Gene expression signatures predict circadian rhythms in oncogenic pathways

Abstract

Day-night environmental cycles together with our own adaptive rhythms in behavior and physiology lead to rhythmicity of various processes on the cellular level, including cell signaling. Despite many implications of such daily changes in signaling, the quantification of such rhythms and estimates of peak phases of pathway activities in various tissues are missing. Governed mainly by posttranslational modifications, a pathway activity might not be well quantified via the expression level of pathway components. Instead, a gene expression signatures approach can be used to score activity of various pathways. Here, we apply such gene expression signatures on circadian time series transcriptomics data to infer rhythmicity in cellular signaling. We show that, across multiple datasets, the gene expression signatures predict the presence of rhythmicity in EGFR, PI3K and p53 pathways in mouse liver. With the focus on EGFR pathway, we pinpoint the most influential signature genes for the overall rhythmicity in the activity scores for this pathway. These findings suggest that time of the day is an important factor to consider in studies on signaling. Simultaneously, this study provides a new paradigm to use circadian transcriptomics to get at temporal dynamics of pathway activation.