Histone deacetylase inhibitors (HDACIs) are therapeutic drugs that inhibit deacetylase activity, thereby increasing acetylation of many proteins, including histones. HDACIs have antineoplastic effects in preclinical and clinical trials and are being considered for cancers with unmet therapeutic need, including neuroblastoma (NB). Uncertainty of how HDACI-induced protein acetylation leads to cell death, however, makes it difficult to determine which tumors are likely to be responsive to these agents. Here, we show that NB cells are sensitive to HDACIs, and that the mechanism by which HDACIs induce apoptosis involves Bax. In these cells, Bax associates with cytoplasmic Ku70, a protein that typically increases chemotherapy resistance. Our data show that in NB cells Ku70 binds to Bax in an acetylation-sensitive manner. Upon HDACI treatment, acetylated Ku70 releases Bax, allowing it to translocate to mitochondria and trigger cytochrome c release, leading to caspase-dependent death. This study shows that Ku70 is an important Bax-binding protein, and that this interaction can be therapeutically regulated in NB cells. Whereas the Bax-binding ability of Ku70 allows it to block apoptosis in response to certain agents, it is also a molecular target for the action of HDACIs, and in this context, a mediator of NB cell death.cAMP-response element-binding protein ͉ histone acetyltransferase

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model. C 2015 AIP Publishing LLC. [http://dx

We consider the Brownian motion of a particle and present a tutorial review over the last 111 years since Einstein’s paper in 1905. We describe Einstein’s model, Langevin’s model and the hydrodynamic models, with increasing sophistication on the hydrodynamic interactions between the particle and the fluid. In recent years, the effects of interfaces on the nearby Brownian motion have been the focus of several investigations. We summarize various results and discuss some of the controversies associated with new findings about the changes in Brownian motion induced by the interface.