Next-Generation Sequencing Reveals Novel Rare Fusion Events with Functional Implication in Prostate Cancer
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Phosphorylation of Synaptojanin Differentially Regulates Endocytosis of Functionally Distinct Synaptic Vesicle Pools
8882 • The Journal of Neuroscience, August 24, 2016 • 36(34):8882–8894 Cellular/Molecular Phosphorylation of Synaptojanin Differentially Regulates Endocytosis of Functionally Distinct Synaptic Vesicle Pools X Junhua Geng,1* Liping Wang,1,2* Joo Yeun Lee,1,4 XChun-Kan Chen,1 and Karen T. Chang1,3,4 1Zilkha Neurogenetic Institute, 2Department of Biochemistry and Molecular Biology, and 3Department of Cell and Neurobiology, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, and 4Neuroscience Graduate Program, University of Southern California, Los Angeles, California 90089 The rapid replenishment of synaptic vesicles through endocytosis is crucial for sustaining synaptic transmission during intense neuronal activity. Synaptojanin (Synj), a phosphoinositide phosphatase, is known to play an important role in vesicle recycling by promoting the uncoating of clathrin following synaptic vesicle uptake. Synj has been shown to be a substrate of the minibrain (Mnb) kinase, a fly homolog of the dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A); however, the functional impacts of Synj phosphorylation by Mnb are not well understood. Here we identify that Mnb phosphorylates Synj at S1029 in Drosophila. We find that phosphorylation of Synj at S1029 enhances Synj phosphatase activity, alters interaction between Synj and endophilin, and promotes efficient endocytosis of the active cycling vesicle pool (also referred to as exo-endo cycling pool) at the expense of reserve pool vesicle endocytosis. Dephosphorylated Synj, on the other hand, is deficient in the endocytosis of the active recycling pool vesicles but maintains reserve pool vesicle endocytosis to restore total vesicle pool size and sustain synaptic transmission. Together, our findings reveal a novel role for Synj in modulating reserve pool vesicle endocytosis and further indicate that dynamic phosphorylation and dephosphorylation of Synj differentially maintain endocytosis of distinct functional synaptic vesicle pools. -
Katalog 2015 Cover Paul Lin *Hinweis Förderung.Indd
Product List 2015 WE LIVE SERVICE Certificates quartett owns two productions sites that are certified according to EN ISO 9001:2008 Quality management systems - Requirements EN ISO 13485:2012 + AC:2012 Medical devices - Quality management systems - Requirements for regulatory purposes GMP Conformity Our quality management guarantees products of highest quality! 2 Foreword to the quartett product list 2015 quartett Immunodiagnostika, Biotechnologie + Kosmetik Vertriebs GmbH welcomes you as one of our new business partners as well as all of our previous loyal clients. You are now member of quartett´s worldwide customers. First of all we would like to introduce ourselves to you. Founded as a family-run company in 1986, quartett ensures for more than a quarter of a century consistent quality of products. Service and support of our valued customers are our daily businesses. And we will continue! In the end 80´s quartett offered radioimmunoassay and enzyme immunoassay kits from different manufacturers in the USA. In the beginning 90´s the company changed its strategy from offering products for routine diagnostic to the increasing field of research and development. Setting up a production plant in 1997 and a second one in 2011 supported this decision. The company specialized its product profile in the field of manufacturing synthetic peptides for antibody production, peptides such as protease inhibitors, biochemical reagents and products for histology, cytology and immunohistology. All products are exclusively manufactured in Germany without outsourcing any production step. Nowadays, we expand into all other diagnostic and research fields and supply our customers in universities, government institutes, pharmaceutical and biotechnological companies, hospitals, and private doctor offices. -
SYNJ2 Antibody (Monoclonal) (M02) Mouse Monoclonal Antibody Raised Against a Partial Recombinant SYNJ2
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 SYNJ2 Antibody (monoclonal) (M02) Mouse monoclonal antibody raised against a partial recombinant SYNJ2. Catalog # AT4124a Specification SYNJ2 Antibody (monoclonal) (M02) - Product Information Application WB Primary Accession O15056 Other Accession NM_003898 Reactivity Human Host mouse Clonality Monoclonal Isotype IgG2a Kappa Calculated MW 165538 SYNJ2 Antibody (monoclonal) (M02) - Additional Information Antibody Reactive Against Recombinant Protein.Western Blot detection against Gene ID 8871 Immunogen (36.74 KDa) . Other Names Synaptojanin-2, Synaptic inositol 1, 5-trisphosphate 5-phosphatase 2, SYNJ2, KIAA0348 Target/Specificity SYNJ2 (NP_003889, 1396 a.a. ~ 1495 a.a) partial recombinant protein with GST tag. MW of the GST tag alone is 26 KDa. Dilution WB~~1:500~1000 Format Clear, colorless solution in phosphate buffered saline, pH 7.2 . Western Blot analysis of SYNJ2 expression in transfected 293T cell line by SYNJ2 Storage monoclonal antibody (M02), clone 2H8. Store at -20°C or lower. Aliquot to avoid repeated freezing and thawing. Lane 1: SYNJ2 transfected lysate(92.2 KDa). Lane 2: Non-transfected lysate. Precautions SYNJ2 Antibody (monoclonal) (M02) is for research use only and not for use in diagnostic or therapeutic procedures. SYNJ2 Antibody (monoclonal) (M02) - Protocols Page 1/3 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Provided below are standard protocols that you may find useful for product applications. • Western Blot • Blocking Peptides • Dot Blot • Immunohistochemistry • Immunofluorescence • Immunoprecipitation • Flow Cytomety • Cell Culture Western blot analysis of SYNJ2 over-expressed 293 cell line, cotransfected with SYNJ2 Validated Chimera RNAi ( (Cat # AT4124a ) SYNJ2 Antibody (monoclonal) (M02) - Background The gene is a member of the inositol-polyphosphate 5-phosphatase family. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Snotarget Shows That Human Orphan Snorna Targets Locate Close to Alternative Splice Junctions
Available online at www.sciencedirect.com Gene 408 (2008) 172–179 www.elsevier.com/locate/gene snoTARGET shows that human orphan snoRNA targets locate close to alternative splice junctions Peter S. Bazeley a, Valery Shepelev b, Zohreh Talebizadeh c, Merlin G. Butler c, Larisa Fedorova d, ⁎ Vadim Filatov e, Alexei Fedorov a,d, a Program in Bioinformatics and Proteomics/Genomics, University of Toledo Health Science Campus, Toledo, OH 43614, USA b Department of Bioinformatics, Institute of Molecular Genetics, RAS, Moscow 123182, Russia c Section of Medical Genetics and Molecular Medicine, Children's Mercy Hospitals and Clinics and University of Missouri, Kansas City School of Medicine, Kansas City, MO, USA d Department of Medicine, University of Toledo Health Science Campus, Toledo, OH 43614, USA e Dinom LLC, 8/44 Pedagogicheskaya st., Moscow 115404, Russia Received 31 July 2007; received in revised form 19 October 2007; accepted 24 October 2007 Available online 21 November 2007 Received by Takashi Gojobori Abstract Among thousands of non-protein-coding RNAs which have been found in humans, a significant group represents snoRNA molecules that guide other types of RNAs to specific chemical modifications, cleavages, or proper folding. Yet, hundreds of mammalian snoRNAs have unknown function and are referred to as “orphan” molecules. In 2006, for the first time, it was shown that a particular orphan snoRNA (HBII-52) plays an important role in the regulation of alternative splicing of the serotonin receptor gene in humans and other mammals. In order to facilitate the investigation of possible involvement of snoRNAs in the regulation of pre-mRNA processing, we developed a new computational web resource, snoTARGET, which searches for possible guiding sites for snoRNAs among the entire set of human and rodent exonic and intronic sequences. -
Conserved and Novel Properties of Clathrin-Mediated Endocytosis in Dictyostelium Discoideum" (2012)
Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2012 Conserved and Novel Properties of Clathrin- Mediated Endocytosis in Dictyostelium Discoideum Laura Macro Follow this and additional works at: http://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons Recommended Citation Macro, Laura, "Conserved and Novel Properties of Clathrin-Mediated Endocytosis in Dictyostelium Discoideum" (2012). Student Theses and Dissertations. Paper 163. This Thesis is brought to you for free and open access by Digital Commons @ RU. It has been accepted for inclusion in Student Theses and Dissertations by an authorized administrator of Digital Commons @ RU. For more information, please contact [email protected]. CONSERVED AND NOVEL PROPERTIES OF CLATHRIN- MEDIATED ENDOCYTOSIS IN DICTYOSTELIUM DISCOIDEUM A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Laura Macro June 2012 © Copyright by Laura Macro 2012 CONSERVED AND NOVEL PROPERTIES OF CLATHRIN- MEDIATED ENDOCYTOSIS IN DICTYOSTELIUM DISCOIDEUM Laura Macro, Ph.D. The Rockefeller University 2012 The protein clathrin mediates one of the major pathways of endocytosis from the extracellular milieu and plasma membrane. Clathrin functions with a network of interacting accessory proteins, one of which is the adaptor complex AP-2, to co-ordinate vesicle formation. Disruption of genes involved in clathrin-mediated endocytosis causes embryonic lethality in multicellular animals suggesting that clathrin-mediated endocytosis is a fundamental cellular process. However, loss of clathrin-mediated endocytosis genes in single cell eukaryotes, such as S.cerevisiae (yeast), does not cause lethality, suggesting that clathrin may convey specific advantages for multicellularity. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Discovery of Driver Non-Coding Splice-Site-Creating Mutations in Cancer
ARTICLE https://doi.org/10.1038/s41467-020-19307-6 OPEN Discovery of driver non-coding splice-site-creating mutations in cancer Song Cao1,2, Daniel Cui Zhou 1,2, Clara Oh1,2, Reyka G. Jayasinghe1,2, Yanyan Zhao1, Christopher J. Yoon1,2, Matthew A. Wyczalkowski 1,2, Matthew H. Bailey 1,2, Terrence Tsou 1,2, Qingsong Gao1,2, ✉ ✉ Andrew Malone 1, Sheila Reynolds3, Ilya Shmulevich3, Michael C. Wendl2,4,5, Feng Chen1,6 & Li Ding1,2,4,6 Non-coding mutations can create splice sites, however the true extent of how such somatic 1234567890():,; non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons. Introns associated with new exon creation are significantly larger than the genome-wide average intron size. We find that some mutation-induced splicing alterations are located in genes important in tumorigenesis (ATRX, BCOR, CDKN2B, MAP3K1, MAP3K4, MDM2, SMAD4, STK11, TP53 etc.), often leading to truncated proteins and affecting gene expression. The pattern emerging from these exon-creating mutations sug- gests that splice sites created by non-coding mutations interact with pre-existing potential splice sites that originally lacked a suitable splicing pair to induce new exon formation. Our study suggests the importance of investigating biological and clinical consequences of noncoding splice-inducing mutations that were previously neglected by conventional anno- tation pipelines. MiSplice will be useful for automatically annotating the splicing impact of coding and non-coding mutations in future large-scale analyses. -
Identification of Six Autophagy-Related-Lncrna Prognostic Biomarkers in Uveal Melanoma
Hindawi Disease Markers Volume 2021, Article ID 2401617, 12 pages https://doi.org/10.1155/2021/2401617 Research Article Identification of Six Autophagy-Related-lncRNA Prognostic Biomarkers in Uveal Melanoma Yao Chen , Lu Chen , Jinwei Wang , Jia Tan, and Sha Wang Hunan Key Laboratory of Ophthalmology, Eye Center of Xiangya Hospital, Central South University, China Correspondence should be addressed to Sha Wang; [email protected] Received 7 June 2021; Revised 16 July 2021; Accepted 23 July 2021; Published 13 August 2021 Academic Editor: Ting Su Copyright © 2021 Yao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Currently, no autophagy-related long noncoding RNA (lncRNA) has been reported to predict the prognosis of uveal melanoma patients. Our study screened for autophagy-related lncRNAs in 80 samples downloaded from The Cancer Genome Atlas (TCGA) database through lncRNA-mRNA coexpression. We used univariate Cox to further filter the lncRNAs. Multivariate Cox regression and LASSO regression were applied to construct an autophagy-associated lncRNA predictive model and calculate the risk score. Clinical risk factors were validated using Cox regression to determine whether they were independent prognostic indicators. Functional enrichment was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The model was built with six predictive autophagy-associated lncRNAs and clustered uveal melanoma patients into high- and low-risk groups. The risk score of our model was a significant independent prognostic factor (hazard ratio = 1:0; p <0:001). -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002)