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Retrocopy Contributions to the Evolution of the Human Genome Robert Baertsch*1, Mark Diekhans1, W James Kent1, David Haussler1 and Jürgen Brosius2
BMC Genomics BioMed Central Research article Open Access Retrocopy contributions to the evolution of the human genome Robert Baertsch*1, Mark Diekhans1, W James Kent1, David Haussler1 and Jürgen Brosius2 Address: 1Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA and 2Institute of Experimental Pathology, ZMBE, University of Münster, Von-Esmarch-Str. 56, D-48149, Münster, Germany Email: Robert Baertsch* - [email protected]; Mark Diekhans - [email protected]; W James Kent - [email protected]; David Haussler - [email protected]; Jürgen Brosius - [email protected] * Corresponding author Published: 8 October 2008 Received: 17 March 2008 Accepted: 8 October 2008 BMC Genomics 2008, 9:466 doi:10.1186/1471-2164-9-466 This article is available from: http://www.biomedcentral.com/1471-2164/9/466 © 2008 Baertsch et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Evolution via point mutations is a relatively slow process and is unlikely to completely explain the differences between primates and other mammals. By contrast, 45% of the human genome is composed of retroposed elements, many of which were inserted in the primate lineage. A subset of retroposed mRNAs (retrocopies) shows strong evidence of expression in primates, often yielding functional retrogenes. Results: To identify and analyze the relatively recently evolved retrogenes, we carried out BLASTZ alignments of all human mRNAs against the human genome and scored a set of features indicative of retroposition. -
Remodeling Adipose Tissue Through in Silico Modulation of Fat Storage For
Chénard et al. BMC Systems Biology (2017) 11:60 DOI 10.1186/s12918-017-0438-9 RESEARCHARTICLE Open Access Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes Thierry Chénard2, Frédéric Guénard3, Marie-Claude Vohl3,4, André Carpentier5, André Tchernof4,6 and Rafael J. Najmanovich1* Abstract Background: Type 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions. Results: We present iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each -
METABOLIC EVOLUTION in GALDIERIA SULPHURARIA By
METABOLIC EVOLUTION IN GALDIERIA SULPHURARIA By CHAD M. TERNES Bachelor of Science in Botany Oklahoma State University Stillwater, Oklahoma 2009 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY May, 2015 METABOLIC EVOLUTION IN GALDIERIA SUPHURARIA Dissertation Approved: Dr. Gerald Schoenknecht Dissertation Adviser Dr. David Meinke Dr. Andrew Doust Dr. Patricia Canaan ii Name: CHAD M. TERNES Date of Degree: MAY, 2015 Title of Study: METABOLIC EVOLUTION IN GALDIERIA SULPHURARIA Major Field: PLANT SCIENCE Abstract: The thermoacidophilic, unicellular, red alga Galdieria sulphuraria possesses characteristics, including salt and heavy metal tolerance, unsurpassed by any other alga. Like most plastid bearing eukaryotes, G. sulphuraria can grow photoautotrophically. Additionally, it can also grow solely as a heterotroph, which results in the cessation of photosynthetic pigment biosynthesis. The ability to grow heterotrophically is likely correlated with G. sulphuraria ’s broad capacity for carbon metabolism, which rivals that of fungi. Annotation of the metabolic pathways encoded by the genome of G. sulphuraria revealed several pathways that are uncharacteristic for plants and algae, even red algae. Phylogenetic analyses of the enzymes underlying the metabolic pathways suggest multiple instances of horizontal gene transfer, in addition to endosymbiotic gene transfer and conservation through ancestry. Although some metabolic pathways as a whole appear to be retained through ancestry, genes encoding individual enzymes within a pathway were substituted by genes that were acquired horizontally from other domains of life. Thus, metabolic pathways in G. sulphuraria appear to be composed of a ‘metabolic patchwork’, underscored by a mosaic of genes resulting from multiple evolutionary processes. -
List of Genes Used in Cell Type Enrichment Analysis
List of genes used in cell type enrichment analysis Metagene Cell type Immunity ADAM28 Activated B cell Adaptive CD180 Activated B cell Adaptive CD79B Activated B cell Adaptive BLK Activated B cell Adaptive CD19 Activated B cell Adaptive MS4A1 Activated B cell Adaptive TNFRSF17 Activated B cell Adaptive IGHM Activated B cell Adaptive GNG7 Activated B cell Adaptive MICAL3 Activated B cell Adaptive SPIB Activated B cell Adaptive HLA-DOB Activated B cell Adaptive IGKC Activated B cell Adaptive PNOC Activated B cell Adaptive FCRL2 Activated B cell Adaptive BACH2 Activated B cell Adaptive CR2 Activated B cell Adaptive TCL1A Activated B cell Adaptive AKNA Activated B cell Adaptive ARHGAP25 Activated B cell Adaptive CCL21 Activated B cell Adaptive CD27 Activated B cell Adaptive CD38 Activated B cell Adaptive CLEC17A Activated B cell Adaptive CLEC9A Activated B cell Adaptive CLECL1 Activated B cell Adaptive AIM2 Activated CD4 T cell Adaptive BIRC3 Activated CD4 T cell Adaptive BRIP1 Activated CD4 T cell Adaptive CCL20 Activated CD4 T cell Adaptive CCL4 Activated CD4 T cell Adaptive CCL5 Activated CD4 T cell Adaptive CCNB1 Activated CD4 T cell Adaptive CCR7 Activated CD4 T cell Adaptive DUSP2 Activated CD4 T cell Adaptive ESCO2 Activated CD4 T cell Adaptive ETS1 Activated CD4 T cell Adaptive EXO1 Activated CD4 T cell Adaptive EXOC6 Activated CD4 T cell Adaptive IARS Activated CD4 T cell Adaptive ITK Activated CD4 T cell Adaptive KIF11 Activated CD4 T cell Adaptive KNTC1 Activated CD4 T cell Adaptive NUF2 Activated CD4 T cell Adaptive PRC1 Activated -
Markers of T Cell Senescence in Humans
International Journal of Molecular Sciences Review Markers of T Cell Senescence in Humans Weili Xu 1,2 and Anis Larbi 1,2,3,4,5,* 1 Biology of Aging Program and Immunomonitoring Platform, Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Immunos Building, Biopolis, Singapore 138648, Singapore; [email protected] 2 School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore 3 Department of Microbiology, National University of Singapore, Singapore 117597, Singapore 4 Department of Geriatrics, Faculty of Medicine, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada 5 Faculty of Sciences, University ElManar, Tunis 1068, Tunisia * Correspondence: [email protected]; Tel.: +65-6407-0412 Received: 31 May 2017; Accepted: 26 July 2017; Published: 10 August 2017 Abstract: Many countries are facing the aging of their population, and many more will face a similar obstacle in the near future, which could be a burden to many healthcare systems. Increased susceptibility to infections, cardiovascular and neurodegenerative disease, cancer as well as reduced efficacy of vaccination are important matters for researchers in the field of aging. As older adults show higher prevalence for a variety of diseases, this also implies higher risk of complications, including nosocomial infections, slower recovery and sequels that may reduce the autonomy and overall quality of life of older adults. The age-related effects on the immune system termed as “immunosenescence” can be exemplified by the reported hypo-responsiveness to influenza vaccination of the elderly. T cells, which belong to the adaptive arm of the immune system, have been extensively studied and the knowledge gathered enables a better understanding of how the immune system may be affected after acute/chronic infections and how this matters in the long run. -
Human ADAM12 Quantikine ELISA
Quantikine® ELISA Human ADAM12 Immunoassay Catalog Number DAD120 For the quantitative determination of A Disintegrin And Metalloproteinase domain- containing protein 12 (ADAM12) concentrations in cell culture supernates, serum, plasma, and urine. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. TABLE OF CONTENTS SECTION PAGE INTRODUCTION .....................................................................................................................................................................1 PRINCIPLE OF THE ASSAY ...................................................................................................................................................2 LIMITATIONS OF THE PROCEDURE .................................................................................................................................2 TECHNICAL HINTS .................................................................................................................................................................2 MATERIALS PROVIDED & STORAGE CONDITIONS ...................................................................................................3 OTHER SUPPLIES REQUIRED .............................................................................................................................................3 PRECAUTIONS .........................................................................................................................................................................4 -
Supplemental Table S1
Entrez Gene Symbol Gene Name Affymetrix EST Glomchip SAGE Stanford Literature HPA confirmed Gene ID Profiling profiling Profiling Profiling array profiling confirmed 1 2 A2M alpha-2-macroglobulin 0 0 0 1 0 2 10347 ABCA7 ATP-binding cassette, sub-family A (ABC1), member 7 1 0 0 0 0 3 10350 ABCA9 ATP-binding cassette, sub-family A (ABC1), member 9 1 0 0 0 0 4 10057 ABCC5 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 1 0 0 0 0 5 10060 ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 1 0 0 0 0 6 79575 ABHD8 abhydrolase domain containing 8 1 0 0 0 0 7 51225 ABI3 ABI gene family, member 3 1 0 1 0 0 8 29 ABR active BCR-related gene 1 0 0 0 0 9 25841 ABTB2 ankyrin repeat and BTB (POZ) domain containing 2 1 0 1 0 0 10 30 ACAA1 acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiol 0 1 0 0 0 11 43 ACHE acetylcholinesterase (Yt blood group) 1 0 0 0 0 12 58 ACTA1 actin, alpha 1, skeletal muscle 0 1 0 0 0 13 60 ACTB actin, beta 01000 1 14 71 ACTG1 actin, gamma 1 0 1 0 0 0 15 81 ACTN4 actinin, alpha 4 0 0 1 1 1 10700177 16 10096 ACTR3 ARP3 actin-related protein 3 homolog (yeast) 0 1 0 0 0 17 94 ACVRL1 activin A receptor type II-like 1 1 0 1 0 0 18 8038 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 1 0 0 0 0 19 8751 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 1 0 0 0 0 20 8728 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 1 0 0 0 0 21 81792 ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 1 0 0 0 0 22 9507 ADAMTS4 ADAM metallopeptidase with thrombospondin type 1 -
ANKRD11 Gene Ankyrin Repeat Domain 11
ANKRD11 gene ankyrin repeat domain 11 Normal Function The ANKRD11 gene provides instructions for making a protein called ankyrin repeat domain 11 (ANKRD11). As its name suggests, this protein contains multiple regions called ankyrin domains; proteins with these domains help other proteins interact with each other. The ANKRD11 protein interacts with certain proteins called histone deacetylases, which are important for controlling gene activity. Through these interactions, ANKRD11 affects when genes are turned on and off. For example, ANKRD11 brings together histone deacetylases and other proteins called p160 coactivators. This association regulates the ability of p160 coactivators to turn on gene activity. ANKRD11 may also enhance the activity of a protein called p53, which controls the growth and division (proliferation) and the self-destruction (apoptosis) of cells. The ANKRD11 protein is found in nerve cells (neurons) in the brain. During embryonic development, ANKRD11 helps regulate the proliferation of these cells and development of the brain. Researchers speculate that the protein may also be involved in the ability of neurons to change and adapt over time (plasticity), which is important for learning and memory. ANKRD11 may function in other cells in the body and appears to be involved in normal bone development. Health Conditions Related to Genetic Changes KBG syndrome Several ANKRD11 gene mutations have been found to cause KBG syndrome, a condition characterized by large upper front teeth and other unusual facial features, skeletal abnormalities, and intellectual disability. Most of these mutations lead to an abnormally short ANKRD11 protein, which likely has little or no function. Reduction of this protein's function is thought to underlie the signs and symptoms of the condition. -
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. -
Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1
BIOLOGY OF REPRODUCTION 78, 184–192 (2008) Published online before print 10 October 2007. DOI 10.1095/biolreprod.107.062943 Identification and Characterization of RHOA-Interacting Proteins in Bovine Spermatozoa1 Sarah E. Fiedler, Malini Bajpai, and Daniel W. Carr2 Department of Medicine, Oregon Health & Sciences University and Veterans Affairs Medical Center, Portland, Oregon 97239 ABSTRACT Guanine nucleotide exchange factors (GEFs) catalyze the GDP for GTP exchange [2]. Activation is negatively regulated by In somatic cells, RHOA mediates actin dynamics through a both guanine nucleotide dissociation inhibitors (RHO GDIs) GNA13-mediated signaling cascade involving RHO kinase and GTPase-activating proteins (GAPs) [1, 2]. Endogenous (ROCK), LIM kinase (LIMK), and cofilin. RHOA can be RHO can be inactivated via C3 exoenzyme ADP-ribosylation, negatively regulated by protein kinase A (PRKA), and it and studies have demonstrated RHO involvement in actin-based interacts with members of the A-kinase anchoring (AKAP) cytoskeletal response to extracellular signals, including lyso- family via intermediary proteins. In spermatozoa, actin poly- merization precedes the acrosome reaction, which is necessary phosphatidic acid (LPA) [2–4]. LPA is known to signal through for normal fertility. The present study was undertaken to G-protein-coupled receptors (GPCRs) [4, 5]; specifically, LPA- determine whether the GNA13-mediated RHOA signaling activated GNA13 (formerly Ga13) promotes RHO activation pathway may be involved in acrosome reaction in bovine through GEFs [4, 6]. Activated RHO-GTP then signals RHO caudal sperm, and whether AKAPs may be involved in its kinase (ROCK), resulting in the phosphorylation and activation targeting and regulation. GNA13, RHOA, ROCK2, LIMK2, and of LIM-kinase (LIMK), which in turn phosphorylates and cofilin were all detected by Western blot in bovine caudal inactivates cofilin, an actin depolymerizer, the end result being sperm. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
CD29 Identifies IFN-Γ–Producing Human CD8+ T Cells With
+ CD29 identifies IFN-γ–producing human CD8 T cells with an increased cytotoxic potential Benoît P. Nicoleta,b, Aurélie Guislaina,b, Floris P. J. van Alphenc, Raquel Gomez-Eerlandd, Ton N. M. Schumacherd, Maartje van den Biggelaarc,e, and Monika C. Wolkersa,b,1 aDepartment of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands; bLandsteiner Laboratory, Oncode Institute, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; cDepartment of Research Facilities, Sanquin Research, 1066 CX Amsterdam, The Netherlands; dDivision of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands; and eDepartment of Molecular and Cellular Haemostasis, Sanquin Research, 1066 CX Amsterdam, The Netherlands Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved February 12, 2020 (received for review August 12, 2019) Cytotoxic CD8+ T cells can effectively kill target cells by producing therefore developed a protocol that allowed for efficient iso- cytokines, chemokines, and granzymes. Expression of these effector lation of RNA and protein from fluorescence-activated cell molecules is however highly divergent, and tools that identify and sorting (FACS)-sorted fixed T cells after intracellular cytokine + preselect CD8 T cells with a cytotoxic expression profile are lacking. staining. With this top-down approach, we performed an un- + Human CD8 T cells can be divided into IFN-γ– and IL-2–producing biased RNA-sequencing (RNA-seq) and mass spectrometry cells. Unbiased transcriptomics and proteomics analysis on cytokine- γ– – + + (MS) analyses on IFN- and IL-2 producing primary human producing fixed CD8 T cells revealed that IL-2 cells produce helper + + + CD8 Tcells.