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

Sumanta Ray, Sanghamitra Bandyopadhyay

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |December 15, 2015
    PubMed
    Summary

    This study introduces a new framework to analyze gene co-expression networks in HIV-1 progression. It identifies key transcription factors and network changes across different HIV stages.

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    Area of Science:

    • Bioinformatics
    • Systems Biology
    • Genomics

    Background:

    • HIV-1 infection progresses through distinct stages: acute, chronic (clinical latency), and acquired immunodeficiency syndrome (AIDS).
    • Gene co-expression network analysis is a powerful tool for understanding gene regulation and biological processes.
    • Understanding stage-specific gene expression patterns is crucial for deciphering HIV-1 pathogenesis.

    Purpose of the Study:

    • To develop a novel computational framework for identifying topological patterns in gene co-expression networks during HIV-1 progression.
    • To detect and characterize changes in modular network structures across different stages of HIV-1 infection.
    • To identify key transcription factors and their expression dynamics associated with HIV-1 progression.

    Main Methods:

    • Development of a novel framework integrating topological, correlation-based, and eigengene-based measures.
    • Application of a rank aggregation scheme to rank gene co-expression modules.
    • Comparative analysis of topological and intramodular properties of HIV infection modules across different disease stages.
    • Eigengene-based analysis to reveal perturbations in modular structures.

    Main Results:

    • Identification of significant changes in the expression patterns of novel transcription factors (e.g., FOXO1, GATA3, STAT1, STAT3) across HIV-1 progression stages.
    • Detection of dynamic alterations in the modular structure of gene co-expression networks correlating with HIV-1 disease progression.
    • Validation of the framework's ability to capture diverse modular structures and provide agreement between different analytical measures.

    Conclusions:

    • The developed framework effectively identifies stage-specific topological patterns and modular changes in HIV-1 gene co-expression networks.
    • Key transcription factors exhibit altered expression profiles that correlate with the progression of HIV-1 infection.
    • This approach provides novel insights into the molecular mechanisms underlying HIV-1 pathogenesis and potential therapeutic targets.