Proteomic Identification of the Transcription Factors Ikaros And

Total Page:16

File Type:pdf, Size:1020Kb

Proteomic Identification of the Transcription Factors Ikaros And European School of Molecular Medicine (SEMM) University of Milan and University of Naples “Federico II” PhD degree in Systems Medicine (curriculum in Molecular Oncology) Settore disciplinare: BIO/11 Proteomic identification of the transcription factors Ikaros and Aiolos as new Myc interactors on chromatin Chiara Veronica Locarno Matricola: R10755 Center for Genomic Science IIT@SEMM, Milan Supervisor: Bruno Amati, PhD IEO, Milan Added Supervisor: Arianna Sabò, PhD IEO, Milan Academic year 2017-2018 Table of contents List of abbreviations ........................................................................................................... 4 List of figures ....................................................................................................................... 8 List of tables ....................................................................................................................... 11 Abstract .............................................................................................................................. 12 1. INTRODUCTION ......................................................................................................... 13 1.1 Myc ........................................................................................................................................ 13 1.1.1 Myc discovery and structure ........................................................................................... 13 1.1.2. Role of Myc in physiological and pathological conditions ............................................ 15 1.1.3. Myc interactions on chromatin ....................................................................................... 17 1.1.3.1. Myc binding to DNA ............................................................................................................ 17 1.1.3.2. Mechanisms of transcriptional control by Myc .................................................................... 19 1.1.4. Myc and cancer therapy ................................................................................................. 21 1.2. B-cell development ............................................................................................................ 22 1.2.1. Early development .......................................................................................................... 22 1.2.2. Maturation ...................................................................................................................... 24 1.2.3. Antibody production and memory ................................................................................. 25 1.2.4. Role of Myc in B lymphopoiesis .................................................................................... 27 1.3. Ikaros and Aiolos ................................................................................................................ 28 1.3.1. Ikaros discovery and structure ........................................................................................ 28 1.3.2. Function of Ikaros and Aiolos in hematopoiesis ............................................................ 30 1.3.3. Expression pattern of Ikaros family members ................................................................ 32 1.3.4. Ikaros and Aiolos role in B lymphocyte development ................................................... 33 1.3.4.1. Priming of the lymphoid lineage ........................................................................................... 33 1.3.4.2. Ikaros role in early B cell progenitors ................................................................................... 34 1.3.4.3. Role of Aiolos in mature B cells ........................................................................................... 35 1.3.5. Mechanisms of action ..................................................................................................... 35 1.3.6. Pathologies and therapies ............................................................................................... 38 1 Aim of the project ............................................................................................................... 40 2. MATERIALS AND METHODS ................................................................................. 41 2.1. Cell lines ................................................................................................................... 41 2.2. Cell transfection ....................................................................................................... 42 2.3. Cell transduction ...................................................................................................... 43 2.4. Proliferation assays .................................................................................................. 43 2.5. Chromatin proteomics (ChroP) ........................................................................................ 44 2.5.1. Chromatin Immunoprecipitation (ChIP) ........................................................................ 44 2.5.2. In-gel digestion for MS .................................................................................................. 45 2.5.3. Liquid chromatography and tandem Mass Spectrometry (LC-MS/MS) ........................ 46 2.5.4. Protein identification by MaxQuant software and data analysis .................................... 47 2.6. ChIP-qPCR ............................................................................................................... 48 2.7. Western Blot (WB) .................................................................................................. 48 2.8. Co-Immunoprecipitation (coIP) ............................................................................... 49 2.9. ChIP-sequencing (ChIP-seq) .................................................................................... 49 2.10. ChIP-seq analysis ................................................................................................... 50 2.11. RNA extraction and analysis: quantitative PCR (qPCR) and RNA-seq. ............... 51 2.12. RNA-seq data analysis ........................................................................................... 51 2.13. Motif analysis ......................................................................................................... 52 2.14. Functional annotation ............................................................................................. 52 2.15. List of primers ................................................................................................................... 53 3. RESULTS ....................................................................................................................... 54 3.1. Identification of Myc interactors through mass spectrometry analysis ...................... 54 3.1.1. Myc modulation in p493-6 cells ..................................................................................... 54 3.1.2. Setting of the label-free ChroP technique. ..................................................................... 56 3.1.3. Identification of new Myc interactors ............................................................................ 59 3.1.4. Validation of Myc interactors ......................................................................................... 62 2 3.2. Ikaros, Aiolos and Myc interaction in p493-6 cells ....................................................... 64 3.2.1. Ikaros and Aiolos coimmunoprecipitate with Myc ........................................................ 64 3.2.2. Genome-wide distribution of Ikaros, Aiolos and Myc ................................................... 69 3.2.3. Identification of direct Myc-regulated genes. ................................................................ 79 3.3. Combined activation of Ikaros and Myc in BH1 cells .................................................. 81 3.3.1. IkarosER and Tet-Myc inducible system ....................................................................... 81 3.3.2. Ikaros and Myc have an opposite effect on cell proliferation ........................................ 89 3.3.3. Transcriptional effect of Ikaros and Myc induction ....................................................... 92 4. DISCUSSION .............................................................................................................. 101 4.1. Identification of new Myc interactors using a MS-based approach. ......................... 101 4.2. Interplay between Ikaros/Aiolos and Myc .................................................................... 104 5. APPENDIX .................................................................................................................. 110 5.1. Experimental design ......................................................................................................... 110 5.2. Materials and methods ..................................................................................................... 112 5.2.1. Cell culture ................................................................................................................... 112 5.2.2. SILAC ChroP ............................................................................................................... 112 5.2.3. MS analysis .................................................................................................................. 112 5.2.4. siRNA tranfection ......................................................................................................... 112
Recommended publications
  • Identification of the Binding Partners for Hspb2 and Cryab Reveals
    Brigham Young University BYU ScholarsArchive Theses and Dissertations 2013-12-12 Identification of the Binding arP tners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non- Redundant Roles for Small Heat Shock Proteins Kelsey Murphey Langston Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Microbiology Commons BYU ScholarsArchive Citation Langston, Kelsey Murphey, "Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins" (2013). Theses and Dissertations. 3822. https://scholarsarchive.byu.edu/etd/3822 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Julianne H. Grose, Chair William R. McCleary Brian Poole Department of Microbiology and Molecular Biology Brigham Young University December 2013 Copyright © 2013 Kelsey Langston All Rights Reserved ABSTRACT Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactors and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston Department of Microbiology and Molecular Biology, BYU Master of Science Small Heat Shock Proteins (sHSP) are molecular chaperones that play protective roles in cell survival and have been shown to possess chaperone activity.
    [Show full text]
  • Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
    Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A.
    [Show full text]
  • Physical and Functional Interaction Between SET1/COMPASS Complex Component CFP-1 and a Sin3s HDAC Complex in C. Elegans
    Physical and functional interaction between SET1/COMPASS complex component CFP-1 and a Sin3S HDAC complex in C. elegans Flore Beurton, Przemyslaw Stempor, Matthieu Caron, Alex Appert, Yan Dong, Ron A-J Chen, David Cluet, Yohann Couté, Marion Herbette, Ni Huang, et al. To cite this version: Flore Beurton, Przemyslaw Stempor, Matthieu Caron, Alex Appert, Yan Dong, et al.. Physical and functional interaction between SET1/COMPASS complex component CFP-1 and a Sin3S HDAC complex in C. elegans. Nucleic Acids Research, Oxford University Press, 2019, 47 (21), pp.11164- 11180. 10.1093/nar/gkz880. hal-02375443 HAL Id: hal-02375443 https://hal.archives-ouvertes.fr/hal-02375443 Submitted on 25 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. bioRxiv preprint doi: https://doi.org/10.1101/436147; this version posted October 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Physical and functional interaction between SET1/COMPASS complex component CFP- 1 and a Sin3 HDAC complex Beurton F.
    [Show full text]
  • TASOR Is a Pseudo-PARP That Directs HUSH Complex Assembly and Epigenetic Transposon Control
    bioRxiv preprint doi: https://doi.org/10.1101/2020.03.09.974832; this version posted March 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. TASOR is a pseudo-PARP that directs HUSH complex assembly and epigenetic transposon control Christopher H. Douse1,‡, Iva A. Tchasovnikarova2,‡, Richard T. Timms2,‡, Anna V. Protasio2, Marta Seczynska2, Daniil M. Prigozhin1, Anna Albecka1,2, Jane Wagstaff3, James C. Williamson2, Stefan M.V. Freund3, Paul J. Lehner2*, Yorgo Modis1,2* 1 Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, CB2 0QH, UK 2 Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge CB2 0AW, UK 3 Structural Studies Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, CB2 0QH, UK Current addresses: Department of Experimental Medical Science, Lund University, Sölvegatan 19, Lund, Sweden (C.H.D.); The Gurdon Institute, Tennis Court Road, Cambridge, UK (I.A.T.); Department of Pathology, Tennis Court Road, Cambridge, UK (A.V.P.); Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA (D.M.P.) ‡These authors contributed equally *co-corresponding authors Keywords: Transcriptional repression; epigenetic silencing; antiretroviral response; genome stability; histone H3 lysine 9 methylation (H3K9me3); transposable element (TE); long interspersed nuclear element-1 (LINE-1); poly-ADP ribose polymerase (PARP); RNA-binding protein; RNA-induced transcriptional silencing; CUT&RUN; CUT&Tag; genome profiling 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.09.974832; this version posted March 11, 2020.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • NBN Gene Analysis and It's Impact on Breast Cancer
    Journal of Medical Systems (2019) 43: 270 https://doi.org/10.1007/s10916-019-1328-z IMAGE & SIGNAL PROCESSING NBN Gene Analysis and it’s Impact on Breast Cancer P. Nithya1 & A. ChandraSekar1 Received: 8 March 2019 /Accepted: 7 May 2019 /Published online: 5 July 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Single Nucleotide Polymorphism (SNP) researches have become essential in finding out the congenital relationship of structural deviations with quantitative traits, heritable diseases and physical responsiveness to different medicines. NBN is a protein coding gene (Breast Cancer); Nibrin is used to fix and rebuild the body from damages caused because of strand breaks (both singular and double) associated with protein nibrin. NBN gene was retrieved from dbSNP/NCBI database and investigated using computational SNP analysis tools. The encrypted region in SNPs (exonal SNPs) were analyzed using software tools, SIFT, Provean, Polyphen, INPS, SNAP and Phd-SNP. The 3’ends of SNPs in un-translated region were also investigated to determine the impact of binding. The association of NBN gene polymorphism leads to several diseases was studied. Four SNPs were predicted to be highly damaged in coding regions which are responsible for the diseases such as, Aplastic Anemia, Nijmegan breakage syndrome, Microsephaly normal intelligence, immune deficiency and hereditary cancer predisposing syndrome (clivar). The present study will be helpful in finding the suitable drugs in future for various diseases especially for breast cancer. Keywords NBN . Single nucleotide polymorphism . Double strand breaks . nsSNP . Associated diseases Introduction NBN has a more complex structure due to its interaction with large proteins formed from the ATM gene which is NBN (Nibrin) is a protein coding gene, it is also known as highly essential in identifying damaged strands of DNA NBS1, Cell cycle regulatory Protein P95, is situated on and facilitating their repair [1].
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Transcriptional Control of Tissue-Resident Memory T Cell Generation
    Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells.
    [Show full text]
  • Construction of a Cerna Network Combining Transcription Factors in Eutopic Endometrial Tissue of Tubal Factor Infertility and Endometriosis-Related Infertility
    Construction of a ceRNA network combining transcription factors in eutopic endometrial tissue of tubal factor infertility and endometriosis-related infertility Junzui Li ( [email protected] ) Xiamen University https://orcid.org/0000-0002-6640-8215 Lulu Ren Xiamen University Medical College Cui Yang Xiamen University Medical College Rongfeng Wu Xiamen University Medical College Zhixiong Huang Xiamen University School of Life Sciences Qionghua Chen Xiamen University Medical College Research article Keywords: TFI, endometriosis-related infertility, DEGs, ceRNA network, TFs Posted Date: December 19th, 2019 DOI: https://doi.org/10.21203/rs.2.19312/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/27 Abstract Purpose Although tubal factor infertility (TFI) and endometriosis-related infertility all can result in female infertility, the pathogenesis of TFI and endometriosis-related infertility were different. The pathophysiologic mechanisms of TFI and endometriosis-related infertility have not been investigated thoroughly. Thus, the aim of the study is to identify the potential crucial genes, pathways, transcription factors (TFs) and long non-coding RNAs (lncRNAs) associated with TFI and endometriosis-related infertility, and further analyze the molecular mechanism implicated in genes. Methods 3 patients with TFI and 3 patients with endometriosis-related infertility were recruited, and microarray hybridization of the eutopic endometrial tissue during the window of implantation (WOI) was performed to examine the expression of mRNAs and lncRNAs. First, differentially expressed genes (DEGs) and differentially expressed lncRNAs (DELs) were screened out based on P < 0.05 and fold change (FC) ≧ 2. Second, gene ontology, pathway and TFs enrichment analyses and PPI network construction of DEGs were performed.
    [Show full text]
  • 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
    [Show full text]
  • 1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
    xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A
    [Show full text]
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
    [Show full text]