Reversion of High-level Mecillinam Resistance to Susceptibility in Escherichia coli During Growth in Urine

Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that model in vivo responses

The rise in infections caused by multidrug-resistant (MDR) bacteria has necessitated a variety of clinical approaches, including the use of antibiotic combinations. Here, we tested the hypothesis that drug-drug interactions vary in different media, and determined which in vitro models best predict drug interactions in the lungs. We systematically studied pair-wise antibiotic interactions in three different media, CAMHB, (a rich lab medium standard for antibiotic susceptibility testing), a urine mimetic medium (UMM), and a minimal medium of M9 salts supplemented with glucose and iron (M9Glu) with three Gram-negative ESKAPE pathogens, Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), and Pseudomonas aeruginosa (Pa). There were pronounced differences in responses to antibiotic combinations between the three bacterial species grown in the same medium. However, within species, PaO1 responded to drug combinations similarly when grown in all three different media, whereas Ab17978 and other Ab clinical isolates responded similarly when grown in CAMHB and M9Glu medium. By contrast, drug interactions in Kp43816, and other Kp clinical isolates poorly correlated across different media. To assess whether any of these media were predictive of antibiotic interactions against Kp in the lungs of mice, we tested three antibiotic combination pairs. In vitro measurements in M9Glu, but not rich medium or UMM, predicted in vivo outcomes. This work demonstrates that antibiotic interactions are highly variable across three Gram-negative pathogens and highlights the importance of growth medium by showing a superior correlation between in vitro interactions in a minimal growth medium and in vivo outcomes.

IMPORTANCE

Drug-resistant bacterial infections are a growing concern and have only continued to increase during the SARS-CoV-2 pandemic. Though not routinely used for Gram-negative bacteria, drug combinations are sometimes used for serious infections and may become more widely used as the prevalence of extremely drug-resistant organisms increases. To date, reliable methods are not available for identifying beneficial drug combinations for a particular infection. Our study shows variability across strains in how drug interactions are impacted by growth conditions. It also demonstrates that testing drug combinations in tissue-relevant growth conditions for some strains better models what happens during infection and may better inform combination therapy selection.

Re-evaluation of FDA-approved antibiotics with increased diagnostic accuracy for assessment of antimicrobial resistance

Accurate assessment of antibiotic susceptibility is critical for treatment of antimicrobial resistant (AMR) infections. Here, we examine whether antimicrobial susceptibility testing in media more physiologically representative of in vivo conditions improves prediction of clinical outcome relative to standard bacteriologic medium. This analysis reveals that ∼15% of minimum inhibitory concentration (MIC) values obtained in physiologic media predicted a change in susceptibility that crossed a clinical breakpoint used to categorize patient isolates as susceptible or resistant. The activities of antibiotics having discrepant results in different media were evaluated in murine sepsis models. Testing in cell culture medium improves the accuracy by which MIC assays predict in vivo efficacy. This analysis identifies several antibiotics for treatment of AMR infections that standard testing failed to identify and those that are ineffective despite indicated use by standard testing. Methods with increased diagnostic accuracy mitigate the AMR crisis via utilizing existing agents and optimizing drug discovery.

Innovative Strategies to Overcome Antimicrobial Resistance and Tolerance

Antimicrobial resistance and tolerance are natural phenomena that arose due to evolutionary adaptation of microorganisms against various xenobiotic agents. These adaptation mechanisms make the current treatment options challenging as it is increasingly difficult to treat a broad range of infections, associated biofilm formation, intracellular and host adapted microbes, as well as persister cells and microbes in protected niches. Therefore, novel strategies are needed to identify the most promising drug targets to overcome the existing hurdles in the treatment of infectious diseases. Furthermore, discovery of novel drug candidates is also much needed, as few novel antimicrobial drugs have been introduced in the last two decades. In this review, we focus on the strategies that may help in the development of innovative small molecules which can interfere with microbial resistance mechanisms. We also highlight the recent advances in optimization of growth media which mimic host conditions and genome scale molecular analyses of microbial response against antimicrobial agents. Furthermore, we discuss the identification of antibiofilm molecules and their mechanisms of action in the light of the distinct physiology and metabolism of biofilm cells. This review thus provides the most recent advances in host mimicking growth media for effective drug discovery and development of antimicrobial and antibiofilm agents.

Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification

The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.

Resistance to Mecillinam and Nine Other Antibiotics for Oral Use in Escherichia coli Isolated from Urine Specimens of Primary Care Patients in Germany, 2019/20

Urinary tract infections (UTIs) are among the most common bacterial infections in humans. Escherichia coli is by far the leading cause of community-acquired UTIs. Pivmecillinam, the oral prodrug of the penicillin derivative mecillinam (amdinocillin), was re-introduced in Germany in March 2016 for first-line treatment of acute uncomplicated cystitis. This study aimed to evaluate the prevalence of resistance to mecillinam in comparison to nine other antibiotics used for oral treatment in E. coli urine isolates after the re-introduction of pivmecillinam. A total of 460 isolates were collected at 23 laboratories of clinical microbiology between October 2019 and March 2020. Forty-six isolates (10.0%) produced an extended-spectrum β-lactamase (ESBL) of the CTX-M family. Resistance to amoxicillin (43.3%) was most widespread, followed by resistance to trimethoprim-sulfamethoxazole (27.0%), amoxicillin-clavulanic acid (18.0%), cefuroxime (11.3%), and ciprofloxacin (11.1%). Twenty-four E. coli isolates (5.2%) were resistant to mecillinam. The concentrations of mecillinam needed to inhibit 50/90% of the ESBL-producing isolates and the remaining isolates were 1/4 mg/L and 0.5/4 mg/L, respectively. The findings support the recommendation to regard pivmecillinam as a first-line option for the treatment of uncomplicated lower UTIs.

Evaluation of Metallo-β-Lactamase Susceptibility Testing in a Physiologic Medium

Research in identifying alternative growth media that better mimic host conditions is gaining ground. Relative to nutrient-rich Mueller-Hinton broth (MHB), data on the influence of physiologic or host-mimicking media on metallo-β-lactamase (MBL) resistance are lacking. The objective was to evaluate meropenem susceptibility against clinical and engineered MBL-harboring Enterobacterales strains in a physiologic medium (urine). Antimicrobial susceptibility testing (AST) by broth microdilution was conducted with a wild-type Klebsiella pneumoniae strain and two engineered isogenic variants harboring K. pneumoniae carbapenemase 2 (KPC-2) or New Delhi MBL 1 (NDM-1), as well as two clinical K. pneumoniae isolates (harboring NDM-1 and VIM-1). MICs were determined in conventional cation-adjusted MHB (caMHB) and sterile-filtered urine samples (18 patients). All KPC- and MBL-harboring isolates were meropenem resistant (MICs of ≥16 mg/liter) in caMHB. AST of the KPC isolate in urine resulted in 50% (9/18 urine samples) essential agreement (i.e., within ±1 dilution, relative to the caMHB MIC), highlighting challenges with the use of urine as a medium capable of supporting AST. In the 9 AST-viable urine samples, meropenem MICs were 2- to 9-fold lower than that in caMHB (MIC of 32 mg/liter) among MBL-harboring isolates. Zinc concentrations determined by inductively coupled plasma mass spectrometry averaged 1.25 mg/liter and ranged from 0.12 to 1.14 mg/liter in caMHB and 18 urine samples, respectively. The full extent of MBL-mediated resistance among K. pneumoniae isolates appears to be attenuated in urine. Factors influencing free bioactive zinc levels warrant further investigation. IMPORTANCE Studies assessing antibiotic susceptibility profiles in nonconventional media are lacking. MBL-mediated resistance has come under scrutiny due to the dependence on extracellular zinc concentrations, which makes the choice of testing medium influential for β-lactam MICs. This study explores human urine as a physiologically relevant matrix with which susceptibility profiles of MBL-harboring isolates can be assessed, relative to conventional broth.

Understanding Beta-Lactam-Induced Lysis at the Single-Cell Level

Mechanical rupture, or lysis, of the cytoplasmic membrane is a common cell death pathway in bacteria occurring in response to β-lactam antibiotics. A better understanding of the cellular design principles governing the susceptibility and response of individual cells to lysis could indicate methods of potentiating β-lactam antibiotics and clarify relevant aspects of cellular physiology. Here, we take a single-cell approach to bacterial cell lysis to examine three cellular features—turgor pressure, mechanosensitive channels, and cell shape changes—that are expected to modulate lysis. We develop a mechanical model of bacterial cell lysis and experimentally analyze the dynamics of lysis in hundreds of single Escherichia coli cells. We find that turgor pressure is the only factor, of these three cellular features, which robustly modulates lysis. We show that mechanosensitive channels do not modulate lysis due to insufficiently fast solute outflow, and that cell shape changes result in more severe cellular lesions but do not influence the dynamics of lysis. These results inform a single-cell view of bacterial cell lysis and underscore approaches of combatting antibiotic tolerance to β-lactams aimed at targeting cellular turgor.

Testing physiologically relevant conditions in minimal inhibitory concentration assays

The minimal inhibitory concentration (MIC) assay uses agar or broth dilution methods to measure, under defined test conditions, the lowest effective concentration of an antimicrobial agent that inhibits visible growth of a bacterium of interest. This assay is used to test the susceptibilities of bacterial isolates and of novel antimicrobial drugs, and is typically done in nutrient-rich laboratory media that have little relevance to in vivo conditions. As an extension to our original protocol on MIC assays (also published in Nature Protocols), here we describe the application of the MIC broth microdilution assay to test antimicrobial susceptibility in conditions that are more physiologically relevant to infections observed in the clinic. Specifically, we describe a platform that can be applied to the preparation of medium that mimics lung and wound exudate or blood conditions for the growth and susceptibility testing of bacteria, including ESKAPE pathogens. This protocol can also be applied to most physiologically relevant liquid medium and aerobic pathogens, and takes 3-4 d to complete.

Antibiotic resistance: turning evolutionary principles into clinical reality

Antibiotic resistance is one of the major challenges facing modern medicine worldwide. The past few decades have witnessed rapid progress in our understanding of the multiple factors that affect the emergence and spread of antibiotic resistance at the population level and the level of the individual patient. However, the process of translating this progress into health policy and clinical practice has been slow. Here, we attempt to consolidate current knowledge about the evolution and ecology of antibiotic resistance into a roadmap for future research as well as clinical and environmental control of antibiotic resistance. At the population level, we examine emergence, transmission and dissemination of antibiotic resistance, and at the patient level, we examine adaptation involving bacterial physiology and host resilience. Finally, we describe new approaches and technologies for improving diagnosis and treatment and minimizing the spread of resistance.

A nutrient-limited screen unmasks rifabutin hyperactivity for extensively drug-resistant Acinetobacter baumannii

Industry screens of large chemical libraries have traditionally relied on rich media to ensure rapid bacterial growth in high-throughput testing. We used eukaryotic, nutrient-limited growth media in a compound screen that unmasked a previously unknown hyperactivity of the old antibiotic, rifabutin (RBT), against highly resistant Acinetobacter baumannii. In nutrient-limited, but not rich, media, RBT was 200-fold more potent than rifampin. RBT was also substantially more effective in vivo. The mechanism of enhanced efficacy was a Trojan horse-like import of RBT, but not rifampin, through fhuE, only in nutrient-limited conditions. These results are of fundamental importance to efforts to discover antibacterial agents.

Use of Supplemented or Human Material to Simulate PD Behavior of Antibiotics at the Target Site In Vitro

In antimicrobial drug development, in vitro antibiotic susceptibility testing is conducted in standard growth media, such as Mueller–Hinton broth (MHB). These growth media provide optimal bacterial growth, but do not consider certain host factors that would be necessary to mimic the in vivo bacterial environment in the human body. The present review aimed to include relevant data published between 1986 and 2019. A database search (PubMed) was done with text keywords, such as “MIC” (minimal inhibitory concentration), “TKC” (time kill curve), “blood”, “body fluid”, “PD” (pharmacodynamic), and “in vitro”, and 53 papers were ultimately selected. Additionally, a literature search for physiologic characteristics of body fluids was conducted. This review gives an excerpt of the complexity of human compartments with their physiologic composition. Furthermore, we present an update of currently available in vitro models operated either with adapted growth media or body fluids themselves. Moreover, the feasibility of testing the activity of antimicrobials in such settings is discussed, and pro and cons for standard practice methods are given. The impact on bacterial killing varies between individual adapted microbiological media, as well as direct pharmacodynamic simulations in body fluids, between bacterial strains, antimicrobial agents, and the compositions of the adjuvants or the biological fluid itself.

Microbial risk factors for treatment failure of pivmecillinam in community-acquired urinary tract infections caused by ESBL-producing Escherichia coli

The aim of this study was to identify microbial risk factors for treatment failure of pivmecillinam in community-acquired urinary tract infections (ca-UTIs) caused by ESBL-producing Escherichia coli. Eighty-nine ESBL-producing E. coli isolated from women suffering from ca-UTIs were included. The susceptibilities to mecillinam were determined using MIC gradient strip. Whole genome sequencing was performed on a MiSeq platform, and genome assembly was performed using SPAdes v3.11.0. Neither mecillinam MICs nor ESBL genotypes were associated with treatment outcome of patients treated with pivmecillinam. Specific STs, however, showed significant differences in treatment outcome. Patients infected with ST131 were more likely to experience treatment failure compared to patients infected with non-ST131 (p 0.02) when adjusted for pivmecillinam dose, mecillinam MIC and severity of infection. Patients infected with ST69 were more often successfully treated compared to patients infected with non-ST69 (p 0.04). Patients infected with bla_CTX-M-15 ST131 strains were more likely to experience treatment failure than those infected with non-bla_CTX-M-15 ST131 strains (p 0.02). The results suggest that specific STs are associated with the clinical efficacy of pivmecillinam. Further studies with a larger number of strains, including a larger number of mecillinam resistant strains, are needed to confirm these results.

Upregulation of PBP1B and LpoB in cysB Mutants Confers Mecillinam (Amdinocillin) Resistance in Escherichia coli

Mecillinam (amdinocillin) is a β-lactam antibiotic that inhibits the essential penicillin-binding protein 2 (PBP2). In clinical isolates of Escherichia coli from urinary tract infections, inactivation of the cysB gene (which encodes the main regulator of cysteine biosynthesis, CysB) is the major cause of resistance. How a nonfunctional CysB protein confers resistance is unknown, however, and in this study we wanted to examine the mechanism of resistance.

Mecillinam (amdinocillin) is a β-lactam antibiotic that inhibits the essential penicillin-binding protein 2 (PBP2). In clinical isolates of Escherichia coli from urinary tract infections, inactivation of the cysB gene (which encodes the main regulator of cysteine biosynthesis, CysB) is the major cause of resistance. How a nonfunctional CysB protein confers resistance is unknown, however, and in this study we wanted to examine the mechanism of resistance. Results show that cysB mutations cause a gene regulatory response that changes the expression of ∼450 genes. Among the proteins that show increased levels are the PBP1B, LpoB, and FtsZ proteins, which are known to be involved in peptidoglycan biosynthesis. Artificial overexpression of either PBP1B or LpoB in a wild-type E. coli strain conferred mecillinam resistance; conversely, inactivation of either the mrcB gene (which encodes PBP1B) or the lpoB gene (which encodes the PBP1B activator LpoB) made cysB mutants susceptible. These results show that expression of the proteins PBP1B and LpoB is both necessary and sufficient to confer mecillinam resistance. The addition of reducing agents to a cysB mutant converted it to full susceptibility, with associated downregulation of PBP1B, LpoB, and FtsZ. We propose a model in which cysB mutants confer mecillinam resistance by inducing a response that causes upregulation of the PBP1B and LpoB proteins. The higher levels of these two proteins can then rescue cells with mecillinam-inhibited PBP2. Our results also show how resistance can be modulated by external conditions such as reducing agents.

Pivmecillinam for Uncomplicated Lower Urinary Tract Infections Caused by Staphylococcus saprophyticus-Cumulative Observational Data from Four Recent Clinical Studies

Objectives: To investigate pivmecillinam´s efficacy in uncomplicated lower urinary tract infection (UTI) caused by Staphylococcus saprophyticus-considered non-susceptible to mecillinam. Methods: Participants with confirmed UTIs caused by S. saprophyticus from four randomized controlled trials, where pivmecillinam was empirically given to females with symptoms of UTIs. The primary outcome was defined as a cumulative clinical effect-symptom resolution during the first eight days of therapy, without a recurrence of UTI symptoms in the long-term follow-up (approximately four weeks). Secondary outcomes included the bacteriological effect-elimination of the causative agent, with or without new uropathogenic bacteria present in the first control urine sample. Significant bacteriuria was defined as ≥103 bacteria/mL. The antibiotic susceptibility testing was done by disc diffusion methodology, according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Results: Seventy-four females (18-55 years) were empirically treated with pivmecillinam for UTIs caused by S. saphrophyticus (mean age 25 years; standard deviation (SD) 5.8). The cumulative clinical effect was 53/74 (72%), and the bacteriological effect was 51/59 (86%). Conclusion: Pivmecillinam showed a high clinical and bacteriological effect in UTIs caused by S. saprophyticus in these four clinical trials. The characterization of non-susceptibility for mecillinam regarding the treatment of UTIs caused by this common pathogen may need to be revised.

Plasticity of Escherichia coli cell wall metabolism promotes fitness and antibiotic resistance across environmental conditions

Although the peptidoglycan cell wall is an essential structural and morphological feature of most bacterial cells, the extracytoplasmic enzymes involved in its synthesis are frequently dispensable under standard culture conditions. By modulating a single growth parameter-extracellular pH-we discovered a subset of these so-called 'redundant' enzymes in Escherichia coli are required for maximal fitness across pH environments. Among these pH specialists are the class A penicillin binding proteins PBP1a and PBP1b; defects in these enzymes attenuate growth in alkaline and acidic conditions, respectively. Genetic, biochemical, and cytological studies demonstrate that synthase activity is required for cell wall integrity across a wide pH range and influences pH-dependent changes in resistance to cell wall active antibiotics. Altogether, our findings reveal previously thought to be redundant enzymes are instead specialized for distinct environmental niches. This specialization may ensure robust growth and cell wall integrity in a wide range of conditions.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Comparing normalization methods and the impact of noise

INTRODUCTION

Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways.

OBJECTIVE

Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets.

METHODS

Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and random dilution factors. Methods were evaluated based on an ability to recover the intensities of the true spectral peaks and the reproducibility of true classifying features from orthogonal projections to latent structures-discriminant analysis model (OPLS-DA).

RESULTS

Most normalization methods (except histogram matching) performed equally well at modest levels of signal variance. Only probabilistic quotient (PQ) and constant sum (CS) maintained the highest level of peak recovery (> 67%) and correlation with true loadings (> 0.6) at maximal noise.

CONCLUSION

PQ and CS performed the best at recovering peak intensities and reproducing the true classifying features for an OPLS-DA model regardless of spectral noise level. Our findings suggest that performance is largely determined by the level of noise in the dataset, while the effect of dilution factors was negligible. A minimal allowable noise level of 20% was also identified for a valid NMR metabolomics dataset.