ASCL1 Is a MYCN- and LMO1-Dependent Member of the Adrenergic Neuroblastoma Core Regulatory Circuitry
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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. -
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. -
Identifying and Mapping Cell-Type-Specific Chromatin PNAS PLUS Programming of Gene Expression
Identifying and mapping cell-type-specific chromatin PNAS PLUS programming of gene expression Troels T. Marstranda and John D. Storeya,b,1 aLewis-Sigler Institute for Integrative Genomics, and bDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544 Edited by Wing Hung Wong, Stanford University, Stanford, CA, and approved January 2, 2014 (received for review July 2, 2013) A problem of substantial interest is to systematically map variation Relating DHS to gene-expression levels across multiple cell in chromatin structure to gene-expression regulation across con- types is challenging because the DHS represents a continuous ditions, environments, or differentiated cell types. We developed variable along the genome not bound to any specific region, and and applied a quantitative framework for determining the exis- the relationship between DHS and gene expression is largely tence, strength, and type of relationship between high-resolution uncharacterized. To exploit variation across cell types and test chromatin structure in terms of DNaseI hypersensitivity and genome- for cell-type-specific relationships between DHS and gene expres- wide gene-expression levels in 20 diverse human cell types. We sion, the measurement units must be placed on a common scale, show that ∼25% of genes show cell-type-specific expression ex- the continuous DHS measure associated to each gene in a well- plained by alterations in chromatin structure. We find that distal defined manner, and all measurements considered simultaneously. regions of chromatin structure (e.g., ±200 kb) capture more genes Moreover, the chromatin and gene-expression relationship may with this relationship than local regions (e.g., ±2.5 kb), yet the local only manifest in a single cell type, making standard measures of regions show a more pronounced effect. -
Microglia Emerge from Erythromyeloid Precursors Via Pu.1- and Irf8-Dependent Pathways
ART ic LE S Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways Katrin Kierdorf1,2, Daniel Erny1, Tobias Goldmann1, Victor Sander1, Christian Schulz3,4, Elisa Gomez Perdiguero3,4, Peter Wieghofer1,2, Annette Heinrich5, Pia Riemke6, Christoph Hölscher7,8, Dominik N Müller9, Bruno Luckow10, Thomas Brocker11, Katharina Debowski12, Günter Fritz1, Ghislain Opdenakker13, Andreas Diefenbach14, Knut Biber5,15, Mathias Heikenwalder16, Frederic Geissmann3,4, Frank Rosenbauer6 & Marco Prinz1,17 Microglia are crucial for immune responses in the brain. Although their origin from the yolk sac has been recognized for some time, their precise precursors and the transcription program that is used are not known. We found that mouse microglia were derived from primitive c-kit+ erythromyeloid precursors that were detected in the yolk sac as early as 8 d post conception. + lo − + − + These precursors developed into CD45 c-kit CX3CR1 immature (A1) cells and matured into CD45 c-kit CX3CR1 (A2) cells, as evidenced by the downregulation of CD31 and concomitant upregulation of F4/80 and macrophage colony stimulating factor receptor (MCSF-R). Proliferating A2 cells became microglia and invaded the developing brain using specific matrix metalloproteinases. Notably, microgliogenesis was not only dependent on the transcription factor Pu.1 (also known as Sfpi), but also required Irf8, which was vital for the development of the A2 population, whereas Myb, Id2, Batf3 and Klf4 were not required. Our data provide cellular and molecular insights into the origin and development of microglia. Microglia are the tissue macrophages of the brain and scavenge dying have the ability to give rise to microglia and macrophages in vitro cells, pathogens and molecules using pattern recognition receptors and in vivo under defined conditions. -
Prospective Isolation of NKX2-1–Expressing Human Lung Progenitors Derived from Pluripotent Stem Cells
The Journal of Clinical Investigation RESEARCH ARTICLE Prospective isolation of NKX2-1–expressing human lung progenitors derived from pluripotent stem cells Finn Hawkins,1,2 Philipp Kramer,3 Anjali Jacob,1,2 Ian Driver,4 Dylan C. Thomas,1 Katherine B. McCauley,1,2 Nicholas Skvir,1 Ana M. Crane,3 Anita A. Kurmann,1,5 Anthony N. Hollenberg,5 Sinead Nguyen,1 Brandon G. Wong,6 Ahmad S. Khalil,6,7 Sarah X.L. Huang,3,8 Susan Guttentag,9 Jason R. Rock,4 John M. Shannon,10 Brian R. Davis,3 and Darrell N. Kotton1,2 2 1Center for Regenerative Medicine, and The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA. 3Center for Stem Cell and Regenerative Medicine, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas, USA. 4Department of Anatomy, UCSF, San Francisco, California, USA. 5Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. 6Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, Massachusetts, USA. 7Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA. 8Columbia Center for Translational Immunology & Columbia Center for Human Development, Columbia University Medical Center, New York, New York, USA. 9Department of Pediatrics, Monroe Carell Jr. Children’s Hospital, Vanderbilt University, Nashville, Tennessee, USA. 10Division of Pulmonary Biology, Cincinnati Children’s Hospital, Cincinnati, Ohio, USA. It has been postulated that during human fetal development, all cells of the lung epithelium derive from embryonic, endodermal, NK2 homeobox 1–expressing (NKX2-1+) precursor cells. -
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 -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
The Transcription Factors C-Myb and GATA-2 Act Independently in The
Proc. Natl. Acad. Sci. USA Vol. 93, pp. 5313-5318, May 1996 Medical Sciences The transcription factors c-myb and GATA-2 act independently in the regulation of normal hematopoiesis PAOLA MELOTrl AND BRUNO CALABRETTA Department of Microbiology and Immunology, Kimmel Cancer Institute, Thomas Jefferson University, 233 South 10th Street, Philadelphia, PA 19107 Communicated by Sidney Weinhouse, Thomas Jefferson University, Philadelphia, PA, January 23, 1996 (received for review, October 20, 1995) ABSTRACT The transcription factors c-myb and GATA-2 erythromyeloid differentiation (7). This process appears to are both required for blood cell development in vivo and in rest in the ability of c-myb to activate the expression of vitro. However, very little is known on their mechanism(s) of hematopoiesis-specific targets such as c-kit,flt-3, GATA-1, and action and whether they impact on complementary or over- CD34, but not GATA-2 (7). The induction of c-kit and flt-3 lapping pathways of hematopoietic proliferation and differ- expression and the dependence of c-myb-transfected ES cells entiation. We report here that embryonic stem (ES) cells on the expression of these cytokine receptors for their prolif- transfected with c-myb or GATA-2 cDNAs, individually or in eration (7) strongly suggest that the up-regulation of growth combination, underwent hematopoietic commitment and dif- factor receptor levels is of fundamental importance for the ferentiation in the absence of added hematopoietic growth expansion of progenitor cells. In turn, such a process is factors but that stimulation with c-kit and flt-3 ligands en- probably a requirement for completion of the differentiation hanced colony formation only in the c-myb transfectants. -
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. -
Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491 -
Taf7l Cooperates with Trf2 to Regulate Spermiogenesis
Taf7l cooperates with Trf2 to regulate spermiogenesis Haiying Zhoua,b, Ivan Grubisicb,c, Ke Zhengd,e, Ying Heb, P. Jeremy Wangd, Tommy Kaplanf, and Robert Tjiana,b,1 aHoward Hughes Medical Institute and bDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, California Institute for Regenerative Medicine Center of Excellence, University of California, Berkeley, CA 94720; cUniversity of California Berkeley–University of California San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA 94720; dDepartment of Animal Biology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104; eState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 210029, People’s Republic of China; and fSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel Contributed by Robert Tjian, September 11, 2013 (sent for review August 20, 2013) TATA-binding protein (TBP)-associated factor 7l (Taf7l; a paralogue (Taf4b; a homolog of Taf4) (9), TBP-related factor 2 (Trf2) (10, of Taf7) and TBP-related factor 2 (Trf2) are components of the core 11), and Taf7l (12, 13). For example, mice bearing mutant or promoter complex required for gene/tissue-specific transcription deficient CREM showed decreased postmeiotic gene expression of protein-coding genes by RNA polymerase II. Previous studies and defective spermiogenesis (14). Mice deficient in Taf4b, reported that Taf7l knockout (KO) mice exhibit structurally abnor- a testis-specific homolog of Taf4, are initially normal but undergo mal sperm, reduced sperm count, weakened motility, and compro- progressive germ-cell loss and become infertile by 3 mo of age −/Y mised fertility. -
ARID5B As a Critical Downstream Target of the TAL1 Complex That Activates the Oncogenic Transcriptional Program and Promotes T-Cell Leukemogenesis
Downloaded from genesdev.cshlp.org on October 10, 2021 - Published by Cold Spring Harbor Laboratory Press ARID5B as a critical downstream target of the TAL1 complex that activates the oncogenic transcriptional program and promotes T-cell leukemogenesis Wei Zhong Leong,1 Shi Hao Tan,1 Phuong Cao Thi Ngoc,1 Stella Amanda,1 Alice Wei Yee Yam,1 Wei-Siang Liau,1 Zhiyuan Gong,2 Lee N. Lawton,1 Daniel G. Tenen,1,3,4 and Takaomi Sanda1,4 1Cancer Science Institute of Singapore, National University of Singapore, 117599 Singapore; 2Department of Biological Sciences, National University of Singapore, 117543 Singapore; 3Harvard Medical School, Boston, Massachusetts 02215, USA; 4Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 117599 Singapore The oncogenic transcription factor TAL1/SCL induces an aberrant transcriptional program in T-cell acute lym- phoblastic leukemia (T-ALL) cells. However, the critical factors that are directly activated by TAL1 and contribute to T-ALL pathogenesis are largely unknown. Here, we identified AT-rich interactive domain 5B (ARID5B) as a col- laborating oncogenic factor involved in the transcriptional program in T-ALL. ARID5B expression is down-regulated at the double-negative 2–4 stages in normal thymocytes, while it is induced by the TAL1 complex in human T-ALL cells. The enhancer located 135 kb upstream of the ARID5B gene locus is activated under a superenhancer in T-ALL cells but not in normal T cells. Notably, ARID5B-bound regions are associated predominantly with active tran- scription. ARID5B and TAL1 frequently co-occupy target genes and coordinately control their expression.