Resolving a neonatal intensive care unit outbreak of meticillin-resistant Staphylococcus aureus to the single-nucleotide variant level using Oxford Nanopore simplex reads and HERRO error correction

Abstract

Our laboratory began prospective genomic surveillance for healthcare-associated organisms in 2022 using Oxford Nanopore Technologies (ONT) as a standalone platform. While effective for early outbreak detection, the lower read accuracy compared to Illumina sequencing has limited single-nucleotide variant (SNV) analysis. This study aimed to determine whether Haplotype-aware ERRor cOrrection (HERRO) of ONT data could permit high-resolution comparison of outbreak isolates. ONT simplex reads were used from isolates involved in a recent outbreak of meticillin-resistant Staphylococcus aureus (MRSA) in our neonatal unit. Raw data were basecalled and adapter-trimmed using Dorado v0.7.0, followed by HERRO correction. Genome assemblies and phylogenies were compared with previous analyses (using Dorado v0.3.4 no HERRO correction, and data generated by Illumina sequencing). Five of nine outbreak isolates were analysed; four had insufficient read lengths (N50 values <10,000 bp) and did not achieve complete chromosome coverage post-HERRO correction. The average chromosome sequencing depth for nanopore data was 147× (range: 44-220×) with an average read N50 of 12,215 bp (interquartile range: 11,439-12,711 bp). The median pairwise SNV distance between outbreak isolates from the original investigation was 51 SNVs (range: 40-68), which decreased to 3 SNVs (range: 1-15) with HERRO correction. Illumina sequencing generated a median SNV distance of 2 (range: 0-13). The HERRO-corrected ONT phylogeny closely matched the Illumina-generated phylogeny. HERRO correction enabled high-resolution analysis of MRSA outbreak isolates comparable to Illumina sequencing. ONT sequencing with HERRO correction represents a viable standalone option for detailed genomic analysis of hospital outbreaks, provided sufficient read lengths are achieved.

Keywords: Genomic surveillance; Infection control; Outbreak detection; Phylogenetic analysis; Single-nucleotide variants.