Highlights d YAP/TEAD non-canonically bind to a group of ERa-bound enhancers d YAP/TEAD are required for estrogen-induced transcription and breast cancer growth d YAP/TEAD regulate enhancer activation by controlling the recruitment of MED1 d TEAD is recruited to ERa active enhancers through protein tethering trans-binding

No abstract

Resistance to cancer treatment can be driven by epigenetic reprogramming of specific transcriptomes in favor of the refractory phenotypes. Here we discover that tamoxifen resistance in breast cancer is driven by a regulatory axis consisting of a master transcription factor, its cofactor, and an epigenetic regulator. The oncogenic histone methyltransferase EZH2 conferred tamoxifen resistance by silencing the expression of the estrogen receptor α (ERα) cofactor GREB1. In clinical specimens, induction of DNA methylation of a particular CpG-enriched region at the GREB1 promoter negatively correlated with GREB1 levels and cell sensitivity to endocrine agents. GREB1 also ensured proper cellular reactions to different ligands by recruiting distinct sets of ERα cofactors to cis-regulatory elements, which explains the contradictory biological effects of GREB1 on breast cancer cell growth in response to estrogen or antiestrogen. In refractory cells, EZH2-dependent repression of GREB1 triggered chromatin reallocation of ERα coregulators, converting the antiestrogen into an agonist. In clinical specimens from patients receiving adjuvant tamoxifen treatment, expression levels of EZH2 and GREB1 were correlated negatively, and taken together better predicted patient responses to endocrine therapy. Overall, our work suggests a new strategy to overcome endocrine resistance in metastatic breast cancer by targeting a particular epigenetic program.

http://bis.zju.edu.cn/MTide.

In the past few decades, despite all the significant achievements in industrial microbial improvement, the approaches of traditional random mutation and selection as well as the rational metabolic engineering based on the local knowledge cannot meet today's needs. With rapid reconstructions and accurate in silico simulations, genome-scale metabolic model (GSMM) has become an indispensable tool to study the microbial metabolism and design strain improvements. In this review, we highlight the application of GSMM in guiding microbial improvements focusing on a systematic strategy and its achievements in different industrial fields. This strategy includes a repetitive process with four steps: essential data acquisition, GSMM reconstruction, constraints-based optimizing simulation, and experimental validation, in which the second and third steps are the centerpiece. The achievements presented here belong to different industrial application fields, including food and nutrients, biopharmaceuticals, biopolymers, microbial biofuel, and bioremediation. This strategy and its achievements demonstrate a momentous guidance of GSMM for metabolic engineering breeding of industrial microbes. More efforts are required to extend this kind of study in the meantime.