Theoretical targets for TCDD: a bioinformatics approach

Knockdown of IL4I1 Improved High Glucose-evoked Insulin Resistance in HepG2 Cells by Alleviating Inflammation and Lipotoxicity Through AHR Activation

Insulin resistance (IR) is one of the leading causes of Type 2 diabetes mellitus (T2DM). Inflammation, as a result of the disordered immune response, plays important roles in IR and T2DM. Interleukin-4-induced gene 1 (IL4I1) has been shown to regulate immune response and be involved in inflammation progress. However, there was little known about its roles in T2DM. Here, high glucose (HG)-treated HepG2 cells were used for T2DM investigation in vitro. Our results indicated that the expression of IL4I1 was up-regulated in peripheral blood samples of T2DM-patients and HG-induced HepG2 cells. The silencing of IL4I1 alleviated the HG-evoked IR through elevating the expressions of p-IRS1, p-AKT and GLUT4, and enhancing glucose consumption. Furthermore, IL4I1 knockdown inhibited inflammatory response by reducing the levels of inflammatory mediators, and suppressed the accumulation of lipid metabolites triglyceride (TG) and palmitate (PA) in HG-induced cells. Notably, IL4I1 expression was positively correlated with aryl hydrocarbon receptor (AHR) in peripheral blood samples of T2DM-patients. The silencing of IL4I1 inhibited the AHR signaling by reducing the HG-induced expressions of AHR and CYP1A1. Subsequent experiments confirmed that 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), an agonist of AHR, reversed the suppressive effects of IL4I1 knockdown on HG-caused inflammation, lipid metabolism and IR in cells. In conclusion, we found that the silencing of IL4I1 attenuated inflammation, lipid metabolism and IR in HG-induced cells via inhibiting AHR signaling, suggesting that IL4I1 might be a potential therapy target for T2DM.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD)-induced suppression of immunity in THP-1-derived macrophages and the possible mechanisms

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a well-known immunotoxic environmental pollutant. However, most immunotoxicology studies of TCDD were based on the animal models and the inner mechanisms have just focused on a few genes/proteins. In this study, the immune functions of THP-1-derived macrophages was measured with in-vitro bioassays after 24-h exposure of TCDD including environmentally relevant concentrations. RNA-seq and Weighted Gene Co-expression Network Analysis were used to characterize the immunotoxicity molecular mechanisms. Our study is the first report on the TCDD-induced effects of cell adhesion, morphology, and multiple cytokines/chemokines production on THP-1 macrophages. After TCDD treatment, we observed an inhibited cell adherence, probably attributed to the suppressed mRNA levels of adhesion molecules ICAM-1, VCAM-1 and CD11b, and a decrease in cell pseudopodia and expression of F-actin. The inflammatory cytokines TNF-α, IL-10 and other 8 cytokines/chemokines regulating granulocytes/T cells and angiogenesis were disrupted by TCDD. Alternative splicing event was found to be a sensitive target for TCDD. Using WGCNA, we identified 10 hub genes (TNF, SRC, FGF2, PTGS2, CDH2, GNG11, BDNF, WNT5A, CXCR5 and RUNX2) highly relevant to these observed phenotypes, suggesting AhR less important in the effects TCDD have on THP-1 macrophages than in other cells. Our findings broaden the understanding of TCDD immunotoxicity on macrophages and provide new potential targets for clarifying the molecular mechanisms.

Computational prediction and in vitro validation of VEGFR1 as a novel protein target for 2,3,7,8-tetrachlorodibenzo-p-dioxin

The toxic manifestations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an environmental contaminant, primarily depend on its ability to activate aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor belonging to the superfamily of basic-helix-loop-helix DNA-binding proteins. In the present study, we aimed to identify novel protein receptor targets for TCDD using computational and in vitro validation experiments. Interestingly, results from computational methods predicted that Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) could be one of the potential targets for TCDD in both mouse and humans. Results from molecular docking studies showed that human VEGFR1 (hVEGFR1) has less affinity towards TCDD compared to the mouse VEGFR1 (mVEGFR1). In vitro validation results showed that TCDD can bind and phosphorylate hVEGFR1. Further, results from molecular dynamic simulation studies showed that hVEGFR1 interaction with TCDD is stable throughout the simulation time. Overall, the present study has identified VEGFR1 as a novel target for TCDD, which provides the basis for further elucidating the role of TCDD in angiogenesis.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Using reverse docking for target identification and its applications for drug discovery

INTRODUCTION

In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity.

AREAS COVERED

In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers.

EXPERT OPINION

There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.

Computational prediction and analysis of breast cancer targets for 6-methyl-1, 3, 8-trichlorodibenzofuran

Breast cancer is one of the most known cancer types caused to the women around the world. Dioxins on the other hand are a wide range of chemical compounds known to cause the effects on human health. Among them, 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) is a relatively non toxic prototypical alkyl polychlorinated dibenzofuran known to act as a highly effective agent for inhibiting hormone-responsive breast cancer growth in animal models. In this study, we have developed a multi-level computational approach to identify possible new breast cancer targets for MCDF. We used PharmMapper Server to predict breast cancer target proteins for MCDF. Search results showed crystal Structure of the Antagonist Form of Glucocorticoid Receptor with highest fit score and AutoLigand analysis showed two potential binding sites, site-A and site-B for MCDF. A molecular docking was performed on these two sites and based on binding energy site-B was selected. MD simulation studies on Glucocorticoid receptor-MCDF complex revealed that MCDF conformation was stable at site-B and the intermolecular interactions were maintained during the course of simulation. In conclusion, our approach couples reverse pharmacophore analysis, molecular docking and molecular dynamics simulations to identify possible new breast cancer targets for MCDF.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.