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Linking gene expression and functional network data in human heart failure.

Camargo A, Azuaje F - PLoS ONE (2007)

Bottom Line: Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed.Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners.We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, Northern Ireland, United Kingdom.

ABSTRACT

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.

Conclusion: 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.

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Related in: MedlinePlus

Plot of t-statistics (cL).Plot of t-statistic (di) representing the score for gene i vs. number of interacting partners (IP Log2) associated with protein encoded by gene i.
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pone-0001347-g002: Plot of t-statistics (cL).Plot of t-statistic (di) representing the score for gene i vs. number of interacting partners (IP Log2) associated with protein encoded by gene i.

Mentions: This section of the study integrated gene expression data with the PPI network to describe potential significant relationships between network connectivity and gene expression patterns (as described in Methods). The first set of results, involving significantly differentially- expressed genes, found that genes represented by network superhubs and hubs tend to have lower range of ‘di’s values (the score of class differentiation). In Figure 2 genes with those characteristics are shown on the farthest right side of the plot. On the contrary, genes represented by network peripherals-A and -B tend to have higher range of ‘di’s values. When proteins encoded by non-significantly differentially-expressed genes were assessed, we found that some of these protein's interacting partners were encoded by several significantly differentially-expressed genes. For instance, GRB2 has 180 interacting partners and was not found to be significantly differentially-expressed in the gene expression data. However, 19 genes encoding its interacting partners, such as ABL1 or BCAR1, were identified as significantly differentially-expressed in the expression data analysis. We analysed the biological role of GRB2, and its corresponding interacting partners, and found that processes such as ‘signal transduction’, “regulation T Cell activation” or “regulation of MAPK activity” were over-represented (P<0.0001). According to KEGG and Reactome, GRB2 is involved in more than 15 pathways. Other proteins whose interacting partners were encoded by more than 15 significantly differentially-expressed genes were TP53, NR3C1, SMAD2, CASP3, ESR1, RB1 and YWHAG (Annex S1 shows complete list), which are involved in functional processes such as apoptosis or cell cycle. These findings stress the importance of performing gene expression analysis in conjunction with interaction networks to help to identify otherwise overlooked potential clinically-relevant targets.


Linking gene expression and functional network data in human heart failure.

Camargo A, Azuaje F - PLoS ONE (2007)

Plot of t-statistics (cL).Plot of t-statistic (di) representing the score for gene i vs. number of interacting partners (IP Log2) associated with protein encoded by gene i.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2147076&req=5

pone-0001347-g002: Plot of t-statistics (cL).Plot of t-statistic (di) representing the score for gene i vs. number of interacting partners (IP Log2) associated with protein encoded by gene i.
Mentions: This section of the study integrated gene expression data with the PPI network to describe potential significant relationships between network connectivity and gene expression patterns (as described in Methods). The first set of results, involving significantly differentially- expressed genes, found that genes represented by network superhubs and hubs tend to have lower range of ‘di’s values (the score of class differentiation). In Figure 2 genes with those characteristics are shown on the farthest right side of the plot. On the contrary, genes represented by network peripherals-A and -B tend to have higher range of ‘di’s values. When proteins encoded by non-significantly differentially-expressed genes were assessed, we found that some of these protein's interacting partners were encoded by several significantly differentially-expressed genes. For instance, GRB2 has 180 interacting partners and was not found to be significantly differentially-expressed in the gene expression data. However, 19 genes encoding its interacting partners, such as ABL1 or BCAR1, were identified as significantly differentially-expressed in the expression data analysis. We analysed the biological role of GRB2, and its corresponding interacting partners, and found that processes such as ‘signal transduction’, “regulation T Cell activation” or “regulation of MAPK activity” were over-represented (P<0.0001). According to KEGG and Reactome, GRB2 is involved in more than 15 pathways. Other proteins whose interacting partners were encoded by more than 15 significantly differentially-expressed genes were TP53, NR3C1, SMAD2, CASP3, ESR1, RB1 and YWHAG (Annex S1 shows complete list), which are involved in functional processes such as apoptosis or cell cycle. These findings stress the importance of performing gene expression analysis in conjunction with interaction networks to help to identify otherwise overlooked potential clinically-relevant targets.

Bottom Line: Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed.Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners.We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, Northern Ireland, United Kingdom.

ABSTRACT

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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus