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      Integrated chromosomal and plasmid sequence analyses reveal diverse modes of carbapenemase gene spread among Klebsiella pneumoniae

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      a , 1 , a , b , c , d , e , f , g , the European Survey of Carbapenemase-Producing Enterobacteriaceae (EuSCAPE) Working Group 2 , the ESCMID Study Group for Epidemiological Markers (ESGEM) 2 , e , f , h , b , c , a , i , 1
      (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab)
      Proceedings of the National Academy of Sciences of the United States of America
      National Academy of Sciences
      Klebsiella pneumoniae, carbapenem resistance, carbapenemase genes, plasmids, genomics

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          Significance

          In many clinically important bacteria, antibiotic resistance genes are primarily carried on plasmids. These can spread horizontally between different strains and species. However, current surveillance systems track chromosomal lineages of bacteria only, leading to an incomplete understanding of how resistance spreads, from within an individual hospital to across country borders. We present an integrated, high-resolution analysis of both chromosome and plasmid sequences using Klebsiella pneumoniae isolates sampled during a European survey. We show that carbapenemase genes, which confer resistance to last-line antibiotics, have spread in diverse ways including via one plasmid/multiple lineages ( bla OXA-48-like), multiple plasmids/multiple lineages ( bla VIM, bla NDM), and multiple plasmids/one lineage ( bla KPC). These different trajectories must be considered in genomic surveillance systems and the design of new interventions.

          Abstract

          Molecular and genomic surveillance systems for bacterial pathogens currently rely on tracking clonally evolving lineages. By contrast, plasmids are usually excluded or analyzed with low-resolution techniques, despite being the primary vectors of antibiotic resistance genes across many key pathogens. Here, we used a combination of long- and short-read sequence data of Klebsiella pneumoniae isolates ( n = 1,717) from a European survey to perform an integrated, continent-wide study of chromosomal and plasmid diversity. This revealed three contrasting modes of dissemination used by carbapenemase genes, which confer resistance to last-line carbapenems. First, bla OXA-48-like genes have spread primarily via the single epidemic pOXA-48–like plasmid, which emerged recently in clinical settings and spread rapidly to numerous lineages. Second, bla VIM and bla NDM genes have spread via transient associations of many diverse plasmids with numerous lineages. Third, bla KPC genes have transmitted predominantly by stable association with one successful clonal lineage (ST258/512) yet have been mobilized among diverse plasmids within this lineage. We show that these plasmids, which include pKpQIL-like and IncX3 plasmids, have a long association (and are coevolving) with the lineage, although frequent recombination and rearrangement events between them have led to a complex array of mosaic plasmids carrying bla KPC. Taken altogether, these results reveal the diverse trajectories of antibiotic resistance genes in clinical settings, summarized as using one plasmid/multiple lineages, multiple plasmids/multiple lineages, and multiple plasmids/one lineage. Our study provides a framework for the much needed incorporation of plasmid data into genomic surveillance systems, an essential step toward a more comprehensive understanding of resistance spread.

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          Most cited references52

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              Basic local alignment search tool.

              A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                6 October 2020
                23 September 2020
                23 September 2020
                : 117
                : 40
                : 25043-25054
                Affiliations
                [1] aCentre for Genomic Pathogen Surveillance, Wellcome Genome Campus , Hinxton, CB10 1SA Cambridge, United Kingdom;
                [2] bInstitute for Infection Prevention and Hospital Epidemiology, Medical Centre, University of Freiburg , 79106 Freiburg, Germany;
                [3] cFaculty of Medicine, University of Freiburg , 79106 Freiburg, Germany;
                [4] dModernizing Medical Microbiology Consortium, Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University , Oxford OX3 9DU, United Kingdom;
                [5] eDepartment of Experimental and Clinical Medicine, University of Florence , 50134 Florence, Italy;
                [6] fClinical Microbiology and Virology Unit, Florence Careggi University Hospital , 50134 Florence, Italy;
                [7] gDepartment of Veterinary Medicine, University of Cambridge , CB3 0ES Cambridge, United Kingdom;
                [8] hMilner Centre for Evolution, Department of Biology and Biochemistry, University of Bath , Bath BA2 7AY, United Kingdom;
                [9] iBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Oxford University , Oxford OX3 7LF, United Kingdom
                Author notes
                1To whom correspondence may be addressed. Email: sophia.david@ 123456sanger.ac.uk or david.aanensen@ 123456bdi.ox.ac.uk .

                Edited by Rita R. Colwell, University of Maryland, College Park, MD, and approved August 17, 2020 (received for review February 23, 2020)

                Author contributions: S.D. and D.M.A. designed research; S.D. and V.C. performed research; E.S.C.-P.E.W.G. and E.S.G.E.M. contributed new reagents/analytic tools; S.D., S.R., A.E.S., T.G., J.P., G.M.R., E.J.F., H.G., and D.M.A. analyzed data; E.S.C.-P.E.W.G. collected the bacterial isolates and epidemiological data; E.S.G.E.M. facilitated the training and capacity building for the collection of bacterial isolates; and S.D., G.M.R., E.J.F., H.G., and D.M.A. wrote the paper.

                3H.G. and D.M.A. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-0115-0954
                https://orcid.org/0000-0003-1592-6394
                https://orcid.org/0000-0002-7069-5958
                https://orcid.org/0000-0002-9971-522X
                Article
                202003407
                10.1073/pnas.2003407117
                7587227
                32968015
                a471e6bd-7d5e-467f-8576-51159f5d29a7
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 12
                Funding
                Funded by: Wellcome 100010269
                Award ID: 098051
                Award Recipient : Sophia David Award Recipient : Victoria Cohen Award Recipient : David M Aanensen
                Funded by: Wellcome 100010269
                Award ID: 099202
                Award Recipient : Sophia David Award Recipient : Victoria Cohen Award Recipient : David M Aanensen
                Funded by: DH | National Institute for Health Research (NIHR) 501100000272
                Award ID: 16/136/111
                Award Recipient : Sophia David Award Recipient : Victoria Cohen Award Recipient : David M Aanensen
                Categories
                Biological Sciences
                Microbiology

                klebsiella pneumoniae,carbapenem resistance,carbapenemase genes,plasmids,genomics

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