Identification and Characterization of Two Novel Compounds: Heterozygous Variants of Lipoprotein Lipase in Two Pedigrees With Type I Hyperlipoproteinemia

Association of HindIII Polymorphism of the Lipoprotein Lipase (LPL) Gene (rs320) and Plasma Metabolic Parameters in a Nigerian Population

Genetic variations in the lipoprotein lipase (LPL) gene including the HindIII polymorphism (rs320) have been reported to modify fat metabolism, adiposity, and body weight. However, little attention has been given to the African population. The present study aimed to investigate the relationship between the rs320 gene polymorphism and a number of metabolic and anthropometric parameters in a sample of the Nigerian population. We recruited 236 participants for the study. The participants were required to sign informed consent forms after which information related to their calorie intake and utilization as well as anthropometric measurements were recorded. Plasma metabolic parameters were subsequently determined using an autoanalyzer. Genotyping for HindIII polymorphism was performed using the PCR-RFLP method. The frequencies (n) of T and G alleles were 0.841 (397) and 0.158 (75), while the frequencies (n) of TT, TG, and GG were 0.691(163), 0.301(71), and 0.01(2), respectively. The population was not in Hardy-Weinberg equilibrium (χ2 = 3.717, df = 1, p = 0.841). The anthropometric parameters, the fasting blood glucose, and low-density lipoprotein cholesterol showed no association with the alleles, while plasma high-density lipoprotein cholesterol and total cholesterol were significantly higher among the G allele carriers. However, triglyceride and total protein were significantly higher among the non-G allele carriers. The LPL HindIII gene polymorphism is associated with changes in plasma lipid profile in a sample of the Nigerian population.

Lipoprotein Lipase: Structure, Function, and Genetic Variation

Biallelic rare pathogenic loss-of-function (LOF) variants in lipoprotein lipase (LPL) cause familial chylomicronemia syndrome (FCS). Heterozygosity for these same variants is associated with a highly variable plasma triglyceride (TG) phenotype ranging from normal to severe hypertriglyceridemia (HTG), with longitudinal variation in phenotype severity seen often in a given carrier. Here, we provide an updated overview of genetic variation in LPL in the context of HTG, with a focus on disease-causing and/or disease-associated variants. We provide a curated list of 300 disease-causing variants discovered in LPL, as well as an exon-by-exon breakdown of the LPL gene and protein, highlighting the impact of variants and the various functional residues of domains of the LPL protein. We also provide a curated list of variants of unknown or uncertain significance, many of which may be upgraded to pathogenic/likely pathogenic classification should an additional case and/or segregation data be reported. Finally, we also review the association between benign/likely benign variants in LPL, many of which are common polymorphisms, and the TG phenotype.

Update of a Genetic Risk Score Predictive of the Plasma Triglyceride Response to an Omega-3 Fatty Acid Supplementation in the FAS Study

A genetic risk score (GRS) predictive of the plasma triglyceride (TG) response to an omega-3 fatty acid (n-3 FA) supplementation has been previously developed in the Fatty Acid Sensor (FAS) Study. Recently, novel single nucleotide polymorphisms (SNPs) interacting with a fish oil supplementation and associated with plasma lipid levels have been identified in the UK Biobank. The aim of this study was to verify whether the addition of SNPs identified in the UK Biobank to the GRS built in the FAS Study improves its capacity to predict the plasma TG response to an n-3 FA supplementation. SNPs interacting with fish oil supplementation in the modulation of plasma lipid levels in the UK Biobank and associated with plasma TG levels have been genotyped in participants of the FAS Study (n = 141). Participants have been supplemented with 5 g fish oil/day for six weeks. Plasma TG concentrations were measured before and after the supplementation. Based on the initial GRS of 31 SNPs (GRS31), we computed three new GRSs by adding new SNPs identified in the UK Biobank: GRS32 (rs55707100), GRS38 (seven new SNPs specifically associated with plasma TG levels), and GRS46 (all 15 new SNPs associated with plasma lipid levels). The initial GRS31 explained 50.1% of the variance in plasma TG levels during the intervention, whereas GRS32, GRS38, and GRS46 explained 49.1%, 45.9%, and 45%, respectively. A significant impact on the probability of being classified as a responder or a nonresponder was found for each of the GRSs analyzed, but none of them outperformed the predictive capacity of GRS31 in any of the metrics analyzed, i.e., accuracy, area under the response operating curve (AUC-ROC), sensitivity, specificity and McFadden's pseudo R². The addition of SNPs identified in the UK Biobank to the initial GRS31 did not significantly improve its capacity to predict the plasma TG response to an n-3 FA supplementation. Thus, GRS31 still remains the most precise tool so far by which to discriminate the individual responsiveness to n-3 FAs. Further studies are needed in the field to increase our knowledge of factors underlying the heterogeneity observed in the metabolic response to an n-3 FA supplementation.

Current Understanding on the Genetic Basis of Key Metabolic Disorders: A Review

Simple Summary

Metabolic disorders (MD) are a challenge to healthcare systems; the emergence of the modern socio-economic system has led to a profound change in lifestyles in terms of dietary habits, exercise regimens, and behavior, all of which complement the genetic factors associated with MD. Diabetes Mellitus and Familial hypercholesterolemia are two of the 14 most widely researched MD, as they pose the greatest challenge to the public healthcare system and have an impact on productivity and the economy. Research findings have led to the development of new therapeutic molecules for the mitigation of MD as well as the invention of experimental strategies, which target the genes themselves via gene editing and RNA interference. Although these approaches may herald the emergence of a new toolbox to treat MD, the current therapeutic approaches still heavily depend on substrate reduction, dietary restrictions based on genetic factors, exercise, and the maintenance of good mental health. The development of orphan drugs for the less common MD such as Krabbe, Farber, Fabry, and Gaucher diseases, remains in its infancy, owing to the lack of investment in research and development, and this has driven the development of personalized therapeutics based on gene silencing and related technologies.

Abstract

Advances in data acquisition via high resolution genomic, transcriptomic, proteomic and metabolomic platforms have driven the discovery of the underlying factors associated with metabolic disorders (MD) and led to interventions that target the underlying genetic causes as well as lifestyle changes and dietary regulation. The review focuses on fourteen of the most widely studied inherited MD, which are familial hypercholesterolemia, Gaucher disease, Hunter syndrome, Krabbe disease, Maple syrup urine disease, Metachromatic leukodystrophy, Mitochondrial encephalopathy lactic acidosis stroke-like episodes (MELAS), Niemann-Pick disease, Phenylketonuria (PKU), Porphyria, Tay-Sachs disease, Wilson’s disease, Familial hypertriglyceridemia (F-HTG) and Galactosemia based on genome wide association studies, epigenetic factors, transcript regulation, post-translational genetic modifications and biomarker discovery through metabolomic studies. We will delve into the current approaches being undertaken to analyze metadata using bioinformatic approaches and the emerging interventions using genome editing platforms as applied to animal models.