Antimicrobial susceptibility prediction from genomes: a dream come true?

Genome-based diagnostics provides relevant information to guide patient treatment and support pathogen and resistance surveillance. Recently, Coll et al. introduced a curated database for predicting antimicrobial resistance (AMR) from Enterococcus faecium genomics data, offering excellent predictive values for susceptibility to important antimicrobials. Challenges to predict resistance to last-resort antimicrobials remain.

Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing

Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples.

Foodborne Toxigenic Agents Investigated in Central Italy: An Overview of a Three-Year Experience (2018-2020)

Foodborne diseases (FBDs) represent a worldwide public health issue, given their spreadability and the difficulty of tracing the sources of contamination. This report summarises the incidence of foodborne pathogens and toxins found in food, environmental and clinical samples collected in relation to diagnosed or suspected FBD cases and submitted between 2018 and 2020 to the Food Microbiology Unit of the Istituto Zooprofilattico Sperimentale del Lazio e della Toscana (IZSLT). Data collected from 70 FBD investigations were analysed: 24.3% of them started with an FBD diagnosis, whereas a further 41.4% involved clinical diagnoses based on general symptomatology. In total, 5.6% of the 340 food samples analysed were positive for the presence of a bacterial pathogen, its toxins or both. Among the positive samples, more than half involved meat-derived products. Our data reveal the probable impact of the COVID-19 pandemic on the number of FBD investigations conducted. In spite of the serious impact of FBDs on human health and the economy, the investigation of many foodborne outbreaks fails to identify the source of infection. This indicates a need for the competent authorities to continue to develop and implement a more fully integrated health network.

Recent Advances in Rapid Antimicrobial Susceptibility Testing

Background

Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.

Content

Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.

Summary

We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Whole-Genome Sequence Datasets: A Powerful Resource for the Food Microbiology Laboratory Toolbox

Whole-genome sequencing (WGS) technologies are rapidly being adopted for routine use in food microbiology laboratories worldwide. Examples of how WGS is used to support food safety testing include gene marker discovery (e.g., virulence and anti-microbial resistance gene determination) and high-resolution typing (e.g., cg/wgMLST analysis). This has led to the establishment of large WGS databases representing the genomes of thousands of different types of food pathogenic and commensal bacteria. This information constitutes an invaluable resource that can be leveraged to develop and validate routine test methods used to support regulatory and industry food safety objectives. For example, well-curated raw and assembled genomic datasets of the key food pathogens (Salmonella enterica, Listeria monocytogenes, and Shiga-toxigenic Escherichia coli) have been used in our laboratory in studies to validate bioinformatics pipelines, as well as new molecular methods as a prelude to the laboratory phase of the “wet lab” validation process. The application of genomic information to food microbiology method development will decrease the cost of test development and lead to the generation of more robust methodologies supporting risk assessment and risk management actions.

A Bioinformatics Whole-Genome Sequencing Workflow for Clinical Mycobacterium tuberculosis Complex Isolate Analysis, Validated Using a Reference Collection Extensively Characterized with Conventional Methods and In Silico Approaches

The use of whole-genome sequencing (WGS) for routine typing of bacterial isolates has increased substantially in recent years. For Mycobacterium tuberculosis (MTB), in particular, WGS has the benefit of drastically reducing the time required to generate results compared to most conventional phenotypic methods. Consequently, a multitude of solutions for analyzing WGS MTB data have been developed, but their successful integration in clinical and national reference laboratories is hindered by the requirement for their validation, for which a consensus framework is still largely absent. We developed a bioinformatics workflow for (Illumina) WGS-based routine typing of MTB complex (MTBC) member isolates allowing complete characterization, including (sub)species confirmation and identification (16S, csb/RD, hsp65), single nucleotide polymorphism (SNP)-based antimicrobial resistance (AMR) prediction, and pathogen typing (spoligotyping, SNP barcoding, and core genome multilocus sequence typing). Workflow performance was validated on a per-assay basis using a collection of 238 in-house-sequenced MTBC isolates, extensively characterized with conventional molecular biology-based approaches supplemented with public data. For SNP-based AMR prediction, results from molecular genotyping methods were supplemented with in silico modified data sets, allowing us to greatly increase the set of evaluated mutations. The workflow demonstrated very high performance with performance metrics of >99% for all assays, except for spoligotyping, where sensitivity dropped to ∼90%. The validation framework for our WGS-based bioinformatics workflow can aid in the standardization of bioinformatics tools by the MTB community and other SNP-based applications regardless of the targeted pathogen(s). The bioinformatics workflow is available for academic and nonprofit use through the Galaxy instance of our institute at https://galaxy.sciensano.be.