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Thesis Reference
Thesis A kinome-wide RNAi screen to identify genes controlling membrane lipid homeostasis in human cells GEHIN, Charlotte Abstract The control of lipid homeostasis is a fundamental process that allows cells to maintain the unique lipid composition of their membrane compartments and to deal with the energetic fluxes from metabolism. If most of enzymes involved in lipid metabolism are characterized, the question of the genetic control of lipid homeostasis is still outstanding. In order to find genes that control the homeostasis of membrane lipids, I combined a large-scale RNAi screen targeting the human knome with the techniques of targeted lipidomic analysis by mass spectrometry to monitor lipid changes in HeLa cells. Data analysis of the screen allowed the characterization of candidate genes involved in the control of membrane lipid homeostasis. In parallel, in the context of the NCCR Chemical Biology, I developed a robotically-assisted siRNA transfection assay and screened a library of chemicals potentially able to transfect siRNA in Human cells at least as efficiently than commercially available compounds. Reference GEHIN, Charlotte. A kinome-wide RNAi screen to identify genes controlling membrane lipid homeostasis in human cells. Thèse de doctorat : Univ. Genève, 2014, no. Sc. 4670 URN : urn:nbn:ch:unige-380353 DOI : 10.13097/archive-ouverte/unige:38035 Available at: http://archive-ouverte.unige.ch/unige:38035 Disclaimer: layout of this document may differ from the published version. 1 / 1 UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES -
Genome-Wide Association and Gene Enrichment Analyses of Meat Sensory Traits in a Crossbred Brahman-Angus
Proceedings of the World Congress on Genetics Applied to Livestock Production, 11. 124 Genome-wide association and gene enrichment analyses of meat tenderness in an Angus-Brahman cattle population J.D. Leal-Gutíerrez1, M.A. Elzo1, D. Johnson1 & R.G. Mateescu1 1 University of Florida, Department of Animal Sciences, 2250 Shealy Dr, 32608 Gainesville, Florida, United States. [email protected] Summary The objective of this study was to identify genomic regions associated with meat tenderness related traits using a whole-genome scan approach followed by a gene enrichment analysis. Warner-Bratzler shear force (WBSF) was measured on 673 steaks, and tenderness and connective tissue were assessed by a sensory panel on 496 steaks. Animals belong to the multibreed Angus-Brahman herd from University of Florida and range from 100% Angus to 100% Brahman. All animals were genotyped with the Bovine GGP F250 array. Gene enrichment was identified in two pathways; the first pathway is involved in negative regulation of transcription from RNA polymerase II, and the second pathway groups several cellular component of the endoplasmic reticulum membrane. Keywords: tenderness, gene enrichment, regulation of transcription, cell growth, cell proliferation Introduction Identification of quantitative trait loci (QTL) for any complex trait, including meat tenderness, is the first most important step in the process of understanding the genetic architecture underlying the phenotype. Given a large enough population and a dense coverage of the genome, a genome-wide association study (GWAS) is usually successful in uncovering major genes and QTLs with large and medium effect on these type of traits. Several GWA studies on Bos indicus (Magalhães et al., 2016; Tizioto et al., 2013) or crossbred beef cattle breeds (Bolormaa et al., 2011b; Hulsman Hanna et al., 2014; Lu et al., 2013) were successful at identifying QTL for meat tenderness; and most of them include the traditional candidate genes µ-calpain and calpastatin. -
Hereditary Spastic Paraplegia: from Genes, Cells and Networks to Novel Pathways for Drug Discovery
brain sciences Review Hereditary Spastic Paraplegia: From Genes, Cells and Networks to Novel Pathways for Drug Discovery Alan Mackay-Sim Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia; a.mackay-sim@griffith.edu.au Abstract: Hereditary spastic paraplegia (HSP) is a diverse group of Mendelian genetic disorders affect- ing the upper motor neurons, specifically degeneration of their distal axons in the corticospinal tract. Currently, there are 80 genes or genomic loci (genomic regions for which the causative gene has not been identified) associated with HSP diagnosis. HSP is therefore genetically very heterogeneous. Finding treatments for the HSPs is a daunting task: a rare disease made rarer by so many causative genes and many potential mutations in those genes in individual patients. Personalized medicine through genetic correction may be possible, but impractical as a generalized treatment strategy. The ideal treatments would be small molecules that are effective for people with different causative mutations. This requires identification of disease-associated cell dysfunctions shared across geno- types despite the large number of HSP genes that suggest a wide diversity of molecular and cellular mechanisms. This review highlights the shared dysfunctional phenotypes in patient-derived cells from patients with different causative mutations and uses bioinformatic analyses of the HSP genes to identify novel cell functions as potential targets for future drug treatments for multiple genotypes. Keywords: neurodegeneration; motor neuron disease; spastic paraplegia; endoplasmic reticulum; Citation: Mackay-Sim, A. Hereditary protein-protein interaction network Spastic Paraplegia: From Genes, Cells and Networks to Novel Pathways for Drug Discovery. Brain Sci. 2021, 11, 403. -
Mouse Tmtc2 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Tmtc2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Tmtc2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Tmtc2 gene (NCBI Reference Sequence: NM_177368 ; Ensembl: ENSMUSG00000036019 ) is located on Mouse chromosome 10. 12 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 12 (Transcript: ENSMUST00000061506). Exon 2 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Tmtc2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-34F6 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: Exon 2 starts from about 3.35% of the coding region. The knockout of Exon 2 will result in frameshift of the gene. The size of intron 1 for 5'-loxP site insertion: 159879 bp, and the size of intron 2 for 3'-loxP site insertion: 42438 bp. The size of effective cKO region: ~1071 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 2 12 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Tmtc2 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. -
Discovery of an O-Mannosylation Pathway Selectively Serving Cadherins and Protocadherins
Discovery of an O-mannosylation pathway selectively serving cadherins and protocadherins Ida Signe Bohse Larsena,1, Yoshiki Narimatsua,1, Hiren Jitendra Joshia,1, Lina Siukstaitea, Oliver J. Harrisonb, Julia Braschb, Kerry M. Goodmanb, Lars Hansena, Lawrence Shapirob,c,d, Barry Honigb,c,d,e, Sergey Y. Vakhrusheva, Henrik Clausena, and Adnan Halima,2,3 aDepartment of Cellular and Molecular Medicine, Faculty of Health Sciences, Copenhagen Center for Glycomics, University of Copenhagen, DK-2200 Copenhagen, Denmark; bDepartment of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032; cZuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032; dDepartment of Systems Biology, Columbia University, New York, NY 10032; and eHoward Hughes Medical Institute, Columbia University, New York, NY 10032 Edited by Stuart A. Kornfeld, Washington University School of Medicine, St. Louis, MO, and approved September 6, 2017 (received for review May 22, 2017) The cadherin (cdh) superfamily of adhesion molecules carry muscular dystrophies that have been designated α-dystroglyca- O-linked mannose (O-Man) glycans at highly conserved sites nopathies because deficient O-Man glycosylation of α-DG dis- localized to specific β-strands of their extracellular cdh (EC) domains. rupts the interaction between the dystrophin glycoprotein complex These O-Man glycans do not appear to be elongated like O-Man and the ECM (7–9). Several studies have also implicated de- glycans found on α-dystroglycan (α-DG), and we recently demon- ficiency of POMT2 with E-cdh dysfunction (10–12), although di- strated that initiation of cdh/protocadherin (pcdh) O-Man glycosyl- rect evidence for a role in glycosylation of cdhs and pcdhs is ation is not dependent on the evolutionary conserved POMT1/ missing. -
Novel Extensions of Label Propagation for Biomarker Discovery in Genomic Data
NOVEL EXTENSIONS OF LABEL PROPAGATION FOR BIOMARKER DISCOVERY IN GENOMIC DATA by Matthew E. Stokes B.S. Systems and Control Engineering, Case Western Reserve University, 2008 M.S. Intelligent Systems Program / Biomedical Informatics, University of Pittsburgh 2011 Submitted to the Graduate Faculty of the Kenneth P. Dietrich School of Arts and Sciences in fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2014 UNIVERSITY OF PITTSBURGH DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation proposal was presented by Matthew E. Stokes on July 17, 2014 and approved by M. Michael Barmada, PhD, Department of Human Genetics Gregory F. Cooper, MD, PhD, Department of Biomedical Informatics and the Intelligent Systems Program Milos Hauskrecht, PhD, Department of Computer Science and the Intelligent Systems Program Dissertation Advisor: Shyam Visweswaran, MD, PhD, Department of Biomedical Informatics and the Intelligent Systems Program ii NOVEL EXTENSIONS OF LABEL PORPAGATION FOR BIOMARKER DISCOVERY IN GENOMIC DATA Matthew E. Stokes, M.S University of Pittsburgh, 2014 Copyright © by Matthew E. Stokes 2014 iii NOVEL EXTENSIONS OF LABEL PROPAGATION FOR BIOMARKER DISCOVERY IN GENOMIC DATA Matthew E. Stokes, PhD University of Pittsburgh, 2014 One primary goal of analyzing genomic data is the identification of biomarkers which may be causative of, correlated with, or otherwise biologically relevant to disease phenotypes. In this work, I implement and extend a multivariate feature ranking algorithm called label propagation (LP) for biomarker discovery in genome-wide single-nucleotide polymorphism (SNP) data. This graph-based algorithm utilizes an iterative propagation method to efficiently compute the strength of association between a SNP and a phenotype. -
Download Ppis for Each Single Seed, Thus Obtaining Each Seed’S Interactome (Ferrari Et Al., 2018)
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.14.425874; this version posted January 16, 2021. 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. Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias Nikoleta Vavouraki1,2, James E. Tomkins1, Eleanna Kara3, Henry Houlden3, John Hardy4, Marcus J. Tindall2,5, Patrick A. Lewis1,4,6, Claudia Manzoni1,7* Author Affiliations 1: Department of Pharmacy, University of Reading, Reading, RG6 6AH, United Kingdom 2: Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AH, United Kingdom 3: Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 4: Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 5: Institute of Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AS, United Kingdom 6: Department of Comparative Biomedical Sciences, Royal Veterinary College, London, NW1 0TU, United Kingdom 7: School of Pharmacy, University College London, London, WC1N 1AX, United Kingdom *Corresponding author: [email protected] Abstract The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein- protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. -
TMTC2 (G-15): Sc-169651
SAN TA C RUZ BI OTEC HNOL OG Y, INC . TMTC2 (G-15): sc-169651 BACKGROUND SOURCE The tetratricopeptide repeat (TPR) motif is a degenerate, 34 amino acid TMTC2 (G-15) is an affinity purified goat polyclonal antibody raised against sequence found in many proteins and acts to mediate protein-protein inter - a peptide mapping within an internal region of TMTC2 of human origin. actions in various pathways. At the sequence level, there can be up to 16 tandem TPR repeats, each of which has a helix-turn-helix shape that stacks PRODUCT on other TPR repeats to achieve ligand binding specificity. TMTC2 (trans - Each vial contains 200 µg IgG in 1.0 ml of PBS with < 0.1% sodium azide membrane and tetratricopeptide repeat containing 2) is an 836 amino acid and 0.1% gelatin. multi-pass membrane protein that contains ten TPR repeats and belongs to the TMTC family. The gene encoding TMTC2 maps to human chromosome 12, Blocking peptide available for competition studies, sc-169651 P, (100 µg which encodes over 1,100 genes and comprises approximately 4.5% of the peptide in 0.5 ml PBS containing < 0.1% sodium azide and 0.2% BSA). human genome. Chromosome 12 is associated with a variety of diseases and afflictions, including hypochondrogenesis, achondrogenesis, Kniest dysplasia, APPLICATIONS Noonan syndrome and trisomy 12p, which causes facial developmental TMTC2 (G-15) is recommended for detection of TMTC2 of mouse, rat and defects and seizure disorders. human origin by Western Blotting (starting dilution 1:200, dilution range 1:100-1:1000), immunofluorescence (starting dilution 1:50, dilution range REFERENCES 1:50-1:500) and solid phase ELISA (starting dilution 1:30, dilution range 1. -
Meta-Analysis Identifies Seven Susceptibility Loci Involved in the Atopic March
ARTICLE Received 20 Jul 2015 | Accepted 6 Oct 2015 | Published 6 Nov 2015 DOI: 10.1038/ncomms9804 OPEN Meta-analysis identifies seven susceptibility loci involved in the atopic march Ingo Marenholz et al.# Eczema often precedes the development of asthma in a disease course called the ‘atopic march’. To unravel the genes underlying this characteristic pattern of allergic disease, we conduct a multi-stage genome-wide association study on infantile eczema followed by childhood asthma in 12 populations including 2,428 cases and 17,034 controls. Here we report two novel loci specific for the combined eczema plus asthma phenotype, which are associated with allergic disease for the first time; rs9357733 located in EFHC1 on chromo- some 6p12.3 (OR 1.27; P ¼ 2.1 Â 10 À 8) and rs993226 between TMTC2 and SLC6A15 on chromosome 12q21.3 (OR 1.58; P ¼ 5.3 Â 10 À 9). Additional susceptibility loci identified at genome-wide significance are FLG (1q21.3), IL4/KIF3A (5q31.1), AP5B1/OVOL1 (11q13.1), C11orf30/LRRC32 (11q13.5) and IKZF3 (17q21). We show that predominantly eczema loci increase the risk for the atopic march. Our findings suggest that eczema may play an important role in the development of asthma after eczema. Correspondence and requests for materials should be addressed to Y.A.L. (email: [email protected]). #A full list of authors and their affiliations appears at the end of the paper. NATURE COMMUNICATIONS | 6:8804 | DOI: 10.1038/ncomms9804 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9804 he atopic or allergic march describes the sequential located in the same region, we selected the best SNP per 1-Mb progression of different allergic conditions frequently window. -
Content Based Search in Gene Expression Databases and a Meta-Analysis of Host Responses to Infection
Content Based Search in Gene Expression Databases and a Meta-analysis of Host Responses to Infection A Thesis Submitted to the Faculty of Drexel University by Francis X. Bell in partial fulfillment of the requirements for the degree of Doctor of Philosophy November 2015 c Copyright 2015 Francis X. Bell. All Rights Reserved. ii Acknowledgments I would like to acknowledge and thank my advisor, Dr. Ahmet Sacan. Without his advice, support, and patience I would not have been able to accomplish all that I have. I would also like to thank my committee members and the Biomed Faculty that have guided me. I would like to give a special thanks for the members of the bioinformatics lab, in particular the members of the Sacan lab: Rehman Qureshi, Daisy Heng Yang, April Chunyu Zhao, and Yiqian Zhou. Thank you for creating a pleasant and friendly environment in the lab. I give the members of my family my sincerest gratitude for all that they have done for me. I cannot begin to repay my parents for their sacrifices. I am eternally grateful for everything they have done. The support of my sisters and their encouragement gave me the strength to persevere to the end. iii Table of Contents LIST OF TABLES.......................................................................... vii LIST OF FIGURES ........................................................................ xiv ABSTRACT ................................................................................ xvii 1. A BRIEF INTRODUCTION TO GENE EXPRESSION............................. 1 1.1 Central Dogma of Molecular Biology........................................... 1 1.1.1 Basic Transfers .......................................................... 1 1.1.2 Uncommon Transfers ................................................... 3 1.2 Gene Expression ................................................................. 4 1.2.1 Estimating Gene Expression ............................................ 4 1.2.2 DNA Microarrays ...................................................... -
Differential Methylation of the TRPA1 Promoter in Pain Sensitivity
ARTICLE Received 21 Nov 2012 | Accepted 21 Nov 2013 | Published 4 Feb 2014 DOI: 10.1038/ncomms3978 OPEN Differential methylation of the TRPA1 promoter in pain sensitivity J.T. Bell1,2, A.K. Loomis3, L.M. Butcher4,F.Gao5, B. Zhang3, C.L. Hyde3, J. Sun5,H.Wu5, K. Ward1, J. Harris1, S. Scollen6, M.N. Davies1,7, L.C. Schalkwyk7, J. Mill7,8, The MuTHER Consortium*, F.M.K. Williams1,N.Li5, P. Deloukas9,10,11, S. Beck4, S.B. McMahon12, J. Wang5,11,13,14, S.L. John3, T.D. Spector1, Chronic pain is a global public health problem, but the underlying molecular mechanisms are not fully understood. Here we examine genome-wide DNA methylation, first in 50 identical twins discordant for heat pain sensitivity and then in 50 further unrelated individuals. Whole- blood DNA methylation was characterized at 5.2 million loci by MeDIP sequencing and assessed longitudinally to identify differentially methylated regions associated with high or low pain sensitivity (pain DMRs). Nine meta-analysis pain DMRs show robust evidence for association (false discovery rate 5%) with the strongest signal in the pain gene TRPA1 (P ¼ 1.2 Â 10 À 13). Several pain DMRs show longitudinal stability consistent with susceptibility effects, have similar methylation levels in the brain and altered expression in the skin. Our approach identifies epigenetic changes in both novel and established candidate genes that provide molecular insights into pain and may generalize to other complex traits. 1 Department of Twin Research and Genetics Epidemiology, Kings College London, London SE1 7EH, UK. 2 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. -
393LN V 393P 344SQ V 393P Probe Set Entrez Gene
393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393