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      Regional Spread of bla NDM-1-Containing Klebsiella pneumoniae ST147 in Post-Acute Care Facilities

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          Abstract

          Background

          Carbapenem-resistant Enterobacterales (CRE) harboring bla KPC have been endemic in Chicago-area healthcare networks for more than a decade. During 2016–2019, a series of regional point-prevalence surveys identified increasing prevalence of bla NDM-containing CRE in multiple long-term acute care hospitals (LTACHs) and ventilator-capable skilled nursing facilities (vSNFs). We performed a genomic epidemiology investigation of bla NDM-producing CRE to understand their regional emergence and spread.

          Methods

          We performed whole-genome sequencing on New Delhi metallo-beta-lactamase (NDM)+ CRE isolates from 4 point-prevalence surveys across 35 facilities (LTACHs, vSNFs, and acute care hospital medical intensive care units) in the Chicago area and investigated the genomic relatedness and transmission dynamics of these isolates over time.

          Results

          Genomic analyses revealed that the rise of NDM+ CRE was due to the clonal dissemination of an sequence type (ST) 147 Klebsiella pneumoniae strain harboring bla NDM-1 on an IncF plasmid. Dated phylogenetic reconstructions indicated that ST147 was introduced into the region around 2013 and likely acquired NDM around 2015. Analyzing the relatedness of strains within and between facilities supported initial increases in prevalence due to intrafacility transmission in certain vSNFs, with evidence of subsequent interfacility spread among LTACHs and vSNFs connected by patient transfer.

          Conclusions

          We identified a regional outbreak of bla NDM-1 ST147 that began in and disseminated across Chicago area post-acute care facilities. Our findings highlight the importance of performing genomic surveillance at post-acute care facilities to identify emerging threats.

          Abstract

          Whole-genome sequencing of carbapenem-resistant Enterobacterales in Chicago area healthcare facilities revealed clonal dissemination of bla NDM-1 Klebsiella pneumoniae sequence type 147 within and between post-acute care facilities.

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          Most cited references52

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Journal
                Clin Infect Dis
                Clin Infect Dis
                cid
                Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
                Oxford University Press (US )
                1058-4838
                1537-6591
                15 October 2021
                17 May 2021
                17 May 2021
                : 73
                : 8
                : 1431-1439
                Affiliations
                [1 ] Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan, USA
                [2 ] Department of Microbiology and Immunology, University of Michigan , Ann Arbor, Michigan, USA
                [3 ] Department of Medicine, Rush University Medical Center , Chicago, Illinois, USA
                [4 ] Department of Medicine, Cook County Health , 4Chicago, Illinois, USA
                [5 ] Department of Pathology, Rush University Medical Center , Chicago, Illinois, USA
                Author notes

                E. S. S. and M. Y. L. contributed equally to this work.

                Correspondence: M. Y. Lin, Department of Medicine, Division of Infectious Diseases, Rush University Medical Center, 600 S Paulina St, Ste 143, Chicago, IL 60612 ( Michael_Lin@ 123456rush.edu ).
                Article
                ciab457
                10.1093/cid/ciab457
                8528401
                33999991
                126d89ea-fae7-4a8f-8dd0-dd16e3137e49
                © The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 18 March 2021
                : 04 May 2021
                : 14 July 2021
                Page count
                Pages: 9
                Funding
                Funded by: CDC Cooperative Agreement;
                Award ID: U54 CK000481
                Funded by: SHEPheRD;
                Award ID: 200-2011-42037
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: DGE 1256260
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: T32AI007528
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: 1R01AI148259-01
                Categories
                Major Articles and Commentaries
                AcademicSubjects/MED00290

                Infectious disease & Microbiology
                ndm,klebsiella pneumonia,st147,genomic epidemiology,carbapenem resistance

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