Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells

BACKGROUND

Abnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear.

METHODS

In this study, we examined how VSMC responds under mechanical stress by using time-course microarray data. A three-phase study was proposed to investigate the stress-induced differentially expressed genes (DEGs) in VSMC. First, DEGs were identified by using the moderated t-statistics test. Second, more DEGs were inferred by using the Gaussian Graphical Model (GGM). Finally, the topological parameters-based method and cluster analysis approach were employed to predict the last batch of DEGs. To identify the potential drugs for vascular diseases involve VSMC proliferation, the drug-gene interaction database, Connectivity Map (cMap) was employed. Success of the predictions were determined using in-vitro data, i.e. MTT and clonogenic assay.

RESULTS

Based on the differential expression calculation, at least 23 DEGs were found, and the findings were qualified by previous studies on VSMC. The results of gene set enrichment analysis indicated that the most often found enriched biological processes are cell-cycle-related processes. Furthermore, more stress-induced genes, well supported by literature, were found by applying graph theory to the gene association network (GAN). Finally, we showed that by processing the cMap input queries with a cluster algorithm, we achieved a substantial increase in the number of potential drugs with experimental IC50 measurements. With this novel approach, we have not only successfully identified the DEGs, but also improved the DEGs prediction by performing the topological and cluster analysis. Moreover, the findings are remarkably validated and in line with the literature. Furthermore, the cMap and DrugBank resources were used to identify potential drugs and targeted genes for vascular diseases involve VSMC proliferation. Our findings are supported by in-vitro experimental IC50, binding activity data and clinical trials.

CONCLUSION

This study provides a systematic strategy to discover potential drugs and target genes, by which we hope to shed light on the treatments of VSMC proliferation associated diseases.

Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn't produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes.

Enantioresolution in electrokinetic chromatography-complete filling technique using sulfated gamma-cyclodextrin. Software-free topological anticipation

Few papers have tried to predict the resolution ability of chiral selectors in capillary electrophoresis for the separation of the enantiomers of chiral compounds. In a previous work, we have used molecular information available on-line to establish enantioresolution levels of basic compounds using highly sulfated β-CD (HS-β-CD) as chiral selector in electrokinetic chromatography-complete filling technique (EKC-CFT). The present study is a continuation of this previous work, introducing some novelties. In this work, the ability of sulfated γ-cyclodextrin (S-γ-CD) as chiral selector in EKC-CFT is modelled for the first time. Thirty-three structurally unrelated cationic and neutral compounds (drugs and pesticides) are studied. Categorical enantioresolution levels (RsC, 0 or 1) are assigned from experimental enantioresolution values obtained at different S-γ-CD concentrations. Novel topological parameters connected to the chiral carbon (C^*-parameters) are introduced. Four C^*-parameters and a topological parameter of the whole molecule (aromatic atom count) are the most important variables according to a discriminant partial least squares-variable selection process. It suggests the preponderance of the topology adjacent to the chiral carbon to anticipate the RsC levels. A software-free anticipation protocol for new molecules is proposed. Over the current set of molecules evaluated, 100% of correct anticipations (resolved and non-resolved compounds) are obtained, while anticipation of some compounds remains undetermined. A criterion is introduced to alert on compounds which should not be anticipated.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Intermolecular interactions in water and ethanol solution of ethyl acetate: Raman, DFT, MEP, FMO, AIM, NCI-RDG, ELF, and LOL analyses

CONTEXT

The intermolecular interactions of ethyl acetate (EtOAc)-water (H2O)/ethanol (EtOH) mixtures were investigated using a combination of Raman spectroscopy and quantum chemical calculations. The computational approach was used to analyze the structure of hydrogen-bonded complexes of ethyl acetate with water/ethanol molecules, based on density functional theory (DFT). The calculated frequencies closely matched the experimental Raman values, with differences being under 4%. Experimental data show that when the concentrations of ethyl acetate in the ethyl acetate/water/ethanol solutions were reduced, almost all Raman spectral bands are blue-shifted. The AIM analysis reveals that all the given complexes possess a positive energy density, indicating that the molecules interact electrostatically. The energy and bond length indicate that the methyl group forms relatively weak hydrogen bonds. Analysis indicates that EtOAc forms weak H-bonding C = O∙∙∙H and C-H∙∙∙O, which are recognized as van der Waals interactions. As the amount of ethyl acetate decreases in the complex, the interaction forces also decrease. This could also explain why the bands are blue-shifted. It was discovered that the title complexes' hydrogen bond energy decreased exponentially as bond length increased.

METHODS

The geometries of the molecular complexes were optimized using the Gaussian 09W program and the B3LYP/6-311 +  + G(d,p) set of functions. The potential energy distribution (PED) analysis was performed using VEDA 4.0 software. Raman spectra were drawn using the Origin 8.5 software. The Multiwfn 3.8 software was used to calculate topological parameters of electron density in molecular systems. GaussView 6.0 and Visual Molecular Dynamics (VMD) 1.9.3 tools were used to visualize all computational results.