Methodology article

Characterizing disease states from topological properties of transcriptional regulatory networks

David P Tuck¹, Harriet M Kluger² and Yuval Kluger^3*

• * Corresponding author: Yuval Kluger kluger@saturn.med.nyu.edu

Author Affiliations

¹ Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA

² Department of Internat Medicine, Yale University School of Medicine, New Haven, Connecticut 06510, USA

³ Department of Cell Biology, New York University School of Medicine, New York, New York 10016, USA

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BMC Bioinformatics 2006, 7:236 doi:10.1186/1471-2105-7-236

Published: 2 May 2006

Abstract

Background

High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome.

Results

We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links.

We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity.

Conclusion

Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.