Discovering Condition Specific Topological Pattern Changes in Coexpression Network: An Application to HIV-1 Progression

Article Type

Research Article

Publication Title

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Abstract

The natural progression of HIV-1 begins with a short acute retroviral syndrome which typically transit to chronic and clinical latency stages and subsequently progresses to a symptomatic, life-threatening immunodeficiency disease known as AIDS. Microarray analysis based on gene coexpression is widely used to investigate the coregulation pattern of a group (or cluster) of genes in a specific phenotype. Moreover, an investigation on the topological patterns across multiple phenotypes can facilitate the understanding of stage specific infection pattern of HIV-1 virus. Here, we develop a novel framework to identify topological patterns of gene co-expression network and detect changes of modular structure across different stages of HIV progression. This is achieved by comparing the topological and intramodular properties of HIV infection modules. To capture the diversity in modular structure, some topological, correlation based, and eigengene based measures are utilized here. We have applied a rank aggregation scheme to rank all the modules to provide a good agreement between these measures. Some novel transcription factors like 'FOXO1', 'GATA3', 'GFI1', 'IRF1', 'IRF7', 'MAX', 'STAT1', 'STAT3', 'XBP1', and 'YY1' that merge from the modules show significant change in expression pattern over HIV progression stages. Moreover, we have performed an eigengene based analysis to reveal the perturbation in modular structure across three stages of HIV-1 progression.

First Page

1086

Last Page

1099

DOI

10.1109/TCBB.2015.2505300

Publication Date

1-11-2016

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