Mechanistic analysis of enhancer sequences in the Estrogen Receptor transcriptional program

BackgroundEstrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development, orchestrating the expression of thousands of genes. Dysregulation of ERα-mediated transcriptional program results in abnormal cell proliferation and cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood.ResultsA subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.ConclusionsWe modeled the sequence-to-expression relationship in ERα-driven enhancers and gained mechanistic insights into the workings of a major transcriptional program. Our model is consistent with the current body of knowledge and its predictions are confirmed by experimental observations. We believe this to be a promising approach to analysis of regulatory sequences and variants.

Native and engineered sensors for Ca2+ and Zn2+: lessons from calmodulin and MTF1

Ca2+ and Zn2+ dynamics have been identified as important drivers of physiological processes. In order for these dynamics to encode function, the cell must have sensors that transduce changes in metal concentration to specific downstream actions. Here we compare and contrast the native metal sensors: calmodulin (CaM), the quintessential Ca2+ sensor and metal-responsive transcription factor 1 (MTF1), a candidate Zn2+ sensor. While CaM recognizes and modulates the activity of hundreds of proteins through allosteric interactions, MTF1 recognizes a single DNA motif that is distributed throughout the genome regulating the transcription of many target genes. We examine how the different inorganic chemistries of these two metal ions may shape these different mechanisms transducing metal ion concentration into changing physiologic activity. In addition to native metal sensors, scientists have engineered sensors to spy on the dynamic changes of metals in cells. The inorganic chemistry of the metals shapes the possibilities in the design strategies of engineered sensors. We examine how different strategies to tune the affinities of engineered sensors mirror the strategies nature developed to sense both Ca2+ and Zn2+ in cells.