DOCSLIB.ORG

Genomics Genetics, Transcriptomics and Epigenetics Subgroup Report

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genomics Genetics, Transcriptomics and Epigenetics Subgroup Report Genomics Genetics, Transcriptomics and Epigenetics Subgroup report Didier Meulendijks, NL Dieter Deforce, BE Hans Ovelgönne, NL Renate König, DE Marjon Pasmooij, NL (subgroup lead) See websites for contact details Heads of Medicines Agencies www.hma.eu European Medicines Agency www.ema.europa.eu An agency of the European Union 1. Summary ................................................................................................. 4 2. Background ............................................................................................. 5 2.1. Genomics ............................................................................................................ 5 2.2. Genetics .............................................................................................................. 5 2.3. Transcriptomics .................................................................................................... 6 2.4. Epigenetics .......................................................................................................... 6 2.5. Microbiomics ........................................................................................................ 8 3. Objectives ............................................................................................... 8 4. Methods ................................................................................................... 9 5. Results of the data characterisation ........................................................ 9 5.1. General overview and history ................................................................................. 9 5.2. European regulatory scientific guidelines ............................................................... 12 5.3. U.S. Food and Drug Administration (FDA) .............................................................. 14 5.4. Data sources ..................................................................................................... 19 5.4.1. Public data sources .......................................................................................... 19 5.4.2. Genomics initiatives ......................................................................................... 23 5.4.3. Conclusions on Data Sources ............................................................................ 26 5.5. Volume ............................................................................................................. 26 5.5.1. Size of the data source ..................................................................................... 26 5.5.2. Structure (terminology, structured vs. unstructured data) .................................... 27 5.6. Veracity ............................................................................................................ 28 5.6.1. Data provenance ............................................................................................. 28 5.6.2. Data Quality ................................................................................................... 28 5.6.3. Completeness (opportunity to capture the data) .................................................. 30 5.6.4. Representativeness .......................................................................................... 31 5.6.5. Analytical tests / variability ............................................................................... 31 5.6.6. Validation ....................................................................................................... 32 5.7. Variability .......................................................................................................... 33 5.7.1. Data heterogeneity .......................................................................................... 33 5.7.2. Data standards ................................................................................................ 34 5.7.3. Data processing .............................................................................................. 36 5.8. Velocity ............................................................................................................. 39 5.8.1. Speed of change .............................................................................................. 39 5.8.2. Rate of accumulation ....................................................................................... 39 5.9. Value ................................................................................................................ 40 5.9.1. Usability of genomics big data ........................................................................... 40 5.9.2. Identify any uncertainties or unknowns which require further exploration ............... 44 5.9.3. Possible gaps in current European guidance ........................................................ 44 5.9.4. Data Accessibility - consider privacy and governance challenges and the limitations to access as this will affect the value from a regulatory context .......................................... 45 5.9.5. Data analytics - discuss current and potential new approaches .............................. 45 5.9.6. Regulatory challenges ...................................................................................... 45 6. Conclusions ........................................................................................... 46 6.1. Summarised key messages ................................................................................. 46 Genomics Genetics, Transcriptomics and Epigenetics Page 2/69 6.2. Specific recommendations from the analysis .......................................................... 47 7. References ............................................................................................ 54 Appendix 1: Definitions ............................................................................. 57 Appendix 2: Genomics initiatives ............................................................... 58 Genomics Genetics, Transcriptomics and Epigenetics Page 3/69 1. Summary This report is a deliverable from the HMA/EMA Big Data Task Force, ‘Genomics’ subgroup, and focuses on the characterisation and mapping of a type of ‘big data’, namely genomics data, including epigenetics and transcriptomics data. In addition, this report describes the potential usability/applicability of genomics data in regulatory processes as well as recommendations from the HMA/EMA Big Data Task Force regarding how to optimise the future use of genomics (big) data in regulatory processes. There are different public data sources (databases) where genomics data can be uploaded and freely accessed. Most publicly available genomics data sources are derived from investigator-initiated (non- industry-driven) initiatives and contain only genomics data, without phenotypic/clinical outcome data linked to the genomics data. Some data sources do contain phenotypic data, mainly data on the presence or absence of genetic/hereditary diseases. Other phenotypic data, in particular clinical outcome data (e.g. data on efficacy or safety of treatments) are currently only sporadically found in available public databases (e.g. PharmGKB, https://www.pharmgkb.org/), although a vast number of initiatives are now being performed which do link genomics data to clinical data. Most genomics data generated by pharmaceutical industry, which is often genomics data linked to clinical outcomes (e.g. from clinical trials), are not publicly available. These data would be of interest to regulators as the data could be used for regulatory purposes (e.g. pharmacovigilance, identification of biomarkers for efficacy, etc.). Incentives to make genomics data available to regulators and/or publicly available could be beneficial for the regulatory system, e.g. it could facilitate more individual patient-based B/R assessment as opposed to population-based B/R assessment. Therefore, it could be considered to request companies upon a marketing authorisation application to make genomics data linked to clinical data from the pivotal clinical trials available in public databases or to the EMA. By doing so, the data would be accessible for further analyses for academic as well as potentially for regulatory purposes. To facilitate data sharing in a secure way, it could be explored whether the EMA should provide a central platform for sharing of clinical trial data, or whether the EMA could provide a portal linking to industry-owned data. To be able to optimally profit from available data, it would be important to link the most important parameters related to phenotype and/or treatment outcome to the genomics data (e.g. adverse events, primary efficacy outcomes). Importantly, various privacy, security and ethical issues need to be addressed before genomics data sharing can become common practice. For example, informed consent would have to be adequately covered in relation to data sharing. Furthermore, to be able to link data from different sources, a system that includes patient identifiers that ensures both adequate linking of data and patient privacy would be needed. Linking clinical and phenotype data across datasets would both empower precision medicine, but also introduce new privacy risks. The latter is especially of concern for rare diseases where there are sometimes only a few patients with a specific mutation worldwide. Genomics data quality can be improved by improving standardisation (e.g. making use of standard operating procedures), by requiring raw data to be shared in addition to processed data, by requiring meta-data to be attached
Load more

Recommended publications
  • Applied Category Theory for Genomics – an Initiative
    Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4].
    [Show full text]
  • The 4D Nucleome Project
    The 4D nucleome project The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Dekker, Job et al. “The 4D Nucleome Project.” Nature 549, no. 7671 (September 2017): 219–226 © 2017 Macmillan Publishers Limited, part of Springer Nature As Published http://dx.doi.org/10.1038/NATURE23884 Publisher Nature Publishing Group Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/114838 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nature. Manuscript Author Author manuscript; Manuscript Author available in PMC 2017 September 27. Published in final edited form as: Nature. 2017 September 13; 549(7671): 219–226. doi:10.1038/nature23884. The 4D Nucleome Project Job Dekker1, Andrew S. Belmont2, Mitchell Guttman3, Victor O. Leshyk4, John T. Lis5, Stavros Lomvardas6, Leonid A. Mirny7, Clodagh C. O’Shea8, Peter J. Park9, Bing Ren10, Joan C. Ritland Politz11, Jay Shendure12, Sheng Zhong4, and the 4D Nucleome Network13 1Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Howard Hughes Medical Institute, Worcester, MA 01605 2Department of Cell and Developmental Biology, University of Illinois, Urbana-Champaign, IL 61801 3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 4Department
    [Show full text]
  • Genetic and Environmental Exposures Constrain Epigenetic Drift Over the Human Life Course
    Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Research Genetic and environmental exposures constrain epigenetic drift over the human life course Sonia Shah,1,9 Allan F. McRae,1,9 Riccardo E. Marioni,1,2,3,9 Sarah E. Harris,2,3 Jude Gibson,4 Anjali K. Henders,5 Paul Redmond,6 Simon R. Cox,3,6 Alison Pattie,6 Janie Corley,6 Lee Murphy,4 Nicholas G. Martin,5 Grant W. Montgomery,5 John M. Starr,3,7 Naomi R. Wray,1,10 Ian J. Deary,3,6,10 and Peter M. Visscher1,3,8,10 1Queensland Brain Institute, The University of Queensland, Brisbane, 4072, Queensland, Australia; 2Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom; 3Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 4Wellcome Trust Clinical Research Facility, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, United Kingdom; 5QIMR Berghofer Medical Research Institute, Brisbane, 4029, Queensland, Australia; 6Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 7Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 8University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, Brisbane, 4072, Queensland, Australia Epigenetic mechanisms such as DNA methylation (DNAm) are essential for regulation of gene expression. DNAm is dy- namic, influenced by both environmental and genetic factors. Epigenetic drift is the divergence of the epigenome as a function of age due to stochastic changes in methylation.
    [Show full text]
  • The International Human Epigenome Consortium (IHEC): a Blueprint for Scientific Collaboration and Discovery
    The International Human Epigenome Consortium (IHEC): A Blueprint for Scientific Collaboration and Discovery Hendrik G. Stunnenberg1#, Martin Hirst2,3,# 1Department of Molecular Biology, Faculties of Science and Medicine, Radboud University, Nijmegen, The Netherlands 2Department of Microbiology and Immunology, Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada V6T 1Z4. 3Canada’s Michael Smith Genome Science Center, BC Cancer Agency, Vancouver, BC, Canada V5Z 4S6 #Corresponding authors [email protected] [email protected] Abstract The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalogue of high-resolution reference epigenomes of major primary human cell types. The studies now presented (cell.com/XXXXXXX) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic data sets to gain insights in the epigenetic control of cell states relevant for human health and disease. One of the great mysteries in developmental biology is how the same genome can be read by cellular machinery to generate the plethora of different cell types required for eukaryotic life. As appreciation grew for the central roles of transcriptional and epigenetic mechanisms in specification of cellular fates and functions, researchers around the world encouraged scientific funding agencies to develop an organized and standardized effort to exploit epigenomic assays to shed additional light on this process (Beck, Olek et al. 1999, Jones and Martienssen 2005, American Association for Cancer Research Human Epigenome Task and European Union 2008). In March 2009, leading scientists and international health research funding agency representatives were invited to a meeting in Bethesda (MD, USA) to gauge the level of interest in an international epigenomics project and to identify potential areas of focus.
    [Show full text]
  • Distinctive Regulatory Architectures of Germline-Active and Somatic Genes in C
    Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Research Distinctive regulatory architectures of germline-active and somatic genes in C. elegans Jacques Serizay, Yan Dong, Jürgen Jänes, Michael Chesney, Chiara Cerrato, and Julie Ahringer The Gurdon Institute and Department of Genetics, University of Cambridge, CB2 1QN Cambridge, United Kingdom RNA profiling has provided increasingly detailed knowledge of gene expression patterns, yet the different regulatory ar- chitectures that drive them are not well understood. To address this, we profiled and compared transcriptional and regu- latory element activities across five tissues of Caenorhabditis elegans, covering ∼90% of cells. We find that the majority of promoters and enhancers have tissue-specific accessibility, and we discover regulatory grammars associated with ubiquitous, germline, and somatic tissue–specific gene expression patterns. In addition, we find that germline-active and soma-specific promoters have distinct features. Germline-active promoters have well-positioned +1 and −1 nucleosomes associated with a periodic 10-bp WW signal (W = A/T). Somatic tissue–specific promoters lack positioned nucleosomes and this signal, have wide nucleosome-depleted regions, and are more enriched for core promoter elements, which largely differ between tissues. We observe the 10-bp periodic WW signal at ubiquitous promoters in other animals, suggesting it is an ancient conserved signal. Our results show fundamental differences in regulatory architectures of germline and somatic tissue–specific genes, uncover regulatory rules for generating diverse gene expression patterns, and provide a tissue-specific resource for future studies. [Supplemental material is available for this article.] Cell type–specific transcription regulation underlies production tissues are achieved and whether expression is governed by dis- of the myriad of different cells generated during development.
    [Show full text]
  • Small Variants Frequently Asked Questions (FAQ) Updated September 2011
    Small Variants Frequently Asked Questions (FAQ) Updated September 2011 Summary Information for each Genome .......................................................................................................... 3 How does Complete Genomics map reads and call variations? ........................................................................... 3 How do I assess the quality of a genome produced by Complete Genomics?................................................ 4 What is the difference between “Gross mapping yield” and “Both arms mapped yield” in the summary file? ............................................................................................................................................................................. 5 What are the definitions for Fully Called, Partially Called, Half-Called and No-Called?............................ 5 In the summary-[ASM-ID].tsv file, how is the number of homozygous SNPs calculated? ......................... 5 In the summary-[ASM-ID].tsv file, how is the number of heterozygous SNPs calculated? ....................... 5 In the summary-[ASM-ID].tsv file, how is the total number of SNPs calculated? .......................................... 5 In the summary-[ASM-ID].tsv file, what regions of the genome are included in the “exome”? .............. 6 In the summary-[ASM-ID].tsv file, how is the number of SNPs in the exome calculated? ......................... 6 In the summary-[ASM-ID].tsv file, how are variations in potentially redundant regions of the genome counted? .....................................................................................................................................................................
    [Show full text]
  • Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis
    fcell-08-00252 April 13, 2020 Time: 17:59 # 1 ORIGINAL RESEARCH published: 16 April 2020 doi: 10.3389/fcell.2020.00252 Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis Xiaoxia Ying1†, Xiyun Jin2†, Pingping Wang2†, Yuzhu He1, Haomiao Zhang1, Xiang Ren1, Songling Chai1, Wenqi Fu1, Pengcheng Zhao1, Chen Chen1, Guowu Ma1* and Huiying Liu1* 1 2 Edited by: School of Stomatology, Dalian Medical University, Dalian, China, School of Life Sciences and Technology, Harbin Institute Yongchun Zuo, of Technology, Harbin, China Inner Mongolia University, China Reviewed by: Osteoporosis is the most common bone metabolic disease, characterized by bone Liang Yu, mass loss and bone microstructure changes due to unbalanced bone conversion, Xidian University, China Yanshuo Chu, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to The University of Texas MD Anderson treat a variety of diseases, including inflammation, cancer and autoimmune diseases. Cancer Center, United States However, excess glucocorticoids can cause osteoporosis. Herein we performed an *Correspondence: Guowu Ma integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA [email protected] analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential Huiying Liu expression analysis revealed 1047 and 844 differentially expressed genes in the two [email protected] datasets. After integrating differentially expressed glucocorticoid-related genes, we †These authors have contributed equally to this work found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate Specialty section: This article was submitted to genes. Enrichment analysis showed that these genes are significantly enriched in the Epigenomics and Epigenetics, osteoporosis related pathways.
    [Show full text]
  • Invitrogen Lentiarray Human CRISPR Library, 96-Well Plate User Guide
    USER GUIDE Invitrogen™ LentiArray™ Human CRISPR Library, 96-well Plate Catalog Numbers A31931, A31932, A31933, A31934, A31935, A31936, A31937, A31938, A31939, A31940, A31941, A31942, A31943, A31944, A31945, A31946, A31947, A31948, and A31949 Doc. Part No. 100044720 Pub. No. MAN0016075 Rev. B WARNING! Read the Safety Data Sheets (SDSs) and follow the handling instructions. Wear appropriate protective eyewear, clothing, and gloves. Safety Data Sheets (SDSs) are available from thermofisher.com/support. Product description Invitrogen™ LentiArray™ Human CRISPR libraries consist of pre-defined collection of gene families for functional genomics screening in an arrayed format. Each library targets a subset of human genes with up to 4 sequence-verified distinct lentiviral gRNA constructs per gene, pooled in a single well in a 96-well format. The gRNAs are based on the latest research on gRNA design. The gRNAs included in the LentiArray™ libraries are designed to knockout all known isoforms of the target genes and are selected for maximum knockout efficiency without sacrificing specificity. Characteristic Description Product Invitrogen™ LentiArray™ Human CRISPR Lentivirus Library (see Table 1, for details) Amount 4 aliquots of 50 µL/well per gene target (200 µL total per gene target) Viral titer • Libraries are delivered with a range of average titer between 2×107–2×108 TU/mL by puromycin antibiotic selection. • We recommend using 1×108 TU/mL for starting MOI calculations, see “MOI determination for screens“ on page 2 for additional guidance. Lentiviral map • gRNA expression is driven by a U6 promoter. • Includes puromycin resistance gene to allow selection of transduced cells. Plate layout • Refer to the associated PDF file for the plate map of the specific LentiArray™ Human CRISPR library and to the associated Excel files for gRNA target information.
    [Show full text]
  • Ecological Developmental Biology and Disease States CHAPTER 5 Teratogenesis: Environmental Assaults on Development 167
    Integrating Epigenetics, Medicine, and Evolution Scott F. Gilbert David Epel Swarthmore College Hopkins Marine Station, Stanford University Sinauer Associates, Inc. • Publishers Sunderland, Massachusetts U.S.A. © Sinauer Associates, Inc. This material cannot be copied, reproduced, manufactured or disseminated in any form without express written permission from the publisher. Brief Contents PART 1 Environmental Signals and Normal Development CHAPTER 1 The Environment as a Normal Agent in Producing Phenotypes 3 CHAPTER 2 How Agents in the Environment Effect Molecular Changes in Development 37 CHAPTER 3 Developmental Symbiosis: Co-Development as a Strategy for Life 79 CHAPTER 4 Embryonic Defenses: Survival in a Hostile World 119 PART 2 Ecological Developmental Biology and Disease States CHAPTER 5 Teratogenesis: Environmental Assaults on Development 167 CHAPTER 6 Endocrine Disruptors 197 CHAPTER 7 The Epigenetic Origin of Adult Diseases 245 PART 3 Toward a Developmental Evolutionary Synthesis CHAPTER 8 The Modern Synthesis: Natural Selection of Allelic Variation 289 CHAPTER 9 Evolution through Developmental Regulatory Genes 323 CHAPTER 10 Environment, Development, and Evolution: Toward a New Synthesis 369 CODA Philosophical Concerns Raised by Ecological Developmental Biology 403 APPENDIX A Lysenko, Kammerer, and the Truncated Tradition of Ecological Developmental Biology 421 APPENDIX B The Molecular Mechanisms of Epigenetic Change 433 APPENDIX C Writing Development Out of the Modern Synthesis 441 APPENDIX D Epigenetic Inheritance Systems:
    [Show full text]
  • DNA Sequencing and Sorting: Identifying Genetic Variations
    BioMath DNA Sequencing and Sorting: Identifying Genetic Variations Student Edition Funded by the National Science Foundation, Proposal No. ESI-06-28091 This material was prepared with the support of the National Science Foundation. However, any opinions, findings, conclusions, and/or recommendations herein are those of the authors and do not necessarily reflect the views of the NSF. At the time of publishing, all included URLs were checked and active. We make every effort to make sure all links stay active, but we cannot make any guaranties that they will remain so. If you find a URL that is inactive, please inform us at [email protected]. DIMACS Published by COMAP, Inc. in conjunction with DIMACS, Rutgers University. ©2015 COMAP, Inc. Printed in the U.S.A. COMAP, Inc. 175 Middlesex Turnpike, Suite 3B Bedford, MA 01730 www.comap.com ISBN: 1 933223 71 5 Front Cover Photograph: EPA GULF BREEZE LABORATORY, PATHO-BIOLOGY LAB. LINDA SHARP ASSISTANT This work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties. DNA Sequencing and Sorting: Identifying Genetic Variations Overview Each of the cells in your body contains a copy of your genetic inheritance, your DNA which has been passed down to you, one half from your biological mother and one half from your biological father. This DNA determines physical features, like eye color and hair color, and can determine susceptibility to medical conditions like hypertension, heart disease, diabetes, and cancer.
    [Show full text]
  • RNA Isolation and Purification for Every Sample, RNA Type, and Application Isolate and Purify RNA with Confidence
    RNA isolation and purification For every sample, RNA type, and application Isolate and purify RNA with confidence RNA isolation is a crucial step in your quest to understand gene expression levels. With all the solutions that Thermo Fisher Scientific has to offer, you can be confident that you’re getting started on the right foot. Go ahead and push the limits of your research. We’ll be there to support you with robust RNA purification kits, trusted RNA tools, and experienced technical support, all backed by nearly 30 years of leadership and innovation in RNA technologies. • Isolate from any sample type, for any application • Obtain high-purity, intact RNA • Achieve high yields, even from small sample quantities Contents Methods of RNA isolation 4 RNA and sample types 5 RNA applications 6 Organic RNA extraction 8 Spin column RNA extraction 10 Lysate-based RNA extraction 11 Automated RNA purification 12 Transcriptome purification 14 mRNA purification 15 MicroRNA and small RNA purification 16 Viral RNA purification 18 RNA from FFPE samples 20 RNA isolation technology guide 22 Avoiding RNA degradation 26 Tips for handling RNA 27 RNA quantitation 28 Gene expression solutions 30 Gene expression research considerations 31 Superior cDNA synthesis for any application 32 Complete kit with flexible priming options 33 Which instrument fits your needs? 34 Which thermal cycler or PCR instrument fits your needs? 35 RNA technical resources 36 Services and support 37 Ordering information 38 Methods of RNA isolation For every application, sample, and RNA type For over three decades, our scientists have developed and professional support. Our RNA isolation products innovative and robust RNA isolation products designed include organic reagents, columns, sample lysate, and to make your job as a scientist easier.
    [Show full text]
  • A Pilot Analysis of DNA Methylation in Candidate Genes in Brain
    cells Article Epigenetic Study in Parkinson’s Disease: A Pilot Analysis of DNA Methylation in Candidate Genes in Brain Luis Navarro-Sánchez 1 , Beatriz Águeda-Gómez 1, Silvia Aparicio 1 and Jordi Pérez-Tur 1,2,3,* 1 Unitat de Genètica Molecular, Instituto de Biomedicina de Valencia, CSIC, 46010 València, Spain; [email protected] (L.N.-S.); [email protected] (B.Á.-G.); [email protected] (S.A.) 2 Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), 46010 València, Spain 3 Unidad Mixta de Genética y Neurología, Instituto de Investigación Sanitaria La Fe, 46026 València, Spain * Correspondence: [email protected]; Tel.: +34-96-339-1755 Received: 25 July 2018; Accepted: 21 September 2018; Published: 26 September 2018 Abstract: Efforts have been made to understand the pathophysiology of Parkinson’s disease (PD). A significant number of studies have focused on genetics, despite the fact that the described pathogenic mutations have been observed only in around 10% of patients; this observation supports the fact that PD is a multifactorial disorder. Lately, differences in miRNA expression, histone modification, and DNA methylation levels have been described, highlighting the importance of epigenetic factors in PD etiology. Taking all this into consideration, we hypothesized that an alteration in the level of methylation in PD-related genes could be related to disease pathogenesis, possibly due to alterations in gene expression. After analysing promoter regions of five PD-related genes in three brain regions by pyrosequencing, we observed some differences in DNA methylation levels (hypo and hypermethylation) in substantia nigra in some CpG dinucleotides that, possibly through an alteration in Sp1 binding, could alter their expression.
    [Show full text]