Open Access Highly Accessed Research article

Exploring pathway interactions in insulin resistant mouse liver

Thomas Kelder12*, Lars Eijssen1, Robert Kleemann3, Marjan van Erk2, Teake Kooistra3 and Chris Evelo1

Author Affiliations

1 Department of Bioinformatics, Maastricht University, Maastricht, The Netherlands

2 TNO, Research group Microbiology & Systems Biology, Zeist, The Netherlands

3 TNO, Metabolic Health Research, Leiden, The Netherlands

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BMC Systems Biology 2011, 5:127  doi:10.1186/1752-0509-5-127

Published: 15 August 2011

Abstract

Background

Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset.

Results

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.

Conclusions

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.