Circadian Signatures in Rat Liver: From Gene Expression to Pathways

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.

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

“…(Note: in the analysis that follows, we assume that the base line expression levels, i.e., in the absence of the drug, are represented by mRNA and protein abundance levels prior to drug administration). In a number of previous publications, we have illustrated the analysis of longitudinal data with a time-varying base line ( Androulakis et al, 2007 ; Yang et al, 2007b , 2008a , 2009b , 2012a , b ; Yang E.H. et al, 2009 ; Almon et al, 2008a , b ; Nguyen et al, 2009 , 2010a , b , 2011 , 2014a ; Ovacik et al, 2010 ; Scheff et al, 2010a ; Swiss et al, 2011 ). However, temporal relations among time-varying quantities are known to be non-trivial, extending far beyond the classic view of correlation ( Qian et al, 2001 ).…”

“…The diversity of the available models describing gene induction by MPL have been expanded to several pharmacogenomics models that explain the response of numerous genes with various dynamic patterns ( Almon et al, 2003 , 2005 , 2007a , b ; Jin et al, 2003 ; Nguyen et al, 2014a ). Our earlier studies in collaboration with Prof. Jusko, characterized global dynamics of the systems that are regulated by CS at the transcriptional level across multiple tissues in adrenalectomized (ADX) and intact male rats, following single and chronic dosing of MPL enabling us to: (a) develop transcription-level understanding of MPL (acute vs. chronic) effects; (b) elaborate on tissue-specific transcriptional differences; (c) assess MPL-induced dose- and tissue-specific transcriptional regulation; and (d) assess circadian dynamics and regulation of intact and MPL-dosed animals ( Yang et al, 2007b , 2008a ; Yang E.H. et al, 2009 ; Almon et al, 2008a ; Nguyen et al, 2010a , b , 2014a ; Ovacik et al, 2010 ; Scheff et al, 2010a , 2011 ).…”

“…This dataset was previously submitted to the GEO (GSE490). We have previously presented extensive analyses and studies of the transcription response to acute and chronic administration of MPL on multiple tissues ( Almon et al, 2008a , b ; Yang et al, 2008a ; Nguyen et al, 2010a , 2014a ; Ovacik et al, 2010 ).…”

“…It is the hope that large scale studies will provide some stratification and generate somewhat more coherent data. Furthermore, deciphering evolution of dynamics following an external perturbation needs to take into consideration that the “homeostatic” dynamics may in fact demonstrate a baseline dynamics response – most likely in the form of circadian variation ( Almon et al, 2008a , b ; Nguyen et al, 2010b , 2014b ; Ovacik et al, 2010 ; Rao et al, 2016b ). Therefore, deviations from homeostasis in response to a pharmacological agent need to consider the homeostatic dynamics as well.…”

A hierarchical Bayesian model for inference of copy number variants and their association to gene expression

“…This strategy helped us to reduce the dependence between the outcome variables in model (2) and also to achieve a dimension reduction of the model space. Methods that employ pathway-based scores of gene expression data have become quite popular in genomics; see, for example, Su, Yoon and Dougherty (2009), Ovacik et al (2010), Chen, Wang and Ishwaran (2010), Drier, Sheffer and Domany (2013), among others. More precisely, we considered the genes that map to each one of the 186 KEGG pathways, using the software Compadre [see Rodriguez et al (2012)].…”