Sequencing-based methods and resources to study antimicrobial resistance

The tetracycline resistome is shaped by selection for specific resistance mechanisms by each antibiotic generation

The history of clinical resistance to tetracycline antibiotics is characterized by cycles whereby the deployment of a new generation of drug molecules is quickly followed by the discovery of a new mechanism of resistance. This suggests mechanism-specific selection by each tetracycline generation; however, the evolutionary dynamics of this remain unclear. Here, we evaluate 24 recombinant Escherichia coli strains expressing tetracycline resistance genes from each mechanism (efflux pumps, ribosomal protection proteins, and enzymatic inactivation) in the context of each tetracycline generation. We employ a high-throughput barcode sequencing protocol that can discriminate between strains in mixed culture and quantify their relative abundances. We find that each mechanism is preferentially selected for by specific antibiotic generations, leading to their expansion. Remarkably, the minimum inhibitory concentration associated with individual genes is secondary to resistance mechanism for inter-mechanism relative fitness, but it does explain intra-mechanism relative fitness. These patterns match the history of clinical deployment of tetracycline drugs and resistance discovery in pathogens.

De novo acquisition of antibiotic resistance in six species of bacteria

Bacteria can become resistant to antibiotics in two ways: by acquiring resistance genes through horizontal gene transfer and by de novo development of resistance upon exposure to non-lethal concentrations. The importance of the second process, de novo build-up, has not been investigated systematically over a range of species and may be underestimated as a result. To investigate the DNA mutation patterns accompanying the de novo antibiotic resistance acquisition process, six bacterial species encountered in the food chain were exposed to step-wise increasing sublethal concentrations of six antibiotics to develop high levels of resistance. Phenotypic and mutational landscapes were constructed based on whole-genome sequencing at two time points of the evolutionary trajectory. In this study, we found that (1) all of the six strains can develop high levels of resistance against most antibiotics; (2) increased resistance is accompanied by different mutations for each bacterium-antibiotic combination; (3) the number of mutations varies widely, with Y. enterocolitica having by far the most; (4) in the case of fluoroquinolone resistance, a mutational pattern of gyrA combined with parC is conserved in five of six species; and (5) mutations in genes coding for efflux pumps are widely encountered in gram-negative species. The overall conclusion is that very similar phenotypic outcomes are instigated by very different genetic changes. The outcome of this study may assist policymakers when formulating practical strategies to prevent development of antimicrobial resistance in human and veterinary health care.IMPORTANCEMost studies on de novo development of antimicrobial resistance have been performed on Escherichia coli. To examine whether the conclusions of this research can be applied to more bacterial species, six species of veterinary importance were made resistant to six antibiotics, each of a different class. The rapid build-up of resistance observed in all six species upon exposure to non-lethal concentrations of antimicrobials indicates a similar ability to adjust to the presence of antibiotics. The large differences in the number of DNA mutations accompanying de novo resistance suggest that the mechanisms and pathways involved may differ. Hence, very similar phenotypes can be the result of various genotypes. The implications of the outcome are to be considered by policymakers in the area of veterinary and human healthcare.

Inactivation of antibiotic resistant bacteria by nitrogen-doped carbon quantum dots through spontaneous generation of intracellular and extracellular reactive oxygen species

The widespread antibiotic resistance has called for alternative antimicrobial agents. Carbon nanomaterials, especially carbon quantum dots (CQDs), may be promising alternatives due to their desirable physicochemical properties and potential antimicrobial activity, but their antimicrobial mechanism remains to be investigated. In this study, nitrogen-doped carbon quantum dots (N-CQDs) were synthesized to inactivate antibiotic-resistant bacteria and treat bacterial keratitis. N-CQDs synthesized via a facile hydrothermal approach displayed a uniform particle size of less than 10 nm, featuring a graphitic carbon structure and functional groups including -OH and -NH2. The N-CQDs demonstrated antimicrobial activity against Staphylococcus aureus (S. aureus) and methicillin-resistant S. aureus, which was both dose- and time-dependent, reducing the survival rate to below 1 %. The antimicrobial activity was confirmed by live/dead staining. In in vivo studies, the N-CQDs were more efficient in treating drug-resistant bacterial keratitis and reducing corneal damage compared to the common antibiotic levofloxacin. The N-CQDs were shown to generate intracellular and extracellular ROS, which potentially caused oxidative stress, membrane disruption, and cell death. This antimicrobial mechanism was supported by scanning and transmission electron microscopy, significant regulation of genes related to oxidative stress, and increased protein and lactate dehydrogenase leakage. This study has provided insight into the development, application, and mechanism of N-CQDs in antimicrobial applications.

Clinical considerations on antimicrobial resistance potential of complex microbiological samples

Antimicrobial resistance (AMR) is one of our greatest public health challenges. Targeted use of antibiotics (ABs) can reduce the occurrence and spread of AMR and boost the effectiveness of treatment. This requires knowledge of the AB susceptibility of the pathogens involved in the disease. Therapeutic recommendations based on classical AB susceptibility testing (AST) are based on the analysis of only a fraction of the bacteria present in the disease process. Next and third generation sequencing technologies allow the identification of antimicrobial resistance genes (ARGs) present in a bacterial community. Using this metagenomic approach, we can map the antimicrobial resistance potential (AMRP) of a complex, multi-bacterial microbial sample. To understand the interpretiveness of AMRP, the concordance between phenotypic AMR properties and ARGs was investigated by analyzing data from 574 Escherichia coli strains of five different studies. The overall results show that for 44% of the studied ABs, phenotypically resistant strains are genotypically associated with a 90% probability of resistance, while for 92% of the ABs, the phenotypically susceptible strains are genotypically susceptible with a 90% probability. ARG detection showed a phenotypic prediction with at least 90% confidence in 67% of ABs. The probability of detecting a phenotypically susceptible strain as resistant based on genotype is below 5% for 92% of ABs. While the probability of detecting a phenotypically resistant strain as susceptible based on genotype is below 5% for 44% of ABs. We can assume that these strain-by-strain concordance results are also true for bacteria in complex microbial samples, and conclude that AMRP obtained from metagenomic ARG analysis can help choose efficient ABs. This is illustrated using AMRP by a canine external otitis sample.

Risk factors for fluoroquinolone- and macrolide-resistance among swine Campylobacter coli using multi-layered chain graphs

Campylobacter spp. resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant Campylobacter have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in Campylobacter coli isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and phenotypic resistance data from 1082 isolates and host exposures obtained through surveys for 18 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58 % of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79 % in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery.

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

High-Throughput Shotgun Metagenomics of Microbial Footprints Uncovers a Cocktail of Noxious Antibiotic Resistance Genes in the Winam Gulf of Lake Victoria, Kenya

Background: A diverse range of pollutants, including heavy metals, agrochemicals, pharmaceutical residues, illicit drugs, personal care products, and other anthropogenic contaminants, pose a significant threat to aquatic ecosystems. The Winam Gulf of Lake Victoria, heavily impacted by surrounding human activities, faces potential contamination from these pollutants. However, studies exploring the presence of antibiotic resistance genes (ARGs) in the lake remain limited. In the current study, a shotgun metagenomics approach was employed to identify ARGs and related pathways. Genomic DNA was extracted from water and sediment samples and sequenced using the high-throughput Illumina NovaSeq platform. Additionally, phenotypic antibiotic resistance was assessed using the disk diffusion method with commonly used antibiotics. Results: The analysis of metagenomes sequences from the Gulf ecosystem and Comprehensive Antibiotic Resistance Database (CARD) revealed worrying levels of ARGs in the lake. The study reported nine ARGs from the 37 high-risk resistant gene families previously documented by the World Health Organization (WHO). Proteobacteria had the highest relative abundance of antibiotic resistance (53%), Bacteriodes (4%), Verrucomicrobia (2%), Planctomycetes Chloroflexi, Firmicutes (2%), and other unclassified bacteria (39%). Genes that target protection, replacement, change, and antibiotic-resistant efflux were listed in order of dominance. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed antibiotic resistance to beta-lactamase and vancomycin. Phenotypic resistance to vancomycin, tetracycline, sulfamethoxazole, erythromycin, trimethoprim, tetracycline, and penicillin was reported through the zone of inhibition. Conclusions: This study highlights that the Winam Gulf of Lake Victoria in Kenya harbors a diverse array of antibiotic-resistant genes, including those conferring multidrug resistance. These findings suggest that the Gulf could be serving as a reservoir for more antibiotic-resistant genes, posing potential risks to both human health and aquatic biodiversity. The insights gained from this research can guide policy development for managing antibiotic resistance in Kenya.

Tracing the transmission of carbapenem-resistant Enterobacterales at the patient: ward environmental nexus

INTRODUCTION

Colonisation and infection with Carbapenem-resistant Enterobacterales (CRE) in healthcare settings poses significant risks, especially for vulnerable patients. Genomic analysis can be used to trace transmission routes, supporting antimicrobial stewardship and informing infection control strategies. Here we used genomic analysis to track the movement and transmission of CREs within clinical and environmental samples.

METHODS

25 isolates were cultured from clinical patient samples or swabs, that tested positive for OXA-48-like variants using the NG-Test® CARBA-5 test and whole genome sequenced (WGS) using Oxford Nanopore Technologies (ONT). 158 swabs and 52 wastewater samples were collected from the ward environment. 60 isolates (matching clinical isolate genera; Klebsiella, Enterobacter, Citrobacter and Escherichia) were isolated from the environmental samples using selective agar. Metagenomic sequencing was undertaken on 36 environmental wastewater and swab samples.

RESULTS

21/25 (84%) clinical isolates had > 1 blaOXA gene and 19/25 (76%) harboured > 1 blaNDM gene. Enterobacterales were most commonly isolated from environmental wastewater samples 27/52 (51.9%), then stick swabs 5/43 (11.6%) and sponge swabs 5/115 (4.3%). 11/60 (18%) environmental isolates harboured > 1 blaOXA gene and 1.9% (1/60) harboured blaNDM-1. blaOXA genes were found in 2/36 (5.5%) metagenomic environmental samples.

CONCLUSIONS

Potential for putative patient-patient and patient-ward transmission was shown. Metagenomic sampling needs optimization to improve sensitivity.

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda

BACKGROUND

Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model's generalizability on another dataset from South Africa.

RESULTS

We trained ten machine learning algorithms on a dataset comprising of 182 MTB isolates with clinical data variables (age, sex, HIV status) and SNP mutations across the entire genome as predictor variables and phenotypic drug-susceptibility data for the four drugs as the outcome variable. Model performance varied across the four anti-TB drugs after a five-fold cross validation. The best model was selected considering the highest Mathews Correlation Coefficient (MCC) and Area Under the Receiver Operating Characteristic Curve (AUC) score as key metrics. The Logistic regression excelled in predicting rifampicin resistance (MCC: 0.83 (95% confidence intervals (CI) 0.73-0.86) and AUC: 0.96 (95% CI 0.95-0.98) and streptomycin (MCC: 0.44 (95% CI 0.27-0.58) and AUC: 0.80 (95% CI 0.74-0.82), Extreme Gradient Boosting (XGBoost) for ethambutol (MCC: 0.65 (95% CI 0.54-0.74) and AUC: 0.90 (95% CI 0.83-0.96) and Gradient Boosting (GBC) for isoniazid (MCC: 0.69 (95% CI 0.61-0.78) and AUC: 0.91 (95% CI 0.88-0.96). The best performing model per drug was only trained on the SNP dataset after excluding the clinical data variables because intergrating them with SNP mutations showed a marginal improvement in the model's performance. Despite the high MCC (0.18 to 0.72) and AUC (0.66 to 0.95) scores for all the best models with the Uganda test dataset, LR model for rifampicin and streptomycin didn't generalize with the South Africa dataset compared to the GBC and XGBoost models. Compared to TB profiler, LR for RIF was very sensitive and the GBC for INH and XGBoost for EMB were very specific on the Uganda dataset. TB profiler outperformed all the best models on the South Africa dataset. We identified key mutations associated with drug resistance for these antibiotics. HIV status was also identified among the top significant features in predicting drug resistance.

CONCLUSION

Leveraging machine learning applications in predicting antimicrobial resistance represents a promising avenue in addressing the global health challenge posed by antimicrobial resistance. This work demonstrates that integration of diverse data types such as genomic and clinical data could improve resistance predictions while using machine learning algorithms, support robust surveillance systems and also inform targeted interventions to curb the rising threat of antimicrobial resistance.

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.

Assessing antimicrobial resistance in pasture-based dairy farms: a 15-month surveillance study in New Zealand

Antimicrobial resistance is a global public and animal health concern. Antimicrobial resistance genes (ARGs) have been detected in dairy farm environments globally; however, few longitudinal studies have utilized shotgun metagenomics for ARG surveillance in pasture-based systems. This 15-month study aimed to undertake a baseline survey using shotgun metagenomics to assess the relative abundance and diversity of ARGs in two pasture-based dairy farm environments in New Zealand with different management practices. There was no statistically significant difference in overall ARG relative abundance between the two dairy farms (P = 0.321) during the study period. Compared with overseas data, the relative abundance of ARG copies per 16S rRNA gene in feces (0.08-0.17), effluent (0.03-0.37), soil (0.20-0.63), and bulk tank milk (0.0-0.12) samples was low. Models comparing the presence or absence of resistance classes found in >10% of all feces, effluent, and soil samples demonstrated no statistically significant associations (P > 0.05) with "season," and only multi-metal (P = 0.020) and tetracycline (P = 0.0003) resistance were significant at the "farm" level. Effluent samples harbored the most diverse ARGs, some with a recognized public health risk, whereas soil samples had the highest ARG relative abundance but without recognized health risks. This highlights the importance of considering the genomic context and risk of ARGs in metagenomic data sets. This study suggests that antimicrobial resistance on pasture-based dairy farms is low and provides essential baseline ARG surveillance data for such farming systems.IMPORTANCEAntimicrobial resistance is a global threat to human and animal health. Despite the detection of antimicrobial resistance genes (ARGs) in dairy farm environments globally, longitudinal surveillance in pasture-based systems remains limited. This study assessed the relative abundance and diversity of ARGs in two New Zealand dairy farms with different management practices and provided important baseline ARG surveillance data on pasture-based dairy farms. The overall ARG relative abundance on these two farms was low, which provides further evidence for consumers of the safety of New Zealand's export products. Effluent samples harbored the most diverse range of ARGs, some of which were classified with a recognized risk to public health, whereas soil samples had the highest ARG relative abundance; however, the soil ARGs were not classified with a recognized public health risk. This emphasizes the need to consider genomic context and risk as well as ARG relative abundance in resistome studies.

Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings

Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins.

Antibiotic Prescribing Practices in a Tertiary Care Teaching Hospital: A Retrospective Cross-Sectional Analysis

Background Antibiotics are among the most frequently prescribed medications in hospitals, yet a significant portion of their use is inappropriate, contributing to the growing global threat of antimicrobial resistance (AMR). In Pakistan, AMR has reached alarming levels with the rise of multi- and extensively drug-resistant bacteria. This study evaluates antibiotic prescribing practices and the use of culture sensitivity (CS) testing to assess the appropriateness of antibiotic therapy. Methods This cross-sectional retrospective study was conducted in a 500-bed tertiary care teaching hospital in Islamabad, Pakistan, analyzing inpatient records from 2020 to 2022. From over 5000 patient files, 1012 met the inclusion criteria and were reviewed. The study assessed the rationality of antibiotic prescriptions based on the evidence of infection (EoI), clinical parameters, and the presence or absence of CS testing. Statistical analyses, including Chi-square tests, logistic regression, and Cox proportional hazards regression, were applied to determine the association between antibiotic use and patient outcomes. Results Among the 1,012 patients analyzed, 91.8% (n = 929) received one or more antibiotics. However, 30% (n = 274) of these prescriptions were issued without any documented EoI. Only 17.5% of patients underwent CS testing. Patients exposed to five or more antibiotics had a 2.5-fold increased risk of ICU mortality (HR = 2.50, p < 0.001). A positive correlation (r = 0.42, p < 0.001) was found between the number of antibiotics prescribed and the length of hospitalization. Conclusion The findings of this study reveal a high rate of inappropriate antibiotics prescribed without EoI and CS testing. The results emphasize the urgent need for comprehensive Antibiotic Stewardship Programs (ASPs) in hospital settings, particularly focusing on mandatory CS testing protocols. By reducing irrational antibiotic use, these initiatives can significantly mitigate the rise of AMR globally, especially in resource-limited settings. Implementing ASPs not only optimizes antibiotic use but also aligns with the WHO recommendations, demonstrating the effectiveness of multifaceted interventions in minimizing resistance and improving patient outcomes.

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.

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume ≈ 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

Comparison of targeted next-generation sequencing and metagenomic next-generation sequencing in the identification of pathogens in pneumonia after congenital heart surgery: a comparative diagnostic accuracy study

BACKGROUND

This study aimed to compare targeted next-generation sequencing (tNGS) with metagenomic next-generation sequencing (mNGS) for pathogen detection in infants with severe postoperative pneumonia after congenital heart surgery.

METHODS

We conducted a retrospective observational study using data from the electronic medical record system of infants who developed severe pneumonia after surgery for congenital heart disease from August 2021 to August 2022. Infants were divided into tNGS and mNGS groups based on the pathogen detection methods. The primary outcome was the efficiency of pathogen detection, and the secondary outcomes were the timeliness and cost of each method.

RESULTS

In the study, 91 infants were included, with tNGS detecting pathogens in 84.6% (77/91) and mNGS in 81.3% (74/91) of cases (P = 0.55). No significant differences were found in sensitivity, specificity, PPA, and NPA between the two methods (P > 0.05). tNGS identified five strains with resistance genes, while mNGS detected one strain. Furthermore, tNGS had a faster detection time (12 vs. 24 h) and lower cost ($150 vs. $500) compared to mNGS.

CONCLUSION

tNGS offers similar sensitivity to mNGS but with greater efficiency and cost-effectiveness, making it a promising approach for respiratory pathogen detection.

Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes

BACKGROUND

Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.

METHODS

We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy.

RESULTS

The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner .

CONCLUSIONS

It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

Sequencing Methods to Study the Microbiome with Antibiotic Resistance Genes in Patients with Pulmonary Infections

Various antibiotic-resistant bacteria (ARB) are known to induce repeated pulmonary infections and increase morbidity and mortality. A thorough knowledge of antibiotic resistance is imperative for clinical practice to treat resistant pulmonary infections. In this study, we used a reads-based method and an assembly-based method according to the metagenomic next-generation sequencing (mNGS) data to reveal the spectra of ARB and corresponding antibiotic resistance genes (ARGs) in samples from patients with pulmonary infections. A total of 151 clinical samples from 144 patients with pulmonary infections were collected for retrospective analysis. The ARB and ARGs detection performance was compared by the reads-based method and assembly-based method with the culture method and antibiotic susceptibility testing (AST), respectively. In addition, ARGs and the attribution relationship of common ARB were analyzed by the two methods. The comparison results showed that the assembly-based method could assist in determining pathogens detected by the reads-based method as true ARB and improve the predictive capabilities (46% > 13%). ARG-ARB network analysis revealed that assembly-based method could promote determining clear ARG-bacteria attribution and 101 ARGs were detected both in two methods. 25 ARB were obtained by both methods, of which the most predominant ARB and its ARGs in the samples of pulmonary infections were Acinetobacter baumannii (ade), Pseudomonas aeruginosa (mex), Klebsiella pneumoniae (emr), and Stenotrophomonas maltophilia (sme). Collectively, our findings demonstrated that the assembly-based method could be a supplement to the reads-based method and uncovered pulmonary infection-associated ARB and ARGs as potential antibiotic treatment targets.

BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.

Comparative genomic sequencing to characterize Mycoplasma pneumoniae genome, typing, and drug resistance

To analyze the characteristics of Mycoplasma pneumoniae as well as macrolide antibiotic resistance through whole-genome sequencing and comparative genomics. Thirteen clinical strains isolated from 2003 to 2019 were selected, 10 of which were resistant to erythromycin (MIC >64 µg/mL), including 8 P1-type I and 2 P1-type II. Three were sensitive (<1 µg/mL) and P1-type II. One resistant strain had an A→G point mutation at position 2064 in region V of the 23S rRNA, the others had it at position 2063, while the three sensitive strains had no mutation here. Genome assembly and comparative genome analysis revealed a high level of genome consistency within the P1 type, and the primary differences in genome sequences concentrated in the region encoding the P1 protein. In P1-type II strains, three specific gene mutations were identified: C162A and A430G in L4 gene and T1112G mutation in the CARDS gene. Clinical information showed seven cases were diagnosed with severe pneumonia, all of which were infected with drug-resistant strains. Notably, BS610A4 and CYM219A1 exhibited a gene multi-copy phenomenon and shared a conserved functional domain with the DUF31 protein family. Clinically, the patients had severe refractory pneumonia, with pleural effusion, necessitating treatment with glucocorticoids and bronchoalveolar lavage. The primary variations between strains occur among different P1-types, while there is a high level of genomic consistency within P1-types. Three mutation loci associated with specific types were identified, and no specific genetic alterations directly related to clinical presentation were observed.IMPORTANCEMycoplasma pneumoniae is an important pathogen of community-acquired pneumonia, and macrolide resistance brings difficulties to clinical treatment. We analyzed the characteristics of M. pneumoniae as well as macrolide antibiotic resistance through whole-genome sequencing and comparative genomics. The work addressed primary variations between strains that occur among different P1-types, while there is a high level of genomic consistency within P1-types. In P1-type II strains, three specific gene mutations were identified: C162A and A430G in L4 gene and T1112G mutation in the CARDS gene. All the strains isolated from severe pneumonia cases were drug-resistant, two of which exhibited a gene multi-copy phenomenon, sharing a conserved functional domain with the DUF31 protein family. Three mutation loci associated with specific types were identified, and no specific genetic alterations directly related to clinical presentation were observed.

Dual elemental doping activated signaling pathway of angiogenesis and defective heterojunction engineering for effective therapy of MRSA-infected wounds

Multi-drug resistant bacterial infections pose a significant threat to human health. Thus, the development of effective bactericidal strategies is a pressing concern. In this study, a ternary heterostructure (Zn-CN/P-GO/BiS) comprised of Zn-doped graphite phase carbon nitride (g-C3N4), phosphorous-doped graphene oxide (GO) and bismuth sulphide (Bi2S3) is constructed for efficiently treating methicillin-resistant Staphylococcus aureus (MRSA)-infected wound. Zn doping-induced defect sites in g-C3N4 results in a reduced band gap (ΔE) and a smaller energy gap (ΔEST) between the singlet state S1 and triplet state T1, which favours two-photon excitation and accelerates electron transfer. Furthermore, the formation of an internal electric field at the ternary heterogeneous interface optimizes the charge transfer pathway, inhibits the recombination of electron-hole pairs, improves the photodynamic effect of g-C3N4, and enhances its catalytic performance. Therefore, the Zn-CN/P-GO/BiS significantly augments the production of reactive oxygen species and heat under 808 nm NIR (0.67 W cm-2) irradiation, leading to the elimination of 99.60% ± 0.07% MRSA within 20 min. Additionally, the release of essential trace elements (Zn and P) promotes wound healing by activating hypoxia-inducible factor-1 (HIF-1) and peroxisome proliferator-activated receptors (PPAR) signaling pathways. This work provides unique insight into the rapid antibacterial applications of trace element doping and two-photon excitation.

Subtask-Aware Representation Learning for Predicting Antibiotic Resistance Gene Properties via Gating-Controlled Mechanism

The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction.

Metagenomic approach revealed the mobility and co-occurrence of antibiotic resistomes between non-intensive aquaculture environment and human

BACKGROUND

Aquaculture is an important food source worldwide. The extensive use of antibiotics in intensive large-scale farms has resulted in resistance development. Non-intensive aquaculture is another aquatic feeding model that is conducive to ecological protection and closely related to the natural environment. However, the transmission of resistomes in non-intensive aquaculture has not been well characterized. Moreover, the influence of aquaculture resistomes on human health needs to be further understood. Here, metagenomic approach was employed to identify the mobility of aquaculture resistomes and estimate the potential risks to human health.

RESULTS

The results demonstrated that antibiotic resistance genes (ARGs) were widely present in non-intensive aquaculture systems and the multidrug type was most abundant accounting for 34%. ARGs of non-intensive aquaculture environments were mainly shaped by microbial communities accounting for 51%. Seventy-seven genera and 36 mobile genetic elements (MGEs) were significantly associated with 23 ARG types (p < 0.05) according to network analysis. Six ARGs were defined as core ARGs (top 3% most abundant with occurrence frequency > 80%) which occupied 40% of ARG abundance in fish gut samples. Seventy-one ARG-carrying contigs were identified and 75% of them carried MGEs simultaneously. The qacEdelta1 and sul1 formed a stable combination and were detected simultaneously in aquaculture environments and humans. Additionally, 475 high-quality metagenomic-assembled genomes (MAGs) were recovered and 81 MAGs carried ARGs. The multidrug and bacitracin resistance genes were the most abundant ARG types carried by MAGs. Strikingly, Fusobacterium_A (opportunistic human pathogen) carrying ARGs and MGEs were identified in both the aquaculture system and human guts, which indicated the potential risks of ARG transfer.

CONCLUSIONS

The mobility and pathogenicity of aquaculture resistomes were explored by a metagenomic approach. Given the observed co-occurrence of resistomes between the aquaculture environment and human, more stringent regulation of resistomes in non-intensive aquaculture systems may be required. Video Abstract.

Gut resistome linked to sexual preference and HIV infection

BACKGROUND

People living with HIV (PLWH) are at increased risk of acquisition of multidrug resistant organisms due to higher rates of predisposing factors. The gut microbiome is the main reservoir of the collection of antimicrobial resistance determinants known as the gut resistome. In PLWH, changes in gut microbiome have been linked to immune activation and HIV-1 associated complications. Specifically, gut dysbiosis defined by low microbial gene richness has been linked to low Nadir CD4 + T-cell counts. Additionally, sexual preference has been shown to strongly influence gut microbiome composition in PLWH resulting in different Prevotella or Bacteroides enriched enterotypes, in MSM (men-who-have-sex-with-men) or no-MSM, respectively. To date, little is known about gut resistome composition in PLWH due to the scarcity of studies using shotgun metagenomics. The present study aimed to detect associations between different microbiome features linked to HIV-1 infection and gut resistome composition.

RESULTS

Using shotgun metagenomics we characterized the gut resistome composition of 129 HIV-1 infected subjects showing different HIV clinical profiles and 27 HIV-1 negative controls from a cross-sectional observational study conducted in Barcelona, Spain. Most no-MSM showed a Bacteroides-enriched enterotype and low microbial gene richness microbiomes. We did not identify differences in resistome diversity and composition according to HIV-1 infection or immune status. However, gut resistome was more diverse in MSM group, Prevotella-enriched enterotype and gut micorbiomes with high microbial gene richness compared to no-MSM group, Bacteroides-enriched enterotype and gut microbiomes with low microbial gene richness. Additionally, gut resistome beta-diversity was different according to the defined groups and we identified a set of differentially abundant antimicrobial resistance determinants based on the established categories.

CONCLUSIONS

Our findings reveal a significant correlation between gut resistome composition and various host variables commonly associated with gut microbiome, including microbiome enterotype, microbial gene richness, and sexual preference. These host variables have been previously linked to immune activation and lower Nadir CD4 + T-Cell counts, which are prognostic factors of HIV-related comorbidities. This study provides new insights into the relationship between antibiotic resistance and clinical characteristics of PLWH.

Genetic basis of clarithromycin resistance in Bacillus anthracis

The high-consequence pathogen Bacillus anthracis causes human anthrax and often results in lethal infections without the rapid administration of effective antimicrobial treatment. Antimicrobial resistance profiling is therefore critical to inform post-exposure prophylaxis and treatment decisions, especially during emergencies such as outbreaks or where intentional release is suspected. Whole-genome sequencing using a rapid long-read sequencer can uncover antimicrobial resistance patterns if genetic markers of resistance are known. To identify genomic markers associated with antimicrobial resistance, we isolated B. anthracis derived from the avirulent Sterne strain with elevated minimal inhibitory concentrations to clarithromycin. Mutants were characterized both phenotypically through broth microdilution susceptibility testing and observations during culturing, as well as genotypically with whole-genome sequencing. We identified two different in-frame insertions in the L22 ribosomal protein-encoding gene rplV, which were subsequently confirmed to be involved in clarithromycin resistance through the reversion of the mutant gene to the parent (drug-susceptible) sequence. Detection of the rplV insertions was possible with rapid long-read sequencing, with a time-to-answer within 3 h. The mutations associated with clarithromycin resistance described here will be used in conjunction with known genetic markers of resistance for other antimicrobials to strengthen the prediction of antimicrobial resistance in B. anthracis.IMPORTANCEThe disease anthrax, caused by the pathogen Bacillus anthracis, is extremely deadly if not treated quickly and appropriately. Clarithromycin is an antibiotic recommended for the treatment and post-exposure prophylaxis of anthrax by the Centers for Disease Control and Prevention; however, little is known about the ability of B. anthracis to develop resistance to clarithromycin or the mechanism of that resistance. The characterization of clarithromycin-resistant isolates presented here provides valuable information for researchers and clinicians in the event of a release of the resistant strain. Additionally, knowledge of the genetic basis of resistance provides a foundation for susceptibility prediction through rapid genome sequencing to inform timely treatment decisions.

Molecular rapid diagnostic testing for bloodstream infections: Nanopore targeted sequencing with pathogen-specific primers

BACKGROUND

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs).

METHODS

We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023.

RESULTS

The positivity rate for NTS (69.5 %, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7 %, 289/387; p = 0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9 %, 131/387; p < 0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n = 54), Pseudomonas aeruginosa (n = 36), Escherichia coli (n = 36), Enterococcus faecium(n = 30), Acinetobacter baumannii(n = 26), Staphylococcus aureus(n = 23), and Human cytomegalovirus (n = 37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0 % (95 % CI 79.5 %-87.7 %) and 90.1 % (95 % CI 81.7 %-88.5 %), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2 % (359/387), whereas the consistency between NTS and BC was only 60.2 % (233/387). In 80.6 % (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95 % of samples can be tested and analyzed in approximately 7 h, allowing for early patient diagnosis.

CONCLUSIONS

NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences

BACKGROUND

Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.

RESULTS

In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.

CONCLUSIONS

ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Role of RelA-synthesized (p)ppGpp and ROS-induced mutagenesis in de novo acquisition of antibiotic resistance in E. coli

The stringent response of bacteria to starvation and stress also fulfills a role in addressing the threat of antibiotics. Within this stringent response, (p)ppGpp, synthesized by RelA or SpoT, functions as a global alarmone. However, the effect of this (p)ppGpp on resistance development is poorly understood. Here, we show that knockout of relA or rpoS curtails resistance development against bactericidal antibiotics. The emergence of mutated genes associated with starvation and (p)ppGpp, among others, indicates the activation of stringent responses. The growth rate is decreased in ΔrelA-resistant strains due to the reduced ability to synthesize (p)ppGpp and the persistence of deacylated tRNA impeding protein synthesis. Sluggish cellular activity causes decreased production of reactive oxygen species (ROS), thereby reducing oxidative damage, leading to weakened DNA mismatch repair, potentially reducing the generation of mutations. These findings offer new targets for mitigating antibiotic resistance development, potentially achieved through inhibiting (p)ppGpp or ROS synthesis.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes

MOTIVATION

The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance.

RESULTS

In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs.

AVAILABILITY AND IMPLEMENTATION

The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation

BACKGROUND

The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection.

OBJECTIVE

In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed.

METHODS

The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients' admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model's predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning-based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic.

RESULTS

The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance.

CONCLUSIONS

The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Rapid detection of mexX in Pseudomonas aeruginosa based on CRISPR-Cas13a coupled with recombinase polymerase amplification

The principal pathogen responsible for chronic urinary tract infections, immunocompromised hosts, and cystic fibrosis patients is Pseudomonas aeruginosa, which is difficult to eradicate. Due to the extensive use of antibiotics, multidrug-resistant P. aeruginosa has evolved, complicating clinical therapy. Therefore, a rapid and efficient approach for detecting P. aeruginosa strains and their resistance genes is necessary for early clinical diagnosis and appropriate treatment. This study combines recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats-association protein 13a (CRISPR-Cas13a) to establish a one-tube and two-step reaction systems for detecting the mexX gene in P. aeruginosa. The test times for one-tube and two-step RPA-Cas13a methods were 5 and 40 min (including a 30 min RPA amplification reaction), respectively. Both methods outperform Quantitative Real-time Polymerase Chain Reactions (qRT-PCR) and traditional PCR. The limit of detection (LoD) of P. aeruginosa genome in one-tube and two-step RPA-Cas13a is 10 aM and 1 aM, respectively. Meanwhile, the designed primers have a high specificity for P. aeruginosa mexX gene. These two methods were also verified with actual samples isolated from industrial settings and demonstrated great accuracy. Furthermore, the results of the two-step RPA-Cas13a assay could also be visualized using a commercial lateral flow dipstick with a LoD of 10 fM, which is a useful adjunt to the gold-standard qRT-PCR assay in field detection. Taken together, the procedure developed in this study using RPA and CRISPR-Cas13a provides a simple and fast way for detecting resistance genes.

Metabolism-Triggered Plasmonic Nanosensor for Bacterial Detection and Antimicrobial Susceptibility Testing of Clinical Isolates

Antimicrobial resistance (AMR) is predicted to become the leading cause of death worldwide in the coming decades. Rapid and on-site antibiotic susceptibility testing (AST) is crucial for guiding appropriate antibiotic choices to combat AMR. With this in mind, we have designed a simple and efficient plasmonic nanosensor consisting of Cu2+ and cysteine-modified AuNP (Au/Cys) that utilizes the metabolic activity of bacteria toward Cu2+ for bacterial detection and AST. When Cu2+ is present, it induces the aggregation of Au/Cys. However, in the presence of bacteria, Cu2+ is metabolized to varying extents, resulting in distinct levels of aggregation. Moreover, the metabolic activity of bacteria can be influenced by their antibiotic susceptibility, allowing us to differentiate between susceptible and resistant strains through direct color changes from the Cu2+-Au/Cys platform over approximately 3 h. These color changes can be easily detected using naked-eye observation, smartphone analysis, or absorption readout. We have validated the platform using four clinical isolates and six types of antibiotics, demonstrating a clinical sensitivity and specificity of 95.8%. Given its simplicity, low cost, high speed, and high accuracy, the plasmonic nanosensor holds great potential for point-of-care detection of antibiotic susceptibility across various settings.

Metagenomics reveals the temporal dynamics of the rumen resistome and microbiome in goat kids

BACKGROUND

The gut microbiome of domestic animals carries antibiotic resistance genes (ARGs) which can be transmitted to the environment and humans, resulting in challenges of antibiotic resistance. Although it has been reported that the rumen microbiome of ruminants may be a reservoir of ARGs, the factors affecting the temporal dynamics of the rumen resistome are still unclear. Here, we collected rumen content samples of goats at 1, 7, 14, 28, 42, 56, 70, and 84 days of age, analyzed their microbiome and resistome profiles using metagenomics, and assessed the temporal dynamics of the rumen resistome in goats at the early stage of life under a conventional feeding system.

RESULTS

In our results, the rumen resistome of goat kids contained ARGs to 41 classes, and the richness of ARGs decreased with age. Four antibiotic compound types of ARGs, including drugs, biocides, metals, and multi-compounds, were found during milk feeding, while only drug types of ARGs were observed after supplementation with starter feed. The specific ARGs for each age and their temporal dynamics were characterized, and the network inference model revealed that the interactions among ARGs were related to age. A strong correlation between the profiles of rumen resistome and microbiome was found using Procrustes analysis. Ruminal Escherichia coli within Proteobacteria phylum was the main carrier of ARGs in goats consuming colostrum, while Prevotella ruminicola and Fibrobacter succinogenes associated with cellulose degradation were the carriers of ARGs after starter supplementation. Milk consumption was likely a source of rumen ARGs, and the changes in the rumen resistome with age were correlated with the microbiome modulation by starter supplementation.

CONCLUSIONS

Our data revealed that the temporal dynamics of the rumen resistome are associated with the microbiome, and the reservoir of ARGs in the rumen during early life is likely related to age and diet. It may be a feasible strategy to reduce the rumen and its downstream dissemination of ARGs in ruminants through early-life dietary intervention. Video Abstract.

Altered microbiota, antimicrobial resistance genes, and functional enzyme profiles in the rumen of yak calves fed with milk replacer

IMPORTANCE

Yaks, as ruminants inhabiting high-altitude environments, possess a distinct rumen microbiome and are resistant to extreme living conditions. This study investigated the microbiota, resistome, and functional gene profiles in the rumen of yaks fed milk or milk replacer (MR), providing insights into the regulation of the rumen microbiome and the intervention of antimicrobial resistance in yaks through dietary methods. The abundance of Prevotella members increased significantly in response to MR. Tetracycline resistance was the most predominant. The rumen of yaks contained multiple antimicrobial resistance genes (ARGs) originating from different bacteria, which could be driven by MR, and these ARGs displayed intricate and complex interactions. MR also induced changes in functional genes. The enzymes associated with fiber degradation and butyrate metabolism were activated and showed close correlations with Prevotella members and butyrate concentration. This study allows us to deeply understand the ruminal microbiome and ARGs of yaks and their relationship with rumen bacteria in response to different milk sources.

Reactive oxygen species accelerate de novo acquisition of antibiotic resistance in E. coli

Reactive oxygen species (ROS) produced as a secondary effect of bactericidal antibiotics are hypothesized to play a role in killing bacteria. If correct, ROS may play a role in development of de novo resistance. Here we report that single-gene knockout strains with reduced ROS scavenging exhibited enhanced ROS accumulation and more rapid acquisition of resistance when exposed to sublethal levels of bactericidal antibiotics. Consistent with this observation, the ROS scavenger thiourea in the medium decelerated resistance development. Thiourea downregulated the transcriptional level of error-prone DNA polymerase and DNA glycosylase MutM, which counters the incorporation and accumulation of 8-hydroxy-2'-deoxyguanosine (8-HOdG) in the genome. The level of 8-HOdG significantly increased following incubation with bactericidal antibiotics but decreased after treatment with the ROS scavenger thiourea. These observations suggest that in E. coli sublethal levels of ROS stimulate de novo development of resistance, providing a mechanistic basis for hormetic responses induced by antibiotics.

Metagenomic identification of pathogens and antimicrobial-resistant genes in bacterial positive blood cultures by nanopore sequencing

Nanopore sequencing workflows have attracted increasing attention owing to their fast, real-time, and convenient portability. Positive blood culture samples were collected from patients with bacterial bloodstream infection and tested by nanopore sequencing. This study compared the sequencing results for pathogen taxonomic profiling and antimicrobial resistance genes to those of species identification and phenotypic drug susceptibility using traditional microbiology testing. A total of 37 bacterial positive blood culture results of strain genotyping by nanopore sequencing were consistent with those of mass spectrometry. Among them, one mixed infection of bacteria and fungi was identified using nanopore sequencing and confirmatory quantitative polymerase chain reaction. The amount of sequencing data was 21.89 ± 8.46 MB for species identification, and 1.0 MB microbial strain data enabled accurate determination. Data volumes greater than or equal to 94.6 MB nearly covered all the antimicrobial resistance genes of the bacteria in our study. In addition, the results of the antimicrobial resistance genes were compared with those of phenotypic drug susceptibility testing for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Therefore, the nanopore sequencing platform for rapid identification of causing pathogens and relevant antimicrobial resistance genes complementary to conventional blood culture outcomes may optimize antimicrobial stewardship management for patients with bacterial bloodstream infection.

Development of a novel integrated isothermal amplification system for detection of bacteria-spiked blood samples

Bloodstream infection (BSI) caused by bacteria is highly pathogenic and lethal, and easily develops whole-body inflammatory state. Immediate identification of disease-causing bacteria can improve patient prognosis. Traditional testing methods are not only time-consuming, but such tests are limited to laboratories. Recombinase polymerase amplification combined with lateral flow dipstick (RPA-LFD) holds great promise for rapid nucleic acid detection, but the uncapping operation after amplification easily contaminates laboratories. Therefore, the establishment of a more effective integrated isothermal amplification system has become an urgent problem to be solved. In this study, we designed and fabricated a hermetically sealed integrated isothermal amplification system. Combining with this system, a set of RPA-LFD assays for detecting S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI were established and evaluated. The whole process could be completed in less than 15 min and the results can be visualized by the naked eye. The developed RPA-LFD assays displayed a good sensitivity, and no cross-reactivity was observed in seven similar bacterial genera. The results obtained with 60 clinical samples indicated that the developed RPA-LFD assays had high specifcity and sensitivity for identifying S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI. In conclusion, our results showed that the developed RPA-LFD assay is an alternative to existing PCR-based methods for detection of S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI in primary hospitals.

NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.

Metagenomic analysis of ecological niche overlap and community collapse in microbiome dynamics

Species utilizing the same resources often fail to coexist for extended periods of time. Such competitive exclusion mechanisms potentially underly microbiome dynamics, causing breakdowns of communities composed of species with similar genetic backgrounds of resource utilization. Although genes responsible for competitive exclusion among a small number of species have been investigated in pioneering studies, it remains a major challenge to integrate genomics and ecology for understanding stable coexistence in species-rich communities. Here, we examine whether community-scale analyses of functional gene redundancy can provide a useful platform for interpreting and predicting collapse of bacterial communities. Through 110-day time-series of experimental microbiome dynamics, we analyzed the metagenome-assembled genomes of co-occurring bacterial species. We then inferred ecological niche space based on the multivariate analysis of the genome compositions. The analysis allowed us to evaluate potential shifts in the level of niche overlap between species through time. We hypothesized that community-scale pressure of competitive exclusion could be evaluated by quantifying overlap of genetically determined resource-use profiles (metabolic pathway profiles) among coexisting species. We found that the degree of community compositional changes observed in the experimental microbiome was correlated with the magnitude of gene-repertoire overlaps among bacterial species, although the causation between the two variables deserves future extensive research. The metagenome-based analysis of genetic potential for competitive exclusion will help us forecast major events in microbiome dynamics such as sudden community collapse (i.e., dysbiosis).

MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data

Novel methodologies are now essential for identification of antibiotic resistant pathogens in order to resist them. Here, we are presenting a model, MACI (Machine learning-based Antibiotic resistance gene-specific drug Class Identification) that can take metagenomic fragments as input and predict the drug class of antibiotic resistant genes. In our study, we trained a model using the Comprehensive Antibiotic Resistance Database, containing 5138 representative sequences across 134 drug classes. Among these classes, 23 dominated, contributing 85% of the sequence data. The model achieved an average precision of 0.8389 ± 0.0747 and recall of 0.8197 ± 0.0782 for these 23 drug classes. Additionally, it exhibited higher performance (precision and recall: 0.8817 ± 0.0540 and 0.8620 ± 0.0493) for predicting multidrug resistant classes compared to single drug resistant categories (0.7923 ± 0.0669 and 0.7737 ± 0.0794). The model also showed promising results when tested on an independent data. We then analysed these 23 drug classes to identify class-specific overlapping nucleotide patterns. Five significant drug classes, viz. "Carbapenem; cephalosporin; penam", "cephalosporin", "cephamycin", "cephalosporin; monobactam; penam; penem", and "fluoroquinolone" were identified, and their patterns aligned with the functional domains of antibiotic resistance genes. These class-specific patterns play a pivotal role in rapidly identifying drug classes with antibiotic resistance genes. Further analysis revealed that bacterial species containing these five drug classes are associated with well-known multidrug resistance properties.

Revolutionizing Medical Microbiology: How Molecular and Genomic Approaches Are Changing Diagnostic Techniques

Molecular and genomic approaches have revolutionized medical microbiology by offering faster and more accurate diagnostic techniques for infectious diseases. Traditional methods, which include culturing microbes and biochemical testing, are time-consuming and may not detect antibiotic-resistant strains. In contrast, molecular and genomic methods, including polymerase chain reaction (PCR)-based techniques and whole-genome sequencing, provide rapid and precise detection of pathogens, early-stage diseases, and antibiotic-resistant strains. These approaches have advantages such as high sensitivity and specificity, the potential for targeted therapies, and personalized medicine. However, implementing molecular and genomic techniques faces challenges related to cost, equipment, expertise, and data analysis. Ethical and legal considerations regarding patient privacy and genetic data usage also arise. Nonetheless, the future of medical microbiology lies in the widespread adoption of molecular and genomic approaches, which can lead to improved patient outcomes and the identification of antibiotic-resistant strains. Continued advancements, education, and exploration of ethical implications are necessary to fully harness the potential of molecular and genomic techniques in medical microbiology.

Microbiota of long-term indwelling hemodialysis catheters during renal transplantation perioperative period: a cross-sectional metagenomic microbial community analysis

Background: Catheter-related infection (CRI) is a major complication in patients undergoing hemodialysis. The lack of high-throughput research on catheter-related microbiota makes it difficult to predict the occurrence of CRI. Thus, this study aimed to delineate the microbial structure and diversity landscape of hemodialysis catheter tips among patients during the perioperative period of kidney transplantation (KTx) and provide insights into predicting the occurrence of CRI.Methods: Forty patients at the Department of Transplantation undergoing hemodialysis catheter removal were prospectively included. Samples, including catheter tip, catheter outlet skin swab, catheter blood, peripheral blood, oropharynx swab, and midstream urine, from the separate pre- and post-KTx groups were collected and analyzed using metagenomic next-generation sequencing (mNGS). All the catheter tips and blood samples were cultured conventionally.Results: The positive detection rates for bacteria using mNGS and traditional culture were 97.09% (200/206) and 2.65% (3/113), respectively. Low antibiotic-sensitivity biofilms with colonized bacteria were detected at the catheter tip. In asymptomatic patients, no statistically significant difference was observed in the catheter tip microbial composition and diversity between the pre- and post-KTx group. The catheter tip microbial composition and diversity were associated with fasting blood glucose levels. Microorganisms at the catheter tip most likely originated from catheter outlet skin and peripheral blood.Conclusions: The long-term colonization microbiota at the catheter tip is in a relatively stable state and is not readily influenced by KTx. It does not act as the source of infection in all CRIs, but could reflect hematogenous infection to some extent.

Polymyxin resistance caused by large-scale genomic inversion due to IS26 intramolecular translocation in Klebsiella pneumoniae

Polymyxin B and polymyxin E (colistin) are presently considered the last line of defense against human infections caused by multidrug-resistant Gram-negative organisms such as carbapenemase-producer Enterobacterales, Acinetobacter baumannii, and Klebsiella pneumoniae. Yet resistance to this last-line drugs is a major public health threat and is rapidly increasing. Polymyxin S2 (S2) is a polymyxin B analogue previously synthesized in our institute with obviously high antibacterial activity and lower toxicity than polymyxin B and colistin. To predict the possible resistant mechanism of S2 for wide clinical application, we experimentally induced bacterial resistant mutants and studied the preliminary resistance mechanisms. Mut-S, a resistant mutant of K. pneumoniae ATCC BAA-2146 (Kpn2146) induced by S2, was analyzed by whole genome sequencing, transcriptomics, mass spectrometry and complementation experiment. Surprisingly, large-scale genomic inversion (LSGI) of approximately 1.1 Mbp in the chromosome caused by IS26 mediated intramolecular transposition was found in Mut-S, which led to mgrB truncation, lipid A modification and hence S2 resistance. The resistance can be complemented by plasmid carrying intact mgrB. The same mechanism was also found in polymyxin B and colistin induced drug-resistant mutants of Kpn2146 (Mut-B and Mut-E, respectively). This is the first report of polymyxin resistance caused by IS26 intramolecular transposition mediated mgrB truncation in chromosome in K. pneumoniae. The findings broaden our scope of knowledge for polymyxin resistance and enriched our understanding of how bacteria can manage to survive in the presence of antibiotics.

Characterization and Molecular Mechanism of Aminoglycoside-6-Adenyl Transferase Associated with Aminoglycoside Resistance from Elizabethkingia meningoseptica

Purpose

Elizabethkingia meningoseptica (EM) is a multi-drug-resistant bacterium of global concern for its role in nosocomial infection and is generally resistant to aminoglycoside antibiotics. In the whole genome of an EM strain (FMS-007), an aminoglycoside-6-adenyl transferase gene (ant(6)FMS-007) was predicted. This study aimed to characterize the biochemical function of ANT(6)FMS-007 and analyze the relationship between genotype and phenotype of ant(6) in clinical EM isolates, so as to provide evidence for clinical precision drug use. This study could establish a method for the verification of known or unknown functionally resistant genes.

Methods

A total of 42 EM clinical isolates were collected from clinical departments during 2015-2023. The phenotype of aminoglycoside antibiotics was analyzed by broth microdilution (BMD) and Kirby-Bauer (K-B) methods. The whole-length ant(6) from EM clinical isolates was analyzed by polymerase chain reaction (PCR) and sequencing. The biochemical function of predictive ANT(6)FMS-007 from the FMS-007 whole genome was identified by 3D plate experiment and mass spectrometry analysis. Candidate active sites were predicted by multi-species sequence alignment and molecular docking, and other important sites were identified in the comparison of ant(6) genotypes and phenotypes of EM clinical isolates. Drug susceptibility test was used to verify the function of these sites.

Results

The predictive ANT(6)FMS-007 protein could inactivate STR by modifying STR with ATP to form STR-AMP. Four active sites (Asp-38, Asp-42, Lys-95, and Lys-213) of ANT(6)FMS-007 were identified. Thirty-one EM clinical isolates (74%) carried the ant(6) gene. Eight EM clinical isolates containing the ant(6) gene had MIC values (<=32μg/mL) lower by at least 16-fold than FMS-007 (512μg/mL) for STR, and N59H and K204Q were the common mutations in the ant(6) gene.

Conclusion

This assay verified the biochemical function of the predictive gene ant(6)FMS-007 and could provide an alternative method to study resistant gene function in multi-drug-resistant bacteria. The inconsistency between genotype and phenotype of resistant genes indicated that the combination of resistance gene detection and functional analysis could better provide precision medicine for clinical use.

LSPR-Based Biosensing Enables the Detection of Antimicrobial Resistance Genes

The development of rapid, simple, and accurate bioassays for the detection of nucleic acids has received increasing demand in recent years. Here, localized surface plasmon resonance (LSPR) spectroscopy for the detection of an antimicrobial resistance gene, sulfhydryl variable β-lactamase (blaSHV), which confers resistance against a broad spectrum of β-lactam antibiotics is used. By performing limit of detection experiments, a 23 nucleotide (nt) long deoxyribonucleic acid (DNA) sequence down to 25 nm was detected, whereby the signal intensity is inversely correlated with sequence length (23, 43, 63, and 100 nt). In addition to endpoint measurements of hybridization events, the setup also allowed to monitor the hybridization events in real-time, and consequently enabled to extract kinetic parameters of the studied binding reaction. Performing LSPR measurements using single nucleotide polymorphism (SNP) variants of blaSHV revealed that these sequences can be distinguished from the fully complementary sequence. The possibility to distinguish such sequences is of utmost importance in clinical environments, as it allows to identify mutations essential for enzyme function and thus, is crucial for the correct treatment with antibiotics. Taken together, this system provides a robust, label-free, and cost-efficient analytical tool for the detection of nucleic acids and will enable the surveillance of antimicrobial resistance determinants.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Antimicrobial resistance gene lack in tick-borne pathogenic bacteria

Tick-borne infections, including those of bacterial origin, are significant public health issues. Antimicrobial resistance (AMR), which is one of the most pressing health challenges of our time, is driven by specific genetic determinants, primarily by the antimicrobial resistance genes (ARGs) of bacteria. In our work, we investigated the occurrence of ARGs in the genomes of tick-borne bacterial species that can cause human infections. For this purpose, we processed short/long reads of 1550 bacterial isolates of the genera Anaplasma (n = 20), Bartonella (n = 131), Borrelia (n = 311), Coxiella (n = 73), Ehrlichia (n = 13), Francisella (n = 959) and Rickettsia (n = 43) generated by second/third generation sequencing that have been freely accessible at the NCBI SRA repository. From Francisella tularensis, 98.9% of the samples contained the FTU-1 beta-lactamase gene. However, it is part of the F. tularensis representative genome as well. Furthermore, 16.3% of them contained additional ARGs. Only 2.2% of isolates from other genera (Bartonella: 2, Coxiella: 8, Ehrlichia: 1, Rickettsia: 2) contained any ARG. We found that the odds of ARG occurrence in Coxiella samples were significantly higher in isolates related to farm animals than from other sources. Our results describe a surprising lack of ARGs in these bacteria and suggest that Coxiella species in farm animal settings could play a role in the spread of AMR.

pH Window for High Selectivity of Ionizable Antimicrobial Polymers toward Bacteria

Antimicrobial polymers exhibit great potential for treating drug-resistant bacteria; however, designing antimicrobial polymers that can selectively kill bacteria and cause relatively low toxicity to normal tissues/cells remains a key challenge. Here, we report a pH window for ionizable polymers that exhibit high selectivity toward bacteria. Ionizable polymer PC6A showed the greatest selectivity (131.6) at pH 7.4, exhibiting low hemolytic activity and high antimicrobial activity against bacteria, whereas a very high or low protonation degree (PD) produced relatively low selectivity (≤35.6). Bactericidal mechanism of PC6A primarily comprised membrane lysis without inducing drug resistance even after consecutive incubation for 32 passages. Furthermore, PC6A demonstrated synergistic effects in combination with antibiotics at pH 7.4. Hence, this study provides a strategy for designing selective antimicrobial polymers.

[Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance]

Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.

Selection of Antibiotic-Resistant Bacterial Strains and Identification of Genomic Alterations by Whole-Genome Sequencing: Using Staphylococcus aureus and Oxacillin Resistance as an Example

Here, we discuss methods for the selection of antibiotic-resistant bacteria and the use of high-throughput whole-genome sequencing for the identification of the underlying mutations. We comment on sample requirements and the choice of specific DNA preparation methods depending on the strain used and briefly introduce a workflow we use for the selection of Staphylococcus aureus strains with increased oxacillin resistance and identification of genomic alterations.