Evaluating the genome and resistome of extensively drug-resistant Klebsiella pneumoniae using native DNA and RNA Nanopore sequencing

Abstract

Background

Klebsiella pneumoniae frequently harbours multidrug resistance, and current diagnostics struggle to rapidly identify appropriate antibiotics to treat these bacterial infections. The MinION device can sequence native DNA and RNA in real time, providing an opportunity to compare the utility of DNA and RNA for prediction of antibiotic susceptibility. However, the effectiveness of bacterial direct RNA sequencing and base-calling has not previously been investigated. This study interrogated the genome and transcriptome of 4 extensively drug-resistant (XDR) K. pneumoniae clinical isolates; however, further antimicrobial susceptibility testing identified 3 isolates as pandrug-resistant (PDR).

Results

The majority of acquired resistance (≥75%) resided on plasmids including several megaplasmids (≥100 kb). DNA sequencing detected most resistance genes (≥70%) within 2 hours of sequencing. Neural network–based base-calling of direct RNA achieved up to 86% identity rate, although ≤23% of reads could be aligned. Direct RNA sequencing (with ∼6 times slower pore translocation) was able to identify (within 10 hours) ≥35% of resistance genes, including those associated with resistance to aminoglycosides, β-lactams, trimethoprim, and sulphonamide and also quinolones, rifampicin, fosfomycin, and phenicol in some isolates. Direct RNA sequencing also identified the presence of operons containing up to 3 resistance genes. Polymyxin-resistant isolates showed a heightened transcription of phoPQ (≥2-fold) and the pmrHFIJKLM operon (≥8-fold). Expression levels estimated from direct RNA sequencing displayed strong correlation (Pearson: 0.86) compared to quantitative real-time PCR across 11 resistance genes.

Conclusion

Overall, MinION sequencing rapidly detected the XDR/PDR K. pneumoniae resistome, and direct RNA sequencing provided accurate estimation of expression levels of these genes.

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Now published in GigaScience doi: 10.1093/gigascience/giaa002

Miranda E. Pitt 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Miranda E. PittFor correspondence: miranda.pitt@imb.uq.edu.au l.coin@imb.uq.edu.auSon H. Nguyen 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Son H. NguyenTânia P.S. Duarte 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Tânia P.S. DuarteMark A.T. Blaskovich 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Mark A.T. BlaskovichLachlan J.M. Coin 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, AustraliaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lachlan J.M. CoinFor correspondence: miranda.pitt@imb.uq.edu.au l.coin@imb.uq.edu.au

A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa002 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

These peer reviews were as follows:

Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102080 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102081 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.102082

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