Trichostatin A enhances acetylation as well as protein stability of ERα through induction of p300 protein

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract Introduction Trichostatin A (TSA) is a well-characterized histone deacetylase (HDAC) inhibitor. TSA modifies the balance between HDAC and histone acetyltransferase activities that is important in chromatin remodeling and gene expression. Although several previous studies have demonstrated the role of TSA in regulation of estrogen receptor alpha (ERα), the precise mechanism by which TSA affects ERα activity remains unclear. Methods Transient transfection was performed using the Welfect-EX™Plus procedure. The mRNA expression was determined using RT-PCR. Protein expression and interaction were determined by western blotting and immunoprecipitation. The transfection of siRNAs was performed using the Oligofectamine™ reagent procedure. Results TSA treatment increased acetylation of ERα in a dose-dependent manner. The TSA-induced acetylation of ERα was accompanied by an increased stability of ERα protein. Interestingly, TSA also increased the acetylation and the stability of p300 protein. Overexpression of p300 induced acetylation and stability of ERα by blocking ubiquitination. Knockdown of p300 by RNA interference decreased acetylation as well as the protein level of ERα, indicating that p300 mediated the TSA-induced stabilization of ERα. Conclusions We report that TSA enhanced acetylation as well as the stability of the ERα protein by modulating stability of p300. These results may provide the molecular basis for pharmacological functions of HDAC inhibitors in the treatment of human breast cancer

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis