Dataset Information

Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population.

ABSTRACT: The existence of genetic traits might explain the susceptibility to develop hypercholesterolemia and the inter-individual differences in statin response. This study was performed to evaluate whether individuals' polymorphisms in HMG-CoA and KIF6 genes are independently associated with hypercholesterolemia, other lipid-associated traits, and statin response in unselected individuals enrolled in the Brisighella heart study (Survey 2012). A total of 1622 individuals, of which 183 under statin medication, were genotyped for a total of five polymorphisms (KIF6 rs20455, rs9471077, rs9462535; HMG-CoA rs3761740, rs3846662). The relationships between the five loci and clinical characteristics were analyzed. The principal basic parameters calculated on 12 h fasting blood included total cholesterol (TC), High Density Lipoprotein Cholesterol (HDL-C), Low-Density Lipoprotein Cholesterol (LDL-C), and triglycerides (TG). Hypercholesterolemia was defined as a TC >200 mg/dL or use of lipid-lowering medication. 965 individuals were characterized by hypercholesterolemia; these subjects were significantly older (p < 0.001), with body mass index (BMI) and waist circumference significantly higher (p < 0.001) compared to the others. HMG-CoA rs3846662 GG genotype was significantly over-represented in the hypercholesterolemic group (p = 0.030). HMG-CoA rs3846662 genotype was associated with the level of TC and LDL-C. Furthermore, in the same subset of untreated subjects, we observed a significant correlation between the KIF6 rs20455 and HDL-C. KIF6 variants were associated with a significantly lower (rs20455) or higher (rs9471077 and rs9462535) risk of obesity, in males only. No association between responsiveness to statins and the polymorphisms under investigation were observed. Our results showed associations between HMG-CoA rs3846662 and KIF6 rs20455 and lipid phenotypes, which may have an influence on dyslipidemia-related events. Moreover, this represents the first study implicating KIF6 variants with obesity in men, and point to the possible involvement of this genetic locus in the known gender-related differences in coronary artery disease.

SUBMITTER: Angelini S

PROVIDER: S-EPMC5795999 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population.

Angelini Sabrina S Rosticci Martina M Massimo Gianmichele G Musti Muriel M Ravegnini Gloria G Consolini Nicola N Sammarini Giulia G D'Addato Sergio S Rizzoli Elisabetta E Botbayev Dauren D Borghi Claudio C Cantelli-Forti Giorgio G Cicero Arrigo F AF Hrelia Patrizia P

International journal of molecular sciences 20171224 1

The existence of genetic traits might explain the susceptibility to develop hypercholesterolemia and the inter-individual differences in statin response. This study was performed to evaluate whether individuals' polymorphisms in <i>HMG-CoA</i> and <i>KIF6</i> genes are independently associated with hypercholesterolemia, other lipid-associated traits, and statin response in unselected individuals enrolled in the Brisighella heart study (Survey 2012). A total of 1622 individuals, of which 183 unde ...[more]

PMID: 29295555

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Project description:HMG-CoA reductase (HMGR) is the target of statins, cholesterol-lowering drugs prescribed to millions of patients worldwide. More recent research indicates that HMGR could be a useful target in the development of antimicrobial agents. Over the last seven decades, researchers have proposed a series of increasingly complex reaction mechanisms for this biomedically important enzyme. The maturation of the mechanistic proposals for HMGR have paralleled advances in a diverse set of research areas, such as molecular biology and computational chemistry. Thus, the development of the HMGR mechanism provides a useful case study for following the advances in state-of-the-art methods in enzyme mechanism research. Similarly, the questions raised by these mechanism proposals reflect the limitations of the methods used to develop them. The mechanism of HMGR, a four-electron oxidoreductase, is unique and far more complex than originally thought. The reaction contains multiple chemical steps, coupled to large-scale domain motions of the homodimeric enzyme. The first proposals for the HMGR mechanism were based on kinetic and labeling experiments, drawing analogies to the mechanism of known dehydrogenases. Advances in molecular biology and bioinformatics enabled researchers to use site-directed mutagenesis experiments and protein sequencing to identify catalytically important glutamate, aspartate, and histidine residues. These studies, in turn, have generated new and more complicated mechanistic proposals. With the development of protein crystallography, researchers solved HMGR crystal structures to reveal an unexpected lysine residue at the center of the active site. The many crystal structures of HMGR led to increasingly complex mechanistic proposals, but the inherent limitations of the protein crystallography left a number of questions unresolved. For example, the protonation state of the glutamate residue within the active site cannot be clearly determined from the crystal structure. The differing protonation state of this residue leads to different proposed mechanisms for the enzyme. As computational analysis of large biomolecules has become more feasible, the application of methods such as hybrid quantum mechanics/molecular mechanics (QM/MM) calculations to the HMGR mechanism have led to the most detailed mechanistic proposal yet. As these methodologies continue to improve, they prove to be very powerful for the study of enzyme mechanisms in conjunction with protein crystallography. Nevertheless, even the most current mechanistic proposal for HMGR remains incomplete due to limitations of the current computational methodologies. Thus, HMGR serves as a model for how the combination of increasingly sophisticated experimental and computational methods can elucidate very complex enzyme mechanisms.

Dataset Information

Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population.

Publications

Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population.

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