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Exploring pathway interactions in insulin resistant mouse liver

DOI: 10.1186/1752-0509-5-127

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Abstract:

We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar.Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.Biological pathways provide a powerful medium to explore and reduce the complexity of large datasets. Pathways organize genes, proteins, metabolites and their interactions into functional groups, often visualized as diagrams or networks. A commonly employed analysis technique using pathways is enrichment analysis, where pathways are represented as gene sets and where the aim is to find those sets that are enriched with entities of interest, such as differentially expressed genes [1]. More recent techniques also include connectivity within a pathway to measure its impact [2]. Such techniques allow a resea

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