WGS to predict antibiotic MICs for Neisseria gonorrhoeae

Quantifying the impact of genetic determinants of antibiotic resistance on bacterial lineage dynamics

The dynamics of antimicrobial resistance in bacteria are informed by the fitness advantages conferred by genetic determinants of resistance in the presence of antibiotic pressure and the potential fitness costs in its absence. However, frameworks for quantitative estimates of real-world fitness impact have been lacking, given multidrug resistance, multiple pathways to resistance, and uncertainty around drug exposures. Here, we addressed these challenges through analysis of genome sequences from clinical isolates of Neisseria gonorrhoeae collected over 20 years from across the United States, together with national data on antibiotic treatment. Using a hierarchical Bayesian phylodynamic model, we quantified the contributions of resistance determinants to strain success. Resistance mutations had a fitness benefit when the cognate antibiotic was in use but did not always incur a fitness cost otherwise. Two fluoroquinolone-resistance conferring mutations at the same site in gyrA had divergent fitness impact after fluoroquinolones were no longer used for treatment, findings supported by in vitro competition experiments. Fitness costs were alleviated by loss of costly resistance determinants and counterbalanced by gain of new fitness-conferring resistance determinants. Quantifying the extent to which the resistance determinants explained each lineage's dynamics highlighted gaps and pointed to opportunities for investigation into other genetic and environmental drivers. This work thus establishes a method for linking pathogen genomics and antibiotic use patterns to quantify the fitness impact of resistance determinants and the factors shaping ecological trends.

Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant Acinetobacter baumannii from whole genome sequencing and gene expression

Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susceptibility phenotypes based on WGS data. This study aimed to establish an artificial intelligence-based computational approach in predicting antimicrobial susceptibilities of multidrug-resistant Acinetobacter baumannii from WGS and gene expression data. Antimicrobial susceptibility testing (AST) was performed using the broth microdilution method for 10 antimicrobial agents. In silico multilocus sequence typing (MLST), antimicrobial resistance genes, and phylogeny based on cgSNP and cgMLST strategies were analyzed. High-throughput qPCR was performed to measure the expression level of antimicrobial resistance (AMR) genes. Most isolates exhibited a high level of resistance to most of the tested antimicrobial agents, with the majority belonging to the IC2/CC92 lineage. Phylogenetic analysis revealed undetected transmission events or local outbreaks. The percentage agreements between AMR phenotype and genotype ranged from 70.08% to 89.96%, with the coefficient of agreement (κ) extending from 0.025 and 0.881. The prediction of AST employed by deep neural network models achieved an accuracy of up to 98.64% on the testing data set. Additionally, several linear regression models demonstrated high prediction accuracy, reaching up to 86.15% within an error range of one gradient, indicating a linear relationship between certain gene expressions and the corresponding antimicrobial MICs. In conclusion, neural network-based predictions could be used as a tool for the surveillance of antimicrobial resistance in multidrug-resistant A. baumannii.

Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning

Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of in silico analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic.

IMPORTANCE

In the age of next-generation sequencing (NGS), while data-driven methods such as genome-wide association study (GWAS) and machine learning (ML) excel at finding patterns, functional validation can be challenging due to the high numbers of candidate variants. We designed an integrative approach combining a GWAS on S. pneumoniae clinical isolates, followed by whole-genome transformation coupled with NGS to functionally characterize a large set of GWAS candidates. Our study validated several phenotypic folA mutations beyond the standard Ile100Leu mutation, and showed that the overexpression of the sulA locus produces trimethoprim (TMP) resistance in Streptococcus pneumoniae. These validated loci, when used to build ML models, were found to be the best inputs for predicting TMP minimal inhibitory concentrations. Integrative approaches can bridge the genotype-phenotype gap by biological insights that can be incorporated in ML models for accurate prediction of drug susceptibility.

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.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Phenotypic and genotypic characterization of Neisseria gonorrhoeae isolates among individuals at high risk for sexually transmitted diseases in Zurich, Switzerland

BACKGROUND

While ceftriaxone resistance remains scarce in Switzerland, global Neisseria gonorrhoeae (NG) antimicrobial resistance poses an urgent threat. This study describes clinical characteristics in MSM (men who have sex with men) diagnosed with NG infection and analyses NG resistance by phenotypic and genotypic means.

METHODS

Data of MSM enrolled in three clinical cohorts with a positive polymerase chain reaction test (PCR) for NG were analysed between January 2019 and December 2021 and linked with antibiotic susceptibility testing. Bacterial isolates were subjected to whole genome sequencing (WGS).

RESULTS

Of 142 participants, 141 (99%) were MSM and 118 (84%) living with HIV. Participants were treated with ceftriaxone (N = 79), azithromycin (N = 2), or a combination of both (N = 61). No clinical or microbiological failures were observed. From 182 positive PCR samples taken, 23 were available for detailed analysis. Based on minimal inhibitory concentrations (MICs), all isolates were susceptible to ceftriaxone, gentamicin, cefixime, cefpodoxime, ertapenem, zoliflodacin, and spectinomycin. Resistance to azithromycin, tetracyclines and ciprofloxacin was observed in 10 (43%), 23 (100%) and 11 (48%) of the cases, respectively. Analysis of WGS data revealed combinations of resistance determinants that matched with the corresponding phenotypic resistance pattern of each isolate.

CONCLUSION

Among the MSM diagnosed with NG mainly acquired in Switzerland, ceftriaxone MICs were low for a subset of bacterial isolates studied and no treatment failures were observed. For azithromycin, high occurrences of in vitro resistance were found. Gentamicin, cefixime, cefpodoxime, ertapenem, spectinomycin, and zoliflodacin displayed excellent in vitro activity against the 23 isolates underscoring their potential as alternative agents to ceftriaxone.

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

Background

Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.

Methods

We aimed to predict the minimum inhibitory concentrations (MICs) of 13 antimicrobial agents against Acinetobacter baumannii using three machine learning algorithms (random forest, support vector machine, and XGBoost) combined with k-mer features extracted from WGS data.

Results

A cohort of 339 isolates was used for model construction. The average essential agreement and category agreement of the best models exceeded 90.90% (95%CI, 89.03-92.77%) and 95.29% (95%CI, 94.91-95.67%), respectively; the exceptions being levofloxacin, minocycline and imipenem. The very major error rates ranged from 0.0 to 5.71%. We applied feature selection pipelines to extract the top-ranked 11-mers to optimise training time and computing resources. This approach slightly improved the prediction performance and enabled us to obtain prediction results within 10 min. Notably, when employing these top-ranked 11-mers in an independent test dataset (120 isolates), we achieved an average accuracy of 0.96.

Conclusion

Our study is the first to demonstrate that AMR prediction for A. baumannii using machine learning methods based on k-mer features has competitive performance over traditional workflows; hence, sequence-based AMR prediction and its application could be further promoted. The k-mer-based workflow developed in this study demonstrated high recall/sensitivity and specificity, making it a dependable tool for MIC prediction in clinical settings.

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the "Genome Feature Extractor Pipeline" that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.

Genomics for antimicrobial resistance surveillance to support infection prevention and control in health-care facilities

Integration of genomic technologies into routine antimicrobial resistance (AMR) surveillance in health-care facilities has the potential to generate rapid, actionable information for patient management and inform infection prevention and control measures in near real time. However, substantial challenges limit the implementation of genomics for AMR surveillance in clinical settings. Through a workshop series and online consultation, international experts from across the AMR and pathogen genomics fields convened to review the evidence base underpinning the use of genomics for AMR surveillance in a range of settings. Here, we summarise the identified challenges and potential benefits of genomic AMR surveillance in health-care settings, and outline the recommendations of the working group to realise this potential. These recommendations include the definition of viable and cost-effective use cases for genomic AMR surveillance, strengthening training competencies (particularly in bioinformatics), and building capacity at local, national, and regional levels using hub and spoke models.

Use of genome sequencing to resolve differences in gradient diffusion and agar dilution antimicrobial susceptibility testing performance of Neisseria gonorrhoeae isolates in Alberta, Canada

Agar dilution is the gold standard method for phenotypic antimicrobial susceptibility testing (AST) for Neisseria gonorrhoeae. However, this method is laborious and requires expertise, so laboratories that perform N. gonorrhoeae AST may choose alternative methods such as disk diffusion and gradient diffusion. In this study, we retrospectively compare the performance of gradient diffusion to agar dilution for 2,394 unique N. gonorrhoeae isolates identified in Alberta from 2017 to 2020 against azithromycin, cefixime, ceftriaxone, ciprofloxacin, penicillin, and tetracycline. Genome sequencing was utilized to resolve discrepancies between AST methods, detect antimicrobial resistance markers, and identify trends between error rates and sequence types (STs) of isolates. Over 90% of N. gonorrhoeae isolates were susceptible to azithromycin, cefixime, and ceftriaxone, whereas decreased susceptibility was observed for ciprofloxacin, penicillin, and tetracycline. Categorical (CA) and essential agreement (EA) was poorest between the two methods for penicillin (CA: 86.02%; EA: 77.69%) and tetracycline (CA: 47.22%; EA: 55.96%); however, the low CA was primarily attributed to minor errors. Antimicrobial agents with errors outside of acceptable limits included azithromycin (very major error: 18.42%; major error: 7.73%) and tetracycline (very major error: 6.17%). Genome sequencing on a subset of isolates resolved 30.3% of the azithromycin major errors and confirmed the azithromycin or tetracycline very major errors. Significant associations between certain STs and error types for azithromycin and tetracycline were also identified. Overall, gradient diffusion compared well to agar dilution for cefixime, ceftriaxone, and ciprofloxacin, and genome sequencing was identified as a useful tool to arbitrate discrepant susceptibility testing results between gradient diffusion and agar dilution for N. gonorrhoeae.

Time Required for Nanopore Whole-Genome Sequencing of Neisseria gonorrhoeae for Identification of Phylogenetic Relationships

BACKGROUND

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a global health challenge. Limitations to AMR surveillance reporting, alongside reduction in culture-based susceptibility testing, has resulted in a need for rapid diagnostics and strain detection. We investigated Nanopore sequencing time, and depth, to accurately identify closely related N. gonorrhoeae isolates, compared to Illumina sequencing.

METHODS

N. gonorrhoeae strains collected from a London sexual health clinic were cultured and sequenced with MiSeq and MinION sequencing platforms. Accuracy was determined by comparing variant calls at 68 nucleotide positions (37 resistance-associated markers). Accuracy at varying MinION sequencing depths was determined through retrospective time-stamped read analysis.

RESULTS

Of 22 MinION-MiSeq pairs reaching sufficient sequencing depth, agreement of variant call positions passing quality control criteria was 185/185 (100%; 95% confidence interval [CI], 98.0%-100.0%), 502/503 (99.8%; 95% CI, 98.9%-99.9%), and 564/565 (99.8%; 95% CI, 99.0%-100.0%) at 10x, 30x, and 40x MinION depth, respectively. Isolates identified as closely related by MiSeq, within one yearly evolutionary distance of ≤5 single nucleotide polymorphisms, were accurately identified via MinION.

CONCLUSIONS

Nanopore sequencing shows utility as a rapid surveillance tool, identifying closely related N. gonorrhoeae strains, with just 10x sequencing depth, taking a median time of 29 minutes. This highlights its potential for tracking local transmission and AMR markers.

Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections

Urinary tract infections are one of the most common bacterial infections worldwide; however, increasing antimicrobial resistance in bacterial pathogens is making it challenging for clinicians to correctly prescribe patients appropriate antibiotics. In this study, we present four interpretable machine learning-based decision support algorithms for predicting antimicrobial resistance. Using electronic health record data from a large cohort of patients diagnosed with potentially complicated UTIs, we demonstrate high predictability of antibiotic resistance across four antibiotics - nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. We additionally demonstrate the generalizability of our methods on a separate cohort of patients with uncomplicated UTIs, demonstrating that machine learning-driven approaches can help alleviate the potential of administering non-susceptible treatments, facilitate rapid effective clinical interventions, and enable personalized treatment suggestions. Additionally, these techniques present the benefit of providing model interpretability, explaining the basis for generated predictions.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain)

Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these bacteria. With the development of whole-genome sequencing (WGS) technologies, surveillance based on traditional serotyping and phenotypic tests of resistance is being replaced by genomic surveillance. To introduce WGS as a routine methodology for the surveillance of food-borne Salmonella in the region, we applied this technology to analyze a set of 141 S. enterica isolates obtained from various food sources between 2010 and 2017 in the Comunitat Valenciana (Spain). For this, we performed an evaluation of the most relevant Salmonella typing methods, serotyping and sequence typing, using both traditional and in silico approaches. We extended the use of WGS to detect antimicrobial resistance determinants and predicted minimum inhibitory concentrations (MICs). Finally, to understand possible contaminant sources in this region and their relationship to antimicrobial resistance (AMR), we performed cluster detection combining single-nucleotide polymorphism (SNP) pairwise distances and phylogenetic and epidemiological data. The results of in silico serotyping with WGS data were highly congruent with those of serological analyses (98.5% concordance). Multi-locus sequence typing (MLST) profiles obtained with WGS information were also highly congruent with the sequence type (ST) assignment based on Sanger sequencing (91.9% coincidence). In silico identification of antimicrobial resistance determinants and minimum inhibitory concentrations revealed a high number of resistance genes and possible resistant isolates. A combined phylogenetic and epidemiological analysis with complete genome sequences revealed relationships among isolates indicative of possible common sources for isolates with separate sampling in time and space that had not been detected from epidemiological information. As a result, we demonstrate the usefulness of WGS and in silico methods to obtain an improved characterization of S. enterica enterica isolates, allowing better surveillance of the pathogen in food products and in potential environmental and clinical samples of related interest.

Virotyping and genetic antimicrobial susceptibility testing of porcine ETEC/STEC strains and associated plasmid types

Introduction

Enterotoxigenic Escherichia coli (ETEC) infections are the most common cause of secretory diarrhea in suckling and post-weaning piglets. For the latter, Shiga toxin-producing Escherichia coli (STEC) also cause edema disease. This pathogen leads to significant economic losses. ETEC/STEC strains can be distinguished from general E. coli by the presence of different host colonization factors (e.g., F4 and F18 fimbriae) and various toxins (e.g., LT, Stx2e, STa, STb, EAST-1). Increased resistance against a wide variety of antimicrobial drugs, such as paromomycin, trimethoprim, and tetracyclines, has been observed. Nowadays, diagnosing an ETEC/STEC infection requires culture-dependent antimicrobial susceptibility testing (AST) and multiplex PCRs, which are costly and time-consuming.

Methods

Here, nanopore sequencing was used on 94 field isolates to assess the predictive power, using the meta R package to determine sensitivity and specificity and associated credibility intervals of genotypes associated with virulence and AMR.

Results

Genetic markers associated with resistance for amoxicillin (plasmid-encoded TEM genes), cephalosporins (ampC promoter mutations), colistin (mcr genes), aminoglycosides (aac(3) and aph(3) genes), florfenicol (floR), tetracyclines (tet genes), and trimethoprim-sulfa (dfrA genes) could explain most acquired resistance phenotypes. Most of the genes were plasmid-encoded, of which some collocated on a multi-resistance plasmid (12 genes against 4 antimicrobial classes). For fluoroquinolones, AMR was addressed by point mutations within the ParC and GyrA proteins and the qnrS1 gene. In addition, long-read data allowed to study the genetic landscape of virulence- and AMR-carrying plasmids, highlighting a complex interplay of multi-replicon plasmids with varying host ranges.

Conclusion

Our results showed promising sensitivity and specificity for the detection of all common virulence factors and most resistance genotypes. The use of the identified genetic hallmarks will contribute to the simultaneous identification, pathotyping, and genetic AST within a single diagnostic test. This will revolutionize future quicker and more cost-efficient (meta)genomics-driven diagnostics in veterinary medicine and contribute to epidemiological studies, monitoring, tailored vaccination, and management.

A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction in streptococcus pneumoniae

Streptococcus pneumoniae is one of the major concerns of clinicians and one of the global public health problems. This pathogen is associated with high morbidity and mortality rates and antimicrobial resistance (AMR). In the last few years, reduced genome sequencing costs have made it possible to explore more of the drug resistance of S. pneumoniae, and machine learning (ML) has become a popular tool for understanding, diagnosing, treating, and predicting these phenotypes. Nucleotide k-mers, amino acid k-mers, single nucleotide polymorphisms (SNPs), and combinations of these features have rich genetic information in whole-genome sequencing. This study compares different ML models for predicting AMR phenotype for S. pneumoniae. We compared nucleotide k-mers, amino acid k-mers, SNPs, and their combinations to predict AMR in S. pneumoniae for three antibiotics: Penicillin, Erythromycin, and Tetracycline. 980 pneumococcal strains were downloaded from the European Nucleotide Archive (ENA). Furthermore, we used and compared several machine learning methods to train the models, including random forests, support vector machines, stochastic gradient boosting, and extreme gradient boosting. In this study, we found that key features of the AMR prediction model setup and the choice of machine learning method affected the results. The approach can be applied here to further studies to improve AMR prediction accuracy and efficiency.

Whole-Genome Sequencing-Based Resistome Analysis of Nosocomial Multidrug-Resistant Non-Fermenting Gram-Negative Pathogens from the Balkans

Non-fermenting Gram-negative bacilli (NFGNB), such as Pseudomonas aeruginosa and Acinetobacter baumannii, are among the major opportunistic pathogens involved in the global antibiotic resistance epidemic. They are designated as urgent/serious threats by the Centers for Disease Control and Prevention and are part of the World Health Organization’s list of critical priority pathogens. Also, Stenotrophomonas maltophilia is increasingly recognized as an emerging cause for healthcare-associated infections in intensive care units, life-threatening diseases in immunocompromised patients, and severe pulmonary infections in cystic fibrosis and COVID-19 individuals. The last annual report of the ECDC showed drastic differences in the proportions of NFGNB with resistance towards key antibiotics in different European Union/European Economic Area countries. The data for the Balkans are of particular concern, indicating more than 80% and 30% of invasive Acinetobacter spp. and P. aeruginosa isolates, respectively, to be carbapenem-resistant. Moreover, multidrug-resistant and extensively drug-resistant S. maltophilia from the region have been recently reported. The current situation in the Balkans includes a migrant crisis and reshaping of the Schengen Area border. This results in collision of diverse human populations subjected to different protocols for antimicrobial stewardship and infection control. The present review article summarizes the findings of whole-genome sequencing-based resistome analyses of nosocomial multidrug-resistant NFGNBs in the Balkan countries.

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic resistance. As most isolates in the United States are susceptible to at least one antibiotic, rapid molecular antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, thereby expanding treatment options. With genome sequence and antibiotic resistance phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an opportunity to use statistical methods to develop sequence-based diagnostics that predict antibiotic susceptibility from genotype. N. gonorrhoeae, therefore, provides a useful example illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. We present an overview of this framework, which begins with establishing the assay technology, the performance criteria, the population in which the diagnostic will be used, and the clinical goals, and extends to the choices that must be made to arrive at a set of features with the desired properties for predicting susceptibility phenotype from genotype. While we focus on the example of N. gonorrhoeae, the framework generalizes to other organisms for which large-scale genotype and antibiotic resistance data can be combined to aid in diagnostics development.

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning

Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.

Assessment of Antibiotic Resistance and Efflux Pump Gene Expression in Neisseria Gonorrhoeae Isolates from South Africa by Quantitative Real-Time PCR and Regression Analysis

Introduction

Treatment of gonorrhoea infection is limited by the increasing prevalence of multidrug-resistant strains. Cost-effective molecular diagnostic tests can guide effective antimicrobial stewardship. The aim of this study was to correlate mRNA expression levels in Neisseria gonorrhoeae antibiotic target genes and efflux pump genes to antibiotic resistance in our population.

Methods

This study investigated the expression profile of antibiotic resistance-associated genes (penA, ponA, pilQ, mtrR, mtrA, mtrF, gyrA, parC, parE, rpsJ, 16S rRNA, and 23S rRNA) and efflux pump genes (macAB, norM, and mtrCDE), by quantitative real-time PCR, in clinical isolates from KwaZulu-Natal, South Africa. Whole-genome sequencing was used to determine the presence or absence of mutations.

Results

N. gonorrhoeae isolates, from female and male patients presenting for care at clinics in KwaZulu-Natal, South Africa, were analysed. As determined by binomial regression and ROC analysis, the most significant (p ≤ 0.05) markers for resistance prediction in this population, and their cutoff values, were determined to be mtrC (p = 0.024; cutoff <0.089), gyrA (p = 0.027; cutoff <0.0518), parE (p = 0.036; cutoff <0.0033), rpsJ (p = 0.047; cutoff <0.0012), and 23S rRNA (p = 0.042; cutoff >7.754).

Conclusion

Antimicrobial stewardship includes exploring options to conserve currently available drugs for gonorrhoea treatment. There is the potential to predict an isolate as either susceptible or nonsusceptible based on the mRNA expression level of specific candidate markers, to inform patient management. This real-time qPCR approach, with few targets, can be further investigated for use as a potentially cost-effective diagnostic tool to detect resistance.

Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases

Next-generation sequencing (NGS) technologies have become increasingly available for use in the clinical microbiology diagnostic environment. There are three main applications of these technologies in the clinical microbiology laboratory: whole genome sequencing (WGS), targeted metagenomics sequencing and shotgun metagenomics sequencing. These applications are being utilized for initial identification of pathogenic organisms, the detection of antimicrobial resistance mechanisms and for epidemiologic tracking of organisms within and outside hospital systems. In this review, we analyze these three applications and provide a comprehensive summary of how these applications are currently being used in public health, basic research, and clinical microbiology laboratory environments. In the public health arena, WGS is being used to identify and epidemiologically track food borne outbreaks and disease surveillance. In clinical hospital systems, WGS is used to identify multi-drug-resistant nosocomial infections and track the transmission of these organisms. In addition, we examine how metagenomics sequencing approaches (targeted and shotgun) are being used to circumvent the traditional and biased microbiology culture methods to identify potential pathogens directly from specimens. We also expand on the important factors to consider when implementing these technologies, and what is possible for these technologies in infectious disease diagnosis in the next 5 years.

Using Genomics to Understand the Epidemiology of Infectious Diseases in the Northern Territory of Australia

The Northern Territory (NT) is a geographically remote region of northern and central Australia. Approximately a third of the population are First Nations Australians, many of whom live in remote regions. Due to the physical environment and climate, and scale of social inequity, the rates of many infectious diseases are the highest nationally. Molecular typing and genomic sequencing in research and public health have provided considerable new knowledge on the epidemiology of infectious diseases in the NT. We review the applications of genomic sequencing technology for molecular typing, identification of transmission clusters, phylogenomics, antimicrobial resistance prediction, and pathogen detection. We provide examples where these methodologies have been applied to infectious diseases in the NT and discuss the next steps in public health implementation of this technology.

Summary of the Fourth Annual American Sexually Transmitted Diseases Association Workshop on Improving Sexually Transmitted Infection Control Efforts Through Cross-Sector Collaboration

ABSTRACT

The American Sexually Transmitted Diseases Association has, for several years, been conducting a cross-sector workshop to bring together a variety of stakeholders to develop ideas for collaboratively improving the sexually transmitted infection control efforts in the United States. In this summary, we share the content of discussions and ideas of the fourth annual workshop for future research and potential changes to practice with a focus on diagnostic capacity.

Proceedings of the Clinical Microbiology Open 2018 and 2019 - a Discussion about Emerging Trends, Challenges, and the Future of Clinical Microbiology

Clinical Microbiology Open (CMO), a meeting supported by the American Society for Microbiology's Clinical and Public Health Microbiology Committee (CPHMC) and Corporate Council, provides a unique interactive platform for leaders from diagnostic microbiology laboratories, industry, and federal agencies to discuss the current and future state of the clinical microbiology laboratory. The purpose is to leverage the group's diverse views and expertise to address critical challenges, and discuss potential collaborative opportunities for diagnostic microbiology, through the utilization of varied resources. The first and second CMO meetings were held in 2018 and 2019, respectively. Discussions were focused on the diagnostic potential of innovative technologies and laboratory diagnostic stewardship, including expansion of next-generation sequencing into clinical diagnostics, improvement and advancement of molecular diagnostics, emerging diagnostics, including rapid antimicrobial susceptibility and point of care testing (POCT), harnessing big data through artificial intelligence, and staffing in the clinical microbiology laboratory. Shortly after CMO 2019, the coronavirus disease 2019 (COVID-19) pandemic further highlighted the need for the diagnostic microbiology community to work together to utilize and expand on resources to respond to the pandemic. The issues, challenges, and potential collaborative efforts discussed during the past two CMO meetings proved critical in addressing the COVID-19 response by diagnostic laboratories, industry partners, and federal organizations. Planning for a third CMO (CMO 2022) is underway and will transition from a discussion-based meeting to an action-based meeting. The primary focus will be to reflect on the lessons learned from the COVID-19 pandemic and better prepare for future pandemics.

Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood-while maintaining, if not improving-the current assay's sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

Prediction of Antimicrobial Minimal Inhibitory Concentrations for Neisseria gonorrhoeae using Machine Learning Models

The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R², (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R² value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Comparative Whole-Genome Analysis of Neisseria gonorrhoeae Isolates Revealed Changes in the Gonococcal Genetic Island and Specific Genes as a Link to Antimicrobial Resistance

Comparative whole-genome analysis was performed for Neisseria gonorrhoeae isolates belonging to the Neisseria gonorrhoeae multiantigen sequence typing (NG-MAST) types predominant worldwide — 225, 1407, 2400, 2992, and 4186 — and to genogroup 807, the most common genogroup in the Russian Federation. Here, for the first time, the complete genomes of 25 N. gonorrhoeae isolates from genogroup 807 were obtained. For NG-MAST types 225, 1407, 2400, 2992, and 4186, genomes from the Pathogenwatch database were used. The phylogenetic network constructed for 150 genomes showed that the clustering according to NG-MAST type corresponded to the clustering according to genome. Comparisons of genomes of the six sequence types revealed 8-20 genes specific to each sequence type, including the loci for phase variations and genetic components of the gonococcal genetic island (GGI). NG-MAST type 2992 and 4186 isolates either lacked the GGI or carried critical mutations in genes essential for DNA secretion. In all analyzed genogroup 807 isolates, substitution of the essential atlA gene with the eppA gene was found, accompanied by a change in the traG allele, replacement of the ych gene with ych1, and the absence of the exp1 gene, which is likely to result in loss of GGI functionality. For the NG-MAST type 225, 1407 and 2400 isolates, no premature stop codons or reading frameshifts were found in the genes essential for GGI function. A relationship between isolate susceptibility to ciprofloxacin, penicillin, tetracycline and the presence of lesions in GGI genes necessary for DNA secretion was established. The N. gonorrhoeae evolutionary pathways, which allow a particular sequence type to maintain long-term predominance in the population, may include changes in genes responsible for adhesion and virulence, changes in the GGI structure, preservation of genes carrying drug resistance determinants, and changes in genes associated with host adaptation or encoding enzymes of biochemical pathways.

Neisseria gonorrhoeae physiology and pathogenesis

Neisseria gonorrhoeae is an obligate human pathogen that is the cause of the sexually transmitted disease gonorrhoea. Recently, there has been a surge in gonorrhoea cases that has been exacerbated by the rapid rise in gonococcal multidrug resistance to all useful antimicrobials resulting in this organism becoming a significant public health burden. Therefore, there is a clear and present need to understand the organism's biology through its physiology and pathogenesis to help develop new intervention strategies. The gonococcus initially colonises and adheres to host mucosal surfaces utilising a type IV pilus that helps with microcolony formation. Other adhesion strategies include the porin, PorB, and the phase variable outer membrane protein Opa. The gonococcus is able to subvert complement mediated killing and opsonisation by sialylation of its lipooligosaccharide and deploys a series of anti-phagocytic mechanisms. N. gonorrhoeae is a fastidious organism that is able to grow on a limited number of primary carbon sources such as glucose and lactate. The utilization of lactate by the gonococcus has been implicated in a number of pathogenicity mechanisms. The bacterium lives mainly in microaerobic environments and can grow both aerobically and anaerobically with the aid of nitrite. The gonococcus does not produce siderophores for scavenging iron but can utilize some produced by other bacteria, and it is able to successful chelate iron from host haem, transferrin and lactoferrin. The gonococcus is an incredibly versatile human pathogen; in the following chapter, we detail the intricate mechanisms used by the bacterium to invade and survive within the host.

Infection prevention and control insights from a decade of pathogen whole-genome sequencing

Pathogen whole-genome sequencing has become an important tool for understanding the transmission and epidemiology of infectious diseases. It has improved our understanding of sources of infection and transmission routes for important healthcare-associated pathogens, including Clostridioides difficile and Staphylococcus aureus. Transmission from known infected or colonized patients in hospitals may explain fewer cases than previously thought and multiple introductions of these pathogens from the community may play a greater a role. The findings have had important implications for infection prevention and control. Sequencing has identified heterogeneity within pathogen species, with some subtypes transmitting and persisting in hospitals better than others. It has identified sources of infection in healthcare-associated outbreaks of food-borne pathogens, Candida auris and Mycobacterium chimera, as well as individuals or groups involved in transmission and historical sources of infection. SARS-CoV-2 sequencing has been central to tracking variants during the COVID-19 pandemic and has helped understand transmission to and from patients and healthcare workers despite prevention efforts. Metagenomic sequencing is an emerging technology for culture-independent diagnosis of infection and antimicrobial resistance. In future, sequencing is likely to become more accessible and widely available. Real-time use in hospitals may allow infection prevention and control teams to identify transmission and to target interventions. It may also provide surveillance and infection control benchmarking. Attention to ethical and wellbeing issues arising from sequencing identifying individuals involved in transmission is important. Pathogen whole-genome sequencing has provided an incredible new lens to understand the epidemiology of healthcare-associated infection and to better control and prevent these infections.

Application and Challenge of 3rd Generation Sequencing for Clinical Bacterial Studies

Over the past 25 years, the powerful combination of genome sequencing and bioinformatics analysis has played a crucial role in interpreting information encoded in bacterial genomes. High-throughput sequencing technologies have paved the way towards understanding an increasingly wide range of biological questions. This revolution has enabled advances in areas ranging from genome composition to how proteins interact with nucleic acids. This has created unprecedented opportunities through the integration of genomic data into clinics for the diagnosis of genetic traits associated with disease. Since then, these technologies have continued to evolve, and recently, long-read sequencing has overcome previous limitations in terms of accuracy, thus expanding its applications in genomics, transcriptomics and metagenomics. In this review, we describe a brief history of the bacterial genome sequencing revolution and its application in public health and molecular epidemiology. We present a chronology that encompasses the various technological developments: whole-genome shotgun sequencing, high-throughput sequencing, long-read sequencing. We mainly discuss the application of next-generation sequencing to decipher bacterial genomes. Secondly, we highlight how long-read sequencing technologies go beyond the limitations of traditional short-read sequencing. We intend to provide a description of the guiding principles of the 3rd generation sequencing applications and ongoing improvements in the field of microbial medical research.

First National Genomic Epidemiological Study of Neisseria gonorrhoeae Strains Spreading Across Sweden in 2016

The increasing transmission and antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a global health concern with worrying trends of decreasing susceptibility to also the last-line extended-spectrum cephalosporin (ESC) ceftriaxone. A dramatic increase of reported gonorrhea cases has been observed in Sweden from 2016 and onward. The aim of the present study was to comprehensively investigate the genomic epidemiology of all cultured N. gonorrhoeae isolates in Sweden during 2016, in conjunction with phenotypic AMR and clinical and epidemiological data of patients. In total, 1279 isolates were examined. Etest and whole-genome sequencing (WGS) were performed, and epidemiological data obtained from the Public Health Agency of Sweden. Overall, 51.1%, 1.7%, and 1.3% resistance to ciprofloxacin, cefixime, and azithromycin, respectively, was found. No isolates were resistant to ceftriaxone, however, 9.3% of isolates showed a decreased susceptibility to ceftriaxone and 10.5% to cefixime. In total, 44 penA alleles were found of which six were mosaic (n = 92). Using the typing schemes of MLST, NG-MAST, and NG-STAR; 133, 422, and 280 sequence types, respectively, and 93 NG-STAR clonal complexes were found. The phylogenomic analysis revealed two main lineages (A and B) with lineage A divided into two main sublineages (A1 and A2). Resistance and decreased susceptibility to ESCs and azithromycin and associated AMR determinants, such as mosaic penA and mosaic mtrD, were predominantly found in sublineage A2. Resistance to cefixime and azithromycin was more prevalent among heterosexuals and MSM, respectively, and both were predominantly spread through domestic transmission. Continuous surveillance of the spread and evolution of N. gonorrhoeae, including phenotypic AMR testing and WGS, is essential for enhanced knowledge regarding the dynamic evolution of N. gonorrhoeae and gonorrhea epidemiology.

A Bayesian approach to modeling antimicrobial multidrug resistance

Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.

Strengths and caveats of identifying resistance genes from whole genome sequencing data

INTRODUCTION

Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist.

AREAS COVERED

We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction.

EXPERT OPINION

AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.

Antimicrobial resistance prediction in Neisseria gonorrhoeae: Current status and future prospects

INTRODUCTION

Several nucleic acid amplification tests (NAATs), mostly real-time PCRs, to detect antimicrobial resistance (AMR) determinants and predict AMR in Neisseria gonorrhoeae are promising, and some may be ready to apply at the point-of-care (POC), but important limitations remain with most NAATs. Next-generation sequencing (NGS) can overcome many of these limitations.Areas covered: Recent advances, with main focus on publications since 2017, in the development and use of NAATs and NGS to predict gonococcal AMR for surveillance and clinical use, and pros and cons of these tests as well as future perspectives for appropriate use of molecular AMR prediction for N. gonorrhoeae.Expert Commentary: NAATs and/or NGS for AMR prediction should supplement culture-based AMR surveillance, which will remain because it detects also AMR due to unknown AMR determinants, and translation into POC tests is imperative for the end-goal of individualized treatment, sparing ceftriaxone±azithromycin. Several challenges for direct testing of clinical, especially pharyngeal, specimens and for accurate prediction of cephalosporins and azithromycin resistance, especially using NAATs, remain. The choice of AMR prediction assay needs to carefully consider the intended use of the assay; limitations intrinsic to the AMR prediction technology, algorithms and specific to chosen methodology; specimen types analyzed; and cost-effectiveness.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Linear regression equations to predict β-lactam, macrolide, lincosamide and fluoroquinolone minimum inhibitory concentrations from molecular antimicrobial resistance determinants in Streptococcus pneumoniae

Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial minimum inhibitory concentration (MIC) for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim/sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolated from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n=439) and USA (n=607 and n=747) isolates. The MIC for β-lactam antimicrobials were fully explained by amino acid substitutions in motif regions of the penicillin binding proteins PBP1a, PPB2b, and PBP2x. Accuracy of predicted MICs within one doubling dilution to phenotypically determined MICs for penicillin was 97.4%, ceftriaxone 98.2%; erythromycin 94.8%; clarithromycin 96.6%; clindamycin 98.2%; levofloxacin 100%; and trimethoprim/sulfamethoxazole 98.8%; with an overall sensitivity of 95.8% and specificity of 98.0%. Accuracy of predicted MICs to the phenotypically determined MICs was similar to phenotype-only MIC comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular determinants will facilitate the transition from routine phenotypic testing to whole genome sequencing analysis and can fill the surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to better monitor the dynamics of S. pneumoniae.

Utility of Whole Genome Sequencing in Assessing and Enhancing Partner Notification of Neisseria gonorrhoeae Infection

BACKGROUND

Gonorrhea is a sexually transmitted infection of global concern. We investigated whole-genome sequencing (WGS) as a tool to measure and enhance partner notification (PN) in gonorrhea management.

METHODS

Between May and November 2018, all N. gonorrhoeae isolated from patients attending Leeds Sexual Health, United Kingdom, underwent WGS. Reports listing sequences within 20 single-nucleotide polymorphisms (SNPs) of study isolates within a database containing select isolates from April 1, 2016, to November 15, 2018, were issued to clinicians. The proportion of cases with a potential transmission partner identified by PN was determined from patient and PN data. The WGS reports were reviewed to identify additional cases within 6 SNPs or less and verified for PN concordance.

RESULTS

Three hundred eighty isolates from 377 cases were successfully sequenced; 292 had traceable/contactable partners and 69 (18%) had a potential transmission partner identified by PN. Concordant PN and WGS links were identified in 47 partner pairs. Of 308 cases with no transmission partner by PN, 185 (60%) had a case within 6 SNPs or less; examination of these cases' PN data identified 7 partner pairs with previously unrecognized PN link, giving a total of 54 pairs; all had 4 or less SNP differences. The WGS clusters confirmed gaps in partner finding, at individual and group levels. Despite the clinic providing sexual health services to the whole city, 35 cases with multiple partners had no genetically related case, suggesting multiple undiagnosed infections.

CONCLUSIONS

Whole-genome sequencing could improve gonorrhea PN and control by identifying new links and clusters with significant gaps in partner finding.

Antimicrobial Resistance Mechanisms, Multilocus Sequence Typing, and NG-STAR Sequence Types of Diverse Neisseria gonorrhoeae Isolates in KwaZulu-Natal, South Africa

Antimicrobial resistance (AMR) is a major challenge to managing infectious diseases. Africa has the highest incidence of gonorrhoea, but there is a lack of comprehensive data from sparse surveillance programs. This study investigated the molecular epidemiology and AMR profiles of Neisseria gonorrhoeae isolates in KwaZulu-Natal province (KZN), South Africa. Repository isolates from patients attending public health care clinics for sexually transmitted infection (STI) care were used for phenotypic and genotypic analysis. An Etest was performed to determine antimicrobial susceptibility. Whole-genome sequencing (WGS) was used to determine epidemiology and to predict susceptibility by detecting resistance-associated genes and mutations. Among the 61 isolates, multiple sequence types were identified. Six isolates were novel, as determined by multilocus sequence typing. N. gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR) determined 48 sequence types, of which 35 isolates had novel antimicrobial profiles. Two novel penA alleles and eight novel mtrR alleles were identified. Point mutations were detected in gyrA, parC, mtrR, penA, ponA, and porB1. This study revealed a high prevalence of AMR (penicillin 67%, tetracycline 89%, and ciprofloxacin 52%). However, spectinomycin, cefixime, ceftriaxone, and azithromycin remained 100% effective. This study is one of the first to comprehensively describe the epidemiology and AMR of N. gonorrhoeae in KZN, South Africa and Africa, using WGS. KZN has a wide strain diversity and most of these sequence types have been detected in multiple countries; however, more than half of our isolates have novel antimicrobial profiles. Continued surveillance is crucial to monitor the emergence of resistance to cefixime, ceftriaxone, and azithromycin.

Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data

Minimal inhibitory concentration (MIC) is defined as the lowest concentration of an antimicrobial agent that can inhibit the visible growth of a particular microorganism after overnight incubation. Clinically, antibiotic doses for specific infections are determined according to the fraction of MIC. Therefore, credible assessment of MICs will provide a physician valuable information on the choice of therapeutic strategy. Early and precise usage of antibiotics is the key to an infection therapy. Compared with the traditional culture-based method, the approach of whole genome sequencing to identify MICs can shorten the experimental time, thereby improving clinical efficacy. Klebsiella pneumoniae is one of the most significant members of the genus Klebsiella in the Enterobacteriaceae family and also a common non-social pathogen. Meropenem is a broad-spectrum antibacterial agent of the carbapenem family, which can produce antibacterial effects of most Gram-positive and -negative bacteria. In this study, we used single-nucleotide polymorphism (SNP) information and nucleotide k-mers count based on metagenomic data to predict MICs of meropenem against K. pneumoniae. Then, features of 110 sequenced K. pneumoniae genome data were combined and modeled with XGBoost algorithm and deep neural network (DNN) algorithm to predict MICs. We first use the XGBoost classification model and the XGBoost regression model. After five runs, the average accuracy of the test set was calculated. The accuracy of using nucleotide k-mers to predict MICs of the XGBoost classification model and XGBoost regression model was 84.5 and 89.1%. The accuracy of SNP in predicting MIC was 80 and 81.8%, respectively. The results show that XGBoost regression is better than XGBoost classification in both nucleotide k-mers and SNPs to predict MICs. We further selected 40 nucleotide k-mers and 40 SNPs with the highest correlation with MIC values as features to retrain the XGBoost regression model and DNN regression model. After 100 and 1,000 runs, the results show that the accuracy of the two models was improved. The accuracy of the XGBoost regression model for k-mers, SNPs, and k-mers & SNPs was 91.1, 85.2, and 91.3%, respectively. The accuracy of the DNN regression model was 91.9, 87.1, and 91.8%, respectively. Through external verification, some of the selected features were found to be related to drug resistance.

Prediction of ceftriaxone MIC in Neisseria gonorrhoeae using DNA microarray technology and regression analysis

BACKGROUND

Decreased susceptibility of Neisseria gonorrhoeae to extended-spectrum cephalosporins is a major concern. Elucidation of the phenotypic and genetic characteristics of such isolates is a priority task.

METHODS

We developed a method for predicting the N. gonorrhoeae ceftriaxone susceptibility level (MICcro) by identifying genetic determinants of resistance using low-density hydrogel microarrays and a regression equation. A training dataset, containing 5631 isolates from the Pathogenwatch database and 181 isolates obtained in the Russian Federation during 2018-19, was used to build a regression model. The regression equation was tested on 14 WHO reference strains. Ceftriaxone resistance determinants for the 448 evaluated clinical isolates collected in Russia were identified using microarray analysis, and MICcro values were calculated using the regression equation and compared with those measured by the serial dilution method.

RESULTS

The regression equation for calculating MICcro values included 20 chromosomal resistance determinants. The greatest contributions to the increase in MICcro were shown to be PBP2: Ala-501→Pro, Ala-311→Val, Gly-545→Ser substitutions, Asp(345-346) insertion; and PorB: Gly-120→Arg substitution. The substitutions PBP2: Ala-501→Thr/Val, PorB: Gly-120→Asn/Asp/Lys and PBP1: Leu-421→Pro had weaker effects. For 94.4% of the isolates in the evaluation set, the predicted MICcro was within one doubling dilution of the experimentally determined MICcro. No ceftriaxone-resistant isolates were identified in the analysed samples from Russia, and no interpretative errors were detected in the MICcro calculations.

CONCLUSIONS

The developed strategy for predicting ceftriaxone MIC can be used for the continuous surveillance of known and emerging resistant N. gonorrhoeae isolates.

Recent advances in understanding and combatting Neisseria gonorrhoeae: a genomic perspective

The sexually transmitted infection (STI) gonorrhoea remains a major global public health concern. The World Health Organization (WHO) estimates that 87 million new cases in individuals who were 15 to 49 years of age occurred in 2016. The growing number of gonorrhoea cases is concerning given the rise in gonococci developing antimicrobial resistance (AMR). Therefore, a global action plan is needed to facilitate surveillance. Indeed, the WHO has made surveillance leading to the elimination of STIs (including gonorrhoea) a global health priority. The availability of whole genome sequence data offers new opportunities to combat gonorrhoea. This can be through (i) enhanced surveillance of the global prevalence of AMR, (ii) improved understanding of the population biology of the gonococcus, and (iii) opportunities to mine sequence data in the search for vaccine candidates. Here, we review the current status in Neisseria gonorrhoeae genomics. In particular, we explore how genomics continues to advance our understanding of this complex pathogen.

Comparison of phenotypic antimicrobial susceptibility testing results and WGS-derived genotypic resistance profiles for a cohort of ESBL-producing Escherichia coli collected from Canadian hospitals: CANWARD 2007-18

OBJECTIVES

To determine whether the genotypic resistance profile inferred from WGS could accurately predict phenotypic resistance for ESBL-producing Escherichia coli isolated from patient samples in Canadian hospital laboratories.

METHODS

As part of the ongoing CANWARD study, 671 E. coli were collected and phenotypically confirmed as ESBL producers using CLSI M100 disc testing criteria. Isolates were sequenced using the Illumina MiSeq platform, resulting in 636 high-quality genomes for comparison. Using a rules-based approach, the genotypic resistance profile was compared with the phenotypic resistance interpretation generated using the CLSI broth microdilution method for ceftriaxone, ciprofloxacin, gentamicin and trimethoprim/sulfamethoxazole.

RESULTS

The most common genes associated with non-susceptibility to ceftriaxone, gentamicin and trimethoprim/sulfamethoxazole were CTX-M-15 (n = 391), aac(3)-IIa + aac(6')-Ib-cr (n = 121) and dfrA17 + sul1 (n = 169), respectively. Ciprofloxacin non-susceptibility was most commonly attributed to alterations in both gyrA (S83L + D87N) and parC (S80I + E84V), with (n = 187) or without (n = 197) aac(6')-Ib-cr. Categorical agreement (susceptible or non-susceptible) between actual and predicted phenotype was 95.6%, 98.9%, 97.6% and 88.8% for ceftriaxone, ciprofloxacin, gentamicin and trimethoprim/sulfamethoxazole, respectively. Only ciprofloxacin results (susceptible or non-susceptible) were predicted with major error (ME) and very major error (VME) rates of <3%: ciprofloxacin (ME, 1.5%; VME, 1.1%); gentamicin (ME, 0.8%-31.7%; VME, 4.8%); ceftriaxone (ME, 81.8%; VME, 3.0%); and trimethoprim/sulfamethoxazole (ME, 0.9%-23.0%; VME, 5.2%-8.5%).

CONCLUSIONS

Our rules-based approach for predicting a resistance phenotype from WGS performed well for ciprofloxacin, with categorical agreement of 98.9%, an ME rate of 1.5% and a VME rate of 1.1%. Although high categorical agreements were also obtained for gentamicin, ceftriaxone and trimethoprim/sulfamethoxazole, ME and/or VME rates were ≥3%.

Effect of genetic background on the evolution of Vancomycin-Intermediate Staphylococcus aureus (VISA)

Vancomycin-intermediate Staphylococcus aureus (VISA) typically arises through accumulation of chromosomal mutations that alter cell-wall thickness and global regulatory pathways. Genome-based prediction of VISA requires understanding whether strain background influences patterns of mutation that lead to resistance. We used an iterative method to experimentally evolve three important methicillin-resistant S. aureus (MRSA) strain backgrounds-(CC1, CC5 and CC8 (USA300)) to generate a library of 120 laboratory selected VISA isolates. At the endpoint, isolates had vancomycin MICs ranging from 4 to 10 μg/mL. We detected mutations in more than 150 genes, but only six genes (already known to be associated with VISA from prior studies) were mutated in all three background strains (walK, prs, rpoB, rpoC, vraS, yvqF). We found evidence of interactions between loci (e.g., vraS and yvqF mutants were significantly negatively correlated) and rpoB, rpoC, vraS and yvqF were more frequently mutated in one of the backgrounds. Increasing vancomycin resistance was correlated with lower maximal growth rates (a proxy for fitness) regardless of background. However, CC5 VISA isolates had higher MICs with fewer rounds of selection and had lower fitness costs than the CC8 VISA isolates. Using multivariable regression, we found that genes differed in their contribution to overall MIC depending on the background. Overall, these results demonstrated that VISA evolved through mutations in a similar set of loci in all backgrounds, but the effect of mutation in common genes differed with regard to fitness and contribution to resistance in different strains.

Bioinformatics tools used for whole-genome sequencing analysis of Neisseria gonorrhoeae: a literature review

Whole-genome sequencing (WGS) data are well established for the investigation of gonococcal transmission, antimicrobial resistance prediction, population structure determination and population dynamics. A variety of bioinformatics tools, repositories, services and platforms have been applied to manage and analyze Neisseria gonorrhoeae WGS datasets. This review provides an overview of the various bioinformatics approaches and resources used in 105 published studies (as of 30 April 2021). The challenges in the analysis of N. gonorrhoeae WGS datasets, as well as future bioinformatics requirements, are also discussed.

Developing a model to predict individualised treatment for gonorrhoea: a modelling study

OBJECTIVE

To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone.

DESIGN

A modelling study.

SETTING

England and Wales.

PARTICIPANTS

Individuals accessing sentinel health services.

INTERVENTION

Developing an Excel model which uses participants' demographic, behavioural and clinical characteristics to predict susceptibility to legacy antibiotics. Model parameters were calculated using data for 2015-2017 from the Gonococcal Resistance to Antimicrobials Surveillance Programme.

MAIN OUTCOME MEASURES

Estimated number of doses of ceftriaxone saved, and number of people delayed effective treatment, by model use in clinical practice. Model outputs are the predicted risk of resistance to ciprofloxacin, azithromycin, penicillin and cefixime, in groups of individuals with different combinations of characteristics (gender, sexual orientation, number of recent sexual partners, age, ethnicity), and a treatment recommendation.

RESULTS

Between 2015 and 2017, 8013 isolates were collected: 64% from men who have sex with men, 18% from heterosexual men and 18% from women. Across participant subgroups, stratified by all predictors, resistance prevalence was high for ciprofloxacin (range: 11%-51%) and penicillin (range: 6%-33%). Resistance prevalence for azithromycin and cefixime ranged from 0% to 13% and for ceftriaxone it was 0%. Simulating model use, 88% of individuals could be given cefixime and 10% azithromycin, saving 97% of ceftriaxone doses, with 1% of individuals delayed effective treatment.

CONCLUSIONS

Using demographic and behavioural characteristics, we could not reliably identify a participant subset in which ciprofloxacin or penicillin would be effective. Cefixime resistance was almost universally low; however, substituting ceftriaxone for near-uniform treatment with cefixime risks re-emergence of resistance to cefixime and ceftriaxone. Several subgroups had low azithromycin resistance, but widespread azithromycin monotherapy risks resistance at population level. However, this dataset had limitations; further exploration of individual characteristics to predict resistance to a wider range of legacy antibiotics may still be appropriate.

Phylogenomic analysis reveals persistence of gonococcal strains with reduced-susceptibility to extended-spectrum cephalosporins and mosaic penA-34

The recent emergence of strains of Neisseria gonorrhoeae associated with treatment failures to ceftriaxone, the foundation of current treatment options, has raised concerns over a future of untreatable gonorrhea. Current global data on gonococcal strains suggest that several lineages, predominately characterized by mosaic penA alleles, are associated with elevated minimum inhibitory concentrations (MICs) to extended spectrum cephalosporins (ESCs). Here we report on whole genome sequences of 813 N. gonorrhoeae isolates collected through the Gonococcal Isolate Surveillance Project in the United States. Phylogenomic analysis revealed that one persisting lineage (Clade A, multi-locus sequence type [MLST] ST1901) with mosaic penA-34 alleles, contained the majority of isolates with elevated MICs to ESCs. We provide evidence that an ancestor to the globally circulating MLST ST1901 clones potentially emerged around the early to mid-20th century (1944, credibility intervals [CI]: 1935-1953), predating the introduction of cephalosporins, but coinciding with the use of penicillin. Such results indicate that drugs with novel mechanisms of action are needed as these strains continue to persist and disseminate globally.