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基于综合生物信息学策略的细胞壁合成网络模块高通量筛选

High-Throughput Screen for Cell Wall Synthesis Network Module in Based on Integrated Bioinformatics Strategy.

作者信息

Luo Xizi, Pan Jiahui, Meng Qingyu, Huang Juanjuan, Wang Wenfang, Zhang Nan, Wang Guoqing

机构信息

Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, China.

College of Mathematics, Jilin University, Changchun, China.

出版信息

Front Bioeng Biotechnol. 2020 Jun 30;8:607. doi: 10.3389/fbioe.2020.00607. eCollection 2020.

Abstract

BACKGROUND

is one of the deadliest pathogens in humans. Co-infection of with HIV and the emergence of multi-drug-resistant tuberculosis (TB) constitute a serious global threat. However, no effective anti-TB drugs are available, with the exception of first-line drugs such as isoniazid. The cell wall of , which is primarily responsible for the lack of effective anti-TB drugs and the escape of the bacteria from host immunity, is an important drug target. The core components of the cell wall of are peptidoglycan, arabinogalactan, and mycotic acid. However, the functional genome and metabolic regulation pathways for the cell wall are still unknown. In this study, we used the biclustering algorithm integrated into cMonkey, sequence alignment, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and other bioinformatics methods to scan the whole genome of as well as to identify and statistically analyze the genes related to the synthesis of the cell wall.

METHOD

We performed high-throughput genome-wide screening for using Biocarta, KEGG, National Cancer Institute Pathway Interaction Database (NCI-PID), HumanCyc, and Reactome. We then used the Database of Origin and Registration (DOOR) established in our laboratory to classify the collection of operons for cell wall synthetic genes. We used the cMonkey double clustering algorithm to perform clustering analysis on the gene expression profile of for cell wall synthesis. Finally, we visualized the results using Cytoscape.

RESULT AND CONCLUSION

Through bioinformatics and statistical analyses, we identified 893 H37Rv cell wall synthesis genes, distributed in 20 pathways, involved in 46 different functions related to cell wall synthesis, and clustered in 386 modules. We identified important pivotal genes and proteins in the cell wall synthesis pathway such as , a class of operons containing genes involved in cell wall synthesis such as ID6951, and a class of operons indispensable for the survival of the bacteria. In addition, we found 41 co-regulatory modules for cell wall synthesis and five co-expression networks of molecular complexes involved in peptidoglycan biosynthesis, membrane transporter synthesis, and other cell wall processes.

摘要

背景

[病原体名称]是人类最致命的病原体之一。[病原体名称]与艾滋病毒的合并感染以及多重耐药结核病(TB)的出现构成了严重的全球威胁。然而,除了异烟肼等一线药物外,没有有效的抗结核药物。[病原体名称]的细胞壁是缺乏有效抗结核药物以及细菌逃避宿主免疫的主要原因,是一个重要的药物靶点。[病原体名称]细胞壁的核心成分是肽聚糖、阿拉伯半乳聚糖和霉菌酸。然而,[病原体名称]细胞壁的功能基因组和代谢调控途径仍然未知。在本研究中,我们使用集成到cMonkey中的双聚类算法、序列比对、基因本体论(GO)、京都基因与基因组百科全书(KEGG)以及其他生物信息学方法来扫描[病原体名称]的全基因组,并识别和统计分析与[病原体名称]细胞壁合成相关的基因。

方法

我们使用Biocarta、KEGG、美国国立癌症研究所通路相互作用数据库(NCI-PID)、HumanCyc和Reactome对[病原体名称]进行全基因组高通量筛选。然后,我们使用我们实验室建立的起源和注册数据库(DOOR)对[病原体名称]细胞壁合成基因的操纵子集合进行分类。我们使用cMonkey双聚类算法对[病原体名称]细胞壁合成的基因表达谱进行聚类分析。最后,我们使用Cytoscape对结果进行可视化。

结果与结论

通过生物信息学和统计分析,我们鉴定出893个[病原体名称]H37Rv细胞壁合成基因,分布在20条途径中,涉及与细胞壁合成相关的46种不同功能,并聚类在386个模块中。我们在细胞壁合成途径中鉴定出重要的关键基因和蛋白质,例如一类包含参与细胞壁合成的基因(如ID6951)的操纵子,以及一类对细菌生存不可或缺的操纵子。此外,我们发现了41个细胞壁合成的共调控模块以及5个参与肽聚糖生物合成、膜转运蛋白合成和其他细胞壁过程的分子复合物共表达网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8f6/7338375/e364870a7478/fbioe-08-00607-g001.jpg

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