DOCSLIB.ORG

Application of RNA-Seq in Ecotoxicogenomics: Exploring the Effects of Ibuprofen Exposure on Rainbow Trout and C

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Application of RNA-Seq in Ecotoxicogenomics: Exploring the Effects of Ibuprofen Exposure on Rainbow Trout and C Application of RNA-Seq in Ecotoxicogenomics: Exploring the Effects of Ibuprofen Exposure on Rainbow Trout and C. elegans by Lorraine Lok-Lun Yu Brown B.Sc., University of British Columbia, 2007 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Molecular Biology and Biochemistry Faculty of Science Lorraine Lok-Lun Yu Brown 2013 SIMON FRASER UNIVERSITY Fall 2013 Approval Name: Lorraine Lok-Lun Yu Brown Degree: Master of Science Title of Thesis: Application of RNA-Seq in Ecotoxicogenomics: Exploring the Effects of Ibuprofen Exposure on Rainbow Trout and C. elegans Examining Committee: Chair: Nicholas Harden Professor Fiona Brinkman Senior Supervisor Professor William Davidson Supervisor Professor Steven Jones Supervisor Professor Christopher Kennedy Internal Examiner Professor Department of Biological Sciences Date Defended/Approved: December 16, 2013 ii Partial Copyright Licence iii Ethics Statement iv Abstract RNA-Seq was applied in this ecotoxicogenomics study to investigate the effects of ibuprofen in two species, rainbow trout (Oncorhynchus mykiss), a fish routinely used in ecotoxicology tests, and Caenorhabditis elegans, a well-studied nematode with immense genomics information. Exposure to environmentally relevant levels of ibuprofen resulted in gene expression changes relating to stress, prostaglandin synthesis, reproduction and development in both species. In fish, we observed sex-dependent differences in vitellogenin and prostaglandin synthase gene expression, highlighting the importance of genetic sex determination of juvenile fish used in bioassays. In worms, we saw a decrease in progeny production count. Our results suggest that ibuprofen may have negative impacts on reproduction in both species but requires further investigation. This study demonstrated the benefits and challenges of using RNA-Seq in ecotoxicology and the value of studying diverse species, including traditional models and more microscopic organisms, to better understand toxicant impact on an entire ecosystem. Keywords: RNA-Seq; rainbow trout; C. elegans; ibuprofen; toxicogenomics v Dedication To my family vi Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Fiona Brinkman, for taking a chance on me and for being willing to branch out to a field (environmental toxicology) that was somewhat new to her when I first started. I am truly grateful for her support, guidance, and patience. Without her, all of this would not be possible. Thank you to Graham van Aggelen for always encouraging me to pursue my goals. Thank you to my committee members, Dr. Willie Davidson and Dr. Steven Jones, for providing constructive feedback and guidance. Thank you to Dr. Heather Osachoff, for being a positive role model for me and for inspiring me to pursue a graduate degree. Thank you to the Brinkman Lab staff members and students: Bhav Dhillon, Amir Foroushani, Erin Gill, Emma Griffiths, Matthew Laird, Raymond Lo, David Lynn, Mike Peabody, Thea Van Rossum, Geoff Winsor, and Matthew Whiteside. Thank you to the staff at Environment Canada: Norm Berke, Joy Bruno, Craig Buday, Michelle Linssen- Sauvé, Georgina Lush, Grant Schroeder, and Rachel Skirrow. Also thank you to all the co-op students and volunteers who have helped me with this project: Ishai Kones, Richard Chea, and Melanie Pylatuk. I am grateful for the funding provided by SFU via Graduate Student Fellowships. Finally, thank you to Aron, for his endless support and encouragement; my parents, Kin Kong and Wai Fan, and my sister, Rosa, for their unconditional love and support. vii Table of Contents Approval .......................................................................................................................... ii Partial Copyright Licence ............................................................................................... iii Ethics Statement ............................................................................................................ iv Abstract ........................................................................................................................... v Dedication ...................................................................................................................... vi Acknowledgements ....................................................................................................... vii Table of Contents .......................................................................................................... viii List of Tables ................................................................................................................... x List of Figures.................................................................................................................. x List of Acronyms ............................................................................................................ xii Chapter 1. Introduction ............................................................................................... 1 Introduction to the Research Problem and Objectives ..................................................... 1 Next Generation Sequencing: RNA-Seq .......................................................................... 2 Overview ................................................................................................................ 2 Template preparation ............................................................................................. 2 Sequencing and imaging (Illumina example) .......................................................... 3 Data analysis .......................................................................................................... 3 Benefits and challenges of RNA-Seq ...................................................................... 7 Recent applications of RNA-Seq in environmental toxicogenomics ........................ 9 Ibuprofen: A Non-Steroidal Anti-Inflammatory Drug ....................................................... 12 Overview of NSAIDs and their mode of action ...................................................... 12 Presence of ibuprofen in the environment ............................................................ 12 Known effects of ibuprofen on non-target organisms ............................................ 13 The endocrine system and endocrine disruption ................................................... 14 Vitellogenesis in endocrine disruption ................................................................... 16 Background Information of Test Species ....................................................................... 16 Rainbow trout (Oncorhynchus mykiss) ................................................................. 16 Caenorhabditis elegans ........................................................................................ 17 Research Objectives ..................................................................................................... 18 Chapter 2. Effects of Ibuprofen Exposure in Rainbow Trout ................................. 19 Acknowledgements ....................................................................................................... 19 Introduction ................................................................................................................... 20 Materials and Methods .................................................................................................. 21 Exposure bioassays and tissue collection ............................................................. 21 Water quality parameters and chemistry analysis ................................................. 22 RNA and DNA extraction ...................................................................................... 22 RNA-Seq .............................................................................................................. 22 QPCR ................................................................................................................... 24 Genetic sex identification ...................................................................................... 25 Results .......................................................................................................................... 26 Water quality parameters and chemistry analysis ................................................. 26 Genetic sex of test fish ......................................................................................... 27 viii RNA-Seq .............................................................................................................. 28 RNA-Seq gene expression ................................................................................... 29 Functional gene set enrichment analysis .............................................................. 35 qPCR gene expression ......................................................................................... 37 Discussion ..................................................................................................................... 39 General gene expression changes ....................................................................... 39 qPCR validation .................................................................................................... 40 Stress response ................................................................................................... 41 Prostaglandin biosynthesis pathway ....................................................................
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Protein-Coding Variants Implicate Novel Genes Related to Lipid Homeostasis Contributing to Body-Fat Distribution
    ARTICLES https://doi.org/10.1038/s41588-018-0334-2 Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adi- pose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants. entral body-fat distribution, as assessed by waist-to-hip ratio 47 assuming an additive model and one under a recessive model (WHR), is a heritable and a well-established risk factor for (Table 1 and Supplementary Figs. 1–4). Due to possible heteroge- Cadverse metabolic outcomes1–6. Lower values of WHR are neity, we also performed European-only meta-analysis. Here, four associated with lower risk of cardiometabolic diseases such as type additional coding variants were significant (three novel) assum- 2 diabetes7,8, or differences in bone structure and gluteal muscle ing an additive model (Table 1 and Supplementary Figs.
    [Show full text]
  • T Cell Self-Reactivity During Thymic Development Dictates the Timing of Positive Selection
    bioRxiv preprint doi: https://doi.org/10.1101/2021.01.18.427079; this version posted January 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Title: T cell self-reactivity during thymic development dictates the timing of positive 2 selection 3 4 5 Authors: 6 Lydia K. Lutes1, Zoë Steier2, Laura L. McIntyre1, Shraddha Pandey1, James Kaminski3,#, 7 Ashley R. Hoover1,§, Silvia Ariotti1,†, Aaron Streets2,3,4, Nir Yosef2,3,4,5,6,, Ellen A. Robey1‡ 8 9 Author Affiliations: 10 1 Division of Immunology and Pathogenesis, Department of Molecular and Cell Biology, University of 11 California, Berkeley 12 2 Department of Bioengineering, University of California, Berkeley 13 3 Center for Computational Biology, University of California, Berkeley 14 4 Chan Zuckerberg Biohub, San Francisco, California 15 5 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 16 6 Ragon Institute of MGH, MIT and Harvard 17 ‡ To whom correspondence may be addressed. E-mail: [email protected]. 18 19 Present addresses: # Division of Pediatric Hematology/Oncology, Boston Children’s Hospital; Department of 20 Medical Oncology, Dana Farber Cancer Institute, and Harvard Medical School, Boston, MA; § Oklahoma 21 Medical Research Foundation, Oklahoma, United States; † Instituto de Medicina Molecular, João Lobo Atunes, 22 Faculdade de Medicina da Universidade de Lisboa, Av. Professor Egas Moniz, Lisbon, 1649-028, Portugal. 23 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.18.427079; this version posted January 21, 2021.
    [Show full text]
  • Protein Dysregulation in Immune Cells of Multiple Sclerosis Patients
    Master’s Thesis 2020 60 ECTS Faculty of Chemistry, Biotechnology and Food Sciences Protein Dysregulation in Immune Cells of Multiple Sclerosis Patients Oda Glomstad Kråbøl Biotechnology Faculty of Chemistry, Biotechnology and Food Sciences Protein Dysregulation in Immune Cells of Multiple Sclerosis Patients Oda Glomstad Kråbøl Main supervisor: Tone Berge Co-supervisor: Chiara Cappelletti Internal supervisor: Siv Kjølsrud Bøhn Oslo University Hospital – University of Oslo Multiple Sclerosis Research Group and The Norwegian University of Life Sciences, Faculty of Chemistry, Biotechnology and Food Sciences June 2020 ©Oda Glomstad Kråbøl Protein Dysregulation in Immune Cells of Multiple Sclerosis Patients https://nmbu.brage.unit.no Acknowledgements This thesis was carried out at the MS research group in the Neuroscience Research Unit of Oslo University Hospital (OUS) from August 2019 to June 2020. The thesis is a part of a Master’s Degree in Biotechnology at the Norwegian University of Life Sciences (NMBU), main affiliation being the Faculty of Chemistry, Biotechnology and Food Sciences (KBM). Firstly, I would like to thank Professor Tone Berge for being my supervisor, and for arranging this interesting project. Her expertise and enthusiasm have given me great insights into the challenging yet fascinating multiple sclerosis research field. The guidance I have received throughout this time has been exceptional! I am likewise thankful for my co-supervisor Chiara Cappelletti for devoting her time supervising me in the lab and answering all my questions. She has provided me with protocols as well as theoretical and practical explanations for everything I have needed, which has been invaluable. I would also like to thank the MS research group members for making me feel welcome and included.
    [Show full text]
  • Protein-Coding Variants Implicate Novel Genes Related to Lipid Homeostasis Contributing to Body-Fat Distribution
    ARTICLES https://doi.org/10.1038/s41588-018-0334-2 Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adi- pose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants. entral body-fat distribution, as assessed by waist-to-hip ratio 47 assuming an additive model and one under a recessive model (WHR), is a heritable and a well-established risk factor for (Table 1 and Supplementary Figs. 1–4). Due to possible heteroge- Cadverse metabolic outcomes1–6. Lower values of WHR are neity, we also performed European-only meta-analysis. Here, four associated with lower risk of cardiometabolic diseases such as type additional coding variants were significant (three novel) assum- 2 diabetes7,8, or differences in bone structure and gluteal muscle ing an additive model (Table 1 and Supplementary Figs.
    [Show full text]
  • Jachetti Suppl Table 1 80814.Xlsx
    Uniprot name Protein name Gene name Peptides OT1 - TPIN-SC(CD62Lhi)/OT1 - TNE-SC(CD62Llo) OT1 - TPIN-SC(CD62Lhi)/OT1 - TPIN-SC(CD62Llo) Log2 (ratio) Counts Log10 (Summed Significance B Log2 (ratio) Counts Log10 (Summed Significance B Intensities) Intensites) O08797 Protein Serpinb9 Serpinb9 5 1,6950 8 6,3029 0,0000 -0,2205 7 6,7278 0,5314 E9Q5W7 L-selectin Sell 2 1,4661 2 5,6553 0,0000 0,9395 4 6,3054 0,3946 Q544R1 Proline synthase co-transcribeD Prosc 2 1,4208 2 6,1679 0,0000 0,5063 2 5,7559 0,7523 bacterial homolog protein O09159 Lysosomal alpha-mannosiDase Man2b1 3 1,1965 2 5,4603 0,0000 NaN 1 5,1854 1,0000 E9PUM4 Talin-2 Tln2 19 1,1659 2 7,2477 0,0000 NaN 0 NaN 1,0000 Q3V157 Signal transDucer anD activator of Stat4 5 0,9376 3 5,7622 0,0000 0,1255 10 6,6896 0,8677 transcription 4 A2RSY6 TRMT1-like protein Trmt1l 2 0,8718 2 5,6063 0,0000 NaN 0 NaN 1,0000 P07356 Annexin A2 Anxa2 16 0,8307 15 6,9356 0,0000 0,9899 19 7,4857 0,4938 Q3UD32 Cathepsin S Ctss 4 0,8148 6 6,4179 0,0000 0,5122 5 6,7013 0,7468 Q8BV49 Pyrin anD HIN Domain-containing Pyhin1 6 0,7389 4 6,3847 0,0001 0,0098 5 6,9368 0,7255 protein 1 A2A4L8 SWI/SNF-relateD matrix- Smarce1 2 0,7367 2 5,5580 0,0001 NaN 1 6,0717 1,0000 associateD actin-DepenDent regulator of chromatin subfamily E member 1 Q7TPR4 Alpha-actinin-1 Actn1 37 0,6753 16 6,8461 0,0000 1,0007 16 7,3022 0,4868 Q6IRU2 Tropomyosin alpha-4 chain Tpm4 8 0,6405 5 6,6265 0,0000 0,5613 5 6,8943 0,8061 E9Q2W5 YTH Domain family protein 3 YthDf2 2 0,6162 2 7,8124 0,0000 NaN 1 7,4648 1,0000 O08738 Caspase-6 Casp6 3 0,5895 2
    [Show full text]
  • Systematic Prioritization of Candidate Genes in Disease Loci Identifies TRAFD1 As a 2 Master Regulator of IFN Signalling in Celiac Disease
    bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.973487; this version posted March 5, 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. 1 Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a 2 master regulator of IFN signalling in celiac disease # # 3 Adriaan van der Graaf1 , Maria Zorro1 , Annique Claringbould1, Urmo Vosa1,2, Raul Aguirre- 4 Gamboa1, Chan Li1,3, Joram Mooiweer1, Isis Ricano-Ponce1, Zuzanna Borek4,5, Frits 5 Koning6, Yvonne Kooy-Winkelaar6, Ludvig Sollid3, Shuo-Wang Qiao3, BIOS consortium, 6 Vinod Kumar1,7, Yang Li1,7,8, Lude Franke1, Sebo Withoff1, Cisca Wijmenga1,3, Serena # # 7 Sanna1,9 *, Iris Jonkers1,3 *. 8 Affiliations 9 1 Department of Genetics, University Medical Center Groningen, University of Groningen, 10 Groningen, the Netherlands 11 2 Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia 12 3 Department of Immunology, K.G. Jebsen Coeliac Disease Research Centre, University of 13 Oslo, Oslo, Norway 14 4 Deutsches Rheumaforschungszentrum Berlin (DRFZ), An Institute of the Leibniz 15 Association, Berlin, Germany 16 5 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, 17 Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of 18 Gastroenterology, Infectious Diseases and Rheumatology, Berlin, Germany 19 6 Department of Immunohematology and Blood Transfusion, Leiden University
    [Show full text]
  • Influence of Host Genetics in Shaping the Rumen Bacterial Community In
    www.nature.com/scientificreports OPEN Infuence of host genetics in shaping the rumen bacterial community in beef cattle Waseem Abbas1,4, Jeremy T. Howard1,4, Henry A. Paz1,2, Kristin E. Hales3, James E. Wells3, Larry A. Kuehn3, Galen E. Erickson1, Matthew L. Spangler1* & Samodha C. Fernando1* In light of recent host-microbial association studies, a consensus is evolving that species composition of the gastrointestinal microbiota is a polygenic trait governed by interactions between host genetic factors and the environment. Here, we investigated the efect of host genetic factors in shaping the bacterial species composition in the rumen by performing a genome-wide association study. Using a common set of 61,974 single-nucleotide polymorphisms found in cattle genomes (n = 586) and corresponding rumen bacterial community composition, we identifed operational taxonomic units (OTUs), Families and Phyla with high heritability. The top associations (1-Mb windows) were located on 7 chromosomes. These regions were associated with the rumen microbiota in multiple ways; some (chromosome 19; position 3.0–4.0 Mb) are associated with closely related taxa (Prevotellaceae, Paraprevotellaceae, and RF16), some (chromosome 27; position 3.0–4.0 Mb) are associated with distantly related taxa (Prevotellaceae, Fibrobacteraceae, RF16, RFP12, S24-7, Lentisphaerae, and Tenericutes) and others (chromosome 23; position 0.0–1.0) associated with both related and unrelated taxa. The annotated genes associated with identifed genomic regions suggest the associations observed are directed toward selective absorption of volatile fatty acids from the rumen to increase energy availability to the host. This study demonstrates that host genetics afects rumen bacterial community composition.
    [Show full text]
  • Can We Treat Congenital Blood Disorders by Transplantation Of
    Can we Treat Congenital Blood Disorders by Transplantation of Stem Cells, Gene Therapy to the Fetus? Panicos Shangaris University College London 2019 A thesis submitted for the degree of Doctor of Philosophy 1 Declaration I, Panicos Shangaris confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 2 Abstract Congenital diseases such as blood disorders are responsible for over a third of all pediatric hospital admissions. In utero transplantation (IUT) could cure affected fetuses but so far in humans, successful IUT has been limited to fetuses with severe immunologic defects, due to the maternal immune system and a functionally developed fetal immune system. I hypothesised that using autologous fetal cells could overcome the barriers to engraftment. Previous studies show that autologous haematopoietic progenitors can be easily derived from amniotic fluid (AF), and they can engraft long term into fetal sheep. In normal mice, I demonstrated that IUT of mouse AFSC results in successful haematopoietic engraftment in immune-competent mice. Congenic AFSCs appear to have a significant advantage over allogenic AFSCs. This was seen both by their end haematopoietic potential and the immune response of the host. Expansion of haematopoietic stem cells (HSC) has been a complicated and demanding process. To achieve adequate numbers for autologous stem cells for IUT, HSCs need to be expanded efficiently. I expanded and compared AFSCs, fetal liver stem cells and bone marrow stem cells. Culturing and expanding fetal and adult stem cells in embryonic stem cell conditions maintained their haematopoietic potential.
    [Show full text]
  • 2015-04-10 Introduction
    UNIVERSITY OF CALIFORNIA, SAN DIEGO Enhancers and Transcriptional Regulation in CD4+ T Cells A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Karmel Alon Allison Committee in charge: Professor Christopher K. Glass, Chair Professor Stephen M. Hedrick, Co-Chair Professor John T. Chang Professor Terry Gaasterland Professor Bing Ren 2015 ! Copyright Karmel Alon Allison, 2015 All rights reserved. ! The Dissertation of Karmel Alon Allison is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Co-Chair Chair University of California, San Diego 2015 iii! ! DEDICATION To my husband, David, who loved, supported, and funded me throughout this journey, to my son, Ezra, who was a very good little boy and let Mommy work, and to God, who is and does all things. iv! ! TABLE OF CONTENTS Signature Page........................................................................................................... iii Dedication.................................................................................................................. iv Table of Contents....................................................................................................... v List of Figures............................................................................................................ vi Acknowledgements.................................................................................................... viii Vita............................................................................................................................
    [Show full text]
  • Protein Interactions in Mrna Methylation Complexes
    University of New Orleans ScholarWorks@UNO Senior Honors Theses Undergraduate Showcase 5-2013 Protein Interactions in mRNA Methylation Complexes Yazan Ali Alqara University of New Orleans Follow this and additional works at: https://scholarworks.uno.edu/honors_theses Part of the Biology Commons Recommended Citation Alqara, Yazan Ali, "Protein Interactions in mRNA Methylation Complexes" (2013). Senior Honors Theses. 28. https://scholarworks.uno.edu/honors_theses/28 This Honors Thesis-Unrestricted is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Honors Thesis-Unrestricted in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Honors Thesis-Unrestricted has been accepted for inclusion in Senior Honors Theses by an authorized administrator of ScholarWorks@UNO. For more information, please contact [email protected]. Protein Interactions in mRNA Methylation Complexes An Honors Thesis Presented to the Department of Biological Sciences of the University of New Orleans In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science, with Honors in Biology by Yazan Ali Alqara May 2013 Acknowledgments I can wholeheartedly say that committing to this research project is one of the best decisions I have made to date. The determination it takes to commit to research is beyond comparison. Even the slightest possibility that I could contribute to the scientific realm of discoveries has kept my curiosity stimulated throughout the interval of my project.
    [Show full text]