A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

Central metabolism is a key player in E. coli biofilm stimulation by sub-MIC antibiotics.

Exposure of Escherichia coli to sub-inhibitory antibiotics stimulates biofilm formation through poorly characterized mechanisms. Using a high-throughput Congo Red binding assay to report on biofilm matrix production, we screened ~4000 E. coli K12 deletion mutants for deficiencies in this biofilm stimulation response. We screened using three different antibiotics to identify core components of the biofilm stimulation response. Mutants lacking acnA, nuoE, or lpdA failed to respond to sub-MIC cefixime and novobiocin, implicating central metabolism and aerobic respiration in biofilm stimulation. These genes are members of the ArcA/B regulon-controlled by a respiration-sensitive two-component system. Mutants of arcA and arcB had a 'pre-activated' phenotype, where biofilm formation was already high relative to wild type in vehicle control conditions, and failed to increase further with the addition of sub-MIC cefixime. Using a tetrazolium dye and an in vivo NADH sensor, we showed spatial co-localization of increased metabolic activity with sub-lethal concentrations of the bactericidal antibiotics cefixime and novobiocin. Supporting a role for respiratory stress, the biofilm stimulation response to cefixime and novobiocin was inhibited when nitrate was provided as an alternative electron acceptor. Deletion of a gene encoding part of the machinery for respiring nitrate abolished its ameliorating effects, and nitrate respiration increased during growth with sub-MIC cefixime. Finally, in probing the generalizability of biofilm stimulation, we found that the stimulation response to translation inhibitors, unlike other antibiotic classes, was minimally affected by nitrate supplementation, suggesting that targeting the ribosome stimulates biofilm formation in distinct ways. By characterizing the biofilm stimulation response to sub-MIC antibiotics at a systems level, we identified multiple avenues for design of therapeutics that impair bacterial stress management.

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.

The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded ß-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

A multiplexable assay for screening antibiotic lethality against drug-tolerant bacteria.

Antibiotic screens typically rely on growth inhibition to characterize compound bioactivity-an approach that cannot be used to assess the bactericidal activity of antibiotics against bacteria in drug-tolerant states. To address this limitation, we developed a multiplexed assay that uses metabolism-sensitive staining to report on the killing of antibiotic-tolerant bacteria. This method can be used with diverse bacterial species and applied to genome-scale investigations to identify therapeutic targets against tolerant pathogens.

Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery.

Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.

Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small-Molecule Inhibitors of Escherichia coli.

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 molecules (actives) from a small-molecule screen in efflux-compromised Escherichia coli We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli Furthermore, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first efflux susceptibility machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico Here, SERF demonstrated high accuracy in identifying such molecules. Furthermore, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side-chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side-chain modification to design novel antimicrobials evading efflux pumps.

Assembly and clustering of natural antibiotics guides target identification.

Antibiotics are essential for numerous medical procedures, including the treatment of bacterial infections, but their widespread use has led to the accumulation of resistance, prompting calls for the discovery of antibacterial agents with new targets. A majority of clinically approved antibacterial scaffolds are derived from microbial natural products, but these valuable molecules are not well annotated or organized, limiting the efficacy of modern informatic analyses. Here, we provide a comprehensive resource defining the targets, chemical origins and families of the natural antibacterial collective through a retrobiosynthetic algorithm. From this we also detail the directed mining of biosynthetic scaffolds and resistance determinants to reveal structures with a high likelihood of having previously unknown modes of action. Implementing this pipeline led to investigations of the telomycin family of natural products from Streptomyces canus, revealing that these bactericidal molecules possess a new antibacterial mode of action dependent on the bacterial phospholipid cardiolipin.

Antagonism screen for inhibitors of bacterial cell wall biogenesis uncovers an inhibitor of undecaprenyl diphosphate synthase.

Drug combinations are valuable tools for studying biological systems. Although much attention has been given to synergistic interactions in revealing connections between cellular processes, antagonistic interactions can also have tremendous value in elucidating genetic networks and mechanisms of drug action. Here, we exploit the power of antagonism in a high-throughput screen for molecules that suppress the activity of targocil, an inhibitor of the wall teichoic acid (WTA) flippase in Staphylococcus aureus. Well-characterized antagonism within the WTA biosynthetic pathway indicated that early steps would be sensitive to this screen; however, broader interactions with cell wall biogenesis components suggested that it might capture additional targets. A chemical screening effort using this approach identified clomiphene, a widely used fertility drug, as one such compound. Mechanistic characterization revealed the target was the undecaprenyl diphosphate synthase, an enzyme that catalyzes the synthesis of a polyisoprenoid essential for both peptidoglycan and WTA synthesis. The work sheds light on mechanisms contributing to the observed suppressive interactions of clomiphene and in turn reveals aspects of the biology that underlie cell wall synthesis in S. aureus. Further, this effort highlights the utility of antagonistic interactions both in high-throughput screening and in compound mode of action studies. Importantly, clomiphene represents a lead for antibacterial drug discovery.

Identification of Two Phosphate Starvation-induced Wall Teichoic Acid Hydrolases Provides First Insights into the Degradative Pathway of a Key Bacterial Cell Wall Component.

The cell wall of most Gram-positive bacteria contains equal amounts of peptidoglycan and the phosphate-rich glycopolymer wall teichoic acid (WTA). During phosphate-limited growth of the Gram-positive model organism Bacillus subtilis 168, WTA is lost from the cell wall in a response mediated by the PhoPR two-component system, which regulates genes involved in phosphate conservation and acquisition. It has been thought that WTA provides a phosphate source to sustain growth during starvation conditions; however, WTA degradative pathways have not been described for this or any condition of bacterial growth. Here, we uncover roles for the Bacillus subtilis PhoP regulon genes glpQ and phoD as encoding secreted phosphodiesterases that function in WTA metabolism during phosphate starvation. Unlike the parent 168 strain, ΔglpQ or ΔphoD mutants retained WTA and ceased growth upon phosphate limitation. Characterization of GlpQ and PhoD enzymatic activities, in addition to X-ray crystal structures of GlpQ, revealed distinct mechanisms of WTA depolymerization for the two enzymes; GlpQ catalyzes exolytic cleavage of individual monomer units, and PhoD catalyzes endo-hydrolysis at nonspecific sites throughout the polymer. The combination of these activities appears requisite for the utilization of WTA as a phosphate reserve. Phenotypic characterization of the ΔglpQ and ΔphoD mutants revealed altered cell morphologies and effects on autolytic activity and antibiotic susceptibilities that, unexpectedly, also occurred in phosphate-replete conditions. Our findings offer novel insight into the B. subtilis phosphate starvation response and implicate WTA hydrolase activity as a determinant of functional properties of the Gram-positive cell envelope.

A cell-based approach to characterize antimicrobial compounds through kinetic dose response.

The rapid spread of antibiotic resistance has created a pressing need for the development of novel drug screening platforms. Herein, we report on the use of cell-based kinetic dose response curves for small molecule characterization in antibiotic discovery efforts. Kinetically monitoring bacterial growth at sub-inhibitory concentrations of antimicrobial small molecules generates unique dose response profiles. We show that clustering of profiles by growth characteristics can classify antibiotics by mechanism of action. Furthermore, changes in growth kinetics have the potential to offer insight into the mechanistic action of novel molecules and can be used to predict off-target effects generated through structure-activity relationship studies. Kinetic dose response also allows for detection of unstable compounds early in the lead development process. We propose that this kinetic approach is a rapid and cost-effective means to gather critical information on antimicrobial small molecules during the hit selection and lead development pipeline.

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli.

Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152. This mobile CRISPRi library can be conjugated into other ordered genetic libraries to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two complementary crosses: (1) an Lpp deletion into every CRISPRi knockdown strain and (2) the lolA knockdown plasmid into the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed suppressing interactions for the loci in question.

A platform for predicting mechanism of action based on bacterial transcriptional responses identifies an unusual DNA gyrase inhibitor.

In the search for much-needed new antibacterial chemical matter, a myriad of compounds have been reported in academic and pharmaceutical screening endeavors. Only a small fraction of these, however, are characterized with respect to mechanism of action (MOA). Here, we describe a pipeline that categorizes transcriptional responses to antibiotics and provides hypotheses for MOA. 3D-printed imaging hardware PFIboxes) profiles responses of Escherichia coli promoter-GFP fusions to more than 100 antibiotics. Notably, metergoline, a semi-synthetic ergot alkaloid, mimics a DNA replication inhibitor. In vitro supercoiling assays confirm this prediction, and a potent analog thereof (MLEB-1934) inhibits growth at 0.25 µg/mL and is highly active against quinolone-resistant strains of methicillin-resistant Staphylococcus aureus. Spontaneous suppressor mutants map to a seldom explored allosteric binding pocket, suggesting a mechanism distinct from DNA gyrase inhibitors used in the clinic. In all, the work highlights the potential of this platform to rapidly assess MOA of new antibacterial compounds.

The dynamic nature of bacterial surfaces: implications for metal-membrane interaction.

Bacterial envelopes are chemically complex, diverse structures. Chemical and physical influences from cellular microenvironments force lipids, proteins, and sugars to organize dynamically. This constant reorganization serves to maintain compartmentalization and function, but also affects the influence of charged functional groups that drive electrochemical interactions with metal ions. The interactions of metal species with cell walls are of particular interest because (i) metals must be taken up or excluded to maintain cell function, and (ii) electrochemical interactions between charged metals and anionic ligands are inevitable. In this review we explore the associations of metals with metal-reactive ligands found within bacterial envelopes, and outward to include those within biofilm matrics. The mechanisms that underpin metal binding to these ligands have not been well considered with respect to the dynamic organization of the biological structures themselves. Bacteria respond sensitively and rapidly to growth environment with de novo syntheses of chemical constituents, which can impact metal interactions. We discuss causes of membrane chemical variability as observed in laboratory experiments, and offer consequences for this adaptability in natural settings. The structural impacts of metal ion associations with bacterial envelopes are often overlooked. This review explores how dynamic bacterial surface chemistry influences metal binding and, in turn, how metal ions impact membrane organization in laboratory and natural conditions.

A standardized nomenclature for resistance-modifying agents in the Comprehensive Antibiotic Resistance Database.

IMPORTANCE: While increasing rates of antimicrobial resistance undermine our current arsenal of antibiotics, resistance-modifying agents (RMAs) hold promise to extend the lifetime of these important molecules. We here provide a standardized nomenclature for RMAs within the Comprehensive Antibiotic Resistance Database in aid of RMA discovery, data curation, and genome mining.

Inhibiting fatty acid synthesis overcomes colistin resistance.

Treating multidrug-resistant infections has increasingly relied on last-resort antibiotics, including polymyxins, for example colistin. As polymyxins are given routinely, the prevalence of their resistance is on the rise and increases mortality rates of sepsis patients. The global dissemination of plasmid-borne colistin resistance, driven by the emergence of mcr-1, threatens to diminish the therapeutic utility of polymyxins from an already shrinking antibiotic arsenal. Restoring sensitivity to polymyxins using combination therapy with sensitizing drugs is a promising approach to reviving its clinical utility. Here we describe the ability of the biotin biosynthesis inhibitor, MAC13772, to synergize with colistin exclusively against colistin-resistant bacteria. MAC13772 indirectly disrupts fatty acid synthesis (FAS) and restores sensitivity to the last-resort antibiotic, colistin. Accordingly, we found that combinations of colistin and other FAS inhibitors, cerulenin, triclosan and Debio1452-NH3, had broad potential against both chromosomal and plasmid-mediated colistin resistance in chequerboard and lysis assays. Furthermore, combination therapy with colistin and the clinically relevant FabI inhibitor, Debio1452-NH3, showed efficacy against mcr-1 positive Klebsiella pneumoniae and colistin-resistant Escherichia coli systemic infections in mice. Using chemical genomics, lipidomics and transcriptomics, we explored the mechanism of the interaction. We propose that inhibiting FAS restores colistin sensitivity by depleting lipid synthesis, leading to changes in phospholipid composition. In all, this work reveals a surprising link between FAS and colistin resistance.

Inspiratory limb carbon dioxide entrainment during high-frequency oscillatory ventilation: characterization in a mechanical test lung and swine model.

BACKGROUND: High-frequency oscillatory ventilation (HFOV) has been utilized as a rescue oxygenation therapy in adults with ARDS over the last decade. The HFOV oscillating piston can generate negative pressure during the exhalation cycle, which has been termed active exhalation. We hypothesized that this characteristic of HFOV entrains CO(2) into the inspiratory limb of the circuit and increases the total dead space. The purpose of this study was to determine if retrograde CO(2) entrainment occurs and how it is altered by HFOV parameter settings. METHODS: An HFOV was interfaced to a cuffed endotracheal tube and connected to a mechanical test lung. Negative pressure changes within the circuit's inspiratory limb were measured while HFOV settings were manipulated. Retrograde CO(2) entrainment was evaluated by insufflating CO(2) into the test lung to achieve 40 mm Hg at the carina. Inspiratory limb CO(2) entrainment was measured at incremental distances from the Y-piece. HFOV settings and cuff leak were varied to assess their effect on CO(2) entrainment. Control experiments were conducted using a conventional ventilator. Test lung results were validated on a large hypercapnic swine. RESULTS: Negative pressure was detectable within the inspiratory limb of the HFOV circuit and varied inversely with mean airway pressure (P(-)(aw)) and directly with oscillatory pressure amplitude (ΔP). CO(2) was readily detectable within the inspiratory limb and was proportional to the negative pressure that was generated. Factors that decreased CO(2) entrainment in both the test lung and swine included low ΔP, high mean airway pressure, high oscillatory frequency (Hz), high bias flow, and endotracheal tube cuff leak placement. CO(2) entrainment was also reduced by utilizing a higher bias flow strategy at any targeted mean airway pressure. CONCLUSIONS: Retrograde CO(2) entrainment occurs during HFOV use and can be manipulated with the ventilator settings. This phenomenon may have clinical implications on the development or persistence of hypercapnia.

Chemical Screen for Vancomycin Antagonism Uncovers Probes of the Gram-Negative Outer Membrane.

The outer membrane of Gram-negative bacteria is a formidable permeability barrier which allows only a small subset of chemical matter to penetrate. This outer membrane barrier can hinder the study of cellular processes and compound mechanism of action, as many compounds including antibiotics are precluded from entry despite having intracellular targets. Consequently, outer membrane permeabilizing compounds are invaluable tools in such studies. Many existing compounds known to perturb the outer membrane also impact inner membrane integrity, such as polymyxins and their derivatives, making these probes nonspecific. We performed a screen of ∼140 000 diverse synthetic compounds, for those that antagonized the growth inhibitory activity of vancomycin at 15 °C in Escherichia coli, to enrich for chemicals capable of perturbing the outer membrane. This led to the discovery that liproxstatin-1, an inhibitor of ferroptosis in human cells, and MAC-0568743, a novel cationic amphiphile, could potentiate the activity of large-scaffold antibiotics with low permeation into Gram-negative bacteria at 37 °C. Liproxstatin-1 and MAC-0568743 were found to physically disrupt the integrity of the outer membrane through interactions with lipopolysaccharide in the outer leaflet of the outer membrane. We showed that these compounds selectively disrupt the outer membrane while minimally impacting inner membrane integrity, particularly at the concentrations needed to potentiate Gram-positive-targeting antibiotics. Further exploration of these molecules and their structural analogues is a promising avenue for the development of outer membrane specific probes.

A comprehensive guide to dynamic analysis of microbial gene expression using the 3D-printed PFIbox and a fluorescent reporter library.

Temporally resolved assays of bacterial gene expression using printed fluorescence imaging boxes (PFIboxes) are non-destructive, inexpensive and simple to prepare. Herein, we describe a full experimental pipeline wherein PFIbox parts are modified and 3D printed, electronics assembled and used to study transcriptional responses of Escherichia coli to chemical stressors. A chemical probe is added to agar growth medium, and a promoter-fluorophore fusion library is arrayed in high density on the agar slab. With high temporal resolution, the reporter library is imaged in PFIboxes, then quantified using promoter activity as a measure of gene expression. PFIboxes have advantages over conventional transcriptomic approaches such as RNA-seq, as the non-destructive nature permits a high-resolution temporal dimension in the data. This results in rapid measurement of transcriptional responses to chemical or physical stimuli. Each time-course gene expression assay costs about US$2 to run, in triplicate, using this method. Printing time depends on printer and settings, but once printed, PFIboxes can be fully assembled, programmed and loaded with samples in less than 1 h. Experimental durations and sampling frequency are set according to user need, but can be run in the duration of a microbial growth curve.

Genetic and Chemical-Genetic Interactions Map Biogenesis and Permeability Determinants of the Outer Membrane of Escherichia coli.

Gram-negative bacteria are intrinsically resistant to many antibiotics due to their outer membrane barrier. Although the outer membrane has been studied for decades, there is much to uncover about the biology and permeability of this complex structure. Investigating synthetic genetic interactions can reveal a great deal of information about genetic function and pathway interconnectivity. Here, we performed synthetic genetic arrays (SGAs) in Escherichia coli by crossing a subset of gene deletion strains implicated in outer membrane permeability with nonessential gene and small RNA (sRNA) deletion collections. Some 155,400 double-deletion strains were grown on rich microbiological medium with and without subinhibitory concentrations of two antibiotics excluded by the outer membrane, vancomycin and rifampin, to probe both genetic interactions and permeability. The genetic interactions of interest were synthetic sick or lethal (SSL) gene deletions that were detrimental to the cell in combination but had a negligible impact on viability individually. On average, there were ∼30, ∼36, and ∼40 SSL interactions per gene under no-drug, rifampin, and vancomycin conditions, respectively; however, many of these involved frequent interactors. Our data sets have been compiled into an interactive database called the Outer Membrane Interaction (OMI) Explorer, where genetic interactions can be searched, visualized across the genome, compared between conditions, and enriched for gene ontology (GO) terms. A set of SSL interactions revealed connectivity and permeability links between enterobacterial common antigen (ECA) and lipopolysaccharide (LPS) of the outer membrane. This data set provides a novel platform to generate hypotheses about outer membrane biology and permeability.IMPORTANCE Gram-negative bacteria are a major concern for public health, particularly due to the rise of antibiotic resistance. It is important to understand the biology and permeability of the outer membrane of these bacteria in order to increase the efficacy of antibiotics that have difficulty penetrating this structure. Here, we studied the genetic interactions of a subset of outer membrane-related gene deletions in the model Gram-negative bacterium E. coli We systematically combined these mutants with 3,985 nonessential gene and small RNA deletion mutations in the genome. We examined the viability of these double-deletion strains and probed their permeability characteristics using two antibiotics that have difficulty crossing the outer membrane barrier. An understanding of the genetic basis for outer membrane integrity can assist in the development of new antibiotics with favorable permeability properties and the discovery of compounds capable of increasing outer membrane permeability to enhance the activity of existing antibiotics.