Targeting the DEK Oncogene in Head and Neck Squamous Cell Carcinoma: Functional and Transcriptional Consequences
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The T-ALL Related Gene BCL11B Regulates the Initial Stages of Human T-Cell Differentiation
Leukemia (2017) 31, 2503–2514 © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 0887-6924/17 www.nature.com/leu ORIGINAL ARTICLE The T-ALL related gene BCL11B regulates the initial stages of human T-cell differentiation VL Ha1, A Luong1,FLi2, D Casero3, J Malvar1,YMKim1,4, R Bhatia5, GM Crooks3,6,7,8 and C Parekh1,4 The initial stages of T-cell differentiation are characterized by a progressive commitment to the T-cell lineage, a process that involves the loss of alternative (myelo-erythroid, NK, B) lineage potentials. Aberrant differentiation during these stages can result in T-cell acute lymphoblastic leukemia (T-ALL). However, the mechanisms regulating the initial stages of human T-cell differentiation are obscure. Through loss of function studies, we showed BCL11B, a transcription factor recurrently mutated T-ALL, is essential for T-lineage commitment, particularly the repression of NK and myeloid potentials, and the induction of T-lineage genes, during the initial stages of human T-cell differentiation. In gain of function studies, BCL11B inhibited growth of and induced a T-lineage transcriptional program in T-ALL cells. We found previously unknown differentiation stage-specific DNA binding of BCL11B at multiple T-lineage genes; target genes showed BCL11B-dependent expression, suggesting a transcriptional activator role for BCL11B at these genes. Transcriptional analyses revealed differences in the regulatory actions of BCL11B between human and murine thymopoiesis. Our studies show BCL11B is a key regulator of the initial stages of human T-cell differentiation and delineate the BCL11B transcriptional program, enabling the dissection of the underpinnings of normal T-cell differentiation and providing a resource for understanding dysregulations in T-ALL. -
Epha4/Tie2 Crosstalk Regulates Leptomeningeal Collateral Remodeling Following Ischemic Stroke
EphA4/Tie2 crosstalk regulates leptomeningeal collateral remodeling following ischemic stroke Benjamin Okyere, … , John B. Matson, Michelle H. Theus J Clin Invest. 2019. https://doi.org/10.1172/JCI131493. Research In-Press Preview Neuroscience Vascular biology Leptomeningeal anastomoses or pial collateral vessels play a critical role in cerebral blood flow (CBF) restoration following ischemic stroke. The magnitude of this adaptive response is postulated to be controlled by the endothelium, although the underlying molecular mechanisms remain under investigation. Here we demonstrated that endothelial genetic deletion, using EphA4f/f/Tie2-Cre and EphA4f/f/VeCahderin-CreERT2 mice and vessel painting strategies, implicated EphA4 receptor tyrosine kinase as a major suppressor of pial collateral remodeling, CBF and functional recovery following permanent middle cerebral artery occlusion. Pial collateral remodeling is limited by the cross talk between EphA4-Tie2 signaling in vascular endothelial cells, which is mediated through p-Akt regulation. Furthermore, peptide inhibition of EphA4 resulted in acceleration of the pial arteriogenic response. Our findings demonstrate EphA4 is a negative regulator of Tie2 receptor signaling which limits pial collateral arteriogenesis following cerebrovascular occlusion. Therapeutic targeting of EphA4 and/or Tie2 represents an attractive new strategy for improving collateral function, neural tissue health and functional recovery following ischemic stroke. Find the latest version: https://jci.me/131493/pdf 1 EphA4/Tie2 -
Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma
Anatomy and Pathology Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma Sarah L. Lake,1 Sarah E. Coupland,1 Azzam F. G. Taktak,2 and Bertil E. Damato3 PURPOSE. To detect deletions and loss of heterozygosity of disease is fatal in 92% of patients within 2 years of diagnosis. chromosome 3 in a rare subset of fatal, disomy 3 uveal mela- Clinical and histopathologic risk factors for UM metastasis noma (UM), undetectable by fluorescence in situ hybridization include large basal tumor diameter (LBD), ciliary body involve- (FISH). ment, epithelioid cytomorphology, extracellular matrix peri- ϩ ETHODS odic acid-Schiff-positive (PAS ) loops, and high mitotic M . Multiplex ligation-dependent probe amplification 3,4 5 (MLPA) with the P027 UM assay was performed on formalin- count. Prescher et al. showed that a nonrandom genetic fixed, paraffin-embedded (FFPE) whole tumor sections from 19 change, monosomy 3, correlates strongly with metastatic death, and the correlation has since been confirmed by several disomy 3 metastasizing UMs. Whole-genome microarray analy- 3,6–10 ses using a single-nucleotide polymorphism microarray (aSNP) groups. Consequently, fluorescence in situ hybridization were performed on frozen tissue samples from four fatal dis- (FISH) detection of chromosome 3 using a centromeric probe omy 3 metastasizing UMs and three disomy 3 tumors with Ͼ5 became routine practice for UM prognostication; however, 5% years’ metastasis-free survival. to 20% of disomy 3 UM patients unexpectedly develop metas- tases.11 Attempts have therefore been made to identify the RESULTS. Two metastasizing UMs that had been classified as minimal region(s) of deletion on chromosome 3.12–15 Despite disomy 3 by FISH analysis of a small tumor sample were found these studies, little progress has been made in defining the key on MLPA analysis to show monosomy 3. -
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. -
SUPPLEMENTAL DATA Supplemental Materials And
SUPPLEMENTAL DATA Supplemental Materials and Methods Cells and Cell Culture Human breast carcinoma cell lines, MDA-MB-231 and MCF7, were purchased from American Type Tissue Culture Collection (ATCC). 231BoM-1833, 231BrM-2a, CN34, CN34-BoM2d, CN34-BrM2c and MCF7- BoM2d cell lines were kindly provided by Dr. Joan Massagué (Memorial Sloan-Kettering Cancer Center) (1-3). Luciferase-labeled cells were generated by infecting the lentivirus carrying the firefly luciferase gene. The immortalized mouse bone microvascular endothelial cell (mBMEC) was a generous gift from Dr. Isaiah J. Fidler (M.D. Anderson Cancer Center) (4). MCF10A and MCF10DCIS.com cells were purchased from ATCC and Asterand, respectively. MDA-MB-231, its variant cells, MCF7 and MCF-BoM2d cells were cultured in DMEM medium supplemented with 10% FBS and antibiotics. CN34 and its variant cells were cultured in Medium199 supplemented with 2.5% FBS, 10 µg/ml insulin, 0.5 µg/ml hydrocortisone, 20 ng/ml EGF, 100 ng/ml cholera toxin and antibiotics. MCF10DCIS.com cells were cultured in RPMI-1640 medium supplemented with 10% FBS and antibiotics. MCF10A cells were cultured in MEGM mammary epithelial cell growth medium (Lonza). mBMEC was maintained at 8% CO2 at 33 °C in DMEM with 10% FBS, 2 mM L-glutamine, 1 mM sodium pyruvate, 1% non-essential amino acids and 1% vitamin mixture. Bone marrow stromal fibroblast cell lines HS5 and HS27A, and osteoblast cell line, hFOB1.19, were purchased from ATCC. Bone marrow derived human mesenchymal stem cells, BM-hMSC, were isolated for enrichment of plastic adherent cells from unprocessed bone marrow (Lonza) which was depleted of red blood cells. -
Meta-Analysis of Nasopharyngeal Carcinoma
BMC Genomics BioMed Central Research article Open Access Meta-analysis of nasopharyngeal carcinoma microarray data explores mechanism of EBV-regulated neoplastic transformation Xia Chen†1,2, Shuang Liang†1, WenLing Zheng1,3, ZhiJun Liao1, Tao Shang1 and WenLi Ma*1 Address: 1Institute of Genetic Engineering, Southern Medical University, Guangzhou, PR China, 2Xiangya Pingkuang associated hospital, Pingxiang, Jiangxi, PR China and 3Southern Genomics Research Center, Guangzhou, Guangdong, PR China Email: Xia Chen - [email protected]; Shuang Liang - [email protected]; WenLing Zheng - [email protected]; ZhiJun Liao - [email protected]; Tao Shang - [email protected]; WenLi Ma* - [email protected] * Corresponding author †Equal contributors Published: 7 July 2008 Received: 16 February 2008 Accepted: 7 July 2008 BMC Genomics 2008, 9:322 doi:10.1186/1471-2164-9-322 This article is available from: http://www.biomedcentral.com/1471-2164/9/322 © 2008 Chen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Epstein-Barr virus (EBV) presumably plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC), but the molecular mechanism of EBV-dependent neoplastic transformation is not well understood. The combination of bioinformatics with evidences from biological experiments paved a new way to gain more insights into the molecular mechanism of cancer. Results: We profiled gene expression using a meta-analysis approach. Two sets of meta-genes were obtained. Meta-A genes were identified by finding those commonly activated/deactivated upon EBV infection/reactivation. -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
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. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
BTG2: a Rising Star of Tumor Suppressors (Review)
INTERNATIONAL JOURNAL OF ONCOLOGY 46: 459-464, 2015 BTG2: A rising star of tumor suppressors (Review) BIjING MAO1, ZHIMIN ZHANG1,2 and GE WANG1 1Cancer Center, Institute of Surgical Research, Daping Hospital, Third Military Medical University, Chongqing 400042; 2Department of Oncology, Wuhan General Hospital of Guangzhou Command, People's Liberation Army, Wuhan, Hubei 430070, P.R. China Received September 22, 2014; Accepted November 3, 2014 DOI: 10.3892/ijo.2014.2765 Abstract. B-cell translocation gene 2 (BTG2), the first 1. Discovery of BTG2 in TOB/BTG gene family gene identified in the BTG/TOB gene family, is involved in many biological activities in cancer cells acting as a tumor The TOB/BTG genes belong to the anti-proliferative gene suppressor. The BTG2 expression is downregulated in many family that includes six different genes in vertebrates: TOB1, human cancers. It is an instantaneous early response gene and TOB2, BTG1 BTG2/TIS21/PC3, BTG3 and BTG4 (Fig. 1). plays important roles in cell differentiation, proliferation, DNA The conserved domain of BTG N-terminal contains two damage repair, and apoptosis in cancer cells. Moreover, BTG2 regions, named box A and box B, which show a high level of is regulated by many factors involving different signal path- homology to the other domains (1-5). Box A has a major effect ways. However, the regulatory mechanism of BTG2 is largely on cell proliferation, while box B plays a role in combination unknown. Recently, the relationship between microRNAs and with many target molecules. Compared with other family BTG2 has attracted much attention. MicroRNA-21 (miR-21) members, BTG1 and BTG2 have an additional region named has been found to regulate BTG2 gene during carcinogenesis. -
Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of