Linking gene expression and functional network data in human heart failure.
BACKGROUND
Gene expression profiling and the analysis of protein-protein interaction (PPI) networks may support the identification of disease bio-markers and potential drug targets. Thus, a step forward in the development of systems approaches to medicine is the integrative analysis of these data sources in specific pathological conditions. We report such an integrative bioinformatics analysis in human heart failure (HF). A global PPI network in HF was assembled, which by itself represents a useful compendium of the current status of human HF-relevant interactions. This provided the basis for the analysis of interaction connectivity patterns in relation to a HF gene expression data set.


RESULTS
Relationships between the significance of the differentiation of gene expression and connectivity degrees in the PPI network were established. In addition, relationships between gene co-expression and PPI network connectivity were analysed. Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed. Genes that are not significantly differentially expressed may encode proteins that exhibit diverse network connectivity patterns. Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners. Genes encoding network hubs may exhibit weak co-expression with the genes encoding their interacting protein partners. We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes. Gene Ontology (GO) analysis established that highly-connected proteins are likely to be engaged in higher level GO biological process terms, while low-connectivity proteins tend to be engaged in more specific disease-related processes.


CONCLUSIONS
This investigation supports the hypothesis that the integrative analysis of differential gene expression and PPI network analysis may facilitate a better understanding of functional roles and the identification of potential drug targets in human heart failure.
Camargo
Anyela
A
School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, Northern Ireland, United Kingdom.
Azuaje
Francisco
F
D020869:Gene Expression Profiling; D006333:Heart Failure / Q000235:genetics* / Q000503:physiopathology; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis