Characterization of MNAR expression

We have previously demonstrated that modulator of nongenomic action of estrogen receptor (MNAR) integrates action of estrogen receptor alpha (ERalpha), and potentially some other nuclear receptors (NRs), in regulation of Src/Ras/Raf/mitogen-activated protein kinase (MAPK) signaling pathway. MNAR is a scaffolding protein that contains 10 LXXLL type motifs that can interact with NRs and 3 PXXP type motifs that can bind to SH3 domains present in kinases and other signaling molecules. Formation of ER-MNAR-cSrc complex leads to activation of Src and downstream Ras/Raf/MAPK pathway. The goal for this study was to compare MNAR expression in various cell lines, to optimize methods that can be used to manipulate its expression and to evaluate MNAR cellular distribution. We found that MNAR is differentially expressed. The highest levels of its expression were found in fast proliferating cells, such as breast adenocarcinoma (MCF-7)-, T cell lymphoma (Jurkat)-, prostate carcinoma (LNCaP)- and osteosarcoma (SaOS2)-derived cell lines. MNAR was undetectable in African green monkey kidney cells (COS-7) and Chinese hamster ovary cells (CHO-K1). We established and optimized a protocol to knockdown MNAR using siRNA and to overexpress it in MCF-7 cells. Exogenously expressed MNAR was found in both cytoplasmic and nuclear fractions, the majority of MNAR, however, was found in the cytoplasmic fraction. Presence of MNAR in the cell nucleus indicates that it may play a role in regulation of gene expression.     

Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation

In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, with further information on the variance structure giving an efficient estimator. The proposed method can be used to handle a variety of continuous and discrete outcomes. A test built on this approach is also developed for model simplification in order to improve efficiency. Simulations are carried out to compare the proposed estimation procedure with other methods. In combination with sensitivity analysis, our approach can be used to fit parsimonious semi-parametric pattern-mixture models to outcomes that are potentially MNAR. We apply the proposed method to an epidemiologic cohort study to examine cognition decline among elderly.     

Developmental expression of MNAR mRNA in the mouse brain

During the development of the central nervous system, estrogen influences cellular differentiation and determines the functional connectivity of distinct neural networks. Estrogens generally act through nuclear estrogen receptors (ERs). Recent research has additionally revealed rapid estrogen effects requiring the binding of estrogen to membrane/cytoplasmic ERs and the activation of intracellular signaling systems such as the Src/MAPK cascade. The scaffold protein MNAR/PELP1 appears to be the designated functional mediator of such non-genomic estrogen effects between non-nuclear ERs and Src/MAPKs. In this study, we demonstrate the expression and differential regulation of MNAR mRNA in the developing male and female mouse brain by quantitative polymerase chain reaction. In the midbrain and hypothalamus, a gradual decline in MNAR mRNA levels has been observed prenatally with the highest values at embryonic day 15 and lowest at postnatal day 15. In the cortex, mRNA levels do not fluctuate until postnatal day 7 but decrease thereafter. No differences in MNAR expression between sexes have been detected. Analysis of neuronal and astroglia-enriched cell cultures has revealed the presence of MNAR in both cell types.     

Transit of hormonal and EGF receptor-dependent signals through cholesterol-rich membranes

The functional consequences of changes in membrane lipid composition that coincide with malignant growth are poorly understood. Sufficient data have been acquired from studies of lipid binding proteins, post-translational modifications of signaling proteins, and biochemical inhibition of lipidogenic pathways to indicate that growth and survival pathways might be substantially re-directed by alterations in the lipid content of membranes. Cholesterol and glycosphingolipids segregate into membrane patches that exhibit a liquid-ordered state in comparison to membrane domains containing relatively lower amounts of these classes of lipids. These "lipid raft" structures, which may vary in size and stability in different cell types, both accumulate and exclude signaling proteins and have been implicated in signal transduction through a number of cancer-relevant pathways. In prostate cancer cells, signaling from epidermal growth factor receptor (EGFR) to the serine-threonine kinase Akt1, as well as from IL-6 to STAT3, have been demonstrated to be influenced by experimental interventions that target cholesterol homeostasis. The recent finding that classical steroid hormone receptors also reside in these microdomains, and thus may function within these structures in a signaling capacity independent of their role as nuclear factors, suggests a novel means of cross-talk between receptor tyrosine kinase-derived and steroidogenic signals. Potential points of intersection between components of the EGFR family of receptor tyrosine kinases and androgen receptor signaling pathways, which may be sensitive to disruptions in cholesterol metabolism, are discussed. Understanding the manner in which these pathways converge within cholesterol-rich membranes may present new avenues for therapeutic intervention in hormone-dependent cancers.     

Treatment of nonignorable missing data when modeling unobserved heterogeneity with finite mixture models

Multiple imputation has become a widely accepted technique to deal with the problem of incomplete data. Typically, imputation of missing values and the statistical analysis are performed separately. Therefore, the imputation model has to be consistent with the analysis model. If the data are analyzed with a mixture model, the parameter estimates are usually obtained iteratively. Thus, if the data are missing not at random, parameter estimation and treatment of missingness should be combined. We solve both problems by simultaneously imputing values using the data augmentation method and estimating parameters using the EM algorithm. This iterative procedure ensures that the missing values are properly imputed given the current parameter estimates. Properties of the parameter estimates were investigated in a simulation study. The results are illustrated using data from the National Health and Nutrition Examination Survey.