Discovery of antibiotics that selectively kill metabolically dormant bacteria

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies

Antibiotic tolerance, or the ability of bacteria to survive antibiotic treatment in the absence of genetic resistance, has been linked to chronic and recurrent infections. Tolerant cells are often characterized by a low metabolic state, against which most clinically used antibiotics are ineffective. Here, we show that tolerance readily evolves against antibiotics that are strongly dependent on bacterial metabolism, but does not arise against antibiotics whose efficacy is only minimally affected by metabolic state. We identify a mechanism of tolerance evolution in E. coli involving deletion of the sodium-proton antiporter gene nhaA, which results in downregulated metabolism and upregulated stress responses. Additionally, we find that cycling of antibiotics with different metabolic dependencies interrupts evolution of tolerance in vitro, increasing the lifetime of treatment efficacy. Our work highlights the potential for limiting the occurrence and extent of tolerance by accounting for antibiotic dependencies on bacterial metabolism.

Increased energy demand from anabolic-catabolic processes drives β-lactam antibiotic lethality

β-Lactam antibiotics disrupt the assembly of peptidoglycan (PG) within the bacterial cell wall by inhibiting the enzymatic activity of penicillin-binding proteins (PBPs). It was recently shown that β-lactam treatment initializes a futile cycle of PG synthesis and degradation, highlighting major gaps in our understanding of the lethal effects of PBP inhibition by β-lactam antibiotics. Here, we assess the downstream metabolic consequences of treatment of Escherichia coli with the β-lactam mecillinam and show that lethality from PBP2 inhibition is a specific consequence of toxic metabolic shifts induced by energy demand from multiple catabolic and anabolic processes, including accelerated protein synthesis downstream of PG futile cycling. Resource allocation into these processes is coincident with alterations in ATP synthesis and utilization, as well as a broadly dysregulated cellular redox environment. These results indicate that the disruption of normal anabolic-catabolic homeostasis by PBP inhibition is an essential factor for β-lactam antibiotic lethality.

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.

Targeting Antibiotic Tolerance, Pathogen by Pathogen

Antibiotic tolerance, the capacity of genetically susceptible bacteria to survive the lethal effects of antibiotic treatment, plays a critical and underappreciated role in the disease burden of bacterial infections. Here, we take a pathogen-by-pathogen approach to illustrate the clinical significance of antibiotic tolerance and discuss how the physiology of specific pathogens in their infection environments impacts the mechanistic underpinnings of tolerance. We describe how these insights are leading to the development of species-specific therapeutic strategies for targeting antibiotic tolerance and highlight experimental platforms that are enabling us to better understand the complexities of drug-tolerant pathogens in in vivo settings.