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Sökning: WFRF:(Neville Matt) > (2015)

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1.
  • Amisten, Stefan, et al. (författare)
  • An atlas of G-protein coupled receptor expression and function in human subcutaneous adipose tissue.
  • 2015
  • Ingår i: Pharmacology and Therapeutics. - : Elsevier. - 0163-7258. ; 146:Sep 19, s. 61-93
  • Forskningsöversikt (refereegranskat)abstract
    • G-protein coupled receptors (GPCRs) are involved in the regulation of adipose tissue function, but the total number of GPCRs expressed by human subcutaneous adipose tissue, as well as their function and interactions with drugs, is poorly understood. We have constructed an atlas of all GPCRs expressed by human subcutaneous adipose tissue: the 'adipose tissue GPCRome', to support the exploration of novel control nodes in metabolic and endocrine functions. This atlas describes how adipose tissue GPCRs regulate lipolysis, insulin resistance and adiponectin and leptin secretion. We also discuss how adipose tissue GPCRs interact with their endogenous ligands and with GPCR-targeting drugs, with a focus on how drug/receptor interactions may affect lipolysis, and present a model predicting how GPCRs with unknown effects on lipolysis might modulate cAMP-regulated lipolysis. Subcutaneous adipose tissue expresses 163 GPCRs, a majority of which have unknown effects on lipolysis, insulin resistance and adiponectin and leptin secretion. These GPCRs are activated by 180 different endogenous ligands, and are the targets of a large number of clinically used drugs. We identified 119 drugs, acting on 23 GPCRs, that are predicted to stimulate lipolysis and 173 drugs, acting on 25 GPCRs, that are predicted to inhibit lipolysis. This atlas highlights knowledge gaps in the current understanding of adipose tissue GPCR function, and identifies GPCR/ligand/drug interactions that might affect lipolysis, which is important for understanding and predicting metabolic side effects of drugs. This approach may aid in the design of new, safer therapeutic agents, with fewer undesired effects on lipid homeostasis.
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2.
  • Mahajan, Anubha, et al. (författare)
  • Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus.
  • 2015
  • Ingår i: PLoS Genetics. - : Public Library of Science. - 1553-7404 .- 1553-7390. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
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