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. Using this model, we demonstrate that macrophage-mediated inflammation is regulated by endothelial cell activity. Furthermore, endothelial inflammation occurs independently of macrophages following exposure to CAR T-cell products and the induced endothelial inflammation potentiates macrophage-mediated inflammatory signaling, leading to a hyperinflammatory environment. While corticosteroids, the current gold standard of care, attenuate the resulting macrophage inflammatory signaling, the endothelial activity remains refractory to this treatment strategy. Utilizing a network model, coupled to in vitro secretion profiling, we identified STAT3 programming as critical in regulating this endothelial behavior. Lastly, we demonstrate how targeting STAT3 activity can abrogate endothelial inflammation and attenuate this otherwise hyperinflammatory environment. Our results demonstrate that endothelial cells play a central role in the pathophysiology of CAR T-cell toxicities and targeting the mechanisms driving the endothelial response can guide future clinical management.

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. Two of the 4,417,942 base-pair long H37Rv assemblies are 100% identical, with the third differing by a single nucleotide. Compared to the existing H37Rv reference, the new sequence contains ~6.4 kb additional base pairs, encoding ten new regions that include insertions in PE/PPE genes and new paralogs of esxN and esxJ, which are differentially expressed compared to the reference genes. New sequencing and de novo assemblies with Bact-Builder confirm that all 10 regions, plus small additional polymorphisms, are also present in the commonly used H37Rv strains NR123, TMC102, and H37Rv1998. Thus, Bact-Builder shows promise as an improved method to perform accurate and reproducible de novo assemblies of bacterial genomes, and our work provides important updates to the primary M. tuberculosis reference genome.

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. Longitudinal unbiased proteomic analysis of plasma and single-cell transcriptomics of peripheral blood cells conducted prior to and at multiple times after convalescent plasma transfusion identified the key biological processes associated with the transition from severe disease to disease-free state. These included (i) temporally ordered upward and downward changes in plasma proteins reestablishing homeostasis and (ii) post-transfusion disappearance of a subset of monocytes characterized by hyperactivated Interferon responses and decreased TNF-α signaling. Monitoring specific dysfunctional myeloid cell subsets in peripheral blood may provide prognostic keys in COVID-19.

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. Here we review emerging technologies poised to transform understanding into why antibiotics may fail in the clinic. We discuss how these technologies led to the discovery that nucleotide metabolism regulates antibiotic drug responses and why these are relevant to human infections. We highlight opportunities for how studies into nucleotide metabolism may enhance understanding of antibiotic failure mechanisms.

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. aureus pneumonia. To gain a better understanding of the genetic elements required for S. aureus pathogenesis in the airway, we performed an unbiased genome-wide transposon sequencing (Tn-seq) screen in a model of acute murine pneumonia. We identified 136 genes important for bacterial survival during infection, with a high proportion involved in metabolic processes. Phenotyping 80 individual deletion mutants through diverse in vitro and in vivo assays demonstrated that metabolism is linked to several processes, which include biofilm formation, growth, and resistance to host stressors. We further validated the importance of 23 mutations in pneumonia. Multivariate and principal-component analyses identified two key metabolic mechanisms enabling infection in the airway, growth (e.g., the ability to replicate and form biofilms) and resistance to host stresses. As deep validation of these hypotheses, we investigated the role of pyruvate carboxylase, which was important across multiple infection models and confirmed a connection between growth and resistance to host cell killing. Pathogenesis is conventionally understood in terms of the host-pathogen interactions that enable a pathogen to neutralize a host's immune response. We demonstrate with the important bacterial pathogen S. aureus that microbial metabolism influences key traits important for in vivo infection, independent from host immunomodulation. IMPORTANCE Staphylococcus aureus is an important bacterial pathogen that causes significant morbidity and mortality, infecting numerous bodily sites, including the respiratory tract. To identify the bacterial requirements for lung infection, we conducted a genome-wide screen in a mouse model of acute pneumonia. We discovered that metabolic genes were overrepresented in those required for lung infection. In contrast to the conventional view of pathogenesis focusing on immunomodulation, we demonstrate through phenotyping of deletion mutants in several functional assays that replicative ability and tolerance against host defenses form two key metabolic dimensions of bacterial infection. These dimensions are independent for most pathways but are coupled in central carbon metabolism and highlight the critical role of bacterial metabolism in survival against host defenses during infection.

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. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance, and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Although the use of ML for understanding, diagnosing, treating, and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.

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 Escherichia coli rely on growth-dependent selection, which may provide a limited view of the antibiotic resistance landscape. We sequenced and analyzed E. coli adapted to representative antibiotics at increasingly heightened metabolic states. This revealed various underappreciated noncanonical genes, such as those related to central carbon and energy metabolism, which are implicated in antibiotic resistance. These metabolic alterations lead to lower basal respiration, which prevents antibiotic-mediated induction of tricarboxylic acid cycle activity, thus avoiding metabolic toxicity and minimizing drug lethality. Several of the identified metabolism-specific mutations are overrepresented in the genomes of >3500 clinical E. coli pathogens, indicating clinical relevance.

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 efficacy1-3. However, the two are interrelated as bacterial growth inherently imposes a metabolic burden4; thus, determining individual contributions from each is challenging5,6. Indeed, faster growth is often correlated with increased antibiotic efficacy7,8; however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic efficacy, are lacking9. Here, we measured growth and metabolism in parallel across a broad range of coupled and uncoupled conditions to determine their relative contribution to antibiotic lethality. We show that when growth and metabolism are uncoupled, antibiotic lethality uniformly depends on the bacterial metabolic state at the time of treatment, rather than growth rate. We further reveal a critical metabolic threshold below which antibiotic lethality is negligible. These findings were general for a wide range of conditions, including nine representative bactericidal drugs and a diverse range of Gram-positive and Gram-negative species (Escherichia coli, Acinetobacter baumannii and Staphylococcus aureus). This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development.

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. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Quantification of DNA by a Thermal-Durable Biosensor Modified with Conductive Poly(3,4-ethylenedioxythiophene).

The general clinical procedure for viral DNA detection or gene mutation diagnosis following polymerase chain reaction (PCR) often involves gel electrophoresis and DNA sequencing, which is usually time-consuming. In this study, we have proposed a facile strategy to construct a DNA biosensor, in which the platinum electrode was modified with a dual-film of electrochemically synthesized poly(3,4-ethylenedioxythiophene) (PEDOT) resulting in immobilized gold nanoparticles, with the gold nanoparticles easily immobilized in a uniform distribution. The DNA probe labeled with a SH group was then assembled to the fabricated electrode and employed to capture the target DNA based on the complementary sequence. The hybridization efficiency was evaluated with differential pulse voltammetry (DPV) in the presence of daunorubicin hydrochloride. Our results demonstrated that the peak current in DPV exhibited a linear correlation the concentration of target DNA that was complementary to the probe DNA. Moreover, the electrode could be reused by heating denaturation and re-hybridization, which only brought slight signal decay. In addition, the addition of the oxidized form of nicotinamide adenine dinucleotide (NAD⁺) could dramatically enhance the sensitivity by more than 5.45-fold, and the limit-of-detection reached about 100 pM.

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 treatment elicits microbiome-independent changes in local metabolites, but not those distal to the infection site, by acting directly on host cells. The metabolites induced during treatment, such as AMP, reduce antibiotic efficacy and enhance phagocytic killing. Moreover, antibiotic treatment impairs immune function by inhibiting respiratory activity in immune cells. Collectively, these results highlight the immunomodulatory potential of antibiotics and reveal the local metabolic microenvironment to be an important determinant of infection resolution.

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. Moreover, environmental factors such as oxygen availability, extracellular metabolites, population heterogeneity and multidrug contexts alter antibiotic efficacy by impacting bacterial metabolism and stress responses. Here we review recent studies on antibiotic efficacy and highlight insights gained on the involvement of cellular respiration, redox stress and altered metabolism in antibiotic lethality. We discuss the complexity found in natural environments and highlight knowledge gaps in antibiotic lethality that may be addressed using systems approaches.

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. Expression of the chimeric periplasmic-cytoplasmic MalE-LacZ72-47 protein is an historically important lethal stress originally identified during seminal genetic experiments that defined the SecY-dependent protein translocation system. Multiple, independent lines of evidence presented here indicate that the predominant mechanism for MalE-LacZ lethality shares attributes with the ROS-dependent component of antibiotic lethality. MalE-LacZ lethality requires molecular oxygen, and its expression induces ROS production. The increased susceptibility of mutants sensitive to oxidative stress to MalE-LacZ lethality indicates that ROS contribute causally to cell death rather than simply being produced by dying cells. Observations that support the proposed mechanism of cell death include MalE-LacZ expression being bacteriostatic rather than bactericidal in cells that overexpress MutT, a nucleotide sanitizer that hydrolyzes 8-oxo-dGTP to the monophosphate, or that lack MutM and MutY, DNA glycosylases that process base pairs involving 8-oxo-dGTP. Our studies suggest stress-induced physiological changes that favor this mode of ROS-dependent death.

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. We identify fumarate as a tobramycin potentiator that activates cellular respiration and generates a proton motive force by stimulating the tricarboxylic acid (TCA) cycle. In contrast, we find that glyoxylate induces phenotypic tolerance by inhibiting cellular respiration with acetyl-coenzyme A diversion through the glyoxylate shunt, despite drug import. Collectively, this work demonstrates that TCA cycle activity is important for both aminoglycoside uptake and downstream lethality and identifies a potential strategy for potentiating aminoglycoside treatment of P. aeruginosa infections.

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. We leveraged the opposing phenotypes of bacteriostatic and bactericidal drugs in combination to investigate their activity. Growth inhibition from bacteriostatic antibiotics was associated with suppressed cellular respiration whereas cell death from most bactericidal antibiotics was associated with accelerated respiration. In combination, suppression of cellular respiration by the bacteriostatic antibiotic was the dominant effect, blocking bactericidal killing. Global metabolic profiling of bacteriostatic antibiotic treatment revealed that accumulation of metabolites involved in specific drug target activity was linked to the buildup of energy metabolites that feed the electron transport chain. Inhibition of cellular respiration by knockout of the cytochrome oxidases was sufficient to attenuate bactericidal lethality whereas acceleration of basal respiration by genetically uncoupling ATP synthesis from electron transport resulted in potentiation of the killing effect of bactericidal antibiotics. This work identifies a link between antibiotic-induced cellular respiration and bactericidal lethality and demonstrates that bactericidal activity can be arrested by attenuated respiration and potentiated by accelerated respiration. Our data collectively show that antibiotics perturb the metabolic state of bacteria and that the metabolic state of bacteria impacts antibiotic 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. Here we robustly test the hypothesis using biochemical, enzymatic, and biophysical assays along with genetic and phenotypic experiments. We first used a novel intracellular H2O2 sensor, together with a chemically diverse panel of fluorescent dyes sensitive to an array of reactive species to demonstrate that antibiotics broadly induce redox stress. Subsequent gene-expression analyses reveal that complex antibiotic-induced oxidative stress responses are distinct from canonical responses generated by supraphysiological levels of H2O2. We next developed a method to quantify cellular respiration dynamically and found that bactericidal antibiotics elevate oxygen consumption, indicating significant alterations to bacterial redox physiology. We further show that overexpression of catalase or DNA mismatch repair enzyme, MutS, and antioxidant pretreatment limit antibiotic lethality, indicating that reactive oxygen species causatively contribute to antibiotic killing. Critically, the killing efficacy of antibiotics was diminished under strict anaerobic conditions but could be enhanced by exposure to molecular oxygen or by the addition of alternative electron acceptors, indicating that environmental factors play a role in killing cells physiologically primed for death. This work provides direct evidence that, downstream of their target-specific interactions, bactericidal antibiotics induce complex redox alterations that contribute to cellular damage and death, thus supporting an evolving, expanded model of antibiotic lethality.

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

ß-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. Spatially-targeted FRET reporters for PKA activity identified slower PKA activation and lower isoproterenol sensitivity in the nucleus (t50=10.6±0.7 min; EC50=89.0 nmol/L) than in the cytosol (t50=3.71±0.25 min; EC50=1.22 nmol/L). These differences were not explained by cAMP or AKAP-based compartmentation. A computational model of cytosolic and nuclear PKA activity was developed and predicted that differences in nuclear PKA dynamics and magnitude are regulated by slow PKA catalytic subunit diffusion, while differences in isoproterenol sensitivity are regulated by nuclear expression of protein kinase inhibitor (PKI). These were validated by FRET and immunofluorescence. The model also predicted differential phosphorylation of PKA substrates regulating cell contractility and hypertrophy. Ca(2+) and cell hypertrophy measurements validated these predictions and identified higher isoproterenol sensitivity for contractile enhancements (EC50=1.84 nmol/L) over cell hypertrophy (EC50=85.9 nmol/L). Over-expression of spatially targeted PKA catalytic subunit to the cytosol or nucleus enhanced contractile and hypertrophic responses, respectively. We conclude that restricted PKA catalytic subunit diffusion is an important PKA compartmentation mechanism and the nucleus comprises a novel PKA signaling microdomain, insulating hypertrophic from contractile ß-adrenergic signaling responses.