Primepcr™Assay Validation Report
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Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Proteomics of Lipid Accumulation and DGAT Inhibition in Hepg2 Liver Carcinoma Cells
Proteomics of lipid accumulation and DGAT inhibition in HepG2 liver carcinoma cells. By Bhumika Bhatt-Wessel A thesis submitted to Victoria University of Wellington in fulfilment of the requirement for the degree of Doctor of Philosophy In Cell and Molecular Biology. Victoria University of Wellington 2017. i ABSTRACT Non-alcoholic fatty liver disease (NAFLD) is a manifestation of the metabolic syndrome in the liver. It is marked by hepatocyte accumulation of triacylglycerol (TAG) rich lipid droplets. In some patients, the disease progresses to non-alcoholic steatohepatitis (NASH), characterized by cellular damage, inflammation and fibrosis. In some cases, cirrhosis and liver failure may occur. However, the pathogenesis of NAFLD is still unclear. The present project is based on the hypothesis that hepatocytes are equipped with mechanisms that allow them to manage lipid accumulation to a certain extent. Continued or increased lipid accumulation beyond this triggers molecular mechanisms such as oxidative stress, lipid peroxidation and cell death that aggravate the condition and cause disease progression. The aim of this project is to study the effects of lipid accumulation on the cells using proteomics approach to identify proteins involved in the disease progression. A cell culture model was used in the study. HepG2 cells, a human liver carcinoma cell line, were treated with a mixture of fatty acids (FA) to induce lipid accumulation. The lipid accumulation in HepG2 cells was measured with Oil red O assay and the effect of lipid accumulation on the proliferation of the cells was measured using an MTT cell proliferation assay. HepG2 cells treated with 1 mM FA mixture for 6 hours induced lipid accumulation 1.4 times of control with 90% of cell proliferation capacity of the control cells. -
As a Risk Gene for Schizophrenia X Chen Et Al
Molecular Psychiatry (2011) 16, 1117–1129 & 2011 Macmillan Publishers Limited All rights reserved 1359-4184/11 www.nature.com/mp ORIGINAL ARTICLE GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5)asa risk gene for schizophrenia X Chen1,2, G Lee1, BS Maher1, AH Fanous1,3,4, J Chen1, Z Zhao5, A Guo5, E van den Oord1,6, PF Sullivan7, J Shi8, DF Levinson8, PV Gejman9, A Sanders9, J Duan9, MJ Owen10, NJ Craddock10, MC O’Donovan10, J Blackman10, D Lewis10, GK Kirov10, W Qin11, S Schwab11, D Wildenauer11, K Chowdari12, V Nimgaonkar12, RE Straub13, DR Weinberger13, FA O’Neill14, D Walsh15, M Bronstein16, A Darvasi16, T Lencz17, AK Malhotra17, D Rujescu18, I Giegling18, T Werge19, T Hansen19, A Ingason19, MM No¨ethen20, M Rietschel21,22, S Cichon20,23, S Djurovic24, OA Andreassen24, RM Cantor25, R Ophoff 25,26, A Corvin27, DW Morris27, M Gill27, CN Pato28, MT Pato28, A Macedo29, HMD Gurling30, A McQuillin30, J Pimm30, C Hultman31, P Lichtenstein31, P Sklar32,33, SM Purcell32,33, E Scolnick32,33, D St Clair 34, DHR Blackwood35 and KS Kendler1,2 and the GROUP investigators, the International Schizophrenia Consortium36 1Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; 2Department of Molecular and Human Genetics, Virginia Commonwealth University, Richmond, VA, USA; 3Washington VA Medical Center, Washington, DC, USA; 4Department of Psychiatry, Georgetown University School of Medicine, Washington, DC, USA; 5Departments -
Strong Synaptic Transmission Impact by Copy Number Variations in Schizophrenia
Strong synaptic transmission impact by copy number variations in schizophrenia Joseph T. Glessnera, Muredach P. Reillyb, Cecilia E. Kima, Nagahide Takahashic, Anthony Albanoa, Cuiping Houa, Jonathan P. Bradfielda, Haitao Zhanga, Patrick M. A. Sleimana, James H. Florya, Marcin Imielinskia, Edward C. Frackeltona, Rosetta Chiavaccia, Kelly A. Thomasa, Maria Garrisa, Frederick G. Otienoa, Michael Davidsond, Mark Weiserd, Abraham Reichenberge, Kenneth L. Davisc,JosephI.Friedmanc, Thomas P. Cappolab, Kenneth B. Marguliesb, Daniel J. Raderb, Struan F. A. Granta,f,g, Joseph D. Buxbaumc, Raquel E. Gurh, and Hakon Hakonarsona,f,g,1 aCenter for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104; bPenn Cardiovascular Institute, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; cConte Center for the Neuroscience of Mental Disorders and Department of Psychiatry, Mount Sinai School of Medicine, New York, NY, 10029; dSheba Medical Center, Tel Hashomer, 52621, Israel; eMount Sinai and Institute of Psychiatry, King’s College, London, SE5 8AF, United Kingdom; fDivision of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104; gDepartment of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; and hSchizophrenia Center, Neuropsychiatry Division, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 Edited by James R. Lupski, Baylor College of Medicine, Houston, TX, and accepted by the Editorial Board April 13, 2010 (received for review January 7, 2010) Schizophrenia is a psychiatric disorder with onset in late adoles- contribute to the complex etiology underlying various psychiatric cence and unclear etiology characterized by both positive and ne- and neurodevelopmental disorders (13, 14). Whereas rare recurrent gative symptoms, as well as cognitive deficits. -
Functional Dependency Analysis Identifies Potential Druggable
cancers Article Functional Dependency Analysis Identifies Potential Druggable Targets in Acute Myeloid Leukemia 1, 1, 2 3 Yujia Zhou y , Gregory P. Takacs y , Jatinder K. Lamba , Christopher Vulpe and Christopher R. Cogle 1,* 1 Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL 32610-0278, USA; yzhou1996@ufl.edu (Y.Z.); gtakacs@ufl.edu (G.P.T.) 2 Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610-0278, USA; [email protected]fl.edu 3 Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610-0278, USA; cvulpe@ufl.edu * Correspondence: [email protected]fl.edu; Tel.: +1-(352)-273-7493; Fax: +1-(352)-273-5006 Authors contributed equally. y Received: 3 November 2020; Accepted: 7 December 2020; Published: 10 December 2020 Simple Summary: New drugs are needed for treating acute myeloid leukemia (AML). We analyzed data from genome-edited leukemia cells to identify druggable targets. These targets were necessary for AML cell survival and had favorable binding sites for drug development. Two lists of genes are provided for target validation, drug discovery, and drug development. The deKO list contains gene-targets with existing compounds in development. The disKO list contains gene-targets without existing compounds yet and represent novel targets for drug discovery. Abstract: Refractory disease is a major challenge in treating patients with acute myeloid leukemia (AML). Whereas the armamentarium has expanded in the past few years for treating AML, long-term survival outcomes have yet to be proven. To further expand the arsenal for treating AML, we searched for druggable gene targets in AML by analyzing screening data from a lentiviral-based genome-wide pooled CRISPR-Cas9 library and gene knockout (KO) dependency scores in 15 AML cell lines (HEL, MV411, OCIAML2, THP1, NOMO1, EOL1, KASUMI1, NB4, OCIAML3, MOLM13, TF1, U937, F36P, AML193, P31FUJ). -
A SARS-Cov-2 Protein Interaction Map Reveals Targets for Drug Repurposing
Article A SARS-CoV-2 protein interaction map reveals targets for drug repurposing https://doi.org/10.1038/s41586-020-2286-9 A list of authors and affiliations appears at the end of the paper Received: 23 March 2020 Accepted: 22 April 2020 A newly described coronavirus named severe acute respiratory syndrome Published online: 30 April 2020 coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than Check for updates 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efcacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and eforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identifed the human proteins that physically associated with each of the SARS-CoV-2 proteins using afnity-purifcation mass spectrometry, identifying 332 high-confdence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. -
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 -
Network-Based Analysis of Key Regulatory Genes Implicated in Type
www.nature.com/scientificreports OPEN Network‑based analysis of key regulatory genes implicated in Type 2 Diabetes Mellitus and Recurrent Miscarriages in Turner Syndrome Anam Farooqui1, Alaa Alhazmi2, Shaful Haque3, Naaila Tamkeen4, Mahboubeh Mehmankhah1, Safa Tazyeen1, Sher Ali5 & Romana Ishrat1* The information on the genotype–phenotype relationship in Turner Syndrome (TS) is inadequate because very few specifc candidate genes are linked to its clinical features. We used the microarray data of TS to identify the key regulatory genes implicated with TS through a network approach. The causative factors of two common co‑morbidities, Type 2 Diabetes Mellitus (T2DM) and Recurrent Miscarriages (RM), in the Turner population, are expected to be diferent from that of the general population. Through microarray analysis, we identifed nine signature genes of T2DM and three signature genes of RM in TS. The power‑law distribution analysis showed that the TS network carries scale‑free hierarchical fractal attributes. Through local‑community‑paradigm (LCP) estimation we fnd that a strong LCP is also maintained which means that networks are dynamic and heterogeneous. We identifed nine key regulators which serve as the backbone of the TS network. Furthermore, we recognized eight interologs functional in seven diferent organisms from lower to higher levels. Overall, these results ofer few key regulators and essential genes that we envisage have potential as therapeutic targets for the TS in the future and the animal models studied here may prove useful in the validation of such targets. Te medical systems and scientists throughout the world are under an unprecedented challenge to meet the medical needs of much of the world’s population that are sufering from chromosomal anomalies. -
Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility. -
Individual Functions and Substrate Specificities of Importin Alpha
From the Max-Delbrück-Center for Molecular Medicine Berlin Head of the research group: Prof. Dr. M. Bader “Individual functions and substrate specificities of importin α subtypes” Dissertation for Fulfillment of Requirements for the Doctoral Degree of the University of Lübeck from the Department of Natural Sciences Submitted by Stefanie Hügel from Hennigsdorf Lübeck 2013 First referee: Prof. Dr. M. Bader Second referee: Prof. Dr. N. Tautz Date of oral examination: 21.02.13 Approved for printing. Lübeck, 22.02.2013 2 1 TABLE OF CONTENTS 1 TABLE OF CONTENTS _______________________________________________________ 3 2 ABSTRACT _______________________________________________________________ 6 3 ZUSAMMENFASSUNG ______________________________________________________ 7 4 INTRODUCTION ___________________________________________________________ 9 4.1 Nucleocytoplasmic protein transport ____________________________________________ 9 4.1.1 The nuclear pore complex – gateway to the nucleus ______________________________________ 10 4.1.1 Transport factors __________________________________________________________________ 11 4.1.2 Nothing happens without RanGTP _____________________________________________________ 12 4.2 The classical nuclear protein import pathway ____________________________________ 13 4.2.1 Structure and function of importin α ___________________________________________________ 13 4.2.2 Importin α:β mediated nuclear protein import___________________________________________ 15 4.2.3 The importin α protein family ________________________________________________________ -
Bioinformatic Analysis of Chicken Chemokines
BIOINFORMATIC ANALYSIS OF CHICKEN CHEMOKINES, CHEMOKINE RECEPTORS, AND TOLL-LIKE RECEPTOR 21 A Thesis by JIXIN WANG Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE August 2006 Major Subject: Poultry Science BIOINFORMATIC ANALYSIS OF CHICKEN CHEMOKINES, CHEMOKINE RECEPTORS, AND TOLL-LIKE RECEPTOR 21 A Thesis by JIXIN WANG Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved by: Co-Chairs of Committee, James J. Zhu Luc R. Berghman Committee Member, David L. Adelson Head of Department, Alan R. Sams August 2006 Major Subject: Poultry Science iii ABSTRACT Bioinformatic Analysis of Chicken Chemokines, Chemokine Receptors, and Toll-Like Receptor 21. (August 2006) Jixin Wang, B.S., Tarim University of Agriculture and Reclamation; M.S., South China Agricultural University Co-Chairs of Advisory Committee: Dr. James J. Zhu Dr. Luc R. Berghman Chemokines triggered by Toll-like receptors (TLRs) are small chemoattractant proteins, which mainly regulate leukocyte trafficking in inflammatory reactions via interaction with G protein-coupled receptors. Forty-two chemokines and 19 cognate receptors have been found in the human genome. Prior to this study, only 11 chicken chemokines and 7 receptors had been reported. The objectives of this study were to identify systematically chicken chemokines and their cognate receptor genes in the chicken genome and to annotate these genes and ligand-receptor binding by a comparative genomics approach. Twenty-three chemokine and 14 chemokine receptor genes were identified in the chicken genome. -
UI Health Care Letterhead Template
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Brownstein, Catherine A. et al. "An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge." Genome Biology 15:.3 (2014). p.1-18. As Published http://dx.doi.org/10.1186/gb-2014-15-3-r53 Publisher BioMed Central Ltd. Version Final published version Citable link http://hdl.handle.net/1721.1/88017 Terms of Use Creative Commons Attribution Detailed Terms http://creativecommons.org/licenses/by/2.0 University of Iowa Hospitals and Clinics Department of Otolaryngology – Head & Neck Surgery 200 Hawkins Drive, 21151-PFP Iowa City, IA 52242-1078 319-356-3612 Tel 319-356-4108 Fax www.uihealthcare.com September 24, 2012 Katherine Flannery Program Manager Harvard Medical School Center for Biomedical Informatics Email: [email protected] RE: CLARITY Challenge Dear Katherine, Accompanying this letter is our CLARITY Challenge reports. As stipulated in the protocol and instructions, our reports are not focused on identifying unrelated findings associated with other health issues or diseases. Pursuant to these specific aims and objectives, we have completed the following: 1. Analysis of whole genome and whole exome sequence data on three families; 2. Identification of potential alterations associated with the phenotype in the proband; 3. Determination and identification of key components for reporting these results; 4.