Deciphering the Primary Role of Au⋅⋅⋅H-X Hydrogen Bonding in Gold Catalysis

Au⋅⋅⋅H-X (X=N or C) hydrogen bonding is gaining increasing interest, both in the study of its intrinsic nature and in their operability in different fields. While the role of these interactions has been studied in the stabilization of gold(I) complexes, their role during the minimum free energy reaction pathway of a given catalytic process remains unexplored. We report herein that complex [Au(C≡CPh)(pip)] (pip=piperidine) catalyses the A3 -coupling reaction for the synthesis of propargylamines, thanks to the ability of Au(I) to promote weak hydrogen bonding interactions with the reactants along the free energy profile. Density Functional Theory (DFT) calculations show that these Au⋅⋅⋅H-X interactions play a directing role in the catalysed A3 -coupling. Topological non-covalent interactions (NCI), interaction region indicator (IRI) and quantum theory of atoms in molecules (QTAIM) analysis in real space of the electron density provide a description of these interactions accurately.

How Hepatitis C Virus Leads to Hepatocellular Carcinoma: A Network-Based Study

BACKGROUND

Hepatitis C virus (HCV) has been known as a major cause of hepatocellular carcinoma (HCC) worldwide. However, the distinct molecular mechanisms underlying the effects of HCV proteins on the HCC progression have remained unclear.

OBJECTIVES

In the present study, we studied the possible role of HCV in the HCC initiation and invasion using topological analysis of protein-protein interaction (PPI) networks.

MATERIALS AND METHODS

After analysis with GEO2R, a PPI network of differentially expressed genes (DEGs) was constructed for both chronic HCV and HCC samples. The STRING and GeneMANIA databases were used to determine the putative interactions between DEGs. In parallel, the functional annotation of DEGs was performed using g: Profiler web tool. The topological analysis and network visualization was carried outperformed using Cytoscape software and the top hub genes were identified. We determined the hub genes-related miRNAs using miRTarBase server and reconstructed a miRNA-Hubgene network.

RESULTS

Based on the topological analysis of miRNA-Hubgene network, we identified the key hub miRNAs. In order to identify the most important common sub-network, we aligned two PPI networks using NETAL tool. The c-Jun gene was identified as the most important hub gene in both HCV and HCC networks. Furthermore, the hsa-miR-34a, hsa-miR-155, hsa-miR-24, hsa-miR-744 and hsa-miR-92a were recognized as the most important hub miRNAs with positive correlation in the chronic HCV and HCC samples. Functional annotation of differentially expressed miRNAs (DEMs) using the tool for annotations of human miRNAs (TAM) revealed that there is a considerable overlap between miRNA gene expression profiles of HCV-infected and HCC cells.

CONCLUSIONS

Our results revealed the possible crucial genes and miRNAs involved in the initiation and progression of HCC cells infected with HCV.