Analytical Performance Validation of Next-Generation Sequencing Based Clinical Microbiology Assays Using a K-mer Analysis Workflow

Abstract

Next-generation sequencing (NGS) enables clinical microbiology assays such as molecular typing of bacterial isolates which is now routinely applied for infection control and epidemiology. Additionally, feasibility for NGS-based identification of antimicrobial resistance (AMR) markers as well as genetic prediction of antibiotic susceptibility testing results has been demonstrated. Various bioinformatics approaches enabling NGSbased clinical microbiology assays exist, but standardized, computationally efficient and scalable sample-to-results workflows including validated quality control parameters are still lacking. Bioinformatics analysis workflows based on k-mers have been shown to allow for fast and efficient analysis of large genomics data sets as obtained from microbial sequencing applications. We here demonstrate applicability of k-mer based clinical microbiology assays for whole-genome sequencing (WGS) including variant calling, taxonomic identification, bacterial typing as well as AMR marker detection. The wet-lab and dry-lab workflows were developed and validated in line with Clinical Laboratory Improvement Act (CLIA) guidelines for laboratory-developed tests (LDTs) on multi-drug resistant ESKAPE pathogens. The developed k-mer based workflow demonstrated ≥99.39% repeatability, ≥99.09% reproducibility and ≥99.76% accuracy for variant calling and applied assays as determined by intra-day and inter-day triplicate measurements. The limit of detection (LOD) across assays was found to be at 20× sequencing depth and 15× for AMR marker detection. Thorough benchmarking of the k-mer based workflow revealed analytical performance criteria are comparable to stateof-the-art alignment based workflows across clinical microbiology assays. Diagnostic sensitivity and specificity for multilocus sequence typing (MLST) and phylogenetic analysis were 100% for both approaches. For AMR marker detection, sensitivity and specificity were 95.29 and 99.78% for the k-mer based workflow as compared to 95.17 and 99.77% for the alignment-based approach. Summarizing, results illustrate that k-mer based analysis workflows enable a broad range of clinical microbiology assays, potentially not only for WGS-based typing and AMR gene detection but also genetic prediction of antibiotic susceptibility testing results.

Genomic and Phenotypic Analysis of Linezolid-Resistant Staphylococcus epidermidis in a Tertiary Hospital in Innsbruck, Austria

“…One-hundred fifty-four culture plates with bacterial isolates were sent from the Medical University of Innsbruck to the Ares Genetics laboratory. DNA extraction, library preparation, and WGS were conducted as reported previously [ 24 ]. In brief, single colonies were picked from the plates and resuspended in lysis buffer.…”

“…QC and de novo genome assembly of raw data were performed for all 154 isolates as described previously [ 24 ]. Seven assemblies failed internal QC due to gene duplication rates exceeding 60% (5 cases) and low-quality assembly with N50 < 5000 (2 cases).…”

“…Genes and point mutations associated with linezolid resistance were extracted from ARESdb [ 26 ] and by literature search. cfr (AM408573, AJ879565), optrA (ALMH01000001, ALZI01000051), poxtA (MF095097), Lsa(A) (AAO43110), Vga(B) (AAB95639), and efflux pumps lmrS (F6MF49), AcrA (P0AE06), EmrD-3 (A0A0H3AF79), and EmrE (CAA77936) were identified via sequence alignment [ 24 ]. Known point mutations in 23S rRNA, L3, and L4 ( Table S1 ) were manually inspected.…”

Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation

“…However, in recent years some have been developed and used [ 56 , 57 ]. This lack of consensus for data analysis highlights the importance of metrics evaluation and workflow validation [ 18 , 19 , 20 ] to ensure its quality.…”

“…All in all, the integration of microbiology, genomics and bioinformatics knowledge is essential to ensure the quality of the data, validate the analytical results and provide a reliable interpretation of the obtained information, integrating metadata. Several bioinformatics approaches have been tested and validated [ 18 , 19 , 20 ] and here, more than introducing a workflow, we present what we consider important as key considerations for robust bioinformatics data analysis and reliable results interpretation in the context of whole genome sequencing applied to pathogen source tracking in food industry.…”