Effect of Chemotherapeutic Agents on the Expression of Retinoid Receptors and Markers of Cancer Stem Cells and Epithelial-Mesenchymal Transition.

A large body of evidence suggests that cancer stem cells (CSCs) and epithelial-mesenchymal transition (EMT), as well as expression and function of retinoid receptors, are pivotal features of tumor initiation, progression, and chemoresistance. This is also true for pancreatic ductal adenocarcinoma (PDAC), which represents a clinical challenge due to poor prognosis and increasing incidence. Understanding the above features of cancer cells could open new avenues for PDAC treatment strategies. The aim of this study was to investigate the relation between CSCs, EMT, and retinoid receptors in PDAC after treatment with the chemotherapeutic agents - gemcitabine and 5-fluorouracil. First, we demonstrated the difference in the expression levels of CSC and EMT markers and retinoid receptors in the untreated Mia PaCa-2 and Panc1 cells that also differed in the frequency of spontaneous apoptosis and distribution between the cell cycle phases. Chemotherapy reduced the number of cancer cells in the S phase. Gemcitabine and 5-fluorouracil modulated expression of CSC markers, E-cadherin, and RXRß in Panc1 but not in Mia PaCa-2 cells. We suggest that these effects could be attributed to the difference in the basal levels of expression of the investigated genes. The obtained data could be interesting in the context of future preclinical research.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.