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Open Access Highly Accessed Research article

Circadian signatures in rat liver: from gene expression to pathways

Meric A Ovacik1, Siddharth Sukumaran2, Richard R Almon23, Debra C DuBois23, William J Jusko3 and Ioannis P Androulakis4*

Author Affiliations

1 Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA

2 Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA

3 Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA

4 Biomedical Engineering Department, Rutgers University Piscataway, NJ 08854, USA

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BMC Bioinformatics 2010, 11:540  doi:10.1186/1471-2105-11-540

Published: 1 November 2010

Abstract

Background

Circadian rhythms are 24 hour oscillations in many behavioural, physiological, cellular and molecular processes that are controlled by an endogenous clock which is entrained to environmental factors including light, food and stress. Transcriptional analyses of circadian patterns demonstrate that genes showing circadian rhythms are part of a wide variety of biological pathways.

Pathway activity method can identify the significant pattern of the gene expression levels within a pathway. In this method, the overall gene expression levels are translated to a reduced form, pathway activity levels, via singular value decomposition (SVD). A given pathway represented by pathway activity levels can then be as analyzed using the same approaches used for analyzing gene expression levels. We propose to use pathway activity method across time to identify underlying circadian pattern of pathways.

Results

We used synthetic data to demonstrate that pathway activity analysis can evaluate the underlying circadian pattern within a pathway even when circadian patterns cannot be captured by the individual gene expression levels. In addition, we illustrated that pathway activity formulation should be coupled with a significance analysis to distinguish biologically significant information from random deviations. Next, we performed pathway activity level analysis on a rich time series of transcriptional profiling in rat liver. The over-represented five specific patterns of pathway activity levels, which cannot be explained by random event, exhibited circadian rhythms. The identification of the circadian signatures at the pathway level identified 78 pathways related to energy metabolism, amino acid metabolism, lipid metabolism and DNA replication and protein synthesis, which are biologically relevant in rat liver. Further, we observed tight coordination between cholesterol biosynthesis and bile acid biosynthesis as well as between folate biosynthesis, one carbon pool by folate and purine-pyrimidine metabolism. These coupled pathways are parts of a sequential reaction series where the product of one pathway is the substrate of another pathway.

Conclusions

Rather than assessing the importance of a single gene beforehand and map these genes onto pathways, we instead examined the orchestrated change within a pathway. Pathway activity level analysis could reveal the underlying circadian dynamics in the microarray data with an unsupervised approach and biologically relevant results were obtained.