Differential Expression of Elastic Fibre Components in Intrinsically Aged Skin
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Genome Analysis and Knowledge
Dahary et al. BMC Medical Genomics (2019) 12:200 https://doi.org/10.1186/s12920-019-0647-8 SOFTWARE Open Access Genome analysis and knowledge-driven variant interpretation with TGex Dvir Dahary1*, Yaron Golan1, Yaron Mazor1, Ofer Zelig1, Ruth Barshir2, Michal Twik2, Tsippi Iny Stein2, Guy Rosner3,4, Revital Kariv3,4, Fei Chen5, Qiang Zhang5, Yiping Shen5,6,7, Marilyn Safran2, Doron Lancet2* and Simon Fishilevich2* Abstract Background: The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. Results: We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. -
Genome-Wide Screen of Otosclerosis in Population Biobanks
medRxiv preprint doi: https://doi.org/10.1101/2020.11.15.20227868; this version posted November 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 Genome-wide Screen of Otosclerosis in 2 Population Biobanks: 18 Loci and Shared 3 Heritability with Skeletal Structure 4 Joel T. Rämö1, Tuomo Kiiskinen1, Juha Karjalainen1,2,3,4, Kristi Krebs5, Mitja Kurki1,2,3,4, Aki S. 5 Havulinna6, Eija Hämäläinen1, Paavo Häppölä1, Heidi Hautakangas1, FinnGen, Konrad J. 6 Karczewski1,2,3,4, Masahiro Kanai1,2,3,4, Reedik Mägi5, Priit Palta1,5, Tõnu Esko5, Andres Metspalu5, 7 Matti Pirinen1,7,8, Samuli Ripatti1,2,7, Lili Milani5, Antti Mäkitie9, Mark J. Daly1,2,3,4,10, and Aarno 8 Palotie1,2,3,4 9 1. Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of 10 Helsinki, Helsinki, Finland 11 2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, 12 Massachusetts, USA 13 3. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, 14 USA 15 4. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA 16 5. Estonian Genome Center, University of Tartu, Tartu, Estonia, Institute of Molecular and Cell Biology, 17 University of Tartu, Tartu, Estonia 18 6. Finnish Institute for Health and Welfare, Helsinki, Finland 19 7. Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland 20 8. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Ltbp2 Null Mutations in an Autosomal Recessive
LTBP2 NULL MUTATIONS IN AN AUTOSOMAL RECESSIVE OCULAR SYNDROME WITH MEGALOCORNEA, SPHEROPHAKIA, AND SECONDARY GLAUCOMA Julie Desir, Yves Sznajer, Fanny Depasse, Françoise Roulez, Marc Schrooyen, Françoise Meire, Marc J Abramowicz To cite this version: Julie Desir, Yves Sznajer, Fanny Depasse, Françoise Roulez, Marc Schrooyen, et al.. LTBP2 NULL MUTATIONS IN AN AUTOSOMAL RECESSIVE OCULAR SYNDROME WITH MEGALO- CORNEA, SPHEROPHAKIA, AND SECONDARY GLAUCOMA. European Journal of Human Ge- netics, Nature Publishing Group, 2010, n/a (n/a), pp.n/a-n/a. 10.1038/ejhg.2010.11. hal-00511180 HAL Id: hal-00511180 https://hal.archives-ouvertes.fr/hal-00511180 Submitted on 24 Aug 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. LTBP2 NULL MUTATIONS IN AN AUTOSOMAL RECESSIVE OCULAR SYNDROME WITH MEGALOCORNEA, SPHEROPHAKIA, AND SECONDARY GLAUCOMA Julie Désir (1,2), Yves Sznajer (2,3), Fanny Depasse (4), Françoise Roulez (5), Marc Schrooyen (4), Françoise Meire (4,5,6), Marc Abramowicz (1,2) 1. IRIBHM, Université Libre de Bruxelles (ULB), Brussels, Belgium 2. Department of Medical Genetics, Hôpital Erasme-ULB, Brussels, Belgium 3. Clinical Genetics Unit, HUDERF-ULB, Brussels, Belgium 4. Ophthalmology Department, Hôpital Erasme-ULB, Brussels, Belgium 5. -
Grant Application Form Please Complete the Following Form for IETF
©2007 IETF Grant Application Form Please complete the following form for IETF grant applications. This form and all the attachments below must be combined into one document before submitting electronically. Grant submissions will not be accepted otherwise. Attachments Required 1. Specific aims of the proposal (1 page maximum). 2. Rationale of the proposal and relevance to essential tremor (1-2 pages maximum). 3. Preliminary data, if available should be incorporated into the Rationale/Relevance section. Preliminary data are not required for a proposal. However, if preliminary data are referred to in the proposal rationale, or have been used to formulate the hypotheses to be tested, such information must be formally presented in this section. 4. Research methods and procedures (1-2 pages maximum). 5. Anticipated results (half-page maximum). 6. Detailed budget and justification (1 page maximum). 7. Biographic sketch of principal investigator and all professional personnel participating in the project (standard NIH format, including biosketch and other support). 8. Copies of relevant abstracts and/or articles that have been published, are in press, or have been submitted for publication. 9. Completed conflict of interest questionnaire. Project Title: ____________________________________________________________________________ Sponsoring Institution: ____________________________________________________________________ Principal Investigator: Last Name: _______________________________ First Name: ______________________ Middle Initial: __ Degree(s): -
Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing -
(12) United States Patent (10) Patent No.: US 7.873,482 B2 Stefanon Et Al
US007873482B2 (12) United States Patent (10) Patent No.: US 7.873,482 B2 Stefanon et al. (45) Date of Patent: Jan. 18, 2011 (54) DIAGNOSTIC SYSTEM FOR SELECTING 6,358,546 B1 3/2002 Bebiak et al. NUTRITION AND PHARMACOLOGICAL 6,493,641 B1 12/2002 Singh et al. PRODUCTS FOR ANIMALS 6,537,213 B2 3/2003 Dodds (76) Inventors: Bruno Stefanon, via Zilli, 51/A/3, Martignacco (IT) 33035: W. Jean Dodds, 938 Stanford St., Santa Monica, (Continued) CA (US) 90403 FOREIGN PATENT DOCUMENTS (*) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 WO WO99-67642 A2 12/1999 U.S.C. 154(b) by 158 days. (21)21) Appl. NoNo.: 12/316,8249 (Continued) (65) Prior Publication Data Swanson, et al., “Nutritional Genomics: Implication for Companion Animals'. The American Society for Nutritional Sciences, (2003).J. US 2010/O15301.6 A1 Jun. 17, 2010 Nutr. 133:3033-3040 (18 pages). (51) Int. Cl. (Continued) G06F 9/00 (2006.01) (52) U.S. Cl. ........................................................ 702/19 Primary Examiner—Edward Raymond (58) Field of Classification Search ................... 702/19 (74) Attorney, Agent, or Firm Greenberg Traurig, LLP 702/23, 182–185 See application file for complete search history. (57) ABSTRACT (56) References Cited An analysis of the profile of a non-human animal comprises: U.S. PATENT DOCUMENTS a) providing a genotypic database to the species of the non 3,995,019 A 1 1/1976 Jerome human animal Subject or a selected group of the species; b) 5,691,157 A 1 1/1997 Gong et al. -
Bioinformatic Analysis Reveals the Importance of Epithelial-Mesenchymal Transition in the Development of Endometriosis
www.nature.com/scientificreports OPEN Bioinformatic analysis reveals the importance of epithelial- mesenchymal transition in the development of endometriosis Meihong Chen1,6, Yilu Zhou2,3,6, Hong Xu4, Charlotte Hill2, Rob M. Ewing2,3, Deming He1, Xiaoling Zhang1 ✉ & Yihua Wang2,3,5 ✉ Background: Endometriosis is a frequently occurring disease in women, which seriously afects their quality of life. However, its etiology and pathogenesis are still unclear. Methods: To identify key genes/ pathways involved in the pathogenesis of endometriosis, we recruited 3 raw microarray datasets (GSE11691, GSE7305, and GSE12768) from Gene Expression Omnibus database (GEO), which contain endometriosis tissues and normal endometrial tissues. We then performed in-depth bioinformatic analysis to determine diferentially expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and protein-protein interaction (PPI) network analysis. The fndings were further validated by immunohistochemistry (IHC) staining in endometrial tissues from endometriosis or control patients. Results: We identifed 186 DEGs, of which 118 were up-regulated and 68 were down-regulated. The most enriched DEGs in GO functional analysis were mainly associated with cell adhesion, infammatory response, and extracellular exosome. We found that epithelial-mesenchymal transition (EMT) ranked frst in the Hallmark pathway enrichment. EMT may potentially be induced by infammatory cytokines such as CXCL12. IHC confrmed the down-regulation of E-cadherin (CDH1) and up-regulation of CXCL12 in endometriosis tissues. Conclusions: Utilizing bioinformatics and patient samples, we provide evidence of EMT in endometriosis. Elucidating the role of EMT will improve the understanding of the molecular mechanisms involved in the development of endometriosis. Endometriosis is a frequently occurring gynaecological disease characterised by chronic pelvic pain, dysmenor- rhea and infertility1. -
Gene Regulation Underlies Environmental Adaptation in House Mice
Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Research Gene regulation underlies environmental adaptation in house mice Katya L. Mack,1 Mallory A. Ballinger,1 Megan Phifer-Rixey,2 and Michael W. Nachman1 1Department of Integrative Biology and Museum of Vertebrate Zoology, University of California, Berkeley, California 94720, USA; 2Department of Biology, Monmouth University, West Long Branch, New Jersey 07764, USA Changes in cis-regulatory regions are thought to play a major role in the genetic basis of adaptation. However, few studies have linked cis-regulatory variation with adaptation in natural populations. Here, using a combination of exome and RNA- seq data, we performed expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses to study the genetic architecture of regulatory variation in wild house mice (Mus musculus domesticus) using individuals from five pop- ulations collected along a latitudinal cline in eastern North America. Mice in this transect showed clinal patterns of variation in several traits, including body mass. Mice were larger in more northern latitudes, in accordance with Bergmann’s rule. We identified 17 genes where cis-eQTLs were clinal outliers and for which expression level was correlated with latitude. Among these clinal outliers, we identified two genes (Adam17 and Bcat2) with cis-eQTLs that were associated with adaptive body mass variation and for which expression is correlated with body mass both within and between populations. Finally, we per- formed a weighted gene co-expression network analysis (WGCNA) to identify expression modules associated with measures of body size variation in these mice. -
Technical Manual Human LTBP2 (Latent-Transforming Growth Factor
Technical Manual Human LTBP2 (Latent-transforming growth factor beta-binding protein 2) ELISA Kit • Catalogue Code: HUFI01553 • Sandwich ELISA Kit • Research Use Only Contents 1. Key features and Sample Types .......................................................................................................... 3 2. Storage & Expiry .................................................................................................................................. 3 3. Description and Principle .................................................................................................................... 4 4. Kit Contents ......................................................................................................................................... 5 5. Workflow Overview ............................................................................................................................ 6 6. Sample Preparation............................................................................................................................. 7 7. Standard and Reagent Preparation..................................................................................................... 8 1. Wash Buffer: ................................................................................................................................... 9 2. Standard Dilution: ........................................................................................................................... 9 3. Preparation of biotin-labelled antibody working -
Functional Interpretation of Gene Lists
Functional interpretation of gene lists Bing Zhang Department of Biomedical Informatics Vanderbilt University [email protected] Microarray data analysis workflow log2(ratio) 92546_r_at 92545_f_at 96055_at 102105_f_at 102700_at -log10(p value) 161361_s_at Microarray data Differential 92202_g_at expression 103548_at 100947_at 101869_s_at 102727_at 160708_at …... Normalization Clustering Lists of genes with potential biological interest 2 Applied Bioinformatics, Spring 2013 Omics studies generate gene/protein lists n Genomics q Genome Wide Association Study (GWAS) q Next generation sequencing (NGS) n Transcriptomics q mRNA profiling ……….. n Microarrays ……….. ……….. n Serial analysis of gene expression (SAGE) ……….. n RNA-Seq ………. q Protein-DNA interaction ………. n Chromatin immunoprecipitation ………. n Proteomics ………. ……… q Protein profiling n LC-MS/MS ……… ……… q Protein-protein interaction n Yeast two hybrid n Affinity pull-down/LC-MS/MS 3 Applied Bioinformatics, Spring 2013 Microarray experiment comparing metastatic and non- metastatic colon cancer cell lines Parental cell line Affymetrix RNA Mouse430_2 Metastatic cell line Smith et al., Gastroenterology, 138:958-968, 2010 4 Applied Bioinformatics, Spring 2013 Data matrix and differential expression analysis 863 significant probe set IDs (adjp<0.01 and fold-change>2), out of 45,101 probe sets *because the data are log2 based, fold-change>2 means abs(logFC)>1 5 Applied Bioinformatics, Spring 2013 Understanding a gene list n Level I 1451263_a_at 1436486_x_at q What are the genes behind the IDs and 1451780_at what do we know about the function of the 1438237_at 1417023_a_at genes? 1441054_at 1416203_at 1416295_a_at 1435012_x_at 1416069_at 1436485_s_at 1438148_at 1452740_at 1422184_a_at …… 6 Applied Bioinformatics, Spring 2013 One-gene-at-a-time information systems 7 Applied Bioinformatics, Spring 2013 Biomart: a batch information retrieval system n In contrast to the “one-gene-at-a-time” systems, e.g. -
Heterogeneity Between Primary Colon Carcinoma and Paired Lymphatic and Hepatic Metastases
MOLECULAR MEDICINE REPORTS 6: 1057-1068, 2012 Heterogeneity between primary colon carcinoma and paired lymphatic and hepatic metastases HUANRONG LAN1, KETAO JIN2,3, BOJIAN XIE4, NA HAN5, BINBIN CUI2, FEILIN CAO2 and LISONG TENG3 Departments of 1Gynecology and Obstetrics, and 2Surgical Oncology, Taizhou Hospital, Wenzhou Medical College, Linhai, Zhejiang; 3Department of Surgical Oncology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang; 4Department of Surgical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang; 5Cancer Chemotherapy Center, Zhejiang Cancer Hospital, Zhejiang University of Chinese Medicine, Hangzhou, Zhejiang, P.R. China Received January 26, 2012; Accepted May 8, 2012 DOI: 10.3892/mmr.2012.1051 Abstract. Heterogeneity is one of the recognized characteris- Introduction tics of human tumors, and occurs on multiple levels in a wide range of tumors. A number of studies have focused on the Intratumor heterogeneity is one of the recognized charac- heterogeneity found in primary tumors and related metastases teristics of human tumors, which occurs on multiple levels, with the consideration that the evaluation of metastatic rather including genetic, protein and macroscopic, in a wide range than primary sites could be of clinical relevance. Numerous of tumors, including breast, colorectal cancer (CRC), non- studies have demonstrated particularly high rates of hetero- small cell lung cancer (NSCLC), prostate, ovarian, pancreatic, geneity between primary colorectal tumors and their paired gastric, brain and renal clear cell carcinoma (1). Over the past lymphatic and hepatic metastases. It has also been proposed decade, a number of studies have focused on the heterogeneity that the heterogeneity between primary colon carcinomas and found in primary tumors and related metastases with the their paired lymphatic and hepatic metastases may result in consideration that the evaluation of metastatic rather than different responses to anticancer therapies.