Semaphorin 7A impairs barrier function in cultured human corneal epithelial cells in a manner dependent on nuclear factor-kappa B.

AIM: To evaluate the role of semaphorin 7A (Sema7A) and its associated regulatory mechanisms in modulating the barrier function of cultured human corneal epithelial cells (HCEs). METHODS: Barrier models of HCEs were treated with recombinant human Sema7A at concentrations of 0, 125, 250, or 500 ng/mL for 24, 48, or 72h in vitro. Transepithelial electrical resistance (TEER) as well as Dextran-fluorescein isothiocyanate (FITC) permeability assays were conducted to assess barrier function. To quantify tight junctions (TJs) such as occludin and zonula occludens-1 (ZO-1) at the mRNA level, reverse transcription-polymerase chain reaction (RT-PCR) analysis was performed. Immunoblotting was used to examine the activity of the nuclear factor-kappa B (NF-κB) signaling pathway and the production of TJs proteins. Immunofluorescence analyses were employed to localize the TJs. Enzyme-linked immunosorbent assay (ELISA) and RT-PCR were utilized to observe changes in interleukin (IL)-1ß levels. To investigate the role of NF-κB signaling activation and IL-1ß in Sema7A's anti-barrier mechanism, we employed 0.1 µmol/L IκB kinase 2 (IKK2) inhibitor IV or 500 ng/mL IL-1 receptor (IL-1R) antagonist. RESULTS: Treatment with Sema7A resulted in decreased TEER and increased permeability of Dextran-FITC in HCEs through down-regulating mRNA and protein levels of TJs in a time- and dose-dependent manner, as well as altering the localization of TJs. Furthermore, Sema7A stimulated the activation of inhibitor of kappa B alpha (IκBα) and expression of IL-1ß. The anti-barrier function of Sema7A was significantly suppressed by treatment with IKK2 inhibitor IV or IL-1R antagonists. CONCLUSION: Sema7A disrupts barrier function through its influence on NF-κB-mediated expression of TJ proteins, as well as the expression of IL-1ß. These findings suggest that Sema7A could be a potential therapeutic target for the diseases in corneal epithelium.

17ß-estradiol inhibits TGF-ß-induced collagen gel contraction mediated by human Tenon fibroblasts via Smads and MAPK signaling pathways.

AIM: To investigate the impact of 17ß-estradiol on the collagen gels contraction (CGC) and inflammation induced by transforming growth factor (TGF)-ß in human Tenon fibroblasts (HTFs). METHODS: HTFs were three-dimensionally cultivated in type I collagen-generated gels with or without TGF-ß (5 ng/mL), 17ß-estradiol (12.5 to 100 µmol/L), or progesterone (12.5 to 100 µmol/L). Then, the collagen gel diameter was determined to assess the contraction, and the development of stress fibers was analyzed using immunofluorescence staining. Immunoblot and gelatin zymography assays were used to analyze matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) being released into culture supernatants. Enzyme-linked immunosorbent assay (ELISA) and reverse transcription-quantitative polymerase chain reaction (RT-PCR) were used to detect interleukin (IL)-6, monocyte chemoattractant proteins (MCP)-1, and vascular endothelial growth factor (VEGF) in HTFs at the translational and transcriptional levels. The phosphorylation levels of Sma- and Mad-related proteins (Smads), mitogen-activated protein kinases (MAPKs), and protein kinase B (AKT) were measured by immunoblotting. Statistical analysis was performed using either the Tukey-Kramer test or Student's unpaired t-test to compare the various treatments. RESULTS: The CGC caused by TGF-ß in HTFs was significantly inhibited by 17ß-estradiol (25 to 100 µmol/L), and a statistically significant difference was observed when comparing the normal control group with 17ß-estradiol concentrations exceeding 25 µmol/L (P<0.05). The suppressive impact of 17ß-estradiol became evident 24h after administration and peaked at 72h (P<0.05), whereas progesterone had no impact. Moreover, 17ß-estradiol attenuated the formation of stress fibers, and the production of MMP-3 and MMP-1 in HTFs stimulated by TGF-ß. The expression of MCP-1, IL-6, and VEGF mRNA and protein in HTFs were suppressed by 100 µmol/L 17ß-estradiol (P<0.01). Additionally, the phosphorylation of Smad2 Smad3, p38, and extracellular signal-regulated kinase (ERK) were downregulated (P <0.01). CONCLUSION: 17ß-estradiol significantly inhibits the CGC and inflammation caused by TGF-ß in HTFs. This inhibition is likely related to the suppression of stress fibers, inhibition of MMPs, and attenuation of Smads and MAPK (ERK and p38) signaling. 17ß-estradiol may have potential clinical benefits in preventing scar development and inflammation in the conjunctiva.

LncRNA HOTAIR Enhances Epithelial-to-mesenchymal Transition to Promote the Migration and Invasion of Liver Cancer by Regulating NUAK1 via Epigenetic Inhibition miR-145-5p Expression.

LncRNA HOTAIR play important roles in the epigenetic regulation of carcinogenesis and progression in liver cancer. Previous studies suggest that the overexpression of HOTAIR predicts poor prognosis. In this study, through transcriptome sequencing data and in vitro experiments, we found that HOTAIR were more highly expressed and there is significantly positive relationship between HOTAIR and NUAK1 in liver cancer tissues and cell lines. miR-145-5p was downregulated and showed negative correlation with HOTAIR and NUAK1. Transfect Sh-HOTAIR, LZRS-HOTAIR, miR-145 mimic, miR-145 inhibitor to change the expression of HOTAIR and miR-145-5p. The addition of HTH-01-015 inhibits the expression of NUAK1. HOTAIR knockdown, miR-145-5p upregulation and NUAK1 inhibition all repressed migration, invasion and metastasis and reversed the epithelial-to-mesenchymal transition in SNU-387 and HepG2 cells. We also showed that HOTAIR recruiting and binding PRC2 (EZH2) epigenetically represses miR-145-5p, which controls the target NUAK1, thus contributing to liver cancer cell-EMT process and accelerating tumor metastasis. Moreover, it is demonstrated that HOTAIR crosstalk with miR-145-5p/NUAK1 during epigenetic regulation. Our findings indicate that HOTAIR/miR-145-5p/NUAK1 axis acts as an EMT regulator and may be candidate prognostic biomarker and targets for new therapies in liver cancer.

Inhibitory effects of luteolin on TLR3-mediated inflammation caused by TAK/NF-κB signaling in human corneal fibroblasts.

AIM: To study the role of luteolin (LUT) in the expression of toll-like receptors 3 (TLR3) ligand polyI:C stimulated inflammatory factors in human corneal fibroblasts (HCFs). METHODS: HCFs cells were cultivated with or without LUT or polyI:C. The expression levels of interleukin (IL)-6, IL-8, monocyte chemotactic protein-1 (MCP-1), vascular cell adhesion molecule (VCAM)-1, as well as intercellular adhesion molecule (ICAM)-1 were measured using enzyme-linked immunosorbent assay (ELISA), immunoblotting or reverse transcription-quantitative polymerase chain reaction (PCR) analyses. Immunoblotting was used to assess toll-interleukin-1 receptor-domain-containing adapter-inducing interferon-ß (TRIF), TLR3, transforming growth factor-b-activated kinase 1 (TAK1), tumor necrosis factor receptor-associated factor 6 (TRAF6), the transcription factor AP-1, as well as transcription factor nuclear factor (NF-κB)-inhibitory protein IκB-α degradation and phosphorylation. Immunofluorescence assays were used to localize the cellular location of the p65 subunit of NF-κB. RESULTS: Corneal fibroblasts exposed to polyI:C demonstrated decreased VCAM-1, ICAM-1, MCP-1, IL-6, and IL-8 expression levels upon exposure to LUT in a time-dependent and concentration-dependent manner. LUT was observed to suppress polyI:C-triggered expression of TLR3, the translocation of NF-κB p65 into cell nuclei, as well as the phosphorylation of TAK, c-Jun, and IκB-α, while no impact on the expression levels of TRIF and TRAF6 were observed. CONCLUSION: LUT suppress the expression of proinflammatory adhesion molecules, chemokines, and cytokines in polyI:C exposed HCFs. These effects are likely mediated through TAK/NF-κB signal attenuation. Therefore, LUT is a candidate molecule that can prevent the TLR3-mediated inflammation response associated with corneal viral infection.

Spectroscopic Evidence for a Spin- and Valley-Polarized Metallic State in a Nonmagic-Angle Twisted Bilayer Graphene.

In the magic-angle twisted bilayer graphene (MA-TBG), strong electron-electron (e-e) correlations caused by the band-flattening lead to many exotic quantum phases such as superconductivity, correlated insulator, ferromagnetism, and quantum anomalous Hall effects, when its low-energy van Hove singularities (VHSs) are partially filled. Here our high-resolution scanning tunneling microscope and spectroscopy measurements demonstrate that the e-e correlation in a nonmagic-angle TBG with a twist angle θ = 1.49° still plays an important role in determining its electronic properties. Our most interesting observation on that sample is when one of its VHSs is partially filled, the one associated peak in the spectrum splits into four peaks. Simultaneously, the spatial symmetry of electronic states around the split VHSs is broken by the e-e correlation. Our analysis based on the continuum model suggests that such a one-to-four split of the VHS originates from the formation of an interaction-driven spin-valley-polarized metallic state near the VHS, which is a symmetry-breaking phase that has not been identified in the MA-TBG or in other systems.

Drug Target Group Prediction with Multiple Drug Networks.

BACKGROUND: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. METHODS: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. RESULTS AND CONCLUSION: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

lncRNA SLC16A1-AS1 as a novel prognostic biomarker in non-small cell lung cancer.

Long non-coding RNAs (lncRNAs) have proved to act as crucial biomarkers in tumors. Novel biomarkers in non-small cell lung cancer (NSCLC) need to be investigated badly. To identify the differentially expressed lncRNAs between NSCLC tissue and adjacent tissue, microarray analysis was performed. lncRNA SLC16A1-AS1 was significantly less expressed in NSCLC tissue than that in adjacent tissue. Gain-of-function experiments was performed to determine the biological functions of SLC16A1-AS. In situhybridization and survival analysis were applied in lung cancer tissue samples to determine the prognostic role of SLC16A1-AS1. It was showed that SLC16A1-AS1 was remarkably downregulated in NSCLC tissues and cell lines. Functionally, SLC16A1-AS1 overexpression could inhibit the viability and proliferation of lung cancer cell, block the cell cycle and promote cell apoptosis in vitro which may result from reduced phosphorylation of rat sarcoma (RAS)/ proto-oncogene serine/threonine-protein kinase (RAF)/ mitogen-activated protein kinase kinase (MEK)/ extracellular regulated protein kinases (ERK) pathway caused by elevated expression of SLC16A1-AS1. Clinical sample analysis showed that SLC16A1-AS1 had a favorable impact on the overall survival and progression-free survival of patients with NSCLC. Our results suggested that SLC16A1-AS1 may act as a potential biomarker for patients with NSCLC.

iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs.

MOTIVATION: The anatomical therapeutic chemical (ATC) classification system plays an increasingly important role in drug repositioning and discovery. The correct identification of classes in each level of such system that a given drug may belong to is an essential problem. Several multi-label classifiers have been proposed in this regard. Although they provided satisfactory performance, the feature extraction procedures were still rough. More refined features may further improve the predicted quality. RESULTS: In this article, we provide a novel multi-label classifier, called iATC-NRAKEL, to predict drug ATC classes in the first level. To obtain more informative drug features, we employed the drug association information in STITCH and KEGG, which was organized by seven drug networks. The powerful network embedding algorithm, Mashup, was adopted to extract informative drug features. The obtained features were fed into the RAndom k-labELsets (RAKEL) algorithm with support vector machine as the basic classification algorithm to construct the classifier. The 10-fold cross-validation of the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 76.56 and 74.51%, respectively. The comparison results indicated that iATC-NRAKEL was much superior to all previous reported classifiers. Finally, the contribution of each network was analyzed. AVAILABILITY AND IMPLEMENTATION: The codes of iATC-NRAKEL are available at https://github.com/zhou256/iATC-NRAKEL.

High-Magnetic-Field Tunneling Spectra of ABC-Stacked Trilayer Graphene on Graphite.

ABC-stacked trilayer graphene (TLG) was predicted to exhibit novel many-body phenomena due to the existence of almost dispersionless flat bands near the charge neutrality point. Here, using high-magnetic-field scanning tunneling microscopy, we present Landau Level (LL) spectroscopy measurements of high-quality ABC-stacked TLG on graphite. We observe an approximately linear magnetic-field scaling of valley splitting and spin splitting in the ABC-stacked TLG. Our experiment indicates that the spin splitting decreases dramatically with increasing the LL index. When the lowest LL is partially filled, we find an obvious enhancement of the spin splitting, attributing to strong many-body effects. Moreover, we observe linear energy scaling of the inverse lifetime of quasiparticles, providing an additional evidence for the strong electron-electron interactions in the ABC-stacked TLG. These results imply that interesting broken-symmetry states and novel electron correlated effects could emerge in the ABC-stacked TLG in the presence of high magnetic fields.

A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information.

AIM AND OBJECTIVE: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been uncovered. However, their completeness is still on the way. A number of prediction methods have been built to assign chemicals into certain metabolic pathway, which can further be used to predict novel latent chemicals for a given metabolic pathway. However, they did not make use of chemical properties in a system level to construct prediction models. METHOD: In this study, we applied a network integration method, which can extract topological features from different chemical networks, representing chemical associations from their different properties, and fused several high-dimension vector representations into a low-dimension vector representation for each chemical. The compact vector representations were fed into the Support Vector Machine (SVM) to construct the prediction model. To tackle the problem that one chemical can participate in more than one pathway type, we construct an SVM-based binary prediction model for each pathway type to determine whether a given chemical can participate in the pathway type. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) was adopted to weaken the influence of imbalanced dataset. RESULTS AND CONCLUSION: Each binary model gave a quite good performance and was superior to the classic prediction model, indicating that the proposed models can be useful tools for integrating heterogeneous information to assign chemicals into correct metabolic pathways.