Genome-Based Prediction of Bacterial Antibiotic Resistance

Characterization of acquired β-lactamases in Pseudomonas aeruginosa and quantification of their contributions to resistance

Pseudomonas aeruginosa is a highly problematic opportunistic pathogen that causes a range of different infections. Infections are commonly treated with β-lactam antibiotics, including cephalosporins, monobactams, penicillins, and carbapenems, with carbapenems regarded as antibiotics of last resort. Isolates of P. aeruginosa can contain horizontally acquired bla genes encoding β-lactamase enzymes, but the extent to which these contribute to β-lactam resistance in this species has not been systematically quantified. The overall aim of this research was to address this knowledge gap by quantifying the frequency of β-lactamase-encoding genes in P. aeruginosa and by determining the effects of β-lactamases on susceptibility of P. aeruginosa to β-lactams. Genome analysis showed that β-lactamase-encoding genes are present in 3% of P. aeruginosa but are enriched in carbapenem-resistant isolates (35%). To determine the substrate antibiotics, 10 β-lactamases were expressed from an integrative plasmid in the chromosome of P. aeruginosa reference strain PAO1. The β-lactamases reduced susceptibility to a variety of clinically used antibiotics, including carbapenems (meropenem, imipenem), penicillins (ticarcillin, piperacillin), cephalosporins (ceftazidime, cefepime), and a monobactam (aztreonam). Different enzymes acted on different β-lactams. β-lactamases encoded by the genomes of P. aeruginosa clinical isolates had similar effects to the enzymes expressed in strain PAO1. Genome engineering was used to delete β-lactamase-encoding genes from three carbapenem-resistant clinical isolates and increased susceptibility to substrate β-lactams. Our findings demonstrate that acquired β-lactamases play an important role in β-lactam resistance in P. aeruginosa, identifying substrate antibiotics for a range of enzymes and quantifying their contributions to resistance.IMPORTANCEPseudomonas aeruginosa is an extremely problematic pathogen, with isolates that are resistant to the carbapenem class of β-lactam antibiotics being in critical need of new therapies. Genes encoding β-lactamase enzymes that degrade β-lactam antibiotics can be present in P. aeruginosa, including carbapenem-resistant isolates. Here, we show that β-lactamase genes are over-represented in carbapenem-resistant isolates, indicating their key role in resistance. We also show that different β-lactamases alter susceptibility of P. aeruginosa to different β-lactam antibiotics and quantify the effects of selected enzymes on β-lactam susceptibility. This research significantly advances the understanding of the contributions of acquired β-lactamases to antibiotic resistance, including carbapenem resistance, in P. aeruginosa and by implication in other species. It has potential to expedite development of methods that use whole genome sequencing of infecting bacteria to inform antibiotic treatment, allowing more effective use of antibiotics, and facilitate the development of new antibiotics.

Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae

BACKGROUND

Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning for the rapid detection of ceftazidime-avibactam (CZA) resistance in KP to improve clinical decision-making processes.

METHODS

Out of 107,721 bacterial samples, 675 specimens of KP with suspected multi-drug resistance were selected. These specimens were collected from a tertiary hospital and four secondary hospitals between 2022 and 2023 to evaluate CZA resistance. We used MALDI-TOF MS and machine learning to develop an AI-CDSS with enhanced speed of resistance detection.

RESULTS

Machine learning models, especially light gradient boosting machines (LGBM), exhibited an area under the curve (AUC) of 0.95, indicating high accuracy. The predictive models formed the core of our newly developed AI-CDSS, enabling clinical decisions quicker than traditional methods using culture and antibiotic susceptibility testing by a day.

CONCLUSIONS

The study confirms that MALDI-TOF MS, integrated with machine learning, can swiftly detect CZA resistance. Incorporating this insight into an AI-CDSS could transform clinical workflows, giving healthcare professionals immediate, crucial insights for shaping treatment plans. This approach promises to be a template for future anti-resistance strategies, emphasizing the vital importance of advanced diagnostics in enhancing public health outcomes.

Prediction of antimicrobial resistance in Klebsiella pneumoniae using genomic and metagenomic next-generation sequencing data

OBJECTIVES

Klebsiella pneumoniae is a significant pathogen with increasing resistance and high mortality rates. Conventional antibiotic susceptibility testing methods are time-consuming. Next-generation sequencing has shown promise for predicting antimicrobial resistance (AMR). This study aims to develop prediction models using whole-genome sequencing data and assess their feasibility with metagenomic next-generation sequencing data from clinical samples.

METHODS

On the basis of 4170 K. pneumoniae genomes, the main genetic characteristics associated with AMR were identified using a LASSO regression model. Consequently, the prediction model was established, validated and optimized using clinical isolate read simulation sequences. To evaluate the efficacy of the model, clinical specimens were collected.

RESULTS

Four predictive models for amikacin, ciprofloxacin, levofloxacin, and piperacillin/tazobactam, initially had positive predictive values (PPVs) of 92%, 98%, 99%, 94%, respectively, when they were originally constructed. When applied to clinical specimens, their PPVs were 96%, 96%, 95%, and 100%, respectively. Meanwhile, there were negative predictive values (NPVs) of 100% for ciprofloxacin and levofloxacin, and 'not applicable' (NA) for amikacin and piperacillin/tazobactam. Our method achieved antibacterial phenotype classification accuracy rates of 95.92% for amikacin, 96.15% for ciprofloxacin, 95.31% for levofloxacin and 100% for piperacillin/tazobactam. The sequence-based prediction antibiotic susceptibility testing (AST) reported results in an average time of 19.5 h, compared with the 67.9 h needed for culture-based AST, resulting in a significant reduction of 48.4 h.

CONCLUSIONS

These preliminary results demonstrated that the performance of prediction model for a clinically significant antimicrobial-species pair was comparable to that of phenotypic methods, thereby encouraging the expansion of sequence-based susceptibility prediction and its clinical validation and application.

Comparison of commercial next-generation sequencing assays to conventional culture methods for bacterial identification and antimicrobial susceptibility of samples obtained from clinical cases of canine superficial bacterial folliculitis

BACKGROUND

Bacterial identification and antimicrobial susceptibility testing is an important step in timely therapeutic decisions for canine superficial bacterial folliculitis (SBF), commonly caused by Staphylococcus pseudintermedius. Next-generation sequencing (NGS) offers the appeal of potentially expedited results with complete detection of bacterial organisms and associated resistance genes compared to culture. Limited studies exist comparing the two methodologies for clinical samples.

HYPOTHESIS/OBJECTIVES

To compare and contrast genotypic and phenotypic methods for bacterial identification and antimicrobial susceptibility from cases of canine SBF.

ANIMALS

Twenty-four client-owned dogs with lesions consistent with SBF were enrolled.

MATERIALS AND METHODS

A sterile culturette swab was used to sample dogs with SBF lesions. The swab was rinsed in 0.9 mL of sterile phosphate-buffered saline and vortexed to create a homogenous solution. Two swabs for NGS laboratories (Labs) and one swab for culture (Culture Lab) were randomly sampled from this solution and submitted for bacterial identification and antimicrobial susceptibility.

RESULTS

No statistical difference regarding turnaround time for NGS Labs compared to Culture Lab was found. NGS Lab 1 identified more organisms than NGS Lab 2 and Culture Lab, which were both statistically significant. There was no statistical difference in detection frequency for Staphylococcus spp. among all laboratories. There was poor agreement for the presence of meticillin resistance and most antimicrobials among all laboratories.

CONCLUSIONS AND CLINICAL RELEVANCE

Utilisation of NGS as a replacement for traditional culture when sampling canine SBF lesions is not supported at this time.

Metrological evaluation of DNA extraction method effects on the bacterial microbiome and resistome in sputum

Targeted high-throughput sequencing (HTS) has revolutionized the way we look at bacterial communities. It can be used for the species-specific detection of bacteria as well as for the determination of the microbiome and resistome and can be applied to samples from almost any environment. However, the results of targeted HTS can be influenced by many factors, which poses a major challenge for its use in clinical diagnostics. In this study, we investigated the impact of the DNA extraction method on the determination of the bacterial microbiome and resistome by targeted HTS using principles from metrology and diagnostics such as repeatability and analytical sensitivity. Sputum samples spiked with Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa at three different concentrations (103-106 cells/mL) were used. DNA was extracted from each sample on 2 separate days in three replicates each using three different extraction methods based on cetrimonium bromide, magnetic beads, and silica membranes. All three spiked bacteria were detected in sputum, and the DNA extraction method had no significant effect on detection. However, the DNA extraction method had significant effects on the composition of the microbiome and the resistome. The sequencing results were repeatable in the majority of cases. The silica membrane-based DNA extraction kit provided the most repeatable results and the highest diversity of the microbiome and resistome. Targeted HTS has been shown to be a reliable tool for determining the microbiome and resistome; however, the method of DNA extraction should be carefully selected to minimize its impact on the results.

IMPORTANCE

High-throughput sequencing (HTS) is one of the crucial new technologies that gives us insights into previously hidden parts of microbial communities. The DNA extraction method is an important step that can have a major impact on the results, and understanding this impact is of paramount importance for their reliable interpretation. Our results are of great value for the interpretation of sputum microbiome and resistome results obtained by targeted HTS. Our findings allow for a more rational design of future microbiome studies, which would lead to higher repeatability of results and easier comparison between different laboratories. This could also facilitate the introduction of targeted HTS in clinical microbiology for reliable identification of pathogenic bacteria and testing for antimicrobial resistance (AMR). As AMR is a major threat to public health, the improved methods for determining AMR would bring great benefits to both the healthcare system and society as a whole.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

Genome-Based Analysis of Genetic Diversity, Antimicrobial Susceptibility, and Virulence Gene Distribution in Salmonella Pullorum Isolates from Poultry in China

Pullorum disease, caused by Salmonella enterica serovar Pullorum (S. Pullorum) infection, is a major pathogenic threat to the poultry industry. In this study, 40 S. Pullorum isolates from seven provinces of China were comprehensively analyzed in terms of antigenic type and antimicrobial susceptibility, and their drug-resistance genes and virulence genes were identified with whole-genome sequencing (WGS). We show that all these isolates were standard antigenic types, with ST92 the predominant genotype (92.5%). Disk diffusion assays revealed high resistance rates to streptomycin (92.5%), ciprofloxacin (82.5%), and ampicillin (80%), and the resistance rates to streptomycin, gentamicin, ampicillin, and cefotaxime were higher in isolates from sick chickens than in those from healthy chickens. In addition, gyrA mutations and eight acquired resistance genes were identified, with aac(6')-Iaa the most prevalent, followed by blaTEM1β, sul2, and the GyrA S83F mutation. The resistance phenotypes to streptomycin, ampicillin, and ciprofloxacin correlated strongly with the presence of the aac(6')-Iaa resistance gene, blaTEM1β resistance gene, and gyrA mutations, respectively. Analysis of the virulence genes showed that the isolates expressed numerous factors associated with secretion systems, including SPI-1 and SPI-2. Overall, this study extends our understanding of the epidemiology and antibiotic resistance of S. Pullorum in China.

Penicillin-binding protein 3 sequence variations reduce susceptibility of Pseudomonas aeruginosa to β-lactams but inhibit cell division

BACKGROUND

β-lactam antibiotics, which inhibit penicillin-binding protein 3 (PBP3) that is required for cell division, play a key role in treating P. aeruginosa infections. Some sequence variations in PBP3 have been associated with β-lactam resistance but the effects of variations on antibiotic susceptibility and on cell division have not been quantified. Antibiotic efflux can also reduce susceptibility.

OBJECTIVES

To quantify the effects of PBP3 variations on β-lactam susceptibility and cell morphology in P. aeruginosa.

METHODS

Nineteen PBP3 variants were expressed from a plasmid in the reference strain P. aeruginosa PAO1 and genome engineering was used to construct five mutants expressing PBP3 variants from the chromosome. The effects of the variations on β-lactam minimum inhibitory concentration (MIC) and cell morphology were measured.

RESULTS

Some PBP3 variations reduced susceptibility to a variety of β-lactam antibiotics including meropenem, ceftazidime, cefepime and ticarcillin with different variations affecting different antibiotics. None of the tested variations reduced susceptibility to imipenem or piperacillin. Antibiotic susceptibility was further reduced when PBP3 variants were expressed in mutant bacteria overexpressing the MexAB-OprM efflux pump, with some variations conferring clinical levels of resistance. Some PBP3 variations, and sub-MIC levels of β-lactams, reduced bacterial growth rates and inhibited cell division, causing elongated cells.

CONCLUSIONS

PBP3 variations in P. aeruginosa can increase the MIC of multiple β-lactam antibiotics, although not imipenem or piperacillin. PBP3 variations, or the presence of sub-lethal levels of β-lactams, result in elongated cells indicating that variations reduce the activity of PBP3 and may reduce bacterial fitness.

Infections in long-duration space missions

As government space agencies and private companies announce plans for deep space exploration and colonisation, prioritisation of medical preparedness is becoming crucial. Among all medical conditions, infections pose one of the biggest threats to astronaut health and mission success. To gain a comprehensive understanding of these risks, we review the measured and estimated incidence of infections in space, effect of space environment on the human immune system and microbial behaviour, current preventive and management strategies for infections, and future perspectives for diagnosis and treatment. This information will enable space agencies to enhance their comprehension of the risk of infection in space, highlight gaps in knowledge, aid better crew preparation, and potentially contribute to sepsis management in terrestrial settings, including not only isolated or austere environments but also conventional clinical settings.

Rapid inference of antibiotic resistance and susceptibility for Klebsiella pneumoniae by clinical shotgun metagenomic sequencing

OBJECTIVES

The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data.

METHODS

We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating "possible species," "gene copy number calculation" and "species-specific kmers." The performance of the method was evaluated on retrospective case studies.

RESULTS

Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial.

CONCLUSIONS

In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.

From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Acute Retinal Necrosis Associated with Pseudorabies Virus Infection: A Case Report and Literature Review

PURPOSE

To analyze a case of acute retinal necrosis (ARN) associated with pseudorabies virus (PRV) infection and discusses the clinical characteristics of PRV-induced ARN (PRV-ARN).

METHODS

Case report and literature review of ocular features in PRV-ARN.

RESULTS

A 52-year-old female diagnosed with encephalitis presented with bilateral vision loss, mild anterior uveitis, vitreous opacity, occlusive retinal vasculitis, and retinal detachment in her left eye. The result of metagenomic next-generation sequencing (mNGS) indicated that both cerebrospinal fluids and vitreous fluid tested positive for PRV.

CONCLUSION

PRV, a zoonosis, can infect both humans and mammals. Patients affected with PRV may experience severe encephalitis and oculopathy, and the infection has been associated with high mortality and disability. ARN is the most common ocular disease, which develops rapidly following encephalitis and is characterized by five figures: bilateral onset, rapid progression, severe visual impairment, poor response to systemic antiviral drugs, and an unfavorable prognosis.

New insight in molecular detection of Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis, is a pathogenic bacterium that has claimed millions of lives since the Middle Ages. According to the World Health Organization's report, tuberculosis ranks among the ten deadliest diseases worldwide. The presence of an extensive array of genes and diverse proteins within the cellular structure of this bacterium has provided us with a potent tool for diagnosis. While the culture method remains the gold standard for tuberculosis diagnosis, it is possible that molecular diagnostic methods, emphasis on the identification of mutation genes (e.g., rpoB and gyrA) and single nucleotide polymorphisms, could offer a safe and reliable alternative. Over the past few decades, as our understanding of molecular genetics has expanded, methods have been developed based on gene expansion and detection. These methods typically commence with DNA amplification through nucleic acid targeted techniques such as polymerase chain reaction. Various molecular compounds and diverse approaches have been employed in molecular assays. In this review, we endeavor to provide an overview of molecular assays for the diagnosis of tuberculosis with their properties (utilization, challenges, and functions). The ultimate goal is to explore the potential of replacing traditional bacterial methods with these advanced molecular diagnostic techniques.

Integrating DNA/RNA microbe detection and host response for accurate diagnosis, treatment and prognosis of childhood infectious meningitis and encephalitis

BACKGROUND

Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy.

METHODS

Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL).

RESULTS

Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM.

CONCLUSIONS

This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

BACKGROUND

Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P. aeruginosa genomes remains an unsolved problem.

METHODS

We first curated the most comprehensive database yet of known P. aeruginosa AMR variants. Next, we performed comparative genomics and microbial genome-wide association study analysis across a Global isolate Dataset (n = 1877) with paired antimicrobial phenotype and genomic data to identify novel AMR variants. Finally, the performance of our P. aeruginosa AMR database, implemented in our AMR detection and prediction tool, ARDaP, was compared with three previously published in silico AMR gene detection or phenotype prediction tools-abritAMR, AMRFinderPlus, ResFinder-across both the Global Dataset and an analysis-naïve Validation Dataset (n = 102).

RESULTS

Our AMR database comprises 3639 mobile AMR genes and 728 chromosomal variants, including 75 previously unreported chromosomal AMR variants, 10 variants associated with unusual antimicrobial susceptibility, and 281 chromosomal variants that we show are unlikely to confer AMR. Our pipeline achieved a genotype-phenotype balanced accuracy (bACC) of 85% and 81% across 10 clinically relevant antibiotics when tested against the Global and Validation Datasets, respectively, vs. just 56% and 54% with abritAMR, 58% and 54% with AMRFinderPlus, and 60% and 53% with ResFinder. ARDaP's superior performance was predominantly due to the inclusion of chromosomal AMR variants, which are generally not identified with most AMR identification tools.

CONCLUSIONS

Our ARDaP software and associated AMR variant database provides an accurate tool for predicting AMR phenotypes in P. aeruginosa, far surpassing the performance of current tools. Implementation of ARDaP for routine AMR prediction from P. aeruginosa genomes and metagenomes will improve AMR identification, addressing a critical facet in combatting this treatment-refractory pathogen. However, knowledge gaps remain in our understanding of the P. aeruginosa resistome, particularly the basis of colistin AMR.

Insights on the genomic diversity, virulence and resistance profile of a Campylobacter jejuni strain isolated from a hospitalized patient in Brazil

Campylobacteriosis is currently recognized as one of the major causes of foodborne bacterial diseases worldwide. In Brazil, there is insufficient data to estimate the impact of Campylobacter in public health. The aim of this present study was to characterize a C. jejuni CJ-HBSJRP strain isolated from a hospitalized patient in Brazil by its ability to invade human Caco-2 epithelial cells, to survive in U937 human macrophages, and to assess its phenotypic antimicrobial resistance profile. In addition, prophages, virulence and antimicrobial resistance genes were search using whole-genome sequencing data. The genetic relatedness was evaluated by MLST and cgMLST analysis by comparison with 29 other C. jejuni genomes isolated from several countries. The CJ-HBSJRP strain showed an invasion percentage of 50% in Caco-2 polarized cells, 37.5% of survivability in U937 cells and was phenotypically resistant to ampicillin, ciprofloxacin and nalidixic acid. A total of 94 virulence genes related to adherence, biofilm, chemotaxis, immune modulation, invasion process, metabolism, motility and toxin were detected. The resistance genes blaOXA-605 (blaOXA-61), cmeB and mutations in the QRDR region of gyrA were also found and none prophages were detected. The MLST analysis showed 23 different STs among the strains studied. Regarding cgMLST analysis, the CJ-HBSJRP strain was genetically distinct and did not group closely to any other isolate. The results obtained reinforce the pathogenic potential of the CJHBSJRP strain and highlighted the need for more careful attention to Campylobacter spp. infections in Brazil since this pathogen has been the most commonly reported zoonosis in several countries worldwide.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Navigating the network: a narrative overview of AMR surveillance and data flow in the United States

The antimicrobial resistance (AMR) surveillance landscape in the United States consists of a data flow that starts in the clinical setting and is maintained by a network of national and state public health laboratories. These organizations are well established, with robust methodologies to test and confirm antimicrobial susceptibility. Still, the bridge that guides the flow of data is often one directional and caught in a constant state of rush hour that can only be refined with improvements to infrastructure and automation in the data flow. Moreover, there is an absence of information in the literature explaining the processes clinical laboratories use to coalesce and share susceptibility test data for AMR surveillance, further complicated by variability in testing procedures. This knowledge gap limits our understanding of what is needed to improve and streamline data sharing from clinical to public health laboratories. Successful models of AMR surveillance display attributes like 2-way communication between clinical and public health laboratories, centralized databases, standardized data, and the use of electronic health records or data systems, highlighting areas of opportunity and improvement. This article explores the roles and processes of the organizations involved in AMR surveillance in the United States and identifies current knowledge gaps and opportunities to improve communication between them through standardization, communication, and modernization of data flow.

Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients

BACKGROUND

Antimicrobial resistance (AMR) is a major threat to children's health, particularly in respiratory infections. Accurate identification of pathogens and AMR is crucial for targeted antibiotic treatment. Metagenomic next-generation sequencing (mNGS) shows promise in directly detecting microorganisms and resistance genes in clinical samples. However, the accuracy of AMR prediction through mNGS testing needs further investigation for practical clinical decision-making.

METHODS

We aimed to evaluate the performance of mNGS in predicting AMR for severe pneumonia in pediatric patients. We conducted a retrospective analysis at a tertiary hospital from May 2022 to May 2023. Simultaneous mNGS and culture were performed on bronchoalveolar lavage fluid samples obtained from pediatric patients with severe pneumonia. By comparing the results of mNGS detection of microorganisms and antibiotic resistance genes with those of culture, sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

RESULTS

mNGS detected bacterial in 71.7% cases (86/120), significantly higher than culture (58/120, 48.3%). Compared to culture, mNGS demonstrated a sensitivity of 96.6% and a specificity of 51.6% in detecting pathogenic microorganisms. Phenotypic susceptibility testing (PST) of 19 antibiotics revealed significant variations in antibiotics resistance rates among different bacteria. Sensitivity prediction of mNGS for carbapenem resistance was higher than penicillins and cephalosporin (67.74% vs. 28.57%, 46.15%), while specificity showed no significant difference (85.71%, 75.00%, 75.00%). mNGS also showed a high sensitivity of 94.74% in predicting carbapenem resistance in Acinetobacter baumannii.

CONCLUSIONS

mNGS exhibits variable predictive performance among different pathogens and antibiotics, indicating its potential as a supplementary tool to conventional PST. However, mNGS currently cannot replace conventional PST.

Metallo-beta-lactamases: mechanisms, treatment challenges, and future prospects

INTRODUCTION

Metallo-beta-lactamases (MBLs) are responsible for resistance to almost all beta-lactam antibiotics. Found predominantly in Gram-negative bacteria, they severely limit treatment options. Understanding the epidemiology, risk factors, treatment, and prevention of infections caused by MBL-producing organisms is essential to reduce their burden.

AREAS COVERED

The origins and structure of MBLs are discussed. We describe the mechanisms of action that differentiate MBLs from other beta-lactamases. We discuss the global epidemiology of MBL-producing organisms and their impact on patients' outcomes. By exposing the mechanisms of transmission of MBLs among bacterial populations, we emphasize the importance of infection prevention and control.

EXPERT OPINION

MBLs are spreading globally and challenging the majority of available antibacterial agents. Genotypic tests play an important role in the identification of MBL production. Phenotypic tests are less specific but may be used in low-resource settings, where MBLs are more predominant. Infection prevention and control are critical to reduce the spread of organisms producing MBL in healthcare systems. New combinations such as avibactam-aztreonam and new agents such as cefiderocol have shown promising results for the treatment of infections caused by MBL-producing organisms. New antibiotic and non-antibiotic agents are being developed and may improve the management of infections caused by MBL-producing organisms.

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of Resistant E. coli Infections

Drug-resistant Gram-negative bacterial infections, on average, increase the length of stay (LOS) in U.S. hospitals by 5 days, translating to approximately $15,000 per patient. We used statistical and machine-learning models to explore the relationship between antibiotic usage and antibiotic resistance over time and to predict the clinical and financial costs associated with resistant E. coli infections. We acquired data on antibiotic utilization and the resistance/sensitivity of 4776 microbial cultures at a Kaiser Permanente facility from April 2013 to December 2019. The ARIMA (autoregressive integrated moving average), neural networks, and random forest time series algorithms were employed to model antibiotic resistance trends. The models' performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE). The best performing model was then used to predict antibiotic resistance rates for the year 2020. The ARIMA model with cefazolin, followed by the one with cephalexin, provided the lowest RMSE and MAE values without signs of overfitting across training and test datasets. The study showed that reducing cefazolin usage could decrease the rate of resistant E. coli infections. Although piperacillin/tazobactam did not perform as well as cefazolin in our time series models, it performed reasonably well and, due to its broad spectrum, might be a practical target for interventions in antimicrobial stewardship programs (ASPs), at least for this particular facility. While a more generalized model could be developed with data from multiple facilities, this study acts as a framework for ASP clinicians to adopt statistical and machine-learning approaches, using region-specific data to make effective interventions.

Genomic characteristics of cfr and fexA carrying Staphylococcus aureus isolated from pig carcasses in Korea

The emergence of transferable linezolid resistance genes poses significant challenges to public health, as it does not only confer linezolid resistance but also reduces susceptibility to florfenicol, which is widely used in the veterinary field. This study evaluated the genetic characteristics of linezolid-resistant Staphylococcus aureus strains isolated from pig carcasses and further clarified potential resistance and virulence mechanisms in a newly identified sequence type. Of more than 2500 strains isolated in a prior study, 15 isolated from pig carcasses exhibited linezolid resistance (minimum inhibitory concentration ≥ 8 mg/L). The strains were characterized in detail by genomic analysis. Linezolid-resistant S. aureus strains exhibited a high degree of genetic lineage diversity, with one strain (LNZ_R_SAU_64) belonging to ST8004, which has not been reported previously. The 15 strains carried a total of 21 antibiotic resistance genes, and five carried mecA associated with methicillin resistance. All strains harbored cfr and fexA, which mediate resistance to linezolid, phenicol, and other antibiotics. Moreover, the strains carried enterotoxin gene clusters, including the hemolysin, leukotoxin, and protease genes, which are associated with humans or livestock. Some genes were predicted to be carried in plasmids or flanked by ISSau9 and the transposon Tn554, thus being transmittable between staphylococci. Strains carrying the plasmid replicon repUS5 displayed high sequence similarity (99%) to the previously reported strain pSA737 in human clinical samples in the United States. The results illustrate the need for continuous monitoring of the prevalence and transmission of linezolid-resistant S. aureus isolated from animals and their products.

The role of hypermutation and collateral sensitivity in antimicrobial resistance diversity of Pseudomonas aeruginosa populations in cystic fibrosis lung infection

Pseudomonas aeruginosa is an opportunistic pathogen which causes chronic, drug-resistant lung infections in cystic fibrosis (CF) patients. In this study, we explore the role of genomic diversification and evolutionary trade-offs in antimicrobial resistance (AMR) diversity within P. aeruginosa populations sourced from CF lung infections. We analyzed 300 clinical isolates from four CF patients (75 per patient) and found that genomic diversity is not a consistent indicator of phenotypic AMR diversity. Remarkably, some genetically less diverse populations showed AMR diversity comparable to those with significantly more genetic variation. We also observed that hypermutator strains frequently exhibited increased sensitivity to antimicrobials, contradicting expectations from their treatment histories. Investigating potential evolutionary trade-offs, we found no substantial evidence of collateral sensitivity among aminoglycoside, beta-lactam, or fluoroquinolone antibiotics, nor did we observe trade-offs between AMR and growth in conditions mimicking CF sputum. Our findings suggest that (i) genomic diversity is not a prerequisite for phenotypic AMR diversity, (ii) hypermutator populations may develop increased antimicrobial sensitivity under selection pressure, (iii) collateral sensitivity is not a prominent feature in CF strains, and (iv) resistance to a single antibiotic does not necessarily lead to significant fitness costs. These insights challenge prevailing assumptions about AMR evolution in chronic infections, emphasizing the complexity of bacterial adaptation during infection.IMPORTANCEUpon infection in the cystic fibrosis (CF) lung, Pseudomonas aeruginosa rapidly acquires genetic mutations, especially in genes involved in antimicrobial resistance (AMR), often resulting in diverse, treatment-resistant populations. However, the role of bacterial population diversity within the context of chronic infection is still poorly understood. In this study, we found that hypermutator strains of P. aeruginosa in the CF lung undergoing treatment with tobramycin evolved increased sensitivity to tobramycin relative to non-hypermutators within the same population. This finding suggests that antimicrobial treatment may only exert weak selection pressure on P. aeruginosa populations in the CF lung. We further found no evidence for collateral sensitivity in these clinical populations, suggesting that collateral sensitivity may not be a robust, naturally occurring phenomenon for this microbe.

Genome-based assessment of antimicrobial resistance reveals the lineage specificity of resistance and resistance gene profiles in Riemerella anatipestifer from China

Riemerella anatipestifer (R. anatipestifer) is an important pathogen that causes severe systemic infections in domestic ducks, resulting in substantial economic losses for China's waterfowl industry. Controlling R. anatipestifer with antibiotics is extremely challenging due to its multidrug resistance. Notably, large-scale studies on antimicrobial resistance (AMR) and the corresponding genetic determinants in R. anatipestifer remain scarce. To solve this dilemma, more than 400 nonredundant R. anatipestifer isolates collected from 22 provinces in China between 1994 and 2021 were subjected to broth dilution antibiotic susceptibility assays, and their resistance-associated genetic determinants were characterized by whole-genome sequencing. While over 90% of the isolates was resistant to sulfamethoxazole, kanamycin, gentamicin, ofloxacin, norfloxacin, and trimethoprim, 88.48% of the isolates was resistant to the last-resort drug (tigecycline). Notably, R. anatipestifer resistance to oxacillin, norfloxacin, ofloxacin, and tetracycline was found to increase relatively over time. Genome-wide analysis revealed the alarmingly high prevalence of blaOXA-like (93.05%) and tet(X) (90.64%) genes and the uneven distribution of resistance genes among lineages. Overall, this study reveals a serious AMR situation regarding R. anatipestifer in China, with a high prevalence and high diversity of antimicrobial resistance genes, providing important data for the rational use of antibiotics in veterinary practice.IMPORTANCERiemerella anatipestifer (R. anatipestifer), an important waterfowl pathogen, has caused substantial economic losses worldwide, especially in China. Antimicrobial resistance (AMR) is a major challenge in controlling this pathogen. Although a few studies have reported antimicrobial resistance in R. anatipestifer, comprehensive data remain a gap. This study aims to address the lack of information on R. anatipestifer AMR and its genetic basis. By analyzing more than 400 isolates collected over two decades, this study reveals alarming levels of resistance to several antibiotics, including drugs of last resort. The study also revealed the lineage-specificity of resistance profiles and resistance gene profiles. Overall, this study provides new insights and updated data support for understanding AMR and its genetic determinants in R. anatipestifer.

Hound: a novel tool for automated mapping of genotype to phenotype in bacterial genomes assembled de novo

Increasing evidence suggests that microbial species have a strong within species genetic heterogeneity. This can be problematic for the analysis of prokaryote genomes, which commonly relies on a reference genome to guide the assembly process. Differences between reference and sample genomes will therefore introduce errors in final assembly, jeopardizing the detection from structural variations to point mutations-critical for genomic surveillance of antibiotic resistance. Here we present Hound, a pipeline that integrates publicly available tools to assemble prokaryote genomes de novo, detect user-given genes by similarity to report mutations found in the coding sequence, promoter, as well as relative gene copy number within the assembly. Importantly, Hound can use the query sequence as a guide to merge contigs, and reconstruct genes that were fragmented by the assembler. To showcase Hound, we screened through 5032 bacterial whole-genome sequences isolated from farmed animals and human infections, using the amino acid sequence encoded by blaTEM-1, to detect and predict resistance to amoxicillin/clavulanate which is driven by over-expression of this gene. We believe this tool can facilitate the analysis of prokaryote species that currently lack a reference genome, and can be scaled either up to build automated systems for genomic surveillance or down to integrate into antibiotic susceptibility point-of-care diagnostics.

Metagenome and Resistome Analysis of Beta-Lactam-Resistant Bacteria Isolated from River Waters in Surabaya, Indonesia

Antimicrobial agents are administered to humans and livestock, and bacterial antimicrobial resistance (AMR) and antimicrobial agents are released into the environment. In this study, to investigate the trend of AMR in humans, livestock, and the environment, we performed a metagenomic analysis of multidrug-resistant bacteria with CHROMagar ESBL in environmental river water samples, which were collected using syringe filter units from waters near hospitals, downtown areas, residential areas, and water treatment plants in Surabaya, Indonesia. Our results showed that Acinetobacter, Pseudomonas, Aeromonas, Enterobacter, Escherichia, and Klebsiella grew in CHROMagar ESBL; they were most frequently detected in water samples from rivers surrounding hospitals contaminated with various AMR genes (ARGs) in high levels. These results identified bacteria as ARG reservoirs and revealed that hospitals could be sources for various ARGs disseminated into the environment. In conclusion, this study details a novel metagenomic analysis of collected bacteria in environmental water samples using a syringe filter unit for an AMR epidemiological study based on the One Health approach.

Core and variable antimicrobial resistance genes in the gut microbiomes of Chinese and European pigs

Monitoring the prevalence of antimicrobial resistance genes (ARGs) is vital for addressing the global crisis of antibiotic-resistant bacterial infections. Despite its importance, the characterization of ARGs and microbiome structures, as well as the identification of indicators for routine ARG monitoring in pig farms, are still lacking, particularly concerning variations in antimicrobial exposure in different countries or regions. Here, metagenomics and random forest machine learning were used to elucidate the ARG profiles, microbiome structures, and ARG contamination indicators in pig manure under different antimicrobial pressures between China and Europe. Results showed that Chinese pigs exposed to high-level antimicrobials exhibited higher total and plasmid-mediated ARG abundances compared to those in European pigs ( P<0.05). ANT(6)-Ib, APH(3')-IIIa, and tet(40) were identified as shared core ARGs between the two pig populations. Furthermore, the core ARGs identified in pig populations were correlated with those found in human populations within the same geographical regions. Lactobacillus and Prevotella were identified as the dominant genera in the core microbiomes of Chinese and European pigs, respectively. Forty ARG markers and 43 biomarkers were able to differentiate between the Chinese and European pig manure samples with accuracies of 100% and 98.7%, respectively. Indicators for assessing ARG contamination in Chinese and European pigs also achieved high accuracy ( r=0.72-0.88). Escherichia flexneri in both Chinese and European pig populations carried between 21 and 37 ARGs. The results of this study emphasize the importance of global collaboration in reducing antimicrobial resistance risk and provide validated indicators for evaluating the risk of ARG contamination in pig farms.

Genetic features of antimicrobial drug-susceptible extraintestinal pathogenic Escherichia coli pandemic sequence type 95

IMPORTANCE

Despite the increasing prevalence of antibiotic-resistant Escherichia coli strains that cause urinary tract and bloodstream infections, a major pandemic lineage of extraintestinal pathogenic E. coli (ExPEC) ST95 has a comparatively low frequency of drug resistance. We compared the genomes of 1,749 ST95 isolates to identify genetic features that may explain why most strains of ST95 resist becoming drug-resistant. Identification of such genomic features could contribute to the development of novel strategies to prevent the spread of antibiotic-resistant genes and devise new measures to control antibiotic-resistant infections.

Deciphering complex antibiotic resistance patterns in Helicobacter pylori through whole genome sequencing and machine learning

Introduction

Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments. Investigating the genotype-phenotype connection for Hp using next-generation sequencing could enhance our understanding of this resistance.

Methods

In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole-genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the development of support vector machine models.

Results

Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively.

Discussion

Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections. The application of whole-genome sequencing for Hp presents a promising pathway for advancing personalized medicine in this context.

A Mini-review on Helicobacter pylori with Gastric Cancer and Available Treatments

Helicobacter pylori (H. pylori) is the most thoroughly researched etiological component for stomach inflammation and malignancies. Even though there are conventional recommendations and treatment regimens for eradicating H. pylori, failure rates continue to climb. Antibiotic resistance contributes significantly to misdiagnoses, false positive results, and clinical failures, all of which raise the chance of infection recurrence. This review aims to explore the molecular mechanisms underlying drug resistance in H. pylori and discuss novel approaches for detecting genotypic resistance. Modulation of drug uptake/ efflux, biofilm, and coccoid development. Newer genome sequencing approaches capable of detecting H. pylori genotypic resistance are presented. Prolonged infection in the stomach causes major problems such as gastric cancer. The review discusses how H. pylori causes stomach cancer, recent biomarkers such as miRNAs, molecular pathways in the development of gastric cancer, and diagnostic methods and clinical trials for the disease. Efforts have been made to summarize the recent advancements made toward early diagnosis and novel therapeutic approaches for H. pylori-induced gastric cancer.

Biofilm antimicrobial susceptibility testing: where are we and where could we be going?

Our knowledge about the fundamental aspects of biofilm biology, including the mechanisms behind the reduced antimicrobial susceptibility of biofilms, has increased drastically over the last decades. However, this knowledge has so far not been translated into major changes in clinical practice. While the biofilm concept is increasingly on the radar of clinical microbiologists, physicians, and healthcare professionals in general, the standardized tools to study biofilms in the clinical microbiology laboratory are still lacking; one area in which this is particularly obvious is that of antimicrobial susceptibility testing (AST). It is generally accepted that the biofilm lifestyle has a tremendous impact on antibiotic susceptibility, yet AST is typically still carried out with planktonic cells. On top of that, the microenvironment at the site of infection is an important driver for microbial physiology and hence susceptibility; but this is poorly reflected in current AST methods. The goal of this review is to provide an overview of the state of the art concerning biofilm AST and highlight the knowledge gaps in this area. Subsequently, potential ways to improve biofilm-based AST will be discussed. Finally, bottlenecks currently preventing the use of biofilm AST in clinical practice, as well as the steps needed to get past these bottlenecks, will be discussed.

Virulence attributes of successful methicillin-resistant Staphylococcus aureus lineages

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe and often fatal infections. MRSA epidemics have occurred in waves, whereby a previously successful lineage has been replaced by a more fit and better adapted lineage. Selection pressures in both hospital and community settings are not uniform across the globe, which has resulted in geographically distinct epidemiology. This review focuses on the mechanisms that trigger the establishment and maintenance of current, dominant MRSA lineages across the globe. While the important role of antibiotic resistance will be mentioned throughout, factors which influence the capacity of S. aureus to colonize and cause disease within a host will be the primary focus of this review. We show that while MRSA possesses a diverse arsenal of toxins including alpha-toxin, the success of a lineage involves more than just producing toxins that damage the host. Success is often attributed to the acquisition or loss of genetic elements involved in colonization and niche adaptation such as the arginine catabolic mobile element, as well as the activity of regulatory systems, and shift metabolism accordingly (e.g., the accessory genome regulator, agr). Understanding exactly how specific MRSA clones cause prolonged epidemics may reveal targets for therapies, whereby both core (e.g., the alpha toxin) and acquired virulence factors (e.g., the Panton-Valentine leukocidin) may be nullified using anti-virulence strategies.

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

IMPORTANCE

Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Direct prediction of carbapenem resistance in Pseudomonas aeruginosa by whole genome sequencing and metagenomic sequencing

Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P. aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa, including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa, the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa.

Rapid Minimum Inhibitory Concentration (MIC) Analysis Using Lyophilized Reagent Beads in a Novel Multiphase, Single-Vessel Assay

Antimicrobial resistance (AMR) is a global threat fueled by incorrect (and overuse) of antibiotic drugs, giving rise to the evolution of multi- and extreme drug-resistant bacterial strains. The longer time to antibiotic administration (TTA) associated with the gold standard bacterial culture method has been responsible for the empirical usage of antibiotics and is a key factor in the rise of AMR. While polymerase chain reaction (PCR) and other nucleic acid amplification methods are rapidly replacing traditional culture methods, their scope has been restricted mainly to detect genotypic determinants of resistance and provide little to no information on phenotypic susceptibility to antibiotics. The work presented here aims to provide phenotypic antimicrobial susceptibility testing (AST) information by pairing short growth periods (~3-4 h) with downstream PCR assays to ultimately predict minimum inhibitory concentration (MIC) values of antibiotic treatment. To further simplify the dual workflows of the AST and PCR assays, these reactions are carried out in a single-vessel format (PCR tube) using novel lyophilized reagent beads (LRBs), which store dried PCR reagents along with primers and enzymes, and antibiotic drugs separately. The two reactions are separated in space and time using a melting paraffin wax seal, thus eliminating the need to transfer reagents across different consumables and minimizing user interactions. Finally, these two-step single-vessel reactions are multiplexed by using a microfluidic manifold that allows simultaneous testing of an unknown bacterial sample against different antibiotics at varying concentrations. The LRBs used in the microfluidic system showed no interference with the bacterial growth and PCR assays and provided an innovative platform for rapid point-of-care diagnostics (POC-Dx).

Comparison of carbapenem-susceptible and carbapenem-resistant Enterobacterales at nine sites in the USA, 2013-2016: a resource for antimicrobial resistance investigators

Carbapenem-resistant Enterobacterales (CRE) are an urgent public health threat. Genomic sequencing is an important tool for investigating CRE. Through the Division of Healthcare Quality Promotion Sentinel Surveillance system, we collected CRE and carbapenem-susceptible Enterobacterales (CSE) from nine clinical laboratories in the USA from 2013 to 2016 and analysed both phenotypic and genomic sequencing data for 680 isolates. We describe the molecular epidemiology and antimicrobial susceptibility testing (AST) data of this collection of isolates. We also performed a phenotype-genotype correlation for the carbapenems and evaluated the presence of virulence genes in Klebsiella pneumoniae complex isolates. These AST and genomic sequencing data can be used to compare and contrast CRE and CSE at these sites and serve as a resource for the antimicrobial resistance research community.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Joining Forces against Antibiotic Resistance: The One Health Solution

Antibiotic resistance is a significant global health concern that affects both human and animal populations. The One Health approach acknowledges the interconnectedness of human health, animal health, and the environment. It emphasizes the importance of collaboration and coordination across these sectors to tackle complex health challenges such as antibiotic resistance. In the context of One Health, antibiotic resistance refers to the ability of bacteria to withstand the efficacy of antibiotics, rendering them less effective or completely ineffective in treating infections. The emergence and spread of antibiotic-resistant bacteria pose a threat to human and animal health, as well as to the effectiveness of medical treatments and veterinary interventions. In particular, One Health recognizes that antibiotic use in human medicine, animal agriculture, and the environment are interconnected factors contributing to the development and spread of antibiotic resistance. For example, the misuse and overuse of antibiotics in human healthcare, including inappropriate prescribing and patient non-compliance, can contribute to the selection and spread of resistant bacteria. Similarly, the use of antibiotics in livestock production for growth promotion and disease prevention can contribute to the development of antibiotic resistance in animals and subsequent transmission to humans through the food chain. Addressing antibiotic resistance requires a collaborative One Health approach that involves multiple participants, including healthcare professionals, veterinarians, researchers, and policymakers.

Staphylococcus aureus Induced Wound Infections Which Antimicrobial Resistance, Methicillin- and Vancomycin-Resistant: Assessment of Emergence and Cross Sectional Study

Background

Wound infection is a prevalent concern in the medical field, being is a multi-step process involving several biological processes. Methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA) infections often occur in areas of damaged skin, such as abrasions and open wounds.

Methods

This research aims to light the incidence of MRSA and VRSA in wound swabs, the antimicrobial susceptibility configuration of isolated S. aureus patterns in pus/wound samples collected from Saudi Arabian tertiary hospital. The cross section study, β- lactamase detection, VRSA genotyping, MAR index, D-test and VRSA genotyping are methods, which used for completed this research.

Results

Patients of several ages and genders delivered specimens from two hospitals in the Al jouf area, in the northern province of Saudi Arabia. S. aureus was found in 188 (34.7%) of the 542 wounds. The traumatized wounds provided 71 isolates (38.8%), surgical wound provided 49 isolates (26.8%) and abscess were represented 16 by isolates (8.7%). In the study, 123 (65.4%) out of 188 were MRSA, 60 (31.9%) were MSSA, and five (2.7%) were VRSA. Linezolid and rifampin were found to be the most effective antimicrobials with 100% in vitro antibacterial activity against S. aureus isolates. The Multiple antimicrobials resistance (MAR) index revealed 73 isolates (38.9%) with a MAR index greater than 0.2, and 115 (61.1%) less than 0.2. The D-test showed that of MLSb phenotypes among S. aureus, 22 (11.7%) strains were D-test positive (MLSbi phenotype), 53 (28.2%) strains were constitutive MLSc phenotypes, and 17 (9%) strains were shown to have MSb phenotypes. All VRSA isolates (n=5) were found to be positive for vanA, and no vanB positive isolates were detected in the study.

Conclusion

Regular monitoring and an antimicrobials stewardship program should be in place to provide critical information that can be utilized for empirical therapy and future prevention strategies.

Comparison of 6 DNA extraction methods for isolation of high yield of high molecular weight DNA suitable for shotgun metagenomics Nanopore sequencing to detect bacteria

BACKGROUND

Oxford Nanopore Technologies (ONT) offers an accessible platform for long-read sequencing, which improves the reconstruction of genomes and helps to resolve complex genomic contexts, especially in the case of metagenome analysis. To take the best advantage of long-read sequencing, DNA extraction methods must be able to isolate pure high molecular weight (HMW) DNA from complex metagenomics samples, without introducing any bias. New methods released on the market, and protocols developed at the research level, were specifically designed for this application and need to be assessed.

RESULTS

In this study, with different bacterial cocktail mixes, analyzed as pure or spiked in a synthetic fecal matrix, we evaluated the performances of 6 DNA extraction methods using various cells lysis and purification techniques, from quick and easy, to more time-consuming and gentle protocols, including a portable method for on-site application. In addition to the comparison of the quality, quantity and purity of the extracted DNA, the performance obtained when doing Nanopore sequencing on a MinION flow cell was also tested. From the obtained results, the Quick-DNA HMW MagBead Kit (Zymo Research) was selected as producing the best yield of pure HMW DNA. Furthermore, this kit allowed an accurate detection, by Nanopore sequencing, of almost all the bacterial species present in a complex mock community.

CONCLUSION

Amongst the 6 tested methods, the Quick-DNA HMW MagBead Kit (Zymo Research) was considered as the most suitable for Nanopore sequencing and would be recommended for bacterial metagenomics studies using this technology.

The impact of applying various de novo assembly and correction tools on the identification of genome characterization, drug resistance, and virulence factors of clinical isolates using ONT sequencing

Oxford Nanopore sequencing technology (ONT) is currently widely used due to its affordability, simplicity, and reliability. Despite the advantage ONT has over next-generation sequencing in detecting resistance genes in mobile genetic elements, its relatively high error rate (10-15%) is still a deterrent. Several bioinformatic tools are freely available for raw data processing and obtaining complete and more accurate genome assemblies. In this study, we evaluated the impact of using mix-and-matched read assembly (Flye, Canu, Wtdbg2, and NECAT) and read correction (Medaka, NextPolish, and Racon) tools in generating complete and accurate genome assemblies, and downstream genomic analysis of nine clinical Escherichia coli isolates. Flye and Canu assemblers were the most robust in genome assembly, and Medaka and Racon correction tools significantly improved assembly parameters. Flye functioned well in pan-genome analysis, while Medaka increased the number of core genes detected. Flye, Canu, and NECAT assembler functioned well in detecting antimicrobial resistance genes (AMR), while Wtdbg2 required correction tools for better detection. Flye was the best assembler for detecting and locating both virulence and AMR genes (i.e., chromosomal vs. plasmid). This study provides insight into the performance of several read assembly and read correction tools for analyzing ONT sequencing reads for clinical isolates.

Alternate Antimicrobial Therapies and Their Companion Tests

New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with the emergence of resistance, resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies with clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers, and phages. Clinical diagnostics optimize the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic-based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious diseases include detection, monitoring of response, and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy, this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives with the use of alternate therapeutics, with a particular focus on companion diagnostic applications in infectious diseases.

Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review

Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.

Detection of Extended-spectrum β-lactamase-producing Escherichia coli isolates by isothermal amplification and association of their virulence genes and phylogroups with extraintestinal infection

Extraintestinal pathogenic Escherichia coli (ExPEC) producing extended-spectrum β-lactamases (ESBL) cause serious human infections due to their virulence and multidrug resistance (MDR) profiles. We characterized 144 ExPEC strains (collected from a tertiary cancer institute) in terms of antimicrobial susceptibility spectrum, ESBL variants, virulence factors (VF) patterns, and Clermont's phylogroup classification. The developed multiplex recombinase polymerase amplification and thermophilic helicase-dependent amplification (tHDA) assays for bla_CTX-M, bla_OXA, bla_SHV, and bla_TEM detection, respectively, were validated using PCR-sequencing results. All ESBL-ExPEC isolates carried bla_CTX-M genes with following prevalence frequency of variants: bla_CTX-M-15 (50.5%) > bla_CTX-M-55 (17.9%) > bla_CTX-M-27 (16.8%) > bla_CTX-M-14 (14.7%). The multiplex recombinase polymerase amplification assay had 100% sensitivity, and specificity for bla_CTX-M, bla_OXA, bla_SHV, while tHDA had 86.89% sensitivity, and 100% specificity for bla_TEM. The VF genes showed the following prevalence frequency: traT (67.4%) > ompT (52.6%) > iutA (50.5%) > fimH (47.4%) > iha (33.7%) > hlyA (26.3%) > papC (12.6%) > cvaC (3.2%), in ESBL-ExPEC isolates which belonged to phylogroups A (28.4%), B2 (28.4%), and F (22.1%). The distribution of traT, ompT, and hlyA and phylogroup B2 were significantly different (P < 0.05) between ESBL-ExPEC and non-ESBL-ExPEC isolates. Thus, these equipment-free isothermal resistance gene amplification assays contribute to effective treatment and control of virulent ExPEC, especially antimicrobial resistance strains.

Antibiotic Resistance/Susceptibility Profiles of Staphylococcus equorum Strains from Cheese, and Genome Analysis for Antibiotic Resistance Genes

In food, bacteria carrying antibiotic resistance genes could play a prominent role in the spread of resistance. Staphylococcus equorum populations can become large in a number of fermented foods, yet the antibiotic resistance properties of this species have been little studied. In this work, the resistance/susceptibility (R/S) profile of S. equorum strains (n = 30) from cheese to 16 antibiotics was determined by broth microdilution. The minimum inhibitory concentration (MIC) for all antibiotics was low in most strains, although higher MICs compatible with acquired genes were also noted. Genome analysis of 13 strains showed the S. equorum resistome to be composed of intrinsic mechanisms, acquired mutations, and acquired genes. As such, a plasmidic cat gene providing resistance to chloramphenicol was found in one strain; this was able to provide resistance to Staphylococcus aureus after electroporation. An msr(A) polymorphic gene was identified in five strains. The Mrs(A) variants were associated with variable resistance to erythromycin. However, the genetic data did not always correlate with the phenotype. As such, all strains harbored a polymorphic fosB/fosD gene, although only one acquired copy was associated with strong resistance to fosfomycin. Similarly, a plasmid-associated blaR1-blaZI operon encoding a penicillinase system was identified in five ampicillin- and penicillin G-susceptible strains. Identified genes not associated with phenotypic resistance further included mph(C) in two strains and norA in all strains. The antibiotic R/S status and gene content of S. equorum strains intended to be employed in food systems should be carefully determined.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

The presence of antibiotics and multidrug-resistant Staphylococcus aureus reservoir in a low-order stream spring in central Brazil

The disposal of industrial effluents strongly influences low-order streams, which makes them fragile ecosystems that can be impacted by contamination. In central Brazil, the Extrema River spring targets the dumping of pharmaceutical products from the surrounding industries. So, this work aimed to investigate the presence of antibiotics in Extrema River spring samples and the isolation of Staphylococcus aureus, a potential multidrug-resistant bacteria, verifying the antimicrobial resistance profile of these isolates. Three campaigns were carried out in different locals (P1-P3) between October and December 2021, in the dry and rainy seasons. The high-performance liquid chromatography-tandem mass spectrometry (LCMS) approach indicated the presence of sulfamethoxazole (≥ 1 ng/L), metronidazole (< 0.5 ng/L), and chloramphenicol (< 5 ng/L) in the water samples in November (rainy season). S. aureus was isolated in P1 (n = 128), P2 (n = 168), and P3 (n = 36), with greater resistance to trimethoprim-sulfamethoxazole (90%), clindamycin (70%), and gentamicin (60%). The presence of antibiotics in the Extrema River spring may cause S. aureus antibiotic resistance development. The presence of antibiotics and the high percentage of isolated multidrug-resistant S. aureus in the Extrema River spring cause concern and indicate the clandestine dumping of effluents from nearby pharmaceutical industries. Since preserving the springs of low-order streams is important for the environment and public health, we encourage monitoring the wastewater from Extrema River's nearby pharmaceutical industries and preserving the spring of this river.