Role of bile salt in regulating Mcl-1 phosphorylation and chemoresistance in hepatocellular carcinoma cells

<p>Abstract</p> <p>Background</p> <p>Glycochenodeoxycholate (GCDA) is one of the major human bile salts. Bile salts stimulate cell survival and proliferation through the mitogen-activated protein kinase, but the downstream signaling mechanism(s) remains enigmatic. Mcl-1 is an antiapoptotic molecule of the Bcl2 family that is extensively overexpressed in tumor tissues of patients with hepatocellular carcinoma (HCC).</p> <p>Results</p> <p>Here we found that exposure of HepG2 cells to GCDA results in activation of ERK1 and ERK2 and phosphorylation of Mcl-1 in a PD98059 (MEK inhibitor)-sensitive manner. GCDA stimulates Mcl-1 phosphorylation in cells expressing WT but not T163A Mcl-1 mutant, indicating that GCDA-induced Mcl-1 phosphorylation occurs exclusively at the T163 site in its PEST region. GCDA-induced Mcl-1 phosphorylation at T163 enhances the half-life of Mcl-1. Treatment of HepG2 cells with GCDA facilitates Mcl-1 dissociation from Mule (a physiological Mcl-1 ubiquitin E3 ligase). Specific depletion of Mcl-1 from HepG2 cells by RNA interference increases sensitivity of HepG2 cells to chemotherapeutic drugs (<it>i.e</it>. cisplatin and irinotecan). In addition to activation of the ERK/Mcl-1 survival pathway, GCDA can also induce dose-dependent apurinic/apyrimidinic (AP) sites of DNA lesions, which may partially neutralize its survival activity.</p> <p>Conclusion</p> <p>Our findings suggest that bile salt may function as a survival agonist and/or potential carcinogen in the development of HCC. Molecular approaches that inactivate Mcl-1 by blocking its T163 phosphorylation may represent new strategies for treatment of HCC.</p

Cationic Polybutyl Cyanoacrylate Nanoparticles for DNA Delivery

To enhance the intracellular delivery potential of plasmid DNA using nonviral vectors, we used polybutyl cyanoacrylate (PBCA) and chitosan to prepare PBCA nanoparticles (NPs) by emulsion polymerization and prepared NP/DNA complexes through the complex coacervation of nanoparticles with the DNA. The object of our work is to evaluate the characterization and transfection efficiency of PBCA-NPs. The NPs have a zeta potential of 25.53 mV at pH 7.4 and size about 200 nm. Electrophoretic analysis suggested that the NPs with positive charges could protect the DNA from nuclease degradation and cell viability assay showed that the NPs exhibit a low cytotoxicity to human hepatocellular carcinoma (HepG2) cells. Qualitative and quantitative analysis of transfection in HepG2 cells by the nanoparticles carrying plasmid DNA encoding for enhanced green fluorescent protein (EGFP-N1) was done by digital fluorescence imaging microscopy system and fluorescence-activated cell sorting (FACS). Qualitative results showed highly efficient expression of GFP that remained stable for up to 96 hours. Quantitative results from FACS showed that PBCA-NPs were significantly more effective in transfecting HepG2 cells after 72 hours postincubation. The results of this study suggested that PBCA-NPs have favorable properties for nonviral delivery

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes.

BackgroundRupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.MethodsIn this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.Results38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.ConclusionWe identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture

Effects of Spermidine on Gut Microbiota Modulation in Experimental Abdominal Aortic Aneurysm Mice

Accumulating evidence in recent years has demonstrated the important role of gut microbiota in maintaining cardiovascular function. However, their functions in abdominal aortic aneurysm (AAA) are largely unexplored. In this study, we established a porcine pancreatic elastase-infused experimental AAA mouse model and explored gut microbiota modulation using 16S rDNA sequencing. Here, we found that a significant alteration to gut microbiota composition and function occurred in AAA. The functional change in the gut microbiome revealed dysregulated biosynthesis metabolism and transport of spermidine in AAA. Furthermore, exogenous spermidine was administrated via drinking water and attenuated the progression of experimental AAA disease, which supports our recent study that spermidine alleviates systemic inflammation and AAA. These effects were associated with remitted gut microbiota dysbiosis and metabolism in AAA progression as demonstrated by 16S rDNA gene analysis. In addition, several bacterial florae, such as Bacteroides, Parabacteroides and Prevotella, were identified to be associated with the progression of AAA. Our results uncovered altered gut microbial profiles in AAA and highlighted the potential therapeutic use of spermidine in the treatment of gut microbiota dysbiosis and AAA

Drug target genes among DEMGs (Common targets in bold).

Drug target genes among DEMGs (Common targets in bold).</p

WGCNA on AAA conditions.

(A) Based on the full gene expression of the AAA case, average linkage hierarchical clustering analysis revealed no outliers. (B) and (C) Soft-thresholding power analysis was used to obtain the scale-free fit index of network topology, and the optimum soft threshold is 10. (D) Hierarchical cluster analysis was conducted to detect co-expression clusters with corresponding color assignments. Each color represents a module in the constructed gene co-expression network. (E) Calculated correlation coefficients between the modules and subgroups. The magnitude of the correlation is indicated by the shade of the color. (F) Significant correlation existed in the module membership (MM) and gene significance (GS) of the turquoise module.</p

Flowchart of this study.

DEMGs, differentially expressed mechanosensitive genes; RAW, Regional Aortic Weakness; SVM, support vector machine; ICS, inflammatory composite score.</p

Construction of Mechanical sensitivity score and nomogram.

(A) Venn diagram showing overlapping of three gene sets to obtain the signature genes. (B) ROC curves for Triple cross-validation show the strong performance of linear SVM (AAA samples in the merged dataset). (C) C1 had a higher mechanical sensitivity score than C2. (D) Use of hub gene shared with signature gene for the next step of analysis. (E) Construction of clinical diagnostic nomogram based on expression of CAV1, GJA1, and TAGLN. (F) Calibration curve showed the validity of the nomogram (P > 0.05).</p

Enrichment analysis of DEMGs and validation and interaction analysis of hub genes.

(A, B) GO and KEGG enrichment analysis of DEMGs. € The Venn diagram shows the overlap of results generated by four different algorithms to obtain hub genes. (D) ROC curves for Triple cross-validation show the strong performance of the ‘rbf’ SVM model in GSE98278 (large- and intermediate-sized AAA). (E) Representative gene relationship network diagram of hub genes. Connections with a correlation of less than 0.3 are not shown. The shade of color indicates the absolute value of the correlation coefficient. (F) The transcription factor SRF regulates three hub genes.</p