The Apolipoprotein L Family of Programmed Cell Death and Immunity Genes Rapidly Evolved in Primates at Discrete Sites of Host–Pathogen Interactions
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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. -
Gene Expression Polarization
Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression This information is current as of September 27, 2021. Fernando O. Martinez, Siamon Gordon, Massimo Locati and Alberto Mantovani J Immunol 2006; 177:7303-7311; ; doi: 10.4049/jimmunol.177.10.7303 http://www.jimmunol.org/content/177/10/7303 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2006/11/03/177.10.7303.DC1 Material http://www.jimmunol.org/ References This article cites 61 articles, 22 of which you can access for free at: http://www.jimmunol.org/content/177/10/7303.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 27, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2006 by The American Association of Immunologists All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression1 Fernando O. -
The Expression of the Human Apolipoprotein Genes and Their Regulation by Ppars
CORE Metadata, citation and similar papers at core.ac.uk Provided by UEF Electronic Publications The expression of the human apolipoprotein genes and their regulation by PPARs Juuso Uski M.Sc. Thesis Biochemistry Department of Biosciences University of Kuopio June 2008 Abstract The expression of the human apolipoprotein genes and their regulation by PPARs. UNIVERSITY OF KUOPIO, the Faculty of Natural and Environmental Sciences, Curriculum of Biochemistry USKI Juuso Oskari Thesis for Master of Science degree Supervisors Prof. Carsten Carlberg, Ph.D. Merja Heinäniemi, Ph.D. June 2008 Keywords: nuclear receptors; peroxisome proliferator-activated receptor; PPAR response element; apolipoprotein; lipid metabolism; high density lipoprotein; low density lipoprotein. Lipids are any fat-soluble, naturally-occurring molecules and one of their main biological functions is energy storage. Lipoproteins carry hydrophobic lipids in the water and salt-based blood environment for processing and energy supply in liver and other organs. In this study, the genomic area around the apolipoprotein genes was scanned in silico for PPAR response elements (PPREs) using the in vitro data-based computer program. Several new putative REs were found in surroundings of multiple lipoprotein genes. The responsiveness of those apolipoprotein genes to the PPAR ligands GW501516, rosiglitazone and GW7647 in the HepG2, HEK293 and THP-1 cell lines were tested with real-time PCR. The APOA1, APOA2, APOB, APOD, APOE, APOF, APOL1, APOL3, APOL5 and APOL6 genes were found to be regulated by PPARs in direct or secondary manners. Those results provide new insights in the understanding of lipid metabolism and so many lifestyle diseases like atherosclerosis, type 2 diabetes, heart disease and stroke. -
CD56+ T-Cells in Relation to Cytomegalovirus in Healthy Subjects and Kidney Transplant Patients
CD56+ T-cells in Relation to Cytomegalovirus in Healthy Subjects and Kidney Transplant Patients Institute of Infection and Global Health Department of Clinical Infection, Microbiology and Immunology Thesis submitted in accordance with the requirements of the University of Liverpool for the degree of Doctor in Philosophy by Mazen Mohammed Almehmadi December 2014 - 1 - Abstract Human T cells expressing CD56 are capable of tumour cell lysis following activation with interleukin-2 but their role in viral immunity has been less well studied. The work described in this thesis aimed to investigate CD56+ T-cells in relation to cytomegalovirus infection in healthy subjects and kidney transplant patients (KTPs). Proportions of CD56+ T cells were found to be highly significantly increased in healthy cytomegalovirus-seropositive (CMV+) compared to cytomegalovirus-seronegative (CMV-) subjects (8.38% ± 0.33 versus 3.29%± 0.33; P < 0.0001). In donor CMV-/recipient CMV- (D-/R-)- KTPs levels of CD56+ T cells were 1.9% ±0.35 versus 5.42% ±1.01 in D+/R- patients and 5.11% ±0.69 in R+ patients (P 0.0247 and < 0.0001 respectively). CD56+ T cells in both healthy CMV+ subjects and KTPs expressed markers of effector memory- RA T-cells (TEMRA) while in healthy CMV- subjects and D-/R- KTPs the phenotype was predominantly that of naïve T-cells. Other surface markers, CD8, CD4, CD58, CD57, CD94 and NKG2C were expressed by a significantly higher proportion of CD56+ T-cells in healthy CMV+ than CMV- subjects. Functional studies showed levels of pro-inflammatory cytokines IFN-γ and TNF-α, as well as granzyme B and CD107a were significantly higher in CD56+ T-cells from CMV+ than CMV- subjects following stimulation with CMV antigens. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
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. -
Insights Into the Molecular Mechanisms of Apoptosis Induced
I nsights into the molecular mechanisms of apoptosis induced by glucose deprivation Raffaella Iurlaro ADVERTIMENT . La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions d'ús: La difusió d’aquesta tesi per mitjà del servei TDX ( www.tdx.cat ) i a través del Dipòsit Digital de la UB ( diposit.ub.edu ) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposici ó des d’un lloc aliè al servei TDX ni al Dipòsit Digital de la UB . No s’autoritza la presentació del seu contingut en una finestra o marc aliè a TDX o al Dipòsit Digital de la UB (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora. ADVERTENCIA . La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR ( www.tdx.cat ) y a través del Repositorio Digital de la UB ( diposit.ub.edu ) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR o al Repositorio Digital de la UB . -
Epigenetic Aging Is Accelerated in Alcohol Use Disorder and Regulated by Genetic Variation in APOL2
www.nature.com/npp ARTICLE OPEN Epigenetic aging is accelerated in alcohol use disorder and regulated by genetic variation in APOL2 Audrey Luo1, Jeesun Jung1, Martha Longley1, Daniel B. Rosoff1, Katrin Charlet1,2, Christine Muench 1, Jisoo Lee1, Colin A. Hodgkinson3, David Goldman3, Steve Horvath4,5, Zachary A. Kaminsky6 and Falk W. Lohoff1 To investigate the potential role of alcohol use disorder (AUD) in aging processes, we employed Levine’s epigenetic clock (DNAm PhenoAge) to estimate DNA methylation age in 331 individuals with AUD and 201 healthy controls (HC). We evaluated the effects of heavy, chronic alcohol consumption on epigenetic age acceleration (EAA) using clinical biomarkers, including liver function test enzymes (LFTs) and clinical measures. To characterize potential underlying genetic variation contributing to EAA in AUD, we performed genome-wide association studies (GWAS) on EAA, including pathway analyses. We followed up on relevant top findings with in silico expression quantitative trait loci (eQTL) analyses for biological function using the BRAINEAC database. There was a 2.22-year age acceleration in AUD compared to controls after adjusting for gender and blood cell composition (p = 1.85 × 10−5). This association remained significant after adjusting for race, body mass index, and smoking status (1.38 years, p = 0.02). Secondary analyses showed more pronounced EAA in individuals with more severe AUD-associated phenotypes, including elevated gamma- glutamyl transferase (GGT) and alanine aminotransferase (ALT), and higher number of heavy drinking days (all ps < 0.05). The genome-wide meta-analysis of EAA in AUD revealed a significant single nucleotide polymorphism (SNP), rs916264 (p = 5.43 × 10−8), in apolipoprotein L2 (APOL2) at the genome-wide level. -
Genetics/ Genomics and HIV Nephropathy
Genetics/ Genomics and HIV Nephropathy Sarala Naicker MB ChB(Natal), FRCP(Lond), PhD(Natal) Emeritus Professor of Medicine School of Clinical Medicine University of the Witwatersrand Johannesburg KDIGOSouth Africa KDIGO Conference Yaounde, Cameroon 18-20 March 2017 CKD prevalence § Esmated 3,2million people on RRT, with CKD incidence growing by 6% annually (WHO) § Cumulave lifeme risk for CKD varies by ancestry § African descent are the most affected (4X more likely than of European origin) § HIV CKD 18-50X increase in KDIGO people of African descent Rates adjusted for age and gender (USRDS 2012) More details Map of early human migrations[25] 1. Homo sapiens 2. Neanderthals 3. Early hominins NordNordWest - File:Spreading homo sapiens ru.svg by Urutseg Spreading of Homo sapiens •Migraon out of Africa Public Domain • File:SpreadingKDIGO homo sapiens la.svg • Created: 12 August 2014 About | Discussion | Help Human diversity, migration and origins KDIGO Floyd Reed and Sarah Tishkoff Renal histology in HIV infection- Africa JHB1 JHB2 JHB3 DBN4 CT5 CT6 Nigeria7 N 99 84 636 37 145 192 10 HIVAN (%) 27 15 25.8 83 55 57.3 70 IC Disease (%) 21 9 9.6 8.3 21.9* Other GN (%) 41 27 27.3 7 15.9 12.5 [FSGS] [13] [14.5] Tubulo-Int 21 6.3 10 13 70 Disease (%) KDIGO Other (%) 10 15 4.9 16 1. Gerntholtz et al. Kidney Int. 2006; 69: 1885-1891 Abbreviations 2. Rahmanian S. MMed, Wits 2015 3. Diana et al. WCN 2015 poster JHB= Johannesburg 4. Han et al. Kidney Int. 2006; 69: 2243-2250 DBN= Durban 6. -
Nº Ref Uniprot Proteína Péptidos Identificados Por MS/MS 1 P01024
Document downloaded from http://www.elsevier.es, day 26/09/2021. This copy is for personal use. Any transmission of this document by any media or format is strictly prohibited. Nº Ref Uniprot Proteína Péptidos identificados 1 P01024 CO3_HUMAN Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 por 162MS/MS 2 P02751 FINC_HUMAN Fibronectin OS=Homo sapiens GN=FN1 PE=1 SV=4 131 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 128 4 P0C0L4 CO4A_HUMAN Complement C4-A OS=Homo sapiens GN=C4A PE=1 SV=1 95 5 P04275 VWF_HUMAN von Willebrand factor OS=Homo sapiens GN=VWF PE=1 SV=4 81 6 P02675 FIBB_HUMAN Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 78 7 P01031 CO5_HUMAN Complement C5 OS=Homo sapiens GN=C5 PE=1 SV=4 66 8 P02768 ALBU_HUMAN Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 66 9 P00450 CERU_HUMAN Ceruloplasmin OS=Homo sapiens GN=CP PE=1 SV=1 64 10 P02671 FIBA_HUMAN Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 58 11 P08603 CFAH_HUMAN Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 56 12 P02787 TRFE_HUMAN Serotransferrin OS=Homo sapiens GN=TF PE=1 SV=3 54 13 P00747 PLMN_HUMAN Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 48 14 P02679 FIBG_HUMAN Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 47 15 P01871 IGHM_HUMAN Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 41 16 P04003 C4BPA_HUMAN C4b-binding protein alpha chain OS=Homo sapiens GN=C4BPA PE=1 SV=2 37 17 Q9Y6R7 FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 30 18 O43866 CD5L_HUMAN CD5 antigen-like OS=Homo -
New Mesh Headings for 2018 Single Column After Cutover
New MeSH Headings for 2018 Listed in alphabetical order with Heading, Scope Note, Annotation (AN), and Tree Locations 2-Hydroxypropyl-beta-cyclodextrin Derivative of beta-cyclodextrin that is used as an excipient for steroid drugs and as a lipid chelator. Tree locations: beta-Cyclodextrins D04.345.103.333.500 D09.301.915.400.375.333.500 D09.698.365.855.400.375.333.500 AAA Domain An approximately 250 amino acid domain common to AAA ATPases and AAA Proteins. It consists of a highly conserved N-terminal P-Loop ATPase subdomain with an alpha-beta-alpha conformation, and a less-conserved C- terminal subdomain with an all alpha conformation. The N-terminal subdomain includes Walker A and Walker B motifs which function in ATP binding and hydrolysis. Tree locations: Amino Acid Motifs G02.111.570.820.709.275.500.913 AAA Proteins A large, highly conserved and functionally diverse superfamily of NTPases and nucleotide-binding proteins that are characterized by a conserved 200 to 250 amino acid nucleotide-binding and catalytic domain, the AAA+ module. They assemble into hexameric ring complexes that function in the energy-dependent remodeling of macromolecules. Members include ATPASES ASSOCIATED WITH DIVERSE CELLULAR ACTIVITIES. Tree locations: Acid Anhydride Hydrolases D08.811.277.040.013 Carrier Proteins D12.776.157.025 Abuse-Deterrent Formulations Drug formulations or delivery systems intended to discourage the abuse of CONTROLLED SUBSTANCES. These may include physical barriers to prevent chewing or crushing the drug; chemical barriers that prevent extraction of psychoactive ingredients; agonist-antagonist combinations to reduce euphoria associated with abuse; aversion, where controlled substances are combined with others that will produce an unpleasant effect if the patient manipulates the dosage form or exceeds the recommended dose; delivery systems that are resistant to abuse such as implants; or combinations of these methods.