DOCSLIB.ORG

The Photoreceptor Program in Group 3 Medulloblastoma: Role of the Tfs NRL and CRX

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Photoreceptor Program in Group 3 Medulloblastoma: Role of the Tfs NRL and CRX The photoreceptor program in Group 3 Medulloblastoma: Role of the TFs NRL and CRX Celio Pouponnot Institut Curie UMR3347 CNRS / U1021 INSERM Institut Curie - C. Pouponnot - 1 21 février 2018 Medulloblastoma (MB) MB cerebellum Pediatric tumor of the cerebellum (Median age 7 yrs) Most frequent Malignant brain tumor of childhood Treatment : surgery, chemotherapy, radiotherapy WNT G4 è70-80% overall survival at 5 years SHH èImportant secondary effects G3 2 21 février 2018 Medulloblastoma (MB) Ø Based on gene expression profil : 4 different molecular groups MB subtype WNT HH 3 4 incidence 10% 25% 25% 40% Overall survival Very good Intermediate Bad Intermediate (meta+++) (meta++) MYC amplification MYCN & CDK6 Driver pathway WNT HH OTX2 amplification amplification MYC & GABAergic & Expression Photoreceptor Neuronal & WNT signaling SHH signaling photoreceptor glutamatergic signature markers signature signature • Cancers often express aberrant differentiation programs unrelated to the tissue of origin •Although express as a result of cancer cell plasticity, they are not thought to actively participate to cancer progression. •Group 3 MB expresses an aberrant photoreceptor differentiation of unknown significance. 3 3 21 février 2018 Group 3 are enriched in active enhancer/SE controling photoreceptor genes 4 21 février 2018 Group 3 are enriched in active enhancer/SE controling photoreceptor genes Same enrichment observed in the analysis of enhancer-gene target in MB TF/SE interaction identifies two master regulators of the photoreceptor lineage: CRX and NRL 5 21 février 2018 NRL or CRX are involved in MB growth CRX KD NRL KD CRX or NRL KD increases survival of orthotpicallygrafted mice Institut Curie 6 21 février 2018 NRL in Medulloblastoma MAF NRL TA EHR DB LZ Role of NRL in medulloblastoma NRL belongs to the TF of the oncogenic MAF family (Multiple Myeloma) NRL expression is restricted to the photoreceptors of the retina (not expressed in the cerebellum) Role in photoreceptor terminal differentiation (KO NRL: lack of a subset of photoreceptors) Mutations of NRL in human (retinitis pigmentosa) è NRL has not yet been demonstrated to be involved in oncogenesis Role of NRL in medulloblastoma? Institut Curie 7 21 février 2018 NRL is not expressed during cerebellar development Institut Curie 8 21 février 2018 Transcriptomic analysis of NRL KD in D458MED shNRL Mean shNRL Ctl rep1 rep2 rep3 NCOA1 ZNF385A Up CYB5R1 PURB HNRNPU CRX FXR2 GNGT1 ARR3 RPS20 MRFAP1L1 NSF AIPL1 FAM57B NDUFS8 UNC119 NDRG3 ATP1A3 ELOVL2 SMUG1 STXBP1 PBX1 ABCA4 Down PDE6B CPE EPB41L2 PPP5C MEGF9 GNB3 PDC ALDOC ACTR1B RP1 GNGT2 RCVRN VTN SLC6A6 PDE6A RBP3 PLEKHB1 GUCA1A −27 0 4.8 Photoreceptor genes Institut Curie 9 21 février 2018 Transcriptomic analysis of NRL KD in D458MED shNRL Mean shNRL Mean shNRL Ctl shNRL Ctl rep1 rep2 rep3 rep1 rep2 rep3 NCOA1 SMARCD3 ZNF385A LOC645513 RREB1 Up CYB5R1 PURB AMOTL2 HNRNPU TBC1D8 CRX JAG2 FXR2 Up CREBL2 GNGT1 DOK6 ARR3 CPLX3 RPS20 ERLIN1 MRFAP1L1 AK1 NSF FGD4 AIPL1 DDX31 FAM57B NDUFS8 IMPG2 UNC119 FLJ27352 NDRG3 SSX2IP ATP1A3 ARL4D ELOVL2 RNF5 SMUG1 TMEM57 STXBP1 GTPBP8 PBX1 KCTD21 ABCA4 Down PDE6B MXRA7 CKB CPE ICK EPB41L2 Down SPATA20 PPP5C NLK MEGF9 GNB3 ADCK1 PDC GNB3 ALDOC USH2A ACTR1B MAK RP1 ARL6 GNGT2 RD3 RCVRN KIAA1107 VTN NRN1 SLC6A6 TESK2 PDE6A FSTL5 RBP3 CNTN1 PLEKHB1 GUCA1A NRL −27 0 4.8 −7.1 0 3.4 Photoreceptor genes Group3 genes NRL regulates the photoreceptor differentation program and some of the group 3 specific genes Institut Curie 10 21 février 2018 In vitro KD of NRL Ø Growth curves/Viability assay D458Med: DAOY : endogenous NRL expression no endogenous NRL expression 5 12,0 12,0 shCtl 8,0 8,0 shNRL #1 4,0 4,0 Count x10 Count shNRL #2 0,0 0,0 Cell 0 2 4 6 8 0 2 4 6 8 Day Day PDX culture 1 PDX culture 2 3,0E+04 2,0E+05 NI 2,0E+04 shCtl 1,0E+05 1,0E+04 shNRL #1 Luminescence 0,0E+00 0,0E+00 0 2 4 6 0 2 4 6 shNRL #2 Day Day NRL is required for cell viability/growth 11 Institut Curie 11 21 février 2018 NRL protects MB cells from apoptosis Ø Apoptosis : cleaved caspase 3 (FACS) 60 *** 40 *** 20 NRL KD 0 induces apoptosis in MB cell % apoptotic cells apoptotic % shCtl shCtl lines and in PDXs shNRL #1 shNRL #2 shNRL #1 shNRL #2 shNRL D458Med DAOY PDX culture 1 PDX culture 2 100 ** 100 ** ** 80 80 60 * 60 40 40 20 20 % apoptotic cells apoptotic % 0 0 NI #1 NI shCtl shCtl shNRL #1 shNRL shNRL #2 shNRL shNRL #2 shNRL shNRL 12 Institut Curie 12 21 février 2018 Transcriptomic analysis of NRL KD in D458MED List of NRL correlated genes in human MB samples Institut Curie 13 21 février 2018 Transcriptomic analysis of NRL KD in D458MED List of NRL correlated genes in human MB Top 100 genes samples Institut Curie 14 21 février 2018 Transcriptomic analysis of NRL KD in D458MED shNRL Mean shNRL Ctl rep1 rep2 rep3 RALGPS2 ARHGAP28 PALMD Up LOC100130155 ATP5A1 CRX BCL2L1 GNGT1 ENOSF1 IMPG2 SSX2IP BCMO1 ARL4D PCK2 AIPL1 List of NRL correlated Genes significantly EML1 CALCOCO2 genes in human MB Top 100 genes deregulated ABCA4 PDE6B samples following NRL KD C14orf129 KCNH6 CTBS HSBP1L1 Down MNAT1 GNB3 USH2A MAK PHYHD1 PDC SLC1A7 CERS4 ARL6 RD3 RP1 DBP EPS8 GNGT2 RCVRN VTN GPR160 NRL −6.2 0 4.6 NRL correlated genes A Subset of the top 100 genes the expression of which is correlated to that of NRL are deregulated in KD NRL cells. Institut Curie 15 21 février 2018 NRL and BCL-XL expression are correlated Fattet dataset Wnt HH 3 4 XL - NRL Fattet dataset BCL-XL GSE12992 XL expression of BCLof -1.5 0 2.8 - Log2 expression Log2 corr.score: 0.719 / rank: 31/18901 genes) BCL Log2 expression of Wnt HH 3 4 NRL Kool dataset Wnt HH 3 4 XL - NRL Kool dataset BCL-XL GSE10327 XL expression of BCLof -1.5 0 2.4 - corr.score: 0.349 / rank: 925/18901 genes expression Log2 Log2 expression of BCL NRL Wnt HH 3 4 Northcott dataset #1 Wnt HH 3 4 XL - NRL Northcott dataset #1 BCL-XL GSE21140 XL expression - of BCLof -1.5 0 3.1 Log2 expression expression Log2 corr.score: 0.622 / rank 131/22370 genes BCL Log2 expression of NRL Wnt HH 3 4 Northcott dataset #2 HH 3 4 XL - Northcott dataset #2 NRL BCL-XL GSE37385 XL expression - of BCLof -1.3 0 3.7 Log2 expression Log2 corr.score: 0.516 / rank: 82/15896 genes BCL Log2 expression of Wnt HH 3 4 NRL Institut Curie 16 21 février 2018 BCL-XL is a NRL direct target gene -1694 -1681 MARE consensus TGCTGACTCAGCA ChIP known MAF hRhodopsin TGCCGATTCAGCC hInsulin TGCAGCCTCAGCC target genes 20,0 hBCL-XL TGCTGCCTCAGCC 10,0 +1 BCL-XL promoter Fold induction Fold 0,0 Ctl Ctl Ctl Ctl NRL Western-Blot NRL NRL NRL promoter 3’UTR promoter 3’UTR DAOY D458MED PDX culture 1 shNRL shNRL D458Med DAOY Empty vector NRL shCtl #1 #2 shCtl #1 #2 NRL BCL-XL β-actin NRL binds to the BCL-XL promoter and induces its expression Institut Curie 17 21 février 2018 IS BCL-XL a potential therapeutic target in MB? shBCL-XL 100 - Ctl #1 #2 cells 80 60 BCL-XL 40 apoptotic 20 β-actin % 0 - Ctl #1 #2 shBCL-XL EC shbclxl D458D458MED shBclxL PDX2 shCtrl (1/8) 100 Ctl (0/7) shCtrl (0/7) 100 Ctl (1/8) shBCL-XL #1 (0/8) shBclxL#3 (0/8) shBclxL#3 (4/8) (%) ) shBCL-XL #1 (3/8) ) (%) % shBclxL#5 (7/8) ( % shBCL-XL #2 (1/8) shBclxL#5 (1/8) shBCL-XL #2 (6/8) ( l l a a v i v 50 i 50 v v Survival r r Survival u u S S 0 0 0 20 40 60 0 20 40 60 DayDay DayDay KD of BCL-XL induces apoptosis in MB and increases survival 18 Institut Curie 18 21 février 2018 BCL2 proteins, as potential therapeutic targets Ø BCL inhibition using pharmacological agents : TW37 è Target the BH3-binding groove BH3 domain of Bcl Bcl-2 anti-apoptotic in Bcl-2 anti-apoptotic proteins pro-apoptotic protein protein In vivo experiments Inhibition of Tumor regression tumor formation Institut Curie 19 21 février 2018 19 TW37 induces MB regression In vivo experiments : tumor regression TW37 TW37 100 0 10 12 16 18 5 Ctl (n=12) Ctl TW37 Ctl TW37 10 Start p<0.005 Number of of Number 1 TW37 (n=11) photons/s x10 0,1 D9 D19 7 12 17 22 before treatment after treatment Day 1 0,8 0,6 Inhibitor of BCL anti-apoptotic 0,4 Survival 0,2 family induces tumor regression p<0.0001 0 0 20 40 60 80 100 Stop TW37 treatment Institut Curie 20 21 février 2018 Model NRL and CRX are master regulators of the Group 3 MB photoreceptor program Photoreceptor transcriptional program is a dependency in Group 3 MB NRL control cell cycle by inducing CCND2 expression NRL protects MB from apoptosis by inducing BCL-XL expression BCL-XL as a potential therapeutic target in Group 3 MB Institut Curie 21 21 février 2018 Team Signaling and Cancer progression Alexandra Chloé Bertrand Morgane Alain Magalie Sabine Charlène Celio Collaborations: O. Ayrault/ F.Do z / O. Delattre (Institut Curie) Paul Northcott (St Jude) R.J. Weschler-Reya (Burhnam Institute) Charles Lin (Baylor College of Medecine) C. Haberler (Inst. Of Neurology) FranckInstitutInstitut Bourdeaut CurieCurie --nomnom dede(Institut l’émetteur l'émetteur - -Curie)TitreTitre dede lala présentationprésentation 22 21 février 2018 Group 3 MB is enriched in photoreceptor genes Kool et al., 2008 and Cho et al.
Load more

Recommended publications
  • Core Transcriptional Regulatory Circuitries in Cancer
    Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage.
    [Show full text]
  • The Title of the Dissertation
    UNIVERSITY OF CALIFORNIA SAN DIEGO Novel network-based integrated analyses of multi-omics data reveal new insights into CD8+ T cell differentiation and mouse embryogenesis A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Kai Zhang Committee in charge: Professor Wei Wang, Chair Professor Pavel Arkadjevich Pevzner, Co-Chair Professor Vineet Bafna Professor Cornelis Murre Professor Bing Ren 2018 Copyright Kai Zhang, 2018 All rights reserved. The dissertation of Kai Zhang is approved, and it is accept- able in quality and form for publication on microfilm and electronically: Co-Chair Chair University of California San Diego 2018 iii EPIGRAPH The only true wisdom is in knowing you know nothing. —Socrates iv TABLE OF CONTENTS Signature Page ....................................... iii Epigraph ........................................... iv Table of Contents ...................................... v List of Figures ........................................ viii List of Tables ........................................ ix Acknowledgements ..................................... x Vita ............................................. xi Abstract of the Dissertation ................................. xii Chapter 1 General introduction ............................ 1 1.1 The applications of graph theory in bioinformatics ......... 1 1.2 Leveraging graphs to conduct integrated analyses .......... 4 1.3 References .............................. 6 Chapter 2 Systematic
    [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]
  • Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
    Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7
    [Show full text]
  • TWIST1 and TWIST2 Regulate Glycogen Storage and Inflammatory
    J M MUDRY and others TWIST regulation of metabolism 224:3 303–313 Research in muscle TWIST1 and TWIST2 regulate glycogen storage and inflammatory genes in skeletal muscle Correspondence 1 1 2 1,2 Jonathan M Mudry , Julie Massart , Ferenc L M Szekeres and Anna Krook should be addressed to A Krook 1Section for Integrative Physiology, Department of Molecular Medicine and Surgery, and 2Section for Integrative Email Physiology, Department of Physiology and Pharmacology, Karolinska Institutet, SE-171 77 Stockholm, Sweden [email protected] Abstract TWIST proteins are important for development of embryonic skeletal muscle and play a role Key Words in the metabolism of tumor and white adipose tissue. The impact of TWIST on metabolism " TWIST in skeletal muscle is incompletely studied. Our aim was to assess the impact of TWIST1 " metabolism and TWIST2 overexpression on glucose and lipid metabolism. In intact mouse muscle, " glycogen overexpression of Twist reduced total glycogen content without altering glucose uptake. " skeletal muscle Expression of TWIST1 or TWIST2 reduced Pdk4 mRNA, while increasing mRNA levels of Il6, Tnfa, and Il1b. Phosphorylation of AKT was increased and protein abundance of acetyl CoA carboxylase (ACC) was decreased in skeletal muscle overexpressing TWIST1 or TWIST2. Glycogen synthesis and fatty acid oxidation remained stable in C2C12 cells overexpressing TWIST1 or TWIST2. Finally, skeletal muscle mRNA levels remain unaltered in ob/ob mice, type 2 diabetic patients, or in healthy subjects before and after 3 months of exercise training. Journal of Endocrinology Collectively, our results indicate that TWIST1 and TWIST2 are expressed in skeletal muscle. Overexpression of these proteins impacts proteins in metabolic pathways and mRNA level of cytokines.
    [Show full text]
  • Accompanies CD8 T Cell Effector Function Global DNA Methylation
    Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer, Benjamin G. Barwick, Benjamin A. Youngblood, Rafi Ahmed and Jeremy M. Boss This information is current as of October 1, 2021. J Immunol 2013; 191:3419-3429; Prepublished online 16 August 2013; doi: 10.4049/jimmunol.1301395 http://www.jimmunol.org/content/191/6/3419 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130139 Material 5.DC1 References This article cites 81 articles, 25 of which you can access for free at: http://www.jimmunol.org/content/191/6/3419.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 1, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer,* Benjamin G. Barwick,* Benjamin A. Youngblood,*,† Rafi Ahmed,*,† and Jeremy M.
    [Show full text]
  • A Range of Clinical Phenotypes Associated with Mutations in CRX, a Photoreceptor Transcription-Factor Gene Melanie M
    Am. J. Hum. Genet. 63:1307–1315, 1998 A Range of Clinical Phenotypes Associated with Mutations in CRX, a Photoreceptor Transcription-Factor Gene Melanie M. Sohocki,1 Lori S. Sullivan,1,2 Helen A. Mintz-Hittner,2 David Birch,3 John R. Heckenlively,4 Carol L. Freund,5 Roderick R. McInnes,5 and Stephen P. Daiger1,2 1Human Genetics Center, School of Public Health, and 2Department of Ophthalmology and Visual Science, The University of Texas Health Science Center, Houston; 3Retina Foundation of the Southwest, Dallas; 4Jules Stein Eye Institute, University of California, Los Angeles; and 5Program in Developmental Biology, Research Institute, Hospital for Sick Children, Toronto Summary is the diagnosis of some of these diseases difficult because of overlapping phenotypes, but mutations within a single Mutations in the retinal-expressed gene CRX (cone-rod gene may cause very different phenotypes. For example, homeobox gene) have been associated with dominant different mutations in one gene, peripherin/RDS, have cone-rod dystrophy and with de novo Leber congenital been associated with several forms of retinal degenera- amaurosis. However, CRX is a transcription factor for tion, such as cone-rod dystrophy, cone degeneration, and several retinal genes, including the opsins and the gene retinitis pigmentosa, affecting both the macula and the for interphotoreceptor retinoid binding protein. Because panretinal structures (Weleber et al. 1993; Wells et al. loss of CRX function could alter the expression of a 1993; Keen et al. 1994; Nakazawa et al. 1994, 1996). number of other retinal proteins, we screened for mu- Recently, mutations within the photoreceptor-ex- tations in the CRX gene in probands with a range of pressed gene CRX (cone-rod homeobox gene; MIM degenerative retinal diseases.
    [Show full text]
  • An All-To-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine
    bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 2019. 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. An All-to-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine Guang Song1,6, Guohua Wang2,6, Ximei Luo2,3,6, Ying Cheng4, Qifeng Song1, Jun Wan3, Cedric Moore1, Hongjun Song5, Peng Jin4, Jiang Qian3,7,*, Heng Zhu1,7,8,* 1Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China 3Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 4Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA 5Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA 19104, USA 6These authors contributed equally 7Senior author 8Lead Contact *Correspondence: [email protected] (H.Z.), [email protected] (J.Q.). 1 bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 2019. 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. SUMMARY Epigenetic modifications of DNA in mammals play important roles in many biological processes. Identification of readers of these epigenetic marks is a critical step towards understanding the underlying molecular mechanisms. Here, we report the invention and application of an all-to-all approach, dubbed Digital Affinity Profiling via Proximity Ligation (DAPPL), to simultaneously profile human TF-DNA interactions using mixtures of random DNA libraries carrying four different epigenetic modifications (i.e., 5-methylcytosine, 5- hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine).
    [Show full text]
  • 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.
    [Show full text]
  • Gene Networks Activated by Specific Patterns of Action Potentials in Dorsal Root Ganglia Neurons Received: 10 August 2016 Philip R
    www.nature.com/scientificreports OPEN Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons Received: 10 August 2016 Philip R. Lee1,*, Jonathan E. Cohen1,*, Dumitru A. Iacobas2,3, Sanda Iacobas2 & Accepted: 23 January 2017 R. Douglas Fields1 Published: 03 March 2017 Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. Adaptation in the nervous system in response to external stimuli requires synthesis of new gene products in order to elicit long lasting changes in processes such as development, response to injury, learning, and memory1. Information in the environment is coded in the pattern of action-potential firing, therefore gene transcription must be regulated by the pattern of neuronal firing.
    [Show full text]
  • Genequery™ Human Circadian Rhythm Qpcr Array Kit (GQH-CIR) Catalog #GK060
    GeneQuery™ Human Circadian Rhythm qPCR Array Kit (GQH-CIR) Catalog #GK060 Product Description ScienCell's GeneQuery™ Human Circadian Rhythm qPCR Array Kit (GQH-CIR) profiles 88 key genes involved in the regulating changes that occur on a roughly 24-hr cycle. Circadian rhythms are primarily responses to changes in light and are found in most living organisms. Not to be confused with biological clocks, circadian rhythms can influence sleep patterns, hormone release, body temperature, and other homeostatic functions. Below are brief examples of how included genes may be grouped according to their function: Clock regulation: ARNTL, BHLHE40, CLOCK, PER1, RORA Casein kinases: CSNK1A1, CSNK1D, CSNK1E, CSNK2A1 CREB pathway: CAMK2A, HTR7, MAPK1, PRKACB, AANAT Transcription factors: SMAD4, IRF1, NFIL3, RORC, SP1, GeneQuery™ qPCR array kits are qPCR ready in a 96-well plate format, with each well containing one primer set that recognizes and efficiently amplifies a specific target gene's cDNA. The carefully designed primers ensure that: (i) the optimal annealing temperature in qPCR analysis is 65°C (with 2 mM Mg2+ and no DMSO); (ii) the primer set recognizes all known transcript variants of the target gene, unless otherwise noted; and (iii) only one gene is amplified. Each primer set has been validated by qPCR with melt curve analysis and gel electrophoresis. GeneQuery™ qPCR Array Kit Controls Each GeneQuery™ plate contains eight controls (Figure 1): Five target housekeeping genes (β-actin, GAPDH, LDHA, NONO, and PPIH), which enable normalization of data. The Genomic DNA (gDNA) Control (GDC), which detects gDNA contamination in cDNA samples. This primer set targets a non-transcribed region of the genome.
    [Show full text]
  • Estrogen Promotes Mandibular Condylar Fibrocartilage
    www.nature.com/scientificreports OPEN Estrogen Promotes Mandibular Condylar Fibrocartilage Chondrogenesis and Inhibits Received: 27 November 2017 Accepted: 18 May 2018 Degeneration via Estrogen Published: xx xx xxxx Receptor Alpha in Female Mice Jennifer L. Robinson 1,3, Paola Soria2, Manshan Xu2, Mark Vrana1, Jefrey Luchetti1, Helen H. Lu3, Jing Chen2 & Sunil Wadhwa2 Temporomandibular joint degenerative disease (TMJ-DD) is a chronic form of TMJ disorder that specifcally aficts people over the age of 40 and targets women at a higher rate than men. Prevalence of TMJ-DD in this population suggests that estrogen loss plays a role in the disease pathogenesis. Thus, the goal of the present study was to determine the role of estrogen on chondrogenesis and homeostasis via estrogen receptor alpha (ERα) during growth and maturity of the joint. Young and mature WT and ERαKO female mice were subjected to ovariectomy procedures and then given placebo or estradiol treatment. The efect of estrogen via ERα on fbrocartilage morphology, matrix production, and protease activity was assessed. In the young mice, estrogen via ERα promoted mandibular condylar fbrocartilage chondrogenesis partly by inhibiting the canonical Wnt signaling pathway through upregulation of sclerostin (Sost). In the mature mice, protease activity was partly inhibited with estrogen treatment via the upregulation and activity of protease inhibitor 15 (Pi15) and alpha-2- macroglobulin (A2m). The results from this work provide a mechanistic understanding of estradiol on TMJ growth and homeostasis and can be utilized for development of therapeutic targets to promote regeneration and inhibit degeneration of the mandibular condylar fbrocartilage. Temporomandibular joint degenerative disease (TMJ-DD) is marked by degradation and premature calcifcation of the extracellular matrix (ECM) of the articular mandibular condylar fbrocartilage.
    [Show full text]