miR-22 has a potent anti-tumour role with therapeutic potential in acute myeloid leukaemia

MicroRNAs are subject to precise regulation and have key roles in tumorigenesis. In contrast to the oncogenic role of miR-22 reported in myelodysplastic syndrome (MDS) and breast cancer, here we show that miR-22 is an essential anti-tumour gatekeeper in de novo acute myeloid leukaemia (AML) where it is significantly downregulated. Forced expression of miR-22 significantly suppresses leukaemic cell viability and growth in vitro, and substantially inhibits leukaemia development and maintenance in vivo. Mechanistically, miR-22 targets multiple oncogenes, including CRTC1, FLT3 and MYCBP, and thus represses the CREB and MYC pathways. The downregulation of miR-22 in AML is caused by TET1/GFI1/EZH2/SIN3A-mediated epigenetic repression and/or DNA copy-number loss. Furthermore, nanoparticles carrying miR-22 oligos significantly inhibit leukaemia progression in vivo. Together, our study uncovers a TET1/GFI1/EZH2/SIN3A/miR-22/CREB-MYC signalling circuit and thereby provides insights into epigenetic/genetic mechanisms underlying the pathogenesis of AML, and also highlights the clinical potential of miR-22-based AML therapy

SH3 Domain-Peptide Binding Energy Calculations Based on Structural Ensemble and Multiple Peptide Templates

SH3 domains mediate signal transduction by recognizing short peptides. Understanding of the driving forces in peptide recognitions will help us to predict the binding specificity of the domain-peptide recognition and to understand the molecular interaction networks of cells. However, accurate calculation of the binding energy is a tough challenge. In this study, we propose three ideas for improving our ability to predict the binding energy between SH3 domains and peptides: (1) utilizing the structural ensembles sampled from a molecular dynamics simulation trajectory, (2) utilizing multiple peptide templates, and (3) optimizing the sequence-structure mapping. We tested these three ideas on ten previously studied SH3 domains for which SPOT analysis data were available. The results indicate that calculating binding energy using the structural ensemble was most effective, clearly increasing the prediction accuracy, while the second and third ideas tended to give better binding energy predictions. We applied our method to the five SH3 targets in DREAM4 Challenge and selected the best performing method

Structure-Based Rational Design of a Toll-like Receptor 4 (TLR4) Decoy Receptor with High Binding Affinity for a Target Protein

Repeat proteins are increasingly attracting much attention as alternative scaffolds to immunoglobulin antibodies due to their unique structural features. Nonetheless, engineering interaction interface and understanding molecular basis for affinity maturation of repeat proteins still remain a challenge. Here, we present a structure-based rational design of a repeat protein with high binding affinity for a target protein. As a model repeat protein, a Toll-like receptor4 (TLR4) decoy receptor composed of leucine-rich repeat (LRR) modules was used, and its interaction interface was rationally engineered to increase the binding affinity for myeloid differentiation protein 2 (MD2). Based on the complex crystal structure of the decoy receptor with MD2, we first designed single amino acid substitutions in the decoy receptor, and obtained three variants showing a binding affinity (KD) one-order of magnitude higher than the wild-type decoy receptor. The interacting modes and contributions of individual residues were elucidated by analyzing the crystal structures of the single variants. To further increase the binding affinity, single positive mutations were combined, and two double mutants were shown to have about 3000- and 565-fold higher binding affinities than the wild-type decoy receptor. Molecular dynamics simulations and energetic analysis indicate that an additive effect by two mutations occurring at nearby modules was the major contributor to the remarkable increase in the binding affinities

Interactions of synthetic polymers with cell membranes: Cell penetration of polycationic polymers and multivalent effects of targeted nanodevices.

This dissertation describes biological interactions of synthetic polymers relevant to two major biomedical applications, gene delivery and targeted drug delivery. The first part describes biological interactions of polycationic polymers that have been commonly used as gene delivery/cell transfection agents. This study reveals that polycationic polymers such as amine terminated poly(amidoamine) (PAMAM) dendrimers, poly-L-lysine, polyethylenimine, diethylaminoethyl-dextran induce membrane permeabilization in living cells. Exposure of cells to the polycationic polymers caused enzyme leakage out of the cells, polymer internalization into the cells, and diffusion of small molecular probes in and out of the cells. In contrast, charge neutral acetamide and negatively charged carboxylate terminated PAMAM dendrimers, as well as polyethyleneglycol and poly(vinyl alcohol) do not enter the cells or cause permeability of the cell membranes. By combining our previous AFM observation and a variety of in vitro tests presented here, we conclude that a nanoscale hole formation mechanism is an important pathway for polycationic polymer-cell interactions. The second part of this study deals with multivalent interaction of cancer cell targeting dendritic nanodevices with a receptor protein target. Dendrimer-based anti-cancer nanotherapeutics containing ∼5 folate molecules have shown in vitro and in vivo efficacy in cancer cell targeting. Multivalent interactions have been inferred from observed targeting efficacy but have not been experimentally proven. This study provides quantitative and systematic evidence for multivalent interactions between these nanodevices and folate binding protein (FBP). A series of the nanodevices were synthesized by conjugation with different amounts of folate. Dissociation constants ( KD) between the nanodevices and FBP measured by SPR are dramatically enhanced through multivalency (∼2,500-170,000 fold). Qualitative evidence is also provided for a multivalent targeting effect to KB cells using flow cytometry. These data support the hypothesis that multivalent enhancement of KD, not an enhanced rate of endocytosis, is the key factor resulting in the improved biological targeting by these drug delivery platforms, providing a design guide for future receptor targeting agents.Ph.D.Applied SciencesBiomedical engineeringPolymer chemistryPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/126053/2/3224905.pd

Exchange Rates, Monetary Policies, and Macroeconomic Stability

Three aggregated stochastic models are developed which incorporate the major features of small open economy with structural changes in different stages of economic development. For Model 1 and 2, both analytic and empirical studies are made. The optimal policy mix between exchange rate and monetary policies for achieving economic stability is analyzed when the economy faces unexpected shocks on demand and supply. Empirical studies of Korea concludes that it is not optimal to absorb the external shocks through full accommodation of exchange rates. Since Model 3 assumes rigid price and capital mobility, exchange rates are no longer policy variables and determined within the system. The optimal monetary policies under those conditions are analytically derived but empirical studies are postponed since capital flows are under strict control in Korea

Application of the parallel diagonal dominant algorithm for the incompressible Navier-Stokes equations

The accuracy and the applicability of the parallel diagonal dominant (PDD) algorithm are explored for highly scalable computation of the incompressible Navier-Stokes equations which are integrated using a fully-implicit fractional-step method in parallel computational environments. The PDD algorithm is known to be applicable only for an evenly diagonal dominant matrix. In the present study, however, it is shown mathematically that the PDD algorithm is utilizable even for non-diagonal dominant matrices derived from discretization of incompressible momentum equations. The order of accuracy and the error characteristics are investigated in detail in terms of the Courant-Friedrichs-Lewy (CFL) number and the grid spacing by conducting simulations of decaying vortices in both two and three dimensions, flow in a lid-driven cavity, and flow over a circular cylinder. In order to reduce communication cost, which is one of bottlenecks in parallel computation, an aggregative data communication method is combined with the PDD algorithm. Parallel performance of the present PDD-based method is investigated by measuring the speedup, efficiency, overhead, and serial fraction. (C) 2020 Elsevier Inc. All rights reserved.11Nsciescopu

Inverse Approach of Parameter Optimization for Nonlinear Meta-Model Using Finite Element Simulation

Accurate and efficient estimation and prediction of the nonlinear behavior of materials during plastic working is a major issue in academic and industrial settings. Studies on property meta-models are being conducted to estimate and predict plastic working results. However, accurately representing strong nonlinear properties using power-law and exponential models, which are typical meta-models, is difficult. The combination meta-model can be used to solve this problem, but the possible number of parameters increases. This causes a cost problem when using FE simulation. In this study, the accuracy of the nonlinear properties of materials and the number of iterations were compared for three typical meta-models and the proposed advanced meta-models considering stress–strain properties. A material property test was conducted using ASTM E8/E8M, and the meta-model was initialized using ASTM E646 and MATLAB Curve Fitting Toolbox. A finite element (FE) simulation was conducted for the meta-models, and the test and simulation results were compared in terms of the engineering stress–strain curve and the root-mean-square error (RMSE). In addition, an inverse method was applied for the FE simulation to estimate the true stress–strain properties, and the results were analyzed in terms of the RMSE and the number of iterations and simulations. Finally, the need for an advanced meta-model that exhibits strong nonlinearity was suggested

Control of a fly-inspired flyer in complex flow using computational fluid dynamics and reinforcement learning

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KRDS: a web server for evaluating drug resistance mutations in kinases by molecular docking

Abstract Kinases are major targets of anti-cancer therapies owing to their importance in signaling processes that regulate cell growth and proliferation. However, drug resistance has emerged as a major obstacle to cancer therapy. Resistance to drugs has various underlying mechanisms, including the acquisition of mutations at drug binding sites and the resulting reduction in drug binding affinity. Therefore, the identification of mutations that are relevant to drug resistance may be useful to overcome this issue. We hypothesized that these mutations can be identified by combining recent advances in computational methods for protein structure modeling and ligand docking simulation. Hence, we developed a web-based tool named the Kinase Resistance Docking System (KRDS) that enables the assessment of the effects of mutations on kinase-ligand interactions. KRDS receives a list of mutations in kinases, generates structural models of the mutants, performs docking simulations, and reports the results to users. The changes in docking scores and docking conformations can be analyzed to infer the effects of mutations on drug binding and drug resistance. We expect our tool to improve our understanding of drug binding mechanisms and facilitate the development of effective new drugs to overcome resistance related to kinases; it may be particularly useful for biomedical researchers who are not familiar with computational environments. Our tool is available at http://bcbl.kaist.ac.kr/KRDS/