Mouse Ankrd27 Knockout Project (CRISPR/Cas9)
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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. -
ARTICLE Doi:10.1038/Nature10523
ARTICLE doi:10.1038/nature10523 Spatio-temporal transcriptome of the human brain Hyo Jung Kang1*, Yuka Imamura Kawasawa1*, Feng Cheng1*, Ying Zhu1*, Xuming Xu1*, Mingfeng Li1*, Andre´ M. M. Sousa1,2, Mihovil Pletikos1,3, Kyle A. Meyer1, Goran Sedmak1,3, Tobias Guennel4, Yurae Shin1, Matthew B. Johnson1,Zˇeljka Krsnik1, Simone Mayer1,5, Sofia Fertuzinhos1, Sheila Umlauf6, Steven N. Lisgo7, Alexander Vortmeyer8, Daniel R. Weinberger9, Shrikant Mane6, Thomas M. Hyde9,10, Anita Huttner8, Mark Reimers4, Joel E. Kleinman9 & Nenad Sˇestan1 Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment. -
RNA-Seq Transcriptome Reveals Different Molecular Responses
Zhao et al. BMC Genomics (2020) 21:475 https://doi.org/10.1186/s12864-020-06885-4 RESEARCH ARTICLE Open Access RNA-Seq transcriptome reveals different molecular responses during human and mouse oocyte maturation and fertilization Zheng-Hui Zhao1,2, Tie-Gang Meng1,3, Ang Li1, Heide Schatten4, Zhen-Bo Wang1,2* and Qing-Yuan Sun1,3* Abstract Background: Female infertility is a worldwide concern and the etiology of infertility has not been thoroughly demonstrated. Although the mouse is a good model system to perform functional studies, the differences between mouse and human also need to be considered. The objective of this study is to elucidate the different molecular mechanisms underlying oocyte maturation and fertilization between human and mouse. Results: A comparative transcriptome analysis was performed to identify the differentially expressed genes and associated biological processes between human and mouse oocytes. In total, 8513 common genes, as well as 15, 165 and 6126 uniquely expressed genes were detected in human and mouse MII oocytes, respectively. Additionally, the ratios of non-homologous genes in human and mouse MII oocytes were 37 and 8%, respectively. Functional categorization analysis of the human MII non-homologous genes revealed that cAMP-mediated signaling, sister chromatid cohesin, and cell recognition were the major enriched biological processes. Interestingly, we couldn’t detect any GO categories in mouse non-homologous genes. Conclusions: This study demonstrates that human and mouse oocytes exhibit significant differences in gene expression profiles during oocyte maturation, which probably deciphers the differential molecular responses to oocyte maturation and fertilization. The significant differences between human and mouse oocytes limit the generalizations from mouse to human oocyte maturation. -
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Viewer (IGV), UCSC As Described [23, 28]
Jahan et al. Epigenetics & Chromatin (2016) 9:19 DOI 10.1186/s13072-016-0068-2 Epigenetics & Chromatin RESEARCH Open Access The chicken erythrocyte epigenome Sanzida Jahan, Wayne Xu, Shihua He, Carolina Gonzalez, Geneviève P. Delcuve and James R. Davie* Abstract Background: Transcriptional regulation is impacted by multiple layers of genome organization. A general feature of transcriptionally active chromatin is sensitivity to DNase I and association with acetylated histones. However, very few of these active DNase I-sensitive domains, such as the chicken erythrocyte β-globin domain, have been identified and characterized. In chicken polychromatic erythrocytes, dynamically acetylated histones associated with DNase I-sensi- tive, transcriptionally active chromatin prevent histone H1/H5-induced insolubility at physiological ionic strength. Results: Here, we identified and mapped out all the transcriptionally active chromosomal domains in the chicken polychromatic erythrocyte genome by combining a powerful chromatin fractionation method with next-generation DNA and RNA sequencing. Two classes of transcribed chromatin organizations were identified on the basis of the extent of solubility at physiological ionic strength. Highly transcribed genes were present in multigenic salt-soluble chromatin domains ranging in length from 30 to over 150 kb. We identified over 100 highly expressed genes that were organized in broad dynamically highly acetylated, salt-soluble chromatin domains. Highly expressed genes were associated with H3K4me3 and H3K27ac and produced discernible antisense transcripts. The moderately- and low- expressing genes had highly acetylated, salt-soluble chromatin regions confined to the ′5 end of the gene. Conclusions: Our data provide a genome-wide profile of chromatin signatures in relation to expression levels in chicken polychromatic erythrocytes. -
1 Novel Expression Signatures Identified by Transcriptional Analysis
ARD Online First, published on October 7, 2009 as 10.1136/ard.2009.108043 Ann Rheum Dis: first published as 10.1136/ard.2009.108043 on 7 October 2009. Downloaded from Novel expression signatures identified by transcriptional analysis of separated leukocyte subsets in SLE and vasculitis 1Paul A Lyons, 1Eoin F McKinney, 1Tim F Rayner, 1Alexander Hatton, 1Hayley B Woffendin, 1Maria Koukoulaki, 2Thomas C Freeman, 1David RW Jayne, 1Afzal N Chaudhry, and 1Kenneth GC Smith. 1Cambridge Institute for Medical Research and Department of Medicine, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK 2Roslin Institute, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK Correspondence should be addressed to Dr Paul Lyons or Prof Kenneth Smith, Department of Medicine, Cambridge Institute for Medical Research, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0XY, UK. Telephone: +44 1223 762642, Fax: +44 1223 762640, E-mail: [email protected] or [email protected] Key words: Gene expression, autoimmune disease, SLE, vasculitis Word count: 2,906 The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in Annals of the Rheumatic Diseases and any other BMJPGL products to exploit all subsidiary rights, as set out in their licence (http://ard.bmj.com/ifora/licence.pdf). http://ard.bmj.com/ on September 29, 2021 by guest. Protected copyright. 1 Copyright Article author (or their employer) 2009. -
Chromatin Occupancy and Target Genes of the Haematopoietic Master Transcription Factor MYB Roza B
www.nature.com/scientificreports OPEN Chromatin occupancy and target genes of the haematopoietic master transcription factor MYB Roza B. Lemma1,2,8, Marit Ledsaak1,3,8, Bettina M. Fuglerud1,4,5, Geir Kjetil Sandve6, Ragnhild Eskeland1,3,7 & Odd S. Gabrielsen1* The transcription factor MYB is a master regulator in haematopoietic progenitor cells and a pioneer factor afecting diferentiation and proliferation of these cells. Leukaemic transformation may be promoted by high MYB levels. Despite much accumulated molecular knowledge of MYB, we still lack a comprehensive understanding of its target genes and its chromatin action. In the present work, we performed a ChIP-seq analysis of MYB in K562 cells accompanied by detailed bioinformatics analyses. We found that MYB occupies both promoters and enhancers. Five clusters (C1–C5) were found when we classifed MYB peaks according to epigenetic profles. C1 was enriched for promoters and C2 dominated by enhancers. C2-linked genes were connected to hematopoietic specifc functions and had GATA factor motifs as second in frequency. C1 had in addition to MYB-motifs a signifcant frequency of ETS-related motifs. Combining ChIP-seq data with RNA-seq data allowed us to identify direct MYB target genes. We also compared ChIP-seq data with digital genomic footprinting. MYB is occupying nearly a third of the super-enhancers in K562. Finally, we concluded that MYB cooperates with a subset of the other highly expressed TFs in this cell line, as expected for a master regulator. Te transcription factor c-Myb (approved human symbol MYB), encoded by the MYB proto-oncogene, is highly expressed in haematopoietic progenitor cells and plays a key role in regulating the expression of genes involved in diferentiation and proliferation of myeloid and lymphoid progenitors 1–5. -
A Discovery Tool for the Analysis of Chromatin Structure and Dynamics During Differentiation
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Developmental Cell Resource NA-Seq: A Discovery Tool for the Analysis of Chromatin Structure and Dynamics during Differentiation Gaetano Gargiulo,1,6 Samuel Levy,2,6 Gabriele Bucci,1 Mauro Romanenghi,1 Lorenzo Fornasari,3 Karen Y. Beeson,2 Susanne M. Goldberg,2 Matteo Cesaroni,1 Marco Ballarini,3 Fabio Santoro,3 Natalie Bezman,4 Gianmaria Frige` ,1 Philip D. Gregory,4 Michael C. Holmes,4 Robert L. Strausberg,2 Pier Giuseppe Pelicci,1 Fyodor D. Urnov,4 and Saverio Minucci1,5,* 1Department of Experimental Oncology, IFOM-IEO Campus, European Institute of Oncology (IEO), Via Ripamonti 435, 20141 Milan, Italy 2J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA 3Congenia, Genextra Group, Piazzetta Bossi 4, 20121 Milan, Italy 4Sangamo BioSciences, Richmond, CA 94804, USA 5Department of Biomolecular Sciences and Biotechnology, University of Milan, Via Celoria 26, 20133 Milan, Italy 6These authors contributed equally to this work *Correspondence: [email protected] DOI 10.1016/j.devcel.2009.02.002 SUMMARY been generated in lymphoid T cells (Barski et al., 2007; Boyle et al., 2008a; Schones et al., 2008). In addition, binding sites It is well established that epigenetic modulation of for several transcription factors (TFs) have been analyzed in genome accessibility in chromatin occurs during cell lines. These analyses revealed that the DNA primary biological processes. Here we describe a method sequence is insufficient to determine whether a given factor based on restriction enzymes and next-generation will be bound in vivo. -
Chromatin Conformation Links Distal Target Genes to CKD Loci
BASIC RESEARCH www.jasn.org Chromatin Conformation Links Distal Target Genes to CKD Loci Maarten M. Brandt,1 Claartje A. Meddens,2,3 Laura Louzao-Martinez,4 Noortje A.M. van den Dungen,5,6 Nico R. Lansu,2,3,6 Edward E.S. Nieuwenhuis,2 Dirk J. Duncker,1 Marianne C. Verhaar,4 Jaap A. Joles,4 Michal Mokry,2,3,6 and Caroline Cheng1,4 1Experimental Cardiology, Department of Cardiology, Thoraxcenter Erasmus University Medical Center, Rotterdam, The Netherlands; and 2Department of Pediatrics, Wilhelmina Children’s Hospital, 3Regenerative Medicine Center Utrecht, Department of Pediatrics, 4Department of Nephrology and Hypertension, Division of Internal Medicine and Dermatology, 5Department of Cardiology, Division Heart and Lungs, and 6Epigenomics Facility, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands ABSTRACT Genome-wide association studies (GWASs) have identified many genetic risk factors for CKD. However, linking common variants to genes that are causal for CKD etiology remains challenging. By adapting self-transcribing active regulatory region sequencing, we evaluated the effect of genetic variation on DNA regulatory elements (DREs). Variants in linkage with the CKD-associated single-nucleotide polymorphism rs11959928 were shown to affect DRE function, illustrating that genes regulated by DREs colocalizing with CKD-associated variation can be dysregulated and therefore, considered as CKD candidate genes. To identify target genes of these DREs, we used circular chro- mosome conformation capture (4C) sequencing on glomerular endothelial cells and renal tubular epithelial cells. Our 4C analyses revealed interactions of CKD-associated susceptibility regions with the transcriptional start sites of 304 target genes. Overlap with multiple databases confirmed that many of these target genes are involved in kidney homeostasis. -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19 -
Role of Gigaxonin in the Regulation of Intermediate Filaments: a Study Using Giant Axonal Neuropathy Patient-Derived Induced Pluripotent Stem Cell-Motor Neurons
Role of Gigaxonin in the Regulation of Intermediate Filaments: a Study Using Giant Axonal Neuropathy Patient-Derived Induced Pluripotent Stem Cell-Motor Neurons Bethany Johnson-Kerner Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2013 © 2012 Bethany Johnson-Kerner All rights reserved Abstract Role of Gigaxonin in the Regulation of Intermediate Filaments: a Study Using Giant Axonal Neuropathy Patient-Derived Induced Pluripotent Stem Cell-Motor Neurons Bethany Johnson-Kerner Patients with giant axonal neuropathy (GAN) exhibit loss of motor and sensory function and typically live for less than 30 years. GAN is caused by autosomal recessive mutations leading to low levels of gigaxonin, a ubiquitously-expressed cytoplasmic protein whose cellular roles are poorly understood. GAN pathology is characterized by aggregates of intermediate filaments (IFs) in multiple tissues. Disorganization of the neuronal intermediate filament (nIF) network is a feature of several neurodegenerative disorders, including amyotrophic lateral sclerosis, Parkinson’s disease and axonal Charcot-Marie-Tooth disease. In GAN such changes are often striking: peripheral nerve biopsies show enlarged axons with accumulations of neurofilaments; so called “giant axons.” Interestingly, IFs also accumulate in other cell types in patients. These include desmin in muscle fibers, GFAP (glial fibrillary acidic protein) in astrocytes, and vimentin in multiple cell types including primary cultures of biopsied fibroblasts. These findings suggest that gigaxonin may be a master regulator of IFs, and understanding its function(s) could shed light on GAN as well as the numerous other diseases in which IFs accumulate. -
Prevalence of Chromosomal Rearrangements Involving Non-ETS Genes in Prostate Cancer
INTERNATIONAL JOURNAL OF ONCOLOGY 46: 1637-1642, 2015 Prevalence of chromosomal rearrangements involving non-ETS genes in prostate cancer Martina KLUTH1*, RAMI GALAL1*, ANTJE KROHN1, JOACHIM WEISCHENFELDT4, CHRISTINA TSOURLAKIS1, LISA PAUSTIAN1, RAMIN Ahrary1, MALIK AHMED1, SEKANDER SCHERZAI1, ANNE MEYER1, HÜSEYIN SIRMA1, JAN KORBEL4, GUIDO SAUTER1, THORSTEN SCHLOMM2,3, RONALD SIMON1 and SARAH MINNER1 1Institute of Pathology, 2Martini-Clinic, Prostate Cancer Center, and 3Department of Urology, Section for Translational Prostate Cancer Research, University Medical Center Hamburg-Eppendorf; 4Genome Biology Unit, European Molecular Biology Laboratory (EMBL), D-69117 Heidelberg, Germany Received November 25, 2014; Accepted December 30, 2014 DOI: 10.3892/ijo.2015.2855 Abstract. Prostate cancer is characterized by structural rear- tumors that can be surgically treated in a curative manner, rangements, most frequently including translocations between ~20% of the tumors will progress to metastatic and hormone androgen-dependent genes and members of the ETS family refractory disease, accounting for >250.000 deaths per year of transcription factor like TMPRSS2:ERG. In a recent whole worldwide (1). Targeted therapies that would allow for an genome sequencing study we identified 140 gene fusions that effective treatment after failure of androgen withdrawal were unrelated to ETS genes in 11 prostate cancers. The aim therapy are lacking. of the present study was to estimate the prevalence of non-ETS Recent whole genome sequencing studies have shown that gene fusions. We randomly selected 27 of these rearrange- the genomic landscape of prostate cancer differs markedly ments and analyzed them by fluorescencein situ hybridization from that of other solid tumor types. Whereas, for example, (FISH) in a tissue microarray format containing 500 prostate breast or colon cancer is characterized by high-grade genetic cancers. -
Signatures of Adaptive Evolution in Platyrrhine Primate Genomes 5 6 Hazel Byrne*, Timothy H
1 2 Supplementary Materials for 3 4 Signatures of adaptive evolution in platyrrhine primate genomes 5 6 Hazel Byrne*, Timothy H. Webster, Sarah F. Brosnan, Patrícia Izar, Jessica W. Lynch 7 *Corresponding author. Email [email protected] 8 9 10 This PDF file includes: 11 Section 1: Extended methods & results: Robust capuchin reference genome 12 Section 2: Extended methods & results: Signatures of selection in platyrrhine genomes 13 Section 3: Extended results: Robust capuchins (Sapajus; H1) positive selection results 14 Section 4: Extended results: Gracile capuchins (Cebus; H2) positive selection results 15 Section 5: Extended results: Ancestral Cebinae (H3) positive selection results 16 Section 6: Extended results: Across-capuchins (H3a) positive selection results 17 Section 7: Extended results: Ancestral Cebidae (H4) positive selection results 18 Section 8: Extended results: Squirrel monkeys (Saimiri; H5) positive selection results 19 Figs. S1 to S3 20 Tables S1–S3, S5–S7, S10, and S23 21 References (94 to 172) 22 23 Other Supplementary Materials for this manuscript include the following: 24 Tables S4, S8, S9, S11–S22, and S24–S44 1 25 1) Extended methods & results: Robust capuchin reference genome 26 1.1 Genome assembly: versions and accessions 27 The version of the genome assembly used in this study, Sape_Mango_1.0, was uploaded to a 28 Zenodo repository (see data availability). An assembly (Sape_Mango_1.1) with minor 29 modifications including the removal of two short scaffolds and the addition of the mitochondrial 30 genome assembly was uploaded to NCBI under the accession JAGHVQ. The BioProject and 31 BioSample NCBI accessions for this project and sample (Mango) are PRJNA717806 and 32 SAMN18511585.