Title
Exploring pathway interactions in insulin resistant mouse liver
Author
Kelder, T.
Eijssen, L.
Kleemann, R.
van Erk, M.
Kooistra, T.
Evelo, C.
Publication year
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. © 2011 Kelder et al; licensee BioMed Central Ltd.
Subject
Life
MHR - Metabolic Health Research MSB - Microbiology and Systems Biology
EELS - Earth, Environmental and Life Sciences
Health
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http://resolver.tudelft.nl/uuid:0c3a3383-4171-4ad5-ac07-54f60cb6e0ed
DOI
https://doi.org/10.1186/1752-0509-5-127
TNO identifier
436037
ISSN
1752-0509
Source
BMC Systems Biology, 5
Article number
No.: 127
Document type
article