Intricate interplay of CRISPR-Cas systems, anti-CRISPR proteins, and antimicrobial resistance genes in a globally successful multi-drug resistant <i>Klebsiella pneumoniae</i> clone

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Abstract

Background

Klebsiella pneumoniae is one of the most prevalent pathogens responsible for multiple infections in healthcare settings and the community. K. pneumoniae CG147, primarily including ST147 (the founder ST), ST273, and ST392, is one of the most globally successful MDR clone linked to various carbapenemases.

Methods

One hundred and one CG147 strains were sequenced and additional 911 publicly available CG147 genome sequences were included for analysis. The molecular epidemiology, population structure, and time phylogeny were investigated. The virulome, resistome, and mobilome were analyzed, and the recombination in the capsular region was studied. The CRISPR-Cas and anti-CRISPR were identified. The interplay between CRISPR-Cas, anti-CRISPR, and carbapenemase-encoding plasmids was analyzed and experimentally validated.

Results

We analyzed 1012 global CG147 genomes, with 80.4% encoding at least one carbapenemase (NDM [529/1012, 52.3%], OXA-48-like [182/1012, 17.7%], and KPC [105/1012, 10.4%]). Surprisingly, almost all CG147 strains (99.7%, 1009/1,012) harbor a chromosomal type I-E CRISPR-Cas system, with 41.8% (423/1012) containing an additional plasmid-borne type IV-A3 CRISPR-Cas system, and both target IncF plasmids, e.g., the most prevalent KPC-encoding pKpQIL-like plasmids. We found the presence of IV-A3 CRISPR-Cas system showed a negative correlation with the presence of KPC. Interestingly, a prophage-encoding anti-CRISPR AcrIE8.1 and a plasmid-borne anti-CRISPR AcrIE9.2 were detected in 40.1% (406/1012) and 54.2% (548/1012) of strains, respectively, which displayed positive correlations with the presence of a carbapenemase. Plasmid transfer experiments confirmed that the I-E and IV-A3 CRISPR-Cas systems significantly decreased ( p < 0.001) KPC-encoding pKpQIL plasmid conjugation frequencies, while the AcrIE8.1 and AcrIE9.2 significantly increased ( p < 0.001) pKpQIL conjugation frequencies and protected plasmids from elimination by CRISPR-Cas I-E system.

Conclusions

Our results indicated a complex interplay between CRISPR-Cas, anti-CRISPR, and mobile genetic elements that shape the evolution of CG147. Our findings advance the understanding of multi-drug resistance mechanisms and will aid in preventing the emergence of future MDR clones.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-025-01428-6.

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Most cited references 51

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Fast and accurate short read alignment with Burrows–Wheeler transform

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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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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Alexandros Stamatakis (2014)

Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Barry N. Kreiswirth: barry.kreiswirth@hmh-cdi.org

Liang Chen:

ORCID: http://orcid.org/0000-0001-5845-2235

liangch@buffalo.edu

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 30 January 2025

Publication date PMC-release: 30 January 2025

Publication date Collection: 2025

Volume: 17

Electronic Location Identifier: 9

Affiliations

[1 ]Center for Discovery and Innovation, Hackensack Meridian Health, ( https://ror.org/04p5zd128) Nutley, NJ USA

[2 ]School of Microbiology, University of Antioquia, ( https://ror.org/03bp5hc83) Medellín, Colombia

[3 ]Guangdong Laboratory for Lingnan Modern Agriculture, National Risk Assessment Laboratory for Antimicrobial Resistance of Animal Original Bacteria, College of Veterinary Medicine, South China Agricultural University, ( https://ror.org/05v9jqt67) Guangzhou, China

[4 ]Guangdong Provincial Key Laboratory of Veterinary Pharmaceutics, Development and Safety Evaluation, South China Agricultural University, ( https://ror.org/05v9jqt67) Guangzhou, China

[5 ]Cummings School of Medicine, University of Calgary, ( https://ror.org/03yjb2x39) Calgary, AB Canada

[6 ]Alberta Precision Laboratories, Calgary, AB Canada

[7 ]University of Pretoria, ( https://ror.org/00g0p6g84) Pretoria, Gauteng South Africa

[8 ]Department of Medical Sciences, Hackensack Meridian School of Medicine, ( https://ror.org/04p5zd128) Nutley, NJ USA

[9 ]Present address: School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, ( https://ror.org/01y64my43) Buffalo, NY USA

Author information

Liang Chen http://orcid.org/0000-0001-5845-2235

Article

Publisher ID: 1428

DOI: 10.1186/s13073-025-01428-6

PMC ID: 11781037

PubMed ID: 39885543

SO-VID: e34d324e-e59b-49fb-965e-a7a064cd67b9

License:

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

History

Date received : 29 April 2024

Date accepted : 2 January 2025

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ScienceOpen disciplines: Molecular medicine

Keywords: klebsiella pneumoniae,cg147,crispr-cas,anti-crispr,genomic epidemiology

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ScienceOpen disciplines: Molecular medicine

Keywords: klebsiella pneumoniae, cg147, crispr-cas, anti-crispr, genomic epidemiology

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Correction: Intricate interplay of CRISPR-Cas systems, anti-CRISPR proteins, and antimicrobial resistance genes in a globally successful multi-drug resistant Klebsiella pneumoniae clone
Authors: Jianping Jiang, Astrid V Cienfuegos-Gallet, Tengfei Long …

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Intricate interplay of CRISPR-Cas systems, anti-CRISPR proteins, and antimicrobial resistance genes in a globally successful multi-drug resistant Klebsiella pneumoniae clone

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Related collections

Genome Engineering using CRISPR

Most cited references 51

The Sequence Alignment/Map format and SAMtools

Fast and accurate short read alignment with Burrows–Wheeler transform

RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

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