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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

Scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi).(A) significantly differentially-expressed genes. (B) Non-significantly differentially-expressed genes. (C) KHFGs. (D) Independent testing data set.
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pone-0001347-g003: Scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi).(A) significantly differentially-expressed genes. (B) Non-significantly differentially-expressed genes. (C) KHFGs. (D) Independent testing data set.

Mentions: In this section gene expression data were integrated with the topology of the PPI network to assess significant co-expression levels (as detailed in Methods). We found that genes represented by network hubs and superhubs are not necessarily significantly co-expressed with their attributed protein-coding partners (IPs), than other types of nodes. For example, genes MAPK1 and FXR2, represented by network superhubs, were significantly co-expressed with 15.5% and 10.2% of the genes encoding their IPs respectively. On the other hand, genes represented by network peripheral-A and -B may be strongly correlated with their interacting partners. For example, ALDOB, represented by a network peripheral-A, was significantly co-expressed with all the genes encoding its IPs (i.e. 100% significant co-expression level). Table 2 shows more details of the difference between these categories in terms of mean cL values. No statistical significance difference between category means were found at P = 0.05. However, note that only network peripherals-A or –B showed cases with cL = 100%. The global trend, as shown in Figure 3, is that the higher the number of node connections the greater the tendency to display low cL values. Figure 3A shows a scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi) (as defined in Methods). Similar trend was observed when non-significantly differentially expressed genes were analysed. For example, HAP1 and SIN3A, represented by network peripherals-A, were significantly co-expressed with all their partners, IPs. Figure 3B plots (IPi) versus (cLi) of non-significantly differentially-expressed genes. When analysing nodes representing KHFGs, results showed that in general cLi of these genes was low. In fact, none of the 40 KHFGs, which had a corresponding transcript in the DCM expression data, obtained a cLi>50% (Figure 3C). For example, the cLi for PRKCA, represented by a network superhub, was equal to 16.2% (i.e. this gene's expression pattern was significantly co-expressed with only a few of the genes coding for its IPs).


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

Camargo A, Azuaje F - PLoS ONE (2007)

Scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi).(A) significantly differentially-expressed genes. (B) Non-significantly differentially-expressed genes. (C) KHFGs. (D) Independent testing data set.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0001347-g003: Scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi).(A) significantly differentially-expressed genes. (B) Non-significantly differentially-expressed genes. (C) KHFGs. (D) Independent testing data set.
Mentions: In this section gene expression data were integrated with the topology of the PPI network to assess significant co-expression levels (as detailed in Methods). We found that genes represented by network hubs and superhubs are not necessarily significantly co-expressed with their attributed protein-coding partners (IPs), than other types of nodes. For example, genes MAPK1 and FXR2, represented by network superhubs, were significantly co-expressed with 15.5% and 10.2% of the genes encoding their IPs respectively. On the other hand, genes represented by network peripheral-A and -B may be strongly correlated with their interacting partners. For example, ALDOB, represented by a network peripheral-A, was significantly co-expressed with all the genes encoding its IPs (i.e. 100% significant co-expression level). Table 2 shows more details of the difference between these categories in terms of mean cL values. No statistical significance difference between category means were found at P = 0.05. However, note that only network peripherals-A or –B showed cases with cL = 100%. The global trend, as shown in Figure 3, is that the higher the number of node connections the greater the tendency to display low cL values. Figure 3A shows a scatter plot of the number interacting partners (IPi) for a gene i, versus its significant co-expression level (cLi) (as defined in Methods). Similar trend was observed when non-significantly differentially expressed genes were analysed. For example, HAP1 and SIN3A, represented by network peripherals-A, were significantly co-expressed with all their partners, IPs. Figure 3B plots (IPi) versus (cLi) of non-significantly differentially-expressed genes. When analysing nodes representing KHFGs, results showed that in general cLi of these genes was low. In fact, none of the 40 KHFGs, which had a corresponding transcript in the DCM expression data, obtained a cLi>50% (Figure 3C). For example, the cLi for PRKCA, represented by a network superhub, was equal to 16.2% (i.e. this gene's expression pattern was significantly co-expressed with only a few of the genes coding for its IPs).

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