Dynein binds to beta-catenin and may tether microtubules at adherens junctions

Interactions between microtubule and actin networks are thought to be crucial for mechanical and signalling events at the cell cortex. Cytoplasmic dynein has been proposed to mediate many of these interactions. Here, we report that dynein is localized to the cortex at adherens junctions in cultured epithelial cells and that this localization is sensitive to drugs that disrupt the actin cytoskeleton. Dynein is recruited to developing contacts between cells, where it localizes with the junctional proteins beta-catenin and E-cadherin. Microtubules project towards these early contacts and we hypothesize that dynein captures and tethers microtubules at these sites. Dynein immunoprecipitates with beta-catenin, and biochemical analysis shows that dynein binds directly to beta-catenin. Overexpression of beta-catenin disrupts the cellular localization of dynein and also dramatically perturbs the organization of the cellular microtubule array. In cells overexpressing beta-catenin, the centrosome becomes disorganized and microtubules no longer appear to be anchored at the cortex. These results identify a novel role for cytoplasmic dynein in capturing and tethering microtubules at adherens junctions, thus mediating cross-talk between actin and microtubule networks at the cell cortex.     

Opposing Kinesin and Myosin-I Motors Drive Membrane Deformation and Tubulation along Engineered Cytoskeletal Networks

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GeneTools – application for functional annotation and statistical hypothesis testing

Background  

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions.     

NIX initiates mitochondrial fragmentation via DRP1 to drive epidermal differentiation

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A Highlights from MBoC Selection: Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for data sets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background signal. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared with current methods. This algorithm, called Cega, for “find the object,” produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.