Co-Occupancy by Multiple Cardiac Transcription Factors Identifies
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Methylation of the Homeobox Gene, HOPX, Is Frequently Detected in Poorly Differentiated Colorectal Cancer
ANTICANCER RESEARCH 31: 2889-2892 (2011) Methylation of the Homeobox Gene, HOPX, Is Frequently Detected in Poorly Differentiated Colorectal Cancer YOSHIKUNI HARADA, KAZUHIRO KIJIMA, KAZUKI SHINMURA, MAKIKO SAKATA, KAZUMA SAKURABA, KAZUAKI YOKOMIZO, YOUHEI KITAMURA, ATSUSHI SHIRAHATA, TETSUHIRO GOTO, HIROKI MIZUKAMI, MITSUO SAITO, GAKU KIGAWA, HIROSHI NEMOTO and KENJI HIBI Gastroenterological Surgery, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-ku, Yokohama 227-8501, Japan Abstract. Background: Homeodomein only protein x pathogenesis of colorectal cancer (4-8). It is also known that (HOPX) gene methylation has frequently been detected in gene promoter hypermethylation is involved in the cancer tissues. The methylation status of the HOPX gene in development and progression of cancer (9). An investigation colorectal cancer was examined and compared to the of genetic changes is important in order to clarify the clinocopathological findings. Materials and Methods: tumorigenic pathway of colorectal cancer (10). Eighty-nine tumor samples and corresponding normal tissues The homeodomain only protein x (HOPX) gene, also were obtained from colorectal cancer patients who known as NECC1 (not expressed in choriocarcinoma clone underwent surgery at our hospital. The methylation status of 1), LAGY (lung cancer-associated gene Y), and OB1 (odd the HOPX gene in these samples was examined by homeobox 1 protein), was initially identified as an essential quantitative methylation-specific PCR (qMSP). Subsequently, gene for the modulation of cardiac growth and development the clinicopathological findings were correlated with the (11). Three spliced transcript variants, HOPX-α, HOPX-β methylation status of the HOPX gene. Results: HOPX gene and HOPX-γ, encode the same protein, which contains a methylation was found in 46 (52%) out of the 89 colorectal putative homeodomain motif that acts as an adapter protein carcinomas, suggesting that it was frequently observed in to mediate transcriptional repression (12). -
Uterine Double-Conditional Inactivation of Smad2 and Smad3 in Mice Causes Endometrial Dysregulation, Infertility, and Uterine Cancer
Uterine double-conditional inactivation of Smad2 and Smad3 in mice causes endometrial dysregulation, infertility, and uterine cancer Maya Krisemana,b, Diana Monsivaisa,c, Julio Agnoa, Ramya P. Masanda, Chad J. Creightond,e, and Martin M. Matzuka,c,f,g,h,1 aDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030; bReproductive Endocrinology and Infertility, Baylor College of Medicine/Texas Children’s Hospital Women’s Pavilion, Houston, TX 77030; cCenter for Drug Discovery, Baylor College of Medicine, Houston, TX 77030; dDepartment of Medicine, Baylor College of Medicine, Houston, TX 77030; eDan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030; fDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030; gDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030; and hDepartment of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX 77030 Contributed by Martin M. Matzuk, December 6, 2018 (sent for review April 30, 2018; reviewed by Milan K. Bagchi and Thomas E. Spencer) SMAD2 and SMAD3 are downstream proteins in the transforming in endometrial function. Notably, members of the transforming growth factor-β (TGF β) signaling pathway that translocate signals growth factor β (TGF β) family are involved in many cellular from the cell membrane to the nucleus, bind DNA, and control the processes and serve as principal regulators of numerous biological expression of target genes. While SMAD2/3 have important roles functions, including female reproduction. Previous studies have in the ovary, we do not fully understand the roles of SMAD2/3 in shown the TGF β family to have key roles in ovarian folliculo- the uterus and their implications in the reproductive system. -
Table S1. List of Proteins in the BAHD1 Interactome
Table S1. List of proteins in the BAHD1 interactome BAHD1 nuclear partners found in this work yeast two-hybrid screen Name Description Function Reference (a) Chromatin adapters HP1α (CBX5) chromobox homolog 5 (HP1 alpha) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins (20-23) HP1β (CBX1) chromobox homolog 1 (HP1 beta) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins HP1γ (CBX3) chromobox homolog 3 (HP1 gamma) Binds histone H3 methylated on lysine 9 and chromatin-associated proteins MBD1 methyl-CpG binding domain protein 1 Binds methylated CpG dinucleotide and chromatin-associated proteins (22, 24-26) Chromatin modification enzymes CHD1 chromodomain helicase DNA binding protein 1 ATP-dependent chromatin remodeling activity (27-28) HDAC5 histone deacetylase 5 Histone deacetylase activity (23,29,30) SETDB1 (ESET;KMT1E) SET domain, bifurcated 1 Histone-lysine N-methyltransferase activity (31-34) Transcription factors GTF3C2 general transcription factor IIIC, polypeptide 2, beta 110kDa Required for RNA polymerase III-mediated transcription HEYL (Hey3) hairy/enhancer-of-split related with YRPW motif-like DNA-binding transcription factor with basic helix-loop-helix domain (35) KLF10 (TIEG1) Kruppel-like factor 10 DNA-binding transcription factor with C2H2 zinc finger domain (36) NR2F1 (COUP-TFI) nuclear receptor subfamily 2, group F, member 1 DNA-binding transcription factor with C4 type zinc finger domain (ligand-regulated) (36) PEG3 paternally expressed 3 DNA-binding transcription factor with -
MKL1 (C) Antibody, Rabbit Polyclonal
Order: (888)-282-5810 (Phone) (818)-707-0392 (Fax) [email protected] Web: www.Abiocode.com MKL1 (C) Antibody, Rabbit Polyclonal Cat#: R2403-2 Lot#: Refer to vial Quantity: 100 ul Application: WB Predicted I Observed M.W.: 99 I 140 kDa Uniprot ID: Q969V6 Background: MKL/myocardin-like protein 1 (MKL1) is a transcriptional coactivator of serum response factor (SRF) with the potential to modulate SRF target genes. MKL1 suppresses TNF-induced cell death by inhibiting activation of caspases; its transcriptional activity is indispensable for the antiapoptotic function. MKL1 may up-regulate antiapoptotic molecules, which in turn inhibit caspase activation. Other Names: MKL/myocardin-like protein 1, Megakaryoblastic leukemia 1 protein, Megakaryocytic acute leukemia protein, Myocardin-related transcription factor A, MRTF-A, KIAA1438, MAL Source and Purity: Rabbit polyclonal antibodies were produced by immunizing animals with a GST-fusion protein containing the C-terminal region of human MKL1. Antibodies were purified by affinity purification using immunogen. Storage Buffer and Condition: Supplied in 1 x PBS (pH 7.4), 100 ug/ml BSA, 40% Glycerol, 0.01% NaN3. Store at -20 °C. Stable for 6 months from date of receipt. Species Specificity: Human Tested Applications: WB: 1:1,000-1:3,000 (detect endogenous protein*) *: The apparent protein size on WB may be different from the calculated M.W. due to modifications. For research use only. Not for therapeutic or diagnostic purposes. Abiocode, Inc., 29397 Agoura Rd., Ste 106, Agoura Hills, CA 91301 Order: (888)-282-5810 (Phone) (818)-707-0392 (Fax) [email protected] Web: www.Abiocode.com Product Data: kDa A B 250 150 100 75 50 37 Fig 1. -
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. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
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. -
Autism Ontario Genetics Webinar EN.Pdf
Autism Ontario Genetics Webinar Thursday October 29, 2020 12:00 – 1:00 pm Panelist Stephen Scherer, PhD - Scientist Ny Hoang, MS, CGC – Genetic Counsellor Ryan Yuen, PhD - Scientist Evdokia Anagnostou, MD - Child Neurologist Autism Spectrum Disorders weak genetic factor Complex Multifactorial Condition strong genetic factor environmental factors Genetic contribution is highly variable Strong genetic Moderate genetic Weaker genetic factor factor factor Examples of genetic factors: SHANK3, NRXN1, CHD8, ARID1B, 16p13.11, 22q11 Genetic contribution is a biological difference chromosome Cell DNA sequence Protein Genetic contribution is a biological difference chromosome Cell DNA sequence genetic variant Protein not working Protein Protein not working DNA sequence genetic variant Deletions & Duplications Single Nucleotide Variations 1q21.1 deletions/ duplications CHD8, ARID1B, SCN2A, SYNGAP1, 16p13.11 deletions/ duplications SHANK3, ANK2, GRIN2B, CHD2 NRXN3, ASTN2, MBD5, PTCHD1 DNA sequence genetic variant Repeat Expansions Example: Fragile X syndrome (FMR1) CGGCGGCGGCGGCGG Normal repeat size: 5-40 CGGCGGCGGCGGCGGCGGCGGCGGCGG Syndrome repeat size: >200 DNA sequence genetic variant Repeat Expansions Fragile X syndrome (FMR1), repeat >200 • Disorders linked to well- CGG defined repeat pattern (motif) Friedreich Ataxia (FXN), repeat >100 • Only one pattern per disorder GAA • Myotonic dystrophy Type 1 (DMPK), repeat >50 Normal repeat size range CTG known Huntington’s Disease (HTT), repeat >35 CAG Spinocerebellar Ataxia Type 10 (ATXN10), repeat >800 -
RBPMS Is Differentially Expressed in High-Grade Serous Ovarian Cancers
1 RBPMS is differentially expressed in high-grade serous ovarian cancers. 2 3 Shahan Mamoor1 1 4 [email protected] East Islip, NY 11730 5 6 Ovarian cancer is the most lethal gynecologic cancer1. We sought to identify genes associated 7 with high-grade serous ovarian cancer (HGSC) by comparing global gene expression profiles of 8 normal ovary with that of primary tumors from women diagnosed with HGSC using published microarray data2,3. We found significant differential expression of RBPMS in high-grade serous 9 ovarian tumors. 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Keywords: ovarian cancer, high-grade serous ovarian cancer, HGSC, targeted therapeutics in 26 ovarian cancer, systems biology of ovarian cancer, RBPMS. 27 28 PAGE 1 OF 13 1 The five-year survival rate for women diagnosed with high-grade serous ovarian cancer is 2 between 30-40% and has not changed significantly in decades4,5. The development of novel, 3 4 targeted therapeutics to treat HGSC can be facilitated by an enhanced understanding of the 5 transcriptional behavior of ovarian tumors relative to that of the normal ovary. We mined 6 2,3 7 published microarray data to compare global gene expression profiles between HGSC ovarian 8 tumors and that of normal ovarian tissue. We identified the gene encoding RBPMS as among the 9 most differentially expressed genes in HGSC tumors of the ovary. RBPMS may be a gene of 10 11 interest when prioritizing the study of target genes and pathways for the development of novel 12 therapeutic interventions in high-grade serous ovarian cancers. -
Wide-Scale Analysis of Human Functional Transcription Factor
Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site Yuval Tabach1,2, Ran Brosh2, Yossi Buganim2, Anat Reiner1, Or Zuk1, Assif Yitzhaky1, Mark Koudritsky1, Varda Rotter2 and Eytan Domany1,* 1Departments of Physics of Complex Systems and 2Molecular Cell Biology, The Weizmann Institute of Science, Rehovot 76100, Israel *Corresponding author. Email: [email protected] Tel: +972-8-9343964 Fax: +972-8- 9344109 Running title: Binding Sites Location Bias Keywords: Transcription factor binding sites, location bias, cell cycle, NF-kB, promoter, Gene Ontology, TATA, myocardin 1 Summary Background: Elucidating basic principles that underlie regulation of gene expression by transcription factors (TFs) is a central challenge of the post-genomic era. Transcription factors regulate expression by binding to specific DNA sequences; such a binding event is functional when it affects gene expression. Functionality of a binding site is reflected in conservation of the binding sequence during evolution and in over represented binding in gene groups with coherent biological functions. Functionality is governed by several parameters such as the TF- DNA binding strength, distance of the binding site from the transcription start site (TSS), DNA packing, and more. Understanding how these parameters control functionality of different TFs in different biological contexts is an essential step towards identifying functional TF binding sites, a must for understanding regulation of transcription. Methodology/Principal Findings: We introduce a novel method to screen the promoters of a set of genes with shared biological function, against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. -
1 Early Patterning and Specification of Cardiac Progenitors In
Early Patterning and Specification of Cardiac Progenitors in Gastrulating Mesoderm W. Patrick Devine1,2,3,4, Joshua D. Wythe1,2, Matthew George1,2,5 , Kazuko Koshiba- Takeuchi1,2, Benoit G. Bruneau1,2,4,5 1. Gladstone Institute of Cardiovascular Disease, San Francisco, CA, 94158 USA 2. Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA 94158, USA 3. Department of Pathology, University of California, San Francisco, CA 94143 USA 4. Cardiovascular Research Institute, University of California, San Francisco, CA 94158 USA 5. Developmental and Stem Cell Biology Program, University of San Francisco, CA 94143, USA 6. Department of Pediatrics, University of California, San Francisco, CA 94143 USA Competing interests statement: The authors declare no competing interests. 1 Abstract Mammalian heart development requires precise allocation of cardiac progenitors. The existence of a multipotent progenitor for all anatomic and cellular components of the heart has been predicted but its identity and contribution to the two cardiac progenitor "fields" has remained undefined. Here we show, using clonal genetic fate mapping, that Mesp1+ cells in gastrulating mesoderm are rapidly specified into committed cardiac precursors fated for distinct anatomic regions of the heart. We identify Smarcd3 as a marker of early specified cardiac precursors and identify within these precursors a compartment boundary at the future junction of the left and right ventricles that arises prior to morphogenesis. Our studies define the timing and hierarchy of cardiac progenitor specification and demonstrate that the cellular and anatomical fate of mesoderm-derived cardiac cells is specified very early. These findings will be important to understand the basis of congenital heart defects and to derive cardiac regeneration strategies.