Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

CARD*Shark: automated prioritization of literature curation for the Comprehensive Antibiotic Resistance Database

Scientific literature is published at a rate that makes manual data extraction a highly time-consuming task. The Comprehensive Antibiotic Resistance Database (CARD) utilizes literature to curate information on antimicrobial resistance genes and to enable time-efficient triage of publications we have developed a classification algorithm for identifying publications describing first reports of new resistance genes. Trained on publications contained in the CARD, CARD*Shark downloads, processes and identifies publications recently added to PubMed that should be reviewed by biocurators. With CARD*Shark, we can minimize the monthly scope of articles a biocurator reviews from hundreds of articles to a few dozen, drastically improving the speed of curation while ensuring no relevant publications are overlooked. Database URL http://card.mcmaster.ca.

Rapid fluoroquinolone resistance detection in Pseudomonas aeruginosa using mismatch amplification mutation assay-based real-time PCR

Background. Antimicrobial resistance (AMR) is an ever-increasing global health concern. One crucial facet in tackling the AMR epidemic is earlier and more accurate AMR diagnosis, particularly in the dangerous and highly multi-drug-resistant ESKAPE pathogen, Pseudomonas aeruginosa .

Objectives. We aimed to develop two SYBR Green-based mismatch amplification mutation assays (SYBR-MAMAs) targeting GyrA T83I (gyrA248) and GyrA D87N, D87Y and D87H (gyrA259). Together, these variants cause the majority of fluoroquinolone (FQ) AMR in P. aeruginosa .

Methods. Following assay validation, the gyrA248 and gyrA259 SYBR-MAMAs were tested on 84 Australian clinical P. aeruginosa isolates, 46 of which demonstrated intermediate/full ciprofloxacin resistance according to antimicrobial susceptibility testing.

Results. Our two SYBR-MAMAs correctly predicted an AMR phenotype in the majority (83%) of isolates with intermediate/full FQ resistance. All FQ-sensitive strains were predicted to have a sensitive phenotype. Whole-genome sequencing confirmed 100 % concordance with SYBR-MAMA genotypes.

Conclusions. Our GyrA SYBR-MAMAs provide a rapid and cost-effective method for same-day identification of FQ AMR in P. aeruginosa . An additional SYBR-MAMA targeting the GyrB S466Y/S466F variants would increase FQ AMR prediction to 91 %. Clinical implementation of our assays will permit more timely treatment alterations in cases where decreased FQ susceptibility is identified, leading to improved patient outcomes and antimicrobial stewardship.

Importance of Microbiome of Fecal Samples Obtained from Adolescents with Different Weight Conditions on Resistance Gene Transfer

Antimicrobial resistance (AMR) is a relevant public health problem worldwide, and microbiome bacteria may contribute to the horizontal gene transfer associated with antimicrobial resistance. The microbiome of fecal samples from Mexican adolescents were analyzed and correlated with eating habits, and the presence of AMR genes on bacteria in the microbiome was evaluated. Fecal samples from adolescents were collected and processed to extract genomic DNA. An Illumina HiSeq 1500 system was used to determine resistance genes and the microbiome of adolescents through the amplification of gene resistance and the V3–V4 regions of RNA, respectively. Analysis of the microbiome from fecal samples taken from 18 obese, overweight, and normal-weight adolescents revealed that the Firmicutes was the most frequent phylum, followed by Bacteroidetes, Actinobacteria, Proteobacteria and Verrucomicrobia. The following species were detected as the most frequent in the samples: F. prausnitzii, P. cori, B. adolescentis, E. coli and A. muciniphila. The presence of Bacteroides, Prevotella and Ruminococcus was used to establish the enterotype; enterotype 1 was more common in women and enterotype 2 was more common in men. Twenty-nine AMR genes were found for β-lactamases, fluoroquinolones, aminoglycosides, macrolide, lincosamides, streptogramin (MLS), tetracyclines and sulfonamides. The presence of microorganisms in fecal samples that harbor AMR genes that work against antimicrobials frequently used for the treatment of microbial infections such as b-lactams, macrolides, aminoglycosides, MLS, and tetracyclines is of great concern, as these organisms may be an important reservoir for horizontal AMR gene transfer.

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

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

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.