High precisionNeisseria gonorrhoeaevariant and antimicrobial resistance calling from metagenomic Nanopore sequencing

Abstract

The rise of antimicrobial resistant Neisseria gonorrhoeae is a significant public health concern. Against this background, rapid culture-independent diagnostics may allow targeted treatment and prevent onward transmission. We have previously shown metagenomic sequencing of urine samples from men with urethral gonorrhoea can recover near-complete N. gonorrhoeae genomes. However, disentangling the N. gonorrhoeae genome from metagenomic samples and robustly identifying antimicrobial resistance determinants from error-prone Nanopore sequencing is a substantial bioinformatics challenge.Here we demonstrate an N. gonorrhoeae diagnostic workflow for analysis of metagenomic sequencing data obtained from clinical samples using R9.4.1 Nanopore sequencing. We compared results from simulated and clinical infections with data from known reference strains and Illumina sequencing of isolates cultured from the same patients. We evaluated three Nanopore variant callers and developed a random forest classifier to filter called SNPs. Clair was the most suitable variant caller after SNP filtering. A minimum depth of 20x reads was required to confidently identify resistant determinants over the entire genome. Our findings show that metagenomic Nanopore sequencing can provide reliable diagnostic information in N. gonorrhoeae infection.

Targeted amplification and MinION nanopore sequencing of key azole and echinocandin resistance determinants of clinically relevant Candida spp. from blood culture bottles

“…2017; Sanderson et al . 2020). In our study, we generated a high‐depth coverage of >2000×/loci obtained within a 12‐h runtime, enabling confident prediction of resistance SNPs in FKS and ERG .…”

Clinical Metagenomic Sequencing for Species Identification and Antimicrobial Resistance Prediction in Orthopaedic Device Infection

“…Detecting S. aureus AMR determinants AMR determinants were detected using methods previously described [16]. Briefly, chromosomal variants were called after aligning reads to a reference genome (MRSA252) using minimap2 [21] (version 2.17-r941).…”

“…However, lack of comprehensive antimicrobial susceptibility prediction from orthopaedic metagenomic sequencing has limited its application. We [16] and other authors [17][18][19] have applied longread sequencing to link antimicrobial resistance (AMR) determinants to individual species within metagenomic samples. However, these approaches require extraction of sufficient pathogen DNA from clinical samples, which is often difficult given the overwhelming amount of human DNA frequently present.…”

“…Briefly, chromosomal variants were called after aligning reads to a reference genome (MRSA252) using minimap2 [21] (version 2.17-r941). Variants identified using Clair [22] (git commit 54c7dd4) were filtered with a trained random-forest classifier described previously [16] then compared to a database of resistance conferring SNPs [23] (Table S1). Mobile genetic elements containing AMR genes were detected by minimap2 overlaps with a catalogue of genes adapted from previous studies [23] (Table S2).…”

Development of an amplicon nanopore sequencing strategy for detection of mutations conferring intermediate resistance to vancomycin in Staphylococcus aureus strains

“…Another method that extrapolates antibiotic resistance from pneumococcal sequence type takes only five minutes (21). No nanopore method has yet been developed for direct detection of resistance-causing mutations from PCR amplicons, but such mutations have been detected in metagenomic sequence data collected from urine containing Neisseria gonorrhoeae (22). Additionally, genomic prediction of antibiotic resistance must still be calibrated thoroughly against culture-based methods (23).…”