A Genomic View of Estrogen Actions in Human Breast Cancer Cells by Expression Profiling of the Hormone-Responsive Transcriptome
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CCT3 Rabbit Pab
Leader in Biomolecular Solutions for Life Science CCT3 Rabbit pAb Catalog No.: A6547 3 Publications Basic Information Background Catalog No. The protein encoded by this gene is a molecular chaperone that is a member of the A6547 chaperonin containing TCP1 complex (CCT), also known as the TCP1 ring complex (TRiC). This complex consists of two identical stacked rings, each containing eight different Observed MW proteins. Unfolded polypeptides enter the central cavity of the complex and are folded 60kDa in an ATP-dependent manner. The complex folds various proteins, including actin and tubulin. Alternate transcriptional splice variants have been characterized for this gene. Calculated MW In addition, a pseudogene of this gene has been found on chromosome 8. 56kDa/60kDa Category Primary antibody Applications WB,IHC,IF Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 7203 P49368 IHC 1:50 - 1:200 Immunogen 1:50 - 1:200 IF Recombinant fusion protein containing a sequence corresponding to amino acids 1-300 of human CCT3 (NP_005989.3). Synonyms CCT3;CCT-gamma;CCTG;PIG48;TCP-1-gamma;TRIC5 Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts of various cell lines, using CCT3 antibody (A6547) at 1:1000 dilution. Secondary antibody: HRP Goat Anti-Rabbit IgG (H+L) (AS014) at 1:10000 dilution. Lysates/proteins: 25ug per lane. Blocking buffer: 3% nonfat dry milk in TBST. -
Establishing the Pathogenicity of Novel Mitochondrial DNA Sequence Variations: a Cell and Molecular Biology Approach
Mafalda Rita Avó Bacalhau Establishing the Pathogenicity of Novel Mitochondrial DNA Sequence Variations: a Cell and Molecular Biology Approach Tese de doutoramento do Programa de Doutoramento em Ciências da Saúde, ramo de Ciências Biomédicas, orientada pela Professora Doutora Maria Manuela Monteiro Grazina e co-orientada pelo Professor Doutor Henrique Manuel Paixão dos Santos Girão e pela Professora Doutora Lee-Jun C. Wong e apresentada à Faculdade de Medicina da Universidade de Coimbra Julho 2017 Faculty of Medicine Establishing the pathogenicity of novel mitochondrial DNA sequence variations: a cell and molecular biology approach Mafalda Rita Avó Bacalhau Tese de doutoramento do programa em Ciências da Saúde, ramo de Ciências Biomédicas, realizada sob a orientação científica da Professora Doutora Maria Manuela Monteiro Grazina; e co-orientação do Professor Doutor Henrique Manuel Paixão dos Santos Girão e da Professora Doutora Lee-Jun C. Wong, apresentada à Faculdade de Medicina da Universidade de Coimbra. Julho, 2017 Copyright© Mafalda Bacalhau e Manuela Grazina, 2017 Esta cópia da tese é fornecida na condição de que quem a consulta reconhece que os direitos de autor são pertença do autor da tese e do orientador científico e que nenhuma citação ou informação obtida a partir dela pode ser publicada sem a referência apropriada e autorização. This copy of the thesis has been supplied on the condition that anyone who consults it recognizes that its copyright belongs to its author and scientific supervisor and that no quotation from the -
ANP32A and ANP32B Are Key Factors in the Rev Dependent CRM1 Pathway
bioRxiv preprint doi: https://doi.org/10.1101/559096; this version posted February 24, 2019. 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. 1 ANP32A and ANP32B are key factors in the Rev dependent CRM1 pathway 2 for nuclear export of HIV-1 unspliced mRNA 3 Yujie Wang1, Haili Zhang1, Lei Na 1, Cheng Du1, Zhenyu Zhang1, Yong-Hui Zheng1,2, Xiaojun Wang1* 4 1State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, the Chinese 5 Academy of Agricultural Sciences, Harbin 150069, China 6 2Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, 7 USA. 8 * Address correspondence to Xiaojun Wang, [email protected]. 9 10 11 12 13 14 15 16 17 18 19 20 21 1 bioRxiv preprint doi: https://doi.org/10.1101/559096; this version posted February 24, 2019. 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. 22 Abstract 23 The nuclear export receptor CRM1 is an important regulator involved in the shuttling of various cellular 24 and viral RNAs between the nucleus and the cytoplasm. HIV-1 Rev interacts with CRM1 in the late phase of 25 HIV-1 replication to promote nuclear export of unspliced and single spliced HIV-1 transcripts. However, the 26 knowledge of cellular factors that are involved in the CRM1-dependent viral RNA nuclear export remains 27 inadequate. Here, we identified that ANP32A and ANP32B mediate the export of unspliced or partially spliced 28 viral mRNA via interacting with Rev and CRM1. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
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. -
Transcriptional Regulation of RKIP in Prostate Cancer Progression
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Sciences Transcriptional Regulation of RKIP in Prostate Cancer Progression Submitted by: Sandra Marie Beach In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Sciences Examination Committee Major Advisor: Kam Yeung, Ph.D. Academic William Maltese, Ph.D. Advisory Committee: Sonia Najjar, Ph.D. Han-Fei Ding, M.D., Ph.D. Manohar Ratnam, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: May 16, 2007 Transcriptional Regulation of RKIP in Prostate Cancer Progression Sandra Beach University of Toledo ACKNOWLDEGMENTS I thank my major advisor, Dr. Kam Yeung, for the opportunity to pursue my degree in his laboratory. I am also indebted to my advisory committee members past and present, Drs. Sonia Najjar, Han-Fei Ding, Manohar Ratnam, James Trempe, and Douglas Pittman for generously and judiciously guiding my studies and sharing reagents and equipment. I owe extended thanks to Dr. William Maltese as a committee member and chairman of my department for supporting my degree progress. The entire Department of Biochemistry and Cancer Biology has been most kind and helpful to me. Drs. Roy Collaco and Hong-Juan Cui have shared their excellent technical and practical advice with me throughout my studies. I thank members of the Yeung laboratory, Dr. Sungdae Park, Hui Hui Tang, Miranda Yeung for their support and collegiality. The data mining studies herein would not have been possible without the helpful advice of Dr. Robert Trumbly. I am also grateful for the exceptional assistance and shared microarray data of Dr. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
Gpr161 Anchoring of PKA Consolidates GPCR and Camp Signaling
Gpr161 anchoring of PKA consolidates GPCR and cAMP signaling Verena A. Bachmanna,1, Johanna E. Mayrhofera,1, Ronit Ilouzb, Philipp Tschaiknerc, Philipp Raffeinera, Ruth Röcka, Mathieu Courcellesd,e, Federico Apeltf, Tsan-Wen Lub,g, George S. Baillieh, Pierre Thibaultd,i, Pia Aanstadc, Ulrich Stelzlf,j, Susan S. Taylorb,g,2, and Eduard Stefana,2 aInstitute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, 6020 Innsbruck, Austria; bDepartment of Chemistry and Biochemistry, University of California, San Diego, CA 92093; cInstitute of Molecular Biology, University of Innsbruck, 6020 Innsbruck, Austria; dInstitute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada H3C 3J7; eDépartement de Biochimie, Université de Montréal, Montreal, QC, Canada H3C 3J7; fOtto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; gDepartment of Pharmacology, University of California, San Diego, CA 92093; hInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; iDepartment of Chemistry, Université de Montréal, Montreal, QC, Canada H3C 3J7; and jInstitute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, 8010 Graz, Austria Contributed by Susan S. Taylor, May 24, 2016 (sent for review February 18, 2016; reviewed by John J. G. Tesmer and Mark von Zastrow) Scaffolding proteins organize the information flow from activated G accounts for nanomolar binding affinities to PKA R subunit dimers protein-coupled receptors (GPCRs) to intracellular effector cascades (12, 13). Moreover, additional components of the cAMP signaling both spatially and temporally. By this means, signaling scaffolds, such machinery, such as GPCRs, adenylyl cyclases, and phosphodiester- as A-kinase anchoring proteins (AKAPs), compartmentalize kinase ases, physically interact with AKAPs (1, 5, 11, 14). -
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. -
Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1
BIOLOGY OF REPRODUCTION 78, 184–192 (2008) Published online before print 10 October 2007. DOI 10.1095/biolreprod.107.062943 Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1 Sarah E. Fiedler, Malini Bajpai, and Daniel W. Carr2 Department of Medicine, Oregon Health & Sciences University and Veterans Affairs Medical Center, Portland, Oregon 97239 ABSTRACT Guanine nucleotide exchange factors (GEFs) catalyze the GDP for GTP exchange [2]. Activation is negatively regulated by In somatic cells, RHOA mediates actin dynamics through a both guanine nucleotide dissociation inhibitors (RHO GDIs) GNA13-mediated signaling cascade involving RHO kinase and GTPase-activating proteins (GAPs) [1, 2]. Endogenous (ROCK), LIM kinase (LIMK), and cofilin. RHOA can be RHO can be inactivated via C3 exoenzyme ADP-ribosylation, negatively regulated by protein kinase A (PRKA), and it and studies have demonstrated RHO involvement in actin-based interacts with members of the A-kinase anchoring (AKAP) cytoskeletal response to extracellular signals, including lyso- family via intermediary proteins. In spermatozoa, actin poly- merization precedes the acrosome reaction, which is necessary phosphatidic acid (LPA) [2–4]. LPA is known to signal through for normal fertility. The present study was undertaken to G-protein-coupled receptors (GPCRs) [4, 5]; specifically, LPA- determine whether the GNA13-mediated RHOA signaling activated GNA13 (formerly Ga13) promotes RHO activation pathway may be involved in acrosome reaction in bovine through GEFs [4, 6]. Activated RHO-GTP then signals RHO caudal sperm, and whether AKAPs may be involved in its kinase (ROCK), resulting in the phosphorylation and activation targeting and regulation. GNA13, RHOA, ROCK2, LIMK2, and of LIM-kinase (LIMK), which in turn phosphorylates and cofilin were all detected by Western blot in bovine caudal inactivates cofilin, an actin depolymerizer, the end result being sperm. -
GENOME EDITING in AFRICA’S AGRICULTURE 2021 an EARLY TAKE-OFF TABLE of CONTENTS Abbreviations and Acronyms 3
GENOME EDITING IN AFRICA’S AGRICULTURE 2021 AN EARLY TAKE-OFF TABLE OF CONTENTS Abbreviations and Acronyms 3 1. Introduction 4 1.1 Milestones in Plant Breeding 5 1.2 How CRISPR genome editing works in agriculture 6 2 . Genome editing projects and experts in eastern Africa 7 2.1 Kenya 8 2.2 Ethiopia 14 2.3 Uganda 15 3 . Gene editing projects and experts in southern Africa 17 3.1 South Africa 18 4. Gene editing projects and experts in West Africa 19 4.1 Nigeria 20 5. Gene editing projects and experts in Central Africa 21 5.1 Cameroon 22 6. Gene editing projects and experts in North Africa 23 6.1 Egypt 24 7. Conclusion 25 8. CRISPR genome editing: inside a crop breeder’s toolkit 26 9. Regulatory Approaches for Genome Edited Products in Various Countries 27 10. Communicating about Genome Editing in Africa 28 2 ABBREVIATIONS AND ACRONYMS CRISPR Clustered Regularly Interspaced Short Palindromic Repeats DNA Deoxyribonucleic acid GM Genetically Modified GMO Genetically Modified Organism HDR Homology Directed Repair ISAAA International Service for the Acquisition of Agri-biotech Applications NHEJ Non Homologous End Joining PCR Polymerase Chain Reaction RNA Ribonucleic Acid LGS1 Low germination stimulant 1 3 1.0 INTRODUCTION Genome editing (also referred to as gene editing) comprises a arm is provided with the CRISPR-Cas9 cassette, homology-directed group of technologies that give scientists the ability to change an repair (HDR) will occur, otherwise the cell will employ non- organism’s DNA. These technologies allow addition, removal or homologous end joining (NHEJ) to create small indels at the cut site alteration of genetic material at particular locations in the genome.