RESUMO
The glucocorticoid receptor (GR) is a potent metabolic regulator and a major drug target. While GR is known to play integral roles in circadian biology, its rhythmic genomic actions have never been characterized. Here we mapped GR's chromatin occupancy in mouse livers throughout the day and night cycle. We show how GR partitions metabolic processes by time-dependent target gene regulation and controls circulating glucose and triglycerides differentially during feeding and fasting. Highlighting the dominant role GR plays in synchronizing circadian amplitudes, we find that the majority of oscillating genes are bound by and depend on GR. This rhythmic pattern is altered by high-fat diet in a ligand-independent manner. We find that the remodeling of oscillatory gene expression and postprandial GR binding results from a concomitant increase of STAT5 co-occupancy in obese mice. Altogether, our findings highlight GR's fundamental role in the rhythmic orchestration of hepatic metabolism.
Assuntos
Cromatina/metabolismo , Relógios Circadianos , Ritmo Circadiano , Dieta Hiperlipídica , Gorduras na Dieta/metabolismo , Metabolismo Energético , Fígado/metabolismo , Obesidade/metabolismo , Receptores de Glucocorticoides/metabolismo , Animais , Glicemia/metabolismo , Relógios Circadianos/genética , Ritmo Circadiano/genética , Gorduras na Dieta/administração & dosagem , Gorduras na Dieta/sangue , Modelos Animais de Doenças , Metabolismo Energético/genética , Jejum/metabolismo , Regulação da Expressão Gênica , Glucocorticoides/metabolismo , Gluconeogênese , Ligantes , Masculino , Camundongos Endogâmicos C57BL , Camundongos Knockout , Obesidade/sangue , Obesidade/genética , PPAR alfa/genética , PPAR alfa/metabolismo , Período Pós-Prandial , Receptores de Glucocorticoides/deficiência , Receptores de Glucocorticoides/genética , Fator de Transcrição STAT5/genética , Fator de Transcrição STAT5/metabolismo , Via Secretória , Transdução de Sinais , Fatores de Tempo , Transcrição Gênica , Triglicerídeos/sangueRESUMO
Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.