Chemical-Chemical Combinations Map Uncharted Interactions in Escherichia coli under Nutrient Stress

Nutrient stress is a target for new antibiotics

Novel approaches are required to address the looming threat of pan-resistant Gram-negative pathogens and forestall the rise of untreatable infections. Unconventional targets that are uniquely important during infection and tractable to high-throughput drug discovery methods hold high potential for innovation in antibiotic discovery programs. In this context, inhibitors of bacterial nutrient stress are particularly exciting candidates for future antibiotic development. Amino acid, nucleotide, and vitamin biosynthesis pathways are critical for bacterial growth in nutrient-limiting conditions in the laboratory and the host. Although historically dismissed as dispensable for pathogens, a wealth of transposon mutagenesis and single-mutant studies have emerged which demonstrate that several such pathways are critical for infection. Indeed, high-throughput screens of diverse synthetic compounds and natural products have uncovered inhibitors of nutrient biosynthesis. Herein, we review bacterial nutrient biosynthesis and its role during host infection. Further, we explore screening platforms developed to search for inhibitors of these targets and highlight successes among these. Finally, we feature important and sometimes surprising connections between bacterial nutrient biosynthesis, antibiotic activity, and antibiotic resistance.

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity

Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate, and serine for maximum growth, growth rate, and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

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.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

Re-sensitizing Ampicillin and Kanamycin-Resistant E. coli and S. aureus Using Synergistic Metal Micronutrients-Antibiotic Combinations

Due to the recent emergence of multi-drug resistant strains, the development of novel antimicrobial agents has become a critical issue. The use of micronutrient transition metals is a promising approach to overcome this problem since these compounds exhibit significant toxicity at low concentrations in prokaryotic cells. In this work, we demonstrate that at concentrations lower than their minimal inhibitory concentrations and in combination with different antibiotics, it is possible to mitigate the barriers to employ metallic micronutrients as therapeutic agents. Here, we show that when administered as a combinatorial treatment, Cu²⁺, Zn²⁺, Co²⁺, Cd²⁺, and Ni²⁺ increase susceptibility of Escherichia coli and Staphylococcus aureus to ampicillin and kanamycin. Furthermore, ampicillin-resistant E. coli is re-sensitized to ampicillin when the ampicillin is administered in combination with Cu²⁺, Cd²⁺, or Ni². Similarly, Cu²⁺, Zn²⁺, or Cd²⁺ re-sensitize kanamycin-resistant E. coli and S. aureus to kanamycin when administered in a combinatorial treatment with those transition metals. Here, we demonstrate that for both susceptible and resistant bacteria, transition-metal micronutrients, and antibiotics interact synergistically in combinatorial treatments and exhibit increased effects when compared to the treatment with the antibiotic alone. Moreover, in vitro and in vivo assays, using a murine topical infection model, showed no toxicological effects of either treatment at the administered concentrations. Lastly, we show that combinatorial treatments can clear a murine topical infection caused by an antibiotic-resistant strain. Altogether, these results suggest that antibiotic-metallic micronutrient combinatorial treatments will play an important role in future developments of antimicrobial agents and treatments against infections caused by both susceptible and resistant strains.

Mimicking the human environment in mice reveals that inhibiting biotin biosynthesis is effective against antibiotic-resistant pathogens

To revitalize the antibiotic pipeline, it is critical to identify and validate new antimicrobial targets¹. In Mycobacteria tuberculosis and Francisella tularensis, biotin biosynthesis is a key fitness determinant during infection^2-5, making it a high-priority target. However, biotin biosynthesis has been overlooked for priority pathogens such as Acinetobacter baumannii, Klebsiella pneumoniae and Pseudomonas aeruginosa. This can be attributed to the lack of attenuation observed for biotin biosynthesis genes during transposon mutagenesis studies in mouse infection models^6-9. Previous studies did not consider the 40-fold higher concentration of biotin in mouse plasma compared to human plasma. Here, we leveraged the unique affinity of streptavidin to develop a mouse infection model with human levels of biotin. Our model suggests that biotin biosynthesis is essential during infection with A. baumannii, K. pneumoniae and P. aeruginosa. Encouragingly, we establish the capacity of our model to uncover in vivo activity for the biotin biosynthesis inhibitor MAC13772. Our model addresses the disconnect in biotin levels between humans and mice, and explains the failure of potent biotin biosynthesis inhibitors in standard mouse infection models.

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.