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Network signatures link hepatic effects of anti-diabetic interventions with systemic disease parameters.

Kelder T, Verschuren L, van Ommen B, van Gool AJ, Radonjic M - BMC Syst Biol (2014)

Bottom Line: We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al.Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317.This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.

View Article: PubMed Central - HTML - PubMed

Affiliation: TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands. thomas@edgeleap.com.

ABSTRACT

Background: Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To optimally treat T2DM, pharmacological interventions should, ideally, target key nodes in this network that act as determinants of disease progression.

Results: We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al. Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317. By combining knowledge-based and data-driven networks with a random walks based algorithm, we extracted network signatures that link the DLI and two drug interventions to dyslipidemia-related disease parameters.

Conclusions: This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.

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Identified network signatures. Network signatures of the top relevance scores representing the centrality of the node in linking the three interventions with disease parameters through co-expression modules A, B, and C. A) The signatures for DLI (left bar) and corresponding relevance scores for fenofibrate (FF) and T0901317 (T09) (right bars). B) The signatures for fenofibrate (FF, left bar) and corresponding relevance scores for DLI and T0901317 (T09) (right bars). C) The signatures for T0901317 (T09, left bar) and corresponding relevance scores for DLI and fenofibrate (FF) (right bars). Each signature contains the union of the top 10 genes with highest relevance scores for connecting the intervention targets to genes from either module A, B, or C. Each column in the heatmap represents the relevance scores for the paths to a module, each row represents a gene in the network. The genes are sorted by the maximum of the relevance scores across the signatures for module A, B, and C. Cells are shaded by relevance score (darker is a higher relevance score) and colored by direction of regulation by the intervention (red is upregulated, blue is downregulated). Colored boxes left of the gene symbols indicate for each gene the co-expression module membership. Colored asterisks on the right of the gene symbols indicate when the gene was considered as intervention target).
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Figure 2: Identified network signatures. Network signatures of the top relevance scores representing the centrality of the node in linking the three interventions with disease parameters through co-expression modules A, B, and C. A) The signatures for DLI (left bar) and corresponding relevance scores for fenofibrate (FF) and T0901317 (T09) (right bars). B) The signatures for fenofibrate (FF, left bar) and corresponding relevance scores for DLI and T0901317 (T09) (right bars). C) The signatures for T0901317 (T09, left bar) and corresponding relevance scores for DLI and fenofibrate (FF) (right bars). Each signature contains the union of the top 10 genes with highest relevance scores for connecting the intervention targets to genes from either module A, B, or C. Each column in the heatmap represents the relevance scores for the paths to a module, each row represents a gene in the network. The genes are sorted by the maximum of the relevance scores across the signatures for module A, B, and C. Cells are shaded by relevance score (darker is a higher relevance score) and colored by direction of regulation by the intervention (red is upregulated, blue is downregulated). Colored boxes left of the gene symbols indicate for each gene the co-expression module membership. Colored asterisks on the right of the gene symbols indicate when the gene was considered as intervention target).

Mentions: For each of the three selected co-expression modules we identified the most relevant paths between intervention targets and any of the module nodes in the corresponding intervention network using the kWalks algorithm. This resulted in a relevance score for each node and edge, representing the expected number of times it is visited by random walks between the intervention and module nodes. These scores provide a ranked network signature for each intervention, highlighting the genes that have the most relevant position in the network in connecting DLI, fenofibrate and T0901317 interventions with co-expression module genes associated to disease parameters atherosclerosis, plasma cholesterol levels, liver weight, and plasma triglyceride levels (FigureĀ 2).


Network signatures link hepatic effects of anti-diabetic interventions with systemic disease parameters.

Kelder T, Verschuren L, van Ommen B, van Gool AJ, Radonjic M - BMC Syst Biol (2014)

Identified network signatures. Network signatures of the top relevance scores representing the centrality of the node in linking the three interventions with disease parameters through co-expression modules A, B, and C. A) The signatures for DLI (left bar) and corresponding relevance scores for fenofibrate (FF) and T0901317 (T09) (right bars). B) The signatures for fenofibrate (FF, left bar) and corresponding relevance scores for DLI and T0901317 (T09) (right bars). C) The signatures for T0901317 (T09, left bar) and corresponding relevance scores for DLI and fenofibrate (FF) (right bars). Each signature contains the union of the top 10 genes with highest relevance scores for connecting the intervention targets to genes from either module A, B, or C. Each column in the heatmap represents the relevance scores for the paths to a module, each row represents a gene in the network. The genes are sorted by the maximum of the relevance scores across the signatures for module A, B, and C. Cells are shaded by relevance score (darker is a higher relevance score) and colored by direction of regulation by the intervention (red is upregulated, blue is downregulated). Colored boxes left of the gene symbols indicate for each gene the co-expression module membership. Colored asterisks on the right of the gene symbols indicate when the gene was considered as intervention target).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4363943&req=5

Figure 2: Identified network signatures. Network signatures of the top relevance scores representing the centrality of the node in linking the three interventions with disease parameters through co-expression modules A, B, and C. A) The signatures for DLI (left bar) and corresponding relevance scores for fenofibrate (FF) and T0901317 (T09) (right bars). B) The signatures for fenofibrate (FF, left bar) and corresponding relevance scores for DLI and T0901317 (T09) (right bars). C) The signatures for T0901317 (T09, left bar) and corresponding relevance scores for DLI and fenofibrate (FF) (right bars). Each signature contains the union of the top 10 genes with highest relevance scores for connecting the intervention targets to genes from either module A, B, or C. Each column in the heatmap represents the relevance scores for the paths to a module, each row represents a gene in the network. The genes are sorted by the maximum of the relevance scores across the signatures for module A, B, and C. Cells are shaded by relevance score (darker is a higher relevance score) and colored by direction of regulation by the intervention (red is upregulated, blue is downregulated). Colored boxes left of the gene symbols indicate for each gene the co-expression module membership. Colored asterisks on the right of the gene symbols indicate when the gene was considered as intervention target).
Mentions: For each of the three selected co-expression modules we identified the most relevant paths between intervention targets and any of the module nodes in the corresponding intervention network using the kWalks algorithm. This resulted in a relevance score for each node and edge, representing the expected number of times it is visited by random walks between the intervention and module nodes. These scores provide a ranked network signature for each intervention, highlighting the genes that have the most relevant position in the network in connecting DLI, fenofibrate and T0901317 interventions with co-expression module genes associated to disease parameters atherosclerosis, plasma cholesterol levels, liver weight, and plasma triglyceride levels (FigureĀ 2).

Bottom Line: We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al.Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317.This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.

View Article: PubMed Central - HTML - PubMed

Affiliation: TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands. thomas@edgeleap.com.

ABSTRACT

Background: Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To optimally treat T2DM, pharmacological interventions should, ideally, target key nodes in this network that act as determinants of disease progression.

Results: We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al. Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317. By combining knowledge-based and data-driven networks with a random walks based algorithm, we extracted network signatures that link the DLI and two drug interventions to dyslipidemia-related disease parameters.

Conclusions: This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.

Show MeSH
Related in: MedlinePlus