RESUMEN
Growing evidence supports the importance of the p53 tumor suppressor in metabolism but the mechanisms underlying p53-mediated control of metabolism remain poorly understood. Here, we identify the multifunctional E4F1 protein as a key regulator of p53 metabolic functions in adipocytes. While E4F1 expression is upregulated during obesity, E4f1 inactivation in mouse adipose tissue results in a lean phenotype associated with insulin resistance and protection against induced obesity. Adipocytes lacking E4F1 activate a p53-dependent transcriptional program involved in lipid metabolism. The direct interaction between E4F1 and p53 and their co-recruitment to the Steaoryl-CoA Desaturase-1 locus play an important role to regulate monounsaturated fatty acids synthesis in adipocytes. Consistent with the role of this E4F1-p53-Steaoryl-CoA Desaturase-1 axis in adipocytes, p53 inactivation or diet complementation with oleate partly restore adiposity and improve insulin sensitivity in E4F1-deficient mice. Altogether, our findings identify a crosstalk between E4F1 and p53 in the control of lipid metabolism in adipocytes that is relevant to obesity and insulin resistance.
Asunto(s)
Adipocitos/metabolismo , Tejido Adiposo/metabolismo , Obesidad/genética , Proteínas Represoras/genética , Estearoil-CoA Desaturasa/genética , Proteína p53 Supresora de Tumor/genética , Ubiquitina-Proteína Ligasas/genética , Adipocitos/patología , Tejido Adiposo/patología , Adulto , Anciano , Animales , Índice de Masa Corporal , Ácidos Grasos Monoinsaturados/metabolismo , Femenino , Regulación de la Expresión Génica , Humanos , Resistencia a la Insulina , Metabolismo de los Lípidos/genética , Masculino , Ratones , Ratones Noqueados , Persona de Mediana Edad , Obesidad/metabolismo , Obesidad/patología , Proteínas Represoras/deficiencia , Proteínas Represoras/metabolismo , Transducción de Señal , Estearoil-CoA Desaturasa/metabolismo , Proteína p53 Supresora de Tumor/metabolismo , Ubiquitina-Proteína Ligasas/deficiencia , Ubiquitina-Proteína Ligasas/metabolismoRESUMEN
Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.