Role of the SH3 domain of Lck in its trafficking and localization in T-cells

T cell development and activation are governed by signaling through the T cell receptor (TCR). Lck, a member of the Src-family of non-receptor protein tyrosine kinases, plays a pivotal role in normal T cell development and activation. The SH3 domain of Lck is a protein-protein binding domain that interacts with proline-rich sequences with the consensus sequence PxxP. A crippling point mutation within the SH3 domain of Lck (W97A) resulted in a mutant form of Lck that exhibited a distinct subcellular localization. I showed that while WTLck-GFP localized primarily to the plasma membrane and the Golgi, W97ALck-GFP showed decreased Golgi localization. This indicates that the SH3 domain of Lck influences its trafficking and localization in T cells. Using GST pulldown assays coupled with mass spectrometry, I identified several proteins that bound specifically to WTLck-SH3 and not to W97ALck-SH3. One of these proteins was dynamin2 (Dyn2). Dyn2 plays a pivotal role in receptor endocytosis and the regulation of actin dynamics. I demonstrated that Lck and Dyn2 interact at the Golgi. I further showed that the Golgi recruitment and accumulation of Dyn2 is dependent on the SH3 domain of Lck. This Golgi accumulation of Dyn2 subsequently led to Golgi fragmentation and vesiculation. Since Golgi fragmentation is pivotal in mediating Golgi positioning, it follows that this process positively regulates MTOC and Golgi polarization to the immune synapse (IS). Accordingly, I demonstrated that the W97ALck expressing cells exhibited a defect in MTOC polarization to the IS. Taken together, I propose that Lck-Dyn2 interaction serves to mediate stable IS formation through MTOC and Golgi polarization. This is the first report of a novel role for Lck as an adaptor protein in mediating TCR signaling

GUItars: a GUI tool for analysis of high-throughput RNA interference screening data.

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/

siRNA counts classified by effect sizes.

<p>GUItars output with gene counts ranked based upon the criteria presented by Zhang <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049386#pone.0049386-Zhang4" target="_blank">[12]</a>. Data is generated from a 12-plate luminescence-based assay with 3840 total genes.</p

General workflow of high-throughput data analysis with GUItars.

<p>General workflow of high-throughput data analysis with GUItars.</p

Graphical outputs demonstrated on a 12-plate siRNA screen analyzed with the robust SSMD method with GUItars.

<p>(A) Raw data (left) and log₂-transformed data (right) histograms of each plate showing the original data distribution and effect of data transformation (one representative plate is shown). (B) Original scale (left) and rescaled (right) heat maps of each plate helping to capture systematic errors (one representative plate is shown). (C) Column-wise plate-series plot. (D) Screen-wise line plot for average control readings showing a clear separation between negative control and positive controls that is consistent throughout the screen. (E) Screen-wise SSMD score scatter plots with cutoff lines at 1.28 and −1.28 for signal-increasing and signal-decreasing hits, respectively. (F) Hit distribution heat maps for signal-increasing (top) and signal-decreasing (bottom) hits. (G) Screen-wise hit counts for signal-increasing (top) and signal-decreasing (bottom) hits.</p

User input files required by GUItars.

<p>Three separate input files are required by GUItars: A “data file directory” containing individual files for each plate, an “annotation file” with first two columns containing RNAi source plate ID and assay plate well ID with a single header line, and a “plate ID file” with a single header line. An “annotation file” and a “plate ID” file are mandatory only if the “hit mapping to the RNAi annotation file” option is checked.</p

Excel readable output file.

<p>Individual tab-delimited output files as well as a comprehensive Excel file are generated with the following information: Plate QC calculations before and after control outlier knockout, scores for all wells classified by well type, scores for hit wells classified by hit type (i.e., signal-increasing or signal-decreasing), and annotated hit list (optional) with corresponding scores.</p

Cyclin-Dependent Kinase 4 Phosphorylates and Positively Regulates PAX3-FOXO1 in Human Alveolar Rhabdomyosarcoma Cells

<div><p>Alveolar rhabdomyosarcoma (ARMS) is an aggressive childhood muscle sarcoma with a 5-year survival rate of less than 30%. More than 80% of ARMSs harbor a PAX3-FOXO1 fusion transcription factor. However, expression of PAX3-FOXO1 in muscle cells alone is not sufficient and requires the loss of function of <i>Ink4a/ARF</i> to promote malignant proliferation of muscle cells <i>in vitro</i> or initiate ARMS tumor formation <i>in vivo</i>. This prompted us to examine the signaling pathways required to activate the function of PAX3-FOXO1 and to explore the functional interaction between the <i>Ink4a/ARF</i> and PAX3-FOXO1 signaling pathways. Here we report that inhibition of cyclin-dependent kinase 4 (Cdk4) by fascaplysin (a small molecule selective inhibitor of Cdk4/cyclin D1 that we identified in a screen for compounds that inhibit PAX3-FOXO1) led to inhibition of the transcriptional activity of PAX3-FOXO1 in ARMS cell line Rh30. Consistent with this finding, activation of Cdk4 enhanced the activity of PAX3-FOXO1. <i>In vitro</i> kinase assays revealed that Cdk4 directly phosphorylated PAX3-FOXO1 at Ser⁴³⁰. Whereas fascaplysin did not affect the protein level of PAX3-FOXO1, it did increase the cytoplasmic level of PAX3-FOXO1 in a portion of cells. Our findings indicate that Cdk4 phosphorylates and positively regulates PAX3-FOXO1 and suggest that inhibition of Cdk4 activity should be explored as a promising avenue for developing therapy for ARMS.</p> </div