External validation of WGS-based antimicrobial susceptibility prediction tools, KOVER-AMR and ResFinder 4.1 for Escherichia coli clinical isolates

Exploring uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli: an observational analysis

BACKGROUND

Antimicrobial resistance (AMR) in Escherichia coli is a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to variants in a curated reference database, with the implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study, we evaluated the performance of the AMRFinder tool and, subsequently, the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility phenotype predictions in E coli.

METHODS

In this cross-sectional study of international genome sequence data, we assembled a global dataset of 9001 E coli sequences from five publicly available data collections predominantly deriving from human bloodstream infections from: Norway, Oxfordshire (UK), Thailand, the UK, and Sweden. 8555 of these sequences had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes (relevant to amoxicillin-clavulanic acid, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, piperacillin-tazobactam, and trimethoprim) extracted using the National Center for Biotechnology Information AMRFinder tool (using both default and strict [100%] coverage and identity filters). We assessed the predictive value of the presence of these genes for predicting resistance or susceptibility against US Food and Drug Administration thresholds for major and very major errors. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (≥70% match) and antibiotic-resistance genes (ARGs; 100% match) and categorised these according to their frequency in the dataset. Accumulation curves were simulated and correlations between gene frequency in the Oxfordshire and other datasets calculated using the Spearman coefficient. Firth regression was used to model the association between the presence of blaTEM-1 variants and amoxicillin-clavulanic acid or piperacillin-tazobactam resistance, adjusted for the presence of other relevant ARGs.

FINDINGS

The performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet US Food and Drug Administration thresholds for any of the seven antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, most explainable resistance was associated with the presence of a small number of genes. There was a proportion of resistance that could not be explained by known ARGs; this ranged from 75·1% for amoxicillin-clavulanic acid to 3·4% for ciprofloxacin. Only 18 199 (51·5%) of the 35 343 ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1042 unique ARGs, of which 126 (12·1%) were present ten times or more, 313 (30·0%) were present between two and nine times, and 603 (57·9%) were present only once. Simulated accumulation curves revealed that discovery of new (100% match) ARGs present more than once in the dataset plateaued relatively quickly, whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0·76 [95% CI 0·73-0·80], p<0·0001) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed six times in Oxfordshire always being found elsewhere. Finally, using the example of blaTEM-1, we showed that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences; for example, two common, uncatalogued blaTEM-1 alleles with only synonymous mutations compared with the known reference were associated with reduced resistance to amoxicillin-clavulanic acid (adjusted odds ratio 0·58 [95% CI 0·35-0·95], p=0·031) and piperacillin-tazobactam (0·50 [95% CI 0·29-0·82], p=0·005).

INTERPRETATION

We highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery.

FUNDING

National Institute for Health and Care Research, Wellcome, and UK Medical Research Council.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

Suboptimal bioinformatic predictions of antimicrobial resistance from whole-genome sequences in multidrug-resistant Corynebacterium isolates

Herein, we combined different bioinformatics tools and databases (BV-BRC, ResFinder, RAST, and KmerResistance) to perform a prediction of antimicrobial resistance (AMR) in the genomic sequences of 107 Corynebacterium striatum isolates for which trustable antimicrobial susceptibility (AST) phenotypes could be retrieved. Then, the reliabilities of the AMR predictions were evaluated by different metrics: area under the ROC curve (AUC); Major Error Rates (MERs) and Very Major Error Rates (VMERs); Matthews Correlation Coefficient (MCC); F1-Score; and Accuracy. Out of 15 genes that were reliably detected in the C. striatum isolates, only tetW yielded predictive values for tetracycline resistance that were acceptable considering Food and Drug Administration (FDA)'s criteria for quality (MER < 3.0% and VMER with a 95% C.I. ≤1.5-≤7.5); this was accompanied by a MCC score higher than 0.9 for this gene. Noteworthy, our results indicate that other commonly used metrics (AUC, F1-score, and Accuracy) may render overoptimistic evaluations of AMR-prediction reliabilities on imbalanced datasets. Accordingly, out of 10 genes tested by PCR on additional multidrug-resistant Corynebacterium spp. isolates (n = 18), the tetW gene rendered the best agreement values with AST profiles (94.11%). Overall, our results indicate that genome-based AMR prediction can still be challenging for MDR clinical isolates of emerging Corynebacterium spp.

Capture of armA by a novel ISCR element, ISCR28

ISCR28 is a fully functional and active member of the IS91-like family of insertion sequences. ISCR28 is 1,708-bp long and contains a 1,293-bp long putative open reading frame that codes a transposase. Sixty ISCR28-containing sequences from GenBank generated 27 non-repeat genetic contexts, all of which represented naturally occurring biological events that had occurred in a wide range of gram-negative organisms. Insertion of ISCR28 into target DNA preferred the presence of a 5'-GXXT-3' sequence at its terIS (replication terminator) end. Loss of the first 4 bp of its oriIS (origin of replication) likely caused ISCR28 to be trapped in ISApl1-based transposons or similar structures. Loss of terIS and fusion with a mobile element upstream likely promoted co-transfer of ISCR28 and the downstream resistance genes. ArmA and its downstream intact ISCR28 can be excised from recombinant pKD46 plasmids forming circular intermediates, further elucidating its activity as a transposase.

Genomic and phenotypic inconsistencies in Pseudomonas aeruginosa resistome among intensive care patients

Objective

Pseudomonas aeruginosa, a difficult-to-manage nosocomial pathogen, poses a serious threat to clinical outcomes in intensive care (ICU) patients due to its high antimicrobial resistance (AMR). To promote effective management, it is essential to investigate the genomic and phenotypic differences in AMR expression of the isolates.

Methods

A prospective observational study was conducted from July 2022 to April 2023 at Liepaja Regional Hospital in Latvia. The study included all adult patients who were admitted to the ICU and had a documented infection with P. aeruginosa, as confirmed by standard laboratory microbiological testing and short-read sequencing. Since ResFinder is the only sequencing-based database offering antibacterial susceptibility testing (AST) data for each antibiotic, we conducted a comparison of the resistance profile with the results of phenotypic testing, evaluating if ResFinder met the US Food and Drug Administration (FDA) requirements for approval as a new AMR diagnostic test. Next, to improve precision, AST data from ResFinder was compared with two other databases - AMRFinderPlus and RGI. Additionally, data was gathered from environmental samples to inform the implementation of appropriate infection control measures in real time.

Results

Our cohort consisted of 33 samples from 29 ICU patients and 34 environmental samples. The presence of P. aeruginosa infection was found to be associated with unfavourable clinical outcomes. A third of the patient samples were identified as multi-drug resistant isolates. Apart from resistance against colistin, significant discrepancies were observed when phenotypic data were compared to genotypic data. For example, the aminoglycoside resistance prediction of ResFinder yielded a major errors value of 3.03% for amikacin, which was marginally above the FDA threshold. Among the three positive environmental samples, one sample exhibited multiple AMR genes similar to the patient samples in its cluster.

Conclusion

Our findings underscore the importance of utilizing a combination of diagnostic methods for the identification of resistance mechanisms, clusters, and environmental reservoirs in ICUs.

Accessory genes define species-specific routes to antibiotic resistance

A deeper understanding of the relationship between the antimicrobial resistance (AMR) gene carriage and phenotype is necessary to develop effective response strategies against this global burden. AMR phenotype is often a result of multi-gene interactions; therefore, we need approaches that go beyond current simple AMR gene identification tools. Machine-learning (ML) methods may meet this challenge and allow the development of rapid computational approaches for AMR phenotype classification. To examine this, we applied multiple ML techniques to 16,950 bacterial genomes across 28 genera, with corresponding MICs for 23 antibiotics with the aim of training models to accurately determine the AMR phenotype from sequenced genomes. This resulted in a >1.5-fold increase in AMR phenotype prediction accuracy over AMR gene identification alone. Furthermore, we revealed 528 unique (often species-specific) genomic routes to antibiotic resistance, including genes not previously linked to the AMR phenotype. Our study demonstrates the utility of ML in predicting AMR phenotypes across diverse clinically relevant organisms and antibiotics. This research proposes a rapid computational method to support laboratory-based identification of the AMR phenotype in pathogens.

Prevalence and genetic characteristics of Salmonella enterica serovar Meleagridis from animals and humans

Salmonella enterica serovar Meleagridis (S. Meleagridis) is a non-typhoidal Salmonella serotype commonly found in food and humans. In this study, we investigated 61 Chinese S. Meleagridis isolates from various sources, predominantly from pigs and pig products. Additionally, the serotype was also identified in samples from human infections. Whole-genome sequencing analysis of these isolates, combined with 10 isolates from other countries, demonstrated that the Chinese isolates formed a distinct Cluster C, further divided into two subclusters (Cluster C-1 and Cluster C-2) based on cgMLST analysis. CRISPR typing divided the 61 isolates into three CRISPR types (MCT1, MCT2, MCT3), belonging to Cluster I (96.7%, 59/61) and Cluster II (3.3%, 2/61), which corresponded to Cluster C-2 and Cluster C-1, respectively. Among the 48 identified spacers, the spacer SoeB5 was the only target differentiating MCT1 and MCT2 isolates of Cluster I. MelB12 and MelB13, identified in US and Denmark isolates, were not found among the 61 Chinese isolates. Examination of antimicrobial resistance gene profiles and their genetic contexts uncovered the presence of IncR plasmids in 43 (70.5%, 43/61) isolates within Cluster C, conferring resistance to tetracycline and trimethoprim/sulfamethoxazole. Homology analysis of spacers showed that 12 spacers exhibited similarity to sequences in phages or plasmids. Additionally, five spacers showed homology to sequences in plasmids from other Salmonella serotypes, suggesting their potential role in helping S. Meleagridis resist against Salmonella isolates carrying similar plasmids. The comprehensive analysis of CRISPR, cgMLST, and antimicrobial resistance in S. Meleagridis highlights the pig reservoir as a crucial factor in the evolution and transmission of this serotype to humans.

Complete genome analysis of Bacillus velezensis TS5 and its potential as a probiotic strain in mice

Introduction

In recent years, a large number of studies have shown that Bacillus velezensis has the potential as an animal feed additive, and its potential probiotic properties have been gradually explored.

Methods

In this study, Illumina NovaSeq PE150 and Oxford Nanopore ONT sequencing platforms were used to sequence the genome of Bacillus velezensis TS5, a fiber-degrading strain isolated from Tibetan sheep. To further investigate the potential of B. velezensis TS5 as a probiotic strain, in vivo experiments were conducted using 40 five-week-old male specific pathogen-free C57BL/6J mice. The mice were randomly divided into four groups: high fiber diet control group (H group), high fiber diet probiotics group (HT group), low fiber diet control group (L group), and low fiber diet probiotics group (LT group). The H and HT groups were fed high-fiber diet (30%), while the L and LT groups were fed low-fiber diet (5%). The total bacteria amount in the vegetative forms of B. velezensis TS5 per mouse in the HT and LT groups was 1 × 109 CFU per day, mice in the H and L groups were given the same volume of sterile physiological saline daily by gavage, and the experiment period lasted for 8 weeks.

Results

The complete genome sequencing results of B. velezensis TS5 showed that it contained 3,929,788 nucleotides with a GC content of 46.50%. The strain encoded 3,873 genes that partially related to stress resistance, adhesion, and antioxidants, as well as the production of secondary metabolites, digestive enzymes, and other beneficial nutrients. The genes of this bacterium were mainly involved in carbohydrate metabolism, amino acid metabolism, vitamin and cofactor metabolism, biological process, and molecular function, as revealed by KEGG and GO databases. The results of mouse tests showed that B. velezensis TS5 could improve intestinal digestive enzyme activity, liver antioxidant capacity, small intestine morphology, and cecum microbiota structure in mice.

Conclusion

These findings confirmed the probiotic effects of B. velezensis TS5 isolated from Tibetan sheep feces and provided the theoretical basis for the clinical application and development of new feed additives.

Ultrafast detection of β-lactamase resistance in Klebsiella pneumoniae from blood culture by nanopore sequencing

Aim: This study aimed to assess the ultra-fast method using MinION™ sequencing for rapid identification of β-lactamase-producing Klebsiella pneumoniae clinical isolates from positive blood cultures. Methods: Spiked-blood positive blood cultures were extracted using the ultra-fast method and automated DNA extraction for MinION sequencing. Raw reads were analyzed for β-lactamase resistance genes. Multilocus sequence typing and β-lactamase variant characterization were performed after assembly. Results: The ultra-fast method identified clinically relevant β-lactamase resistance genes in less than 1 h. Multilocus sequence typing and β-lactamase variant characterization required 3-6 h. Sequencing quality showed no direct correlation with pore number or DNA concentration. Conclusion: Nanopore sequencing, specifically the ultra-fast method, is promising for the rapid diagnosis of bloodstream infections, facilitating timely identification of multidrug-resistant bacteria in clinical samples.

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

Several bioinformatics genotyping algorithms are now commonly used to characterize antimicrobial resistance (AMR) gene profiles in whole-genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance and SRST2, run with the ResFinder database) and their outputs compared. For simulation tests where 3079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3076 (99.9 %) simulations, ABRicate for 3054 (99.2 %), ARIBA for 2783 (90.4 %) and SRST2 for 2108 (68.5 %). For simulation tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance identified the correct alleles in 35 338/46 318 (76.3 %) simulations, ABRicate identified them in 11 842/46 318 (25.6 %) simulations, ARIBA identified them in 1679/46 318 (3.6 %) simulations and SRST2 identified them in 2000/46 318 (4.3 %) simulations. In real data, across all methods, 1392/1818 (76 %) isolates had discrepant allele calls for at least 1 gene. In addition to highlighting areas for improvement in challenging scenarios, (e.g. identification of AMR genes at <10× coverage, identifying multiple closely related AMR genes present in the same sample), our evaluations identified some more systematic errors that could be readily soluble, such as repeated misclassification (i.e. naming) of genes as shorter variants of the same gene present within the reference resistance gene database. Such naming errors accounted for at least 2530/4321 (59 %) of the discrepancies seen in real data. Moreover, many of the remaining discrepancies were likely 'artefactual', with reporting of cut-off differences accounting for at least 1430/4321 (33 %) discrepants. Whilst we found that comparing outputs generated by running multiple algorithms on the same dataset could identify and resolve these algorithmic artefacts, the results of our evaluations emphasize the need for developing new and more robust genotyping algorithms to further improve accuracy and performance.