TET2 Repression by Androgen Hormone Regulates Global Hydroxymethylation Status and Prostate Cancer Progression
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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. -
14-3-3Sigma Negatively Regulates the Stability and Subcellular Localization of Cop1
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 12-2010 14-3-3SIGMA NEGATIVELY REGULATES THE STABILITY AND SUBCELLULAR LOCALIZATION OF COP1 Chun-Hui Su Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Biology Commons Recommended Citation Su, Chun-Hui, "14-3-3SIGMA NEGATIVELY REGULATES THE STABILITY AND SUBCELLULAR LOCALIZATION OF COP1" (2010). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 101. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/101 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]. 14-3-3SIGMA NEGATIVELY REGULATES THE STABILITY AND SUBCELLULAR LOCALIZATION OF COP1 By Chun-Hui Su, M.S. APPROVED: ______________________________ Mong-Hong Lee, Supervisory Professor ______________________________ Randy -
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. -
Transcription Factors E2A, FOXO1 and FOXP1 Regulate Recombination Activating Gene Expression in Cancer Cells
Transcription Factors E2A, FOXO1 and FOXP1 Regulate Recombination Activating Gene Expression in Cancer Cells Zhengshan Chen1, Yanna Xiao1, Junjun Zhang1, Jing Li1, Yuxuan Liu2, Yingying Zhao2, Changchun Ma1, Jin Luo1, Yamei Qiu1, Guowei Huang1, Christine Korteweg1, Jiang Gu1,2* 1 Department of Pathology, Shantou University Medical College, Shantou, China, 2 Department of Pathology, Peking (Beijing) University Health Science Center, Beijing, China Abstract It has long been accepted that immunoglobulins (Igs) were produced by B lymphoid cells only. Recently Igs have been found to be expressed in various human cancer cells and promote tumor growth. Recombination activating gene 1 (RAG1) and RAG2, which are essential enzymes for initiating variable-diversity-joining segment recombination, have also been found to be expressed in cancer cells. However, the mechanism of RAG activation in these cancer cells has not been elucidated. Here, we investigated the regulatory mechanism of RAG expression in four human cancer cell lines by analyzing transcription factors that induce RAG activation in B cells. By RT-PCR, Western blot and immunofluorescence, we found that transcription factors E2A, FOXO1 and FOXP1 were expressed and localized to the nuclei of these cancer cells. Over- expression of E2A, FOXO1 or Foxp1 increased RAG expression, while RNA interference of E2A, FOXO1 or FOXP1 decreased RAG expression in the cancer cells. Chromatin immunoprecipitation experiments showed acetylation of RAG enhancer (Erag) and E2A, FOXO1 or FOXP1 were bound to Erag in vivo. These results indicate that in these cancer cells the transcription factors E2A, FOXO1 and FOXP1 regulate RAG expression, which initiates Ig gene rearrangement much in the way similar to B lymphocytes. -
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 -
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int. -
FOXP1 Acts Through a Negative Feedback Loop to Suppress FOXO-Induced Apoptosis
Cell Death and Differentiation (2013) 20, 1219–1229 & 2013 Macmillan Publishers Limited All rights reserved 1350-9047/13 www.nature.com/cdd FOXP1 acts through a negative feedback loop to suppress FOXO-induced apoptosis R van Boxtel1,5, C Gomez-Puerto1,6, M Mokry2,6, A Eijkelenboom3, KE van der Vos1, EES Nieuwenhuis2, BMT Burgering3, EW-F Lam4 and PJ Coffer*,1,2 Transcriptional activity of Forkhead box transcription factor class O (FOXO) proteins can result in a variety of cellular outcomes depending on cell type and activating stimulus. These transcription factors are negatively regulated by the phosphoinositol 3-kinase (PI3K)–protein kinase B (PKB) signaling pathway, which is thought to have a pivotal role in regulating survival of tumor cells in a variety of cancers. Recently, it has become clear that FOXO proteins can promote resistance to anti-cancer therapeutics, designed to inhibit PI3K–PKB activity, by inducing the expression of proteins that provide feedback at different levels of this pathway. We questioned whether such a feedback mechanism may also exist directly at the level of FOXO-induced transcription. To identify critical modulators of FOXO transcriptional output, we performed gene expression analyses after conditional activation of key components of the PI3K–PKB–FOXO signaling pathway and identified FOXP1 as a direct FOXO transcriptional target. Using chromatin immunoprecipitation followed by next-generation sequencing, we show that FOXP1 binds enhancers that are pre-occupied by FOXO3. By sequencing the transcriptomes of cells in which FOXO is specifically activated in the absence of FOXP1, we demonstrate that FOXP1 can modulate the expression of a specific subset of FOXO target genes, including inhibiting expression of the pro-apoptotic gene BIK. -
The NANOG Transcription Factor Induces Type 2 Deiodinase Expression and Regulates the Intracellular Activation of Thyroid Hormone in Keratinocyte Carcinomas
Cancers 2020, 12 S1 of S18 Supplementary Materials: The NANOG Transcription Factor Induces Type 2 Deiodinase Expression and Regulates the Intracellular Activation of Thyroid Hormone in Keratinocyte Carcinomas Annarita Nappi, Emery Di Cicco, Caterina Miro, Annunziata Gaetana Cicatiello, Serena Sagliocchi, Giuseppina Mancino, Raffaele Ambrosio, Cristina Luongo, Daniela Di Girolamo, Maria Angela De Stefano, Tommaso Porcelli, Mariano Stornaiuolo and Monica Dentice Figure S1. Strategy for the mutagenesis of Dio2 promoter. (A) Schematic representation of NANOG Binding Site within the Dio2 promoter region. (B) Schematic diagram for site‐directed mutagenesis of NANOG Binding Site on Dio2 promoter region by Recombinant PCR. (C) Representation of the mutated NANOG Binding Site on Dio2 promoter region. (D) Electropherogram of the NANOG Binding Site mutation within the Dio2 promoter. Cancers 2020, 12 S2 of S18 Figure S2. Strategy for the silencing of NANOG expression. (A) Cloning strategies for the generation of NANOG shRNA expression vectors. (B) Electropherograms of the NANOG shRNA sequences cloned into pcDNA3.1 vector. (C) Validation of effective NANOG down-modulation by two different NANOG shRNA vectors was assessed by Western Blot analysis of NANOG expression in BCC cells. (D) Quantification of NANOG protein levels versus Tubulin levels in the same experiment as in C is represented by histograms. Cancers 2020, 12 S3 of S18 Figure S3. The CD34+ cells are characterized by the expression of typical epithelial stemness genes. The mRNA levels of a panel of indicated stemness markers of epidermis were measured by Real Time PCR in the same experiment indicated in figure 3F and G. Cancers 2020, 12 S4 of S18 Figure S4. -
Targeting EZH2 Increases Therapeutic Efficacy of PD-1 Check-Point Blockade in Models of Prostate Cancer Supplement Figures and T
Targeting EZH2 Increases Therapeutic Efficacy of PD-1 Check-Point Blockade in Models of Prostate Cancer Supplement Figures and Tables 1 Fig. S1. (A) Schema and genotyping PCR example for the creation of EM and EMC genetically engineered mice. (B) Three-dimensional PCa organoids generated from EM mice (without PSACreERT2) alleles. When treated with tamoxifen, demonstrates no loss of H3K27me3 or EDU staining, indicating specificity of tamoxifen-PSACreERT2 mediated deletion of the Ezh2 set domain. (C) Principle component analysis (PCA) following chemical and genetic inhibition of Ezh2 catalytic function results in significant changes in gene expression. 2 Fig. S2. (A) A 29-gene signature derived from Fig. 1C demonstrates complete independence from a previously published polycomb repression signature. (B) Our 29 gene signature demonstrates significant correlation with a previously published polycomb repression signature in 2 independent human PCa gene expression datasets. (C) EZH2 activity is not determined by EZH2 mRNA expression. 3 Fig. S3. A 29-gene signature derived from Fig 1C was used to generate signature scores for each patient within four independent human prostate cancer RNA-seq datasets. Patients were ranked highest score to lowest score and subject to quartile separation. First (blue) and fourth (red) quartiles were analyzed by supervised clustering to demonstrate expression differences within patients with most lowest EZH2 activity and most highest EZH2 activity. 4 Fig. S4. Genes representing IFN signaling (STAT1, IRF9), Th1 chemokines (CXCL10, CXCL11), and MHC Class I molecules (B2M, HLA-A) were shown to be enriched in PCa patients with low EZH2 activity. 5 Fig. S5. Treatment of 22Rv1 human 2D cell lines with the demonstrated conditions for 96 hours show that EZH2 inhibition increases expression of dsRNA (green = dsRNA, blue = nuclei). -
Role of Estrogen Receptor in Breast Cancer Cell Gene Expression
4046 MOLECULAR MEDICINE REPORTS 13: 4046-4050, 2016 Role of estrogen receptor in breast cancer cell gene expression YABING ZHENG1, XIYING SHAO1, YUAN HUANG1, LEI SHI1, BO CHEN2, XIAOJIA WANG1, HONGJIAN YANG3, ZHANHONG CHEN1 and XIPING ZHANG3 Departments of 1Medical Oncology (Breast), 2Pathology and 3Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China Received April 28, 2015; Accepted February 23, 2016 DOI: 10.3892/mmr.2016.5018 Abstract. The aim of the present study was to establish the Europe in 2012, and the number of breast cancer-associated underlying regulatory mechanism of estrogen receptor (ER) mortalities is 131,000 (6). Furthermore, breast cancer is in breast cancer cell gene expression. A gene expression the most common cause of cancer-associated mortality in profile accession( no. GSE11324) was downloaded from the females. Therefore, it is essential to understand its molecular Gene Expression Omnibus (GEO) database. Differentially mechanism and develop more effective therapeutic methods expressed genes (DEGs) from an estrogen treatment group and for breast cancer treatment. a control group were identified. Chromatin immunoprecipita- The estrogen receptor (ER) is critical in determining the tion with high-throughput sequencing data (series GSE25710) phenotype of human breast cancers and is one of the most was obtained from the GEO for the ER binding sites, and important therapeutic targets (7). Furthermore, certain studies binding and expression target analysis was performed. A total have suggested that activation of ER is responsible for various of 3,122 DEGs were obtained and ER was demonstrated to biological processes, including cell growth and differentia- exhibit inhibition and activation roles during the regulation tion, and programmed cell death (8,9). -
Detection of Differentially Expressed Candidate Genes for a Fatty Liver QTL
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Kobayashi et al. BMC Genetics (2016) 17:73 DOI 10.1186/s12863-016-0385-2 RESEARCH ARTICLE Open Access Detection of differentially expressed candidate genes for a fatty liver QTL on mouse chromosome 12 Misato Kobayashi1, Miyako Suzuki1, Tamio Ohno2, Kana Tsuzuki1, Chie Taguchi1, Soushi Tateishi1, Teruo Kawada3, Young-il Kim3, Atsushi Murai1 and Fumihiko Horio1,4* Abstract Background: The SMXA-5 mouse is an animal model of high-fat diet-induced fatty liver. The major QTL for fatty liver, Fl1sa on chromosome 12, was identified in a SM/J × SMXA-5 intercross. The SMXA-5 genome consists of the SM/J and A/J genomes, and the A/J allele of Fl1sa is a fatty liver-susceptibility allele. The existence of the responsible genes for fatty liver within Fl1sa was confirmed in A/J-12SM consomic mice. The aim of this study was to identify candidate genes for Fl1sa, and to investigate whether the identified genes affect the lipid metabolism. Results: A/J-12SM mice showed a significantly lower liver triglyceride content compared to A/J mice when fed the high-fat diet for 7 weeks. We detected differences in the accumulation of liver lipids in response to the high-fat diet between A/J and A/J-12SM consomic mice. To identify candidate genes for Fl1sa, we performed DNA microarray analysis using the livers of A/J-12SM and A/J mice fed the high-fat diet. The mRNA levels of three genes (Iah1, Rrm2, Prkd1) in the chromosomal region of Fl1sa were significantly different between the strains. -
Autism and Cancer Share Risk Genes, Pathways, and Drug Targets
TIGS 1255 No. of Pages 8 Forum Table 1 summarizes the characteristics of unclear whether this is related to its signal- Autism and Cancer risk genes for ASD that are also risk genes ing function or a consequence of a second for cancers, extending the original finding independent PTEN activity, but this dual Share Risk Genes, that the PI3K-Akt-mTOR signaling axis function may provide the rationale for the (involving PTEN, FMR1, NF1, TSC1, and dominant role of PTEN in cancer and Pathways, and Drug TSC2) was associated with inherited risk autism. Other genes encoding common Targets for both cancer and ASD [6–9]. Recent tumor signaling pathways include MET8[1_TD$IF],[2_TD$IF] genome-wide exome-sequencing studies PTK7, and HRAS, while p53, AKT, mTOR, Jacqueline N. Crawley,1,2,* of de novo variants in ASD and cancer WNT, NOTCH, and MAPK are compo- Wolf-Dietrich Heyer,3,4 and have begun to uncover considerable addi- nents of signaling pathways regulating Janine M. LaSalle1,4,5 tional overlap. What is surprising about the the nuclear factors described above. genes in Table 1 is not necessarily the Autism is a neurodevelopmental number of risk genes found in both autism Autism is comorbid with several mono- and cancer, but the shared functions of genic neurodevelopmental disorders, disorder, diagnosed behaviorally genes in chromatin remodeling and including Fragile X (FMR1), Rett syndrome by social and communication genome maintenance, transcription fac- (MECP2), Phelan-McDermid (SHANK3), fi de cits, repetitive behaviors, tors, and signal transduction pathways 15q duplication syndrome (UBE3A), and restricted interests. Recent leading to nuclear changes [7,8].