Identification Et Classification De Composés Reprotoxiques Par Des Approches De Toxicogénomique Prédictive Thomas Darde

Total Page:16

File Type:pdf, Size:1020Kb

Identification Et Classification De Composés Reprotoxiques Par Des Approches De Toxicogénomique Prédictive Thomas Darde Identification et classification de composés reprotoxiques par des approches de toxicogénomique prédictive Thomas Darde To cite this version: Thomas Darde. Identification et classification de composés reprotoxiques par des approches detox- icogénomique prédictive. Médecine humaine et pathologie. Université Rennes 1, 2017. Français. NNT : 2017REN1B022. tel-01751766 HAL Id: tel-01751766 https://tel.archives-ouvertes.fr/tel-01751766 Submitted on 29 Mar 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ANNÉE 2017 THÈSE / UNIVERSITÉ DE RENNES 1 sous le sceau de l’Université Bretagne Loire pour le grade de DOCTEUR DE L’UNIVERSITÉ DE RENNES 1 Mention : Biologie et Sciences de la Santé École doctorale Biologie-Santé présentée par Thomas DARDE Préparée à l’IRSET Inserm U1085 Équipe « Physiology and physiopathology of the urogenital tract » UFR Sciences de le Vie et de l’Environnement Identification et Thèse dirigée par : classification de Frédéric CHALMEL Chargé de recherche - Inserm U 1085 IRSET composés Antoine ROLLAND reprotoxiques par Maître de conférence - Inserm U 1085 IRSET des approches de et soutenue à Rennes le 3 octobre 2017 toxicogénomique devant le jury composé de : Karine AUDOUZE Maitre de conférences – Université Paris Diderot prédictive Rapporteur Odile LECOMPTE Maitre de conférences – iCube Rapporteur Farzad PAKDEL Directeur de recherche – Inserm U1085 IRSET Président du jury Serge NEF Professeur - University of Geneva Medical School Examinateur Christian DELAMARCHE Professeur – Université Rennes 1 Examinateur Frédéric CHALMEL Chargé de recherche – Inserm U1085 IRSET Directeur de thèse i Table des matières Table des matières ....................................................................................................................... ii Liste des tableaux ....................................................................................................................... vi Liste des figures ....................................................................................................................... viii Liste des abréviations .................................................................................................................. x Remerciements ......................................................................................................................... xiv Préambule ................................................................................................................................... 1 1. Introduction ......................................................................................................................... 3 1.1. Le système endocrinien et ses hormones .................................................................... 3 1.1.1. Qu’est-ce que le système endocrinien ? .............................................................. 3 1.1.2. Présentation des différents types d’hormones ..................................................... 5 1.1.3. Le mécanisme d’action des hormones ................................................................ 7 1.1.4. La régulation du système endocrinien ................................................................ 9 1.1.5. Les glandes endocrines et leurs hormones ........................................................ 11 1.2. Le testicule ................................................................................................................ 19 1.2.1. Généralités sur l’anatomie du testicule adulte .................................................. 19 1.2.2. Fonction exocrine du testicule .......................................................................... 20 1.2.3. Fonction endocrine du testicule ........................................................................ 25 1.2.4. Mécanismes de régulation de la stéroïdogenèse ............................................... 28 1.3. Les perturbateurs endocriniens ................................................................................. 31 1.3.1. Histoire de la perturbation endocrinienne ......................................................... 31 1.3.2. Un souci de définition ....................................................................................... 34 1.3.3. Mécanisme(s) d’action d’un perturbateur endocrinien ..................................... 35 1.3.4. Effets des perturbateurs endocriniens sur l’Homme ......................................... 36 1.3.5. Présentation de perturbateurs endocriniens connus .......................................... 46 1.4. Évaluation des risques ............................................................................................... 53 1.4.1. La règlementation européenne .......................................................................... 53 1.4.2. Règlementation non adaptée aux PEs? ............................................................. 56 ii 1.4.3. Outils de détection de toxicités ......................................................................... 58 1.4.4. Tests spécifiques de détection des perturbateurs endocriniens ......................... 61 1.5. La toxicologie prédictive .......................................................................................... 67 1.5.1. Outils de toxicologie prédictive ........................................................................ 67 1.5.2. Méthodes de prédiction ..................................................................................... 73 2. Objectifs de ma thèse ........................................................................................................ 91 3. Matériels et méthodes ....................................................................................................... 93 3.1. Ressources informatiques et environnement de travail ............................................ 93 3.1.1. Langages de programmation ............................................................................. 93 3.1.2. Environnement web .......................................................................................... 96 3.2. Outils et ressources bio-informatiques .................................................................... 101 3.2.1. Suites logicielles ............................................................................................. 101 3.2.2. Navigateurs de génome ................................................................................... 101 3.2.3. Galaxy ............................................................................................................. 102 3.2.4. Vocabulaires contrôlés et ontologies .............................................................. 104 3.2.5. Bases de données ............................................................................................ 105 3.2.6. Description des différents types de fichiers .................................................... 107 3.3. Estimateurs mathématiques .................................................................................... 111 3.3.1. Estimateurs de distances ................................................................................. 111 3.3.2. Évaluation d’un modèle statistique de classification ...................................... 113 3.4. Méthodes statistiques .............................................................................................. 117 3.4.1. Analyse en composantes principales .............................................................. 117 3.4.2. Enrichissement – Loi hypergéométrique ........................................................ 122 3.4.3. Méthodes de prédiction de classe ................................................................... 123 3.5. Méthodes appliquées dans le cadre du projet ChemPSy ........................................ 127 3.5.1. Méthodes appliquées sur les données de la CTD ............................................ 127 3.5.2. Méthodes appliquées sur les données issues de GEO ..................................... 129 4. Résultats et discussion .................................................................................................... 137 4.1. ChemPSy : un système de priorisation pour les substances chimiques .................. 137 4.1.1. Résultats obtenus à partir de la CTD .............................................................. 137 iii 4.1.2. Résultats obtenus à partir de GEO .................................................................. 140 4.1.3. Conclusion et perspectives du projet ChemPSy ............................................. 159 4.2. TOXsIgN : un espace de dépôt pour les signatures toxicogénomique ................... 163 4.2.1. Manuscrit en préparation ................................................................................ 165 4.2.2. Résultats et discussion .................................................................................... 191 4.2.3. Conclusion et perspectives .............................................................................. 196 4.3. The ReproGenomics Viewer (RGV) : un navigateur de génome pour les données de reprogénomique .................................................................................................................
Recommended publications
  • Universidade Estadual De Campinas Instituto De Biologia
    UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA CAMPINAS 2016 1 VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA Dissertação apresentada ao Instituto de Biologia da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do Título de Mestra em Biologia Funcional e Molecular na área de concentração de Bioquímica. Dissertation presented to the Institute of Biology of the University of Campinas in partial fulfillment of the requirements for the degree of Master in Functional and Molecular Biology, in the area of Biochemistry. ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA DISSERTAÇÃO DEFENDIDA PELA ALUNA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA E ORIENTADA PELO DANIEL MARTINS-DE-SOUZA. Orientador: Daniel Martins-de-Souza CAMPINAS 2016 2 Agência(s) de fomento e nº(s) de processo(s): CNPq, 151787/2F2014-0 Ficha catalográfica Universidade Estadual de Campinas Biblioteca do Instituto de Biologia Mara Janaina de Oliveira - CRB 8/6972 Saia-Cereda, Verônica Aparecida Monteiro, 1988- Sa21p O proteoma do corpo caloso da esquizofrenia / Verônica Aparecida Monteiro Saia Cereda. – Campinas, SP : [s.n.], 2016. Orientador: Daniel Martins de Souza. Dissertação (mestrado) – Universidade Estadual de Campinas, Instituto de Biologia. 1. Esquizofrenia. 2. Espectrometria de massas. 3. Corpo caloso.
    [Show full text]
  • The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid
    The Capacity of Long-Term in Vitro Proliferation of Acute Myeloid Leukemia Cells Supported Only by Exogenous Cytokines Is Associated with a Patient Subset with Adverse Outcome Annette K. Brenner, Elise Aasebø, Maria Hernandez-Valladares, Frode Selheim, Frode Berven, Ida-Sofie Grønningsæter, Sushma Bartaula-Brevik and Øystein Bruserud Supplementary Material S2 of S31 Table S1. Detailed information about the 68 AML patients included in the study. # of blasts Viability Proliferation Cytokine Viable cells Change in ID Gender Age Etiology FAB Cytogenetics Mutations CD34 Colonies (109/L) (%) 48 h (cpm) secretion (106) 5 weeks phenotype 1 M 42 de novo 241 M2 normal Flt3 pos 31.0 3848 low 0.24 7 yes 2 M 82 MF 12.4 M2 t(9;22) wt pos 81.6 74,686 low 1.43 969 yes 3 F 49 CML/relapse 149 M2 complex n.d. pos 26.2 3472 low 0.08 n.d. no 4 M 33 de novo 62.0 M2 normal wt pos 67.5 6206 low 0.08 6.5 no 5 M 71 relapse 91.0 M4 normal NPM1 pos 63.5 21,331 low 0.17 n.d. yes 6 M 83 de novo 109 M1 n.d. wt pos 19.1 8764 low 1.65 693 no 7 F 77 MDS 26.4 M1 normal wt pos 89.4 53,799 high 3.43 2746 no 8 M 46 de novo 26.9 M1 normal NPM1 n.d. n.d. 3472 low 1.56 n.d. no 9 M 68 MF 50.8 M4 normal D835 pos 69.4 1640 low 0.08 n.d.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Downloaded from Here
    bioRxiv preprint doi: https://doi.org/10.1101/017566; this version posted November 19, 2015. 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. 1 1 Testing for ancient selection using cross-population allele 2 frequency differentiation 1;∗ 3 Fernando Racimo 4 1 Department of Integrative Biology, University of California, Berkeley, CA, USA 5 ∗ E-mail: [email protected] 6 1 Abstract 7 A powerful way to detect selection in a population is by modeling local allele frequency changes in a 8 particular region of the genome under scenarios of selection and neutrality, and finding which model is 9 most compatible with the data. Chen et al. [2010] developed a composite likelihood method called XP- 10 CLR that uses an outgroup population to detect departures from neutrality which could be compatible 11 with hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive 12 to recent selection and may miss selective events that happened a long time ago. To overcome this, 13 we developed an extension of XP-CLR that jointly models the behavior of a selected allele in a three- 14 population tree. Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that 15 occurred before two populations split from each other, and can distinguish between those events and 16 events that occurred specifically in each of the populations after the split.
    [Show full text]
  • Whole Exome Sequencing for the Identification of CYP3A7 Variants
    www.nature.com/scientificreports OPEN Whole exome sequencing for the identifcation of CYP3A7 variants associated with tacrolimus Received: 21 February 2018 Accepted: 9 November 2018 concentrations in kidney Published: xx xx xxxx transplant patients Minji Sohn1, Myeong Gyu Kim1,2, Nayoung Han1, In-Wha Kim1, Jungsoo Gim3, Sang-Il Min4, Eun Young Song5, Yon Su Kim6, Hun Soon Jung7, Young Kee Shin1, Jongwon Ha4 & Jung Mi Oh 1 The purpose of this study was to identify genotypes associated with dose-adjusted tacrolimus trough concentrations (C0/D) in kidney transplant recipients using whole-exome sequencing (WES). This study included 147 patients administered tacrolimus, including seventy-fve patients in the discovery set and seventy-two patients in the replication set. The patient genomes in the discovery set were sequenced using WES. Also, known tacrolimus pharmacokinetics-related intron variants were genotyped. Tacrolimus C0/D was log-transformed. Sixteen variants were identifed including novel CYP3A7 rs12360 and rs10211 by ANOVA. CYP3A7 rs2257401 was found to be the most signifcant variant among the periods by ANOVA. Seven variants including CYP3A7 rs2257401, rs12360, and rs10211 were analyzed by SNaPshot in the replication set and the efects on tacrolimus C0/D were verifed. A linear mixed model (LMM) was further performed to account for the efects of the variants and clinical factors. The combined set LMM showed that only CYP3A7 rs2257401 was associated with tacrolimus C0/D after adjusting for patient age, albumin, and creatinine. The CYP3A7 rs2257401 genotype variant showed a signifcant diference on the tacrolimus C0/D in those expressing CYP3A5, showing its own efect.
    [Show full text]
  • Phenomexcan: Mapping the Genome to the Phenome Through the Transcriptome
    bioRxivbioRxiv preprint preprint doi: doi: https://doi.org/10.1101/833210 https://doi.org/10.1101/833210. ;The this copyrightversion posted holder March for this 13, preprint 2020. (whichThe copyright was not holder peer-reviewed) for this preprint is the author/funder.(which was not It certified by peer review) is the author/funder,is made who hasavailable granted under bioRxiv a CC-BY a license 4.0 International to display the license preprint. in perpetuity. It is made available under aCC-BY 4.0 International license. PhenomeXcan: Mapping the genome to the phenome through the transcriptome Authors: Milton Pividori1†, Padma S. Rajagopal2†, Alvaro Barbeira1, Yanyu Liang1, Owen Melia1, Lisa Bastarache3, 4, YoSon Park5, The GTEx Consortium6, Xiaoquan Wen7*, Hae K. Im1* Affiliations: 1. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA. 2. Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL, USA. 3. Department of Biomedical Informatics, Department of Medicine, Vanderbilt University, Nashville, TN, USA. 4. Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. 5 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 6. Please see Supplementary Materials for full author list 7. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. * Correspondence to [email protected], [email protected] † Both authors contributed equally to this manuscript One-Sentence Summary: PhenomeXcan is a gene-based resource of gene-trait associations with biological context that supports translational research. 1 bioRxivbioRxiv preprint preprint doi: doi: https://doi.org/10.1101/833210 https://doi.org/10.1101/833210.
    [Show full text]
  • Spatial Maps of Prostate Cancer Transcriptomes Reveal an Unexplored Landscape of Heterogeneity
    ARTICLE DOI: 10.1038/s41467-018-04724-5 OPEN Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity Emelie Berglund1, Jonas Maaskola1, Niklas Schultz2, Stefanie Friedrich3, Maja Marklund1, Joseph Bergenstråhle1, Firas Tarish2, Anna Tanoglidi4, Sanja Vickovic 1, Ludvig Larsson1, Fredrik Salmeń1, Christoph Ogris3, Karolina Wallenborg2, Jens Lagergren5, Patrik Ståhl1, Erik Sonnhammer3, Thomas Helleday2 & Joakim Lundeberg 1 1234567890():,; Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor micro- environment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies. 1 Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden. 2 Department of Oncology-Pathology, Karolinska Institutet (KI), Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden. 3 Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Tomtebodavägen 23, Solna 17165, Sweden.
    [Show full text]
  • WO 2016/070129 Al 6 May 2016 (06.05.2016) W 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 2016/070129 Al 6 May 2016 (06.05.2016) W P O P C T (51) International Patent Classification: (74) Agent: BAKER, C , Hunter; Wolf, Greenfield & Sacks, A61K 9/00 (2006.01) C07K 14/435 (2006.01) P.C., 600 Atlantic Avenue, Boston, MA 02210-2206 (US). (21) International Application Number: (81) Designated States (unless otherwise indicated, for every PCT/US20 15/058479 kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, (22) International Filing Date: BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, 30 October 2015 (30.10.201 5) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, (25) Filing Language: English HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, (26) Publication Language: English MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, (30) Priority Data: PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, 14/529,010 30 October 2014 (30. 10.2014) US 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. (71) Applicant: PRESIDENT AND FELLOWS OF HAR¬ VARD COLLEGE [US/US]; 17 Quincy Street, Cam (84) Designated States (unless otherwise indicated, for every bridge, MA 02138 (US).
    [Show full text]
  • MOL #82305 TITLE PAGE Title: Induced CYP3A4 Expression In
    Downloaded from molpharm.aspetjournals.org at ASPET Journals on September 28, 2021 1 This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted.
    [Show full text]
  • Biallelic Variants in ASNA1, Encoding a Cytosolic Targeting Factor of Tail-Anchored Proteins, Cause Rapidly Progressive Pediatric Cardiomyopathy
    August22019 Circulation: Genomic and Precision Medicine ORIGINAL ARTICLE Biallelic Variants in ASNA1, Encoding a Cytosolic Targeting Factor of Tail-Anchored Proteins, Cause Rapidly Progressive Pediatric Cardiomyopathy BACKGROUND: Pediatric cardiomyopathies are a clinically and genetically Judith M.A. Verhagen, heterogeneous group of heart muscle disorders associated with high MD, PhD morbidity and mortality. Although knowledge of the genetic basis of et al pediatric cardiomyopathy has improved considerably, the underlying cause remains elusive in a substantial proportion of cases. METHODS: Exome sequencing was used to screen for the causative genetic defect in a pair of siblings with rapidly progressive dilated cardiomyopathy and death in early infancy. Protein expression was assessed in patient samples, followed by an in vitro tail-anchored protein insertion assay and functional analyses in zebrafish. Downloaded from http://ahajournals.org by on December 1, 2019 RESULTS: We identified compound heterozygous variants in the highly conserved ASNA1 gene (arsA arsenite transporter, ATP-binding, homolog), which encodes an ATPase required for post-translational membrane insertion of tail-anchored proteins. The c.913C>T variant on the paternal allele is predicted to result in a premature stop codon p.(Gln305*), and likely explains the decreased protein expression observed in myocardial tissue and skin fibroblasts. The c.488T>C variant on the maternal allele results in a valine to alanine substitution at residue 163 (p.Val163Ala). Functional studies showed that this variant leads to protein misfolding as well as less effective tail-anchored protein insertion. Loss of asna1 in zebrafish resulted in reduced cardiac contractility and early lethality. In contrast to wild-type mRNA, injection of either mutant mRNA failed to rescue this phenotype.
    [Show full text]
  • Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants
    Computational prediction of the pathogenic status of cancer-specific somatic variants By: Nikta Feizi A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba In partial fulfillment of the requirements of the degree of MASTER OF SCIENCE Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg, Manitoba, Canada Copyright © 2019 by Nikta Feizi 1 Abstract The ever-increasing shift in cancer genetic testing is accompanied by a demand for interpretation of the results. In-silico interpretation approaches are shown to be promising in increasing the clinical utilization of genetic tests by classifying variants into well-defined pathogenic and non-pathogenic clinical groups. Current prediction tools are mostly trained based on the characteristics of germ-line variants and utilized for classifying both gremline and somatic variants, which may result in biased predictions in the event of classifying somatic variants. Considering the critical role of somatic variants in cancer occurrence and progression establishing cancer specific prediction tools which are designed solely based on the characteristics of somatic variants is of high importance. To this aim, we established a gold standard dataset exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer (COSMIC). To label the pathogenic (positive) variants we introduced a bi-dimensional recurrence score spanning both the frequency of a given mutation across a dataset and the number of cancer types the mutation affects. To label the non-pathogenic (negative) variants we collected the somatic SNVs with the minor allele frequency of equal or greater than 1% in at least one of 26 populations from 1000 Genomes project that are also defined in COSMIC.
    [Show full text]
  • Downloaded from Here
    bioRxiv preprint doi: https://doi.org/10.1101/017566; this version posted April 6, 2015. 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. 1 1 Testing for ancient selection using cross-population allele 2 frequency differentiation 1;∗ 3 Fernando Racimo 4 1 Department of Integrative Biology, University of California, Berkeley, CA, USA 5 ∗ E-mail: [email protected] 6 1 Abstract 7 A powerful way to detect selection in a population is by modeling local allele frequency changes in a 8 particular region of the genome under scenarios of selection and neutrality, and finding which model is 9 most compatible with the data. Chen et al. [1] developed a composite likelihood method called XP-CLR 10 that uses an outgroup population to detect departures from neutrality which could be compatible with 11 hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive to recent 12 selection and may miss selective events that happened a long time ago. To overcome this, we developed 13 an extension of XP-CLR that jointly models the behavior of a selected allele in a three-population three. 14 Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that occurred before two 15 populations split from each other, and can distinguish between those events and events that occurred 16 specifically in each of the populations after the split.
    [Show full text]