Cycloastragenol restrains keratinocyte hyperproliferation by promoting autophagy via the miR-145/STC1/Notch1 axis in psoriasis

Psoriasis is characterized by inflammation and hyperproliferation of epidermal keratinocytes. Cycloastragenol (CAG) is an active molecule of Astragalus membranaceus that potentially plays a repressive role in psoriasis. Activated cell autophagy is an effective pathway for alleviating psoriasis progression. Thus, we investigated the role of CAG in the proliferation and autophagy of interleukin (IL)-22-stimulated keratinocytes. A psoriasis model was established by stimulating HaCaT cells with IL-22. Gene or protein expression levels were measured by qRT-PCR or western blot. Autophagy flux was observed with mRFP-GFP-LC3 adenovirus transfection assay under confocal microscopy. Stanniocalcin-1 (STC1) secretion levels were determined using ELISA kits. The apoptosis rate was assessed using flow cytometry. Interactions between miR-145 and STC1 or STC1 and Notch1 were validated by luciferase reporter gene assays, RIP, and Co-IP assays. CAG repressed cell proliferation and promoted apoptosis and autophagy in IL-22-stimulated HaCaT cells. Additionally, CAG promoted autophagy by enhancing miR-145. STC1 silencing ameliorated autophagy repression in IL-22-treated HaCaT cells. Moreover, miR-145 negatively regulated STC1, and STC1 was found to activate Notch1. Lastly, STC1 overexpression reversed CAG-promoted autophagy. CAG alleviated keratinocyte hyperproliferation through autophagy enhancement via regulating the miR-145/STC1/Notch1 axis in psoriasis.</p

The discovery of novel HDAC3 inhibitors via virtual screening and <i>in vitro</i> bioassay

<p>Histone deacetylase 3 (HDAC3) is a potential target for the treatment of human diseases such as cancers, diabetes, chronic inflammation and neurodegenerative diseases. Previously, we proposed a virtual screening (VS) pipeline named “Hypo1_FRED_SAHA-3” for the discovery of HDAC3 inhibitors (HDAC3Is) and had thoroughly validated it by theoretical calculations. In this study, we attempted to explore its practical utility in a large-scale VS campaign. To this end, we used the VS pipeline to hierarchically screen the Specs chemical library. In order to facilitate compound cherry-picking, we then developed a knowledge-based pose filter (PF) by using our in-house quantitative structure activity relationship- (QSAR-) modelling approach and coupled it with FRED and Autodock Vina. Afterward, we purchased and tested 11 diverse compounds for their HDAC3 inhibitory activity <i>in vitro</i>. The bioassay has identified compound <b>2</b> (Specs ID: AN-979/41971160) as a HDAC3I (IC₅₀ = 6.1 μM), which proved the efficacy of our workflow. As a medicinal chemistry study, we performed a follow-up substructure search and identified two more hit compounds of the same chemical type, i.e. <b>2–1</b> (AQ-390/42122119, IC₅₀ = 1.3 μM) and <b>2–2</b> (AN-329/43450111, IC₅₀ = 12.5 μM). Based on the chemical structures and activities, we have demonstrated the essential role of the capping group in maintaining the activity for this class of HDAC3Is. In addition, we tested the hit compounds for their <i>in vitro</i> activities on other HDACs, including HDAC1, HDAC2, HDAC8, HDAC4 and HDAC6. We have identified these compounds are HDAC1/2/3 selective inhibitors, of which compound <b>2</b> show the best selectivity profile. Taken together, the present study is an experimental validation and an update to our earlier VS strategy. The identified hits could be used as starting structures for the development of highly potent and selective HDAC3Is.</p

An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD

The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein–ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein–ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein–ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) coupling PFE with Chemgauss4 significantly improves the early enrichment of Chemgauss4 itself and (2) PFEs show greater consistency in boosting early enrichment and larger overall enrichment than our prior PFs. In addition, we analyzed the pairwise topological similarities among cognate ligands used to construct PFEs and found that it is the higher chemical diversity of the cognate ligands that leads to the improved performance of PFEs. Taken together, the results so far prove that the incorporation of knowledge from diverse protein–ligand interfaces by ensemble modeling is able to enhance the screening competence of SBVS scoring functions

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of “artificial enrichment”, “analogue bias”. In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far

Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families

Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24 609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of “artificial enrichment” and “analogue bias”. We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the “2D bias” and “LBVS favorable” effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/

Dioxin-like Potency of HO- and MeO- Analogues of PBDEs’ the Potential Risk through Consumption of Fish from Eastern China

Polybrominated diphenyl ethers (PBDEs) and their analogues, such as hydroxylated PBDE (HO-PBDEs) and methoxylated PBDE (MeO-PBDEs) are of interest due to their wide distribution, bioaccumulation and potential toxicity to humans and wildlife. While information on the toxicity/biological potencies of PBDEs was available, information on analogues of PBDEs was limited. Dioxin-like toxicity of 34 PBDEs analogues was evaluated by use of the H4IIE-<i>luc</i>, rat hepatoma transactivation bioassay in 384-well plate format at concentrations ranging from 0 to 10 000 ng/mL. Among the 34 target analogues of PBDEs studied here, 19 activated the aryl hydrocarbon receptor (AhR) and induced significant dioxin-like responses in H4IIE-<i>luc</i> cells. Efficacies of the analogues of PBDEs ranged from 5.0% to 101.8% of the maximum response caused by 2,3,7,8-tetrachlorodibenzo-<i>p</i>-dioxin (TCDD-max) and their respective 2,3,7,8-TCDD potency factors (ReP_{H4IIE‑<i>luc</i>}) ranged from 7.35 × 10^–12 to 4.00 × 10^–4, some of which were equal to or more potent than some mono-<i>ortho</i>-substituted PCBs (TEF-_WHO = 3 × 10^–5). HO-PBDEs exhibited greater dioxin-like activity than did the corresponding MeO-PBDEs. Analogues of PBDEs were detected mostly in marine organisms. Of these 11 detected analogues of PBDEs, 6 were found to have measurable dioxin-like potency. Though some analogues of PBDEs exhibited significant dioxin-like potency as measured by responses of the H4IIE-<i>luc</i> transactivation assay, concentrations of 2,3,7,8-tetrachlorodibenzo-<i>p-</i>dioxin (TCDD) equivalents (^{PBDEs analogues}TEQ_{H4IIE‑<i>luc</i>}), calculated as the sum of the product of concentrations of individual PBDE and their ReP_{H4IIE‑<i>luc</i>}, were less than the tolerance limit proposed by European Union and the oral reference dose (RfD) derived by U.S. Environmental Protection Agency, respectively. (Hazard Quotients (HQ) < 0.005) Additional investigations should be conducted to evaluate the toxic potencies of these chemicals, especially for 2′-MeO-BDE-28, 4-HO-BDE-90, 6-HO-BDE-47, and 6-MeO-BDE-47, which had been detected in other environmental media, including human blood

Characterization of Cancer Associated Mucin Type O-Glycans Using the Exchange Sialylation Properties of Mammalian Sialyltransferase ST3Gal-II

Our previous studies suggest that the α2,3sialylated T-antigen (NeuAcα2,3Galβ1,3GalNac-) and associated glycan structures are likely to be elevated during cancer. An easy and reliable strategy to label mucinous glycans that contain such carbohydrates can enable the identification of novel glycoproteins that are cancer associated. To this end, the present study demonstrates that the exchange sialylation property of mammalian ST3Gal-II can facilitate the labeling of mucin glycoproteins in cancer cells, tumor specimens, and glycoproteins in cancer sera. Results show that (i) the radiolabeled mucin glycoproteins of each of the cancer cell lines studied (T47D, MCF7, LS180, LNCaP, SKOV₃, HL60, DU4475, and HepG₂) is distinct either in terms of the specific glycans presented or their relative distribution. While some cell lines like T47D had only one single sialylated O-glycan, others like LS180 and DU4475 contained a complex mixture of mucinous carbohydrates. (ii) [¹⁴C]­sialyl labeling of primary tumor cells identified a 25–35 kDa mucin glycoprotein unique to pancreatic tumor. Labeled glycoproteins for other cancers had higher molecular weight. (iii) Studies of [¹⁴C] sialylated human sera showed larger mucin glycopeptides and >2-fold larger mucin-type chains in human serum compared to [¹⁴C]­sialyl labeled glycans of fetuin. Overall, the exchange sialylation property of ST3Gal-II provides an efficient avenue to identify mucinous proteins for applications in glycoproteomics and cancer research

MOESM1 of Resolvin D1 mitigates energy metabolism disorder after ischemia–reperfusion of the rat lung

Additional file 1: Figure S1. Product information

Additional file 1: Table S1. of Parenting style, resilience, and mental health of community-dwelling elderly adults in China

Datasets. (XLSX 121 kb