npj Antimicrobials and Resistance

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

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

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

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

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

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

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

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

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

Phage therapy is increasingly proposed for carbapenem-resistant Klebsiella pneumoniae, but most Klebsiella phages bind a single capsule type, so a phage is useful only where its capsule type circulates. We asked whether published phages cover the capsule types found in Pakistani patients. We typed 254 clinical K. pneumoniae genomes from Pakistan and curated the phage and depolymerase literature into 34 phage-capsule records covering 27 phages and 15 capsule types. Nearly half the cohort was unmatched: 121 of 254 isolates (47.6%), spanning 51 of 66 capsule types, belong to types for which no phage has been described, and 67 of those isolates carry a carbapenemase. Collapsing each BioProject to one isolate per type raises the unmatched fraction to 51.8%. To ask why, we typed 475 genomes sampled on identical criteria from India, Bangladesh and Nepal, China, Europe and the USA. Two capsule types separated sharply. KL81 accounted for 6.3% of South Asian genomes and none of 275 from China, Europe or the USA, and has no published phage; KL107 showed the reverse, 9.8% in those regions and absent from South Asia. Both survived collapsing (P = 0.0014 and P = 0.0002). Six further differences did not, and are attributable to clonal expansion within single studies. Phages are isolated against locally available hosts, so the capsule types entering the global phage collection track the regions doing the isolating. KL81 is a defined target for phage isolation in South Asia.

High attrition rates across clinical drug development impose immense clinical and financial burdens, yet prospectively forecasting whether an individual trial will achieve its primary endpoint remains challenging. Here we present a machine-learning system that predicts clinical trial success across phases I-IV, independently modeling studies with efficacy versus safety/toxicity primary endpoints. To prevent lookahead bias and outcome leakage, models are trained on registration-time features, including target and mechanism-of-action precedent, drug modality and novelty, trial design and operational characteristics, and frozen literature-derived target-choice embeddings. Each prediction yields a continuous Predictive Trial Index (PTI) between 0 and 1 reflecting success probability. On a test set of trials initiated in 2021 or later, the efficacy model achieved an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI 0.812-0.845; n = 2,347) and the safety/toxicity model reached 0.809 (95% CI 0.787-0.831; n = 1,485). These models are embedded in inClinico Agent, an agentic AI system integrating frontier large language models (LLMs) with deterministic scoring engines, retrieval tools and specialized clinical skills. We also introduce ClinBench, a prospective benchmark of 2,040 ongoing trials evaluated by inClinico Agent and five frontier LLMs, establishing a leakage-free foundation for tracking prospective predictive accuracy as trials reach completion.

Urinary tract infections (UTIs) are frequently caused by Escherichia coli and are increasingly associated with antimicrobial resistance, highlighting the need for alternative therapies. Although phage therapy is a promising approach, the rational selection of phages and phage cocktails remains challenging, particularly when detailed genomic information on the infecting strain is unavailable. In this study, we isolated 18 phages from environmental samples collected in Sherbrooke, Quebec, Canada. Genome sequencing identified 15 pure lytic phages representing diverse taxonomic groups and revealed prophage-derived contaminants in three isolates, underscoring the importance of genomic validation during phage discovery. Host range was determined using efficiency-of-plating (EOP) assays against UTI-associated E. coli isolates. Based on host-range overlap and cumulative EOP values, we developed a phenotype-based framework for phage cocktail design and classified combinations as redundant or complementary. Complementary cocktails composed of phages with distinct host ranges and high infection effectiveness consistently outperformed redundant combinations in time-kill assays, demonstrating that host-range diversity and lytic effectiveness are independent properties that should be considered separately during cocktail design. To evaluate cocktail performance under physiologically relevant conditions, the most effective combinations were tested in artificial urine. Both cocktails exhibited reduced activity compared with standard laboratory media, indicating that environmental conditions strongly influence phage-host interactions. Together, our results demonstrate that host-range overlap and cumulative infection effectiveness provide a practical framework for identifying effective phage combinations in the absence of detailed bacterial genomic information and may facilitate the rational development of phage therapies for UTIs.

Antibiotic resistance is increasingly recognized as a major threat to global health, yet the contribution of non-antibiotics to this problem remains poorly defined. Here, we established a transcriptional chemical genomics approach to map how 2,415 host-relevant small molecules influence stress-responsive promoters and fitness of the foodborne pathogen Campylobacter jejuni. This screen uncovered widespread activation of the CmeABC efflux pump by diverse compounds, including several human-targeted drugs. Notably, ibuprofen, one of the most widely used non-steroidal anti-inflammatory drugs worldwide, induced efflux pump expression and thereby enhanced bacterial tolerance to ciprofloxacin. We also identified multiple commensal-sparing human-targeted drugs with growth-inhibitory activity against C. jejuni, highlighting opportunities for pathogen-targeted drug repurposing. Together, our findings reveal that non-antibiotic drugs can both promote resistance-associated phenotypes and selectively suppress pathogen growth. This underscores the need to consider collateral drug effects on bacterial physiology when designing strategies to curb the burden of antimicrobial resistance.

Cryptic pockets are druggable sites that are absent in a protein's resting structure and form only upon backbone rearrangement, posing a challenge for detection from static apo structures. Existing methods rely on generating open conformations through sampling or complex prediction, limiting applicability. Here we present a novel detector that reads cryptic pockets directly from a structural world-model latent representation of a single apo structure, without conformational sampling or external pocket finders. Evaluated on CryptoBench and CryptoBank datasets, the method localizes cryptic sites with top-1 accuracy as high as 0.848 and top-5 accuracy exceeding 0.93, and successfully recovers an allosteric site on held-out WRN helicase structures. The detector complements existing approaches and improves with training data scale. These findings suggest that structural world-model latents encode conformational flexibility, enabling effective cryptic pocket identification from apo structures alone, facilitating drug discovery on challenging targets. Benchmarked against the co-folding engine OpenDDE, the detector finds pockets directly rather than by first predicting a bound complex: on 190 targets held out of both training sets it recovers 119 sites OpenDDE misses, against 2 in the other direction, and at top-5 co-folding's recovered sites are a subset of ours. It runs on any structure from the apo coordinates alone, including the mmCIF-only entries all recent depositions carry, and its accuracy keeps climbing as the training corpus grows. The intended use is prospective cryptic-site nomination on the targets that sequence and static structure leave without a starting point.

Co-administration of phages and antibiotics has been proposed as a therapeutic approach against antibiotic-resistant bacteria. The relationship, however, between antibiotic resistance and phage resistance in clinical isolates is unclear. Here, we examine associations between phage and antibiotic resistance profiles across a panel of Pseudomonas aeruginosa clinical isolates from the Centers for Disease Control (CDC) and Food and Drug Administration (FDA) Antimicrobial Resistance Isolate (ARI) Bank comprising 55 clinical strains with full genome sequences and antibiotic susceptibility testing (AST) data for 11 clinically relevant antibiotics. As phages in this study, we use three well-characterized, morphologically distinct phages, OMKO1, Luz19, and PAML31-1. We screen for phage resistance using a growth suppression assay, then conduct statistical analysis against antibiotic MIC (Minimum Inhibitory Concentration) data provided by the CDC to define association patterns across this dataset. We find multiple significant susceptibility correlations between pairs of antibiotics and phages, and a positive overall association between average phage resistance and antibiotic resistance across the 55 strains, even controlling for phylogenetic associations (=0.358, p<0.005). We conclude that phage and antibiotic resistance are positively associated across this clinical isolate collection, suggesting that the two resistance phenotypes are not independent in P. aeruginosa. These findings have implications for the development of phage-antibiotic cocktails.

Enterobacterales pathogens that encode multiple carbapenemase genes are an emerging public health threat. The genomic and plasmid context of the multi-carbapenemase producers has not yet been explored at scale. We analysed 1501 publicly-available global genomes that encoded two or more genes from the five prevalent carbapenemase families (IMP, KPC, NDM, OXA-48-like, and VIM). We observed 241 distinct allelic combinations, with NDM/OXA-48-like (25.6%), multiple KPC (25.3%), KPC/NDM (18.8%), and multiple NDM (10.9%) being the most common groups. We found that many genomes were part of high-risk lineages (48.0%). Most genomes carried carbapenemase genes on two plasmids (59.2%) or one plasmid (23.5%), and most genomes encoded different carbapenemase families (56.2%) rather than copies of the same allele (36.2%). We observed multiple mechanisms of multi-carbapenemase evolution including plasmid co-integration, convergence with virulence, and generation of novel allele diversity through duplication. Multi-carbapenemase plasmids had more AMR genes that conferred resistance to more AMR classes compared to plasmids encoding a single carbapenemase. The proportion of cross-class combinations (KPC/NDM and NDM/OXA-48-like) increased significantly over time, with NDM-5/OXA-181 (3.3-fold) and NDM-4/OXA-181 (8.9-fold) combinations enriched. These findings provide valuable insights into the genomic features of multi-carbapenemase Enterobacterales to support the development of effective surveillance programmes.

Flow cytometry is central to clinical hematology, generating extensive archives comprising billions of single-cell profiles linked to expert-derived diagnostic summaries. While these records represent a largely untapped repository of collective expert knowledge, the underlying single-cell annotations required for supervised machine learning are typically not recorded in a standardized, accessible format. We present IMPGAT (implicit gating), a weakly supervised multiple-instance learning framework that reconstructs single-cell gating strategies directly from existing sample-level cell-type frequency distributions. The model utilizes an induced set attention mechanism to map single-cell marker profiles to specific cell populations, requiring neither explicit cell-level labels nor unsupervised clustering during training. We evaluate the framework on acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) cohorts, totaling 711 measurements from 268 individuals and 240 million single cells. In ALL, the model's quantification of cell-type abundances shows no statistically significant difference from human inter-expert variability. In AML, the framework achieves blast quantification on par with a recently established supervised state-of-the-art model that relies on explicit single-cell annotations for training. Error analysis shows that mismatches between model predictions and expert gating primarily involve expert-labeled non-cellular events, reflecting the non-incorporation of any explicit physical prior knowledge (such as the geometric relationships traditionally used to identify doublets and debris in scatter cytograms) in model training. Conversely, model certainty correlates with phenotypic borderline cases where manual bisection is inherently difficult. Our approach enables the systematic utilization of retrospective clinical records for the development of automated, standardized gating logic and is applicable to broader high-throughput contexts, such as large-scale immune monitoring or phenotypic drug screening.

Carbapenem-resistant Escherichia coli (CREC) is an emerging global health threat associated with limited treatment options and increased mortality. Here, analysis of 20,331 E. coli genomes from urinary tract infection (UTI) and urine samples identifies phylogroup A sequence type 361 (ST361) as an emerging CREC clone associated with UTI. Phylogenetic and phylodynamic analyses reveal two dominant ST361 clades that emerged in Asia around 2006 and subsequently disseminated intercontinentally. ST361 exhibits near pan-drug resistance, with blaNDM-5 conferring carbapenem resistance. In silico capsule and O-antigen typing reveals a highly conserved Klebsiella-like surface polysaccharide serotype in 95.9% of ST361 genomes, which we show contributes to virulence in UTI and catheter-associated UTI mouse models. Analysis of the mobile genetic elements harbouring blaNDM-5 in ST361 and other CREC further reveals a dynamic interplay between plasmid transfer and insertion elements that contributes to blaNDM-5 dissemination. Together, these findings identify ST361 as an emerging UTI-associated CREC clone and reveal the convergence of clonal expansion, virulence-associated surface architecture and mobile genetic elements in its spread.

The use of bacteriophage (phage) to treat bacterial infections is undergoing a resurgence due to the rise of antibiotic resistance. Pathogenic Escherichia coli, including extraintestinal pathotypes such as uropathogenic E. coli (UPEC), pose a substantial clinical burden with the challenge of multi-drug resistance strains, making it important to progress alternative treatment options such as those based on phage. One major hurdle is selecting effective phage combinations against an infecting strain while accounting for the multiple mechanisms that determine phage susceptibility and bacterial resistance. We previously analysed over 9000 interactions between 31 phage and 314 sequenced E. coli and built machine learning models to predict phage activity against "unseen" E. coli based on these scores and bacterial genome sequence data. However, this approach did not leverage phage gene content, limiting its ability to generalise across phage. Here, we advance this framework by combining bacterial and phage pangenomes into a single predictive model, allowing gene content across related phage as well as bacteria to be incorporated as machine learning features. This unified approach increased predictive accuracy relative to the single-phage models and produced a single model capable of predicting previously unseen phage-E. coli interactions. In leave-phage-out analyses, it also predicted activity for phages whose interaction data were entirely excluded from training. The genomic features most consistently contributing to prediction included bacterial determinants of surface recognition and anti-phage defence, alongside phage-associated features, indicating that this combined model, termed "PanPhage", captures genetic information relevant to multiple stages of the phage-host interaction.

The emergence of antimicrobial resistance restricts the clinical efficacy of therapeutic antibiotic drugs. Elucidating the molecular mechanisms by which bacteria evade antibiotic activities will lead to novel therapeutic approaches. Therefore, it is critical to understand the mechanisms that govern susceptibility to antibiotics. Fluoroquinolones are a class of antibiotics widely used to treat both Gram-positive and Gram-negative bacterial infections. Resistance to fluoroquinolones is widespread in multiple pathogenic bacteria, limiting their therapeutic usefulness. Resistance to fluoroquinolones is well-known to be mediated by Point mutations in the genes encoding the cellular targets of fluoroquinolones, type-II topoisomerases, leads to clinically relevant resistance. However, several recent studies have revealed that dysregulation of translation influences fitness during fluoroquinolone exposure. For example, several screens have identified insertions in gene that encodes elongation factor P (EF-P) increases survival to fluoroquinolones. Here, we sought to understand the impact of EF-P on fluoroquinolone survival using the Gram-positive model organism Bacillus subtilis. We found that loss of EF-P increases the survival of B. subtilis exposed to fluoroquinolones by several orders of magnitude. Furthermore, we demonstrate that fluoroquinolone sensitivity is dependent on post-translational modification of EF-P, indicating that it is dependent on EF-P activity. Remarkably, loss of EF-P increases survival of cells lacking RecA, a key DNA repair protein, which is crucial for the survival of fluoroquinolone drugs. Using transcriptomics, we identify key cellular response pathways differentially regulated in cells lacking EF-P during fluoroquinolone treatment. We conclude that EF-P regulates diverse mechanisms that drive the susceptibility to fluoroquinolones.