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.

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.

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.

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.

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.

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.

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.

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments.

Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux.IMPORTANCE While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3-5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

Genetic and Chemical Screening in Human Blood Serum Reveals Unique Antibacterial Targets and Compounds against Klebsiella pneumoniae.

Antibiotics halt the growth of bacteria by targeting core, essential physiology that is required for life on standard microbiological media. Many more biochemical and virulence processes, however, are required for bacteria to cause infection in a host. Indeed, chemical inhibitors of the latter processes are overlooked using conventional antibiotic drug discovery approaches. Here, we use human blood serum as an alternative growth medium to explore new targets and compounds. High-throughput screening of genetic and chemical libraries identified compounds targeting biological activities required by Klebsiella pneumoniae to grow in serum, such as nucleobase biosynthesis and iron acquisition, and showed that serum can chemically transform compounds to reveal cryptic antibacterial activity. One of these compounds, ruthenium red, was effective in a rat bloodstream infection model. Our data demonstrate that human serum is an effective tool to find new chemical matter to address the current antibiotic resistance crisis.

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.

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.

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.

Potentiation of Antibiotics against Gram-Negative Bacteria by Polymyxin B Analogue SPR741 from Unique Perturbation of the Outer Membrane.

Therapeutics targeting Gram-negative bacteria have the challenge of overcoming a formidable outer membrane (OM) barrier. Here, we characterize the action of SPR741, a novel polymyxin B (PMB) analogue shown to potentiate several large-scaffold antibiotics in Gram-negative pathogens. Probing the surface topology of Escherichia coli using atomic force microscopy revealed substantial OM disorder at concentrations of SPR741 that lead to antibiotic potentiation. Conversely, very little cytoplasmic membrane depolarization was observed at these same concentrations, indicating that SPR741 acts predominately on the OM. Truncating the lipopolysaccharide (LPS) core with genetic perturbations uniquely sensitized E. coli to SPR741, suggesting that LPS core residues keep SPR741 at the OM, where it can potentiate a codrug, rather than permit its entry to the cytoplasmic membrane. Further, a promoter activity assay revealed that SPR741 challenge induced the expression of RcsAB, a stress sensor for OM perturbation. Together, these results indicate that SPR741 interacts predominately with the OM, in contrast to the dual action of PMB and colistin at both the outer and cytoplasmic membranes.

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.

A macrophage-based screen identifies antibacterial compounds selective for intracellular Salmonella Typhimurium.

Salmonella Typhimurium (S. Tm) establishes systemic infection in susceptible hosts by evading the innate immune response and replicating within host phagocytes. Here, we sought to identify inhibitors of intracellular S. Tm replication by conducting parallel chemical screens against S. Tm growing in macrophage-mimicking media and within macrophages. We identify several compounds that inhibit Salmonella growth in the intracellular environment and in acidic, ion-limited media. We report on the antimicrobial activity of the psychoactive drug metergoline, which is specific against intracellular S. Tm. Screening an S. Tm deletion library in the presence of metergoline reveals hypersensitization of outer membrane mutants to metergoline activity. Metergoline disrupts the proton motive force at the bacterial cytoplasmic membrane and extends animal survival during a systemic S. Tm infection. This work highlights the predictive nature of intracellular screens for in vivo efficacy, and identifies metergoline as a novel antimicrobial active against Salmonella.

Open-Source High-Throughput Phenomics of Bacterial Promoter-Reporter Strains.

Open-source electronics are becoming more prevalent in biological sciences, enabling novel and unique means of data acquisition. Here, we present 3D-printed, open-source tools to acquire fluorescence phenotypes with high temporal resolution. Printed fluorescence imaging boxes (PFIboxes) cost approximately 200 US dollars to assemble, can be placed in incubators or hypoxic chambers, and accurately read high-density colony arrays of microorganisms. We demonstrate the utility of PFIboxes using a time course gene expression approach, examining global Escherichia coli promoter activity using a fluorescent reporter library across a diverse panel of 15 antibiotics, each at several concentrations. Many secondary and indirect effects were observed when E. coli was challenged with various drugs, including increased gene expression in carbon metabolism processes. Further, kinetic data acquisition enabled non-destructive time course gene expression, clustering of which revealed patterns of co-expression. In all, PFIboxes provide an open solution to gene expression, for about 2 US dollars per treatment condition, including technical replicates.