DOCSLIB.ORG

Pathway and Network Analysis of Transcriptomic and Genomic Data.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Pathway and Network Analysis of Transcriptomic and Genomic Data.Pdf 저작자표시-비영리-변경금지 2.0 대한민국 이용자는 아래의 조건을 따르는 경우에 한하여 자유롭게 l 이 저작물을 복제, 배포, 전송, 전시, 공연 및 방송할 수 있습니다. 다음과 같은 조건을 따라야 합니다: 저작자표시. 귀하는 원저작자를 표시하여야 합니다. 비영리. 귀하는 이 저작물을 영리 목적으로 이용할 수 없습니다. 변경금지. 귀하는 이 저작물을 개작, 변형 또는 가공할 수 없습니다. l 귀하는, 이 저작물의 재이용이나 배포의 경우, 이 저작물에 적용된 이용허락조건 을 명확하게 나타내어야 합니다. l 저작권자로부터 별도의 허가를 받으면 이러한 조건들은 적용되지 않습니다. 저작권법에 따른 이용자의 권리는 위의 내용에 의하여 영향을 받지 않습니다. 이것은 이용허락규약(Legal Code)을 이해하기 쉽게 요약한 것입니다. Disclaimer Doctoral Thesis Pathway and Network Analysis of Transcriptomic and Genomic Data Sora Yoon Department of Biological Sciences Graduate School of UNIST 2019 Pathway and Network Analysis of Transcriptomic and Genomic Data Sora Yoon Department of Biological Sciences Graduate School of UNIST Abstract The development of high-throughput technologies has enabled to produce omics data and it has facilitated the systemic analysis of biomolecules in cells. In addition, thanks to the vast amount of knowledge in molecular biology accumulated for decades, numerous biological pathways have been categorized as gene-sets. Using these omics data and pre-defined gene-sets, the pathway analysis identifies genes that are collectively altered on a gene-set level under a phenotype. It helps the biological interpretation of the phenotype, and find phenotype-related genes that are not detected by single gene- based approach. Besides, the high-throughput technologies have contributed to construct various biological networks such as the protein-protein interactions (PPIs), metabolic/cell signaling networks, gene-regulatory networks and gene co-expression networks. Using these networks, we can visualize the relationships among gene-set members and find the hub genes, or infer new biological regulatory modules. Overall, this thesis/dissertation describes three approaches to enhance the performance of pathway and/or network analysis of transcriptomic and genomic data. First, a simple but effective method that improves the gene-permuting gene-set enrichment analysis (GSEA) of RNA-sequencing data will be addressed, which is especially useful for small replicate data. By taking absolute statistic, it greatly reduced the false positive rate caused by inter-gene correlation within gene-sets, and improved the overall discriminatory ability in gene-permuting GSEA. Next, a powerful competitive gene-set analysis tool for GWAS summary data, named GSA-SNP2, will be introduced. The z-score method applied with adjusted gene score greatly improved sensitivity compared to existing competitive gene-set analysis methods while exhibiting decent false positive control. The performance was validated using both simulation and real data. In addition, GSA-SNP2 visualizes protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further mechanistic study. Finally, a novel approach to predict condition-specific miRNA target network by biclustering a large collection of mRNA fold-change data for sequence-specific targets will be introduced. The bicluster targets exhibited on average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.2%) by filtering them using functional network information. The analysis of cancer-related biclusters revealed that PI3K/Akt signaling pathway is strongly enriched in targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Among them, five independent prognostic miRNAs were identified, and repressions of bicluster targets and pathway activity by mir-29 were experimentally validated. The BiMIR database provides a useful resource to search for miRNA regulation modules for 459 human miRNAs. i ii Table of Contents Abstract ............................................................................................................................................ i Table of Contents ............................................................................................................................ iii List of Figures ................................................................................................................................. vi List of Tables ................................................................................................................................ viii Chapter I: Introduction ................................................................................................................... 1 1.1 Omics data ................................................................................................................................ 1 1.1.1 Genomic data ......................................................................................................................... 1 1.1.1.1 Genome-wide association study ........................................................................................... 2 1.1.2 Transcriptomic data ................................................................................................................ 4 1.1.2.1 Microarray and RNA-sequencing ......................................................................................... 4 1.1.2.2 Issues in RNA-sequencing data analysis .............................................................................. 5 1.2 Pathway analysis ....................................................................................................................... 6 1.2.1 Pathway databases .................................................................................................................. 7 1.2.2 Pathway analysis methods ...................................................................................................... 7 1.2.2.1 Over-representation analysis ................................................................................................ 7 1.2.2.2 Functional class sorting ....................................................................................................... 7 1.2.2.2.1 Gene-set enrichment analysis ............................................................................................ 8 1.2.2.3 Pathway topology-based method .......................................................................................... 9 1.2.3 Competitive and self-contained gene-set analysis .................................................................... 9 1.3 Biological network .................................................................................................................. 12 1.4 Research overview .................................................................................................................. 13 Chapter II: Improving gene-set enrichment analysis of RNA-seq data with small replicates .... 15 2.1 Abstract................................................................................................................................... 15 2.2 Introduction............................................................................................................................. 15 2.3 Materials and Methods ............................................................................................................ 16 2.3.1 Absolute gene-permuting GSEA and filtering ....................................................................... 16 2.3.2 Simulation of the read count data with the inter-gene correlation........................................... 18 2.3.3 Biological relevance measure of a gene-set ........................................................................... 20 2.3.4 RNA-seq data handling and gene-set condition ..................................................................... 21 2.3.5 Gene-set size ........................................................................................................................ 21 iii 2.3.6 AbsfilterGSEA R package .................................................................................................... 21 2.4 Results .................................................................................................................................... 21 2.4.1 Comparison of gene-permuting GSEA methods for simulated read count data ...................... 21 2.4.2 Comparison of GSEA methods for RNA-seq data ................................................................. 25 2.4.3 Effects of the absolute filtering on false positive control and biological relevance ................. 26 2.5 Discussion ............................................................................................................................... 27 2.6 Supplementary information of Chapter II ................................................................................. 29 Chapter III: A powerful pathway enrichment and network analysis tool for GWAS summary data ................................................................................................................................................ 34 3.1 Abstract................................................................................................................................... 34 3.2 Introduction............................................................................................................................. 34 3.3 Materials and Methods ............................................................................................................ 36 3.3.1 Algorithm of GSA-SNP2 ...................................................................................................... 36 3.3.2 Competitive pathway analysis tools .....................................................................................
Load more

Recommended publications
  • Pseudouridine Synthases Modify Human Pre-Mrna Co-Transcriptionally and Affect Splicing
    bioRxiv preprint doi: https://doi.org/10.1101/2020.08.29.273565; this version posted August 31, 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. Pseudouridine synthases modify human pre-mRNA co-transcriptionally and affect splicing Authors: Nicole M. Martinez1, Amanda Su1, Julia K. Nussbacher2,3,4, Margaret C. Burns2,3,4, Cassandra Schaening5, Shashank Sathe2,3,4, Gene W. Yeo2,3,4* and Wendy V. Gilbert1* Authors and order TBD with final revision. Affiliations: 1Yale School of Medicine, Department of Molecular Biophysics & Biochemistry, New Haven, CT 06520, USA. 2Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92037, USA. 3Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA. 4Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA 92037, USA. 5Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. *Correspondence to: [email protected], [email protected] Abstract: Eukaryotic messenger RNAs are extensively decorated with modified nucleotides and the resulting epitranscriptome plays important regulatory roles in cells 1. Pseudouridine (Ψ) is a modified nucleotide that is prevalent in human mRNAs and can be dynamically regulated 2–5. However, it is unclear when in their life cycle RNAs become pseudouridylated and what the endogenous functions of mRNA pseudouridylation are. To determine if pseudouridine is added co-transcriptionally, we conducted pseudouridine profiling 2 on chromatin-associated RNA to reveal thousands of intronic pseudouridines in nascent pre-mRNA at locations that are significantly associated with alternatively spliced exons, enriched near splice sites, and overlap hundreds of binding sites for regulatory RNA binding proteins.
    [Show full text]
  • Vfabstract-Booklet-2017---Nda-Removed.Pdf
    WELCOME Dear CRINA Research Day Attendee: Thank you for joining us at the fourth annual CRINA Research Day. Last year, at our third event we welcomed over 250 attendees and featured more than 100 posters from many departments and faculties across campus. We are happy to announce that many of those attendees signed up to be members of CRINA, forming the core of our cancer research community. One year later, we continue to build our cancer research community by hosting a cancer-themed Research Day yet again. This year, we have continued to provide trainees with an opportunity to organize the program and present their work orally to our cancer research community at the University of Alberta. We hope that you continue to explore what the University of Alberta has to offer in the cancer research sphere and grow your network of collaborators through future CRINA Research Days. CRINA as an institute has a well-established reporting structure with operations committees and advisory boards. At our core, we continue to strengthen connections within our cancer research community by hosting events throughout the year such as seminars and symposia. Our leadership team is working on defining University of Alberta cancer research strengths in terms of research excellence and available infrastructure and platforms, with plans to build on these strengths to accelerate discovery and innovation. CRINA also represents the interests of its members as a unified voice on the provincial stage, working with AHS, AIHS and the ACF. Our ultimate goal is to establish our Institute as a national leader in cancer research and patient care, wherein clinical outcomes are addressed with scientific inquiry and where research drives innovations in cancer prevention, treatment and survivorship.
    [Show full text]
  • Stroke Genetics and Genomics
    Faculdade de Medicina da Universidade de Lisboa Unidade Neurológica de Investigação Clínica PhD Thesis Stroke Genetics and Genomics Tiago Krug Coelho Host Institution: Instituto Gulbenkian de Ciência Supervisor at Instituto Gulbenkian de Ciência: Doctor Sofia Oliveira Supervisor at Faculdade de Medicina da Universidade de Lisboa: Professor José Ferro PhD in Biomedical Sciences Specialization in Neurosciences 2010 Stroke Genetics and Genomics A ciência tem, de facto, um único objectivo: a verdade. Não esgota perfeitamente a sua tarefa se não descobre a causa do todo. Chiara Lubich i Stroke Genetics and Genomics ii Stroke Genetics and Genomics A impressão desta dissertação foi aprovada pela Comissão Coordenadora do Conselho Científico da Faculdade de Medicina de Lisboa em reunião de 28 de Setembro de 2010. iii Stroke Genetics and Genomics iv Stroke Genetics and Genomics As opiniões expressas são da exclusiva responsabilidade do seu autor. v Stroke Genetics and Genomics vi Stroke Genetics and Genomics Abstract ABSTRACT This project presents a comprehensive approach to the identification of new genes that influence the risk for developing stroke. Stroke is the leading cause of death in Portugal and the third leading cause of death in the developed world. It is even more disabling than lethal, and the persistent neurological impairment and physical disability caused by stroke have a very high socioeconomic cost. Moreover, the number of affected individuals is expected to increase with the current aging of the population. Stroke is a “brain attack” cutting off vital blood and oxygen to the brain cells and it is a complex disease resulting from environmental and genetic factors.
    [Show full text]
  • Genomic and Transcriptomic Investigations Into the Feed Efficiency Phenotype of Beef Cattle
    Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published version when available. Title Genomic and transcriptomic investigations into the feed efficiency phenotype of beef cattle Author(s) Higgins, Marc Publication Date 2019-03-06 Publisher NUI Galway Item record http://hdl.handle.net/10379/15008 Downloaded 2021-09-25T18:07:39Z Some rights reserved. For more information, please see the item record link above. Genomic and Transcriptomic Investigations into the Feed Efficiency Phenotype of Beef Cattle Marc Higgins, B.Sc., M.Sc. A thesis submitted for the Degree of Doctor of Philosophy to the Discipline of Biochemistry, School of Natural Sciences, National University of Ireland, Galway. Supervisor: Dr. Derek Morris Discipline of Biochemistry, School of Natural Sciences, National University of Ireland, Galway. Supervisor: Dr. Sinéad Waters Teagasc, Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange. Submitted November 2018 Table of Contents Declaration ................................................................................................................ vii Funding .................................................................................................................... viii Acknowledgements .................................................................................................... ix Abstract ......................................................................................................................
    [Show full text]
  • Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma
    Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen Submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in the Graduate School of Arts and Sciences Columbia University 2013 ! 2013 James Chen All rights reserved ABSTRACT Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma. TABLE OF CONTENTS CHAPTER 1… Introduction and Background 1 Glioblastoma and the Mesenchymal Subtype 3 Systems Biology and Master Regulators 9 Thesis Project: Genetics and Genomics 20 CHAPTER 2… TCGA Data Processing 23 CHAPTER 3… DIGGIn Part 1 – Selecting f-CNVs 33 Mutual Information 40 Application and Analysis 45 CHAPTER 4… DIGGIn Part 2 – Selecting drivers 52 CHAPTER 5… KLHL9 Manuscript 63 Methods 90 CHAPTER 5a… Revisions work-in-progress 105 CHAPTER 6… Discussion 109 APPENDICES… 132 APPEND01 – TCGA classifications 133 APPEND02 – GBM f-CNV list 136 APPEND03 – MES f-CNV candidate drivers 152 APPEND04 – Scripts 149 APPEND05 – Manuscript Figures and Legends 175 APPEND06 – Manuscript Supplemental Materials 185 i ACKNOWLEDGEMENTS I would like to thank the Califano Lab and my mentor, Andrea Califano, for their intellectual and motivational support during my stay in their lab.
    [Show full text]
  • Identification of Novel Cellular Factors Involved in HIV-1 Latency
    Identification of novel cellular factors involved in HIV-1 latency Vorgelegt beim Fachbereich Biologie der Technischen Universität Darmstadt zur Erlangung des akademischen Grades Doctor rerum naturalium Dissertation von Alexandra Borch 1. Referentin: Prof. Dr. Beatrix Süß 2. Referent: Prof. Dr. Alexander Löwer 3. Referent: Prof. Dr. Klaus Cichutek Darmstadt 2019 Die vorliegende Arbeit wurde unter der Leitung von Prof. Dr. Klaus Cichutek in der Arbeitsgruppe von Dr. Renate König „Host-Pathogen Interactions“ am Paul-Ehrlich-Institut in Langen angefertigt. Die Betreuung seitens der Technischen Universität Darmstadt erfolgte durch Prof. Dr. Beatrix Süß vom Fachbereich Biologie. Borch, Alexandra Identification of novel cellular factors involved in HIV-1 latency Beim Fachbereich Biologie, Technische Universität Darmstadt Jahr der Veröffentlichung der Dissertation auf TUprints:2020 URN: urn:nbn:de:tuda-tuprints-112896 Datum der Einreichung: 02.10.2019 Datum der mündlichen Prüfung: 13.12.2019 Veröffentlicht unter CC BY-SA 4.0 International https://creativecommons.org/licenses/ Summary Summary Despite ongoing efforts, HIV-1 (the causative agent of AIDS) remains an unresolved health threat. Even though current therapy approaches efficiently block ongoing viral replication they cannot cure the infection due to the presence of a latent reservoir. It is of crucial importance to understand that this viral reservoir can fuel new rounds of viral replication and spread the infection. The viral reservoir is defined as cells (best-characterized are resting memory CD4+ T cells) harboring a replication-competent provirus while not producing new progeny virus, a state that can be reversed. Strategies to eradicate the viral reservoir include the so-called ‘shock and kill’ approach, which is a two-step process, aiming in the first step to reactivate the latent reservoir, leading to the production of new viruses.
    [Show full text]
  • Tumors Normals
    ! ii! Dedications and acknowledgements ! The text of chapter three of this dissertation are a reprint of the material as it appears in Mazor T., Pankov A., et al., DNA Methylation and Somatic Mutations Converge on the Cell Cycle and Define Similar Evolutionary Histories in Brain Tumors. Cancer Cell, 2015. 28(3): p. 307-317 and Mazor T., Pankov A, Song J S, Costello J F. Intratumoral Heterogeneity of the Epigenome. Accepted at Cancer Cell, 2016. I would like to start by thanking the fantastic researchers that I had the great priviledge of working with throughout my graduate degree. I would specifically like to thank Tali Mazor, Julia Ye, and Jacob Freimer for sharing their wisdom and technical expertise to create an extraordinary collaborative environment that combined the experimental and computational aspects of genomics. The successful completion of our projects would not have been possible without their extensive knowledge. I would also like to expressed my gratitude to Dr. Robert Blelloch for his dedicated mentorship and ongoing support in helping to design the novel techniques used for our projects with his lab. A special thanks to Dr. Howard Rosen and Dr. John Kornak for providing continuing guidance on a non-genomics project to identify shrinking brain regions in dementia. They allowed me to push the boundaries of my learning and expand how I think about the applications of my work. I would especially like to thank the many researchers whose incredible skills have inspired me to become a better, more well-rounded scientist. I was incredibly priviledged to have Dr. Adam Olshen, Dr.
    [Show full text]
  • Investigating the Impact of Telomere Dysfunction on the Chronic Lymphocytic Leukaemia Genome
    INVESTIGATING THE IMPACT OF TELOMERE DYSFUNCTION ON THE CHRONIC LYMPHOCYTIC LEUKAEMIA GENOME Laura Escudero-Monreal A thesis submitted for the degree of Doctor of Philosophy September 2017 Institute of Cancer and Genetics School of Medicine Cardiff University Funded by Cancer Research Wales and Cardiff University I dedicate this Ph.D. thesis to everyone who suffers from cancer, for I hope one day, science can fight and win this battle. Laura Escudero-Monreal NOTICE OF SUBMISSION DECLARATION This work has not been submitted in substance for any other degree or award at this or any other university or place of learning, nor is being submitted concurrently in candidature for any degree or other award. Signed: Date: STATEMENT 1 This thesis is being submitted in partial fulfilment of the requirements for the degree of PhD. Signed: Date: STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated, and the thesis has not been edited by a third party beyond what is permitted by Cardiff University’s Policy on the Use of Third Party Editors by Research Degree Students. Other sources are acknowledged by explicit references. The views expressed are my own. Signed: Date: STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available online in the University’s Open Access repository and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed: Date: STATEMENT 4: PREVIOUSLY APPROVED BAR ON ACCESS I hereby give consent for my thesis, if accepted, to be available online in the University’s Open Access repository and for inter-library loans after expiry of a bar on access previously approved by the Academic Standards & Quality Committee.
    [Show full text]
  • A Grainyhead-Like 2/Ovo-Like 2 Pathway Regulates Renal Epithelial Barrier Function and Lumen Expansion
    BASIC RESEARCH www.jasn.org A Grainyhead-Like 2/Ovo-Like 2 Pathway Regulates Renal Epithelial Barrier Function and Lumen Expansion † ‡ | Annekatrin Aue,* Christian Hinze,* Katharina Walentin,* Janett Ruffert,* Yesim Yurtdas,*§ | Max Werth,* Wei Chen,* Anja Rabien,§ Ergin Kilic,¶ Jörg-Dieter Schulzke,** †‡ Michael Schumann,** and Kai M. Schmidt-Ott* *Max Delbrueck Center for Molecular Medicine, Berlin, Germany; †Experimental and Clinical Research Center, and Departments of ‡Nephrology, §Urology, ¶Pathology, and **Gastroenterology, Charité Medical University, Berlin, Germany; and |Berlin Institute of Urologic Research, Berlin, Germany ABSTRACT Grainyhead transcription factors control epithelial barriers, tissue morphogenesis, and differentiation, but their role in the kidney is poorly understood. Here, we report that nephric duct, ureteric bud, and collecting duct epithelia express high levels of grainyhead-like homolog 2 (Grhl2) and that nephric duct lumen expansion is defective in Grhl2-deficient mice. In collecting duct epithelial cells, Grhl2 inactivation impaired epithelial barrier formation and inhibited lumen expansion. Molecular analyses showed that GRHL2 acts as a transcrip- tional activator and strongly associates with histone H3 lysine 4 trimethylation. Integrating genome-wide GRHL2 binding as well as H3 lysine 4 trimethylation chromatin immunoprecipitation sequencing and gene expression data allowed us to derive a high-confidence GRHL2 target set. GRHL2 transactivated a group of genes including Ovol2, encoding the ovo-like 2 zinc finger transcription factor, as well as E-cadherin, claudin 4 (Cldn4), and the small GTPase Rab25. Ovol2 induction alone was sufficient to bypass the requirement of Grhl2 for E-cadherin, Cldn4,andRab25 expression. Re-expression of either Ovol2 or a combination of Cldn4 and Rab25 was sufficient to rescue lumen expansion and barrier formation in Grhl2-deficient collecting duct cells.
    [Show full text]
  • Diagnóstico Molecular Do Transtorno Do Espectro Autista Através Do Sequenciamento Completo De Exoma Molecular Diagnosis Of
    Universidade de São Paulo Tatiana Ferreira de Almeida Diagnóstico molecular do transtorno do espectro autista através do sequenciamento completo de exoma Molecular diagnosis of autism spectrum disorder through whole exome sequencing São Paulo 2018 Universidade de São Paulo Tatiana Ferreira de Almeida Diagnóstico molecular do transtorno do espectro autista através do sequenciamento completo de exoma Molecular diagnosis of autism spectrum disorder through whole exome sequencing Tese apresentada ao Instituto de Biociências da Universidade de São Paulo, para a obtenção de Título de Doutor em Ciências, na Área de Biologia/Genética. Versão corrigida. Orientador(a): Maria Rita dos Santos Passos-Bueno São Paulo ii 2018 Dedico esta tese à ciência, e a todos os seus amantes. O, reason not the need! Our basest beggars Are in the poorest thing superfluous. Allow not nature more than nature needs, Man’s life is cheap as beast’s. Thou art a lady: If only to go warm were gorgeous, Why, nature needs not what thou gorgeous wear’st Which scarcely keeps thee warm. But, for true need- You heavens, give me that patience, patience I need! You see me here, you gods, a poor old man, As full of grief as age; wretched in both.” iii King Lear, William Shakespeare, 1608 Agradecimentos Agradeço em primeiro lugar ao meu grande amor Danilo, sem ele, há 11 anos, essa jornada não teria começado, a vida não teria me surpreendido e eu estaria de volta em casa, com todos os peixinhos do mar, e infeliz pelo resto da minha vida. Agradeço minha família, principalmente minha mãe Flávia e meu pai Airton por sacrificarem com mínimos protestos, as melhores horas que passaríamos juntos para que eu cumprisse meu trabalho.
    [Show full text]
  • Mirnas Documented to Play a Role in Hematopoietic Cell Lineage. Our
    Table S1: miRNAs documented to play a role in hematopoietic cell lineage. Our review of the literature summarizing miRNAs known to be involved in the development and proliferation of the hematopoietic lineage cells. miRNA Expression/function/target/regulator References miR-150 Elevated during developmental stages of B and T cell maturation. 16-19 Controls B cell differentiation, present in mature, resting T and B cells but decreased upon activation of naïve T or B cells. Plays a role in establishing lymphocyte identity. Very little is known about function in T cells. Regulators: Foxp3 Target: C-Myb miR-146a/b Upregulated in macropgahe inflammatory response. Differentially 17, 20 upregulated in murine Th1 subset but abolished in Th2 subset. Upregulated in response to TCRs stimulation, as well as by IL-1 and TNF. Highly expressed in murine T-regs and could play a role in establishing lymphocyte identity. Modulates activation induced cell death in activated T cells. Negative regulator of TLR and cytokine signaling pathway. Endotoxin tolerance. Antiviral role. Targets: IRAK1, IRAK2, TRAF6, FAF1 miR-16-1 Promote apoptosis by targeting Bcl2 expression, act as tumor 22 cluster suppressor RNAs. May block differentiation of later stage hematopoietic progenitor cells to mature cells. Downregulated in CLL. Target: BCL2. miR-155 Regulator of T and B cell maturation and innate immune response. 23-29 Expressed in primary mediastinal B-cell lymphoma, T and B cells, macrophages and DCs. Upregulated during B cell activation. Involved in T cell differentiation and indicated as a positive regulator of cytokine production. Activated by stimulating TLR3 and INFab receptors in bone derived macrophages (regulation of antimicrobial defense).
    [Show full text]
  • Xu Jialin Thesis 2018
    GENETIC MODIFIERS OF TELOMERE MAINTENANCE AND THEIR CONTRIBUTIONS TO PHENOTYPIC VARIATIONS IN TELOMERE BIOLOGY DISORDERS by Jialin Xu B.Sc., Fudan University, 2009 M.Sc., Fudan University, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pharmaceutical Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2019 © Jialin Xu, 2019 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Genetic modifiers of telomere maintenance and their contributions to phenotypic variations in telomere biology disorders. submitted by Jialin Xu in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Pharmaceutical Sciences Examining Committee: Dr. Judy Wong Supervisor Dr. Andrew Sandford Supervisory Committee Member Supervisory Committee Member Dr. Suzanne Vercauteran University Examiner Dr. Louis Lefebvre University Examiner Additional Supervisory Committee Members: Dr. Bruce Carleton Supervisory Committee Member Supervisory Committee Member ii Abstract Telomeres, the protective caps at the end of human chromosomes, are shortened during cellular proliferation in normal aging. Telomere biology disorders (TBDs) refer to a spectrum of tissue degenerative disorders caused by accelerated shortening of telomeres secondary to genetic defects in telomere biology genes. Defects in eleven genes involved in telomere length maintenance have been found to cause TBDs. Accelerated telomere shortening leads to premature aging at the cellular level and regenerative defects at the tissue level. TBD-related genetic defects, in concert with intrinsic tissue turn-over rate and various environmental insults that precipitate telomere shortening, determine the aging process of specific tissues and the clinical presentations of TBDs.
    [Show full text]