficiently reconvert these metabolites into active NTX in human urine. Together, our findings establish a mechanistic framework linking NTX antibacterial activity, host detoxification, and pathogen-dependent metabolite reactivation, and providing a molecular explanation for NTXs enduring therapeutic potential and favourable safety profile.