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This article is part of the supplement: 22nd International Conference on Genome Informatics: Systems Biology

Open Access Proceedings

Differentially co-expressed interacting protein pairs discriminate samples under distinct stages of HIV type 1 infection

Dukyong Yoon1, Hyosil Kim1, Haeyoung Suh-Kim2, Rae Woong Park1* and KiYoung Lee13*

Author Affiliations

1 Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea

2 Department of Anatomy, Ajou University School of Medicine, Suwon 443-749, Korea

3 Departments of Medicine and Bioengineering, University of California San Diego, La Jolla, California 92093, USA

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BMC Systems Biology 2011, 5(Suppl 2):S1  doi:10.1186/1752-0509-5-S2-S1

Published: 14 December 2011

Abstract

Background

Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs.

Methods

Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature.

Results

Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70–100% accuracy) was much higher than that of DEGs (35–45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to “translation,” “RNA splicing,” “mRNA, RNA, and nucleic acid transport,” and “DNA metabolism.” Through the HIV-1 stage-related modules, changing stage-specific patterns of protein interactions could be observed.

Conclusions

DEP-based method discriminated the HIV-1 infection stages clearly, and revealed a HIV-1 stage-specific gene signature. The proposed DEP-based method might complement existing DEG-based approaches in various microarray expression analyses.