Ghrelin knockout mice show decreased voluntary alcohol consumption and reduced ethanol-induced conditioned place preference

Recent work suggests that stomach-derived hormone ghrelin receptor (GHS-R1A) antagonism may reduce motivational aspects of ethanol intake. In the current study we hypothesized that the endogenous GHS-R1A agonist ghrelin modulates alcohol reward mechanisms. For this purpose ethanol-induced conditioned place preference (CPP), ethanol-induced locomotor stimulation and voluntary ethanol consumption in a two-bottle choice drinking paradigm were examined under conditions where ghrelin and its receptor were blocked, either using ghrelin knockout (KO) mice or the specific ghrelin receptor (GHS-R1A) antagonist “JMV2959”. We showed that ghrelin KO mice displayed lower ethanol-induced CPP than their wild-type (WT) littermates. Consistently, when injected during CPP-acquisition, JMV2959 reduced CPP-expression in C57BL/6 mice. In addition, ethanol-induced locomotor stimulation was lower in ghrelin KO mice. Moreover, GHS-R1A blockade, using JMV2959, reduced alcohol-stimulated locomotion only in WT but not in ghrelin KO mice. When alcohol consumption and preference were assessed using the two-bottle choice test, both genetic deletion of ghrelin and pharmacological antagonism of the GHS-R1A (JMV2959) reduced voluntary alcohol consumption and preference. Finally, JMV2959-induced reduction of alcohol intake was only observed in WT but not in ghrelin KO mice. Taken together, these results suggest that ghrelin neurotransmission is necessary for the stimulatory effect of ethanol to occur, whereas lack of ghrelin leads to changes that reduce the voluntary intake as well as conditioned reward by ethanol. Our findings reveal a major, novel role for ghrelin in mediating ethanol behavior, and add to growing evidence that ghrelin is a key mediator of the effects of multiple abused drugs.     

Large and prolonged in vivo response of pancreatic secretion to phenylethylamide and phenylethylester derivatives of Boc-[Nle-Nle]CCK(26–33) in the rat

Supramaximal doses of cholecystokinin (CCK) induce in vitro submaximal biological responses (i.e., smaller by 50% than the response to a maximal dose of CCK), desensitization and residual stimulation, and in vivo secretory inhibition and edematous pancreatitis. It has been reported previously that supramaximal doses of Boc-[Nle²⁸-Nle³¹]CCK(27–32)/-phenylethylester (JMV180) do not produce these effects. The aim of this study was to analyze the in vivo response of pancreatic secretion of the rat to a wide dose range of Boc-[Nle²⁸-Nle³¹]CCK(26–33) (JMV118), an analog of CCK8 with the same activity spectrum as CCK8, to JMV180 and to Boc-[Nle²⁸-Nle³¹]CCK(27–32)-phenylethylamide (JMV170). The three peptides were administered as intravenous infusions and as bolus intravenous injections. In the case of infusions, the same maximal effect was observed with all three peptides. It was obtained with 22.5 pmol/kg · min of JMV118; JMV180 and JMV170 were about 700 times less potent. In the case of bolus injections, the maximal response to JMV118 was observed with 450 pmol/kg, and the response peaked 10–15 min after the injection. Higher doses of JMV118 induced a secretory peak that was smaller and delayed relative to the moment of injection. JMV180 and JMV170 were about 500 times less potent: the maximal response was observed with 218700 pmol/kg and peaked 10–15 min after the injection. Larger doses of JMV180 and JMV170 produced neither supramaximal inhibition nor a delayed peak response, but induced a sustained stimulation of pancreatic secretion that could last more than 3 h after the injection. These data indicate that single large doses of JMV180 and JMV170 can produce a large and long-lasting stimulation of pancreatic secretion in vivo, a goal that cannot be reached with JMV118 or CCK8.     

Estimation of individual genetic effects from binary observations on relatives applied to a family history of respiratory illnesses and chronic lung disease of newborns

This paper considers methods for estimating the relationship between a binary response Y and the genetic effects responsible for a second binary trait Z. The responses Y are observed only for target individuals, and the responses Z are observed only for the relatives of these targets. The analysis consists of two parts. The first part concerns the analysis of the family data Z and the second part estimates the relation between the genetic effects and Y. For the family data, a generalized linear mixed model with a logit link and Gaussian genetic (random) effects is used. Estimates of the variances of the genetic effects are obtained by using a pseudo-profile log-likelihood method. Estimation of the log likelihood involves averaging over n-dimensional normal distributions, which is done by importance sampling. The methods used in the second part are straightforward. The methods are applied to a data set containing chronic lung disease (CLDN) responses of newborns and asthma (AS), allergy (AL), chronic bronchitis (CB) and eczema (EC) responses observed for the relatives of these newborns. The clinical question is whether genetic effects of AS, AL, CB, and EC have an effect on the risk for CLDN. It can be concluded that for AS, AL, CB, and EC, the influence of genetic effects is significant. However, these genetic predispositions have no significant effect on CLDN.     

Mutational analyses of HAMP helices suggest a dynamic bundle model of input–output signalling in chemoreceptors

To test the gearbox model of HAMP signalling in the Escherichia coli serine receptor, Tsr, we generated a series of amino acid replacements at each residue of the AS1 and AS2 helices. The residues most critical for Tsr function defined hydrophobic packing faces consistent with a four-helix bundle. Suppression patterns of helix lesions conformed to the predicted packing layers in the bundle. Although the properties and patterns of most AS1 and AS2 lesions were consistent with both proposed gearbox structures, some mutational features specifically indicate the functional importance of an x-da bundle over an alternative a-d bundle. These genetic data suggest that HAMP signalling could simply involve changes in the stability of its x-da bundle. We propose that Tsr HAMP controls output signals by modulating destabilizing phase clashes between the AS2 helices and the adjoining kinase control helices. Our model further proposes that chemoeffectors regulate HAMP bundle stability through a control cable connection between the transmembrane segments and AS1 helices. Attractant stimuli, which cause inward piston displacements in chemoreceptors, should reduce cable tension, thereby stabilizing the HAMP bundle. This study shows how transmembrane signalling and HAMP input–output control could occur without the helix rotations central to the gearbox model.     

Pharmacological evidences for DFK167-sensitive presenilin-independent γ-secretase-like activity

Amyloid‐β (Aβ) peptides production is thought to be a key event in the neurodegenerative process ultimately leading to Alzheimer’s disease (AD) pathology. A bulk of studies concur to propose that the C‐terminal moiety of Aβ is released from its precursor β‐amyloid precursor protein by a high molecular weight enzymatic complex referred to as γ‐secretase, that is composed of at least, nicastrin (NCT), Aph‐1, Pen‐2, and presenilins (PS) 1 or 2. They are thought to harbor the γ‐secretase catalytic activity. However, several lines of evidence suggest that additional γ‐secretase‐like activities could potentially contribute to Aβ production. By means of a quenched fluorimetric substrate (JMV2660) mimicking the β‐amyloid precursor protein sequence targeted by γ‐secretase, we first show that as expected, this probe allows monitoring of an activity detectable in several cell systems including the neuronal cell line telencephalon specific murine neurons (TSM1). This activity is reduced by DFK167, N‐[N‐(3,5‐difluorophenacetyl)‐L‐alanyl]‐S‐phenylglycine t‐butyl ester (DAPT), and LY68458, three inhibitors known to functionally interact with PS. Interestingly, JMV2660 but not the unrelated peptide JMV2692, inhibits Aβ production in an in vitroγ‐secretase assay as expected from a putative substrate competitor. This activity is enhanced by PS1 and PS2 mutations known to be responsible for familial forms of AD and reduced by aspartyl mutations inactivating PS or in cells devoid of PS or NCT. However, we clearly establish that residual JMV2660‐hydrolysing activity could be recovered in PS‐ and NCT‐deficient fibroblasts and that this activity remained inhibited by DFK167. Overall, our study describes the presence of a proteolytic activity displaying γ‐secretase‐like properties but independent of PS and still blocked by DFK167, suggesting that the PS‐dependent complex could not be the unique γ‐secretase activity responsible for Aβ production and delineates PS‐independent γ‐secretase activity as a potential additional therapeutic target to fight AD pathology.     

A bivariate cure-mixture approach for modeling familial association in diseases

For modeling correlation in familial diseases with variable ages at onset, we propose a bivariate model that incorporates two types of pairwise association, one between the lifetime risk or the overall susceptibility of two individuals and one between the ages at onset between two susceptible individuals. For estimation, we consider a two-stage estimation procedure similar to that of Shih (1998, Biometrics 54, 1115-1128). We evaluate the properties of the estimators through simulations and compare the performance with that from a bivariate survival model that allows correlation between ages at onset only. We apply the methodology to breast cancer using the kinship data from the Washington Ashkenazi Study. We also discuss potential applications of the proposed method in the area of cure modeling.     

Comparative activity of two cholecystokinin analogues with partial agonist activity: effects on food intake and brain monoamines

Two analogues of the C-terminal heptapeptide of cholecystokinin have been synthesized, in which the C-terminal phenylalanine residue has been replaced by a phenylethylester (JMV 180) or a phenylethylamide (JMV 170) group. They have been shown to present partial agonist CCK activity on pancreatic amylase release. In this study, the effects of the two peptides were investigated on food intake and brain monoamine metabolism after intraperitoneal (IP) and intracerebroventricular (ICV) administration. Neither peptide was active on feeding after IP administration but both decreased food intake after ICV injection, with a slightly higher potency for JMV 170. JMV 180 induced no change in monoamine metabolism whatever the route of administration. JMV 170 IP decreased cortical levels of dopamine and its metabolites. This effect was stronger after ICV injection and was accompanied by changes in serotonergic metabolism in the hypothalamus and cortex. Contrary to CCK8 S, which is more active on feeding after peripheral injection, the feeding effects of the analogues obtained by modification of the C-terminal phenylalanine residue appear to involve a central site of action. Furthermore, phenylethylamide substitution (JMV 170) gives rise to greater potency on monoaminergic variations than replacement with a phenylethylester (JMV 180) and the effect is enhanced following central administration.     

A general approach for two-stage analysis of multilevel clustered non-Gaussian data

In this article, we propose a two-stage approach to modeling multilevel clustered non-Gaussian data with sufficiently large numbers of continuous measures per cluster. Such data are common in biological and medical studies utilizing monitoring or image-processing equipment. We consider a general class of hierarchical models that generalizes the model in the global two-stage (GTS) method for nonlinear mixed effects models by using any square-root-n-consistent and asymptotically normal estimators from stage 1 as pseudodata in the stage 2 model, and by extending the stage 2 model to accommodate random effects from multiple levels of clustering. The second-stage model is a standard linear mixed effects model with normal random effects, but the cluster-specific distributions, conditional on random effects, can be non-Gaussian. This methodology provides a flexible framework for modeling not only a location parameter but also other characteristics of conditional distributions that may be of specific interest. For estimation of the population parameters, we propose a conditional restricted maximum likelihood (CREML) approach and establish the asymptotic properties of the CREML estimators. The proposed general approach is illustrated using quartiles as cluster-specific parameters estimated in the first stage, and applied to the data example from a collagen fibril development study. We demonstrate using simulations that in samples with small numbers of independent clusters, the CREML estimators may perform better than conditional maximum likelihood estimators, which are a direct extension of the estimators from the GTS method.     

Evaluation of methylation analysis for diagnostic testing in 258 referrals suspected of Prader-Willi or Angelman syndromes

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are distinct neurodevelopmental disorders with interrelated genetic mechanisms because genomic imprinting within the chromosome 15q11–13 region affects both the PWS and the AS locus. Methylation analysis is one method of distinguishing between the maternally and paternally inherited chromosome 15. Here we present clinical and molecular data on a large series of 258 referred patients, evaluated with methylation analysis: 115 with suspected PWS and 143 with suspected AS. In these patients, the clinical phenotype was graded into three groups: classical (group 1); not classical but possible (group 2); not classical and unlikely (group 3). For PWS, a fourth group consisted of hypotonic babies. DNA methylation analysis confirmed the diagnosis of PWS in 30 patients (26%) and AS in 28 patients (20%). For 21 PWS patients the mechanism was established: 15 had deletions, 4 had uniparental disomy (UPD) and 2 a presumed imprinting defect. Clinically all those with an abnormal methylation pattern had the classical phenotype and none of those with a normal methylation pattern had classical PWS. For 23 AS patients in whom a mechanism was established, 17 had a deletion, 3 had UPD and 3 had a presumed imprinting defect. There was greater clinical overlap in AS, with 26 classical AS patients having a normal methylation pattern while an abnormal methylation pattern was seen in one patient from group 2. In addition, there were a further 40 patients with a normal methylation pattern in whom AS was still a possible diagnosis. Our conclusion is that methylation analysis provides an excellent screening test for both syndromes, providing ∼99% diagnosis for PWS and for AS, a 75% diagnostic rate, supplemented for the remaining 25% with an essential basic starting point to further investigations. Received: 10 February 1998 / Accepted: 7 July 1998     

Quadratic inference functions for varying-coefficient models with longitudinal data

Nonparametric smoothing methods are used to model longitudinal data, but the challenge remains to incorporate correlation into nonparametric estimation procedures. In this article, we propose an efficient estimation procedure for varying-coefficient models for longitudinal data. The proposed procedure can easily take into account correlation within subjects and deal directly with both continuous and discrete response longitudinal data under the framework of generalized linear models. The proposed approach yields a more efficient estimator than the generalized estimation equation approach when the working correlation is misspecified. For varying-coefficient models, it is often of interest to test whether coefficient functions are time varying or time invariant. We propose a unified and efficient nonparametric hypothesis testing procedure, and further demonstrate that the resulting test statistics have an asymptotic chi-squared distribution. In addition, the goodness-of-fit test is applied to test whether the model assumption is satisfied. The corresponding test is also useful for choosing basis functions and the number of knots for regression spline models in conjunction with the model selection criterion. We evaluate the finite sample performance of the proposed procedures with Monte Carlo simulation studies. The proposed methodology is illustrated by the analysis of an acquired immune deficiency syndrome (AIDS) data set.     

Concepts of association between cancer and ionising radiation: accounting for specific biological mechanisms

The probability that an observed cancer was caused by radiation exposure is usually estimated using cancer rates and risk models from radioepidemiological cohorts and is called assigned share (AS). This definition implicitly assumes that an ongoing carcinogenic process is unaffected by the studied radiation exposure. However, there is strong evidence that radiation can also accelerate an existing clonal development towards cancer. In this work, we define different association measures that an observed cancer was newly induced, accelerated, or retarded. The measures were quantified exemplarily by Monte Carlo simulations that track the development of individual cells. Three biologically based two-stage clonal expansion (TSCE) models were applied. In the first model, radiation initiates cancer development, while in the other two, radiation has a promoting effect, i.e. radiation accelerates the clonal expansion of pre-cancerous cells. The parameters of the TSCE models were derived from breast cancer data from the atomic bomb survivors of Hiroshima and Nagasaki. For exposure at age 30, all three models resulted in similar estimates of AS at age 60. For the initiation model, estimates of association were nearly identical to AS. However, for the promotion models, the cancerous clonal development was frequently accelerated towards younger ages, resulting in associations substantially higher than AS. This work shows that the association between a given cancer and exposure in an affected person depends on the underlying biological mechanism and can be substantially larger than the AS derived from classic radioepidemiology.

     

A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.     

Improving Power for Testing Genetic Association in Case–Control Studies by Reducing the Alternative Space

Summary .  To detect association between a genetic marker and a disease in case–control studies, the Cochran–Armitage trend test is typically used. The trend test is locally optimal when the genetic model is correctly specified. However, in practice, the underlying genetic model, and hence the optimal trend test, are usually unknown. In this case, Pearson's chi-squared test, the maximum of three trend test statistics (optimal for the recessive, additive, and dominant models), and the test based on genetic model selection (GMS) are useful. In this article, we first modify the existing GMS method so that it can be used when the risk allele is unknown. Then we propose a new approach by excluding a genetic model that is not supported by the data. Using either the model selection or exclusion, the alternative space is reduced conditional on the observed data, and hence the power to detect a true association can be increased. Simulation results are reported and the proposed methods are applied to the genetic markers identified from the genome-wide association studies conducted by the Wellcome Trust Case–Control Consortium. The results demonstrate that the genetic model exclusion approach usually performs better than existing methods under its worst situation across scientifically plausible genetic models we considered.     

A transitional model for longitudinal binary data subject to nonignorable missing data

Binary longitudinal data are often collected in clinical trials when interest is on assessing the effect of a treatment over time. Our application is a recent study of opiate addiction that examined the effect of a new treatment on repeated urine tests to assess opiate use over an extended follow-up. Drug addiction is episodic, and a new treatment may affect various features of the opiate-use process such as the proportion of positive urine tests over follow-up and the time to the first occurrence of a positive test. Complications in this trial were the large amounts of dropout and intermittent missing data and the large number of observations on each subject. We develop a transitional model for longitudinal binary data subject to nonignorable missing data and propose an EM algorithm for parameter estimation. We use the transitional model to derive summary measures of the opiate-use process that can be compared across treatment groups to assess treatment effect. Through analyses and simulations, we show the importance of properly accounting for the missing data mechanism when assessing the treatment effect in our example.     

Arginine 336 and asparagine 333 of the human cholecystokinin-A receptor binding site interact with the penultimate aspartic acid and the C-terminal amide of cholecystokinin.

The cholecystokinin-A receptor (CCK-AR) is a G protein-coupled receptor that mediates important central and peripheral cholecystokinin actions. Residues of the CCK-AR binding site that interact with the C-terminal part of CCK that is endowed with biological activity are still unknown. Here we report on the identification of Arg-336 and Asn-333 of CCK-AR, which interact with the Asp-8 carboxylate and the C-terminal amide of CCK-9, respectively. Identification of the two amino acids was achieved by dynamics-based docking of CCK in a refined three-dimensional model of CCK-AR using, as constraints, previous results that demonstrated that Trp-39/Gln-40 and Met-195/Arg-197 interact with the N terminus and the sulfated tyrosine of CCK, respectively. Arg-336-Asp-8 and Asn-333-amide interactions were pharmacologically assessed by mutational exchange of Arg-336 and Asn-333 in the receptor or reciprocal elimination of the partner chemical functions in CCK. This study also allowed us to demonstrate that (i) the identified interactions are crucial for stabilizing the high affinity phospholipase C-coupled state of the CCK-AR.CCK complex, (ii) Arg-336 and Asn-333 are directly involved in interactions with nonpeptide antagonists SR-27,897 and L-364,718, and (iii) Arg-336 but not Asn-333 is directly involved in the binding of the peptide antagonist JMV 179 and the peptide partial agonist JMV 180. These data will be used to obtain an integrated dynamic view of the molecular processes that link agonist binding to receptor activation.     

Model Selection in Estimating Equations

Model selection is a necessary step in many practical regression analyses. But for methods based on estimating equations, such as the quasi-likelihood and generalized estimating equation (GEE) approaches, there seem to be few well-studied model selection techniques. In this article, we propose a new model selection criterion that minimizes the expected predictive bias (EPB) of estimating equations. A bootstrap smoothed cross-validation (BCV) estimate of EPB is presented and its performance is assessed via simulation for overdispersed generalized linear models. For illustration, the method is applied to a real data set taken from a study of the development of ewe embryos.     

Direct repression of KNOX loci by the ASYMMETRIC LEAVES1 complex of Arabidopsis

KNOTTED1-like homeobox (KNOX) genes promote stem cell activity and must be repressed to form determinate lateral organs. Stable KNOX gene silencing during organogenesis is known to involve the predicted DNA binding proteins ASYMMETRIC LEAVES1 (AS1) and AS2 as well as the chromatin-remodeling factor HIRA. However, the mechanism of silencing is unknown. Here, we show that AS1 and AS2 form a repressor complex that binds directly to the regulatory motifs CWGTTD and KMKTTGAHW present at two sites in the promoters of the KNOX genes BREVIPEDICELLUS (BP) and KNAT2. The two binding sites act nonredundantly, and interaction between AS1-AS2 complexes at these sites is required to repress BP. Promoter deletion analysis further indicates that enhancer elements required for BP expression in the leaf are located between the AS1-AS2 complex binding sites. We propose that AS1-AS2 complexes interact to create a loop in the KNOX promoter and, likely through recruitment of HIRA, form a repressive chromatin state that blocks enhancer activity during organogenesis. Our model for AS1-AS2-mediated KNOX gene silencing is conceptually similar to the action of an insulator. This regulatory mechanism may be conserved in simple leafed species of monocot and dicot lineages and constitutes a potential key determinant in the evolution of compound leaves.     

Combining assays for estimating prevalence of human herpesvirus 8 infection using multivariate mixture models

For many diseases, it is difficult or impossible to establish a definitive diagnosis because a perfect "gold standard" may not exist or may be too costly to obtain. In this paper, we propose a method to use continuous test results to estimate prevalence of disease in a given population and to estimate the effects of factors that may influence prevalence. Motivated by a study of human herpesvirus 8 among children with sickle-cell anemia in Uganda, where 2 enzyme immunoassays were used to assess infection status, we fit 2-component multivariate mixture models. We model the component densities using parametric densities that include data transformation as well as flexible transformed models. In addition, we model the mixing proportion, the probability of a latent variable corresponding to the true unknown infection status, via a logistic regression to incorporate covariates. This model includes mixtures of multivariate normal densities as a special case and is able to accommodate unusual shapes and skewness in the data. We assess model performance in simulations and present results from applying various parameterizations of the model to the Ugandan study.     

Statistical tests for identifying differentially expressed genes in time-course microarray experiments

MOTIVATION: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data. RESULTS: We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.     

Stochastic segmentation models for array-based comparative genomic hybridization data analysis

Array-based comparative genomic hybridization (array-CGH) is a high throughput, high resolution technique for studying the genetics of cancer. Analysis of array-CGH data typically involves estimation of the underlying chromosome copy numbers from the log fluorescence ratios and segmenting the chromosome into regions with the same copy number at each location. We propose for the analysis of array-CGH data, a new stochastic segmentation model and an associated estimation procedure that has attractive statistical and computational properties. An important benefit of this Bayesian segmentation model is that it yields explicit formulas for posterior means, which can be used to estimate the signal directly without performing segmentation. Other quantities relating to the posterior distribution that are useful for providing confidence assessments of any given segmentation can also be estimated by using our method. We propose an approximation method whose computation time is linear in sequence length which makes our method practically applicable to the new higher density arrays. Simulation studies and applications to real array-CGH data illustrate the advantages of the proposed approach.