Identification of a novel LMF1 nonsense mutation responsible for severe hypertriglyceridemia by targeted next-generation sequencing.

BACKGROUND: Severe hypertriglyceridemia (HTG) may result from mutations in genes affecting the intravascular lipolysis of triglyceride (TG)-rich lipoproteins. OBJECTIVE: The aim of this study was to develop a targeted next-generation sequencing panel for the molecular diagnosis of disorders characterized by severe HTG. METHODS: We developed a targeted customized panel for next-generation sequencing Ion Torrent Personal Genome Machine to capture the coding exons and intron/exon boundaries of 18 genes affecting the main pathways of TG synthesis and metabolism. We sequenced 11 samples of patients with severe HTG (TG>885 mg/dL-10 mmol/L): 4 positive controls in whom pathogenic mutations had previously been identified by Sanger sequencing and 7 patients in whom the molecular defect was still unknown. RESULTS: The customized panel was accurate, and it allowed to confirm genetic variants previously identified in all positive controls with primary severe HTG. Only 1 patient of 7 with HTG was found to be carrier of a homozygous pathogenic mutation of the third novel mutation of LMF1 gene (c.1380C>G-p.Y460X). The clinical and molecular familial cascade screening allowed the identification of 2 additional affected siblings and 7 heterozygous carriers of the mutation. CONCLUSIONS: We showed that our targeted resequencing approach for genetic diagnosis of severe HTG appears to be accurate, less time consuming, and more economical compared with traditional Sanger resequencing. The identification of pathogenic mutations in candidate genes remains challenging and clinical resequencing should mainly intended for patients with strong clinical criteria for monogenic severe HTG.

Myristic acid is associated to low plasma HDL cholesterol levels in a Mediterranean population and increases HDL catabolism by enhancing HDL particles trapping to cell surface proteoglycans in a liver hepatoma cell model.

BACKGROUND: HDL-C plasma levels are modulated by dietary fatty acid (FA), but studies investigating dietary supplementation in FA gave contrasting results. Saturated FA increased HDL-C levels only in some studies. Mono-unsaturated FA exerted a slight effect while poly-unsaturated FA mostly increased plasma HDL-C. AIMS: This study presents two aims: i) to investigate the relationship between HDL-C levels and plasma FA composition in a Sicilian population following a "Mediterranean diet", ii) to investigate if FA that resulted correlated with plasma HDL-C levels in the population study and/or very abundant in the plasma were able to affect HDL catabolism in an "in vitro" model of cultured hepatoma cells (HepG2). RESULTS: plasma HDL-C levels in the population correlated negatively with myristic acid (C14:0, ß = -0.24, p < 0.01), oleic acid (C18:1n9, ß = -0.22, p < 0.01) and cis-11-Eicosenoic (C20:1n9, ß = -0.19, p = 0.01) and positively with palmitoleic acid (C16:1, ß = +0.19, p = 0.03). HepG2 cells were conditioned with FA before evaluating HDL binding kinetics, and only C14:0 increased HDL binding by a non-saturable pathway. After removal of heparan sulphate proteoglycans (HSPG) by heparinases HDL binding dropped by 29% only in C14:0 conditioned cells (p < 0.05). C14:0 showed also the highest internalization of HDL-derived cholesteryl esters (CE, +32% p = 0.01 vs. non-conditioned cells). CONCLUSIONS: C14:0 was correlated with decreased plasma HDL-C levels in a Mediterranean population. C14:0 might reduce HDL-C levels by increasing HDL trapping to cell surface HSPG and CE stripping from bound HDL. Other mechanisms are to be investigated to explain the effects of other FA on HDL metabolism.

FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease.

Lipidomic analysis is able to measure simultaneously thousands of compounds belonging to a few lipid classes. In each lipid class, compounds differ only by the acyl radical, ranging between C10:0 (capric acid) and C24:0 (lignoceric acid). Although some metabolites have a peculiar pathological role, more often compounds belonging to a single lipid class exert the same biological effect. Here, we present a lipidomics workflow that extracts the tandem mass spectrometry data from individual files and uses them to group compounds into structurally homogeneous clusters by chemical structure hierarchical clustering analysis (CHCA). The case-to-control peak area ratios of the metabolites are then analyzed within clusters. We created two freely available applications to assist the workflow: FragClust to generate the tables to be subjected to CHCA, and TestClust to perform statistical analysis on clustered data. We used the lipidomics data from the plasma of Alzheimer's disease (AD) patients in comparison with healthy controls to test the workflow. To date, the search for plasma biomarkers in AD has not provided reliable results. This article shows that the workflow is helpful to understand the behavior of whole lipid classes in plasma of AD patients.