Multilocus genetic determinants of LDL particle size in coronary artery disease families.

Recent interest in atherosclerosis has focused on the genetic determinants of low-density lipoprotein (LDL) particle size, because of (i) the association of small dense LDL particles with a three-fold increased risk for coronary artery disease (CAD) and (ii) the recent report of linkage of the trait to the LDL receptor (chromosome 19). By utilizing nonparametric quantitative sib-pair and relative-pair analysis methods in CAD families, we tested for linkage of a gene or genes controlling LDL particle sizes with the genetic loci for the major apolipoproteins and enzymes participating in lipoprotein metabolism. We confirmed evidence for linkage to the LDL receptor locus (P=.008). For six candidate gene loci, including apolipoprotein(apo)B, apoAII, apo(a), apoE-CI-CII, lipoprotein lipase, and high-density lipoprotein-binding protein, no evidence for linkage was observed by sib-pair linkage analyses (P values ranged from .24 to .81). However, in addition, we did find tentative evidence for linkage with the apoAI-CIII-AIV locus (chromosome 11) (P=.06) and significant evidence for linkage of the cholesteryl ester transfer protein locus (chromosome 16) (P=.01) and the manganese superoxide dismutase locus (chromosome 6) (P=.001), thus indicating multilocus determination of this atherogenic trait.     

Effects of Brefeldin A on Galactosphingolipid Synthesis in an Immortalized Schwann Cell Line: Evidence for Different Intracellular Locations of Galactosylceramide Sulfotransferase and Ceramide Galactosyltransferase Activities

Abstract: Brefeldin A (BFA) has been used extensively to study the intracellular transport and processing of proteins and sphingolipids because of its dramatic alteration of the structural and functional organization of the Golgi. We have examined the effect of BFA on the synthesis of galactosylceramide sulfate (SGalCer) and its immediate precursor galactosylceramide (GalCer) in an immortalized Schwann cell line (S16) to determine the intracellular sites of synthesis of these two related glycolipids. During a 6-h labeling period, a dose-dependent inhibition of [³⁵S]sulfate incorporation into SGalCer was observed with 95% inhibition occurring at 0.5 µg/ml BFA. Labeling of newly synthesized galactosphingolipids with [³H]-palmitic acid for 6 h in the presence of BFA resulted in increased incorporation of label into GalCer containing nonhydroxy fatty acids (NFA-GalCer) to 162% of control values, whereas labeling of GalCer containing 2-hydroxy fatty acids (HFA-GalCer) was reduced to 63% of control. After 24 h, these values were at 366 and 91%, respectively. These results indicate that at least some of the HFA-GalCer was initially synthesized at a location distal to the BFA block and separate from the site of NFA-GalCer synthesis. Examination of [³H]palmitic acid incorporation into free ceramides showed an increase of 133 and 161% for hydroxy and nonhydroxy fatty acid ceramides, respectively, in cells treated for 6 h with BFA in comparison with levels found in untreated control cells, indicating that BFA did not block fatty acid 2-hydroxylation or the formation of HFA ceramide. Incorporation of [³H]palmitic acid into glucosylceramide and GM3 was increased over control levels whereas labeling of GM2 was inhibited, consistent with what has been reported previously for the effect of BFA on these glycolipids in other cell types. These results suggest that there are at least two separate intracellular sites for the galactosylation of HFA and NFA ceramide, respectively, which can be distinguished by their sensitivity to BFA. Our results also indicate that the site of GalCer sulfation is not redistributed to the endoplasmic reticulum in the presence of BFA and therefore may be localized to the distal Golgi or trans-Golgi network.     

Lipid Binding to Amyloid β-Peptide Aggregates: Preferential Binding of Cholesterol as Compared with Phosphatidylcholine and Fatty Acids

Abstract: Amyloid β-peptide (Aβ) aggregates are one of the key neuropathological characteristics of Alzheimer's disease. Aβ belongs to a group of proteins that aggregate and form β-sheets, and some of these proteins bind cholesterol and other lipids. The purpose of the experiments reported here was to determine if cholesterol, fatty acids, and phosphatidylcholine (PC) would bind to Aβ_1–40 and if such binding would be dependent on aggregation of Aβ_1–40. Lipid binding was determined using fluorescent-labeled lipids. Incubation of Aβ_1–40 for 0, 1, 3, 6, 21, and 24 h resulted in aggregation of the peptide with formation of dimers, trimers (1–24 h), and polymers (6–24 h) as determined by sodium dodecyl sulfate-gel electrophoresis. No change in the fluorescence of the lipids was observed when lipids were added to Aβ_1–40 that had been incubated for 0, 1, or 3 h. However, the fluorescence intensities of cholesterol, saturated fatty acids, and PC were significantly increased (p < 0.0001) when added to Aβ_1–40 that had been incubated for 6, 21, and 24 h in which Aβ_1–40 polymers were detected. The binding affinity of cholesterol to Aβ_1–40 polymers (K_D of 3.24 ± 0.315 × 10^?9M) was markedly higher as compared with the other lipids (stearic acid, 9.42 ± 0.41 × 10^?8M; PC, 7.07 ± 0.12 × 10^?7M). The results of this study indicate that Aβ_1–40 polymers bind lipids and have a higher affinity for cholesterol than PC or saturated fatty acids. Aggregated Aβ_1–40 may affect lipid transport between cells or remove specific lipids from membranes, and such effects could contribute to neuronal dysfunction.     

Integrated Methods to Solve the Biological Basis of Common Diseases

Biological variations influence population variations of many common traits. Identification of the biological basis of many common diseases has been particularly difficult, but new reagents and analytical tools will greatly facilitate this process. The goal of this review is to discuss how to identify the biological basis of common traits by using mouse models. No single method will work for all traits. Understanding complex problems will require broadly based holistic approaches that use a wide array of tools and resources. A multiplicity of developed methods together provide the tools needed to identify the biological basis of any common trait. These tools, whole-genome linkage maps, maps of expressed genes, and statistical methods, deal with the complexities of multiple loci or correlated traits. This review provides some criteria for making choices about the likely productive approaches at each stage in the process of finding genes that influence common traits.     

Intragenic duplication and divergence in the spectrin superfamily of proteins

Many structural, signaling, and adhesion molecules contain tandemly repeated amino acid motifs. The alpha-actinin/spectrin/dystrophin superfamily of F-actin-crosslinking proteins contains an array of triple alpha-helical motifs (spectrin repeats). We present here the complete sequence of the novel beta-spectrin isoform beta(Heavy)- spectrin (beta H). The sequence of beta H supports the origin of alpha- and beta-spectrins from a common ancestor, and we present a novel model for the origin of the spectrins from a homodimeric actin-crosslinking precursor. The pattern of similarity between the spectrin repeat units indicates that they have evolved by a series of nested, nonuniform duplications. Furthermore, the spectrins and dystrophins clearly have common ancestry, yet the repeat unit is of a different length in each family. Together, these observations suggest a dynamic period of increase in repeat number accompanied by homogenization within each array by concerted evolution. However, today, there is greater similarity of homologous repeats between species than there is across repeats within species, suggesting that concerted evolution ceased some time before the arthropod/vertebrate split. We propose a two-phase model for the evolution of the spectrin repeat arrays in which an initial phase of concerted evolution is subsequently retarded as each new protein becomes constrained to a specific length and the repeats diverge at the DNA level. This evolutionary model has general applicability to the origins of the many other proteins that have tandemly repeated motifs.      

Regulation of rat ovarian cell growth and steroid secretion

A cultured rat ovarian cell line (31 A-F(2)) was used to study the effect of growth factors (epidermal growth factor [EGF] and fibroblast growth factor [FGF]), a survival factor (ovarian growth factor [OGF]), a hormone (insulin), and an iron-binding protein (transferring) on cell proliferation and steroid production under defined culture conditions. EGF and insulin were shown to be mitogenic (half-maximal response at 0.12 nM and 0.11 muM, respectively) for 31A-F(2) cells incubated in serum-free medium. EGF induced up to three doublings in the cell population, whereas insulin induced an average of one cell population doubling. FGF, OGF, and transferrin were found not to have any prominent effect on cell division when incubated individually with 31A-F(2) cells in serum-free medium. However, a combination of EGF, OGF, insulin, and transferrin stimulated cell division to the same approximate extent as cells incubated in the presence of 5 percent fetal calf serum. EGF or insulin did not significantly affect total cell cholesterol levels (relative to cells incubated in serum-free medium) when incubated individually with 31A-F(2) cells. However, cell cholesterol levels were increased by the addition of OGF (250 percent), FGF (370 percent), or a combination of insulin and EGF (320 percent). Progesterone secretion from 31A-F(2) cells was enhanced by EGF (25 percent), FGF (80 percent), and insulin (115 percent). However, the addition of a mitogenic mixture of EGF, OGF, insulin, and transferrin suppressed progesterone secretion 150 percent) below that of control cultures. These studies have permitted us to determine that EGF and insulin are mitogenic factors that are required for the growth of 31A-F(2) cells and that OGF and transferrin are positive cofactors that enhance growth. Also, additional data suggest that cholesterol and progesterone production in 31A-F(2) cells can be regulated by peptide growth factors and the hormone insulin.