DOCSLIB.ORG

Increased Genomic Burden of Germline Copy Number Variants Is Associated with Early Onset Breast Cancer: Australian Breast Cancer Family Registry Logan C

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Increased Genomic Burden of Germline Copy Number Variants Is Associated with Early Onset Breast Cancer: Australian Breast Cancer Family Registry Logan C Walker et al. Breast Cancer Research (2017) 19:30 DOI 10.1186/s13058-017-0825-6 RESEARCH ARTICLE Open Access Increased genomic burden of germline copy number variants is associated with early onset breast cancer: Australian breast cancer family registry Logan C. Walker1, John F. Pearson2, George A. R. Wiggins1, Graham G. Giles3, John L. Hopper4* and Melissa C. Southey5 Abstract Background: Women with breast cancer who have multiple affected relatives are more likely to have inherited genetic risk factors for the disease. All the currently known genetic risk factors for breast cancer account for less than half of the average familial risk. Furthermore, the genetic factor(s) underlying an increased cancer risk for many women from multiple-case families remain unknown. Rare genomic duplications and deletions, known as copy number variants (CNVs), cover more than 10% of a human genome, are often not assessed in studies of genetic predisposition, and could account for some of the so-called “missing heritability”. Methods: We carried out a hypothesis-generating case-control study of breast cancer diagnosed before age 40 years (200 cases, 293 controls) using population-based cases from the Australian Breast Cancer Family Study. Genome-wide scanning for CNVs was performed using the Human610-Quad BeadChip and fine-mapping was conducted using PennCNV. Results: We identified deletions overlapping two known cancer susceptibility genes, (BRCA1 and BLM), and a duplication overlapping SMARCB1, associated with risk. The number of deletions across the genome was 1.5-fold higher for cases than controls (P =10-16), and 2-fold higher when only rare deletions overlapping genes (frequency <1%) were assessed (P =5×10-4). Association tests of CNVs, followed by experimental validation of CNV calls, found deletions overlapping the OR4C11 and OR4P4 genes were associated with breast cancer (P =0.02andP = 0.03, respectively). Conclusion: These results suggest rare CNVs might have a role in breast cancer susceptibility, at least for disease at a young age. Keywords: Breast cancer, Early onset, Copy number variants, Inherited susceptibility, Genome-wide association analysis Background currently only account for around 48% of average famil- Breast cancer is the most common malignancy among ial risk, and account for a lower proportion of the famil- women in the developed world, and is increasing rapidly ial risk of disease at a young age, and the vast majority in the developing world. A proportion of women with of women from multiple-case families do not have a breast cancer have multiple affected relatives and are known genetic explanation for their increased cancer therefore more likely to have inherited genetic factors risk [1, 2]. Thus, for a substantial fraction of women, in- that increase their risk of developing the disease. All the cluding young affected women and those with a family currently known genetic risk factors for breast cancer history, the cause of their disease remains unexplained. Copy number variants (CNVs) are estimated to cover – * Correspondence: [email protected] 5 10% of the human genome [3] and, based on nucleotide 4Centre for Epidemiology and Biostatistics, Melbourne School of Population coverage, are responsible for the majority of genetic vari- and Global Health, University of Melbourne, Melbourne, Victoria, Australia ability in human populations. CNVs have been reported to Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Walker et al. Breast Cancer Research (2017) 19:30 Page 2 of 8 disrupt genes known to be involved in breast cancer sus- Genotyping and identification of CNVs ceptibility, including BRCA1, BRCA2, TP53 and CHEK2 All DNA samples were genotyped with the Human610- [4], and could similarly alter other genes involved in path- Quad BeadChip (Illumina, Inc, San Diego, CA, USA) ways related to breast cancer susceptibility. Furthermore, with approximately 610,000 markers (including approxi- several array-based studies have reported candidate rare mately 20,000 non-polymorphic markers) for single nu- CNVs that overlap genes; variants in these might contrib- cleotide polymorphism (SNP) and CNV analysis. Samples ute to breast cancer susceptibility [5–7]. However, there were processed using Illumina’s recommended protocol has been a notable lack of consistency across these studies, for Infinium HD assays. Data for each array were normal- probably because many women carry rare CNVs or be- ised using GenomeStudio 2011.1 software (Illumina). cause of false CNV calls caused by technical issues [8]. Probe information, including genomic location, signal in- Evidence from some studies has suggested that the fre- tensity (Norm R), allele frequency (Norm theta), log R ra- quency and size of germline CNVs are increased in tios (LRRs), and B allele frequencies (BAF), for each women with breast cancer [5, 7, 9], and that this sample was calculated and exported from GenomeStudio. might be strongest for CNVs that overlap gene re- All samples had a call rate >95%. The CNV calls were gen- gions [5, 7]. Thus, functional disruption of genes by erated using the PennCNV program (version 27 Aug. CNVs across the genome might contribute to the 2009), using the default program parameters, library files, genetic basis of breast cancer risk. and genomic wave adjustment. Only a few studies have examined the relationship be- Quality control procedures were performed to remove tween common germ-line CNVs and breast cancer risk. poor-quality array data (Additional file 1: Figure S1). A large genome-wide association study (2000 breast can- Samples were excluded if they met the following criteria: cer cases and 3000 controls) from the Wellcome Trust log R ratio standard deviation >0.28; B allele frequency Case Control Consortium suggested that such CNVs drift >0.01; waviness factor deviating from 0 by >0.04, or were unlikely to have a major role in the genetic basis of with the number of CNV calls exceeding 70. To reduce breast cancer [10]. However, more recent genome-wide false positives, CNV calls were excluded if they con- association studies of common CNVs (mean allele fre- tained <5 probes, and/or were ≥1000 kb in size. A total quency (MAF) ≥5%) in Chinese and European women of 200 cases and 293 controls passed quality control identified a deletion in the APOBEC3 gene cluster asso- steps and were assessed in the study. CNV data used in ciated with up to 1.3-fold and up to 2.3-fold increase in the study are shown in Additional file 2: Table S1. risk of breast cancer associated with hemizygous and homozygous deletions, respectively [11, 12]. Identification of genes overlapping CNVs and defining To better understand the role of CNVs in breast can- rare CNVs cer risk we have conducted a hypothesis-generating To avoid examining multiple isoforms from genes, we study of breast cancer at a young age (diagnosed before annotated 39,544 UCSC RefSeq (NCBI36/Hg18) tran- the age of 40 years). Our study aimed to assess whether scripts using the SOURCE database [27] and defined the CNVs across the genome are more frequent in such genomic intervals for a total of 18,791 unique genes. breast cancer cases when compared with unaffected Thus, each gene interval encompassed the start and end controls, and whether cancer susceptibility genes are of all associated RefSeq transcripts (Additional file 1: disrupted by rare CNVs. Figure S2). CNVs and gene regions that were estimated to overlap by at least one base pair were identified in a genome-wide scan using Intersect and Join tools from Methods the Galaxy web server [28–30]. Subjects Because putative CNV calls do not typically conform to We conducted a hypothesis-generating study of 258 discrete genomic regions in different women, we used the women who were diagnosed with breast cancer before genome coordinates of 18,791 RefSeq gene (NCBI36/Hg18) the age of 40 years from the population-based Australian boundaries to define a CNV region (Additional file 1: Breast Cancer Family Study [13–15]. These cases had Figure S1). Each of these regions therefore represented a clus- been previously screened for germline mutations in ter of one or more CNVs overlapping a well-characterised BRCA1, ATM, CHEK2, PALB2, TP53, BRCA2, CDH1, gene in the human genome and was used to measure the and FANCM [13, 14, 16–25]. A total of 348 women un- frequency of CNVs in our study. Rare CNVs were defined as affected with breast cancer (controls) were selected from thosewithafrequency<1%inthetotalsample. participants in the Australian Mammographic Density Twins and Sisters Study, a cross-sectional study of twins CNV validation by quantitative PCR and their sisters [15, 26]. All study participants provided DNA samples were used to experimentally validate written informed consent. putative CNVs at 12 genomic regions using Human Walker et al. Breast Cancer Research (2017) 19:30 Page 3 of 8 TaqMan® Copy Number Reference Assays (Thermo- compared with controls and that this feature was due fisher Scientific Inc). Primer and probe sequences are to the inheritance or de novo formation of genomic presented in Additional file 3: Table S2. RNaseP was deletions. used as an endogenous reference gene. All assays To assess the potential disruption of CNVs to func- were carried out in triplicate.
Load more

Recommended publications
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
    [Show full text]
  • SNP Genotypes of Olfactory Receptor Genes Associated with Olfactory Ability in German Shepherd Dogs
    SHORT COMMUNICATION doi: 10.1111/age.12389 SNP genotypes of olfactory receptor genes associated with olfactory ability in German Shepherd dogs † ‡ – M. Yang*, G.-J. Geng , W. Zhang , L. Cui§, H.-X. Zhang and J.-L. Zheng‡ † *Police-dog Technology Department, National Police University of China, Shenyang, Liaoning 110034, China. Technology Department, ‡ Shenyang Traffic Police Detachment, Shenyang, Liaoning 110001, China. Forensic Medicine Department, National Police University of China, Shenyang, Liaoning 110854, China. §Document Inspection Department, National Police University of China, Shenyang, Liaoning – 110854, China. Mark Inspection Department, National Police University of China, Shenyang, Liaoning 110854, China. Summary To find out the relationship between SNP genotypes of canine olfactory receptor genes and olfactory ability, 28 males and 20 females from German Shepherd dogs in police service were scored by odor detection tests and analyzed using the Beckman GenomeLab SNPstream. The representative 22 SNP loci from the exonic regions of 12 olfactory receptor genes were investigated, and three kinds of odor (human, ice drug and trinitrotoluene) were detected. The results showed that the SNP genotypes at the OR10H1-like:c.632C>T, OR10H1-like:c.770A>T, OR2K2-like:c.518G>A, OR4C11-like: c.511T>G and OR4C11-like:c.692G>A loci had a statistically significant effect on the scenting abilities (P < 0.001). The kind of odor influenced the performances of the dogs (P < 0.001). In addition, there were interactions between genotype and the kind of odor at the following loci: OR10H1-like:c.632C>T, OR10H1-like:c.770A>T, OR4C11-like:c.511T>G and OR4C11-like:c.692G>A(P< 0.001).
    [Show full text]
  • Copy Number Variation in Fetal Alcohol Spectrum Disorder
    Biochemistry and Cell Biology Copy number variation in fetal alcohol spectrum disorder Journal: Biochemistry and Cell Biology Manuscript ID bcb-2017-0241.R1 Manuscript Type: Article Date Submitted by the Author: 09-Nov-2017 Complete List of Authors: Zarrei, Mehdi; The Centre for Applied Genomics Hicks, Geoffrey G.; University of Manitoba College of Medicine, Regenerative Medicine Reynolds, James N.; Queen's University School of Medicine, Biomedical and Molecular SciencesDraft Thiruvahindrapuram, Bhooma; The Centre for Applied Genomics Engchuan, Worrawat; Hospital for Sick Children SickKids Learning Institute Pind, Molly; University of Manitoba College of Medicine, Regenerative Medicine Lamoureux, Sylvia; The Centre for Applied Genomics Wei, John; The Centre for Applied Genomics Wang, Zhouzhi; The Centre for Applied Genomics Marshall, Christian R.; The Centre for Applied Genomics Wintle, Richard; The Centre for Applied Genomics Chudley, Albert; University of Manitoba Scherer, Stephen W.; The Centre for Applied Genomics Is the invited manuscript for consideration in a Special Fetal Alcohol Spectrum Disorder Issue? : Keyword: Fetal alcohol spectrum disorder, FASD, copy number variations, CNV https://mc06.manuscriptcentral.com/bcb-pubs Page 1 of 354 Biochemistry and Cell Biology 1 Copy number variation in fetal alcohol spectrum disorder 2 Mehdi Zarrei,a Geoffrey G. Hicks,b James N. Reynolds,c,d Bhooma Thiruvahindrapuram,a 3 Worrawat Engchuan,a Molly Pind,b Sylvia Lamoureux,a John Wei,a Zhouzhi Wang,a Christian R. 4 Marshall,a Richard F. Wintle,a Albert E. Chudleye,f and Stephen W. Scherer,a,g 5 aThe Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital 6 for Sick Children, Toronto, Ontario, Canada 7 bRegenerative Medicine Program, University of Manitoba, Winnipeg, Canada 8 cCentre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.
    [Show full text]
  • Single Cell Derived Clonal Analysis of Human Glioblastoma Links
    SUPPLEMENTARY INFORMATION: Single cell derived clonal analysis of human glioblastoma links functional and genomic heterogeneity ! Mona Meyer*, Jüri Reimand*, Xiaoyang Lan, Renee Head, Xueming Zhu, Michelle Kushida, Jane Bayani, Jessica C. Pressey, Anath Lionel, Ian D. Clarke, Michael Cusimano, Jeremy Squire, Stephen Scherer, Mark Bernstein, Melanie A. Woodin, Gary D. Bader**, and Peter B. Dirks**! ! * These authors contributed equally to this work.! ** Correspondence: [email protected] or [email protected]! ! Supplementary information - Meyer, Reimand et al. Supplementary methods" 4" Patient samples and fluorescence activated cell sorting (FACS)! 4! Differentiation! 4! Immunocytochemistry and EdU Imaging! 4! Proliferation! 5! Western blotting ! 5! Temozolomide treatment! 5! NCI drug library screen! 6! Orthotopic injections! 6! Immunohistochemistry on tumor sections! 6! Promoter methylation of MGMT! 6! Fluorescence in situ Hybridization (FISH)! 7! SNP6 microarray analysis and genome segmentation! 7! Calling copy number alterations! 8! Mapping altered genome segments to genes! 8! Recurrently altered genes with clonal variability! 9! Global analyses of copy number alterations! 9! Phylogenetic analysis of copy number alterations! 10! Microarray analysis! 10! Gene expression differences of TMZ resistant and sensitive clones of GBM-482! 10! Reverse transcription-PCR analyses! 11! Tumor subtype analysis of TMZ-sensitive and resistant clones! 11! Pathway analysis of gene expression in the TMZ-sensitive clone of GBM-482! 11! Supplementary figures and tables" 13" "2 Supplementary information - Meyer, Reimand et al. Table S1: Individual clones from all patient tumors are tumorigenic. ! 14! Fig. S1: clonal tumorigenicity.! 15! Fig. S2: clonal heterogeneity of EGFR and PTEN expression.! 20! Fig. S3: clonal heterogeneity of proliferation.! 21! Fig.
    [Show full text]
  • Genome-Wide Profiling of Druggable Active Tumor Defense Mechanisms to Enhance Cancer Immunotherapy
    bioRxiv preprint doi: https://doi.org/10.1101/843185; this version posted November 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. Genome-wide profiling of druggable active tumor defense mechanisms to enhance cancer immunotherapy Rigel J. Kishton1,2,*,#, Shashank J. Patel1,2,†,*, Suman K. Vodnala1,2, Amy E. Decker3, Yogin Patel1,2, Madhusudhanan Sukumar1,2, Tori N. Yamamoto1,2,4, Zhiya Yu1,2, Michelle Ji1,2, Amanda N. Henning1,2, Devikala Gurusamy1,2, Douglas C. Palmer1,2, Winifred Lo1, Anna Pasetto1, Parisa Malekzadeh1, Drew C. Deniger1, Kris C. Wood3, Neville E. Sanjana5,6, Nicholas P. Restifo1,2, #, § 1Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA 2Center for Cell-Based Therapy, National Cancer Institute, Bethesda, MD 20892, USA 3Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, NC, USA 4Immunology Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA 5New York Genome Center, New York, NY 10013 USA 6Department of Biology, New York University, New York, NY 10003, USA *These authors contributed equally to this work. †Present address: NextCure Inc., Beltsville, MD 20705, USA §Present address: Lyell Immunopharma, South San Francisco, CA 94080, USA #Corresponding authors. NPR: [email protected]. RJK: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/843185; this version posted November 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.
    [Show full text]
  • Clinical, Molecular, and Immune Analysis of Dabrafenib-Trametinib
    Supplementary Online Content Chen G, McQuade JL, Panka DJ, et al. Clinical, molecular and immune analysis of dabrafenib-trametinib combination treatment for metastatic melanoma that progressed during BRAF inhibitor monotherapy: a phase 2 clinical trial. JAMA Oncology. Published online April 28, 2016. doi:10.1001/jamaoncol.2016.0509. eMethods. eReferences. eTable 1. Clinical efficacy eTable 2. Adverse events eTable 3. Correlation of baseline patient characteristics with treatment outcomes eTable 4. Patient responses and baseline IHC results eFigure 1. Kaplan-Meier analysis of overall survival eFigure 2. Correlation between IHC and RNAseq results eFigure 3. pPRAS40 expression and PFS eFigure 4. Baseline and treatment-induced changes in immune infiltrates eFigure 5. PD-L1 expression eTable 5. Nonsynonymous mutations detected by WES in baseline tumors This supplementary material has been provided by the authors to give readers additional information about their work. © 2016 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 eMethods Whole exome sequencing Whole exome capture libraries for both tumor and normal samples were constructed using 100ng genomic DNA input and following the protocol as described by Fisher et al.,3 with the following adapter modification: Illumina paired end adapters were replaced with palindromic forked adapters with unique 8 base index sequences embedded within the adapter. In-solution hybrid selection was performed using the Illumina Rapid Capture Exome enrichment kit with 38Mb target territory (29Mb baited). The targeted region includes 98.3% of the intervals in the Refseq exome database. Dual-indexed libraries were pooled into groups of up to 96 samples prior to hybridization.
    [Show full text]
  • Positive Selection, Relaxation, and Acceleration in the Evolution of the Human and Chimp Genome
    Positive Selection, Relaxation, and Acceleration in the Evolution of the Human and Chimp Genome Leonardo Arbiza1, Joaquı´n Dopazo2, Herna´n Dopazo1* 1 Pharmacogenomics and Comparative Genomics Unit, Centro de Investigacio´nPrı´ncipe Felipe (CIPF), Valencia, Spain, 2 Functional Genomics Unit, Bioinformatics Department, Centro de Investigacio´nPrı´ncipe Felipe (CIPF), Valencia, Spain For years evolutionary biologists have been interested in searching for the genetic bases underlying humanness. Recent efforts at a large or a complete genomic scale have been conducted to search for positively selected genes in human and in chimp. However, recently developed methods allowing for a more sensitive and controlled approach in the detection of positive selection can be employed. Here, using 13,198 genes, we have deduced the sets of genes involved in rate acceleration, positive selection, and relaxation of selective constraints in human, in chimp, and in their ancestral lineage since the divergence from murids. Significant deviations from the strict molecular clock were observed in 469 human and in 651 chimp genes. The more stringent branch-site test of positive selection detected 108 human and 577 chimp positively selected genes. An important proportion of the positively selected genes did not show a significant acceleration in rates, and similarly, many of the accelerated genes did not show significant signals of positive selection. Functional differentiation of genes under rate acceleration, positive selection, and relaxation was not statistically significant between human and chimp with the exception of terms related to G-protein coupled receptors and sensory perception. Both of these were over-represented under relaxation in human in relation to chimp.
    [Show full text]
  • The Mouse Solitary Odorant Receptor Gene Promoters As Models for the Study of Odorant Receptor Gene Choice
    RESEARCH ARTICLE The Mouse Solitary Odorant Receptor Gene Promoters as Models for the Study of Odorant Receptor Gene Choice Andrea Degl'Innocenti1,2*, Marta Parrilla1☯, Bettina Harr3☯, Meike Teschke3☯ 1 Max-Planck-Institut für Biophysik, Frankfurt am Main, Germany, 2 Unità di Biologia Cellulare e dello Sviluppo, Dipartimento di Biologia, Università di Pisa, Pisa, Italy, 3 Abteilung Evolutionsgenetik, Max-Planck- Institut für Evolutionsbiologie, Plön, Germany ☯ These authors contributed equally to this work. * [email protected] Abstract OPEN ACCESS Citation: Degl'Innocenti A, Parrilla M, Harr B, Background Teschke M (2016) The Mouse Solitary Odorant In vertebrates, several anatomical regions located within the nasal cavity mediate olfaction. Receptor Gene Promoters as Models for the Study of Odorant Receptor Gene Choice. PLoS ONE 11(1): Among these, the main olfactory epithelium detects most conventional odorants. Olfactory e0144698. doi:10.1371/journal.pone.0144698 sensory neurons, provided with cilia exposed to the air, detect volatile chemicals via an Editor: Johannes Reisert, Monell Chemical Senses extremely large family of seven-transmembrane chemoreceptors named odorant receptors. Center, UNITED STATES Their genes are expressed in a monogenic and monoallelic fashion: a single allele of a sin- Received: June 26, 2015 gle odorant receptor gene is transcribed in a given mature neuron, through a still uncharac- terized molecular mechanism known as odorant receptor gene choice. Accepted: November 23, 2015 Published: January 21, 2016 Copyright: © 2016 Degl'Innocenti et al. This is an Aim open access article distributed under the terms of the Odorant receptor genes are typically arranged in genomic clusters, but a few are isolated Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any (we call them solitary) from the others within a region broader than 1 Mb upstream and medium, provided the original author and source are downstream with respect to their transcript's coordinates.
    [Show full text]
  • The Hypothalamus As a Hub for SARS-Cov-2 Brain Infection and Pathogenesis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The hypothalamus as a hub for SARS-CoV-2 brain infection and pathogenesis Sreekala Nampoothiri1,2#, Florent Sauve1,2#, Gaëtan Ternier1,2ƒ, Daniela Fernandois1,2 ƒ, Caio Coelho1,2, Monica ImBernon1,2, Eleonora Deligia1,2, Romain PerBet1, Vincent Florent1,2,3, Marc Baroncini1,2, Florence Pasquier1,4, François Trottein5, Claude-Alain Maurage1,2, Virginie Mattot1,2‡, Paolo GiacoBini1,2‡, S. Rasika1,2‡*, Vincent Prevot1,2‡* 1 Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, DistAlz, UMR-S 1172, Lille, France 2 LaBoratorY of Development and PlasticitY of the Neuroendocrine Brain, FHU 1000 daYs for health, EGID, School of Medicine, Lille, France 3 Nutrition, Arras General Hospital, Arras, France 4 Centre mémoire ressources et recherche, CHU Lille, LiCEND, Lille, France 5 Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Center for Infection and ImmunitY of Lille (CIIL), Lille, France. # and ƒ These authors contriButed equallY to this work. ‡ These authors directed this work *Correspondence to: [email protected] and [email protected] Short title: Covid-19: the hypothalamic hypothesis 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Primepcr™Assay Validation Report
    PrimePCR™Assay Validation Report Gene Information Gene Name olfactory receptor, family 4, subfamily C, member 11 Gene Symbol OR4C11 Organism Human Gene Summary Olfactory receptors interact with odorant molecules in the nose to initiate a neuronal response that triggers the perception of a smell. The olfactory receptor proteins are members of a large family of G-protein-coupled receptors (GPCR) arising from single coding-exon genes. Olfactory receptors share a 7-transmembrane domain structure with many neurotransmitter and hormone receptors and are responsible for the recognition and G protein-mediated transduction of odorant signals. The olfactory receptor gene family is the largest in the genome. The nomenclature assigned to the olfactory receptor genes and proteins for this organism is independent of other organisms. Gene Aliases OR11-136, OR4C11P RefSeq Accession No. NC_000011.9, NT_167190.1 UniGene ID Hs.553620 Ensembl Gene ID ENSG00000172188 Entrez Gene ID 219429 Assay Information Unique Assay ID qHsaCED0018972 Assay Type SYBR® Green Detected Coding Transcript(s) ENST00000302231 Amplicon Context Sequence ACAGAAAGGCAATCTTAAGGCCAGGATAATCTGAGCTGTAGAGTGTATTAAAGAC CCTATCCAGGCAAGAACAATCAGGATGATGCAGACCTGCTGGCTCATGATGGTT GGG Amplicon Length (bp) 82 Chromosome Location 11:55371349-55371460 Assay Design Exonic Purification Desalted Validation Results Efficiency (%) 99 R2 0.9996 cDNA Cq Target not expressed in universal RNA Page 1/5 PrimePCR™Assay Validation Report cDNA Tm (Celsius) Target not expressed in universal RNA gDNA Cq Target not expressed
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
    [Show full text]
  • WO 2016/004387 Al 7 January 2016 (07.01.2016) P O P C T
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2016/004387 Al 7 January 2016 (07.01.2016) P O P C T (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every A61P 35/00 (2006.01) kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, (21) International Application Number: BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, PCT/US20 15/039 108 DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, (22) International Filing Date: HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, 2 July 2015 (02.07.2015) KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (25) Filing Language: English PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, (26) Publication Language: English SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (30) Priority Data: 62/020,3 10 2 July 2014 (02.07.2014) US (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (71) Applicant: H. LEE MOFFITT CANCER CENTER GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, AND RESEARCH INSTITUTE, INC. [US/US]; 12902 TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, Magnolia Dr., Tampa, FL 336 12-9497 (US).
    [Show full text]