SUPPLEMENTARY DATA Supplementary Table 1. Top Ten
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Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin a and Affects the Migration and Invasion Potential of Breast Cancer Cells
Published OnlineFirst February 28, 2010; DOI: 10.1158/0008-5472.CAN-08-1108 Tumor and Stem Cell Biology Cancer Research Cyclin D1/Cyclin-Dependent Kinase 4 Interacts with Filamin A and Affects the Migration and Invasion Potential of Breast Cancer Cells Zhijiu Zhong, Wen-Shuz Yeow, Chunhua Zou, Richard Wassell, Chenguang Wang, Richard G. Pestell, Judy N. Quong, and Andrew A. Quong Abstract Cyclin D1 belongs to a family of proteins that regulate progression through the G1-S phase of the cell cycle by binding to cyclin-dependent kinase (cdk)-4 to phosphorylate the retinoblastoma protein and release E2F transcription factors for progression through cell cycle. Several cancers, including breast, colon, and prostate, overexpress the cyclin D1 gene. However, the correlation of cyclin D1 overexpression with E2F target gene regulation or of cdk-dependent cyclin D1 activity with tumor development has not been identified. This suggests that the role of cyclin D1 in oncogenesis may be independent of its function as a cell cycle regulator. One such function is the role of cyclin D1 in cell adhesion and motility. Filamin A (FLNa), a member of the actin-binding filamin protein family, regulates signaling events involved in cell motility and invasion. FLNa has also been associated with a variety of cancers including lung cancer, prostate cancer, melanoma, human bladder cancer, and neuroblastoma. We hypothesized that elevated cyclin D1 facilitates motility in the invasive MDA-MB-231 breast cancer cell line. We show that MDA-MB-231 motility is affected by disturbing cyclin D1 levels or cyclin D1-cdk4/6 kinase activity. -
Regulation of Cardiac Progenitors by Combination of Mesp1 and ETS
REGULATION OF CARDIAC PROGENITORS BY COMBINATION OF MESP1 AND ETS TRANSCRIPTION FACTORS A Dissertation by KUO-CHAN WENG Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Chair of Committee, Fen Wang Co-Chair of Committee, Robert J. Schwartz Committee Members, James F. Martin Jiang Chang Head of Department, Fen Wang May 2014 Major Subject: Medical Science Copyright 2014 Kuo-Chan Weng ABSTRACT Heart disease remains the leading cause of death worldwide. By understanding the regulating networks during cardiac development we can exploit those networks to manipulate adult cells into cardiac progenitors and provide an alternative for repairing diseased hearts. Mesp1 is considered to have critical roles during cardiac development but the molecular mechanisms need to be further studied. The roles of ETS transcription factors have been primarily limited to hematopoietic differentiation and cancer progression. The ETS transcription factors are known to have proliferating roles and were hypothesized to also be involved in cardiac differentiation and may potentially be used for cell reprogramming. The first part of this study characterizes the expression pattern of Mesp1 protein in early mouse embryo from E6.5 to E9.5 and provides a full expression profile in differentiating embryoid bodies in vitro from the undifferentiated stage to Day10. Our work showed Mesp1 expresses in the posterior region of E6.5 embryo then starts migrating through the primitive streak toward anterior mesoderm and endoderm in E7.5. A Mesp1 linage tracing ES cell line was established, and it allowed us to trace the Mesp1 derived cell population. -
Detailed Review Paper on Retinoid Pathway Signalling
1 1 Detailed Review Paper on Retinoid Pathway Signalling 2 December 2020 3 2 4 Foreword 5 1. Project 4.97 to develop a Detailed Review Paper (DRP) on the Retinoid System 6 was added to the Test Guidelines Programme work plan in 2015. The project was 7 originally proposed by Sweden and the European Commission later joined the project as 8 a co-lead. In 2019, the OECD Secretariat was added to coordinate input from expert 9 consultants. The initial objectives of the project were to: 10 draft a review of the biology of retinoid signalling pathway, 11 describe retinoid-mediated effects on various organ systems, 12 identify relevant retinoid in vitro and ex vivo assays that measure mechanistic 13 effects of chemicals for development, and 14 Identify in vivo endpoints that could be added to existing test guidelines to 15 identify chemical effects on retinoid pathway signalling. 16 2. This DRP is intended to expand the recommendations for the retinoid pathway 17 included in the OECD Detailed Review Paper on the State of the Science on Novel In 18 vitro and In vivo Screening and Testing Methods and Endpoints for Evaluating 19 Endocrine Disruptors (DRP No 178). The retinoid signalling pathway was one of seven 20 endocrine pathways considered to be susceptible to environmental endocrine disruption 21 and for which relevant endpoints could be measured in new or existing OECD Test 22 Guidelines for evaluating endocrine disruption. Due to the complexity of retinoid 23 signalling across multiple organ systems, this effort was foreseen as a multi-step process. -
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. -
Molecular Subtypes of Diffuse Large B-Cell Lymphoma Arise by Distinct Genetic Pathways
Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways Georg Lenza, George W. Wrightb, N. C. Tolga Emrea, Holger Kohlhammera, Sandeep S. Davea, R. Eric Davisa, Shannon Cartya, Lloyd T. Lama, A. L. Shaffera, Wenming Xiaoc, John Powellc, Andreas Rosenwaldd, German Ottd,e, Hans Konrad Muller-Hermelinkd, Randy D. Gascoynef, Joseph M. Connorsf, Elias Campog, Elaine S. Jaffeh, Jan Delabiei, Erlend B. Smelandj,k, Lisa M. Rimszal, Richard I. Fisherm,n, Dennis D. Weisenburgero, Wing C. Chano, and Louis M. Staudta,q aMetabolism Branch, bBiometric Research Branch, cCenter for Information Technology, and hLaboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892; dDepartment of Pathology, University of Wu¨rzburg, 97080 Wu¨rzburg, Germany; eDepartment of Clinical Pathology, Robert-Bosch-Krankenhaus, 70376 Stuttgart, Germany; fBritish Columbia Cancer Agency, Vancouver, British Columbia, Canada V5Z 4E6; gHospital Clinic, University of Barcelona, 08036 Barcelona, Spain; iPathology Clinic and jInstitute for Cancer Research, Rikshospitalet Hospital, Oslo, Norway; kCentre for Cancer Biomedicine, Faculty Division the Norwegian Radium Hospital, N-0310, Oslo, Norway; lDepartment of Pathology, University of Arizona, Tucson, AZ 85724; mSouthwest Oncology Group, 24 Frank Lloyd Wright Drive, Ann Arbor, MI 48106 ; nJames P. Wilmot Cancer Center, University of Rochester, Rochester, NY 14642; and oDepartments of Pathology and Microbiology, University of Nebraska, Omaha, NE 68198 Edited by Ira -
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. -
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 -
NVND-2019 Book
2nd International Conference on Neurovascular and Neurodegenerative Diseases October 28-30 2019 Media Partner Venue Paris Marriott Charles de Gaulle Airport Hotel 5 Allee du Verger, Zone Hoteliere Roissy en France, 95700 France Day- 1 Monday | October 28, 2019 Keynote Presentations Cerebrovascular Lesions in Pick Complex Diseases: A Neuropathological Study with a 7.0-tesla Magnetic Resonance Imaging Jacques De Reuck Université Lille 2, INSERM U1171, Degenerative & Vascular Cognitive Disorders, CHR Lille, France Abstract Introduction: Pick complex refers to a spectrum of diseases that have in common the presence of tau inclusions. The main neuropathological phenotypes comprise tau-frontotemporal lobar degeneration (Tau-FTLD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). The present neuropathological study investigates their incidence of cerebrovascular lesions. Material and Methods: seventy patients underwent an autopsy and post-mortem MRI. The brains consisted of 14 with Tau-FTLD, 22 with PSP, 6 with CBD and 28 controls, who had no history of a brain disease. A whole coronal section of a cerebral hemisphere, at the level of the mamillary body, was taken for the semi-quantitative evaluation of the small cerebrovascular lesions such as white matter changes (WMCs), cortical micro-bleeds (CoMBs), and cortical micro-infarcts (CoMIs). In addition the severity and the distribution of WMCs, CoMIs and CoMBs were examined with 7.0-tesla MRI on three coronal sections of a cerebral hemisphere. Results: on neuropathological examination severe WMCs and more CoMBs are observed in Tau-FTLD, while the latter are also more frequent in CBD. The MRI examination shows that severe WMCs are present in the frontal sections not only of the Tau-FTLD but also to a lesser degree of the PSP and CBD brains. -
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 -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
Supplemental Table S1 (A): Microarray Datasets Characteristics
Supplemental table S1 (A): Microarray datasets characteristics Title Summary Samples Literature ref. GEO ref. Acquisition of granule Gene expression profiling of 27 (1) GSE 11859 neuron precursor identity cerebellar tumors generated and Hedgehog‐induced from various early and late medulloblastoma in mice. stage CNS progenitor cells Medulloblastomas derived Study of mouse 5 (2) GSE 7212 from Cxcr6 mutant mice medulloblastoma in response respond to treatment with to inhibitor of Smoothened a Smoothened inhibitor Expression profiles of Identification of distinct classes 10 (3) GSE 9299 mouse medulloblastoma of up‐regulated or down‐ 339 & 340 regulated genes during Hh dependent tumorigenesis Genetic alterations in Identification of differently 10 (4) GSE 6463 mouse medulloblastomas expressed genes among CGNPs 339 & and generation of tumors and CGNPs transfected with 340 from cerebellar granule retroviruses that express nmyc neuron precursors or cyclin‐d1 Patched heterozygous Analysis of granule cell 14 (5) GSE 2426 model of medulloblastoma precursors, pre‐neoplastic cells, GDS1110 and tumor cells 1. Schuller U, Heine VM, Mao J, Kho AT, Dillon AK, Han YG, et al. Acquisition of granule neuron precursor identity is a critical determinant of progenitor cell competence to form Shh‐induced medulloblastoma. Cancer Cell 2008;14:123‐134. 2. Sasai K, Romer JT, Kimura H, Eberhart DE, Rice DS, Curran T. Medulloblastomas derived from Cxcr6 mutant mice respond to treatment with a smoothened inhibitor. Cancer Res 2007;67:3871‐3877. 3. Mao J, Ligon KL, Rakhlin EY, Thayer SP, Bronson RT, Rowitch D, et al. A novel somatic mouse model to survey tumorigenic potential applied to the Hedgehog pathway. Cancer Res 2006;66:10171‐10178.