Investigation on critical structural motifs of ligands for triggering glucocorticoid receptor nuclear migration through molecular docking simulations

Molecular docking and dynamic simulation approach to decipher steroidal sapogenins (genus Trillium) derived agonists for glucocorticoid receptor

Steroidal sapogenins (SS) are structural analogues of steroidal drugs, which are frequently used for the treatment of several diseases including reproductive, malignancies, neurological, and inflammation-related diseases. The glucocorticoid receptor (GR) is a nuclear receptor that regulates development, metabolism, and inflammation, in response to steroidal ligands. Therefore, GR is considered as a potential therapeutic target for steroidal agents to the treatment of inflammation-related diseases. We hypothesized that SS may act as an agonist for GR due to structural similarity with corticosteroids. In this study, we carried out in silico screening of various SS from the genus Trillium to check their potential as an agonist for GR. Our data suggest that out of 42 SS, only 7 molecules have interacted with GR. However, molecular mechanics with generalized Born and surface area (MM-GBSA) analysis revealed that only two SS (SS 38 and SS 39) molecules bind favorably to GR. Among these, SS 38 (docking score: -9.722 Kcal/mol and MM-GBSA ΔG_bind: -50.192 Kcal/mol) and SS 39 (docking score: -11.20 Kcal/mol and MM-GBSA ΔG_bind: -58.937 Kcal/mol) have best docking and MM-GBSA scores. Molecular dynamics (MD) simulation studies of SS 38, SS 39, and dexamethasone-GR complex revealed that both SS shows hydrogen bonding and hydrophobic interaction with GR over the 120 ns simulation with mild fluctuations. The current study suggests that SS 38 and SS 39 may be further explored as a potential agonist to treat several disease conditions mediated by GR.

Potential Dissociative Glucocorticoid Receptor Activity for Protopanaxadiol and Protopanaxatriol

Glucocorticoids are steroid hormones that regulate inflammation, growth, metabolism, and apoptosis via their cognate receptor, the glucocorticoid receptor (GR). GR, acting mainly as a transcription factor, activates or represses the expression of a large number of target genes, among them, many genes of anti-inflammatory and pro-inflammatory molecules, respectively. Transrepression activity of glucocorticoids also accounts for their anti-inflammatory activity, rendering them the most widely prescribed drug in medicine. However, chronic and high-dose use of glucocorticoids is accompanied with many undesirable side effects, attributed predominantly to GR transactivation activity. Thus, there is a high need for selective GR agonist, capable of dissociating transrepression from transactivation activity. Protopanaxadiol and protopanaxatriol are triterpenoids that share structural and functional similarities with glucocorticoids. The molecular mechanism of their actions is unclear. In this study applying induced-fit docking analysis, luciferase assay, immunofluorescence, and Western blot analysis, we showed that protopanaxadiol and more effectively protopanaxatriol are capable of binding to GR to activate its nuclear translocation, and to suppress the nuclear factor-kappa beta activity in GR-positive HeLa and HEK293 cells, but not in GR-low level COS-7 cells. Interestingly, no transactivation activity was observed, whereas suppression of the dexamethasone-induced transactivation of GR and induction of apoptosis in HeLa and HepG2 cells were observed. Thus, our results indicate that protopanaxadiol and protopanaxatriol could be considered as potent and selective GR agonist.

A Natural Mutation in Helix 5 of the Ligand Binding Domain of Glucocorticoid Receptor Enhances Receptor-Ligand Interaction

The glucocorticoid receptor (GR) is a central player in the neuroendocrine stress response; it mediates feedback regulation of the hypothalamus-pituitary-adrenal (HPA) axis and physiological actions of glucocorticoids in the periphery. Despite intensive investigations of GR in the context of receptor-ligand interaction, only recently the first naturally occurring gain-of-function substitution, Ala610Val, of the ligand binding domain was identified in mammals. We showed that this mutation underlies a major quantitative trait locus for HPA axis activity in pigs, reducing cortisol production by about 40-50 percent. To unravel the molecular mechanisms behind this gain of function, receptor-ligand interactions were evaluated in silico, in vitro and in vivo. In accordance with previously observed phenotypic effects, the mutant Val610 GR showed significantly increased activation in response to glucocorticoid and non-glucocorticoid steroids, and, as revealed by GR-binding studies in vitro and in pituitary glands, enhanced ligand binding. Concordantly, the protein structure prediction depicted reduced binding distances between the receptor and ligand, and altered interactions in the ligand binding pocket. Consequently, the Ala610Val substitution opens up new structural information for the design of potent GR ligands and to examine effects of the enhanced GR responsiveness to glucocorticoids on the entire organism.

Surveying and benchmarking techniques to analyse DNA gel fingerprint images

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.

iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition

MOTIVATION

Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post-genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences. Although some efforts have been made in this regard, they were limited at only identifying whether a query DNA element being of an enhancer or not. According to the distinct levels of biological activities and regulatory effects on target genes, however, enhancers should be further classified into strong and weak ones in strength.

RESULTS

In view of this, a two-layer predictor called ' IENHANCER-2L: ' was proposed by formulating DNA elements with the 'pseudo k-tuple nucleotide composition', into which the six DNA local parameters were incorporated. To the best of our knowledge, it is the first computational predictor ever established for identifying not only enhancers, but also their strength. Rigorous cross-validation tests have indicated that IENHANCER-2L: holds very high potential to become a useful tool for genome analysis.

AVAILABILITY AND IMPLEMENTATION

For the convenience of most experimental scientists, a web server for the two-layer predictor was established at http://bioinformatics.hitsz.edu.cn/iEnhancer-2L/, by which users can easily get their desired results without the need to go through the mathematical details.

CONTACT

bliu@gordonlifescience.org, bliu@insun.hit.edu.cn, xlan@stanford.edu, kcchou@gordonlifescience.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association analysis between the distributions of histone modifications and gene expression in the human embryonic stem cell

It is well known that histone modifications are associated with gene expression. In order to further study this relationship, 16 kinds of Chip-seq histone modification data and mRNA-seq data of the human embryonic stem cell H1 are chosen. The distributions of histone modifications in the regions flanking transcription start sites (TSSs) for highly expressed and lowly expressed genes are computed, respectively. And four types of distributions of histone modifications in regions flanking TSSs and the spatial patterning of the correlations between histone modifications and gene expression are detected. Our results suggest that the correlations between the regions overlapped by peaks are higher than the non-overlapped ones for each histone modification. In addition, to obtain the effect of the cooperative action of histone modification on gene expression, five histone modification clusters are found in highly expressed and lowly expressed genes, histone modification and gene expression interaction network is constructed. To further explore which region is the main target region for the specific histone modification, the human genes are divided into five functional regions. The results indicate that histone modifications are mostly located in the promoters of highly expressed genes versus the exons of lowly expressed genes, and exons have a smaller range of normalized tag counts than other gene elements in the two groups of genes. Finally, the type specificity and regional bias of histone modifications for 11 key transcription factor genes regulating the stem cell renewal are analyzed.