Chromatin represents a repressive barrier to the process of ligand-dependent transcriptional activity of nuclear receptors. Here, we show that H3K27 methylation imposes ligand-dependent regulation of the oestrogen receptor α (ERα)-dependent apoptotic response via Bcl-2 in breast cancer cells. The activation of BCL2 transcription is dependent on the simultaneous inactivation of the H3K27 methyltransferase, EZH2, and the demethylation of H3K27 at a poised enhancer by the ERα-dependent recruitment of JMJD3 in hormone-dependent breast cancer cells. We also provide evidence that this pathway is modified in cells resistant to anti-oestrogen (AE), which constitutively express BCL2. We show that the lack of H3K27 methylation at BCL2 regulatory elements due to the inactivation of EZH2 by the HER2 pathway leads to this constitutive activation of BCL2 in these AE-resistant cells. Our results describe a mechanism in which the epigenetic state of chromatin affects ligand dependency during ERα-regulated gene expression.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3209777PMC
http://dx.doi.org/10.1038/emboj.2011.284DOI Listing

Publication Analysis

Top Keywords

poised enhancer
8
ligand dependency
8
h3k27 methylation
8
breast cancer
8
cancer cells
8
activation bcl2
8
h3k27
5
bcl2
5
h3k27 demethylation
4
demethylation jmjd3
4

Similar Publications

The utilization of dual-working-electrode mode of interdigitated array (IDA) electrodes and other two-electrode systems has revolutionized electrochemical detection by enabling the simultaneous and independent detection of two species, accompanied by the exhibition of unique characteristics. In contrast to conventional dual-potential electrodes, such as the rotating ring disk electrodes (RRDE), IDA electrodes demonstrate analogous yet vastly improved performance, characterized by remarkable collection efficiency and sensitivity. Notably, due to the distinctive microscale structure of IDA electrode, the special "feedback" effect makes IDA a unique signal amplifier.

View Article and Find Full Text PDF

Digital Mini-LED Lighting Using Organic Thin-Film Transistors Reaching over 100,000 Nits of Luminance.

Nanomaterials (Basel)

January 2025

Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

This paper demonstrates the use of organic thin-film transistors (OTFTs) to drive active digital mini light-emitting diode (mini-LED) backlights, aiming to achieve exceptional display performance. Our findings reveal that OTFTs can effectively power mini-LED backlights, reaching brightness levels exceeding 100,000 nits. This approach not only enhances image quality but also improves energy efficiency.

View Article and Find Full Text PDF

Monte Carlo Simulations in Nanomedicine: Advancing Cancer Imaging and Therapy.

Nanomaterials (Basel)

January 2025

Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada.

Monte Carlo (MC) simulations have become important in advancing nanoparticle (NP)-based applications for cancer imaging and therapy. This review explores the critical role of MC simulations in modeling complex biological interactions, optimizing NP designs, and enhancing the precision of therapeutic and diagnostic strategies. Key findings highlight the ability of MC simulations to predict NP bio-distribution, radiation dosimetry, and treatment efficacy, providing a robust framework for addressing the stochastic nature of biological systems.

View Article and Find Full Text PDF

This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.

View Article and Find Full Text PDF

Artificial intelligence (AI) is a rapidly transforming drug discovery and development process, significantly impacting the pharmaceutical industry and enhancing human health. This review article examines the tremendous role of AI in analyzing complex biological data, optimizing research processes, and reducing costs of production. Implementation of AI in the pharmaceutical sector can store a vast dataset of manufacturing processes, identify potential disease targets, simulate physiological conditions, and predict drug interactions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!