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Open Access Research article

Patterns of human gene expression variance show strong associations with signaling network hierarchy

Kakajan Komurov* and Prahlad T Ram

Author Affiliations

Department of Systems Biology, The University of Texas M.D. Anderson Cancer Center, 7435 Fannin St, Houston, TX 77054 USA

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BMC Systems Biology 2010, 4:154  doi:10.1186/1752-0509-4-154

Published: 12 November 2010

Abstract

Background

Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV) of genes and their relationship to cellular functions and physiological responses is poorly understood.

Results

To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes.

Conclusion

Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.