Actin polymerization-dependent activation of Cas-L promotes immunological synapse stability

This work was supported by National Institutes of Health Common Fund through a Nanomedicine Development Center PN2EY016586 (MLD, MPS). OH and KA were Cas-L coordinates T-cell actin cytoskeleton 2 supported by NIH grants R01 AI068963-01A2 and R01 AI088106-01A1. The Wellcome Trust and the Kennedy Institute of Rheumatology Trust supported MLD

EGF-induced PIP2 hydrolysis releases and activates cofilin locally in carcinoma cells

Lamellipodial protrusion and directional migration of carcinoma cells towards chemoattractants, such as epidermal growth factor (EGF), depend upon the spatial and temporal regulation of actin cytoskeleton by actin-binding proteins (ABPs). It is generally hypothesized that the activity of many ABPs are temporally and spatially regulated by PIP2; however, this is mainly based on in vitro–binding and structural studies, and generally in vivo evidence is lacking. Here, we provide the first in vivo data that directly visualize the spatial and temporal regulation of cofilin by PIP2 in living cells. We show that EGF induces a rapid loss of PIP2 through PLC activity, resulting in a release and activation of a membrane-bound pool of cofilin. Upon release, we find that cofilin binds to and severs F-actin, which is coincident with actin polymerization and lamellipod formation. Moreover, our data provide evidence for how PLC is involved in the formation of protrusions in breast carcinoma cells during chemotaxis and metastasis towards EGF

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p