RESUMO
We used genome-wide sequencing methods to study stimulus-dependent enhancer function in mouse cortical neurons. We identified approximately 12,000 neuronal activity-regulated enhancers that are bound by the general transcriptional co-activator CBP in an activity-dependent manner. A function of CBP at enhancers may be to recruit RNA polymerase II (RNAPII), as we also observed activity-regulated RNAPII binding to thousands of enhancers. Notably, RNAPII at enhancers transcribes bi-directionally a novel class of enhancer RNAs (eRNAs) within enhancer domains defined by the presence of histone H3 monomethylated at lysine 4. The level of eRNA expression at neuronal enhancers positively correlates with the level of messenger RNA synthesis at nearby genes, suggesting that eRNA synthesis occurs specifically at enhancers that are actively engaged in promoting mRNA synthesis. These findings reveal that a widespread mechanism of enhancer activation involves RNAPII binding and eRNA synthesis.
Assuntos
Elementos Facilitadores Genéticos/genética , Regulação da Expressão Gênica/genética , Neurônios/metabolismo , Transcrição Gênica/genética , Animais , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Proteína de Ligação a CREB/metabolismo , Sequência Consenso/genética , Proteínas do Citoesqueleto/genética , Genes Reporter , Genes fos/genética , Histonas/metabolismo , Metilação , Camundongos , Camundongos Endogâmicos C57BL , Proteínas do Tecido Nervoso/genética , RNA Polimerase II/metabolismo , RNA não Traduzido/biossíntese , RNA não Traduzido/genéticaRESUMO
BACKGROUND: The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial. RESULTS: We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence (R² = 0.90). CONCLUSION: We provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.GEO Accession Number: GSE32679.