Comparative Oncogenomics Identifies Breast Tumors Enriched in Functional
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
Comparative Oncogenomics Identifies Tyrosine Kinase FES As a Tumor Suppressor in Melanoma
RESEARCH ARTICLE The Journal of Clinical Investigation Comparative oncogenomics identifies tyrosine kinase FES as a tumor suppressor in melanoma Michael Olvedy,1,2 Julie C. Tisserand,3 Flavie Luciani,1,2 Bram Boeckx,4,5 Jasper Wouters,6,7 Sophie Lopez,3 Florian Rambow,1,2 Sara Aibar,6,7 Bernard Thienpont,4,5 Jasmine Barra,1,2 Corinna Köhler,1,2 Enrico Radaelli,8 Sophie Tartare-Deckert,9 Stein Aerts,6,7 Patrice Dubreuil,3 Joost J. van den Oord,10 Diether Lambrechts,4,5 Paulo De Sepulveda,3 and Jean-Christophe Marine1,2 1Laboratory for Molecular Cancer Biology, Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium. 2Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium. 3INSERM, Aix Marseille University, CNRS, Institut Paoli-Calmettes, CRCM, Equipe Labellisée Ligue Contre le Cancer, Marseille, France. 4Laboratory for Translational Genetics, Center for Cancer Biology, VIB, Leuven, Belgium. 5Laboratory for Translational Genetics, and 6Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven, Belgium. 7Laboratory of Computational Biology, and 8Mouse Histopathology Core Facility, VIB Center for Brain & Disease Research, Leuven, Belgium. 9Centre Méditerranéen de Médecine Moléculaire (C3M), INSERM, U1065, Université Côte d’Azur, Nice, France. 10Laboratory of Translational Cell and Tissue Research, Department of Pathology, KU Leuven and UZ Leuven, Leuven, Belgium. Identification and functional validation of oncogenic drivers are essential steps toward -
BNIPL Antibody - Middle Region Rabbit Polyclonal Antibody Catalog # AI13432
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 BNIPL antibody - middle region Rabbit Polyclonal Antibody Catalog # AI13432 Specification BNIPL antibody - middle region - Product Information Application WB Primary Accession Q7Z465 Other Accession NM_138278, NP_612122 Reactivity Human, Mouse, Rat, Rabbit, Horse, Bovine, Guinea Pig, Dog Predicted Human, Mouse, Rat, Rabbit, Pig, WB Suggested Anti-BNIPL Antibody Titration: Horse, Bovine, 0.2-1 μg/ml Guinea Pig, Dog Positive Control: OVCAR-3 cell lysate Host Rabbit Clonality Polyclonal Calculated MW 40kDa KDa BNIPL antibody - middle region - BNIPL antibody - middle region - Additional References Information Zhou Y.T.,et al.J. Biol. Chem. Gene ID 149428 277:7483-7492(2002). Qin W.,et al.Biochem. Biophys. Res. Commun. Alias Symbol BNIP-S, BNIPL-1, 308:379-385(2003). BNIPL-2, PP753 Gregory S.G.,et al.Nature 441:315-321(2006). Other Names Shen L.,et al.FEBS Lett. 540:86-90(2003). Bcl-2/adenovirus E1B 19 kDa-interacting protein 2-like protein, BNIPL Format Liquid. Purified antibody supplied in 1x PBS buffer with 0.09% (w/v) sodium azide and 2% sucrose. Reconstitution & Storage Add 50 ul of distilled water. Final anti-BNIPL antibody concentration is 1 mg/ml in PBS buffer with 2% sucrose. For longer periods of storage, store at 20°C. Avoid repeat freeze-thaw cycles. Precautions BNIPL antibody - middle region is for research use only and not for use in diagnostic or therapeutic procedures. Page 1/2 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 BNIPL antibody - middle region - Protein Information Name BNIPL Function May be a bridge molecule between BCL2 and ARHGAP1/CDC42 in promoting cell death. -
Rare Variants in the DNA Repair Pathway and the Risk of Colorectal Cancer Marco Matejcic1, Hiba A
Author Manuscript Published OnlineFirst on February 24, 2021; DOI: 10.1158/1055-9965.EPI-20-1457 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Rare variants in the DNA repair pathway and the risk of colorectal cancer Marco Matejcic1, Hiba A. Shaban1, Melanie W. Quintana2, Fredrick R. Schumacher3,4, Christopher K. Edlund5, Leah Naghi6, Rish K. Pai7, Robert W. Haile8, A. Joan Levine8, Daniel D. Buchanan9,10,11, Mark A. Jenkins12, Jane C. Figueiredo13, Gad Rennert14, Stephen B. Gruber15, Li Li16, Graham Casey17, David V. Conti18†, Stephanie L. Schmit1,19† † These authors contributed equally to this work. Affiliations 1 Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA 2 Berry Consultants, Austin, TX, 78746, USA 3 Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA 4 Seidman Cancer Center, University Hospitals, Cleveland, OH 44106, USA 5 Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 6 Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, NY 10467, USA 7 Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA 8 Department of Medicine, Research Center for Health Equity, Cedars-Sinai Samuel Oschin Comprehensive Cancer Center, Los Angeles, CA 90048, USA 9 Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010, Australia 10 Victorian Comprehensive Cancer Centre, University of Melbourne, Centre for Cancer Research, Parkville, Victoria 3010, Australia 1 Downloaded from cebp.aacrjournals.org on September 29, 2021. -
A Sleeping Beauty Mutagenesis Screen Reveals a Tumor Suppressor
A Sleeping Beauty mutagenesis screen reveals a tumor PNAS PLUS suppressor role for Ncoa2/Src-2 in liver cancer Kathryn A. O’Donnella,b,1,2, Vincent W. Kengc,d,e, Brian Yorkf, Erin L. Reinekef, Daekwan Seog, Danhua Fanc,h, Kevin A. T. Silversteinc,h, Christina T. Schruma,b, Wei Rose Xiea,b,3, Loris Mularonii,j, Sarah J. Wheelani,j, Michael S. Torbensonk, Bert W. O’Malleyf, David A. Largaespadac,d,e, and Jef D. Boekea,b,i,2 Departments of aMolecular Biology and Genetics, iOncology, jDivision of Biostatistics and Bioinformatics, and kPathology and bThe High Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205; cMasonic Cancer Center, dDepartment of Genetics, Cell Biology, and Development, eCenter for Genome Engineering, and hBiostatistics and Bioinformatics Core, University of Minnesota, Minneapolis, MN 55455; fDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030; and gLaboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892 Edited by Harold Varmus, National Cancer Institute, Bethesda, MD, and approved March 23, 2012 (received for review September 21, 2011) The Sleeping Beauty (SB) transposon mutagenesis system is a pow- alterations have been documented in liver cancer, the key genetic erful tool that facilitates the discovery of mutations that accelerate alterations that drive hepatocellular transformation remain tumorigenesis. In this study, we sought to identify mutations that poorly understood. Nevertheless, a unifying feature of human cooperate with MYC, one of the most commonly dysregulated HCC is amplification or overexpression of the MYC oncogene (8, genes in human malignancy. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
The Orchestra of Lipid-Transfer Proteins at the Crossroads Between Metabolism and Signaling
Progress in Lipid Research 61 (2016) 30–39 Contents lists available at ScienceDirect Progress in Lipid Research journal homepage: www.elsevier.com/locate/plipres Review The orchestra of lipid-transfer proteins at the crossroads between metabolism and signaling Antonella Chiapparino a, Kenji Maeda a,1, Denes Turei a,b, Julio Saez-Rodriguez b,2,Anne-ClaudeGavina,c,⁎ a European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany b European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Cambridge CB10 1SD, UK c European Molecular Biology Laboratory (EMBL), Molecular Medicine Partnership Unit (MMPU), Meyerhofstrasse 1, D-69117 Heidelberg, Germany article info abstract Article history: Within the eukaryotic cell, more than 1000 species of lipids define a series of membranes essential for cell func- Received 29 September 2015 tion. Tightly controlled systems of lipid transport underlie the proper spatiotemporal distribution of membrane Accepted 15 October 2015 lipids, the coordination of spatially separated lipid metabolic pathways, and lipid signaling mediated by soluble Available online 1 December 2015 proteins that may be localized some distance away from membranes. Alongside the well-established vesicular transport of lipids, non-vesicular transport mediated by a group of proteins referred to as lipid-transfer proteins Keywords: (LTPs) is emerging as a key mechanism of lipid transport in a broad range of biological processes. More than a Signaling lipid Biological membranes hundred LTPs exist in humans and these can be divided into at least ten protein families. LTPs are widely distrib- Non-vesicular lipid trafficking uted in tissues, organelles and membrane contact sites (MCSs), as well as in the extracellular space. -
Oncogenomics: Clinical Pathogenesis Approach
International Journal of Genetics, ISSN: 0975–2862, Volume 1, Issue 2, 2009, pp-25-43 Oncogenomics: Clinical pathogenesis approach Gupta G. 1, Gupta N. 2, Trivedi S. 3, Patil P. 5, Gupta M. 2, Jhadav A. 4, Vamsi K.K.6, Khairnar Y. 4, Boraste A. 4, Mujapara A.K. 7, Joshi B.8 1S.D.S.M. College Palghar, Mumbai 2Sindhu Mahavidyalaya Panchpaoli Nagpur 3V.V.P. Engineering College, Rajkot, Gujrat 4Padmashree Dr. D.Y. Patil University, Navi Mumbai, 400614, India 5Dr. D. Y. Patil ACS College, Pimpri, Pune 6Rai foundations College CBD Belapur Navi Mumbai 7Sir PP Institute of Science, Bhavnagar 8Rural College of Pharmacy, D.S Road, Bevanahalli, Banglore Abstract - Oncogenomics is new research sub-field of genomics, which applies high throughput technologies to characterize genes associated with cancer and synonymous with cancer genomics. The goal of oncogenomics is to identify new oncogenes or tumor suppressor genes that may provide new insights into cancer diagnosis, predicting clinical outcome of cancers, and new targets for cancer therapies. Oncoproteomics is the term used to describe the application of proteomic technologies in oncology and parallels the related field of oncogenomics. It is now contributing to the development of personalized management of cancer. Genomics has generated a wealth of data that is now being used to identify additional molecular alterations associated with cancer development. Mapping these alterations in the cancer genome is a critical first step in dissecting oncological pathways. Keywords - Biomarkers, Drug development, Drug resistance, Gene therapy, Oncogenomics Introduction Oncogenomics applies high throughput technologies to characterize genes associated transducing cellular signals of cell growth or with cancer and is used to identify new death. -
Augmenter of Liver Regeneration Essential for Growth and Beyond
Cytokine and Growth Factor Reviews 45 (2019) 65–80 Contents lists available at ScienceDirect Cytokine and Growth Factor Reviews journal homepage: www.elsevier.com/locate/cytogfr Augmenter of liver regeneration: Essential for growth and beyond T ⁎ Sara Ibrahima,b, Thomas S. Weissa,b, a Children’s University Hospital (KUNO), University of Regensburg, Regensburg, Germany b Center for Liver Cell Research, University of Regensburg Hospital, Regensburg, Germany ARTICLE INFO ABSTRACT Keywords: Liver regeneration is a well-orchestrated process that is triggered by tissue loss due to trauma or surgical re- Augmenter of liver regeneration section and by hepatocellular death induced by toxins or viral infections. Due to the central role of the liver for GFER body homeostasis, intensive research was conducted to identify factors that might contribute to hepatic growth Isoforms and regeneration. Using a model of partial hepatectomy several factors including cytokines and growth factors Function that regulate this process were discovered. Among them, a protein was identified to specifically support liver Regulation regeneration and therefore was named ALR (Augmenter of Liver Regeneration). ALR protein is encoded by GFER Location (growth factor erv1-like) gene and can be regulated by various stimuli. ALR is expressed in different tissues in three isoforms which are associated with multiple functions: The long forms of ALR were found in the inner- mitochondrial space (IMS) and the cytosol. Mitochondrial ALR (23 kDa) was shown to cooperate with Mia40 to insure adequate protein folding during import into IMS. On the other hand short form ALR, located mainly in the cytosol, was attributed with anti-apoptotic and anti-oxidative properties as well as its inflammation and me- tabolism modulating effects. -
Newly Identified Gon4l/Udu-Interacting Proteins
www.nature.com/scientificreports OPEN Newly identifed Gon4l/ Udu‑interacting proteins implicate novel functions Su‑Mei Tsai1, Kuo‑Chang Chu1 & Yun‑Jin Jiang1,2,3,4,5* Mutations of the Gon4l/udu gene in diferent organisms give rise to diverse phenotypes. Although the efects of Gon4l/Udu in transcriptional regulation have been demonstrated, they cannot solely explain the observed characteristics among species. To further understand the function of Gon4l/Udu, we used yeast two‑hybrid (Y2H) screening to identify interacting proteins in zebrafsh and mouse systems, confrmed the interactions by co‑immunoprecipitation assay, and found four novel Gon4l‑interacting proteins: BRCA1 associated protein‑1 (Bap1), DNA methyltransferase 1 (Dnmt1), Tho complex 1 (Thoc1, also known as Tho1 or HPR1), and Cryptochrome circadian regulator 3a (Cry3a). Furthermore, all known Gon4l/Udu‑interacting proteins—as found in this study, in previous reports, and in online resources—were investigated by Phenotype Enrichment Analysis. The most enriched phenotypes identifed include increased embryonic tissue cell apoptosis, embryonic lethality, increased T cell derived lymphoma incidence, decreased cell proliferation, chromosome instability, and abnormal dopamine level, characteristics that largely resemble those observed in reported Gon4l/udu mutant animals. Similar to the expression pattern of udu, those of bap1, dnmt1, thoc1, and cry3a are also found in the brain region and other tissues. Thus, these fndings indicate novel mechanisms of Gon4l/ Udu in regulating CpG methylation, histone expression/modifcation, DNA repair/genomic stability, and RNA binding/processing/export. Gon4l is a nuclear protein conserved among species. Animal models from invertebrates to vertebrates have shown that the protein Gon4-like (Gon4l) is essential for regulating cell proliferation and diferentiation. -
Oncogenomics
Oncogene (2002) 21, 7901 – 7911 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Groups of p53 target genes involved in specific p53 downstream effects cluster into different classes of DNA binding sites Hua Qian1,4, Ting Wang1, Louie Naumovski2, Charles D Lopez3 and Rainer K Brachmann*,1,5 1Division of Oncology, Department of Medicine, Washington University School of Medicine, 660 S Euclid Avenue, Box 8069, St Louis, Missouri, MO 63110, USA; 2Division of Hematology, Oncology and Stem Cell Transplantation, Stanford University, 269 Campus Drive, CCSR Room 1215, Stanford, California, CA 94305, USA; 3Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health Sciences University, 3181 SW Sam Jackson Park Road, Baird Hall Room 3030, Portland, Oregon, OR 97201, USA The tumor suppressor protein p53, once activated, can and/or apoptosis (Prives and Hall, 1999; Vogelstein et cause either cell cycle arrest or apoptosis through al., 2000). Direct target genes of p53 have one or more transactivation of target genes with p53 DNA binding p53 DNA binding site(s) (DBS) very similar to the p53 sites (DBS). To investigate the role of p53 DBS in the consensus DBS that consists of two copies of the 10 regulation of this profound, yet poorly understood base pair (bp) motif 5’-PuPuPuC(A/T)(T/A)GPyPyPy- decision of life versus death, we systematically studied 3’ separated by 0 – 13 bp (el-Deiry et al., 1992; Funk et all known and potential p53 DBS. We analysed the DBS al., 1992). However, only one p53 DBS, Type IV separated from surrounding promoter regions in yeast Collagenase, is a perfect match for the p53 consensus and mammalian assays with and without DNA damage. -
Duke University Dissertation Template
Gene-Environment Interactions in Cardiovascular Disease by Cavin Keith Ward-Caviness Graduate Program in Computational Biology and Bioinformatics Duke University Date:_______________________ Approved: ___________________________ Elizabeth R. Hauser, Supervisor ___________________________ William E. Kraus ___________________________ Sayan Mukherjee ___________________________ H. Frederik Nijhout Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate Program in Computational Biology and Bioinformatics in the Graduate School of Duke University 2014 i v ABSTRACT Gene-Environment Interactions in Cardiovascular Disease by Cavin Keith Ward-Caviness Graduate Program in Computational Biology and Bioinformatics Duke University Date:_______________________ Approved: ___________________________ Elizabeth R. Hauser, Supervisor ___________________________ William E. Kraus ___________________________ Sayan Mukherjee ___________________________ H. Frederik Nijhout An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate Program in Computational Biology and Bioinformatics in the Graduate School of Duke University 2014 Copyright by Cavin Keith Ward-Caviness 2014 Abstract In this manuscript I seek to demonstrate the importance of gene-environment interactions in cardiovascular disease. This manuscript contains five studies each of which contributes to our understanding of the joint impact of genetic variation