Plasma glycomics predict cardiovascular disease in patients with ART-controlled HIV infections

Despite effective control of HIV infection with antiretroviral drugs, individuals with HIV have high incidences of secondary diseases. These sequelae, such as cardiovascular disease (CVD), are poorly understood and represent a major health burden. To date, predictive biomarkers of HIV-associated secondary disease have been elusive, making preventative clinical management essentially impossible. Here, we applied a newly developed and easy to deploy, multitarget, and high-throughput glycomic analysis to banked HIV+ human plasma samples to determine whether the glycome may include biomarkers that predict future HIV-associated cardiovascular events or CVD diagnoses. Using 324 patient samples, we identified a glycomic fingerprint that was predictive of future CVD events but independent of CD4 counts, diabetes, age, and birth sex, suggesting that the plasma glycome may serve as a biomarker for specific HIV-associated sequelae. Our findings constitute the discovery of novel glycan biomarkers that could classify patients with HIV with elevated risk for CVD and reveal the untapped prognostic potential of the plasma glycome in human disease.-Oswald, D. M., Sim, E. S., Baker, C., Farhan, O., Debanne, S. M., Morris, N. J., Rodriguez, B. G., Jones, M. B., Cobb, B. A. Plasma glycomics predict cardiovascular disease in patients with ART-controlled HIV infections.

Heritability and longitudinal outcomes of spelling skills in individuals with histories of early speech and language disorders

This study examined the spelling skills in middle childhood and adolescence in individuals with histories of early childhood speech sound disorders (SSD) with and without language impairment (LI). Youth without such histories were also included (No SSD/LI group). The heritability of spelling skills at each age level was estimated. Children with SSD were classified as SSD-only, SSD with LI but without childhood apraxia of speech (SSD + LI/ No CAS), and CAS and LI (CAS + LI). The SSD-only group did not differ in spelling from the No SSD/LI group, suggesting that SSD-only did not increase risk for poor spelling. The SSD + LI/No CAS and CAS + LI groups had poorer spelling skills than the SSD-only and No SSD/LI groups. Spelling was associated with phonological awareness in the middle childhood and adolescent samples and with rapid automatized naming in the adolescent sample. Heritability of spelling skills was stronger in adolescence than in middle childhood. Differences in the correlates of spelling and in heritability at the two ages suggest developmental changes in the factors contributing to spelling.

Genome-wide association study identifies three novel loci in Fuchs endothelial corneal dystrophy

The structure of the cornea is vital to its transparency, and dystrophies that disrupt corneal organization are highly heritable. To understand the genetic aetiology of Fuchs endothelial corneal dystrophy (FECD), the most prevalent corneal disorder requiring transplantation, we conducted a genome-wide association study (GWAS) on 1,404 FECD cases and 2,564 controls of European ancestry, followed by replication and meta-analysis, for a total of 2,075 cases and 3,342 controls. We identify three novel loci meeting genome-wide significance (P<5 × 10-8): KANK4 rs79742895, LAMC1 rs3768617 and LINC00970/ATP1B1 rs1200114. We also observe an overwhelming effect of the established TCF4 locus. Interestingly, we detect differential sex-specific association at LAMC1, with greater risk in women, and TCF4, with greater risk in men. Combining GWAS results with biological evidence we expand the knowledge of common FECD loci from one to four, and provide a deeper understanding of the underlying pathogenic basis of FECD.

Model-Free Linkage Analysis of a Quantitative Trait

Model-free methods of linkage analysis for quantitative traits are a class of easily implemented, computationally efficient and statistically robust approaches to searching for linkage to a quantitative trait. By "model-free" we refer to methods of linkage analysis that do not fully specify a genetic model (i.e., the causal allele frequency, and penetrance functions). In this chapter we briefly survey the methods that are available, and then we discuss the necessary steps to implement an analysis using the programs GENIBD, SIBPAL and RELPAL in the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) software suite.

Structural Equation Modeling

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees

Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates. The single nucleotide polymorphism (SNP) genotype information from genome-wide association studies (GWAS) data was used for the test of association. The adjustment for hypertension treatment effect was done by the censored regression approach. Two different longitudinal data models, autoregressive model and latent growth curve model, were used to fit the longitudinal BP traits. The test of association for SNP was done using a novel score test within the SEM framework of strum. We found the 10 SNPs within the GWAS suggestive P value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general SEM framework in strum is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data.

strum: an R package for structural modeling of latent variables for general pedigrees

BACKGROUND

Structural equation modeling (SEM) is an extremely general and powerful approach to account for measurement error and causal pathways when analyzing data, and it has been used in wide range of applied sciences. There are many commercial and freely available software packages for SEM. However, it is difficult to use any of the packages to analyze general pedigree data, and SEM packages for genetics are limited in their application.

RESULTS

We present the new R package strum to serve the need of a suitable SEM software tool for genetic analysis. It implements a general framework for SEM within the context of general pedigree data. This context requires specialized considerations such as familial correlations and ascertainment. Our package is an extraordinarily flexible tool capable of modeling genetic association, linkage analysis, polygenic effects, shared environment, and ascertainment combined with confirmatory factor analysis and general SEM. It also provides a convenient tool for model visualization, and integrates tools for simulating pedigree data. The various features of this package are tested through a simulation study to evaluate performance, and our results show that strum is very reliable and robust in terms of the accuracy and coverage of parameter estimates.

CONCLUSIONS

strum is a valuable new tool for genetic analysis. It can be easily used with general pedigree data, incorporating both measurement and structural models, giving it some significant advantages over other software packages. It also includes a built-in approach for handling ascertainment, a helpful integrated tool for genetic data simulation, and built-in tools for model visualization, providing a significant addition to biomedical research.

How meaningful are heritability estimates of liability?

It is commonly acknowledged that estimates of heritability from classical twin studies have many potential shortcomings. Despite this, in the post-GWAS era, these heritability estimates have come to be a continual source of interest and controversy. While the heritability estimates of a quantitative trait are subject to a number of biases, in this article we will argue that the standard statistical approach to estimating the heritability of a binary trait relies on some additional untestable assumptions which, if violated, can lead to badly biased estimates. The ACE liability threshold model assumes at its heart that each individual has an underlying liability or propensity to acquire the binary trait (e.g., disease), and that this unobservable liability is multivariate normally distributed. We investigated a number of different scenarios violating this assumption such as the existence of a single causal diallelic gene and the existence of a dichotomous exposure. For each scenario, we found that substantial asymptotic biases can occur, which no increase in sample size can remove. Asymptotic biases as much as four times larger than the true value were observed, and numerous cases also showed large negative biases. Additionally, regions of low bias occurred for specific parameter combinations. Using simulations, we also investigated the situation where all of the assumptions of the ACE liability model are met. We found that commonly used sample sizes can lead to biased heritability estimates. Thus, even if we are willing to accept the meaningfulness of the liability construct, heritability estimates under the ACE liability threshold model may not accurately reflect the heritability of this construct. The points made in this paper should be kept in mind when considering the meaningfulness of a reported heritability estimate for any specific disease.

A variance component based multi-marker association test using family and unrelated data

Background

Incorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples.

Results

The proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotype scores, which avoids the power loss when the effects are in different directions or have different association strengths. The method is also able to effectively leverage the LD information in a region and it can produce a test statistic with an adaptively estimated number of degrees of freedom. Our method can readily allow for the adjustment of non-genetic contributions to the familial similarity, as well as multiple covariates.

Conclusions

We demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package “fassoc”, which provides a useful tool for data analysis and exploration.

A Parametric Survival Model When a Covariate is Subject to Left-Censoring

PROBLEM STATEMENT

Modeling survival data with a set of covariates usually assumes that the values of the covariates are fully observed. However, in a variety of applications, some values of a covariate may be left-censored due to inadequate instrument sensitivity to quantify the biospecimen. When data are left-censored, the true values are missing but are known to be smaller than the detection limit. The most commonly used ad-hoc method to deal with nondetect values is to substitute the nondetect values by the detection limit. Such ad-hoc analysis of survival data with an explanatory variable subject to left-censoring may provide biased and inefficient estimators of hazard ratios and survivor functions.

METHOD

We consider a parametric proportional hazards model to analyze time-to-event data. We propose a likelihood method for the estimation and inference of model parameters. In this likelihood approach, instead of replacing the nondetect values by the detection limit, we adopt a numerical integration technique to evaluate the observed data likelihood in the presence of a left-censored covariate. Monte Carlo simulations were used to demonstrate various properties of the proposed regression estimators including the consistency and efficiency.

RESULTS

The simulation study shows that the proposed likelihood approach provides approximately unbiased estimators of the model parameters. The proposed method also provides estimators that are more efficient than those obtained under the ad-hoc method. Also, unlike the ad-hoc estimators, the coverage probabilities of the proposed estimators are at their nominal level. Analysis of a large cohort study, genetic and inflammatory marker of sepsis study, shows discernibly different results based on the proposed method.

CONCLUSION

Naive use of detection limit in a parametric survival model may provide biased and inefficient estimators of hazard ratios and survivor functions. The proposed likelihood approach provides approximately unbiased and efficient estimators of hazard ratios and survivor functions.

Power of single- vs. multi-marker tests of association

Current genome-wide association studies still heavily rely on a single-marker strategy, in which each single nucleotide polymorphism (SNP) is tested individually for association with a phenotype. Although methods and software packages that consider multimarker models have become available, they have been slow to become widely adopted and their efficacy in real data analysis is often questioned. Based on conducting extensive simulations, here we endeavor to provide more insights into the performance of simple multimarker association tests as compared to single-marker tests. The results reveal the power advantage as well as disadvantage of the two- vs. the single-marker test. Power differentials depend on the correlation structure among tag SNPs, as well as that between tag SNPs and causal variants. A two-marker test has relatively better performance than single-marker tests when the correlation of the two adjacent markers is high. However, using HapMap data, two-marker tests tended to have a greater chance of being less powerful than single-marker tests, due to constraints on the number of actual possible haplotypes in the HapMap data. Yet, the average power difference was small whenever the one-marker test is more powerful, while there were many situations where the two-marker test can be much more powerful. These findings can be useful to guide analyses of future studies.

Structural equation modeling

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of general pedigrees. Here, we review the theory of SEM for both unrelated and family data, the available software for SEM, and provide an example of SEM analysis.

Model-free linkage analysis of a quantitative trait

Model-free methods of linkage analysis for quantitative traits are a class of easily implemented, computationally efficient, and statistically robust approaches to searching for linkage to a quantitative trait. By "model-free" we refer to methods of linkage analysis that do not fully specify a genetic model (i.e., the causal allele frequency and penetrance functions). In this chapter, we briefly survey the methods that are available, and then we discuss the necessary steps to implement an analysis using the programs GENIBD, SIBPAL, and RELPAL in the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) software suite.

A framework for structural equation models in general pedigrees

BACKGROUND/AIMS

Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed.

METHODS

This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used.

RESULTS

A simulation study shows that our method is computationally feasible, and has good statistical properties.

CONCLUSION

Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.

Single-marker and two-marker association tests for unphased case-control genotype data, with a power comparison

In case-control single nucleotide polymorphism (SNP) data, the allele frequency, Hardy Weinberg Disequilibrium, and linkage disequilibrium (LD) contrast tests are three distinct sources of information about genetic association. While all three tests are typically developed in a retrospective context, we show that prospective logistic regression models may be developed that correspond conceptually to the retrospective tests. This approach provides a flexible framework for conducting a systematic series of association analyses using unphased genotype data and any number of covariates. For a single stage study, two single-marker tests and four two-marker tests are discussed. The true association models are derived and they allow us to understand why a model with only a linear term will generally fit well for a SNP in weak LD with a causal SNP, whatever the disease model, but not for a SNP in high LD with a non-additive disease SNP. We investigate the power of the association tests using real LD parameters from chromosome 11 in the HapMap CEU population data. Among the single-marker tests, the allelic test has on average the most power in the case of an additive disease, but for dominant, recessive, and heterozygote disadvantage diseases, the genotypic test has the most power. Among the four two-marker tests, the Allelic-LD contrast test, which incorporates linear terms for two markers and their interaction term, provides the most reliable power overall for the cases studied. Therefore, our result supports incorporating an interaction term as well as linear terms in multi-marker tests.

Mendelian randomization in family data

The phrase "mendelian randomization" has become associated with the use of genetic polymorphisms to uncover causal relationships between phenotypic variables. The statistical methods useful in mendelian randomization are known as instrumental variable techniques. We present an approach to instrumental variable estimation that is useful in family data and is robust to the use of weak instruments. We illustrate our method to measure the causal influence of low-density lipoprotein on high-density lipoprotein, body mass index, triglycerides, and systolic blood pressure. We use the Framingham Heart Study data as distributed to participants in the Genetics Analysis Workshop 16.

Comparison of univariate and multivariate linkage analysis of traits related to hypertension

Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis.

Modeling the complex gene x environment interplay in the simulated rheumatoid arthritis GAW15 data using latent variable structural equation modeling

Rheumatoid arthritis is a complex disease that appears to involve multiple genetic and environmental factors. Using the Genetic Analysis Workshop 15 simulated rheumatoid arthritis data and the structural equation modeling framework, we tested hypothesized "causal" rheumatoid arthritis model(s) by employing a novel latent gene construct approach that models individual genes as latent variables defined by multiple dense and non-dense single-nucleotide polymorphisms (SNPs). Our approach produced valid latent gene constructs, particularly with dense SNPs, which when coupled with other factors involved in rheumatoid arthritis, were able to generate good fitting models by certain goodness of fit indices. We observed that Gene F, C, DR, sex and smoking were significant predictors of rheumatoid arthritis but Genes A and E were not, which was generally, but not entirely, consistent with how the data were simulated. Our approach holds promise in unravelling complex diseases and improves upon current "one SNP (haplotype)-at-a-time" regression approaches by decreasing the number of statistical tests while minimizing problems with multicolinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity, and resolve multiplicative latent gene interactions. Moreover, because some a priori biological information is needed to form an initial substantive model, our approach may be most appropriate for candidate gene SNP panel applications.

Comparison of affected sibling-pair linkage methods to identify gene x gene interaction in GAW15 simulated data

Non-parametric linkage methods have had limited success in detecting gene by gene interactions. Using affected sibling-pair (ASP) data from all replicates of the simulated data from Problem 3, we assessed the statistical power of three approaches to identify the gene x gene interaction between two loci on different chromosomes. The first method conditioned on linkage at the primary disease susceptibility locus (DR), to find linkage to a simulated effect modifier at Locus A with a mean allele sharing test. The second approach used a regression-based mean test to identify either the presence of interaction between the two loci or linkage to the A locus in the presence of linkage to DR. The third method applied a conditional logistic model designed to test for the presence of interacting loci. The first approach had decreased power over an unconditional linkage analysis, supporting the idea that gene x gene interaction cannot be detected with ASP data. The regression-based mean test and the conditional logistic model had the lowest power to detect gene x gene interaction, possibly because of the complex recoding of the tri-allelic DR locus for use as a covariate. We conclude that the ASP approaches tested have low power to successfully identify the interaction between the DR and A loci despite the large sample size, which may be due to the low prevalence of the high-risk DR genotypes. Additionally, the lack of data on discordant sibships may have decreased the power to identify gene x gene interactions.

Cloning and preliminary characterization of a 121 kDa protein with multiple predicted C2 domains

In the course of characterizing proteins present in a preparation of vesicles from rat adipocytes containing glucose transporters, we examined a protein that migrated at 115 kDa upon SDS gel electrophoresis (designated vp115). Sequences of tryptic peptides were obtained, and from this information the cDNA for rat vp115 was cloned. The cDNA encodes an open reading frame for a protein of 121 kDa. Computer-aided sequence analysis predicted that vp115 has a potential membrane-inserted or membrane-spanning domain toward its amino terminus, followed by five C2 domains. Immunoblotting revealed that vp115 was not actually a component of the glucose transporter-containing vesicles, was most abundant in the plasma membranes and high density microsome fractions of rat adipocytes, and was expressed in all the major rat tissues.

Cloning and characterization of a 22 kDa protein from rat adipocytes: a new member of the reticulon family

In the course of our examination of proteins associated with the GLUT4-containing vesicles of rat adipocytes we have identified a new 22 kDa member of the family of endoplasmic reticulum (ER) proteins known as reticulons. The protein, which we refer to as vp20, was purified from a preparation of GLUT4-containing vesicles of rat adipocytes, and tryptic peptides were micro-sequenced. From this information a cDNA encoding a single open reading frame for a protein of 22 kDa was cloned. This protein is homologous to known members of the reticulon protein family. vp20 has two hydrophobic stretches of about 35 amino acids that could be membrane spanning domains and an ER retention motif at its carboxy-terminus. vp20 was most abundant in the high density microsome fraction of adipocytes, which is the fraction most enriched in ER. Only a small fraction of vp20 was present in the GLUT4 vesicle population, and that fraction appears to be due to ER vesicles that were non-specifically bound to the adsorbent. Analysis of tissue distribution of vp20 in rats revealed that it is concentrated in muscle, fat and the brain.

Sortilin is the major 110-kDa protein in GLUT4 vesicles from adipocytes

Vesicles containing the glucose transporter GLUT4 from rat adipocytes contain a major protein of 110 kDa. We have isolated this protein, obtained the sequences of peptides, and cloned a large portion of its cDNA. This revealed that the protein is sortilin, a novel membrane protein that was cloned in another context from a human source while this work was in progress. Subcellular fractionation of rat and 3T3-L1 adipocytes, together with GLUT4 vesicle isolation, showed that sortilin was primarily located in the low density microsomes in vesicles containing GLUT4. Insulin caused a 1.7-fold increase in the amount of sortilin at the plasma membranes of 3T3-L1 adipocytes, as assessed by cell surface biotinylation. The expression of sortilin in 3T3-L1 cells occurred only upon differentiation. Previous characterization of sortilin has led to the suggestion that it functions to sort lumenal proteins from the trans Golgi. The significance of its insulin-stimulated increase at the cell surface and of its expression upon differentiation will require definitive delineation of its function.

Membrane amine oxidase cloning and identification as a major protein in the adipocyte plasma membrane

A 97-kDa protein present in the glucose transporter (GLUT4 isotype)-containing vesicles from rat adipocytes has been isolated, the sequences of two tryptic peptides were obtained, and on the basis of these its cDNA partially cloned. The 97-kDa protein is almost certainly identical to a major integral glycoprotein of this size in the rat adipocyte plasma membrane, since its predicted N-terminal sequence is the same as that recently determined for this glycoprotein by amino acid sequencing. Moreover, the predicted partial sequence (322 amino acids) of the 97-kDa protein is highly homologous to the corresponding region of a human placental amine oxidase, which was cloned simultaneously and proposed to be a secreted protein. The amino acid sequence of the 97-kDa rat/human amine oxidase indicates that the protein consists of a very short N-terminal cytoplasmic domain followed by a single transmembrane segment and a large extracellular domain containing the catalytic site. Thus this study establishes the 97-kDa rat/human amine oxidase as the first integral membrane amine oxidase to be cloned. The membrane amine oxidase was more abundant in the plasma membranes than the low density microsomes of the adipocyte, and in contrast to some other proteins found in GLUT4 vesicles, it did not redistribute to the plasma membrane in response to treatment of the cells with insulin.

Insulin stimulates cell surface aminopeptidase activity toward vasopressin in adipocytes

We previously discovered that insulin stimulates the marked translocation of a novel membrane aminopeptidase, designated vp165 for vesicle protein of 165 kDa, to the cell surface in adipocytes. To examine the hypothesis that this enzyme acts on peptide hormones, we assessed the relative affinity of the enzyme for 22 peptide hormones by measuring the inhibitory effect of each on the hydrolysis of a fluorogenic substrate, and we directly assayed the cleavage of four of these. Angiotensin III, angiotensin IV, and Lys-bradykinin bound to the enzyme with half-saturation constants between 20 and 600 nM and were cleaved by vp165. Vasopressin bound with lower affinity but at saturation was cleaved more rapidly. Subsequently, the effect of insulin on the rates of cleavage of 125I-labeled vasopressin by intact 3T3-L1 and rat adipocytes was determined. With both cell types, vasopressin cleavage was stimulated approximately threefold. These findings indicate that a physiological role for vp165 may be the processing of peptide hormones and that insulin could enhance the cleavage of extracellular substrates by eliciting the translocation of vp165 to the cell surface.

Streptozotocin-induced diabetes elicits the phosphorylation of hepatocyte Gi2 alpha at the protein kinase C site but not at the protein kinase A-controlled site

Streptozotocin-induced diabetes caused a profound increase in the steady-state level of phosphorylation of the alpha-subunit of the adenylate cyclase inhibitory protein Gi2 in hepatocytes. Unlike hepatocytes from control animals, those from streptozotocin-diabetic animals showed no increase in the phosphorylation of Gi2 alpha in response to a challenge with the protein kinase C activator phorbol myristate acetate. However, a stimulatory effect of 8-bromo-cAMP on Gi2 alpha phosphorylation was evident in hepatocytes from diabetic animals but this was severely reduced compared with that observed in hepatocytes from normal animals. Two-dimensional tryptic phosphopeptide mapping showed that Gi2 alpha in resting hepatocytes from diabetic animals was phosphorylated exclusively at the protein kinase C site (C-site) but no labelling was evident at the protein kinase A-regulated site (AN-site). Treatment of hepatocytes from diabetic animals with phorbol myristate acetate did not change this pattern of labelling. In contrast, challenge of hepatocytes from diabetic animals with 8-bromo-cAMP led to the appearance of a new labelled phosphopeptide that was consistent with labelling at the AN-site. Analysis of the C-site and AN-site phosphopeptides from hepatocytes of diabetic animals treated with 8-bromo-cAMP showed that the increase in labelling of Gi2 alpha caused by this ligand could be attributed almost entirely to labelling at the AN-site. Thus streptozotocin diabetes appears to cause enhanced labelling of hepatocyte Gi2 alpha by exclusively increasing phosphorylation at the C-site. It is suggested that the increased labelling at the C-site reflects an augmentation of the protein kinase C signalling system in hepatocytes from streptozotocin-induced diabetic animals. This may have wide-spread functional consequences for these cells and may result either from an increased protein kinase C activity and/or a reduction in protein phosphatase 1 and/or 2A activity.

Characterization of the insulin-regulated membrane aminopeptidase in 3T3-L1 adipocytes

A novel membrane aminopeptidase has been identified as a major protein in vesicles from rat adipocytes containing the glucose transporter isotype Glut4. In this study we have characterized this aminopeptidase, referred to as vp165, in 3T3-L1 adipocytes. The subcellular distributions of vp165 and Glut4 were determined by immunoisolation of vesicles with antibodies against both proteins, by immunofluorescence, and by subcellular fractionation and immunoblotting. Relative amounts of vp165 at the cell surface in basal and insulin-treated cells were assayed by cell surface biotinylation. These experiments showed that vp165 and Glut4 were entirely colocalized and that vp165 increased markedly at the cell surface in response to insulin, in a way similar to Glut4. When intact cells were assayed with a novel, membrane-impermeant fluorogenic substrate for vp165, we found that insulin stimulated aminopeptidase activity at the cell surface. This observation provides direct evidence for the functional consequence of vp165 translocation.

Insulin inhibits the phosphorylation of alpha-Gi-2 in intact hepatocytes

Challenge of intact hepatocytes with insulin reduced the level of phosphorylated alpha-Gi-2 found under basal (resting) conditions. At maximally effective concentrations of insulin the steady-state labelling of alpha-Gi-2 was reduced by approximately 21%. Insulin achieved this in a time- and dose-dependent fashion, exhibiting an IC50 value of 109 +/- 22 pM. The increased labelling of alpha-Gi-2 seen after challenge of cells with phorbol 12-myristate 13-acetate was also attenuated by insulin. Treatment of hepatocytes with the protein phosphatase inhibitor okadaic acid increased the labelling of alpha-Gi-2 in a fashion which was insensitive to the action of insulin. It is suggested that insulin may reduce the level of phosphorylation of alpha-Gi-2 by stimulating intracellular protein phosphatase activity and that this action may offer a molecular explanation for the ability of insulin to inhibit adenylate cyclase activity in hepatocytes by increasing the level of non-phosphorylated alpha-Gi-2.

Multi-site phosphorylation of the inhibitory guanine nucleotide regulatory protein Gi-2 occurs in intact rat hepatocytes

A phosphorylated form of alpha-Gi-2 (the alpha-subunit of Gi-2), immunoprecipitated from hepatocytes under basal conditions, migrated as a single species of pI approximately 5.7, the labelling of which increased approximately 2-fold in cells challenged with either vasopressin or phorbol 12-myristate 13-acetate (PMA); agents which activate protein kinase C. In contrast, treatment of hepatocytes with 8-bromo-cyclic AMP produced a more acidic species of phosphorylated alpha-Gi-2 having a pI of approximately 5.4 and whose labelling was increased approximately 3-fold. Trypsin digestion of labelled alpha-Gi-2 isolated from hepatocytes under basal conditions identified, on two-dimensional peptide analyses, three positively charged phosphoserine-containing peptides (C1, C2 and C3), with only peptides C1 and C2 being evident upon less extensive digestion with trypsin. These are suggested to reflect a single site of phosphorylation, with proteolysis by trypsin being incomplete, and where C2 is larger than C1, which is larger than C3. An identical pattern of tryptic phosphopeptides was seen in hepatocytes treated with either vasopressin or PMA, although labelling of this group of peptides was increased by approximately 2-fold compared with the basal state. In contrast, treatment of hepatocytes with glucagon, 8-bromo-cyclic AMP or forskolin not only resulted in increased labelling of the 'basal' sites approximately 3-fold, but identified a novel positively charged tryptic phosphoserine-containing peptide (AN). All four tryptic peptides were susceptible to proteolysis by V8 protease. Treatment of labelled alpha-Gi-2 from basal and PMA-treated cells produced a pattern of peptides which was identical with those found when the tryptic phosphopeptide was treated with V8 protease. We tentatively suggest that, on alpha-Gi-2, Ser144 is phosphorylated through the action of protein kinase C and Ser207 is phosphorylated upon elevation of the intracellular concentrations of cyclic AMP.

Regulation of hepatocyte adenylate cyclase by amylin and CGRP: a single receptor displaying apparent negative cooperatively towards CGRP and simple saturation kinetics for amylin, a requirement for phosphodiesterase inhibition to observe elevated hepatocyte cyclic AMP levels and the phosphorylation of Gi-2

Challenge of intact hepatocytes with amylin only succeeded in elevating intracellular cyclic AMP levels and activating phosphorylase in the presence of the cAMP phosphodiesterase inhibitor IBMX. Both amylin and CGRP similarly activated adenylate cyclase, around 5-fold, although approximately 400-fold higher levels of amylin were required to elicit half maximal activation. Amylin activated adenylate cyclase though apparently simple Michaelien kinetics whereas CGRP elicited activation by kinetics indicative of apparent negative co-operativity. Use of the antagonist CGPP(8-37) showed that both CGRP and amylin activated hepatocyte adenylate cyclase through a common receptor by a mnemonical mechanism where it was proposed that the receptor co-existed in interconvertible high and low affinity states for CGRP. It is suggested that this model may serve as a paradigm for G-protein linked receptors in general. Amylin failed to both stimulate inositol phospholipid metabolism in hepatocytes and to elicit the desensitization of glucagon-stimulated adenylate cyclase. Amylin did, however, elicit the phosphorylation of the inhibitory guanine nucleotide regulatory protein Gi-2 in hepatocytes and prevented the action of insulin in reducing the level of phosphorylation of this G-protein.

A mnemonical or negative-co-operativity model for the activation of adenylate cyclase by a common G-protein-coupled calcitonin-gene-related neuropeptide (CGRP)/amylin receptor

Both amylin and calcitonin-gene-related neuropeptide (CGRP) activated adenylate cyclase activity in hepatocyte membranes around 5-fold in a dose-dependent fashion, with EC50 values of 120 +/- 14 and 0.3 +/- 0.14 nM respectively. Whereas amylin exhibited normal activation kinetics (Hill coefficient, h approximately 1), CGRP showed kinetics indicative of either multiple sites/receptor species having different affinities for this ligand or a single receptor species exhibiting apparent negative co-operativity (h approximately 0.21). The CGRP antagonist CGRP-(8-37)-peptide inhibited adenylate cyclase stimulated by EC50 concentrations of either amylin or CGRP. Inhibition by CGRP-(8-37) was selective in that markedly lower concentrations were required to block the action of amylin (IC50 = 3 +/- 1 nM) compared with that of CGRP itself (IC50 = 120 +/- 11 nM). Dose-effect data for inhibition of CGRP action by CGRP-(8-37) showed normal saturation kinetics (h approximately 1), whereas CGRP-(8-37) inhibited amylin-stimulated adenylate cyclase activity in a fashion which was indicative of either multiple sites or apparent negative co-operativity (h approximately 0.24). Observed changes in the kinetics of inhibition by CGRP-(8-37) of CGRP, but not amylin-stimulated adenylate cyclase, at concentrations of agonists below their EC50 values militated against a model of two distinct populations of non-interacting receptors each able to bind both amylin and CGRP. A kinetic model is proposed whereby a single receptor, capable of being activated by both CGRP and amylin, obeys either a mnemonical kinetic mechanism or one of negative co-operativity with respect to CGRP but not to amylin. The relative merits of these two models are discussed together with a proposal suggesting that the activation of adenylate cyclase by various G-protein-linked receptors may be described by a mnemonical model mechanism.

The occurrence of three isoenzymes of protein kinase C (alpha, beta and gamma) in retinas of different species

The localisation and immunochemical identification of 3 different forms of protein kinase C (PKC-alpha, PKC-beta and PKC-gamma) in retinas of different species were analysed by immunohistochemistry and SDS-PAGE-Western blotting, respectively. Only in some cases was there a correlation between the findings from each procedure. One reason for the lack of correlation could be the small amounts of PKC present in some retinas, which made detection possible only by first concentrating the antigen by SDS-PAGE and then carrying out Western blotting. Another possible reason is that an antibody recognises unknown antigens immunohistochemically, but, because of their specific characteristics, they are denatured when subjected to SDS-PAGE and Western blotting and therefore remain undetected. PKC-beta immunoreactivity is present in rabbit, frog and goldfish retinas but absent from the rat retina. However, SDS-PAGE and Western blotting experiments showed that the PKC-beta isoenzyme is absent from the fish retina but present in the rat retina. PKC-beta immunoreactivity in rabbit retina is present in ganglion and/or amacrine cells; in the frog retina the enzyme is associated with some bipolar cells. In the goldfish retina, PKC-beta is associated with a large population of cells in the ganglion cell layer as well as with some amacrine cell bodies. PKC-alpha is present primarily in bipolar cells of rat, fish and rabbit retinas and was not detected by immunohistochemistry or blotting experiments in the frog retina. SDS-PAGE and Western blotting of retinal extracts from different species showed that PKC-gamma occurs in the rabbit where it was associated with ganglion and/or amacrine cells.(ABSTRACT TRUNCATED AT 250 WORDS)

Protein kinase C (alpha and beta) immunoreactivity in rabbit and rat retina: effect of phorbol esters and transmitter agonists on immunoreactivity and the translocation of the enzyme from cytosolic to membrane compartments

Using a monoclonal antibody against protein kinase C (PKC) that recognises the isoenzymes alpha, beta I, and beta II, positive immunoreactivity was observed throughout the cytoplasm of bipolar cells in both rat and rabbit retinas. PKC immunoreactivity was also associated with the outer segment of photoreceptors in the rabbit retina and presumed amacrine cells in the rat retina. The PKC immunoreactivity in the retina was unaffected in content or localisation in rats kept in continuous dark or light conditions over a period of 6 days. The localisation of PKC immunoreactivity in retinas was similar in 6-day-old, 16 day-old, or adult rabbits. However, the content of PKC was lowest at the youngest stage and highest in the adult rabbit retinas. Of the two active phorbol esters studied, only phorbol 12,13-dibutyrate (PDbut) at a concentration of 1 microM caused the PKC immunoreactivity in rabbit retina bipolar cells to be "transported" from the perikarya towards the axonal terminal processes. Biochemical analyses showed that most of the cytosolic PKC was translocated to the membrane compartment following such treatment. The other phorbol ester, phorbol 12-myristate 13-acetate, even at a concentration of 10 microM did not cause a similar transport of PKC immunoreactivity in the bipolar cells, although a partial translocation of the enzyme could be followed biochemically. Both the translocation and transport of PKC by PDbut could be reversed by simply incubating the retinas in physiological solution for 60 min. The "transport" and translocation processes were not obviously affected by the transport inhibitor colchicine or by known PKC inhibitor such as staurosporine, H-7, sphingosine, or polymyxin B. In addition, agonists known to stimulate inositol phosphates in the retina, viz., carbachol, noradrenaline, and quisqualate, or 4-aminopyridine did not cause a translocation or "transport" of PKC as observed for the phorbol esters.

DARPP-32 like protein in specific snail (Helix aspersa) neurones

Monoclonal antibodies to DARPP-32 recognise an antigen which is present in specific neurones in the snail (Helix aspersa). Consecutive sections 10-microns-thick processed for the localisation of DARPP-32 and tyrosine-hydroxylase immunoreactivity did not show a coexistence in any neuronal structures. DARPP-32 positive cells were, however, often morphologically closely associated with tyrosine-hydroxylase positive cells, implying a functional relationship consistent with the proposed role of DARPP-32. Immunochemical analysis of the DARPP-32 immunoreactive material in the snail nervous system shows that the substance has a molecular weight of 28 kDa and therefore different from the DARPP-32 protein found in the rat brain.

Particle size analysis of microcapsules

To standardize the preparation of three-walled w/o/w microcapsules it was necessary to assess their particle size distribution, since they occur as both unicored and multicored types. The log-normal law, reported to be obeyed by many particulate systems, including microcapsules, was not applicable to the size data. A curve-fitting computer program using least squares minimization was used to assess the size distribution data. The models tested were the unimodal and bimodal log-normal distributions, the bimodal form being the most appropriate. Using the equations to the curves of best fit, the modal size and standard deviation of each population were estimated, and the relative percentages of unicored and multicored microcapsules could be deduced from an analysis of the bimodal curves in which they were represented as the sum of two constituent unimodal distributions.

Three-ply walled w/o/w microcapsules formed by a multiple emulsion technique

A new method of microencapsulation is described. Interfacial rheological studies had shown the formation of a rigid bipolymer film at the interface between an aqueous solution of a water-soluble polymer and a non-aqueous solution of an oil-soluble polymer. This led to the idea that small spherical bodies might be formed on making a w/o/w emulsion from these solutions. The present work has shown that ethyl cellulose/acacia microcapsules are formed when the organic solvent ethyl acetate is removed from the multiple emulsion drops. These microcapsules may be obtained as a free-flowing powder.

Rostroconchia: A New Class of Bivalved Mollusks

Four Paleozoic bivalved genera are assigned to the new molluscan class Rostroconchia: Eopteria, Euchasma, Conocardium, and Pseudoconocardium. These mollusks have ani uncoiled univalved larval shell; an untorted bivalved adult shell; no hinge teeth, ligament, or adductor muscles; and a fused, almost inflexible. hinge. Rostroconchianis developed separately from the pelecypods through the ribeirioids, but are regarded as more closely related to the Pelecypoda and Scaphopoda than to other known classes of mollusks.