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用于干预策略的疾病特异性模块和枢纽基因:一种基于共表达网络的恶性疟原虫临床分离株研究方法。

Disease specific modules and hub genes for intervention strategies: A co-expression network based approach for Plasmodium falciparum clinical isolates.

作者信息

Subudhi Amit Kumar, Boopathi Pon Arunachalam, Pandey Isha, Kaur Ramandeep, Middha Sheetal, Acharya Jyoti, Kochar Sanjay K, Kochar Dhanpat K, Das Ashis

机构信息

Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India.

Department of Medicine, S.P. Medical College, Bikaner, Rajasthan, India.

出版信息

Infect Genet Evol. 2015 Oct;35:96-108. doi: 10.1016/j.meegid.2015.08.007. Epub 2015 Aug 4.

Abstract

Systems biology approaches that are based on gene expression and bioinformatics analysis have been successful in predicting the functions of many genes in Plasmodium falciparum, a protozoan parasite responsible for most of the deaths due to malaria. However, approaches that can provide information about the biological processes that are active in this parasite in vivo during complicated malaria conditions have been scarcely deployed. Here we report the analysis of a weighted gene co-expression based network for P. falciparum, from non-cerebral clinical complications. Gene expression profiles of 20 P. falciparum clinical isolates were utilized to construct the same. A total of 20 highly interacting modules were identified post network creation. In 12 of these modules, at least 10% of the member genes, were found to be differentially regulated in parasites from patient isolates showing complications, when compared with those from patients with uncomplicated disease. Enrichment analysis helped identify biological processes like oxidation-reduction, electron transport chain, protein synthesis, ubiquitin dependent catabolic processes, RNA binding and purine nucleotide metabolic processes as associated with these modules. Additionally, for each module, highly connected hub genes were identified. Detailed functional analysis of many of these, which have known annotated functions underline their importance in parasite development and survival. This suggests, that other hub genes with unknown functions may also be playing crucial roles in parasite biology, and, are potential candidates for intervention strategies.

摘要

基于基因表达和生物信息学分析的系统生物学方法已成功预测了恶性疟原虫中许多基因的功能,恶性疟原虫是一种原生动物寄生虫,导致了大部分疟疾死亡病例。然而,在复杂疟疾情况下能够提供有关该寄生虫体内活跃生物过程信息的方法却很少被采用。在此,我们报告了对来自非脑型临床并发症的恶性疟原虫基于加权基因共表达网络的分析。利用20个恶性疟原虫临床分离株的基因表达谱构建了该网络。网络构建后共鉴定出20个高度相互作用的模块。在其中12个模块中,与无并发症疾病患者的寄生虫相比,发现至少10%的成员基因在有并发症的患者分离株的寄生虫中受到差异调节。富集分析有助于确定与这些模块相关的生物过程,如氧化还原、电子传递链、蛋白质合成、泛素依赖性分解代谢过程、RNA结合和嘌呤核苷酸代谢过程。此外,对于每个模块,都鉴定出了高度连接的枢纽基因。对其中许多具有已知注释功能的基因进行详细功能分析,突显了它们在寄生虫发育和生存中的重要性。这表明,其他功能未知的枢纽基因可能也在寄生虫生物学中发挥关键作用,并且是干预策略的潜在候选对象。

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