Cytoplasmic Localized Ubiquitin Ligase Cullin 7 Binds to P53 and Promotes Cell Growth by Antagonizing P53 Function
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The Cardiomyocyte Cell Cycle," Novartis Found Symposium 274 (2006): 196-276
DePauw University Scholarly and Creative Work from DePauw University Biology Faculty publications Biology 2006 The ac rdiomyocyte cell cycle Pascal J. Lafontant DePauw University, [email protected] Follow this and additional works at: http://scholarship.depauw.edu/bio_facpubs Part of the Biology Commons Recommended Citation Lafontant, Pascal J. E. and Loren J. Field. "The cardiomyocyte cell cycle," Novartis Found Symposium 274 (2006): 196-276. This Article is brought to you for free and open access by the Biology at Scholarly and Creative Work from DePauw University. It has been accepted for inclusion in Biology Faculty publications by an authorized administrator of Scholarly and Creative Work from DePauw University. For more information, please contact [email protected]. NIH Public Access Author Manuscript Novartis Found Symp. Author manuscript; available in PMC 2009 January 20. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Novartis Found Symp. 2006 ; 274: 196±276. The cardiomyocyte cell cycle Pascal J. E. Lafontant and Loren J. Field From the Wells Center for Pediatric Research and Krannert Institute of Cardiology, Indiana University School of Medicine, Indianapolis, IN 46202-5225. Abstract Many forms of cardiac disease are characterized by cardiomyocyte death due to necrosis, apoptosis and/or oncosis. Recently, the notion of promoting cardiac regeneration as a means to replace damaged heart tissue has engendered considerable interest. One approach to accomplish heart muscle regeneration entails promoting cardiomyocyte cell cycle activity in the surviving myocardium. Genetically modified mice have provided useful model systems to test the efficacy of specific pathways to promote cardiomyocyte proliferation in normal and diseased hearts. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
Expression Profiling of KLF4
Expression Profiling of KLF4 AJCR0000006 Supplemental Data Figure S1. Snapshot of enriched gene sets identified by GSEA in Klf4-null MEFs. Figure S2. Snapshot of enriched gene sets identified by GSEA in wild type MEFs. 98 Am J Cancer Res 2011;1(1):85-97 Table S1: Functional Annotation Clustering of Genes Up-Regulated in Klf4 -Null MEFs ILLUMINA_ID Gene Symbol Gene Name (Description) P -value Fold-Change Cell Cycle 8.00E-03 ILMN_1217331 Mcm6 MINICHROMOSOME MAINTENANCE DEFICIENT 6 40.36 ILMN_2723931 E2f6 E2F TRANSCRIPTION FACTOR 6 26.8 ILMN_2724570 Mapk12 MITOGEN-ACTIVATED PROTEIN KINASE 12 22.19 ILMN_1218470 Cdk2 CYCLIN-DEPENDENT KINASE 2 9.32 ILMN_1234909 Tipin TIMELESS INTERACTING PROTEIN 5.3 ILMN_1212692 Mapk13 SAPK/ERK/KINASE 4 4.96 ILMN_2666690 Cul7 CULLIN 7 2.23 ILMN_2681776 Mapk6 MITOGEN ACTIVATED PROTEIN KINASE 4 2.11 ILMN_2652909 Ddit3 DNA-DAMAGE INDUCIBLE TRANSCRIPT 3 2.07 ILMN_2742152 Gadd45a GROWTH ARREST AND DNA-DAMAGE-INDUCIBLE 45 ALPHA 1.92 ILMN_1212787 Pttg1 PITUITARY TUMOR-TRANSFORMING 1 1.8 ILMN_1216721 Cdk5 CYCLIN-DEPENDENT KINASE 5 1.78 ILMN_1227009 Gas2l1 GROWTH ARREST-SPECIFIC 2 LIKE 1 1.74 ILMN_2663009 Rassf5 RAS ASSOCIATION (RALGDS/AF-6) DOMAIN FAMILY 5 1.64 ILMN_1220454 Anapc13 ANAPHASE PROMOTING COMPLEX SUBUNIT 13 1.61 ILMN_1216213 Incenp INNER CENTROMERE PROTEIN 1.56 ILMN_1256301 Rcc2 REGULATOR OF CHROMOSOME CONDENSATION 2 1.53 Extracellular Matrix 5.80E-06 ILMN_2735184 Col18a1 PROCOLLAGEN, TYPE XVIII, ALPHA 1 51.5 ILMN_1223997 Crtap CARTILAGE ASSOCIATED PROTEIN 32.74 ILMN_2753809 Mmp3 MATRIX METALLOPEPTIDASE -
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. -
Supporting Information
Supporting Information Barshis et al. 10.1073/pnas.1210224110 SI Materials and Methods and “ssu-parc.fasta” downloaded on September 14, 2011 from mRNA Extraction. Total RNA was extracted from each sample www.arb-silva.de), and potential Symbiodinium contamination using a modified TRIzol (GibcoBRL/Invitrogen) protocol. Ap- was removed based significant nucleotide similarity (BLASTN, proximately 150–200 mg of coral tissue and skeleton was placed ≥100 bp and ≥70% identity) to ESTs from Symbiodinium sp. in 1 mL of TRIzol and homogenized for 2 min by vortexing with KB8 (clade A) and Symbiodinium sp. MF1.04b (clade B) (6) ∼100 μL of 0.5-mm Zirconia/Silica Beads (BioSpec Products). and the related dinoflagellate Polarella glacialis. Finally, the re- Resulting tissue/TRIzol slurry was removed by centrifugation, sulting contigs were compared (via BLASTX) to the NCBI non- and the standard TRIzol extraction was performed according to redundant protein database (nr; downloaded on June 7, 2011 manufacturer’s specifications with the replacement of 250 μLof from www.ncbi.nlm.nih.gov). The nonredundant (nr) results were 100% (vol/vol) isopropanol with 250 μL of high-salt buffer (0.8 used to remove any additional sequences likely to be noncoral, M Na citrate, 1.2 M NaCl) during the final precipitation step. based on similarity to alveolates, fungi, bacteria, or Archaea, as Resulting RNA pellet was resuspended in 12 μL of diethylpyro- determined using the metagenome analyzer (MEGAN) Version 4 = = = carbonate (DEPC)-treated H2O. mRNA was isolated from total (min. support 1, min. score 200, top percent 20) (7). RNA using the Micro-FastTrack mRNA isolation kit (In- The remaining, putatively coral, contigs were then “meta- vitrogen) and an overnight precipitation at −80 °C. -
Report 99517
Comprehensive Skeletal Dysplasias and Disorders Panel Plus REFERRING HEALTHCARE PROFESSIONAL NAME HOSPITAL PATIENT NAME DOB AGE GENDER ORDER ID 6 PRIMARY SAMPLE TYPE SAMPLE COLLECTION DATE CUSTOMER SAMPLE ID SUMMARY OF RESULTS PRIMARY FINDINGS The patient is heterozygous for TRPV4 c.2396C>T, p.(Pro799Leu), which is classified as pathogenic. Del/Dup (CNV) analysis Negative for explaining the patient’s phenotype. PRIMARY FINDINGS: SEQUENCE ALTERATIONS GENE TRANSCRIPT NOMENCLATURE GENOTYPE CONSEQUENCE INHERITANCE CLASSIFICATION TRPV4 NM_021625.4 c.2396C>T, p.(Pro799Leu) HET missense_variant AD Pathogenic ID ASSEMBLY POS REF/ALT GRCh37/hg19 12:110222183 G/A PHENOTYPE Brachyolmia (autosomal dominant type), Charcot-Marie-Tooth disease, Familial Digital arthropathy with brachydactyly, gnomAD AC/AN POLYPHEN SIFT MUTTASTER Hereditary motor and sensory neuropathy, 0/0 possibly damaging deleterious disease causing Metatropic dysplasia, Parastremmatic dwarfism, Spinal muscular atrophy, Spondyloepiphyseal dysplasia Maroteaux type, Spondylometaphyseal dysplasia Kozlowski type SEQUENCING PERFORMANCE METRICS PANEL GENES EXONS / REGIONS BASES BASES > 20X MEDIAN PERCENT COVERAGE > 20X Comprehensive Skeletal Dysplasias and 252 4053 816901 812241 199 99.43 Disorders Panel Blueprint Genetics Oy, Keilaranta 16 A-B, 02150 Espoo, Finland VAT number: FI22307900, CLIA ID Number: 99D2092375, CAP Number: 9257331 TARGET REGION AND GENE LIST The Blueprint Genetics Comprehensive Skeletal Dysplasias and Disorders Panel (version 4, Oct 19, 2019) Plus Analysis includes sequence -
Visual Data Mining : Background, Techniques, and Drug Discovery
Visual Data Mining: Background, Techniques, and Drug Discovery Applications Mihael Ankerst The Boeing Company Georges Grinstein UMass Lowell and AnVil Inc. Daniel Keim AT&T Research and University of Konstanz A color version of the tutorial notes can be found via http://www.fmi.uni-konstanz.de/~keim KDD’2002 Conference Emails and URLs Data Exploration • Definition Mihael Ankerst – [email protected] Data Exploration is the process of searching and analyzing – http://www.visualclassification.com/ankerst databases to find implicit but potentially useful information Daniel A. Keim – [email protected] • more formally – [email protected] Data Exploration is the process of finding a – http://www.fmi.uni-konstanz.de/~keim • subset D‘ of the database D and George Grinstein – [email protected] • hypotheses Hu(D‘,C) – http://genome.uml.edu that a user U considers useful in an application context C – http://www.anvilinfo.com Mihael Ankerst, The Boeing Company -- Daniel A. Keim, AT&T and Univ. of Konstanz Mihael Ankerst, The Boeing Company -- Daniel A. Keim, AT&T and Univ. of Konstanz Georges Grinstein, UMass Lowell and AnVil Inc. 2 Georges Grinstein, UMass Lowell and AnVil Inc. 5 Overview Abilities of Humans and Computers Part I: Visualization Techniques 1. Introduction 2. Visual Data Exploration Techniques abilities of Data Storage 3. Distortion and Interaction Techniques the computer Numerical Computation 4. Visual Data Mining Systems Searching Part II: Specific Visual Data Mining Techniques 1. Association Rules Planning 2. Classification Diagnosis Logic 3. Clustering Prediction 4. Text Mining 5. Tightly Integrated Visualization Perception Part III: Drug Discovery Applications Creativity 1. -
Biological Data Integration Using Semantic Web Technologies
Biological data integration using Semantic Web technologies Pasquier C Phone: +33 492 07 6947 Fax: +33 492 07 6432 Email: [email protected] Institute of Signaling, Developmental Biology & Cancer CNRS - UMR 6543, University of Nice Sophia-Antipolis Parc Valrose, 06108 NICE cedex 2, France. Summary Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling. Keywords Data integration, Semantic Web, OWL, RDF, SPARQL, Knowledge Base System (KBS) Introduction Biology is now an information-intensive science and research in genomics, transcriptomics and proteomics heavily depend on the availability and the efficient use of information. When data were structured and organized as a collection of records in dedicated, self-sufficient databases, information was retrieved by performing queries on the database using a specialized query language; for example SQL (Structured Query Language) for relational databases or OQL (Object Query Language) for object databases. -
Uman Enome News
uman enome news ISSN: 1050-6101 Vol. 7, No.2, July-August 1995 Optical Mapping Offers Fast, Accurate Method for Generating Restriction Maps New Approach Eliminates Electrophoresis, Is Amenable to Automation evelopment of cheaper and faster technologies for large-scale Dgenome mapping has been a major priority in the first 5 years of the Human Genome Project. Although many efforts have focused on improving standard gel electrophoresis and hybridization methods, a new approach using optical detection of single DNA mole.cules shows great promise for rapid construction of ordered genome maps based on restriction endonuclease cutting sites. l -4 Restriction endonucleases-enzymes that cut DNA molecules at specific sites in the genome-have played a major role in allowing investigators to identify and characterize various loci on a DNA molecule. Unlike maps based on STSs (a sequence-based landmark), restriction maps provide the precise genomic distances that are essential for efficient sequencing and for determining the spatial relationships of specific loci. Compared with hybridization-based fingerprinting approaches, ordered restriction maps offer relatively unambiguous clone characterization, which is useful for determining overlapping areas in contig formation, establishing minimum tiling paths for sequencing (coverage of a region), and characterizing genetic lesions with respect to various structural alterations. Image of a human chromosome 11 YAC clone (425 kb) cleaved by restriction endonucleases, Despite the broad applications of restriction maps, however, associated stained with a fluorochrome, and visualized by techniques for their generation have changed little over the last 10 years fluorescence microscopy. (White bar at lower left because of their reliance on tedious electrophoresis methods. -
Towards a Sustainable Funding Model for the Uniprot Use Case[Version 2
F1000Research 2018, 6(ELIXIR):2051 Last updated: 25 OCT 2018 RESEARCH ARTICLE Funding knowledgebases: Towards a sustainable funding model for the UniProt use case [version 2; referees: 3 approved] Chiara Gabella , Christine Durinx , Ron Appel ELIXIR-Switzerland, SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland First published: 27 Nov 2017, 6(ELIXIR):2051 (doi: Open Peer Review v2 10.12688/f1000research.12989.1) Latest published: 22 Mar 2018, 6(ELIXIR):2051 (doi: 10.12688/f1000research.12989.2) Referee Status: Abstract Invited Referees Millions of life scientists across the world rely on bioinformatics data resources 1 2 3 for their research projects. Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases. Despite the increasing need for such highly accurate and reliable sources of scientific version 2 information, most of them do not have secured funding over the near future and published often depend on short-term grants that are much shorter than their planning 22 Mar 2018 horizon. Additionally, they are often evaluated as research projects rather than as research infrastructure components. version 1 In this work, twelve funding models for data resources are described and published report report report applied on the case study of the Universal Protein Resource (UniProt), a key 27 Nov 2017 resource for protein sequences and functional information knowledge. We show that most of the models present inconsistencies with open access or Helen Berman, Rutgers, The State equity policies, and that while some models do not allow to cover the total 1 costs, they could potentially be used as a complementary income source. -
Cullin 7 in Tumor Development: a Novel Potential Anti-Cancer Target
572 Neoplasma 2021; 68(3): 572–579 doi:10.4149/neo_2021_201207N1324 Cullin 7 in tumor development: a novel potential anti-cancer target Yumiao LI1,#, Aiming ZANG1,#, Jiejun FU2, Youchao JIA1,* 1Department of Medical Oncology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Cancer Radiotherapy and Chemotherapy, Baoding, Hebei, China; 2Department of Cell Biology and Genetics, Guangxi Medical University, Key Laboratory of Longevity and Aging-related Diseases of Chinese Ministry of Education, Center for Translational Medicine and School of Preclinical Medicine, Nanning, Guangxi, China *Correspondence: [email protected] #Contributed equally to this work. Received December 7, 2020 / Accepted January 25, 2021 As a core scaffold protein, Cullin 7 (Cul7) forms Skp1-Cullin-F-box (SCF) E3 ubiquitin ligase complexes with the regulator of cullins-1 (ROC1), S-phase kinase associated protein 1 (Skp1) and F-Box, and WD repeat domain containing 8 (Fbxw8). Alternatively, Cul7 can form a CRL7SMU1 complex with suppressor of Mec-8 and Unc-52 protein homolog (SMU1), damage-specific DNA binding protein 1 (DDB1), and ring finger protein 40 (RNF40), to promote cell growth. The mutations of Cul7 cause the 3-M dwarf syndrome, indicating Cul7 plays an important role in growth and development in humans and mice. Moreover, Cul7 regulates cell transformation, tumor protein p53 activity, cell senescence, and apoptosis, mutations in Cul7 are also involved in the development of tumors, indicating the characteristics of an oncogene. Cul7 is highly expressed in breast cancer, lung cancer, hepatocellular carcinoma, pancreatic cancer, ovarian cancer, and other malignant tumors where Cul7 promotes tumor development, cell transformation, and cell survival by regulating complex signaling pathways associated with protein degradation. -
Clinical, Molecular, and Immune Analysis of Dabrafenib-Trametinib
Supplementary Online Content Chen G, McQuade JL, Panka DJ, et al. Clinical, molecular and immune analysis of dabrafenib-trametinib combination treatment for metastatic melanoma that progressed during BRAF inhibitor monotherapy: a phase 2 clinical trial. JAMA Oncology. Published online April 28, 2016. doi:10.1001/jamaoncol.2016.0509. eMethods. eReferences. eTable 1. Clinical efficacy eTable 2. Adverse events eTable 3. Correlation of baseline patient characteristics with treatment outcomes eTable 4. Patient responses and baseline IHC results eFigure 1. Kaplan-Meier analysis of overall survival eFigure 2. Correlation between IHC and RNAseq results eFigure 3. pPRAS40 expression and PFS eFigure 4. Baseline and treatment-induced changes in immune infiltrates eFigure 5. PD-L1 expression eTable 5. Nonsynonymous mutations detected by WES in baseline tumors This supplementary material has been provided by the authors to give readers additional information about their work. © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 eMethods Whole exome sequencing Whole exome capture libraries for both tumor and normal samples were constructed using 100ng genomic DNA input and following the protocol as described by Fisher et al.,3 with the following adapter modification: Illumina paired end adapters were replaced with palindromic forked adapters with unique 8 base index sequences embedded within the adapter. In-solution hybrid selection was performed using the Illumina Rapid Capture Exome enrichment kit with 38Mb target territory (29Mb baited). The targeted region includes 98.3% of the intervals in the Refseq exome database. Dual-indexed libraries were pooled into groups of up to 96 samples prior to hybridization.