1 Supplementary Figure Legends Supplementary Figure 1. Magnified

Total Page:16

File Type:pdf, Size:1020Kb

1 Supplementary Figure Legends Supplementary Figure 1. Magnified 1 Supplementary Figure Legends Supplementary Figure 1. Magnified image for the colony-formation assays shown in Fig. 3A. Supplementary Figure 2. The mRNA expression level of NR1I2 in endogenously NR1I2-positive cells (SK-N-KP) as well as stably transfected cells (SMS-KAN-B1, SMS-KAN-B2, and GOTO-A1) and their mock-transfected control (KAN-vector and GOTO-vector). Supplementary Figure 3. Representative results of RT-PCR analysis revealed up-regulation of CYP3A4, a known transcriptional target of NR1I2, in VP-16-NR1/2, underscoring the reliability of our system for detecting target genes. 2 Supplementary Table Up-regulated genes in NR1I2-stably expressing NB cells Ratio Acc. number Gene symbol Description Chr. Location 1st 2nd Cell cycle / Cell death /Differentiation NM 003914 1 CCNA1 cyclin A1 13q12.3-q13 1.89 1.92 NM 003503 1 CDC7L1 CDC7 cell division cycle 7-like 1 (S. cerevisiae) 1p22 1.84 1.86 NM 004208 1 PDCD8 programmed cell death 8 (apoptosis-inducing factor) Xq25-q26 2.00 2.03 NM 000465 1 BARD1 BRCA1 associated RING domain 1 2q34-q35 1.90 1.93 NM 000127 1 EXT1 exostoses (multiple) 1 8q24.11-q24.13 1.87 1.89 NM 016341 1 PLCE1 phospholipase C, epsilon 1 10q23 2.89 2.94 NM_004613_1 TGM2 transglutaminase 2 (C polypeptide, protein-glutamine-gamma- 20q12 1.59 1.62 glutamyltransferase) NM 004907 1 ETR101 immediate early protein 19p13.13 1.65 1.67 NM 003389 1 CORO2A coronin, actin binding protein, 2A 9q22.3 1.71 1.73 NM 005876 1 APEG1 aortic preferentially expressed protein 1 2q36.3 1.88 1.91 Signaling pathway NM 013324 1 CISH cytokine inducible SH2-containing protein 3p21.3 2.14 2.17 NM 000455 1 STK11 serine/threonine kinase 11 (Peutz-Jeghers syndrome) 19p13.3 1.62 1.64 NM 005167 1 ARHC ras homolog gene family, member C 1p21-p13 1.50 1.52 NM 001667 1 ARL2 ADP-ribosylation factor-like 2 11q13 1.51 1.53 NM 006189 1 OMP olfactory marker protein 11q13.5 1.55 1.58 XM 032838 1 GUCY1A3 guanylate cyclase 1, soluble, alpha 3 4q31.1-q31.2 1.60 1.63 Cell adhesion NM 031500 1 PCDHA4 protocadherin alpha 4 5q31 1.94 1.97 NM 024003 1 L1CAM L1 cell adhesion molecule Xq28 1.87 1.89 NM 018930 1 PCDHB10 protocadherin beta 10 5q31 1.63 1.66 NM 016522 1 HNT neurotrimin 11q25 1.55 1.57 NM 003259 1 ICAM5 intercellular adhesion molecule 5, telencephalin 19p13.2 1.52 1.54 Transcription-related genes NM 030380 1 GLI2 GLI-Kruppel family member GLI2 2q14 2.68 2.72 NM 002359 1 MAFG v-maf musculoaponeurotic fibrosarcoma oncogene homolog G (avian) 17q25 2.51 2.55 NM 005170 1 ASCL2 achaete-scute complex-like 2 (Drosophila) 11p15.5 2.43 2.46 NM 014553 1 LBP-9 LBP protein; likely ortholog of mouse CRTR-1 2q14 1.93 1.95 NM 018489 1 ASH1 hypothetical protein ASH1 1q21.2 1.81 1.83 NM 006756 1 TCEA1 transcription elongation factor A (SII), 1 3p22-p21.3 1.54 1.56 NM 002729 1 HHEX hematopoietically expressed homeobox 10q24.1 1.55 1.57 NM 003935 1 TOP3B topoisomerase (DNA) III beta 22q11.22 1.59 1.61 NM 015339 1 ADNP activity-dependent neuroprotector 20q13.13-q13.2 1.61 1.63 NM 004182 1 UXT ubiquitously-expressed transcript Xp11.23- 2.90 2.94 NM 003893 1 LDB1 LIM domain binding 1 10q24-q25 1.58 1.60 NM 005663 1 WHSC2 Wolf-Hirschhorn syndrome candidate 2 4p16.3 1.65 1.67 Receptor / membrane protein NM_006840_1 LILRB5 leukocyte immunoglobulin-like receptor, subfamily B (with TM and 19q13.4 1.73 1.76 ITIM domains), member 5 NM_004631_1 LRP8 low density lipoprotein receptor-related protein 8, apolipoprotein e 1p34 1.52 1.54 receptor NM_022965_1 FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric 4p16.3 1.56 1.59 dwarfism) NM 025141 1 BLP2 BBP-like protein 2 15q26.3 1.51 1.53 NM 016447 1 MPP6 membrane protein, palmitoylated 6 (MAGUK p55 subfamily member 7p15 1.67 1.70 NM 005727 1 TSPAN-1 tetraspan 1 1p34.1 1.67 1.69 NM_000484_1 APP amyloid beta (A4) precursor protein (protease nexin-II, Alzheimer 21q21.3 2.03 2.05 Ion channel / transpor NM 012456 1 TIMM10 translocase of inner mitochondrial membrane 10 homolog (yeast) 11q12.1-q12.3 1.87 1.89 NM 003172 1 SURF1 surfeit 1 9q34.2 1.92 1.95 NM 006188 1 OCM oncomodulin 7p13-p11 1.7 1.72 NM 002243 1 KCNJ15 potassium inwardly-rectifying channel, subfamily J, member 15 21q22.2 1.88 1.91 NM 004588 1 SCN2B sodium channel, voltage-gated, type II, beta polypeptide 11q23 1.52 1.54 NM 021625 1 TRPV4 transient receptor potential cation channel, subfamily V, member 4 12q24.1 1.77 1.80 NM 005829 1 AP3S2 adaptor-related protein complex 3, sigma 2 subunit 15q25.2 2.51 2.55 NM 018484 1 SLC22A11 solute carrier family 22 (organic anion/cation transporter), member 11 11q13.1 1.66 1.68 NM 006224 1 PITPN phosphotidylinositol transfer protein 17p13.3 1.73 1.75 NM 005063 1 SCD stearoyl-CoA desaturase (delta-9-desaturase) 10q23-q24 1.74 1.77 NM 016176 1 Cab45 calcium binding protein Cab45 precursor 1p36.33 1.84 1.86 3 (Table. Continued) Ratio Acc. number Gene symbol Description Chr. Location 1st 2nd Homeostasis / metabolic enzymes NM 004776 1 B4GALT5 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 5 20q13.1-q13.2 1.54 1.56 NM 019894 1 TMPRSS4 transmembrane protease, serine 4 11q23.3 1.55 1.57 NM_021599_1 ADAMTS2 a disintegrin-like and metalloprotease (reprolysin type) with 5qter 1.58 1.60 thrombospondin type 1 motif, 2 NM 004567 1 PFKFB4 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 3p21-p22 4.90 4.97 NM 000854 1 GSTT2 glutathione S-transferase theta 2 22q11.23 3.23 3.28 NM 000300 1 PLA2G2A phospholipase A2, group IIA (platelets, synovial fluid) 1p35 3.01 3.05 NM0001201 EPHX1 epoxide hydrolase 1, microsomal (xenobiotic) 1q42.1 2.22 2.25 NM0026291 PGAM1 phosphoglycerate mutase 1 (brain) 10q25.3 2.13 2.16 NM0011901 BCAT2 branched chain aminotransferase 2, mitochondrial 19q13 1.61 1.63 NM 002905 1 RDH5 retinol dehydrogenase 5 (11-cis and 9-cis) 12q13-q14 1.62 1.64 NM0122601 HPCL2 2-hydroxyphytanoyl-CoA lyase 3p25.1 1.72 1.74 NM 005476 1 GNE UDP-N-acetylglucosamine-2-epimerase/N-acetylmannosamine kinase 9p11.2 1.87 1.90 NM0000391 APOA1 apolipoprotein A-I 11q23-q24 1.98 2.00 Ribosomal proteins NM 001020 1 RPS16 ribosomal protein S16 19q13.1 1.56 1.59 NM 052969 1 RPL39L ribosomal protein L39-like 3q27 2.30 2.34 DNA replication / repair NM 002553 1 ORC5L origin recognition complex, subunit 5-like (yeast) 7q22.1 1.67 1.69 NM_001983_1 ERCC1 excision repair cross-complementing rodent repair deficiency, 19q13.2-q13.3 1.82 1.84 complementation group 1 Hormone / Growth factor NM 007179 1 INSL6 insulin-like 6 9p24 1.66 1.68 NM0054481 BMP15 bone morphogenetic protein 15 Xp11.2 1.50 1.52 Unknown function NM0223331 TIAL1 TIA1 cytotoxic granule-associated RNA binding protein-like 1 10q 2.34 2.37 NM 014841 1 SNAP91 synaptosomal-associated protein, 91 kD homolog (mouse) 6q15 3.38 3.43 NM 025136 1 OPA3 optic atrophy 3 (autosomal recessive, with chorea and spastic 19q13.32 2.46 2.49 NM 012332 1 MT-ACT48 Mitochondrial Acyl-CoA Thioesterase Xp22.13 1.59 1.61 NM 013344 1 LZLP leucine zipper-like protein 11q13.1 1.58 1.61 NM 000607 1 ORM1 orosomucoid 1 9q31-q32 1.58 1.60 NM 006455 1 SC65 nucleolar autoantigen (55kD) similar to rat synaptonemal complex 17q21.1 1.56 1.58 NM0184751 TPARL TPA regulated locus 4q12 1.56 1.58 NM_021622_1 PLEKHA1 pleckstrin homology domain-containing, family A (phosphoinositide 10q26.3 1.54 1.56 binding specific) member 1 NM 006269 1 RP1 retinitis pigmentosa 1 (autosomal dominant) 8q11-q13 1.54 1.56 NM 014419 1 DKKL1 soggy-1 gene 19q13.33 1.53 1.56 NM 023036 1 DNAI2 dynein, axonemal, intermediate polypeptide 2 17q25 1.52 1.54 NM 001549 1 IFIT4 interferon-induced protein with tetratricopeptide repeats 4 10q24 1.52 1.54 NM 024614 1 FLJ13197 hypothetical protein FLJ13197 4p14 2.95 2.99 NM 023928 1 FLJ12389 hypothetical protein FLJ12389 similar to acetoacetyl-CoA synthetase 12q24.31 2.16 2.19 NM 018671 1 IRO039700 hypothetical protein IRO039700 15q26.1 2.12 2.15 NM 014674 1 KIAA0212 KIAA0212 gene product 3p26.1 2.01 2.04 NM 024706 1 FLJ13479 hypothetical protein FLJ13479 16p11.1 1.92 1.94 BC002509 1 MGC2941 hypothetical protein MGC2941 17p13.2 1.90 1.93 NM 018618 1 PRO2121 hypothetical protein PRO2121 1p36.33 1.89 1.91 NM 024668 1 FLJ20288 hypothetical protein FLJ20288 5q31.3 1.81 1.83 NM 025260 1 C6orf25 chromosome 6 open reading frame 25 6p21.31 1.74 1.76 NM 016068 1 LOC51024 CGI-135 protein 7q11.22 1.67 1.69 NM 024843 1 FLJ23462 duodenal cytochrome b 2q31.1 1.64 1.67 NM0175661 DKFZp434G05 hypothetical protein DKFZp434G0522 16q24.3 1.56 1.58 NM 032638 1 MGC2306 hypothetical protein MGC2306 3q22.1 1.55 1.58 NM 014746 1 KIAA0161 KIAA0161 gene product 2p25.3 1.54 1.56 NM0175661 DKFZp434G05 hypothetical protein DKFZp434G0522 16q24.3 1.56 1.58 NM 032638 1 MGC2306 hypothetical protein MGC2306 3q22.1 1.55 1.58 NM 014746 1 KIAA0161 KIAA0161 gene product 2p25.3 1.54 1.56 NM 018198 1 FLJ10737 hypothetical protein FLJ10737 1p36.23 1.51 1.54 NM 017877 1 FLJ20555 hypothetical protein FLJ20555 2p23.3 1.51 1.53 NM 018540 1 PRO2831 hypothetical protein PRO2831 6p21.1 1.51 1.53 Supplementary Figure 1 Empty NR1I2 VP-NR1I2 IMR32 SMS-KAN Misawa et al.
Recommended publications
  • 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]
  • 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,
    [Show full text]
  • High-Throughput Bioinformatics Approaches to Understand Gene Expression Regulation in Head and Neck Tumors
    High-throughput bioinformatics approaches to understand gene expression regulation in head and neck tumors by Yanxiao Zhang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in The University of Michigan 2016 Doctoral Committee: Associate Professor Maureen A. Sartor, Chair Professor Thomas E. Carey Assistant Professor Hui Jiang Professor Ronald J. Koenig Associate Professor Laura M. Rozek Professor Kerby A. Shedden c Yanxiao Zhang 2016 All Rights Reserved I dedicate this thesis to my family. For their unfailing love, understanding and support. ii ACKNOWLEDGEMENTS I would like to express my gratitude to Dr. Maureen Sartor for her guidance in my research and career development. She is a great mentor. She patiently taught me when I started new in this field, granted me freedom to explore and helped me out when I got lost. Her dedication to work, enthusiasm in teaching, mentoring and communicating science have inspired me to feel the excite- ment of research beyond novel scientific discoveries. I’m also grateful to have an interdisciplinary committee. Their feedback on my research progress and presentation skills is very valuable. In particular, I would like to thank Dr. Thomas Carey and Dr. Laura Rozek for insightful discussions on the biology of head and neck cancers and human papillomavirus, Dr. Ronald Koenig for expert knowledge on thyroid cancers, Dr. Hui Jiang and Dr. Kerby Shedden for feedback on the statistics part of my thesis. I would like to thank all the past and current members of Sartor lab for making the lab such a lovely place to stay and work in.
    [Show full text]
  • Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
    Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................
    [Show full text]
  • Rna-Sequencing Applications: Gene Expression Quantification and Methylator Phenotype Identification
    The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 8-2013 RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION Guoshuai Cai Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Computational Biology Commons, and the Medicine and Health Sciences Commons Recommended Citation Cai, Guoshuai, "RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION" (2013). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 386. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/386 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION
    [Show full text]
  • Cep-2020-00633.Pdf
    Clin Exp Pediatr Vol. 64, No. 5, 208–222, 2021 Review article CEP https://doi.org/10.3345/cep.2020.00633 Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis Seoung Wan Nam, MD, PhD1,*, Kwang Seob Lee, MD2,*, Jae Won Yang, MD, PhD3,*, Younhee Ko, PhD4, Michael Eisenhut, MD, FRCP, FRCPCH, DTM&H5, Keum Hwa Lee, MD, MS6,7,8, Jae Il Shin, MD, PhD6,7,8, Andreas Kronbichler, MD, PhD9 1Department of Rheumatology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea; 2Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; 3Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Korea; 4Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea; 5Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, UK; 6Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea; 7Division of Pediatric Nephrology, Severance Children’s Hospital, Seoul, Korea; 8Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea; 9Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria 1,3) The publication of genetic epidemiology meta-analyses has analyses have redundant duplicate topics and many errors. increased rapidly, but it has been suggested that many of the Although there has been an impressive increase in meta-analyses statistically significant results are false positive. In addition, from China, particularly those on genetic associa tions, most most such meta-analyses have been redundant, duplicate, and claimed candidate gene associations are likely false-positives, erroneous, leading to research waste. In addition, since most suggesting an urgent global need to incorporate genome-wide claimed candidate gene associations were false-positives, cor- data and state-of-the art statistical inferences to avoid a flood of rectly interpreting the published results is important.
    [Show full text]
  • Metastatic Genes Targeted by an Antioxidant in an Established Radiation- and Estrogen-Breast Cancer Model
    1590 INTERNATIONAL JOURNAL OF ONCOLOGY 51: 1590-1600, 2017 Metastatic genes targeted by an antioxidant in an established radiation- and estrogen-breast cancer model GLORIA M. CALAF1,2 and DEBASISH ROY3 1Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Chile; 2Center for Radiological Research, Columbia University Medical Center, New York, NY; 3Department of Natural Sciences, Hostos College, The City University of New York, Bronx, NY, USA Received March 16, 2017; Accepted August 23, 2017 DOI: 10.3892/ijo.2017.4125 Abstract. Breast cancer remains the second most common Introduction disease worldwide. Radiotherapy, alone or in combination with chemotherapy, is widely used after surgery as a treatment for Breast cancer remains the second most common cancer cancer with proven therapeutic efficacy manifested by reduced worldwide with nearly 1.7 million new cases in 2012 (1). In incidence of loco-regional and distant recurrences. However, cancer treatment, radiotherapy, alone or in a combination clinical evidence indicates that relapses occurring after radio- with chemotherapy, is widely used after surgery with proven therapy are associated with increased metastatic potential therapeutic efficacy manifested by reduced incidence of and poor prognosis in the breast. Among the anticarcinogenic loco-regional and distant recurrences (2-4). However, clinical and antiproliferative agents, curcumin is a well-known major evidence indicates that relapses occurring after radiotherapy dietary natural yellow pigment derived from the rhizome of the are associated with increased metastatic potential and poor herb Curcuma longa (Zingiberaceae). The aim of the present prognosis in breast (5,6) and other tissues (7,8). This has also study was to analyze the differential expression of metastatic been confirmed experimentally in tumors growing within a genes in radiation- and estrogen-induced breast cancer cell previously irradiated mammary tissue that is more invasive model and the effect of curcumin on such metastatic genes in and metastasized (9-11).
    [Show full text]
  • (B6;129.Cg-Gt(ROSA)26Sor Tm20(CAG-Ctgf-GFP)Jsd) Were Crossed with Female Foxd1cre/+ Heterozygote Mice 1, and Experimental Mice Were Selected As Foxd1cre/+; Rs26cig/+
    Supplemental Information SI Methods Animal studies Heterozygote mice (B6;129.Cg-Gt(ROSA)26Sor tm20(CAG-Ctgf-GFP)Jsd) were crossed with female Foxd1Cre/+ heterozygote mice 1, and experimental mice were selected as Foxd1Cre/+; Rs26CIG/+. In some studies Coll-GFPTg or TCF/Lef:H2B-GFPTg mice or Foxd1Cre/+; Rs26tdTomatoR/+ mice were used as described 2; 3. Left kidneys were subjected to ureteral obstruction using a posterior surgical approach as described 2. In some experiments recombinant mouse DKK1 (0.5mg/kg) or an equal volume of vehicle was administered by daily IP injection. In the in vivo ASO experiment, either specific Lrp6 (TACCTCAATGCGATTT) or scrambled negative control ASO (AACACGTCTATACGC) (30mg/kg) (Exiqon, LNA gapmers) was administered by IP injection on d-1, d1, d4, and d7. In other experiments anti-CTGF domain-IV antibodies (5mg/kg) or control IgG were administered d-1, d1 and d6. All animal experiments were performed under approved IACUC protocols held at the University of Washington and Biogen. Recombinant protein and antibody generation and characterization Human CTGF domain I (sequence Met1 CPDEPAPRCPAGVSLVLDGCGCCRVCAKQLGELCTERDPCDPHKGLFC), domain I+II (sequence Met1CPDEPAPRCPAGVSLVLDGCGCCRVCAKQLGELCTERDPCDPHKGLFCCIFGGT VYRSGESFQSSCKYQCTCLDGAVGCMPLCSMDVRLPSPDCPFPRRVKLPGKCCEE) were cloned and expressed in 293 cells, and purified by Chelating SFF(Ni) Column, tested for single band by SEC and PAGE, and tested for absence of contamination. Domain-IV (sequence GKKCIRTPKISKPIKFELSGCTSMKTYRAKFCGVCTDGRCCTPHRTTTLPVEFKCPDGE VMKKNMMFIKTCACHYNCPGDNDIFESLYYRKMY) was purchased from Peprotech. Mouse or human DKK1 was generated from the coding sequence with some modifications and a tag. Secreted protein was harvested from 293 cells, and purified by nickel column, and tested for activity in a supertopflash (STF) assay 4. DKK1 showed EC50 of 0.69nM for WNT3a-induced WNT signaling in STF cells.
    [Show full text]
  • Gene List HTG Edgeseq Mouse Mrna Tumor Response Panel
    Gene List HTG EdgeSeq Mouse mRNA Tumor Response Panel Mouse ANT1 Atf5 C1qbp Ccl7 Cd52 Clcf1 Cxcl15 Egf Fgf15 Gli3 Heyl Igf2r Il34 ANT2 Atg10 C1ra Ccl8 Cd53 Clec4a1 Cxcl16 Egfr Fgf16 Glycam1 Hfe Igfbp3 Il3ra ANT3 Atg12 C1s1 Ccl9 Cd55 Clec4a2 Cxcl2 Egr1 Fgf17 Gna11 Hgf Igfbp4 Il4 ANT4 Atg16l1 C2 Ccna1 Cd59a Clec4a3 Cxcl3 Egr2 Fgf18 Gnaq Hhex Igll1 Il4ra POS1 Atg5 C3 Ccna2 Cd59b Clec4a4 Cxcl5 Egr3 Fgf2 Gnas Hhip Igtp Il5 POS2 Atg7 C3ar1 Ccnb1 Cd6 Clec4e Cxcl9 Eif2a Fgf20 Gnb1 Hif1a Iigp1 Il5ra POS3 Atm C4a Ccnb3 Cd63 Clec4n Cxcr1 Eif2b4 Fgf21 Gng11 Hist1h3b Ikbkap Il6 POS4 Atr C4b Ccnd1 Cd68 Clec5a Cxcr2 Eif4ebp1 Fgf22 Gng12 Hist2h3b Ikbkb Il6ra A2m Atrx C4bp Ccnd2 Cd69 Clec7a Cxcr3 Elane Fgf23 Gng2 Hist2h3c1_ Ikbke Il6st Abca1 Atxn1 C5ar1 Ccnd3 Cd7 Cltc Cxcr4 Elk1 Fgf3 Gng4 Hist2h3c2 Ikbkg Il7 Abcb10 Axin1 C6 Ccne1 Cd70 Clu Cxcr5 Endog Fgf4 Gng7 Hlx Ikzf1 Il7r Abcb1a Axin2 C7 Ccne2 Cd74 Cma1 Cxcr6 Eng Fgf5 Gngt1 Hmga1_ Ikzf2 Il9 Abcb5 Axl C8a Ccno Cd79a Cmah Cxxc4 Entpd1 Fgf6 Gp1bb Hmga1b Ikzf3 Ilf3 Abcf1 B2m C8b Ccr1 Cd79b Cmklr1 Cybb Eomes Fgf7 Gpc4 Hmga2 Ikzf4 Inhba Abcg1 Bad C8g Ccr10 Cd80 Cmpk2 Cyfip2 Ep300 Fgf8 Gpi1 Hmgb1 Il10 Inhbb Abl1 Bag1 C9 Ccr2 Cd81 Cntfr Cyld Epcam Fgf9 Gpr183 Hmgb2 Il10ra Inpp5d Abl2 Bag3 Cacna1c Ccr3 Cd82 Col11a1 Cysltr1 Epha2 Fgfbp1 Grb2 Hmgn1 Il10rb Insr Ackr2 Baiap3 Cacna1d Ccr4 Cd83 Col11a2 Cysltr2 Epo Fgfr1 Gria3 Hmox1 Il11 Irak1 Ackr4 Bak1 Cacna1e Ccr5 Cd84 Col1a1 Dach1 Epor Fgfr2 Grin1 Hnf1a Il11ra1 Irak2 Acvr1b Bambi Cacna1g Ccr6 Cd86 Col1a2 Dad1 Epsti1 Fgfr3 Grin2a Hoxa10 Il11ra2 Irak3 Acvr1c Banf1
    [Show full text]
  • Gene List HTG Edgeseq Immune Response Panel
    Gene List HTG EdgeSeq Immune Response Panel A2M AGER ARAF BIRC6 CAVIN1 CD1D CDK4 COPS2 CXCL5 AAMP AGO1 ARF1 BIRC7 CAVIN2 CD2 CDK6 COPS5 CXCL6 ABCA1 AGO2 ARF5 BLK CBL CD209 CDK8 CPA3 CXCL8 ABCB1 AGT ARG1 BLNK CBLB CD22 CDK9 CPEB4 CXCL9 ABCC2 AHI1 ARHGAP30 BMP8A CCDC116 CD226 CDKAL1 CPT1A CXCR1 ABCC3 AHR ARHGEF1 BMPER CCDC60 CD24 CDKN1A CPT1B CXCR2 ABCC5 AHSA1 ARID5B BORCS5 CCDC88B CD244 CDKN1B CPT2 CXCR3 ABCC8 AHSA2P ARL3 BRAF CCL11 CD247 CDKN1C CR2 CXCR4 ABCF1 AICDA ARPC2 BRD8 CCL13 CD27 CDKN2A CRCP CXCR5 ABHD6 AIG1 ARRB2 BRF1 CCL14 CD274 CDKN2B CREB1 CXCR6 ABL1 AIM2 ASPA BRWD1 CCL15 CD276 CDKN2C CREB3 CXorf21 ACAA2 AIRE ATF1 BSN CCL16 CD28 CDKN2D CREB3L2 CYBA ACADL AKT1 ATF2 BST2 CCL17 CD33 CEACAM1 CREB3L4 CYBB ACADM AKT1S1 ATF3_activating BTC CCL18 CD34 CEACAM3 CREBBP CYC1 ACE AKT2 ATF3_repressing BTK CCL19 CD36 CEACAM4 CREM CYLD ACE2 AKT3 ATF4 BTLA CCL2 CD38 CEACAM6 CRK CYP1B1 ACIN1 ALAS1 ATF6 BTNL2 CCL20 CD3D CEACAM8 CRLF2 CYP27A1 ACKR1 ALAS2 ATG101 BUB1 CCL21 CD3E CEBPA CRLS1 CYP4A11_ ACKR2 ALOX15 ATG16L1 BUB1B CCL22 CD3G CEBPB CRP CYP4A22 ACLY ALOX5 ATG4B C19orf33 CCL23 CD4 CEBPD CRTC3 CYP51A1 ACOT1_ACOT2 ALOX5AP ATG5 C1orf53 CCL24 CD40 CEBPG CRYGD CYP7A1 ACOT13 ALPL ATG9A C1QA CCL26 CD40LG CELF1 CSF1 DAB2IP ACOX1 AMIGO3 ATM C1QB CCL27 CD44 CELF2 CSF1R DACT1 ACOX3 ANG ATP2A2 C1QBP CCL3 CD46 CENPO CSF2 DAP ACOXL ANGPT1 ATP2B4 C1QTNF6 CCL3L_family CD48 CEP250 CSF2RA DAXX ACP5 ANGPT2 ATP5F1B C1S CCL4 CD5 CEP57 CSF2RB DBP ACPP ANGPTL1 ATP6V0A1 C2 CCL5 CD52 CFB CSF3 DCLRE1B ACSL1 ANGPTL4 ATP6V0C C3 CCL7 CD55 CFD CSF3R DDAH1 ACSL3 ANK3
    [Show full text]
  • 180034V1.Full.Pdf
    bioRxiv preprint doi: https://doi.org/10.1101/180034; this version posted August 30, 2017. 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. Transcriptional Architecture of Synaptic Communication Delineates Cortical GABAergic Neuron Identity Anirban Paul, Megan Crow, Ricardo Raudales, Jesse Gillis, Z. Josh Huang * One Bungtown Road, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA *corresponding and lead author Contact Information: Anirban Paul: [email protected] Megan Crow: [email protected] Ricardo Raudales: [email protected] Jesse Gillis: [email protected] Z. Josh Huang: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/180034; this version posted August 30, 2017. 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. 1 Abstract 2 Understanding the organizational logic of neural circuits requires deciphering the biological 3 basis of neuron type diversity and identity, but there is no consensus on defining a neuron type. 4 We analyzed single cell transcriptomes of anatomically and physiologically characterized 5 cortical ground truth populations and conducted a computational genomic screen for 6 transcription profiles that distinguish them. We discovered that cardinal GABAergic neuron 7 types are delineated by a transcriptional architecture that encodes their synaptic communication 8 patterns. This architecture comprises 6 categories of ~40 gene families including cell adhesion 9 molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and 10 vesicular release components, and transcription factors. Combinatorial expression of select 11 members across families shapes a multi-layered molecular scaffold along cell membrane that 12 may customize synaptic connectivity patterns and input-output signaling properties.
    [Show full text]
  • Transcriptomic Analysis of Lncap Tumor Xenograft To
    nutrients Article Transcriptomic Analysis of LNCaP Tumor Xenograft to Elucidate the Components and Mechanisms Contributed by Tumor Environment as Targets for Dietary Prostate Cancer Prevention Studies Lu Yu 1, Robert W. Li 2 , Haiqiu Huang 1 , Quynhchi Pham 3, Liangli Yu 1 and Thomas T. Y. Wang 3,* 1 Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; [email protected] (L.Y.); [email protected] (H.H.); [email protected] (L.Y.) 2 Animal Parasitic Diseases Laboratory, Beltsville Area Research Center, ARS, USDA, Beltsville, MD 20705, USA; [email protected] 3 Diet, Genomics and Immunology Laboratory, Beltsville Human Nutrition Research Center, ARS, USDA, Beltsville, MD 20705, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-(301)-504-8459 Abstract: LNCaP athymic xenograft model has been widely used to allow researchers to examine the effects and mechanisms of experimental treatments such as diet and diet-derived cancer preventive and therapeutic compounds on prostate cancer. However, the biological characteristics of human LNCaP cells before/after implanting in athymic mouse and its relevance to clinical human prostate outcomes remain unclear and may dictate interpretation of biological efficacies/mechanisms of Citation: Yu, L.; Li, R.W.; Huang, H.; diet/diet-derived experimental treatments. In this study, transcriptome profiles and pathways of hu- Pham, Q.; Yu, L.; Wang, T.T.Y. man prostate LNCaP cells before (in vitro) and after (in vivo) implanting into xenograft mouse were Transcriptomic Analysis of LNCaP compared using RNA-sequencing technology (RNA-seq) followed by bioinformatic analysis. A shift Tumor Xenograft to Elucidate the from androgen-responsive to androgen nonresponsive status was observed when comparing LNCaP Components and Mechanisms xenograft tumor to culture cells.
    [Show full text]