DOCSLIB.ORG

Genome-Wide Association Study Identifies Novel Genetic Variants Contributing to Variation in Blood Metabolite Levels

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genome-Wide Association Study Identifies Novel Genetic Variants Contributing to Variation in Blood Metabolite Levels ARTICLE Received 15 Aug 2014 | Accepted 20 Apr 2015 | Published 12 Jun 2015 DOI: 10.1038/ncomms8208 Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels Harmen H.M. Draisma1,2,3,*, Rene´ Pool1,2,4,*, Michael Kobl5, Rick Jansen6, Ann-Kristin Petersen5, Anika A.M. Vaarhorst4,7,8, Idil Yet9, Toomas Haller10,Ays¸e Demirkan11,12,To˜nu Esko10,13,14,15, Gu Zhu16, Stefan Bo¨hringer17, Marian Beekman7, Jan Bert van Klinken11, Werner Ro¨misch-Margl18, Cornelia Prehn19, Jerzy Adamski19,20,21, Anton J.M. de Craen22, Elisabeth M. van Leeuwen12, Najaf Amin12, Harish Dharuri11, Harm-Jan Westra23, Lude Franke23, Eco J.C. de Geus1,2, Jouke Jan Hottenga1,2, Gonneke Willemsen1,2, Anjali K. Henders16, Grant W. Montgomery16, Dale R. Nyholt16,24, John B. Whitfield16, Brenda W. Penninx2,25, Tim D. Spector9, Andres Metspalu10, P. Eline Slagboom4,7, Ko Willems van Dijk11,26, Peter A.C. ‘t Hoen11, Konstantin Strauch5,27, Nicholas G. Martin16, Gert-Jan B. van Ommen11, Thomas Illig28,29,30, Jordana T. Bell9, Massimo Mangino9, Karsten Suhre18,31,32, Mark I. McCarthy33,34,35, Christian Gieger5,28,36, Aaron Isaacs12, Cornelia M. van Duijn4,8,12,** & Dorret I. Boomsma1,2,4,** Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, Po1.09 Â 10 À 9) associations between single-nucleo- tide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N ¼ 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research. 1 Department of Biological Psychology, VU University Amsterdam, van der Boechorststraat 1, Amsterdam 1081 BT, The Netherlands. 2 The EMGO þ Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, Amsterdam 1081 BT, The Netherlands. 3 Neuroscience Campus Amsterdam, De Boelelaan 1085, Amsterdam 1081 HV, The Netherlands. 4 BBMRI- NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), S4-P, Postbus 9600, Leiden 2300 RC, The Netherlands. 5 Institute of Genetic Epidemiology, Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, Ingolsta¨dter Landstrae 1, Neuherberg 85764, Germany. 6 Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, VUmc, A.J. Ernststraat 1187, Amsterdam 1081 HL, The Netherlands. 7 Department of Molecular Epidemiology, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands. 8 Netherlands Consortium for Healthy Aging, Leiden University Medical Center, Leiden, The Netherlands. 9 Department of Twin Research and Genetic Epidemiology, King’s College London, Westminster Bridge Road, London SE1 7EH, UK. 10 Estonian Genome Center, University of Tartu, 23b Riia Street, Tartu 51010, Estonia. 11 Department of Human Genetics, Leiden University Medical Center, S4-P, PO Box 9600, Leiden 2300 RC, The Netherlands. 12 Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, P.O. Box 2040, Rotterdam 3000 CA, The Netherlands. 13 Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, 300 Longwood Ave, Boston MA02115, Massachusetts, USA. 14 Medical and Population Genetics Program, Broad Institute of MITand Harvard, Cambridge, Massachusetts 2142, USA. 15 Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, NRB 0330, Boston MA 02115, Massachusetts, USA. 16 Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane 4006, Australia. 17 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands. 18 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstrae 1, Neuherberg 85764, Germany. 19 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstrae1, Neuherberg 85764, Germany. 20 German Center for Diabetes Research at Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstr. 1, Neuherberg 85764, Germany. 21 Lehrstuhl fu¨r Experimentelle Genetik, Technische Universita¨t Mu¨nchen, Freising-Weihenstephan 85350, Germany. 22 Department of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands. 23 Department of Genetics, CB50, University Medical Center Groningen, University of Groningen, P.O. Box 30001, Groningen 9700 RB, The Netherlands. 24 Statistical and Genomic Epidemiology, Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Ave, Kelvin Grove QLD 4059, Queensland, Australia. 25 Department of Psychiatry, VU University Medical Center, A.J. Ernststraat 1187, Amsterdam 1081 HL, The Netherlands. 26 Department of Endocrinology, Leiden University Medical Center, S4-P, PO Box 9600, Leiden 2300 RC, The Netherlands. 27 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universita¨t, Munich 81377, Germany. 28 Research Unit of Molecular Epidemiology, Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstrae 1, Neuherberg 85764, Germany. 29 Hannover Unified Biobank, Hannover Medical School, Hannover 30625, Germany. 30 Institute for Human Genetics, Hannover Medical School, Carl-Neuberg-Strasse 1, Hannover 30625, Germany. 31 Faculty of Biology, Ludwig-Maximilians-Universita¨t, Planegg-Martinsried 82152, Germany. 32 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), PO Box 24144, Education City—Qatar Foundation, Doha, Qatar. 33 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. 34 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK. 35 Oxford National Institute for Health Research Biomedical Research Centre, The Joint Research Office, Block 60, Churchill Hospital, Old Road, Headington OX3 7LE, UK. 36 Institute of Epidemiology II, Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, Ingolsta¨dter Landstrae 1, Neuherberg 85764, Germany. * These authors contributed equally to this work. ** These authors jointly supervised this work. Correspondence and requests for materials should be addressed to H.H.M.D. (email: [email protected]). NATURE COMMUNICATIONS | 6:7208 | DOI: 10.1038/ncomms8208 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8208 etabolite levels in human blood reflect the physiological metabolome-wide significance (Z-test, Po1.09 Â 10 À 9), which state of the body, and may differ between individuals reduced to 123 associations involving 59 independent SNPs and Mbecause of variation in genetic makeup and environ- 85 different metabolites. Of these 123 associations (listed in mental exposure1. The study of the genetic contribution to Supplementary Data 2), 4 represented secondary association variation in metabolite levels is an important basis for improved signals according to approximate conditional analysis. Regional aetiological understanding, prevention, diagnosis and treatment association plots, showing the association signals in the regions of complex disorders1,2. Modern high-throughput metabolomics surrounding the lead metabolomic SNPs, are given for all 123 enables the cost-effective measurement of large metabolite panels associations in Supplementary Fig. 3. SNPs representing in blood samples obtained from many individuals. The data independent association signals were aggregated into 31 genomic generated by such metabolomic experiments have been combined loci, which are listed in Supplementary Data 3. Figure 2 depicts all with genotypic data in several recent genome-wide association associations between loci and metabolites as detected in the (GWA) studies2–12. Indeed, the combined investigation of large discovery phase. numbers of genetic variants and large numbers of metabolic traits Five independent SNPs had not been associated with variation is beginning to draw a systems-wide overview of genetic in serum metabolite levels in previous GWA studies (see Table 1). influences on human metabolism11. However, the heritability To further interpret the association of the remaining 54 SNPs estimates from twin and family studies9–11,13 suggest that with serum metabolite concentrations, we compared our findings additional genetic variants influencing variation in serum with those from 11 published GWA studies2–12 for which at metabolite levels remain to be found in GWA studies. least one of the included metabolites overlapped with the In the current study, we set out to further characterize
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]
  • Glutaminase 2 Induces Interleukin-2 Production in CD4+ T Cells by Supporting Antioxidant Defense
    Glutaminase 2 Induces Interleukin-2 Production in CD4+ T Cells by Supporting Antioxidant Defense The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Orite, Seo Yeon Kim. 2019. Glutaminase 2 Induces Interleukin-2 Production in CD4+ T Cells by Supporting Antioxidant Defense. Master's thesis, Harvard Medical School. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:42057407 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use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
    [Show full text]
  • Genome-Wide Analysis of Transcriptional Bursting-Induced Noise in Mammalian Cells
    bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019. 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. Title: Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells Authors: Hiroshi Ochiai1*, Tetsutaro Hayashi2, Mana Umeda2, Mika Yoshimura2, Akihito Harada3, Yukiko Shimizu4, Kenta Nakano4, Noriko Saitoh5, Hiroshi Kimura6, Zhe Liu7, Takashi Yamamoto1, Tadashi Okamura4,8, Yasuyuki Ohkawa3, Itoshi Nikaido2,9* Affiliations: 1Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-0046, Japan 2Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama, 351-0198, Japan 3Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-0054, Japan 4Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 5Division of Cancer Biology, The Cancer Institute of JFCR, Tokyo, 135-8550, Japan 6Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8503, Japan 7Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, 20147, USA 8Section of Animal Models, Department of Infectious Diseases, National Center for Global Health and Medicine (NCGM), Tokyo, 812-0054, Japan 9Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako, 351-0198, Japan *Corresponding authors Corresponding authors e-mail addresses Hiroshi Ochiai, [email protected] Itoshi Nikaido, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/736207; this version posted August 15, 2019.
    [Show full text]
  • Identification the Ferroptosis-Related Gene Signature in Patients with Esophageal Adenocarcinoma
    Zhu et al. Cancer Cell Int (2021) 21:124 https://doi.org/10.1186/s12935-021-01821-2 Cancer Cell International PRIMARY RESEARCH Open Access Identifcation the ferroptosis-related gene signature in patients with esophageal adenocarcinoma Lei Zhu1,2,3†, Fugui Yang1,2†, Lingwei Wang1,2, Lin Dong1,2, Zhiyuan Huang1,2, Guangxue Wang2, Guohan Chen1* and Qinchuan Li1,2* Abstract Background: Ferroptosis is a recently recognized non-apoptotic cell death that is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in esophageal adenocarcinoma (EAC) remains unclear. This study aims to explore the ferroptosis-related genes (FRG) expression profles and their prognostic values in EAC. Methods: The FRG data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regressions were used to identify the prognostic FRG, and the predictive ROC model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investi- gate the bioinformatics functions of signifcantly diferent genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and TIMER database. Finally, SDG were verifed in clinical EAC specimens and normal esophageal mucosal tissues. Results: Twenty-eight signifcantly diferent FRG were screened from 78 EAC and 9 normal tissues. Enrichment analyses showed these SDG were mainly related to the iron-related pathways and metabolisms of ferroptosis. Gene network demonstrated the TP53, G6PD, NFE2L2 and PTGS2 were the hub genes in the biology of ferroptosis.
    [Show full text]
  • The Fundamental Role of the P53 Pathway in Tumor Metabolism and Its Implication in Tumor Therapy
    Author Manuscript Published OnlineFirst on February 3, 2012; DOI: 10.1158/1078-0432.CCR-11-3040 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. The fundamental role of the p53 pathway in tumor metabolism and its implication in tumor therapy Lan Shen1，Xiang Sun2，Zhenhong Fu3，Guodong Yang1，Jianying Li1，and Libo Yao1,* 1 Department of Biochemistry and Molecular Biology, The State Key Laboratory of Cancer Biology, The Fourth Military Medical University, Xi’an 710032, The People’s Republic of China 2 Department of Prosthodontics, School of Stomatology, The Fourth Military Medical University, Xi’an 710032, The People’s Republic of China 3 Department of Cardiology, The General Hospital of the People’s Liberation Army, Beijing 100853, The People’s Republic of China L. Shen, X. Sun and Z-H. Fu contributed equally to this work. * Corresponding author: Department of Biochemistry and Molecular Biology, The State Key Laboratory of Cancer Biology, The Fourth Military Medical University, Xi’an 710032, The People’s Republic of China. Phone: 86-29-84774513; Fax: 86-29-84773947; E-mail: [email protected]. Statement of translational relevance The tumor suppressor p53 is one of the most highly studied in the field of cancer research due to its functions on tumor cell survival and apoptosis. However, tumor metabolic reprogramming fuels cancer cell malignant growth and proliferation. In this review, we systematically document the mechanisms of p53 in tumor metabolism regulation. On this basis, we analyzed the therapeutic strategy whereby p53 helps to prevent tumor malignant metabolic phenotype, bioenergetic, and biosynthetic processes, and blocking the reprogramming of tumor metabolism will provide new strategies for tumor therapy.
    [Show full text]
  • Characterization of the Scavenger Cell Proteome in Mouse and Rat Liver
    Biol. Chem. 2021; 402(9): 1073–1085 Martha Paluschinski, Cheng Jun Jin, Natalia Qvartskhava, Boris Görg, Marianne Wammers, Judith Lang, Karl Lang, Gereon Poschmann, Kai Stühler and Dieter Häussinger* Characterization of the scavenger cell proteome in mouse and rat liver + https://doi.org/10.1515/hsz-2021-0123 The data suggest that the population of perivenous GS Received January 25, 2021; accepted July 4, 2021; scavenger cells is heterogeneous and not uniform as previ- published online July 30, 2021 ously suggested which may reflect a functional heterogeneity, possibly relevant for liver regeneration. Abstract: The structural-functional organization of ammonia and glutamine metabolism in the liver acinus involves highly Keywords: glutaminase; glutamine synthetase; liver specialized hepatocyte subpopulations like glutamine syn- zonation; proteomics; scavenger cells. thetase (GS) expressing perivenous hepatocytes (scavenger cells). However, this cell population has not yet been char- acterized extensively regarding expression of other genes and Introduction potential subpopulations. This was investigated in the present study by proteome profiling of periportal GS-negative and There is a sophisticated structural-functional organization in perivenous GS-expressing hepatocytes from mouse and rat. the liver acinus with regard to ammonium and glutamine Apart from established markers of GS+ hepatocytes such as metabolism (Frieg et al. 2021; Gebhardt and Mecke 1983; glutamate/aspartate transporter II (GLT1) or ammonium Häussinger 1983, 1990). Periportal hepatocytes express en- transporter Rh type B (RhBG), we identified novel scavenger zymes required for urea synthesis such as the rate-controlling cell-specific proteins like basal transcription factor 3 (BTF3) enzyme carbamoylphosphate synthetase 1 (CPS1) and liver- and heat-shock protein 25 (HSP25).
    [Show full text]
  • (KGA) and Its Regulation by Raf-Mek-Erk Signaling in Cancer Cell Metabolism
    Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism K. Thangavelua,1, Catherine Qiurong Pana,b,1, Tobias Karlbergc, Ganapathy Balajid, Mahesh Uttamchandania,d,e, Valiyaveettil Sureshd, Herwig Schülerc, Boon Chuan Lowa,b,2, and J. Sivaramana,2 Departments of aBiological Sciences and dChemistry, National University of Singapore, Singapore 117543; bMechanobiology Institute Singapore, National University of Singapore, Singapore 117411; cStructural Genomics Consortium, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm SE-17177, Sweden; and eDefence Medical and Environmental Research Institute, DSO National Laboratories, Singapore 117510 Edited by John Kuriyan, University of California, Berkeley, CA, and approved March 22, 2012 (received for review October 11, 2011) Besides thriving on altered glucose metabolism, cancer cells un- a substrate for the ubiquitin ligase anaphase-promoting complex/ dergo glutaminolysis to meet their energy demands. As the first cyclosome (APC/C)-Cdh1, linking glutaminolysis to cell cycle enzyme in catalyzing glutaminolysis, human kidney-type glutamin- progression (12). In comparison, function and regulation of LGA is ase isoform (KGA) is becoming an attractive target for small not well studied, although it was recently shown to be linked to p53 pathway (13, 14). Although intense efforts are being made to de- molecules such as BPTES [bis-2-(5 phenylacetamido-1, 2, 4-thiadia- fi fi velop a speci c KGA inhibitor such as BPTES [bis-2-(5-phenyl- zol-2-yl) ethyl sul de], although the regulatory mechanism of KGA acetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide] (15), its mechanism remains unknown.
    [Show full text]
  • Genomic Epidemiology and Recent Update on Nucleic Acid–Based Diagnostics for COVID-19
    Current Tropical Medicine Reports https://doi.org/10.1007/s40475-020-00212-3 COVID-19 IN THE TROPICS: IMPACT AND SOLUTIONS (AJ RODRIGUEZ-MORALES, SECTION EDITOR)) Genomic Epidemiology and Recent Update on Nucleic Acid–Based Diagnostics for COVID-19 Ali A. Rabaan1 & Shamsah H. Al-Ahmed2 & Ranjit Sah3 & Jaffar A. Al-Tawfiq4,5,6 & Shafiul Haque7 & Harapan Harapan8,9,10 & Kovy Arteaga-Livias11,12 & D. Katterine Bonilla Aldana13,14 & Pawan Kumar 15 & Kuldeep Dhama16 & Alfonso J. Rodriguez-Morales12,13,14,17 Accepted: 10 September 2020 # Springer Nature Switzerland AG 2020 Abstract Purpose of the Review The SARS-CoV-2 genome has been sequenced and the data is made available in the public domain. Molecular epidemiological investigators have utilized this information to elucidate the origin, mode of transmission, and contact tracing of SARS-CoV-2. The present review aims to highlight the recent advancements in the molecular epidemiological studies along with updating recent advancements in the molecular (nucleic acid based) diagnostics for COVID-19, the disease caused by SARS-CoV-2. Recent Findings Epidemiological studies with the integration of molecular genetics principles and tools are now mainly focused on the elucidation of molecular pathology of COVID-19. Molecular epidemiological studies have discovered the mutability of SARS-CoV-2 which is of utmost importance for the development of therapeutics and vaccines for COVID-19. The whole world is now participating in the race for development of better and rapid diagnostics and therapeutics for COVID-19. Several molecular diagnostic techniques have been developed for accurate and precise diagnosis of COVID-19. Summary Novel genomic techniques have helped in the understanding of the disease pathology, origin, and spread of COVID- 19.
    [Show full text]
  • Role and Limitations of Epidemiology in Establishing a Causal Association Eduardo L
    Seminars in Cancer Biology 14 (2004) 413–426 Role and limitations of epidemiology in establishing a causal association Eduardo L. Franco a,∗, Pelayo Correa b, Regina M. Santella c, Xifeng Wu d, Steven N. Goodman e, Gloria M. Petersen f a Departments of Epidemiology and Oncology, McGill University, 546 Pine Avenue West, Montreal, QC, Canada H2W1S6 b Department of Pathology, Louisiana State University Health Sciences Center, New Orleans, LA, USA c Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA d Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA e Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA f Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA Abstract Cancer risk assessment is one of the most visible and controversial endeavors of epidemiology. Epidemiologic approaches are among the most influential of all disciplines that inform policy decisions to reduce cancer risk. The adoption of epidemiologic reasoning to define causal criteria beyond the realm of mechanistic concepts of cause-effect relationships in disease etiology has placed greater reliance on controlled observations of cancer risk as a function of putative exposures in populations. The advent of molecular epidemiology further expanded the field to allow more accurate exposure assessment, improved understanding of intermediate endpoints, and enhanced risk prediction by incorporating the knowledge on genetic susceptibility. We examine herein the role and limitations of epidemiology as a discipline concerned with the identification of carcinogens in the physical, chemical, and biological environment. We reviewed two examples of the application of epidemiologic approaches to aid in the discovery of the causative factors of two very important malignant diseases worldwide, stomach and cervical cancers.
    [Show full text]
  • Molecular Epidemiology and Whole-Genome Analysis of Bovine Foamy Virus in Japan
    viruses Article Molecular Epidemiology and Whole-Genome Analysis of Bovine Foamy Virus in Japan Hirohisa Mekata 1,* , Tomohiro Okagawa 2, Satoru Konnai 2,3 and Takayuki Miyazawa 4 1 Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan 2 Department of Advanced Pharmaceutics, Faculty of Veterinary Medicine, Hokkaido University, Sapporo 060-0818, Japan; [email protected] (T.O.); [email protected] (S.K.) 3 Department of Disease Control, Faculty of Veterinary Medicine, Hokkaido University, Sapporo 060-0818, Japan 4 Laboratory of Virus-Host Coevolution, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan; [email protected] * Correspondence: [email protected]; Tel./Fax: +81-985-58-7881 Abstract: Bovine foamy virus (BFV) is a member of the foamy virus family in cattle. Information on the epidemiology, transmission routes, and whole-genome sequences of BFV is still limited. To understand the characteristics of BFV, this study included a molecular survey in Japan and the determination of the whole-genome sequences of 30 BFV isolates. A total of 30 (3.4%, 30/884) cattle were infected with BFV according to PCR analysis. Cattle less than 48 months old were scarcely infected with this virus, and older animals had a significantly higher rate of infection. To reveal the possibility of vertical transmission, we additionally surveyed 77 pairs of dams and 3-month-old calves in a farm already confirmed to have BFV. We confirmed that one of the calves born from a dam with BFV was infected.
    [Show full text]
  • Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings
    Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Nishihara, Reiko, Tyler J. VanderWeele, Kenji Shibuya, Murray A. Mittleman, Molin Wang, Alison E. Field, Edward Giovannucci, Paul Lochhead, and Shuji Ogino. 2015. “Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings.” European Journal of Epidemiology 30 (10): 1129–35. https://doi.org/10.1007/ s10654-015-0088-4. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41392032 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP HHS Public Access Author manuscript Author Manuscript Author ManuscriptEur J Epidemiol Author Manuscript. Author Author Manuscript manuscript; available in PMC 2016 October 07. Published in final edited form as: Eur J Epidemiol. 2015 October ; 30(10): 1129–1135. doi:10.1007/s10654-015-0088-4. Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings Reiko Nishiharaa,b,c, Tyler J. VanderWeeled,e, Kenji Shibuyac, Murray A. Mittlemand,f, Molin Wangd,e,g, Alison E. Fieldd,g,h,i, Edward Giovannuccia,d,g, Paul Lochheadi,j, and Shuji Oginob,d,k aDepartment of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, Massachusetts 02115 USA bDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Boston, Massachusetts 02215 USA cDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan dDepartment of Epidemiology, Harvard T.H.
    [Show full text]
  • Transcription Factor Gata3 Expression Is Induced by Gls2 Overexpression in a Glioblastoma Cell Line but Is Gls2-Independent in Patient-Derived Glioblastoma
    JOURNAL OF PHYSIOLOGY AND PHARMACOLOGY 2017, 68, 2, 209-214 www.jpp.krakow.pl E. MAJEWSKA1, R. ROLA2, M. BARCZEWSKA3, J. MARQUEZ4, J. ALBRECHT1, M. SZELIGA1 TRANSCRIPTION FACTOR GATA3 EXPRESSION IS INDUCED BY GLS2 OVEREXPRESSION IN A GLIOBLASTOMA CELL LINE BUT IS GLS2-INDEPENDENT IN PATIENT-DERIVED GLIOBLASTOMA 1Department of Neurotoxicology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland; 2Department of Neurosurgery and Paediatric Neurosurgery, Medical University of Lublin, Lublin, Poland; 3Department of Neurosurgery, Faculty of Medical Sciences, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga, Malaga, Spain Phosphate-activated glutaminase (GA), a ubiquitous glutamine-metabolizing enzyme, is encoded by two genes, GLS and GLS2. In mammalian cancers, GLS isoforms are perceived as molecules promoting cell proliferation and invasion, whereas the role of GLS2 isoforms seems to be more complex and cell type-specific. Previous studies have shown abundance of GLS and lack of GLS2 transcripts in T98G human glioblastoma (GBM) cell line and patient-derived GBM. Transfection with GAB sequence, the whole GLS2 cDNA transcript, suppressed malignant phenotype of T98G cells. Microarray analysis revealed upregulation of GATA3, the product of which has been implicated in suppressing growth of some peripheral cancers. In this study we confirmed a significant upregulation of GATA3 expression in the transfected cells both at mRNA and protein level. Considerable expression of GATA3 was also observed in GBM tissues (previously shown as not expressing GLS2), while only traces or no GATA3 was detected in (GLS2-expressing) non-tumorigenic brain samples.
    [Show full text]