Regulation of Gene Expression Dynamics During Developmental Transitions by the Ikaros Transcription Factor
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The Molecular Role of Non-Canonical Notch Signaling Via Deltex-1 in High Grade Glioma
THE MOLECULAR ROLE OF NON-CANONICAL NOTCH SIGNALING VIA DELTEX-1 IN HIGH GRADE GLIOMA Inauguraldissertation zur Erlangung der Würde eines Doktors der Philosophie vorgelegt der Philosophisch-Naturwissenschaftlichen Fakultät der Universität Basel von Roland Martin Huber aus Siegershausen TG Basel, 2011 Genehmigt von der Philosophisch-Naturwissenschaftlichen Fakultät auf Antrag von: Prof. Dr. Markus A. Rüegg (Fakultätsverantwortlicher) Prof. Dr. Adrian Merlo (Dissertationsleiter) Dr. Brian A. Hemmings, FRS (Korreferent) Basel, den 21.06.2011 Prof. Dr. Martin Spiess Dekan “It’s not enough to say we are doing our best. We must succeed in doing what is necessary.” Winston Spencer Churchill Acknowledgement My first and outmost thanks go to my family for raising me to become a curious, independent and critical person interested in the beauty of nature and natural sciences. All I am today roots in my family. I am deeply grateful to Prof. Adrian Merlo for his trust in me and my work, his well measured guidance and help, his willingness to share his views and thoughts on science but also philosophy, and for his critical and thorough supervision of my thesis. Many people were involved in this piece of work in some way or the other and I would like to take the time to say thank you to all the members of the lab who were an invaluable help and also good friends; to Dr. Brian A. Hemmings for his generosity of giving me a place to work in difficult times as well as for his scientific support; to all the members of the CCRP glioma group for help, reagents and valuable comments and discussions; to all the members of my thesis committee for their help and valuable time; to Prof. -
Genome-Scale Single-Cell Mechanical Phenotyping Reveals Disease-Related Genes Involved in Mitotic Rounding
ARTICLE DOI: 10.1038/s41467-017-01147-6 OPEN Genome-scale single-cell mechanical phenotyping reveals disease-related genes involved in mitotic rounding Yusuke Toyoda 1,2, Cedric J. Cattin3, Martin P. Stewart 3,4,5, Ina Poser1, Mirko Theis6, Teymuras V. Kurzchalia1, Frank Buchholz1,6, Anthony A. Hyman1 & Daniel J. Müller 3 To divide, most animal cells drastically change shape and round up against extracellular confinement. Mitotic cells facilitate this process by generating intracellular pressure, which the contractile actomyosin cortex directs into shape. Here, we introduce a genome-scale microcantilever- and RNAi-based approach to phenotype the contribution of > 1000 genes to the rounding of single mitotic cells against confinement. Our screen analyzes the rounding force, pressure and volume of mitotic cells and localizes selected proteins. We identify 49 genes relevant for mitotic rounding, a large portion of which have not previously been linked to mitosis or cell mechanics. Among these, depleting the endoplasmic reticulum-localized protein FAM134A impairs mitotic progression by affecting metaphase plate alignment and pressure generation by delocalizing cortical myosin II. Furthermore, silencing the DJ-1 gene uncovers a link between mitochondria-associated Parkinson’s disease and mitotic pressure. We conclude that mechanical phenotyping is a powerful approach to study the mechanisms governing cell shape. 1 Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany. 2 Division of Cell Biology, Life Science Institute, Kurume University, Hyakunen-Kohen 1-1, Kurume, Fukuoka 839-0864, Japan. 3 Department of Biosystems Science and Engineering (D-BSSE), Eidgenössische Technische Hochschule (ETH) Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. -
Supplementary Information – Postema Et Al., the Genetics of Situs Inversus Totalis Without Primary Ciliary Dyskinesia
1 Supplementary information – Postema et al., The genetics of situs inversus totalis without primary ciliary dyskinesia Table of Contents: Supplementary Methods 2 Supplementary Results 5 Supplementary References 6 Supplementary Tables and Figures Table S1. Subject characteristics 9 Table S2. Inbreeding coefficients per subject 10 Figure S1. Multidimensional scaling to capture overall genomic diversity 11 among the 30 study samples Table S3. Significantly enriched gene-sets under a recessive mutation model 12 Table S4. Broader list of candidate genes, and the sources that led to their 13 inclusion Table S5. Potential recessive and X-linked mutations in the unsolved cases 15 Table S6. Potential mutations in the unsolved cases, dominant model 22 2 1.0 Supplementary Methods 1.1 Participants Fifteen people with radiologically documented SIT, including nine without PCD and six with Kartagener syndrome, and 15 healthy controls matched for age, sex, education and handedness, were recruited from Ghent University Hospital and Middelheim Hospital Antwerp. Details about the recruitment and selection procedure have been described elsewhere (1). Briefly, among the 15 people with radiologically documented SIT, those who had symptoms reminiscent of PCD, or who were formally diagnosed with PCD according to their medical record, were categorized as having Kartagener syndrome. Those who had no reported symptoms or formal diagnosis of PCD were assigned to the non-PCD SIT group. Handedness was assessed using the Edinburgh Handedness Inventory (EHI) (2). Tables 1 and S1 give overviews of the participants and their characteristics. Note that one non-PCD SIT subject reported being forced to switch from left- to right-handedness in childhood, in which case five out of nine of the non-PCD SIT cases are naturally left-handed (Table 1, Table S1). -
Supplementary Tables S1-S3
Supplementary Table S1: Real time RT-PCR primers COX-2 Forward 5’- CCACTTCAAGGGAGTCTGGA -3’ Reverse 5’- AAGGGCCCTGGTGTAGTAGG -3’ Wnt5a Forward 5’- TGAATAACCCTGTTCAGATGTCA -3’ Reverse 5’- TGTACTGCATGTGGTCCTGA -3’ Spp1 Forward 5'- GACCCATCTCAGAAGCAGAA -3' Reverse 5'- TTCGTCAGATTCATCCGAGT -3' CUGBP2 Forward 5’- ATGCAACAGCTCAACACTGC -3’ Reverse 5’- CAGCGTTGCCAGATTCTGTA -3’ Supplementary Table S2: Genes synergistically regulated by oncogenic Ras and TGF-β AU-rich probe_id Gene Name Gene Symbol element Fold change RasV12 + TGF-β RasV12 TGF-β 1368519_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 42.22 5.53 75.28 1373000_at sushi-repeat-containing protein, X-linked 2 (predicted) Srpx2 19.24 25.59 73.63 1383486_at Transcribed locus --- ARE 5.93 27.94 52.85 1367581_a_at secreted phosphoprotein 1 Spp1 2.46 19.28 49.76 1368359_a_at VGF nerve growth factor inducible Vgf 3.11 4.61 48.10 1392618_at Transcribed locus --- ARE 3.48 24.30 45.76 1398302_at prolactin-like protein F Prlpf ARE 1.39 3.29 45.23 1392264_s_at serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpine1 ARE 24.92 3.67 40.09 1391022_at laminin, beta 3 Lamb3 2.13 3.31 38.15 1384605_at Transcribed locus --- 2.94 14.57 37.91 1367973_at chemokine (C-C motif) ligand 2 Ccl2 ARE 5.47 17.28 37.90 1369249_at progressive ankylosis homolog (mouse) Ank ARE 3.12 8.33 33.58 1398479_at ryanodine receptor 3 Ryr3 ARE 1.42 9.28 29.65 1371194_at tumor necrosis factor alpha induced protein 6 Tnfaip6 ARE 2.95 7.90 29.24 1386344_at Progressive ankylosis homolog (mouse) -
Introducing a New Compendium of Molecular Interactions and a New Visualization Tool for the Study of Gene Regulatory Networks Lepoivre Et Al
TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks Lepoivre et al. Lepoivre et al. BMC Bioinformatics 2012, 13:19 http://www.biomedcentral.com/1471-2105/13/19 (31 January 2012) Lepoivre et al. BMC Bioinformatics 2012, 13:19 http://www.biomedcentral.com/1471-2105/13/19 SOFTWARE Open Access TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks Cyrille Lepoivre1,2, Aurélie Bergon1,2, Fabrice Lopez1,2,3, Narayanan B Perumal4, Catherine Nguyen1,2,3, Jean Imbert1,2,3 and Denis Puthier1,5* Abstract Background: Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. Results: We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. -
Transdifferentiation of Human Mesenchymal Stem Cells
Transdifferentiation of Human Mesenchymal Stem Cells Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Tatjana Schilling aus San Miguel de Tucuman, Argentinien Würzburg, 2007 Eingereicht am: Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Martin J. Müller Gutachter: PD Dr. Norbert Schütze Gutachter: Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am: Hiermit erkläre ich ehrenwörtlich, dass ich die vorliegende Dissertation selbstständig angefertigt und keine anderen als die von mir angegebenen Hilfsmittel und Quellen verwendet habe. Des Weiteren erkläre ich, dass diese Arbeit weder in gleicher noch in ähnlicher Form in einem Prüfungsverfahren vorgelegen hat und ich noch keinen Promotionsversuch unternommen habe. Gerbrunn, 4. Mai 2007 Tatjana Schilling Table of contents i Table of contents 1 Summary ........................................................................................................................ 1 1.1 Summary.................................................................................................................... 1 1.2 Zusammenfassung..................................................................................................... 2 2 Introduction.................................................................................................................... 4 2.1 Osteoporosis and the fatty degeneration of the bone marrow..................................... 4 2.2 Adipose and bone -
WO 2016/040794 Al 17 March 2016 (17.03.2016) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2016/040794 Al 17 March 2016 (17.03.2016) P O P C T (51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, C12N 1/19 (2006.01) C12Q 1/02 (2006.01) BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, C12N 15/81 (2006.01) C07K 14/47 (2006.01) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, (21) International Application Number: KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, PCT/US20 15/049674 MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (22) International Filing Date: PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, 11 September 2015 ( 11.09.201 5) SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of regional protection available): ARIPO (BW, GH, (30) Priority Data: GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, 62/050,045 12 September 2014 (12.09.2014) US TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, (71) Applicant: WHITEHEAD INSTITUTE FOR BIOMED¬ DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, ICAL RESEARCH [US/US]; Nine Cambridge Center, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, Cambridge, Massachusetts 02142-1479 (US). -
Entrez ID Gene Name Fold Change Q-Value Description
Entrez ID gene name fold change q-value description 4283 CXCL9 -7.25 5.28E-05 chemokine (C-X-C motif) ligand 9 3627 CXCL10 -6.88 6.58E-05 chemokine (C-X-C motif) ligand 10 6373 CXCL11 -5.65 3.69E-04 chemokine (C-X-C motif) ligand 11 405753 DUOXA2 -3.97 3.05E-06 dual oxidase maturation factor 2 4843 NOS2 -3.62 5.43E-03 nitric oxide synthase 2, inducible 50506 DUOX2 -3.24 5.01E-06 dual oxidase 2 6355 CCL8 -3.07 3.67E-03 chemokine (C-C motif) ligand 8 10964 IFI44L -3.06 4.43E-04 interferon-induced protein 44-like 115362 GBP5 -2.94 6.83E-04 guanylate binding protein 5 3620 IDO1 -2.91 5.65E-06 indoleamine 2,3-dioxygenase 1 8519 IFITM1 -2.67 5.65E-06 interferon induced transmembrane protein 1 3433 IFIT2 -2.61 2.28E-03 interferon-induced protein with tetratricopeptide repeats 2 54898 ELOVL2 -2.61 4.38E-07 ELOVL fatty acid elongase 2 2892 GRIA3 -2.60 3.06E-05 glutamate receptor, ionotropic, AMPA 3 6376 CX3CL1 -2.57 4.43E-04 chemokine (C-X3-C motif) ligand 1 7098 TLR3 -2.55 5.76E-06 toll-like receptor 3 79689 STEAP4 -2.50 8.35E-05 STEAP family member 4 3434 IFIT1 -2.48 2.64E-03 interferon-induced protein with tetratricopeptide repeats 1 4321 MMP12 -2.45 2.30E-04 matrix metallopeptidase 12 (macrophage elastase) 10826 FAXDC2 -2.42 5.01E-06 fatty acid hydroxylase domain containing 2 8626 TP63 -2.41 2.02E-05 tumor protein p63 64577 ALDH8A1 -2.41 6.05E-06 aldehyde dehydrogenase 8 family, member A1 8740 TNFSF14 -2.40 6.35E-05 tumor necrosis factor (ligand) superfamily, member 14 10417 SPON2 -2.39 2.46E-06 spondin 2, extracellular matrix protein 3437 -
Comprehensive Analysis Reveals Novel Gene Signature in Head and Neck Squamous Cell Carcinoma: Predicting Is Associated with Poor Prognosis in Patients
5892 Original Article Comprehensive analysis reveals novel gene signature in head and neck squamous cell carcinoma: predicting is associated with poor prognosis in patients Yixin Sun1,2#, Quan Zhang1,2#, Lanlin Yao2#, Shuai Wang3, Zhiming Zhang1,2 1Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; 2School of Medicine, Xiamen University, Xiamen, China; 3State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China Contributions: (I) Conception and design: Y Sun, Q Zhang; (II) Administrative support: Z Zhang; (III) Provision of study materials or patients: Y Sun, Q Zhang; (IV) Collection and assembly of data: Y Sun, L Yao; (V) Data analysis and interpretation: Y Sun, S Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Zhiming Zhang. Department of Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China. Email: [email protected]. Background: Head and neck squamous cell carcinoma (HNSC) remains an important public health problem, with classic risk factors being smoking and excessive alcohol consumption and usually has a poor prognosis. Therefore, it is important to explore the underlying mechanisms of tumorigenesis and screen the genes and pathways identified from such studies and their role in pathogenesis. The purpose of this study was to identify genes or signal pathways associated with the development of HNSC. Methods: In this study, we downloaded gene expression profiles of GSE53819 from the Gene Expression Omnibus (GEO) database, including 18 HNSC tissues and 18 normal tissues. -
RNA Sequencing Analyses Reveal Differentially Expressed Genes and Pathways As Notch2 Targets in B-Cell Lymphoma
www.oncotarget.com Oncotarget, 2020, Vol. 11, (No. 48), pp: 4527-4540 Research Paper RNA sequencing analyses reveal differentially expressed genes and pathways as Notch2 targets in B-cell lymphoma Ashok Arasu1, Pavithra Balakrishnan1 and Thirunavukkarasu Velusamy1 1Department of Biotechnology, School of Biotechnology and Genetic Engineering, Bharathiar University, Coimbatore, India Correspondence to: Thirunavukkarasu Velusamy, email: [email protected] Keywords: SMZL; Notch2; RNA sequencing; PI3K/AKT; NF-kB Received: August 04, 2020 Accepted: October 17, 2020 Published: December 01, 2020 Copyright: © 2020 Arasu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Splenic marginal zone lymphoma (SMZL) is a low grade, indolent B-cell neoplasm that comprises approximately 10% of all lymphoma. Notch2, a pivotal gene for marginal zone differentiation is found to be mutated in SMZL. Deregulated Notch2 signaling has been involved in tumorigenesis and also in B-cell malignancies. However the role of Notch2 and the downstream pathways that it influences for development of B-cell lymphoma remains unclear. In recent years, RNA sequencing (RNA-Seq) has become a functional and convincing technology for profiling gene expression and to discover new genes and transcripts that are involved in disease development in a single experiment. In the present study, using transcriptome sequencing approach, we have identified key genes and pathways that are probably the underlying cause in the development of B-cell lymphoma. We have identified a total of 15,083 differentially expressed genes (DEGs) and 1067 differentially expressed transcripts (DETs) between control and Notch2 knockdown B cells. -
Cell Cycle Arrest Through Indirect Transcriptional Repression by P53: I Have a DREAM
Cell Death and Differentiation (2018) 25, 114–132 Official journal of the Cell Death Differentiation Association OPEN www.nature.com/cdd Review Cell cycle arrest through indirect transcriptional repression by p53: I have a DREAM Kurt Engeland1 Activation of the p53 tumor suppressor can lead to cell cycle arrest. The key mechanism of p53-mediated arrest is transcriptional downregulation of many cell cycle genes. In recent years it has become evident that p53-dependent repression is controlled by the p53–p21–DREAM–E2F/CHR pathway (p53–DREAM pathway). DREAM is a transcriptional repressor that binds to E2F or CHR promoter sites. Gene regulation and deregulation by DREAM shares many mechanistic characteristics with the retinoblastoma pRB tumor suppressor that acts through E2F elements. However, because of its binding to E2F and CHR elements, DREAM regulates a larger set of target genes leading to regulatory functions distinct from pRB/E2F. The p53–DREAM pathway controls more than 250 mostly cell cycle-associated genes. The functional spectrum of these pathway targets spans from the G1 phase to the end of mitosis. Consequently, through downregulating the expression of gene products which are essential for progression through the cell cycle, the p53–DREAM pathway participates in the control of all checkpoints from DNA synthesis to cytokinesis including G1/S, G2/M and spindle assembly checkpoints. Therefore, defects in the p53–DREAM pathway contribute to a general loss of checkpoint control. Furthermore, deregulation of DREAM target genes promotes chromosomal instability and aneuploidy of cancer cells. Also, DREAM regulation is abrogated by the human papilloma virus HPV E7 protein linking the p53–DREAM pathway to carcinogenesis by HPV.Another feature of the pathway is that it downregulates many genes involved in DNA repair and telomere maintenance as well as Fanconi anemia. -
Analyzing the Mirna-Gene Networks to Mine the Important Mirnas Under Skin of Human and Mouse
Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 5469371, 9 pages http://dx.doi.org/10.1155/2016/5469371 Research Article Analyzing the miRNA-Gene Networks to Mine the Important miRNAs under Skin of Human and Mouse Jianghong Wu,1,2,3,4,5 Husile Gong,1,2 Yongsheng Bai,5,6 and Wenguang Zhang1 1 College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China 2Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China 3Inner Mongolia Prataculture Research Center, Chinese Academy of Science, Hohhot 010031, China 4State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China 5Department of Biology, Indiana State University, Terre Haute, IN 47809, USA 6The Center for Genomic Advocacy, Indiana State University, Terre Haute, IN 47809, USA Correspondence should be addressed to Yongsheng Bai; [email protected] and Wenguang Zhang; [email protected] Received 11 April 2016; Revised 15 July 2016; Accepted 27 July 2016 Academic Editor: Nicola Cirillo Copyright © 2016 Jianghong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Genetic networks provide new mechanistic insights into the diversity of species morphology. In this study, we have integrated the MGI, GEO, and miRNA database to analyze the genetic regulatory networks under morphology difference of integument of humans and mice. We found that the gene expression network in the skin is highly divergent between human and mouse.