A Plausible Microtubule-Based Mechanism for Cell Division Orientation in Plant Embryogenesis

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DDB2 induces nuclear accumulation of the hepatitis B virus X protein independently of binding to DDB1

The hepatitis B virus (HBV) X protein (HBx) is critical for the life cycle of the virus. HBx associates with several host cell proteins including the DDB1 subunit of the damaged-DNA binding protein DDB. Recent studies on the X protein encoded by the woodchuck hepadnavirus have provided correlative evidence indicating that the interaction with DDB1 is important for establishment of infection by the virus. In addition, the interaction with DDB1 has been implicated in the nuclear localization of HBx. Because the DDB2 subunit of DDB is required for the nuclear accumulation of DDB1, we investigated the role of DDB2 in the nuclear accumulation of HBx. Here we show that expression of DDB2 increases the nuclear levels of HBx. Several C-terminal deletion mutants of DDB2 that fail to bind DDB1 are able to associate with HBx, suggesting that DDB2 may associate with HBx independently of binding to DDB1. We also show that DDB2 enhances the nuclear accumulation of HBx independently of binding to DDB1, since a mutant that does not bind DDB1 is able to enhance the nuclear accumulation of HBx. HBV infection is associated with liver pathogenesis. We show that the nuclear levels of DDB1 and DDB2 are tightly regulated in hepatocytes. Studies with regenerating mouse liver indicate that during late G1 phase the nuclear levels of both subunits of DDB are transiently increased, followed by a sharp decrease in S phase. Taken together, these results suggest that DDB1 and DDB2 would participate in the nuclear functions of HBx effectively only during the late-G1 phase of the cell cycle.     

Choroid plexus ependymal cells enhance neurite outgrowth from dorsal root ganglion neurons in vitro

The epithelial cells of the choroid plexus are a continuation of the ventricular ependymal cells and are regarded as modified ependymal cells. The present study was carried out to determine the influence of choroid plexus ependymal cells (CPECs) on axonal growth in vitro. Choroid plexuses were dissected from the fourth ventricle of postnatal day-1–10 mice, mechanically dissociated, and plated in fibronectin-coated culture dishes. CPECs had spread into monolayers with few endothelial cells in 3-week cultures. Some macrophages were scattered on the monolayer of CPECs. Dorsal root ganglia (DRG) were excised from mouse fetuses of 14-day gestation, dissociated with trypsin and cocultured on the CPEC monolayers. For comparison, dissociated DRG neurons were cocultured on astrocyte monolayers or cultured on laminin-coated plates. After 4.5 h culturing, the cultures were fixed and immunohistochemically double-stained for neurites and CPECs using antibodies against β-tubulin III and S-100 β, respectively. It was demonstrated that neurons extended many long neurites with elaborate branching on the surface of S-100-stained CPECs. In contrast, DRG neurons cultured on the astrocytes and on the laminin-coated plates had much shorter primary neurites with fewer branches than those cultured on the CPECs. The total length of neurites including primary neurites and their branches, of a single DRG neuron was 285 ± 14, 395 ± 15 and 565 ± 12 μm on the laminin-coated plates, on astrocytes and on CPECs, respectively. Scanning electron microscopy revealed extension of neurites with well-developed growth cones on the ependymal cells. These results suggest that CPECs have a great capacity to promote neurite outgrowth from DRG neurons in vitro.     

Estimating average treatment effects with a double-index propensity score

We consider estimating average treatment effects (ATE) of a binary treatment in observational data when data-driven variable selection is needed to select relevant covariates from a moderately large number of available covariates $\mathbf{X}$. To leverage covariates among $\mathbf{X}$ predictive of the outcome for efficiency gain while using regularization to fit a parametric propensity score (PS) model, we consider a dimension reduction of $\mathbf{X}$ based on fitting both working PS and outcome models using adaptive LASSO. A novel PS estimator, the Double-index Propensity Score (DiPS), is proposed, in which the treatment status is smoothed over the linear predictors for $\mathbf{X}$ from both the initial working models. The ATE is estimated by using the DiPS in a normalized inverse probability weighting estimator, which is found to maintain double robustness and also local semiparametric efficiency with a fixed number of covariates p. Under misspecification of working models, the smoothing step leads to gains in efficiency and robustness over traditional doubly robust estimators. These results are extended to the case where p diverges with sample size and working models are sparse. Simulations show the benefits of the approach in finite samples. We illustrate the method by estimating the ATE of statins on colorectal cancer risk in an electronic medical record study and the effect of smoking on C-reactive protein in the Framingham Offspring Study.