Epithelial-to-mesenchymal transition drives a pro-metastatic Golgi compaction process through scaffolding protein PAQR11

Tumor cells gain metastatic capacity through a Golgi phosphoprotein 3-dependent (GOLPH3-dependent) Golgi membrane dispersal process that drives the budding and transport of secretory vesicles. Whether Golgi dispersal underlies the prometastatic vesicular trafficking that is associated with epithelial-to-mesenchymal transition (EMT) remains unclear. Here, we have shown that, rather than causing Golgi dispersal, EMT led to the formation of compact Golgi organelles with improved ribbon linking and cisternal stacking. Ectopic expression of the EMT-activating transcription factor ZEB1 stimulated Golgi compaction and relieved microRNA-mediated repression of the Golgi scaffolding protein PAQR11. Depletion of PAQR11 dispersed Golgi organelles and impaired anterograde vesicle transport to the plasma membrane as well as retrograde vesicle tethering to the Golgi. The N-terminal scaffolding domain of PAQR11 was associated with key regulators of Golgi compaction and vesicle transport in pull-down assays and was required to reconstitute Golgi compaction in PAQR11-deficient tumor cells. Finally, high PAQR11 levels were correlated with EMT and shorter survival in human cancers, and PAQR11 was found to be essential for tumor cell migration and metastasis in EMT-driven lung adenocarcinoma models. We conclude that EMT initiates a PAQR11-mediated Golgi compaction process that drives metastasis

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes

T Cell Activation and Senescence Predict Subclinical Carotid Artery Disease in HIV-Infected Women

Background. Individuals infected with human immunodeficiency virus (HIV) have increased risk of cardiovascular events. It is unknown whether T cell activation and senescence, 2 immunologic sequelae of HIV infection, are associated with vascular disease among HIV-infected adults

A Bayesian method for inferring quantitative information from FRET data

<p>Abstract</p> <p>Background</p> <p>Understanding biological networks requires identifying their elementary protein interactions and establishing the timing and strength of those interactions. Fluorescence microscopy and Förster resonance energy transfer (FRET) have the potential to reveal such information because they allow molecular interactions to be monitored in living cells, but it is unclear how best to analyze FRET data. Existing techniques differ in assumptions, manipulations of data and the quantities they derive. To address this variation, we have developed a versatile Bayesian analysis based on clear assumptions and systematic statistics.</p> <p>Results</p> <p>Our algorithm infers values of the FRET efficiency and dissociation constant, <it>K_d</it>, between a pair of fluorescently tagged proteins. It gives a posterior probability distribution for these parameters, conveying more extensive information than single-value estimates can. The width and shape of the distribution reflects the reliability of the estimate and we used simulated data to determine how measurement noise, data quantity and fluorophore concentrations affect the inference. We are able to show why varying concentrations of donors and acceptors is necessary for estimating <it>K_d</it>. We further demonstrate that the inference improves if additional knowledge is available, for example of the FRET efficiency, which could be obtained from separate fluorescence lifetime measurements.</p> <p>Conclusions</p> <p>We present a general, systematic approach for extracting quantitative information on molecular interactions from FRET data. Our method yields both an estimate of the dissociation constant and the uncertainty associated with that estimate. The information produced by our algorithm can help design optimal experiments and is fundamental for developing mathematical models of biochemical networks.</p

CD28null CD4 T-cell expansions in autoimmune disease suggest a link with cytomegalovirus infection

Immunosenescence is thought to contribute to the increase of autoimmune diseases in older people. Immunosenescence is often associated with the presence of an expanded population of CD4 T cells lacking expression of CD28 (CD28null). These highly cytotoxic CD4 T cells were isolated from disease-affected tissues in patients with rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, or other chronic inflammatory diseases and their numbers appeared to be linked to disease severity. However, we recently demonstrated that the common herpes virus, cytomegalovirus (CMV), not ageing, is the major driver of this subset of cytotoxic T cells. In this review, we discuss how CMV might potentiate and exacerbate autoimmune disease through the expansion of CD28null CD4 T cells

ASACUSA Status Report

ASACUSA progress during 2009 and plans for 201