Trends in Genetics
Volume 26, Issue 7, July 2010, Pages 326-333
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Review
From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases

https://doi.org/10.1016/j.tig.2010.05.001Get rights and content

Understanding diseases requires identifying the differences between healthy and affected tissues. Gene expression data have revolutionized the study of diseases by making it possible to simultaneously consider thousands of genes. The identification of disease-associated genes requires studying the genes in the context of the regulatory systems they are involved in. A major goal is to identify specific regulatory networks that are dysfunctional in a given disease state. Although we still have not reached a stage where the elucidation of differential regulatory networks is commonly feasible, recent advances have described the first steps towards this goal – the identification of differential coexpression networks. This review describes the shift from differential gene expression to differential networking and outlines how this shift will affect the study of the genetic basis of disease.

Section snippets

Dysfunctional networks in disease

To understand the roles of genes in complex human diseases, genes need to be studied in the context of the regulatory systems they are involved in [1]. Regulatory systems inside cells can be effectively abstracted into networks (Box 1). Such regulatory networks hold the potential to provide the cellular context of all genes of interest and give a means to identify specific subnetworks that are dysfunctional in a given disease state. It is therefore not surprising that several recent

From differential expression to differential coexpression

Recent investigations have gone beyond testing for differential expression and aim to elucidate dysfunctional regulatory networks in disease. Instead of focusing on differences in mean gene expression levels, the goal is to identify differences in their coexpression patterns (commonly quantified by pairwise correlations) in healthy and disease-affected samples (Box 4). Pairwise relationships between gene expression levels result from regulatory relationships among the genes, and identifying

From differential coexpression to differential networking

Identifying differential coexpression is the first step towards identifying differential gene networks. As Bill Shipley insightfully states in his book Cause and Correlation in Biology[47]: ‘As with shadows, these correlational patterns are incomplete – and potentially ambiguous – projections of the original causal processes. As with shadows, we can infer much about the underlying causal process if we can learn to study their details, to sharpen their contours, and especially if we can study

Acknowledgements

I kindly thank Paolo Uva, Diogo Camacho, three anonymous reviewers and the editor for critical reading of the manuscript and their insightful suggestions. This work was supported in part by the Regional Authorities of Sardinia (see: http://www.sardegnaricerche.it/).

Glossary

Differential coexpression
the observation that the correlation (or other measure of association) between the expression levels of two (or more) genes is significantly different (higher or lower) in case (e.g. disease) and control (e.g. healthy) samples.
Differential expression
the observation that the mean expression level of a given gene (or set of genes) is significantly different (higher or lower) between case and control samples.
False discovery rate (FDR)
expected ratio of false positive

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