开发以肺炎克雷伯菌菌株为例的CRISPR-Cas系统的搜索和分析方法,作为创建个性化噬菌体治疗的基础

L. A. Stepanenko, B. Sukhov, V. V. Bedinskaya, A. Borisenko, T. V. Kon’kova
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摘要

本文提出了一种利用生物信息学研究方法在肺炎克雷伯菌基因组中搜索和分析细菌CRISPR- cas系统结构,并通过CRISPR盒中的间隔物筛选噬菌体的算法。目的是利用生物信息学研究方法确定和研究以肺炎克雷伯菌菌株为例的细菌CRISPR-Cas系统的结构,以开发靶向噬菌体的选择方法。研究对象包括从GenBank数据库下载的150个全基因组序列。其中,在52株菌株中检测到CRISPR-Cas系统,占34.7%。在研究菌株的CRISPR- cas系统中使用几种搜索算法,分别在46.2%和53.8%的病例中确定存在一个和两个CRISPR盒。在所有病例中,在卡带旁发现了一套完整的i型- i - e亚型Cas基因。共鉴定出1659个间隔片段,其中281个间隔片段在两个或两个以上的CRISPR位点重复,505个间隔片段没有重复。磁带中的间隔片数量从4个到64个不等。对耐抗生素菌株和医院菌株的CRISPR卡带中间隔成分的分析提供了它们的进化史和它们的CRISPR系统靶向的噬菌体的信息。开发的生物信息学分析算法能够为个性化噬菌体治疗技术的开发创造平台。
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Developing approaches for search and analysis of CRISPR-Cas systems on the example of Klebsiella pneumoniae strains as a basis for creating personalized bacteriophage therapy
This paper proposes an algorithm for searching and analyzing the structures of CRISPR-Cas systems of bacteria and screening bacteriophages through spacers in CRISPR cassettes using bioinformatic research methods in the genomes of Klebsiella pneumoniae strains. The aim was to determine and study the structure of CRISPR-Cas systems of bacteria on the example of Klebsiella pneumoniae strains using bioinformatic research methods in order to develop approaches for the selection of target bacteriophages. The research object included 150 genome-wide sequences downloaded from the GenBank database. Of these sequences, CRISPR-Cas systems were detected in 52 strains, which amounted to 34.7%. Using several search algorithms in the CRISPR-Cas systems of the studied strains, the presence of one and two CRISPR cassette was determined in 46.2 and 53.8% of cases, respectively. In all the cases, a complete set of Cas genes characteristic of Type-I Subtype-I-E systems was identified next to the cassettes. The total number of the identified spacers was 1659, of which 281 spacers were repeated in two or more CRISPR loci, while 505 spacers had no repeats. The number of spacers in the cassettes ranged from 4 to 64. The analysis of the spacer composition in CRISPR cassettes of antibiotic-resistant and hospital strains provided information on their evolutionary history and on the bacteriophages which are targeted by their CRISPR systems. The developed bioinformatic analysis algorithm enables creating a platform for the development of personalized bacteriophage therapy technologies.
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