CcorGsDb: um banco de dados de genes correlacionados com o ritmo circadiano no sistema nervoso central de camundongos

Abstract

Circadian rhythms are controlled by a set of core genes, called clock genes, which are ubiquitously expressed. Each tissue, however, has a high specificity in the set of clockcontrolled genes (CCGs) that participate in related circadian outputs. Normally, the identification of CCGs is based on time series datasets, which are costly, laborious and in some instances impossible to perform. A recent approach based on correlation patterns of circadian markers has been used to capture CCGs in datasets without temporal information. However, this machine learning method still demands that the samples are collected throughout the circadian cycle. Moreover, not every gene with circadian function has an oscillatory pattern at transcript level. Thus, other strategies for identifying CCGs with spatial resolution could be advantageous. In this study, we identified potential CCGs in the mouse Central Nervous System, including 12 specific regions, using Weighted Gene Co-expression Network Analysis (WGCNA) of in situ hybridization expression data available at the Allen Mouse Brain Atlas. Significant modules were filtered based on their correlations with circadian markers. Clock Correlated Genes (CCorGs) datasets, evaluated in those regions with previous time course experiments, are enriched for genes with higher rAMP. Several CCorGs are targets of drugs with short time action on the CNS and may be candidates for chronopharmacological studies. To our knowledge, this is the first study of co-expression analysis to search for CCGs in the CNS. The strategy used has the potential to identify important genes for circadian rhythms in specific tissues, overcoming methodological limitations considered in other studies.