Advances in molecular-based methods complementary to conventional blood culture diagnostics and antimicrobial stewardship programmes may optimize infection management by allowing rapid identification of pathogens and relevant antimicrobial resistance genes. Rapid diagnosis of the causing microorganism and relevant resistance determinants is important for early administration and modification of appropriate antimicrobial therapy. Ultimately, this may lead to improved quality and cost-effectiveness of health care, as well as reduced antimicrobial resistance selection.

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.

No abstract

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P<0.05) fewer indels and mismatches per 100 kbp. However, the hybrid and LR assemblies had slightly higher genome fractions. For LR assemblies, Canu followed by Racon, and Medaka polishing was the most accurate approach. The cgSNP approach, based on either reads or assemblies, was more robust than the cgMLST approach, especially for LR sequence data. In conclusion, anti-TB drug-resistance prediction, particularly with only LR sequence data, remains challenging, especially for first-line drugs. In addition, SR assemblies appear more accurate than LR ones, and reproducible phylogeny can be achieved using cgSNP approaches.

Identification and phenotypic drug-susceptibility testing for mycobacteria are time-consuming and challenging but essential for managing mycobacterial infections. Next-generation sequencing (NGS) technologies can increase diagnostic speed and quality, but standardization is still lacking for many aspects (e.g., unbiased extraction, host depletion, bioinformatic analysis). Targeted PCR approaches directly on sample material are limited by the number of targets that can be included. Unbiased shotgun metagenomics on direct material is hampered by the massive amount of host DNA, which should be removed to improve the microbial detection sensitivity. For this reason, we developed a method for NGS-based diagnosis of mycobacteria directly from patient material. As a model, we used the non-tuberculous mycobacterium (NTM) Mycobacterium abscessus. We first compared the efficiency of three different DNA extraction kits for isolating DNA (quality and concentration). The two most efficient kits were then used in a follow-up study using artificial sputum. Finally, one extraction kit was selected and further evaluated for DNA isolation from a patients’ sputum mixture spiked with M. abscessus at three concentrations (final concentrations 108, 107, 106  CFU/ml). The spiked sputum samples were processed with and without saponin treatment (ST) in combination with DNAse treatment prior to bacterial DNA extraction to evaluate the recovery of bacteria and depletion of host DNA by PCR and Illumina sequencing.While Ct values of the qPCR targeting mycobacterial ITS DNA remained rather stable, Ct values in the qPCR targeting the human β-actin gene increased by five Ct values in ST samples. In subsequent Illumina sequencing, a decrease of 89% of reads mapped to the human genome was observed in ST samples. The percentage of reads mapped to M. abscessus (108 CFU/ml) increased by 89%, and the sequencing depth increased two times when undergoing ST.In conclusion, the sensitivity of M. abscessus detection in artificial sputum was increased using a saponin pre-treatment step. The saponin followed by the DNase I treatment approach could be efficiently applied to detect and characterize mycobacterial infections, including tuberculosis, directly from sputum.

Outbreak management of extended spectrum β-lactamase (ESBL)-producing pathogens requires rapid and accurate diagnosis. However, conventional screening is slow and labor-intensive. The vast majority of the screened samples are negative and detection of non-outbreak-related resistant micro-organisms often complicates outbreak management. In a CTX-M-15-producing Escherichia coli outbreak, 149 fecal samples and rectal eSwabs were collected by a cross-sectional survey in a Dutch nursing home. Samples were processed by routine diagnostic methods. Retrospectively, ESBL-producing bacteria and resistance genes were detected directly from eSwab medium by an accelerated workflow without prior enrichment cultures by an amplicon-based next-generation sequencing (NGS) method, and culture. A total of 27 (18.1%) samples were positive in either test. Sensitivity for CTX-M detection was 96.3% for the phenotypic method and 85.2% for the NGS method, and the specificity was 100% for both methods, as confirmed by micro-array. This resulted in a positive predictive value (PPV) of 100% for both methods, and a negative predictive value (NPV) of 99.2% and 96.8% for the phenotypic method and the NGS method, respectively. Time to result was four days and 14 h for the phenotypic method and the NGS method, respectively. In conclusion, the sensitivity without enrichment shows promising results for further use of amplicon-based NGS for screening during outbreaks.

No abstract