Centre-specific bacterial pathogen typing affects infection-control decision making

ASGARD+: A New Modular Platform for Bacterial Antibiotic-Resistant Analysis

ASGARD+ (Accelerated Sequential Genome-analysis and Antibiotic Resistance Detection) is a command-line platform for automatic identification of antibiotic-resistance genes in bacterial genomes, providing an easy-to-use interface to process big batches of sequence files from whole genome sequencing, with minimal configuration. It also provides a CPU-optimization algorithm that reduces the processing time. This tool consists of two main protocols. The first one, ASGARD, is based on the identification and annotation of antimicrobial resistance elements directly from the short reads using different public databases. SAGA, enables the alignment, indexing, and mapping of whole-genome samples against a reference genome for the detection and call of variants, as well as the visualization of the results through the construction of a tree of SNPs. The application of both protocols is performed using just one short command and one configuration file based on JSON syntax, which modulates each pipeline step, allowing the user to do as many interventions as needed on the different software tools that are adapted to the pipeline. The modular ASGARD+ allows researchers with little experience in bioinformatic analysis and command-line use to quickly explore bacterial genomes in depth, optimizing analysis times and obtaining accurate results. © 2023 Wiley Periodicals LLC. Basic Protocol 1: ASGARD+ installation Basic Protocol 2: Configuration files general setup Basic Protocol 3: ASGARD execution Support Protocol: Results visualization with Phandango Basic Protocol 4: SAGA execution Alternative Protocol 1: Container installation Alternative Protocol 2: Run ASGARD and SAGA in container.

An ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance

Realising the promise of genomics to revolutionise identification and surveillance of antimicrobial resistance (AMR) has been a long-standing challenge in clinical and public health microbiology. Here, we report the creation and validation of abritAMR, an ISO-certified bioinformatics platform for genomics-based bacterial AMR gene detection. The abritAMR platform utilises NCBI's AMRFinderPlus, as well as additional features that classify AMR determinants into antibiotic classes and provide customised reports. We validate abritAMR by comparing with PCR or reference genomes, representing 1500 different bacteria and 415 resistance alleles. In these analyses, abritAMR displays 99.9% accuracy, 97.9% sensitivity and 100% specificity. We also compared genomic predictions of phenotype for 864 Salmonella spp. against agar dilution results, showing 98.9% accuracy. The implementation of abritAMR in our institution has resulted in streamlined bioinformatics and reporting pathways, and has been readily updated and re-verified. The abritAMR tool and validation datasets are publicly available to assist laboratories everywhere harness the power of AMR genomics in professional practice.

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

Throughout the entirety of human history, bacterial pathogens have played an important role and even shaped the fate of civilizations. The application of genomics within the last 27 years has radically changed the way we understand the biology and evolution of these pathogens. In this review, we discuss how the short- (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing technologies have shaped the discipline of bacterial pathogen genomics, in terms of fundamental research (i.e., evolution of pathogenicity), forensics, food safety, and routine clinical microbiology. We have mined and discuss some of the most prominent data/bioinformatics resources such as NCBI pathogens, PATRIC, and Pathogenwatch. Based on this mining, we present some of the most popular sequencing technologies, hybrid approaches, assemblers, and annotation pipelines. A small number of bacterial pathogens are of very high importance, and we also present the wealth of the genomic data for these species (i.e., which ones they are, the number of antimicrobial resistance genes per genome, the number of virulence factors). Finally, we discuss how this discipline will probably be transformed in the near future, especially by transitioning into metagenome-assembled genomes (MAGs), thanks to long-read sequencing.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

Background

Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles.

Methods

We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision.

Results

The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences.

Conclusions

This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08437-4.

Implementation of the Infection Risk Scan (IRIS) in nine hospitals in the Belgian-Dutch border region (i-4-1-Health project)

Background

A tool, the Infection Risk Scan has been developed to measure the quality of infection control and antimicrobial use. This tool measures various patient-, ward- and care-related variables in a standardized way. We describe the implementation of this tool in nine hospitals in the Dutch/Belgian border area and the obtained results.

Methods

The IRIS consists of a set of objective and reproducible measurements: patient comorbidities, (appropriate) use of indwelling medical devices, (appropriate) use of antimicrobial therapy, rectal carriage of Extended-spectrum beta-lactamase producing Enterobacterales and their clonal relatedness, environmental contamination, hand hygiene performance, personal hygiene of health care workers and presence of infection prevention preconditions. The Infection Risk Scan was implemented by an expert team. In each setting, local infection control practitioners were trained to achieve a standardized implementation of the tool and an unambiguous assessment of data.

Results

The IRIS was implemented in 34 wards in six Dutch and three Belgian hospitals. The tool provided ward specific results and revealed differences between wards and countries. There were significant differences in the prevalence of ESBL-E carriage between countries (Belgium: 15% versus The Netherlands: 9.6%), environmental contamination (median adenosine triphosphate (ATP) level Belgium: 431 versus median ATP level The Netherlands: 793) and calculated hand hygiene actions based on alcohol based handrub consumption (Belgium: 12.5/day versus The Netherlands: 6.3/day) were found.

Conclusion

The Infection risk Scan was successfully implemented in multiple hospitals in a large cross-border project and provided data that made the quality of infection control and antimicrobial use more transparent. The observed differences provide potential targets for improvement of the quality of care.