REGULATION OF HEPATIC ER STRESS BY THE E3 UBIQUITIN LIGASE GP78 IN ZEBRAFISH

Mammalian gp78 is an E3 ubiquitin ligase that is anchored at the membrane of the endoplasmic reticulum (ER). It regulates protein homeostasis by polyubiquitinating and targeting proteins for proteasomal degradation under both physiologic and stress conditions. To further test its role in vivo, we analyzed the gross embryonic morphology of zebrafish embryos in which gp78 was knocked down using morpholinos and in transgenic fish overexpressing wild-type gp78 or dominant-negative gp78. We show that gp78 is highly conserved among vertebrates. Zebrafish gp78, similar to human gp78, can colocalize with mouse MmUBC7 in HeLa cells. In vitro ubiquitination assays confirmed that zebrafish gp78 is indeed an E3 ubiquitin ligase. Although gp78 was maternally and constitutively expressed during embryonic development, with relatively high expression levels in several tissues, such as liver and brain, the knockdown of endogenous gp78 or overexpression of wild-type or dominant-negative gp78 did not result in developmental defects, suggesting compensation by other E3 ubiquitin ligases during embryonic development. ER-associated protein degradation (ERAD) activity by the unfolded protein response (UPR) represents one of the mechanisms for restoring ER homeostasis. However, the significance of gp78 in the regulation of hepatic ER stress in vivo remains elusive. Here we report that zebrafish gp78 plays a key role in the regulation of hepatic ER stress under tunicamycin-induced stress, but not under physiologic conditions. Tunicamycin treatment induced ER stress and upregulated the expression of several key components of the gp78-mediated ERAD complex in the liver. Moreover, hepatic-specific overexpression of the dominant-negative form of gp78 (gp78-R2M) rendered livers more sensitive to tunicamycin-induced ER stress, suggesting a role for gp78-mediated ERAD in the regulation of hepatic protein homeostasis. Moreover, the overexpression of gp78-R2M enhanced the expression of sterol response element binding protein (Srebp) target genes in response to ER stress, while this was not observed in fish overexpressing wild-type gp78. Together, these data indicate that gp78 plays a critical role in the regulation of hepatic ER stress and lipid metabolism

Estimation and Model Selection of Semiparametric Multivariate Survival Functions under General Censorship

Many models of semiparametric multivariate survival functions are characterized by nonparametric marginal survival functions and parametric copula functions, where different copulas imply different dependence structures. This paper considers estimation and model selection for these semiparametric multivariate survival functions, allowing for misspecified parametric copulas and data subject to general censoring. We first establish convergence of the two-step estimator of the copula parameter to the pseudo-true value defined as the value of the parameter that minimizes the KLIC between the parametric copula induced multivariate density and the unknown true density. We then derive its root--n asymptotically normal distribution and provide a simple consistent asymptotic variance estimator by accounting for the impact of the nonparametric estimation of the marginal survival functions. These results are used to establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application of the model selection test to the Loss-ALAE insurance data set is provided.Multivariate survival models, Misspecified copulas, Penalized pseudo-likelihood ratio, Fixed or random censoring, Kaplan-Meier estimator

Unfolding-model-based visualization: theory, method and applications

Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional Euclidian space, in which respondents and items are represented by ideal points, with personperson, item-item, and person-item similarities being captured by the Euclidian distances between the points. In this paper, we study the visualization of multidimensional unfolding from a statistical perspective. We cast multidimensional unfolding into an estimation problem, where the respondent and item ideal points are treated as parameters to be estimated. An estimator is then proposed for the simultaneous estimation of these parameters. Asymptotic theory is provided for the recovery of the ideal points, shedding lights on the validity of model-based visualization. An alternating projected gradient descent algorithm is proposed for the parameter estimation. We provide two illustrative examples, one on users’ movie rating and the other on senate roll call voting