Somatic Mutations in Early Onset Luminal Breast Cancer
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Mouse Shcbp1 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Shcbp1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Shcbp1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Shcbp1 gene (NCBI Reference Sequence: NM_011369 ; Ensembl: ENSMUSG00000022322 ) is located on Mouse chromosome 8. 13 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 13 (Transcript: ENSMUST00000022945). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Shcbp1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP24-78D8 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a knock-out allele exhibit normal viability, fertility and T cell development but show decreased susceptibility to experimental autoimmune encephalomyelitis. Exon 4 starts from about 19.36% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 2817 bp, and the size of intron 4 for 3'-loxP site insertion: 10568 bp. The size of effective cKO region: ~709 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 4 13 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Shcbp1 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Psychostimulant-Regulated Plasticity in Interneurons of the Nucleus Accumbens
Psychostimulant-Regulated Plasticity in Interneurons of the Nucleus Accumbens by David A. Gallegos Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Anne E. West, Supervisor ___________________________ Jorg Grandl ___________________________ Debra Silver ___________________________ Gregory Crawford ___________________________ Hiro Matsunami Psychostimulant-Regulated Epigenetic Plasticity in Interneurons of the Nucleus Accumbens submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2019 ABSTRACT Psychostimulant-Regulated Epigenetic Plasticity in Interneurons of the Nucleus Accumbens by David A. Gallegos Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Anne E. West, Supervisor ___________________________ Jorg Grandl ___________________________ Debra Silver ___________________________ Gregory Crawford ___________________________ Hiro Matsunami An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2019 Copyright by David Andres Gallegos 2019 Abstract Exposure to psychostimulant drugs of abuse exerts lasting influences on brain function via the regulation of immediate and persistent gene transcription. These changes in gene transcription drive the development of addictive-like -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Focus on the Small Heat Shock Protein HSPB1 Autofagie in De Erfelij
Faculteit Faculteit Farmaceutische, Biomedische en Diergeneeskundige wetenschappen Biochemie en Biotechnologie Autophagy in inherited peripheral neuropathies: Focus on the small heat shock protein HSPB1 Autofagie in de erfelijke perifere neuropathieën: Focus op de kleine heat shock proteïne HSPB1 Proefschrift voorgelegd tot het behalen van de graad van Doctor in de Wetenschappen: Biochemie en Biotechnologie aan de Universiteit Antwerpen. te verdedigen door Mansour HAIDAR Promotor Prof. Dr. Vincent Timmerman Antwerpen, 2018 1 2 “Haud igitur redit ad Nihilum res ulla, sed omnes Discidio redeunt in corpora materiai” Lucretius, De Rerum Natura, Book I. 250 3 4 Members of the jury Chair Prof. Dr. Wim Vanden Berghe, PhD (UA, Antwerp, Belgium) Promotor Prof. Dr. Vincent Timmerman, PhD (UA, Antwerp, Belgium) Internal jury member Prof. Dr. Wim Martinet, PhD (UA, Antwerp, Belgium) External jury members Prof. Dr. Joy Irobi (UHasselt, Hasselt, Belgium) Prof. Dr. Maurizio D’Antonio (San Raffaele Institute, Milan, Italy) Prof. Dr. Ir. Winnok De Vos (UA, Antwerp, Belgium) 5 6 Table of Contents Summary/Samenvatting 9 Rationale and Aims 13 Introduction Chapter 1 Autophagy as an emerging common pathomechanism in inherited 15 peripheral neuropathies Chapter 2 Small heat shock proteins: Their role in proteostasis 79 and neurodegeneration Results Chapter 3 HSPB1 is required for Autophagy: Insights from CMT-causing mutations 103 Chapter 4 An interactomics study of HSPB1 wild-type and mutant links it to the 129 autophagy receptor P62 Discussion 179 List of abbreviations 195 Curriculum Vitae 199 Acknowledgements 203 7 8 Summary Inherited peripheral neuropathies (IPNs) are genetically heterogeneous disorders affecting mainly the peripheral nervous system and with over 1500 mutations in more than 80 affected genes discovered so far. -
Identify Key Genes and Pathways in Pancreatic Ductal Adenocarcinoma Via Bioinformatics Analysis
Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis Huatian Luo Fujian Medical University https://orcid.org/0000-0002-6505-825X Da-qiu Chen Fujian Medical University Jing-jing Pan Fujian Medical University Zhang-wei Wu Fujian Medical University Can Yang Fujian Medical University Hao-jie Xu Fujian Medical University Shang-hua Xu Fujian Medical University Hong-da Cai Fujian Medical University Shi Chen ( [email protected] ) Fujian Provincial Hospital Yao-dong Wang ( [email protected] ) Fujian Medical University Research article Keywords: Bioinformatical analysis, Microarray, Pancreatic cancer, Differentially expressed gene Posted Date: April 7th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-19826/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/14 Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression proles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Proling Interactive Analysis (GEPIA) was performed. -
Essential Genes and Their Role in Autism Spectrum Disorder
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Essential Genes And Their Role In Autism Spectrum Disorder Xiao Ji University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, and the Genetics Commons Recommended Citation Ji, Xiao, "Essential Genes And Their Role In Autism Spectrum Disorder" (2017). Publicly Accessible Penn Dissertations. 2369. https://repository.upenn.edu/edissertations/2369 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2369 For more information, please contact [email protected]. Essential Genes And Their Role In Autism Spectrum Disorder Abstract Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific oler in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
CSE642 Final Version
Eindhoven University of Technology MASTER Dimensionality reduction of gene expression data Arts, S. Award date: 2018 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Eindhoven University of Technology MASTER THESIS Dimensionality Reduction of Gene Expression Data Author: S. (Sako) Arts Daily Supervisor: dr. V. (Vlado) Menkovski Graduation Committee: dr. V. (Vlado) Menkovski dr. D.C. (Decebal) Mocanu dr. N. (Nikolay) Yakovets May 16, 2018 v1.0 Abstract The focus of this thesis is dimensionality reduction of gene expression data. I propose and test a framework that deploys linear prediction algorithms resulting in a reduced set of selected genes relevant to a specified case. Abstract In cancer research there is a large need to automate parts of the process of diagnosis, this is mainly to reduce cost, make it faster and more accurate. -
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 -
GWAS for Meat and Carcass Traits
G3: Genes|Genomes|Genetics Early Online, published on July 11, 2019 as doi:10.1534/g3.119.400452 GWAS for meat and carcass traits using imputed sequence level genotypes in pooled F2- designs in pigs Clemens Falker-Gieske,*,1,2 Iulia Blaj,†,2 Siegfried Preuß,‡ Jörn Bennewitz,‡ Georg Thaller,† Jens Tetens*,§ *Department of Animal Sciences, Georg-August-University, 37077 Göttingen, Germany. †Institute of Animal Breeding and Husbandry, Kiel University, 24118 Kiel, Germany. ‡Institute of Animal Husbandry and Breeding, University of Hohenheim, 70599 Stuttgart, Germany. §Center for Integrated Breeding Research, Georg-August-University, 37077 Göttingen, Germany. 1 © The Author(s) 2013. Published by the Genetics Society of America. Running title: Sequence level GWAS in pooled F2 pigs Keywords Genome wide association study Whole genome sequencing Imputation Meat, carcass, and production traits Variant calling 1Corresponding author: Clemens Falker-Gieske, Georg-August-University Goettingen, Department of Animal Sciences, Division Functional Breeding, Burckhardtweg 2, 37077 Goettingen, (+49) 551-39-23669 2contributed equally. 2 ABSTRACT In order to gain insight into the genetic architecture of economically important traits in pigs and to derive suitable genetic markers to improve these traits in breeding programs, many studies have been conducted to map quantitative trait loci. Shortcomings of these studies were low mapping resolution, large confidence intervals for quantitative trait loci-positions and large linkage disequilibrium blocks. Here, we overcome these shortcomings by pooling four large F2 designs to produce smaller linkage disequilibrium blocks and by resequencing the founder generation at high coverage and the F1 generation at low coverage for subsequent imputation of the F2 generation to whole genome sequencing marker density.