Identification of an antibiotic from an HTS targeting EF-Tu:tRNA interaction: a prospective topical treatment for MRSA skin infections.

Because of the urgent need for new antibiotics to treat drug-resistant bacterial pathogens, we employed an assay that rapidly screens large quantities of compounds for their ability to interfere with bacterial protein synthesis, in particular, the delivery of amino acids to the ribosome via tRNA and elongation factor Tu (EF-Tu). We have identified a drug lead, named MGC-10, which kills Gram-positive bacteria, including methicillin-resistant (MRSA), with a MIC of 6 µM, while being harmless to mammalian cells in that concentration range. The antibacterial activity of MGC-10 was broad against over 50 strains of antibiotic-resistant samples obtained from hospital infections, where MGC-10 inhibited all tested strains of MRSA.

Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline.

Multidrug-resistant tuberculosis (MDR-TB), defined as resistance to the first-line drugs isoniazid and rifampin, is a growing source of global mortality and threatens global control of tuberculosis disease. The diarylquinoline bedaquiline has recently emerged as a highly efficacious drug against MDR-TB and kills Mycobacterium tuberculosis by inhibiting mycobacterial ATP synthase. However, the mechanisms underlying bedaquiline's efficacy against MDR-TB remain unknown.

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.

(Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs.

An in vitro model of the macrophage-endothelial interface to characterize CAR T-cell induced cytokine storm.

Chimeric Antigen Receptor (CAR) T-cell therapy is a highly effective treatment for B-cell malignancies but limited in use due to clinically significant hyperinflammatory toxicities. Understanding the pathophysiologic mechanisms which mediate these toxicities can help identify novel management strategies. Here we report a novel in vitro model of the macrophage-endothelial interface to study the effects of CAR T-cell-induced cytokine storm.

A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome.

H37Rv is the most widely used Mycobacterium tuberculosis strain, and its genome is globally used as the M. tuberculosis reference sequence. Here, we present Bact-Builder, a pipeline that uses consensus building to generate complete and accurate bacterial genome sequences and apply it to three independently cultured and sequenced H37Rv aliquots of a single laboratory stock.

Longitudinal Analysis of Biologic Correlates of COVID-19 Resolution: Case Report.

While the biomarkers of COVID-19 severity have been thoroughly investigated, the key biological dynamics associated with COVID-19 resolution are still insufficiently understood. We report a case of full resolution of severe COVID-19 due to convalescent plasma transfusion. Following transfusion, the patient showed fever remission, improved respiratory status, and rapidly decreased viral burden in respiratory fluids and SARS-CoV-2 RNAemia.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure.

Nucleotide metabolism plays a central role in bacterial physiology, producing the nucleic acids necessary for DNA replication and RNA transcription. Recent studies demonstrate that nucleotide metabolism also proactively contributes to antibiotic-induced lethality in bacterial pathogens and that disruptions to nucleotide metabolism contributes to antibiotic treatment failure in the clinic. As antimicrobial resistance continues to grow unchecked, new approaches are needed to study the molecular mechanisms responsible for antibiotic efficacy.

Growth and Stress Tolerance Comprise Independent Metabolic Strategies Critical for Staphylococcus aureus Infection.

Staphylococcus aureus is an important pathogen that leads to high morbidity and mortality. Although S. aureus produces many factors important for pathogenesis, few have been validated as playing a role in the pathogenesis of S.

Clinically relevant mutations in core metabolic genes confer antibiotic resistance.

Although metabolism plays an active role in antibiotic lethality, antibiotic resistance is generally associated with drug target modification, enzymatic inactivation, and/or transport rather than metabolic processes. Evolution experiments of rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed adapted to representative antibiotics at increasingly heightened metabolic states.

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training data set and then applied to a test data set on which predictive performance is evaluated.

Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate.

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy. However, the two are interrelated as bacterial growth inherently imposes a metabolic burden; thus, determining individual contributions from each is challenging. Indeed, faster growth is often correlated with increased antibiotic efficacy; however, the concurrent role of metabolism in that relationship has not been well characterized.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model.

Antibiotic-Induced Changes to the Host Metabolic Environment Inhibit Drug Efficacy and Alter Immune Function.

Bactericidal antibiotics alter microbial metabolism as part of their lethality and can damage mitochondria in mammalian cells. In addition, antibiotic susceptibility is sensitive to extracellular metabolites, but it remains unknown whether metabolites present at an infection site can affect either treatment efficacy or immune function. Here, we quantify local metabolic changes in the host microenvironment following antibiotic treatment for a peritoneal Escherichia coli infection.

Antibiotic efficacy-context matters.

Antibiotic lethality is a complex physiological process, sensitive to external cues. Recent advances using systems approaches have revealed how events downstream of primary target inhibition actively participate in antibiotic death processes. In particular, altered metabolism, translational stress and DNA damage each contribute to antibiotic-induced cell death.

Lethality of MalE-LacZ hybrid protein shares mechanistic attributes with oxidative component of antibiotic lethality.

Downstream metabolic events can contribute to the lethality of drugs or agents that interact with a primary cellular target. In bacteria, the production of reactive oxygen species (ROS) has been associated with the lethal effects of a variety of stresses including bactericidal antibiotics, but the relative contribution of this oxidative component to cell death depends on a variety of factors. Experimental evidence has suggested that unresolvable DNA problems caused by incorporation of oxidized nucleotides into nascent DNA followed by incomplete base excision repair contribute to the ROS-dependent component of antibiotic lethality.

Carbon Sources Tune Antibiotic Susceptibility in Pseudomonas aeruginosa via Tricarboxylic Acid Cycle Control.

Metabolically dormant bacteria present a critical challenge to effective antimicrobial therapy because these bacteria are genetically susceptible to antibiotic treatment but phenotypically tolerant. Such tolerance has been attributed to impaired drug uptake, which can be reversed by metabolic stimulation. Here, we evaluate the effects of central carbon metabolite stimulations on aminoglycoside sensitivity in the pathogen Pseudomonas aeruginosa.

Antibiotic efficacy is linked to bacterial cellular respiration.

Bacteriostatic and bactericidal antibiotic treatments result in two fundamentally different phenotypic outcomes--the inhibition of bacterial growth or, alternatively, cell death. Most antibiotics inhibit processes that are major consumers of cellular energy output, suggesting that antibiotic treatment may have important downstream consequences on bacterial metabolism. We hypothesized that the specific metabolic effects of bacteriostatic and bactericidal antibiotics contribute to their overall efficacy.

Antibiotics induce redox-related physiological alterations as part of their lethality.

Deeper understanding of antibiotic-induced physiological responses is critical to identifying means for enhancing our current antibiotic arsenal. Bactericidal antibiotics with diverse targets have been hypothesized to kill bacteria, in part by inducing production of damaging reactive species. This notion has been supported by many groups but has been challenged recently.

PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling.

β-Adrenergic signaling is spatiotemporally heterogeneous in the cardiac myocyte, conferring exquisite control to sympathetic stimulation. Such heterogeneity drives the formation of protein kinase A (PKA) signaling microdomains, which regulate Ca(2+) handling and contractility. Here, we test the hypothesis that the nucleus independently comprises a PKA signaling microdomain regulating myocyte hypertrophy.

Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP.

Understanding how specific cyclic AMP (cAMP) signals are organized and relayed to their effectors in different compartments of the cell to achieve functional specificity requires molecular tools that allow precise manipulation of cAMP in these compartments. Here we characterize a new method using bicarbonate-activatable and genetically targetable soluble adenylyl cyclase to control the location, kinetics and magnitude of the cAMP signal. Using this live-cell cAMP manipulation in conjunction with fluorescence imaging and mechanistic modeling, we uncovered the activation of a resident pool of protein kinase A (PKA) holoenzyme in the nuclei of HEK-293 cells, modifying the existing dogma of cAMP-PKA signaling in the nucleus.

Phospholemman is a negative feed-forward regulator of Ca2+ in β-adrenergic signaling, accelerating β-adrenergic inotropy.

Sympathetic stimulation enhances cardiac contractility by stimulating β-adrenergic signaling and protein kinase A (PKA). Recently, phospholemman (PLM) has emerged as an important PKA substrate capable of regulating cytosolic Ca(2+) transients. However, it remains unclear how PLM contributes to β-adrenergic inotropy.