Genomic surveillance of multidrug-resistant organisms based on long-read sequencing

Multidrug-resistant organisms (MDRO) pose a significant threat to public health worldwide. The ability to identify antimicrobial resistance determinants, to assess changes in molecular types, and to detect transmission are essential for surveillance and infection prevention of MDRO. Molecular characterization based on long-read sequencing has emerged as a promising alternative to short-read sequencing. The

We demonstrate that molecular characterization of automatically extracted DNA followed by long-read sequencing is as accurate compared to short-read sequencing and suitable for typing and outbreak analysis as part of genomic surveillance of MDRO. However, the analysis of P. aeruginosa requires further improvement which may be obtained by other basecalling algorithms. The low implementation costs and rapid library preparation for long-read sequencing of MDRO extends its applicability to resource-constrained settings and low-income countries worldwide.

Genomic DNA of 356 MDRO was automatically extracted using the Maxwell-RSC48. The MDRO included 106 Klebsiella pneumoniae isolates, 85 Escherichia coli, 15 Enterobacter cloacae complex, 10 Citrobacter freundii, 34 Pseudomonas aeruginosa, 16 Acinetobacter baumannii, and 69 methicillin-resistant Staphylococcus aureus (MRSA), of which 24 were from an outbreak. MDRO were sequenced using both short-read (Illumina NextSeq 550) and long-read (Nanopore Rapid Barcoding Kit-24-V14, R10.4.1) whole-genome sequencing (WGS). Basecalling was performed for two distinct models using Dorado-0.3.2 duplex mode. Long-read data was assembled using Flye, Canu, Miniasm, Unicycler, Necat, Raven, and Redbean assemblers. Long-read WGS data with > 40 × coverage was used for multi-locus sequence typing (MLST), whole-genome MLST (wgMLST), whole-genome single-nucleotide polymorphisms (wgSNP), in silico multiple locus variable-number of tandem repeat analysis (iMLVA) for MRSA, and identification of resistance genes (ABRicate).

Comparison of wgMLST profiles based on long-read and short-read WGS data revealed > 95% of wgMLST profiles within the species-specific cluster cut-off, except for P. aeruginosa. The wgMLST profiles obtained by long-read and short-read WGS differed only one to nine wgMLST alleles or SNPs for K. pneumoniae, E. coli, E. cloacae complex, C. freundii, A. baumannii complex, and MRSA. For P. aeruginosa, differences were up to 27 wgMLST alleles between long-read and short-read wgMLST and 0-10 SNPs. MLST sequence types and iMLVA types were concordant between long-read and short-read WGS data and conventional MLVA typing. Antimicrobial resistance genes were detected in long-read sequencing data with high sensitivity/specificity (92-100%/99-100%). Long-read sequencing enabled analysis of an MRSA outbreak. Conclusions: We demonstrate that molecular characterization of automatically extracted DNA followed by long-read sequencing is as accurate compared to short-read sequencing and suitable for typing and outbreak analysis as part of genomic surveillance of MDRO. However, the analysis of P. aeruginosa requires further improvement which may be obtained by other basecalling algorithms. The low implementation costs and rapid library preparation for long-read sequencing of MDRO extends its applicability to resource-constrained settings and low-income countries worldwide.