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      Looking beyond GWAS: allele-specific transcription factor binding drives the association of GALNT2 to HDL-C plasma levels

      research-article
      , , ,
      Lipids in Health and Disease
      BioMed Central
      AS-SNPs, GWAS, HDL-C, GALNT2

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          Abstract

          Background

          Plasma levels of high-density lipoprotein cholesterol (HDL-C) have been associated to cardiovascular disease. The high heritability of HDL-C plasma levels has been an incentive for several genome wide association studies (GWASs) which identified, among others, variants in the first intron of the GALNT2 gene strongly associated to HDL-C levels. However, the lead GWAS SNP associated to HDL-C levels in this genomic region, rs4846914, is located outside of transcription factor (TF) binding sites defined by chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) experiments in the ENCODE project and is therefore unlikely to be functional. In this study we apply a bioinformatics approach which rely on the premise that ChIP-seq reads can identify allele specific binding of a TF at cell specific regulatory elements harboring allele specific SNPs (AS-SNPs). EMSA and luciferase assays were used to validate the allele specific binding and to test the enhancer activity of the regulatory element harboring the AS-SNP rs4846913 as well as the neighboring rs2144300 which are in high LD with rs4846914.

          Findings

          Using luciferase assays we found that rs4846913 and the neighboring rs2144300 displayed allele specific enhancer activity. We propose that an inhibitor binds preferentially to the rs4846913-C allele with an inhibitory boost from the synergistic binding of other TFs at the neighboring SNP rs2144300. These events influence the transcription level of GALNT2.

          Conclusions

          The results suggest that rs4846913 and rs2144300 drive the association to HDL-C plasma levels through an inhibitory regulation of GALNT2 rather than the reported lead GWAS SNP rs4846914.

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          Most cited references7

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          Newly identified loci that influence lipid concentrations and risk of coronary artery disease.

          To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
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            Control of mucin-type O-glycosylation: a classification of the polypeptide GalNAc-transferase gene family.

            Glycosylation of proteins is an essential process in all eukaryotes and a great diversity in types of protein glycosylation exists in animals, plants and microorganisms. Mucin-type O-glycosylation, consisting of glycans attached via O-linked N-acetylgalactosamine (GalNAc) to serine and threonine residues, is one of the most abundant forms of protein glycosylation in animals. Although most protein glycosylation is controlled by one or two genes encoding the enzymes responsible for the initiation of glycosylation, i.e. the step where the first glycan is attached to the relevant amino acid residue in the protein, mucin-type O-glycosylation is controlled by a large family of up to 20 homologous genes encoding UDP-GalNAc:polypeptide GalNAc-transferases (GalNAc-Ts) (EC 2.4.1.41). Therefore, mucin-type O-glycosylation has the greatest potential for differential regulation in cells and tissues. The GalNAc-T family is the largest glycosyltransferase enzyme family covering a single known glycosidic linkage and it is highly conserved throughout animal evolution, although absent in bacteria, yeast and plants. Emerging studies have shown that the large number of genes (GALNTs) in the GalNAc-T family do not provide full functional redundancy and single GalNAc-T genes have been shown to be important in both animals and human. Here, we present an overview of the GalNAc-T gene family in animals and propose a classification of the genes into subfamilies, which appear to be conserved in evolution structurally as well as functionally.
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              SLiCE: a novel bacterial cell extract-based DNA cloning method

              We describe a novel cloning method termed SLiCE (Seamless Ligation Cloning Extract) that utilizes easy to generate bacterial cell extracts to assemble multiple DNA fragments into recombinant DNA molecules in a single in vitro recombination reaction. SLiCE overcomes the sequence limitations of traditional cloning methods, facilitates seamless cloning by recombining short end homologies (≥15 bp) with or without flanking heterologous sequences and provides an effective strategy for directional subcloning of DNA fragments from Bacteria Artificial Chromosomes (BACs) or other sources. SLiCE is highly cost effective as a number of standard laboratory bacterial strains can serve as sources for SLiCE extract. In addition, the cloning efficiencies and capabilities of these strains can be greatly improved by simple genetic modifications. As an example, we modified the DH10B Escherichia coli strain to express an optimized λ prophage Red recombination system. This strain, termed PPY, facilitates SLiCE with very high efficiencies and demonstrates the versatility of the method.
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                Author and article information

                Contributors
                marco.cavalli@igp.uu.se
                claes.wadelius@igp.uu.se
                Journal
                Lipids Health Dis
                Lipids Health Dis
                Lipids in Health and Disease
                BioMed Central (London )
                1476-511X
                27 January 2016
                27 January 2016
                2016
                : 15
                : 18
                Affiliations
                Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
                Author information
                http://orcid.org/0000-0003-1143-1431
                Article
                183
                10.1186/s12944-016-0183-x
                4728761
                26817450
                22babff5-f2ee-49cc-92e1-17bd83f810f6
                © Cavalli et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 3 July 2015
                : 15 January 2016
                Categories
                Short Report
                Custom metadata
                © The Author(s) 2016

                Biochemistry
                as-snps,gwas,hdl-c,galnt2
                Biochemistry
                as-snps, gwas, hdl-c, galnt2

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