Insulin modulates norepinephrine-mediated melatonin synthesis in cultured rat pineal gland

The mammalian pineal gland synthesizes melatonin in a circadian manner, peaking during the dark phase. This synthesis is primarily regulated by sympathetic innervations via noradrenergic fibers, but is also modulated by many peptidergic and hormonal systems. A growing number of studies reveal a complex role for melatonin in influencing various physiological processes, including modulation of insulin secretion and action. In contrast, a role for insulin as a modulator of mclatonin synthesis has not been investigated previously. The aim of the current study was to determine whether insulin modulates norepinephrine (NE)-mediated melatonin synthesis. The results demonstrate that insulin (10(-8)M) potentiated norepinephrine-mediated melatonin synthesis and tryptophan hydroxylase (TPOH) activity in ex vivo incubated pineal glands. When ex vivo incubated pineal glands were synchronized (12h NE-stimulation, followed by 12h incubation in the absence of NE), insulin potentiated NE-mediated melatonin synthesis and arylalkylamine-N-acetyltransferase (AANAT) activity. Insulin did not affect the activity of hydroxyindole-O-methyltranferase (HIOMT), nor the gene expression of tpoh, aanat, or hiomt, under any of the conditions investigated. We conclude that insulin potentiates NE-mediated melatonin synthesis in cultured rat pineal gland, potentially through post-transcriptional events. (C) 2007 Elsevier Inc. All rights reserved.

Posttranscriptional regulation of sodium-iodide symporter mRNA expression in the rat thyroid gland by acute iodide administration

Serrano-Nascimento C, Calil-Silveira J, Nunes MT. Posttranscriptional regulation of sodium-iodide symporter mRNA expression in the rat thyroid gland by acute iodide administration. Am J Physiol Cell Physiol 298: C893-C899, 2010. First published January 27, 2010; doi:10.1152/ajpcell.00224.2009.-Iodide is an important regulator of thyroid activity. Its excess elicits the Wolff-Chaikoff effect, characterized by an acute suppression of thyroid hormone synthesis, which has been ascribed to serum TSH reduction or TGF-beta increase and production of iodolipids in the thyroid. These alterations take hours/days to occur, contrasting with the promptness of Wolff-Chaikoff effect. We investigated whether acute iodide administration could trigger events that precede those changes, such as reduction of sodium-iodide symporter (NIS) mRNA abundance and adenylation, and if perchlorate treatment could counteract them. Rats subjected or not to methylmercaptoimidazole treatment (0.03%) received NaI (2,000 mu g/0.5 ml saline) or saline intraperitoneally and were killed 30 min up to 24 h later. Another set of animals was treated with iodide and perchlorate, in equimolar doses. NIS mRNA content was evaluated by Northern blotting and real-time PCR, and NIS mRNA poly(A) tail length by rapid amplification of cDNA ends-poly(A) test (RACE-PAT). We observed that NIS mRNA abundance and poly(A) tail length were significantly reduced in all periods of iodide treatment. Perchlorate reversed these effects, indicating that iodide was the agent that triggered the modifications observed. Since the poly(A) tail length of mRNAs is directly associated with their stability and translation efficiency, we can assume that the rapid decay of NIS mRNA abundance observed was due to a reduction of its stability, a condition in which its translation could be impaired. Our data show for the first time that iodide regulates NIS mRNA expression at posttranscriptional level, providing a new mechanism by which iodide exerts its autoregulatory effect on thyroid.

Distribution of small Rho GTPases in the developing rat submandibular gland

During the rat submandibular gland (SMG) development, organogenesis and cytodifferentiation depend on the actin cytoskeleton, which is regulated by small Rho GTPases. These proteins link cell surface receptors to pathways that regulate cell motility, polarity, gene expression, vesicular trafficking, proliferation and apoptosis. The aim of this study was to evaluate, by immunohistochemistry, the distribution pattern of RhoA, RhoB, RhoC, Rac1 and Cdc42 during cytodifferentiation of the rat SMG and in male adults. All GTPases were found in epithelial and mesenchymal tissues throughout gland development. Rac1 appeared to be important for parenchyma expansion at the beginning of cytodifferentiation, while RhoC, Cdc42 and the inactive phosphorylated form of Rac1 seemed associated with lumen formation and cell polarization in terminal tubules. RhoA and RhoB labeling was evident throughout development. All GTPases were differentially expressed in the adult gland, suggesting that they play specific roles during differentiation and function of the rat SMG.

Adhesion and protease activity in cell lines from human salivary gland tumors are regulated by the laminin-derived peptide AG73, syndecan-1 and beta 1 integrin

We studied the induction of protease activity by the laminin alpha 1-derived peptide AG73 in cells from adenoid cystic carcinoma (CAC2) and myoepithelioma (M1), respectively a malignant and a benign salivary gland tumors. Laminin alpha 1 chain and MMP9 were immunolocalized in adenoid cystic carcinoma and myoepithelioma in vivo and in vitro. Cells grown inside AG73-enriched laminin-111 exhibited large spaces in the extracellular matrix, suggestive of remodeling. The broad spectrum MMP inhibitor GM6001 decreased spaces induced by AG73 in CAC2 and M I cells. This result strongly suggests that AG73-mediated matrix remodeling involves matrix metalloproteinases. CAC2 and M1 cells cultured on AG73 showed a dose-dependent increase of MMP9 secretion, as detected by zymography. Furthermore, siRNA silencing of MMP9 decreased remodeling in 3D cultures. We searched for AG73 receptors regulating MMP9 activity in our cell lines. CAC2 and M1 cells grown on AG73 exhibited colocalization of syndecan-1 and beta 1 integrin. siRNA knockdown of syndecan-1 expression in these cells resulted in decreased adhesion to AG73 and reduced protease and remodeling activity. We investigated syndecan-1 co-receptors in both cell lines. Silencing beta 1 integrin inhibited adhesion to AG73, matrix remodeling and protease activity. Double-knockdown experiments were carried out to further explore syndecan-1 and beta 1 integrin cooperation. CAC2 cells transfected with both syndecan-1 and beta 1 integrin siRNA oligos showed significant decrease in adhesion to AG73. Simultaneous silencing of receptors also induced a decrease in protease activity. Our results suggest that syndecan-1 and beta 1 integrin signaling downstream of AG73 regulate adhesion and MMP production by CAC2 and M1 cells. (c) 2008 Elsevier B.V./International Society of Matrix Biology. All rights reserved.

Insulin temporal sensitivity and its signaling pathway in the rat pineal gland

Aims: In our previous work, we reported that the insulin potentiating effect on melatonin synthesis is regulated by a post-transcriptional mechanism. However, the major proteins of the insulin signaling pathway (ISP) and the possible pathway component recruited on the potentiating effect of insulin had not been characterized. A second question raised was whether windows of sensitivity to insulin exist in the pineal gland due to insulin rhythmic secretion pattern. Main methods: Melatonin content from norepinephrine(NE)-synchronized pineal gland cultures was quantified by high performance liquid chromatography with electrochemical detection and arylalkylamine-N-acetyltransferase (AANAT) activity was assayed by radiometry. Immunoblotting and immunoprecipitation techniques were performed to establish the ISP proteins expression and the formation of 14-3-3: AANAT complex, respectively. Key findings: The temporal insulin susceptibility protocol revealed two periods of insulin potentiating effect, one at the beginning and another one at the end of the in vitro induced ""night"". In some Timed-insulin Stimulation (TSs), insulin also promoted a reduction on melatonin synthesis, showing its dual action in cultured pineal glands. The major ISP components, such as IR beta, IGF-1R, IRS-1, IRS-2 and PI3K(p85), as well tyrosine phosphorylation of pp85 were characterized within pineal glands. Insulin is not involved in the 14-3-3:AANAT complex formation. The blockage of PI3K by LY 294002 reduced melatonin synthesis and AANAT activity. Significance: The present study demonstrated windows of differential insulin sensitivity, a functional ISP and the PI3K-dependent insulin potentiating effect on NE-mediated melatonin synthesis, supporting the hypothesis of a crosstalk between noradrenergic and insulin pathways in the rat pineal gland. (C) 2010 Elsevier Inc. All rights reserved.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.