Studies into the role of LRP1 (low-density lipoprotein receptor-related protein 1) in human lipid metabolism are scarce. Although it is known that a common variant in LRP1 (rs116133520) is significantly associated with HDL-C (high-density lipoprotein cholesterol), the mechanism underlying this observation is unclear. In this study, we set out to study the functional effects of 2 rare LRP1 variants identified in subjects with extremely low HDL-C levels.

GALNT2, encoding polypeptide N-acetylgalactosaminyltransferase 2 (GalNAc-T2), was initially discovered as a regulator of high-density lipoprotein metabolism. GalNAc-T2 is known to exert these effects through post-translational modification, i.e., O-linked glycosylation of secreted proteins with established roles in plasma lipid metabolism. It has recently become clear that loss of GALNT2 in rodents, cattle, nonhuman primates, and humans should be regarded as a novel congenital disorder of glycosylation that affects development and body weight. The role of GALNT2 in metabolic abnormalities other than plasma lipids, including insulin sensitivity and energy homeostasis, is poorly understood.

The angiopoietin-like protein ANGPTL8 (A8) is one of 3 ANGPTLs (A8, A3, A4) that coordinate changes in triglyceride (TG) delivery to tissues by inhibiting lipoprotein lipase (LPL), an enzyme that hydrolyzes TG. Previously we showed that A8, which is expressed in liver and adipose tissue, is required to redirect dietary TG from oxidative to storage tissues following food intake. Here we show that A8 from liver and adipose tissue have different roles in this process. Mice lacking hepatic A8 have no circulating A8, high intravascular LPL activity, low plasma TG levels, and evidence of decreased delivery of dietary lipids to adipose tissue. In contrast, mice lacking A8 in adipose tissue have higher postprandial TG levels and similar intravascular LPL activity and plasma A8 levels and higher levels of plasma TG. Expression of A8, together with A4, in cultured cells reduced A4 secretion and A4-mediated LPL inhibition. Thus, hepatic A8 (with A3) acts in an endocrine fashion to inhibit intravascular LPL in oxidative tissues, whereas A8 in adipose tissue enhances LPL activity by autocrine/paracrine inhibition of A4. These combined actions of A8 ensure that TG stores are rapidly replenished and sufficient energy is available until the next meal.

ANGPTL8 (A8) plays a key role in determining the tissue fate of circulating triglycerides (TGs). Plasma A8 levels are associated with several parameters of glucose and TG metabolism, but the causality of these relationships and the contribution of genetic variants to differences in A8 levels have not been explored.

Genome-wide association studies have confirmed the involvement of non-coding angiopoietin-like 3 (ANGPTL3) gene variants with coronary artery disease, levels of low-density lipoprotein cholesterol (LDL-C), triglycerides and ANGPTL3 mRNA transcript. Extensive linkage disequilibrium at the locus, however, has hindered efforts to identify the potential functional variants. Using regulatory annotations from ENCODE, combined with functional in vivo assays such as allele-specific formaldehyde-assisted isolation of regulatory elements, statistical approaches including eQTL/lipid colocalisation, and traditional in vitro methodologies including electrophoretic mobility shift assay and luciferase reporter assays, variants affecting the ANGPTL3 regulome were examined. From 253 variants associated with ANGPTL3 mRNA expression, and/or lipid traits, 46 were located within liver regulatory elements and potentially functional. One variant, rs10889352, demonstrated allele-specific effects on DNA-protein interactions, reporter gene expression and chromatin accessibility, in line with effects on LDL-C levels and expression of ANGPTL3 mRNA. The ANGPTL3 gene lies within DOCK7, although the variant is within non-coding regions outside of ANGPTL3, within DOCK7, suggesting complex long-range regulatory effects on gene expression. This study illustrates the power of combining multiple genome-wide datasets with laboratory data to localise functional non-coding variation and provides a model for analysis of regulatory variants from GWAS.