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      Protocadherin-18 Is a Novel Differentiation Marker and an Inhibitory Signaling Receptor for CD8 + Effector Memory T Cells

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          Abstract

          CD8 + tumor infiltrating T cells (TIL) lack effector-phase functions due to defective proximal TCR-mediated signaling previously shown to result from inactivation of p56 lck kinase. We identify a novel interacting partner for p56 lck in nonlytic TIL, Protocadherin-18 (‘pcdh18’), and show that pcdh18 is transcribed upon in vitro or in vivo activation of all CD8 + central memory T cells (CD44 +CD62L hiCD127 +) coincident with conversion into effector memory cells (CD44 +CD62L loCD127 +). Expression of pcdh18 in primary CD8 + effector cells induces the phenotype of nonlytic TIL: defective proximal TCR signaling, cytokine secretion, and cytolysis, and enhanced AICD. pcdh18 contains a motif (centered at Y842) shared with src kinases (QGQYQP) that is required for the inhibitory phenotype. Thus, pcdh18 is a novel activation marker of CD8 + memory T cells that can function as an inhibitory signaling receptor and restrict the effector phase.

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          Identifying biological themes within lists of genes with EASE.

          EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.
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            EXPANDER – an integrative program suite for microarray data analysis

            Background Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. Results Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. Conclusion EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at
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              CLICK and EXPANDER: a system for clustering and visualizing gene expression data.

              Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. http://www.cs.tau.ac.il/~rshamir/expander/expander.html
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                2 May 2012
                : 7
                : 5
                : e36101
                Affiliations
                [1 ]Department of Cell Biology, New York University Langone School of Medicine New York, New York, United States of America
                [2 ]New York University Cancer Institute, New York University Langone School of Medicine New York, New York, United States of America
                [3 ]Department of Pathology, New York University Langone School of Medicine New York, New York, United States of America
                [4 ]Center for Cancer and Immunology Research, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's Research Institute, Children's National Medical Center, Washington, District of Columbia, United States of America
                National Cancer Institute (INCA), Brazil
                Author notes

                Conceived and designed the experiments: EJV-C NM JCB ABF. Performed the experiments: EJV-C NM JCB RB GC PL JM SR ABF. Analyzed the data: EJV-C JCB RB PL ABF. Contributed reagents/materials/analysis tools: JM SR PL RB. Wrote the paper: EJV-C ABF.

                Article
                PONE-D-12-05202
                10.1371/journal.pone.0036101
                3342238
                22567129
                2da548bb-3e5d-4ba9-8705-fb7071e5b80c
                Vazquez-Cintron et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 22 February 2012
                : 26 March 2012
                Page count
                Pages: 15
                Categories
                Research Article
                Biology
                Developmental Biology
                Cell Differentiation
                Genetics
                Gene Expression
                DNA transcription
                Molecular Genetics
                Gene Regulation
                Immunology
                Immune Cells
                T Cells
                Immunologic Subspecialties
                Tumor Immunology
                Molecular Cell Biology
                Gene Expression
                Medicine
                Oncology
                Basic Cancer Research
                Tumor Physiology

                Uncategorized
                Uncategorized

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