Metabolic constraints on the evolution of antibiotic resistance

Unveiling triclosan biodegradation: Novel metabolic pathways, genomic insights, and global environmental adaptability of Pseudomonas sp. strain W03

The polychlorinated aromatic antimicrobial agent triclosan (TCS) is widely used to indiscriminately and rapidly kill microorganisms. The global use of TCS has led to widespread environmental contamination, posing significant threats to ecosystem and human health. Here we reported a newly isolated Pseudomonas sp. W03 for degrading TCS metabolically at concentrations up to 10 mg/L. This strain exhibited optimal degradation activity at 30°C and pH 7.0, and retained substantial activity at pH 4.0, although it was sensitive to alkaline conditions. Genomic analysis of strain W03 revealed a circular chromosome comprising 6075,907 bp with a GC content of 65.08 %. A novel TCS degradation pathway, involving dechlorination, oxidation, ether bond fission, and reoxidation processes, was identified. Also, the study mapped the global distribution of analogous Pseudomonas using 16S rRNA gene sequences, revealing their widespread presence in diverse aquatic environments, with a significant abundance in wastewater systems. These findings indicated that these bacteria play a critical ecological role in both natural and engineered environments, particularly in the degradation of organic pollutants. This study enhances our understanding of microbial degradation of emerging contaminants and presents a promising candidate for bioremediation strategies aimed at mitigating TCS-related water pollution.

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.

Dynamic impact of polyethylene terephthalate nanoplastics on antibiotic resistance and microplastics degradation genes in the rhizosphere of Oryza sativa L

This study examined the effects of polyethylene terephthalate (PET) nanoplastics on the rhizosphere of Oryza sativa L., focusing on dynamic changes and interactions among microbial communities, antibiotic resistance genes (ARGs) and microplastic degradation genes (MDGs). PET exposure altered the structure and function of soil microbial, enabling specific microbial groups to thrive in polluted environments. High-dose PET treatments markedly increased the abundance and dissemination of ARGs, primarily via resistance mechanisms such as antibiotic efflux and target alteration. By providing additional carbon sources and surfaces for microbial attachment, PET stimulated the growth of microorganisms harboring MDGs, resulting in an increase in MDGs abundance. The elevated expression of MDGs facilitated the propagation of ARGs, with overlapping host microorganisms suggesting that certain microbial groups exhibit dual metabolic capabilities, enabling them to endure both antibiotic and microplastic pressures. Toxic byproducts of microplastic degradation, such as mono-ethylhexyl phthalate, further promoted ARGs dissemination by increasing horizontal gene transfer frequency. Structural equation modeling revealed that PET indirectly influenced ARGs and MDGs expression by altering soil C/N ratio, available phosphorus, and enzyme activities. Thus, nanoscale PET exacerbates ecological risks to soil microbial communities by driving co-propagation of ARGs and MDGs, highlighting the persistent threat of composite pollution to agroecosystems.

Metabolic mutations reduce antibiotic susceptibility of E. coli by pathway-specific bottlenecks

Metabolic variation across pathogenic bacterial strains can impact their susceptibility to antibiotics and promote the evolution of antimicrobial resistance (AMR). However, little is known about how metabolic mutations influence metabolism and which pathways contribute to antibiotic susceptibility. Here, we measured the antibiotic susceptibility of 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. Across all mutants, we observed modest increases of the minimal inhibitory concentration (twofold to tenfold) without any cases of major resistance. Most mutants that showed reduced susceptibility to either of the two tested antibiotics carried mutations in metabolic genes. The effect of metabolic mutations on antibiotic susceptibility was antibiotic- and pathway-specific: mutations that reduced susceptibility against the β-lactam antibiotic carbenicillin converged on purine nucleotide biosynthesis, those against the aminoglycoside gentamicin converged on the respiratory chain. In addition, metabolic mutations conferred tolerance to carbenicillin by reducing growth rates. These results, along with evidence that metabolic bottlenecks are common among clinical E. coli isolates, highlight the contribution of metabolic mutations for AMR.

Influence of soil type on bacterial growth and tolerance to experimentally added human antibiotics

The human antibiotics cefuroxime (CXM) and azithromycin (AZI) are among the most commonly prescribed. A significant portion of both are excreted and has been detected in sewage treatment plant effluents. The increasing use of such effluents in crops for irrigation and as fertilisers poses a threat to soil microbiota because of the presence of antibiotics. The lack of studies on CXM and AZI in soils hinders our understanding of their potential toxic effects on soil bacterial communities and ecosystem services. This study significantly contributes to the literature by quantifying the toxicity of CXM and AZI at varying concentrations in 12 different crop soils and tracking their evolution over time. The study also examined whether antibiotic pressure led to the development of more tolerant bacterial communities. The results of this study are the values of the logarithm of the antibiotic concentration at which 50 % of bacterial growth is inhibited (Log IC50) and indicate that both antibiotics are toxic to soil bacteria. The direct toxicity of CXM (1 day after contamination) was higher (Log IC50: 0.9 = 7.9 mg kg-1) than that of AZI (Log IC50: 3.4 = 2362 mg kg-1). However, bacterial growth was less affected by CXM over time, whereas AZI remained toxic in some soils until day 42 (Log IC50: 3.2 = 1533 mg kg-1 and 3.4 = 2291 mg kg-1, respectively). The overall results indicate that selective pressure exerted by antibiotics generates antibiotic tolerance in soils, even at the lowest antibiotic concentration studied (7.8 mg kg-1). The general trend was to increase tolerance to higher antibiotic concentrations up to the highest concentration studied (2000 mg kg-1). However, the degree of tolerance developed was highly dependent on soil type. More studies should be conducted to quantitatively assess the toxic and tolerance-developing effects of antibiotics in soils. Such information will be valuable for identifying which antibiotics pose a threat to the soil microbiota and consequently to human health.

Vancomycin‐intermediate Staphylococcus aureus employs CcpA‐GlmS metabolism regulatory cascade to resist vancomycin

Vancomycin (VAN)‐intermediate Staphylococcus aureus (VISA) is a critical cause of VAN treatment failure worldwide. Multiple genetic changes are reportedly associated with VISA formation, whereas VISA strains often present common phenotypes, such as reduced autolysis and thickened cell wall. However, how mutated genes lead to VISA common phenotypes remains unclear. Here, we show a metabolism regulatory cascade (CcpA‐GlmS), whereby mutated two‐component systems (TCSs) link to the common phenotypes of VISA. We found that ccpA deletion decreased VAN resistance in VISA strains with diverse genetic backgrounds. Metabolic alteration in VISA was associated with ccpA upregulation, which was directly controlled by TCSs WalKR and GraSR. RNA‐sequencing revealed the crucial roles of CcpA in changing the carbon flow and nitrogen flux of VISA to promote VAN resistance. A gate enzyme (GlmS) that drives carbon flow to the cell wall precursor biosynthesis was upregulated in VISA. CcpA directly controlled glmS expression. Blocking CcpA sensitized VISA strains to VAN treatment in vitro and in vivo. Overall, this work uncovers a link between the formation of VISA phenotypes and commonly mutated genes. Inhibition of CcpA‐GlmS cascade is a promising strategy to restore the therapeutic efficiency of VAN against VISA infections.

Synthetic Approaches, Properties, and Applications of Acylals in Preparative and Medicinal Chemistry

Diesters of geminal diols (R-CH(O-CO-R')2, RR'C(OCOR″)2, etc. with R = H, aryl or alkyl) are termed acylals according to IUPAC recommendations (Rule P-65.6.3.6 Acylals) if the acids involved are carboxylic acids. Similar condensation products can be obtained from various other acidic structures as well, but these related "non-classical acylals", as one might call them, differ in various aspects from classical acylals and will not be discussed in this article. Carboxylic acid diesters of geminal diols play a prominent role in organic chemistry, not only in their application as protective groups for aldehydes and ketones but also as precursors in the total synthesis of natural compounds and in a variety of organic reactions. What is more, acylals are useful as a key structural motif in clinically validated prodrug approaches. In this review, we summarise the syntheses and chemical properties of such classical acylals and show what potentially under-explored possibilities exist in the field of drug design, especially prodrugs, and classify this functional group in medicinal chemistry.

Enhanced fatty acid biosynthesis by Sigma28 in stringent responses contributes to multidrug resistance and biofilm formation in Helicobacter pylori

The metabolic state of bacteria significantly contributes to their resistance to antibiotics; however, the specific metabolic mechanisms conferring antimicrobial resistance in Helicobacter pylori remain largely understudied. Employing transcriptomic and non-targeted metabolomics, we characterized the metabolic reprogramming of H. pylori when challenged with antibiotic agents. We observed a notable increase in both genetic and key proteomic components involved in fatty acid biosynthesis. Inhibition of this pathway significantly enhanced the antibiotic susceptibility of the sensitive and multidrug-resistant H. pylori strains while also disrupting their biofilm-forming capacities. Further analysis revealed that antibiotic treatment induced a stringent response, triggering the expression of the hp0560-hp0557 operon regulated by Sigma28 (σ28). This activation in turn stimulated the fatty acid biosynthetic pathway, thereby enhancing the antibiotic tolerance of H. pylori. Our findings reveal a novel adaptive strategy employed by H. pylori to withstand antibiotic stress.

Conserved Evolutionary Trajectory Can Be Perturbed to Prevent Resistance Evolution under Norfloxacin Pressure by Forcing Mycobacterium smegmatis on Alternate Evolutionary Paths

Antibiotic resistance is a pressing health issue, with the emergence of resistance in bacteria outcompeting the discovery of novel drug candidates. While many studies have used Adaptive Laboratory Evolution (ALE) to understand the determinants of resistance, the influence of the drug dosing profile on the evolutionary trajectory remains understudied. In this study, we employed ALE on Mycobacterium smegmatis exposed to various concentrations of Norfloxacin using both cyclic constant and stepwise increasing drug dosages to examine their impact on the resistance mechanisms selected. Mutations in an efflux pump regulator, LfrR, were found in all of the evolved populations irrespective of the drug profile and population bottleneck, indicating a conserved efflux-based resistance mechanism. This mutation appeared early in the evolutionary trajectory, providing low-level resistance when present alone, with a further increase in resistance resulting from successive accumulation of other mutations. Notably, drug target mutations, similar to those observed in clinical isolates, were only seen above a threshold of greater than 4× the minimum inhibitory concentration (MIC). A combination of three mutations in the genes, lfrR, MSMEG_1959, and MSMEG_5045, was conserved across multiple lineages, leading to high-level resistance and preceding the appearance of drug target mutations. Interestingly, in populations evolved from parental strains lacking the lfrA efflux pump, the primary target of the lfrR regulator, no lfrR gene mutations are selected. Furthermore, evolutional trajectories originating from the ΔlfrA strain displayed early arrest in some lineages and the absence of target gene mutations in those that evolved, albeit delayed. Thus, blocking or inhibiting the expression of efflux pumps can arrest or delay the fixation of drug target mutations, potentially limiting the maximum attainable resistance levels.

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.

Beta-lactamase dependent and independent evolutionary paths to high-level ampicillin resistance

The incidence of beta-lactam resistance among clinical isolates is a major health concern. A key method to study the emergence of antibiotic resistance is adaptive laboratory evolution. However, in the case of the beta-lactam ampicillin, bacteria evolved in laboratory settings do not recapitulate clinical-like resistance levels, hindering efforts to identify major evolutionary paths and their dependency on genetic background. Here, we used the Microbial Evolution and Growth Arena (MEGA) plate to select ampicillin-resistant Escherichia coli mutants with varying degrees of resistance. Whole-genome sequencing of resistant isolates revealed that ampicillin resistance was acquired via a combination of single-point mutations and amplification of the gene encoding beta-lactamase AmpC. However, blocking AmpC-mediated resistance revealed latent adaptive pathways: strains deleted for ampC were able to adapt through combinations of changes in genes involved in multidrug resistance encoding efflux pumps, transcriptional regulators, and porins. Our results reveal that combinations of distinct genetic mutations, accessible at large population sizes, can drive high-level resistance to ampicillin even independently of beta-lactamases.

Quorum-sensing regulation of phenazine production heightens Pseudomonas aeruginosa resistance to ciprofloxacin

Quorum sensing is a type of cell-cell communication that modulates various biological activities of bacteria. Previous studies indicate that quorum sensing contributes to the evolution of bacterial resistance to antibiotics, but the underlying mechanisms are not fully understood. In this study, we grew Pseudomonas aeruginosa in the presence of sub-lethal concentrations of ciprofloxacin, resulting in a large increase in ciprofloxacin minimal inhibitory concentration. We discovered that quorum sensing-mediated phenazine biosynthesis was significantly enhanced in the resistant isolates, where the quinolone circuit was the predominant contributor to this phenomenon. We found that production of pyocyanin changed carbon flux and showed that the effect can be partially inhibited by the addition of pyruvate to cultures. This study illustrates the role of quorum sensing-mediated phenotypic resistance and suggests a strategy for its prevention.

Exposure of Helicobacter pylori to clarithromycin in vitro resulting in the development of resistance and triggers metabolic reprogramming associated with virulence and pathogenicity

In H. pylori infection, antibiotic-resistance is one of the most common causes of treatment failure. Bacterial metabolic activities, such as energy production, bacterial growth, cell wall construction, and cell-cell communication, all play important roles in antimicrobial resistance mechanisms. Identification of microbial metabolites may result in the discovery of novel antimicrobial therapeutic targets and treatments. The purpose of this work is to assess H. pylori metabolomic reprogramming in order to reveal the underlying mechanisms associated with the development of clarithromycin resistance. Previously, four H. pylori isolates were induced to become resistant to clarithromycin in vitro by incrementally increasing the concentrations of clarithromycin. Bacterial metabolites were extracted using the Bligh and Dyer technique and analyzed using metabolomic fingerprinting based on Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (LC-Q-ToF-MS). The data was processed and analyzed using the MassHunter Qualitative Analysis and Mass Profiler Professional software. In parental sensitivity (S), breakpoint isolates (B), and induced resistance isolates (R) H. pylori isolates, 982 metabolites were found. Furthermore, based on accurate mass, isotope ratios, abundances, and spacing, 292 metabolites matched the metabolites in the Agilent METLIN precise Mass-Personal Metabolite Database and Library (AM-PCDL). Several metabolites associated with bacterial virulence, pathogenicity, survival, and proliferation (L-leucine, Pyridoxone [Vitamine B6], D-Mannitol, Sphingolipids, Indoleacrylic acid, Dulcitol, and D-Proline) were found to be elevated in generated resistant H. pylori isolates when compared to parental sensitive isolates. The elevated metabolites could be part of antibiotics resistance mechanisms. Understanding the fundamental metabolome changes in the course of progressing from clarithromycin-sensitive to breakpoint to resistant in H. pylori clinical isolates may be a promising strategy for discovering novel alternatives therapeutic targets.

Potential environmental risks of field bio/non-degradable microplastic from mulching residues in farmland: Evidence from metagenomic analysis of plastisphere

The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.

The Effect of the Stringent Response and Oxidative Stress Response on Fitness Costs of De Novo Acquisition of Antibiotic Resistance

Resistance evolution during exposure to non-lethal levels of antibiotics is influenced by various stress responses of bacteria which are known to affect growth rate. Here, we aim to disentangle how the interplay between resistance development and associated fitness costs is affected by stress responses. We performed de novo resistance evolution of wild-type strains and single-gene knockout strains in stress response pathways using four different antibiotics. Throughout resistance development, the increase in minimum inhibitory concentration (MIC) is accompanied by a gradual decrease in growth rate, most pronounced in amoxicillin or kanamycin. By measuring biomass yield on glucose and whole-genome sequences at intermediate and final time points, we identified two patterns of how the stress responses affect the correlation between MIC and growth rate. First, single-gene knockout E. coli strains associated with reactive oxygen species (ROS) acquire resistance faster, and mutations related to antibiotic permeability and pumping out occur earlier. This increases the metabolic burden of resistant bacteria. Second, the ΔrelA knockout strain, which has reduced (p)ppGpp synthesis, is restricted in its stringent response, leading to diminished growth rates. The ROS-related mutagenesis and the stringent response increase metabolic burdens during resistance development, causing lower growth rates and higher fitness costs.

Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency

Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.

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.

Rolling the evolutionary dice: Neisseria commensals as proxies for elucidating the underpinnings of antibiotic resistance mechanisms and evolution in human pathogens

Species within the genus Neisseria are adept at sharing adaptive allelic variation, with commensal species repeatedly transferring resistance to their pathogenic relative Neisseria gonorrhoeae. However, resistance in commensals is infrequently characterized, limiting our ability to predict novel and potentially transferable resistance mechanisms that ultimately may become important clinically. Unique evolutionary starting places of each Neisseria species will have distinct genomic backgrounds, which may ultimately control the fate of evolving populations in response to selection as epistatic and additive interactions coerce lineages along divergent evolutionary trajectories. Alternatively, similar genetic content present across species due to shared ancestry may constrain existing adaptive solutions. Thus, identifying the paths to resistance across commensals may aid in characterizing the Neisseria resistome-or the reservoir of alleles within the genus as well as its depth. Here, we use in vitro evolution of four commensal species to investigate the potential and repeatability of resistance evolution to two antimicrobials, the macrolide azithromycin and the β-lactam penicillin. After 20 days of selection, commensals evolved resistance to penicillin and azithromycin in 11/16 and 12/16 cases, respectively. Almost all cases of resistance emergence converged on mutations within ribosomal components or the mtrRCDE efflux pump for azithromycin-based selection and mtrRCDE, penA, and rpoB for penicillin selection, thus supporting constrained adaptive solutions despite divergent evolutionary starting points across the genus for these particular drugs. Though drug-selected loci were limited, we do identify novel resistance-imparting mutations. Continuing to explore paths to resistance across different experimental conditions and genomic backgrounds, which could shunt evolution down alternative evolutionary trajectories, will ultimately flesh out the full Neisseria resistome.IMPORTANCENeisseria gonorrhoeae is a global threat to public health due to its rapid acquisition of antibiotic resistance to all first-line treatments. Recent work has documented that alleles acquired from close commensal relatives have played a large role in the emergence of resistance to macrolides and beta-lactams within gonococcal populations. However, commensals have been relatively underexplored for the resistance genotypes they may harbor. This leaves a gap in our understanding of resistance that could be rapidly acquired by the gonococcus through a known highway of horizontal gene exchange. Here, we characterize resistance mechanisms that can emerge in commensal Neisseria populations via in vitro selection to multiple antimicrobials and begin to define the number of paths to resistance. This study, and other similar works, may ultimately aid both surveillance efforts and clinical diagnostic development by nominating novel and conserved resistance mechanisms that may be at risk of rapid dissemination to pathogen populations.

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.

Comparative proteomics unveils the bacteriostatic mechanisms of Ga(III) on the regulation of metabolic pathways in Pseudomonas aeruginosa

Gallium has a long history as a chemotherapeutic agent. The mechanisms of action of Ga(III)-based anti-infectives are different from conventional antibiotics, which primarily result from the chemical similarities of Ga(III) with Fe(III) and substitution of gallium into iron-dependent biological pathways. However, more aspects of the molecular mechanisms of Ga(III) against human pathogens, especially the effects on bacterial metabolic processes, remain to be understood. Herein, by using conventional quantitative proteomics, we identified the protein changes of Pseudomonas aeruginosa (P. aeruginosa) in response to Ga(NO3)3 treatment. We show that Ga(III) exhibits bacteriostatic mode of action against P. aeruginosa through affecting the expressions of a number of key enzymes in the main metabolic pathways, including glycolysis, TCA cycle, amino acid metabolism, and protein and nucleic acid biosynthesis. In addition, decreased expressions of proteins associated with pathogenesis and virulence of P. aeruginosa were also identified. Moreover, the correlations between protein expressions and metabolome changes in P. aeruginosa upon Ga(III) treatment were identified and discussed. Our findings thus expand the understanding on the antimicrobial mechanisms of Ga(III) that shed light on enhanced therapeutic strategies. BIOLOGICAL SIGNIFICANCE: Mounting evidence suggest that the efficacy and resistance of clinical antibiotics are closely related to the metabolic homeostasis in bacterial pathogens. Ga(III)-based compounds have been repurposed as antibacterial therapeutic candidates against antibiotics resistant pathogens, and represent a safe and promising treatment for clinical human infections, while more thorough understandings of how bacteria respond to Ga(III) treatment are needed. In the present study, we provide evidences at the proteome level that indicate Ga(III)-induced metabolic perturbations in P. aeruginosa. We identified and discussed the interference of Ga(III) on the expressions and activities of enzymes in the main metabolic pathways in P. aeruginosa. In view of our previous report that the antimicrobial efficacy of Ga(III) could be modulated according to Ga(III)-induced metabolome changes in P. aeruginosa, our current analyses may provide theoretical basis at the proteome level for the development of efficient gallium-based therapies by exploiting bacterial metabolic mechanisms.

Unveiling abundance-dependent metabolic phenotypes of microbial communities

IMPORTANCE

In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.

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.

Combined metabolomics and transcriptomics analysis reveals the mechanism of antibiotic resistance of Salmonella enterica serovar Typhimurium after acidic stress

Drug-resistant Salmonella is widely distributed in the meat production chain, endangering food safety and public health. Acidification of meat products during processing can induce acid stress, which may alter antibiotic resistance. Our study investigated the effects of acid stress on the antibiotic resistance and metabolic profile of Salmonella Typhimurium, and explored the underlying mechanisms using metabolomic and transcriptomic analysis. We found that acid-stressed 14028s was more sensitive to small molecule hydrophobic antibiotics (SMHA) while more resistant to meropenem (MERO). Metabolomic analysis revealed that enhanced sensitivity to SMHA was correlated with increased purine metabolism and tricarboxylic acid cycle. Transcriptomic analysis revealed the downregulation of chemotaxis-related genes, which are also associated with SMHA sensitivity. We also found a significant downregulation of the ompF gene, which encodes a major outer membrane protein OmpF of Salmonella. The decreased expression of OmpF porin hindered the influx of MERO, leading to enhanced resistance of the bacteria to the drug. Our findings contribute to greatly improve the understanding of the relationship between Salmonella metabolism, gene expression, and changes in drug resistance after acid stress, while providing a structural framework for exploring the relationship between bacterial stress responses and antibiotic resistance.

Inactivation of chlorine-resistant bacteria (CRB) via various disinfection methods: Resistance mechanism and relation with carbon source metabolism

With the widespread use of chlorine disinfection, chlorine-resistant bacteria (CRB) in water treatment systems have gained public attention. Bacterial chlorine resistance has been found positively correlated with extracellular polymeric substance (EPS) secretion. In this study, we selected the most suitable CRB controlling method against eight bacterial strains with different chlorine resistance among chloramine, ozone, and ultraviolet (UV) disinfection, analyzed the resistance mechanisms, clarified the contribution of EPS to disinfection resistance, and explored the role of carbon source metabolism capacity. Among all the disinfectants, UV disinfection showed the highest disinfection capacity by achieving the highest average and median log inactivation rates for the tested strains. For Bacillus cereus CR19, the strain with the highest chlorine resistance, 40 mJ/cm2 UV showed a 1.90 log inactivation, which was much higher than that of 2 mg-Cl2/L chlorine (0.67 log), 2 mg-Cl2/L chloramine (1.68 log), and 2 mg/L ozone (0.19 log). Meanwhile, the UV resistance of the bacteria did not correlate with EPS secretion. These characteristics render UV irradiation the best CRB controlling disinfection method. Chloramine was found to have a generally high inactivation efficiency for bacteria with high chlorine-resistance, but a low inactivation efficiency for low chlorine-resistant ones. Although EPS consumed up to 56.7% of chloramine which an intact bacterial cell consumed, EPS secretion could not explain chloramine resistance. Thus, chloramine is an acceptable CRB control method. Similar to chlorine, ozone generally selected high EPS-secreting bacteria, with EPS consuming up to 100% ozone. Therefore, ozone is not an appropriate method for controlling CRB with high EPS secretion. EPS played an important role in all types of disinfection resistance, and can be considered the main mechanism for bacterial chlorine and ozone disinfection resistance. However, as EPS was not the main resistance mechanism in UV and chloramine disinfection, CRB with high EPS secretion were inactivated more effectively. Furthermore, carbon source metabolism was found related to the multiple resistance of bacteria. Those with low carbon source metabolism capacity tended to have higher multiple resistance, especially to chlorine, ozone, and UV light. Distinctively, among the tested gram-negative bacteria, in contrast to other disinfectants, chloramine resistance was negatively correlated with EPS secretion and positively correlated with carbon source metabolism capacity, suggesting a special disinfection mechanism.

Rolling the evolutionary dice: Neisseria commensals as proxies for elucidating the underpinnings of antibiotic resistance mechanisms and evolution in human pathogens

Species within the genus Neisseria are especially adept at sharing adaptive allelic variation across species' boundaries, with commensal species repeatedly transferring resistance to their pathogenic relative N. gonorrhoeae. However, resistance in commensal Neisseria is infrequently characterized at both the phenotypic and genotypic levels, limiting our ability to predict novel and potentially transferable resistance mechanisms that ultimately may become important clinically. Unique evolutionary starting places of each Neisseria species will have distinct genomic backgrounds, which may ultimately control the fate of evolving populations in response to selection, as epistatic and additive interactions may coerce lineages along divergent evolutionary trajectories. However alternatively, similar genetic content present across species due to shared ancestry may constrain the adaptive solutions that exist. Thus, identifying the paths to resistance across commensals may aid in characterizing the Neisseria resistome - or the reservoir of alleles within the genus, as well as its depth. Here, we use in vitro evolution of four commensal species to investigate the potential for and repeatability of resistance evolution to two antimicrobials, the macrolide azithromycin and the β-lactam penicillin. After 20 days of selection, commensals evolved elevated minimum inhibitory concentrations (MICs) to penicillin and azithromycin in 11/16 and 12/16 cases respectively. Almost all cases of resistance emergence converged on mutations within ribosomal components or the mtrRCDE efflux pump for azithromycin-based selection, and mtrRCDE or penA for penicillin selection; thus, supporting constrained adaptive solutions despite divergent evolutionary starting points across the genus for these particular drugs. However, continuing to explore the paths to resistance across different experimental conditions and genomic backgrounds, which could shunt evolution down alternative evolutionary trajectories, will ultimately flesh out the full Neisseria resistome.

Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture

The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.

Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source

BACKGROUND

Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources.

METHODS

We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus.

RESULTS

Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa.

CONCLUSIONS

Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens.

Time-lapse proteomics unveil constant high exposure of non-antibiotic drug induces synthetic susceptibility towards regular antibiotics

Antibiotic resistance is a significant threat to the human race, as regular consumption of antibiotics may lead to antibiotic-resistant bacterial strains. Non-antibiotic drugs also have an extensive impact on bacterial strains, where persistent uptake alters the survival mechanisms of bacteria that could lead to cross-resistance towards other antibiotics. Here, we use time-lapse proteomics shift assays to examine Gram-negative (E. coli. O157:H7 and P. aeruginosa) and Gram-positive (E. faecalis and S. aureus) strains of bacteria for short and continuous exposure to the non-antibiotic drug Hydroxychloroquine (HCQ). Proteomic transitions from wild type to HCQ-exposed strains revealed bacterial transitions and their survival adaptabilities, which were different across all strains. In addition to their structural differences, some shared pathways were enriched among Gram-negative and positive strains. We also validated the cross-resistance and sensitivity towards 24 regularly prescribed antibiotics, indicating that long-term exposure to non-antibiotic drugs may induce general proteomics alterations in the bacterial strains, promoting antibiotic resistance. We validated that HCQ exposure renders Gram-negative strains resistant to Β-lactam and susceptible to macrolides and folic acid. In contrast, Gram-positive strains become susceptible to Β-lactam and resistant to aminoglycosides. Exposure to non-antibiotic drugs causes resistance or susceptibility toward other antibiotics, providing clinicians a reason to overcome antibiotic resistance.

Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of TetR E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of TetR E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized TetR E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (TetR) Escherichia coli, we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of TetR E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics.

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.

Host species and microplastics differentiate the crop root endophytic antibiotic resistome

The increasing One-Health concept calls for a more in-depth understanding of the dissemination of antibiotic resistance in plant microbiomes. While there is considerable published evidence that microplastics can promote the spread of antibiotic resistance genes (ARGs) in the environment, whether and how microplastics impact the plant endophytic resistome are largely unknown. Here we examined the ARGs along the soil-root continuum of maize and wheat under the pressure of microplastics. Amendment with heavy metals was also included as they can apply the selective pressure for ARG spread as well. The crop species and genotypes had significant effects on the root endophytic ARG abundance and diversity. The greatest ARG abundance was observed in the maize ZD958 endophytes (0.215 copies per 16S rRNA gene), followed by the maize XY335 (0.092 copies per 16S rRNA gene). For each crop genotype, amendment with microplastics and heavy metals significantly increased the ARG abundances and changed their profiles in root endophytes. The endophytic ARG variances were closely associated with the endophytic microbiome, the rhizosphere bacterial communities and resistome. Additionally, the level of endophytic ARGs was positively relevant to the abundance of mobile genetic elements (MGEs). These findings suggested that the root endophytic resistome was primarily affected by the crop species, and microplastics might show enhancement effects on the endophytic resistome via changing the root-associated microbiome and facilitating the MGE mediation. Overall, this study, for the first time, highlights the root endophytic ARG emergence and dissemination induced by microplastics.

CanB is a metabolic mediator of antibiotic resistance in Neisseria gonorrhoeae

The evolution of the obligate human pathogen Neisseria gonorrhoeae has been shaped by selective pressures from diverse host niche environments and antibiotics. The varying prevalence of antibiotic resistance across N. gonorrhoeae lineages suggests that underlying metabolic differences may influence the likelihood of acquisition of specific resistance mutations. We hypothesized that the requirement for supplemental CO₂, present in approximately half of isolates, reflects one such example of metabolic variation. Here, using a genome-wide association study and experimental investigations, we show that CO₂ dependence is attributable to a single substitution in a β-carbonic anhydrase, CanB. CanB^19E is necessary and sufficient for growth in the absence of CO₂, and the hypomorphic CanB^19G variant confers CO₂ dependence. Furthermore, ciprofloxacin resistance is correlated with CanB^19G in clinical isolates, and the presence of CanB^19G increases the likelihood of acquisition of ciprofloxacin resistance. Together, our results suggest that metabolic variation has affected the acquisition of fluoroquinolone resistance.

Comparison of freeze-thaw and sonication cycle-based methods for extracting AMR-associated metabolites from Staphylococcus aureus

Emerging antimicrobial resistance (AMR) among Gram-positive pathogens, specifically in Staphylococcus aureus (S. aureus), is becoming a leading public health concern demanding effective therapeutics. Metabolite modulation can improve the efficacy of existing antibiotics and facilitate the development of effective therapeutics. However, it remained unexplored for drug-resistant S. aureus (gentamicin and methicillin-resistant), primarily due to the dearth of optimal metabolite extraction protocols including a protocol for AMR-associated metabolites. Therefore, in this investigation, we have compared the performance of the two most widely used methods, i.e., freeze-thaw cycle (FTC) and sonication cycle (SC), alone and in combination (FTC + SC), and identified the optimal method for this purpose. A total of 116, 119, and 99 metabolites were identified using the FTC, SC, and FTC + SC methods, respectively, leading to the identification of 163 metabolites cumulatively. Out of 163, 69 metabolites were found to be associated with AMR in published literature consisting of the highest number of metabolites identified by FTC (57) followed by SC (54) and FTC + SC (40). Thus, the performances of FTC and SC methods were comparable with no additional benefits of combining both. Moreover, each method showed biasness toward specific metabolite(s) or class of metabolites, suggesting that the choice of metabolite extraction method shall be decided based on the metabolites of interest in the investigation.

Harnessing Interactional Sensory Genes for Rationally Reprogramming Chaotic Metabolism

Rationally controlling cellular metabolism is of great importance but challenging owing to its highly complex and chaotic nature. Natural existing sensory proteins like histidine kinases (HKs) are understood as "sensitive nodes" of biological networks that can trigger disruptive metabolic reprogramming (MRP) upon perceiving environmental fluctuation. Here, the "sensitive node" genes were adopted to devise a global MRP platform consisting of a CRISPR interference-mediated dual-gene combinational knockdown toolbox and survivorship-based metabolic interaction decoding algorithm. The platform allows users to decode the interfering effects of n × n gene pairs while only requiring the synthesis of n pairs of primers. A total of 35 HK genes and 24 glycine metabolic genes were selected as the targets to determine the effectiveness of our platform in a Vibrio sp. FA2. The platform was applied to decode the interfering impact of HKs on antibiotic resistance in strain FA2. A pattern of combined knockdown of HK genes (sasA_8 and 04288) was demonstrated to be capable of reducing antibiotic resistance of Vibrio by 108-fold. Patterns of combined knockdown of glycine pathway genes (e.g., gcvT and ltaE) and several HK genes (e.g., cpxA and btsS) were also revealed to increase glycine production. Our platform may enable an efficient and rational approach for global MRP based on the elucidation of high-order gene interactions. A web-based 1-stop service (https://smrp.sjtu.edu.cn) is also provided to simplify the implementation of this smart strategy in a broad range of cells.

A Hydrazine-Hydrazone Adamantine Compound Shows Antimycobacterial Activity and Is a Probable Inhibitor of MmpL3

Tuberculosis remains an important cause of morbidity and mortality throughout the world. Notably, an important number of multi drug resistant cases is an increasing concern. This problem points to an urgent need for novel compounds with antimycobacterial properties and to improve existing therapies. Whole-cell-based screening for compounds with activity against Mycobacterium tuberculosis complex strains in the presence of linezolid was performed in this study. A set of 15 bioactive compounds with antimycobacterial activity in vitro were identified with a minimal inhibitory concentration of less than 2 µg/mL. Among them, compound 1 is a small molecule with a chemical structure consisting of an adamantane moiety and a hydrazide–hydrazone moiety. Whole genome sequencing of spontaneous mutants resistant to the compounds suggested compound 1 to be a new inhibitor of MmpL3. This compound binds to the same pocket as other already published MmpL3 inhibitors, without disturbing the proton motive force of M. bovis BCG and M. smegmatis. Compound 1 showed a strong activity against a panel ofclinical strains of M. tuberculosis in vitro. This compound showed no toxicity against mammalian cells and protected Galleria mellonella larvae against M. bovis BCG infection. These results suggest that compound 1 is a promising anti-TB agent with the potential to improve TB treatment in combination with standard TB therapies.

Biofilm antimicrobial susceptibility through an experimental evolutionary lens

Experimental evolution experiments in which bacterial populations are repeatedly exposed to an antimicrobial treatment, and examination of the genotype and phenotype of the resulting evolved bacteria, can help shed light on mechanisms behind reduced susceptibility. In this review we present an overview of why it is important to include biofilms in experimental evolution, which approaches are available to study experimental evolution in biofilms and what experimental evolution has taught us about tolerance and resistance in biofilms. Finally, we present an emerging consensus view on biofilm antimicrobial susceptibility supported by data obtained during experimental evolution studies.

Biosynthesis of Silver Nanoparticles Using Seasonal Samples of Sonoran Desert Propolis: Evaluation of Its Antibacterial Activity against Clinical Isolates of Multi-Drug Resistant Bacteria

Multi-drug resistant (MDR) bacteria have gained importance as a health problem worldwide, and novel antibacterial agents are needed to combat them. Silver nanoparticles (AgNPs) have been studied as a potent antimicrobial agent, capable of countering MDR bacteria; nevertheless, their conventional synthesis methods can produce cytotoxicity and environmental hazards. Biosynthesis of silver nanoparticles has emerged as an alternative to reduce the cytotoxic and environmental problems derived from their chemical synthesis, using natural products as a reducing and stabilizing agent. Sonoran Desert propolis (SP) is a poplar-type propolis rich in polyphenolic compounds with remarkable biological activities, such as being antioxidant, antiproliferative, and antimicrobial, and is a suitable candidate for synthesis of AgNPs. In this study, we synthesized AgNPs using SP methanolic extract (SP-AgNPs) and evaluated the reduction capacity of their seasonal samples and main chemical constituents. Their cytotoxicity against mammalian cell lines and antibacterial activity against multi-drug resistant bacteria were assessed. Quercetin and galangin showed the best-reduction capacity for synthesizing AgNPs, as well as the seasonal sample from winter (SPw-AgNPs). The SPw-AgNPs had a mean size of around 16.5 ± 5.3 nm, were stable in different culture media, and the presence of propolis constituents was confirmed by FT-IR and HPLC assays. The SPw-AgNPs were non-cytotoxic to ARPE-19 and HeLa cell lines and presented remarkable antibacterial and antibiofilm activity against multi-drug resistant clinical isolates, with E. coli 34 and ATCC 25922 being the most susceptible (MBC = 25 μg/mL), followed by E. coli 2, 29, 37 and PNG (MBC = 50 μg/mL), and finally E. coli 37 and S. aureus ATCC 25923 (MBC = 100 μg/mL). These results demonstrated the efficacy of SP as a reducing and stabilizing agent for synthesis of AgNPs and their capacity as an antibacterial agent.

Antimicrobial Resistance and Its Spread Is a Global Threat

Antimicrobial resistance (AMR) is a challenge to human wellbeing the world over and is one of the more serious public health concerns. AMR has the potential to emerge as a serious healthcare threat if left unchecked, and could put into motion another pandemic. This establishes the need for the establishment of global health solutions around AMR, taking into account microdata from different parts of the world. The positive influences in this regard could be establishing conducive social norms, charting individual and group behavior practices that favor global human health, and lastly, increasing collective awareness around the need for such action. Apart from being an emerging threat in the clinical space, AMR also increases treatment complexity, posing a real challenge to the existing guidelines around the management of antibiotic resistance. The attribute of resistance development has been linked to many genetic elements, some of which have complex transmission pathways between microbes. Beyond this, new mechanisms underlying the development of AMR are being discovered, making this field an important aspect of medical microbiology. Apart from the genetic aspects of AMR, other practices, including misdiagnosis, exposure to broad-spectrum antibiotics, and lack of rapid diagnosis, add to the creation of resistance. However, upgrades and innovations in DNA sequencing technologies with bioinformatics have revolutionized the diagnostic industry, aiding the real-time detection of causes of AMR and its elements, which are important to delineating control and prevention approaches to fight the threat.

Biodegradable microplastics induced the dissemination of antibiotic resistance genes and virulence factors in soil: A metagenomic perspective

Concerns about the ecological safety of both conventional and biodegradable microplastics have grown due to the inadequate end-of-life treatments of plastics. In this study, the effects of conventional and biodegradable microplastics on the spread of antibiotic resistance genes (ARGs) and virulence factors (VFs) were estimated in a soil microcosm experiment. The gene profiles and their respective bacterial hosts in soil were evaluated by metagenomic sequencing methods. The abundances of ARGs and VFs in polybutylene succinate (PBS) treated soils were statistically higher than the values in the control and conventional microplastic treatments. In comparison with the control, application of conventional microplastics showed negligible effects on ARG and VF profiles in the soil, while biodegradable microplastic amendments significantly changed the compositions of ARGs and VFs. The host-tracking analysis suggested application of microplastics broadened the bacterial hosts of ARGs and VFs in the soil. The percentage of Proteobacteria as ARG hosts increased from 38.5% in the control soils to 58.2% in microplastic exposed soil. The genus Bradyrhizobium was the dominant host of ARGs and VFs in biodegradable microplastic treatments, while conventional microplastics increased the percentages of Pseudomonas as the bacterial hosts. This study enhances the understanding of the effects of conventional and biodegradable microplastics on the propagation and hosts of ARGs and VFs in the terrestrial environment, providing essential insights into the risk assessment and management of plastics.

Emergence of nutriments as a nascent complementary therapy against antimicrobial resistance

With these growing and evolving years, antimicrobial resistance has become a great subject of interest. The idea of using natural productive ways can be an effective measure against antimicrobial resistance. The growing prevalence of antimicrobial resistance indicates that advanced natural approaches are a topic of concern for fighting the resistance. Many natural products including essential oils, flavonoids, alkaloids and botanicals have been demonstrated as effective bactericidal agents. In this review, we will discuss in detail about the relevance of such natural products to tackle the problem of antimicrobial resistance, antibiotic adjuvants that aim towards non-essential bacterial targets to reduce the prevalence of resistant bacterial infections, latest bioinformatics approach towards antibacterial drug discovery along with an understanding of biogenic nanoparticles in antimicrobial activity.

Evolution of Habitat-Dependent Antibiotic Resistance in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic human pathogen that usually causes difficult-to-treat infections due to its low intrinsic antibiotic susceptibility and outstanding capacity for becoming resistant to antibiotics. In addition, it has a remarkable metabolic versatility, being able to grow in different habitats, from natural niches to different and changing inpatient environments. Study of the environmental conditions that shape genetic and phenotypic changes of P. aeruginosa toward antibiotic resistance supposes a novelty, since experimental evolution assays are usually performed with well-defined antibiotics in regular laboratory growth media. Therefore, in this work we address the extent to which the nutrients’ availability may constrain the evolution of antibiotic resistance. We determined that P. aeruginosa genetic trajectories toward resistance to tobramycin, ceftazidime, and ceftazidime-avibactam are different when evolving in laboratory rich medium, urine, or synthetic sputum. Furthermore, our study, linking genotype with phenotype, showed a clear impact of each analyzed environment on both the fitness and resistance level associated with particular resistance mutations. This indicates that the phenotype associated with specific resistance mutations is variable and dependent on the bacterial metabolic state in each particular habitat. Our results support that the design of evolution-based strategies to tackle P. aeruginosa infections should be based on robust patterns of evolution identified within each particular infection and body location.

IMPORTANCE Predicting evolution toward antibiotic resistance (AR) and its associated trade-offs, such as collateral sensitivity, is important to design evolution-based strategies to tackle AR. However, the effect of nutrients' availability on such evolution, particularly those that can be found under in vivo infection conditions, has been barely addressed. We analyzed the evolutionary patterns of P. aeruginosa in the presence of antibiotics in different media, including urine and synthetic sputum, whose compositions are similar to the ones in infections, finding that AR evolution differs, depending on growth conditions. Furthermore, the representative mutants isolated under each condition tested render different AR levels and fitness costs, depending on nutrients’ availability, supporting the idea that environmental constraints shape the phenotypes associated with specific AR mutations. Consequently, the selection of AR mutations that render similar phenotypes is environment dependent. The analysis of evolution patterns toward AR requires studying growth conditions mimicking those that bacteria face during in vivo evolution.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Plant pathogenic bacterium can rapidly evolve tolerance to an antimicrobial plant allelochemical

Crop losses to plant pathogens are a growing threat to global food security and more effective control strategies are urgently required. Biofumigation, an agricultural technique where Brassica plant tissues are mulched into soils to release antimicrobial plant allelochemicals called isothiocyanates (ITCs), has been proposed as an environmentally friendly alternative to agrochemicals. Whilst biofumigation has been shown to suppress a range of plant pathogens, its effects on plant pathogenic bacteria remain largely unexplored. Here, we used a laboratory model system to compare the efficacy of different types of ITCs against Ralstonia solanacearum plant bacterial pathogen. Additionally, we evaluated the potential for ITC-tolerance evolution under high, intermediate, and low transfer frequency ITC exposure treatments. We found that allyl-ITC was the most efficient compound at suppressing R. solanacearum growth, and its efficacy was not improved when combined with other types of ITCs. Despite consistent pathogen growth suppression, ITC tolerance evolution was observed in the low transfer frequency exposure treatment, leading to cross-tolerance to ampicillin beta-lactam antibiotic. Mechanistically, tolerance was linked to insertion sequence movement at four positions in genes that were potentially associated with stress responses (H-NS histone like protein), cell growth and competitiveness (acyltransferase), iron storage ([2-Fe-2S]-binding protein) and calcium ion sequestration (calcium-binding protein). Interestingly, pathogen adaptation to the growth media also indirectly selected for increased ITC tolerance through potential adaptations linked with metabolism and antibiotic resistance (dehydrogenase-like protein) and transmembrane protein movement (Tat pathway signal protein). Together, our results suggest that R. solanacearum can rapidly evolve tolerance to allyl-ITC plant allelochemical which could constrain the long-term efficiency of biofumigation biocontrol and potentially shape pathogen evolution with plants.

Iron-Induced Respiration Promotes Antibiotic Resistance in Actinomycete Bacteria

The bacterial response to antibiotics eliciting resistance is one of the key challenges in global health. Despite many attempts to understand intrinsic antibiotic resistance, many of the underlying mechanisms still remain elusive. In this study, we found that iron supplementation promoted antibiotic resistance in Streptomyces coelicolor. Iron-promoted resistance occurred specifically against bactericidal antibiotics, irrespective of the primary target of antibiotics. Transcriptome profiling revealed that some genes in the central metabolism and respiration were upregulated under iron-replete conditions. Iron supported the growth of S. coelicolor even under anaerobic conditions. In the presence of potassium cyanide, which reduces aerobic respiration of cells, iron still promoted respiration and antibiotic resistance. This suggests the involvement of a KCN-insensitive type of respiration in the iron effect. This phenomenon was also observed in another actinobacterium, Mycobacterium smegmatis. Taken together, these findings provide insight into a bacterial resistance strategy that mitigates the activity of bactericidal antibiotics whose efficacy accompanies oxidative damage by switching the respiration mode.