An Atlas of Human Gene Expression from Massively Parallel Signature Sequencing (MPSS)
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The Landscape of Genomic Imprinting Across Diverse Adult Human Tissues
Downloaded from genome.cshlp.org on October 3, 2021 - Published by Cold Spring Harbor Laboratory Press Research The landscape of genomic imprinting across diverse adult human tissues Yael Baran,1 Meena Subramaniam,2 Anne Biton,2 Taru Tukiainen,3,4 Emily K. Tsang,5,6 Manuel A. Rivas,7 Matti Pirinen,8 Maria Gutierrez-Arcelus,9 Kevin S. Smith,5,10 Kim R. Kukurba,5,10 Rui Zhang,10 Celeste Eng,2 Dara G. Torgerson,2 Cydney Urbanek,11 the GTEx Consortium, Jin Billy Li,10 Jose R. Rodriguez-Santana,12 Esteban G. Burchard,2,13 Max A. Seibold,11,14,15 Daniel G. MacArthur,3,4,16 Stephen B. Montgomery,5,10 Noah A. Zaitlen,2,19 and Tuuli Lappalainen17,18,19 1The Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel; 2Department of Medicine, University of California San Francisco, San Francisco, California 94158, USA; 3Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA; 4Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA; 5Department of Pathology, Stanford University, Stanford, California 94305, USA; 6Biomedical Informatics Program, Stanford University, Stanford, California 94305, USA; 7Wellcome Trust Center for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom; 8Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; 9Department of Genetic Medicine and Development, University of Geneva, 1211 Geneva, Switzerland; -
Bioinformatics Analysis and Identi Cation of Potential Biomarkers In
Bioinformatics Analysis and Identication of Potential Biomarkers in Gastric Cancer Jingdi Yang Nanchang University - Donghu Campus Bo Peng Nanchang University - Donghu Campus Xianzheng Qin Nanchang University Medical College: Medical College of Nanchang University Tian Zhou ( [email protected] ) Nanchang University Research Keywords: gastric cancer, differentially expressed genes, microarray, Kaplan-Meier analysis, COL1A1, BGN Posted Date: November 20th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-111288/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/24 Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer. Methods: Three gene expression proles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Proling Interactive Analysis and Kaplan-Meier plotter were used to verify the identied hub genes. Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were signicantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. -
Mouse Fscn3 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Fscn3 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Fscn3 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Fscn3 gene (NCBI Reference Sequence: NM_019569 ; Ensembl: ENSMUSG00000029707 ) is located on Mouse chromosome 6. 7 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 6 (Transcript: ENSMUST00000031719). Exon 2 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Fscn3 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP24-176I9 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 9.71% of the coding region. The knockout of Exon 2 will result in frameshift of the gene. The size of intron 1 for 5'-loxP site insertion: 1817 bp, and the size of intron 2 for 3'-loxP site insertion: 839 bp. The size of effective cKO region: ~1197 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 7 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Homology arm Exon of mouse Fscn3 cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Ovarian Gene Expression in the Absence of FIGLA, an Oocyte
BMC Developmental Biology BioMed Central Research article Open Access Ovarian gene expression in the absence of FIGLA, an oocyte-specific transcription factor Saurabh Joshi*1, Holly Davies1, Lauren Porter Sims2, Shawn E Levy2 and Jurrien Dean1 Address: 1Laboratory of Cellular and Developmental Biology, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA and 2Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA Email: Saurabh Joshi* - [email protected]; Holly Davies - [email protected]; Lauren Porter Sims - [email protected]; Shawn E Levy - [email protected]; Jurrien Dean - [email protected] * Corresponding author Published: 13 June 2007 Received: 11 December 2006 Accepted: 13 June 2007 BMC Developmental Biology 2007, 7:67 doi:10.1186/1471-213X-7-67 This article is available from: http://www.biomedcentral.com/1471-213X/7/67 © 2007 Joshi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Ovarian folliculogenesis in mammals is a complex process involving interactions between germ and somatic cells. Carefully orchestrated expression of transcription factors, cell adhesion molecules and growth factors are required for success. We have identified a germ-cell specific, basic helix-loop-helix transcription factor, FIGLA (Factor In the GermLine, Alpha) and demonstrated its involvement in two independent developmental processes: formation of the primordial follicle and coordinate expression of zona pellucida genes. Results: Taking advantage of Figla null mouse lines, we have used a combined approach of microarray and Serial Analysis of Gene Expression (SAGE) to identify potential downstream target genes. -
Systems and Chemical Biology Approaches to Study Cell Function and Response to Toxins
Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences Presented by MSc. Yingying Jiang born in Shandong, China Oral-examination: Systems and chemical biology approaches to study cell function and response to toxins Referees: Prof. Dr. Rob Russell Prof. Dr. Stefan Wölfl CONTRIBUTIONS The chapter III of this thesis was submitted for publishing under the title “Drug mechanism predominates over toxicity mechanisms in drug induced gene expression” by Yingying Jiang, Tobias C. Fuchs, Kristina Erdeljan, Bojana Lazerevic, Philip Hewitt, Gordana Apic & Robert B. Russell. For chapter III, text phrases, selected tables, figures are based on this submitted manuscript that has been originally written by myself. i ABSTRACT Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragment- chemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. -
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. -
Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis
fgene-12-697514 July 5, 2021 Time: 19:1 # 1 ORIGINAL RESEARCH published: 09 July 2021 doi: 10.3389/fgene.2021.697514 Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis Zi-An Chen1, Yu-Feng Sun1, Quan-Xu Wang1, Hui-Hui Ma1, Zhi-Zhao Ma2* and Chuan-Jie Yang1* 1 Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China, 2 Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated. Methods: All UC datasets published in the GEO database were analyzed and Edited by: Shulan Tian, summarized. Subsequently, the robust rank aggregation (RRA) method was used to Mayo Clinic, United States identify differentially expressed genes (DEGs) between UC patients and controls. Gene Reviewed by: functional annotation and PPI network analysis were performed to illustrate the potential Espiridión Ramos-Martínez, Universidad Nacional Autónoma functions of the DEGs. Some important functional modules from the protein-protein de México, Mexico interaction (PPI) network were identified by molecular complex detection (MCODE), Panwen Wang, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and Mayo Clinic Arizona, United States analyses were performed. The results of CytoHubba, a plug for integrated algorithm for *Correspondence: Zhi-Zhao Ma biomolecular interaction networks combined with RRA analysis, were used to identify [email protected] the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium Chuan-Jie Yang [email protected] salt (DSS) solution to verify the expression of hub genes. -
S41467-020-16223-7.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-16223-7 OPEN Trefoil factors share a lectin activity that defines their role in mucus Michael A. Järvå 1,2, James P. Lingford1,2, Alan John1,2, Niccolay Madiedo Soler1,2, Nichollas E. Scott 3 & ✉ Ethan D. Goddard-Borger 1,2 The mucosal epithelium secretes a host of protective disulfide-rich peptides, including the trefoil factors (TFFs). The TFFs increase the viscoelasticity of the mucosa and promote cell 1234567890():,; migration, though the molecular mechanisms underlying these functions have remained poorly defined. Here, we demonstrate that all TFFs are divalent lectins that recognise the GlcNAc-α-1,4-Gal disaccharide, which terminates some mucin-like O-glycans. Degradation of this disaccharide by a glycoside hydrolase abrogates TFF binding to mucins. Structural, mutagenic and biophysical data provide insights into how the TFFs recognise this dis- accharide and rationalise their ability to modulate the physical properties of mucus across different pH ranges. These data reveal that TFF activity is dependent on the glycosylation state of mucosal glycoproteins and alludes to a lectin function for trefoil domains in other human proteins. 1 The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia. 2 Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia. 3 Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, ✉ Parkville, VIC 3010, Australia. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:2265 | https://doi.org/10.1038/s41467-020-16223-7 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16223-7 he three human TFFs (TFF1, TFF2, and TFF3)1 are ubi- considering the glycosylation state of mucosal proteins when Tquitous in mucosal environments. -
Análise Integrativa De Perfis Transcricionais De Pacientes Com
UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. -
Toxicogenomics Applications of New Functional Genomics Technologies in Toxicology
\-\w j Toxicogenomics Applications of new functional genomics technologies in toxicology Wilbert H.M. Heijne Proefschrift ter verkrijging vand egraa dva n doctor opgeza gva nd e rector magnificus vanWageninge n Universiteit, Prof.dr.ir. L. Speelman, in netopenbaa r te verdedigen op maandag6 decembe r200 4 des namiddagst e half twee ind eAul a - Table of contents Abstract Chapter I. page 1 General introduction [1] Chapter II page 21 Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach[2] Chapter III page 48 Bromobenzene-induced hepatotoxicity atth etranscriptom e level PI Chapter IV page 67 Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis[4] Chapter V page 88 Liver gene expression profiles in relation to subacute toxicity in rats exposed to benzene[5] Chapter VI page 115 Toxicogenomics analysis of liver gene expression in relation to subacute toxicity in rats exposed totrichloroethylen e [6] Chapter VII page 135 Toxicogenomics analysis ofjoin t effects of benzene and trichloroethylene mixtures in rats m Chapter VII page 159 Discussion and conclusions References page 171 Appendices page 187 Samenvatting page 199 Dankwoord About the author Glossary Abbreviations List of genes Chapter I General introduction Parts of this introduction were publishedin : Molecular Biology in Medicinal Chemistry, Heijne etal., 2003 m NATO Advanced Research Workshop proceedings, Heijne eral., 2003 81 Chapter I 1. General introduction 1.1 Background /.1.1 Toxicologicalrisk -
Noelia Díaz Blanco
Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr. -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.