DOCSLIB.ORG

A Novel Nuclear Role for the Mitochondrial Hydroxylase Clk-1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Novel Nuclear Role for the Mitochondrial Hydroxylase Clk-1 A novel nuclear role for the mitochondrial hydroxylase Clk-1 A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Life Sciences 2011 Richard Mark Monaghan 1 (ii) – Table of Contents Section Title Page (i) Title page 1 (ii) Table of contents 2 (iii) List of tables and figures 6 (iv) Abstract 10 (v) Declaration and copyright statement 11 (vi) Acknowledgements 12 (vii) Abbreviations 13 1. Introduction 24 1.1 Energy at the heart of the cell 25 1.1.1 Biological Signals 25 1.1.2 The significance of energy 26 1.1.3 Reactive oxygen species – not always on the attack 27 1.1.4 Mitochondria – archaic timers of life 28 1.2 Cellular respiration 29 1.2.1 Sites of cellular energy production 29 1.2.2 Glycolysis and cancer cell metabolism 30 1.2.3 Mitochondria – centres of metabolic discourse 31 1.2.4 Mitochondrial-nuclear retrograde signalling 32 1.3 An energy centred signalling network 34 1.3.1 Insulin-like growth factor and organism energy homeostasis 34 1.3.2 Forkhead box transcription factors, O subgroup 37 1.3.3 Sirtuins 38 1.3.4 AMP-dependent protein kinase 39 1.3.5 Oxygen sensing – hypoxia inducible factors 40 1.3.6 SAPK-interacting protein 1 42 1.4 Development, metabolic rates and epigenetics 44 2 1.4.1 Regulation of gene expression during development 44 1.4.2 DNA methylation – another level of sequence information 45 1.4.3 Histones and their modifications 46 1.4.4 Metabolism and epigenetics 47 1.5 Ubiquinone and Clk-1 49 1.5.1 Conservation of a lipid hydroxylase 49 1.5.2 Monooxygenases and dioxygenases 52 1.5.3 Altered developmental rates in clk-1 mutants 52 1.5.4 Metabolic changes in long lived Clk-1+/- mice 55 1.5.5 Other Clk-1 research 56 1.5.6 Conclusion 56 1.6 Project Aims 57 2. Materials and methods 58 2.1 Reagents, constructs and antibodies 59 2.2 Molecular cloning techniques and bacterial protein preparation 59 2.3 siRNAs 65 2.4 Tissue culture, transfections, cell lysis and pull down techniques 65 2.5 SDS-PAGE and western blotting 66 2.6 Kinase assays 66 2.7 Mass spectrometry techniques 67 2.8 Clk-1 antibody generation 68 2.9 Microscopy and immunofluorescence 69 2.10 Fractionation techniques 69 2.11 Chromatin immunoprecipitation and promoter microarray 69 2.12 Quantitative PCR and calculations 70 2.13 Functional clustering and enrichment analysis 71 2.14 Cell survival and reactive oxygen species assays 72 2.15 RNA extraction and expression microarrays 72 2.16 Bisulfite sequencing 73 2.17 Mitochondrial isolation and genome IPs 73 2.18 DNA immunoprecipitations 73 3 3. Characterising the Clk-1 N-terminal domain 75 3.1 Introduction 76 3.2 Stimuli that increase the pool of uncleaved Clk-1 76 3.3 Clk-1 N-terminal domain is a potential PKA/PKC substrate 79 3.4 PKC phosphorylation of Clk-1 on serines 22 and 37 81 The effect of PMA and oxidative stress on overexpressed 3.5 uncleaved Clk-1 is not through direct phosphorylation of Clk-1 85 N-terminal domain by PKC Endogenous uncleaved Clk-1 is stabilised by oxidative stress but 3.6 85 not PMA 3.7 Discussion 88 4. Dual mitochondrial and nuclear localisation of Clk-1 90 4.1 Introduction 91 4.2 Clk-1 localises to both the mitochondria and nucleus 91 The N-terminal domain of Clk-1 contains determinants of both 4.3 92 nuclear and mitochondrial localisation 4.4 An arginine point mutant that disrupts Clk-1 nuclear localisation 96 Clk-1 N-terminus could potentially act as a nuclear retention 4.5 99 signal Endogenous uncleaved Clk-1 is predominantly found in the 4.6 103 nuclear-associated fraction 4.7 Discussion 104 5. Clk-1 associates with chromatin 109 5.1 Introduction 110 5.2 Association of uncleaved Clk-1 with chromatin 111 5.3 HIF1α stabilisation and levels of uncleaved Clk-1 112 5.4 Clk-1 ChIP-chip 115 5.5 Discussion 123 6. Functional analysis of nuclear Clk-1 127 4 6.1 Introduction 128 6.2 Elucidating Clk-1 nuclear-specific functions 129 Comparison of the gene expression profile of wild type Clk-1 and 6.3 132 Clk-1 R28A expressing cells Expression of Clk-1 R28A induces changes in gene expression 6.4 140 without knockdown of endogenous Clk-1 Nuclear Clk-1 is responsible for maintenance of specific 6.5 147 epigenetic marks 6.6 Further analysis of chromatin changes regulated by nuclear Clk-1 153 6.7 Function of Clk-1 at regulated genomic loci 157 6.8 Discussion 165 7. Discussion 168 7.1 Introduction 169 7.2 Upstream regulation of Clk-1 170 7.3 Clk-1 as an epigenetic regulator of metabolism and differentiation 176 7.4 Conclusion 181 8. References 183 9. Appendices 231 9.1 Electronic data files 232 Word count – 75 589 5 (iii) – List of Tables and Figures Cellular metabolic pathways, their location, interactions and Figure 1.1 33 cofactor production Conserved energy/redox sensitive signalling networks, their effects Figure 1.2 36 on cell and mitochondrial function, and interaction with each other Metabolic pathways and their cofactors associated with enzymatic Figure 1.3 48 modifications of histone and DNA epigenetic marks Figure 1.4 Clk-1 amino acid sequence conservation across eukarya 50 Figure 1.5 Clk-1 synthesis, import and mitochondrial function 53 Table 2.1 Antibodies 60 Table 2.2 Buffer list 61 Table 2.3 Molecular cloning primers 62 Stimuli that regulate the level of uncleaved Clk-1 expressed in 293T Figure 3.1 77 cells Figure 3.2 Interaction of Clk-1 and Sin1 after PMA treatment 80 Screen of mTOR/Sin1-associated kinases to determine if they Figure 3.3 82 phosphorylate Clk-1 Figure 3.4 Clk-1 is not an mTOR substrate in vitro 83 The major PKA and PKC phosphorylation sites lie within the N- Figure 3.5 84 terminal domain of Clk-1 PMA and H O do not appear to regulate Clk-1 cleavage through Figure 3.6 2 2 86 stimulated PKC phosphorylation of the Clk-1 N-terminal domain Figure 3.7 H2O2 but not PMA affects endogenous Clk-1 cleavage 87 Cellular localisation of ubiquinone biosynthesis pathway members Figure 4.1 93 Clk-1 and Coq6 Determinants of mitochondrial and nuclear localisation for N- and Figure 4.2 94 C-terminally tagged Clk-1 GFP-constructs Figure 4.3 Localisation of Clk-1-GFP after hydrogen peroxide treatment 95 Localisation of Clk-1 N-terminal domain fused to GFP and of the Figure 4.4 97 mitochondrial peptidase mutant Summary of localisation of Clk-1 N-terminal domain deletion Figure 4.5 mutants and amino acid conservation of the N-terminus domain in 98 higher eukaryotes 6 Cellular localisation of Clk-1 R28A mutant compared to wild type Figure 4.6 100 Clk-1 Cellular localisation of Clk-1 N-terminal deletion mutants tagged Figure 4.7 102 with Myc and the SV40 nuclear localisation sequence Figure 4.8 Cellular fractionation of endogenous cleaved and uncleaved Clk-1 105 Overexpressed uncleaved Clk-1 can localise with the chromatin- Figure 5.1 113 associated fraction in 293T cells Effect of anoxia and hypoxia mimetics on Clk-1 cleavage compared Figure 5.2 114 to HIF1α stabilisation Integrated genome browser results for selected anti-Clk-1 ChIP- Figure 5.3 117 chip identified promoters Functional annotation grouping of genes with promoters identified Figure 5.4 119 in anti-Clk-1 ChIP-chip promoter microarray Figure 5.5 Anti-Clk-1 chromatin immunoprecipitation from 293T cells 121 Anti-Clk-1 chromatin immunoprecipitation from 293T cells following Figure 5.6 122 CoCl2 treatment Anti-Clk-1 chromatin immunoprecipitation from 293T cells following Figure 5.7 124 knockdown of Clk-1 Stable cell lines expressing Clk-1 wild type or Clk-1 R28A mutant Figure 6.1 under control of a Tet repressor and siRNA targeting Clk-1 5’- and 130 3’-UTRs Reduced cell survival in 293 TRex cells rescued by Clk-1 R28A Figure 6.2 131 expression or transfected with Clk-1 siRNA Increased ROS levels in Clk-1 R28A expressing cells compared to Figure 6.3 133 Clk-1 wild type expressing cells Functional annotation grouping of genes differentially expressed Figure 6.4 135 between Clk-1 wild type and Clk-1 R28A expressing 293 TRex cells Differences in mRNA levels between Clk-1 wild type and Clk-1 Figure 6.5 138 R28A expressing cells Comparison of Clk-1 ChIP-chip positives and gene expression Figure 6.6 differences between Clk-1 wild type and Clk-1 R28A stably 139 expressing cells Functional clustering of genes with mRNA differences between Clk- Figure 6.7 1 wild type and Clk-1 R28A expressing cells that also display Clk-1 141 ChIP promoter enrichment Gene expression changes between Clk-1 wild type and Clk-1 R28A Figure 6.8.1 143 expressing cells Gene expression changes between Clk-1 wild type and Clk-1 R28A Figure 6.8.2 145 expressing following oxidative stress treatment 7 Leakage of expression from stably transfected Clk-1 constructs Figure 6.9 146 under control of a Tet repressor Clk-1 dimerisation during oxidative stress appears independent of Figure 6.10 148 disulfide bridging. Overexpression of Clk-1 or nuclear-only Clk-1 does not rescue the Figure 6.11 151 phenotype of Clk-1 R28A expressing cells Rescue of GSTP1 expression in cells stably expressing Clk-1 R28A Figure 6.12 154 with 5-azacytidine.
Load more

Recommended publications
  • PARSANA-DISSERTATION-2020.Pdf
    DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
    [Show full text]
  • Frontiers in Integrative Genomics and Translational Bioinformatics
    BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Frontiers in Integrative Genomics and Translational Bioinformatics BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Copyright © òýÔ Hindawi Publishing Corporation. All rights reserved. is is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Frontiers in Integrative Genomics and Translational Bioinformatics, Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Volume òýÔ , Article ID Þò ¥ÀÔ, ç pages Building Integrated Ontological Knowledge Structures with Ecient Approximation Algorithms, Yang Xiang and Sarath Chandra Janga Volume òýÔ , Article ID ýÔ ò, Ô¥ pages Predicting Drug-Target Interactions via Within-Score and Between-Score, Jian-Yu Shi, Zun Liu, Hui Yu, and Yong-Jun Li Volume òýÔ , Article ID ç ýÀç, À pages RNAseq by Total RNA Library Identies Additional RNAs Compared to Poly(A) RNA Library, Yan Guo, Shilin Zhao, Quanhu Sheng, Mingsheng Guo, Brian Lehmann, Jennifer Pietenpol, David C. Samuels, and Yu Shyr Volume òýÔ , Article ID âòÔçý, À pages Construction of Pancreatic Cancer Classier Based on SVM Optimized by Improved FOA, Huiyan Jiang, Di Zhao, Ruiping Zheng, and Xiaoqi Ma Volume òýÔ , Article ID ÞÔýòç, Ôò pages OperomeDB: A Database of Condition-Specic Transcription Units in Prokaryotic Genomes, Kashish Chetal and Sarath Chandra Janga Volume òýÔ , Article ID çÔòÔÞ, Ôý pages How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective, Lei Wang, ChienWei Chiang, Hong Liang, Hengyi Wu, Weixing Feng, Sara K.
    [Show full text]
  • Supplemental Figure 1. Vimentin
    Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672
    [Show full text]
  • The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A
    www.nature.com/scientificreports OPEN The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A. Canham , Amy Van Deusen , Daniel R. Brison , Paul A. De Sousa , 3 5 6 5 7 Published: 26 November 2015 Janet Downie , Liani Devito , Zoe A. Hewitt , Dusko Ilic , Susan J. Kimber , Harry D. Moore6, Helen Murray3 & Tilo Kunath1 The application of human embryonic stem cell (hESC) derivatives to regenerative medicine is now becoming a reality. Although the vast majority of hESC lines have been derived for research purposes only, about 50 lines have been established under Good Manufacturing Practice (GMP) conditions. Cell types differentiated from these designated lines may be used as a cell therapy to treat macular degeneration, Parkinson’s, Huntington’s, diabetes, osteoarthritis and other degenerative conditions. It is essential to know the genetic stability of the hESC lines before progressing to clinical trials. We evaluated the molecular karyotype of 25 clinical-grade hESC lines by whole-genome single nucleotide polymorphism (SNP) array analysis. A total of 15 unique copy number variations (CNVs) greater than 100 kb were detected, most of which were found to be naturally occurring in the human population and none were associated with culture adaptation. In addition, three copy-neutral loss of heterozygosity (CN-LOH) regions greater than 1 Mb were observed and all were relatively small and interstitial suggesting they did not arise in culture. The large number of available clinical-grade hESC lines with defined molecular karyotypes provides a substantial starting platform from which the development of pre-clinical and clinical trials in regenerative medicine can be realised.
    [Show full text]
  • The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc Oncogenesis
    The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis By Yuting Sun This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy at the University of New South Wales Children’s Cancer Institute Australia for Medical Research School of Women’s and Children’s Health, Faculty of Medicine University of New South Wales Australia August 2014 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Sun First name: Yuting Other name/s: Abbreviation for degree as given in the University calendar: PhD School : School of·Women's and Children's Health Faculty: Faculty of Medicine Title: The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis. Abstract 350 words maximum: (PLEASE TYPE) N-Myc Induces neuroblastoma by regulating the expression of target genes and proteins, and N-Myc protein is degraded by Fbxw7 and NEDD4 and stabilized by Aurora A. The class lla histone deacetylase HDAC5 suppresses gene transcription, and blocks myoblast and leukaemia cell differentiation. While histone H3 lysine 4 (H3K4) trimethylation at target gene promoters is a pre-requisite for Myc· induced transcriptional activation, WDRS, as a histone H3K4 methyltransferase presenter, is required for H3K4 methylation and transcriptional activation mediated by a histone H3K4 methyltransferase complex. Here, I investigated the roles of HDAC5 and WDR5 in N-Myc overexpressing neuroblastoma. I have found that N-Myc upregulates HDAC5 protein expression, and that HDAC5 represses NEDD4 gene expression, increases Aurora A gene expression and consequently upregulates N-Myc protein expression in neuroblastoma cells.
    [Show full text]
  • 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]
  • Coordinate Regulation of Long Non-Coding Rnas and Protein-Coding Genes in Germ- Free Mice Joseph Dempsey, Angela Zhang and Julia Yue Cui*
    Dempsey et al. BMC Genomics (2018) 19:834 https://doi.org/10.1186/s12864-018-5235-3 RESEARCHARTICLE Open Access Coordinate regulation of long non-coding RNAs and protein-coding genes in germ- free mice Joseph Dempsey, Angela Zhang and Julia Yue Cui* Abstract Background: Long non-coding RNAs (lncRNAs) are increasingly recognized as regulators of tissue-specific cellular functions and have been shown to regulate transcriptional and translational processes, acting as signals, decoys, guides, and scaffolds. It has been suggested that some lncRNAs act in cis to regulate the expression of neighboring protein-coding genes (PCGs) in a mechanism that fine-tunes gene expression. Gut microbiome is increasingly recognized as a regulator of development, inflammation, host metabolic processes, and xenobiotic metabolism. However, there is little known regarding whether the gut microbiome modulates lncRNA gene expression in various host metabolic organs. The goals of this study were to 1) characterize the tissue-specific expression of lncRNAs and 2) identify and annotate lncRNAs differentially regulated in the absence of gut microbiome. Results: Total RNA was isolated from various tissues (liver, duodenum, jejunum, ileum, colon, brown adipose tissue, white adipose tissue, and skeletal muscle) from adult male conventional and germ-free mice (n = 3 per group). RNA-Seq was conducted and reads were mapped to the mouse reference genome (mm10) using HISAT. Transcript abundance and differential expression was determined with Cufflinks using the reference databases NONCODE 2016 for lncRNAs and UCSC mm10 for PCGs. Although the constitutive expression of lncRNAs was ubiquitous within the enterohepatic (liver and intestine) and the peripheral metabolic tissues (fat and muscle) in conventional mice, differential expression of lncRNAs by lack of gut microbiota was highly tissue specific.
    [Show full text]
  • ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed
    ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed Probe Sets in Livers of GK Rats. A. Immune/Inflammatory (67 probe sets, 63 genes) Age Strain Probe ID Gene Name Symbol Accession Gene Function 5 WKY 1398390_at small inducible cytokine B13 precursor Cxcl13 AA892854 chemokine activity; lymph node development 5 WKY 1389581_at interleukin 33 Il33 BF390510 cytokine activity 5 WKY *1373970_at interleukin 33 Il33 AI716248 cytokine activity 5 WKY 1369171_at macrophage stimulating 1 (hepatocyte growth factor-like) Mst1; E2F2 NM_024352 serine-throenine kinase; tumor suppression 5 WKY 1388071_x_at major histocompatability antigen Mhc M24024 antigen processing and presentation 5 WKY 1385465_at sialic acid binding Ig-like lectin 5 Siglec5 BG379188 sialic acid-recognizing receptor 5 WKY 1393108_at major histocompatability antigen Mhc BM387813 antigen processing and presentation 5 WKY 1388202_at major histocompatability antigen Mhc BI395698 antigen processing and presentation 5 WKY 1371171_at major histocompatability antigen Mhc M10094 antigen processing and presentation 5 WKY 1370382_at major histocompatability antigen Mhc BI279526 antigen processing and presentation 5 WKY 1371033_at major histocompatability antigen Mhc AI715202 antigen processing and presentation 5 WKY 1383991_at leucine rich repeat containing 8 family, member E Lrrc8e BE096426 proliferation and activation of lymphocytes and monocytes. 5 WKY 1383046_at complement component factor H Cfh; Fh AA957258 regulation of complement cascade 4 WKY 1369522_a_at CD244 natural killer
    [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]
  • Dottorando: Dr.Ssa Valentina TINAGLIA
    Università degli Studi di Milano Scuola di Dottorato in Medicina Molecolare Dipartimento di Scienze e Tecnologie Biomediche Curriculum di Genomica, Proteomica e Tecnologie Correlate Ciclo XXIV Settore Disciplinare: BIO-10 Anno Accademico 2010/2011 Dottorando: Dr.ssa Valentina TINAGLIA Matricola: R08079 INTEGRATED GENOMICS ANALYSIS OF GENE AND MICRORNA EXPRESSION PROFILES IN CLEAR CELL RENAL CARCINOMA CELL LINES Direttore della Scuola: Ch.mo Prof. Mario Clerici Tutore: Prof.ssa Cristina Battaglia Un grazie speciale a Mamma, Papà ed Enzo per la loro infinita pazienza e il loro amore. CONTENTS SOMMARIO .................................................................................................................................... V ABSTRACT .................................................................................................................................. VII 1 INTRODUCTION ...................................................................................................................... 1 1.1 Renal Cell Carcinoma ...................................................................................................... 1 1.1.1 Epidemiology ........................................................................................................... 1 1.1.2 Clinical features ....................................................................................................... 1 1.1.3 Clinical cytogenetic and molecular characteristics of renal tumors .................... 3 1.1.3.1 Familial renal cell carcinoma .............................................................................................
    [Show full text]
  • Table SI. Primer List of Genes Used for Reverse Transcription‑Quantitative PCR Validation
    Table SI. Primer list of genes used for reverse transcription‑quantitative PCR validation. Genes Forward (5'‑3') Reverse (5'‑3') Length COL1A1 AGTGGTTTGGATGGTGCCAA GCACCATCATTTCCACGAGC 170 COL6A1 CCCCTCCCCACTCATCACTA CGAATCAGGTTGGTCGGGAA 65 COL2A1 GGTCCTGCAGGTGAACCC CTCTGTCTCCTTGCTTGCCA 181 DCT CTACGAAACCAGGATGACCGT ACCATCATTGGTTTGCCTTTCA 192 PDE4D ATTGCCCACGATAGCTGCTC GCAGATGTGCCATTGTCCAC 181 RP11‑428C19.4 ACGCTAGAAACAGTGGTGCG AATCCCCGGAAAGATCCAGC 179 GPC‑AS2 TCTCAACTCCCCTCCTTCGAG TTACATTTCCCGGCCCATCTC 151 XLOC_110310 AGTGGTAGGGCAAGTCCTCT CGTGGTGGGATTCAAAGGGA 187 COL1A1, collagen type I alpha 1; COL6A1, collagen type VI, alpha 1; COL2A1, collagen type II alpha 1; DCT, dopachrome tautomerase; PDE4D, phosphodiesterase 4D cAMP‑specific. Table SII. The differentially expressed mRNAs in the ParoAF_Control group. Gene ID logFC P‑Value Symbol Description ENSG00000165480 ‑6.4838 8.32E‑12 SKA3 Spindle and kinetochore associated complex subunit 3 ENSG00000165424 ‑6.43924 0.002056 ZCCHC24 Zinc finger, CCHC domain containing 24 ENSG00000182836 ‑6.20215 0.000817 PLCXD3 Phosphatidylinositol‑specific phospholipase C, X domain containing 3 ENSG00000174358 ‑5.79775 0.029093 SLC6A19 Solute carrier family 6 (neutral amino acid transporter), member 19 ENSG00000168916 ‑5.761 0.004046 ZNF608 Zinc finger protein 608 ENSG00000134343 ‑5.56371 0.01356 ANO3 Anoctamin 3 ENSG00000110400 ‑5.48194 0.004123 PVRL1 Poliovirus receptor‑related 1 (herpesvirus entry mediator C) ENSG00000124882 ‑5.45849 0.022164 EREG Epiregulin ENSG00000113448 ‑5.41752 0.000577 PDE4D Phosphodiesterase
    [Show full text]
  • Spatial Protein Interaction Networks of the Intrinsically Disordered Transcription Factor C(%3$
    Spatial protein interaction networks of the intrinsically disordered transcription factor C(%3$ Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) im Fach Biologie/Molekularbiologie eingereicht an der Lebenswissenschaftlichen Fakultät der Humboldt-Universität zu Berlin Von Evelyn Ramberger, M.Sc. Präsidentin der Humboldt-Universität zu Berlin Prof. Dr.-Ing.Dr. Sabine Kunst Dekan der Lebenswissenschaftlichen Fakultät der Humboldt-Universität zu Berlin Prof. Dr. Bernhard Grimm Gutachter: 1. Prof. Dr. Achim Leutz 2. Prof. Dr. Matthias Selbach 3. Prof. Dr. Gunnar Dittmar Tag der mündlichen Prüfung: 12.8.2020 For T. Table of Contents Selbstständigkeitserklärung ....................................................................................1 List of Figures ............................................................................................................2 List of Tables ..............................................................................................................3 Abbreviations .............................................................................................................4 Zusammenfassung ....................................................................................................6 Summary ....................................................................................................................7 1. Introduction ............................................................................................................8 1.1. Disordered proteins
    [Show full text]