Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Ultra-rapid, quantitative, label-free antibiotic susceptibility testing via optically detected purine metabolites

In order to facilitate the best antimicrobial prescribing practices and to help reduce the increasing global threat of antibiotic resistance, there is an urgent need for the development of novel and truly rapid (≤1 h) antibiotic susceptibility testing (AST) platforms. A 785 nm surface enhanced Raman spectroscopy (SERS) based phenotypic methodology is described that results in accurate minimum inhibitory concentration (MIC) determinations for all tested strain/antibiotic pairs. The SERS-AST procedure results in accurate MICs in ∼1 h, including a 30-min incubation period, and is effective for both Gram positive and negative species, and for antibiotics with different initial primary targets of antibiotic activity, and for both bactericidal and bacteriostatic antibiotics. The molecular level mechanism of this methodology is described. Bacterial SERS spectra are due to secreted purine nucleotide degradation products (principally adenine, guanine, xanthine and hypoxanthine) resulting from water washing induced bacterial stringent response and the resulting (p)ppGpp alarmone mediates nucleobase formation from unneeded tRNA and rRNA. The rewiring of metabolic responses resulting from the secondary metabolic effects of antibiotic exposure during the 30-min incubation period accounts for the dose dependence of the SERS spectral intensities which are used to accurately yield the MIC. This is the fastest demonstrated AST method yielding MICs.

Beyond antibiotics: exploring multifaceted approaches to combat bacterial resistance in the modern era: a comprehensive review

Antibiotics represent one of the most significant medical breakthroughs of the twentieth century, playing a critical role in combating bacterial infections. However, the rapid emergence of antibiotic resistance has become a major global health crisis, significantly complicating treatment protocols. This paper provides a narrative review of the current state of antibiotic resistance, synthesizing findings from primary research and comprehensive review articles to examine the various mechanisms bacteria employ to counteract antibiotics. One of the primary sources of antibiotic resistance is the improper use of antibiotics in the livestock industry. The emergence of drug-resistant microorganisms from human activities and industrial livestock production has presented significant environmental and public health concerns. Today, resistant nosocomial infections occur following long-term hospitalization of patients, causing the death of many people, so there is an urgent need for alternative treatments. In response to this crisis, non-antibiotic therapeutic strategies have been proposed, including bacteriophages, probiotics, postbiotics, synbiotics, fecal microbiota transplantation (FMT), nanoparticles (NPs), antimicrobial peptides (AMPs), antibodies, traditional medicines, and the toxin-antitoxin (TA) system. While these approaches offer innovative solutions for addressing bacterial infections and preserving the efficacy of antimicrobial therapies, challenges such as safety, cost-effectiveness, regulatory hurdles, and large-scale implementation remain. This review examines the potential and limitations of these strategies, offering a balanced perspective on their role in managing bacterial infections and mitigating the broader impact of antibiotic resistance.

Epigenetic modifications and metabolic gene mutations drive resistance evolution in response to stimulatory antibiotics

The antibiotic resistance crisis, fueled by misuse and bacterial evolution, is a major global health threat. Traditional perspectives tie resistance to drug target mechanisms, viewing antibiotics as mere growth inhibitors. New insights revealed that low-dose antibiotics may also serve as signals, unexpectedly promoting bacterial growth. Yet, the development of resistance under these conditions remains unknown. Our study investigated resistance evolution under stimulatory antibiotics and uncovered new genetic mechanisms of resistance linked to metabolic remodeling. We documented a shift from a fast, reversible mechanism driven by methylation in central metabolic pathways to a slower, stable mechanism involving mutations in key metabolic genes. Both mechanisms contribute to a metabolic profile transition from glycolysis to rapid gluconeogenesis. In addition, our findings demonstrated that rising environmental temperatures associated with metabolic evolution accelerated this process, increasing the prevalence of metabolic gene mutations, albeit with a trade-off in interspecific fitness. These findings expand beyond the conventional understanding of resistance mechanisms, proposing a broader metabolic mechanism within the selective window of stimulatory sub-MIC antibiotics, particularly in the context of climate change.

The role of bacterial metabolism in antimicrobial resistance

The relationship between bacterial metabolism and antibiotic treatment is complex. On the one hand, antibiotics leverage cell metabolism to function. On the other hand, increasing research has highlighted that the metabolic state of the cell also impacts all aspects of antibiotic biology, from drug efficacy to the evolution of antimicrobial resistance (AMR). Given that AMR is a growing threat to the current global antibiotic arsenal and ability to treat infectious diseases, understanding these relationships is key to improving both public and human health. However, quantifying the contribution of metabolism to antibiotic activity and subsequent bacterial evolution has often proven challenging. In this Review, we discuss the complex and often bidirectional relationships between metabolism and the various facets of antibiotic treatment and response. We first summarize how antibiotics leverage metabolism for their function. We then focus on the converse of this relationship by specifically delineating the unique contribution of metabolism to three distinct but related arms of antibiotic biology: antibiotic efficacy, AMR evolution and AMR mechanisms. Finally, we note the relevance of metabolism in clinical contexts and explore the future of metabolic-based strategies for personalized antimicrobial therapies. A deeper understanding of these connections is crucial for the broader scientific community to address the growing crisis of AMR and develop future effective therapeutics.

In-cell NMR reveals metabolic adaptations in central carbon pathways driving antibiotic tolerance in Salmonella Typhimurium

Antibiotic tolerance presents a significant challenge in eradicating bacterial infections, as tolerant strains can survive antibiotic treatment, contributing to the recurrence of infections and the development of resistance. However, unlike antibiotic resistance, tolerance is not detectable by standard susceptibility assays such as minimal inhibitory concentration (MIC) tests. Consequently, antibiotic tolerance often goes unnoticed in clinical settings. Bacterial metabolism is closely linked to antibiotic efficacy, and thus presents as a potential target for novel diagnostic methods. Recent advancements in nuclear magnetic resonance (NMR) spectroscopy, including dynamic nuclear polarization (DNP-NMR), enable a non-invasive real-time approach to analyzing bacterial metabolism. In this study, we applied both 1H and in-cell 13C NMR spectroscopy to investigate metabolic adaptations in a tolerance-evolved Salmonella Typhimurium strain, C10, developed through ten cycles of ampicillin treatment. Our results demonstrated that despite similar MICs and growth rates, the C10 strain exhibited a 25-fold increase in tolerance compared to the wild-type, while exhibiting lower metabolic activity. Under ampicillin stress, however, the C10 strain maintained higher metabolic activity and demonstrated greater resilience in glucose consumption and metabolite production relative to the wild-type. Using DNP-NMR, rapid metabolic shifts in the C10 strain were identified within 10 minutes of exposure to high concentrations of ampicillin, characterized by accumulation of key metabolites such as pyruvate and acetate. Overall, our findings underscore the potential of real-time NMR-based analyses to provide deeper insights into antibiotic tolerance and distinguish between susceptible and tolerant bacterial strains.

Recent Advances in Antimicrobial Resistance: Insights from Escherichia coli as a Model Organism

Antimicrobial resistance (AMR) represents a critical global health threat, and a thorough understanding of resistance mechanisms in Escherichia coli is needed to guide effective treatment interventions. This review explores recent advances for investigating AMR in E. coli, including machine learning for resistance pattern analysis, laboratory evolution to generate resistant mutants, mutant library construction, and genome sequencing for in-depth characterization. Key resistance mechanisms are discussed, including drug inactivation, target modification, altered transport, and metabolic adaptation. Additionally, we highlight strategies to mitigate the spread of AMR, such as dynamic resistance monitoring, innovative therapies like phage therapy and CRISPR-Cas technology, and tighter regulation of antibiotic use in animal production systems. This review provides actionable insights into E. coli resistance mechanisms and identifies promising directions for future antibiotic development and AMR management.

Modulation of TCR stimulation and pifithrin-α improve the genomic safety profile of CRISPR-engineered human T cells

CRISPR-engineered chimeric antigen receptor (CAR) T cells are at the forefront of novel cancer treatments. However, several reports describe the occurrence of CRISPR-induced chromosomal aberrations. So far, measures to increase the genomic safety of T cell products focused mainly on the components of the CRISPR-Cas9 system and less on T cell-intrinsic features, such as their massive expansion after T cell receptor (TCR) stimulation. Here, we describe driving forces of indel formation in primary human T cells. Increased T cell activation and proliferation speed correlate with larger deletions. Editing of non-activated T cells reduces the risk of large deletions with the downside of reduced knockout efficiencies. Alternatively, the addition of the small-molecule pifithrin-α limits large deletions, chromosomal translocations, and aneuploidy in a p53-independent manner while maintaining the functionality of CRISPR-engineered T cells, including CAR T cells. Controlling T cell activation and pifithrin-α treatment are easily implementable strategies to improve the genomic integrity of CRISPR-engineered T cells.

Mass spectrometry-based metabolomics for the investigation of antibiotic-bacterial interactions

With the development of analytical technologies especially mass spectrometry, metabolomics is becoming increasingly hot in the field of studying antibiotic-bacterial interactions. On the one hand, metabolomics can reveal metabolic perturbations in bacteria in the presence of antibiotics and expose metabolic mechanisms. On the other hand, through in-depth analysis of bacterial metabolic profiles, biomarkers and bioactive secondary metabolites with great potential as drug precursors can be discovered. This review focuses on the experimental workflow of bacterial metabolomics and its application to study the interaction between bacteria and antibiotics. Metabolomics improves the understanding of antibiotic lethality, reveals metabolic perturbations in antibiotic-resistant bacteria, guides the diagnosis and antibiotic treatment of infectious diseases, and aids in the exploration of antibacterial metabolites in nature. Furthermore, current limitations and directions for future developments in this area are discussed.

Preserving the efficacy of antibiotics to tackle antibiotic resistance

Different international agencies recognize that antibiotic resistance is one of the most severe human health problems that humankind is facing. Traditionally, the introduction of new antibiotics solved this problem but various scientific and economic reasons have led to a shortage of novel antibiotics at the pipeline. This situation makes mandatory the implementation of approaches to preserve the efficacy of current antibiotics. The concept is not novel, but the only action taken for such preservation had been the 'prudent' use of antibiotics, trying to reduce the selection pressure by reducing the amount of antibiotics. However, even if antibiotics are used only when needed, this will be insufficient because resistance is the inescapable outcome of antibiotics' use. A deeper understanding of the alterations in the bacterial physiology upon acquisition of resistance and during infection will help to design improved strategies to treat bacterial infections. In this article, we discuss the interconnection between antibiotic resistance (and antibiotic activity) and bacterial metabolism, particularly in vivo, when bacteria are causing infection. We discuss as well how understanding evolutionary trade-offs, as collateral sensitivity, associated with the acquisition of resistance may help to define evolution-based therapeutic strategies to fight antibiotic resistance and to preserve currently used antibiotics.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

AI-2 quorum sensing-induced galactose metabolism activation in Streptococcus suis enhances capsular polysaccharide-associated virulence

Bacteria utilize intercellular communication to orchestrate essential cellular processes, adapt to environmental changes, develop antibiotic tolerance, and enhance virulence. This communication, known as quorum sensing (QS), is mediated by the exchange of small signalling molecules called autoinducers. AI-2 QS, regulated by the metabolic enzyme LuxS (S-ribosylhomocysteine lyase), acts as a universal intercellular communication mechanism across gram-positive and gram-negative bacteria and is crucial for diverse bacterial processes. In this study, we demonstrated that in Streptococcus suis (S. suis), a notable zoonotic pathogen, AI-2 QS enhances galactose utilization, upregulates the Leloir pathway for capsular polysaccharide (CPS) precursor production, and boosts CPS synthesis, leading to increased resistance to macrophage phagocytosis. Additionally, our molecular docking and dynamics simulations suggest that, similar to S. pneumoniae, FruA, a fructose-specific phosphoenolpyruvate phosphotransferase system prevalent in gram-positive pathogens, may also function as an AI-2 membrane surface receptor in S. suis. In conclusion, our study demonstrated the significance of AI-2 in the synthesis of galactose metabolism-dependent CPS in S. suis. Additionally, we conducted a preliminary analysis of the potential role of FruA as a membrane surface receptor for S. suis AI-2.

SOS genes are rapidly induced while translesion synthesis polymerase activity is temporally regulated

The DNA damage inducible SOS response in bacteria serves to increase survival of the species at the cost of mutagenesis. The SOS response first initiates error-free repair followed by error-prone repair. Here, we have employed a multi-omics approach to elucidate the temporal coordination of the SOS response. Escherichia coli was grown in batch cultivation in bioreactors to ensure highly controlled conditions, and a low dose of the antibiotic ciprofloxacin was used to activate the SOS response while avoiding extensive cell death. Our results show that expression of genes involved in error-free and error-prone repair were both induced shortly after DNA damage, thus, challenging the established perception that the expression of error-prone repair genes is delayed. By combining transcriptomics and a sub-proteomics approach termed signalomics, we found that the temporal segregation of error-free and error-prone repair is primarily regulated after transcription, supporting the current literature. Furthermore, the heterology index (i.e., the binding affinity of LexA to the SOS box) was correlated to the maximum increase in gene expression and not to the time of induction of SOS genes. Finally, quantification of metabolites revealed increasing pyrimidine pools as a late feature of the SOS response. Our results elucidate how the SOS response is coordinated, showing a rapid transcriptional response and temporal regulation of mutagenesis on the protein and metabolite levels.

Mutation-driven resistance development in wastewater E. coli upon low-level cephalosporins: Pharmacophore contribution and novel mechanism

Cephalosporins have been widely applied in clinical and veterinary settings and detected at increasing concentrations in water environments. They potentially induce high-level antibiotic resistance at environmental concentrations. This study characterized how typical wastewater bacteria developed heritable antibiotic resistance under exposure to different cephalosporins, including pharmacophore-resistance correlation, resistance mechanism, and occurrence of resistance-relevant mutations in different water environments. Wastewater-isolated E. coli JX1 was exposed to eight cephalosporins individually at 25 µg/L for 60 days. Multidrug resistance developed and diverse mutations arose in selected mutants, where a single mutation in ATP phosphoribosyltransferase encoding gene (hisG) resulted in up to 128-fold increase in resistance to meropenem. Molprint2D pharma RQSAR analysis revealed that hydrogen-bond acceptors and hydrophobic groups in the R1 and R2 substituents of cephalosporins contributed positively to antibiotic resistance. Some of these pharmacophores may persist during bio- or photo-degradation in the environment. hisG mutation confers a novel resistance mechanism by inhibiting fatty acid degradation, and its variants were more abundant in water-related E. coli (especially in the effluent of wastewater treatment plants) compared with those in non-water environments. These results suggest that specific degradation of particular pharmacophores in cephalosporins could be useful for controlling resistance development, and mutations in previously unreported resistance genes (e.g., hisG) can lead to overlooked antibiotic resistance risks in water environments.

Exogenous methionine contributes to reversing the resistance of Streptococcus suis to macrolides

Streptococcus suis (S. suis) has been increasingly recognized as a porcine zoonotic pathogen that threatens the health of both pigs and humans. Multidrug-resistant Streptococcus suis is becoming increasingly prevalent, and novel strategies to treat bacterial infections caused by these organisms are desperately needed. In the present study, an untargeted metabolomics analysis showed that the significant decrease in methionine content and the methionine biosynthetic pathway were significantly affected by the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis in drug-resistant S. suis. The addition of L-methionine restored the bactericidal activity of macrolides, doxycycline, and ciprofloxacin on S. suis in vivo and in vitro. Further studies showed that the exogenous addition of methionine affects methionine metabolism by reducing S-adenosylmethionine synthetase activity and the contents of S-adenosylmethionine, S-adenosyl homocysteine, and S-ribose homocysteine. Methionine can decrease the total methylation level and methylesterase activity in multidrug resistant S. suis. The drug transport proteins and efflux pump genes were significantly downregulated in S. suis by exogenous L-methionine. Moreover, the exogenous addition of methionine can reduce the survival of S. suis by affecting oxidative stress and metal starvation in bacteria. Thus, L-methionine may influence the development of resistance in S. suis through methyl metabolism and metal starvation. This study provides a new perspective on the mitigation of drug resistance in S. suis.IMPORTANCEBacterial antibiotic resistance has become a severe threat to human and animal health. Increasing the efficacy of existing antibiotics is a promising strategy against antibiotic resistance. Here, we report that L-methionine enhances the efficacy of macrolides, doxycycline, and ciprofloxacin antibiotics in killing Streptococcus suis, including multidrug-resistant pathogens. We investigated the mechanism of action of exogenous methionine supplementation in restoring macrolides in Streptococcus suis and the role of the methionine cycle pathway on methylation levels, efflux pump genes, oxidative stress, and metal starvation in Streptococcus suis. It provides a theoretical basis for the rational use of macrolides in clinical practice and also identifies a possible target for restoring drug resistance in Streptococcus suis.

Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies

Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.

The Combination of Antibiotic and Non-Antibiotic Compounds Improves Antibiotic Efficacy against Multidrug-Resistant Bacteria

Bacterial antibiotic resistance, especially the emergence of multidrug-resistant (MDR) strains, urgently requires the development of effective treatment strategies. It is always of interest to delve into the mechanisms of resistance to current antibiotics and target them to promote the efficacy of existing antibiotics. In recent years, non-antibiotic compounds have played an important auxiliary role in improving the efficacy of antibiotics and promoting the treatment of drug-resistant bacteria. The combination of non-antibiotic compounds with antibiotics is considered a promising strategy against MDR bacteria. In this review, we first briefly summarize the main resistance mechanisms of current antibiotics. In addition, we propose several strategies to enhance antibiotic action based on resistance mechanisms. Then, the research progress of non-antibiotic compounds that can promote antibiotic-resistant bacteria through different mechanisms in recent years is also summarized. Finally, the development prospects and challenges of these non-antibiotic compounds in combination with antibiotics are discussed.

Chance Favors the Prepared Genomes: Horizontal Transfer Shapes the Emergence of Antibiotic Resistance Mutations in Core Genes

Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

SERS-based rapid susceptibility testing of commonly administered antibiotics on clinically important bacteria species directly from blood culture of bacteremia patients

Bloodstream infections are a growing public health concern due to emerging pathogens and increasing antimicrobial resistance. Rapid antibiotic susceptibility testing (AST) is urgently needed for timely and optimized choice of antibiotics, but current methods require days to obtain results. Here, we present a general AST protocol based on surface-enhanced Raman scattering (SERS-AST) for bacteremia caused by eight clinically relevant Gram-positive and Gram-negative pathogens treated with seven commonly administered antibiotics. Our results show that the SERS-AST protocol achieves a high level of agreement (96% for Gram-positive and 97% for Gram-negative bacteria) with the widely deployed VITEK 2 diagnostic system. The protocol requires only five hours to complete per blood-culture sample, making it a rapid and effective alternative to conventional methods. Our findings provide a solid foundation for the SERS-AST protocol as a promising approach to optimize the choice of antibiotics for specific bacteremia patients. This novel protocol has the potential to improve patient outcomes and reduce the spread of antibiotic resistance.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Co-assembled nanocomplexes comprising epigallocatechin gallate and berberine for enhanced antibacterial activity against multidrug resistant Staphylococcus aureus

Berberine (BBR), a major alkaloid in Coptis chinensis, and (-)-epigallocatechin-3-gallate (EGCG), a major catechin in green tea, are two common phytochemicals with numerous health benefits, including antibacterial efficacy. However, the limited bioavailability restricts their application. Advancement in the co-assembly technology to form nanocomposite nanoparticles precisely controls the morphology, electrical charge, and functionalities of the nanomaterials. Here, we have reported a simple one-step method for preparing a novel nanocomposite BBR-EGCG nanoparticles (BBR-EGCG NPs). These BBR-EGCG NPs exhibit improved biocompatibility and greater antibacterial effects both in vitro and in vivo relative to free-BBR and first-line antibiotics (i.e., benzylpenicillin potassium and ciprofloxacin). Furthermore, we demonstrated a synergistic bactericidal effect for BBR when combined with EGCG. We also evaluated the antibacterial activity of BBR and the possible synergism with EGCG in MRSA-infected wounds. A potential mechanism for synergism between S. aureus and MRSA was also explored through ATP determination, the interaction between nanoparticles and bacteria, and, then, transcription analysis. Furthermore, our experiments on S. aureus and MRSA confirmed the biofilm-scavenging effect of BBR-EGCG NPs. More importantly, toxicity analysis revealed that the BBR-EGCG NPs had no toxic effects on the major organs of mice. Finally, we proposed a green method for the fabrication of BBR-EGCG combinations, which may provide an alternative approach to treating infections with MRSA without using antibiotics.

Shengjiang Xiexin Decoction ameliorates antibiotic-associated diarrhea by altering the gut microbiota and intestinal metabolic homeostasis

BACKGROUND

Antibiotic-associated diarrhea (AAD) has had a significant increase in the last years, with limited available effective therapies. Shengjiang Xiexin Decoction (SXD), a classic traditional Chinese medicine formula for treating diarrhea, is a promising alternative for reducing the incidence of AAD.

PURPOSE

This study aimed to explore the therapeutic effect of SXD on AAD and to investigate its potential therapeutic mechanism by integrated analysis of the gut microbiome and intestinal metabolic profile.

METHODS

16S rRNA sequencing analysis of the gut microbiota and untargeted-metabolomics analysis of feces were performed. The mechanism was further explored by fecal microbiota transplantation (FMT).

RESULTS

SXD could effectively ameliorate AAD symptoms and restore intestinal barrier function. In addition, SXD could significantly improve the diversity of the gut microbiota and accelerate the recovery of the gut microbiota. At the genus level, SXD significantly increased the relative abundance of Bacteroides spp (p < 0.01) and decreased the relative abundance of Escherichia_Shigela spp (p < 0.001). Untargeted metabolomics showed that SXD significantly improved gut microbiota and host metabolic function, particularly bile acid metabolism and amino acid metabolism.

CONCLUSION

This study demonstrated that SXD could extensively modulate the gut microbiota and intestinal metabolic homeostasis to treat AAD.

Polymyxin combination therapy for multidrug-resistant, extensively-drug resistant, and difficult-to-treat drug-resistant gram-negative infections: is it superior to polymyxin monotherapy?

INTRODUCTION

The increasing prevalence of infections with multidrug-resistant (MDR), extensively-drug resistant (XDR) or difficult-to-treat drug resistant (DTR) Gram-negative bacilli (GNB), including Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae, Enterobacter species, and Escherichia coli poses a severe challenge.

AREAS COVERED

The rapid growing of multi-resistant GNB as well as the considerable deceleration in development of new anti-infective agents have made polymyxins (e.g. polymyxin B and colistin) a mainstay in clinical practices as either monotherapy or combination therapy. However, whether the polymyxin-based combinations lead to better outcomes remains unknown. This review mainly focuses on the effect of polymyxin combination therapy versus monotherapy on treating GNB-related infections. We also provide several factors in designing studies and their impact on optimizing polymyxin combinations.

EXPERT OPINION

An abundance of recent in vitro and preclinical in vivo data suggest clinical benefit for polymyxin-drug combination therapies, especially colistin plus meropenem and colistin plus rifampicin, with synergistic killing against MDR, XDR, and DTR P. aeruginosa, K. pneumoniae and A. baumannii. The beneficial effects of polymyxin-drug combinations (e.g. colistin or polymyxin B + carbapenem against carbapenem-resistant K. pneumoniae and carbapenem-resistant A. baumannii, polymyxin B + carbapenem + rifampin against carbapenem-resistant K. pneumoniae, and colistin + ceftolozan/tazobactam + rifampin against PDR-P. aeruginosa) have often been shown in clinical setting by retrospective studies. However, high-certainty evidence from large randomized controlled trials is necessary. These clinical trials should incorporate careful attention to patient's sample size, characteristics of patient's groups, PK/PD relationships and dosing, rapid detection of resistance, MIC determinations, and therapeutic drug monitoring.

Molecular Signatures of the Eagle Effect Induced by the Artificial Siderophore Conjugate LP-600 in E. coli

Achieving cellular uptake is a central challenge for novel antibiotics targeting Gram-negative bacterial pathogens. One strategy is to hijack the bacterial iron transport system by siderophore-antibiotic conjugates that are actively imported into the cell. This was realized with the MECAM-ampicillin conjugate LP-600 we recently reported that was highly active against E. coli. In the present study, we investigate a paradoxical regrowth of E. coli upon treatment of LP-600 at concentrations 16-32 times above the minimum inhibitory concentration (MIC). The phenomenon, coined "Eagle-effect" in other systems, was not due to resistance formation, and it occurred for the siderophore conjugate but not for free ampicillin. To investigate the molecular imprint of the Eagle effect, a combined transcriptome and untargeted metabolome analysis was conducted. LP-600 induced the expression of genes involved in iron acquisition, SOS response, and the e14 prophage upon regrowth conditions. The Eagle effect was diminished in the presence of sulbactam, which we ascribe to a putative synergistic antibiotic action but not to β-lactamase inhibition. The study highlights the relevance of the Eagle effect for siderophore conjugates. Through the first systematic -omics investigations, it also demonstrates that the Eagle effect manifests not only in a paradoxical growth but also in unique gene expression and metabolite profiles.

Potent antibiotic design via guided search from antibacterial activity evaluations

MOTIVATION

The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules.

RESULTS

By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance.

AVAILABILITY AND IMPLEMENTATION

Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning

Introduction. The resistance rate of Klebsiella pneumoniae (K. pneumoniae) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice.Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data.Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k-mer features, and analyse their function for understanding its resistance mechanism.Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blastn, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regression, SVM, GBDT and XGBoost) to evaluate their phenotype prediction ability.Results. A total of 16 670 imipenem resistance features were predicted from genotype-phenotype model. The 30 potential genes were identified by annotating the resistance features and corresponded to known antibiotic-related genes (mdtM, dedA, rne, etc.). GO and KEGG pathway analyses indicated the possible association of imipenem resistance with metabolism process and cell membrane. CRYCAGCDN and CGRDAAAN were found from the imipenem resistance features, which were widely presented in the reported β-lactam resistance genes (bla _SHV, bla _CTX-M, bla _TEM, etc.), and YCYAGCMCAST with metabolic functions (organic substance metabolic process, nitrogen compound metabolic process and cellular metabolic process) was identified from the top 50 resistance features. The 25 resistance genes in the training dataset included 19 genes in the external dataset, which verified the accuracy of prediction. The area under curve values of logistics regression, SVM, GBDT and XGBoost were 0.965, 0.966, 0.969 and 0.969, respectively, indicating that the imipenem resistance features have a strong prediction power.Conclusion. Machine-learning methods could effectively predict the imipenem resistance feature in K. pneumoniae, and provide resistance sequence profiles for predicting resistance phenotype and exploring potential resistance mechanisms. It provides an important insight into the potential therapeutic strategies of K. pneumoniae resistance to imipenem, and speed up the application of machine learning in routine diagnosis.

Systems Biology: New Insight into Antibiotic Resistance

Over the past few decades, antimicrobial resistance (AMR) has emerged as an important threat to public health, resulting from the global propagation of multidrug-resistant strains of various bacterial species. Knowledge of the intrinsic factors leading to this resistance is necessary to overcome these new strains. This has contributed to the increased use of omics technologies and their extrapolation to the system level. Understanding the mechanisms involved in antimicrobial resistance acquired by microorganisms at the system level is essential to obtain answers and explore options to combat this resistance. Therefore, the use of robust whole-genome sequencing approaches and other omics techniques such as transcriptomics, proteomics, and metabolomics provide fundamental insights into the physiology of antimicrobial resistance. To improve the efficiency of data obtained through omics approaches, and thus gain a predictive understanding of bacterial responses to antibiotics, the integration of mathematical models with genome-scale metabolic models (GEMs) is essential. In this context, here we outline recent efforts that have demonstrated that the use of omics technology and systems biology, as quantitative and robust hypothesis-generating frameworks, can improve the understanding of antibiotic resistance, and it is hoped that this emerging field can provide support for these new efforts.

In silico genome-scale metabolic modeling and in vitro static time-kill studies of exogenous metabolites alone and with polymyxin B against Klebsiella pneumoniae

Multidrug-resistant (MDR) Klebsiella pneumoniae is a top-prioritized Gram-negative pathogen with a high incidence in hospital-acquired infections. Polymyxins have resurged as a last-line therapy to combat Gram-negative “superbugs”, including MDR K. pneumoniae. However, the emergence of polymyxin resistance has increasingly been reported over the past decades when used as monotherapy, and thus combination therapy with non-antibiotics (e.g., metabolites) becomes a promising approach owing to the lower risk of resistance development. Genome-scale metabolic models (GSMMs) were constructed to delineate the altered metabolism of New Delhi metallo-β-lactamase- or extended spectrum β-lactamase-producing K. pneumoniae strains upon addition of exogenous metabolites in media. The metabolites that caused significant metabolic perturbations were then selected to examine their adjuvant effects using in vitro static time–kill studies. Metabolic network simulation shows that feeding of 3-phosphoglycerate and ribose 5-phosphate would lead to enhanced central carbon metabolism, ATP demand, and energy consumption, which is converged with metabolic disruptions by polymyxin treatment. Further static time–kill studies demonstrated enhanced antimicrobial killing of 10 mM 3-phosphoglycerate (1.26 and 1.82 log₁₀ CFU/ml) and 10 mM ribose 5-phosphate (0.53 and 0.91 log₁₀ CFU/ml) combination with 2 mg/L polymyxin B against K. pneumoniae strains. Overall, exogenous metabolite feeding could possibly improve polymyxin B activity via metabolic modulation and hence offers an attractive approach to enhance polymyxin B efficacy. With the application of GSMM in bridging the metabolic analysis and time–kill assay, biological insights into metabolite feeding can be inferred from comparative analyses of both results. Taken together, a systematic framework has been developed to facilitate the clinical translation of antibiotic-resistant infection management.

Link Between Antibiotic Persistence and Antibiotic Resistance in Bacterial Pathogens

Both, antibiotic persistence and antibiotic resistance characterize phenotypes of survival in which a bacterial cell becomes insensitive to one (or even) more antibiotic(s). However, the molecular basis for these two antibiotic-tolerant phenotypes is fundamentally different. Whereas antibiotic resistance is genetically determined and hence represents a rather stable phenotype, antibiotic persistence marks a transient physiological state triggered by various stress-inducing conditions that switches back to the original antibiotic sensitive state once the environmental situation improves. The molecular basics of antibiotic resistance are in principle well understood. This is not the case for antibiotic persistence. Under all culture conditions, there is a stochastically formed, subpopulation of persister cells in bacterial populations, the size of which depends on the culture conditions. The proportion of persisters in a bacterial population increases under different stress conditions, including treatment with bactericidal antibiotics (BCAs). Various models have been proposed to explain the formation of persistence in bacteria. We recently hypothesized that all physiological culture conditions leading to persistence converge in the inability of the bacteria to re-initiate a new round of DNA replication caused by an insufficient level of the initiator complex ATP-DnaA and hence by the lack of formation of a functional orisome. Here, we extend this hypothesis by proposing that in this persistence state the bacteria become more susceptible to mutation-based antibiotic resistance provided they are equipped with error-prone DNA repair functions. This is - in our opinion - in particular the case when such bacterial populations are exposed to BCAs.

Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review

BACKGROUND

The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies.

OBJECTIVE

Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.

Identification of metabolite extraction method for targeted exploration of antimicrobial resistance associated metabolites of Klebsiella pneumoniae

Antimicrobial resistant Klebsiella pneumoniae (K. pneumoniae), as being a pathogen of critical clinical concern, urgently demands effective therapeutic options. However, the discovery of novel antibiotics over the last three decades has declined drastically and necessitates exploring novel strategies. Metabolomic modulation has been the promising approach for the development of effective therapeutics to deal with AMR; however, only limited efforts have been made to-date, possibly due to the unavailability of suitable metabolites extraction protocols. Therefore, in order to establish a detailed metabolome of K. pneumoniae and identify a method for targeted exploration of metabolites that are involved in the regulation of AMR associated processes, metabolites were extracted using multiple methods of metabolites extraction (freeze-thaw cycle (FTC) and sonication cycle (SC) method alone or in combination (FTC followed by SC; FTC + SC)) from K. pneumoniae cells and then identified using an orbitrap mass analyzer (ESI-LC-MS/MS). A total of 151 metabolites were identified by using FTC, 132 metabolites by using FTC+SC, 103 metabolites by using SC and 69 metabolites common among all the methods used which altogether enabled the identification of 199 unique metabolites. Of these 199, 70 metabolites were known to have an association with AMR phenotype and among these, the FTC + SC method yielded better (identified 55 metabolites), quantitatively and qualitatively compared to FTC and SC alone (identified 51 and 41 metabolites respectively). Each method of metabolite extraction showed a definite degree of biasness and specificity towards chemical classes of metabolites and jointly contributed to the development of a detailed metabolome of the pathogen. FTC method was observed to give higher metabolomic coverage as compared to SC alone and FTC + SC. However, FTC + SC resulted in the identification of a higher number of AMR associated metabolites of K. pneumoniae compared to FTC and SC alone.

Integrated genomics and chemical biology herald an era of sophisticated antibacterial discovery, from defining essential genes to target elucidation

The golden age of antibiotic discovery in the 1940s-1960s saw the development and deployment of many different classes of antibiotics, revolutionizing the field of medicine. Since that time, our ability to discover antibiotics of novel structural classes or mechanisms has not kept pace with the ever-growing threat of antibiotic resistance. Recently, advances at the intersection of genomics and chemical biology have enabled efforts to better define the vulnerabilities of essential gene targets, to develop sophisticated whole-cell chemical screening methods that reveal target biology early, and to elucidate small molecule targets and modes of action more effectively. These new technologies have the potential to expand the chemical diversity of antibiotic candidates, as well as the breadth of targets. We illustrate how the latest tools of genomics and chemical biology are being integrated to better understand pathogen vulnerabilities and antibiotic mechanisms in order to inform a new era of antibiotic discovery.

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble of 3D conformers that are accessible to a molecule. Property prediction could be improved by using conformer ensembles as input, but there is no large-scale dataset that contains graphs annotated with accurate conformers and experimental data. Here we use advanced sampling and semi-empirical density functional theory (DFT) to generate 37 million molecular conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also labeled with high-quality DFT free energies in an implicit water solvent, and 534 ensembles are further optimized with DFT. GEOM will assist in the development of models that predict properties from conformer ensembles, and generative models that sample 3D conformations.

Measurement(s)	Conformer geometries and properties
Technology Type(s)	Computational Chemistry

Tracking Uptake and Metabolism of Xenometallomycins Using a Multi-Isotope Tagging Strategy

Synthetic and naturally occurring siderophores and their conjugates provide access to the bacterial cytoplasm via active membrane transport. Previously, we displaced iron with the radioactive isotope ⁶⁷Ga to quantify and track in vitro and in vivo uptake and distribution of siderophore Trojan Horse antibiotic conjugates. Here, we introduce a multi-isotope tagging strategy to individually elucidate the fate of metal cargo and the ligand construct with radioisotopes ⁶⁷Ga and ¹²⁴I. We synthesized gallium(III) model complexes of a ciprofloxacin-functionalized linear desferrichrome (Ga-D6) and deferoxamine (Ga-D7) incorporating an iodo-tyrosine linker to enable radiolabeling using the metal-binding (⁶⁷Ga) and the cargo-conjugation site (¹²⁴I). Radiochemical experiments with Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa wt strains show that ⁶⁷Ga-D6/D7 and Ga-D6-¹²⁴I/D7-¹²⁴I have comparable uptake, indicating intact complex import and siderophore-mediated uptake. In naive mice, ⁶⁷Ga-D6/D7 and Ga-D6-¹²⁴I/D7-¹²⁴I demonstrate predominantly renal clearance; urine metabolite analysis indicates in vivo dissociation of Ga(III) is a likely mechanism of degradation for ⁶⁷Ga-D6/D7 when compared to ligand radiolabeled compounds, Ga-D6-¹²⁴I/D7-¹²⁴I, which remain >60% intact in urine. Cumulatively, this work demonstrates that a multi-isotope tagging strategy effectively elucidates the in vitro uptake, pharmacokinetics, and in vivo stability of xenometallomycins with modular chemical structures.

Collateral sensitivity to pleuromutilins in vancomycin-resistant Enterococcus faecium

The acquisition of resistance to one antibiotic sometimes leads to collateral sensitivity to a second antibiotic. Here, we show that vancomycin resistance in Enterococcus faecium is associated with a remarkable increase in susceptibility to pleuromutilin antibiotics (such as lefamulin), which target the bacterial ribosome. The trade-off between vancomycin and pleuromutilins is mediated by epistasis between the van gene cluster and msrC, encoding an ABC-F protein that protects bacterial ribosomes from antibiotic targeting. In mouse models of vancomycin-resistant E. faecium colonization and septicemia, pleuromutilin treatment reduces colonization and improves survival more effectively than standard therapy (linezolid). Our findings suggest that pleuromutilins may be useful for the treatment of vancomycin-resistant E. faecium infections.

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. TEASER: Novel treatment strategies are needed to address the emerging threat of antimicrobial resistance (AMR) in bacterial pathogens. Metabolomics approaches may help to unravel the biochemical underpinnings of AMR, thereby facilitating the discovery of metabolism-associated drug targets and treatment strategies.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Ceragenins and Antimicrobial Peptides Kill Bacteria through Distinct Mechanisms

Ceragenins are a family of synthetic amphipathic molecules designed to mimic the properties of naturally occurring cationic antimicrobial peptides (CAMPs). Although ceragenins have potent antimicrobial activity, whether their mode of action is similar to that of CAMPs has remained elusive. Here, we reported the results of a comparative study of the bacterial responses to two well-studied CAMPs, LL37 and colistin, and two ceragenins with related structures, CSA13 and CSA131. Using transcriptomic and proteomic analyses, we found that Escherichia coli responded similarly to both CAMPs and ceragenins by inducing a Cpx envelope stress response. However, whereas E. coli exposed to CAMPs increased expression of genes involved in colanic acid biosynthesis, bacteria exposed to ceragenins specifically modulated functions related to phosphate transport, indicating distinct mechanisms of action between these two classes of molecules. Although traditional genetic approaches failed to identify genes that confer high-level resistance to ceragenins, using a Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) approach we identified E. coli essential genes that when knocked down modify sensitivity to these molecules. Comparison of the essential gene-antibiotic interactions for each of the CAMPs and ceragenins identified both overlapping and distinct dependencies for their antimicrobial activities. Overall, this study indicated that, while some bacterial responses to ceragenins overlap those induced by naturally occurring CAMPs, these synthetic molecules target the bacterial envelope using a distinctive mode of action. IMPORTANCE The development of novel antibiotics is essential because the current arsenal of antimicrobials will soon be ineffective due to the widespread occurrence of antibiotic resistance. The development of naturally occurring cationic antimicrobial peptides (CAMPs) for therapeutics to combat antibiotic resistance has been hampered by high production costs and protease sensitivity, among other factors. The ceragenins are a family of synthetic CAMP mimics that kill a broad spectrum of bacterial species but are less expensive to produce, resistant to proteolytic degradation, and seemingly resistant to the development of high-level resistance. Determining how ceragenins function may identify new essential biological pathways of bacteria that are less prone to the development of resistance and will further our understanding of the design principles for maximizing the effects of synthetic CAMPs.

Combining CRISPRi and metabolomics for functional annotation of compound libraries

Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.

The Serum and Fecal Metabolomic Profiles of Growing Kittens Treated with Amoxicillin/Clavulanic Acid or Doxycycline

Simple Summary

This study investigated the impact of antibiotic treatment οn the serum and fecal metabolome (the collection of all small molecules produced by the gut bacteria and the host) of young cats. Thirty 2-month-old cats with an upper respiratory tract infection were treated with either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days. In addition, another 15 control cats that did not receive antibiotics were included. Blood was collected on days 0 (before treatment), 20/28 (last day of treatment), and 300 (10 months after the end of treatment), while feces were collected on days 0, 20/28, 60, 120, and 300. Seven serum and fecal metabolites differed between cats treated with antibiotics and control cats at the end of treatment period. Ten months after treatment, no metabolites differed from healthy cats, suggesting that amoxicillin/clavulanic acid or doxycycline treatment only temporarily affects the abundance of the serum and fecal metabolome.

Abstract

The long-term impact of antibiotics on the serum and fecal metabolome of kittens has not yet been investigated. Therefore, the objective of this study was to evaluate the serum and fecal metabolome of kittens with an upper respiratory tract infection (URTI) before, during, and after antibiotic treatment and compare it with that of healthy control cats. Thirty 2-month-old cats with a URTI were randomly assigned to receive either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days, and 15 cats of similar age were enrolled as controls. Fecal samples were collected on days 0, 20/28, 60, 120, and 300, while serum was collected on days 0, 20/28, and 300. Untargeted and targeted metabolomic analyses were performed on both serum and fecal samples. Seven metabolites differed significantly in antibiotic-treated cats compared to controls on day 20/28, with two differing on day 60, and two on day 120. Alterations in the pattern of serum amino acids, antioxidants, purines, and pyrimidines, as well as fecal bile acids, sterols, and fatty acids, were observed in antibiotic-treated groups that were not observed in control cats. However, the alterations caused by either amoxicillin/clavulanic acid or doxycycline of the fecal and serum metabolome were only temporary and were resolved by 10 months after their withdrawal.

Synergism of imipenem with fosfomycin associated with the active cell wall recycling and heteroresistance in Acinetobacter calcoaceticus-baumannii complex

The carbapenem-resistant Acinetobacter calcoaceticus-baumannii (ACB) complex has become an urgent threat worldwide. Here, we determined antibiotic combinations and the feasible synergistic mechanisms against three couples of ACB (A. baumannii (AB250 and A10), A. pittii (AP1 and AP23), and A. nosocomialis (AN4 and AN12)). Imipenem with fosfomycin, the most effective in the time-killing assay, exhibited synergism to all strains except AB250. MurA, a fosfomycin target encoding the first enzyme in the de novo cell wall synthesis, was observed with the wild-type form in all isolates. Fosfomycin did not upregulate murA, indicating the MurA-independent pathway (cell wall recycling) presenting in all strains. Fosfomycin more upregulated the recycling route in synergistic strain (A10) than non-synergistic strain (AB250). Imipenem in the combination dramatically downregulated the recycling route in A10 but not in AB250, demonstrating the additional effect of imipenem on the recycling route, possibly resulting in synergism by the agitation of cell wall metabolism. Moreover, heteroresistance to imipenem was observed in only AB250. Our results indicate that unexpected activity of imipenem on the active cell wall recycling concurrently with the presence of heteroresistance subpopulation to imipenem may lead to the synergism of imipenem and fosfomycin against the ACB isolates.

Unraveling individual and combined toxicity of nano/microplastics and ciprofloxacin to Synechocystis sp. at the cellular and molecular levels

Although nanoplastics/microplastics (NPs/MPs) may interact with co-contaminants (e.g. antibiotics) in aquatic systems, little is known about their combined toxicity. Here, we compared the individual toxicity of NPs/MPs or ciprofloxacin (CIP, a very commonly detected antibiotic) and their combined toxicity toward a unicellular cyanobacterium Synechocystis sp. in terms of the cellular responses and metabolomic analysis. We found that CIP exhibited an antagonistic effect with NPs/MPs due to its adsorption onto the surface of NPs/MPs. Particle size-dependent toxic effects of NPs/MPs were observed. Reactive oxygen species (ROS) was verified as an important factor for NPs/MPs to inhibit cell growth, other than for CIP. Metabolomics further revealed that Synechocystis sp. up-regulated glycerophospholipids, amino acids, nucleotides, and carbohydrates to tolerate CIP pressure. NPs/MPs downregulated the TCA cycle and glycerophospholipids metabolism and impaired the primary production and membrane integrity via adhesion with Synechocystis sp.. Additionally, the toxicity of NPs/MPs throughout ten growth cycles at a sublethal concentration unveiled its potential risks in interfering with metabolism. Collectively, our findings provide insights into the joint ecotoxicity of NPs/MPs and antibiotics, and highlight the potential risks of co-pollutants at environmental relevant concentrations.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Insights into the mechanism of the interference of sulfadiazine on soil microbial community and function

The accumulation of sulfonamides in the soil environment possessed the potential to change soil microbial community and function. Metabolomics is capable of providing insights into the carbon metabolic pool and molecular mechanisms associated with external stressors. Here we evaluated alternations in soil bacterial community and soil metabolites profiles under sulfadiazine (SDZ) exposure and proposed a potential mechanism that SDZ accumulation in soil affected soil organic matter (SOM) cycling. Sequencing analysis showed that the relative abundance of bacterial species associated with carbon cycling significantly decreased under high concentrations of SDZ exposure. Untargeted metabolomics analysis showed that 78 metabolites were significantly changed with the presence of SDZ in soil. The combination of functional predictions and pathway analysis both demonstrated that high concentrations of SDZ exposure could cause disturbance in anabolism and catabolism. Moreover, the noticeable decline in the relative content of carbohydrates under high concentrations of SDZ exposure might weaken physical separation and provide more chances for microbes to degrade SOM. The above results provided evidence that SDZ accumulation in soil held the potential to disturb SOM cycling. These findings spread our understanding about the environmental risk of antibiotic in the soil environment beyond the dissemination of antibiotic resistance.

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

Deciphering the mechanism of action of antitubercular compounds with metabolomics

Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.