WIPF1 antagonizes the tumor suppressive effect of miR-141/200c and is associated with poor survival in patients with PDAC

Abstract Background Aberrant expression of Wiskott–Aldrich syndrome protein interacting protein family member 1 (WIPF1) contributes to the invasion and metastasis of several malignancies. However, the role of WIPF1 in human pancreatic ductal adenocarcinoma (PDAC) remains poorly understood. Methods Human pancreatic cancer samples from PDAC patients were collected for methylation analysis. Bioinformatic prediction program and luciferase reporter assay were used to identify microRNAs regulating WIPF1 expression. The association between WIPF1 expression and the overall survival (OS) of patients with PDAC was evaluated by using The Cancer Genome Atlas (TCGA) database. The roles of miR-141/200c and WIPF1 on the invasion and metastasis of PDAC cells were investigated both in vitro and in vivo. Results We found that compared to the surrounding non-cancerous tissues, there was significantly increased methylation of miR-200c and miR-141 in human PDAC tissues that resulted in their silencing, whereas the members of the other cluster of miR-200 family including miR-200a, miR-200b and miR-429 were hypomethylated. Our data show that forced expression of miR-141 or miR-200c suppressed invasion and metastasis of PDAC cells both in vitro and in xenograft and identified WIPF1 as a direct target of miR-141 and miR-200c. Both miR-141 and miR-200c inhibit WIPF1 by directly interacting with its 3′-untranslated region. Remarkably, silencing of WIPF1 blocked PDAC growth and metastasis both in vitro and in vivo, whereas forced WIPF1 overexpression antagonized the tumor suppressive effect of miR-141/200c. Additionally, by using TCGA database we showed that high expression of WIPF1 correlated with poor survival in patients with PDAC. Moreover, we show that miR-141 and miR-200c blocked YAP/TAZ expression by suppressing WIPF1. Conclusions We have identified WIPF1 as an oncoprotein in PDAC and a direct target of miR-141/miR-200c. We have also defined the miR-141/200c-WIPF1-YAP/TAZ as a novel signaling pathway that is involved in the regulation of the invasion and metastasis of human PDAC cells

Computational Insights into Allosteric Conformational Modulation of P-Glycoprotein by Substrate and Inhibitor Binding

The ATP-binding cassette (ABC) transporter P-glycoprotein (P-gp) is a physiologically essential membrane protein that protects many tissues against xenobiotic molecules, but limits the access of chemotherapeutics into tumor cells, thus contributing to multidrug resistance. The atomic-level mechanism of how substrates and inhibitors differentially affect the ATP hydrolysis by P-gp remains to be elucidated. In this work, atomistic molecular dynamics simulations in an explicit membrane/water environment were performed to explore the effects of substrate and inhibitor binding on the conformational dynamics of P-gp. Distinct differences in conformational changes that mainly occurred in the nucleotide-binding domains (NBDs) were observed from the substrate- and inhibitor-bound simulations. The binding of rhodamine-123 can increase the probability of the formation of an intermediate conformation, in which the NBDs were closer and better aligned, suggesting that substrate binding may prime the transporter for ATP hydrolysis. By contrast, the inhibitor QZ-Leu stabilized NBDs in a much more separated and misaligned conformation, which may result in the deficiency of ATP hydrolysis. The significant differences in conformational modulation of P-gp by substrate and inhibitor binding provided a molecular explanation of how these small molecules exert opposite effects on the ATPase activity. A further structural analysis suggested that the allosteric communication between transmembrane domains (TMDs) and NBDs was primarily mediated by two intracellular coupling helices. Our computational simulations provide not only valuable insights into the transport mechanism of P-gp substrates, but also for the molecular design of P-gp inhibitors

Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images

The VHSE-based prediction of proteasomal cleavage sites.

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue's electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites

Comparative Transcriptome Analyses Revealed Conserved and Novel Responses to Cold and Freezing Stress in Brassica napus L

Oil rapeseed (Brassica napus L.) is a typical winter biennial plant, with high cold tolerance during vegetative stage. In recent years, more and more early-maturing rapeseed varieties were planted across China. Unfortunately, the early-maturing rapeseed varieties with low cold tolerance have higher risk of freeze injury in cold winter and spring. Little is known about the molecular mechanisms for coping with different low-temperature stress conditions in rapeseed. In this study, we investigated 47,328 differentially expressed genes (DEGs) of two early-maturing rapeseed varieties with different cold tolerance treated with cold shock at chilling (4°) and freezing (−4°) temperatures, as well as chilling and freezing stress following cold acclimation or control conditions. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that two conserved (the primary metabolism and plant hormone signal transduction) and two novel (plant-pathogen interaction pathway and circadian rhythms pathway) signaling pathways were significantly enriched with differentially-expressed transcripts. Our results provided a foundation for understanding the low-temperature stress response mechanisms of rapeseed. We also propose new ideas and candidate genes for genetic improvement of rapeseed tolerance to cold stresses

Membrane-assisted tariquidar access and binding mechanisms of human ATP-binding cassette transporter P-glycoprotein

The human multidrug transporter P-glycoprotein (P-gp) is physiologically essential and of key relevance to biomedicine. Recent structural studies have shed light on the mode of inhibition of the third-generation inhibitors for human P-gp, but the molecular mechanism by which these inhibitors enter the transmembrane sites remains poorly understood. In this study, we utilized all-atom molecular dynamics (MD) simulations to characterize human P-gp dynamics under a potent inhibitor, tariquidar, bound condition, as well as the atomic-level binding pathways in an explicit membrane/water environment. Extensive unbiased simulations show that human P-gp remains relatively stable in tariquidar-free and bound states, while exhibiting a high dynamic binding mode at either the drug-binding pocket or the regulatory site. Free energy estimations by partial nudged elastic band (PNEB) simulations and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method identify two energetically favorable binding pathways originating from the cytoplasmic gate with an extended tariquidar conformation. Interestingly, free tariquidar in the lipid membrane predominantly adopts extended conformations similar to those observed at the regulatory site. These results suggest that membrane lipids may preconfigure tariquidar into an active ligand conformation for efficient binding to the regulatory site. However, due to its conformational plasticity, tariquidar ultimately moves toward the drug-binding pocket in both pathways, explaining how it acts as a substrate at low concentrations. Our molecular findings propose a membrane-assisted mechanism for the access and binding of the third-generation inhibitors to the binding sites of human P-gp, and offer deeper insights into the molecule design of more potent inhibitors against P-gp-mediated drug resistance

Additional file 1: of WIPF1 antagonizes the tumor suppressive effect of miR-141/200c and is associated with poor survival in patients with PDAC

Table S1. The primer sequences used for polymerase chain reaction. Table S2. The nucleotide sequence of the primers used for qRT-PCR. Table S3. The sequence of miR-200c mimic, miR-141 mimic, anti-miR-200c mimic (Has-miR-200c inhibitor), and anti-miR-141 mimic (Has-miR-141 inhibitor) used for lentivirus transfection and luciferase reporter assay. Table S4. Characteristics of patients with pancreatic cancer (N = 37). Table S5. Characteristics of patients with pancreatic cancer from the TCGA database (N = 177). Figure S1. Identifying miR-141/200c target genes using the TargetScan software program. Figure S2. The levels of CpG methylation of the promoter region of miR-200a/200b/429 in PDAC. Figure S3. Lentiviral expression of miR-141 and miR-200c and their inhibitors in pancreatic cancer cell lines. Figure S4. The effect of miR-141 and miR-200c inhibitors on cell migration and invasion in vitro and tumor growth in xenograft. Figure S5. miR-141 and miR-200c inhibit the expression of WIPF1 in HPDE cell line. Figure S6. Lentiviral expression of shWIPF1 in pancreatic cancer cell lines. Figure S7. WIPF1 antagonizes the inhibitory effect of miR-141/200c on cell migration, invasion and metastasis of PDAC. (DOCX 17513 kb

De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors