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.
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•A deep learning model is trained to predict antibiotics based on structure•Halicin is predicted as an antibacterial molecule from the Drug Repurposing Hub•Halicin shows broad-spectrum antibiotic activities in mice•More antibiotics with distinct structures are predicted from the ZINC15 database
A trained deep neural network predicts antibiotic activity in molecules that are structurally different from known antibiotics, among which Halicin exhibits efficacy against broad-spectrum bacterial infections in mice.
Bacterial Metabolism and Antibiotic Efficacy Stokes, Jonathan M.; Lopatkin, Allison J.; Lobritz, Michael A. ...
Cell metabolism,
08/2019, Letnik:
30, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that ...collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis; (2) the metabolic state of bacteria influences their susceptibility to antibiotics; and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks.
The metabolic state of bacteria significantly contributes to the efficacy of antibiotics. In this Perspective, Stokes et al. highlight the close relationship between bacterial cell metabolism and antibiotic efficacy, leveraging prior observations to describe areas for further exploration, with the goal of developing next-generation antibiotics that can optimally exploit the complex metabolic networks of bacteria.
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing ...computational revolution—artificial intelligence—offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models—namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high‐quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.
In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high‐quality screening datasets and interdisciplinary collaboration.
Plasmid-borne colistin resistance mediated by mcr-1 may contribute to the dissemination of pan-resistant Gram-negative bacteria. Here, we show that mcr-1 confers resistance to colistin-induced lysis ...and bacterial cell death, but provides minimal protection from the ability of colistin to disrupt the Gram-negative outer membrane. Indeed, for colistin-resistant strains of Enterobacteriaceae expressing plasmid-borne mcr-1, clinically relevant concentrations of colistin potentiate the action of antibiotics that, by themselves, are not active against Gram-negative bacteria. The result is that several antibiotics, in combination with colistin, display growth-inhibition at levels below their corresponding clinical breakpoints. Furthermore, colistin and clarithromycin combination therapy displays efficacy against mcr-1-positive Klebsiella pneumoniae in murine thigh and bacteremia infection models at clinically relevant doses. Altogether, these data suggest that the use of colistin in combination with antibiotics that are typically active against Gram-positive bacteria poses a viable therapeutic alternative for highly drug-resistant Gram-negative pathogens expressing mcr-1.
The vast majority of bactericidal antibiotics display poor efficacy against bacterial persisters, cells that are in a metabolically repressed state. Molecules that retain their bactericidal functions ...against such bacteria often display toxicity to human cells, which limits treatment options for infections caused by persisters. Here, we leverage insight into metabolism-dependent bactericidal antibiotic efficacy to design antibiotic combinations that sterilize both metabolically active and persister cells, while minimizing the antibiotic concentrations required. These rationally designed antibiotic combinations have the potential to improve treatments for chronic and recurrent infections.
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•Antibiotics are strongly or weakly dependent on metabolism (SDM or WDM)•Combinations of SDM and WDM antibiotics sterilize bacteria, while dose-sparing•SDM and WDM drug interactions are undetectable in growth-inhibition assays
Zheng et al. rationally combine antibiotics using insights on bacterial metabolism to identify drug-drug combinations that sterilize bacterial cultures of both metabolically active and persister cells, while dose-sparing toxic antibiotics.
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. 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
pathogens, indicating clinical relevance.
Significance
COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs ...for this purpose has been met with difficulties, synergistic drug combinations offer a promising alternative. However, the lack of high-quality training data pertaining to drug combinations makes it challenging to use existing machine learning methods for effective novel combination prediction tasks. Our proposed approach addresses this challenge by leveraging additional readily available data, such as drug−target interactions, thus enabling an effective in silico search for synergistic combinations against SARS-CoV-2.
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.
Bactericidal antibiotics kill bacteria by perturbing various cellular targets and processes. Disruption of the primary antibiotic-binding partner induces a cascade of molecular events, leading to ...overproduction of reactive metabolic by-products. It remains unclear, however, how these molecular events contribute to bacterial cell death. Here, we take a single-cell physical biology approach to probe antibiotic function. We show that aminoglycosides and fluoroquinolones induce cytoplasmic condensation through membrane damage and subsequent outflow of cytoplasmic contents as part of their lethality. A quantitative model of membrane damage and cytoplasmic leakage indicates that a small number of nanometer-scale membrane defects in a single bacterium can give rise to the cellular-scale phenotype of cytoplasmic condensation. Furthermore, cytoplasmic condensation is associated with the accumulation of reactive metabolic by-products and lipid peroxidation, and pretreatment of cells with the antioxidant glutathione attenuates cytoplasmic condensation and cell death. Our work expands our understanding of the downstream molecular events that are associated with antibiotic lethality, revealing cytoplasmic condensation as a phenotypic feature of antibiotic-induced bacterial cell death.
A poor understanding of the mechanisms by which antibiotics traverse the outer membrane remains a considerable obstacle to the development of novel Gram-negative antibiotics. Herein, we demonstrate ...that the Gram-negative bacterium Escherichia coli becomes susceptible to the narrow-spectrum antibiotic vancomycin during growth at low temperatures. Heterologous expression of an Enterococcus vanHBX vancomycin resistance cluster in E. coli confirmed that the mechanism of action was through inhibition of peptidoglycan biosynthesis. To understand the nature of vancomycin permeability, we screened for strains of E. coli that displayed resistance to vancomycin at low temperature. Surprisingly, we observed that mutations in outer membrane biosynthesis suppressed vancomycin activity. Subsequent chemical analysis of lipopolysaccharide from vancomycin-sensitive and -resistant strains confirmed that suppression was correlated with truncations in the core oligosaccharide of lipopolysaccharide. These unexpected observations challenge the current understanding of outer membrane permeability, and provide new chemical insights into the susceptibility of E. coli to glycopeptide antibiotics.
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•E. coli is susceptible to glycopeptide antibiotics during cold stress•Glycopeptides function through the same mechanism in E. coli as in Gram-positives•Paradoxically, genetic inhibition of outer membrane biosynthesis confers resistance•Glycopeptide suppression is correlated with truncations in lipopolysaccharide core
Stokes et al. show that E. coli is susceptible to glycopeptide antibiotics during cold stress and that this phenotype is reversed through inhibition of outer membrane biosynthesis, providing new chemical insights into the susceptibility of E. coli to these antibiotics.
Atrial fibrillation (AF) and left ventricular systolic dysfunction (LVSD) frequently co-exist despite adequate rate control. Existing randomized studies of AF and LVSD of varying etiologies have ...reported modest benefits with a rhythm control strategy.
The goal of this study was to determine whether catheter ablation (CA) for AF could improve LVSD compared with medical rate control (MRC) where the etiology of the LVSD was unexplained, apart from the presence of AF.
This multicenter, randomized clinical trial enrolled patients with persistent AF and idiopathic cardiomyopathy (left ventricular ejection fraction LVEF ≤45%). After optimization of rate control, patients underwent cardiac magnetic resonance (CMR) to assess LVEF and late gadolinium enhancement, indicative of ventricular fibrosis, before randomization to either CA or ongoing MRC. CA included pulmonary vein isolation and posterior wall isolation. AF burden post-CA was assessed by using an implanted loop recorder, and adequacy of MRC was assessed by using serial Holter monitoring. The primary endpoint was change in LVEF on repeat CMR at 6 months.
A total of 301 patients were screened; 68 patients were enrolled between November 2013 and October 2016 and randomized with 33 in each arm (accounting for 2 dropouts). The average AF burden post-CA was 1.6 ± 5.0% at 6 months. In the intention-to-treat analysis, absolute LVEF improved by 18 ± 13% in the CA group compared with 4.4 ± 13% in the MRC group (p < 0.0001) and normalized (LVEF ≥50%) in 58% versus 9% (p = 0.0002). In those undergoing CA, the absence of late gadolinium enhancement predicted greater improvements in absolute LVEF (10.7%; p = 0.0069) and normalization at 6 months (73% vs. 29%; p = 0.0093).
AF is an underappreciated reversible cause of LVSD in this population despite adequate rate control. The restoration of sinus rhythm with CA results in significant improvements in ventricular function, particularly in the absence of ventricular fibrosis on CMR. This outcome challenges the current treatment paradigm that rate control is the appropriate strategy in patients with AF and LVSD. (Catheter Ablation Versus Medical Rate Control in Atrial Fibrillation and Systolic Dysfunction CAMERA-MRI; ACTRN12613000880741).
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