Using Functional Linkage Gene Networks to Study Human Diseases

Using Functional Linkage Gene Networks to Study Human Diseases

Bolan Linghu (Novartis Institutes for BioMedical Research, USA), Guohui Liu (Millennium Pharmaceuticals Inc, USA) and Yu Xia (Boston University, USA)
DOI: 10.4018/978-1-60960-491-2.ch012

Abstract

A major challenge in the post-genomic era is to understand the specific cellular functions of individual genes and how dysfunctions of these genes lead to different diseases. As an emerging area of systems biology, gene networks have been used to shed light on gene function and human disease. In this chapter, first the existence of functional association for genes working in a common biological process or implicated in a common disease is demonstrated. Next, approaches to construct the functional linkage gene network (FLN) based on genomic and proteomic data integration are reviewed. Finally, two FLN-based applications related to diseases are reviewed: prediction of new disease genes and therapeutic targets, and identification of disease-disease associations at the molecular level. Both of these applications bring new insights into the molecular mechanisms of diseases, and provide new opportunities for drug discovery.
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Introduction And Background

With the development of sequencing technologies, whole genome sequencing has been achieved for diverse species (Flicek et al., 2008). For a fully sequenced organism, most protein-encoding genes can be readily identified by available bioinformatics approaches (Flicek et al., 2008). By contrast, it remains a challenging task to understand the specific biological functions of these genes, and how dysfunctions of these genes lead to diverse human disease phenotypes.

A particular cellular function usually requires the collaboration between a specific group of genes or proteins. Rather than acting alone, these genes interact and communicate with each other in diverse ways, but all for the common purpose of maintaining the normal status of a specific biological process (Kanehisa et al., 2004). On the other hand, when one or more genes involved in a particular biological process are dysfunctional, the normal status of the biological process might be perturbed, which might further cause the organism to show abnormal physiological phenotypes referred to as a disease (Goh et al., 2007). Correspondingly, therapeutic drugs aim to target the genes or proteins involved in these perturbed biological processes such that the normal status of the biological processes can be reestablished (Janga & Tzakos, 2009). Therefore, for gene function and human disease research, it is very important to consider individual genes as functional related components within a coherent biological system.

Recent network-based approaches have demonstrated great success in representing functional relationships among genes with applications to understand gene function and human disease (Ahmed & Xing, 2009; Franke et al., 2006; Huttenhower et al., 2009; Kohler et al., 2008; Lage et al., 2007; I. Lee et al., 2008b; Linghu et al., 2008; Linghu et al., 2009; McGary et al., 2007; Oti & Brunner, 2007; Oti et al., 2006; Schadt, 2009). In these networks, nodes represent genes, and edges represent functional associations between linked genes. These networks are referred to as functional linkage gene networks (FLN). In this chapter, we first review the molecular basis for genes working as a functional group, and demonstrate the existence of functional associations between genes implicated in a common disease. Next, we review ways to construct different types of FLNs, as well as two important FLN-based applications related to human diseases: prediction of new disease genes and therapeutic targets, and identification of disease-disease associations at the molecular level (Figure 1).

Key Terms in this Chapter

Drug Discovery: Identify new compounds to target one or more proteins related to a disease with therapeutic effects.

Gene Networks: A graph representation of gene-gene associations with nodes representing genes and edges representing functional associations between genes.

Disease-Disease Associations: Existence of overlap for the underlying molecular mechanisms between diseases.

Diseases: Clinical phenotypes when one or more normal biological processes are perturbed in human.

Disease Gene Prediction: Predict new genes related to the molecular mechanisms of a disease.

Human Gene Networks: Gene networks composed of human genes.

Data Integration: Integration of diverse types of functional genomics and proteomics data to gain a comprehensive view of gene function.

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