利用CRISPR-Cas9富集和牛津纳米孔技术对肠杆菌进行靶向测序。

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2025-02-18 Epub Date: 2025-01-08 DOI:10.1128/msystems.01413-24
Hugh Cottingham, Louise M Judd, Jessica A Wisniewski, Ryan R Wick, Thomas D Stanton, Ben Vezina, Nenad Macesic, Anton Y Peleg, Iruka N Okeke, Kathryn E Holt, Jane Hawkey
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引用次数: 0

摘要

与传统的基于培养的方法相比,直接从患者样本中测序DNA可以更快地表征病原体,但通常无法产生有效下游分析的序列数据。CRISPR-Cas9富集旨在提高低丰度序列的产量,但尚未与牛津纳米孔技术公司(ONT)深入探讨用于临床细菌流行病学。我们设计了CRISPR-Cas9引导RNA,通过靶向多位点序列型(MLST)和转移RNA (tRNA)基因,以及常见的抗菌素耐药(AMR)基因和耐药相关整合子基因intI1,来丰富人类病原体肺炎克雷伯菌。我们验证了20株肺炎克雷伯菌分离株的富集性能,发现指南在两个分离株中除一个AMR基因外的所有保守位点上都成功富集。MLST基因的富集导致在这些区域深度为30倍或以上的10个分离株中有8个在所有7个位点上都有正确的等位基因召唤。然后,我们比较了富集和未富集的三个人类粪便样本的测序,这些样本中含有不同丰度的肺炎克雷伯菌。与未富集测序相比,富集测序产生的AMR和MLST读取数分别是未富集测序的56倍和11.3倍,所需的计算存储空间约为未富集测序的三分之一。由于ONT测序产生的长读数,靶向intI1基因通常导致检测到10-20个近端耐药基因。我们证明,CRISPR-Cas9富集与ONT测序相结合,能够比未富集的患者样本测序改善基因组表征结果。该方法可通过识别感染高危菌株的患者,为感染控制策略提供信息。重要性:了解复杂样品中的细菌可能具有挑战性,因为它们的丰度很低,这通常会导致分析数据不足。为了提高有害细菌的检测,我们实施了一项技术,旨在增加目标病原体的数据量,并与现代DNA测序技术相结合。我们的技术使用CRISPR-Cas9靶向肺炎克雷伯菌细菌病原体中的特定基因序列,并提高人类粪便样本的回收率。我们发现我们的富集方法明显优于传统方法,产生更多来自目标基因的数据。此外,我们开发了新的计算技术来进一步增强分析,为从复杂的生物样品中表征病原体提供了一种彻底的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Targeted sequencing of Enterobacterales bacteria using CRISPR-Cas9 enrichment and Oxford Nanopore Technologies.

Sequencing DNA directly from patient samples enables faster pathogen characterization compared to traditional culture-based approaches, but often yields insufficient sequence data for effective downstream analysis. CRISPR-Cas9 enrichment is designed to improve the yield of low abundance sequences but has not been thoroughly explored with Oxford Nanopore Technologies (ONT) for use in clinical bacterial epidemiology. We designed CRISPR-Cas9 guide RNAs to enrich the human pathogen Klebsiella pneumoniae, by targeting multi-locus sequence type (MLST) and transfer RNA (tRNA) genes, as well as common antimicrobial resistance (AMR) genes and the resistance-associated integron gene intI1. We validated enrichment performance in 20 K. pneumoniae isolates, finding that guides generated successful enrichment across all conserved sites except for one AMR gene in two isolates. Enrichment of MLST genes led to a correct allele call in all seven loci for 8 out of 10 isolates that had depth of 30× or more in these regions. We then compared enriched and unenriched sequencing of three human fecal samples spiked with K. pneumoniae at varying abundance. Enriched sequencing generated 56× and 11.3× the number of AMR and MLST reads, respectively, compared to unenriched sequencing, and required approximately one-third of the computational storage space. Targeting the intI1 gene often led to detection of 10-20 proximal resistance genes due to the long reads produced by ONT sequencing. We demonstrated that CRISPR-Cas9 enrichment combined with ONT sequencing enabled improved genomic characterization outcomes over unenriched sequencing of patient samples. This method could be used to inform infection control strategies by identifying patients colonized with high-risk strains.

Importance: Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen Klebsiella pneumoniae and improve recovery from human stool samples. We found our enrichment method to significantly outperform traditional methods, generating far more data originating from our target genes. Additionally, we developed new computational techniques to further enhance the analysis, providing a thorough method for characterizing pathogens from complex biological samples.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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