Characterization and Abundance of Plasmid-Dependent Alphatectivirus Bacteriophages.

Antimicrobial resistance (AMR) is a major public health threat, exacerbated by the ability of bacteria to rapidly disseminate antimicrobial resistance genes (ARG). Since conjugative plasmids of the incompatibility group P (IncP) are ubiquitous mobile genetic elements that often carry ARG and are broad-host-range, they are important targets to prevent the dissemination of AMR. Plasmid-dependent phages infect plasmid-carrying bacteria by recognizing components of the conjugative secretion system as receptors. We sought to isolate plasmid-dependent phages from wastewater using an avirulent strain of Salmonella enterica carrying the conjugative IncP plasmid pKJK5. Irrespective of the site, we only obtained bacteriophages belonging to the genus Alphatectivirus. Eleven isolates were sequenced, their genomes analyzed, and their host range established using S. enterica, Escherichia coli, and Pseudomonas putida carrying diverse conjugative plasmids. We confirmed that Alphatectivirus are abundant in domestic and hospital wastewater using culture-dependent and culture-independent approaches. However, these results are not consistent with their low or undetectable occurrence in metagenomes. Therefore, overall, our results emphasize the importance of performing phage isolation to uncover diversity, especially considering the potential of plasmid-dependent phages to reduce the spread of ARG carried by conjugative plasmids, and to help combat the AMR crisis.

When less is more: shortening the Lpp protein leads to increased vancomycin resistance in Escherichia coli.

Vancomycin is a naturally occurring cell-wall-targeting glycopeptide antibiotic. Due to the low potency of this antibiotic against Gram-negative pathogens, such as Escherichia coli, there is a limited knowledge about interactions between vancomycin and this group of bacteria. Here, we show that an in-frame 63 bp deletion of the lpp gene caused a fourfold increase in vancomycin resistance in E. coli. The resulting protein, LppΔ21, is 21 amino acids shorter than the wild-type Lpp, a helical structural lipoprotein that controls the width of the periplasmic space through its length. The mutant remains susceptible to synergistic growth inhibition by combination of furazolidone and vancomycin; with furazolidone decreasing the vancomycin MIC by eightfold. These findings have clinical relevance, given that the vancomycin concentration required to select the lpp mutation is reachable during typical vancomycin oral administration for treating Clostridioides difficile infections. Combination therapy with furazolidone, however, is likely to prevent emergence and outgrowth of the lpp-mutated Gram-negative coliforms, avoiding exacerbation of the patient's condition during the treatment.

Robust and Interpretable General Movement Assessment Using Fidgety Movement Detection.

Fidgety movements occur in infants between the age of 9 to 20 weeks post-term, and their absence are a strong indicator that an infant has cerebral palsy. Prechtl's General Movement Assessment method evaluates whether an infant has fidgety movements, but requires a trained expert to conduct it. Timely evaluation facilitates early interventions, and thus computer-based methods have been developed to aid domain experts. However, current solutions rely on complex models or high-dimensional representations of the data, which hinder their interpretability and generalization ability. To address that we propose [Formula: see text], a method that detects fidgety movements and uses them towards an assessment of the quality of an infant's general movements. [Formula: see text] is true to the domain expert process, more accurate, and highly interpretable due to its fine-grained scoring system. The main idea behind [Formula: see text] is to specify signal properties of fidgety movements that are measurable and quantifiable. In particular, we measure the movement direction variability of joints of interest, for movements of small amplitude in short video segments. [Formula: see text] also comprises a strategy to reduce those measurements to a single score that quantifies the quality of an infant's general movements; the strategy is a direct translation of the qualitative procedure domain experts use to assess infants. This brings [Formula: see text] closer to the process a domain expert applies to decide whether an infant produced enough fidgety movements. We evaluated [Formula: see text] on the largest clinical dataset reported, where it showed to be interpretable and more accurate than many methods published to date.

Sequential Organ Failure Assessment (SOFA) Score for predicting mortality in patients with sepsis in Vietnamese intensive care units: a multicentre, cross-sectional study.

OBJECTIVES: To compare the accuracy of the Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) Scores in predicting mortality among intensive care unit (ICU) patients with sepsis in a low-income and middle-income country. DESIGN: A multicentre, cross-sectional study. SETTING: A total of 15 adult ICUs throughout Vietnam. PARTICIPANTS: We included all patients aged ≥18 years who were admitted to ICUs for sepsis and who were still in ICUs from 00:00 to 23:59 of the specified study days (ie, 9 January, 3 April, 3 July and 9 October of the year 2019). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was hospital all-cause mortality (hospital mortality). We also defined the secondary outcome as all-cause deaths in the ICU (ICU mortality). RESULTS: Of 252 patients, 40.1% died in hospitals, and 33.3% died in ICUs. SOFA Score (areas under the receiver operating characteristic curve (AUROC): 0.688 (95% CI 0.618 to 0.758); cut-off value≥7.5; PAUROC<0.001) and APACHE II Score (AUROC: 0.689 (95% CI 0.622 to 0.756); cut-off value ≥20.5; PAUROC<0.001) both had a poor discriminatory ability for predicting hospital mortality. However, the discriminatory ability for predicting ICU mortality of SOFA (AUROC: 0.713 (95% CI 0.643 to 0.783); cut-off value≥9.5; PAUROC<0.001) was fair and was better than that of APACHE II Score (AUROC: 0.672 (95% CI 0.603 to 0.742); cut-off value≥18.5; PAUROC<0.001). A SOFA Score≥8 (adjusted OR (AOR): 2.717; 95% CI 1.371 to 5.382) and an APACHE II Score≥21 (AOR: 2.668; 95% CI 1.338 to 5.321) were independently associated with an increased risk of hospital mortality. Additionally, a SOFA Score≥10 (AOR: 2.194; 95% CI 1.017 to 4.735) was an independent predictor of ICU mortality, in contrast to an APACHE II Score≥19, for which this role did not. CONCLUSIONS: In this study, SOFA and APACHE II Scores were worthwhile in predicting mortality among ICU patients with sepsis. However, due to better discrimination for predicting ICU mortality, the SOFA Score was preferable to the APACHE II Score in predicting mortality.Clinical trials registry - India: CTRI/2019/01/016898.

Predictive validity of the quick Sequential Organ Failure Assessment (qSOFA) score for the mortality in patients with sepsis in Vietnamese intensive care units.

BACKGROUND: The simple scoring systems for predicting the outcome of sepsis in intensive care units (ICUs) are few, especially for limited-resource settings. Therefore, this study aimed to evaluate the accuracy of the quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score in predicting the mortality of ICU patients with sepsis in Vietnam. METHODS: We did a multicenter cross-sectional study of patients with sepsis (≥18 years old) presenting to 15 adult ICUs throughout Vietnam on the specified days (i.e., 9th January, 3rd April, 3rd July, and 9th October) representing the different seasons of 2019. The primary and secondary outcomes were the hospital and ICU all-cause mortalities, respectively. The area under the receiver operating characteristic curve (AUROC) was calculated to determine the discriminatory ability of the qSOFA score for deaths in the hospital and ICU. The cut-off value of the qSOFA scores was determined by the receiver operating characteristic curve analysis. Upon ICU admission, factors associated with the hospital and ICU mortalities were assessed in univariable and multivariable logistic models. RESULTS: Of 252 patients, 40.1% died in the hospital, and 33.3% died in the ICU. The qSOFA score had a poor discriminatory ability for both the hospital (AUROC: 0.610 [95% CI: 0.538 to 0.681]; cut-off value: ≥2.5; sensitivity: 34.7%; specificity: 84.1%; PAUROC = 0.003) and ICU (AUROC: 0.619 [95% CI: 0.544 to 0.694]; cutoff value: ≥2.5; sensitivity: 36.9%; specificity: 83.3%; PAUROC = 0.002) mortalities. However, multivariable logistic regression analyses show that the qSOFA score of 3 was independently associated with the increased risk of deaths in both the hospital (adjusted odds ratio, AOR: 3.358; 95% confidence interval, CI: 1.756 to 6.422) and the ICU (AOR: 3.060; 95% CI: 1.651 to 5.671). CONCLUSION: In our study, despite having a poor discriminatory value, the qSOFA score seems worthwhile in predicting mortality in ICU patients with sepsis in limited-resource settings. CLINICAL TRIAL REGISTRATION: Clinical trials registry-India: CTRI/2019/01/016898.

A large chromosomal inversion affects antimicrobial sensitivity of Escherichia coli to sodium deoxycholate.

Resistance to antimicrobials is normally caused by mutations in the drug targets or genes involved in antimicrobial activation or expulsion. Here we show that an Escherichia coli strain, named DOC14, selected for increased resistance to the bile salt sodium deoxycholate, has no mutations in any ORF, but instead has a 2.1 Mb chromosomal inversion. The breakpoints of the inversion are two inverted copies of an IS5 element. Besides lowering deoxycholate susceptibility, the IS5-mediated chromosomal inversion in the DOC14 mutant was found to increase bacterial survival upon exposure to ampicillin and vancomycin, and sensitize the cell to ciprofloxacin and meropenem, but does not affect bacterial growth or cell morphology in a rich medium in the absence of antibacterial molecules. Overall, our findings support the notion that a large chromosomal inversion can benefit bacterial cells under certain conditions, contributing to genetic variability available for selection during evolution. The DOC14 mutant paired with its isogenic parental strain form a useful model as bacterial ancestors in evolution experiments to study how a large chromosomal inversion influences the evolutionary trajectory in response to various environmental stressors.

Gastric rupture following multiple blunt trauma.

Gastric injury due to trauma is a rare complication that occurs in approximately 0.04%-1.2% of all instances of abdominal trauma. When imaging trauma cases, certain areas can be obscured by several inevitable reasons. Despite its rarity, the high mortality rate of a gastric injury requires an early and accurate diagnosis. We present the case of an 18-year-old male who suffered a gastric rupture of the greater curvature following a road traffic collision before providing a brief review of the literature.

The three ghosts of medical AI: Can the black-box present deliver?

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.

Factors relating to mortality in septic patients in Vietnamese intensive care units from a subgroup analysis of MOSAICS II study.

Sepsis is the most common cause of in-hospital deaths, especially from low-income and lower-middle-income countries (LMICs). This study aimed to investigate the mortality rate and associated factors from sepsis in intensive care units (ICUs) in an LMIC. We did a multicenter cross-sectional study of septic patients presenting to 15 adult ICUs throughout Vietnam on the 4 days representing the different seasons of 2019. Of 252 patients, 40.1% died in hospital and 33.3% died in ICU. ICUs with accredited training programs (odds ratio, OR: 0.309; 95% confidence interval, CI 0.122-0.783) and completion of the 3-h sepsis bundle (OR: 0.294; 95% CI 0.083-1.048) were associated with decreased hospital mortality. ICUs with intensivist-to-patient ratio of 1:6 to 8 (OR: 4.533; 95% CI 1.621-12.677), mechanical ventilation (OR: 3.890; 95% CI 1.445-10.474) and renal replacement therapy (OR: 2.816; 95% CI 1.318-6.016) were associated with increased ICU mortality, in contrast to non-surgical source control (OR: 0.292; 95% CI 0.126-0.678) which was associated with decreased ICU mortality. Improvements are needed in the management of sepsis in Vietnam such as increasing resources in critical care settings, making accredited training programs more available, improving compliance with sepsis bundles of care, and treating underlying illness and shock optimally in septic patients.

A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos.

The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movements in videos is pivotal for improved cerebral palsy screening. Most existing methods use appearance-based features and are thus sensitive to strong but irrelevant signals caused by background clutter or a moving camera. Moreover, these features are computed over the whole frame, thus they measure gross whole body movements rather than specific joint/limb motion. Addressing these challenges, we develop and validate a new method for fidgety movement assessment from consumer-grade videos using human poses extracted from short clips. Human poses capture only relevant motion profiles of joints and limbs and are thus free from irrelevant appearance artifacts. The dynamics and coordination between joints are modeled using spatio-temporal graph convolutional networks. Frames and body parts that contain discriminative information about fidgety movements are selected through a spatio-temporal attention mechanism. We validate the proposed model on the cerebral palsy screening task using a real-life consumer-grade video dataset collected at an Australian hospital through the Cerebral Palsy Alliance, Australia. Our experiments show that the proposed method achieves the ROC-AUC score of 81.87%, significantly outperforming existing competing methods with better interpretability.

Early Moves: a protocol for a population-based prospective cohort study to establish general movements as an early biomarker of cognitive impairment in infants.

INTRODUCTION: The current diagnostic pathways for cognitive impairment rarely identify babies at risk before 2 years of age. Very early detection and timely targeted intervention has potential to improve outcomes for these children and support them to reach their full life potential. Early Moves aims to identify early biomarkers, including general movements (GMs), for babies at risk of cognitive impairment, allowing early intervention within critical developmental windows to enable these children to have the best possible start to life. METHOD AND ANALYSIS: Early Moves is a double-masked prospective cohort study that will recruit 3000 term and preterm babies from a secondary care setting. Early Moves will determine the diagnostic value of abnormal GMs (at writhing and fidgety age) for mild, moderate and severe cognitive delay at 2 years measured by the Bayley-4. Parents will use the Baby Moves smartphone application to video their babies' GMs. Trained GMs assessors will be masked to any risk factors and assessors of the primary outcome will be masked to the GMs result. Automated scoring of GMs will be developed through applying machine-based learning to the data and the predictive value for an abnormal GM will be investigated. Screening algorithms for identification of children at risk of cognitive impairment, using the GM assessment (GMA), and routinely collected social and environmental profile data will be developed to allow more accurate prediction of cognitive outcome at 2 years. A cost evaluation for GMA implementation in preparation for national implementation will be undertaken including exploring the relationship between cognitive status and healthcare utilisation, medical costs, health-related quality of life and caregiver burden. ETHICS AND DISSEMINATION: Ethics approval has been granted by the Medical Research Ethics Committee of Joondalup Health Services and the Health Service Human Research Ethics Committee (1902) of Curtin University (HRE2019-0739). TRIAL REGISTRATION NUMBER: ACTRN12619001422112.

In vitro synergy of 5-nitrofurans, vancomycin and sodium deoxycholate against Gram-negative pathogens.

Introduction. There is an urgent need for effective therapies against bacterial infections, especially those caused by antibiotic-resistant Gram-negative pathogens.Hypothesis. Synergistic combinations of existing antimicrobials show promise due to their enhanced efficacies and reduced dosages which can mitigate adverse effects, and therefore can be used as potential antibacterial therapy.Aim. In this study, we sought to characterize the in vitro interaction of 5-nitrofurans, vancomycin and sodium deoxycholate (NVD) against pathogenic bacteria.Methodology. The synergy of the NVD combination was investigated in terms of growth inhibition and bacterial killing using checkerboard and time-kill assays, respectively.Results. Using a three-dimensional checkerboard assay, we showed that 5-nitrofurans, sodium deoxycholate and vancomycin interact synergistically in the growth inhibition of 15 out of 20 Gram-negative strains tested, including clinically significant pathogens such as carbapenemase-producing Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii, and interact indifferently against the Gram-positive strains tested. The time-kill assay further confirmed that the triple combination was bactericidal in a synergistic manner.Conclusion. This study demonstrates the synergistic effect of 5-nitrofurans, sodium deoxycholate and vancomycin against Gram-negative pathogens and highlights the potential of the combination as a treatment for Gram-negative and Gram-positive infections.

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome.

BACKGROUND: Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure their relative abundance. In this paper, we propose a sparse neural encoder-decoder network to predict metabolite abundances from microbe abundances. RESULTS: Using paired data from a cohort of inflammatory bowel disease (IBD) patients, we show that our neural encoder-decoder model outperforms linear univariate and multivariate methods in terms of accuracy, sparsity, and stability. Importantly, we show that our neural encoder-decoder model is not simply a black box designed to maximize predictive accuracy. Rather, the network's hidden layer (i.e., the latent space, comprised only of sparsely weighted microbe counts) actually captures key microbe-metabolite relationships that are themselves clinically meaningful. Although this hidden layer is learned without any knowledge of the patient's diagnosis, we show that the learned latent features are structured in a way that predicts IBD and treatment status with high accuracy. CONCLUSIONS: By imposing a non-negative weights constraint, the network becomes a directed graph where each downstream node is interpretable as the additive combination of the upstream nodes. Here, the middle layer comprises distinct microbe-metabolite axes that relate key microbial biomarkers with metabolite biomarkers. By pre-processing the microbiome and metabolome data using compositional data analysis methods, we ensure that our proposed multi-omics workflow will generalize to any pair of -omics data. To the best of our knowledge, this work is the first application of neural encoder-decoders for the interpretable integration of multi-omics biological data.

Novel mechanisms of TolC-independent decreased bile-salt susceptibility in Escherichia coli.

Bile salts, including sodium deoxycholate (DOC), are secreted into the intestine to aid fat digestion and contribute to antimicrobial protection. Gram-negative pathogens such as Escherichia coli, however, are highly resistant to DOC, using multiple mechanisms of which the multidrug efflux pump AcrAB-TolC is the dominant one. Given that TolC-mediated efflux masks the interaction of DOC with potential targets, we sought to identify those targets by identifying genes whose mutations cause an increase in the MIC to DOC relative to the ∆tolC parental strain, that lacks TolC-associated functional efflux pumps. Using a mutant screen, we isolated twenty independent spontaneous mutants that had a higher MICDOC than the E. coli parental ∆tolC strain. Whole genome sequencing of these mutants mapped most mutations to the ptsI or cyaA gene. Analysis of knock-out mutants and complementation showed that elimination of PtsI, a component of the carbohydrate phosphotransferase system, or one of the two key proteins involved in cAMP synthesis and signaling, adenylate cyclase (CyaA) or cAMP receptor protein (Crp) causes low-level increased resistance of a ∆tolC E. coli strain to DOC.

In vitro synergy between sodium deoxycholate and furazolidone against enterobacteria.

BACKGROUND: Antimicrobial combinations have been proven as a promising approach in the confrontation with multi-drug resistant bacterial pathogens. In the present study, we identify and characterize a synergistic interaction of broad-spectrum nitroreductase-activated prodrugs 5-nitrofurans, with a secondary bile salt, Sodium Deoxycholate (DOC) in growth inhibition and killing of enterobacteria. RESULTS: Using checkerboard assay, we show that combination of nitrofuran furazolidone (FZ) and DOC generates a profound synergistic effect on growth inhibition in several enterobacterial species including Escherichia coli, Salmonella enterica, Citrobacter gillenii and Klebsiella pneumoniae. The Fractional Inhibitory Concentration Index (FICI) for DOC-FZ synergy ranges from 0.125 to 0.35 that remains unchanged in an ampicillin-resistant E. coli strain containing a ß-lactamase-producing plasmid. Findings from the time-kill assay further highlight the synergy with respect to bacterial killing in E. coli and Salmonella. We further characterize the mechanism of synergy in E. coli K12, showing that disruption of the tolC or acrA genes that encode components of multidrug efflux pumps causes, respectively, a complete or partial loss, of the DOC-FZ synergy. This finding indicates the key role of TolC-associated efflux pumps in the DOC-FZ synergy. Overexpression of Nitric Oxide-detoxifying enzyme Hmp results in a three-fold increase in FICI for DOC-FZ interaction, suggesting a role of nitric oxide in the synergy. We further demonstrate that DOC-FZ synergy is largely independent of NfsA and NfsB, the two major activation enzymes of the nitrofuran prodrugs. CONCLUSIONS: This study is to our knowledge the first report of nitrofuran-deoxycholate synergy against Gram-negative bacteria, offering potential applications in antimicrobial therapeutics. The mechanism of DOC-FZ synergy involves FZ-mediated inhibition of TolC-associated efflux pumps that normally remove DOC from bacterial cells. One possible route contributing to that effect is via FZ-mediated nitric oxide production.

Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach.

Polymer-assisted flocculation-dewatering of mineral processing tailings (MPT) is crucial for its environmental disposal. To reduce the number of laboratory experiments, this study proposes a novel and hybrid machine learning (ML) method for the prediction of the flocculation-dewatering performance. The proposed ML method utilizes principle component analysis (PCA) for the dimension-reduction of the input space. Then, ML prediction is performed using the combination of particle swarm optimisation (PSO) and adaptive neuro-fuzzy inference system (ANFIS). Monte Carlo simulations are used for the converged results. An experimental dataset of 102 data instances is prepared. 17 variables are chosen as inputs and the initial settling rate (ISR) is chosen as the output. Along with the raw dataset, two new datasets are prepared based on the cumulative sum of variance, namely PCA99 with 9 variables and PCA95 with 7 variables. The results show that Monte Carlo simulations need to be performed for over 100 times to reach the converged results. Based on the statistic indicators, it is found that the ML prediction on PCA99 and PCA95 is better than that on the raw dataset (average correlation coefficient is 0.85 for the raw dataset, 0.89 for the PCA99 dataset and 0.88 for the PCA95 dataset). Overall speaking, ML prediction has good prediction performance and it can be employed by the mine site to improve the efficiency and cost-effectiveness. This study presents a benchmark study for the prediction of ISR, which, with better consolidation and development, can become important tools for analysing and modelling flocculate-settling experiments.

Using the IPTG-Inducible Pgrac212 Promoter for Overexpression of Human Rhinovirus 3C Protease Fusions in the Cytoplasm of Bacillus subtilis Cells.

Expression and secretion of recombinant proteins in the endotoxin-free bacterium, Bacillus subtilis, has been thoroughly studied, but overexpression in the cytoplasm has been limited to only a few proteins. Here, we used the robust IPTG-inducible promoter, Pgrac212, to overexpress human rhinovirus 3C protease (HRV3C) in the cytoplasm of B. subtilis cells. A novel solubility tag, the N-terminal domain of the lysS gene of B. subtilis coding for a lysyl-tRNA synthetase was placed at the N terminus with a cleavage site for the endoprotease HRV3C, followed by His-HRV3C or His-GST-HRV3C. The recombinant protease was purified by using a Ni-NTA column. In this study, the His-HRV3C and His-GST-HRV3C proteases were overexpressed in the cytoplasm of B. subtilis at 11% and 16% of the total cellular proteins, respectively. The specific protease activities were 8065 U/mg for His-HRV3C and 3623 U/mg for His-GST-HRV3C. The purified enzymes were used to cleave two different substrates followed by purification of the two different protein targets, the green fluorescent protein and the beta-galactosidase. In conclusion, the combination of an inducible promoter Pgrac212 and a solubility tag allowed the overexpression of the HRV3C protease in the cytoplasm of B. subtilis. The resulting fusion protein was purified using a nickel column and was active in cleaving target proteins to remove the fusion tags. This study offers an effective method for producing recombinant proteins in the cytoplasm of endotoxin-free bacteria.

Novel 5-Nitrofuran-Activating Reductase in Escherichia coli.

The global spread of multidrug-resistant enterobacteria warrants new strategies to combat these pathogens. One possible approach is the reconsideration of "old" antimicrobials, which remain effective after decades of use. Synthetic 5-nitrofurans such as furazolidone, nitrofurantoin, and nitrofurazone are such a class of antimicrobial drugs. Recent epidemiological data showed a very low prevalence of resistance to this antimicrobial class among clinical Escherichia coli isolates in various parts of the world, forecasting the increasing importance of its uses to battle antibiotic-resistant enterobacteria. However, although they have had a long history of clinical use, a detailed understanding of the 5-nitrofurans' mechanisms of action remains limited. Nitrofurans are known as prodrugs that are activated in E. coli by reduction catalyzed by two redundant nitroreductases, NfsA and NfsB. Furazolidone, nevertheless, retains relatively significant antibacterial activity in the nitroreductase-deficient ΔnfsA ΔnfsBE. coli strain, indicating the presence of additional activating enzymes and/or antibacterial activity of the unreduced form. Using genome sequencing, genetic, biochemical, and bioinformatic approaches, we discovered a novel 5-nitrofuran-activating enzyme, AhpF, in E. coli The discovery of a new nitrofuran-reducing enzyme opens new avenues for overcoming 5-nitrofuran resistance, such as designing nitrofuran analogues with higher affinity for AhpF or screening for adjuvants that enhance AhpF expression.