Dual Function of CD81 in Influenza Virus Uncoating and Budding

As an obligatory pathogen, influenza virus co-opts host cell machinery to harbor infection and to produce progeny viruses. In order to characterize the virus-host cell interactions, several genome-wide siRNA screens and proteomic analyses have been performed recently to identify host factors involved in influenza virus infection. CD81 has emerged as one of the top candidates in two siRNA screens and one proteomic study. The exact role played by CD81 in influenza infection, however, has not been elucidated thus far. In this work, we examined the effect of CD81 depletion on the major steps of the influenza infection. We found that CD81 primarily affected virus infection at two stages: viral uncoating during entry and virus budding. CD81 marked a specific endosomal population and about half of the fused influenza virus particles underwent fusion within the CD81-positive endosomes. Depletion of CD81 resulted in a substantial defect in viral fusion and infection. During virus assembly, CD81 was recruited to virus budding site on the plasma membrane, and in particular, to specific sub-viral locations. For spherical and slightly elongated influenza virus, CD81 was localized at both the growing tip and the budding neck of the progeny viruses. CD81 knockdown led to a budding defect and resulted in elongated budding virions with a higher propensity to remain attached to the plasma membrane. Progeny virus production was markedly reduced in CD81-knockdown cells even when the uncoating defect was compensated. In filamentous virus, CD81 was distributed at multiple sites along the viral filament. Taken together, these results demonstrate important roles of CD81 in both entry and budding stages of the influenza infection cycle

第912回千葉医学会整形外科例会

<p>(A) Emission spectrum of CaAl₂O₄:Eu²⁺, Nd³⁺ crystals curves depending on H₃BO₃ concentration. (B) Decay curves depending on H₃BO₃ concentration. (C) Magnified views of the graph in (B). (D) Decay curves in log scale depending on H₃BO₃ concentration. (E) Relative initial intensity measured at 5s (relative values where the value of control sample #1 is 1.0) depending on H₃BO₃ concentration.</p

CatSperζ regulates the structural continuity of sperm Ca2+ signaling domains and is required for normal fertility

We report that the Gm7068 (CatSpere) and Tex40 (CatSperz) genes encode novel subunits of a 9-subunit CatSper ion channel complex. Targeted disruption of CatSperz reduces CatSper current and sperm rheotactic efficiency in mice, resulting in severe male subfertility. Normally distributed in linear quadrilateral nanodomains along the flagellum, the complex lacking CatSperζ is disrupted at ~0.8 μm intervals along the flagellum. This disruption renders the proximal flagellum inflexible and alters the 3D flagellar envelope, thus preventing sperm from reorienting against fluid flow in vitro and efficiently migrating in vivo. Ejaculated CatSperz-null sperm cells retrieved from the mated female uterus partially rescue in vitro fertilization (IVF) that failed with epididymal spermatozoa alone. Human CatSperε is quadrilaterally arranged along the flagella, similar to the CatSper complex in mouse sperm. We speculate that the newly identified CatSperζ subunit is a late evolutionary adaptation to maximize fertilization inside the mammalian female reproductive tract. DOI: http://dx.doi.org/10.7554/eLife.23082.00

Recent Developments in Lanthanide-Doped Alkaline Earth Aluminate Phosphors with Enhanced and Long-Persistent Luminescence

Lanthanide-activated alkaline earth aluminate phosphors are excellent luminescent materials that are designed to overcome the limitations of conventional sulfide-based phosphors. The increasing research attention on these phosphors over the past decade has led to a drastic improvement in their phosphorescence efficiencies and resulted in a wide variety of phosphorescence colors, which can facilitate applications in various areas. This review article discusses the development of lanthanide-activated alkaline earth aluminate phosphors with a focus on the various synthesis methods, persistent luminescence mechanisms, activator and coactivator effects, and the effects of compositions. Particular attention has been devoted to alkaline earth aluminate phosphors that are extensively used, such as strontium-, calcium-, and barium-based aluminates. The role of lanthanide ions as activators and coactivators in phosphorescence emissions was also emphasized. Finally, we address recent techniques involving nanomaterial engineering that have also produced lanthanide-activated alkaline earth aluminate phosphors with long-persistent luminescence

Ultrastructural Studies by Correlative Stochastic Optical Reconstruction Microscopy and Electron Microscopy

Fluorescence light microscopy (LM) and electron microscopy (EM) are two of the most widely used imaging modalities for probing cellular structures. In this dissertation I present our works in both developing methods of several correlative super-resolution fluorescence light microscopy (LM) and electron microscopy (EM) assays by combining stochastic optical reconstruction microscopy (STORM), a super-resolution imaging technique with several different EM imaging modalities and applying super-resolution microscopy to investigate the distributions and interactions of purine biosynthetic enzymes organization complex called purinosomes within the cell. The first work contained in this dissertation is to develop Correlative fluorescence light microscopy and electron microscopy allows the imaging of spatial distributions of specific biomolecules in the context of cellular ultrastructure. Recent development of super-resolution fluorescence microscopy allows the location of molecules to be determined with nanometer-scale spatial resolution. However, correlative super-resolution fluorescence microscopy and electron microscopy (EM) still remains challenging because the optimal specimen preparation and imaging conditions for super-resolution fluorescence microscopy and EM are often not compatible. Here, we have developed several experiment protocols for correlative stochastic optical reconstruction microscopy (STORM) and EM methods, both for un-embedded samples by applying EM-specific sample preparations after STORM imaging and for embedded and sectioned samples by optimizing the fluorescence under EM fixation, staining and embedding conditions. We demonstrated these methods using a variety of cellular targets. In the second part of this dissertation, I focus on the study of dynamic purine biosynthetic enzymes organization complex called purinosomes. Purine biosynthetic enzymes are assembled into dynamic multi-enzyme complex called purinosomes. However, spatial or temporal control of these structures remains unknown. Here, we explored the endogenous purinosomes in medically important HGPRT-deficient LND fibroblasts in order to understand the de novo purine biosynthesis. Using super-resolution microscopy we investigated the interaction of purinosomes and mitochondria or microtubules using photoactivatable fluorescent protein, mMaple3 and LND fibroblast as an ideal model system for the endogenous purinosomes formation in order to avoid possible protein aggregation problems. The STORM images with this ideal model system revealed a highly correlated spatial distribution of endogenous purinosomes with mitochondria or microtubules, suggesting direct physical associations between two structures. In addition to identifying endogenous purinosome association with other cellular components, we also demonstrated that mTOR directly influenced the purinosome association with mitochondria. Inhibition of mTOR decouples spatial correlation of purinosomes with mitochondria. These data provide strong evidences for physical and functional association of endogenous purinosomes with mitochondria and microtubules.Chemistry and Chemical Biolog

Development of catalytic reactor designs for enhanced CO oxidation.

The catalytic removal of pollutants including nitrogen oxides (NOx), hydrocarbons (HC's) and carbon monoxide (CO) in the exhaust of automobiles is generally performed by using monolithic supports coated with noble metal catalysts, notably platinum and rhodium (Pt/Rh) adsorbed onto a washcoat. This is typically achieved to within 90-98% conversion efficiency for average entry conditions (50<Re<400 at actual conditions), with pressure drops not exceeding 1.25-2 kN/m2. The monolith is a honeycomb structure, essentially composed of many parallel channels of square cross section. This therefore acts as a high surface area reactor. One of its drawbacks lies in the amount of precious metal requirements. With the increasing demand and price of these it is likely that in the future they may contribute to the cost of manufacture even more significantly. Detailed analysis shows that the overall rate of reaction of the monolith reactor is usually mass transfer rather than kinetically limited. Thus any boost in the mass transfer rate should increase conversion. Conversely for there to be any reduction in overall surface area or precious metal content there would have to be an increase in mass transfer rate. The effect of increasing mass transfer was studied by two methods namely by axially segmenting the ceramic monolith core sample (consisting of 62 cells/cm 2 of1.04 mm channels) and secondly by inserting static mixers into a catalyst coated pipe (ie."Active Transport Catalytic Reactor" (ATCR)). This was carried out for carbon monoxide oxidation over a commercially prepared catalyst supplied by Johnson Matthey. The intrinsic kinetics of this reaction were determined experimentally in a differential reactor. Conversions and pressure drops were measured for each system for varying Reynolds numbers from 73-440 (S.T.P.) in the channel and 160-2140 (S.T.P.) in the pipe, under stoichiometric reactant concentrations, and for steady state fully warmed up reactor conditions ranging from 250°C to 4(X)°C. A one dimensional model is presented and its predictions compared to the experimental data for conversion and outlet gas temperature. Good agreement between experimental and theoretical data for the ATCR was found using the one-dimensional model for the conditions investigated. Also the model was found to be sufficiently accurate in predicting monolith conversions (ie. less than 10% difference between experiment and theory) and exit gas temperatures (ie. average of 4% difference) for high temperatures of 371°C and above. Pressure drops were also successfully predicted for both segmented monoliths as well as ATCR systems. Monolith segmentation was found to be successful in both enhancing CO oxidation as well as reducing the total catalyst requirements with the result that up to 30% saving of catalyst was possible. A simple optimization process using the theoretical data for the ATCR showed that up to 65% saving in reactor surface area (and hence catalyst requirements) is possible. Thus the novel idea of carrying out heterogeneous reactions within an ATCR shows promising results and indeed there is much scope for future research and possible applications

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging