SIRT1 Activation by a c-MYC Oncogenic Network Promotes the Maintenance and Drug Resistance of Human FLT3-ITD Acute Myeloid Leukemia Stem Cells

SummaryThe FLT3-ITD mutation is frequently observed in acute myeloid leukemia (AML) and is associated with poor prognosis. In such patients, FLT3 tyrosine kinase inhibitors (TKIs) are only partially effective and do not eliminate the leukemia stem cells (LSCs) that are assumed to be the source of treatment failure. Here, we show that the NAD-dependent SIRT1 deacetylase is selectively overexpressed in primary human FLT3-ITD AML LSCs. This SIRT1 overexpression is related to enhanced expression of the USP22 deubiquitinase induced by c-MYC, leading to reduced SIRT1 ubiquitination and enhanced stability. Inhibition of SIRT1 expression or activity reduced the growth of FLT3-ITD AML LSCs and significantly enhanced TKI-mediated killing of the cells. Therefore, these results identify a c-MYC-related network that enhances SIRT1 protein expression in human FLT3-ITD AML LSCs and contributes to their maintenance. Inhibition of this oncogenic network could be an attractive approach for targeting FLT3-ITD AML LSCs to improve treatment outcomes

Deep Learning-Based Survival Analysis for High-Dimensional Survival Data

With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, multilayer deep neural network (DNN) models have made remarkable achievements. Thus, a Cox-based DNN prediction survival model (DNNSurv model), which was built with Keras and TensorFlow, was developed. However, its results were only evaluated on the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluated the prediction performance of the DNNSurv model using ultra-high-dimensional and high-dimensional survival datasets and compared it with three popular ML survival prediction models (i.e., random survival forest and the Cox-based LASSO and Ridge models). For this purpose, we also present the optimal setting of several hyperparameters, including the selection of a tuning parameter. The proposed method demonstrated via data analysis that the DNNSurv model performed well overall as compared with the ML models, in terms of the three main evaluation measures (i.e., concordance index, time-dependent Brier score, and the time-dependent AUC) for survival prediction performance

A Smart Farm DNN Survival Model Considering Tomato Farm Effect

Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to solve problems such as planning the production schedule of crops and optimizing the yield and quality. This helps farmers plan their labor and resources more efficiently. In this paper, our concern is to predict the time-to-harvest (i.e., survival time) of tomatoes on a smart farm. For this, it is first necessary to develop a deep learning modeling approach that takes into account the farm effect on the tomato plants, as each farm has multiple tomato plant subjects and outcomes on the same farm can be correlated. In this paper, we propose deep neural network (DNN) survival models to account for the farm effect as a fixed effect using one-hot encoding. The tomato data used in our study were collected on a weekly basis using the Internet of Things (IoT). We compare the predictive performance of our proposed method with that of existing DNN and statistical survival modeling methods. The results show that our proposed DNN method outperforms the existing methods in terms of the root mean squared error (RMSE), concordance index (C-index), and Brier score

Discovery and Structure-Based Design of Macrocyclic Peptides Targeting STUB1

STIP1 homology and U-Box containing protein 1 (STUB1) plays a key role in maintaining cell health during stress and aging. Recent evidence suggested STUB1 also helps regulate immunity with the potential of clearing malignant cells. Indeed, we and others have shown that STUB1 is a pivotal negative regulator of interferon gamma sensing – a process critical to the immunosurveillance of tumors and pathogens. Thus far, investigation of STUB1’s role relies mostly on genetic approaches as pharmacological inhibitors of this protein are lacking. Identification of a STUB1 tool compound is important as it would allow therapeutically relevant target validation in a broader sense. Accordingly, we leveraged phage display and computational modeling to identify and refine STUB1 binders. Screening of >10E9 macrocyclic peptides resulted in several conserved motifs as well as structurally diverse leads. Co-crystal structure of the peptide hit and STUB1 has enabled us to employ structure-based in silico design for further optimization. Of the modifications employed, replacing the hydrophilic solvent-exposed region of the macrocyclic peptides with a hydrophobic scaffold improved cellular permeability, while the binding conformation was maintained. Further substitution of the permeability-limiting terminal aspartic acid with a tetrazole bioisostere retained the binding to certain extent while improving permeability, suggesting a path forward. The current lead, although not optimal for cellular study, provides a valuable template for further development into selective tool compounds for STUB1 to enable target validation

Molecular Simulations Identify Binding Poses and Approximate Affinities of Stapled α‑Helical Peptides to MDM2 and MDMX

Traditionally, computing the binding affinities of proteins to even relatively small and rigid ligands by free-energy methods has been challenging due to large computational costs and significant errors. Here, we apply a new molecular simulation acceleration method called MELD (Modeling by Employing Limited Data) to study the binding of stapled α-helical peptides to the MDM2 and MDMX proteins. We employ free-energy-based molecular dynamics simulations (MELD-MD) to identify binding poses and calculate binding affinities. Even though stapled peptides are larger and more complex than most protein ligands, the MELD-MD simulations can identify relevant binding poses and compute relative binding affinities. MELD-MD appears to be a promising method for computing the binding properties of peptide ligands with proteins