Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data

Large-scale genomic analysis reveals significant role of insertion sequences in antimicrobial resistance of Acinetobacter baumannii

Acinetobacter baumannii, a prominent nosocomial pathogen renowned for its extensive resistance to antimicrobial agents, poses a significant challenge in the accurate prediction of antimicrobial resistance (AMR) from genomic data. Despite thorough researches on the molecular mechanisms of AMR, gaps remain in our understanding of key contributors. This study utilized rule-based and three machine learning models to predict AMR phenotypes, aiming to decipher key genomic factors associated with AMR. Genomes and antibiotic resistance phenotypes from 1,012 public isolates were employed for model construction and training. To validate the models, a data set comprising 164 self-collected strains underwent next-generation sequencing, nanopore long-read sequencing, and antimicrobial susceptibility testing using the broth dilution method. It was found that the presence of antibiotic resistance genes (ARGs) alone was insufficient to accurately predict AMR phenotype for the majority of antibiotics (90%, 18 out of 20) in the public data set. Conversely, it was observed that combining ARGs with insertion sequence (IS) elements significantly enhanced predictive performance. The Random Forest model was found to outperform the support vector machine (SVM), logistic regression model, and rule-based method across all 20 antibiotics, with accuracies ranging from 83.80% to 97.70%. In the validation data set, even higher accuracies were achieved, ranging from 85.63% to 99.31%. Furthermore, conserved sequence patterns between IS elements and ARGs were validated using self-collected long-read sequencing data, substantially enhancing the accuracy of AMR prediction in A. baumannii. This study underscores the pivotal role of IS elements in AMR.

IMPORTANCE

The interplay between insertion sequences (ISs) and antibiotic resistance genes (ARGs) in Acinetobacter baumannii contributes to resistance against specific antibiotics. Conventionally, genetic variations and ARGs have been utilized for predicting resistance phenotypes, with the potential pivotal role of IS elements largely overlooked. Our study advances this approach by integrating both rule-based and machine learning models to predict AMR in A. baumannii. This significantly enhances the accuracy of AMR prediction, emphasizing the pivotal function of IS elements in antibiotic resistance. Notably, we uncover a series of conserved sequence patterns linking IS elements and ARGs, which outperform ARGs alone in phenotypic prediction. Our findings are crucial for bioinformatics strategies aimed at studying and tracking AMR, offering novel insights into combating the escalating AMR challenge.

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.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

The application status of sequencing technology in global respiratory infectious disease diagnosis

Next-generation sequencing (NGS) has revolutionized clinical microbiology, particularly in diagnosing respiratory infectious diseases and conducting epidemiological investigations. This narrative review summarizes conventional methods for routine respiratory infection diagnosis, including culture, smear microscopy, immunological assays, image techniques as well as polymerase chain reaction(PCR). In contrast to conventional methods, there is a new detection technology, sequencing technology, and here we mainly focus on the next-generation sequencing NGS, especially metagenomic NGS(mNGS). NGS offers significant advantages over traditional methods. Firstly, mNGS eliminates assumptions about pathogens, leading to faster and more accurate results, thus reducing diagnostic time. Secondly, it allows unbiased identification of known and novel pathogens, offering broad-spectrum coverage. Thirdly, mNGS not only identifies pathogens but also characterizes microbiomes, analyzes human host responses, and detects resistance genes and virulence factors. It can complement targeted sequencing for bacterial and fungal classification. Unlike traditional methods affected by antibiotics, mNGS is less influenced due to the extended survival of pathogen DNA in plasma, broadening its applicability. However, barriers to full integration into clinical practice persist, primarily due to cost constraints and limitations in sensitivity and turnaround time. Despite these challenges, ongoing advancements aim to improve cost-effectiveness and efficiency, making NGS a cornerstone technology for global respiratory infection diagnosis.

Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis

Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.

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.

Whole genome sequencing of multidrug-resistant Klebsiella pneumoniae from poultry in Noakhali, Bangladesh: Assessing risk of transmission to humans in a pilot study

BACKGROUND

Multi-drug resistant (MDR) Klebsiella pneumoniae is a public health concern due to its presence in Bangladeshi poultry products and its ability to spread resistance genes. This study genetically characterizes a distinct MDR K. pneumoniae isolate from the gut of poultry in Noakhali, Bangladesh, offering insights into its resistance mechanisms and public health impact.

METHODS

Klebsiella pneumoniae isolates from broiler and layer poultry were identified using biochemical and molecular analyses. Eleven isolates were tested for antibiotic sensitivity and categorized by their Multiple Antibiotic Resistance Index (MARI) profiles. The isolate with the highest MARI was selected for whole-genome sequencing using Illumina technology. The sequencing data were analyzed for genome annotation, pan-genome analysis, genome similarities, sequence type identification, and the identification of genetic determinants of resistance and virulence genes.

RESULT

We identified 10 MARI profiles among 11 K. pneumoniae isolates, with values ranging from 0.64 to 0.94. The highest MARI of 0.94 was found in an isolate from a layer poultry. This isolate's genome, 5401,789 base pairs long with 89.6 % coverage, showed potential inter-species dissemination, as indicated by core genome phylogenetic analysis. It possessed genes conferring resistance to fluoroquinolones, aminoglycosides, β-lactams, folate pathway antagonists, fosfomycin, macrolides, quinolones, rifamycin, tetracyclines, and polymyxins, including colistin.

CONCLUSION

Poultry serve as reservoirs for MDR K. pneumoniae, which can spread to other species and pose significant health risks. Rigorous monitoring of antibiotic use and genetic characterization of MDR bacterial isolates are essential to mitigate this threat.

Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae

Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.

Antimicrobial resistance and clonal relationships of Salmonella enterica Serovar Gallinarum biovar pullorum strains isolated in China based on whole genome sequencing

BACKGROUND

Pullorum disease is a serious problem in many countries. Caused by Salmonella enterica serovar Gallinarum biovar Pullorum (S. Pullorum), it creates huge economic losses in the poultry industry. Although pullorum disease has been well-controlled in many developed countries, it is still a critical problem in developing countries. However, there is still a lack of information on S. Pullorum strains isolated from different regions and sources in China. The objective of this study was to supply the antimicrobial resistance patterns and clonal relationships of S. Pullorum from breeder chicken farms.

METHODS

In this study, a total of 114 S. Pullorum strains recovered from 11 provinces and municipalities in China between 2020 and 2021 were selected. These 114 S. Pullorum strains were analyzed using whole genome sequencing (WGS). Antimicrobial resistance (AMR) was tested both by genotypic prediction using the WGS method and using disc diffusion to assess phenotypic AMR.

RESULTS

These 114 sequenced S. Pullorum strains were divided into three sequence types (STs), the dominant STs was ST92 (104/114). Further core genome multi-locus sequence typing analysis indicated that 114 S. Pullorum strains may have a close relationship, which could be clonally transmitted among different provinces and municipalities. Our results showed a close relationship between the S. Pullorum strains found in different regions, indicating these strains may have been transmitted in China a long time ago. Nearly all S. Pullorum strains 94.74% (n = 108) were resistant to at least one antimicrobial class, and 35.96% of the examined Salmonella strains were considered multiple drug resistant.

CONCLUSION

Overall, this study showed that S. Pullorum strains in China have a close genetic relationship in terms of antimicrobial resistance, suggesting widespread clonal transmission.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Whole-genome sequencing of multidrug-resistant Klebsiella pneumoniae with capsular serotype K2 isolates from mink in China

BACKGROUND

Klebsiella pneumoniae is a zoonotic opportunistic pathogen, and also one of the common pathogenic bacteria causing mink pneumonia. The aim of this study was to get a better understanding of the whole-genome of multi-drug resistant Klebsiella pneumoniae with K2 serotype in China. This study for the first time to analyze Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, resistance and virulence genes of Klebsiella pneumoniae in mink.

RESULTS

The isolate was Klebsiella pneumoniae with serotype K2 and ST6189 by PCR method. The string test was positive and showed high mucus phenotype. There was one plasmid with IncFIB replicons in the genome. The virulence factors including capsule, lipopolysaccharide, adhesin, iron uptake system, urease, secretory system, regulatory gene (rcsA, rcsB), determinants of pili adhesion, enolase and magnesium ion absorption related genes. The strain was multi-drug resistant. A total of 26  resistance genes, including beta-lactam, aminoglycosides, tetracycline, fluoroquinolones, sulfonamides, amide alcohols, macrolides, rifampicin, fosfomycin, vancomycin, diaminopyrimidines and polymyxin. Multidrug-resistant efflux protein AcrA, AcrB, TolC, were predicted in the strain.

CONCLUSION

It was the first to identify that serotype K2 K. pneumonia with ST6189 isolated from mink in China. The finding indicated that hypervirulent and multi-drug resistant K. pneumoniae was exist in Chinese mink. The whole-genome of K. pneumoniae isolates have importance in mink farming practice.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance

The erythromycin resistance RNA methyltransferase (erm) confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of erm is often induced by the macrolide-mediated ribosome stalling in the upstream co-transcribed leader sequence, thereby triggering a conformational switch of the intergenic RNA hairpins to allow the translational initiation of erm. We investigated the evolutionary emergence of the upstream erm regulatory elements and the impact of allelic variation on erm expression and the MLS phenotype. Through systematic profiling of the upstream regulatory sequences across all known erm operons, we observed that specific erm subfamilies, such as ermB and ermC, have independently evolved distinct configurations of small upstream ORFs and palindromic repeats. A population-wide genomic analysis of the upstream ermB regions revealed substantial non-random allelic variation at numerous positions. Utilizing machine learning-based classification coupled with RNA structure modeling, we found that many alleles cooperatively influence the stability of alternative RNA hairpin structures formed by the palindromic repeats, which, in turn, affects the inducibility of ermB expression and MLS phenotypes. Subsequent experimental validation of 11 randomly selected variants demonstrated an impressive 91% accuracy in predicting MLS phenotypes. Furthermore, we uncovered a mixed distribution of MLS-sensitive and MLS-resistant ermB loci within the evolutionary tree, indicating repeated and independent evolution of MLS resistance. Taken together, this study not only elucidates the evolutionary processes driving the emergence and development of MLS resistance but also highlights the potential of using non-coding genomic allele data to predict antibiotic resistance phenotypes.

IMPORTANCE

Antibiotic resistance (AR) poses a global health threat as the efficacy of available antibiotics has rapidly eroded due to the widespread transmission of AR genes. Using Erm-dependent MLS resistance as a model, this study highlights the significance of non-coding genomic allelic variations. Through a comprehensive analysis of upstream regulatory elements within the erm family, we elucidated the evolutionary emergence and development of AR mechanisms. Leveraging population-wide machine learning (ML)-based genomic analysis, we transformed substantial non-random allelic variations into discernible clusters of elements, enabling precise prediction of MLS phenotypes from non-coding regions. These findings offer deeper insight into AR evolution and demonstrate the potential of harnessing non-coding genomic allele data for accurately predicting AR phenotypes.

Resistome Analysis of Klebsiella pneumoniae Complex from Residential Aged Care Facilities Demonstrates Intra-facility Clonal Spread of Multidrug-Resistant Isolates

Antimicrobial-resistant Klebsiella pneumoniae is one of the predominant pathogens in healthcare settings. However, the prevalence and resistome of this organism within residential aged care facilities (RACFs), which are potential hotspots for antimicrobial resistance, remain unexplored. Here, we provide a phenotypic and molecular characterization of antimicrobial-resistant K. pneumoniae isolated from RACFs. K. pneumoniae was isolated from urine, faecal and wastewater samples and facility swabs. The antimicrobial susceptibility profiles of all the isolates were determined and the genomic basis for resistance was explored with whole-genome sequencing on a subset of isolates. A total of 147 K. pneumoniae were isolated, displaying resistance against multiple antimicrobials. Genotypic analysis revealed the presence of beta-lactamases and the ciprofloxacin-resistance determinant QnrB4 but failed to confirm the basis for the observed cephalosporin resistance. Clonal spread of the multidrug-resistant, widely disseminated sequence types 323 and 661 was observed. This study was the first to examine the resistome of K. pneumoniae isolates from RACFs and demonstrated a complexity between genotypic and phenotypic antimicrobial resistance. The intra-facility dissemination and persistence of multidrug-resistant clones is concerning, given that residents are particularly vulnerable to antimicrobial resistant infections, and it highlights the need for continued surveillance and interventions to reduce the risk of outbreaks.

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.

Assessment of in vitro colistin susceptibility of carbapenem-resistant clinical Gram-negative bacterial isolates using four commercially available systems & Whole-genome sequencing: A diagnostic accuracy study

AIM

To analyze the diagnostic utility of commercially available platforms and Whole-genome sequencing (WGS) for accurate determination of colistin susceptibility test results.

MATERIAL & METHODS

An exploratory diagnostic accuracy study was conducted in which sixty carbapenem-resistant Gram-negative bacteria were subjected to identification and AST using MALDI-TOF MS & MicroScan walkaway 96 Plus. Additional AST was performed using the BD Phoenix system and Mikrolatest colistin kit. The test isolates were subjected to Vitek-2 and WGS at CRL, Bengaluru.

RESULTS

There was no statistically significant agreement between the colistin susceptibility results obtained by WGS, with those of commercial phenotypic platforms. The MicroScan 96 Plus had the highest sensitivity (31 %) & NPV (77 %), and the BD Phoenix system had the highest specificity (97 %) and PPV (50 %), respectively, for determining colistin resistance.

CONCLUSION

The utility of WGS as a tool in AMR surveillance and validation of phenotypic AST methods should be explored further.

The clinical and epidemiological impacts of whole genomic sequencing on bacterial and virological agents

Whole Genome Sequencing (WGS) is a molecular biology tool consisting in the sequencing of the entire genome of a given organism. Due to its ability to provide the finest available resolution of bacterial and virological genetics, it is used at several levels in the field of infectiology. On an individual scale and through application of a single technique, it enables the typological identification and characterization of strains, the characterization of plasmids, and enhanced search for resistance genes and virulence factors. On a collective scale, it enables the characterization of strains and the determination of phylogenetic links between different microorganisms during community outbreaks and healthcare-associated epidemics. The information provided by WGS enables real-time monitoring of strain-level epidemiology on a worldwide scale, and facilitates surveillance of the resistance dissemination and the introduction or emergence of pathogenic variants in humans or their environment. There are several possible approaches to completion of an entire genome. The choice of one method rather than another is essentially dictated by the matrix, either a clinical sample or a culture isolate, and the clinical objective. WGS is an advanced technology that remains costly despite a gradual decrease in its expenses, potentially hindering its implementation in certain laboratories and thus its use in routine microbiology. Even though WGS is making steady inroads as a reference method, efforts remain needed in view of so harmonizing its interpretations and decreasing the time to generation of conclusive results.

Associations between Isolation Source, Clonal Composition, and Antibiotic Resistance Genes in Escherichia coli Collected in Washington State, USA

Antimicrobial resistance (AMR) is a global health problem stemming from the use of antibiotics in humans, animals, and the environment. This study used whole-genome sequencing (WGS) of E. coli to explore patterns of AMR across sectors in Washington State, USA (WA). The WGS data from 1449 E. coli isolates were evaluated for isolation source (humans, animals, food, or the environment) and the presence of antibiotic resistance genes (ARGs). We performed sequence typing using PubMLST and used ResFinder to identify ARGs. We categorized isolates as being pan-susceptible, resistant, or multidrug-resistant (MDR), defined as carrying resistance genes for at least three or more antimicrobial drug classes. In total, 60% of isolates were pan-susceptible, while 18% were resistant, and 22% exhibited MDR. The proportion of resistant isolates varied significantly according to the source of the isolates (p < 0.001). The greatest resistance was detected in isolates from humans and then animals, while environmental isolates showed the least resistance. This study demonstrates the feasibility of comparing AMR across various sectors in Washington using WGS and a One Health approach. Such analysis can complement other efforts for AMR surveillance and potentially lead to targeted interventions and monitoring activities to reduce the overall burden of AMR.

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.

Comparative genomics reveals the correlations of stress response genes and bacteriophages in developing antibiotic resistance of Staphylococcus saprophyticus

IMPORTANCE

Staphylococcus saprophyticus is the second most common bacteria associated with urinary tract infections (UTIs) in women. The antimicrobial treatment regimen for uncomplicated UTI is normally nitrofurantoin, trimethoprim-sulfamethoxazole (TMP-SMX), or a fluoroquinolone without routine susceptibility testing of S. saprophyticus recovered from urine specimens. However, TMP-SMX-resistant S. saprophyticus has been detected recently in UTI patients, as well as in our cohort. Herein, we investigated the understudied resistance patterns of this pathogenic species by linking genomic antibiotic resistance gene (ARG) content to susceptibility phenotypes. We describe ARG associations with known and novel SCCmec configurations as well as phage elements in S. saprophyticus, which may serve as intervention or diagnostic targets to limit resistance transmission. Our analyses yielded a comprehensive database of phenotypic data associated with the ARG sequence in clinical S. saprophyticus isolates, which will be crucial for resistance surveillance and prediction to enable precise diagnosis and effective treatment of S. saprophyticus UTIs.

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.

Phenotypic and genotypic analysis of antimicrobial resistance in Nocardia species

BACKGROUND

Antimicrobial resistance is common in Nocardia species but data regarding the molecular mechanisms beyond their resistance traits are limited. Our study aimed to determine the species distribution, the antimicrobial susceptibility profiles, and investigate the associations between the resistance traits and their genotypic determinants.

METHODS

The study included 138 clinical strains of Nocardia from nine Israeli microbiology laboratories. MIC values of 12 antimicrobial agents were determined using broth microdilution. WGS was performed on 129 isolates of the eight predominant species. Bioinformatic analysis included phylogeny and determination of antimicrobial resistance genes and mutations.

RESULTS

Among the isolates, Nocardia cyriacigeorgica was the most common species (36%), followed by Nocardia farcinica (16%), Nocardia wallacei (13%), Nocardia abscessus (9%) and Nocardia brasiliensis (8%). Linezolid was active against all isolates, followed by trimethoprim/sulfamethoxazole (93%) and amikacin (91%). Resistance to other antibiotics was species-specific, often associated with the presence of resistance genes or mutations: (1) aph(2″) in N. farcinica and N. wallacei (resistance to tobramycin); (ii) blaAST-1 in N. cyriacigeorgica and Nocardia neocaledoniensis (resistance to amoxicillin/clavulanate); (iii) blaFAR-1 in N. farcinica (resistance to ceftriaxone); (iv) Ser83Ala substitution in the gyrA gene in four species (resistance to ciprofloxacin); and (v) the 16S rRNA m1A1408 methyltransferase in N. wallacei isolates (correlating with amikacin resistance).

CONCLUSIONS

Our study provides a comprehensive understanding of Nocardia species diversity, antibiotic resistance patterns, and the molecular basis of antimicrobial resistance. Resistance appears to follow species-related patterns, suggesting a lesser role for de novo evolution or transmission of antimicrobial resistance.

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

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.

Advances in the Applications of Bioinformatics and Chemoinformatics

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.

Advances in the Microbiological Diagnosis of Prosthetic Joint Infections

A significant number of prosthetic joint infections (PJI) are culture-negative and/or misinterpreted as aseptic failures in spite of the correct implementation of diagnostic culture techniques, such as tissue sample processing in a bead mill, prolonged incubation time, or sonication of removed implants. Misinterpretation may lead to unnecessary surgery and needless antimicrobial treatment. The diagnostic value of non-culture techniques has been investigated in synovial fluid, periprosthetic tissues, and sonication fluid. Different feasible improvements, such as real-time technology, automated systems and commercial kits are now available to support microbiologists. In this review, we describe non-culture techniques based on nucleic acid amplification and sequencing methods. Polymerase chain reaction (PCR) is a frequently used technique in most microbiology laboratories which allows the detection of a nucleic acid fragment by sequence amplification. Different PCR types can be used to diagnose PJI, each one requiring the selection of appropriate primers. Henceforward, thanks to the reduced cost of sequencing and the availability of next-generation sequencing (NGS), it will be possible to identify the whole pathogen genome sequence and, additionally, to detect all the pathogen sequences present in the joint. Although these new techniques have proved helpful, strict conditions need to be observed in order to detect fastidious microorganisms and rule out contaminants. Specialized microbiologists should assist clinicians in interpreting the result of the analyses at interdisciplinary meetings. New technologies will gradually be made available to improve the etiologic diagnoses of PJI, which will remain an important cornerstone of treatment. Strong collaboration among all specialists involved is essential for the correct diagnosis of PJI.

Antibiotic resistance and virulence characteristics of four carbapenem-resistant Klebsiella pneumoniae strains coharbouring blaKPC and blaNDM based on whole genome sequences from a tertiary general teaching hospital in central China between 2019 and 2021

OBJECTIVE

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infection is a worldwide health issue that poses a serious threat to public health. This study summarizes the clinical features of four patients with CRKP coproducing NDM and KPC infections and further analyses the molecular typing, resistance and virulence factors of the four CRKP strains.

METHODS

Of the twenty-two CRKP isolates, four strains coharbouring bla_KPC and bla_NDM isolated from four patients were screened by Sanger sequencing between October 2019 and April 2021. Demographics, clinical and pathological data of the four patients were collected through electronic medical records. Antimicrobial susceptibility testing, biofilm formation assays and serum bactericidal assays were performed on the four isolates. The antibiotic resistance and virulence genes were investigated by whole-genome sequencing. Sequence types (STs) were determined by multilocus sequence typing, and serotypes were identified by wzi gene sequencing.

RESULTS

Three patients recovered, and one patient stopped treatment. Four strains were multiple carbapenemase producers: KPC-2, NDM-4, SME-5 and IMI-4 coproducer; KPC-2, NDM-1 and SME-3 coproducer; KPC-2, NDM-1 and IMI-3 coproducer; KPC-2 and NDM-5 coproducer. They also harboured ESBL genes and mutations in the efflux pump regulator genes. They were multidrug resistant but sensitive to tigecycline and colistin. Four isolates had moderate biofilm-forming abilities and carried various virulence genes, including siderophores, type 1 fimbriae and E. coli common pilus. Only the NO. 3 strain was resistant to the serum. The STs and serotypes of the four strains were ST11 and KL64, ST337 and none, ST307 and KL102KL149KL155, and ST29 and K54, respectively.

CONCLUSION

Four CRKP strains coharbouring bla_KPC and bla_NDM also carried other carbapenemase genes. Notably, the NO. 1 isolate carrying four carbapenemase genes has not been reported globally until now. Four strains exhibited a high level of resistance to multiple antibiotics. Additionally, three of the four patients were exposed to invasive medical devices that provided an environment for biofilm formation. Meanwhile, three strains with adhesion genes as moderate biofilm formers might form biofilms resulting in long hospital stays, increasing therapeutic difficulty, and even treatment failure. This study reminds clinicians that CRKP strains with multiple carbapenemase genes emerged in our hospital, and stronger measures should be taken to the control of nosocomial infections.

Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing

Besides phenotypic antimicrobial susceptibility testing (AST), whole genome sequencing (WGS) is a promising alternative approach for detection of resistance phenotypes. The aim of this study was to investigate the concordance between WGS-based resistance prediction and phenotypic AST results for enterococcal clinical isolates using a user-friendly online tools and databases. A total of 172 clinical isolates (34 E. faecalis, 138 E. faecium) received at the French National Reference Center for enterococci from 2017 to 2020 were included. AST was performed by disc diffusion or MIC determination for 14 antibiotics according to CA-SFM/EUCAST guidelines. The genome of all strains was sequenced using the Illumina technology (MiSeq) with bioinformatic analysis from raw reads using online tools ResFinder 4.1 and LRE-finder 1.0. For both E. faecalis and E. faecium, performances of WGS-based genotype to predict resistant phenotypes were excellent (concordance > 90%), particularly for antibiotics commonly used for treatment of enterococcal infections such as ampicillin, gentamicin, vancomycin, teicoplanin, and linezolid. Note that 100% very major errors were found for quinupristin-dalfopristin, tigecycline, and rifampicin for which resistance mutations are not included in databases. Also, it was not possible to predict phenotype from genotype for daptomycin for the same reason. WGS combined with online tools could be easily used by non-expert clinical microbiologists as a rapid and reliable tool for prediction of phenotypic resistance to first-line antibiotics among enterococci. Nonetheless, some improvements should be made such as the implementation of resistance mutations in the database for some antibiotics.

A Cross-Validated Feature Selection (CVFS) approach for extracting the most parsimonious feature sets and discovering potential antimicrobial resistance (AMR) biomarkers

Understanding genes and their underlying mechanisms is critical in deciphering how antimicrobial-resistant (AMR) bacteria withstand detrimental effects of antibiotic drugs. At the same time the genes related to AMR phenotypes may also serve as biomarkers for predicting whether a microbial strain is resistant to certain antibiotic drugs. We developed a Cross-Validated Feature Selection (CVFS) approach for robustly selecting the most parsimonious gene sets for predicting AMR activities from bacterial pan-genomes. The core idea behind the CVFS approach is interrogating features among non-overlapping sub-parts of the datasets to ensure the representativeness of the features. By randomly splitting the dataset into disjoint sub-parts, conducting feature selection within each sub-part, and intersecting the features shared by all sub-parts, the CVFS approach is able to achieve the goal of extracting the most representative features for yielding satisfactory AMR activity prediction accuracy. By testing this idea on bacterial pan-genome datasets, we showed that this approach was able to extract the most succinct feature sets that predicted AMR activities very well, indicating the potential of these genes as AMR biomarkers. The functional analysis demonstrated that the CVFS approach was able to extract both known AMR genes and novel ones, suggesting the capabilities of the algorithm in selecting relevant features and highlighting the potential of the novel genes in expanding the antimicrobial resistance gene databases.

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.

Genomic characterization of Salmonella enterica serovar Kentucky and London recovered from food and human salmonellosis in Zhejiang Province, China (2016–2021)

Increasing human salmonellosis caused by Salmonella enterica serovar Kentucky and London has raised serious concerns. To better understand possible health risks, insights were provided into specific genetic traits and antimicrobial resistance of 88 representative isolates from human and food sources in Zhejiang Province, China, during 2016–2021. Phylogenomic analysis revealed consistent clustering of isolates into the respective serovar or sequence types, and identified plausible interhost transmission via distinct routes. Each serovar exhibited remarkable diversity in host range and disease-causing potential by cgMLST analyses, and approximately half (48.6%, 17/35) of the food isolates were phylogenetically indistinguishable to those of clinical isolates in the same region. S. London and S. Kentucky harbored serovar-specific virulence genes contributing to their functions in pathogenesis. The overall resistance genotypes correlated with 97.7% sensitivity and 60.2% specificity to the identified phenotypes. Resistance to ciprofloxacin, cefazolin, tetracycline, ampicillin, azithromycin, chloramphenicol, as well as multidrug resistance, was common. High-level dual resistance to ciprofloxacin and cephalosporins in S. Kentucky ST198 isolates highlights evolving threats of antibiotic resistance. These findings underscored the necessity for the development of effective strategies to mitigate the risk of food contamination by Salmonella host-restricted serovars.

Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance

The hospital environment significantly contributes to the onset of healthcare-associated infections (HAIs), which represent one of the most frequent complications occurring in healthcare facilities worldwide. Moreover, the increased antimicrobial resistance (AMR) characterizing HAI-associated microbes is one of the human health’s main concerns, requiring the characterization of the contaminating microbial population in the hospital environment. The monitoring of surface microbiota in hospitals is generally addressed by microbial cultural isolation. However, this has some important limitations mainly relating to the inability to define the whole drug-resistance profile of the contaminating microbiota and to the long time period required to obtain the results. Hence, there is an urgent need to implement environmental surveillance systems using more effective methods. Molecular approaches, including next-generation sequencing and PCR assays, may be useful and effective tools to monitor microbial contamination, especially the growing AMR of HAI-associated pathogens. Herein, we summarize the results of our recent studies using culture-based and molecular analyses in 12 hospitals for adults and children over a 5-year period, highlighting the advantages and disadvantages of the techniques used.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.

One Day in Denmark: Comparison of Phenotypic and Genotypic Antimicrobial Susceptibility Testing in Bacterial Isolates From Clinical Settings

Antimicrobial susceptibility testing (AST) should be fast and accurate, leading to proper interventions and therapeutic success. Clinical microbiology laboratories rely on phenotypic methods, but the continuous improvement and decrease in the cost of whole-genome sequencing (WGS) technologies make them an attractive alternative. Studies evaluating the performance of WGS-based prediction of antimicrobial resistance (AMR) for selected bacterial species have shown promising results. There are, however, significant gaps in the literature evaluating the applicability of WGS as a diagnostics method in real-life clinical settings against the range of bacterial pathogens experienced there. Thus, we compared standard phenotypic AST results with WGS-based predictions of AMR profiles in bacterial isolates without preselection of defined species, to evaluate the applicability of WGS as a diagnostics method in clinical settings. We collected all bacterial isolates processed by all Danish Clinical Microbiology Laboratories in 1 day. We randomly selected 500 isolates without any preselection of species. We performed AST through standard broth microdilution (BMD) for 488 isolates (n = 6,487 phenotypic AST results) and compared results with in silico antibiograms obtained through WGS (Illumina NextSeq) followed by bioinformatics analyses using ResFinder 4.0 (n = 5,229 comparisons). A higher proportion of AMR was observed for Gram-negative bacteria (10.9%) than for Gram-positive bacteria (6.1%). Comparison of BMD with WGS data yielded a concordance of 91.7%, with discordant results mainly due to phenotypically susceptible isolates harboring genetic AMR determinants. These cases correspond to 6.2% of all isolate-antimicrobial combinations analyzed and to 6.8% of all phenotypically susceptible combinations. We detected fewer cases of phenotypically resistant isolates without any known genetic resistance mechanism, particularly 2.1% of all combinations analyzed, which corresponded to 26.4% of all detected phenotypic resistances. Most discordances were observed for specific combinations of species-antimicrobial: macrolides and tetracycline in streptococci, ciprofloxacin and β-lactams in combination with β-lactamase inhibitors in Enterobacterales, and most antimicrobials in Pseudomonas aeruginosa. WGS has the potential to be used for surveillance and routine clinical microbiology. However, in clinical microbiology settings and especially for certain species and antimicrobial agent combinations, further developments in AMR gene databases are needed to ensure higher concordance between in silico predictions and expected phenotypic AMR profiles.

Genomic dissection of Klebsiella pneumoniae infections in hospital patients reveals insights into an opportunistic pathogen

Klebsiella pneumoniae is a major cause of opportunistic healthcare-associated infections, which are increasingly complicated by the presence of extended-spectrum beta-lactamases (ESBLs) and carbapenem resistance. We conducted a year-long prospective surveillance study of K. pneumoniae clinical isolates in hospital patients. Whole-genome sequence (WGS) data reveals a diverse pathogen population, including other species within the K. pneumoniae species complex (18%). Several infections were caused by K. variicola/K. pneumoniae hybrids, one of which shows evidence of nosocomial transmission. A wide range of antimicrobial resistance (AMR) phenotypes are observed, and diverse genetic mechanisms identified (mainly plasmid-borne genes). ESBLs are correlated with presence of other acquired AMR genes (median n = 10). Bacterial genomic features associated with nosocomial onset are ESBLs (OR 2.34, p = 0.015) and rhamnose-positive capsules (OR 3.12, p < 0.001). Virulence plasmid-encoded features (aerobactin, hypermucoidy) are observed at low-prevalence (<3%), mostly in community-onset cases. WGS-confirmed nosocomial transmission is implicated in just 10% of cases, but strongly associated with ESBLs (OR 21, p < 1 × 10-11). We estimate 28% risk of onward nosocomial transmission for ESBL-positive strains vs 1.7% for ESBL-negative strains. These data indicate that K. pneumoniae infections in hospitalised patients are due largely to opportunistic infections with diverse strains, with an additional burden from nosocomially-transmitted AMR strains and community-acquired hypervirulent strains.

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

BACKGROUND

Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples.

RESULTS

We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols.

CONCLUSIONS

AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Inferring antibiotic susceptibility from metagenomic data: dream or reality?

BACKGROUND

The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48h after sampling, at best. Recently, clinical metagenomics (CMg), the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. CMg indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality.

OBJECTIVES

The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data.

SOURCES

We conducted a narrative review using published articles available in the NCBI Pubmed database.

CONTENT

We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host.

IMPLICATIONS

Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.

Hospital outbreak of carbapenem-resistant Enterobacterales associated with a blaOXA-48 plasmid carried mostly by Escherichia coli ST399

A hospital outbreak of carbapenem-resistant Enterobacterales was detected by routine surveillance. Whole genome sequencing and subsequent analysis revealed a conserved promiscuous bla_OXA-48 carrying plasmid as the defining factor within this outbreak. Four different species of Enterobacterales were involved in the outbreak. Escherichia coli ST399 accounted for 35 of all the 55 isolates. Comparative genomics analysis using publicly available E. coli ST399 genomes showed that the outbreak E. coli ST399 isolates formed a unique clade. We developed a mathematical model of pOXA-48-like plasmid transmission between host lineages and used it to estimate its conjugation rate, giving a lower bound of 0.23 conjugation events per lineage per year. Our analysis suggests that co-evolution between the pOXA-48-like plasmid and E. coli ST399 could have played a role in the outbreak. This is the first study to report carbapenem-resistant E. coli ST399 carrying blaOXA-48 as the main cause of a plasmid-borne outbreak within a hospital setting. Our findings suggest complementary roles for both plasmid conjugation and clonal expansion in the emergence of this outbreak.

Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach

Background

Predicting which pathogens might exhibit antimicrobial resistance (AMR) based on genomics data is one of the promising ways to swiftly and precisely identify AMR pathogens. Currently, the most widely used genomics approach is through identifying known AMR genes from genomic information in order to predict whether a pathogen might be resistant to certain antibiotic drugs. The list of known AMR genes, however, is still far from comprehensive and may result in inaccurate AMR pathogen predictions. We thus felt the need to expand the AMR gene set and proposed a pan-genome-based feature selection method to identify potential gene sets for AMR prediction purposes.

Results

By building pan-genome datasets and extracting gene presence/absence patterns from four bacterial species, each with more than 2000 strains, we showed that machine learning models built from pan-genome data can be very promising for predicting AMR pathogens. The gene set selected by the eXtreme Gradient Boosting (XGBoost) feature selection approach further improved prediction outcomes, and an incremental approach selecting subsets of XGBoost-selected features brought the machine learning model performance to the next level. Investigating selected gene sets revealed that on average about 50% of genes had no known function and very few of them were known AMR genes, indicating the potential of the selected gene sets to expand resistance gene repertoires.

Conclusions

We demonstrated that a pan-genome-based feature selection approach is suitable for building machine learning models for predicting AMR pathogens. The extracted gene sets may provide future clues to expand our knowledge of known AMR genes and provide novel hypotheses for inferring bacterial AMR mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04666-2.

Linear plasmids in Klebsiella and other Enterobacteriaceae

Linear plasmids are extrachromosomal DNA elements that have been found in a small number of bacterial species. To date, the only linear plasmids described in the family Enterobacteriaceae belong to Salmonella, first found in Salmonella enterica Typhi. Here, we describe a collection of 12 isolates of the Klebsiella pneumoniae species complex in which we identified linear plasmids. Screening of assembly graphs assembled from public read sets identified linear plasmid structures in a further 13 K. pneumoniae species complex genomes. We used these 25 linear plasmid sequences to query all bacterial genome assemblies in the National Center for Biotechnology Information database, and discovered an additional 61 linear plasmid sequences in a variety of Enterobacteriaceae species. Gene content analysis divided these plasmids into five distinct phylogroups, with very few genes shared across more than two phylogroups. The majority of linear plasmid-encoded genes are of unknown function; however, each phylogroup carried its own unique toxin–antitoxin system and genes with homology to those encoding the ParAB plasmid stability system. Passage in vitro of the 12 linear plasmid-carrying Klebsiella isolates in our collection (which include representatives of all five phylogroups) indicated that these linear plasmids can be stably maintained, and our data suggest they can transmit between K. pneumoniae strains (including members of globally disseminated multidrug-resistant clones) and also between diverse Enterobacteriaceae species. The linear plasmid sequences, and representative isolates harbouring them, are made available as a resource to facilitate future studies on the evolution and function of these novel plasmids.

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

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.

ResFinder - an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes

Antimicrobial resistance (AMR) is one of the most important health threats globally. The ability to accurately identify resistant bacterial isolates and the individual antimicrobial resistance genes (ARGs) is essential for understanding the evolution and emergence of AMR and to provide appropriate treatment. The rapid developments in next-generation sequencing technologies have made this technology available to researchers and microbiologists at routine laboratories around the world. However, tools available for those with limited experience with bioinformatics are lacking, especially to enable researchers and microbiologists in low- and middle-income countries (LMICs) to perform their own studies. The CGE-tools (Center for Genomic Epidemiology) including ResFinder (https://cge.cbs.dtu.dk/services/ResFinder/) was developed to provide freely available easy to use online bioinformatic tools allowing inexperienced researchers and microbiologists to perform simple bioinformatic analyses. The main purpose was and is to provide these solutions for people involved in frontline diagnosis especially in LMICs. Since its original publication in 2012, ResFinder has undergone a number of improvements including improvement of the code and databases, inclusion of point mutations for selected bacterial species and predictions of phenotypes also for selected species. As of 28 September 2021, 820 803 analyses have been performed using ResFinder from 61 776 IP-addresses in 171 countries. ResFinder clearly fulfills a need for several people around the globe and we hope to be able to continue to provide this service free of charge in the future. We also hope and expect to provide further improvements including phenotypic predictions for additional bacterial species.

Genomic diversity of antimicrobial resistance in non-typhoidal Salmonella in Victoria, Australia

Non-typhoidal Salmonella (NTS) is the second most common cause of foodborne bacterial gastroenteritis in Australia with antimicrobial resistance (AMR) increasing in recent years. Whole-genome sequencing (WGS) provides opportunities for in silico detection of AMR determinants. The objectives of this study were two-fold: (1) establish the utility of WGS analyses for inferring phenotypic resistance in NTS, and (2) explore clinically relevant genotypic AMR profiles to third generation cephalosporins (3GC) in NTS lineages. The concordance of 2490 NTS isolates with matched WGS and phenotypic susceptibility data against 13 clinically relevant antimicrobials was explored. In silico serovar prediction and typing was performed on assembled reads and interrogated for known AMR determinants. The surrounding genomic context, plasmid determinants and co-occurring AMR patterns were further investigated for multidrug resistant serovars harbouring bla CMY-2, bla CTX-M-55 or bla CTX-M-65. Our data demonstrated a high correlation between WGS and phenotypic susceptibility testing. Phenotypic-genotypic concordance was observed between 2440/2490 (98.0 %) isolates, with overall sensitivity and specificity rates >98 % and positive and negative predictive values >97 %. The most common AMR determinants were bla TEM-1, sul2 , tet (A), strA-strB and floR . Phenotypic resistance to cefotaxime and azithromycin was low and observed in 6.2 % (151/2486) and 0.9 % (16/1834) of the isolates, respectively. Several multi-drug resistant NTS lineages were resistant to 3GC due to different genetic mechanisms including bla CMY-2, bla CTX-M-55 or bla CTX-M-65. This study shows WGS can enhance existing AMR surveillance in NTS datasets routinely produced in public health laboratories to identify emerging AMR in NTS. These approaches will be critical for developing capacity to detect emerging public health threats such as resistance to 3GC.

Prediction of antimicrobial resistance in clinical Enterococcus faecium isolates using a rules-based analysis of whole genome sequences

Background: Enterococcus faecium is a major cause of clinical infections, often due to multidrug-resistant (MDR) strains. Whole genome sequencing (WGS) is a powerful tool to study MDR bacteria and their antimicrobial resistance (AMR) mechanisms. Here we use WGS to characterize E. faecium clinical isolates and test the feasibility of rules-based genotypic prediction of AMR. Methods: Clinical isolates were divided into derivation and validation sets. Phenotypic susceptibility testing for ampicillin, vancomycin, high-level gentamicin, ciprofloxacin, levofloxacin, doxycycline, tetracycline, and linezolid was performed using the VITEK 2 automated system, with confirmation and discrepancy resolution by broth microdilution, disk diffusion, or gradient diffusion when needed. WGS was performed to identify isolate lineage and AMR genotype. AMR prediction rules were derived by analyzing the genotypic-phenotypic relationship in the derivation set. Results: Phylogenetic analysis demonstrated that 88% of isolates in the collection belonged to hospital-associated clonal complex 17. Additionally, 12% of isolates had novel sequence types. When applied to the validation set, the derived prediction rules demonstrated an overall positive predictive value of 98% and negative predictive value of 99% compared to standard phenotypic methods. Most errors were falsely resistant predictions for tetracycline and doxycycline. Further analysis of genotypic-phenotypic discrepancies revealed potentially novel pbp5 and tet(M) alleles that provide insight into ampicillin and tetracycline class resistance mechanisms. The prediction rules demonstrated generalizability when tested on an external dataset. Conclusions: Known AMR genes and mutations can predict E. faecium phenotypic susceptibility with high accuracy for most routinely tested antibiotics, providing opportunities for advancing molecular diagnostics.

Aminoglycoside-resistance gene signatures are predictive of aminoglycoside MICs for carbapenem-resistant Klebsiella pneumoniae

BACKGROUND

Aminoglycoside-containing regimens may be an effective treatment option for infections caused by carbapenem-resistant Klebsiella pneumoniae (CR-Kp), but aminoglycoside-resistance genes are common in these strains. The relationship between the aminoglycoside-resistance genes and aminoglycoside MICs remains poorly defined.

OBJECTIVES

To identify genotypic signatures capable of predicting aminoglycoside MICs for CR-Kp.

METHODS

Clinical CR-Kp isolates (n = 158) underwent WGS to detect aminoglycoside-resistance genes. MICs of amikacin, gentamicin, plazomicin and tobramycin were determined by broth microdilution (BMD). Principal component analysis was used to initially separate isolates based on genotype. Multiple linear regression was then used to generate models that predict aminoglycoside MICs based on the aminoglycoside-resistance genes. Last, the performance of the predictive models was tested against a validation cohort of 29 CR-Kp isolates.

RESULTS

Among the original 158 CR-Kp isolates, 91.77% (145/158) had at least one clinically relevant aminoglycoside-resistance gene. As a group, 99.37%, 84.81%, 82.28% and 10.76% of the CR-Kp isolates were susceptible to plazomicin, amikacin, gentamicin and tobramycin, respectively. The first two principal components explained 72.23% of the total variance in aminoglycoside MICs and separated isolates into four groups with aac(6')-Ib, aac(6')-Ib', aac(6')-Ib+aac(6')-Ib' or no clinically relevant aminoglycoside-resistance genes. Regression models predicted aminoglycoside MICs with adjusted R2 values of 56%-99%. Within the validation cohort, the categorical agreement when comparing the observed BMD MICs with the predicated MICs was 96.55%, 89.66%, 86.21% and 82.76% for plazomicin, gentamicin, amikacin and tobramycin, respectively.

CONCLUSIONS

Susceptibility to each aminoglycoside varies in CR-Kp. Detection of aminoglycoside-resistance genes may be useful to predict aminoglycoside MICs for CR-Kp.