Hepatocyte growth factor is associated with greater risk of extracoronary calcification: results from the multiethnic study of atherosclerosis

Hepatocyte growth factor as indicator for subclinical atherosclerosis

Background: Hepatocyte growth factor (HGF) is a pleiotropic cytokine mainly produced by mesenchymal cells. After endothelial damage by oxidized low-density lipoprotein (LDL), HGF is produced and released into the circulation in response. Due to this mechanism HGF has been proposed as possible clinical biomarker for clinical as well as subclinical atherosclerosis. Patients and methods: The conducted study is an observational, single centre, cohort study, including 171 patients with at least one cardiovascular risk factor or already established cardiovascular disease (CVD). Each patient underwent 3D plaque volumetry of the carotid and femoral arteries as well as physical examination and record of the medical history. Additionally, plasma HGF and further laboratory parameters like high sensitivity C-reactive protein and LDL-cholesterol were determined. Results: 169 patients were available for statistical analysis. In bivariate correlation, HGF showed a highly significant correlation with total plaque volume (TPV, r=0.48; p<0.001). In receiver operating characteristic (ROC) analysis for high TPV, HGF showed an area under the curve (AUC) of 0.68 (CI 95%: 0.59-0.77, p<0.001) with a sensitivity of 78% and a specificity of 52% to predict high TPV at a cut-off of 959 ng/ml. In the ROC-analysis for the presence of CVD, HGF demonstrated an AUC of 0.65 (95% CI 0.55-0.73; p=0.01) with a sensitivity of 77% and a specificity of 52%. Conclusions: Higher plasma levels of HGF are associated with higher atherosclerotic plaque volume as measured by 3D-ultrasound.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.